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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from itertools import chain
import torch
from fairseq import optim, utils
class DynamicLossScaler(object):
def __init__(
self, init_scale=2.**15, scale_factor=2., scale_window=2000,
tolerance=0.05, threshold=None,
):
self.loss_scale = init_scale
self.scale_factor = scale_factor
self.scale_window = scale_window
self.tolerance = tolerance
self.threshold = threshold
self._iter = 0
self._last_overflow_iter = -1
self._last_rescale_iter = -1
self._overflows_since_rescale = 0
def update_scale(self, overflow):
iter_since_rescale = self._iter - self._last_rescale_iter
if overflow:
self._last_overflow_iter = self._iter
self._overflows_since_rescale += 1
pct_overflow = self._overflows_since_rescale / float(iter_since_rescale)
if pct_overflow >= self.tolerance:
self._decrease_loss_scale()
self._last_rescale_iter = self._iter
self._overflows_since_rescale = 0
elif (self._iter - self._last_overflow_iter) % self.scale_window == 0:
self.loss_scale *= self.scale_factor
self._last_rescale_iter = self._iter
self._iter += 1
def _decrease_loss_scale(self):
self.loss_scale /= self.scale_factor
if self.threshold is not None:
self.loss_scale = max(self.loss_scale, self.threshold)
@staticmethod
def has_overflow(grad_norm):
# detect inf and nan
if grad_norm == float('inf') or grad_norm != grad_norm:
return True
return False
class _FP16OptimizerMixin(object):
def __init__(self, *args, **kwargs):
# forward __init__ call to the next class in mro(method resolution order)
super().__init__(*args, **kwargs)
@classmethod
def build_fp32_params(cls, params):
# create FP32 copy of parameters and grads
total_param_size = sum(p.data.numel() for p in params)
fp32_params = params[0].new(0).float().new(total_param_size)
offset = 0
for p in params:
numel = p.data.numel()
fp32_params[offset:offset+numel].copy_(p.data.view(-1))
offset += numel
fp32_params = torch.nn.Parameter(fp32_params)
fp32_params.grad = fp32_params.data.new(total_param_size)
return fp32_params
def state_dict(self):
"""Return the optimizer's state dict."""
state_dict = self.fp32_optimizer.state_dict()
state_dict['loss_scale'] = self.scaler.loss_scale
return state_dict
def load_state_dict(self, state_dict, optimizer_overrides=None):
"""Load an optimizer state dict.
In general we should prefer the configuration of the existing optimizer
instance (e.g., learning rate) over that found in the state_dict. This
allows us to resume training from a checkpoint using a new set of
optimizer args.
"""
if 'loss_scale' in state_dict:
self.scaler.loss_scale = state_dict['loss_scale']
self.fp32_optimizer.load_state_dict(state_dict, optimizer_overrides)
def backward(self, loss):
"""Computes the sum of gradients of the given tensor w.r.t. graph leaves.
Compared to :func:`fairseq.optim.FairseqOptimizer.backward`, this
function additionally dynamically scales the loss to avoid gradient
underflow.
"""
loss = loss * self.scaler.loss_scale
loss.backward()
self._needs_sync = True
def _sync_fp16_grads_to_fp32(self, multiply_grads=1.):
if self._needs_sync:
# copy FP16 grads to FP32
offset = 0
for p in self.fp16_params:
if not p.requires_grad:
continue
grad_data = p.grad.data if p.grad is not None else p.data.new_zeros(p.data.shape)
numel = grad_data.numel()
self.fp32_params.grad.data[offset:offset+numel].copy_(grad_data.view(-1))
offset += numel
# correct for dynamic loss scaler
self.fp32_params.grad.data.mul_(multiply_grads / self.scaler.loss_scale)
self._needs_sync = False
def multiply_grads(self, c):
"""Multiplies grads by a constant ``c``."""
if self._needs_sync:
self._sync_fp16_grads_to_fp32(c)
else:
self.fp32_params.grad.data.mul_(c)
def clip_grad_norm(self, max_norm):
"""Clips gradient norm and updates dynamic loss scaler."""
self._sync_fp16_grads_to_fp32()
grad_norm = utils.clip_grad_norm_(self.fp32_params.grad.data, max_norm)
# detect overflow and adjust loss scale
overflow = DynamicLossScaler.has_overflow(grad_norm)
self.scaler.update_scale(overflow)
if overflow:
if self.scaler.loss_scale <= self.min_loss_scale:
# Use FloatingPointError as an uncommon error that parent
# functions can safely catch to stop training.
raise FloatingPointError((
'Minimum loss scale reached ({}). Your loss is probably exploding. '
'Try lowering the learning rate, using gradient clipping or '
'increasing the batch size.'
).format(self.min_loss_scale))
raise OverflowError('setting loss scale to: ' + str(self.scaler.loss_scale))
return grad_norm
def step(self, closure=None):
"""Performs a single optimization step."""
self._sync_fp16_grads_to_fp32()
self.fp32_optimizer.step(closure)
# copy FP32 params back into FP16 model
offset = 0
for p in self.fp16_params:
if not p.requires_grad:
continue
numel = p.data.numel()
p.data.copy_(self.fp32_params.data[offset:offset+numel].view_as(p.data))
offset += numel
def zero_grad(self):
"""Clears the gradients of all optimized parameters."""
for p in self.fp16_params:
p.grad = None
self._needs_sync = False
class FP16Optimizer(_FP16OptimizerMixin, optim.FairseqOptimizer):
"""
Wrap an *optimizer* to support FP16 (mixed precision) training.
"""
def __init__(self, args, params, fp32_optimizer, fp32_params):
super().__init__(args)
self.fp16_params = params
self.fp32_optimizer = fp32_optimizer
self.fp32_params = fp32_params
if getattr(args, 'fp16_scale_window', None) is None:
if len(args.update_freq) > 1:
raise ValueError(
'--fp16-scale-window must be given explicitly when using a '
'custom --update-freq schedule'
)
scale_window = 2**14 / args.distributed_world_size / args.update_freq[0]
else:
scale_window = args.fp16_scale_window
self.scaler = DynamicLossScaler(
init_scale=args.fp16_init_scale,
scale_window=scale_window,
tolerance=args.fp16_scale_tolerance,
threshold=args.threshold_loss_scale,
)
self.min_loss_scale = self.args.min_loss_scale
@classmethod
def build_optimizer(cls, args, params):
"""
Args:
args (argparse.Namespace): fairseq args
params (iterable): iterable of parameters to optimize
"""
fp32_params = cls.build_fp32_params(params)
fp32_optimizer = optim.build_optimizer(args, [fp32_params])
return cls(args, params, fp32_optimizer, fp32_params)
@property
def optimizer(self):
return self.fp32_optimizer.optimizer
@property
def optimizer_config(self):
return self.fp32_optimizer.optimizer_config
def get_lr(self):
return self.fp32_optimizer.get_lr()
def set_lr(self, lr):
self.fp32_optimizer.set_lr(lr)
class _MemoryEfficientFP16OptimizerMixin(object):
def __init__(self, *args, **kwargs):
# forward __init__ call to the next class in mro(method resolution order)
super().__init__(*args, **kwargs)
def state_dict(self):
"""Return the optimizer's state dict."""
state_dict = self.wrapped_optimizer.state_dict()
state_dict['loss_scale'] = self.scaler.loss_scale
return state_dict
def load_state_dict(self, state_dict, optimizer_overrides=None):
"""Load an optimizer state dict.
In general we should prefer the configuration of the existing optimizer
instance (e.g., learning rate) over that found in the state_dict. This
allows us to resume training from a checkpoint using a new set of
optimizer args.
"""
if 'loss_scale' in state_dict:
self.scaler.loss_scale = state_dict['loss_scale']
self.wrapped_optimizer.load_state_dict(state_dict, optimizer_overrides)
# Hack: PyTorch automatically casts the optimizer state to match the
# type of the current parameters. But with --memory-efficient-fp16 the
# params are FP16 while the optimizer state is FP32 and we don't want
# to cast. A workaround is to manually copy back the original state
# after the optimizer has been loaded.
groups = self.optimizer.param_groups
saved_groups = state_dict['param_groups']
id_map = {
old_id: p
for old_id, p in zip(
chain(*(g['params'] for g in saved_groups)),
chain(*(g['params'] for g in groups))
)
}
for k, v in state_dict['state'].items():
if k in id_map:
param = id_map[k]
self.optimizer.state[param] = v
def backward(self, loss):
"""Computes the sum of gradients of the given tensor w.r.t. graph leaves.
Compared to :func:`fairseq.optim.FairseqOptimizer.backward`, this
function additionally dynamically scales the loss to avoid gradient
underflow.
"""
loss = loss * self.scaler.loss_scale
loss.backward()
self._grads_are_scaled = True
def _unscale_grads(self, multiply_grads=1.):
if self._grads_are_scaled:
self._grads_are_scaled = False
# correct for dynamic loss scaler
self.wrapped_optimizer.multiply_grads(multiply_grads / self.scaler.loss_scale)
else:
assert multiply_grads == 1.
def multiply_grads(self, c):
"""Multiplies grads by a constant *c*."""
if self._grads_are_scaled:
self._unscale_grads(c)
else:
self.wrapped_optimizer.multiply_grads(c)
def clip_grad_norm(self, max_norm):
"""Clips gradient norm and updates dynamic loss scaler."""
self._unscale_grads()
grad_norm = self.wrapped_optimizer.clip_grad_norm(max_norm)
# detect overflow and adjust loss scale
overflow = DynamicLossScaler.has_overflow(grad_norm)
self.scaler.update_scale(overflow)
if overflow:
if self.scaler.loss_scale <= self.min_loss_scale:
# Use FloatingPointError as an uncommon error that parent
# functions can safely catch to stop training.
raise FloatingPointError((
'Minimum loss scale reached ({}). Your loss is probably exploding. '
'Try lowering the learning rate, using gradient clipping or '
'increasing the batch size.'
).format(self.min_loss_scale))
raise OverflowError('setting loss scale to: ' + str(self.scaler.loss_scale))
return grad_norm
def step(self, closure=None):
"""Performs a single optimization step."""
self._unscale_grads()
self.wrapped_optimizer.step(closure)
def zero_grad(self):
"""Clears the gradients of all optimized parameters."""
self.wrapped_optimizer.zero_grad()
self._grads_are_scaled = False
class MemoryEfficientFP16Optimizer(_MemoryEfficientFP16OptimizerMixin, optim.FairseqOptimizer):
"""
Wrap an *optimizer* to support FP16 (mixed precision) training.
Compared to :class:`fairseq.optim.FP16Optimizer`, this version does not
maintain an FP32 copy of the model. We instead expect the optimizer to
convert the gradients to FP32 internally and sync the results back to the
FP16 model params. This significantly reduces memory usage but slightly
increases the time spent in the optimizer.
Since this wrapper depends on specific functionality in the wrapped
optimizer (i.e., on-the-fly conversion of grads to FP32), only certain
optimizers can be wrapped. This is determined by the
*supports_memory_efficient_fp16* property.
"""
def __init__(self, args, params, optimizer):
if not optimizer.supports_memory_efficient_fp16:
raise ValueError(
'Unsupported optimizer: {}'.format(optimizer.__class__.__name__)
)
super().__init__(args)
self.wrapped_optimizer = optimizer
if getattr(args, 'fp16_scale_window', None) is None:
if len(args.update_freq) > 1:
raise ValueError(
'--fp16-scale-window must be given explicitly when using a '
'custom --update-freq schedule'
)
scale_window = 2**14 / args.distributed_world_size / args.update_freq[0]
else:
scale_window = args.fp16_scale_window
self.scaler = DynamicLossScaler(
init_scale=args.fp16_init_scale,
scale_window=scale_window,
tolerance=args.fp16_scale_tolerance,
threshold=args.threshold_loss_scale,
)
self.min_loss_scale = self.args.min_loss_scale
@classmethod
def build_optimizer(cls, args, params):
"""
Args:
args (argparse.Namespace): fairseq args
params (iterable): iterable of parameters to optimize
"""
fp16_optimizer = optim.build_optimizer(args, params)
return cls(args, params, fp16_optimizer)
@property
def optimizer(self):
return self.wrapped_optimizer.optimizer
@property
def optimizer_config(self):
return self.wrapped_optimizer.optimizer_config
def get_lr(self):
return self.wrapped_optimizer.get_lr()
def set_lr(self, lr):
self.wrapped_optimizer.set_lr(lr)
| data2vec_vision-main | infoxlm/fairseq/fairseq/optim/fp16_optimizer.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch.optim
from . import FairseqOptimizer, register_optimizer
@register_optimizer('adagrad')
class Adagrad(FairseqOptimizer):
def __init__(self, args, params):
super().__init__(args)
self._optimizer = torch.optim.Adagrad(params, **self.optimizer_config)
@staticmethod
def add_args(parser):
"""Add optimizer-specific arguments to the parser."""
# fmt: off
parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD',
help='weight decay')
# fmt: on
@property
def optimizer_config(self):
"""
Return a kwarg dictionary that will be used to override optimizer
args stored in checkpoints. This allows us to load a checkpoint and
resume training using a different set of optimizer args, e.g., with a
different learning rate.
"""
return {
'lr': self.args.lr[0],
'weight_decay': self.args.weight_decay,
}
| data2vec_vision-main | infoxlm/fairseq/fairseq/optim/adagrad.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import types
import torch
import torch.optim
import torch.distributed as dist
from . import FairseqOptimizer, register_optimizer
@register_optimizer('adam')
class FairseqAdam(FairseqOptimizer):
"""Adam optimizer for fairseq.
Important note: this optimizer corresponds to the "AdamW" variant of
Adam in its weight decay behavior. As such, it is most closely
analogous to torch.optim.AdamW from PyTorch.
"""
def __init__(self, args, params):
super().__init__(args)
if torch.cuda.is_available():
try:
from apex.optimizers import FusedAdam as _FusedAdam # noqa
self._optimizer = FusedAdam(params, **self.optimizer_config)
except ImportError:
self._optimizer = Adam(params, **self.optimizer_config)
else:
self._optimizer = Adam(params, **self.optimizer_config)
@staticmethod
def add_args(parser):
"""Add optimizer-specific arguments to the parser."""
# fmt: off
parser.add_argument('--adam-betas', default='(0.9, 0.999)', metavar='B',
help='betas for Adam optimizer')
parser.add_argument('--adam-eps', type=float, default=1e-8, metavar='D',
help='epsilon for Adam optimizer')
parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD',
help='weight decay')
# fmt: on
@property
def optimizer_config(self):
"""
Return a kwarg dictionary that will be used to override optimizer
args stored in checkpoints. This allows us to load a checkpoint and
resume training using a different set of optimizer args, e.g., with a
different learning rate.
"""
return {
'lr': self.args.lr[0],
'betas': eval(self.args.adam_betas),
'eps': self.args.adam_eps,
'weight_decay': self.args.weight_decay,
}
def average_params(self):
"""Reduce Params is only used during BMUF distributed training."""
state_dict = self.optimizer.state_dict()
total_gpus = float(dist.get_world_size())
for _, value in state_dict["state"].items():
value["exp_avg"] /= total_gpus
value["exp_avg_sq"] /= total_gpus
dist.all_reduce(value["exp_avg"], op=dist.ReduceOp.SUM)
dist.all_reduce(value["exp_avg_sq"], op=dist.ReduceOp.SUM)
class Adam(torch.optim.Optimizer):
"""Implements Adam algorithm.
This implementation is modified from torch.optim.Adam based on:
`Fixed Weight Decay Regularization in Adam`
(see https://arxiv.org/abs/1711.05101)
It has been proposed in `Adam: A Method for Stochastic Optimization`_.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square (default: (0.9, 0.999))
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-8)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
amsgrad (boolean, optional): whether to use the AMSGrad variant of this
algorithm from the paper `On the Convergence of Adam and Beyond`_
.. _Adam\: A Method for Stochastic Optimization:
https://arxiv.org/abs/1412.6980
.. _On the Convergence of Adam and Beyond:
https://openreview.net/forum?id=ryQu7f-RZ
"""
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
weight_decay=0, amsgrad=False):
defaults = dict(lr=lr, betas=betas, eps=eps,
weight_decay=weight_decay, amsgrad=amsgrad)
super(Adam, self).__init__(params, defaults)
@property
def supports_memory_efficient_fp16(self):
return True
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad.data.float()
if grad.is_sparse:
raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
amsgrad = group['amsgrad']
p_data_fp32 = p.data.float()
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p_data_fp32)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
if amsgrad:
# Maintains max of all exp. moving avg. of sq. grad. values
state['max_exp_avg_sq'] = torch.zeros_like(p_data_fp32)
else:
state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)
if amsgrad:
state['max_exp_avg_sq'] = state['max_exp_avg_sq'].type_as(p_data_fp32)
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
if amsgrad:
max_exp_avg_sq = state['max_exp_avg_sq']
beta1, beta2 = group['betas']
state['step'] += 1
# Decay the first and second moment running average coefficient
exp_avg.mul_(beta1).add_(1 - beta1, grad)
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
if amsgrad:
# Maintains the maximum of all 2nd moment running avg. till now
torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
# Use the max. for normalizing running avg. of gradient
denom = max_exp_avg_sq.sqrt().add_(group['eps'])
else:
denom = exp_avg_sq.sqrt().add_(group['eps'])
bias_correction1 = 1 - beta1 ** state['step']
bias_correction2 = 1 - beta2 ** state['step']
step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
if group['weight_decay'] != 0:
p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
p.data.copy_(p_data_fp32)
return loss
class FusedAdam(torch.optim.Optimizer):
"""
Implements Adam algorithm. Currently GPU-only. Requires Apex to be installed via
``python setup.py install --cuda_ext --cpp_ext``.
It has been proposed in `Adam: A Method for Stochastic Optimization`_.
Compared to the original version in Apex, the fairseq version casts grads
and params to FP32 internally to support ``--memory-efficient-fp16``.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups.
lr (float, optional): learning rate. (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square. (default: (0.9, 0.999))
eps (float, optional): term added to the denominator to improve
numerical stability. (default: 1e-8)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
amsgrad (boolean, optional): whether to use the AMSGrad variant of this
algorithm from the paper `On the Convergence of Adam and Beyond`_
(default: False) NOT SUPPORTED in FusedAdam!
eps_inside_sqrt (boolean, optional): in the 'update parameters' step,
adds eps to the bias-corrected second moment estimate before
evaluating square root instead of adding it to the square root of
second moment estimate as in the original paper. (default: False)
.. _Adam\: A Method for Stochastic Optimization:
https://arxiv.org/abs/1412.6980
.. _On the Convergence of Adam and Beyond:
https://openreview.net/forum?id=ryQu7f-RZ
"""
def __init__(self, params,
lr=1e-3, bias_correction = True,
betas=(0.9, 0.999), eps=1e-8, eps_inside_sqrt=False,
weight_decay=0., max_grad_norm=0., amsgrad=False):
global fused_adam_cuda
import importlib
fused_adam_cuda = importlib.import_module("fused_adam_cuda")
if amsgrad:
raise RuntimeError('FusedAdam does not support the AMSGrad variant.')
defaults = dict(lr=lr, bias_correction=bias_correction,
betas=betas, eps=eps, weight_decay=weight_decay,
max_grad_norm=max_grad_norm)
super(FusedAdam, self).__init__(params, defaults)
self.eps_mode = 0 if eps_inside_sqrt else 1
@property
def supports_memory_efficient_fp16(self):
return True
def step(self, closure=None, grads=None, scale=1., grad_norms=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
grads (list of tensors, optional): weight gradient to use for the
optimizer update. If gradients have type torch.half, parameters
are expected to be in type torch.float. (default: None)
output params (list of tensors, optional): A reduced precision copy
of the updated weights written out in addition to the regular
updated weights. Have to be of same type as gradients. (default: None)
scale (float, optional): factor to divide gradient tensor values
by before applying to weights. (default: 1)
"""
loss = None
if closure is not None:
loss = closure()
if grads is None:
grads_group = [None]*len(self.param_groups)
# backward compatibility
# assuming a list/generator of parameter means single group
elif isinstance(grads, types.GeneratorType):
grads_group = [grads]
elif type(grads[0])!=list:
grads_group = [grads]
else:
grads_group = grads
if grad_norms is None:
grad_norms = [None]*len(self.param_groups)
for group, grads_this_group, grad_norm in zip(self.param_groups, grads_group, grad_norms):
if grads_this_group is None:
grads_this_group = [None]*len(group['params'])
# compute combined scale factor for this group
combined_scale = scale
if group['max_grad_norm'] > 0:
# norm is in fact norm*scale
clip = ((grad_norm / scale) + 1e-6) / group['max_grad_norm']
if clip > 1:
combined_scale = clip * scale
bias_correction = 1 if group['bias_correction'] else 0
for p, grad in zip(group['params'], grads_this_group):
#note: p.grad should not ever be set for correct operation of mixed precision optimizer that sometimes sends None gradients
if p.grad is None and grad is None:
continue
if grad is None:
grad = p.grad.data
if grad.is_sparse:
raise RuntimeError('FusedAdam does not support sparse gradients, please consider SparseAdam instead')
p_data_fp32 = p.data.float()
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p_data_fp32)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
else:
state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)
exp_avg = state['exp_avg']
exp_avg_sq = state['exp_avg_sq']
beta1, beta2 = group['betas']
state['step'] += 1
out_p = p.data
fused_adam_cuda.adam(p_data_fp32,
out_p,
exp_avg,
exp_avg_sq,
grad,
group['lr'],
beta1,
beta2,
group['eps'],
combined_scale,
state['step'],
self.eps_mode,
bias_correction,
group['weight_decay'])
return loss
| data2vec_vision-main | infoxlm/fairseq/fairseq/optim/adam.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch.optim
from . import FairseqOptimizer, register_optimizer
@register_optimizer('adadelta')
class Adadelta(FairseqOptimizer):
def __init__(self, args, params):
super().__init__(args)
self._optimizer = torch.optim.Adadelta(params, **self.optimizer_config)
@staticmethod
def add_args(parser):
"""Add optimizer-specific arguments to the parser."""
# fmt: off
parser.add_argument('--adadelta-rho', type=float, default=0.9, metavar='RHO',
help='coefficient used for computing a running average of squared gradients')
parser.add_argument('--adadelta-eps', type=float, default=1e-6, metavar='EPS',
help='term added to the denominator to improve numerical stability')
parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='WD',
help='weight decay')
parser.add_argument('--anneal-eps', action='store_true', help='flag to anneal eps')
# fmt: on
@property
def optimizer_config(self):
"""
Return a kwarg dictionary that will be used to override optimizer
args stored in checkpoints. This allows us to load a checkpoint and
resume training using a different set of optimizer args, e.g., with a
different learning rate.
"""
return {
'lr': self.args.lr[0],
'rho': self.args.adadelta_rho,
'eps': self.args.adadelta_eps,
'weight_decay': self.args.weight_decay,
}
| data2vec_vision-main | infoxlm/fairseq/fairseq/optim/adadelta.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import FairseqLRScheduler, register_lr_scheduler
@register_lr_scheduler('fixed')
class FixedSchedule(FairseqLRScheduler):
"""Decay the LR on a fixed schedule."""
def __init__(self, args, optimizer):
super().__init__(args, optimizer)
# set defaults
args.warmup_updates = getattr(args, 'warmup_updates', 0) or 0
self.lr = args.lr[0]
if args.warmup_updates > 0:
self.warmup_factor = 1. / args.warmup_updates
else:
self.warmup_factor = 1
@staticmethod
def add_args(parser):
"""Add arguments to the parser for this LR scheduler."""
# fmt: off
parser.add_argument('--force-anneal', '--fa', type=int, metavar='N',
help='force annealing at specified epoch')
parser.add_argument('--lr-shrink', default=0.1, type=float, metavar='LS',
help='shrink factor for annealing, lr_new = (lr * lr_shrink)')
parser.add_argument('--warmup-updates', default=0, type=int, metavar='N',
help='warmup the learning rate linearly for the first N updates')
# fmt: on
def get_next_lr(self, epoch):
lrs = self.args.lr
if self.args.force_anneal is None or epoch < self.args.force_anneal:
# use fixed LR schedule
next_lr = lrs[min(epoch, len(lrs) - 1)]
else:
# annneal based on lr_shrink
next_lr = lrs[-1] * self.args.lr_shrink ** (epoch + 1 - self.args.force_anneal)
return next_lr
def step(self, epoch, val_loss=None):
"""Update the learning rate at the end of the given epoch."""
super().step(epoch, val_loss)
self.lr = self.get_next_lr(epoch)
self.optimizer.set_lr(self.warmup_factor * self.lr)
return self.optimizer.get_lr()
def step_update(self, num_updates):
"""Update the learning rate after each update."""
if self.args.warmup_updates > 0 and num_updates < self.args.warmup_updates:
self.warmup_factor = (num_updates + 1) / float(self.args.warmup_updates)
self.optimizer.set_lr(self.warmup_factor * self.lr)
return self.optimizer.get_lr()
| data2vec_vision-main | infoxlm/fairseq/fairseq/optim/lr_scheduler/fixed_schedule.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch.optim.lr_scheduler
from . import FairseqLRScheduler, register_lr_scheduler
@register_lr_scheduler('reduce_lr_on_plateau')
class ReduceLROnPlateau(FairseqLRScheduler):
"""
Decay the LR by a factor every time the validation loss plateaus.
Also comes with optional warmup phase, where we linearly increase the learning rate
from some initial learning rate (``--warmup-init-lr``) until the configured
learning rate (``--lr``). Thereafter the lr is adjusted according to original reduce_on_plateau scheme
During warmup::
lrs = torch.linspace(args.warmup_init_lr, args.lr, args.warmup_updates)
lr = lrs[update_num]
"""
def __init__(self, args, optimizer):
super().__init__(args, optimizer)
if len(args.lr) > 1:
raise ValueError(
'Cannot use a fixed learning rate schedule with reduce_lr_on_plateau.'
' Consider --lr-scheduler=fixed instead.'
)
self.lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
self.optimizer.optimizer, patience=0, factor=args.lr_shrink,
threshold=args.lr_threshold)
warmup_end_lr = args.lr[0]
"""if no warm up, sets initial lr to be args.lr[0]"""
if args.warmup_init_lr < 0:
args.warmup_init_lr = 0 if args.warmup_updates > 0 else warmup_end_lr
""" linearly warmup for the first args.warmup_updates"""
if args.warmup_updates > 0:
self.lr_step = (warmup_end_lr - args.warmup_init_lr) / args.warmup_updates
""" this flag is either set from arg when no warm up, or set by step_update() when warmup finishes"""
self.warmup_end = True if args.warmup_updates <= 0 else False
""" initial learning rate"""
"""this self.lr is used only during init and/or warm up period"""
self.lr = args.warmup_init_lr
self.optimizer.set_lr(self.lr)
@staticmethod
def add_args(parser):
"""Add arguments to the parser for this LR scheduler."""
# fmt: off
parser.add_argument('--lr-shrink', default=0.1, type=float, metavar='LS',
help='shrink factor for annealing, lr_new = (lr * lr_shrink)')
parser.add_argument('--lr-threshold', default=1e-4, type=float, metavar='LT',
help='Threshold for measuring the new optimum, \
to only focus on significant changes')
parser.add_argument('--warmup-updates', default=0, type=int, metavar='N',
help='warmup the learning rate linearly for the first N updates')
parser.add_argument('--warmup-init-lr', default=-1, type=float, metavar='LR',
help='initial learning rate during warmup phase; default is args.lr')
# fmt: on
def state_dict(self):
"""Return the LR scheduler state dict."""
return {
'best': self.lr_scheduler.best,
'last_epoch': self.lr_scheduler.last_epoch,
}
def load_state_dict(self, state_dict):
"""Load an LR scheduler state dict."""
self.lr_scheduler.best = state_dict['best']
if 'last_epoch' in state_dict:
self.lr_scheduler.last_epoch = state_dict['last_epoch']
def step(self, epoch, val_loss=None):
"""Update the learning rate at the end of the given epoch if warmup finishes"""
""" otherwise no update of lr on epoch boundaries"""
if val_loss is not None and self.warmup_end is True:
self.lr_scheduler.step(val_loss, epoch)
else:
self.lr_scheduler.last_epoch = epoch
return self.optimizer.get_lr()
def step_update(self, num_updates):
"""Update the learning rate after each update."""
""" if there is warmup"""
if self.args.warmup_updates > 0:
if num_updates <= self.args.warmup_updates:
self.lr = self.args.warmup_init_lr + num_updates*self.lr_step
self.optimizer.set_lr(self.lr)
else:
if self.warmup_end is False:
self.warmup_end = True
"""else do nothing """
return self.optimizer.get_lr()
| data2vec_vision-main | infoxlm/fairseq/fairseq/optim/lr_scheduler/reduce_lr_on_plateau.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import importlib
import os
from fairseq import registry
from fairseq.optim.lr_scheduler.fairseq_lr_scheduler import FairseqLRScheduler
build_lr_scheduler, register_lr_scheduler, LR_SCHEDULER_REGISTRY = registry.setup_registry(
'--lr-scheduler',
base_class=FairseqLRScheduler,
default='fixed',
)
# automatically import any Python files in the optim/lr_scheduler/ directory
for file in os.listdir(os.path.dirname(__file__)):
if file.endswith('.py') and not file.startswith('_'):
module = file[:file.find('.py')]
importlib.import_module('fairseq.optim.lr_scheduler.' + module)
| data2vec_vision-main | infoxlm/fairseq/fairseq/optim/lr_scheduler/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import FairseqLRScheduler, register_lr_scheduler
@register_lr_scheduler('polynomial_decay')
class PolynomialDecaySchedule(FairseqLRScheduler):
"""Decay the LR on a fixed schedule."""
def __init__(self, args, optimizer):
super().__init__(args, optimizer)
# set defaults
args.warmup_updates = getattr(args, 'warmup_updates', 0) or 0
self.lr = args.lr[0]
if args.warmup_updates > 0:
self.warmup_factor = 1. / args.warmup_updates
else:
self.warmup_factor = 1
self.end_learning_rate = args.end_learning_rate
self.total_num_update = args.total_num_update
self.power = args.power
self.optimizer.set_lr(self.warmup_factor * self.lr)
@staticmethod
def add_args(parser):
"""Add arguments to the parser for this LR scheduler."""
parser.add_argument('--force-anneal', '--fa', type=int, metavar='N',
help='force annealing at specified epoch')
parser.add_argument('--warmup-updates', default=0, type=int, metavar='N',
help='warmup the learning rate linearly for the first N updates')
parser.add_argument('--end-learning-rate', default=0.0, type=float)
parser.add_argument('--power', default=1.0, type=float)
parser.add_argument('--total-num-update', default=1000000, type=int)
def get_next_lr(self, epoch):
lrs = self.args.lr
if self.args.force_anneal is None or epoch < self.args.force_anneal:
# use fixed LR schedule
next_lr = lrs[min(epoch, len(lrs) - 1)]
else:
# annneal based on lr_shrink
next_lr = self.optimizer.get_lr()
return next_lr
def step(self, epoch, val_loss=None):
"""Update the learning rate at the end of the given epoch."""
super().step(epoch, val_loss)
self.lr = self.get_next_lr(epoch)
self.optimizer.set_lr(self.warmup_factor * self.lr)
return self.optimizer.get_lr()
def step_update(self, num_updates):
"""Update the learning rate after each update."""
if self.args.warmup_updates > 0 and num_updates <= self.args.warmup_updates:
self.warmup_factor = num_updates / float(self.args.warmup_updates)
lr = self.warmup_factor * self.lr
elif num_updates >= self.total_num_update:
lr = self.end_learning_rate
else:
warmup = self.args.warmup_updates
lr_range = self.lr - self.end_learning_rate
pct_remaining = 1 - (num_updates - warmup) / (self.total_num_update - warmup)
lr = lr_range * pct_remaining ** (self.power) + self.end_learning_rate
self.optimizer.set_lr(lr)
return self.optimizer.get_lr()
| data2vec_vision-main | infoxlm/fairseq/fairseq/optim/lr_scheduler/polynomial_decay_schedule.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import FairseqLRScheduler, register_lr_scheduler
@register_lr_scheduler('inverse_sqrt')
class InverseSquareRootSchedule(FairseqLRScheduler):
"""Decay the LR based on the inverse square root of the update number.
We also support a warmup phase where we linearly increase the learning rate
from some initial learning rate (``--warmup-init-lr``) until the configured
learning rate (``--lr``). Thereafter we decay proportional to the number of
updates, with a decay factor set to align with the configured learning rate.
During warmup::
lrs = torch.linspace(args.warmup_init_lr, args.lr, args.warmup_updates)
lr = lrs[update_num]
After warmup::
decay_factor = args.lr * sqrt(args.warmup_updates)
lr = decay_factor / sqrt(update_num)
"""
def __init__(self, args, optimizer):
super().__init__(args, optimizer)
if len(args.lr) > 1:
raise ValueError(
'Cannot use a fixed learning rate schedule with inverse_sqrt.'
' Consider --lr-scheduler=fixed instead.'
)
warmup_end_lr = args.lr[0]
if args.warmup_init_lr < 0:
args.warmup_init_lr = 0 if args.warmup_updates > 0 else warmup_end_lr
# linearly warmup for the first args.warmup_updates
self.lr_step = (warmup_end_lr - args.warmup_init_lr) / args.warmup_updates
# then, decay prop. to the inverse square root of the update number
self.decay_factor = warmup_end_lr * args.warmup_updates**0.5
# initial learning rate
self.lr = args.warmup_init_lr
self.optimizer.set_lr(self.lr)
@staticmethod
def add_args(parser):
"""Add arguments to the parser for this LR scheduler."""
# fmt: off
parser.add_argument('--warmup-updates', default=4000, type=int, metavar='N',
help='warmup the learning rate linearly for the first N updates')
parser.add_argument('--warmup-init-lr', default=-1, type=float, metavar='LR',
help='initial learning rate during warmup phase; default is args.lr')
# fmt: on
def step(self, epoch, val_loss=None):
"""Update the learning rate at the end of the given epoch."""
super().step(epoch, val_loss)
# we don't change the learning rate at epoch boundaries
return self.optimizer.get_lr()
def step_update(self, num_updates):
"""Update the learning rate after each update."""
if num_updates < self.args.warmup_updates:
self.lr = self.args.warmup_init_lr + num_updates*self.lr_step
else:
self.lr = self.decay_factor * num_updates**-0.5
self.optimizer.set_lr(self.lr)
return self.lr
| data2vec_vision-main | infoxlm/fairseq/fairseq/optim/lr_scheduler/inverse_square_root_schedule.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .. import FairseqOptimizer
class FairseqLRScheduler(object):
def __init__(self, args, optimizer):
super().__init__()
if not isinstance(optimizer, FairseqOptimizer):
raise ValueError('optimizer must be an instance of FairseqOptimizer')
self.args = args
self.optimizer = optimizer
self.best = None
@staticmethod
def add_args(parser):
"""Add arguments to the parser for this LR scheduler."""
pass
def state_dict(self):
"""Return the LR scheduler state dict."""
return {'best': self.best}
def load_state_dict(self, state_dict):
"""Load an LR scheduler state dict."""
self.best = state_dict['best']
def step(self, epoch, val_loss=None):
"""Update the learning rate at the end of the given epoch."""
if val_loss is not None:
if self.best is None:
self.best = val_loss
else:
self.best = min(self.best, val_loss)
def step_update(self, num_updates):
"""Update the learning rate after each update."""
return self.optimizer.get_lr()
| data2vec_vision-main | infoxlm/fairseq/fairseq/optim/lr_scheduler/fairseq_lr_scheduler.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import FairseqLRScheduler, register_lr_scheduler
import math
@register_lr_scheduler('tri_stage')
class TriStageLRSchedule(FairseqLRScheduler):
"""Tristage learning rate schedulr
Implement the learning rate scheduler in https://arxiv.org/pdf/1904.08779.pdf
Similar to inverse_squre_root scheduler, but tri_stage learning rate employs
three stages LR scheduling:
- warmup stage, starting from `lr` * `init_lr_scale`, linearly
increased to `lr` in `warmup_steps` iterations
- hold stage, after `warmup_steps`, keep the LR as `lr` for `hold_steps`
iterations
- decay stage, after hold stage, decay LR exponetially to
`lr` * `final_lr_scale` in `decay_steps`;
after that LR is keep as `final_lr_scale` * `lr`
During warmup::
init_lr = args.init_lr_scale * args.lr
lrs = torch.linspace(init_lr, args.lr, args.warmup_steps)
lr = lrs[update_num]
During hold::
lr = args.lr
During decay::
decay_factor = - math.log(args.final_lr_scale) / args.decay_steps
lr = args.lr * exp(- (update_num - warmup_steps - decay_steps) * decay_factor)
After that::
lr = args.lr * args.final_lr_scale
"""
def __init__(self, args, optimizer):
super().__init__(args, optimizer)
if len(args.lr) > 1:
raise ValueError(
'Cannot use a fixed learning rate schedule with tri-stage lr.'
' Consider --lr-scheduler=fixed instead.'
)
# calculate LR at each point
self.peak_lr = args.lr[0]
self.init_lr = args.init_lr_scale * args.lr[0]
self.final_lr = args.final_lr_scale * args.lr[0]
# remember the steps at each stage
self.warmup_steps = args.warmup_steps
self.hold_steps = args.hold_steps
self.decay_steps = args.decay_steps
self.warmup_rate = (self.peak_lr - self.init_lr) / self.warmup_steps
self.decay_factor = -math.log(args.final_lr_scale) / args.decay_steps
# initial learning rate
self.lr = self.init_lr
self.optimizer.set_lr(self.lr)
@staticmethod
def add_args(parser):
"""Add arguments to the parser for this LR scheduler."""
# fmt: off
parser.add_argument(
'--warmup-steps',
default=4000,
type=int,
metavar='N',
help='warmup the learning rate linearly for the first N updates'
)
parser.add_argument(
'--hold-steps',
default=20000,
type=int,
metavar='N',
help='steps in hold stage.'
)
parser.add_argument(
'--decay-steps',
default=60000,
type=int,
metavar='N',
help='steps in decay stages'
)
parser.add_argument(
'--init-lr-scale',
default=0.01,
type=float,
help="""
initial learning rate scale during warmup phase; default is 0.01""")
parser.add_argument(
'--final-lr-scale',
default=0.01,
type=float,
help="final learning rate scale; default to 0.01"
)
# fmt: on
def _decide_stage(self, update_step):
"""
return stage, and the corresponding steps within the current stage
"""
if update_step < self.warmup_steps:
# warmup state
return 0, update_step
offset = self.warmup_steps
if update_step < offset + self.hold_steps:
# hold stage
return 1, update_step - offset
offset += self.hold_steps
if update_step <= offset + self.decay_steps:
# decay stage
return 2, update_step - offset
offset += self.decay_steps
# still here ? constant lr stage
return 3, update_step - offset
def step(self, epoch, val_loss=None):
"""Update the learning rate at the end of the given epoch."""
super().step(epoch, val_loss)
# we don't change the learning rate at epoch boundaries
return self.optimizer.get_lr()
def step_update(self, num_updates):
"""Update the learning rate after each update."""
stage, steps_in_stage = self._decide_stage(num_updates)
if stage == 0:
self.lr = self.init_lr + self.warmup_rate * steps_in_stage
elif stage == 1:
self.lr = self.peak_lr
elif stage == 2:
self.lr = self.peak_lr * math.exp(-self.decay_factor * steps_in_stage)
elif stage == 3:
self.lr = self.final_lr
else:
raise ValueError("Undefined stage")
self.optimizer.set_lr(self.lr)
return self.lr
| data2vec_vision-main | infoxlm/fairseq/fairseq/optim/lr_scheduler/tri_stage_lr_scheduler.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
from . import FairseqLRScheduler, register_lr_scheduler
@register_lr_scheduler('cosine')
class CosineSchedule(FairseqLRScheduler):
"""Assign LR based on a cyclical schedule that follows the cosine function.
See https://arxiv.org/pdf/1608.03983.pdf for details.
We also support a warmup phase where we linearly increase the learning rate
from some initial learning rate (``--warmup-init-lr``) until the configured
max learning rate (``--max-lr``).
During warmup::
lrs = torch.linspace(args.warmup_init_lr, args.lr, args.warmup_updates)
lr = lrs[update_num]
After warmup::
lr = lr_min + 0.5*(lr_max - lr_min)*(1 + cos(t_curr / t_i))
where ``t_curr`` is current percentage of updates within the current period
range and ``t_i`` is the current period range, which is scaled by ``t_mul``
after every iteration.
"""
def __init__(self, args, optimizer):
super().__init__(args, optimizer)
if len(args.lr) > 1:
raise ValueError(
'Cannot use a fixed learning rate schedule with cosine.'
' Consider --lr-scheduler=fixed instead.'
)
warmup_end_lr = args.max_lr
if args.warmup_init_lr < 0:
args.warmup_init_lr = args.lr[0]
self.min_lr = args.lr[0]
self.max_lr = args.max_lr
assert self.max_lr > self.min_lr, 'max_lr must be more than lr'
self.t_mult = args.t_mult
self.period = args.lr_period_updates
if self.period <= 0:
assert args.max_update >= 0, 'Either --max_update or --lr-period-updates must be set'
self.period = args.max_update - args.warmup_updates
if args.warmup_updates > 0:
# linearly warmup for the first args.warmup_updates
self.lr_step = (warmup_end_lr - args.warmup_init_lr) / args.warmup_updates
else:
self.lr_step = 1
self.warmup_updates = args.warmup_updates
self.lr_shrink = args.lr_shrink
# initial learning rate
self.lr = args.warmup_init_lr
self.optimizer.set_lr(self.lr)
@staticmethod
def add_args(parser):
"""Add arguments to the parser for this LR scheduler."""
# fmt: off
parser.add_argument('--warmup-updates', default=0, type=int, metavar='N',
help='warmup the learning rate linearly for the first N updates')
parser.add_argument('--warmup-init-lr', default=-1, type=float, metavar='LR',
help='initial learning rate during warmup phase; default is args.lr')
parser.add_argument('--max-lr', type=float, metavar='LR',
help='max learning rate, must be more than args.lr')
parser.add_argument('--t-mult', default=1, type=float, metavar='LR',
help='factor to grow the length of each period')
parser.add_argument('--lr-period-updates', default=-1, type=float, metavar='LR',
help='initial number of updates per period')
parser.add_argument('--lr-shrink', default=0.1, type=float, metavar='LS',
help='shrink factor for annealing')
# fmt: on
def step(self, epoch, val_loss=None):
"""Update the learning rate at the end of the given epoch."""
super().step(epoch, val_loss)
# we don't change the learning rate at epoch boundaries
return self.optimizer.get_lr()
def step_update(self, num_updates):
"""Update the learning rate after each update."""
if num_updates < self.args.warmup_updates:
self.lr = self.args.warmup_init_lr + num_updates * self.lr_step
else:
curr_updates = num_updates - self.args.warmup_updates
if self.t_mult != 1:
i = math.floor(math.log(1 - curr_updates / self.period * (1 - self.t_mult), self.t_mult))
t_i = self.t_mult ** i * self.period
t_curr = curr_updates - (1 - self.t_mult ** i) / (1 - self.t_mult) * self.period
else:
i = math.floor(curr_updates / self.period)
t_i = self.period
t_curr = curr_updates - (self.period * i)
lr_shrink = self.lr_shrink ** i
min_lr = self.min_lr * lr_shrink
max_lr = self.max_lr * lr_shrink
self.lr = min_lr + 0.5 * (max_lr - min_lr) * (1 + math.cos(math.pi * t_curr / t_i))
self.optimizer.set_lr(self.lr)
return self.lr
| data2vec_vision-main | infoxlm/fairseq/fairseq/optim/lr_scheduler/cosine_lr_scheduler.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
from . import FairseqLRScheduler, register_lr_scheduler
@register_lr_scheduler('triangular')
class TriangularSchedule(FairseqLRScheduler):
"""Assign LR based on a triangular cyclical schedule.
See https://arxiv.org/pdf/1506.01186.pdf for details.
"""
def __init__(self, args, optimizer):
super().__init__(args, optimizer)
if len(args.lr) > 1:
raise ValueError(
'Cannot use a fixed learning rate schedule with triangular.'
' Consider --lr-scheduler=fixed instead.'
)
lr = args.lr[0]
assert args.max_lr > lr, 'max_lr must be more than lr'
self.min_lr = lr
self.max_lr = args.max_lr
self.stepsize = args.lr_period_updates // 2
self.lr_shrink = args.lr_shrink
self.shrink_min = args.shrink_min
# initial learning rate
self.lr = self.min_lr
self.optimizer.set_lr(self.lr)
@staticmethod
def add_args(parser):
"""Add arguments to the parser for this LR scheduler."""
# fmt: off
parser.add_argument('--max-lr', required=True, type=float, metavar='LR',
help='max learning rate, must be more than args.lr')
parser.add_argument('--lr-period-updates', default=5000, type=float, metavar='LR',
help='initial number of updates per period (cycle length)')
parser.add_argument('--lr-shrink', default=0.1, type=float, metavar='LS',
help='shrink factor for annealing')
parser.add_argument('--shrink-min', action='store_true',
help='if set, also shrinks min lr')
# fmt: on
def step(self, epoch, val_loss=None):
"""Update the learning rate at the end of the given epoch."""
super().step(epoch, val_loss)
# we don't change the learning rate at epoch boundaries
return self.optimizer.get_lr()
def step_update(self, num_updates):
"""Update the learning rate after each update."""
cycle = math.floor(num_updates / (2 * self.stepsize))
lr_shrink = self.lr_shrink ** cycle
max_lr = self.max_lr * lr_shrink
if self.shrink_min:
min_lr = self.min_lr * lr_shrink
else:
min_lr = self.min_lr
x = abs(num_updates / self.stepsize - 2 * (cycle + 1) + 1)
self.lr = min_lr + (max_lr - min_lr) * max(0, (1 - x))
self.optimizer.set_lr(self.lr)
return self.lr
| data2vec_vision-main | infoxlm/fairseq/fairseq/optim/lr_scheduler/triangular_lr_scheduler.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import List, Optional
import torch
from torch import Tensor
@torch.jit.script
def script_skip_tensor_list(x: List[Tensor], mask):
res = [xi[mask] if xi.size(0) == mask.size(0) else xi[:, mask] for xi in x]
outputs = []
for i, t in enumerate(res):
if t.numel() != 0:
outputs.append(t)
else:
outputs.append(x[i])
return outputs
@torch.jit.script
def script_skip_tensor(x: Tensor, mask):
# None case
if x.size(0) == 0:
return x
res = x[mask] if x.size(0) == mask.size(0) else x[:, mask]
if res.numel() == 0:
return x
else:
return res
@torch.jit.script
def expand_2d_or_3d_tensor(x, trg_dim: int, padding_idx: int):
"""
Expand 2D/3D tensor on dim=1
"""
if x is None:
return None
assert x.dim() == 2 or x.dim() == 3
assert trg_dim >= x.size(1), (trg_dim, x.size())
if trg_dim == x.size(1):
return x
dims = [x.size(0), trg_dim - x.size(1)]
if x.dim() == 3:
dims.append(x.size(2))
x = torch.cat([x, torch.zeros(dims).to(x).fill_(padding_idx)], 1)
return x
@torch.jit.script
def coalesce(x: Optional[Tensor], y: Tensor) -> Tensor:
return x if x is not None else y
@torch.jit.script
def fill_tensors(x: Optional[Tensor], mask, y: Optional[Tensor], padding_idx: int) -> Optional[Tensor]:
"""
Filling tensor x with y at masked positions (dim=0).
"""
if x is None or x.size()[0] == 0 or y is None:
return x
assert x.dim() == y.dim() and mask.size(0) == x.size(0)
assert x.dim() == 2 or (x.dim() == 3 and x.size(2) == y.size(2))
n_selected = mask.sum()
if n_selected == 0:
return x
assert n_selected == y.size(0)
if n_selected == x.size(0):
return y
if x.size(1) < y.size(1):
x = expand_2d_or_3d_tensor(x, y.size(1), padding_idx)
x[mask] = y
elif x.size(1) > y.size(1):
x[mask] = torch.tensor(padding_idx).type_as(x)
if x.dim() == 2:
x[mask, :y.size(1)] = y
else:
x[mask, :y.size(1), :] = y
else:
x[mask] = y
return x
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/model_utils.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Base classes for various fairseq models.
"""
from typing import Dict, List, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.checkpoint_utils import prune_state_dict
from fairseq.data import Dictionary
from fairseq.models import FairseqDecoder, FairseqEncoder
class BaseFairseqModel(nn.Module):
"""Base class for fairseq models."""
def __init__(self):
super().__init__()
self._is_generation_fast = False
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
pass
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
raise NotImplementedError('Model must implement the build_model method')
def get_targets(self, sample, net_output):
"""Get targets from either the sample or the net's output."""
return sample['target']
def get_normalized_probs(self, net_output, log_probs, sample=None):
"""Get normalized probabilities (or log probs) from a net's output."""
if hasattr(self, 'decoder'):
return self.decoder.get_normalized_probs(net_output, log_probs, sample)
elif torch.is_tensor(net_output):
logits = net_output.float()
if log_probs:
return F.log_softmax(logits, dim=-1)
else:
return F.softmax(logits, dim=-1)
raise NotImplementedError
def extract_features(self, *args, **kwargs):
"""Similar to *forward* but only return features."""
return self(*args, **kwargs)
def max_positions(self):
"""Maximum length supported by the model."""
return None
def load_state_dict(self, state_dict, strict=True, args=None):
"""Copies parameters and buffers from *state_dict* into this module and
its descendants.
Overrides the method in :class:`nn.Module`. Compared with that method
this additionally "upgrades" *state_dicts* from old checkpoints.
"""
self.upgrade_state_dict(state_dict)
new_state_dict = prune_state_dict(state_dict, args)
return super().load_state_dict(new_state_dict, strict)
def upgrade_state_dict(self, state_dict):
"""Upgrade old state dicts to work with newer code."""
self.upgrade_state_dict_named(state_dict, '')
def upgrade_state_dict_named(self, state_dict, name):
"""Upgrade old state dicts to work with newer code.
Args:
state_dict (dict): state dictionary to upgrade, in place
name (str): the state dict key corresponding to the current module
"""
assert state_dict is not None
def do_upgrade(m, prefix):
if len(prefix) > 0:
prefix += '.'
for n, c in m.named_children():
name = prefix + n
if hasattr(c, 'upgrade_state_dict_named'):
c.upgrade_state_dict_named(state_dict, name)
elif hasattr(c, 'upgrade_state_dict'):
c.upgrade_state_dict(state_dict)
do_upgrade(c, name)
do_upgrade(self, name)
def make_generation_fast_(self, **kwargs):
"""Optimize model for faster generation."""
if self._is_generation_fast:
return # only apply once
self._is_generation_fast = True
# remove weight norm from all modules in the network
def apply_remove_weight_norm(module):
try:
nn.utils.remove_weight_norm(module)
except ValueError: # this module didn't have weight norm
return
self.apply(apply_remove_weight_norm)
seen = set()
def apply_make_generation_fast_(module):
if module != self and hasattr(module, 'make_generation_fast_') \
and module not in seen:
seen.add(module)
module.make_generation_fast_(**kwargs)
self.apply(apply_make_generation_fast_)
def train(mode=True):
if mode:
raise RuntimeError('cannot train after make_generation_fast')
# this model should no longer be used for training
self.eval()
self.train = train
def prepare_for_onnx_export_(self, **kwargs):
"""Make model exportable via ONNX trace."""
seen = set()
def apply_prepare_for_onnx_export_(module):
if module != self and hasattr(module, 'prepare_for_onnx_export_') \
and module not in seen:
seen.add(module)
module.prepare_for_onnx_export_(**kwargs)
self.apply(apply_prepare_for_onnx_export_)
@classmethod
def from_pretrained(cls, model_name_or_path, checkpoint_file='model.pt', data_name_or_path='.', **kwargs):
"""
Load a :class:`~fairseq.models.FairseqModel` from a pre-trained model
file. Downloads and caches the pre-trained model file if needed.
The base implementation returns a
:class:`~fairseq.hub_utils.GeneratorHubInterface`, which can be used to
generate translations or sample from language models. The underlying
:class:`~fairseq.models.FairseqModel` can be accessed via the
*generator.models* attribute.
Other models may override this to implement custom hub interfaces.
Args:
model_name_or_path (str): either the name of a pre-trained model to
load or a path/URL to a pre-trained model state dict
checkpoint_file (str, optional): colon-separated list of checkpoint
files in the model archive to ensemble (default: 'model.pt')
data_name_or_path (str, optional): point args.data to the archive
at the given path/URL. Can start with '.' or './' to reuse the
model archive path.
"""
from fairseq import hub_utils
x = hub_utils.from_pretrained(
model_name_or_path,
checkpoint_file,
data_name_or_path,
archive_map=cls.hub_models(),
**kwargs,
)
print(x['args'])
return hub_utils.GeneratorHubInterface(x['args'], x['task'], x['models'])
@classmethod
def hub_models(cls):
return {}
class FairseqEncoderDecoderModel(BaseFairseqModel):
"""Base class for encoder-decoder models.
Args:
encoder (FairseqEncoder): the encoder
decoder (FairseqDecoder): the decoder
"""
def __init__(self, encoder, decoder):
super().__init__()
self.encoder = encoder
self.decoder = decoder
assert isinstance(self.encoder, FairseqEncoder)
assert isinstance(self.decoder, FairseqDecoder)
def forward(self, src_tokens, src_lengths, prev_output_tokens, **kwargs):
"""
Run the forward pass for an encoder-decoder model.
First feed a batch of source tokens through the encoder. Then, feed the
encoder output and previous decoder outputs (i.e., teacher forcing) to
the decoder to produce the next outputs::
encoder_out = self.encoder(src_tokens, src_lengths)
return self.decoder(prev_output_tokens, encoder_out)
Args:
src_tokens (LongTensor): tokens in the source language of shape
`(batch, src_len)`
src_lengths (LongTensor): source sentence lengths of shape `(batch)`
prev_output_tokens (LongTensor): previous decoder outputs of shape
`(batch, tgt_len)`, for teacher forcing
Returns:
tuple:
- the decoder's output of shape `(batch, tgt_len, vocab)`
- a dictionary with any model-specific outputs
"""
encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
decoder_out = self.decoder(prev_output_tokens, encoder_out=encoder_out, **kwargs)
return decoder_out
def forward_decoder(self, prev_output_tokens, **kwargs):
return self.decoder(prev_output_tokens, **kwargs)
def extract_features(self, src_tokens, src_lengths, prev_output_tokens, **kwargs):
"""
Similar to *forward* but only return features.
Returns:
tuple:
- the decoder's features of shape `(batch, tgt_len, embed_dim)`
- a dictionary with any model-specific outputs
"""
encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
features = self.decoder.extract_features(prev_output_tokens, encoder_out=encoder_out, **kwargs)
return features
def output_layer(self, features, **kwargs):
"""Project features to the default output size (typically vocabulary size)."""
return self.decoder.output_layer(features, **kwargs)
def max_positions(self):
"""Maximum length supported by the model."""
return (self.encoder.max_positions(), self.decoder.max_positions())
def max_decoder_positions(self):
"""Maximum length supported by the decoder."""
return self.decoder.max_positions()
class FairseqModel(FairseqEncoderDecoderModel):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
utils.deprecation_warning(
'FairseqModel is deprecated, please use FairseqEncoderDecoderModel '
'or BaseFairseqModel instead',
stacklevel=4,
)
class FairseqMultiModel(BaseFairseqModel):
"""Base class for combining multiple encoder-decoder models."""
def __init__(self, encoders, decoders):
super().__init__()
assert encoders.keys() == decoders.keys()
self.keys = list(encoders.keys())
for key in self.keys:
assert isinstance(encoders[key], FairseqEncoder)
assert isinstance(decoders[key], FairseqDecoder)
self.models = nn.ModuleDict({
key: FairseqModel(encoders[key], decoders[key])
for key in self.keys
})
@staticmethod
def build_shared_embeddings(
dicts: Dict[str, Dictionary],
langs: List[str],
embed_dim: int,
build_embedding: callable,
pretrained_embed_path: Optional[str] = None,
):
"""
Helper function to build shared embeddings for a set of languages after
checking that all dicts corresponding to those languages are equivalent.
Args:
dicts: Dict of lang_id to its corresponding Dictionary
langs: languages that we want to share embeddings for
embed_dim: embedding dimension
build_embedding: callable function to actually build the embedding
pretrained_embed_path: Optional path to load pretrained embeddings
"""
shared_dict = dicts[langs[0]]
if any(dicts[lang] != shared_dict for lang in langs):
raise ValueError(
'--share-*-embeddings requires a joined dictionary: '
'--share-encoder-embeddings requires a joined source '
'dictionary, --share-decoder-embeddings requires a joined '
'target dictionary, and --share-all-embeddings requires a '
'joint source + target dictionary.'
)
return build_embedding(
shared_dict, embed_dim, pretrained_embed_path
)
def forward(self, src_tokens, src_lengths, prev_output_tokens, **kwargs):
decoder_outs = {}
for key in self.keys:
encoder_out = self.models[key].encoder(src_tokens, src_lengths, **kwargs)
decoder_outs[key] = self.models[key].decoder(
prev_output_tokens, encoder_out, **kwargs,
)
return decoder_outs
def max_positions(self):
"""Maximum length supported by the model."""
return {
key: (self.models[key].encoder.max_positions(), self.models[key].decoder.max_positions())
for key in self.keys
}
def max_decoder_positions(self):
"""Maximum length supported by the decoder."""
return min(model.decoder.max_positions() for model in self.models.values())
@property
def encoder(self):
return self.models[self.keys[0]].encoder
@property
def decoder(self):
return self.models[self.keys[0]].decoder
class FairseqLanguageModel(BaseFairseqModel):
"""Base class for decoder-only models.
Args:
decoder (FairseqDecoder): the decoder
"""
def __init__(self, decoder):
super().__init__()
self.decoder = decoder
assert isinstance(self.decoder, FairseqDecoder)
def forward(self, src_tokens, **kwargs):
"""
Run the forward pass for a decoder-only model.
Feeds a batch of tokens through the decoder to predict the next tokens.
Args:
src_tokens (LongTensor): tokens on which to condition the decoder,
of shape `(batch, tgt_len)`
src_lengths (LongTensor): source sentence lengths of shape `(batch)`
Returns:
tuple:
- the decoder's output of shape `(batch, seq_len, vocab)`
- a dictionary with any model-specific outputs
"""
return self.decoder(src_tokens, **kwargs)
def forward_decoder(self, prev_output_tokens, **kwargs):
return self.decoder(prev_output_tokens, **kwargs)
def extract_features(self, src_tokens, **kwargs):
"""
Similar to *forward* but only return features.
Returns:
tuple:
- the decoder's features of shape `(batch, seq_len, embed_dim)`
- a dictionary with any model-specific outputs
"""
return self.decoder.extract_features(src_tokens, **kwargs)
def output_layer(self, features, **kwargs):
"""Project features to the default output size (typically vocabulary size)."""
return self.decoder.output_layer(features, **kwargs)
def max_positions(self):
"""Maximum length supported by the model."""
return self.decoder.max_positions()
def max_decoder_positions(self):
"""Maximum length supported by the decoder."""
return self.decoder.max_positions()
@property
def supported_targets(self):
return {'future'}
class FairseqEncoderModel(BaseFairseqModel):
"""Base class for encoder-only models.
Args:
encoder (FairseqEncoder): the encoder
"""
def __init__(self, encoder):
super().__init__()
self.encoder = encoder
assert isinstance(self.encoder, FairseqEncoder)
def forward(self, src_tokens, src_lengths, **kwargs):
"""
Run the forward pass for a encoder-only model.
Feeds a batch of tokens through the encoder to generate features.
Args:
src_tokens (LongTensor): input tokens of shape `(batch, src_len)`
src_lengths (LongTensor): source sentence lengths of shape `(batch)`
Returns:
the encoder's output, typically of shape `(batch, src_len, features)`
"""
return self.encoder(src_tokens, src_lengths, **kwargs)
def get_normalized_probs(self, net_output, log_probs, sample=None):
"""Get normalized probabilities (or log probs) from a net's output."""
encoder_out = net_output['encoder_out']
if torch.is_tensor(encoder_out):
logits = encoder_out.float()
if log_probs:
return F.log_softmax(logits, dim=-1)
else:
return F.softmax(logits, dim=-1)
raise NotImplementedError
def max_positions(self):
"""Maximum length supported by the model."""
return self.encoder.max_positions()
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/fairseq_model.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch.nn as nn
class FairseqEncoder(nn.Module):
"""Base class for encoders."""
def __init__(self, dictionary):
super().__init__()
self.dictionary = dictionary
def forward(self, src_tokens, src_lengths=None, **kwargs):
"""
Args:
src_tokens (LongTensor): tokens in the source language of shape
`(batch, src_len)`
src_lengths (LongTensor): lengths of each source sentence of shape
`(batch)`
"""
raise NotImplementedError
def reorder_encoder_out(self, encoder_out, new_order):
"""
Reorder encoder output according to `new_order`.
Args:
encoder_out: output from the ``forward()`` method
new_order (LongTensor): desired order
Returns:
`encoder_out` rearranged according to `new_order`
"""
raise NotImplementedError
def max_positions(self):
"""Maximum input length supported by the encoder."""
return 1e6 # an arbitrary large number
def upgrade_state_dict(self, state_dict):
"""Upgrade a (possibly old) state dict for new versions of fairseq."""
return state_dict
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/fairseq_encoder.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import checkpoint_utils
from fairseq.models import (
CompositeEncoder,
FairseqDecoder,
FairseqEncoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
DownsampledMultiHeadAttention,
GradMultiply,
LayerNorm,
LearnedPositionalEmbedding,
LinearizedConvolution,
)
@register_model('fconv_self_att')
class FConvModelSelfAtt(FairseqEncoderDecoderModel):
@classmethod
def hub_models(cls):
return {
'conv.stories.pretrained': {
'path': 'https://dl.fbaipublicfiles.com/fairseq/models/stories_checkpoint.tar.gz',
'checkpoint_file': 'pretrained_checkpoint.pt',
'tokenizer': 'nltk',
},
'conv.stories': {
'path': 'https://dl.fbaipublicfiles.com/fairseq/models/stories_checkpoint.tar.gz',
'checkpoint_file': 'fusion_checkpoint.pt',
'tokenizer': 'nltk',
'pretrained': 'True',
'pretrained_checkpoint': './pretrained_checkpoint.pt',
},
# Test set containing dictionaries
'data.stories': 'https://dl.fbaipublicfiles.com/fairseq/data/stories_test.tar.bz2',
}
def __init__(self, encoder, decoder, pretrained_encoder=None):
super().__init__(encoder, decoder)
self.encoder.num_attention_layers = sum(layer is not None for layer in decoder.attention)
self.pretrained_encoder = pretrained_encoder
if self.pretrained_encoder is None:
encoders = {'encoder': encoder}
else:
encoders = {'encoder': encoder, 'pretrained': self.pretrained_encoder}
# for fusion model, CompositeEncoder contains both pretrained and training encoders
# these are forwarded and then combined in the decoder
self.encoder = CompositeEncoder(encoders)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
# fmt: off
parser.add_argument('--dropout', type=float, metavar='D',
help='dropout probability')
parser.add_argument('--encoder-embed-dim', type=int, metavar='N',
help='encoder embedding dimension')
parser.add_argument('--encoder-layers', type=str, metavar='EXPR',
help='encoder layers [(dim, kernel_size), ...]')
parser.add_argument('--decoder-embed-dim', type=int, metavar='N',
help='decoder embedding dimension')
parser.add_argument('--decoder-layers', type=str, metavar='EXPR',
help='decoder layers [(dim, kernel_size), ...]')
parser.add_argument('--decoder-out-embed-dim', type=int, metavar='N',
help='decoder output embedding dimension')
parser.add_argument('--decoder-attention', type=str, metavar='EXPR',
help='decoder attention [True, ...]')
parser.add_argument('--self-attention', type=str, metavar='EXPR',
help='decoder self-attention layers, ex: [True] + [False]*5')
parser.add_argument('--multihead-attention-nheads', type=int,
help='Number of heads to use in attention')
parser.add_argument('--multihead-self-attention-nheads', type=int,
help='Number of heads to use in self-attention')
parser.add_argument('--encoder-attention', type=str, metavar='EXPR',
help='encoder attention [True, ...]')
parser.add_argument('--encoder-attention-nheads', type=int,
help='Number of heads to use in encoder attention')
parser.add_argument('--project-input', type=str, metavar='EXPR',
help='Use projections in self-attention [True, ...]')
parser.add_argument('--gated-attention', type=str, metavar='EXPR',
help='Use GLU layers in self-attention projections [True, ...]')
parser.add_argument('--downsample', type=str, metavar='EXPR',
help='Use downsampling in self-attention [True, ...]')
parser.add_argument('--pretrained-checkpoint', metavar='DIR',
help='path to load checkpoint from pretrained model')
parser.add_argument('--pretrained', type=str, metavar='EXPR',
help='use pretrained model when training [True, ...]')
# fmt: on
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
trained_encoder, trained_decoder = None, None
pretrained = eval(args.pretrained)
if pretrained:
print("| loading pretrained model")
if not os.path.exists(args.pretrained_checkpoint):
new_pretrained_checkpoint = os.path.join(args.data, args.pretrained_checkpoint)
if os.path.exists(new_pretrained_checkpoint):
args.pretrained_checkpoint = new_pretrained_checkpoint
trained_model = checkpoint_utils.load_model_ensemble(
filenames=[args.pretrained_checkpoint],
task=task,
)[0][0]
trained_decoder = list(trained_model.children())[1]
trained_encoder = list(trained_model.children())[0]
# freeze pretrained model
for param in trained_decoder.parameters():
param.requires_grad = False
for param in trained_encoder.parameters():
param.requires_grad = False
encoder = FConvEncoder(
task.source_dictionary,
embed_dim=args.encoder_embed_dim,
convolutions=eval(args.encoder_layers),
dropout=args.dropout,
max_positions=args.max_source_positions,
attention=eval(args.encoder_attention),
attention_nheads=args.encoder_attention_nheads
)
decoder = FConvDecoder(
task.target_dictionary,
embed_dim=args.decoder_embed_dim,
convolutions=eval(args.decoder_layers),
out_embed_dim=args.decoder_out_embed_dim,
attention=eval(args.decoder_attention),
dropout=args.dropout,
max_positions=args.max_target_positions,
selfattention=eval(args.self_attention),
attention_nheads=args.multihead_attention_nheads,
selfattention_nheads=args.multihead_self_attention_nheads,
project_input=eval(args.project_input),
gated_attention=eval(args.gated_attention),
downsample=eval(args.downsample),
pretrained=pretrained,
trained_decoder=trained_decoder
)
model = FConvModelSelfAtt(encoder, decoder, trained_encoder)
return model
@property
def pretrained(self):
return self.pretrained_encoder is not None
class FConvEncoder(FairseqEncoder):
"""Convolutional encoder"""
def __init__(
self, dictionary, embed_dim=512, max_positions=1024,
convolutions=((512, 3),) * 20, dropout=0.1, attention=False,
attention_nheads=1,
):
super().__init__(dictionary)
self.dropout = dropout
self.num_attention_layers = None
num_embeddings = len(dictionary)
self.padding_idx = dictionary.pad()
self.embed_tokens = Embedding(num_embeddings, embed_dim, self.padding_idx)
self.embed_positions = PositionalEmbedding(
max_positions,
embed_dim,
self.padding_idx,
)
def expand_bool_array(val):
if isinstance(val, bool):
# expand True into [True, True, ...] and do the same with False
return [val] * len(convolutions)
return val
attention = expand_bool_array(attention)
in_channels = convolutions[0][0]
self.fc1 = Linear(embed_dim, in_channels, dropout=dropout)
self.projections = nn.ModuleList()
self.convolutions = nn.ModuleList()
self.attention = nn.ModuleList()
self.attproj = nn.ModuleList()
for i, (out_channels, kernel_size) in enumerate(convolutions):
self.projections.append(
Linear(in_channels, out_channels) if in_channels != out_channels else None
)
self.convolutions.append(
ConvTBC(in_channels, out_channels * 2, kernel_size, dropout=dropout)
)
self.attention.append(
SelfAttention(out_channels, embed_dim, attention_nheads) if attention[i] else None
)
in_channels = out_channels
self.fc2 = Linear(in_channels, embed_dim)
def forward(self, src_tokens, src_lengths):
# embed tokens and positions
x = self.embed_tokens(src_tokens) + self.embed_positions(src_tokens)
x = F.dropout(x, p=self.dropout, training=self.training)
input_embedding = x.transpose(0, 1)
# project to size of convolution
x = self.fc1(x)
encoder_padding_mask = src_tokens.eq(self.padding_idx).t() # -> T x B
if not encoder_padding_mask.any():
encoder_padding_mask = None
# B x T x C -> T x B x C
x = x.transpose(0, 1)
# temporal convolutions
for proj, conv, attention in zip(self.projections, self.convolutions, self.attention):
residual = x if proj is None else proj(x)
if encoder_padding_mask is not None:
x = x.masked_fill(encoder_padding_mask.unsqueeze(-1), 0)
x = F.dropout(x, p=self.dropout, training=self.training)
padding_l = (conv.kernel_size[0] - 1) // 2
padding_r = conv.kernel_size[0] // 2
x = F.pad(x, (0, 0, 0, 0, padding_l, padding_r))
x = conv(x)
x = F.glu(x, dim=2)
if attention is not None:
x = attention(x)
x = (x + residual) * math.sqrt(0.5)
# T x B x C -> B x T x C
x = x.transpose(1, 0)
# project back to size of embedding
x = self.fc2(x)
if encoder_padding_mask is not None:
encoder_padding_mask = encoder_padding_mask.t() # -> B x T
x = x.masked_fill(encoder_padding_mask.unsqueeze(-1), 0)
# scale gradients (this only affects backward, not forward)
x = GradMultiply.apply(x, 1.0 / (2.0 * self.num_attention_layers))
# add output to input embedding for attention
y = (x + input_embedding.transpose(0, 1)) * math.sqrt(0.5)
return {
'encoder_out': (x, y),
'encoder_padding_mask': encoder_padding_mask, # B x T
}
def reorder_encoder_out(self, encoder_out, new_order):
encoder_out['encoder_out'] = tuple(
eo.index_select(0, new_order) for eo in encoder_out['encoder_out']
)
if encoder_out['encoder_padding_mask'] is not None:
encoder_out['encoder_padding_mask'] = \
encoder_out['encoder_padding_mask'].index_select(0, new_order)
if 'pretrained' in encoder_out:
encoder_out['pretrained']['encoder_out'] = tuple(
eo.index_select(0, new_order)
for eo in encoder_out['pretrained']['encoder_out']
)
return encoder_out
def max_positions(self):
"""Maximum input length supported by the encoder."""
return self.embed_positions.max_positions()
class FConvDecoder(FairseqDecoder):
"""Convolutional decoder"""
def __init__(
self, dictionary, embed_dim=512, out_embed_dim=256, max_positions=1024,
convolutions=((512, 3),) * 8, attention=True, dropout=0.1,
selfattention=False, attention_nheads=1, selfattention_nheads=1,
project_input=False, gated_attention=False, downsample=False,
pretrained=False, trained_decoder=None,
):
super().__init__(dictionary)
self.register_buffer('version', torch.Tensor([2]))
self.pretrained = pretrained
self.pretrained_decoder = trained_decoder
self.dropout = dropout
self.need_attn = True
in_channels = convolutions[0][0]
def expand_bool_array(val):
if isinstance(val, bool):
# expand True into [True, True, ...] and do the same with False
return [val] * len(convolutions)
return val
attention = expand_bool_array(attention)
selfattention = expand_bool_array(selfattention)
if not isinstance(attention, list) or len(attention) != len(convolutions):
raise ValueError('Attention is expected to be a list of booleans of '
'length equal to the number of layers.')
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
self.embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
self.embed_positions = PositionalEmbedding(
max_positions,
embed_dim,
padding_idx,
)
self.fc1 = Linear(embed_dim, in_channels, dropout=dropout)
self.projections = nn.ModuleList()
self.convolutions = nn.ModuleList()
self.attention = nn.ModuleList()
self.selfattention = nn.ModuleList()
self.attproj = nn.ModuleList()
for i, (out_channels, kernel_size) in enumerate(convolutions):
self.projections.append(
Linear(in_channels, out_channels) if in_channels != out_channels else None
)
self.convolutions.append(
LinearizedConv1d(
in_channels, out_channels * 2, kernel_size,
padding=(kernel_size - 1), dropout=dropout,
)
)
self.attention.append(
DownsampledMultiHeadAttention(
out_channels, embed_dim, attention_nheads,
project_input=project_input, gated=False, downsample=False,
) if attention[i] else None
)
self.attproj.append(
Linear(out_channels, embed_dim, dropout=dropout) if attention[i] else None
)
self.selfattention.append(
SelfAttention(
out_channels, embed_dim, selfattention_nheads,
project_input=project_input, gated=gated_attention,
downsample=downsample,
) if selfattention[i] else None
)
in_channels = out_channels
self.fc2 = Linear(in_channels, out_embed_dim)
self.fc3 = Linear(out_embed_dim, num_embeddings, dropout=dropout)
# model fusion
if self.pretrained:
# independent gates are learned from the concatenated input
self.gate1 = nn.Sequential(Linear(out_embed_dim*2, out_embed_dim), nn.Sigmoid())
self.gate2 = nn.Sequential(Linear(out_embed_dim*2, out_embed_dim), nn.Sigmoid())
# pretrained and trained models are joined
self.joining = nn.Sequential(
Linear(out_embed_dim*2, out_embed_dim*2),
LayerNorm(out_embed_dim*2),
nn.GLU(),
Linear(out_embed_dim, out_embed_dim*2),
LayerNorm(out_embed_dim*2),
nn.GLU(),
Linear(out_embed_dim, out_embed_dim),
LayerNorm(out_embed_dim)
)
# pretrained model contains an output layer that is nhid -> vocab size
# but the models are combined in their hidden state
# the hook stores the output of the pretrained model forward
self.pretrained_outputs = {}
def save_output():
def hook(a, b, output):
self.pretrained_outputs["out"] = output
return hook
self.pretrained_decoder.fc2.register_forward_hook(save_output())
def forward(self, prev_output_tokens, encoder_out):
trained_encoder_out = encoder_out['pretrained'] if self.pretrained else None
encoder_out = encoder_out['encoder']['encoder_out']
encoder_a, encoder_b = self._split_encoder_out(encoder_out)
# embed positions
positions = self.embed_positions(prev_output_tokens)
# embed tokens and positions
x = self.embed_tokens(prev_output_tokens) + positions
x = F.dropout(x, p=self.dropout, training=self.training)
target_embedding = x.transpose(0, 1)
# project to size of convolution
x = self.fc1(x)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
# temporal convolutions
avg_attn_scores = None
for proj, conv, attention, selfattention, attproj in zip(
self.projections, self.convolutions, self.attention, self.selfattention, self.attproj
):
residual = x if proj is None else proj(x)
x = F.dropout(x, p=self.dropout, training=self.training)
x = conv(x)
x = F.glu(x, dim=2)
# attention
if attention is not None:
r = x
x, attn_scores = attention(attproj(x) + target_embedding, encoder_a, encoder_b)
x = x + r
if not self.training and self.need_attn:
if avg_attn_scores is None:
avg_attn_scores = attn_scores
else:
avg_attn_scores.add_(attn_scores)
if selfattention is not None:
x = selfattention(x)
x = (x + residual) * math.sqrt(0.5)
# T x B x C -> B x T x C
x = x.transpose(0, 1)
# project back to size of vocabulary
x = self.fc2(x)
x = F.dropout(x, p=self.dropout, training=self.training)
if not self.pretrained:
x = self.fc3(x)
# fusion gating
if self.pretrained:
trained_x, _ = self.pretrained_decoder.forward(prev_output_tokens, trained_encoder_out)
y = torch.cat([x, self.pretrained_outputs["out"]], dim=-1)
gate1 = self.gate1(y)
gate2 = self.gate2(y)
gated_x1 = gate1 * x
gated_x2 = gate2 * self.pretrained_outputs["out"]
fusion = torch.cat([gated_x1, gated_x2], dim=-1)
fusion = self.joining(fusion)
fusion_output = self.fc3(fusion)
return fusion_output, avg_attn_scores
else:
return x, avg_attn_scores
def max_positions(self):
"""Maximum output length supported by the decoder."""
return self.embed_positions.max_positions()
def make_generation_fast_(self, need_attn=False, **kwargs):
self.need_attn = need_attn
def _split_encoder_out(self, encoder_out):
"""Split and transpose encoder outputs."""
# transpose only once to speed up attention layers
encoder_a, encoder_b = encoder_out
encoder_a = encoder_a.transpose(0, 1).contiguous()
encoder_b = encoder_b.transpose(0, 1).contiguous()
result = (encoder_a, encoder_b)
return result
class SelfAttention(nn.Module):
def __init__(self, out_channels, embed_dim, num_heads, project_input=False, gated=False, downsample=False):
super().__init__()
self.attention = DownsampledMultiHeadAttention(
out_channels, embed_dim, num_heads, dropout=0, bias=True,
project_input=project_input, gated=gated, downsample=downsample,
)
self.in_proj_q = Linear(out_channels, embed_dim)
self.in_proj_k = Linear(out_channels, embed_dim)
self.in_proj_v = Linear(out_channels, embed_dim)
self.ln = LayerNorm(out_channels)
def forward(self, x):
residual = x
query = self.in_proj_q(x)
key = self.in_proj_k(x)
value = self.in_proj_v(x)
x, _ = self.attention(query, key, value, mask_future_timesteps=True, use_scalar_bias=True)
return self.ln(x + residual)
def Embedding(num_embeddings, embedding_dim, padding_idx):
m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
m.weight.data.normal_(0, 0.1)
return m
def PositionalEmbedding(num_embeddings, embedding_dim, padding_idx):
m = LearnedPositionalEmbedding(num_embeddings, embedding_dim, padding_idx)
m.weight.data.normal_(0, 0.1)
return m
def Linear(in_features, out_features, dropout=0.):
"""Weight-normalized Linear layer (input: N x T x C)"""
m = nn.Linear(in_features, out_features)
m.weight.data.normal_(mean=0, std=math.sqrt((1 - dropout) / in_features))
m.bias.data.zero_()
return m
def LinearizedConv1d(in_channels, out_channels, kernel_size, dropout=0., **kwargs):
"""Weight-normalized Conv1d layer optimized for decoding"""
m = LinearizedConvolution(in_channels, out_channels, kernel_size, **kwargs)
std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels))
m.weight.data.normal_(mean=0, std=std)
m.bias.data.zero_()
return m
def ConvTBC(in_channels, out_channels, kernel_size, dropout=0, **kwargs):
"""Weight-normalized Conv1d layer"""
from fairseq.modules import ConvTBC
m = ConvTBC(in_channels, out_channels, kernel_size, **kwargs)
std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels))
m.weight.data.normal_(mean=0, std=std)
m.bias.data.zero_()
return m
@register_model_architecture('fconv_self_att', 'fconv_self_att')
def base_architecture(args):
args.dropout = getattr(args, 'dropout', 0.1)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512)
args.encoder_layers = getattr(args, 'encoder_layers', '[(512, 3)] * 3')
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
args.decoder_layers = getattr(args, 'decoder_layers', '[(512, 3)] * 8')
args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 256)
args.decoder_attention = getattr(args, 'decoder_attention', 'True')
args.self_attention = getattr(args, 'self_attention', 'False')
args.encoder_attention = getattr(args, 'encoder_attention', 'False')
args.multihead_attention_nheads = getattr(args, 'multihead_attention_nheads', 1)
args.multihead_self_attention_nheads = getattr(args, 'multihead_self_attention_nheads', 1)
args.encoder_attention_nheads = getattr(args, 'encoder_attention_nheads', 1)
args.project_input = getattr(args, 'project_input', 'False')
args.gated_attention = getattr(args, 'gated_attention', 'False')
args.downsample = getattr(args, 'downsample', 'False')
args.pretrained_checkpoint = getattr(args, 'pretrained_checkpoint', '')
args.pretrained = getattr(args, 'pretrained', 'False')
@register_model_architecture('fconv_self_att', 'fconv_self_att_wp')
def fconv_self_att_wp(args):
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256)
args.encoder_layers = getattr(args, 'encoder_layers', '[(128, 3)] * 2 + [(512,3)] * 1')
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 256)
args.decoder_layers = getattr(args, 'decoder_layers', '[(512, 4)] * 4 + [(768, 4)] * 2 + [(1024, 4)] * 1')
args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 256)
args.self_attention = getattr(args, 'self_attention', 'True')
args.multihead_self_attention_nheads = getattr(args, 'multihead_self_attention_nheads', 4)
args.project_input = getattr(args, 'project_input', 'True')
args.gated_attention = getattr(args, 'gated_attention', 'True')
args.downsample = getattr(args, 'downsample', 'True')
base_architecture(args)
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/fconv_self_att.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from fairseq import options
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.fconv import FConvDecoder
@register_model('fconv_lm')
class FConvLanguageModel(FairseqLanguageModel):
def __init__(self, decoder):
super().__init__(decoder)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
parser.add_argument('--dropout', type=float, metavar='D',
help='dropout probability')
parser.add_argument('--decoder-embed-dim', type=int, metavar='N',
help='decoder embedding dimension')
parser.add_argument('--decoder-layers', type=str, metavar='EXPR',
help='decoder layers [(dim, kernel_size), ...]')
parser.add_argument('--decoder-out-embed-dim', type=int, metavar='N',
help='decoder output embedding dimension')
parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR',
help='comma separated list of adaptive softmax cutoff points. '
'Must be used with adaptive_loss criterion')
parser.add_argument('--adaptive-softmax-dropout', type=float, metavar='D',
help='sets adaptive softmax dropout for the tail projections')
parser.add_argument('--decoder-attention', type=str, metavar='EXPR',
help='decoder attention [True, ...]')
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present in older models
base_lm_architecture(args)
if hasattr(args, 'max_target_positions') and not hasattr(args, 'tokens_per_sample'):
args.tokens_per_sample = args.max_target_positions
decoder = FConvDecoder(
dictionary=task.target_dictionary,
embed_dim=args.decoder_embed_dim,
convolutions=eval(args.decoder_layers),
out_embed_dim=args.decoder_embed_dim,
attention=eval(args.decoder_attention),
dropout=args.dropout,
max_positions=args.tokens_per_sample,
share_embed=False,
positional_embeddings=False,
adaptive_softmax_cutoff=(
options.eval_str_list(args.adaptive_softmax_cutoff, type=int)
if args.criterion == 'adaptive_loss' else None
),
adaptive_softmax_dropout=args.adaptive_softmax_dropout,
)
return FConvLanguageModel(decoder)
@register_model_architecture('fconv_lm', 'fconv_lm')
def base_lm_architecture(args):
args.dropout = getattr(args, 'dropout', 0.1)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 128)
args.decoder_layers = getattr(args, 'decoder_layers', '[(1268, 4)] * 13')
args.decoder_attention = getattr(args, 'decoder_attention', 'False')
args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', None)
args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0)
@register_model_architecture('fconv_lm', 'fconv_lm_dauphin_wikitext103')
def fconv_lm_dauphin_wikitext103(args):
layers = '[(850, 6)] * 3'
layers += ' + [(850, 1)] * 1'
layers += ' + [(850, 5)] * 4'
layers += ' + [(850, 1)] * 1'
layers += ' + [(850, 4)] * 3'
layers += ' + [(1024, 4)] * 1'
layers += ' + [(2048, 4)] * 1'
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 280)
args.decoder_layers = getattr(args, 'decoder_layers', layers)
args.decoder_attention = getattr(args, 'decoder_attention', 'False')
args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', '10000,20000,200000')
base_lm_architecture(args)
@register_model_architecture('fconv_lm', 'fconv_lm_dauphin_gbw')
def fconv_lm_dauphin_gbw(args):
layers = '[(512, 5)]'
layers += ' + [(128, 1, 0), (128, 5, 0), (512, 1, 3)] * 3'
layers += ' + [(512, 1, 0), (512, 5, 0), (1024, 1, 3)] * 3'
layers += ' + [(1024, 1, 0), (1024, 5, 0), (2048, 1, 3)] * 6'
layers += ' + [(1024, 1, 0), (1024, 5, 0), (4096, 1, 3)]'
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 128)
args.decoder_layers = getattr(args, 'decoder_layers', layers)
args.decoder_attention = getattr(args, 'decoder_attention', 'False')
args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', '10000,50000,200000')
base_lm_architecture(args)
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/fconv_lm.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from collections import OrderedDict
from fairseq import utils
from fairseq.models import (
FairseqMultiModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
base_architecture,
Embedding,
TransformerModel,
TransformerEncoder,
TransformerDecoder,
)
@register_model('multilingual_transformer')
class MultilingualTransformerModel(FairseqMultiModel):
"""Train Transformer models for multiple language pairs simultaneously.
Requires `--task multilingual_translation`.
We inherit all arguments from TransformerModel and assume that all language
pairs use a single Transformer architecture. In addition, we provide several
options that are specific to the multilingual setting.
Args:
--share-encoder-embeddings: share encoder embeddings across all source languages
--share-decoder-embeddings: share decoder embeddings across all target languages
--share-encoders: share all encoder params (incl. embeddings) across all source languages
--share-decoders: share all decoder params (incl. embeddings) across all target languages
"""
def __init__(self, encoders, decoders):
super().__init__(encoders, decoders)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
TransformerModel.add_args(parser)
parser.add_argument('--share-encoder-embeddings', action='store_true',
help='share encoder embeddings across languages')
parser.add_argument('--share-decoder-embeddings', action='store_true',
help='share decoder embeddings across languages')
parser.add_argument('--share-encoders', action='store_true',
help='share encoders across languages')
parser.add_argument('--share-decoders', action='store_true',
help='share decoders across languages')
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
from fairseq.tasks.multilingual_translation import MultilingualTranslationTask
assert isinstance(task, MultilingualTranslationTask)
# make sure all arguments are present in older models
base_multilingual_architecture(args)
if not hasattr(args, 'max_source_positions'):
args.max_source_positions = 1024
if not hasattr(args, 'max_target_positions'):
args.max_target_positions = 1024
src_langs = [lang_pair.split('-')[0] for lang_pair in task.model_lang_pairs]
tgt_langs = [lang_pair.split('-')[1] for lang_pair in task.model_lang_pairs]
if args.share_encoders:
args.share_encoder_embeddings = True
if args.share_decoders:
args.share_decoder_embeddings = True
def build_embedding(dictionary, embed_dim, path=None):
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
emb = Embedding(num_embeddings, embed_dim, padding_idx)
# if provided, load from preloaded dictionaries
if path:
embed_dict = utils.parse_embedding(path)
utils.load_embedding(embed_dict, dictionary, emb)
return emb
# build shared embeddings (if applicable)
shared_encoder_embed_tokens, shared_decoder_embed_tokens = None, None
if args.share_all_embeddings:
if args.encoder_embed_dim != args.decoder_embed_dim:
raise ValueError(
'--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim')
if args.decoder_embed_path and (
args.decoder_embed_path != args.encoder_embed_path):
raise ValueError('--share-all-embeddings not compatible with --decoder-embed-path')
shared_encoder_embed_tokens = FairseqMultiModel.build_shared_embeddings(
dicts=task.dicts,
langs=task.langs,
embed_dim=args.encoder_embed_dim,
build_embedding=build_embedding,
pretrained_embed_path=args.encoder_embed_path,
)
shared_decoder_embed_tokens = shared_encoder_embed_tokens
args.share_decoder_input_output_embed = True
else:
if args.share_encoder_embeddings:
shared_encoder_embed_tokens = (
FairseqMultiModel.build_shared_embeddings(
dicts=task.dicts,
langs=src_langs,
embed_dim=args.encoder_embed_dim,
build_embedding=build_embedding,
pretrained_embed_path=args.encoder_embed_path,
)
)
if args.share_decoder_embeddings:
shared_decoder_embed_tokens = (
FairseqMultiModel.build_shared_embeddings(
dicts=task.dicts,
langs=tgt_langs,
embed_dim=args.decoder_embed_dim,
build_embedding=build_embedding,
pretrained_embed_path=args.decoder_embed_path,
)
)
# encoders/decoders for each language
lang_encoders, lang_decoders = {}, {}
def get_encoder(lang):
if lang not in lang_encoders:
if shared_encoder_embed_tokens is not None:
encoder_embed_tokens = shared_encoder_embed_tokens
else:
encoder_embed_tokens = build_embedding(
task.dicts[lang], args.encoder_embed_dim, args.encoder_embed_path
)
lang_encoders[lang] = TransformerEncoder(args, task.dicts[lang], encoder_embed_tokens)
return lang_encoders[lang]
def get_decoder(lang):
if lang not in lang_decoders:
if shared_decoder_embed_tokens is not None:
decoder_embed_tokens = shared_decoder_embed_tokens
else:
decoder_embed_tokens = build_embedding(
task.dicts[lang], args.decoder_embed_dim, args.decoder_embed_path
)
lang_decoders[lang] = TransformerDecoder(args, task.dicts[lang], decoder_embed_tokens)
return lang_decoders[lang]
# shared encoders/decoders (if applicable)
shared_encoder, shared_decoder = None, None
if args.share_encoders:
shared_encoder = get_encoder(src_langs[0])
if args.share_decoders:
shared_decoder = get_decoder(tgt_langs[0])
encoders, decoders = OrderedDict(), OrderedDict()
for lang_pair, src, tgt in zip(task.model_lang_pairs, src_langs, tgt_langs):
encoders[lang_pair] = shared_encoder if shared_encoder is not None else get_encoder(src)
decoders[lang_pair] = shared_decoder if shared_decoder is not None else get_decoder(tgt)
return MultilingualTransformerModel(encoders, decoders)
def load_state_dict(self, state_dict, strict=True):
state_dict_subset = state_dict.copy()
for k, _ in state_dict.items():
assert k.startswith('models.')
lang_pair = k.split('.')[1]
if lang_pair not in self.models:
del state_dict_subset[k]
super().load_state_dict(state_dict_subset, strict=strict)
@register_model_architecture('multilingual_transformer', 'multilingual_transformer')
def base_multilingual_architecture(args):
base_architecture(args)
args.share_encoder_embeddings = getattr(args, 'share_encoder_embeddings', False)
args.share_decoder_embeddings = getattr(args, 'share_decoder_embeddings', False)
args.share_encoders = getattr(args, 'share_encoders', False)
args.share_decoders = getattr(args, 'share_decoders', False)
@register_model_architecture('multilingual_transformer', 'multilingual_transformer_iwslt_de_en')
def multilingual_transformer_iwslt_de_en(args):
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 1024)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 4)
args.encoder_layers = getattr(args, 'encoder_layers', 6)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 1024)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 4)
args.decoder_layers = getattr(args, 'decoder_layers', 6)
base_multilingual_architecture(args)
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/multilingual_transformer.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from fairseq.models import register_model, register_model_architecture
from fairseq.models.nonautoregressive_transformer import NATransformerModel
def _sequential_poisoning(s, V, beta=0.33, bos=2, eos=3, pad=1):
# s: input batch
# V: vocabulary size
rand_words = torch.randint(low=4, high=V, size=s.size(), device=s.device)
choices = torch.rand(size=s.size(), device=s.device)
choices.masked_fill_((s == pad) | (s == bos) | (s == eos), 1)
replace = choices < beta / 3
repeat = (choices >= beta / 3) & (choices < beta * 2 / 3)
swap = (choices >= beta * 2 / 3) & (choices < beta)
safe = choices >= beta
for i in range(s.size(1) - 1):
rand_word = rand_words[:, i]
next_word = s[:, i + 1]
self_word = s[:, i]
replace_i = replace[:, i]
swap_i = swap[:, i] & (next_word != 3)
repeat_i = repeat[:, i] & (next_word != 3)
safe_i = safe[:, i] | ((next_word == 3) & (~replace_i))
s[:, i] = (
self_word * (safe_i | repeat_i).long()
+ next_word * swap_i.long()
+ rand_word * replace_i.long()
)
s[:, i + 1] = (
next_word * (safe_i | replace_i).long()
+ self_word * (swap_i | repeat_i).long()
)
return s
def gumbel_noise(input, TINY=1e-8):
return input.new_zeros(*input.size()).uniform_().add_(
TINY).log_().neg_().add_(TINY).log_().neg_()
@register_model("iterative_nonautoregressive_transformer")
class IterNATransformerModel(NATransformerModel):
@staticmethod
def add_args(parser):
NATransformerModel.add_args(parser)
parser.add_argument("--train-step", type=int,
help="number of refinement iterations during training")
parser.add_argument("--dae-ratio", type=float,
help="the probability of switching to the denoising auto-encoder loss")
parser.add_argument("--stochastic-approx", action="store_true",
help="sampling from the decoder as the inputs for next iteration")
@classmethod
def build_model(cls, args, task):
model = super().build_model(args, task)
model.train_step = getattr(args, "train_step", 4)
model.dae_ratio = getattr(args, "dae_ratio", 0.5)
model.stochastic_approx = getattr(args, "stochastic_approx", False)
return model
def forward(
self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs
):
B, T = prev_output_tokens.size()
# encoding
encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
length_out, length_tgt = self.decoder.forward_length_prediction(
encoder_out, tgt_tokens
)
word_ins_outs, word_ins_tgts, word_ins_masks = [], [], []
for t in range(self.train_step):
word_ins_out, word_ins_tgt, word_ins_mask = self.decoder(
prev_output_tokens,
encoder_out=encoder_out,
tgt_tokens=tgt_tokens,
step=t,
)
word_ins_outs.append(word_ins_out)
word_ins_tgts.append(word_ins_tgt)
word_ins_masks.append(word_ins_mask)
if t < (self.train_step - 1):
# prediction for next iteration
if self.stochastic_approx:
word_ins_prediction = (
word_ins_out + gumbel_noise(word_ins_out)
).max(-1)[1]
else:
word_ins_prediction = word_ins_out.max(-1)[1]
prev_output_tokens = prev_output_tokens.masked_scatter(
word_ins_mask, word_ins_prediction[word_ins_mask]
)
if self.dae_ratio > 0:
# we do not perform denoising for the first iteration
corrputed = (
torch.rand(size=(B,), device=prev_output_tokens.device)
< self.dae_ratio
)
corrputed_tokens = _sequential_poisoning(
tgt_tokens[corrputed],
len(self.tgt_dict),
0.33,
self.bos,
self.eos,
self.pad,
)
prev_output_tokens[corrputed] = corrputed_tokens
# concat everything
word_ins_out = torch.cat(word_ins_outs, 0)
word_ins_tgt = torch.cat(word_ins_tgts, 0)
word_ins_mask = torch.cat(word_ins_masks, 0)
return {
"word_ins": {
"out": word_ins_out, "tgt": word_ins_tgt,
"mask": word_ins_mask, "ls": self.args.label_smoothing,
"nll_loss": True
},
"length": {
"out": length_out, "tgt": length_tgt,
"factor": self.decoder.length_loss_factor
}
}
@register_model_architecture(
"iterative_nonautoregressive_transformer", "iterative_nonautoregressive_transformer"
)
def base_architecture(args):
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.activation_dropout = getattr(args, "activation_dropout", 0.0)
args.activation_fn = getattr(args, "activation_fn", "relu")
args.dropout = getattr(args, "dropout", 0.1)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.adaptive_input = getattr(args, "adaptive_input", False)
args.apply_bert_init = getattr(args, "apply_bert_init", False)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
# --- special arguments ---
args.sg_length_pred = getattr(args, "sg_length_pred", False)
args.pred_length_offset = getattr(args, "pred_length_offset", False)
args.length_loss_factor = getattr(args, "length_loss_factor", 0.1)
args.ngram_predictor = getattr(args, "ngram_predictor", 1)
args.src_embedding_copy = getattr(args, "src_embedding_copy", False)
args.train_step = getattr(args, "train_step", 4)
args.dae_ratio = getattr(args, "dae_ratio", 0.5)
args.stochastic_approx = getattr(args, "stochastic_approx", False)
@register_model_architecture(
"iterative_nonautoregressive_transformer",
"iterative_nonautoregressive_transformer_wmt_en_de",
)
def iter_nat_wmt_en_de(args):
base_architecture(args)
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/iterative_nonautoregressive_transformer.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch.nn as nn
from fairseq import utils
class FairseqDecoder(nn.Module):
"""Base class for decoders."""
def __init__(self, dictionary):
super().__init__()
self.dictionary = dictionary
self.onnx_trace = False
def forward(self, prev_output_tokens, encoder_out=None, **kwargs):
"""
Args:
prev_output_tokens (LongTensor): shifted output tokens of shape
`(batch, tgt_len)`, for teacher forcing
encoder_out (dict, optional): output from the encoder, used for
encoder-side attention
Returns:
tuple:
- the decoder's output of shape `(batch, tgt_len, vocab)`
- a dictionary with any model-specific outputs
"""
x, extra = self.extract_features(prev_output_tokens, encoder_out=encoder_out, **kwargs)
x = self.output_layer(x)
return x, extra
def extract_features(self, prev_output_tokens, encoder_out=None, **kwargs):
"""
Returns:
tuple:
- the decoder's features of shape `(batch, tgt_len, embed_dim)`
- a dictionary with any model-specific outputs
"""
raise NotImplementedError
def output_layer(self, features, **kwargs):
"""
Project features to the default output size, e.g., vocabulary size.
Args:
features (Tensor): features returned by *extract_features*.
"""
raise NotImplementedError
def get_normalized_probs(self, net_output, log_probs, sample):
"""Get normalized probabilities (or log probs) from a net's output."""
if hasattr(self, 'adaptive_softmax') and self.adaptive_softmax is not None:
if sample is not None:
assert 'target' in sample
target = sample['target']
else:
target = None
out = self.adaptive_softmax.get_log_prob(net_output[0], target=target)
return out.exp_() if not log_probs else out
logits = net_output[0]
if log_probs:
return utils.log_softmax(logits, dim=-1, onnx_trace=self.onnx_trace)
else:
return utils.softmax(logits, dim=-1, onnx_trace=self.onnx_trace)
def max_positions(self):
"""Maximum input length supported by the decoder."""
return 1e6 # an arbitrary large number
def upgrade_state_dict(self, state_dict):
"""Upgrade a (possibly old) state dict for new versions of fairseq."""
return state_dict
def prepare_for_onnx_export_(self):
self.onnx_trace = True
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/fairseq_decoder.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import importlib
import os
from .fairseq_decoder import FairseqDecoder
from .fairseq_encoder import FairseqEncoder
from .fairseq_incremental_decoder import FairseqIncrementalDecoder
from .fairseq_model import (
BaseFairseqModel,
FairseqEncoderModel,
FairseqEncoderDecoderModel,
FairseqLanguageModel,
FairseqModel,
FairseqMultiModel,
)
from .composite_encoder import CompositeEncoder
from .distributed_fairseq_model import DistributedFairseqModel
MODEL_REGISTRY = {}
ARCH_MODEL_REGISTRY = {}
ARCH_MODEL_INV_REGISTRY = {}
ARCH_CONFIG_REGISTRY = {}
__all__ = [
'BaseFairseqModel',
'CompositeEncoder',
'DistributedFairseqModel',
'FairseqDecoder',
'FairseqEncoder',
'FairseqEncoderDecoderModel',
'FairseqEncoderModel',
'FairseqIncrementalDecoder',
'FairseqLanguageModel',
'FairseqModel',
'FairseqMultiModel',
]
def build_model(args, task):
return ARCH_MODEL_REGISTRY[args.arch].build_model(args, task)
def register_model(name):
"""
New model types can be added to fairseq with the :func:`register_model`
function decorator.
For example::
@register_model('lstm')
class LSTM(FairseqEncoderDecoderModel):
(...)
.. note:: All models must implement the :class:`BaseFairseqModel` interface.
Typically you will extend :class:`FairseqEncoderDecoderModel` for
sequence-to-sequence tasks or :class:`FairseqLanguageModel` for
language modeling tasks.
Args:
name (str): the name of the model
"""
def register_model_cls(cls):
if name in MODEL_REGISTRY:
raise ValueError('Cannot register duplicate model ({})'.format(name))
if not issubclass(cls, BaseFairseqModel):
raise ValueError('Model ({}: {}) must extend BaseFairseqModel'.format(name, cls.__name__))
MODEL_REGISTRY[name] = cls
return cls
return register_model_cls
def register_model_architecture(model_name, arch_name):
"""
New model architectures can be added to fairseq with the
:func:`register_model_architecture` function decorator. After registration,
model architectures can be selected with the ``--arch`` command-line
argument.
For example::
@register_model_architecture('lstm', 'lstm_luong_wmt_en_de')
def lstm_luong_wmt_en_de(args):
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1000)
(...)
The decorated function should take a single argument *args*, which is a
:class:`argparse.Namespace` of arguments parsed from the command-line. The
decorated function should modify these arguments in-place to match the
desired architecture.
Args:
model_name (str): the name of the Model (Model must already be
registered)
arch_name (str): the name of the model architecture (``--arch``)
"""
def register_model_arch_fn(fn):
if model_name not in MODEL_REGISTRY:
raise ValueError('Cannot register model architecture for unknown model type ({})'.format(model_name))
if arch_name in ARCH_MODEL_REGISTRY:
raise ValueError('Cannot register duplicate model architecture ({})'.format(arch_name))
if not callable(fn):
raise ValueError('Model architecture must be callable ({})'.format(arch_name))
ARCH_MODEL_REGISTRY[arch_name] = MODEL_REGISTRY[model_name]
ARCH_MODEL_INV_REGISTRY.setdefault(model_name, []).append(arch_name)
ARCH_CONFIG_REGISTRY[arch_name] = fn
return fn
return register_model_arch_fn
# automatically import any Python files in the models/ directory
models_dir = os.path.dirname(__file__)
for file in os.listdir(models_dir):
path = os.path.join(models_dir, file)
if not file.startswith('_') and not file.startswith('.') and (file.endswith('.py') or os.path.isdir(path)):
model_name = file[:file.find('.py')] if file.endswith('.py') else file
module = importlib.import_module('fairseq.models.' + model_name)
# extra `model_parser` for sphinx
if model_name in MODEL_REGISTRY:
parser = argparse.ArgumentParser(add_help=False)
group_archs = parser.add_argument_group('Named architectures')
group_archs.add_argument('--arch', choices=ARCH_MODEL_INV_REGISTRY[model_name])
group_args = parser.add_argument_group('Additional command-line arguments')
MODEL_REGISTRY[model_name].add_args(group_args)
globals()[model_name + '_parser'] = parser
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
TransformerEncoder,
TransformerModel,
TransformerDecoderLayer
)
from fairseq.modules.transformer_sentence_encoder import init_bert_params
from fairseq.utils import new_arange
# -------------- Helper Functions --------------------------------------------------- #
def _skip(x, mask):
"""
Getting sliced (dim=0) tensor by mask. Supporting tensor and list/dict of tensors.
"""
if isinstance(x, int):
return x
if x is None:
return None
if isinstance(x, torch.Tensor):
if x.size(0) == mask.size(0):
return x[mask]
elif x.size(1) == mask.size(0):
return x[:, mask]
if isinstance(x, list):
return [_skip(x_i, mask) for x_i in x]
if isinstance(x, dict):
return {k: _skip(v, mask) for k, v in x.items()}
raise NotImplementedError
def _skip_encoder_out(encoder, encoder_out, mask):
if not mask.any():
return encoder_out
else:
return encoder.reorder_encoder_out(encoder_out, mask.nonzero().squeeze())
def _fill(x, mask, y, padding_idx):
"""
Filling tensor x with y at masked positions (dim=0).
"""
if x is None:
return y
assert x.dim() == y.dim() and mask.size(0) == x.size(0)
assert x.dim() == 2 or (x.dim() == 3 and x.size(2) == y.size(2))
n_selected = mask.sum()
assert n_selected == y.size(0)
if n_selected == x.size(0):
return y
if x.size(1) < y.size(1):
dims = [x.size(0), y.size(1) - x.size(1)]
if x.dim() == 3:
dims.append(x.size(2))
x = torch.cat([x, x.new_zeros(*dims).fill_(padding_idx)], 1)
x[mask] = y
elif x.size(1) > y.size(1):
x[mask] = padding_idx
if x.dim() == 2:
x[mask, :y.size(1)] = y
else:
x[mask, :y.size(1), :] = y
else:
x[mask] = y
return x
def load_libnat():
try:
from fairseq import libnat
except ImportError as e:
import sys
sys.stderr.write("ERROR: missing libnat. run `pip install --editable .`\n")
raise e
return libnat
def _get_ins_targets(in_tokens, out_tokens, padding_idx, unk_idx):
libnat = load_libnat()
in_seq_len, out_seq_len = in_tokens.size(1), out_tokens.size(1)
in_tokens_list = [
[t for t in s if t != padding_idx] for i, s in enumerate(in_tokens.tolist())
]
out_tokens_list = [
[t for t in s if t != padding_idx]
for i, s in enumerate(out_tokens.tolist())
]
full_labels = libnat.suggested_ed2_path(
in_tokens_list, out_tokens_list, padding_idx
)
mask_inputs = [
[len(c) if c[0] != padding_idx else 0 for c in a[:-1]] for a in full_labels
]
# generate labels
masked_tgt_masks = []
for mask_input in mask_inputs:
mask_label = []
for beam_size in mask_input[1:-1]: # HACK 1:-1
mask_label += [0] + [1 for _ in range(beam_size)]
masked_tgt_masks.append(
mask_label + [0 for _ in range(out_seq_len - len(mask_label))]
)
mask_ins_targets = [
mask_input[1:-1] + [0 for _ in range(in_seq_len - 1 - len(mask_input[1:-1]))]
for mask_input in mask_inputs
]
# transform to tensor
masked_tgt_masks = torch.tensor(
masked_tgt_masks, device=out_tokens.device
).bool()
mask_ins_targets = torch.tensor(mask_ins_targets, device=in_tokens.device)
masked_tgt_tokens = out_tokens.masked_fill(masked_tgt_masks, unk_idx)
return masked_tgt_masks, masked_tgt_tokens, mask_ins_targets
def _get_del_targets(in_tokens, out_tokens, padding_idx):
libnat = load_libnat()
out_seq_len = out_tokens.size(1)
with torch.cuda.device_of(in_tokens):
in_tokens_list = [
[t for t in s if t != padding_idx] for i, s in enumerate(in_tokens.tolist())
]
out_tokens_list = [
[t for t in s if t != padding_idx]
for i, s in enumerate(out_tokens.tolist())
]
full_labels = libnat.suggested_ed2_path(
in_tokens_list, out_tokens_list, padding_idx
)
word_del_targets = [b[-1] for b in full_labels]
word_del_targets = [
labels + [0 for _ in range(out_seq_len - len(labels))]
for labels in word_del_targets
]
# transform to tensor
word_del_targets = torch.tensor(word_del_targets, device=out_tokens.device)
return word_del_targets
def _get_del_ins_targets(in_tokens, out_tokens, padding_idx):
libnat = load_libnat()
in_seq_len, out_seq_len = in_tokens.size(1), out_tokens.size(1)
with torch.cuda.device_of(in_tokens):
in_tokens_list = [
[t for t in s if t != padding_idx] for i, s in enumerate(in_tokens.tolist())
]
out_tokens_list = [
[t for t in s if t != padding_idx]
for i, s in enumerate(out_tokens.tolist())
]
full_labels = libnat.suggested_ed2_path(
in_tokens_list, out_tokens_list, padding_idx
)
word_del_targets = [b[-1] for b in full_labels]
word_del_targets = [
labels + [0 for _ in range(out_seq_len - len(labels))]
for labels in word_del_targets
]
mask_inputs = [
[len(c) if c[0] != padding_idx else 0 for c in a[:-1]] for a in full_labels
]
mask_ins_targets = [
mask_input[1:-1] + [0 for _ in range(in_seq_len - 1 - len(mask_input[1:-1]))]
for mask_input in mask_inputs
]
# transform to tensor
mask_ins_targets = torch.tensor(mask_ins_targets, device=in_tokens.device)
word_del_targets = torch.tensor(word_del_targets, device=out_tokens.device)
return word_del_targets, mask_ins_targets
def _apply_ins_masks(
in_tokens, in_scores, mask_ins_pred, padding_idx, unk_idx, eos_idx
):
in_masks = in_tokens.ne(padding_idx)
in_lengths = in_masks.sum(1)
# HACK: hacky way to shift all the paddings to eos first.
in_tokens.masked_fill_(~in_masks, eos_idx)
mask_ins_pred.masked_fill_(~in_masks[:, 1:], 0)
out_lengths = in_lengths + mask_ins_pred.sum(1)
out_max_len = out_lengths.max()
out_masks = (
new_arange(out_lengths, out_max_len)[None, :]
< out_lengths[:, None]
)
reordering = (mask_ins_pred + in_masks[:, 1:].long()).cumsum(1)
out_tokens = (
in_tokens.new_zeros(in_tokens.size(0), out_max_len)
.fill_(padding_idx)
.masked_fill_(out_masks, unk_idx)
)
out_tokens[:, 0] = in_tokens[:, 0]
out_tokens.scatter_(1, reordering, in_tokens[:, 1:])
out_scores = None
if in_scores is not None:
in_scores.masked_fill_(~in_masks, 0)
out_scores = in_scores.new_zeros(*out_tokens.size())
out_scores[:, 0] = in_scores[:, 0]
out_scores.scatter_(1, reordering, in_scores[:, 1:])
return out_tokens, out_scores
def _apply_ins_words(
in_tokens, in_scores, word_ins_pred, word_ins_scores, unk_idx
):
word_ins_masks = in_tokens.eq(unk_idx)
out_tokens = in_tokens.masked_scatter(word_ins_masks, word_ins_pred[word_ins_masks])
if in_scores is not None:
out_scores = in_scores.masked_scatter(
word_ins_masks, word_ins_scores[word_ins_masks]
)
else:
out_scores = None
return out_tokens, out_scores
def _apply_del_words(
in_tokens, in_scores, in_attn, word_del_pred, padding_idx, bos_idx, eos_idx
):
# apply deletion to a tensor
in_masks = in_tokens.ne(padding_idx)
bos_eos_masks = in_tokens.eq(bos_idx) | in_tokens.eq(eos_idx)
max_len = in_tokens.size(1)
word_del_pred.masked_fill_(~in_masks, 1)
word_del_pred.masked_fill_(bos_eos_masks, 0)
reordering = (
new_arange(in_tokens)
.masked_fill_(word_del_pred, max_len)
.sort(1)[1]
)
out_tokens = in_tokens.masked_fill(word_del_pred, padding_idx).gather(1, reordering)
out_scores = None
if in_scores is not None:
out_scores = in_scores.masked_fill(word_del_pred, 0).gather(1, reordering)
out_attn = None
if in_attn is not None:
_mask = word_del_pred[:, :, None].expand_as(in_attn)
_reordering = reordering[:, :, None].expand_as(in_attn)
out_attn = in_attn.masked_fill(_mask, 0.).gather(1, _reordering)
return out_tokens, out_scores, out_attn
# ------------------------------------------------------------------------------------- #
@register_model("levenshtein_transformer")
class LevenshteinTransformerModel(TransformerModel):
def __init__(self, args, encoder, decoder):
super().__init__(args, encoder, decoder)
self.tgt_dict = decoder.dictionary
self.bos = decoder.dictionary.bos()
self.eos = decoder.dictionary.eos()
self.pad = decoder.dictionary.pad()
self.unk = decoder.dictionary.unk()
@staticmethod
def add_args(parser):
TransformerModel.add_args(parser)
parser.add_argument(
"--apply-bert-init",
action="store_true",
help="use custom param initialization for BERT",
)
parser.add_argument(
"--early-exit",
default="6,6,6",
type=str,
help="number of decoder layers before word_del, mask_ins, word_ins",
)
parser.add_argument(
"--no-share-discriminator",
action="store_true",
help="separate parameters for discriminator",
)
parser.add_argument(
"--no-share-maskpredictor",
action="store_true",
help="separate parameters for mask-predictor",
)
parser.add_argument(
"--share-discriminator-maskpredictor",
action="store_true",
help="share the parameters for both mask-predictor and discriminator",
)
parser.add_argument(
"--sampling-for-deletion",
action='store_true',
help='instead of argmax, use sampling to predict the tokens'
)
@classmethod
def build_decoder(cls, args, tgt_dict, embed_tokens):
decoder = LevenshteinTransformerDecoder(args, tgt_dict, embed_tokens)
if getattr(args, "apply_bert_init", False):
decoder.apply(init_bert_params)
return decoder
@classmethod
def build_encoder(cls, args, src_dict, embed_tokens):
encoder = TransformerEncoder(args, src_dict, embed_tokens)
if getattr(args, "apply_bert_init", False):
encoder.apply(init_bert_params)
return encoder
def forward(
self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs
):
assert tgt_tokens is not None, "forward function only supports training."
# encoding
encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
# generate training labels for insertion
masked_tgt_masks, masked_tgt_tokens, mask_ins_targets = _get_ins_targets(
prev_output_tokens, tgt_tokens, self.pad, self.unk
)
mask_ins_targets = mask_ins_targets.clamp(min=0, max=255) # for safe prediction
mask_ins_masks = prev_output_tokens[:, 1:].ne(self.pad)
mask_ins_out, _ = self.decoder.forward_mask_ins(
prev_output_tokens, encoder_out=encoder_out
)
word_ins_out, _ = self.decoder.forward_word_ins(
masked_tgt_tokens, encoder_out=encoder_out
)
# make online prediction
if self.decoder.sampling_for_deletion:
word_predictions = torch.multinomial(
F.softmax(word_ins_out, -1).view(-1, word_ins_out.size(-1)), 1).view(
word_ins_out.size(0), -1)
else:
word_predictions = F.log_softmax(word_ins_out, dim=-1).max(2)[1]
word_predictions.masked_scatter_(
~masked_tgt_masks, tgt_tokens[~masked_tgt_masks]
)
# generate training labels for deletion
word_del_targets = _get_del_targets(word_predictions, tgt_tokens, self.pad)
word_del_out, _ = self.decoder.forward_word_del(
word_predictions, encoder_out)
word_del_masks = word_predictions.ne(self.pad)
return {
"mask_ins": {
"out": mask_ins_out, "tgt": mask_ins_targets,
"mask": mask_ins_masks, "ls": 0.01,
},
"word_ins": {
"out": word_ins_out, "tgt": tgt_tokens,
"mask": masked_tgt_masks, "ls": self.args.label_smoothing,
"nll_loss": True
},
"word_del": {
"out": word_del_out, "tgt": word_del_targets,
"mask": word_del_masks
}
}
def forward_encoder(self, encoder_inputs):
return self.encoder(*encoder_inputs)
def forward_decoder(
self, decoder_out, encoder_out, eos_penalty=0.0, max_ratio=None, **kwargs
):
output_tokens = decoder_out.output_tokens
output_scores = decoder_out.output_scores
attn = decoder_out.attn
history = decoder_out.history
bsz = output_tokens.size(0)
if max_ratio is None:
max_lens = torch.zeros_like(output_tokens).fill_(255)
else:
if encoder_out.encoder_padding_mask is None:
max_src_len = encoder_out.encoder_out.size(1)
src_lens = encoder_out.encoder_out.new(bsz).fill_(max_src_len)
else:
src_lens = (~encoder_out.encoder_padding_mask).sum(1)
max_lens = (src_lens * max_ratio).clamp(min=10).long()
# delete words
# do not delete tokens if it is <s> </s>
can_del_word = output_tokens.ne(self.pad).sum(1) > 2
if can_del_word.sum() != 0: # we cannot delete, skip
word_del_out, word_del_attn = self.decoder.forward_word_del(
_skip(output_tokens, can_del_word),
_skip_encoder_out(self.encoder, encoder_out, can_del_word)
)
word_del_score = F.log_softmax(word_del_out, 2)
word_del_pred = word_del_score.max(-1)[1].bool()
_tokens, _scores, _attn = _apply_del_words(
output_tokens[can_del_word],
output_scores[can_del_word],
word_del_attn,
word_del_pred,
self.pad,
self.bos,
self.eos,
)
output_tokens = _fill(output_tokens, can_del_word, _tokens, self.pad)
output_scores = _fill(output_scores, can_del_word, _scores, 0)
attn = _fill(attn, can_del_word, _attn, 0.)
if history is not None:
history.append(output_tokens.clone())
# insert placeholders
can_ins_mask = output_tokens.ne(self.pad).sum(1) < max_lens
if can_ins_mask.sum() != 0:
mask_ins_out, _ = self.decoder.forward_mask_ins(
_skip(output_tokens, can_ins_mask),
_skip_encoder_out(self.encoder, encoder_out, can_ins_mask)
)
mask_ins_score = F.log_softmax(mask_ins_out, 2)
if eos_penalty > 0.0:
mask_ins_score[:, :, 0] = mask_ins_score[:, :, 0] - eos_penalty
mask_ins_pred = mask_ins_score.max(-1)[1]
mask_ins_pred = torch.min(
mask_ins_pred, max_lens[can_ins_mask, None].expand_as(mask_ins_pred)
)
_tokens, _scores = _apply_ins_masks(
output_tokens[can_ins_mask],
output_scores[can_ins_mask],
mask_ins_pred,
self.pad,
self.unk,
self.eos,
)
output_tokens = _fill(output_tokens, can_ins_mask, _tokens, self.pad)
output_scores = _fill(output_scores, can_ins_mask, _scores, 0)
if history is not None:
history.append(output_tokens.clone())
# insert words
can_ins_word = output_tokens.eq(self.unk).sum(1) > 0
if can_ins_word.sum() != 0:
word_ins_out, word_ins_attn = self.decoder.forward_word_ins(
_skip(output_tokens, can_ins_word),
_skip_encoder_out(self.encoder, encoder_out, can_ins_word)
)
word_ins_score, word_ins_pred = F.log_softmax(word_ins_out, 2).max(-1)
_tokens, _scores = _apply_ins_words(
output_tokens[can_ins_word],
output_scores[can_ins_word],
word_ins_pred,
word_ins_score,
self.unk,
)
output_tokens = _fill(output_tokens, can_ins_word, _tokens, self.pad)
output_scores = _fill(output_scores, can_ins_word, _scores, 0)
attn = _fill(attn, can_ins_word, word_ins_attn, 0.)
if history is not None:
history.append(output_tokens.clone())
# delete some unnecessary paddings
cut_off = output_tokens.ne(self.pad).sum(1).max()
output_tokens = output_tokens[:, :cut_off]
output_scores = output_scores[:, :cut_off]
attn = None if attn is None else attn[:, :cut_off, :]
return decoder_out._replace(
output_tokens=output_tokens,
output_scores=output_scores,
attn=attn,
history=history
)
def initialize_output_tokens(self, encoder_out, src_tokens):
initial_output_tokens = src_tokens.new_zeros(src_tokens.size(0), 2)
initial_output_tokens[:, 0] = self.bos
initial_output_tokens[:, 1] = self.eos
initial_output_scores = initial_output_tokens.new_zeros(
*initial_output_tokens.size()
).type_as(encoder_out.encoder_out)
return DecoderOut(
output_tokens=initial_output_tokens,
output_scores=initial_output_scores,
attn=None,
step=0,
max_step=0,
history=None
)
class LevenshteinTransformerDecoder(TransformerDecoder):
def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False):
super().__init__(
args, dictionary, embed_tokens, no_encoder_attn=no_encoder_attn
)
self.dictionary = dictionary
self.bos = dictionary.bos()
self.unk = dictionary.unk()
self.eos = dictionary.eos()
self.sampling_for_deletion = getattr(args, "sampling_for_deletion", False)
self.embed_mask_ins = Embedding(256, self.output_embed_dim * 2, None)
self.embed_word_del = Embedding(2, self.output_embed_dim, None)
# del_word, ins_mask, ins_word
self.early_exit = [int(i) for i in args.early_exit.split(',')]
assert len(self.early_exit) == 3
# copy layers for mask-predict/deletion
self.layers_msk = None
if getattr(args, "no_share_maskpredictor", False):
self.layers_msk = nn.ModuleList([
TransformerDecoderLayer(args, no_encoder_attn)
for _ in range(self.early_exit[1])
])
self.layers_del = None
if getattr(args, "no_share_discriminator", False):
self.layers_del = nn.ModuleList([
TransformerDecoderLayer(args, no_encoder_attn)
for _ in range(self.early_exit[0])
])
if getattr(args, "share_discriminator_maskpredictor", False):
assert getattr(args, "no_share_discriminator", False), "must set saperate discriminator"
self.layers_msk = self.layers_del
def extract_features(
self, prev_output_tokens, encoder_out=None, early_exit=None, layers=None, **unused
):
"""
Similar to *forward* but only return features.
Inputs:
prev_output_tokens: Tensor(B, T)
encoder_out: a dictionary of hidden states and masks
Returns:
tuple:
- the decoder's features of shape `(batch, tgt_len, embed_dim)`
- a dictionary with any model-specific outputs
the LevenshteinTransformer decoder has full-attention to all generated tokens
"""
# embed positions
positions = (
self.embed_positions(prev_output_tokens)
if self.embed_positions is not None
else None
)
# embed tokens and positions
x = self.embed_scale * self.embed_tokens(prev_output_tokens)
if self.project_in_dim is not None:
x = self.project_in_dim(x)
if positions is not None:
x += positions
x = F.dropout(x, p=self.dropout, training=self.training)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
attn = None
inner_states = [x]
# decoder layers
decoder_padding_mask = prev_output_tokens.eq(self.padding_idx)
layers = self.layers if layers is None else layers
early_exit = len(layers) if early_exit is None else early_exit
for _, layer in enumerate(layers[: early_exit]):
x, attn = layer(
x,
encoder_out.encoder_out if encoder_out is not None else None,
encoder_out.encoder_padding_mask if encoder_out is not None else None,
self_attn_mask=None,
self_attn_padding_mask=decoder_padding_mask,
)
inner_states.append(x)
if self.layer_norm:
x = self.layer_norm(x)
# T x B x C -> B x T x C
x = x.transpose(0, 1)
if self.project_out_dim is not None:
x = self.project_out_dim(x)
return x, {"attn": attn, "inner_states": inner_states}
def forward_mask_ins(self, prev_output_tokens, encoder_out=None, **unused):
features, extra = self.extract_features(
prev_output_tokens, encoder_out=encoder_out, early_exit=self.early_exit[1], layers=self.layers_msk, **unused
)
features_cat = torch.cat([features[:, :-1, :], features[:, 1:, :]], 2)
return F.linear(features_cat, self.embed_mask_ins.weight), extra['attn']
def forward_word_ins(self, prev_output_tokens, encoder_out=None, **unused):
features, extra = self.extract_features(
prev_output_tokens, encoder_out=encoder_out, early_exit=self.early_exit[2], layers=self.layers, **unused
)
return self.output_layer(features), extra['attn']
def forward_word_del(self, prev_output_tokens, encoder_out=None, **unused):
features, extra = self.extract_features(
prev_output_tokens, encoder_out=encoder_out, early_exit=self.early_exit[0], layers=self.layers_del, **unused
)
return F.linear(features, self.embed_word_del.weight), extra['attn']
@register_model_architecture("levenshtein_transformer", "levenshtein_transformer")
def base_architecture(args):
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.activation_dropout = getattr(args, "activation_dropout", 0.0)
args.activation_fn = getattr(args, "activation_fn", "relu")
args.dropout = getattr(args, "dropout", 0.1)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.adaptive_input = getattr(args, "adaptive_input", False)
args.apply_bert_init = getattr(args, "apply_bert_init", False)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.sampling_for_deletion = getattr(args, "sampling_for_deletion", False)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
args.early_exit = getattr(args, "early_exit", "6,6,6")
args.no_share_discriminator = getattr(args, "no_share_discriminator", False)
args.no_share_maskpredictor = getattr(args, "no_share_maskpredictor", False)
args.share_discriminator_maskpredictor = getattr(args, "share_discriminator_maskpredictor", False)
args.no_share_last_layer = getattr(args, "no_share_last_layer", False)
@register_model_architecture(
"levenshtein_transformer", "levenshtein_transformer_wmt_en_de"
)
def levenshtein_transformer_wmt_en_de(args):
base_architecture(args)
# similar parameters used in the "Attention Is All You Need" paper (Vaswani et al., 2017)
@register_model_architecture(
"levenshtein_transformer", "levenshtein_transformer_vaswani_wmt_en_de_big"
)
def levenshtein_transformer_vaswani_wmt_en_de_big(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024)
args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
args.dropout = getattr(args, "dropout", 0.3)
base_architecture(args)
# default parameters used in tensor2tensor implementation
@register_model_architecture(
"levenshtein_transformer", "levenshtein_transformer_wmt_en_de_big"
)
def levenshtein_transformer_wmt_en_de_big_t2t(args):
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True)
args.attention_dropout = getattr(args, "attention_dropout", 0.1)
args.activation_dropout = getattr(args, "activation_dropout", 0.1)
levenshtein_transformer_vaswani_wmt_en_de_big(args)
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/levenshtein_transformer.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
from typing import Any, Dict
from fairseq import checkpoint_utils
from fairseq.data.legacy.masked_lm_dictionary import MaskedLMDictionary
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
TransformerDecoder,
TransformerEncoder,
TransformerModel,
base_architecture as transformer_base_architecture,
)
@register_model("transformer_from_pretrained_xlm")
class TransformerFromPretrainedXLMModel(TransformerModel):
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
TransformerModel.add_args(parser)
parser.add_argument(
"--pretrained-xlm-checkpoint",
type=str,
metavar="STR",
help="XLM model to use for initializing transformer encoder and/or decoder",
)
parser.add_argument(
"--init-encoder-only",
action="store_true",
help="if set, don't load the XLM weights and embeddings into decoder",
)
parser.add_argument(
"--init-decoder-only",
action="store_true",
help="if set, don't load the XLM weights and embeddings into encoder",
)
@classmethod
def build_model(self, args, task, cls_dictionary=MaskedLMDictionary):
assert hasattr(args, "pretrained_xlm_checkpoint"), (
"You must specify a path for --pretrained-xlm-checkpoint to use "
"--arch transformer_from_pretrained_xlm"
)
assert isinstance(task.source_dictionary, cls_dictionary) and isinstance(
task.target_dictionary, cls_dictionary
), (
"You should use a MaskedLMDictionary when using --arch "
"transformer_from_pretrained_xlm because the pretrained XLM model "
"was trained using data binarized with MaskedLMDictionary. "
"For translation, you may want to use --task "
"translation_from_pretrained_xlm"
)
assert not (
getattr(args, "init_encoder_only", False)
and getattr(args, "init_decoder_only", False)
), "Only one of --init-encoder-only and --init-decoder-only can be set."
return super().build_model(args, task)
@classmethod
def build_encoder(cls, args, src_dict, embed_tokens):
return TransformerEncoderFromPretrainedXLM(args, src_dict, embed_tokens)
@classmethod
def build_decoder(cls, args, tgt_dict, embed_tokens):
return TransformerDecoderFromPretrainedXLM(args, tgt_dict, embed_tokens)
def upgrade_state_dict_with_xlm_weights(
state_dict: Dict[str, Any], pretrained_xlm_checkpoint: str
) -> Dict[str, Any]:
"""
Load XLM weights into a Transformer encoder or decoder model.
Args:
state_dict: state dict for either TransformerEncoder or
TransformerDecoder
pretrained_xlm_checkpoint: checkpoint to load XLM weights from
Raises:
AssertionError: If architecture (num layers, attention heads, etc.)
does not match between the current Transformer encoder or
decoder and the pretrained_xlm_checkpoint
"""
if not os.path.exists(pretrained_xlm_checkpoint):
raise IOError("Model file not found: {}".format(pretrained_xlm_checkpoint))
state = checkpoint_utils.load_checkpoint_to_cpu(pretrained_xlm_checkpoint)
xlm_state_dict = state["model"]
for key in xlm_state_dict.keys():
for search_key in ["embed_tokens", "embed_positions", "layers"]:
if search_key in key:
subkey = key[key.find(search_key):]
assert subkey in state_dict, (
"{} Transformer encoder / decoder "
"state_dict does not contain {}. Cannot "
"load {} from pretrained XLM checkpoint "
"{} into Transformer.".format(
str(state_dict.keys()),
subkey, key, pretrained_xlm_checkpoint)
)
state_dict[subkey] = xlm_state_dict[key]
return state_dict
class TransformerEncoderFromPretrainedXLM(TransformerEncoder):
def __init__(self, args, dictionary, embed_tokens):
super().__init__(args, dictionary, embed_tokens)
if getattr(args, 'init_decoder_only', False):
# Don't load XLM weights for encoder if --init-decoder-only
return
assert hasattr(args, "pretrained_xlm_checkpoint"), (
"--pretrained-xlm-checkpoint must be specified to load Transformer "
"encoder from pretrained XLM"
)
xlm_loaded_state_dict = upgrade_state_dict_with_xlm_weights(
state_dict=self.state_dict(),
pretrained_xlm_checkpoint=args.pretrained_xlm_checkpoint,
)
self.load_state_dict(xlm_loaded_state_dict, strict=True)
class TransformerDecoderFromPretrainedXLM(TransformerDecoder):
def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False):
super().__init__(args, dictionary, embed_tokens, no_encoder_attn)
if getattr(args, 'init_encoder_only', False):
# Don't load XLM weights for decoder if --init-encoder-only
return
assert hasattr(args, "pretrained_xlm_checkpoint"), (
"--pretrained-xlm-checkpoint must be specified to load Transformer "
"decoder from pretrained XLM"
)
xlm_loaded_state_dict = upgrade_state_dict_with_xlm_weights(
state_dict=self.state_dict(),
pretrained_xlm_checkpoint=args.pretrained_xlm_checkpoint,
)
self.load_state_dict(xlm_loaded_state_dict, strict=True)
@register_model_architecture(
"transformer_from_pretrained_xlm", "transformer_from_pretrained_xlm"
)
def base_architecture(args):
transformer_base_architecture(args)
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/transformer_from_pretrained_xlm.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn.functional as F
from fairseq.models.levenshtein_transformer import (
_fill,
_skip,
_skip_encoder_out,
_apply_ins_masks,
_apply_ins_words,
_apply_del_words,
)
class _EnsembleModelEncoder(object):
def __init__(self, models):
self.models = models
def reorder_encoder_out(self, encoder_outs, new_order):
encoder_outs = [
model.encoder.reorder_encoder_out(encoder_out, new_order)
for model, encoder_out in zip(self.models, encoder_outs)
]
return encoder_outs
class BasicEnsembleModel(torch.nn.Module):
"""A wrapper around an ensemble of models."""
def __init__(self, models):
super().__init__()
self.models = torch.nn.ModuleList(models)
self.bos = self.models[0].decoder.dictionary.bos()
self.eos = self.models[0].decoder.dictionary.eos()
self.pad = self.models[0].decoder.dictionary.pad()
self.unk = self.models[0].decoder.dictionary.unk()
self.encoder = _EnsembleModelEncoder(self.models)
def has_encoder(self):
return hasattr(self.models[0], 'encoder')
def max_decoder_positions(self):
return min(m.max_decoder_positions() for m in self.models)
@torch.no_grad()
def forward_encoder(self, encoder_input):
if not self.has_encoder():
return None
return [model.forward_encoder(encoder_input) for model in self.models]
@torch.no_grad()
def forward_decoder(self, *inputs):
raise NotImplementedError
def initialize_output_tokens(self, *inputs):
raise NotImplementedError
class EnsembleLevT(BasicEnsembleModel):
"""A wrapper around an ensemble of models."""
def __init__(self, models):
super().__init__(models)
@torch.no_grad()
def forward_decoder(self, decoder_out, encoder_outs, eos_penalty=0.0, max_ratio=None, **kwargs):
# LevT ensembling
# A pipeline of three steps: deletion, placeholder, and word insertion.
# We need to average scores in each step in a pipeline way because of dependence.
# deletion
output_tokens = decoder_out.output_tokens
output_scores = decoder_out.output_scores
attn = decoder_out.attn
bsz = output_tokens.size(0)
if max_ratio is None:
max_lens = output_tokens.new().fill_(255)
else:
if encoder_outs[0].encoder_padding_mask is None:
src_lens = encoder_outs[0].encoder_out.new(bsz).fill_(encoder_outs[0].encoder_out.size(1))
else:
src_lens = (~encoder_outs[0].encoder_padding_mask).sum(1)
max_lens = (src_lens * max_ratio).clamp(min=10).long()
# delete words
# do not delete tokens if it is <s> </s>
can_del_word = output_tokens.ne(self.pad).sum(1) > 2
if can_del_word.sum() != 0: # we cannot delete, skip
output_tokens, output_scores, attn = self.forward_word_del(
encoder_outs,
output_tokens,
output_scores,
attn,
can_del_word,
)
# insert placeholders
can_ins_mask = output_tokens.ne(self.pad).sum(1) < max_lens
if can_ins_mask.sum() != 0:
output_tokens, output_scores = self.forward_mask_ins(
encoder_outs,
output_tokens,
output_scores,
can_ins_mask,
eos_penalty,
max_lens,
)
# insert words
can_ins_word = output_tokens.eq(self.unk).sum(1) > 0
if can_ins_word.sum() != 0:
output_tokens, output_scores, attn = self.forward_word_ins(
encoder_outs,
output_tokens,
output_scores,
attn,
can_ins_word,
)
# delete some unnecessary paddings
cut_off = output_tokens.ne(self.pad).sum(1).max()
output_tokens = output_tokens[:, :cut_off]
output_scores = output_scores[:, :cut_off]
attn = None if attn is None else attn[:, :cut_off, :]
return decoder_out._replace(
output_tokens=output_tokens,
output_scores=output_scores,
attn=attn,
history=None
)
def forward_word_del(self, encoder_outs, output_tokens, output_scores, attn, can_del_word):
word_del_score_avg = []
word_del_attn_avg = []
for model, encoder_out in zip(self.models, encoder_outs):
word_del_out, word_del_attn = model.decoder.forward_word_del(
_skip(output_tokens, can_del_word),
_skip_encoder_out(model.encoder, encoder_out, can_del_word),
)
word_del_score = F.log_softmax(word_del_out, 2)
word_del_score_avg.append(word_del_score)
word_del_attn_avg.append(word_del_attn)
word_del_score_avg = torch.logsumexp(torch.stack(word_del_score_avg, dim=0), dim=0) - math.log(len(self.models))
word_del_pred = word_del_score_avg.max(-1)[1].bool()
if word_del_attn_avg[0] is not None:
word_del_attn_avg = torch.stack(word_del_attn_avg, dim=0)/len(self.models)
else:
word_del_attn_avg = None
_tokens, _scores, _attn = _apply_del_words(
output_tokens[can_del_word],
output_scores[can_del_word],
word_del_attn_avg,
word_del_pred,
self.pad,
self.bos,
self.eos,
)
output_tokens = _fill(output_tokens, can_del_word, _tokens, self.pad)
output_scores = _fill(output_scores, can_del_word, _scores, 0)
attn = _fill(attn, can_del_word, _attn, 0.)
return output_tokens, output_scores, attn
def forward_mask_ins(self, encoder_outs, output_tokens, output_scores, can_ins_mask, eos_penalty, max_lens):
mask_ins_score_avg = []
for model, encoder_out in zip(self.models, encoder_outs):
mask_ins_out, _ = model.decoder.forward_mask_ins(
_skip(output_tokens, can_ins_mask),
_skip_encoder_out(model.encoder, encoder_out, can_ins_mask),
)
mask_ins_score = F.log_softmax(mask_ins_out, 2)
if eos_penalty > 0.0:
mask_ins_score[:, :, 0] -= eos_penalty
mask_ins_score_avg.append(mask_ins_score)
mask_ins_score_avg = torch.logsumexp(torch.stack(mask_ins_score_avg, dim=0), dim=0) - math.log(len(self.models))
mask_ins_pred = mask_ins_score_avg.max(-1)[1]
mask_ins_pred = torch.min(
mask_ins_pred, max_lens[can_ins_mask, None].expand_as(mask_ins_pred)
)
_tokens, _scores = _apply_ins_masks(
output_tokens[can_ins_mask],
output_scores[can_ins_mask],
mask_ins_pred,
self.pad,
self.unk,
self.eos,
)
output_tokens = _fill(output_tokens, can_ins_mask, _tokens, self.pad)
output_scores = _fill(output_scores, can_ins_mask, _scores, 0)
return output_tokens, output_scores
def forward_word_ins(self, encoder_outs, output_tokens, output_scores, attn, can_ins_word):
word_ins_score_avg = []
word_ins_attn_avg = []
for model, encoder_out in zip(self.models, encoder_outs):
word_ins_out, word_ins_attn = model.decoder.forward_word_ins(
_skip(output_tokens, can_ins_word),
_skip_encoder_out(model.encoder, encoder_out, can_ins_word),
)
word_ins_score = F.log_softmax(word_ins_out, 2)
word_ins_score_avg.append(word_ins_score)
word_ins_attn_avg.append(word_ins_attn)
word_ins_score_avg = torch.logsumexp(torch.stack(word_ins_score_avg, dim=0), dim=0) - math.log(len(self.models))
if word_ins_attn_avg[0] is not None:
word_ins_attn_avg = torch.stack(word_ins_attn_avg, dim=0)/len(self.models)
else:
word_ins_attn_avg = None
word_ins_score_max, word_ins_pred = word_ins_score_avg.max(-1)
_tokens, _scores = _apply_ins_words(
output_tokens[can_ins_word],
output_scores[can_ins_word],
word_ins_pred,
word_ins_score_max,
self.unk,
)
output_tokens = _fill(output_tokens, can_ins_word, _tokens, self.pad)
output_scores = _fill(output_scores, can_ins_word, _scores, 0)
attn = _fill(attn, can_ins_word, word_ins_attn, 0.)
return output_tokens, output_scores, attn
def initialize_output_tokens(self, encoder_outs, src_tokens):
# LevT doesn't do length prediction.
return self.models[0].initialize_output_tokens(encoder_outs[0], src_tokens)
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/nonautoregressive_ensembles.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.iterative_refinement_generator import DecoderOut
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
TransformerEncoder,
TransformerModel,
)
from fairseq.modules.transformer_sentence_encoder import init_bert_params
def _mean_pooling(enc_feats, src_masks):
# enc_feats: T x B x C
# src_masks: B x T or None
if src_masks is None:
enc_feats = enc_feats.mean(0)
else:
src_masks = (~src_masks).transpose(0, 1).type_as(enc_feats)
enc_feats = (
(enc_feats / src_masks.sum(0)[None, :, None]) * src_masks[:, :, None]
).sum(0)
return enc_feats
def _argmax(x, dim):
return (x == x.max(dim, keepdim=True)[0]).type_as(x)
def _uniform_assignment(src_lens, trg_lens):
max_trg_len = trg_lens.max()
steps = (src_lens.float() - 1) / (trg_lens.float() - 1) # step-size
# max_trg_len
index_t = utils.new_arange(trg_lens, max_trg_len).float()
index_t = steps[:, None] * index_t[None, :] # batch_size X max_trg_len
index_t = torch.round(index_t).long().detach()
return index_t
@register_model("nonautoregressive_transformer")
class NATransformerModel(TransformerModel):
def __init__(self, args, encoder, decoder):
super().__init__(args, encoder, decoder)
self.tgt_dict = decoder.dictionary
self.bos = decoder.dictionary.bos()
self.eos = decoder.dictionary.eos()
self.pad = decoder.dictionary.pad()
self.unk = decoder.dictionary.unk()
@staticmethod
def add_args(parser):
TransformerModel.add_args(parser)
parser.add_argument(
"--apply-bert-init",
action="store_true",
help="use custom param initialization for BERT",
)
# length prediction
parser.add_argument("--src-embedding-copy", action="store_true",
help="copy encoder word embeddings as the initial input of the decoder")
parser.add_argument("--pred-length-offset", action="store_true",
help="predicting the length difference between the target and source sentences")
parser.add_argument("--sg-length-pred", action="store_true",
help="stop the gradients back-propagated from the length predictor")
parser.add_argument("--length-loss-factor", type=float,
help="weights on the length prediction loss")
@classmethod
def build_decoder(cls, args, tgt_dict, embed_tokens):
decoder = NATransformerDecoder(args, tgt_dict, embed_tokens)
if getattr(args, "apply_bert_init", False):
decoder.apply(init_bert_params)
return decoder
@classmethod
def build_encoder(cls, args, src_dict, embed_tokens):
encoder = TransformerEncoder(args, src_dict, embed_tokens)
if getattr(args, "apply_bert_init", False):
encoder.apply(init_bert_params)
return encoder
def forward(
self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs
):
# encoding
encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
length_out, length_tgt = self.decoder.forward_length_prediction(
encoder_out, tgt_tokens
)
word_ins_out, word_ins_tgt, word_ins_mask = self.decoder(
prev_output_tokens, encoder_out=encoder_out, tgt_tokens=tgt_tokens
)
return {
"word_ins": {
"out": word_ins_out, "tgt": word_ins_tgt,
"mask": word_ins_mask, "ls": self.args.label_smoothing,
"nll_loss": True
},
"length": {
"out": length_out, "tgt": length_tgt,
"factor": self.decoder.length_loss_factor
}
}
def forward_encoder(self, encoder_inputs):
return self.encoder(*encoder_inputs)
def forward_decoder(self, decoder_out, encoder_out, decoding_format=None, **kwargs):
step = decoder_out.step
output_tokens = decoder_out.output_tokens
output_scores = decoder_out.output_scores
history = decoder_out.history
# execute the decoder
output_masks = output_tokens.ne(self.pad)
_scores, _tokens = self.decoder(
output_tokens,
encoder_out=encoder_out,
decoding_format=decoding_format,
step=step,
)
output_tokens.masked_scatter_(output_masks, _tokens[output_masks])
output_scores.masked_scatter_(output_masks, _scores[output_masks])
if history is not None:
history.append(output_tokens.clone())
return decoder_out._replace(
output_tokens=output_tokens,
output_scores=output_scores,
attn=None,
history=history
)
def initialize_output_tokens(self, encoder_out, src_tokens):
# length prediction
_, length_tgt = self.decoder.forward_length_prediction(encoder_out)
max_length = length_tgt.clamp_(min=2).max()
idx_length = utils.new_arange(src_tokens, max_length)
initial_output_tokens = src_tokens.new_zeros(
src_tokens.size(0), max_length
).fill_(self.pad)
initial_output_tokens.masked_fill_(
idx_length[None, :] < length_tgt[:, None], self.unk
)
initial_output_tokens[:, 0] = self.bos
initial_output_tokens.scatter_(1, length_tgt[:, None] - 1, self.eos)
initial_output_scores = initial_output_tokens.new_zeros(
*initial_output_tokens.size()
).type_as(encoder_out.encoder_out)
return DecoderOut(
output_tokens=initial_output_tokens,
output_scores=initial_output_scores,
attn=None,
step=0,
max_step=0,
history=None
)
class NATransformerDecoder(TransformerDecoder):
def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False):
super().__init__(
args, dictionary, embed_tokens, no_encoder_attn=no_encoder_attn
)
self.dictionary = dictionary
self.bos = dictionary.bos()
self.unk = dictionary.unk()
self.eos = dictionary.eos()
self.encoder_embed_dim = args.encoder_embed_dim
self.sg_length_pred = getattr(args, "sg_length_pred", False)
self.pred_length_offset = getattr(args, "pred_length_offset", False)
self.length_loss_factor = getattr(args, "length_loss_factor", 0.1)
self.src_embedding_copy = getattr(args, "src_embedding_copy", False)
self.embed_length = Embedding(256, self.encoder_embed_dim, None)
def forward(
self,
prev_output_tokens,
encoder_out=None,
tgt_tokens=None,
decoding_format=None,
step=0,
**kwargs
):
features, _ = self.extract_features(
prev_output_tokens,
encoder_out=encoder_out,
embedding_copy=(step == 0) & self.src_embedding_copy,
)
if tgt_tokens is not None:
word_ins_mask = tgt_tokens.ne(self.padding_idx)
word_ins_tgt = tgt_tokens
return self.output_layer(features), word_ins_tgt, word_ins_mask
else:
return F.log_softmax(self.output_layer(features), -1).max(-1)
def extract_features(
self,
prev_output_tokens,
encoder_out=None,
early_exit=None,
embedding_copy=False,
**unused
):
"""
Similar to *forward* but only return features.
Inputs:
prev_output_tokens: Tensor(B, T)
encoder_out: a dictionary of hidden states and masks
Returns:
tuple:
- the decoder's features of shape `(batch, tgt_len, embed_dim)`
- a dictionary with any model-specific outputs
the LevenshteinTransformer decoder has full-attention to all generated tokens
"""
# embedding
if embedding_copy:
src_embd = encoder_out.encoder_embedding
src_mask = encoder_out.encoder_padding_mask
src_mask = (
~src_mask
if src_mask is not None
else prev_output_tokens.new_ones(*src_embd.size()[:2]).bool()
)
x, decoder_padding_mask = self.forward_embedding(
prev_output_tokens,
self.forward_copying_source(
src_embd, src_mask, prev_output_tokens.ne(self.padding_idx)
),
)
else:
x, decoder_padding_mask = self.forward_embedding(prev_output_tokens)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
attn = None
inner_states = [x]
# decoder layers
for i, layer in enumerate(self.layers):
# early exit from the decoder.
if (early_exit is not None) and (i >= early_exit):
break
x, attn = layer(
x,
encoder_out.encoder_out if encoder_out is not None else None,
encoder_out.encoder_padding_mask if encoder_out is not None else None,
self_attn_mask=None,
self_attn_padding_mask=decoder_padding_mask,
)
inner_states.append(x)
if self.layer_norm:
x = self.layer_norm(x)
# T x B x C -> B x T x C
x = x.transpose(0, 1)
if self.project_out_dim is not None:
x = self.project_out_dim(x)
return x, {"attn": attn, "inner_states": inner_states}
def forward_embedding(self, prev_output_tokens, states=None):
# embed positions
positions = (
self.embed_positions(prev_output_tokens)
if self.embed_positions is not None
else None
)
# embed tokens and positions
if states is None:
x = self.embed_scale * self.embed_tokens(prev_output_tokens)
if self.project_in_dim is not None:
x = self.project_in_dim(x)
else:
x = states
if positions is not None:
x += positions
x = F.dropout(x, p=self.dropout, training=self.training)
decoder_padding_mask = prev_output_tokens.eq(self.padding_idx)
return x, decoder_padding_mask
def forward_copying_source(self, src_embeds, src_masks, tgt_masks):
length_sources = src_masks.sum(1)
length_targets = tgt_masks.sum(1)
mapped_inputs = _uniform_assignment(length_sources, length_targets).masked_fill(
~tgt_masks, 0
)
copied_embedding = torch.gather(
src_embeds,
1,
mapped_inputs.unsqueeze(-1).expand(
*mapped_inputs.size(), src_embeds.size(-1)
),
)
return copied_embedding
def forward_length_prediction(self, encoder_out, tgt_tokens=None):
enc_feats = encoder_out.encoder_out # T x B x C
src_masks = encoder_out.encoder_padding_mask # B x T or None
if self.pred_length_offset:
if src_masks is None:
src_lengs = enc_feats.new_ones(enc_feats.size(1)).fill_(
enc_feats.size(0)
)
else:
src_lengs = (~src_masks).transpose(0, 1).type_as(enc_feats).sum(0)
src_lengs = src_lengs.long()
enc_feats = _mean_pooling(enc_feats, src_masks)
if self.sg_length_pred:
enc_feats = enc_feats.detach()
length_out = F.linear(enc_feats, self.embed_length.weight)
if tgt_tokens is not None:
# obtain the length target
tgt_lengs = tgt_tokens.ne(self.padding_idx).sum(1).long()
if self.pred_length_offset:
length_tgt = tgt_lengs - src_lengs + 128
else:
length_tgt = tgt_lengs
length_tgt = length_tgt.clamp(min=0, max=255)
else:
# predict the length target (greedy for now)
# TODO: implementing length-beam
pred_lengs = length_out.max(-1)[1]
if self.pred_length_offset:
length_tgt = pred_lengs - 128 + src_lengs
else:
length_tgt = pred_lengs
return length_out, length_tgt
@register_model_architecture(
"nonautoregressive_transformer", "nonautoregressive_transformer"
)
def base_architecture(args):
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.activation_dropout = getattr(args, "activation_dropout", 0.0)
args.activation_fn = getattr(args, "activation_fn", "relu")
args.dropout = getattr(args, "dropout", 0.1)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.adaptive_input = getattr(args, "adaptive_input", False)
args.apply_bert_init = getattr(args, "apply_bert_init", False)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
# --- special arguments ---
args.sg_length_pred = getattr(args, "sg_length_pred", False)
args.pred_length_offset = getattr(args, "pred_length_offset", False)
args.length_loss_factor = getattr(args, "length_loss_factor", 0.1)
args.src_embedding_copy = getattr(args, "src_embedding_copy", False)
@register_model_architecture(
"nonautoregressive_transformer", "nonautoregressive_transformer_wmt_en_de"
)
def nonautoregressive_transformer_wmt_en_de(args):
base_architecture(args)
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/nonautoregressive_transformer.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import inspect
import torch.nn as nn
from fairseq.legacy_distributed_data_parallel import LegacyDistributedDataParallel
from fairseq.models import BaseFairseqModel
def DistributedFairseqModel(args, model):
"""
Wrap a *model* to support distributed data parallel training.
This is similar to the built-in DistributedDataParallel, but allows
additional configuration of the DistributedDataParallel class to
use, and also provides easier access to the wrapped model by
forwarding requests for missing attributes to the wrapped model.
Args:
args (argparse.Namespace): fairseq args
model (BaseFairseqModel): model to wrap
"""
# determine which DDP class to extend
assert isinstance(model, nn.Module)
if args.ddp_backend == 'c10d':
ddp_class = nn.parallel.DistributedDataParallel
init_kwargs = dict(
module=model,
device_ids=[args.device_id],
output_device=args.device_id,
broadcast_buffers=False,
bucket_cap_mb=args.bucket_cap_mb,
)
# Maintain backward compatibility
if 'check_reduction' in inspect.getargspec(ddp_class)[0]:
init_kwargs['check_reduction'] = True
if 'find_unused_parameters' in inspect.getargspec(ddp_class)[0]:
init_kwargs['find_unused_parameters'] = args.find_unused_parameters
elif args.ddp_backend == 'no_c10d':
ddp_class = LegacyDistributedDataParallel
init_kwargs = dict(
module=model,
world_size=args.distributed_world_size,
buffer_size=2**28,
)
else:
raise ValueError('Unknown --ddp-backend: ' + args.ddp_backend)
class _DistributedFairseqModel(ddp_class):
"""Extend DistributedDataParallel to check for missing
attributes in the wrapped module."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def __getattr__(self, name):
wrapped_module = super().__getattr__('module')
if hasattr(wrapped_module, name):
return getattr(wrapped_module, name)
return super().__getattr__(name)
return _DistributedFairseqModel(**init_kwargs)
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/distributed_fairseq_model.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
This file implements:
Ghazvininejad, Marjan, et al.
"Constant-time machine translation with conditional masked language models."
arXiv preprint arXiv:1904.09324 (2019).
"""
from fairseq.models import register_model, register_model_architecture
from fairseq.models.nonautoregressive_transformer import NATransformerModel
from fairseq.utils import new_arange
def _skeptical_unmasking(output_scores, output_masks, p):
sorted_index = output_scores.sort(-1)[1]
boundary_len = (
(output_masks.sum(1, keepdim=True).type_as(output_scores) - 2) * p
).long()
skeptical_mask = new_arange(output_masks) < boundary_len
return skeptical_mask.scatter(1, sorted_index, skeptical_mask)
@register_model("cmlm_transformer")
class CMLMNATransformerModel(NATransformerModel):
@staticmethod
def add_args(parser):
NATransformerModel.add_args(parser)
def forward(
self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs
):
assert not self.decoder.src_embedding_copy, "do not support embedding copy."
encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
length_out, length_tgt = self.decoder.forward_length_prediction(
encoder_out, tgt_tokens
)
word_ins_out, word_ins_tgt, _ = self.decoder(
prev_output_tokens, encoder_out=encoder_out, tgt_tokens=tgt_tokens
)
word_ins_mask = prev_output_tokens.eq(self.unk)
return {
"word_ins": {
"out": word_ins_out, "tgt": word_ins_tgt,
"mask": word_ins_mask, "ls": self.args.label_smoothing,
"nll_loss": True
},
"length": {
"out": length_out, "tgt": length_tgt,
"factor": self.decoder.length_loss_factor
}
}
def forward_decoder(self, decoder_out, encoder_out, decoding_format=None, **kwargs):
step = decoder_out.step
max_step = decoder_out.max_step
output_tokens = decoder_out.output_tokens
output_scores = decoder_out.output_scores
history = decoder_out.history
# execute the decoder
output_masks = output_tokens.eq(self.unk)
_scores, _tokens = self.decoder(
output_tokens, encoder_out=encoder_out, decoding_format=decoding_format
)
output_tokens.masked_scatter_(output_masks, _tokens[output_masks])
output_scores.masked_scatter_(output_masks, _scores[output_masks])
if history is not None:
history.append(output_tokens.clone())
# skeptical decoding (depend on the maximum decoding steps.)
if (step + 1) < max_step:
skeptical_mask = _skeptical_unmasking(
output_scores, output_tokens.ne(self.pad), 1 - (step + 1) / max_step
)
output_tokens.masked_fill_(skeptical_mask, self.unk)
output_scores.masked_fill_(skeptical_mask, 0.0)
if history is not None:
history.append(output_tokens.clone())
return decoder_out._replace(
output_tokens=output_tokens,
output_scores=output_scores,
attn=None,
history=history
)
@register_model_architecture("cmlm_transformer", "cmlm_transformer")
def base_architecture(args):
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.activation_dropout = getattr(args, "activation_dropout", 0.0)
args.activation_fn = getattr(args, "activation_fn", "relu")
args.dropout = getattr(args, "dropout", 0.1)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", True)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.adaptive_input = getattr(args, "adaptive_input", False)
args.apply_bert_init = getattr(args, "apply_bert_init", False)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
# --- special arguments ---
args.sg_length_pred = getattr(args, "sg_length_pred", False)
args.pred_length_offset = getattr(args, "pred_length_offset", False)
args.length_loss_factor = getattr(args, "length_loss_factor", 0.1)
args.ngram_predictor = getattr(args, "ngram_predictor", 1)
args.src_embedding_copy = getattr(args, "src_embedding_copy", False)
@register_model_architecture("cmlm_transformer", "cmlm_transformer_wmt_en_de")
def iter_nat_wmt_en_de(args):
base_architecture(args)
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/cmlm_transformer.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from collections import namedtuple
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
AdaptiveSoftmax,
LayerNorm,
PositionalEmbedding,
SinusoidalPositionalEmbedding,
TransformerDecoderLayer,
TransformerEncoderLayer,
)
import random
DEFAULT_MAX_SOURCE_POSITIONS = 1024
DEFAULT_MAX_TARGET_POSITIONS = 1024
@register_model('transformer')
class TransformerModel(FairseqEncoderDecoderModel):
"""
Transformer model from `"Attention Is All You Need" (Vaswani, et al, 2017)
<https://arxiv.org/abs/1706.03762>`_.
Args:
encoder (TransformerEncoder): the encoder
decoder (TransformerDecoder): the decoder
The Transformer model provides the following named architectures and
command-line arguments:
.. argparse::
:ref: fairseq.models.transformer_parser
:prog:
"""
@classmethod
def hub_models(cls):
# fmt: off
def moses_subword(path):
return {
'path': path,
'tokenizer': 'moses',
'bpe': 'subword_nmt',
}
def moses_fastbpe(path):
return {
'path': path,
'tokenizer': 'moses',
'bpe': 'fastbpe',
}
return {
'transformer.wmt14.en-fr': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-fr.joined-dict.transformer.tar.bz2'),
'transformer.wmt16.en-de': 'https://dl.fbaipublicfiles.com/fairseq/models/wmt16.en-de.joined-dict.transformer.tar.bz2',
'transformer.wmt18.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/wmt18.en-de.ensemble.tar.gz'),
'transformer.wmt19.en-de': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.ensemble.tar.gz'),
'transformer.wmt19.en-ru': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.ensemble.tar.gz'),
'transformer.wmt19.de-en': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.ensemble.tar.gz'),
'transformer.wmt19.ru-en': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.ensemble.tar.gz'),
'transformer.wmt19.en-de.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.single_model.tar.gz'),
'transformer.wmt19.en-ru.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.single_model.tar.gz'),
'transformer.wmt19.de-en.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.single_model.tar.gz'),
'transformer.wmt19.ru-en.single_model': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.single_model.tar.gz'),
}
# fmt: on
def __init__(self, args, encoder, decoder):
super().__init__(encoder, decoder)
self.args = args
self.supports_align_args = True
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
# fmt: off
parser.add_argument('--activation-fn',
choices=utils.get_available_activation_fns(),
help='activation function to use')
parser.add_argument('--dropout', type=float, metavar='D',
help='dropout probability')
parser.add_argument('--attention-dropout', type=float, metavar='D',
help='dropout probability for attention weights')
parser.add_argument('--activation-dropout', '--relu-dropout', type=float, metavar='D',
help='dropout probability after activation in FFN.')
parser.add_argument('--encoder-embed-path', type=str, metavar='STR',
help='path to pre-trained encoder embedding')
parser.add_argument('--encoder-embed-dim', type=int, metavar='N',
help='encoder embedding dimension')
parser.add_argument('--encoder-ffn-embed-dim', type=int, metavar='N',
help='encoder embedding dimension for FFN')
parser.add_argument('--encoder-layers', type=int, metavar='N',
help='num encoder layers')
parser.add_argument('--encoder-attention-heads', type=int, metavar='N',
help='num encoder attention heads')
parser.add_argument('--encoder-normalize-before', action='store_true',
help='apply layernorm before each encoder block')
parser.add_argument('--encoder-learned-pos', action='store_true',
help='use learned positional embeddings in the encoder')
parser.add_argument('--decoder-embed-path', type=str, metavar='STR',
help='path to pre-trained decoder embedding')
parser.add_argument('--decoder-embed-dim', type=int, metavar='N',
help='decoder embedding dimension')
parser.add_argument('--decoder-ffn-embed-dim', type=int, metavar='N',
help='decoder embedding dimension for FFN')
parser.add_argument('--decoder-layers', type=int, metavar='N',
help='num decoder layers')
parser.add_argument('--decoder-attention-heads', type=int, metavar='N',
help='num decoder attention heads')
parser.add_argument('--decoder-learned-pos', action='store_true',
help='use learned positional embeddings in the decoder')
parser.add_argument('--decoder-normalize-before', action='store_true',
help='apply layernorm before each decoder block')
parser.add_argument('--share-decoder-input-output-embed', action='store_true',
help='share decoder input and output embeddings')
parser.add_argument('--share-all-embeddings', action='store_true',
help='share encoder, decoder and output embeddings'
' (requires shared dictionary and embed dim)')
parser.add_argument('--no-token-positional-embeddings', default=False, action='store_true',
help='if set, disables positional embeddings (outside self attention)')
parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR',
help='comma separated list of adaptive softmax cutoff points. '
'Must be used with adaptive_loss criterion'),
parser.add_argument('--adaptive-softmax-dropout', type=float, metavar='D',
help='sets adaptive softmax dropout for the tail projections')
# args for "Cross+Self-Attention for Transformer Models" (Peitz et al., 2019)
parser.add_argument('--no-cross-attention', default=False, action='store_true',
help='do not perform cross-attention')
parser.add_argument('--cross-self-attention', default=False, action='store_true',
help='perform cross+self-attention')
parser.add_argument('--layer-wise-attention', default=False, action='store_true',
help='perform layer-wise attention (cross-attention or cross+self-attention)')
# args for "Reducing Transformer Depth on Demand with Structured Dropout" (Fan et al., 2019)
parser.add_argument('--encoder-layerdrop', type=float, metavar='D', default=0,
help='LayerDrop probability for encoder')
parser.add_argument('--decoder-layerdrop', type=float, metavar='D', default=0,
help='LayerDrop probability for decoder')
parser.add_argument('--encoder-layers-to-keep', default=None,
help='which layers to *keep* when pruning as a comma-separated list')
parser.add_argument('--decoder-layers-to-keep', default=None,
help='which layers to *keep* when pruning as a comma-separated list')
parser.add_argument('--layernorm-embedding', action='store_true',
help='add layernorm to embedding')
parser.add_argument('--no-scale-embedding', action='store_true',
help='if True, dont scale embeddings')
# fmt: on
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present in older models
base_architecture(args)
if args.encoder_layers_to_keep:
args.encoder_layers = len(args.encoder_layers_to_keep.split(","))
if args.decoder_layers_to_keep:
args.decoder_layers = len(args.decoder_layers_to_keep.split(","))
if getattr(args, 'max_source_positions', None) is None:
args.max_source_positions = DEFAULT_MAX_SOURCE_POSITIONS
if getattr(args, 'max_target_positions', None) is None:
args.max_target_positions = DEFAULT_MAX_TARGET_POSITIONS
src_dict, tgt_dict = task.source_dictionary, task.target_dictionary
def build_embedding(dictionary, embed_dim, path=None):
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
emb = Embedding(num_embeddings, embed_dim, padding_idx)
# if provided, load from preloaded dictionaries
if path:
embed_dict = utils.parse_embedding(path)
utils.load_embedding(embed_dict, dictionary, emb)
return emb
if args.share_all_embeddings:
if src_dict != tgt_dict:
raise ValueError('--share-all-embeddings requires a joined dictionary')
if args.encoder_embed_dim != args.decoder_embed_dim:
raise ValueError(
'--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim')
if args.decoder_embed_path and (
args.decoder_embed_path != args.encoder_embed_path):
raise ValueError('--share-all-embeddings not compatible with --decoder-embed-path')
encoder_embed_tokens = build_embedding(
src_dict, args.encoder_embed_dim, args.encoder_embed_path
)
decoder_embed_tokens = encoder_embed_tokens
args.share_decoder_input_output_embed = True
else:
encoder_embed_tokens = build_embedding(
src_dict, args.encoder_embed_dim, args.encoder_embed_path
)
decoder_embed_tokens = build_embedding(
tgt_dict, args.decoder_embed_dim, args.decoder_embed_path
)
encoder = cls.build_encoder(args, src_dict, encoder_embed_tokens)
decoder = cls.build_decoder(args, tgt_dict, decoder_embed_tokens)
return cls(args, encoder, decoder)
@classmethod
def build_encoder(cls, args, src_dict, embed_tokens):
return TransformerEncoder(args, src_dict, embed_tokens)
@classmethod
def build_decoder(cls, args, tgt_dict, embed_tokens):
return TransformerDecoder(
args,
tgt_dict,
embed_tokens,
no_encoder_attn=getattr(args, 'no_cross_attention', False),
)
@register_model('transformer_align')
class TransformerAlignModel(TransformerModel):
"""
See "Jointly Learning to Align and Translate with Transformer
Models" (Garg et al., EMNLP 2019).
"""
def __init__(self, encoder, decoder, args):
super().__init__(args, encoder, decoder)
self.alignment_heads = args.alignment_heads
self.alignment_layer = args.alignment_layer
self.full_context_alignment = args.full_context_alignment
@staticmethod
def add_args(parser):
# fmt: off
super(TransformerAlignModel, TransformerAlignModel).add_args(parser)
parser.add_argument('--alignment-heads', type=int, metavar='D',
help='Number of cross attention heads per layer to supervised with alignments')
parser.add_argument('--alignment-layer', type=int, metavar='D',
help='Layer number which has to be supervised. 0 corresponding to the bottommost layer.')
parser.add_argument('--full-context-alignment', type=bool, metavar='D',
help='Whether or not alignment is supervised conditioned on the full target context.')
# fmt: on
@classmethod
def build_model(cls, args, task):
# set any default arguments
transformer_align(args)
transformer_model = TransformerModel.build_model(args, task)
return TransformerAlignModel(transformer_model.encoder, transformer_model.decoder, args)
def forward(self, src_tokens, src_lengths, prev_output_tokens):
encoder_out = self.encoder(src_tokens, src_lengths)
return self.forward_decoder(prev_output_tokens, encoder_out)
def forward_decoder(
self,
prev_output_tokens,
encoder_out=None,
incremental_state=None,
features_only=False,
**extra_args,
):
attn_args = {'alignment_layer': self.alignment_layer, 'alignment_heads': self.alignment_heads}
decoder_out = self.decoder(
prev_output_tokens,
encoder_out,
**attn_args,
**extra_args,
)
if self.full_context_alignment:
attn_args['full_context_alignment'] = self.full_context_alignment
_, alignment_out = self.decoder(
prev_output_tokens, encoder_out, features_only=True, **attn_args, **extra_args,
)
decoder_out[1]['attn'] = alignment_out['attn']
return decoder_out
EncoderOut = namedtuple('TransformerEncoderOut', [
'encoder_out', # T x B x C
'encoder_padding_mask', # B x T
'encoder_embedding', # B x T x C
'encoder_states', # List[T x B x C]
])
class TransformerEncoder(FairseqEncoder):
"""
Transformer encoder consisting of *args.encoder_layers* layers. Each layer
is a :class:`TransformerEncoderLayer`.
Args:
args (argparse.Namespace): parsed command-line arguments
dictionary (~fairseq.data.Dictionary): encoding dictionary
embed_tokens (torch.nn.Embedding): input embedding
"""
def __init__(self, args, dictionary, embed_tokens):
super().__init__(dictionary)
self.register_buffer('version', torch.Tensor([3]))
self.dropout = args.dropout
self.encoder_layerdrop = args.encoder_layerdrop
embed_dim = embed_tokens.embedding_dim
self.padding_idx = embed_tokens.padding_idx
self.max_source_positions = args.max_source_positions
self.embed_tokens = embed_tokens
self.embed_scale = 1.0 if args.no_scale_embedding else math.sqrt(embed_dim)
self.embed_positions = PositionalEmbedding(
args.max_source_positions, embed_dim, self.padding_idx,
learned=args.encoder_learned_pos,
) if not args.no_token_positional_embeddings else None
self.layer_wise_attention = getattr(args, 'layer_wise_attention', False)
self.layers = nn.ModuleList([])
self.layers.extend([
TransformerEncoderLayer(args)
for i in range(args.encoder_layers)
])
if args.encoder_normalize_before:
self.layer_norm = LayerNorm(embed_dim)
else:
self.layer_norm = None
if getattr(args, 'layernorm_embedding', False):
self.layernorm_embedding = LayerNorm(embed_dim)
else:
self.layernorm_embedding = None
def forward_embedding(self, src_tokens):
# embed tokens and positions
x = embed = self.embed_scale * self.embed_tokens(src_tokens)
if self.embed_positions is not None:
x = embed + self.embed_positions(src_tokens)
if self.layernorm_embedding:
x = self.layernorm_embedding(x)
x = F.dropout(x, p=self.dropout, training=self.training)
return x, embed
def forward(self, src_tokens, src_lengths, cls_input=None, return_all_hiddens=False, **unused):
"""
Args:
src_tokens (LongTensor): tokens in the source language of shape
`(batch, src_len)`
src_lengths (torch.LongTensor): lengths of each source sentence of
shape `(batch)`
return_all_hiddens (bool, optional): also return all of the
intermediate hidden states (default: False).
Returns:
namedtuple:
- **encoder_out** (Tensor): the last encoder layer's output of
shape `(src_len, batch, embed_dim)`
- **encoder_padding_mask** (ByteTensor): the positions of
padding elements of shape `(batch, src_len)`
- **encoder_embedding** (Tensor): the (scaled) embedding lookup
of shape `(batch, src_len, embed_dim)`
- **encoder_states** (List[Tensor]): all intermediate
hidden states of shape `(src_len, batch, embed_dim)`.
Only populated if *return_all_hiddens* is True.
"""
if self.layer_wise_attention:
return_all_hiddens = True
x, encoder_embedding = self.forward_embedding(src_tokens)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
# compute padding mask
encoder_padding_mask = src_tokens.eq(self.padding_idx)
if not encoder_padding_mask.any():
encoder_padding_mask = None
encoder_states = [] if return_all_hiddens else None
# encoder layers
for layer in self.layers:
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
if not self.training or (dropout_probability > self.encoder_layerdrop):
x = layer(x, encoder_padding_mask)
if return_all_hiddens:
encoder_states.append(x)
if self.layer_norm:
x = self.layer_norm(x)
if return_all_hiddens:
encoder_states[-1] = x
return EncoderOut(
encoder_out=x, # T x B x C
encoder_padding_mask=encoder_padding_mask, # B x T
encoder_embedding=encoder_embedding, # B x T x C
encoder_states=encoder_states, # List[T x B x C]
)
def reorder_encoder_out(self, encoder_out, new_order):
"""
Reorder encoder output according to *new_order*.
Args:
encoder_out: output from the ``forward()`` method
new_order (LongTensor): desired order
Returns:
*encoder_out* rearranged according to *new_order*
"""
if encoder_out.encoder_out is not None:
encoder_out = encoder_out._replace(
encoder_out=encoder_out.encoder_out.index_select(1, new_order)
)
if encoder_out.encoder_padding_mask is not None:
encoder_out = encoder_out._replace(
encoder_padding_mask=encoder_out.encoder_padding_mask.index_select(0, new_order)
)
if encoder_out.encoder_embedding is not None:
encoder_out = encoder_out._replace(
encoder_embedding=encoder_out.encoder_embedding.index_select(0, new_order)
)
if encoder_out.encoder_states is not None:
for idx, state in enumerate(encoder_out.encoder_states):
encoder_out.encoder_states[idx] = state.index_select(1, new_order)
return encoder_out
def max_positions(self):
"""Maximum input length supported by the encoder."""
if self.embed_positions is None:
return self.max_source_positions
return min(self.max_source_positions, self.embed_positions.max_positions())
def buffered_future_mask(self, tensor):
dim = tensor.size(0)
if not hasattr(self, '_future_mask') or self._future_mask is None or self._future_mask.device != tensor.device:
self._future_mask = torch.triu(utils.fill_with_neg_inf(tensor.new(dim, dim)), 1)
if self._future_mask.size(0) < dim:
self._future_mask = torch.triu(utils.fill_with_neg_inf(self._future_mask.resize_(dim, dim)), 1)
return self._future_mask[:dim, :dim]
def upgrade_state_dict_named(self, state_dict, name):
"""Upgrade a (possibly old) state dict for new versions of fairseq."""
if isinstance(self.embed_positions, SinusoidalPositionalEmbedding):
weights_key = '{}.embed_positions.weights'.format(name)
if weights_key in state_dict:
print('deleting {0}'.format(weights_key))
del state_dict[weights_key]
state_dict['{}.embed_positions._float_tensor'.format(name)] = torch.FloatTensor(1)
for i in range(len(self.layers)):
# update layer norms
self.layers[i].upgrade_state_dict_named(state_dict, "{}.layers.{}".format(name, i))
version_key = '{}.version'.format(name)
if utils.item(state_dict.get(version_key, torch.Tensor([1]))[0]) < 2:
# earlier checkpoints did not normalize after the stack of layers
self.layer_norm = None
self.normalize = False
state_dict[version_key] = torch.Tensor([1])
return state_dict
class TransformerDecoder(FairseqIncrementalDecoder):
"""
Transformer decoder consisting of *args.decoder_layers* layers. Each layer
is a :class:`TransformerDecoderLayer`.
Args:
args (argparse.Namespace): parsed command-line arguments
dictionary (~fairseq.data.Dictionary): decoding dictionary
embed_tokens (torch.nn.Embedding): output embedding
no_encoder_attn (bool, optional): whether to attend to encoder outputs
(default: False).
"""
def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False):
super().__init__(dictionary)
self.register_buffer('version', torch.Tensor([3]))
self.dropout = args.dropout
self.decoder_layerdrop = args.decoder_layerdrop
self.share_input_output_embed = args.share_decoder_input_output_embed
input_embed_dim = embed_tokens.embedding_dim
embed_dim = args.decoder_embed_dim
self.output_embed_dim = args.decoder_output_dim
self.padding_idx = embed_tokens.padding_idx
self.max_target_positions = args.max_target_positions
self.embed_tokens = embed_tokens
self.embed_scale = 1.0 if args.no_scale_embedding else math.sqrt(embed_dim)
self.project_in_dim = Linear(input_embed_dim, embed_dim, bias=False) if embed_dim != input_embed_dim else None
self.embed_positions = PositionalEmbedding(
args.max_target_positions, embed_dim, self.padding_idx,
learned=args.decoder_learned_pos,
) if not args.no_token_positional_embeddings else None
self.cross_self_attention = getattr(args, 'cross_self_attention', False)
self.layer_wise_attention = getattr(args, 'layer_wise_attention', False)
self.layers = nn.ModuleList([])
self.layers.extend([
TransformerDecoderLayer(args, no_encoder_attn)
for _ in range(args.decoder_layers)
])
self.adaptive_softmax = None
self.project_out_dim = Linear(embed_dim, self.output_embed_dim, bias=False) \
if embed_dim != self.output_embed_dim and not args.tie_adaptive_weights else None
if args.adaptive_softmax_cutoff is not None:
self.adaptive_softmax = AdaptiveSoftmax(
len(dictionary),
self.output_embed_dim,
options.eval_str_list(args.adaptive_softmax_cutoff, type=int),
dropout=args.adaptive_softmax_dropout,
adaptive_inputs=embed_tokens if args.tie_adaptive_weights else None,
factor=args.adaptive_softmax_factor,
tie_proj=args.tie_adaptive_proj,
)
elif not self.share_input_output_embed:
self.embed_out = nn.Parameter(torch.Tensor(len(dictionary), self.output_embed_dim))
nn.init.normal_(self.embed_out, mean=0, std=self.output_embed_dim ** -0.5)
if args.decoder_normalize_before and not getattr(args, 'no_decoder_final_norm', False):
self.layer_norm = LayerNorm(embed_dim)
else:
self.layer_norm = None
if getattr(args, 'layernorm_embedding', False):
self.layernorm_embedding = LayerNorm(embed_dim)
else:
self.layernorm_embedding = None
def forward(
self,
prev_output_tokens,
encoder_out=None,
incremental_state=None,
features_only=False,
**extra_args
):
"""
Args:
prev_output_tokens (LongTensor): previous decoder outputs of shape
`(batch, tgt_len)`, for teacher forcing
encoder_out (optional): output from the encoder, used for
encoder-side attention
incremental_state (dict): dictionary used for storing state during
:ref:`Incremental decoding`
features_only (bool, optional): only return features without
applying output layer (default: False).
Returns:
tuple:
- the decoder's output of shape `(batch, tgt_len, vocab)`
- a dictionary with any model-specific outputs
"""
x, extra = self.extract_features(
prev_output_tokens,
encoder_out=encoder_out,
incremental_state=incremental_state,
**extra_args
)
if not features_only:
x = self.output_layer(x)
return x, extra
def extract_features(
self,
prev_output_tokens,
encoder_out=None,
incremental_state=None,
full_context_alignment=False,
alignment_layer=None,
alignment_heads=None,
**unused,
):
"""
Similar to *forward* but only return features.
Includes several features from "Jointly Learning to Align and
Translate with Transformer Models" (Garg et al., EMNLP 2019).
Args:
full_context_alignment (bool, optional): don't apply
auto-regressive mask to self-attention (default: False).
alignment_layer (int, optional): return mean alignment over
heads at this layer (default: last layer).
alignment_heads (int, optional): only average alignment over
this many heads (default: all heads).
Returns:
tuple:
- the decoder's features of shape `(batch, tgt_len, embed_dim)`
- a dictionary with any model-specific outputs
"""
if alignment_layer is None:
alignment_layer = len(self.layers) - 1
# embed positions
positions = self.embed_positions(
prev_output_tokens,
incremental_state=incremental_state,
) if self.embed_positions is not None else None
if incremental_state is not None:
prev_output_tokens = prev_output_tokens[:, -1:]
if positions is not None:
positions = positions[:, -1:]
# embed tokens and positions
x = self.embed_scale * self.embed_tokens(prev_output_tokens)
if self.project_in_dim is not None:
x = self.project_in_dim(x)
if positions is not None:
x += positions
if self.layernorm_embedding:
x = self.layernorm_embedding(x)
x = F.dropout(x, p=self.dropout, training=self.training)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
self_attn_padding_mask = None
if self.cross_self_attention or prev_output_tokens.eq(self.padding_idx).any():
self_attn_padding_mask = prev_output_tokens.eq(self.padding_idx)
# decoder layers
attn = None
inner_states = [x]
for idx, layer in enumerate(self.layers):
encoder_state = None
if encoder_out is not None:
if self.layer_wise_attention:
encoder_state = encoder_out.encoder_states[idx]
else:
encoder_state = encoder_out.encoder_out
if incremental_state is None and not full_context_alignment:
self_attn_mask = self.buffered_future_mask(x)
else:
self_attn_mask = None
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
if not self.training or (dropout_probability > self.decoder_layerdrop):
x, layer_attn = layer(
x,
encoder_state,
encoder_out.encoder_padding_mask if encoder_out is not None else None,
incremental_state,
self_attn_mask=self_attn_mask,
self_attn_padding_mask=self_attn_padding_mask,
need_attn=(idx == alignment_layer),
need_head_weights=(idx == alignment_layer),
)
inner_states.append(x)
if layer_attn is not None and idx == alignment_layer:
attn = layer_attn.float()
if attn is not None:
if alignment_heads is not None:
attn = attn[:alignment_heads]
# average probabilities over heads
attn = attn.mean(dim=0)
if self.layer_norm:
x = self.layer_norm(x)
# T x B x C -> B x T x C
x = x.transpose(0, 1)
if self.project_out_dim is not None:
x = self.project_out_dim(x)
return x, {'attn': attn, 'inner_states': inner_states}
def output_layer(self, features, **kwargs):
"""Project features to the vocabulary size."""
if self.adaptive_softmax is None:
# project back to size of vocabulary
if self.share_input_output_embed:
return F.linear(features, self.embed_tokens.weight)
else:
return F.linear(features, self.embed_out)
else:
return features
def max_positions(self):
"""Maximum output length supported by the decoder."""
if self.embed_positions is None:
return self.max_target_positions
return min(self.max_target_positions, self.embed_positions.max_positions())
def buffered_future_mask(self, tensor):
dim = tensor.size(0)
if (
not hasattr(self, '_future_mask')
or self._future_mask is None
or self._future_mask.device != tensor.device
or self._future_mask.size(0) < dim
):
self._future_mask = torch.triu(utils.fill_with_neg_inf(tensor.new(dim, dim)), 1)
return self._future_mask[:dim, :dim]
def upgrade_state_dict_named(self, state_dict, name):
"""Upgrade a (possibly old) state dict for new versions of fairseq."""
if isinstance(self.embed_positions, SinusoidalPositionalEmbedding):
weights_key = '{}.embed_positions.weights'.format(name)
if weights_key in state_dict:
del state_dict[weights_key]
state_dict['{}.embed_positions._float_tensor'.format(name)] = torch.FloatTensor(1)
for i in range(len(self.layers)):
# update layer norms
layer_norm_map = {
'0': 'self_attn_layer_norm',
'1': 'encoder_attn_layer_norm',
'2': 'final_layer_norm'
}
for old, new in layer_norm_map.items():
for m in ('weight', 'bias'):
k = '{}.layers.{}.layer_norms.{}.{}'.format(name, i, old, m)
if k in state_dict:
state_dict['{}.layers.{}.{}.{}'.format(name, i, new, m)] = state_dict[k]
del state_dict[k]
version_key = '{}.version'.format(name)
if utils.item(state_dict.get(version_key, torch.Tensor([1]))[0]) <= 2:
# earlier checkpoints did not normalize after the stack of layers
self.layer_norm = None
self.normalize = False
state_dict[version_key] = torch.Tensor([1])
return state_dict
def Embedding(num_embeddings, embedding_dim, padding_idx):
m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
nn.init.constant_(m.weight[padding_idx], 0)
return m
def Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
if bias:
nn.init.constant_(m.bias, 0.)
return m
@register_model_architecture('transformer', 'transformer')
def base_architecture(args):
args.encoder_embed_path = getattr(args, 'encoder_embed_path', None)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 2048)
args.encoder_layers = getattr(args, 'encoder_layers', 6)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 8)
args.encoder_normalize_before = getattr(args, 'encoder_normalize_before', False)
args.encoder_learned_pos = getattr(args, 'encoder_learned_pos', False)
args.decoder_embed_path = getattr(args, 'decoder_embed_path', None)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', args.encoder_ffn_embed_dim)
args.decoder_layers = getattr(args, 'decoder_layers', 6)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 8)
args.decoder_normalize_before = getattr(args, 'decoder_normalize_before', False)
args.decoder_learned_pos = getattr(args, 'decoder_learned_pos', False)
args.attention_dropout = getattr(args, 'attention_dropout', 0.)
args.activation_dropout = getattr(args, 'activation_dropout', 0.)
args.activation_fn = getattr(args, 'activation_fn', 'relu')
args.dropout = getattr(args, 'dropout', 0.1)
args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', None)
args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0)
args.share_decoder_input_output_embed = getattr(args, 'share_decoder_input_output_embed', False)
args.share_all_embeddings = getattr(args, 'share_all_embeddings', False)
args.no_token_positional_embeddings = getattr(args, 'no_token_positional_embeddings', False)
args.adaptive_input = getattr(args, 'adaptive_input', False)
args.no_cross_attention = getattr(args, 'no_cross_attention', False)
args.cross_self_attention = getattr(args, 'cross_self_attention', False)
args.layer_wise_attention = getattr(args, 'layer_wise_attention', False)
args.decoder_output_dim = getattr(args, 'decoder_output_dim', args.decoder_embed_dim)
args.decoder_input_dim = getattr(args, 'decoder_input_dim', args.decoder_embed_dim)
args.no_scale_embedding = getattr(args, 'no_scale_embedding', False)
args.layernorm_embedding = getattr(args, 'layernorm_embedding', False)
@register_model_architecture('transformer', 'transformer_iwslt_de_en')
def transformer_iwslt_de_en(args):
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 1024)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 4)
args.encoder_layers = getattr(args, 'encoder_layers', 6)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 1024)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 4)
args.decoder_layers = getattr(args, 'decoder_layers', 6)
base_architecture(args)
@register_model_architecture('transformer', 'transformer_wmt_en_de')
def transformer_wmt_en_de(args):
base_architecture(args)
# parameters used in the "Attention Is All You Need" paper (Vaswani et al., 2017)
@register_model_architecture('transformer', 'transformer_vaswani_wmt_en_de_big')
def transformer_vaswani_wmt_en_de_big(args):
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1024)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 4096)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 16)
args.encoder_normalize_before = getattr(args, 'encoder_normalize_before', False)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 1024)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 4096)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 16)
args.dropout = getattr(args, 'dropout', 0.3)
base_architecture(args)
@register_model_architecture('transformer', 'transformer_vaswani_wmt_en_fr_big')
def transformer_vaswani_wmt_en_fr_big(args):
args.dropout = getattr(args, 'dropout', 0.1)
transformer_vaswani_wmt_en_de_big(args)
@register_model_architecture('transformer', 'transformer_wmt_en_de_big')
def transformer_wmt_en_de_big(args):
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
transformer_vaswani_wmt_en_de_big(args)
# default parameters used in tensor2tensor implementation
@register_model_architecture('transformer', 'transformer_wmt_en_de_big_t2t')
def transformer_wmt_en_de_big_t2t(args):
args.encoder_normalize_before = getattr(args, 'encoder_normalize_before', True)
args.decoder_normalize_before = getattr(args, 'decoder_normalize_before', True)
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
args.activation_dropout = getattr(args, 'activation_dropout', 0.1)
transformer_vaswani_wmt_en_de_big(args)
@register_model_architecture('transformer_align', 'transformer_align')
def transformer_align(args):
args.alignment_heads = getattr(args, 'alignment_heads', 1)
args.alignment_layer = getattr(args, 'alignment_layer', 4)
args.full_context_alignment = getattr(args, 'full_context_alignment', False)
base_architecture(args)
@register_model_architecture('transformer_align', 'transformer_wmt_en_de_big_align')
def transformer_wmt_en_de_big_align(args):
args.alignment_heads = getattr(args, 'alignment_heads', 1)
args.alignment_layer = getattr(args, 'alignment_layer', 4)
transformer_wmt_en_de_big(args)
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/transformer.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from fairseq import options, utils
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
Embedding,
TransformerDecoder,
)
from fairseq.modules import (
AdaptiveInput,
CharacterTokenEmbedder,
)
DEFAULT_MAX_TARGET_POSITIONS = 1024
@register_model('transformer_lm')
class TransformerLanguageModel(FairseqLanguageModel):
@classmethod
def hub_models(cls):
def moses_fastbpe(path):
return {
'path': path,
'tokenizer': 'moses',
'bpe': 'fastbpe',
}
return {
'transformer_lm.gbw.adaptive_huge': 'https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_gbw_huge.tar.bz2',
'transformer_lm.wiki103.adaptive': 'https://dl.fbaipublicfiles.com/fairseq/models/lm/adaptive_lm_wiki103.tar.bz2',
'transformer_lm.wmt19.en': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.en.tar.bz2'),
'transformer_lm.wmt19.de': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.de.tar.bz2'),
'transformer_lm.wmt19.ru': moses_fastbpe('https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.ru.tar.bz2'),
}
def __init__(self, decoder):
super().__init__(decoder)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
# fmt: off
parser.add_argument('--activation-fn',
choices=utils.get_available_activation_fns(),
help='activation function to use')
parser.add_argument('--dropout', type=float, metavar='D',
help='dropout probability')
parser.add_argument('--attention-dropout', type=float, metavar='D',
help='dropout probability for attention weights')
parser.add_argument('--activation-dropout', '--relu-dropout', type=float, metavar='D',
help='dropout probability after activation in FFN.')
parser.add_argument('--decoder-embed-dim', type=int, metavar='N',
help='decoder embedding dimension')
parser.add_argument('--decoder-output-dim', type=int, metavar='N',
help='decoder output dimension')
parser.add_argument('--decoder-input-dim', type=int, metavar='N',
help='decoder input dimension')
parser.add_argument('--decoder-ffn-embed-dim', type=int, metavar='N',
help='decoder embedding dimension for FFN')
parser.add_argument('--decoder-layers', type=int, metavar='N',
help='num decoder layers')
parser.add_argument('--decoder-attention-heads', type=int, metavar='N',
help='num decoder attention heads')
parser.add_argument('--decoder-normalize-before', action='store_true',
help='apply layernorm before each decoder block')
parser.add_argument('--no-decoder-final-norm', action='store_true',
help='don\'t add an extra layernorm after the last decoder block')
parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR',
help='comma separated list of adaptive softmax cutoff points. '
'Must be used with adaptive_loss criterion')
parser.add_argument('--adaptive-softmax-dropout', type=float, metavar='D',
help='sets adaptive softmax dropout for the tail projections')
parser.add_argument('--adaptive-softmax-factor', type=float, metavar='N',
help='adaptive input factor')
parser.add_argument('--no-token-positional-embeddings', action='store_true',
help='if set, disables positional embeddings (outside self attention)')
parser.add_argument('--share-decoder-input-output-embed', action='store_true',
help='share decoder input and output embeddings')
parser.add_argument('--character-embeddings', action='store_true',
help='if set, uses character embedding convolutions to produce token embeddings')
parser.add_argument('--character-filters', type=str, metavar='LIST',
default='[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]',
help='size of character embeddings')
parser.add_argument('--character-embedding-dim', default=4, type=int, metavar='N',
help='size of character embeddings')
parser.add_argument('--char-embedder-highway-layers', default=2, type=int, metavar='N',
help='number of highway layers for character token embeddder')
parser.add_argument('--adaptive-input', action='store_true',
help='if set, uses adaptive input')
parser.add_argument('--adaptive-input-factor', type=float, metavar='N',
help='adaptive input factor')
parser.add_argument('--adaptive-input-cutoff', metavar='EXPR',
help='comma separated list of adaptive input cutoff points.')
parser.add_argument('--tie-adaptive-weights', action='store_true',
help='if set, ties the weights of adaptive softmax and adaptive input')
parser.add_argument('--tie-adaptive-proj', action='store_true',
help='if set, ties the projection weights of adaptive softmax and adaptive input')
parser.add_argument('--decoder-learned-pos', action='store_true',
help='use learned positional embeddings in the decoder')
# args for "Reducing Transformer Depth on Demand with Structured Dropout" (Fan et al., 2019)
parser.add_argument('--decoder-layerdrop', type=float, metavar='D', default=0,
help='LayerDrop probability for decoder')
parser.add_argument('--decoder-layers-to-keep', default=None,
help='which layers to *keep* when pruning as a comma-separated list')
parser.add_argument('--layernorm-embedding', action='store_true',
help='add layernorm to embedding')
parser.add_argument('--no-scale-embedding', action='store_true',
help='if True, dont scale embeddings')
# fmt: on
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present in older models
base_lm_architecture(args)
if args.decoder_layers_to_keep:
args.decoder_layers = len(args.decoder_layers_to_keep.split(","))
if getattr(args, 'max_target_positions', None) is None:
args.max_target_positions = getattr(args, 'tokens_per_sample', DEFAULT_MAX_TARGET_POSITIONS)
if args.character_embeddings:
embed_tokens = CharacterTokenEmbedder(
task.source_dictionary, eval(args.character_filters),
args.character_embedding_dim, args.decoder_embed_dim,
args.char_embedder_highway_layers,
)
elif args.adaptive_input:
embed_tokens = AdaptiveInput(
len(task.source_dictionary), task.source_dictionary.pad(), args.decoder_input_dim,
args.adaptive_input_factor, args.decoder_embed_dim,
options.eval_str_list(args.adaptive_input_cutoff, type=int),
)
else:
embed_tokens = Embedding(len(task.source_dictionary), args.decoder_input_dim, task.source_dictionary.pad())
if args.tie_adaptive_weights:
assert args.adaptive_input
assert args.adaptive_input_factor == args.adaptive_softmax_factor
assert args.adaptive_softmax_cutoff == args.adaptive_input_cutoff, '{} != {}'.format(
args.adaptive_softmax_cutoff, args.adaptive_input_cutoff)
assert args.decoder_input_dim == args.decoder_output_dim
decoder = TransformerDecoder(
args, task.target_dictionary, embed_tokens, no_encoder_attn=True,
)
return TransformerLanguageModel(decoder)
@register_model_architecture('transformer_lm', 'transformer_lm')
def base_lm_architecture(args):
# backward compatibility for older model checkpoints
if hasattr(args, 'no_tie_adaptive_proj'):
# previous models defined --no-tie-adaptive-proj, so use the existence of
# that option to determine if this is an "old" model checkpoint
args.no_decoder_final_norm = True # old models always set this to True
if args.no_tie_adaptive_proj is False:
args.tie_adaptive_proj = True
if hasattr(args, 'decoder_final_norm'):
args.no_decoder_final_norm = not args.decoder_final_norm
args.dropout = getattr(args, 'dropout', 0.1)
args.attention_dropout = getattr(args, 'attention_dropout', 0.0)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 2048)
args.decoder_layers = getattr(args, 'decoder_layers', 6)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 8)
args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', None)
args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0)
args.adaptive_softmax_factor = getattr(args, 'adaptive_softmax_factor', 4)
args.decoder_learned_pos = getattr(args, 'decoder_learned_pos', False)
args.activation_fn = getattr(args, 'activation_fn', 'relu')
args.add_bos_token = getattr(args, 'add_bos_token', False)
args.no_token_positional_embeddings = getattr(args, 'no_token_positional_embeddings', False)
args.share_decoder_input_output_embed = getattr(args, 'share_decoder_input_output_embed', False)
args.character_embeddings = getattr(args, 'character_embeddings', False)
args.decoder_output_dim = getattr(args, 'decoder_output_dim', args.decoder_embed_dim)
args.decoder_input_dim = getattr(args, 'decoder_input_dim', args.decoder_embed_dim)
# Model training is not stable without this
args.decoder_normalize_before = True
args.no_decoder_final_norm = getattr(args, 'no_decoder_final_norm', False)
args.adaptive_input = getattr(args, 'adaptive_input', False)
args.adaptive_input_factor = getattr(args, 'adaptive_input_factor', 4)
args.adaptive_input_cutoff = getattr(args, 'adaptive_input_cutoff', None)
args.tie_adaptive_weights = getattr(args, 'tie_adaptive_weights', False)
args.tie_adaptive_proj = getattr(args, 'tie_adaptive_proj', False)
args.no_scale_embedding = getattr(args, 'no_scale_embedding', False)
args.layernorm_embedding = getattr(args, 'layernorm_embedding', False)
@register_model_architecture('transformer_lm', 'transformer_lm_big')
def transformer_lm_big(args):
args.decoder_layers = getattr(args, 'decoder_layers', 12)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 1024)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 4096)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 16)
base_lm_architecture(args)
@register_model_architecture('transformer_lm', 'transformer_lm_wiki103')
@register_model_architecture('transformer_lm', 'transformer_lm_baevski_wiki103')
def transformer_lm_baevski_wiki103(args):
args.decoder_layers = getattr(args, 'decoder_layers', 16)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 8)
args.dropout = getattr(args, 'dropout', 0.3)
args.adaptive_input = getattr(args, 'adaptive_input', True)
args.tie_adaptive_weights = getattr(args, 'tie_adaptive_weights', True)
args.adaptive_input_cutoff = getattr(args, 'adaptive_input_cutoff', '20000,60000')
args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', '20000,60000')
args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0.2)
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
args.activation_dropout = getattr(args, 'activation_dropout', 0.1)
args.no_decoder_final_norm = getattr(args, 'no_decoder_final_norm', True)
args.tie_adaptive_proj = getattr(args, 'tie_adaptive_proj', True)
transformer_lm_big(args)
@register_model_architecture('transformer_lm', 'transformer_lm_gbw')
@register_model_architecture('transformer_lm', 'transformer_lm_baevski_gbw')
def transformer_lm_baevski_gbw(args):
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
args.dropout = getattr(args, 'dropout', 0.1)
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
args.no_decoder_final_norm = getattr(args, 'no_decoder_final_norm', True)
transformer_lm_big(args)
@register_model_architecture('transformer_lm', 'transformer_lm_gpt')
def transformer_lm_gpt(args):
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 768)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 3072)
args.decoder_layers = getattr(args, 'decoder_layers', 12)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 12)
args.dropout = getattr(args, 'dropout', 0.1)
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
args.activation_fn = getattr(args, 'activation_fn', 'gelu')
base_lm_architecture(args)
@register_model_architecture('transformer_lm', 'transformer_lm_gpt2_small')
def transformer_lm_gpt2_small(args):
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 1024)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 4096)
args.decoder_layers = getattr(args, 'decoder_layers', 24)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 16)
args.dropout = getattr(args, 'dropout', 0.1)
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
args.activation_fn = getattr(args, 'activation_fn', 'gelu')
base_lm_architecture(args)
@register_model_architecture('transformer_lm', 'transformer_lm_gpt2_medium')
def transformer_lm_gpt2_medium(args):
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 1280)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 5120)
args.decoder_layers = getattr(args, 'decoder_layers', 36)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 20)
args.dropout = getattr(args, 'dropout', 0.1)
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
args.activation_fn = getattr(args, 'activation_fn', 'gelu')
base_lm_architecture(args)
@register_model_architecture('transformer_lm', 'transformer_lm_gpt2_big')
def transformer_lm_gpt2_big(args):
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 1600)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 6400)
args.decoder_layers = getattr(args, 'decoder_layers', 48)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 25)
args.dropout = getattr(args, 'dropout', 0.1)
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
args.activation_fn = getattr(args, 'activation_fn', 'gelu')
base_lm_architecture(args)
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/transformer_lm.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
AdaptiveSoftmax, BeamableMM, GradMultiply, LearnedPositionalEmbedding,
LinearizedConvolution,
)
@register_model('fconv')
class FConvModel(FairseqEncoderDecoderModel):
"""
A fully convolutional model, i.e. a convolutional encoder and a
convolutional decoder, as described in `"Convolutional Sequence to Sequence
Learning" (Gehring et al., 2017) <https://arxiv.org/abs/1705.03122>`_.
Args:
encoder (FConvEncoder): the encoder
decoder (FConvDecoder): the decoder
The Convolutional model provides the following named architectures and
command-line arguments:
.. argparse::
:ref: fairseq.models.fconv_parser
:prog:
"""
@classmethod
def hub_models(cls):
def moses_subword(path):
return {
'path': path,
'tokenizer': 'moses',
'bpe': 'subword_nmt',
}
return {
'conv.wmt14.en-fr': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/wmt14.v2.en-fr.fconv-py.tar.bz2'),
'conv.wmt14.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/wmt14.en-de.fconv-py.tar.bz2'),
'conv.wmt17.en-de': moses_subword('https://dl.fbaipublicfiles.com/fairseq/models/wmt17.v2.en-de.fconv-py.tar.bz2'),
}
def __init__(self, encoder, decoder):
super().__init__(encoder, decoder)
self.encoder.num_attention_layers = sum(layer is not None for layer in decoder.attention)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
# fmt: off
parser.add_argument('--dropout', type=float, metavar='D',
help='dropout probability')
parser.add_argument('--encoder-embed-dim', type=int, metavar='N',
help='encoder embedding dimension')
parser.add_argument('--encoder-embed-path', type=str, metavar='STR',
help='path to pre-trained encoder embedding')
parser.add_argument('--encoder-layers', type=str, metavar='EXPR',
help='encoder layers [(dim, kernel_size), ...]')
parser.add_argument('--decoder-embed-dim', type=int, metavar='N',
help='decoder embedding dimension')
parser.add_argument('--decoder-embed-path', type=str, metavar='STR',
help='path to pre-trained decoder embedding')
parser.add_argument('--decoder-layers', type=str, metavar='EXPR',
help='decoder layers [(dim, kernel_size), ...]')
parser.add_argument('--decoder-out-embed-dim', type=int, metavar='N',
help='decoder output embedding dimension')
parser.add_argument('--decoder-attention', type=str, metavar='EXPR',
help='decoder attention [True, ...]')
parser.add_argument('--share-input-output-embed', action='store_true',
help='share input and output embeddings (requires'
' --decoder-out-embed-dim and --decoder-embed-dim'
' to be equal)')
# fmt: on
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure that all args are properly defaulted (in case there are any new ones)
base_architecture(args)
encoder_embed_dict = None
if args.encoder_embed_path:
encoder_embed_dict = utils.parse_embedding(args.encoder_embed_path)
utils.print_embed_overlap(encoder_embed_dict, task.source_dictionary)
decoder_embed_dict = None
if args.decoder_embed_path:
decoder_embed_dict = utils.parse_embedding(args.decoder_embed_path)
utils.print_embed_overlap(decoder_embed_dict, task.target_dictionary)
encoder = FConvEncoder(
dictionary=task.source_dictionary,
embed_dim=args.encoder_embed_dim,
embed_dict=encoder_embed_dict,
convolutions=eval(args.encoder_layers),
dropout=args.dropout,
max_positions=args.max_source_positions,
)
decoder = FConvDecoder(
dictionary=task.target_dictionary,
embed_dim=args.decoder_embed_dim,
embed_dict=decoder_embed_dict,
convolutions=eval(args.decoder_layers),
out_embed_dim=args.decoder_out_embed_dim,
attention=eval(args.decoder_attention),
dropout=args.dropout,
max_positions=args.max_target_positions,
share_embed=args.share_input_output_embed,
)
return FConvModel(encoder, decoder)
class FConvEncoder(FairseqEncoder):
"""
Convolutional encoder consisting of `len(convolutions)` layers.
Args:
dictionary (~fairseq.data.Dictionary): encoding dictionary
embed_dim (int, optional): embedding dimension
embed_dict (str, optional): filename from which to load pre-trained
embeddings
max_positions (int, optional): maximum supported input sequence length
convolutions (list, optional): the convolutional layer structure. Each
list item `i` corresponds to convolutional layer `i`. Layers are
given as ``(out_channels, kernel_width, [residual])``. Residual
connections are added between layers when ``residual=1`` (which is
the default behavior).
dropout (float, optional): dropout to be applied before each conv layer
"""
def __init__(
self, dictionary, embed_dim=512, embed_dict=None, max_positions=1024,
convolutions=((512, 3),) * 20, dropout=0.1,
):
super().__init__(dictionary)
self.dropout = dropout
self.num_attention_layers = None
num_embeddings = len(dictionary)
self.padding_idx = dictionary.pad()
self.embed_tokens = Embedding(num_embeddings, embed_dim, self.padding_idx)
if embed_dict:
self.embed_tokens = utils.load_embedding(embed_dict, self.dictionary, self.embed_tokens)
self.embed_positions = PositionalEmbedding(
max_positions,
embed_dim,
self.padding_idx,
)
convolutions = extend_conv_spec(convolutions)
in_channels = convolutions[0][0]
self.fc1 = Linear(embed_dim, in_channels, dropout=dropout)
self.projections = nn.ModuleList()
self.convolutions = nn.ModuleList()
self.residuals = []
layer_in_channels = [in_channels]
for _, (out_channels, kernel_size, residual) in enumerate(convolutions):
if residual == 0:
residual_dim = out_channels
else:
residual_dim = layer_in_channels[-residual]
self.projections.append(Linear(residual_dim, out_channels)
if residual_dim != out_channels else None)
if kernel_size % 2 == 1:
padding = kernel_size // 2
else:
padding = 0
self.convolutions.append(
ConvTBC(in_channels, out_channels * 2, kernel_size,
dropout=dropout, padding=padding)
)
self.residuals.append(residual)
in_channels = out_channels
layer_in_channels.append(out_channels)
self.fc2 = Linear(in_channels, embed_dim)
def forward(self, src_tokens, src_lengths):
"""
Args:
src_tokens (LongTensor): tokens in the source language of shape
`(batch, src_len)`
src_lengths (LongTensor): lengths of each source sentence of shape
`(batch)`
Returns:
dict:
- **encoder_out** (tuple): a tuple with two elements, where the
first element is the last encoder layer's output and the
second element is the same quantity summed with the input
embedding (used for attention). The shape of both tensors is
`(batch, src_len, embed_dim)`.
- **encoder_padding_mask** (ByteTensor): the positions of
padding elements of shape `(batch, src_len)`
"""
# embed tokens and positions
x = self.embed_tokens(src_tokens) + self.embed_positions(src_tokens)
x = F.dropout(x, p=self.dropout, training=self.training)
input_embedding = x
# project to size of convolution
x = self.fc1(x)
# used to mask padding in input
encoder_padding_mask = src_tokens.eq(self.padding_idx).t() # -> T x B
if not encoder_padding_mask.any():
encoder_padding_mask = None
# B x T x C -> T x B x C
x = x.transpose(0, 1)
residuals = [x]
# temporal convolutions
for proj, conv, res_layer in zip(self.projections, self.convolutions, self.residuals):
if res_layer > 0:
residual = residuals[-res_layer]
residual = residual if proj is None else proj(residual)
else:
residual = None
if encoder_padding_mask is not None:
x = x.masked_fill(encoder_padding_mask.unsqueeze(-1), 0)
x = F.dropout(x, p=self.dropout, training=self.training)
if conv.kernel_size[0] % 2 == 1:
# padding is implicit in the conv
x = conv(x)
else:
padding_l = (conv.kernel_size[0] - 1) // 2
padding_r = conv.kernel_size[0] // 2
x = F.pad(x, (0, 0, 0, 0, padding_l, padding_r))
x = conv(x)
x = F.glu(x, dim=2)
if residual is not None:
x = (x + residual) * math.sqrt(0.5)
residuals.append(x)
# T x B x C -> B x T x C
x = x.transpose(1, 0)
# project back to size of embedding
x = self.fc2(x)
if encoder_padding_mask is not None:
encoder_padding_mask = encoder_padding_mask.t() # -> B x T
x = x.masked_fill(encoder_padding_mask.unsqueeze(-1), 0)
# scale gradients (this only affects backward, not forward)
x = GradMultiply.apply(x, 1.0 / (2.0 * self.num_attention_layers))
# add output to input embedding for attention
y = (x + input_embedding) * math.sqrt(0.5)
return {
'encoder_out': (x, y),
'encoder_padding_mask': encoder_padding_mask, # B x T
}
def reorder_encoder_out(self, encoder_out, new_order):
if encoder_out['encoder_out'] is not None:
encoder_out['encoder_out'] = (
encoder_out['encoder_out'][0].index_select(0, new_order),
encoder_out['encoder_out'][1].index_select(0, new_order),
)
if encoder_out['encoder_padding_mask'] is not None:
encoder_out['encoder_padding_mask'] = \
encoder_out['encoder_padding_mask'].index_select(0, new_order)
return encoder_out
def max_positions(self):
"""Maximum input length supported by the encoder."""
return self.embed_positions.max_positions()
class AttentionLayer(nn.Module):
def __init__(self, conv_channels, embed_dim, bmm=None):
super().__init__()
# projects from output of convolution to embedding dimension
self.in_projection = Linear(conv_channels, embed_dim)
# projects from embedding dimension to convolution size
self.out_projection = Linear(embed_dim, conv_channels)
self.bmm = bmm if bmm is not None else torch.bmm
def forward(self, x, target_embedding, encoder_out, encoder_padding_mask):
residual = x
# attention
x = (self.in_projection(x) + target_embedding) * math.sqrt(0.5)
x = self.bmm(x, encoder_out[0])
# don't attend over padding
if encoder_padding_mask is not None:
x = x.float().masked_fill(
encoder_padding_mask.unsqueeze(1),
float('-inf')
).type_as(x) # FP16 support: cast to float and back
# softmax over last dim
sz = x.size()
x = F.softmax(x.view(sz[0] * sz[1], sz[2]), dim=1)
x = x.view(sz)
attn_scores = x
x = self.bmm(x, encoder_out[1])
# scale attention output (respecting potentially different lengths)
s = encoder_out[1].size(1)
if encoder_padding_mask is None:
x = x * (s * math.sqrt(1.0 / s))
else:
s = s - encoder_padding_mask.type_as(x).sum(dim=1, keepdim=True) # exclude padding
s = s.unsqueeze(-1)
x = x * (s * s.rsqrt())
# project back
x = (self.out_projection(x) + residual) * math.sqrt(0.5)
return x, attn_scores
def make_generation_fast_(self, beamable_mm_beam_size=None, **kwargs):
"""Replace torch.bmm with BeamableMM."""
if beamable_mm_beam_size is not None:
del self.bmm
self.add_module('bmm', BeamableMM(beamable_mm_beam_size))
class FConvDecoder(FairseqIncrementalDecoder):
"""Convolutional decoder"""
def __init__(
self, dictionary, embed_dim=512, embed_dict=None, out_embed_dim=256,
max_positions=1024, convolutions=((512, 3),) * 20, attention=True,
dropout=0.1, share_embed=False, positional_embeddings=True,
adaptive_softmax_cutoff=None, adaptive_softmax_dropout=0,
):
super().__init__(dictionary)
self.register_buffer('version', torch.Tensor([2]))
self.dropout = dropout
self.need_attn = True
convolutions = extend_conv_spec(convolutions)
in_channels = convolutions[0][0]
if isinstance(attention, bool):
# expand True into [True, True, ...] and do the same with False
attention = [attention] * len(convolutions)
if not isinstance(attention, list) or len(attention) != len(convolutions):
raise ValueError('Attention is expected to be a list of booleans of '
'length equal to the number of layers.')
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
self.embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
if embed_dict:
self.embed_tokens = utils.load_embedding(embed_dict, self.dictionary, self.embed_tokens)
self.embed_positions = PositionalEmbedding(
max_positions,
embed_dim,
padding_idx,
) if positional_embeddings else None
self.fc1 = Linear(embed_dim, in_channels, dropout=dropout)
self.projections = nn.ModuleList()
self.convolutions = nn.ModuleList()
self.attention = nn.ModuleList()
self.residuals = []
layer_in_channels = [in_channels]
for i, (out_channels, kernel_size, residual) in enumerate(convolutions):
if residual == 0:
residual_dim = out_channels
else:
residual_dim = layer_in_channels[-residual]
self.projections.append(Linear(residual_dim, out_channels)
if residual_dim != out_channels else None)
self.convolutions.append(
LinearizedConv1d(in_channels, out_channels * 2, kernel_size,
padding=(kernel_size - 1), dropout=dropout)
)
self.attention.append(AttentionLayer(out_channels, embed_dim)
if attention[i] else None)
self.residuals.append(residual)
in_channels = out_channels
layer_in_channels.append(out_channels)
self.adaptive_softmax = None
self.fc2 = self.fc3 = None
if adaptive_softmax_cutoff is not None:
assert not share_embed
self.adaptive_softmax = AdaptiveSoftmax(num_embeddings, in_channels, adaptive_softmax_cutoff,
dropout=adaptive_softmax_dropout)
else:
self.fc2 = Linear(in_channels, out_embed_dim)
if share_embed:
assert out_embed_dim == embed_dim, \
"Shared embed weights implies same dimensions " \
" out_embed_dim={} vs embed_dim={}".format(out_embed_dim, embed_dim)
self.fc3 = nn.Linear(out_embed_dim, num_embeddings)
self.fc3.weight = self.embed_tokens.weight
else:
self.fc3 = Linear(out_embed_dim, num_embeddings, dropout=dropout)
def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None, **unused):
if encoder_out is not None:
encoder_padding_mask = encoder_out['encoder_padding_mask']
encoder_out = encoder_out['encoder_out']
# split and transpose encoder outputs
encoder_a, encoder_b = self._split_encoder_out(encoder_out, incremental_state)
if self.embed_positions is not None:
pos_embed = self.embed_positions(prev_output_tokens, incremental_state)
else:
pos_embed = 0
if incremental_state is not None:
prev_output_tokens = prev_output_tokens[:, -1:]
x = self._embed_tokens(prev_output_tokens, incremental_state)
# embed tokens and combine with positional embeddings
x += pos_embed
x = F.dropout(x, p=self.dropout, training=self.training)
target_embedding = x
# project to size of convolution
x = self.fc1(x)
# B x T x C -> T x B x C
x = self._transpose_if_training(x, incremental_state)
# temporal convolutions
avg_attn_scores = None
num_attn_layers = len(self.attention)
residuals = [x]
for proj, conv, attention, res_layer in zip(self.projections, self.convolutions, self.attention,
self.residuals):
if res_layer > 0:
residual = residuals[-res_layer]
residual = residual if proj is None else proj(residual)
else:
residual = None
x = F.dropout(x, p=self.dropout, training=self.training)
x = conv(x, incremental_state)
x = F.glu(x, dim=2)
# attention
if attention is not None:
x = self._transpose_if_training(x, incremental_state)
x, attn_scores = attention(x, target_embedding, (encoder_a, encoder_b), encoder_padding_mask)
if not self.training and self.need_attn:
attn_scores = attn_scores / num_attn_layers
if avg_attn_scores is None:
avg_attn_scores = attn_scores
else:
avg_attn_scores.add_(attn_scores)
x = self._transpose_if_training(x, incremental_state)
# residual
if residual is not None:
x = (x + residual) * math.sqrt(0.5)
residuals.append(x)
# T x B x C -> B x T x C
x = self._transpose_if_training(x, incremental_state)
# project back to size of vocabulary if not using adaptive softmax
if self.fc2 is not None and self.fc3 is not None:
x = self.fc2(x)
x = F.dropout(x, p=self.dropout, training=self.training)
x = self.fc3(x)
return x, avg_attn_scores
def reorder_incremental_state(self, incremental_state, new_order):
super().reorder_incremental_state(incremental_state, new_order)
encoder_out = utils.get_incremental_state(self, incremental_state, 'encoder_out')
if encoder_out is not None:
encoder_out = tuple(eo.index_select(0, new_order) for eo in encoder_out)
utils.set_incremental_state(self, incremental_state, 'encoder_out', encoder_out)
def max_positions(self):
"""Maximum output length supported by the decoder."""
return self.embed_positions.max_positions() if self.embed_positions is not None else float('inf')
def upgrade_state_dict(self, state_dict):
if utils.item(state_dict.get('decoder.version', torch.Tensor([1]))[0]) < 2:
# old models use incorrect weight norm dimension
for i, conv in enumerate(self.convolutions):
# reconfigure weight norm
nn.utils.remove_weight_norm(conv)
self.convolutions[i] = nn.utils.weight_norm(conv, dim=0)
state_dict['decoder.version'] = torch.Tensor([1])
return state_dict
def make_generation_fast_(self, need_attn=False, **kwargs):
self.need_attn = need_attn
def _embed_tokens(self, tokens, incremental_state):
if incremental_state is not None:
# keep only the last token for incremental forward pass
tokens = tokens[:, -1:]
return self.embed_tokens(tokens)
def _split_encoder_out(self, encoder_out, incremental_state):
"""Split and transpose encoder outputs.
This is cached when doing incremental inference.
"""
cached_result = utils.get_incremental_state(self, incremental_state, 'encoder_out')
if cached_result is not None:
return cached_result
# transpose only once to speed up attention layers
encoder_a, encoder_b = encoder_out
encoder_a = encoder_a.transpose(1, 2).contiguous()
result = (encoder_a, encoder_b)
if incremental_state is not None:
utils.set_incremental_state(self, incremental_state, 'encoder_out', result)
return result
def _transpose_if_training(self, x, incremental_state):
if incremental_state is None:
x = x.transpose(0, 1)
return x
def extend_conv_spec(convolutions):
"""
Extends convolutional spec that is a list of tuples of 2 or 3 parameters
(kernel size, dim size and optionally how many layers behind to look for residual)
to default the residual propagation param if it is not specified
"""
extended = []
for spec in convolutions:
if len(spec) == 3:
extended.append(spec)
elif len(spec) == 2:
extended.append(spec + (1,))
else:
raise Exception('invalid number of parameters in convolution spec ' + str(spec) + '. expected 2 or 3')
return tuple(extended)
def Embedding(num_embeddings, embedding_dim, padding_idx):
m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
nn.init.normal_(m.weight, 0, 0.1)
nn.init.constant_(m.weight[padding_idx], 0)
return m
def PositionalEmbedding(num_embeddings, embedding_dim, padding_idx):
m = LearnedPositionalEmbedding(num_embeddings, embedding_dim, padding_idx)
nn.init.normal_(m.weight, 0, 0.1)
nn.init.constant_(m.weight[padding_idx], 0)
return m
def Linear(in_features, out_features, dropout=0):
"""Weight-normalized Linear layer (input: N x T x C)"""
m = nn.Linear(in_features, out_features)
nn.init.normal_(m.weight, mean=0, std=math.sqrt((1 - dropout) / in_features))
nn.init.constant_(m.bias, 0)
return nn.utils.weight_norm(m)
def LinearizedConv1d(in_channels, out_channels, kernel_size, dropout=0, **kwargs):
"""Weight-normalized Conv1d layer optimized for decoding"""
m = LinearizedConvolution(in_channels, out_channels, kernel_size, **kwargs)
std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels))
nn.init.normal_(m.weight, mean=0, std=std)
nn.init.constant_(m.bias, 0)
return nn.utils.weight_norm(m, dim=2)
def ConvTBC(in_channels, out_channels, kernel_size, dropout=0, **kwargs):
"""Weight-normalized Conv1d layer"""
from fairseq.modules import ConvTBC
m = ConvTBC(in_channels, out_channels, kernel_size, **kwargs)
std = math.sqrt((4 * (1.0 - dropout)) / (m.kernel_size[0] * in_channels))
nn.init.normal_(m.weight, mean=0, std=std)
nn.init.constant_(m.bias, 0)
return nn.utils.weight_norm(m, dim=2)
@register_model_architecture('fconv', 'fconv')
def base_architecture(args):
args.dropout = getattr(args, 'dropout', 0.1)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512)
args.encoder_embed_path = getattr(args, 'encoder_embed_path', None)
args.encoder_layers = getattr(args, 'encoder_layers', '[(512, 3)] * 20')
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
args.decoder_embed_path = getattr(args, 'decoder_embed_path', None)
args.decoder_layers = getattr(args, 'decoder_layers', '[(512, 3)] * 20')
args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 256)
args.decoder_attention = getattr(args, 'decoder_attention', 'True')
args.share_input_output_embed = getattr(args, 'share_input_output_embed', False)
@register_model_architecture('fconv', 'fconv_iwslt_de_en')
def fconv_iwslt_de_en(args):
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256)
args.encoder_layers = getattr(args, 'encoder_layers', '[(256, 3)] * 4')
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 256)
args.decoder_layers = getattr(args, 'decoder_layers', '[(256, 3)] * 3')
args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 256)
base_architecture(args)
@register_model_architecture('fconv', 'fconv_wmt_en_ro')
def fconv_wmt_en_ro(args):
args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 512)
base_architecture(args)
@register_model_architecture('fconv', 'fconv_wmt_en_de')
def fconv_wmt_en_de(args):
convs = '[(512, 3)] * 9' # first 9 layers have 512 units
convs += ' + [(1024, 3)] * 4' # next 4 layers have 1024 units
convs += ' + [(2048, 1)] * 2' # final 2 layers use 1x1 convolutions
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 768)
args.encoder_layers = getattr(args, 'encoder_layers', convs)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 768)
args.decoder_layers = getattr(args, 'decoder_layers', convs)
args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 512)
base_architecture(args)
@register_model_architecture('fconv', 'fconv_wmt_en_fr')
def fconv_wmt_en_fr(args):
convs = '[(512, 3)] * 6' # first 6 layers have 512 units
convs += ' + [(768, 3)] * 4' # next 4 layers have 768 units
convs += ' + [(1024, 3)] * 3' # next 3 layers have 1024 units
convs += ' + [(2048, 1)] * 1' # next 1 layer uses 1x1 convolutions
convs += ' + [(4096, 1)] * 1' # final 1 layer uses 1x1 convolutions
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 768)
args.encoder_layers = getattr(args, 'encoder_layers', convs)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 768)
args.decoder_layers = getattr(args, 'decoder_layers', convs)
args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 512)
base_architecture(args)
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/fconv.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import sys
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import (
BaseFairseqModel, register_model, register_model_architecture
)
@register_model('wav2vec')
class Wav2VecModel(BaseFairseqModel):
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
parser.add_argument('--prediction-steps', type=int, metavar='N', help='number of steps ahead to predict')
parser.add_argument('--sample-distance', type=int, metavar='N',
help='sample distance from target. does not work properly with cross-sampling')
parser.add_argument('--cross-sample-negatives', action='store_true',
help='whether to sample negatives across examples in the same batch')
parser.add_argument('--num-negatives', type=int, metavar='N', help='number of negative examples')
parser.add_argument('--conv-feature-layers', type=str, metavar='EXPR',
help='convolutional feature extraction layers [(dim, kernel_size, stride), ...]')
parser.add_argument('--conv-aggregator-layers', type=str, metavar='EXPR',
help='convolutional feature extraction layers [(dim, kernel_size, stride), ...]')
parser.add_argument('--dropout', type=float, metavar='D', help='dropout to apply within the model')
parser.add_argument('--dropout-features', type=float, metavar='D', help='dropout to apply to the features')
parser.add_argument('--dropout-agg', type=float, metavar='D', help='dropout to apply after aggregation step')
parser.add_argument('--encoder', type=str, choices=['cnn'], help='type of encoder to use')
parser.add_argument('--aggregator', type=str, choices=['cnn', 'gru'],
help='type of aggregator to use')
parser.add_argument('--gru-dim', type=int, metavar='N', help='GRU dimensionality')
parser.add_argument('--no-conv-bias', action='store_true',
help='if set, does not learn bias for conv layers')
parser.add_argument('--agg-zero-pad', action='store_true',
help='if set, zero pads in aggregator instead of repl pad')
parser.add_argument('--skip-connections-feat', action='store_true',
help='if set, adds skip connections to the feature extractor')
parser.add_argument('--skip-connections-agg', action='store_true',
help='if set, adds skip connections to the aggregator')
parser.add_argument('--residual-scale', type=float, metavar='D',
help='scales residual by sqrt(value)')
parser.add_argument('--log-compression', action='store_true',
help='if set, adds a log compression to feature extractor')
parser.add_argument('--balanced-classes', action='store_true',
help='if set, loss is scaled to balance for number of negatives')
parser.add_argument('--project-features', choices=['none', 'same', 'new'],
help='if not none, features are projected using the (same or new) aggregator')
parser.add_argument('--non-affine-group-norm', action='store_true',
help='if set, group norm is not affine')
parser.add_argument('--offset', help='if set, introduces an offset from target to predictions. '
'if set to "auto", it is computed automatically from the receptive field')
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present in older models
base_wav2vec_architecture(args)
model = Wav2VecModel(args)
print(model)
return model
def __init__(self, args):
super().__init__()
self.prediction_steps = args.prediction_steps
offset = args.offset
if args.encoder == 'cnn':
feature_enc_layers = eval(args.conv_feature_layers)
self.feature_extractor = ConvFeatureExtractionModel(
conv_layers=feature_enc_layers,
dropout=0.,
log_compression=args.log_compression,
skip_connections=args.skip_connections_feat,
residual_scale=args.residual_scale,
non_affine_group_norm=args.non_affine_group_norm,
)
embed = feature_enc_layers[-1][0]
else:
raise Exception('unknown encoder type ' + args.encoder)
if args.offset == 'auto':
assert args.encoder == 'cnn'
jin = 0
rin = 0
for _, k, stride in feature_enc_layers:
if rin == 0:
rin = k
rin = rin + (k - 1) * jin
if jin == 0:
jin = stride
else:
jin *= stride
offset = math.ceil(rin / jin)
offset = int(offset)
def make_aggregator():
if args.aggregator == 'cnn':
agg_layers = eval(args.conv_aggregator_layers)
agg_dim = agg_layers[-1][0]
feature_aggregator = ConvAggegator(
conv_layers=agg_layers,
embed=embed,
dropout=args.dropout,
skip_connections=args.skip_connections_agg,
residual_scale=args.residual_scale,
non_affine_group_norm=args.non_affine_group_norm,
conv_bias=not args.no_conv_bias,
zero_pad=args.agg_zero_pad,
)
elif args.aggregator == 'gru':
agg_dim = args.gru_dim
feature_aggregator = nn.Sequential(
TransposeLast(),
nn.GRU(
input_size=embed,
hidden_size=agg_dim,
num_layers=1,
dropout=args.dropout,
),
TransposeLast(deconstruct_idx=0),
)
else:
raise Exception('unknown aggregator type ' + args.aggregator)
return feature_aggregator, agg_dim
self.feature_aggregator, agg_dim = make_aggregator()
self.wav2vec_predictions = Wav2VecPredictionsModel(
in_dim=agg_dim,
out_dim=embed,
prediction_steps=args.prediction_steps,
n_negatives=args.num_negatives,
cross_sample_negatives=args.cross_sample_negatives,
sample_distance=args.sample_distance,
dropout=args.dropout,
offset=offset,
balanced_classes=args.balanced_classes,
)
self.dropout_feats = nn.Dropout(p=args.dropout_features)
self.dropout_agg = nn.Dropout(p=args.dropout_agg)
if args.project_features == 'none':
self.project_features = None
elif args.project_features == 'same':
self.project_features = self.feature_aggregator
elif args.project_features == 'new':
self.project_features, _ = make_aggregator()
def forward(self, source):
result = {}
features = self.feature_extractor(source)
x = self.dropout_feats(features)
x = self.feature_aggregator(x)
x = self.dropout_agg(x)
if self.project_features is not None:
features = self.project_features(features)
x, targets = self.wav2vec_predictions(x, features)
result['cpc_logits'] = x
result['cpc_targets'] = targets
return result
def upgrade_state_dict_named(self, state_dict, name):
super().upgrade_state_dict_named(state_dict, name)
def max_positions(self):
"""Maximum length supported by the model."""
return sys.maxsize
def get_logits(self, net_output):
logits = net_output['cpc_logits']
return logits
def get_targets(self, sample, net_output, expand_steps=True):
t = net_output['cpc_targets']
return t.contiguous()
def get_target_weights(self, targets, net_output):
targets = net_output['cpc_targets']
if isinstance(targets, tuple) and targets[-1] is not None:
return targets[-1]
return 1.
class TransposeLast(nn.Module):
def __init__(self, deconstruct_idx=None):
super().__init__()
self.deconstruct_idx = deconstruct_idx
def forward(self, x):
if self.deconstruct_idx is not None:
x = x[self.deconstruct_idx]
return x.transpose(-2, -1)
class Fp32GroupNorm(nn.GroupNorm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self, input):
output = F.group_norm(
input.float(), self.num_groups, self.weight.float() if self.weight is not None else None,
self.bias.float() if self.bias is not None else None, self.eps)
return output.type_as(input)
class Fp32LayerNorm(nn.LayerNorm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self, input):
output = F.layer_norm(
input.float(), self.normalized_shape, self.weight.float() if self.weight is not None else None,
self.bias.float() if self.bias is not None else None, self.eps)
return output.type_as(input)
def norm_block(is_layer_norm, dim, affine=True):
if is_layer_norm:
mod = nn.Sequential(
TransposeLast(),
Fp32LayerNorm(dim, elementwise_affine=affine),
TransposeLast(),
)
else:
mod = Fp32GroupNorm(1, dim, affine=affine)
return mod
class ConvFeatureExtractionModel(nn.Module):
def __init__(self, conv_layers, dropout, log_compression, skip_connections, residual_scale, non_affine_group_norm):
super().__init__()
def block(n_in, n_out, k, stride):
return nn.Sequential(
nn.Conv1d(n_in, n_out, k, stride=stride, bias=False),
nn.Dropout(p=dropout),
norm_block(is_layer_norm=False, dim=n_out, affine=not non_affine_group_norm),
nn.ReLU(),
)
in_d = 1
self.conv_layers = nn.ModuleList()
for i, (dim, k, stride) in enumerate(conv_layers):
self.conv_layers.append(
block(in_d, dim, k, stride))
in_d = dim
self.log_compression = log_compression
self.skip_connections = skip_connections
self.residual_scale = math.sqrt(residual_scale)
def forward(self, x):
# BxT -> BxCxT
x = x.unsqueeze(1)
for conv in self.conv_layers:
residual = x
x = conv(x)
if self.skip_connections and x.size(1) == residual.size(1):
tsz = x.size(2)
r_tsz = residual.size(2)
residual = residual[..., ::r_tsz // tsz][..., :tsz]
x = (x + residual) * self.residual_scale
if self.log_compression:
x = x.abs()
x = x + 1
x = x.log()
return x
class ZeroPad1d(nn.Module):
def __init__(self, pad_left, pad_right):
super().__init__()
self.pad_left = pad_left
self.pad_right = pad_right
def forward(self, x):
return F.pad(x, (self.pad_left, self.pad_right))
class ConvAggegator(nn.Module):
def __init__(self, conv_layers, embed, dropout, skip_connections, residual_scale, non_affine_group_norm, conv_bias,
zero_pad):
super().__init__()
def block(n_in, n_out, k, stride):
# padding dims only really make sense for stride = 1
ka = k // 2
kb = ka - 1 if k % 2 == 0 else ka
pad = ZeroPad1d(ka + kb, 0) if zero_pad else nn.ReplicationPad1d((ka + kb, 0))
return nn.Sequential(
pad,
nn.Conv1d(n_in, n_out, k, stride=stride, bias=conv_bias),
nn.Dropout(p=dropout),
norm_block(False, n_out, affine=not non_affine_group_norm),
nn.ReLU(),
)
in_d = embed
self.conv_layers = nn.ModuleList()
self.residual_proj = nn.ModuleList()
for i, (dim, k, stride) in enumerate(conv_layers):
if in_d != dim and skip_connections:
self.residual_proj.append(
nn.Conv1d(in_d, dim, 1, bias=False),
)
else:
self.residual_proj.append(None)
self.conv_layers.append(
block(in_d, dim, k, stride))
in_d = dim
self.conv_layers = nn.Sequential(*self.conv_layers)
self.skip_connections = skip_connections
self.residual_scale = math.sqrt(residual_scale)
def forward(self, x):
for rproj, conv in zip(self.residual_proj, self.conv_layers):
residual = x
x = conv(x)
if self.skip_connections:
if rproj is not None:
residual = rproj(residual)
x = (x + residual) * self.residual_scale
return x
class Wav2VecPredictionsModel(nn.Module):
def __init__(self, in_dim, out_dim, prediction_steps, n_negatives, cross_sample_negatives, sample_distance,
dropout, offset, balanced_classes):
super().__init__()
self.n_negatives = n_negatives
self.cross_sample_negatives = cross_sample_negatives
self.sample_distance = sample_distance
self.project_to_steps = nn.ConvTranspose2d(in_dim, out_dim, (1, prediction_steps))
self.dropout = nn.Dropout(p=dropout)
self.offset = offset
self.balanced_classes = balanced_classes
def sample_negatives(self, y):
bsz, fsz, tsz = y.shape
y = y.transpose(0, 1) # BCT -> CBT
y = y.contiguous().view(fsz, -1) # CBT => C(BxT)
if self.cross_sample_negatives:
high = tsz * bsz
assert self.sample_distance is None, 'sample distance is not supported with cross sampling'
else:
high = tsz if self.sample_distance is None else min(tsz, self.sample_distance)
neg_idxs = torch.randint(low=0, high=high, size=(bsz, self.n_negatives * tsz))
if self.sample_distance is not None and self.sample_distance < tsz:
neg_idxs += torch.cat(
[torch.arange(start=1, end=tsz - self.sample_distance, device=neg_idxs.device, dtype=neg_idxs.dtype),
torch.arange(start=tsz - self.sample_distance, end=tsz - self.sample_distance * 2 - 1, step=-1,
device=neg_idxs.device, dtype=neg_idxs.dtype)])
if not self.cross_sample_negatives:
for i in range(1, bsz):
neg_idxs[i] += i * high
negs = y[..., neg_idxs.view(-1)]
negs = negs.view(fsz, bsz, self.n_negatives, tsz).permute(2, 1, 0, 3) # to NxBxCxT
return negs
def forward(self, x, y):
negatives = self.sample_negatives(y)
y = y.unsqueeze(0)
targets = torch.cat([y, negatives], dim=0)
x = x.unsqueeze(-1)
x = self.project_to_steps(x) # BxCxTxS
x = self.dropout(x)
x = x.unsqueeze(0).expand(targets.size(0), -1, -1, -1, -1)
copies, bsz, dim, tsz, steps = x.shape
steps = min(steps, tsz - self.offset)
predictions = x.new(bsz * copies * (tsz - self.offset + 1) * steps - ((steps + 1) * steps // 2) * copies * bsz)
labels = torch.zeros_like(predictions)
weights = torch.full_like(labels, 1 / self.n_negatives) if self.balanced_classes else None
start = end = 0
for i in range(steps):
offset = i + self.offset
end = start + (tsz - offset) * bsz * copies
pos_num = (end - start) // copies
predictions[start:end] = (x[..., :-offset, i] * targets[..., offset:]).sum(dim=2).flatten()
labels[start:start + pos_num] = 1.
if weights is not None:
weights[start:start + pos_num] = 1.
start = end
assert end == predictions.numel(), '{} != {}'.format(end, predictions.numel())
if weights is not None:
labels = (labels, weights)
return predictions, labels
@register_model_architecture('wav2vec', 'wav2vec')
def base_wav2vec_architecture(args):
conv_feature_layers = '[(512, 10, 5)]'
conv_feature_layers += ' + [(512, 8, 4)]'
conv_feature_layers += ' + [(512, 4, 2)] * 3'
args.conv_feature_layers = getattr(args, 'conv_feature_layers', conv_feature_layers)
args.conv_aggregator_layers = getattr(args, 'conv_aggregator_layers', '[(512, 3, 1)] * 9')
args.prediction_steps = getattr(args, 'prediction_steps', 12)
args.num_negatives = getattr(args, 'num_negatives', 1)
args.sample_distance = getattr(args, 'sample_distance', None)
args.cross_sample_negatives = getattr(args, 'cross_sample_negatives', False)
args.dropout = getattr(args, 'dropout', 0.)
args.dropout_features = getattr(args, 'dropout_features', 0.)
args.dropout_agg = getattr(args, 'dropout_agg', 0.)
args.encoder = getattr(args, 'encoder', 'cnn')
args.aggregator = getattr(args, 'aggregator', 'cnn')
args.skip_connections_feat = getattr(args, 'skip_connections_feat', False)
args.skip_connections_agg = getattr(args, 'skip_connections_agg', False)
args.residual_scale = getattr(args, 'residual_scale', 0.5)
args.gru_dim = getattr(args, 'gru_dim', 512)
args.no_conv_bias = getattr(args, 'no_conv_bias', False)
args.agg_zero_pad = getattr(args, 'agg_zero_pad', False)
args.log_compression = getattr(args, 'log_compression', False)
args.balanced_classes = getattr(args, 'balanced_classes', False)
args.project_features = getattr(args, 'project_features', 'none')
args.non_affine_group_norm = getattr(args, 'non_affine_group_norm', False)
args.offset = getattr(args, 'offset', 'auto')
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/wav2vec.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import AdaptiveSoftmax
@register_model('lstm')
class LSTMModel(FairseqEncoderDecoderModel):
def __init__(self, encoder, decoder):
super().__init__(encoder, decoder)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
# fmt: off
parser.add_argument('--dropout', type=float, metavar='D',
help='dropout probability')
parser.add_argument('--encoder-embed-dim', type=int, metavar='N',
help='encoder embedding dimension')
parser.add_argument('--encoder-embed-path', type=str, metavar='STR',
help='path to pre-trained encoder embedding')
parser.add_argument('--encoder-freeze-embed', action='store_true',
help='freeze encoder embeddings')
parser.add_argument('--encoder-hidden-size', type=int, metavar='N',
help='encoder hidden size')
parser.add_argument('--encoder-layers', type=int, metavar='N',
help='number of encoder layers')
parser.add_argument('--encoder-bidirectional', action='store_true',
help='make all layers of encoder bidirectional')
parser.add_argument('--decoder-embed-dim', type=int, metavar='N',
help='decoder embedding dimension')
parser.add_argument('--decoder-embed-path', type=str, metavar='STR',
help='path to pre-trained decoder embedding')
parser.add_argument('--decoder-freeze-embed', action='store_true',
help='freeze decoder embeddings')
parser.add_argument('--decoder-hidden-size', type=int, metavar='N',
help='decoder hidden size')
parser.add_argument('--decoder-layers', type=int, metavar='N',
help='number of decoder layers')
parser.add_argument('--decoder-out-embed-dim', type=int, metavar='N',
help='decoder output embedding dimension')
parser.add_argument('--decoder-attention', type=str, metavar='BOOL',
help='decoder attention')
parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR',
help='comma separated list of adaptive softmax cutoff points. '
'Must be used with adaptive_loss criterion')
parser.add_argument('--share-decoder-input-output-embed', default=False,
action='store_true',
help='share decoder input and output embeddings')
parser.add_argument('--share-all-embeddings', default=False, action='store_true',
help='share encoder, decoder and output embeddings'
' (requires shared dictionary and embed dim)')
# Granular dropout settings (if not specified these default to --dropout)
parser.add_argument('--encoder-dropout-in', type=float, metavar='D',
help='dropout probability for encoder input embedding')
parser.add_argument('--encoder-dropout-out', type=float, metavar='D',
help='dropout probability for encoder output')
parser.add_argument('--decoder-dropout-in', type=float, metavar='D',
help='dropout probability for decoder input embedding')
parser.add_argument('--decoder-dropout-out', type=float, metavar='D',
help='dropout probability for decoder output')
# fmt: on
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure that all args are properly defaulted (in case there are any new ones)
base_architecture(args)
if args.encoder_layers != args.decoder_layers:
raise ValueError('--encoder-layers must match --decoder-layers')
def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim):
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
embed_dict = utils.parse_embedding(embed_path)
utils.print_embed_overlap(embed_dict, dictionary)
return utils.load_embedding(embed_dict, dictionary, embed_tokens)
if args.encoder_embed_path:
pretrained_encoder_embed = load_pretrained_embedding_from_file(
args.encoder_embed_path, task.source_dictionary, args.encoder_embed_dim)
else:
num_embeddings = len(task.source_dictionary)
pretrained_encoder_embed = Embedding(
num_embeddings, args.encoder_embed_dim, task.source_dictionary.pad()
)
if args.share_all_embeddings:
# double check all parameters combinations are valid
if task.source_dictionary != task.target_dictionary:
raise ValueError('--share-all-embeddings requires a joint dictionary')
if args.decoder_embed_path and (
args.decoder_embed_path != args.encoder_embed_path):
raise ValueError(
'--share-all-embed not compatible with --decoder-embed-path'
)
if args.encoder_embed_dim != args.decoder_embed_dim:
raise ValueError(
'--share-all-embeddings requires --encoder-embed-dim to '
'match --decoder-embed-dim'
)
pretrained_decoder_embed = pretrained_encoder_embed
args.share_decoder_input_output_embed = True
else:
# separate decoder input embeddings
pretrained_decoder_embed = None
if args.decoder_embed_path:
pretrained_decoder_embed = load_pretrained_embedding_from_file(
args.decoder_embed_path,
task.target_dictionary,
args.decoder_embed_dim
)
# one last double check of parameter combinations
if args.share_decoder_input_output_embed and (
args.decoder_embed_dim != args.decoder_out_embed_dim):
raise ValueError(
'--share-decoder-input-output-embeddings requires '
'--decoder-embed-dim to match --decoder-out-embed-dim'
)
if args.encoder_freeze_embed:
pretrained_encoder_embed.weight.requires_grad = False
if args.decoder_freeze_embed:
pretrained_decoder_embed.weight.requires_grad = False
encoder = LSTMEncoder(
dictionary=task.source_dictionary,
embed_dim=args.encoder_embed_dim,
hidden_size=args.encoder_hidden_size,
num_layers=args.encoder_layers,
dropout_in=args.encoder_dropout_in,
dropout_out=args.encoder_dropout_out,
bidirectional=args.encoder_bidirectional,
pretrained_embed=pretrained_encoder_embed,
)
decoder = LSTMDecoder(
dictionary=task.target_dictionary,
embed_dim=args.decoder_embed_dim,
hidden_size=args.decoder_hidden_size,
out_embed_dim=args.decoder_out_embed_dim,
num_layers=args.decoder_layers,
dropout_in=args.decoder_dropout_in,
dropout_out=args.decoder_dropout_out,
attention=options.eval_bool(args.decoder_attention),
encoder_output_units=encoder.output_units,
pretrained_embed=pretrained_decoder_embed,
share_input_output_embed=args.share_decoder_input_output_embed,
adaptive_softmax_cutoff=(
options.eval_str_list(args.adaptive_softmax_cutoff, type=int)
if args.criterion == 'adaptive_loss' else None
),
)
return cls(encoder, decoder)
class LSTMEncoder(FairseqEncoder):
"""LSTM encoder."""
def __init__(
self, dictionary, embed_dim=512, hidden_size=512, num_layers=1,
dropout_in=0.1, dropout_out=0.1, bidirectional=False,
left_pad=True, pretrained_embed=None, padding_value=0.,
):
super().__init__(dictionary)
self.num_layers = num_layers
self.dropout_in = dropout_in
self.dropout_out = dropout_out
self.bidirectional = bidirectional
self.hidden_size = hidden_size
num_embeddings = len(dictionary)
self.padding_idx = dictionary.pad()
if pretrained_embed is None:
self.embed_tokens = Embedding(num_embeddings, embed_dim, self.padding_idx)
else:
self.embed_tokens = pretrained_embed
self.lstm = LSTM(
input_size=embed_dim,
hidden_size=hidden_size,
num_layers=num_layers,
dropout=self.dropout_out if num_layers > 1 else 0.,
bidirectional=bidirectional,
)
self.left_pad = left_pad
self.padding_value = padding_value
self.output_units = hidden_size
if bidirectional:
self.output_units *= 2
def forward(self, src_tokens, src_lengths):
if self.left_pad:
# nn.utils.rnn.pack_padded_sequence requires right-padding;
# convert left-padding to right-padding
src_tokens = utils.convert_padding_direction(
src_tokens,
self.padding_idx,
left_to_right=True,
)
bsz, seqlen = src_tokens.size()
# embed tokens
x = self.embed_tokens(src_tokens)
x = F.dropout(x, p=self.dropout_in, training=self.training)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
# pack embedded source tokens into a PackedSequence
packed_x = nn.utils.rnn.pack_padded_sequence(x, src_lengths.data.tolist())
# apply LSTM
if self.bidirectional:
state_size = 2 * self.num_layers, bsz, self.hidden_size
else:
state_size = self.num_layers, bsz, self.hidden_size
h0 = x.new_zeros(*state_size)
c0 = x.new_zeros(*state_size)
packed_outs, (final_hiddens, final_cells) = self.lstm(packed_x, (h0, c0))
# unpack outputs and apply dropout
x, _ = nn.utils.rnn.pad_packed_sequence(packed_outs, padding_value=self.padding_value)
x = F.dropout(x, p=self.dropout_out, training=self.training)
assert list(x.size()) == [seqlen, bsz, self.output_units]
if self.bidirectional:
def combine_bidir(outs):
out = outs.view(self.num_layers, 2, bsz, -1).transpose(1, 2).contiguous()
return out.view(self.num_layers, bsz, -1)
final_hiddens = combine_bidir(final_hiddens)
final_cells = combine_bidir(final_cells)
encoder_padding_mask = src_tokens.eq(self.padding_idx).t()
return {
'encoder_out': (x, final_hiddens, final_cells),
'encoder_padding_mask': encoder_padding_mask if encoder_padding_mask.any() else None
}
def reorder_encoder_out(self, encoder_out, new_order):
encoder_out['encoder_out'] = tuple(
eo.index_select(1, new_order)
for eo in encoder_out['encoder_out']
)
if encoder_out['encoder_padding_mask'] is not None:
encoder_out['encoder_padding_mask'] = \
encoder_out['encoder_padding_mask'].index_select(1, new_order)
return encoder_out
def max_positions(self):
"""Maximum input length supported by the encoder."""
return int(1e5) # an arbitrary large number
class AttentionLayer(nn.Module):
def __init__(self, input_embed_dim, source_embed_dim, output_embed_dim, bias=False):
super().__init__()
self.input_proj = Linear(input_embed_dim, source_embed_dim, bias=bias)
self.output_proj = Linear(input_embed_dim + source_embed_dim, output_embed_dim, bias=bias)
def forward(self, input, source_hids, encoder_padding_mask):
# input: bsz x input_embed_dim
# source_hids: srclen x bsz x source_embed_dim
# x: bsz x source_embed_dim
x = self.input_proj(input)
# compute attention
attn_scores = (source_hids * x.unsqueeze(0)).sum(dim=2)
# don't attend over padding
if encoder_padding_mask is not None:
attn_scores = attn_scores.float().masked_fill_(
encoder_padding_mask,
float('-inf')
).type_as(attn_scores) # FP16 support: cast to float and back
attn_scores = F.softmax(attn_scores, dim=0) # srclen x bsz
# sum weighted sources
x = (attn_scores.unsqueeze(2) * source_hids).sum(dim=0)
x = torch.tanh(self.output_proj(torch.cat((x, input), dim=1)))
return x, attn_scores
class LSTMDecoder(FairseqIncrementalDecoder):
"""LSTM decoder."""
def __init__(
self, dictionary, embed_dim=512, hidden_size=512, out_embed_dim=512,
num_layers=1, dropout_in=0.1, dropout_out=0.1, attention=True,
encoder_output_units=512, pretrained_embed=None,
share_input_output_embed=False, adaptive_softmax_cutoff=None,
):
super().__init__(dictionary)
self.dropout_in = dropout_in
self.dropout_out = dropout_out
self.hidden_size = hidden_size
self.share_input_output_embed = share_input_output_embed
self.need_attn = True
self.adaptive_softmax = None
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
if pretrained_embed is None:
self.embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
else:
self.embed_tokens = pretrained_embed
self.encoder_output_units = encoder_output_units
if encoder_output_units != hidden_size:
self.encoder_hidden_proj = Linear(encoder_output_units, hidden_size)
self.encoder_cell_proj = Linear(encoder_output_units, hidden_size)
else:
self.encoder_hidden_proj = self.encoder_cell_proj = None
self.layers = nn.ModuleList([
LSTMCell(
input_size=hidden_size + embed_dim if layer == 0 else hidden_size,
hidden_size=hidden_size,
)
for layer in range(num_layers)
])
if attention:
# TODO make bias configurable
self.attention = AttentionLayer(hidden_size, encoder_output_units, hidden_size, bias=False)
else:
self.attention = None
if hidden_size != out_embed_dim:
self.additional_fc = Linear(hidden_size, out_embed_dim)
if adaptive_softmax_cutoff is not None:
# setting adaptive_softmax dropout to dropout_out for now but can be redefined
self.adaptive_softmax = AdaptiveSoftmax(num_embeddings, hidden_size, adaptive_softmax_cutoff,
dropout=dropout_out)
elif not self.share_input_output_embed:
self.fc_out = Linear(out_embed_dim, num_embeddings, dropout=dropout_out)
def forward(self, prev_output_tokens, encoder_out, incremental_state=None):
x, attn_scores = self.extract_features(
prev_output_tokens, encoder_out, incremental_state
)
return self.output_layer(x), attn_scores
def extract_features(
self, prev_output_tokens, encoder_out, incremental_state=None
):
"""
Similar to *forward* but only return features.
"""
encoder_padding_mask = encoder_out['encoder_padding_mask']
encoder_out = encoder_out['encoder_out']
if incremental_state is not None:
prev_output_tokens = prev_output_tokens[:, -1:]
bsz, seqlen = prev_output_tokens.size()
# get outputs from encoder
encoder_outs, encoder_hiddens, encoder_cells = encoder_out[:3]
srclen = encoder_outs.size(0)
# embed tokens
x = self.embed_tokens(prev_output_tokens)
x = F.dropout(x, p=self.dropout_in, training=self.training)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
# initialize previous states (or get from cache during incremental generation)
cached_state = utils.get_incremental_state(self, incremental_state, 'cached_state')
if cached_state is not None:
prev_hiddens, prev_cells, input_feed = cached_state
else:
num_layers = len(self.layers)
prev_hiddens = [encoder_hiddens[i] for i in range(num_layers)]
prev_cells = [encoder_cells[i] for i in range(num_layers)]
if self.encoder_hidden_proj is not None:
prev_hiddens = [self.encoder_hidden_proj(x) for x in prev_hiddens]
prev_cells = [self.encoder_cell_proj(x) for x in prev_cells]
input_feed = x.new_zeros(bsz, self.hidden_size)
attn_scores = x.new_zeros(srclen, seqlen, bsz)
outs = []
for j in range(seqlen):
# input feeding: concatenate context vector from previous time step
input = torch.cat((x[j, :, :], input_feed), dim=1)
for i, rnn in enumerate(self.layers):
# recurrent cell
hidden, cell = rnn(input, (prev_hiddens[i], prev_cells[i]))
# hidden state becomes the input to the next layer
input = F.dropout(hidden, p=self.dropout_out, training=self.training)
# save state for next time step
prev_hiddens[i] = hidden
prev_cells[i] = cell
# apply attention using the last layer's hidden state
if self.attention is not None:
out, attn_scores[:, j, :] = self.attention(hidden, encoder_outs, encoder_padding_mask)
else:
out = hidden
out = F.dropout(out, p=self.dropout_out, training=self.training)
# input feeding
input_feed = out
# save final output
outs.append(out)
# cache previous states (no-op except during incremental generation)
utils.set_incremental_state(
self, incremental_state, 'cached_state',
(prev_hiddens, prev_cells, input_feed),
)
# collect outputs across time steps
x = torch.cat(outs, dim=0).view(seqlen, bsz, self.hidden_size)
# T x B x C -> B x T x C
x = x.transpose(1, 0)
if hasattr(self, 'additional_fc') and self.adaptive_softmax is None:
x = self.additional_fc(x)
x = F.dropout(x, p=self.dropout_out, training=self.training)
# srclen x tgtlen x bsz -> bsz x tgtlen x srclen
if not self.training and self.need_attn:
attn_scores = attn_scores.transpose(0, 2)
else:
attn_scores = None
return x, attn_scores
def output_layer(self, x):
"""Project features to the vocabulary size."""
if self.adaptive_softmax is None:
if self.share_input_output_embed:
x = F.linear(x, self.embed_tokens.weight)
else:
x = self.fc_out(x)
return x
def reorder_incremental_state(self, incremental_state, new_order):
super().reorder_incremental_state(incremental_state, new_order)
cached_state = utils.get_incremental_state(self, incremental_state, 'cached_state')
if cached_state is None:
return
def reorder_state(state):
if isinstance(state, list):
return [reorder_state(state_i) for state_i in state]
return state.index_select(0, new_order)
new_state = tuple(map(reorder_state, cached_state))
utils.set_incremental_state(self, incremental_state, 'cached_state', new_state)
def max_positions(self):
"""Maximum output length supported by the decoder."""
return int(1e5) # an arbitrary large number
def make_generation_fast_(self, need_attn=False, **kwargs):
self.need_attn = need_attn
def Embedding(num_embeddings, embedding_dim, padding_idx):
m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
nn.init.uniform_(m.weight, -0.1, 0.1)
nn.init.constant_(m.weight[padding_idx], 0)
return m
def LSTM(input_size, hidden_size, **kwargs):
m = nn.LSTM(input_size, hidden_size, **kwargs)
for name, param in m.named_parameters():
if 'weight' in name or 'bias' in name:
param.data.uniform_(-0.1, 0.1)
return m
def LSTMCell(input_size, hidden_size, **kwargs):
m = nn.LSTMCell(input_size, hidden_size, **kwargs)
for name, param in m.named_parameters():
if 'weight' in name or 'bias' in name:
param.data.uniform_(-0.1, 0.1)
return m
def Linear(in_features, out_features, bias=True, dropout=0):
"""Linear layer (input: N x T x C)"""
m = nn.Linear(in_features, out_features, bias=bias)
m.weight.data.uniform_(-0.1, 0.1)
if bias:
m.bias.data.uniform_(-0.1, 0.1)
return m
@register_model_architecture('lstm', 'lstm')
def base_architecture(args):
args.dropout = getattr(args, 'dropout', 0.1)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512)
args.encoder_embed_path = getattr(args, 'encoder_embed_path', None)
args.encoder_freeze_embed = getattr(args, 'encoder_freeze_embed', False)
args.encoder_hidden_size = getattr(args, 'encoder_hidden_size', args.encoder_embed_dim)
args.encoder_layers = getattr(args, 'encoder_layers', 1)
args.encoder_bidirectional = getattr(args, 'encoder_bidirectional', False)
args.encoder_dropout_in = getattr(args, 'encoder_dropout_in', args.dropout)
args.encoder_dropout_out = getattr(args, 'encoder_dropout_out', args.dropout)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
args.decoder_embed_path = getattr(args, 'decoder_embed_path', None)
args.decoder_freeze_embed = getattr(args, 'decoder_freeze_embed', False)
args.decoder_hidden_size = getattr(args, 'decoder_hidden_size', args.decoder_embed_dim)
args.decoder_layers = getattr(args, 'decoder_layers', 1)
args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 512)
args.decoder_attention = getattr(args, 'decoder_attention', '1')
args.decoder_dropout_in = getattr(args, 'decoder_dropout_in', args.dropout)
args.decoder_dropout_out = getattr(args, 'decoder_dropout_out', args.dropout)
args.share_decoder_input_output_embed = getattr(args, 'share_decoder_input_output_embed', False)
args.share_all_embeddings = getattr(args, 'share_all_embeddings', False)
args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', '10000,50000,200000')
@register_model_architecture('lstm', 'lstm_wiseman_iwslt_de_en')
def lstm_wiseman_iwslt_de_en(args):
args.dropout = getattr(args, 'dropout', 0.1)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256)
args.encoder_dropout_in = getattr(args, 'encoder_dropout_in', 0)
args.encoder_dropout_out = getattr(args, 'encoder_dropout_out', 0)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 256)
args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 256)
args.decoder_dropout_in = getattr(args, 'decoder_dropout_in', 0)
args.decoder_dropout_out = getattr(args, 'decoder_dropout_out', args.dropout)
base_architecture(args)
@register_model_architecture('lstm', 'lstm_luong_wmt_en_de')
def lstm_luong_wmt_en_de(args):
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1000)
args.encoder_layers = getattr(args, 'encoder_layers', 4)
args.encoder_dropout_out = getattr(args, 'encoder_dropout_out', 0)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 1000)
args.decoder_layers = getattr(args, 'decoder_layers', 4)
args.decoder_out_embed_dim = getattr(args, 'decoder_out_embed_dim', 1000)
args.decoder_dropout_out = getattr(args, 'decoder_dropout_out', 0)
base_architecture(args)
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/lstm.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from fairseq.models import FairseqEncoder
class CompositeEncoder(FairseqEncoder):
"""
A wrapper around a dictionary of :class:`FairseqEncoder` objects.
We run forward on each encoder and return a dictionary of outputs. The first
encoder's dictionary is used for initialization.
Args:
encoders (dict): a dictionary of :class:`FairseqEncoder` objects.
"""
def __init__(self, encoders):
super().__init__(next(iter(encoders.values())).dictionary)
self.encoders = encoders
for key in self.encoders:
self.add_module(key, self.encoders[key])
def forward(self, src_tokens, src_lengths):
"""
Args:
src_tokens (LongTensor): tokens in the source language of shape
`(batch, src_len)`
src_lengths (LongTensor): lengths of each source sentence of shape
`(batch)`
Returns:
dict:
the outputs from each Encoder
"""
encoder_out = {}
for key in self.encoders:
encoder_out[key] = self.encoders[key](src_tokens, src_lengths)
return encoder_out
def reorder_encoder_out(self, encoder_out, new_order):
"""Reorder encoder output according to new_order."""
for key in self.encoders:
encoder_out[key] = self.encoders[key].reorder_encoder_out(encoder_out[key], new_order)
return encoder_out
def max_positions(self):
return min([self.encoders[key].max_positions() for key in self.encoders])
def upgrade_state_dict(self, state_dict):
for key in self.encoders:
self.encoders[key].upgrade_state_dict(state_dict)
return state_dict
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/composite_encoder.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
import torch.nn.functional as F
from fairseq.models import register_model, register_model_architecture
from fairseq.models.levenshtein_transformer import (
LevenshteinTransformerDecoder,
LevenshteinTransformerModel,
)
from fairseq.models.transformer import Linear, TransformerModel
from fairseq.modules.transformer_sentence_encoder import init_bert_params
from fairseq.utils import new_arange
class NegativeDistanceScore(object):
def __init__(self):
# pre-compute some values
self.scores = {}
self.scores[0.5] = self.compute_score_full(50, 0.5)
self.scores[1.0] = self.compute_score_full(50, 1.0)
self.scores[2.0] = self.compute_score_full(50, 2.0)
def __call__(self, i, L, tau):
if (tau is None) or (tau > 1000):
return 1 / L
if tau in self.scores:
if L < self.scores[tau].shape[0]:
return self.scores[tau][L - 1, i]
return self.compute_score(L, tau)[i]
def compute_score(self, L, tau):
s = np.array([-abs(L / 2 - i) / tau for i in range(L)])
s = np.exp(s - s.max())
return s / s.sum()
def compute_score_full(self, L, tau):
s = -abs(np.arange(0, L - 1)[:, None] / 2 - np.arange(L)[None, :]) / tau
s = np.tril(s, 0) + np.triu(s - float("inf"), 1)
s = np.exp(s - s.max(1, keepdims=True))
return s / s.sum(1, keepdims=True)
neg_scorer = NegativeDistanceScore()
def _get_ins_targets(in_tokens, out_tokens, padding_idx, unk_idx, vocab_size, tau=None):
try:
from fairseq import libnat
except ImportError as e:
import sys
sys.stderr.write('ERROR: missing libnat. run `pip install --editable .`\n')
raise e
B = in_tokens.size(0)
T = in_tokens.size(1)
V = vocab_size
with torch.cuda.device_of(in_tokens):
in_tokens_list = [
[t for t in s if t != padding_idx] for i, s in enumerate(in_tokens.tolist())
]
out_tokens_list = [
[t for t in s if t != padding_idx]
for i, s in enumerate(out_tokens.tolist())
]
full_labels = libnat.suggested_ed2_path(
in_tokens_list, out_tokens_list, padding_idx
)
insert_labels = [a[:-1] for a in full_labels]
# numericalize1
insert_label_tensors = in_tokens.new_zeros(B * (T - 1) * V).float()
insert_index, insert_labels = zip(
*[
(w + (j + i * (T - 1)) * V, neg_scorer(k, len(label), tau))
for i, labels in enumerate(insert_labels)
for j, label in enumerate(labels[1:-1])
for k, w in enumerate(label)
]
) # HACK 1:-1
insert_index, insert_labels = [
torch.tensor(list(a), device=in_tokens.device)
for a in [insert_index, insert_labels]
]
insert_label_tensors.scatter_(0, insert_index.long(), insert_labels)
insert_label_tensors = insert_label_tensors.view(B, T - 1, V)
return insert_label_tensors
def _apply_ins_words(in_tokens, in_scores, word_ins_pred, word_ins_scores, padding_idx):
padding_masks = in_tokens[:, 1:].eq(padding_idx)
word_ins_scores.masked_fill_(padding_masks, 0.0)
word_ins_pred.masked_fill_(padding_masks, padding_idx)
in_coords = new_arange(in_tokens).type_as(in_scores)
# shift all padding predictions to infinite
out_coords = (in_coords[:, 1:] - 0.5).masked_fill(
word_ins_pred.eq(padding_idx), float("inf")
)
out_coords = torch.cat([in_coords, out_coords], 1).sort(-1)[1]
out_tokens = torch.cat([in_tokens, word_ins_pred], 1).gather(1, out_coords)
out_scores = torch.cat([in_scores, word_ins_scores], 1).gather(1, out_coords)
return out_tokens, out_scores
@register_model("insertion_transformer")
class InsertionTransformerModel(LevenshteinTransformerModel):
def __init__(self, args, encoder, decoder):
super().__init__(args, encoder, decoder)
@staticmethod
def add_args(parser):
TransformerModel.add_args(parser)
parser.add_argument(
"--apply-bert-init",
action="store_true",
help="use custom param initialization for BERT",
)
parser.add_argument("--label-tau", default=None, type=float)
@classmethod
def build_decoder(cls, args, tgt_dict, embed_tokens):
decoder = InsertionTransformerDecoder(args, tgt_dict, embed_tokens)
if getattr(args, "apply_bert_init", False):
decoder.apply(init_bert_params)
return decoder
def forward(
self, src_tokens, src_lengths, prev_output_tokens, tgt_tokens, **kwargs
):
assert tgt_tokens is not None, "forward function only supports training."
# encoding
encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs)
# generate training labels for insertion
word_ins_out = self.decoder.forward_word_ins(
prev_output_tokens, encoder_out=encoder_out
)
word_ins_tgt = _get_ins_targets(
prev_output_tokens,
tgt_tokens,
self.pad,
self.unk,
len(self.tgt_dict),
tau=self.decoder.label_tau,
).type_as(word_ins_out)
word_ins_masks = prev_output_tokens[:, 1:].ne(self.pad)
return {
"word_ins": {
"out": word_ins_out, "tgt": word_ins_tgt,
"mask": word_ins_masks, "ls": self.args.label_smoothing,
"nll_loss": True
}
}
def forward_decoder(
self, decoder_out, encoder_out, eos_penalty=0.0, max_ratio=None, **kwargs
):
output_tokens = decoder_out.output_tokens
output_scores = decoder_out.output_scores
history = decoder_out.history
# TODO: decoding for InsertionTransformer
word_ins_out = self.decoder.forward_word_ins(
output_tokens, encoder_out=encoder_out
)
word_ins_score = F.log_softmax(word_ins_out, 2)
if eos_penalty > 0.0:
word_ins_score[:, :, self.pad] -= eos_penalty
word_ins_score, word_ins_pred = word_ins_score.max(-1)
output_tokens, output_scores = _apply_ins_words(
output_tokens, output_scores, word_ins_pred, word_ins_score, self.pad
)
# delete some unnecessary paddings
cut_off = output_tokens.ne(self.pad).sum(1).max()
output_tokens = output_tokens[:, :cut_off]
output_scores = output_scores[:, :cut_off]
if history is not None:
history.append(output_tokens.clone())
return decoder_out._replace(
output_tokens=output_tokens,
output_scores=output_scores,
attn=None,
history=history
)
class InsertionTransformerDecoder(LevenshteinTransformerDecoder):
def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False):
# use the TransformerDecoder's __init__
super(LevenshteinTransformerDecoder, self).__init__(
args, dictionary, embed_tokens, no_encoder_attn=no_encoder_attn
)
self.dictionary = dictionary
self.bos = dictionary.bos()
self.unk = dictionary.unk()
self.eos = dictionary.eos()
self.pool_out = Linear(self.output_embed_dim * 2, self.output_embed_dim)
self.label_tau = getattr(args, "label_tau", None)
def forward_word_ins(self, prev_output_tokens, encoder_out=None):
features = self.extract_features(prev_output_tokens, encoder_out=encoder_out)[0]
features = self.pool_out(
torch.cat([features[:, :-1, :], features[:, 1:, :]], 2)
)
return self.output_layer(features)
def forward_mask_ins(self, *args, **kwargs):
raise NotImplementedError
def forward_word_del(self, *args, **kwargs):
raise NotImplementedError
def forward_word_del_mask_ins(self, *args, **kwargs):
raise NotImplementedError
@register_model_architecture("insertion_transformer", "insertion_transformer")
def base_architecture(args):
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(
args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 6)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
args.attention_dropout = getattr(args, "attention_dropout", 0.0)
args.activation_dropout = getattr(args, "activation_dropout", 0.0)
args.activation_fn = getattr(args, "activation_fn", "relu")
args.dropout = getattr(args, "dropout", 0.1)
args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
args.no_token_positional_embeddings = getattr(
args, "no_token_positional_embeddings", False
)
args.adaptive_input = getattr(args, "adaptive_input", False)
args.apply_bert_init = getattr(args, "apply_bert_init", False)
args.decoder_output_dim = getattr(
args, "decoder_output_dim", args.decoder_embed_dim
)
args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
# special for insertion transformer
args.label_tau = getattr(args, "label_tau", None)
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/insertion_transformer.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
BaseFairseqModel,
FairseqEncoder,
register_model,
register_model_architecture,
)
from fairseq.modules import (
LayerNorm,
SinusoidalPositionalEmbedding,
TransformerSentenceEncoder,
)
from fairseq.modules.transformer_sentence_encoder import init_bert_params
@register_model('masked_lm')
class MaskedLMModel(BaseFairseqModel):
"""
Class for training a Masked Language Model. It also supports an
additional sentence level prediction if the sent-loss argument is set.
"""
def __init__(self, args, encoder):
super().__init__()
self.args = args
self.encoder = encoder
# if specified then apply bert initialization on the model. We need
# to explictly call this to make sure that the output embeddings
# and projection layers are also correctly initialized
if getattr(args, 'apply_bert_init', False):
self.apply(init_bert_params)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
# Arguments related to dropout
parser.add_argument('--dropout', type=float, metavar='D',
help='dropout probability')
parser.add_argument('--attention-dropout', type=float,
metavar='D', help='dropout probability for'
' attention weights')
parser.add_argument('--act-dropout', type=float,
metavar='D', help='dropout probability after'
' activation in FFN')
# Arguments related to hidden states and self-attention
parser.add_argument('--encoder-ffn-embed-dim', type=int, metavar='N',
help='encoder embedding dimension for FFN')
parser.add_argument('--encoder-layers', type=int, metavar='N',
help='num encoder layers')
parser.add_argument('--encoder-attention-heads', type=int, metavar='N',
help='num encoder attention heads')
parser.add_argument('--bias-kv', action='store_true',
help='if set, adding a learnable bias kv')
parser.add_argument('--zero-attn', action='store_true',
help='if set, pads attn with zero')
# Arguments related to input and output embeddings
parser.add_argument('--encoder-embed-dim', type=int, metavar='N',
help='encoder embedding dimension')
parser.add_argument('--share-encoder-input-output-embed',
action='store_true', help='share encoder input'
' and output embeddings')
parser.add_argument('--encoder-learned-pos', action='store_true',
help='use learned positional embeddings in the encoder')
parser.add_argument('--no-token-positional-embeddings',
action='store_true',
help='if set, disables positional embeddings'
' (outside self attention)')
parser.add_argument('--num-segment', type=int, metavar='N',
help='num segment in the input')
# Arguments related to sentence level prediction
parser.add_argument('--sentence-class-num', type=int, metavar='N',
help='number of classes for sentence task')
parser.add_argument('--sent-loss', action='store_true', help='if set,'
' calculate sentence level predictions')
# Arguments related to parameter initialization
parser.add_argument('--apply-bert-init', action='store_true',
help='use custom param initialization for BERT')
# misc params
parser.add_argument('--activation-fn',
choices=utils.get_available_activation_fns(),
help='activation function to use')
parser.add_argument('--pooler-activation-fn',
choices=utils.get_available_activation_fns(),
help='Which activation function to use for pooler layer.')
parser.add_argument('--encoder-normalize-before', action='store_true',
help='apply layernorm before each encoder block')
def forward(self, src_tokens, segment_labels=None, **kwargs):
return self.encoder(src_tokens, segment_labels=segment_labels, **kwargs)
def max_positions(self):
return self.encoder.max_positions
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present in older models
base_architecture(args)
if not hasattr(args, 'max_positions'):
args.max_positions = args.tokens_per_sample
print("Model args: ", args)
encoder = MaskedLMEncoder(args, task.dictionary)
return cls(args, encoder)
class MaskedLMEncoder(FairseqEncoder):
"""
Encoder for Masked Language Modelling.
"""
def __init__(self, args, dictionary):
super().__init__(dictionary)
self.padding_idx = dictionary.pad()
self.vocab_size = dictionary.__len__()
self.max_positions = args.max_positions
self.sentence_encoder = TransformerSentenceEncoder(
padding_idx=self.padding_idx,
vocab_size=self.vocab_size,
num_encoder_layers=args.encoder_layers,
embedding_dim=args.encoder_embed_dim,
ffn_embedding_dim=args.encoder_ffn_embed_dim,
num_attention_heads=args.encoder_attention_heads,
dropout=args.dropout,
attention_dropout=args.attention_dropout,
activation_dropout=args.act_dropout,
max_seq_len=self.max_positions,
num_segments=args.num_segment,
use_position_embeddings=not args.no_token_positional_embeddings,
encoder_normalize_before=args.encoder_normalize_before,
apply_bert_init=args.apply_bert_init,
activation_fn=args.activation_fn,
learned_pos_embedding=args.encoder_learned_pos,
add_bias_kv=args.bias_kv,
add_zero_attn=args.zero_attn,
)
self.share_input_output_embed = args.share_encoder_input_output_embed
self.embed_out = None
self.sentence_projection_layer = None
self.sentence_out_dim = args.sentence_class_num
self.lm_output_learned_bias = None
# Remove head is set to true during fine-tuning
self.load_softmax = not getattr(args, 'remove_head', False)
self.masked_lm_pooler = nn.Linear(
args.encoder_embed_dim, args.encoder_embed_dim
)
self.pooler_activation = utils.get_activation_fn(args.pooler_activation_fn)
self.lm_head_transform_weight = nn.Linear(args.encoder_embed_dim, args.encoder_embed_dim)
self.activation_fn = utils.get_activation_fn(args.activation_fn)
self.layer_norm = LayerNorm(args.encoder_embed_dim)
self.lm_output_learned_bias = None
if self.load_softmax:
self.lm_output_learned_bias = nn.Parameter(torch.zeros(self.vocab_size))
if not self.share_input_output_embed:
self.embed_out = nn.Linear(
args.encoder_embed_dim,
self.vocab_size,
bias=False
)
if args.sent_loss:
self.sentence_projection_layer = nn.Linear(
args.encoder_embed_dim,
self.sentence_out_dim,
bias=False
)
def forward(self, src_tokens, segment_labels=None, **unused):
"""
Forward pass for Masked LM encoder. This first computes the token
embedding using the token embedding matrix, position embeddings (if
specified) and segment embeddings (if specified).
Here we assume that the sentence representation corresponds to the
output of the classification_token (see bert_task or cross_lingual_lm
task for more details).
Args:
- src_tokens: B x T matrix representing sentences
- segment_labels: B x T matrix representing segment label for tokens
Returns:
- a tuple of the following:
- logits for predictions in format B x T x C to be used in
softmax afterwards
- a dictionary of additional data, where 'pooled_output' contains
the representation for classification_token and 'inner_states'
is a list of internal model states used to compute the
predictions (similar in ELMO). 'sentence_logits'
is the prediction logit for NSP task and is only computed if
this is specified in the input arguments.
"""
inner_states, sentence_rep = self.sentence_encoder(
src_tokens,
segment_labels=segment_labels,
)
x = inner_states[-1].transpose(0, 1)
x = self.layer_norm(self.activation_fn(self.lm_head_transform_weight(x)))
pooled_output = self.pooler_activation(self.masked_lm_pooler(sentence_rep))
# project back to size of vocabulary
if self.share_input_output_embed \
and hasattr(self.sentence_encoder.embed_tokens, 'weight'):
x = F.linear(x, self.sentence_encoder.embed_tokens.weight)
elif self.embed_out is not None:
x = self.embed_out(x)
if self.lm_output_learned_bias is not None:
x = x + self.lm_output_learned_bias
sentence_logits = None
if self.sentence_projection_layer:
sentence_logits = self.sentence_projection_layer(pooled_output)
return x, {
'inner_states': inner_states,
'pooled_output': pooled_output,
'sentence_logits': sentence_logits
}
def max_positions(self):
"""Maximum output length supported by the encoder."""
return self.max_positions
def upgrade_state_dict_named(self, state_dict, name):
if isinstance(
self.sentence_encoder.embed_positions,
SinusoidalPositionalEmbedding
):
state_dict[
name + '.sentence_encoder.embed_positions._float_tensor'
] = torch.FloatTensor(1)
if not self.load_softmax:
for k in list(state_dict.keys()):
if (
"embed_out.weight" in k or
"sentence_projection_layer.weight" in k or
"lm_output_learned_bias" in k
):
del state_dict[k]
return state_dict
@register_model_architecture('masked_lm', 'masked_lm')
def base_architecture(args):
args.dropout = getattr(args, 'dropout', 0.1)
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
args.act_dropout = getattr(args, 'act_dropout', 0.0)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 4096)
args.encoder_layers = getattr(args, 'encoder_layers', 6)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 8)
args.bias_kv = getattr(args, 'bias_kv', False)
args.zero_attn = getattr(args, 'zero_attn', False)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1024)
args.share_encoder_input_output_embed = getattr(args, 'share_encoder_input_output_embed', False)
args.encoder_learned_pos = getattr(args, 'encoder_learned_pos', False)
args.no_token_positional_embeddings = getattr(args, 'no_token_positional_embeddings', False)
args.num_segment = getattr(args, 'num_segment', 2)
args.sentence_class_num = getattr(args, 'sentence_class_num', 2)
args.sent_loss = getattr(args, 'sent_loss', False)
args.apply_bert_init = getattr(args, 'apply_bert_init', False)
args.activation_fn = getattr(args, 'activation_fn', 'relu')
args.pooler_activation_fn = getattr(args, 'pooler_activation_fn', 'tanh')
args.encoder_normalize_before = getattr(args, 'encoder_normalize_before', False)
@register_model_architecture('masked_lm', 'bert_base')
def bert_base_architecture(args):
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 768)
args.share_encoder_input_output_embed = getattr(
args, 'share_encoder_input_output_embed', True)
args.no_token_positional_embeddings = getattr(
args, 'no_token_positional_embeddings', False)
args.encoder_learned_pos = getattr(args, 'encoder_learned_pos', True)
args.num_segment = getattr(args, 'num_segment', 2)
args.encoder_layers = getattr(args, 'encoder_layers', 12)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 12)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 3072)
args.bias_kv = getattr(args, 'bias_kv', False)
args.zero_attn = getattr(args, 'zero_attn', False)
args.sentence_class_num = getattr(args, 'sentence_class_num', 2)
args.sent_loss = getattr(args, 'sent_loss', True)
args.apply_bert_init = getattr(args, 'apply_bert_init', True)
args.activation_fn = getattr(args, 'activation_fn', 'gelu')
args.pooler_activation_fn = getattr(args, 'pooler_activation_fn', 'tanh')
args.encoder_normalize_before = getattr(args, 'encoder_normalize_before', True)
base_architecture(args)
@register_model_architecture('masked_lm', 'bert_large')
def bert_large_architecture(args):
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1024)
args.encoder_layers = getattr(args, 'encoder_layers', 24)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 16)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 4096)
bert_base_architecture(args)
@register_model_architecture('masked_lm', 'xlm_base')
def xlm_architecture(args):
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1024)
args.share_encoder_input_output_embed = getattr(
args, 'share_encoder_input_output_embed', True)
args.no_token_positional_embeddings = getattr(
args, 'no_token_positional_embeddings', False)
args.encoder_learned_pos = getattr(args, 'encoder_learned_pos', True)
args.num_segment = getattr(args, 'num_segment', 1)
args.encoder_layers = getattr(args, 'encoder_layers', 6)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 8)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 4096)
args.bias_kv = getattr(args, 'bias_kv', False)
args.zero_attn = getattr(args, 'zero_attn', False)
args.sent_loss = getattr(args, 'sent_loss', False)
args.activation_fn = getattr(args, 'activation_fn', 'gelu')
args.encoder_normalize_before = getattr(args, 'encoder_normalize_before', False)
args.pooler_activation_fn = getattr(args, 'pooler_activation_fn', 'tanh')
args.apply_bert_init = getattr(args, 'apply_bert_init', True)
base_architecture(args)
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/masked_lm.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from fairseq.models import FairseqDecoder
class FairseqIncrementalDecoder(FairseqDecoder):
"""Base class for incremental decoders.
Incremental decoding is a special mode at inference time where the Model
only receives a single timestep of input corresponding to the previous
output token (for teacher forcing) and must produce the next output
*incrementally*. Thus the model must cache any long-term state that is
needed about the sequence, e.g., hidden states, convolutional states, etc.
Compared to the standard :class:`FairseqDecoder` interface, the incremental
decoder interface allows :func:`forward` functions to take an extra keyword
argument (*incremental_state*) that can be used to cache state across
time-steps.
The :class:`FairseqIncrementalDecoder` interface also defines the
:func:`reorder_incremental_state` method, which is used during beam search
to select and reorder the incremental state based on the selection of beams.
To learn more about how incremental decoding works, refer to `this blog
<http://www.telesens.co/2019/04/21/understanding-incremental-decoding-in-fairseq/>`_.
"""
def __init__(self, dictionary):
super().__init__(dictionary)
def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None, **kwargs):
"""
Args:
prev_output_tokens (LongTensor): shifted output tokens of shape
`(batch, tgt_len)`, for teacher forcing
encoder_out (dict, optional): output from the encoder, used for
encoder-side attention
incremental_state (dict, optional): dictionary used for storing
state during :ref:`Incremental decoding`
Returns:
tuple:
- the decoder's output of shape `(batch, tgt_len, vocab)`
- a dictionary with any model-specific outputs
"""
raise NotImplementedError
def extract_features(self, prev_output_tokens, encoder_out=None, incremental_state=None, **kwargs):
"""
Returns:
tuple:
- the decoder's features of shape `(batch, tgt_len, embed_dim)`
- a dictionary with any model-specific outputs
"""
raise NotImplementedError
def reorder_incremental_state(self, incremental_state, new_order):
"""Reorder incremental state.
This should be called when the order of the input has changed from the
previous time step. A typical use case is beam search, where the input
order changes between time steps based on the selection of beams.
"""
seen = set()
def apply_reorder_incremental_state(module):
if module != self and hasattr(module, 'reorder_incremental_state') \
and module not in seen:
seen.add(module)
module.reorder_incremental_state(incremental_state, new_order)
self.apply(apply_reorder_incremental_state)
def set_beam_size(self, beam_size):
"""Sets the beam size in the decoder and all children."""
if getattr(self, '_beam_size', -1) != beam_size:
seen = set()
def apply_set_beam_size(module):
if module != self and hasattr(module, 'set_beam_size') \
and module not in seen:
seen.add(module)
module.set_beam_size(beam_size)
self.apply(apply_set_beam_size)
self._beam_size = beam_size
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/fairseq_incremental_decoder.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import options, utils
from fairseq.models import (
FairseqEncoder,
FairseqIncrementalDecoder,
FairseqEncoderDecoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
AdaptiveSoftmax,
DynamicConv,
LayerNorm,
PositionalEmbedding,
LightweightConv,
MultiheadAttention,
)
@register_model('lightconv')
class LightConvModel(FairseqEncoderDecoderModel):
"""
LightConv and DynamicConv model from `"Pay Less Attention with Lightweight and Dynamic Convolutions" (Wu, et al, 2019)
<https://openreview.net/pdf?id=SkVhlh09tX>`_.
To use LightConv please set --encoder-conv-type lightweight --decoder-conv-type lightweight
To use DynamicConv please set --encoder-conv-type dynamic --decoder-conv-type dynamic
Args:
encoder (LightConvEncoder): the encoder
decoder (LightConvDecoder): the decoder
The LightConv model provides the following named architectures and
command-line arguments:
.. argparse::
:ref: fairseq.models.lightconv_parser
:prog:
"""
def __init__(self, encoder, decoder):
super().__init__(encoder, decoder)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
parser.add_argument('--dropout', type=float, metavar='D',
help='dropout probability')
parser.add_argument('--attention-dropout', type=float, metavar='D',
help='dropout probability for attention weights')
parser.add_argument('--relu-dropout', type=float, metavar='D',
help='dropout probability after ReLU in FFN')
parser.add_argument('--input-dropout', type=float, metavar='D',
help='dropout probability of the inputs')
parser.add_argument('--encoder-embed-path', type=str, metavar='STR',
help='path to pre-trained encoder embedding')
parser.add_argument('--encoder-embed-dim', type=int, metavar='N',
help='encoder embedding dimension')
parser.add_argument('--encoder-conv-dim', type=int, metavar='N',
help='encoder embedding dimension')
parser.add_argument('--encoder-ffn-embed-dim', type=int, metavar='N',
help='encoder embedding dimension for FFN')
parser.add_argument('--encoder-layers', type=int, metavar='N',
help='num encoder layers')
parser.add_argument('--encoder-attention-heads', type=int, metavar='N',
help='num encoder attention heads or LightConv/DynamicConv heads')
parser.add_argument('--encoder-normalize-before', action='store_true',
help='apply layernorm before each encoder block')
parser.add_argument('--encoder-learned-pos', action='store_true',
help='use learned positional embeddings in the encoder')
parser.add_argument('--decoder-embed-path', type=str, metavar='STR',
help='path to pre-trained decoder embedding')
parser.add_argument('--decoder-embed-dim', type=int, metavar='N',
help='decoder embedding dimension')
parser.add_argument('--decoder-conv-dim', type=int, metavar='N',
help='decoder embedding dimension')
parser.add_argument('--decoder-ffn-embed-dim', type=int, metavar='N',
help='decoder embedding dimension for FFN')
parser.add_argument('--decoder-layers', type=int, metavar='N',
help='num decoder layers')
parser.add_argument('--decoder-attention-heads', type=int, metavar='N',
help='num decoder attention heads or LightConv/DynamicConv heads')
parser.add_argument('--decoder-learned-pos', action='store_true',
help='use learned positional embeddings in the decoder')
parser.add_argument('--decoder-normalize-before', action='store_true',
help='apply layernorm before each decoder block')
parser.add_argument('--share-decoder-input-output-embed', action='store_true',
help='share decoder input and output embeddings')
parser.add_argument('--share-all-embeddings', action='store_true',
help='share encoder, decoder and output embeddings'
' (requires shared dictionary and embed dim)')
parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR',
help='comma separated list of adaptive softmax cutoff points. '
'Must be used with adaptive_loss criterion'),
parser.add_argument('--adaptive-softmax-dropout', type=float, metavar='D',
help='sets adaptive softmax dropout for the tail projections')
"""LightConv and DynamicConv arguments"""
parser.add_argument('--encoder-kernel-size-list', type=lambda x: options.eval_str_list(x, int),
help='list of kernel size (default: "[3,7,15,31,31,31,31]")')
parser.add_argument('--decoder-kernel-size-list', type=lambda x: options.eval_str_list(x, int),
help='list of kernel size (default: "[3,7,15,31,31,31]")')
parser.add_argument('--encoder-glu', type=options.eval_bool,
help='glu after in proj')
parser.add_argument('--decoder-glu', type=options.eval_bool,
help='glu after in proj')
parser.add_argument('--encoder-conv-type', default='dynamic', type=str,
choices=['dynamic', 'lightweight'],
help='type of convolution')
parser.add_argument('--decoder-conv-type', default='dynamic', type=str,
choices=['dynamic', 'lightweight'],
help='type of convolution')
parser.add_argument('--weight-softmax', default=True, type=options.eval_bool)
parser.add_argument('--weight-dropout', type=float, metavar='D',
help='dropout probability for conv weights')
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present in older models
base_architecture(args)
if not hasattr(args, 'max_source_positions'):
args.max_source_positions = 1024
if not hasattr(args, 'max_target_positions'):
args.max_target_positions = 1024
src_dict, tgt_dict = task.source_dictionary, task.target_dictionary
def build_embedding(dictionary, embed_dim, path=None):
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
emb = Embedding(num_embeddings, embed_dim, padding_idx)
# if provided, load from preloaded dictionaries
if path:
embed_dict = utils.parse_embedding(path)
utils.load_embedding(embed_dict, dictionary, emb)
return emb
if args.share_all_embeddings:
if src_dict != tgt_dict:
raise RuntimeError('--share-all-embeddings requires a joined dictionary')
if args.encoder_embed_dim != args.decoder_embed_dim:
raise RuntimeError(
'--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim')
if args.decoder_embed_path and (
args.decoder_embed_path != args.encoder_embed_path):
raise RuntimeError('--share-all-embeddings not compatible with --decoder-embed-path')
encoder_embed_tokens = build_embedding(
src_dict, args.encoder_embed_dim, args.encoder_embed_path
)
decoder_embed_tokens = encoder_embed_tokens
args.share_decoder_input_output_embed = True
else:
encoder_embed_tokens = build_embedding(
src_dict, args.encoder_embed_dim, args.encoder_embed_path
)
decoder_embed_tokens = build_embedding(
tgt_dict, args.decoder_embed_dim, args.decoder_embed_path
)
encoder = LightConvEncoder(args, src_dict, encoder_embed_tokens)
decoder = LightConvDecoder(args, tgt_dict, decoder_embed_tokens)
return LightConvModel(encoder, decoder)
class LightConvEncoder(FairseqEncoder):
"""
LightConv encoder consisting of *args.encoder_layers* layers. Each layer
is a :class:`LightConvEncoderLayer`.
Args:
args (argparse.Namespace): parsed command-line arguments
dictionary (~fairseq.data.Dictionary): encoding dictionary
embed_tokens (torch.nn.Embedding): input embedding
"""
def __init__(self, args, dictionary, embed_tokens):
super().__init__(dictionary)
self.dropout = args.dropout
embed_dim = embed_tokens.embedding_dim
self.padding_idx = embed_tokens.padding_idx
self.max_source_positions = args.max_source_positions
self.embed_tokens = embed_tokens
self.embed_scale = math.sqrt(embed_dim)
self.embed_positions = PositionalEmbedding(
args.max_source_positions, embed_dim, self.padding_idx,
learned=args.encoder_learned_pos,
) if not args.no_token_positional_embeddings else None
self.layers = nn.ModuleList([])
self.layers.extend([
LightConvEncoderLayer(args, kernel_size=args.encoder_kernel_size_list[i])
for i in range(args.encoder_layers)
])
self.register_buffer('version', torch.Tensor([2]))
self.normalize = args.encoder_normalize_before
if self.normalize:
self.layer_norm = LayerNorm(embed_dim)
def forward(self, src_tokens, **unused):
"""
Args:
src_tokens (LongTensor): tokens in the source language of shape
`(batch, src_len)`
Returns:
dict:
- **encoder_out** (Tensor): the last encoder layer's output of
shape `(src_len, batch, embed_dim)`
- **encoder_padding_mask** (ByteTensor): the positions of
padding elements of shape `(batch, src_len)`
"""
# embed tokens and positions
x = self.embed_scale * self.embed_tokens(src_tokens)
if self.embed_positions is not None:
x += self.embed_positions(src_tokens)
x = F.dropout(x, p=self.dropout, training=self.training)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
# compute padding mask
encoder_padding_mask = src_tokens.eq(self.padding_idx)
if not encoder_padding_mask.any():
encoder_padding_mask = None
# encoder layers
for layer in self.layers:
x = layer(x, encoder_padding_mask)
if self.normalize:
x = self.layer_norm(x)
return {
'encoder_out': x, # T x B x C
'encoder_padding_mask': encoder_padding_mask, # B x T
}
def reorder_encoder_out(self, encoder_out, new_order):
"""
Reorder encoder output according to *new_order*.
Args:
encoder_out: output from the ``forward()`` method
new_order (LongTensor): desired order
Returns:
*encoder_out* rearranged according to *new_order*
"""
if encoder_out['encoder_out'] is not None:
encoder_out['encoder_out'] = \
encoder_out['encoder_out'].index_select(1, new_order)
if encoder_out['encoder_padding_mask'] is not None:
encoder_out['encoder_padding_mask'] = \
encoder_out['encoder_padding_mask'].index_select(0, new_order)
return encoder_out
def max_positions(self):
"""Maximum input length supported by the encoder."""
if self.embed_positions is None:
return self.max_source_positions
return min(self.max_source_positions, self.embed_positions.max_positions())
class LightConvDecoder(FairseqIncrementalDecoder):
"""
LightConv decoder consisting of *args.decoder_layers* layers. Each layer
is a :class:`LightConvDecoderLayer`.
Args:
args (argparse.Namespace): parsed command-line arguments
dictionary (~fairseq.data.Dictionary): decoding dictionary
embed_tokens (torch.nn.Embedding): output embedding
no_encoder_attn (bool, optional): whether to attend to encoder outputs.
Default: ``False``
"""
def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False, final_norm=True):
super().__init__(dictionary)
self.dropout = args.dropout
self.share_input_output_embed = args.share_decoder_input_output_embed
input_embed_dim = embed_tokens.embedding_dim
embed_dim = args.decoder_embed_dim
output_embed_dim = args.decoder_output_dim
padding_idx = embed_tokens.padding_idx
self.max_target_positions = args.max_target_positions
self.embed_tokens = embed_tokens
self.embed_scale = math.sqrt(embed_dim) # todo: try with input_embed_dim
self.project_in_dim = Linear(input_embed_dim, embed_dim, bias=False) if embed_dim != input_embed_dim else None
self.embed_positions = PositionalEmbedding(
args.max_target_positions, embed_dim, padding_idx,
learned=args.decoder_learned_pos,
) if not args.no_token_positional_embeddings else None
self.layers = nn.ModuleList([])
self.layers.extend([
LightConvDecoderLayer(args, no_encoder_attn, kernel_size=args.decoder_kernel_size_list[i])
for i in range(args.decoder_layers)
])
self.adaptive_softmax = None
self.project_out_dim = Linear(embed_dim, output_embed_dim, bias=False) \
if embed_dim != output_embed_dim and not args.tie_adaptive_weights else None
if args.adaptive_softmax_cutoff is not None:
self.adaptive_softmax = AdaptiveSoftmax(
len(dictionary),
output_embed_dim,
options.eval_str_list(args.adaptive_softmax_cutoff, type=int),
dropout=args.adaptive_softmax_dropout,
adaptive_inputs=embed_tokens if args.tie_adaptive_weights else None,
factor=args.adaptive_softmax_factor,
tie_proj=args.tie_adaptive_proj,
)
elif not self.share_input_output_embed:
self.embed_out = nn.Parameter(torch.Tensor(len(dictionary), output_embed_dim))
nn.init.normal_(self.embed_out, mean=0, std=output_embed_dim ** -0.5)
self.register_buffer('version', torch.Tensor([2]))
self.normalize = args.decoder_normalize_before and final_norm
if self.normalize:
self.layer_norm = LayerNorm(embed_dim)
def forward(self, prev_output_tokens, encoder_out=None, incremental_state=None, **kwargs):
"""
Args:
prev_output_tokens (LongTensor): previous decoder outputs of shape
`(batch, tgt_len)`, for teacher forcing
encoder_out (Tensor, optional): output from the encoder, used for
encoder-side attention
incremental_state (dict): dictionary used for storing state during
:ref:`Incremental decoding`
Returns:
tuple:
- the last decoder layer's output of shape `(batch, tgt_len,
vocab)`
- the last decoder layer's attention weights of shape `(batch,
tgt_len, src_len)`
"""
# embed positions
positions = self.embed_positions(
prev_output_tokens,
incremental_state=incremental_state,
) if self.embed_positions is not None else None
if incremental_state is not None:
prev_output_tokens = prev_output_tokens[:, -1:]
if positions is not None:
positions = positions[:, -1:]
# embed tokens and positions
x = self.embed_scale * self.embed_tokens(prev_output_tokens)
if self.project_in_dim is not None:
x = self.project_in_dim(x)
if positions is not None:
x += positions
x = F.dropout(x, p=self.dropout, training=self.training)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
attn = None
inner_states = [x]
# decoder layers
for layer in self.layers:
x, attn = layer(
x,
encoder_out['encoder_out'] if encoder_out is not None else None,
encoder_out['encoder_padding_mask'] if encoder_out is not None else None,
incremental_state,
)
inner_states.append(x)
if self.normalize:
x = self.layer_norm(x)
# T x B x C -> B x T x C
x = x.transpose(0, 1)
if self.project_out_dim is not None:
x = self.project_out_dim(x)
if self.adaptive_softmax is None:
# project back to size of vocabulary
if self.share_input_output_embed:
x = F.linear(x, self.embed_tokens.weight)
else:
x = F.linear(x, self.embed_out)
return x, {'attn': attn, 'inner_states': inner_states}
def max_positions(self):
"""Maximum output length supported by the decoder."""
if self.embed_positions is None:
return self.max_target_positions
return min(self.max_target_positions, self.embed_positions.max_positions())
def buffered_future_mask(self, tensor):
dim = tensor.size(0)
if not hasattr(self, '_future_mask') or self._future_mask is None or self._future_mask.device != tensor.device:
self._future_mask = torch.triu(utils.fill_with_neg_inf(tensor.new(dim, dim)), 1)
if self._future_mask.size(0) < dim:
self._future_mask = torch.triu(utils.fill_with_neg_inf(self._future_mask.resize_(dim, dim)), 1)
return self._future_mask[:dim, :dim]
class LightConvEncoderLayer(nn.Module):
"""Encoder layer block.
Args:
args (argparse.Namespace): parsed command-line arguments
kernel_size: kernel size of the convolution
"""
def __init__(self, args, kernel_size=0):
super().__init__()
self.embed_dim = args.encoder_embed_dim
self.conv_dim = args.encoder_conv_dim
padding_l = kernel_size // 2 if kernel_size % 2 == 1 else ((kernel_size - 1) // 2, kernel_size // 2)
if args.encoder_glu:
self.linear1 = Linear(self.embed_dim, 2*self.conv_dim)
self.act = nn.GLU()
else:
self.linear1 = Linear(self.embed_dim, self.conv_dim)
self.act = None
if args.encoder_conv_type == 'lightweight':
self.conv = LightweightConv(self.conv_dim, kernel_size, padding_l=padding_l,
weight_softmax=args.weight_softmax,
num_heads=args.encoder_attention_heads,
weight_dropout=args.weight_dropout)
elif args.encoder_conv_type == 'dynamic':
self.conv = DynamicConv(self.conv_dim, kernel_size, padding_l=padding_l,
weight_softmax=args.weight_softmax,
num_heads=args.encoder_attention_heads,
weight_dropout=args.weight_dropout)
else:
raise NotImplementedError
self.linear2 = Linear(self.conv_dim, self.embed_dim)
self.dropout = args.dropout
self.relu_dropout = args.relu_dropout
self.input_dropout = args.input_dropout
self.normalize_before = args.encoder_normalize_before
self.fc1 = Linear(self.embed_dim, args.encoder_ffn_embed_dim)
self.fc2 = Linear(args.encoder_ffn_embed_dim, self.embed_dim)
self.layer_norms = nn.ModuleList([LayerNorm(self.embed_dim) for _ in range(2)])
def forward(self, x, encoder_padding_mask):
"""
Args:
x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
encoder_padding_mask (ByteTensor): binary ByteTensor of shape
`(batch, src_len)` where padding elements are indicated by ``1``.
Returns:
encoded output of shape `(batch, src_len, embed_dim)`
"""
residual = x
x = self.maybe_layer_norm(0, x, before=True)
x = F.dropout(x, p=self.input_dropout, training=self.training)
x = self.linear1(x)
if self.act is not None:
x = self.act(x)
if encoder_padding_mask is not None:
x = x.masked_fill(encoder_padding_mask.transpose(0, 1).unsqueeze(2), 0)
x = self.conv(x)
x = self.linear2(x)
x = F.dropout(x, p=self.dropout, training=self.training)
x = residual + x
x = self.maybe_layer_norm(0, x, after=True)
residual = x
x = self.maybe_layer_norm(1, x, before=True)
x = F.relu(self.fc1(x))
x = F.dropout(x, p=self.relu_dropout, training=self.training)
x = self.fc2(x)
x = F.dropout(x, p=self.dropout, training=self.training)
x = residual + x
x = self.maybe_layer_norm(1, x, after=True)
return x
def maybe_layer_norm(self, i, x, before=False, after=False):
assert before ^ after
if after ^ self.normalize_before:
return self.layer_norms[i](x)
else:
return x
def extra_repr(self):
return 'dropout={}, relu_dropout={}, input_dropout={}, normalize_before={}'.format(
self.dropout, self.relu_dropout, self.input_dropout, self.normalize_before)
class LightConvDecoderLayer(nn.Module):
"""Decoder layer block.
Args:
args (argparse.Namespace): parsed command-line arguments
no_encoder_attn (bool, optional): whether to attend to encoder outputs.
Default: ``False``
kernel_size: kernel size of the convolution
"""
def __init__(self, args, no_encoder_attn=False, kernel_size=0):
super().__init__()
self.embed_dim = args.decoder_embed_dim
self.conv_dim = args.decoder_conv_dim
if args.decoder_glu:
self.linear1 = Linear(self.embed_dim, 2*self.conv_dim)
self.act = nn.GLU()
else:
self.linear1 = Linear(self.embed_dim, self.conv_dim)
self.act = None
if args.decoder_conv_type == 'lightweight':
self.conv = LightweightConv(self.conv_dim, kernel_size, padding_l=kernel_size-1,
weight_softmax=args.weight_softmax,
num_heads=args.decoder_attention_heads,
weight_dropout=args.weight_dropout)
elif args.decoder_conv_type == 'dynamic':
self.conv = DynamicConv(self.conv_dim, kernel_size, padding_l=kernel_size-1,
weight_softmax=args.weight_softmax,
num_heads=args.decoder_attention_heads,
weight_dropout=args.weight_dropout)
else:
raise NotImplementedError
self.linear2 = Linear(self.conv_dim, self.embed_dim)
self.dropout = args.dropout
self.relu_dropout = args.relu_dropout
self.input_dropout = args.input_dropout
self.normalize_before = args.decoder_normalize_before
self.conv_layer_norm = LayerNorm(self.embed_dim)
if no_encoder_attn:
self.encoder_attn = None
self.encoder_attn_layer_norm = None
else:
self.encoder_attn = MultiheadAttention(
self.embed_dim, args.decoder_attention_heads,
dropout=args.attention_dropout, encoder_decoder_attention=True
)
self.encoder_attn_layer_norm = LayerNorm(self.embed_dim)
self.fc1 = Linear(self.embed_dim, args.decoder_ffn_embed_dim)
self.fc2 = Linear(args.decoder_ffn_embed_dim, self.embed_dim)
self.final_layer_norm = LayerNorm(self.embed_dim)
self.need_attn = True
def forward(self, x, encoder_out, encoder_padding_mask, incremental_state,
prev_conv_state=None, prev_attn_state=None, conv_mask=None,
conv_padding_mask=None):
"""
Args:
x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
encoder_padding_mask (ByteTensor): binary ByteTensor of shape
`(batch, src_len)` where padding elements are indicated by ``1``.
Returns:
encoded output of shape `(batch, src_len, embed_dim)`
"""
residual = x
x = self.maybe_layer_norm(self.conv_layer_norm, x, before=True)
if prev_conv_state is not None:
if incremental_state is None:
incremental_state = {}
self.conv._set_input_buffer(incremental_state, prev_conv_state)
x = F.dropout(x, p=self.input_dropout, training=self.training)
x = self.linear1(x)
if self.act is not None:
x = self.act(x)
x = self.conv(x, incremental_state=incremental_state)
x = self.linear2(x)
x = F.dropout(x, p=self.dropout, training=self.training)
x = residual + x
x = self.maybe_layer_norm(self.conv_layer_norm, x, after=True)
attn = None
if self.encoder_attn is not None:
residual = x
x = self.maybe_layer_norm(self.encoder_attn_layer_norm, x, before=True)
if prev_attn_state is not None:
if incremental_state is None:
incremental_state = {}
prev_key, prev_value = prev_attn_state
saved_state = {"prev_key": prev_key, "prev_value": prev_value}
self.encoder_attn._set_input_buffer(incremental_state, saved_state)
x, attn = self.encoder_attn(
query=x,
key=encoder_out,
value=encoder_out,
key_padding_mask=encoder_padding_mask,
incremental_state=incremental_state,
static_kv=True,
need_weights=(not self.training and self.need_attn),
)
x = F.dropout(x, p=self.dropout, training=self.training)
x = residual + x
x = self.maybe_layer_norm(self.encoder_attn_layer_norm, x, after=True)
residual = x
x = self.maybe_layer_norm(self.final_layer_norm, x, before=True)
x = F.relu(self.fc1(x))
x = F.dropout(x, p=self.relu_dropout, training=self.training)
x = self.fc2(x)
x = F.dropout(x, p=self.dropout, training=self.training)
x = residual + x
x = self.maybe_layer_norm(self.final_layer_norm, x, after=True)
return x, attn
def maybe_layer_norm(self, layer_norm, x, before=False, after=False):
assert before ^ after
if after ^ self.normalize_before:
return layer_norm(x)
else:
return x
def make_generation_fast_(self, need_attn=False, **kwargs):
self.need_attn = need_attn
def extra_repr(self):
return 'dropout={}, relu_dropout={}, input_dropout={}, normalize_before={}'.format(
self.dropout, self.relu_dropout, self.input_dropout, self.normalize_before)
def Embedding(num_embeddings, embedding_dim, padding_idx):
m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
nn.init.constant_(m.weight[padding_idx], 0)
return m
def Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
if bias:
nn.init.constant_(m.bias, 0.)
return m
@register_model_architecture('lightconv', 'lightconv')
def base_architecture(args):
args.encoder_embed_path = getattr(args, 'encoder_embed_path', None)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 2048)
args.encoder_layers = getattr(args, 'encoder_layers', 7)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 8)
args.encoder_normalize_before = getattr(args, 'encoder_normalize_before', False)
args.encoder_learned_pos = getattr(args, 'encoder_learned_pos', False)
args.decoder_embed_path = getattr(args, 'decoder_embed_path', None)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', args.encoder_ffn_embed_dim)
args.decoder_layers = getattr(args, 'decoder_layers', 6)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 8)
args.decoder_normalize_before = getattr(args, 'decoder_normalize_before', False)
args.decoder_learned_pos = getattr(args, 'decoder_learned_pos', False)
args.attention_dropout = getattr(args, 'attention_dropout', 0.)
args.relu_dropout = getattr(args, 'relu_dropout', 0.)
args.dropout = getattr(args, 'dropout', 0.1)
args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', None)
args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0)
args.share_decoder_input_output_embed = getattr(args, 'share_decoder_input_output_embed', False)
args.share_all_embeddings = getattr(args, 'share_all_embeddings', False)
args.no_token_positional_embeddings = getattr(args, 'no_token_positional_embeddings', False)
args.decoder_output_dim = getattr(args, 'decoder_output_dim', args.decoder_embed_dim)
args.decoder_input_dim = getattr(args, 'decoder_input_dim', args.decoder_embed_dim)
args.encoder_conv_dim = getattr(args, 'encoder_conv_dim', args.encoder_embed_dim)
args.decoder_conv_dim = getattr(args, 'decoder_conv_dim', args.decoder_embed_dim)
args.encoder_kernel_size_list = getattr(args, 'encoder_kernel_size_list', [3, 7, 15, 31, 31, 31, 31])
args.decoder_kernel_size_list = getattr(args, 'decoder_kernel_size_list', [3, 7, 15, 31, 31, 31])
if len(args.encoder_kernel_size_list) == 1:
args.encoder_kernel_size_list = args.encoder_kernel_size_list * args.encoder_layers
if len(args.decoder_kernel_size_list) == 1:
args.decoder_kernel_size_list = args.decoder_kernel_size_list * args.decoder_layers
assert len(args.encoder_kernel_size_list) == args.encoder_layers, "encoder_kernel_size_list doesn't match encoder_layers"
assert len(args.decoder_kernel_size_list) == args.decoder_layers, "decoder_kernel_size_list doesn't match decoder_layers"
args.encoder_glu = getattr(args, 'encoder_glu', True)
args.decoder_glu = getattr(args, 'decoder_glu', True)
args.input_dropout = getattr(args, 'input_dropout', 0.1)
args.weight_dropout = getattr(args, 'weight_dropout', args.attention_dropout)
@register_model_architecture('lightconv', 'lightconv_iwslt_de_en')
def lightconv_iwslt_de_en(args):
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 512)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 1024)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 4)
args.encoder_layers = getattr(args, 'encoder_layers', 7)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 1024)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 4)
args.decoder_layers = getattr(args, 'decoder_layers', 6)
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
args.weight_dropout = getattr(args, 'weight_dropout', 0.1)
args.encoder_glu = getattr(args, 'encoder_glu', False)
args.decoder_glu = getattr(args, 'decoder_glu', False)
args.input_dropout = getattr(args, 'input_dropout', 0.0)
base_architecture(args)
@register_model_architecture('lightconv', 'lightconv_wmt_en_de')
def lightconv_wmt_en_de(args):
base_architecture(args)
@register_model_architecture('lightconv', 'lightconv_wmt_en_de_big')
def lightconv_wmt_en_de_big(args):
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1024)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 4096)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 16)
args.encoder_normalize_before = getattr(args, 'encoder_normalize_before', False)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 1024)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 4096)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 16)
args.dropout = getattr(args, 'dropout', 0.3)
base_architecture(args)
@register_model_architecture('lightconv', 'lightconv_wmt_en_fr_big')
def lightconv_wmt_en_fr_big(args):
args.dropout = getattr(args, 'dropout', 0.1)
lightconv_wmt_en_de_big(args)
@register_model_architecture('lightconv', 'lightconv_wmt_zh_en_big')
def lightconv_wmt_zh_en_big(args):
args.dropout = getattr(args, 'dropout', 0.2)
args.attention_dropout = getattr(args, 'attention_dropout', 0.2)
args.weight_dropout = getattr(args, 'weight_dropout', 0.2)
lightconv_wmt_en_de_big(args)
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/lightconv.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from fairseq import options
from fairseq.models import (
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.models.lightconv import (
Embedding,
LightConvDecoder,
)
from fairseq.modules import (
AdaptiveInput,
CharacterTokenEmbedder,
)
@register_model('lightconv_lm')
class LightConvLanguageModel(FairseqLanguageModel):
def __init__(self, decoder):
super().__init__(decoder)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
parser.add_argument('--dropout', default=0.1, type=float, metavar='D',
help='dropout probability')
parser.add_argument('--attention-dropout', default=0., type=float, metavar='D',
help='dropout probability for attention weights')
parser.add_argument('--relu-dropout', default=0., type=float, metavar='D',
help='dropout probability after ReLU in FFN')
parser.add_argument('--input-dropout', type=float, metavar='D',
help='dropout probability of the inputs')
parser.add_argument('--decoder-embed-dim', type=int, metavar='N',
help='decoder embedding dimension')
parser.add_argument('--decoder-output-dim', type=int, metavar='N',
help='decoder output dimension')
parser.add_argument('--decoder-input-dim', type=int, metavar='N',
help='decoder input dimension')
parser.add_argument('--decoder-ffn-embed-dim', type=int, metavar='N',
help='decoder embedding dimension for FFN')
parser.add_argument('--decoder-layers', type=int, metavar='N',
help='num decoder layers')
parser.add_argument('--decoder-attention-heads', type=int, metavar='N',
help='num decoder attention heads or LightConv/DynamicConv heads')
parser.add_argument('--decoder-normalize-before', default=False, action='store_true',
help='apply layernorm before each decoder block')
parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR',
help='comma separated list of adaptive softmax cutoff points. '
'Must be used with adaptive_loss criterion')
parser.add_argument('--adaptive-softmax-dropout', type=float, metavar='D',
help='sets adaptive softmax dropout for the tail projections')
parser.add_argument('--adaptive-softmax-factor', type=float, metavar='N',
help='adaptive input factor')
parser.add_argument('--no-token-positional-embeddings', default=False, action='store_true',
help='if set, disables positional embeddings (outside self attention)')
parser.add_argument('--share-decoder-input-output-embed', default=False, action='store_true',
help='share decoder input and output embeddings')
parser.add_argument('--character-embeddings', default=False, action='store_true',
help='if set, uses character embedding convolutions to produce token embeddings')
parser.add_argument('--character-filters', type=str, metavar='LIST',
default='[(1, 64), (2, 128), (3, 192), (4, 256), (5, 256), (6, 256), (7, 256)]',
help='size of character embeddings')
parser.add_argument('--character-embedding-dim', type=int, metavar='N', default=4,
help='size of character embeddings')
parser.add_argument('--char-embedder-highway-layers', type=int, metavar='N', default=2,
help='number of highway layers for character token embeddder')
parser.add_argument('--adaptive-input', default=False, action='store_true',
help='if set, uses adaptive input')
parser.add_argument('--adaptive-input-factor', type=float, metavar='N',
help='adaptive input factor')
parser.add_argument('--adaptive-input-cutoff', metavar='EXPR',
help='comma separated list of adaptive input cutoff points.')
parser.add_argument('--tie-adaptive-weights', action='store_true',
help='if set, ties the weights of adaptive softmax and adaptive input')
parser.add_argument('--tie-adaptive-proj', action='store_true',
help='if set, ties the projection weights of adaptive softmax and adaptive input')
parser.add_argument('--decoder-learned-pos', action='store_true',
help='use learned positional embeddings in the decoder')
"""LightConv and DynamicConv arguments"""
parser.add_argument('--decoder-kernel-size-list', type=lambda x: options.eval_str_list(x, int),
help='list of kernel size (default: "[3,7,15,31,31,31]")')
parser.add_argument('--decoder-glu', type=options.eval_bool,
help='glu after in proj')
parser.add_argument('--decoder-conv-type', default='dynamic', type=str,
choices=['dynamic', 'lightweight'],
help='type of convolution')
parser.add_argument('--weight-softmax', default=True, type=options.eval_bool)
parser.add_argument('--weight-dropout', type=float, metavar='D',
help='dropout probability for conv weights')
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present in older models
base_lm_architecture(args)
if getattr(args, 'max_source_positions', None) is None:
args.max_source_positions = args.tokens_per_sample
if getattr(args, 'max_target_positions', None) is None:
args.max_target_positions = args.tokens_per_sample
if args.character_embeddings:
embed_tokens = CharacterTokenEmbedder(task.dictionary, eval(args.character_filters),
args.character_embedding_dim,
args.decoder_embed_dim,
args.char_embedder_highway_layers,
)
elif args.adaptive_input:
embed_tokens = AdaptiveInput(len(task.dictionary), task.dictionary.pad(), args.decoder_input_dim,
args.adaptive_input_factor, args.decoder_embed_dim,
options.eval_str_list(args.adaptive_input_cutoff, type=int))
else:
embed_tokens = Embedding(len(task.dictionary), args.decoder_input_dim, task.dictionary.pad())
if args.tie_adaptive_weights:
assert args.adaptive_input
assert args.adaptive_input_factor == args.adaptive_softmax_factor
assert args.adaptive_softmax_cutoff == args.adaptive_input_cutoff, '{} != {}'.format(
args.adaptive_softmax_cutoff, args.adaptive_input_cutoff)
assert args.decoder_input_dim == args.decoder_output_dim
decoder = LightConvDecoder(args, task.output_dictionary, embed_tokens, no_encoder_attn=True, final_norm=False)
return LightConvLanguageModel(decoder)
@register_model_architecture('lightconv_lm', 'lightconv_lm')
def base_lm_architecture(args):
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 2048)
args.decoder_layers = getattr(args, 'decoder_layers', 6)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 8)
args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', None)
args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0)
args.adaptive_softmax_factor = getattr(args, 'adaptive_softmax_factor', 4)
args.decoder_learned_pos = getattr(args, 'decoder_learned_pos', False)
args.character_embeddings = getattr(args, 'character_embeddings', False)
args.decoder_output_dim = getattr(args, 'decoder_output_dim', args.decoder_embed_dim)
args.decoder_input_dim = getattr(args, 'decoder_input_dim', args.decoder_embed_dim)
args.decoder_conv_dim = getattr(args, 'decoder_conv_dim', args.decoder_embed_dim)
# The model training is not stable without this
args.decoder_normalize_before = True
args.adaptive_input = getattr(args, 'adaptive_input', False)
args.adaptive_input_factor = getattr(args, 'adaptive_input_factor', 4)
args.adaptive_input_cutoff = getattr(args, 'adaptive_input_cutoff', None)
args.tie_adaptive_weights = getattr(args, 'tie_adaptive_weights', False)
args.tie_adaptive_proj = getattr(args, 'tie_adaptive_proj', False)
args.decoder_kernel_size_list = getattr(args, 'decoder_kernel_size_list', [3, 7, 15, 31, 31, 31])
if len(args.decoder_kernel_size_list) == 1:
args.decoder_kernel_size_list = args.decoder_kernel_size_list * args.decoder_layers
assert len(args.decoder_kernel_size_list) == args.decoder_layers, "decoder_kernel_size_list doesn't match decoder_layers"
args.decoder_glu = getattr(args, 'decoder_glu', True)
args.input_dropout = getattr(args, 'input_dropout', 0.1)
args.weight_dropout = getattr(args, 'weight_dropout', args.attention_dropout)
@register_model_architecture('lightconv_lm', 'lightconv_lm_gbw')
def lightconv_lm_gbw(args):
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 512)
args.dropout = getattr(args, 'dropout', 0.1)
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', 4096)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 16)
base_lm_architecture(args)
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/lightconv_lm.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .hub_interface import * # noqa
from .model import * # noqa
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/bart/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
BART: Denoising Sequence-to-Sequence Pre-training for
Natural Language Generation, Translation, and Comprehension
"""
import torch.nn as nn
from fairseq import utils
from fairseq.models import (
register_model,
register_model_architecture,
)
from fairseq.models.transformer import TransformerModel
from fairseq.modules.transformer_sentence_encoder import init_bert_params
from .hub_interface import BARTHubInterface
@register_model('bart')
class BARTModel(TransformerModel):
@classmethod
def hub_models(cls):
return {
'bart.large': 'http://dl.fbaipublicfiles.com/fairseq/models/bart.large.tar.gz',
'bart.large.mnli': 'http://dl.fbaipublicfiles.com/fairseq/models/bart.large.mnli.tar.gz',
'bart.large.cnn': 'http://dl.fbaipublicfiles.com/fairseq/models/bart.large.cnn.tar.gz',
}
def __init__(self, args, encoder, decoder):
super().__init__(args, encoder, decoder)
# We follow BERT's random weight initialization
self.apply(init_bert_params)
self.classification_heads = nn.ModuleDict()
@staticmethod
def add_args(parser):
super(BARTModel, BARTModel).add_args(parser)
parser.add_argument(
'--pooler-dropout', type=float, metavar='D',
help='dropout probability in the masked_lm pooler layers'
)
parser.add_argument(
'--pooler-activation-fn',
choices=utils.get_available_activation_fns(),
help='activation function to use for pooler layer'
)
@property
def supported_targets(self):
return {'self'}
def forward(
self, src_tokens, src_lengths, prev_output_tokens,
features_only=False, classification_head_name=None, **kwargs
):
if classification_head_name is not None:
features_only = True
encoder_out = self.encoder(
src_tokens,
src_lengths=src_lengths,
**kwargs,
)
x, extra = self.decoder(
prev_output_tokens,
encoder_out=encoder_out,
features_only=features_only,
**kwargs,
)
if classification_head_name is not None:
sentence_representation = x[
src_tokens.eq(self.encoder.dictionary.eos()), :
].view(x.size(0), -1, x.size(-1))[:, -1, :]
x = self.classification_heads[classification_head_name](
sentence_representation
)
return x, extra
@classmethod
def from_pretrained(
cls,
model_name_or_path,
checkpoint_file='model.pt',
data_name_or_path='.',
bpe='gpt2',
**kwargs,
):
from fairseq import hub_utils
x = hub_utils.from_pretrained(
model_name_or_path,
checkpoint_file,
data_name_or_path,
archive_map=cls.hub_models(),
bpe=bpe,
load_checkpoint_heads=True,
**kwargs,
)
return BARTHubInterface(x['args'], x['task'], x['models'][0])
def register_classification_head(self, name, num_classes=None, inner_dim=None, **kwargs):
"""Register a classification head."""
print("Registering classification head: {0}".format(name))
if name in self.classification_heads:
prev_num_classes = self.classification_heads[name].out_proj.out_features
prev_inner_dim = self.classification_heads[name].dense.out_features
if num_classes != prev_num_classes or inner_dim != prev_inner_dim:
print(
'WARNING: re-registering head "{}" with num_classes {} (prev: {}) '
'and inner_dim {} (prev: {})'.format(
name, num_classes, prev_num_classes, inner_dim, prev_inner_dim
)
)
self.classification_heads[name] = BARTClassificationHead(
self.args.encoder_embed_dim,
inner_dim or self.args.encoder_embed_dim,
num_classes,
self.args.pooler_activation_fn,
self.args.pooler_dropout,
)
def upgrade_state_dict_named(self, state_dict, name):
super().upgrade_state_dict_named(state_dict, name)
prefix = name + '.' if name != '' else ''
current_head_names = [] if not hasattr(self, 'classification_heads') else \
self.classification_heads.keys()
# Handle new classification heads present in the state dict.
keys_to_delete = []
for k in state_dict.keys():
if not k.startswith(prefix + 'classification_heads.'):
continue
head_name = k[len(prefix + 'classification_heads.'):].split('.')[0]
num_classes = state_dict[prefix + 'classification_heads.' + head_name + '.out_proj.weight'].size(0)
inner_dim = state_dict[prefix + 'classification_heads.' + head_name + '.dense.weight'].size(0)
if getattr(self.args, 'load_checkpoint_heads', False):
if head_name not in current_head_names:
self.register_classification_head(head_name, num_classes, inner_dim)
else:
if head_name not in current_head_names:
print(
'WARNING: deleting classification head ({}) from checkpoint '
'not present in current model: {}'.format(head_name, k)
)
keys_to_delete.append(k)
elif (
num_classes != self.classification_heads[head_name].out_proj.out_features
or inner_dim != self.classification_heads[head_name].dense.out_features
):
print(
'WARNING: deleting classification head ({}) from checkpoint '
'with different dimensions than current model: {}'.format(head_name, k)
)
keys_to_delete.append(k)
for k in keys_to_delete:
del state_dict[k]
# When finetuning on translation task, remove last row of
# embedding matrix that corresponds to mask_idx token.
loaded_dict_size = state_dict['encoder.embed_tokens.weight'].size(0)
if loaded_dict_size == len(self.encoder.dictionary) + 1 and '<mask>' not in self.encoder.dictionary:
state_dict['encoder.embed_tokens.weight'] = state_dict['encoder.embed_tokens.weight'][:loaded_dict_size-1, :]
state_dict['decoder.embed_tokens.weight'] = state_dict['decoder.embed_tokens.weight'][:loaded_dict_size-1, :]
# Copy any newly-added classification heads into the state dict
# with their current weights.
if hasattr(self, 'classification_heads'):
cur_state = self.classification_heads.state_dict()
for k, v in cur_state.items():
if prefix + 'classification_heads.' + k not in state_dict:
print('Overwriting', prefix + 'classification_heads.' + k)
state_dict[prefix + 'classification_heads.' + k] = v
class BARTClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(
self,
input_dim,
inner_dim,
num_classes,
activation_fn,
pooler_dropout,
):
super().__init__()
self.dense = nn.Linear(input_dim, inner_dim)
self.activation_fn = utils.get_activation_fn(activation_fn)
self.dropout = nn.Dropout(p=pooler_dropout)
self.out_proj = nn.Linear(inner_dim, num_classes)
def forward(self, features, **kwargs):
x = features
x = self.dropout(x)
x = self.dense(x)
x = self.activation_fn(x)
x = self.dropout(x)
x = self.out_proj(x)
return x
@register_model_architecture('bart', 'bart_large')
def bart_large_architecture(args):
args.encoder_embed_path = getattr(args, 'encoder_embed_path', None)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1024)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 4*1024)
args.encoder_layers = getattr(args, 'encoder_layers', 12)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 16)
args.encoder_normalize_before = getattr(args, 'encoder_normalize_before', False)
args.encoder_learned_pos = getattr(args, 'encoder_learned_pos', True)
args.decoder_embed_path = getattr(args, 'decoder_embed_path', None)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', args.encoder_embed_dim)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', args.encoder_ffn_embed_dim)
args.decoder_layers = getattr(args, 'decoder_layers', 12)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 16)
args.decoder_normalize_before = getattr(args, 'decoder_normalize_before', False)
args.decoder_learned_pos = getattr(args, 'decoder_learned_pos', True)
args.attention_dropout = getattr(args, 'attention_dropout', 0.)
args.relu_dropout = getattr(args, 'relu_dropout', 0.)
args.dropout = getattr(args, 'dropout', 0.1)
args.max_target_positions = getattr(args, 'max_target_positions', 1024)
args.max_source_positions = getattr(args, 'max_source_positions', 1024)
args.adaptive_softmax_cutoff = getattr(args, 'adaptive_softmax_cutoff', None)
args.adaptive_softmax_dropout = getattr(args, 'adaptive_softmax_dropout', 0)
args.share_decoder_input_output_embed = getattr(args, 'share_decoder_input_output_embed', True)
args.share_all_embeddings = getattr(args, 'share_all_embeddings', True)
args.decoder_output_dim = getattr(args, 'decoder_output_dim', args.decoder_embed_dim)
args.decoder_input_dim = getattr(args, 'decoder_input_dim', args.decoder_embed_dim)
args.no_scale_embedding = getattr(args, 'no_scale_embedding', True)
args.layernorm_embedding = getattr(args, 'layernorm_embedding', True)
args.activation_fn = getattr(args, 'activation_fn', 'gelu')
args.pooler_activation_fn = getattr(args, 'pooler_activation_fn', 'tanh')
args.pooler_dropout = getattr(args, 'pooler_dropout', 0.0)
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/bart/model.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import copy
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List
from fairseq import utils
from fairseq.data import encoders
class BARTHubInterface(nn.Module):
"""A simple PyTorch Hub interface to BART.
Usage: https://github.com/pytorch/fairseq/tree/master/examples/BART
"""
def __init__(self, args, task, model):
super().__init__()
self.args = args
self.task = task
self.model = model
self.bpe = encoders.build_bpe(args)
self.max_positions = min(utils.resolve_max_positions(
self.task.max_positions(),
self.model.max_positions(),
))
# this is useful for determining the device
self.register_buffer('_float_tensor', torch.tensor([0], dtype=torch.float))
@property
def device(self):
return self._float_tensor.device
def encode(self, sentence: str, *addl_sentences, no_separator=True) -> torch.LongTensor:
"""
BPE-encode a sentence (or multiple sentences).
Every sequence begins with a beginning-of-sentence (`<s>`) symbol.
Every sentence ends with an end-of-sentence (`</s>`).
Example (single sentence): `<s> a b c </s>`
Example (sentence pair): `<s> d e f </s> 1 2 3 </s>`
The BPE encoding follows GPT-2. One subtle detail is that the GPT-2 BPE
requires leading spaces. For example::
>>> bart.encode('Hello world').tolist()
[0, 31414, 232, 2]
>>> bart.encode(' world').tolist()
[0, 232, 2]
>>> bart.encode('world').tolist()
[0, 8331, 2]
"""
tokens = self.bpe.encode(sentence)
if len(tokens.split(' ')) > self.max_positions - 2:
tokens = ' '.join(tokens.split(' ')[:self.max_positions - 2])
bpe_sentence = '<s> ' + tokens + ' </s>'
for s in addl_sentences:
bpe_sentence += (' </s>' if not no_separator else '')
bpe_sentence += ' ' + self.bpe.encode(s) + ' </s>'
tokens = self.task.source_dictionary.encode_line(bpe_sentence, append_eos=False)
return tokens.long()
def decode(self, tokens: torch.LongTensor):
assert tokens.dim() == 1
tokens = tokens.cpu().numpy()
if tokens[0] == self.task.source_dictionary.bos():
tokens = tokens[1:] # remove <s>
eos_mask = (tokens == self.task.source_dictionary.eos())
doc_mask = eos_mask[1:] & eos_mask[:-1]
sentences = np.split(tokens, doc_mask.nonzero()[0] + 1)
sentences = [self.bpe.decode(self.task.source_dictionary.string(s)) for s in sentences]
if len(sentences) == 1:
return sentences[0]
return sentences
def _build_sample(self, src_tokens: List[torch.LongTensor]):
# assert torch.is_tensor(src_tokens)
dataset = self.task.build_dataset_for_inference(
src_tokens,
[x.numel() for x in src_tokens],
)
sample = dataset.collater(dataset)
sample = utils.apply_to_sample(
lambda tensor: tensor.to(self.device),
sample
)
return sample
def sample(self, sentences: List[str], beam: int = 1, verbose: bool = False, **kwargs) -> str:
input = [self.encode(sentence) for sentence in sentences]
hypos = self.generate(input, beam, verbose, **kwargs)
return [self.decode(x['tokens']) for x in hypos]
def generate(self, tokens: List[torch.LongTensor], beam: int = 5, verbose: bool = False, **kwargs) -> torch.LongTensor:
sample = self._build_sample(tokens)
# build generator using current args as well as any kwargs
gen_args = copy.copy(self.args)
gen_args.beam = beam
for k, v in kwargs.items():
setattr(gen_args, k, v)
generator = self.task.build_generator(gen_args)
translations = self.task.inference_step(
generator,
[self.model],
sample,
prefix_tokens=sample['net_input']['src_tokens'].new_zeros((len(tokens), 1)).fill_(self.task.source_dictionary.bos()),
)
if verbose:
src_str_with_unk = self.string(tokens)
print('S\t{}'.format(src_str_with_unk))
def getarg(name, default):
return getattr(gen_args, name, getattr(self.args, name, default))
# Process top predictions
hypos = [x[0] for x in translations]
hypos = [v for _, v in sorted(zip(sample['id'].tolist(), hypos))]
return hypos
def extract_features(self, tokens: torch.LongTensor, return_all_hiddens: bool = False) -> torch.Tensor:
if tokens.dim() == 1:
tokens = tokens.unsqueeze(0)
if tokens.size(-1) > min(self.model.max_positions()):
raise ValueError('tokens exceeds maximum length: {} > {}'.format(
tokens.size(-1), self.model.max_positions()
))
tokens.to(device=self.device),
prev_output_tokens = tokens.clone()
prev_output_tokens[:, 0] = tokens.gather(
1,
(tokens.ne(self.task.source_dictionary.pad()).sum(dim=1)- 1).unsqueeze(-1),
).squeeze()
prev_output_tokens[:, 1:] = tokens[:, :-1]
features, extra = self.model(
src_tokens=tokens,
src_lengths=None,
prev_output_tokens=prev_output_tokens,
features_only=True,
return_all_hiddens=return_all_hiddens,
)
if return_all_hiddens:
# convert from T x B x C -> B x T x C
inner_states = extra['inner_states']
return [inner_state.transpose(0, 1) for inner_state in inner_states]
else:
return features # just the last layer's features
def register_classification_head(
self, name: str, num_classes: int = None, embedding_size: int = None, **kwargs
):
self.model.register_classification_head(
name, num_classes=num_classes, embedding_size=embedding_size, **kwargs
)
def predict(self, head: str, tokens: torch.LongTensor, return_logits: bool = False):
if tokens.dim() == 1:
tokens = tokens.unsqueeze(0)
features = self.extract_features(tokens.to(device=self.device))
sentence_representation = features[
tokens.eq(self.task.source_dictionary.eos()), :
].view(features.size(0), -1, features.size(-1))[:, -1, :]
logits = self.model.classification_heads[head](sentence_representation)
if return_logits:
return logits
return F.log_softmax(logits, dim=-1)
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/bart/hub_interface.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from collections import Counter
from typing import List
import torch
def align_bpe_to_words(roberta, bpe_tokens: torch.LongTensor, other_tokens: List[str]):
"""
Helper to align GPT-2 BPE to other tokenization formats (e.g., spaCy).
Args:
roberta (RobertaHubInterface): RoBERTa instance
bpe_tokens (torch.LongTensor): GPT-2 BPE tokens of shape `(T_bpe)`
other_tokens (List[str]): other tokens of shape `(T_words)`
Returns:
List[str]: mapping from *other_tokens* to corresponding *bpe_tokens*.
"""
assert bpe_tokens.dim() == 1
assert bpe_tokens[0] == 0
def clean(text):
return text.strip()
# remove whitespaces to simplify alignment
bpe_tokens = [roberta.task.source_dictionary.string([x]) for x in bpe_tokens]
bpe_tokens = [clean(roberta.bpe.decode(x) if x not in {'<s>', ''} else x) for x in bpe_tokens]
other_tokens = [clean(str(o)) for o in other_tokens]
# strip leading <s>
bpe_tokens = bpe_tokens[1:]
assert ''.join(bpe_tokens) == ''.join(other_tokens)
# create alignment from every word to a list of BPE tokens
alignment = []
bpe_toks = filter(lambda item: item[1] != '', enumerate(bpe_tokens, start=1))
j, bpe_tok = next(bpe_toks)
for other_tok in other_tokens:
bpe_indices = []
while True:
if other_tok.startswith(bpe_tok):
bpe_indices.append(j)
other_tok = other_tok[len(bpe_tok):]
try:
j, bpe_tok = next(bpe_toks)
except StopIteration:
j, bpe_tok = None, None
elif bpe_tok.startswith(other_tok):
# other_tok spans multiple BPE tokens
bpe_indices.append(j)
bpe_tok = bpe_tok[len(other_tok):]
other_tok = ''
else:
raise Exception('Cannot align "{}" and "{}"'.format(other_tok, bpe_tok))
if other_tok == '':
break
assert len(bpe_indices) > 0
alignment.append(bpe_indices)
assert len(alignment) == len(other_tokens)
return alignment
def align_features_to_words(roberta, features, alignment):
"""
Align given features to words.
Args:
roberta (RobertaHubInterface): RoBERTa instance
features (torch.Tensor): features to align of shape `(T_bpe x C)`
alignment: alignment between BPE tokens and words returned by
func:`align_bpe_to_words`.
"""
assert features.dim() == 2
bpe_counts = Counter(j for bpe_indices in alignment for j in bpe_indices)
assert bpe_counts[0] == 0 # <s> shouldn't be aligned
denom = features.new([bpe_counts.get(j, 1) for j in range(len(features))])
weighted_features = features / denom.unsqueeze(-1)
output = [weighted_features[0]]
largest_j = -1
for bpe_indices in alignment:
output.append(weighted_features[bpe_indices].sum(dim=0))
largest_j = max(largest_j, *bpe_indices)
for j in range(largest_j + 1, len(features)):
output.append(weighted_features[j])
output = torch.stack(output)
assert torch.all(torch.abs(output.sum(dim=0) - features.sum(dim=0)) < 1e-4)
return output
def spacy_nlp():
if getattr(spacy_nlp, '_nlp', None) is None:
try:
from spacy.lang.en import English
spacy_nlp._nlp = English()
except ImportError:
raise ImportError('Please install spacy with: pip install spacy')
return spacy_nlp._nlp
def spacy_tokenizer():
if getattr(spacy_tokenizer, '_tokenizer', None) is None:
try:
nlp = spacy_nlp()
spacy_tokenizer._tokenizer = nlp.Defaults.create_tokenizer(nlp)
except ImportError:
raise ImportError('Please install spacy with: pip install spacy')
return spacy_tokenizer._tokenizer
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/roberta/alignment_utils.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .hub_interface import * # noqa
from .model import * # noqa
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/roberta/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
RoBERTa: A Robustly Optimized BERT Pretraining Approach.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqDecoder,
FairseqLanguageModel,
register_model,
register_model_architecture,
)
from fairseq.modules import (
LayerNorm,
TransformerSentenceEncoder,
)
from fairseq.modules.transformer_sentence_encoder import init_bert_params
from .hub_interface import RobertaHubInterface
@register_model('roberta')
class RobertaModel(FairseqLanguageModel):
@classmethod
def hub_models(cls):
return {
'roberta.base': 'http://dl.fbaipublicfiles.com/fairseq/models/roberta.base.tar.gz',
'roberta.large': 'http://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz',
'roberta.large.mnli': 'http://dl.fbaipublicfiles.com/fairseq/models/roberta.large.mnli.tar.gz',
'roberta.large.wsc': 'http://dl.fbaipublicfiles.com/fairseq/models/roberta.large.wsc.tar.gz',
}
def __init__(self, args, encoder):
super().__init__(encoder)
self.args = args
# We follow BERT's random weight initialization
self.apply(init_bert_params)
self.classification_heads = nn.ModuleDict()
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
parser.add_argument('--encoder-layers', type=int, metavar='L',
help='num encoder layers')
parser.add_argument('--encoder-embed-dim', type=int, metavar='H',
help='encoder embedding dimension')
parser.add_argument('--encoder-ffn-embed-dim', type=int, metavar='F',
help='encoder embedding dimension for FFN')
parser.add_argument('--encoder-attention-heads', type=int, metavar='A',
help='num encoder attention heads')
parser.add_argument('--activation-fn',
choices=utils.get_available_activation_fns(),
help='activation function to use')
parser.add_argument('--pooler-activation-fn',
choices=utils.get_available_activation_fns(),
help='activation function to use for pooler layer')
parser.add_argument('--encoder-normalize-before', action='store_true',
help='apply layernorm before each encoder block')
parser.add_argument('--dropout', type=float, metavar='D',
help='dropout probability')
parser.add_argument('--attention-dropout', type=float, metavar='D',
help='dropout probability for attention weights')
parser.add_argument('--activation-dropout', type=float, metavar='D',
help='dropout probability after activation in FFN')
parser.add_argument('--pooler-dropout', type=float, metavar='D',
help='dropout probability in the masked_lm pooler layers')
parser.add_argument('--max-positions', type=int,
help='number of positional embeddings to learn')
parser.add_argument('--load-checkpoint-heads', action='store_true',
help='(re-)register and load heads when loading checkpoints')
# args for "Reducing Transformer Depth on Demand with Structured Dropout" (Fan et al., 2019)
parser.add_argument('--encoder-layerdrop', type=float, metavar='D', default=0,
help='LayerDrop probability for encoder')
parser.add_argument('--encoder-layers-to-keep', default=None,
help='which layers to *keep* when pruning as a comma-separated list')
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present
base_architecture(args)
if not hasattr(args, 'max_positions'):
args.max_positions = args.tokens_per_sample
encoder = RobertaEncoder(args, task.source_dictionary)
return cls(args, encoder)
def forward(self, src_tokens, features_only=False, return_all_hiddens=False, classification_head_name=None, **kwargs):
if classification_head_name is not None:
features_only = True
x, extra = self.decoder(src_tokens, features_only, return_all_hiddens, **kwargs)
if classification_head_name is not None:
x = self.classification_heads[classification_head_name](x)
return x, extra
def register_classification_head(self, name, num_classes=None, inner_dim=None, **kwargs):
"""Register a classification head."""
if name in self.classification_heads:
prev_num_classes = self.classification_heads[name].out_proj.out_features
prev_inner_dim = self.classification_heads[name].dense.out_features
if num_classes != prev_num_classes or inner_dim != prev_inner_dim:
print(
'WARNING: re-registering head "{}" with num_classes {} (prev: {}) '
'and inner_dim {} (prev: {})'.format(
name, num_classes, prev_num_classes, inner_dim, prev_inner_dim
)
)
self.classification_heads[name] = RobertaClassificationHead(
self.args.encoder_embed_dim,
inner_dim or self.args.encoder_embed_dim,
num_classes,
self.args.pooler_activation_fn,
self.args.pooler_dropout,
)
@property
def supported_targets(self):
return {'self'}
@classmethod
def from_pretrained(cls, model_name_or_path, checkpoint_file='model.pt', data_name_or_path='.', bpe='gpt2', **kwargs):
from fairseq import hub_utils
x = hub_utils.from_pretrained(
model_name_or_path,
checkpoint_file,
data_name_or_path,
archive_map=cls.hub_models(),
bpe=bpe,
load_checkpoint_heads=True,
**kwargs,
)
return RobertaHubInterface(x['args'], x['task'], x['models'][0])
def upgrade_state_dict_named(self, state_dict, name):
super().upgrade_state_dict_named(state_dict, name)
prefix = name + '.' if name != '' else ''
current_head_names = [] if not hasattr(self, 'classification_heads') else \
self.classification_heads.keys()
# Handle new classification heads present in the state dict.
keys_to_delete = []
for k in state_dict.keys():
if not k.startswith(prefix + 'classification_heads.'):
continue
head_name = k[len(prefix + 'classification_heads.'):].split('.')[0]
num_classes = state_dict[prefix + 'classification_heads.' + head_name + '.out_proj.weight'].size(0)
inner_dim = state_dict[prefix + 'classification_heads.' + head_name + '.dense.weight'].size(0)
if getattr(self.args, 'load_checkpoint_heads', False):
if head_name not in current_head_names:
self.register_classification_head(head_name, num_classes, inner_dim)
else:
if head_name not in current_head_names:
print(
'WARNING: deleting classification head ({}) from checkpoint '
'not present in current model: {}'.format(head_name, k)
)
keys_to_delete.append(k)
elif (
num_classes != self.classification_heads[head_name].out_proj.out_features
or inner_dim != self.classification_heads[head_name].dense.out_features
):
print(
'WARNING: deleting classification head ({}) from checkpoint '
'with different dimensions than current model: {}'.format(head_name, k)
)
keys_to_delete.append(k)
for k in keys_to_delete:
del state_dict[k]
# Copy any newly-added classification heads into the state dict
# with their current weights.
if hasattr(self, 'classification_heads'):
cur_state = self.classification_heads.state_dict()
for k, v in cur_state.items():
if prefix + 'classification_heads.' + k not in state_dict:
print('Overwriting', prefix + 'classification_heads.' + k)
state_dict[prefix + 'classification_heads.' + k] = v
@register_model('xlmr')
class XLMRModel(RobertaModel):
@classmethod
def hub_models(cls):
return {
'xlmr.base.v0': 'http://dl.fbaipublicfiles.com/fairseq/models/xlmr.base.v0.tar.gz',
'xlmr.large.v0': 'http://dl.fbaipublicfiles.com/fairseq/models/xlmr.large.v0.tar.gz',
}
@classmethod
def from_pretrained(cls, model_name_or_path, checkpoint_file='model.pt', data_name_or_path='.', bpe='sentencepiece', **kwargs):
from fairseq import hub_utils
x = hub_utils.from_pretrained(
model_name_or_path,
checkpoint_file,
data_name_or_path,
archive_map=cls.hub_models(),
bpe=bpe,
load_checkpoint_heads=True,
**kwargs,
)
return RobertaHubInterface(x['args'], x['task'], x['models'][0])
@register_model('camembert')
class CamembertModel(RobertaModel):
@classmethod
def hub_models(cls):
return {
'camembert.v0': 'http://dl.fbaipublicfiles.com/fairseq/models/camembert.v0.tar.gz',
}
@classmethod
def from_pretrained(cls, model_name_or_path, checkpoint_file='model.pt', data_name_or_path='.', bpe='sentencepiece', **kwargs):
from fairseq import hub_utils
x = hub_utils.from_pretrained(
model_name_or_path,
checkpoint_file,
data_name_or_path,
archive_map=cls.hub_models(),
bpe=bpe,
load_checkpoint_heads=True,
**kwargs,
)
return RobertaHubInterface(x['args'], x['task'], x['models'][0])
class RobertaLMHead(nn.Module):
"""Head for masked language modeling."""
def __init__(self, embed_dim, output_dim, activation_fn, weight=None):
super().__init__()
self.dense = nn.Linear(embed_dim, embed_dim)
self.activation_fn = utils.get_activation_fn(activation_fn)
self.layer_norm = LayerNorm(embed_dim)
if weight is None:
weight = nn.Linear(embed_dim, output_dim, bias=False).weight
self.weight = weight
self.bias = nn.Parameter(torch.zeros(output_dim))
def forward(self, features, masked_tokens=None, **kwargs):
# Only project the unmasked tokens while training,
# saves both memory and computation
if masked_tokens is not None:
features = features[masked_tokens, :]
x = self.dense(features)
x = self.activation_fn(x)
x = self.layer_norm(x)
# project back to size of vocabulary with bias
x = F.linear(x, self.weight) + self.bias
return x
class RobertaClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, input_dim, inner_dim, num_classes, activation_fn, pooler_dropout):
super().__init__()
self.dense = nn.Linear(input_dim, inner_dim)
self.activation_fn = utils.get_activation_fn(activation_fn)
self.dropout = nn.Dropout(p=pooler_dropout)
self.out_proj = nn.Linear(inner_dim, num_classes)
def forward(self, features, **kwargs):
x = features[:, 0, :] # take <s> token (equiv. to [CLS])
x = self.dropout(x)
x = self.dense(x)
x = self.activation_fn(x)
x = self.dropout(x)
x = self.out_proj(x)
return x
class RobertaEncoder(FairseqDecoder):
"""RoBERTa encoder.
Implements the :class:`~fairseq.models.FairseqDecoder` interface required
by :class:`~fairseq.models.FairseqLanguageModel`.
"""
def __init__(self, args, dictionary):
super().__init__(dictionary)
self.args = args
# RoBERTa is a sentence encoder model, so users will intuitively trim
# encoder layers. However, the implementation uses the fairseq decoder,
# so we fix here.
if args.encoder_layers_to_keep:
args.encoder_layers = len(args.encoder_layers_to_keep.split(","))
args.decoder_layers_to_keep = args.encoder_layers_to_keep
args.encoder_layers_to_keep = None
self.sentence_encoder = TransformerSentenceEncoder(
padding_idx=dictionary.pad(),
vocab_size=len(dictionary),
num_encoder_layers=args.encoder_layers,
embedding_dim=args.encoder_embed_dim,
ffn_embedding_dim=args.encoder_ffn_embed_dim,
num_attention_heads=args.encoder_attention_heads,
dropout=args.dropout,
attention_dropout=args.attention_dropout,
activation_dropout=args.activation_dropout,
layerdrop=args.encoder_layerdrop,
max_seq_len=args.max_positions,
num_segments=0,
encoder_normalize_before=True,
apply_bert_init=True,
activation_fn=args.activation_fn,
)
self.lm_head = RobertaLMHead(
embed_dim=args.encoder_embed_dim,
output_dim=len(dictionary),
activation_fn=args.activation_fn,
weight=self.sentence_encoder.embed_tokens.weight,
)
def forward(self, src_tokens, features_only=False, return_all_hiddens=False, masked_tokens=None, **unused):
"""
Args:
src_tokens (LongTensor): input tokens of shape `(batch, src_len)`
features_only (bool, optional): skip LM head and just return
features. If True, the output will be of shape
`(batch, src_len, embed_dim)`.
return_all_hiddens (bool, optional): also return all of the
intermediate hidden states (default: False).
Returns:
tuple:
- the LM output of shape `(batch, src_len, vocab)`
- a dictionary of additional data, where 'inner_states'
is a list of hidden states.
"""
x, extra = self.extract_features(src_tokens, return_all_hiddens=return_all_hiddens)
if not features_only:
x = self.output_layer(x, masked_tokens=masked_tokens)
return x, extra
def extract_features(self, src_tokens, return_all_hiddens=False, **unused):
inner_states, _ = self.sentence_encoder(
src_tokens,
last_state_only=not return_all_hiddens,
)
features = inner_states[-1]
return features, {'inner_states': inner_states if return_all_hiddens else None}
def output_layer(self, features, masked_tokens=None, **unused):
return self.lm_head(features, masked_tokens)
def max_positions(self):
"""Maximum output length supported by the encoder."""
return self.args.max_positions
@register_model_architecture('roberta', 'roberta')
def base_architecture(args):
args.encoder_layers = getattr(args, 'encoder_layers', 12)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 768)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 3072)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 12)
args.activation_fn = getattr(args, 'activation_fn', 'gelu')
args.pooler_activation_fn = getattr(args, 'pooler_activation_fn', 'tanh')
args.dropout = getattr(args, 'dropout', 0.1)
args.attention_dropout = getattr(args, 'attention_dropout', 0.1)
args.activation_dropout = getattr(args, 'activation_dropout', 0.0)
args.pooler_dropout = getattr(args, 'pooler_dropout', 0.0)
args.encoder_layers_to_keep = getattr(args, 'encoder_layers_to_keep', None)
args.encoder_layerdrop = getattr(args, 'encoder_layerdrop', 0.0)
@register_model_architecture('roberta', 'roberta_base')
def roberta_base_architecture(args):
base_architecture(args)
@register_model_architecture('roberta', 'roberta_large')
def roberta_large_architecture(args):
args.encoder_layers = getattr(args, 'encoder_layers', 24)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1024)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 4096)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 16)
base_architecture(args)
@register_model_architecture('roberta', 'xlm')
def xlm_architecture(args):
args.encoder_layers = getattr(args, 'encoder_layers', 16)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1280)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', 1280*4)
args.encoder_attention_heads = getattr(args, 'encoder_attention_heads', 16)
base_architecture(args)
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/roberta/model.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.data import encoders
class RobertaHubInterface(nn.Module):
"""A simple PyTorch Hub interface to RoBERTa.
Usage: https://github.com/pytorch/fairseq/tree/master/examples/roberta
"""
def __init__(self, args, task, model):
super().__init__()
self.args = args
self.task = task
self.model = model
self.bpe = encoders.build_bpe(args)
# this is useful for determining the device
self.register_buffer('_float_tensor', torch.tensor([0], dtype=torch.float))
@property
def device(self):
return self._float_tensor.device
def encode(self, sentence: str, *addl_sentences, no_separator=False) -> torch.LongTensor:
"""
BPE-encode a sentence (or multiple sentences).
Every sequence begins with a beginning-of-sentence (`<s>`) symbol.
Every sentence ends with an end-of-sentence (`</s>`) and we use an
extra end-of-sentence (`</s>`) as a separator.
Example (single sentence): `<s> a b c </s>`
Example (sentence pair): `<s> d e f </s> </s> 1 2 3 </s>`
The BPE encoding follows GPT-2. One subtle detail is that the GPT-2 BPE
requires leading spaces. For example::
>>> roberta.encode('Hello world').tolist()
[0, 31414, 232, 2]
>>> roberta.encode(' world').tolist()
[0, 232, 2]
>>> roberta.encode('world').tolist()
[0, 8331, 2]
"""
bpe_sentence = '<s> ' + self.bpe.encode(sentence) + ' </s>'
for s in addl_sentences:
bpe_sentence += (' </s>' if not no_separator else '')
bpe_sentence += ' ' + self.bpe.encode(s) + ' </s>'
tokens = self.task.source_dictionary.encode_line(bpe_sentence, append_eos=False, add_if_not_exist=False)
return tokens.long()
def decode(self, tokens: torch.LongTensor):
assert tokens.dim() == 1
tokens = tokens.numpy()
if tokens[0] == self.task.source_dictionary.bos():
tokens = tokens[1:] # remove <s>
eos_mask = (tokens == self.task.source_dictionary.eos())
doc_mask = eos_mask[1:] & eos_mask[:-1]
sentences = np.split(tokens, doc_mask.nonzero()[0] + 1)
sentences = [self.bpe.decode(self.task.source_dictionary.string(s)) for s in sentences]
if len(sentences) == 1:
return sentences[0]
return sentences
def extract_features(self, tokens: torch.LongTensor, return_all_hiddens: bool = False) -> torch.Tensor:
if tokens.dim() == 1:
tokens = tokens.unsqueeze(0)
if tokens.size(-1) > self.model.max_positions():
raise ValueError('tokens exceeds maximum length: {} > {}'.format(
tokens.size(-1), self.model.max_positions()
))
features, extra = self.model(
tokens.to(device=self.device),
features_only=True,
return_all_hiddens=return_all_hiddens,
)
if return_all_hiddens:
# convert from T x B x C -> B x T x C
inner_states = extra['inner_states']
return [inner_state.transpose(0, 1) for inner_state in inner_states]
else:
return features # just the last layer's features
def register_classification_head(
self, name: str, num_classes: int = None, embedding_size: int = None, **kwargs
):
self.model.register_classification_head(
name, num_classes=num_classes, embedding_size=embedding_size, **kwargs
)
def predict(self, head: str, tokens: torch.LongTensor, return_logits: bool = False):
features = self.extract_features(tokens.to(device=self.device))
logits = self.model.classification_heads[head](features)
if return_logits:
return logits
return F.log_softmax(logits, dim=-1)
def extract_features_aligned_to_words(self, sentence: str, return_all_hiddens: bool = False) -> torch.Tensor:
"""Extract RoBERTa features, aligned to spaCy's word-level tokenizer."""
from fairseq.models.roberta import alignment_utils
from spacy.tokens import Doc
nlp = alignment_utils.spacy_nlp()
tokenizer = alignment_utils.spacy_tokenizer()
# tokenize both with GPT-2 BPE and spaCy
bpe_toks = self.encode(sentence)
spacy_toks = tokenizer(sentence)
spacy_toks_ws = [t.text_with_ws for t in tokenizer(sentence)]
alignment = alignment_utils.align_bpe_to_words(self, bpe_toks, spacy_toks_ws)
# extract features and align them
features = self.extract_features(bpe_toks, return_all_hiddens=return_all_hiddens)
features = features.squeeze(0)
aligned_feats = alignment_utils.align_features_to_words(self, features, alignment)
# wrap in spaCy Doc
doc = Doc(
nlp.vocab,
words=['<s>'] + [x.text for x in spacy_toks] + ['</s>'],
spaces=[True] + [x.endswith(' ') for x in spacy_toks_ws[:-1]] + [True, False],
)
assert len(doc) == aligned_feats.size(0)
doc.user_token_hooks['vector'] = lambda token: aligned_feats[token.i]
return doc
def fill_mask(self, masked_input: str, topk: int = 5):
masked_token = '<mask>'
assert masked_token in masked_input and masked_input.count(masked_token) == 1, \
"Please add one {0} token for the input, eg: 'He is a {0} guy'".format(masked_token)
text_spans = masked_input.split(masked_token)
text_spans_bpe = (' {0} '.format(masked_token)).join(
[self.bpe.encode(text_span.rstrip()) for text_span in text_spans]
).strip()
tokens = self.task.source_dictionary.encode_line(
'<s> ' + text_spans_bpe + ' </s>',
append_eos=False,
add_if_not_exist=False,
)
masked_index = (tokens == self.task.mask_idx).nonzero()
if tokens.dim() == 1:
tokens = tokens.unsqueeze(0)
with utils.eval(self.model):
features, extra = self.model(
tokens.long().to(device=self.device),
features_only=False,
return_all_hiddens=False,
)
logits = features[0, masked_index, :].squeeze()
prob = logits.softmax(dim=0)
values, index = prob.topk(k=topk, dim=0)
topk_predicted_token_bpe = self.task.source_dictionary.string(index)
topk_filled_outputs = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(' ')):
predicted_token = self.bpe.decode(predicted_token_bpe)
# Quick hack to fix https://github.com/pytorch/fairseq/issues/1306
if predicted_token_bpe.startswith('\u2581'):
predicted_token = ' ' + predicted_token
if " {0}".format(masked_token) in masked_input:
topk_filled_outputs.append((
masked_input.replace(
' {0}'.format(masked_token), predicted_token
),
values[index].item(),
predicted_token,
))
else:
topk_filled_outputs.append((
masked_input.replace(masked_token, predicted_token),
values[index].item(),
predicted_token,
))
return topk_filled_outputs
def disambiguate_pronoun(self, sentence: str) -> bool:
"""
Usage::
>>> disambiguate_pronoun('The _trophy_ would not fit in the brown suitcase because [it] was too big.')
True
>>> disambiguate_pronoun('The trophy would not fit in the brown suitcase because [it] was too big.')
'The trophy'
"""
assert hasattr(self.task, 'disambiguate_pronoun'), \
'roberta.disambiguate_pronoun() requires a model trained with the WSC task.'
with utils.eval(self.model):
return self.task.disambiguate_pronoun(self.model, sentence, use_cuda=self.device.type == 'cuda')
| data2vec_vision-main | infoxlm/fairseq/fairseq/models/roberta/hub_interface.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
class BeamableMM(nn.Module):
"""This module provides an optimized MM for beam decoding with attention.
It leverage the fact that the source-side of the input is replicated beam
times and the target-side of the input is of width one. This layer speeds up
inference by replacing the inputs {(bsz x 1 x nhu), (bsz x sz2 x nhu)}
with smaller inputs {(bsz/beam x beam x nhu), (bsz/beam x sz2 x nhu)}.
"""
def __init__(self, beam_size=None):
super(BeamableMM, self).__init__()
self.beam_size = beam_size
def forward(self, input1, input2):
if (
not self.training and # test mode
self.beam_size is not None and # beam size is set
input1.dim() == 3 and # only support batched input
input1.size(1) == 1 # single time step update
):
bsz, beam = input1.size(0), self.beam_size
# bsz x 1 x nhu --> bsz/beam x beam x nhu
input1 = input1[:, 0, :].unfold(0, beam, beam).transpose(2, 1)
# bsz x sz2 x nhu --> bsz/beam x sz2 x nhu
input2 = input2.unfold(0, beam, beam)[:, :, :, 0]
# use non batched operation if bsz = beam
if input1.size(0) == 1:
output = torch.mm(input1[0, :, :], input2[0, :, :])
else:
output = input1.bmm(input2)
return output.view(bsz, 1, -1)
else:
return input1.bmm(input2)
def set_beam_size(self, beam_size):
self.beam_size = beam_size
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/beamable_mm.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.modules.scalar_bias import scalar_bias
class SingleHeadAttention(nn.Module):
"""
Single-head attention that supports Gating and Downsampling
"""
def __init__(
self, out_channels, embed_dim, head_dim, head_index, dropout=0.,
bias=True, project_input=True, gated=False, downsample=False,
num_heads=1,
):
super().__init__()
self.embed_dim = embed_dim
self.dropout = dropout
self.head_index = head_index
self.head_dim = head_dim
self.project_input = project_input
self.gated = gated
self.downsample = downsample
self.num_heads = num_heads
self.projection = None
k_layers = []
v_layers = []
if self.downsample:
k_layers.append(Downsample(self.head_index))
v_layers.append(Downsample(self.head_index))
out_proj_size = self.head_dim
else:
out_proj_size = self.head_dim * self.num_heads
if self.gated:
k_layers.append(GatedLinear(self.embed_dim, out_proj_size, bias=bias))
self.in_proj_q = GatedLinear(self.embed_dim, out_proj_size, bias=bias)
v_layers.append(GatedLinear(self.embed_dim, out_proj_size, bias=bias))
else:
k_layers.append(Linear(self.embed_dim, out_proj_size, bias=bias))
self.in_proj_q = Linear(self.embed_dim, out_proj_size, bias=bias)
v_layers.append(Linear(self.embed_dim, out_proj_size, bias=bias))
self.in_proj_k = nn.Sequential(*k_layers)
self.in_proj_v = nn.Sequential(*v_layers)
if self.downsample:
self.out_proj = Linear(out_proj_size, self.head_dim, bias=bias)
else:
self.out_proj = Linear(out_proj_size, out_channels, bias=bias)
self.scaling = self.head_dim**-0.5
def forward(
self, query, key, value, mask_future_timesteps=False,
key_padding_mask=None, use_scalar_bias=False,
):
"""Input shape: Time x Batch x Channel
Self-attention can be implemented by passing in the same arguments for
query, key and value. Future timesteps can be masked with the
`mask_future_timesteps` argument. Padding elements can be excluded from
the key by passing a binary ByteTensor (`key_padding_mask`) with shape:
batch x src_len, where padding elements are indicated by 1s.
"""
src_len, bsz, out_channels = key.size()
tgt_len = query.size(0)
assert list(query.size()) == [tgt_len, bsz, out_channels]
assert key.size() == value.size()
if key_padding_mask is not None:
assert key_padding_mask.size(0) == bsz
assert key_padding_mask.size(1) == src_len
if self.downsample:
size = bsz
else:
size = bsz * self.num_heads
k = key
v = value
q = query
if self.project_input:
q = self.in_proj_q(q)
k = self.in_proj_k(k)
v = self.in_proj_v(v)
src_len = k.size()[0]
q *= self.scaling
if not self.downsample:
q = q.view(tgt_len, size, self.head_dim)
k = k.view(src_len, size, self.head_dim)
v = v.view(src_len, size, self.head_dim)
q = q.transpose(0, 1)
k = k.transpose(0, 1)
v = v.transpose(0, 1)
attn_weights = torch.bmm(q, k.transpose(1, 2))
if mask_future_timesteps:
assert query.size() == key.size(), \
'mask_future_timesteps only applies to self-attention'
attn_weights *= torch.tril(
attn_weights.data.new([1]).expand(tgt_len, tgt_len).clone(),
diagonal=-1,
)[:, ::self.head_index + 1 if self.downsample else 1].unsqueeze(0)
attn_weights += torch.triu(
attn_weights.data.new([-math.inf]).expand(tgt_len, tgt_len).clone(),
diagonal=0
)[:, ::self.head_index + 1 if self.downsample else 1].unsqueeze(0)
tgt_size = tgt_len
if use_scalar_bias:
attn_weights = scalar_bias(attn_weights, 2)
v = scalar_bias(v, 1)
tgt_size += 1
if key_padding_mask is not None:
# don't attend to padding symbols
if key_padding_mask.max() > 0:
if self.downsample:
attn_weights = attn_weights.view(bsz, 1, tgt_len, src_len)
else:
attn_weights = attn_weights.view(size, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.masked_fill(
key_padding_mask.unsqueeze(1).unsqueeze(2),
-math.inf,
)
attn_weights = attn_weights.view(size, tgt_len, src_len)
attn_weights = F.softmax(attn_weights, dim=-1)
attn_weights = F.dropout(attn_weights, p=self.dropout, training=self.training)
attn = torch.bmm(attn_weights, v)
if self.downsample:
attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, self.head_dim)
else:
attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, self.embed_dim)
attn = self.out_proj(attn)
return attn, attn_weights
class DownsampledMultiHeadAttention(nn.ModuleList):
"""
Multi-headed attention with Gating and Downsampling
"""
def __init__(
self, out_channels, embed_dim, num_heads, dropout=0., bias=True,
project_input=True, gated=False, downsample=False,
):
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
self.downsample = downsample
self.gated = gated
self.project_input = project_input
assert self.head_dim * num_heads == embed_dim
if self.downsample:
attention_heads = []
for index in range(self.num_heads):
attention_heads.append(
SingleHeadAttention(
out_channels, self.embed_dim, self.head_dim, index,
self.dropout, bias, self.project_input, self.gated,
self.downsample, self.num_heads,
)
)
super().__init__(modules=attention_heads)
self.out_proj = Linear(embed_dim, out_channels, bias=bias)
else:
# either we have a list of attention heads, or just one attention head
# if not being downsampled, we can do the heads with one linear layer instead of separate ones
super().__init__()
self.attention_module = SingleHeadAttention(
out_channels, self.embed_dim, self.head_dim, 1, self.dropout,
bias, self.project_input, self.gated, self.downsample, self.num_heads,
)
def forward(
self, query, key, value, mask_future_timesteps=False,
key_padding_mask=None, use_scalar_bias=False,
):
src_len, bsz, embed_dim = key.size()
tgt_len = query.size(0)
assert embed_dim == self.embed_dim
assert list(query.size()) == [tgt_len, bsz, embed_dim]
assert key.size() == value.size()
tgt_size = tgt_len
if use_scalar_bias:
tgt_size += 1
attn = []
attn_weights = []
if self.downsample:
for attention_head_number in range(self.num_heads):
# call the forward of each attention head
_attn, _attn_weight = self[attention_head_number](
query, key, value, mask_future_timesteps, key_padding_mask, use_scalar_bias,
)
attn.append(_attn)
attn_weights.append(_attn_weight)
full_attn = torch.cat(attn, dim=2)
full_attn = self.out_proj(full_attn)
return full_attn, attn_weights[0].clone()
else:
_attn, _attn_weight = self.attention_module(
query, key, value, mask_future_timesteps, key_padding_mask, use_scalar_bias,
)
attn.append(_attn)
attn_weights.append(_attn_weight)
full_attn = torch.cat(attn, dim=2)
full_attn_weights = torch.cat(attn_weights)
full_attn_weights = full_attn_weights.view(bsz, self.num_heads, tgt_size, src_len)
full_attn_weights = full_attn_weights.sum(dim=1) / self.num_heads
return full_attn, full_attn_weights
class Downsample(nn.Module):
"""
Selects every nth element, where n is the index
"""
def __init__(self, index):
super().__init__()
self.index = index
def forward(self, x):
return x[::self.index+1]
def Linear(in_features, out_features, dropout=0., bias=True):
"""Weight-normalized Linear layer (input: B x T x C)"""
m = nn.Linear(in_features, out_features, bias=bias)
m.weight.data.normal_(mean=0, std=math.sqrt((1 - dropout) / in_features))
m.bias.data.zero_()
return nn.utils.weight_norm(m)
def GatedLinear(in_features, out_features, dropout=0., bias=True):
"""Weight-normalized Linear layer (input: B x T x C) with interspersed GLU units"""
return nn.Sequential(
Linear(in_features, out_features*4, dropout, bias),
nn.GLU(),
Linear(out_features*2, out_features*2, dropout, bias),
nn.GLU(),
Linear(out_features, out_features, dropout, bias)
)
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/downsampled_multihead_attention.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
from torch import nn
from torch.nn import Parameter
import torch.nn.functional as F
from fairseq import utils
class MultiheadAttention(nn.Module):
"""Multi-headed attention.
See "Attention Is All You Need" for more details.
"""
def __init__(self, embed_dim, num_heads, kdim=None, vdim=None, dropout=0., bias=True,
add_bias_kv=False, add_zero_attn=False, self_attention=False,
encoder_decoder_attention=False):
super().__init__()
self.embed_dim = embed_dim
self.kdim = kdim if kdim is not None else embed_dim
self.vdim = vdim if vdim is not None else embed_dim
self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
self.scaling = self.head_dim ** -0.5
self.self_attention = self_attention
self.encoder_decoder_attention = encoder_decoder_attention
assert not self.self_attention or self.qkv_same_dim, 'Self-attention requires query, key and ' \
'value to be of the same size'
self.k_proj = nn.Linear(self.kdim, embed_dim, bias=bias)
self.v_proj = nn.Linear(self.vdim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
if add_bias_kv:
self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim))
self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim))
else:
self.bias_k = self.bias_v = None
self.add_zero_attn = add_zero_attn
self.reset_parameters()
self.onnx_trace = False
self.enable_torch_version = False
if hasattr(F, "multi_head_attention_forward"):
self.enable_torch_version = True
else:
self.enable_torch_version = False
def prepare_for_onnx_export_(self):
self.onnx_trace = True
def reset_parameters(self):
if self.qkv_same_dim:
# Empirically observed the convergence to be much better with
# the scaled initialization
nn.init.xavier_uniform_(self.k_proj.weight, gain=1/math.sqrt(2))
nn.init.xavier_uniform_(self.v_proj.weight, gain=1/math.sqrt(2))
nn.init.xavier_uniform_(self.q_proj.weight, gain=1/math.sqrt(2))
else:
nn.init.xavier_uniform_(self.k_proj.weight)
nn.init.xavier_uniform_(self.v_proj.weight)
nn.init.xavier_uniform_(self.q_proj.weight)
nn.init.xavier_uniform_(self.out_proj.weight)
nn.init.constant_(self.out_proj.bias, 0.)
if self.bias_k is not None:
nn.init.xavier_normal_(self.bias_k)
if self.bias_v is not None:
nn.init.xavier_normal_(self.bias_v)
def forward(
self,
query, key, value,
key_padding_mask=None,
incremental_state=None,
need_weights=True,
static_kv=False,
attn_mask=None,
before_softmax=False,
need_head_weights=False,
):
"""Input shape: Time x Batch x Channel
Args:
key_padding_mask (ByteTensor, optional): mask to exclude
keys that are pads, of shape `(batch, src_len)`, where
padding elements are indicated by 1s.
need_weights (bool, optional): return the attention weights,
averaged over heads (default: False).
attn_mask (ByteTensor, optional): typically used to
implement causal attention, where the mask prevents the
attention from looking forward in time (default: None).
before_softmax (bool, optional): return the raw attention
weights and values before the attention softmax.
need_head_weights (bool, optional): return the attention
weights for each head. Implies *need_weights*. Default:
return the average attention weights over all heads.
"""
if need_head_weights:
need_weights = True
tgt_len, bsz, embed_dim = query.size()
assert embed_dim == self.embed_dim
assert list(query.size()) == [tgt_len, bsz, embed_dim]
if self.enable_torch_version and not self.onnx_trace and incremental_state is None and not static_kv:
return F.multi_head_attention_forward(query, key, value,
self.embed_dim, self.num_heads,
torch.empty([0]),
torch.cat((self.q_proj.bias, self.k_proj.bias, self.v_proj.bias)),
self.bias_k, self.bias_v,
self.add_zero_attn, self.dropout,
self.out_proj.weight, self.out_proj.bias,
self.training, key_padding_mask, need_weights,
attn_mask, use_separate_proj_weight=True,
q_proj_weight=self.q_proj.weight,
k_proj_weight=self.k_proj.weight,
v_proj_weight=self.v_proj.weight)
if incremental_state is not None:
saved_state = self._get_input_buffer(incremental_state)
if 'prev_key' in saved_state:
# previous time steps are cached - no need to recompute
# key and value if they are static
if static_kv:
assert self.encoder_decoder_attention and not self.self_attention
key = value = None
else:
saved_state = None
if self.self_attention:
q = self.q_proj(query)
k = self.k_proj(query)
v = self.v_proj(query)
elif self.encoder_decoder_attention:
# encoder-decoder attention
q = self.q_proj(query)
if key is None:
assert value is None
k = v = None
else:
k = self.k_proj(key)
v = self.v_proj(key)
else:
q = self.q_proj(query)
k = self.k_proj(key)
v = self.v_proj(value)
q *= self.scaling
if self.bias_k is not None:
assert self.bias_v is not None
k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)])
v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)])
if attn_mask is not None:
attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1)
if key_padding_mask is not None:
key_padding_mask = torch.cat(
[key_padding_mask, key_padding_mask.new_zeros(key_padding_mask.size(0), 1)], dim=1)
q = q.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim).transpose(0, 1)
if k is not None:
k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)
if v is not None:
v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)
if saved_state is not None:
# saved states are stored with shape (bsz, num_heads, seq_len, head_dim)
if 'prev_key' in saved_state:
prev_key = saved_state['prev_key'].view(bsz * self.num_heads, -1, self.head_dim)
if static_kv:
k = prev_key
else:
k = torch.cat((prev_key, k), dim=1)
if 'prev_value' in saved_state:
prev_value = saved_state['prev_value'].view(bsz * self.num_heads, -1, self.head_dim)
if static_kv:
v = prev_value
else:
v = torch.cat((prev_value, v), dim=1)
key_padding_mask = self._append_prev_key_padding_mask(
key_padding_mask=key_padding_mask,
prev_key_padding_mask=saved_state.get('prev_key_padding_mask', None),
batch_size=bsz,
src_len=k.size(1),
static_kv=static_kv,
)
saved_state['prev_key'] = k.view(bsz, self.num_heads, -1, self.head_dim)
saved_state['prev_value'] = v.view(bsz, self.num_heads, -1, self.head_dim)
saved_state['prev_key_padding_mask'] = key_padding_mask
self._set_input_buffer(incremental_state, saved_state)
src_len = k.size(1)
# This is part of a workaround to get around fork/join parallelism
# not supporting Optional types.
if key_padding_mask is not None and key_padding_mask.shape == torch.Size([]):
key_padding_mask = None
if key_padding_mask is not None:
assert key_padding_mask.size(0) == bsz
assert key_padding_mask.size(1) == src_len
if self.add_zero_attn:
src_len += 1
k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1)
v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1)
if attn_mask is not None:
attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1)
if key_padding_mask is not None:
key_padding_mask = torch.cat(
[key_padding_mask, torch.zeros(key_padding_mask.size(0), 1).type_as(key_padding_mask)], dim=1)
attn_weights = torch.bmm(q, k.transpose(1, 2))
attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz)
assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len]
if attn_mask is not None:
attn_mask = attn_mask.unsqueeze(0)
if self.onnx_trace:
attn_mask = attn_mask.repeat(attn_weights.size(0), 1, 1)
attn_weights += attn_mask
if key_padding_mask is not None:
# don't attend to padding symbols
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
attn_weights = attn_weights.masked_fill(
key_padding_mask.unsqueeze(1).unsqueeze(2),
float('-inf'),
)
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
if before_softmax:
return attn_weights, v
attn_weights_float = utils.softmax(attn_weights, dim=-1, onnx_trace=self.onnx_trace)
attn_weights = attn_weights_float.type_as(attn_weights)
attn_probs = F.dropout(attn_weights_float.type_as(attn_weights), p=self.dropout, training=self.training)
attn = torch.bmm(attn_probs, v)
assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim]
if (self.onnx_trace and attn.size(1) == 1):
# when ONNX tracing a single decoder step (sequence length == 1)
# the transpose is a no-op copy before view, thus unnecessary
attn = attn.contiguous().view(tgt_len, bsz, embed_dim)
else:
attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
attn = self.out_proj(attn)
if need_weights:
attn_weights = attn_weights_float.view(bsz, self.num_heads, tgt_len, src_len).transpose(1, 0)
if not need_head_weights:
# average attention weights over heads
attn_weights = attn_weights.mean(dim=0)
else:
attn_weights = None
return attn, attn_weights
@staticmethod
def _append_prev_key_padding_mask(
key_padding_mask,
prev_key_padding_mask,
batch_size,
src_len,
static_kv,
):
# saved key padding masks have shape (bsz, seq_len)
if prev_key_padding_mask is not None and static_kv:
key_padding_mask = prev_key_padding_mask
elif prev_key_padding_mask is not None and key_padding_mask is not None:
key_padding_mask = torch.cat((prev_key_padding_mask, key_padding_mask), dim=1)
# During incremental decoding, as the padding token enters and
# leaves the frame, there will be a time when prev or current
# is None
elif prev_key_padding_mask is not None:
filler = torch.zeros(batch_size, src_len - prev_key_padding_mask.size(1)).bool()
if prev_key_padding_mask.is_cuda:
filler = filler.cuda()
key_padding_mask = torch.cat((prev_key_padding_mask, filler), dim=1)
elif key_padding_mask is not None:
filler = torch.zeros(batch_size, src_len - key_padding_mask.size(1)).bool()
if key_padding_mask.is_cuda:
filler = filler.cuda()
key_padding_mask = torch.cat((filler, key_padding_mask), dim=1)
return key_padding_mask
def reorder_incremental_state(self, incremental_state, new_order):
"""Reorder buffered internal state (for incremental generation)."""
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is not None:
for k in input_buffer.keys():
if input_buffer[k] is not None:
input_buffer[k] = input_buffer[k].index_select(0, new_order)
self._set_input_buffer(incremental_state, input_buffer)
def _get_input_buffer(self, incremental_state):
return utils.get_incremental_state(
self,
incremental_state,
'attn_state',
) or {}
def _set_input_buffer(self, incremental_state, buffer):
utils.set_incremental_state(
self,
incremental_state,
'attn_state',
buffer,
)
def apply_sparse_mask(self, attn_weights, tgt_len, src_len, bsz):
return attn_weights
def upgrade_state_dict_named(self, state_dict, name):
prefix = name + '.' if name != '' else ''
items_to_add = {}
keys_to_remove = []
for k in state_dict.keys():
if k.endswith(prefix + 'in_proj_weight'):
# in_proj_weight used to be q + k + v with same dimensions
dim = int(state_dict[k].shape[0] / 3)
items_to_add[prefix + 'q_proj.weight'] = state_dict[k][:dim]
items_to_add[prefix + 'k_proj.weight'] = state_dict[k][dim:2*dim]
items_to_add[prefix + 'v_proj.weight'] = state_dict[k][2*dim:]
keys_to_remove.append(k)
k_bias = prefix + 'in_proj_bias'
if k_bias in state_dict.keys():
dim = int(state_dict[k].shape[0] / 3)
items_to_add[prefix + 'q_proj.bias'] = state_dict[k_bias][:dim]
items_to_add[prefix + 'k_proj.bias'] = state_dict[k_bias][dim:2*dim]
items_to_add[prefix + 'v_proj.bias'] = state_dict[k_bias][2*dim:]
keys_to_remove.append(prefix + 'in_proj_bias')
for k in keys_to_remove:
del state_dict[k]
for key, value in items_to_add.items():
state_dict[key] = value
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/multihead_attention.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import absolute_import, division, print_function, unicode_literals
from collections.abc import Iterable
from itertools import repeat
import torch
import torch.nn as nn
def _pair(v):
if isinstance(v, Iterable):
assert len(v) == 2, "len(v) != 2"
return v
return tuple(repeat(v, 2))
def infer_conv_output_dim(conv_op, input_dim, sample_inchannel):
sample_seq_len = 200
sample_bsz = 10
x = torch.randn(sample_bsz, sample_inchannel, sample_seq_len, input_dim)
# N x C x H x W
# N: sample_bsz, C: sample_inchannel, H: sample_seq_len, W: input_dim
x = conv_op(x)
# N x C x H x W
x = x.transpose(1, 2)
# N x H x C x W
bsz, seq = x.size()[:2]
per_channel_dim = x.size()[3]
# bsz: N, seq: H, CxW the rest
return x.contiguous().view(bsz, seq, -1).size(-1), per_channel_dim
class VGGBlock(torch.nn.Module):
"""
VGG motibated cnn module https://arxiv.org/pdf/1409.1556.pdf
Args:
in_channels: (int) number of input channels (typically 1)
out_channels: (int) number of output channels
conv_kernel_size: convolution channels
pooling_kernel_size: the size of the pooling window to take a max over
num_conv_layers: (int) number of convolution layers
input_dim: (int) input dimension
conv_stride: the stride of the convolving kernel.
Can be a single number or a tuple (sH, sW) Default: 1
padding: implicit paddings on both sides of the input.
Can be a single number or a tuple (padH, padW). Default: None
layer_norm: (bool) if layer norm is going to be applied. Default: False
Shape:
Input: BxCxTxfeat, i.e. (batch_size, input_size, timesteps, features)
Output: BxCxTxfeat, i.e. (batch_size, input_size, timesteps, features)
"""
def __init__(
self,
in_channels,
out_channels,
conv_kernel_size,
pooling_kernel_size,
num_conv_layers,
input_dim,
conv_stride=1,
padding=None,
layer_norm=False,
):
assert (
input_dim is not None
), "Need input_dim for LayerNorm and infer_conv_output_dim"
super(VGGBlock, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.conv_kernel_size = _pair(conv_kernel_size)
self.pooling_kernel_size = _pair(pooling_kernel_size)
self.num_conv_layers = num_conv_layers
self.padding = (
tuple(e // 2 for e in self.conv_kernel_size)
if padding is None
else _pair(padding)
)
self.conv_stride = _pair(conv_stride)
self.layers = nn.ModuleList()
for layer in range(num_conv_layers):
conv_op = nn.Conv2d(
in_channels if layer == 0 else out_channels,
out_channels,
self.conv_kernel_size,
stride=self.conv_stride,
padding=self.padding,
)
self.layers.append(conv_op)
if layer_norm:
conv_output_dim, per_channel_dim = infer_conv_output_dim(
conv_op, input_dim, in_channels if layer == 0 else out_channels
)
self.layers.append(nn.LayerNorm(per_channel_dim))
input_dim = per_channel_dim
self.layers.append(nn.ReLU())
if self.pooling_kernel_size is not None:
pool_op = nn.MaxPool2d(kernel_size=self.pooling_kernel_size, ceil_mode=True)
self.layers.append(pool_op)
self.total_output_dim, self.output_dim = infer_conv_output_dim(
pool_op, input_dim, out_channels
)
def forward(self, x):
for i, _ in enumerate(self.layers):
x = self.layers[i](x)
return x
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/vggblock.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn.functional as F
class MeanPoolGatingNetwork(torch.nn.Module):
"""A simple mean-pooling gating network for selecting experts.
This module applies mean pooling over an encoder's output and returns
reponsibilities for each expert. The encoder format is expected to match
:class:`fairseq.models.transformer.TransformerEncoder`.
"""
def __init__(self, embed_dim, num_experts, dropout=None):
super().__init__()
self.embed_dim = embed_dim
self.num_experts = num_experts
self.fc1 = torch.nn.Linear(embed_dim, embed_dim)
self.dropout = torch.nn.Dropout(dropout) if dropout is not None else None
self.fc2 = torch.nn.Linear(embed_dim, num_experts)
def forward(self, encoder_out):
if not (
hasattr(encoder_out, 'encoder_out')
and hasattr(encoder_out, 'encoder_padding_mask')
and encoder_out.encoder_out.size(2) == self.embed_dim
):
raise ValueError('Unexpected format for encoder_out')
# mean pooling over time
encoder_padding_mask = encoder_out.encoder_padding_mask # B x T
encoder_out = encoder_out.encoder_out.transpose(0, 1) # B x T x C
if encoder_padding_mask is not None:
encoder_out = encoder_out.clone() # required because of transpose above
encoder_out[encoder_padding_mask] = 0
ntokens = torch.sum(~encoder_padding_mask, dim=1, keepdim=True)
x = torch.sum(encoder_out, dim=1) / ntokens.type_as(encoder_out)
else:
x = torch.mean(encoder_out, dim=1)
x = torch.tanh(self.fc1(x))
if self.dropout is not None:
x = self.dropout(x)
x = self.fc2(x)
return F.log_softmax(x, dim=-1, dtype=torch.float32).type_as(x)
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/mean_pool_gating_network.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch.nn as nn
from fairseq import utils
class LearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
Padding ids are ignored by either offsetting based on padding_idx
or by setting padding_idx to None and ensuring that the appropriate
position ids are passed to the forward function.
"""
def __init__(
self,
num_embeddings: int,
embedding_dim: int,
padding_idx: int,
):
super().__init__(num_embeddings, embedding_dim, padding_idx)
self.onnx_trace = False
def forward(self, input, incremental_state=None, positions=None):
"""Input is expected to be of size [bsz x seqlen]."""
assert (
(positions is None) or (self.padding_idx is None)
), "If positions is pre-computed then padding_idx should not be set."
if positions is None:
if incremental_state is not None:
# positions is the same for every token when decoding a single step
# Without the int() cast, it doesn't work in some cases when exporting to ONNX
positions = input.data.new(1, 1).fill_(int(self.padding_idx + input.size(1)))
else:
positions = utils.make_positions(
input, self.padding_idx, onnx_trace=self.onnx_trace,
)
return super().forward(positions)
def max_positions(self):
"""Maximum number of supported positions."""
if self.padding_idx is not None:
return self.num_embeddings - self.padding_idx - 1
else:
return self.num_embeddings
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/learned_positional_embedding.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
class GradMultiply(torch.autograd.Function):
@staticmethod
def forward(ctx, x, scale):
ctx.scale = scale
res = x.new(x)
return res
@staticmethod
def backward(ctx, grad):
return grad * ctx.scale, None
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/grad_multiply.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn.functional as F
from torch import nn
from typing import List, Tuple
from .highway import Highway
from fairseq.data import Dictionary
CHAR_PAD_IDX = 0
CHAR_EOS_IDX = 257
class CharacterTokenEmbedder(torch.nn.Module):
def __init__(
self,
vocab: Dictionary,
filters: List[Tuple[int, int]],
char_embed_dim: int,
word_embed_dim: int,
highway_layers: int,
max_char_len: int = 50,
char_inputs: bool = False
):
super(CharacterTokenEmbedder, self).__init__()
self.onnx_trace = False
self.embedding_dim = word_embed_dim
self.max_char_len = max_char_len
self.char_embeddings = nn.Embedding(257, char_embed_dim, padding_idx=0)
self.symbol_embeddings = nn.Parameter(torch.FloatTensor(2, word_embed_dim))
self.eos_idx, self.unk_idx = 0, 1
self.char_inputs = char_inputs
self.convolutions = nn.ModuleList()
for width, out_c in filters:
self.convolutions.append(
nn.Conv1d(char_embed_dim, out_c, kernel_size=width)
)
last_dim = sum(f[1] for f in filters)
self.highway = Highway(last_dim, highway_layers) if highway_layers > 0 else None
self.projection = nn.Linear(last_dim, word_embed_dim)
assert vocab is not None or char_inputs, "vocab must be set if not using char inputs"
self.vocab = None
if vocab is not None:
self.set_vocab(vocab, max_char_len)
self.reset_parameters()
def prepare_for_onnx_export_(self):
self.onnx_trace = True
def set_vocab(self, vocab, max_char_len):
word_to_char = torch.LongTensor(len(vocab), max_char_len)
truncated = 0
for i in range(len(vocab)):
if i < vocab.nspecial:
char_idxs = [0] * max_char_len
else:
chars = vocab[i].encode()
# +1 for padding
char_idxs = [c + 1 for c in chars] + [0] * (max_char_len - len(chars))
if len(char_idxs) > max_char_len:
truncated += 1
char_idxs = char_idxs[:max_char_len]
word_to_char[i] = torch.LongTensor(char_idxs)
if truncated > 0:
print('Truncated {} words longer than {} characters'.format(truncated, max_char_len))
self.vocab = vocab
self.word_to_char = word_to_char
@property
def padding_idx(self):
return Dictionary().pad() if self.vocab is None else self.vocab.pad()
def reset_parameters(self):
nn.init.xavier_normal_(self.char_embeddings.weight)
nn.init.xavier_normal_(self.symbol_embeddings)
nn.init.xavier_uniform_(self.projection.weight)
nn.init.constant_(self.char_embeddings.weight[self.char_embeddings.padding_idx], 0.)
nn.init.constant_(self.projection.bias, 0.)
def forward(
self,
input: torch.Tensor,
):
if self.char_inputs:
chars = input.view(-1, self.max_char_len)
pads = chars[:, 0].eq(CHAR_PAD_IDX)
eos = chars[:, 0].eq(CHAR_EOS_IDX)
if eos.any():
if self.onnx_trace:
chars = torch.where(eos.unsqueeze(1), chars.new_zeros(1), chars)
else:
chars[eos] = 0
unk = None
else:
flat_words = input.view(-1)
chars = self.word_to_char[flat_words.type_as(self.word_to_char)].type_as(input)
pads = flat_words.eq(self.vocab.pad())
eos = flat_words.eq(self.vocab.eos())
unk = flat_words.eq(self.vocab.unk())
word_embs = self._convolve(chars)
if self.onnx_trace:
if pads.any():
word_embs = torch.where(pads.unsqueeze(1), word_embs.new_zeros(1), word_embs)
if eos.any():
word_embs = torch.where(eos.unsqueeze(1), self.symbol_embeddings[self.eos_idx], word_embs)
if unk is not None and unk.any():
word_embs = torch.where(unk.unsqueeze(1), self.symbol_embeddings[self.unk_idx], word_embs)
else:
if pads.any():
word_embs[pads] = 0
if eos.any():
word_embs[eos] = self.symbol_embeddings[self.eos_idx]
if unk is not None and unk.any():
word_embs[unk] = self.symbol_embeddings[self.unk_idx]
return word_embs.view(input.size()[:2] + (-1,))
def _convolve(
self,
char_idxs: torch.Tensor,
):
char_embs = self.char_embeddings(char_idxs)
char_embs = char_embs.transpose(1, 2) # BTC -> BCT
conv_result = []
for i, conv in enumerate(self.convolutions):
x = conv(char_embs)
x, _ = torch.max(x, -1)
x = F.relu(x)
conv_result.append(x)
x = torch.cat(conv_result, dim=-1)
if self.highway is not None:
x = self.highway(x)
x = self.projection(x)
return x
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/character_token_embedder.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from fairseq.modules import TransformerSentenceEncoderLayer
from fairseq.modules.sparse_multihead_attention import SparseMultiheadAttention
class SparseTransformerSentenceEncoderLayer(TransformerSentenceEncoderLayer):
"""
Implements a Sprase Transformer Encoder Layer (see SparseMultiheadAttention)
"""
def __init__(
self,
embedding_dim: int = 768,
ffn_embedding_dim: int = 3072,
num_attention_heads: int = 8,
dropout: float = 0.1,
attention_dropout: float = 0.1,
activation_dropout: float = 0.1,
activation_fn: str = 'relu',
add_bias_kv: bool = False,
add_zero_attn: bool = False,
export: bool = False,
is_bidirectional: bool = True,
stride: int = 32,
expressivity: int = 8,
) -> None:
super().__init__(
embedding_dim, ffn_embedding_dim, num_attention_heads, dropout,
attention_dropout, activation_dropout, activation_fn, add_bias_kv,
add_zero_attn, export
)
self.self_attn = SparseMultiheadAttention(
self.embedding_dim,
num_attention_heads,
dropout=attention_dropout,
add_bias_kv=add_bias_kv,
add_zero_attn=add_zero_attn,
self_attention=True,
is_bidirectional=is_bidirectional,
stride=stride,
expressivity=expressivity,
)
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/sparse_transformer_sentence_encoder_layer.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import operator
import functools
import torch
import torch.nn.functional as F
from torch import nn
class TiedLinear(nn.Module):
def __init__(self, weight, transpose):
super().__init__()
self.weight = weight
self.transpose = transpose
def forward(self, input):
return F.linear(input, self.weight.t() if self.transpose else self.weight)
class TiedHeadModule(nn.Module):
def __init__(self, weights, input_dim, num_classes):
super().__init__()
tied_emb, _ = weights
self.num_words, emb_dim = tied_emb.size()
self.word_proj = TiedLinear(tied_emb, transpose=False)
if input_dim != emb_dim:
self.word_proj = nn.Sequential(
nn.Linear(input_dim, emb_dim, bias=False),
self.word_proj,
)
self.class_proj = nn.Linear(input_dim, num_classes, bias=False)
self.out_dim = self.num_words + num_classes
self.register_buffer('_float_tensor', torch.FloatTensor(1))
def forward(self, input):
inp_sz = functools.reduce(operator.mul, input.shape[:-1], 1)
out = self._float_tensor.new(inp_sz, self.out_dim)
out[:, :self.num_words] = self.word_proj(input.view(inp_sz, -1))
out[:, self.num_words:] = self.class_proj(input.view(inp_sz, -1))
return out
class AdaptiveSoftmax(nn.Module):
"""
This is an implementation of the efficient softmax approximation for
graphical processing units (GPU), described in the paper "Efficient softmax
approximation for GPUs" (http://arxiv.org/abs/1609.04309).
"""
def __init__(self, vocab_size, input_dim, cutoff, dropout, factor=4., adaptive_inputs=None, tie_proj=False):
super().__init__()
if vocab_size > cutoff[-1]:
cutoff = cutoff + [vocab_size]
else:
assert vocab_size == cutoff[
-1], 'cannot specify cutoff larger than vocab size'
output_dim = cutoff[0] + len(cutoff) - 1
self.vocab_size = vocab_size
self.cutoff = cutoff
self.dropout = dropout
self.input_dim = input_dim
self.factor = factor
self.lsm = nn.LogSoftmax(dim=1)
if adaptive_inputs is not None:
self.head = TiedHeadModule(adaptive_inputs.weights_for_band(0), input_dim, len(cutoff) - 1)
else:
self.head = nn.Linear(input_dim, output_dim, bias=False)
self._make_tail(adaptive_inputs, tie_proj)
def init_weights(m):
if hasattr(m, 'weight') and not isinstance(m, TiedLinear) and not isinstance(m, TiedHeadModule):
nn.init.xavier_uniform_(m.weight)
self.apply(init_weights)
self.register_buffer('version', torch.LongTensor([1]))
def _make_tail(self, adaptive_inputs=None, tie_proj=False):
self.tail = nn.ModuleList()
for i in range(len(self.cutoff) - 1):
dim = int(self.input_dim // self.factor ** (i + 1))
tied_emb, tied_proj = adaptive_inputs.weights_for_band(i + 1) \
if adaptive_inputs is not None else (None, None)
if tied_proj is not None:
if tie_proj:
proj = TiedLinear(tied_proj, transpose=True)
else:
proj = nn.Linear(tied_proj.size(0), tied_proj.size(1), bias=False)
else:
proj = nn.Linear(self.input_dim, dim, bias=False)
m = nn.Sequential(
proj,
nn.Dropout(self.dropout),
nn.Linear(
dim, self.cutoff[i + 1] - self.cutoff[i], bias=False,
) if tied_emb is None else TiedLinear(tied_emb, transpose=False),
)
self.tail.append(m)
def upgrade_state_dict_named(self, state_dict, name):
version_name = name + '.version'
if version_name not in state_dict:
raise Exception('This version of the model is no longer supported')
def adapt_target(self, target):
"""
In order to be efficient, the AdaptiveSoftMax does not compute the
scores for all the word of the vocabulary for all the examples. It is
thus necessary to call the method adapt_target of the AdaptiveSoftMax
layer inside each forward pass.
"""
target = target.view(-1)
new_target = [target.clone()]
target_idxs = []
for i in range(len(self.cutoff) - 1):
mask = target.ge(self.cutoff[i]).mul(target.lt(self.cutoff[i + 1]))
new_target[0][mask] = self.cutoff[0] + i
if mask.any():
target_idxs.append(mask.nonzero().squeeze(1))
new_target.append(target[mask].add(-self.cutoff[i]))
else:
target_idxs.append(None)
new_target.append(None)
return new_target, target_idxs
def forward(self, input, target):
"""
Args:
input: (b x t x d)
target: (b x t)
Returns:
2 lists: output for each cutoff section and new targets by cut off
"""
input = input.contiguous().view(-1, input.size(-1))
input = F.dropout(input, p=self.dropout, training=self.training)
new_target, target_idxs = self.adapt_target(target)
output = [self.head(input)]
for i in range(len(target_idxs)):
if target_idxs[i] is not None:
output.append(self.tail[i](input.index_select(0, target_idxs[i])))
else:
output.append(None)
return output, new_target
def get_log_prob(self, input, target):
"""
Computes the log probabilities for all the words of the vocabulary,
given a 2D tensor of hidden vectors.
"""
bsz, length, dim = input.size()
input = input.contiguous().view(-1, dim)
if target is not None:
_, target_idxs = self.adapt_target(target)
else:
target_idxs = None
head_y = self.head(input)
log_probs = head_y.new_zeros(input.size(0), self.vocab_size)
head_sz = self.cutoff[0] + len(self.tail)
log_probs[:, :head_sz] = self.lsm(head_y)
tail_priors = log_probs[:, self.cutoff[0]: head_sz].clone()
for i in range(len(self.tail)):
start = self.cutoff[i]
end = self.cutoff[i + 1]
if target_idxs is None:
tail_out = log_probs[:, start:end]
tail_out.copy_(self.tail[i](input))
log_probs[:, start:end] = self.lsm(tail_out).add_(tail_priors[:, i, None])
elif target_idxs[i] is not None:
idxs = target_idxs[i]
tail_out = log_probs[idxs, start:end]
tail_out.copy_(self.tail[i](input[idxs]))
log_probs[idxs, start:end] = self.lsm(tail_out).add_(tail_priors[idxs, i, None])
log_probs = log_probs.view(bsz, length, -1)
return log_probs
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/adaptive_softmax.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch.nn.modules.utils import _single
class ConvTBC(torch.nn.Module):
"""1D convolution over an input of shape (time x batch x channel)
The implementation uses gemm to perform the convolution. This implementation
is faster than cuDNN for small kernel sizes.
"""
def __init__(self, in_channels, out_channels, kernel_size, padding=0):
super(ConvTBC, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = _single(kernel_size)
self.padding = _single(padding)
self.weight = torch.nn.Parameter(torch.Tensor(
self.kernel_size[0], in_channels, out_channels))
self.bias = torch.nn.Parameter(torch.Tensor(out_channels))
def forward(self, input):
return torch.conv_tbc(input.contiguous(), self.weight, self.bias, self.padding[0])
def __repr__(self):
s = ('{name}({in_channels}, {out_channels}, kernel_size={kernel_size}'
', padding={padding}')
if self.bias is None:
s += ', bias=False'
s += ')'
return s.format(name=self.__class__.__name__, **self.__dict__)
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/conv_tbc.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch.nn.functional as F
def unfold1d(x, kernel_size, padding_l, pad_value=0):
'''unfold T x B x C to T x B x C x K'''
if kernel_size > 1:
T, B, C = x.size()
x = F.pad(x, (0, 0, 0, 0, padding_l, kernel_size - 1 - padding_l), value=pad_value)
x = x.as_strided((T, B, C, kernel_size), (B*C, C, 1, B*C))
else:
x = x.unsqueeze(3)
return x
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/unfold.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
See "Gaussian Error Linear Units (GELUs)" by Dan Hendrycks and Kevin Gimpel with
the corresponding GitHub repo: https://github.com/hendrycks/GELUs
"""
import math
import torch
def gelu_accurate(x):
if not hasattr(gelu_accurate, "_a"):
gelu_accurate._a = math.sqrt(2 / math.pi)
return 0.5 * x * (1 + torch.tanh(gelu_accurate._a * (x + 0.044715 * torch.pow(x, 3))))
def gelu(x: torch.Tensor) -> torch.Tensor:
if hasattr(torch.nn.functional, 'gelu'):
return torch.nn.functional.gelu(x.float()).type_as(x)
else:
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/gelu.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.modules import (
LayerNorm,
MultiheadAttention,
PositionalEmbedding,
TransformerSentenceEncoderLayer,
)
import random
def init_bert_params(module):
"""
Initialize the weights specific to the BERT Model.
This overrides the default initializations depending on the specified arguments.
1. If normal_init_linear_weights is set then weights of linear
layer will be initialized using the normal distribution and
bais will be set to the specified value.
2. If normal_init_embed_weights is set then weights of embedding
layer will be initialized using the normal distribution.
3. If normal_init_proj_weights is set then weights of
in_project_weight for MultiHeadAttention initialized using
the normal distribution (to be validated).
"""
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=0.02)
if module.bias is not None:
module.bias.data.zero_()
if isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=0.02)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
if isinstance(module, MultiheadAttention):
module.q_proj.weight.data.normal_(mean=0.0, std=0.02)
module.k_proj.weight.data.normal_(mean=0.0, std=0.02)
module.v_proj.weight.data.normal_(mean=0.0, std=0.02)
class TransformerSentenceEncoder(nn.Module):
"""
Implementation for a Bi-directional Transformer based Sentence Encoder used
in BERT/XLM style pre-trained models.
This first computes the token embedding using the token embedding matrix,
position embeddings (if specified) and segment embeddings
(if specified). After applying the specified number of
TransformerEncoderLayers, it outputs all the internal states of the
encoder as well as the final representation associated with the first
token (usually CLS token).
Input:
- tokens: B x T matrix representing sentences
- segment_labels: B x T matrix representing segment label for tokens
Output:
- a tuple of the following:
- a list of internal model states used to compute the
predictions where each tensor has shape B x T x C
- sentence representation associated with first input token
in format B x C.
"""
def __init__(
self,
padding_idx: int,
vocab_size: int,
num_encoder_layers: int = 6,
embedding_dim: int = 768,
ffn_embedding_dim: int = 3072,
num_attention_heads: int = 8,
dropout: float = 0.1,
attention_dropout: float = 0.1,
activation_dropout: float = 0.1,
layerdrop : float = 0.0,
max_seq_len: int = 256,
num_segments: int = 2,
use_position_embeddings: bool = True,
offset_positions_by_padding: bool = True,
encoder_normalize_before: bool = False,
apply_bert_init: bool = False,
activation_fn: str = "relu",
learned_pos_embedding: bool = True,
add_bias_kv: bool = False,
add_zero_attn: bool = False,
embed_scale: float = None,
freeze_embeddings: bool = False,
n_trans_layers_to_freeze: int = 0,
export: bool = False,
traceable: bool = False,
) -> None:
super().__init__()
self.padding_idx = padding_idx
self.vocab_size = vocab_size
self.dropout = dropout
self.layerdrop = layerdrop
self.max_seq_len = max_seq_len
self.embedding_dim = embedding_dim
self.num_segments = num_segments
self.use_position_embeddings = use_position_embeddings
self.apply_bert_init = apply_bert_init
self.learned_pos_embedding = learned_pos_embedding
self.traceable = traceable
self.embed_tokens = nn.Embedding(
self.vocab_size, self.embedding_dim, self.padding_idx
)
self.embed_scale = embed_scale
self.segment_embeddings = (
nn.Embedding(self.num_segments, self.embedding_dim, padding_idx=None)
if self.num_segments > 0
else None
)
self.embed_positions = (
PositionalEmbedding(
self.max_seq_len,
self.embedding_dim,
padding_idx=(self.padding_idx if offset_positions_by_padding else None),
learned=self.learned_pos_embedding,
)
if self.use_position_embeddings
else None
)
self.layers = nn.ModuleList(
[
TransformerSentenceEncoderLayer(
embedding_dim=self.embedding_dim,
ffn_embedding_dim=ffn_embedding_dim,
num_attention_heads=num_attention_heads,
dropout=self.dropout,
attention_dropout=attention_dropout,
activation_dropout=activation_dropout,
activation_fn=activation_fn,
add_bias_kv=add_bias_kv,
add_zero_attn=add_zero_attn,
export=export,
)
for _ in range(num_encoder_layers)
]
)
if encoder_normalize_before:
self.emb_layer_norm = LayerNorm(self.embedding_dim, export=export)
else:
self.emb_layer_norm = None
# Apply initialization of model params after building the model
if self.apply_bert_init:
self.apply(init_bert_params)
def freeze_module_params(m):
if m is not None:
for p in m.parameters():
p.requires_grad = False
if freeze_embeddings:
freeze_module_params(self.embed_tokens)
freeze_module_params(self.segment_embeddings)
freeze_module_params(self.embed_positions)
freeze_module_params(self.emb_layer_norm)
for layer in range(n_trans_layers_to_freeze):
freeze_module_params(self.layers[layer])
def forward(
self,
tokens: torch.Tensor,
segment_labels: torch.Tensor = None,
last_state_only: bool = False,
positions: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
# compute padding mask. This is needed for multi-head attention
padding_mask = tokens.eq(self.padding_idx)
if not self.traceable and not padding_mask.any():
padding_mask = None
x = self.embed_tokens(tokens)
if self.embed_scale is not None:
x *= self.embed_scale
if self.embed_positions is not None:
x += self.embed_positions(tokens, positions=positions)
if self.segment_embeddings is not None and segment_labels is not None:
x += self.segment_embeddings(segment_labels)
if self.emb_layer_norm is not None:
x = self.emb_layer_norm(x)
x = F.dropout(x, p=self.dropout, training=self.training)
# account for padding while computing the representation
if padding_mask is not None:
x *= 1 - padding_mask.unsqueeze(-1).type_as(x)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
inner_states = []
if not last_state_only:
inner_states.append(x)
for layer in self.layers:
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
if not self.training or (dropout_probability > self.layerdrop):
x, _ = layer(x, self_attn_padding_mask=padding_mask)
if not last_state_only:
inner_states.append(x)
# T x B x C -> B x T x C
x = x.transpose(0, 1)
sentence_rep = x[:, 0, :]
if last_state_only:
inner_states = [x]
if self.traceable:
return torch.stack(inner_states), sentence_rep
else:
return inner_states, sentence_rep
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/transformer_sentence_encoder.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .adaptive_input import AdaptiveInput
from .adaptive_softmax import AdaptiveSoftmax
from .beamable_mm import BeamableMM
from .character_token_embedder import CharacterTokenEmbedder
from .conv_tbc import ConvTBC
from .downsampled_multihead_attention import DownsampledMultiHeadAttention
from .dynamic_convolution import DynamicConv, DynamicConv1dTBC
from .gelu import gelu, gelu_accurate
from .grad_multiply import GradMultiply
from .highway import Highway
from .layer_norm import LayerNorm
from .learned_positional_embedding import LearnedPositionalEmbedding
from .lightweight_convolution import LightweightConv, LightweightConv1dTBC
from .linearized_convolution import LinearizedConvolution
from .logsumexp_moe import LogSumExpMoE
from .mean_pool_gating_network import MeanPoolGatingNetwork
from .multihead_attention import MultiheadAttention
from .positional_embedding import PositionalEmbedding
from .scalar_bias import ScalarBias
from .sinusoidal_positional_embedding import SinusoidalPositionalEmbedding
from .transformer_sentence_encoder_layer import TransformerSentenceEncoderLayer
from .transformer_sentence_encoder import TransformerSentenceEncoder
from .unfold import unfold1d
from .transformer_layer import TransformerDecoderLayer, TransformerEncoderLayer
from .vggblock import VGGBlock
__all__ = [
'AdaptiveInput',
'AdaptiveSoftmax',
'BeamableMM',
'CharacterTokenEmbedder',
'ConvTBC',
'DownsampledMultiHeadAttention',
'DynamicConv1dTBC',
'DynamicConv',
'gelu',
'gelu_accurate',
'GradMultiply',
'Highway',
'LayerNorm',
'LearnedPositionalEmbedding',
'LightweightConv1dTBC',
'LightweightConv',
'LinearizedConvolution',
'LogSumExpMoE',
'MeanPoolGatingNetwork',
'MultiheadAttention',
'PositionalEmbedding',
'ScalarBias',
'SinusoidalPositionalEmbedding',
'TransformerSentenceEncoderLayer',
'TransformerSentenceEncoder',
'TransformerDecoderLayer',
'TransformerEncoderLayer',
'VGGBlock',
'unfold1d',
]
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch.nn as nn
from fairseq.modules import TransformerSentenceEncoder
from fairseq.modules.sparse_transformer_sentence_encoder_layer import SparseTransformerSentenceEncoderLayer
class SparseTransformerSentenceEncoder(TransformerSentenceEncoder):
"""
Sparse implementation of the TransformerSentenceEncoder
- see SparseMultiheadAttention
"""
def __init__(
self,
padding_idx: int,
vocab_size: int,
num_encoder_layers: int = 6,
embedding_dim: int = 768,
ffn_embedding_dim: int = 3072,
num_attention_heads: int = 8,
dropout: float = 0.1,
attention_dropout: float = 0.1,
activation_dropout: float = 0.1,
max_seq_len: int = 256,
num_segments: int = 2,
use_position_embeddings: bool = True,
offset_positions_by_padding: bool = True,
encoder_normalize_before: bool = False,
apply_bert_init: bool = False,
activation_fn: str = "relu",
learned_pos_embedding: bool = True,
add_bias_kv: bool = False,
add_zero_attn: bool = False,
embed_scale: float = None,
freeze_embeddings: bool = False,
n_trans_layers_to_freeze: int = 0,
export: bool = False,
is_bidirectional: bool = True,
stride: int = 32,
expressivity: int = 8,
) -> None:
super().__init__(
padding_idx, vocab_size, num_encoder_layers, embedding_dim,
ffn_embedding_dim, num_attention_heads, dropout, attention_dropout,
activation_dropout, max_seq_len, num_segments, use_position_embeddings,
offset_positions_by_padding, encoder_normalize_before, apply_bert_init,
activation_fn, learned_pos_embedding, add_bias_kv, add_zero_attn,
embed_scale, freeze_embeddings, n_trans_layers_to_freeze, export
)
self.layers = nn.ModuleList(
[
SparseTransformerSentenceEncoderLayer(
embedding_dim=self.embedding_dim,
ffn_embedding_dim=ffn_embedding_dim,
num_attention_heads=num_attention_heads,
dropout=self.dropout,
attention_dropout=attention_dropout,
activation_dropout=activation_dropout,
activation_fn=activation_fn,
add_bias_kv=add_bias_kv,
add_zero_attn=add_zero_attn,
export=export,
is_bidirectional=is_bidirectional,
stride=stride,
expressivity=expressivity,
)
for _ in range(num_encoder_layers)
]
)
def freeze_module_params(m):
if m is not None:
for p in m.parameters():
p.requires_grad = False
for layer in range(n_trans_layers_to_freeze):
freeze_module_params(self.layers[layer])
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/sparse_transformer_sentence_encoder.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn.functional as F
from fairseq import utils
from .conv_tbc import ConvTBC
class LinearizedConvolution(ConvTBC):
"""An optimized version of nn.Conv1d.
At training time, this module uses ConvTBC, which is an optimized version
of Conv1d. At inference time, it optimizes incremental generation (i.e.,
one time step at a time) by replacing the convolutions with linear layers.
Note that the input order changes from training to inference.
"""
def __init__(self, in_channels, out_channels, kernel_size, **kwargs):
super().__init__(in_channels, out_channels, kernel_size, **kwargs)
self._linearized_weight = None
self.register_backward_hook(self._clear_linearized_weight)
def forward(self, input, incremental_state=None):
"""
Args:
incremental_state: Used to buffer signal; if not None, then input is
expected to contain a single frame. If the input order changes
between time steps, call reorder_incremental_state.
Input:
Time x Batch x Channel during training
Batch x Time x Channel during inference
"""
if incremental_state is None:
output = super().forward(input)
if self.kernel_size[0] > 1 and self.padding[0] > 0:
# remove future timesteps added by padding
output = output[:-self.padding[0], :, :]
return output
# reshape weight
weight = self._get_linearized_weight()
kw = self.kernel_size[0]
bsz = input.size(0) # input: bsz x len x dim
if kw > 1:
input = input.data
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is None:
input_buffer = input.new(bsz, kw, input.size(2)).zero_()
self._set_input_buffer(incremental_state, input_buffer)
else:
# shift buffer
input_buffer[:, :-1, :] = input_buffer[:, 1:, :].clone()
# append next input
input_buffer[:, -1, :] = input[:, -1, :]
input = input_buffer
with torch.no_grad():
output = F.linear(input.view(bsz, -1), weight, self.bias)
return output.view(bsz, 1, -1)
def reorder_incremental_state(self, incremental_state, new_order):
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is not None:
input_buffer = input_buffer.index_select(0, new_order)
self._set_input_buffer(incremental_state, input_buffer)
def _get_input_buffer(self, incremental_state):
return utils.get_incremental_state(self, incremental_state, 'input_buffer')
def _set_input_buffer(self, incremental_state, new_buffer):
return utils.set_incremental_state(self, incremental_state, 'input_buffer', new_buffer)
def _get_linearized_weight(self):
if self._linearized_weight is None:
kw = self.kernel_size[0]
weight = self.weight.transpose(2, 1).transpose(1, 0).contiguous()
assert weight.size() == (self.out_channels, kw, self.in_channels)
self._linearized_weight = torch.nn.Parameter(weight.view(self.out_channels, -1))
return self._linearized_weight
def _clear_linearized_weight(self, *args):
self._linearized_weight = None
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/linearized_convolution.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.modules import LayerNorm, MultiheadAttention
class TransformerEncoderLayer(nn.Module):
"""Encoder layer block.
In the original paper each operation (multi-head attention or FFN) is
postprocessed with: `dropout -> add residual -> layernorm`. In the
tensor2tensor code they suggest that learning is more robust when
preprocessing each layer with layernorm and postprocessing with:
`dropout -> add residual`. We default to the approach in the paper, but the
tensor2tensor approach can be enabled by setting
*args.encoder_normalize_before* to ``True``.
Args:
args (argparse.Namespace): parsed command-line arguments
"""
def __init__(self, args):
super().__init__()
self.embed_dim = args.encoder_embed_dim
self.self_attn = MultiheadAttention(
self.embed_dim, args.encoder_attention_heads,
dropout=args.attention_dropout, self_attention=True
)
self.self_attn_layer_norm = LayerNorm(self.embed_dim)
self.dropout = args.dropout
self.activation_fn = utils.get_activation_fn(
activation=getattr(args, 'activation_fn', 'relu')
)
self.activation_dropout = getattr(args, 'activation_dropout', 0)
if self.activation_dropout == 0:
# for backwards compatibility with models that use args.relu_dropout
self.activation_dropout = getattr(args, 'relu_dropout', 0)
self.normalize_before = args.encoder_normalize_before
self.fc1 = Linear(self.embed_dim, args.encoder_ffn_embed_dim)
self.fc2 = Linear(args.encoder_ffn_embed_dim, self.embed_dim)
self.final_layer_norm = LayerNorm(self.embed_dim)
def upgrade_state_dict_named(self, state_dict, name):
"""
Rename layer norm states from `...layer_norms.0.weight` to
`...self_attn_layer_norm.weight` and `...layer_norms.1.weight` to
`...final_layer_norm.weight`
"""
layer_norm_map = {
'0': 'self_attn_layer_norm',
'1': 'final_layer_norm'
}
for old, new in layer_norm_map.items():
for m in ('weight', 'bias'):
k = '{}.layer_norms.{}.{}'.format(name, old, m)
if k in state_dict:
state_dict[
'{}.{}.{}'.format(name, new, m)
] = state_dict[k]
del state_dict[k]
def forward(self, x, encoder_padding_mask, attn_mask=None):
"""
Args:
x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
encoder_padding_mask (ByteTensor): binary ByteTensor of shape
`(batch, src_len)` where padding elements are indicated by ``1``.
attn_mask (ByteTensor): binary tensor of shape (T_tgt, T_src), where
T_tgt is the length of query, while T_src is the length of key,
though here both query and key is x here,
attn_mask[t_tgt, t_src] = 1 means when calculating embedding
for t_tgt, t_src is excluded (or masked out), =0 means it is
included in attention
Returns:
encoded output of shape `(seq_len, batch, embed_dim)`
"""
residual = x
x = self.maybe_layer_norm(self.self_attn_layer_norm, x, before=True)
if attn_mask is not None:
attn_mask = attn_mask.masked_fill(attn_mask.bool(), -1e8)
# anything in original attn_mask = 1, becomes -1e8
# anything in original attn_mask = 0, becomes 0
# Note that we cannot use -inf here, because at some edge cases,
# the attention weight (before softmax) for some padded element in query
# will become -inf, which results in NaN in model parameters
# TODO: to formally solve this problem, we need to change fairseq's
# MultiheadAttention. We will do this later on.
x, _ = self.self_attn(query=x, key=x, value=x, key_padding_mask=encoder_padding_mask)
x = F.dropout(x, p=self.dropout, training=self.training)
x = residual + x
x = self.maybe_layer_norm(self.self_attn_layer_norm, x, after=True)
residual = x
x = self.maybe_layer_norm(self.final_layer_norm, x, before=True)
x = self.activation_fn(self.fc1(x))
x = F.dropout(x, p=self.activation_dropout, training=self.training)
x = self.fc2(x)
x = F.dropout(x, p=self.dropout, training=self.training)
x = residual + x
x = self.maybe_layer_norm(self.final_layer_norm, x, after=True)
return x
def maybe_layer_norm(self, layer_norm, x, before=False, after=False):
assert before ^ after
if after ^ self.normalize_before:
return layer_norm(x)
else:
return x
class TransformerDecoderLayer(nn.Module):
"""Decoder layer block.
In the original paper each operation (multi-head attention, encoder
attention or FFN) is postprocessed with: `dropout -> add residual ->
layernorm`. In the tensor2tensor code they suggest that learning is more
robust when preprocessing each layer with layernorm and postprocessing with:
`dropout -> add residual`. We default to the approach in the paper, but the
tensor2tensor approach can be enabled by setting
*args.decoder_normalize_before* to ``True``.
Args:
args (argparse.Namespace): parsed command-line arguments
no_encoder_attn (bool, optional): whether to attend to encoder outputs
(default: False).
"""
def __init__(self, args, no_encoder_attn=False, add_bias_kv=False, add_zero_attn=False):
super().__init__()
self.embed_dim = args.decoder_embed_dim
self.cross_self_attention = getattr(args, 'cross_self_attention', False)
self.self_attn = MultiheadAttention(
embed_dim=self.embed_dim,
num_heads=args.decoder_attention_heads,
dropout=args.attention_dropout,
add_bias_kv=add_bias_kv,
add_zero_attn=add_zero_attn,
self_attention=not self.cross_self_attention,
)
self.dropout = args.dropout
self.activation_fn = utils.get_activation_fn(
activation=getattr(args, 'activation_fn', 'relu')
)
self.activation_dropout = getattr(args, 'activation_dropout', 0)
if self.activation_dropout == 0:
# for backwards compatibility with models that use args.relu_dropout
self.activation_dropout = getattr(args, 'relu_dropout', 0)
self.normalize_before = args.decoder_normalize_before
# use layerNorm rather than FusedLayerNorm for exporting.
# char_inputs can be used to determint this.
# TODO remove this once we update apex with the fix
export = getattr(args, 'char_inputs', False)
self.self_attn_layer_norm = LayerNorm(self.embed_dim, export=export)
if no_encoder_attn:
self.encoder_attn = None
self.encoder_attn_layer_norm = None
else:
self.encoder_attn = MultiheadAttention(
self.embed_dim,
args.decoder_attention_heads,
kdim=getattr(args, 'encoder_embed_dim', None),
vdim=getattr(args, 'encoder_embed_dim', None),
dropout=args.attention_dropout,
encoder_decoder_attention=True,
)
self.encoder_attn_layer_norm = LayerNorm(self.embed_dim, export=export)
self.fc1 = Linear(self.embed_dim, args.decoder_ffn_embed_dim)
self.fc2 = Linear(args.decoder_ffn_embed_dim, self.embed_dim)
self.final_layer_norm = LayerNorm(self.embed_dim, export=export)
self.need_attn = True
self.onnx_trace = False
def prepare_for_onnx_export_(self):
self.onnx_trace = True
def forward(
self,
x,
encoder_out=None,
encoder_padding_mask=None,
incremental_state=None,
prev_self_attn_state=None,
prev_attn_state=None,
self_attn_mask=None,
self_attn_padding_mask=None,
need_attn=False,
need_head_weights=False,
):
"""
Args:
x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
encoder_padding_mask (ByteTensor, optional): binary
ByteTensor of shape `(batch, src_len)` where padding
elements are indicated by ``1``.
need_attn (bool, optional): return attention weights
need_head_weights (bool, optional): return attention weights
for each head (default: return average over heads).
Returns:
encoded output of shape `(seq_len, batch, embed_dim)`
"""
if need_head_weights:
need_attn = True
residual = x
x = self.maybe_layer_norm(self.self_attn_layer_norm, x, before=True)
if prev_self_attn_state is not None:
if incremental_state is None:
incremental_state = {}
prev_key, prev_value = prev_self_attn_state[:2]
saved_state = {"prev_key": prev_key, "prev_value": prev_value}
if len(prev_self_attn_state) >= 3:
saved_state["prev_key_padding_mask"] = prev_self_attn_state[2]
self.self_attn._set_input_buffer(incremental_state, saved_state)
if self.cross_self_attention and not (incremental_state is not None and "prev_key" in self.self_attn._get_input_buffer(incremental_state)):
if self_attn_mask is not None:
self_attn_mask = torch.cat((x.new(x.size(0), encoder_out.size(0)).zero_(), self_attn_mask), dim=1)
if self_attn_padding_mask is not None:
if encoder_padding_mask is None:
encoder_padding_mask = self_attn_padding_mask.new(encoder_out.size(1), encoder_out.size(0)).zero_()
self_attn_padding_mask = torch.cat((encoder_padding_mask, self_attn_padding_mask), dim=1)
y = torch.cat((encoder_out, x), dim=0)
else:
y = x
x, attn = self.self_attn(
query=x,
key=y,
value=y,
key_padding_mask=self_attn_padding_mask,
incremental_state=incremental_state,
need_weights=False,
attn_mask=self_attn_mask,
)
x = F.dropout(x, p=self.dropout, training=self.training)
x = residual + x
x = self.maybe_layer_norm(self.self_attn_layer_norm, x, after=True)
if self.encoder_attn is not None:
residual = x
x = self.maybe_layer_norm(self.encoder_attn_layer_norm, x, before=True)
if prev_attn_state is not None:
if incremental_state is None:
incremental_state = {}
prev_key, prev_value = prev_attn_state[:2]
saved_state = {"prev_key": prev_key, "prev_value": prev_value}
if len(prev_attn_state) >= 3:
saved_state["prev_key_padding_mask"] = prev_attn_state[2]
self.encoder_attn._set_input_buffer(incremental_state, saved_state)
x, attn = self.encoder_attn(
query=x,
key=encoder_out,
value=encoder_out,
key_padding_mask=encoder_padding_mask,
incremental_state=incremental_state,
static_kv=True,
need_weights=need_attn or (not self.training and self.need_attn),
need_head_weights=need_head_weights,
)
x = F.dropout(x, p=self.dropout, training=self.training)
x = residual + x
x = self.maybe_layer_norm(self.encoder_attn_layer_norm, x, after=True)
residual = x
x = self.maybe_layer_norm(self.final_layer_norm, x, before=True)
x = self.activation_fn(self.fc1(x))
x = F.dropout(x, p=self.activation_dropout, training=self.training)
x = self.fc2(x)
x = F.dropout(x, p=self.dropout, training=self.training)
x = residual + x
x = self.maybe_layer_norm(self.final_layer_norm, x, after=True)
if self.onnx_trace and incremental_state is not None:
saved_state = self.self_attn._get_input_buffer(incremental_state)
if self_attn_padding_mask is not None:
self_attn_state = saved_state["prev_key"], saved_state["prev_value"], saved_state["prev_key_padding_mask"]
else:
self_attn_state = saved_state["prev_key"], saved_state["prev_value"]
return x, attn, self_attn_state
return x, attn
def maybe_layer_norm(self, layer_norm, x, before=False, after=False):
assert before ^ after
if after ^ self.normalize_before:
return layer_norm(x)
else:
return x
def make_generation_fast_(self, need_attn=False, **kwargs):
self.need_attn = need_attn
def Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
if bias:
nn.init.constant_(m.bias, 0.)
return m
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/transformer_layer.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.modules import (
LayerNorm,
MultiheadAttention,
)
class TransformerSentenceEncoderLayer(nn.Module):
"""
Implements a Transformer Encoder Layer used in BERT/XLM style pre-trained
models.
"""
def __init__(
self,
embedding_dim: int = 768,
ffn_embedding_dim: int = 3072,
num_attention_heads: int = 8,
dropout: float = 0.1,
attention_dropout: float = 0.1,
activation_dropout: float = 0.1,
activation_fn: str = 'relu',
add_bias_kv: bool = False,
add_zero_attn: bool = False,
export: bool = False,
) -> None:
super().__init__()
# Initialize parameters
self.embedding_dim = embedding_dim
self.dropout = dropout
self.activation_dropout = activation_dropout
# Initialize blocks
self.activation_fn = utils.get_activation_fn(activation_fn)
self.self_attn = MultiheadAttention(
self.embedding_dim,
num_attention_heads,
dropout=attention_dropout,
add_bias_kv=add_bias_kv,
add_zero_attn=add_zero_attn,
self_attention=True
)
# layer norm associated with the self attention layer
self.self_attn_layer_norm = LayerNorm(self.embedding_dim, export=export)
self.fc1 = nn.Linear(self.embedding_dim, ffn_embedding_dim)
self.fc2 = nn.Linear(ffn_embedding_dim, self.embedding_dim)
# layer norm associated with the position wise feed-forward NN
self.final_layer_norm = LayerNorm(self.embedding_dim, export=export)
def forward(
self,
x: torch.Tensor,
self_attn_mask: torch.Tensor = None,
self_attn_padding_mask: torch.Tensor = None,
):
"""
LayerNorm is applied either before or after the self-attention/ffn
modules similar to the original Transformer imlementation.
"""
residual = x
x, attn = self.self_attn(
query=x,
key=x,
value=x,
key_padding_mask=self_attn_padding_mask,
need_weights=False,
attn_mask=self_attn_mask,
)
x = F.dropout(x, p=self.dropout, training=self.training)
x = residual + x
x = self.self_attn_layer_norm(x)
residual = x
x = self.activation_fn(self.fc1(x))
x = F.dropout(x, p=self.activation_dropout, training=self.training)
x = self.fc2(x)
x = F.dropout(x, p=self.dropout, training=self.training)
x = residual + x
x = self.final_layer_norm(x)
return x, attn
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/transformer_sentence_encoder_layer.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import nn
class Highway(torch.nn.Module):
"""
A `Highway layer <https://arxiv.org/abs/1505.00387>`_.
Adopted from the AllenNLP implementation.
"""
def __init__(
self,
input_dim: int,
num_layers: int = 1
):
super(Highway, self).__init__()
self.input_dim = input_dim
self.layers = nn.ModuleList([nn.Linear(input_dim, input_dim * 2)
for _ in range(num_layers)])
self.activation = nn.ReLU()
self.reset_parameters()
def reset_parameters(self):
for layer in self.layers:
# As per comment in AllenNLP:
# We should bias the highway layer to just carry its input forward. We do that by
# setting the bias on `B(x)` to be positive, because that means `g` will be biased to
# be high, so we will carry the input forward. The bias on `B(x)` is the second half
# of the bias vector in each Linear layer.
nn.init.constant_(layer.bias[self.input_dim:], 1)
nn.init.constant_(layer.bias[:self.input_dim], 0)
nn.init.xavier_normal_(layer.weight)
def forward(
self,
x: torch.Tensor
):
for layer in self.layers:
projection = layer(x)
proj_x, gate = projection.chunk(2, dim=-1)
proj_x = self.activation(proj_x)
gate = torch.sigmoid(gate)
x = gate * x + (gate.new_tensor([1]) - gate) * proj_x
return x
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/highway.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from .unfold import unfold1d
def DynamicConv(input_size, kernel_size=1, padding_l=None, num_heads=1,
weight_dropout=0., weight_softmax=False,
renorm_padding=False, bias=False, conv_bias=False,
query_size=None, in_proj=False):
if torch.cuda.is_available():
try:
from fairseq.modules.dynamicconv_layer import DynamicconvLayer
return DynamicconvLayer(input_size, kernel_size=kernel_size,
padding_l=padding_l, num_heads=num_heads,
weight_dropout=weight_dropout,
weight_softmax=weight_softmax, bias=bias)
except ImportError as e:
print(e)
return DynamicConv1dTBC(input_size, kernel_size=kernel_size,
padding_l=padding_l, num_heads=num_heads,
weight_dropout=weight_dropout,
weight_softmax=weight_softmax, bias=bias)
def Linear(in_features, out_features, bias=True):
m = nn.Linear(in_features, out_features, bias)
nn.init.xavier_uniform_(m.weight)
if bias:
nn.init.constant_(m.bias, 0.)
return m
class DynamicConv1dTBC(nn.Module):
'''Dynamic lightweight convolution taking T x B x C inputs
Args:
input_size: # of channels of the input
kernel_size: convolution channels
padding_l: padding to the left when using "same" padding
num_heads: number of heads used. The weight is of shape (num_heads, 1, kernel_size)
weight_dropout: the drop rate of the DropConnect to drop the weight
weight_softmax: normalize the weight with softmax before the convolution
renorm_padding: re-normalize the filters to ignore the padded part (only the non-padding parts sum up to 1)
bias: use bias
conv_bias: bias of the convolution
query_size: specified when feeding a different input as the query
in_proj: project the input and generate the filter together
Shape:
Input: TxBxC, i.e. (timesteps, batch_size, input_size)
Output: TxBxC, i.e. (timesteps, batch_size, input_size)
Attributes:
weight: the learnable weights of the module of shape
`(num_heads, 1, kernel_size)`
bias: the learnable bias of the module of shape `(input_size)`
'''
def __init__(self, input_size, kernel_size=1, padding_l=None, num_heads=1,
weight_dropout=0., weight_softmax=False,
renorm_padding=False, bias=False, conv_bias=False,
query_size=None, in_proj=False):
super().__init__()
self.input_size = input_size
self.query_size = input_size if query_size is None else query_size
self.kernel_size = kernel_size
self.padding_l = padding_l
self.num_heads = num_heads
self.weight_dropout = weight_dropout
self.weight_softmax = weight_softmax
self.renorm_padding = renorm_padding
if in_proj:
self.weight_linear = Linear(self.input_size, self.input_size + num_heads * kernel_size * 1)
else:
self.weight_linear = Linear(self.query_size, num_heads * kernel_size * 1, bias=bias)
if conv_bias:
self.conv_bias = nn.Parameter(torch.Tensor(input_size))
else:
self.conv_bias = None
self.reset_parameters()
@property
def in_proj(self):
return self.weight_linear.out_features == self.input_size + self.num_heads * self.kernel_size
def reset_parameters(self):
self.weight_linear.reset_parameters()
if self.conv_bias is not None:
nn.init.constant_(self.conv_bias, 0.)
def forward(self, x, incremental_state=None, query=None, unfold=None):
'''Assuming the input, x, of the shape T x B x C and producing an output in the shape T x B x C
args:
x: Input of shape T x B x C, i.e. (timesteps, batch_size, input_size)
incremental_state: A dict to keep the state
unfold: unfold the input or not. If not, we use the matrix trick instead
query: use the specified query to predict the conv filters
'''
unfold = x.size(0) > 512 if unfold is None else unfold # use unfold mode as default for long sequence to save memory
unfold = unfold or (incremental_state is not None)
assert query is None or not self.in_proj
if query is None:
query = x
if unfold:
output = self._forward_unfolded(x, incremental_state, query)
else:
output = self._forward_expanded(x, incremental_state, query)
if self.conv_bias is not None:
output = output + self.conv_bias.view(1, 1, -1)
return output
def _forward_unfolded(self, x, incremental_state, query):
'''The conventional implementation of convolutions.
Unfolding the input by having a window shifting to the right.'''
T, B, C = x.size()
K, H = self.kernel_size, self.num_heads
R = C // H
assert R * H == C == self.input_size
if self.in_proj:
proj = self.weight_linear(x)
x = proj.narrow(2, 0, self.input_size).contiguous()
weight = proj.narrow(2, self.input_size, H*K).contiguous().view(T*B*H, -1)
else:
weight = self.weight_linear(query).view(T*B*H, -1)
# renorm_padding is only implemented in _forward_expanded
assert not self.renorm_padding or incremental_state is not None
if incremental_state is not None:
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is None:
input_buffer = x.new()
x_unfold = torch.cat([input_buffer, x.unsqueeze(3)], dim=3)
if self.kernel_size > 1:
self._set_input_buffer(incremental_state, x_unfold[:, :, :, -self.kernel_size+1:])
x_unfold = x_unfold.view(T*B*H, R, -1)
else:
padding_l = self.padding_l
if K > T and padding_l == K-1:
weight = weight.narrow(1, K-T, T)
K, padding_l = T, T-1
# unfold the input: T x B x C --> T' x B x C x K
x_unfold = unfold1d(x, K, padding_l, 0)
x_unfold = x_unfold.view(T*B*H, R, K)
if self.weight_softmax and not self.renorm_padding:
weight = F.softmax(weight, dim=1)
weight = weight.narrow(1, 0, K)
if incremental_state is not None:
weight = weight[:, -x_unfold.size(2):]
K = weight.size(1)
if self.weight_softmax and self.renorm_padding:
weight = F.softmax(weight, dim=1)
weight = F.dropout(weight, self.weight_dropout, training=self.training, inplace=False)
output = torch.bmm(x_unfold, weight.unsqueeze(2)) # T*B*H x R x 1
output = output.view(T, B, C)
return output
def _forward_expanded(self, x, incremental_stat, query):
'''Turn the convolution filters into band matrices and do matrix multiplication.
This is faster when the sequence is short, but less memory efficient.
This is not used in the decoder during inference.
'''
T, B, C = x.size()
K, H = self.kernel_size, self.num_heads
R = C // H
assert R * H == C == self.input_size
if self.in_proj:
proj = self.weight_linear(x)
x = proj.narrow(2, 0, self.input_size).contiguous()
weight = proj.narrow(2, self.input_size, H*K).contiguous().view(T*B*H, -1)
else:
weight = self.weight_linear(query).view(T*B*H, -1)
if not self.renorm_padding:
if self.weight_softmax:
weight = F.softmax(weight, dim=1)
weight = F.dropout(weight, self.weight_dropout, training=self.training, inplace=False)
weight = weight.narrow(1, 0, K).contiguous()
weight = weight.view(T, B*H, K).transpose(0, 1)
x = x.view(T, B*H, R).transpose(0, 1)
if self.weight_softmax and self.renorm_padding:
# turn the convolution filters into band matrices
weight_expanded = weight.new(B*H, T, T+K-1).fill_(float('-inf'))
weight_expanded.as_strided((B*H, T, K), (T*(T+K-1), T+K, 1)).copy_(weight)
weight_expanded = weight_expanded.narrow(2, self.padding_l, T)
# normalize the weight over valid positions like self-attention
weight_expanded = F.softmax(weight_expanded, dim=2)
weight_expanded = F.dropout(weight_expanded, self.weight_dropout, training=self.training, inplace=False)
else:
P = self.padding_l
# For efficieny, we cut the kernel size and reduce the padding when the kernel is larger than the length
if K > T and P == K-1:
weight = weight.narrow(2, K-T, T)
K, P = T, T-1
# turn the convolution filters into band matrices
weight_expanded = weight.new_zeros(B*H, T, T+K-1, requires_grad=False)
weight_expanded.as_strided((B*H, T, K), (T*(T+K-1), T+K, 1)).copy_(weight)
weight_expanded = weight_expanded.narrow(2, P, T) # B*H x T x T
output = torch.bmm(weight_expanded, x)
output = output.transpose(0, 1).contiguous().view(T, B, C)
return output
def reorder_incremental_state(self, incremental_state, new_order):
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is not None:
input_buffer = input_buffer.index_select(1, new_order)
self._set_input_buffer(incremental_state, input_buffer)
def _get_input_buffer(self, incremental_state):
return utils.get_incremental_state(self, incremental_state, 'input_buffer')
def _set_input_buffer(self, incremental_state, new_buffer):
return utils.set_incremental_state(self, incremental_state, 'input_buffer', new_buffer)
def extra_repr(self):
s = '{}, kernel_size={}, padding_l={}, num_heads={}, weight_softmax={}, conv_bias={}, renorm_padding={}, in_proj={}'.format(
self.input_size, self.kernel_size, self.padding_l,
self.num_heads, self.weight_softmax, self.conv_bias is not None, self.renorm_padding,
self.in_proj,
)
if self.query_size != self.input_size:
s += ', query_size={}'.format(self.query_size)
if self.weight_dropout > 0.:
s += ', weight_dropout={}'.format(self.weight_dropout)
return s
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/dynamic_convolution.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import nn
from typing import List
class AdaptiveInput(nn.Module):
def __init__(
self,
vocab_size: int,
padding_idx: int,
initial_dim: int,
factor: float,
output_dim: int,
cutoff: List[int],
):
super().__init__()
if vocab_size > cutoff[-1]:
cutoff = cutoff + [vocab_size]
else:
assert vocab_size == cutoff[
-1], 'cannot specify cutoff larger than vocab size'
self.cutoff = cutoff
self.embedding_dim = output_dim
self.padding_idx = padding_idx
self.embeddings = nn.ModuleList()
for i in range(len(self.cutoff)):
prev = self.cutoff[i - 1] if i > 0 else 0
size = self.cutoff[i] - prev
dim = int(initial_dim // (factor ** i))
seq = nn.Sequential(
nn.Embedding(size, dim, padding_idx),
nn.Linear(dim, output_dim, bias=False)
)
self.embeddings.append(seq)
def init_weights(m):
if isinstance(m, nn.Embedding):
nn.init.normal_(m.weight, mean=0, std=m.weight.shape[1] ** -0.5)
nn.init.constant_(m.weight[padding_idx], 0)
elif hasattr(m, 'weight'):
nn.init.xavier_uniform_(m.weight)
self.apply(init_weights)
self.register_buffer('_float_tensor', torch.FloatTensor(1))
def weights_for_band(self, band: int):
return self.embeddings[band][0].weight, self.embeddings[band][1].weight
def forward(self, input: torch.Tensor):
result = self._float_tensor.new(input.shape + (self.embedding_dim,))
for i in range(len(self.cutoff)):
mask = input.lt(self.cutoff[i])
if i > 0:
mask.mul_(input.ge(self.cutoff[i - 1]))
chunk_input = input[mask] - self.cutoff[i - 1]
else:
chunk_input = input[mask]
if mask.any():
result[mask] = self.embeddings[i](chunk_input)
return result
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/adaptive_input.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.modules.unfold import unfold1d
def LightweightConv(input_size, kernel_size=1, padding_l=None, num_heads=1,
weight_dropout=0., weight_softmax=False, bias=False):
if torch.cuda.is_available():
try:
from fairseq.modules.lightconv_layer import LightconvLayer
return LightconvLayer(input_size, kernel_size=kernel_size,
padding_l=padding_l, num_heads=num_heads,
weight_dropout=weight_dropout,
weight_softmax=weight_softmax, bias=bias)
except ImportError as e:
print(e)
return LightweightConv1dTBC(input_size, kernel_size=kernel_size,
padding_l=padding_l, num_heads=num_heads,
weight_dropout=weight_dropout,
weight_softmax=weight_softmax, bias=bias)
class LightweightConv1d(nn.Module):
'''Lightweight Convolution assuming the input is BxCxT
This is just an example that explains LightConv clearer than the TBC version.
We don't use this module in the model.
Args:
input_size: # of channels of the input and output
kernel_size: convolution channels
padding: padding
num_heads: number of heads used. The weight is of shape
`(num_heads, 1, kernel_size)`
weight_softmax: normalize the weight with softmax before the convolution
Shape:
Input: BxCxT, i.e. (batch_size, input_size, timesteps)
Output: BxCxT, i.e. (batch_size, input_size, timesteps)
Attributes:
weight: the learnable weights of the module of shape
`(num_heads, 1, kernel_size)`
bias: the learnable bias of the module of shape `(input_size)`
'''
def __init__(self, input_size, kernel_size=1, padding=0, num_heads=1,
weight_softmax=False, bias=False, weight_dropout=0.):
super().__init__()
self.input_size = input_size
self.kernel_size = kernel_size
self.num_heads = num_heads
self.padding = padding
self.weight_softmax = weight_softmax
self.weight = nn.Parameter(torch.Tensor(num_heads, 1, kernel_size))
if bias:
self.bias = nn.Parameter(torch.Tensor(input_size))
else:
self.bias = None
self.weight_dropout = weight_dropout
self.reset_parameters()
def reset_parameters(self):
nn.init.xavier_uniform_(self.weight)
if self.bias is not None:
nn.init.constant_(self.bias, 0.)
def forward(self, input):
'''
input size: B x C x T
output size: B x C x T
'''
B, C, T = input.size()
H = self.num_heads
weight = self.weight
if self.weight_softmax:
weight = F.softmax(weight, dim=-1)
weight = F.dropout(weight, self.weight_dropout, training=self.training)
# Merge every C/H entries into the batch dimension (C = self.input_size)
# B x C x T -> (B * C/H) x H x T
# One can also expand the weight to C x 1 x K by a factor of C/H
# and do not reshape the input instead, which is slow though
input = input.view(-1, H, T)
output = F.conv1d(input, weight, padding=self.padding, groups=self.num_heads)
output = output.view(B, C, T)
if self.bias is not None:
output = output + self.bias.view(1, -1, 1)
return output
class LightweightConv1dTBC(nn.Module):
'''Lightweight Convolution assuming the input is TxBxC
Args:
input_size: # of channels of the input
kernel_size: convolution channels
padding_l: padding to the left when using "same" padding
num_heads: number of heads used. The weight is of shape (num_heads, 1, kernel_size)
weight_dropout: the drop rate of the DropConnect to drop the weight
weight_softmax: normalize the weight with softmax before the convolution
bias: use bias
Shape:
Input: TxBxC, i.e. (timesteps, batch_size, input_size)
Output: TxBxC, i.e. (timesteps, batch_size, input_size)
Attributes:
weight: the learnable weights of the module of shape
`(num_heads, 1, kernel_size)`
bias: the learnable bias of the module of shape `(input_size)`
'''
def __init__(self, input_size, kernel_size=1, padding_l=None, num_heads=1,
weight_dropout=0., weight_softmax=False, bias=False):
super().__init__()
self.input_size = input_size
self.kernel_size = kernel_size
self.padding_l = padding_l
self.num_heads = num_heads
self.weight_dropout = weight_dropout
self.weight_softmax = weight_softmax
self.weight = nn.Parameter(torch.Tensor(num_heads, 1, kernel_size))
if bias:
self.bias = nn.Parameter(torch.Tensor(input_size))
else:
self.bias = None
self.reset_parameters()
self.onnx_trace = False
def reset_parameters(self):
nn.init.xavier_uniform_(self.weight)
if self.bias is not None:
nn.init.constant_(self.bias, 0.)
def forward(self, x, incremental_state=None, unfold=False):
'''Assuming the input, x, of the shape T x B x C and producing an output in the shape T x B x C
args:
x: Input of shape T x B x C, i.e. (timesteps, batch_size, input_size)
incremental_state: A dict to keep the state
unfold: unfold the input or not. If not, we use the matrix trick instead
'''
unfold = unfold or (incremental_state is not None)
if unfold:
output = self._forward_unfolded(x, incremental_state)
else:
output = self._forward_expanded(x, incremental_state)
if self.bias is not None:
output = output + self.bias.view(1, 1, -1)
return output
def prepare_for_onnx_export_(self):
self.onnx_trace = True
def _forward_unfolded(self, x, incremental_state):
'''The conventional implementation of convolutions.
Unfolding the input by having a window shifting to the right.'''
T, B, C = x.size()
K, H = self.kernel_size, self.num_heads
R = C // H
assert R * H == C == self.input_size
weight = self.weight.view(H, K)
if incremental_state is not None:
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is None:
input_buffer = x.new()
x_unfold = torch.cat([input_buffer, x.unsqueeze(3)], dim=3)
if self.kernel_size > 1:
self._set_input_buffer(incremental_state, x_unfold[:, :, :, -self.kernel_size+1:])
x_unfold = x_unfold.view(T*B*H, R, -1)
else:
# unfold the input: T x B x C --> T' x B x C x K
x_unfold = unfold1d(x, self.kernel_size, self.padding_l, 0)
x_unfold = x_unfold.view(T*B*H, R, K)
if self.weight_softmax:
weight = utils.softmax(weight, dim=1, onnx_trace=self.onnx_trace).type_as(weight)
if incremental_state is not None:
weight = weight[:, -x_unfold.size(2):]
K = weight.size(1)
weight = weight.view(1, H, K).expand(T*B, H, K).contiguous().view(T*B*H, K, 1)
weight = F.dropout(weight, self.weight_dropout, training=self.training)
output = torch.bmm(x_unfold, weight) # T*B*H x R x 1
output = output.view(T, B, C)
return output
def _forward_expanded(self, x, incremental_state):
'''Turn the convolution filters into band matrices and do matrix multiplication.
This is faster when the sequence is short, but less memory efficient.
This is not used in the decoder during inference.
'''
T, B, C = x.size()
K, H = self.kernel_size, self.num_heads
R = C // H
assert R * H == C == self.input_size
weight = self.weight.view(H, K)
if self.weight_softmax:
weight = utils.softmax(weight, dim=1, onnx_trace=self.onnx_trace).type_as(weight)
weight = weight.view(1, H, K).expand(T*B, H, K).contiguous()
weight = weight.view(T, B*H, K).transpose(0, 1)
x = x.view(T, B*H, R).transpose(0, 1)
P = self.padding_l
if K > T and P == K-1:
weight = weight.narrow(2, K-T, T)
K, P = T, T-1
# turn the convolution filters into band matrices
weight_expanded = weight.new_zeros(B*H, T, T+K-1, requires_grad=False)
weight_expanded.as_strided((B*H, T, K), (T*(T+K-1), T+K, 1)).copy_(weight)
weight_expanded = weight_expanded.narrow(2, P, T)
weight_expanded = F.dropout(weight_expanded, self.weight_dropout, training=self.training)
output = torch.bmm(weight_expanded, x)
output = output.transpose(0, 1).contiguous().view(T, B, C)
return output
def reorder_incremental_state(self, incremental_state, new_order):
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is not None:
input_buffer = input_buffer.index_select(1, new_order)
self._set_input_buffer(incremental_state, input_buffer)
def _get_input_buffer(self, incremental_state):
return utils.get_incremental_state(self, incremental_state, 'input_buffer')
def _set_input_buffer(self, incremental_state, new_buffer):
return utils.set_incremental_state(self, incremental_state, 'input_buffer', new_buffer)
def extra_repr(self):
s = '{}, kernel_size={}, padding_l={}, num_heads={}, weight_softmax={}, bias={}'.format(
self.input_size, self.kernel_size, self.padding_l,
self.num_heads, self.weight_softmax, self.bias is not None
)
if self.weight_dropout > 0.:
s += ', weight_dropout={}'.format(self.weight_dropout)
return s
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/lightweight_convolution.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
class ScalarBias(torch.autograd.Function):
"""
Adds a vector of scalars, used in self-attention mechanism to allow
the model to optionally attend to this vector instead of the past
"""
@staticmethod
def forward(ctx, input, dim, bias_init):
size = list(input.size())
size[dim] += 1
output = input.new(*size).fill_(bias_init)
output.narrow(dim, 1, size[dim] - 1).copy_(input)
ctx.dim = dim
return output
@staticmethod
def backward(ctx, grad):
return grad.narrow(ctx.dim, 1, grad.size(ctx.dim) - 1), None, None
def scalar_bias(input, dim, bias_init=0):
return ScalarBias.apply(input, dim, bias_init)
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/scalar_bias.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
class LogSumExpMoE(torch.autograd.Function):
"""Standard LogSumExp forward pass, but use *posterior* for the backward.
See `"Mixture Models for Diverse Machine Translation: Tricks of the Trade"
(Shen et al., 2019) <https://arxiv.org/abs/1902.07816>`_.
"""
@staticmethod
def forward(ctx, logp, posterior, dim=-1):
ctx.save_for_backward(posterior)
ctx.dim = dim
return torch.logsumexp(logp, dim=dim)
@staticmethod
def backward(ctx, grad_output):
posterior, = ctx.saved_tensors
grad_logp = grad_output.unsqueeze(ctx.dim) * posterior
return grad_logp, None, None
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/logsumexp_moe.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch.nn as nn
from .learned_positional_embedding import LearnedPositionalEmbedding
from .sinusoidal_positional_embedding import SinusoidalPositionalEmbedding
def PositionalEmbedding(
num_embeddings: int,
embedding_dim: int,
padding_idx: int,
learned: bool = False,
):
if learned:
# if padding_idx is specified then offset the embedding ids by
# this index and adjust num_embeddings appropriately
# TODO: The right place for this offset would be inside
# LearnedPositionalEmbedding. Move this there for a cleaner implementation.
if padding_idx is not None:
num_embeddings = num_embeddings + padding_idx + 1
m = LearnedPositionalEmbedding(num_embeddings, embedding_dim, padding_idx)
nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
if padding_idx is not None:
nn.init.constant_(m.weight[padding_idx], 0)
else:
m = SinusoidalPositionalEmbedding(
embedding_dim, padding_idx, init_size=num_embeddings + padding_idx + 1,
)
return m
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/positional_embedding.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
from .multihead_attention import MultiheadAttention
class SparseMultiheadAttention(MultiheadAttention):
""" Sparse Multi-Headed Attention.
"Generating Long Sequences with Sparse Transformers". Implements
fixed factorized self attention, where l=stride and c=expressivity.
A(1) includes all words in the stride window and A(2) takes a summary of c
words from the end of each stride window.
If is_bidirectional=False, we do not include any words past the current word,
as in the paper.
"""
def __init__(self, embed_dim, num_heads, kdim=None, vdim=None, dropout=0., bias=True,
add_bias_kv=False, add_zero_attn=False, self_attention=False,
encoder_decoder_attention=False, stride=32, expressivity=8, is_bidirectional=True):
super().__init__(
embed_dim, num_heads, kdim, vdim, dropout, bias, add_bias_kv,
add_zero_attn, self_attention, encoder_decoder_attention
)
self.is_bidirectional = is_bidirectional
self.stride = stride
self.expressivity = expressivity
assert(self.stride > 0 and self.stride >= self.expressivity)
# Used for Ai(2) calculations - beginning of [l-c, l] range
def compute_checkpoint(self, word_index):
if word_index % self.stride == 0 and word_index != 0:
checkpoint_index = word_index - self.expressivity
else:
checkpoint_index = (
math.floor(word_index / self.stride) * self.stride
+ self.stride - self.expressivity
)
return checkpoint_index
# Computes Ai(2)
def compute_subset_summaries(self, absolute_max):
checkpoint_index = self.compute_checkpoint(0)
subset_two = set()
while checkpoint_index <= absolute_max-1:
summary = set(range(checkpoint_index, min(
checkpoint_index+self.expressivity+1, absolute_max)
))
subset_two = subset_two.union(summary)
checkpoint_index = self.compute_checkpoint(checkpoint_index+self.stride)
return subset_two
# Sparse Transformer Fixed Attention Pattern: https://arxiv.org/pdf/1904.10509.pdf
def compute_fixed_attention_subset(self, word_index, tgt_len):
# +1s account for range function; [min, max) -> [min, max]
if not self.is_bidirectional:
absolute_max = word_index + 1
else:
absolute_max = tgt_len
# Subset 1 - whole window
rounded_index = math.floor((word_index + self.stride) / self.stride) * self.stride
if word_index % self.stride == 0 and word_index != 0:
subset_one = set(range(word_index-self.stride, min(absolute_max, word_index+1)))
else:
subset_one = set(range(max(0, rounded_index - self.stride), min(
absolute_max, rounded_index+1))
)
# Subset 2 - summary per window
# If bidirectional, subset 2 is the same for every index
subset_two = set()
if not self.is_bidirectional:
subset_two = self.compute_subset_summaries(absolute_max)
return subset_one.union(subset_two)
# Compute sparse mask - if bidirectional, can pre-compute and store
def buffered_sparse_mask(self, tensor, tgt_len, src_len):
assert(tgt_len > self.stride)
sparse_mask = torch.empty((tgt_len, src_len)).float().fill_(float('-inf'))
# If bidirectional, subset 2 is the same for every index
subset_summaries = set()
if self.is_bidirectional:
subset_summaries = self.compute_subset_summaries(tgt_len)
for i in range(tgt_len):
fixed_attention_subset = self.compute_fixed_attention_subset(i, tgt_len)
fixed_attention_subset = fixed_attention_subset.union(subset_summaries)
included_word_indices = torch.LongTensor(list(fixed_attention_subset))
sparse_mask[i].index_fill_(0, included_word_indices, 0)
return sparse_mask.type_as(tensor)
def apply_sparse_mask(self, attn_weights, tgt_len, src_len, bsz):
sparse_mask = self.buffered_sparse_mask(attn_weights, tgt_len, src_len)
sparse_mask = sparse_mask.unsqueeze(0).expand(bsz * self.num_heads, tgt_len, src_len)
attn_weights += sparse_mask
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/sparse_multihead_attention.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn as nn
import torch.onnx.operators
from fairseq import utils
class SinusoidalPositionalEmbedding(nn.Module):
"""This module produces sinusoidal positional embeddings of any length.
Padding symbols are ignored.
"""
def __init__(self, embedding_dim, padding_idx, init_size=1024):
super().__init__()
self.embedding_dim = embedding_dim
self.padding_idx = padding_idx
self.weights = SinusoidalPositionalEmbedding.get_embedding(
init_size,
embedding_dim,
padding_idx,
)
self.onnx_trace = False
self.register_buffer('_float_tensor', torch.FloatTensor(1))
def prepare_for_onnx_export_(self):
self.onnx_trace = True
@staticmethod
def get_embedding(num_embeddings, embedding_dim, padding_idx=None):
"""Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
from the description in Section 3.5 of "Attention Is All You Need".
"""
half_dim = embedding_dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0)
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
if embedding_dim % 2 == 1:
# zero pad
emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
if padding_idx is not None:
emb[padding_idx, :] = 0
return emb
def forward(self, input, incremental_state=None, timestep=None, **kwargs):
"""Input is expected to be of size [bsz x seqlen]."""
bsz, seq_len = torch.onnx.operators.shape_as_tensor(input)
max_pos = self.padding_idx + 1 + seq_len
if self.weights is None or max_pos > self.weights.size(0):
# recompute/expand embeddings if needed
self.weights = SinusoidalPositionalEmbedding.get_embedding(
max_pos,
self.embedding_dim,
self.padding_idx,
)
self.weights = self.weights.to(self._float_tensor)
if incremental_state is not None:
# positions is the same for every token when decoding a single step
pos = timestep.view(-1)[0] + 1 if timestep is not None else seq_len
if self.onnx_trace:
return self.weights.index_select(index=self.padding_idx + pos, dim=0).unsqueeze(1).repeat(bsz, 1, 1)
return self.weights[self.padding_idx + pos, :].expand(bsz, 1, -1)
positions = utils.make_positions(input, self.padding_idx, onnx_trace=self.onnx_trace)
if self.onnx_trace:
flat_embeddings = self.weights.detach().index_select(0, positions.view(-1))
embedding_shape = torch.cat((bsz.view(1), seq_len.view(1), torch.LongTensor([-1])))
embeddings = torch.onnx.operators.reshape_from_tensor_shape(flat_embeddings, embedding_shape)
return embeddings
return self.weights.index_select(0, positions.view(-1)).view(bsz, seq_len, -1).detach()
def max_positions(self):
"""Maximum number of supported positions."""
return int(1e5) # an arbitrary large number
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/sinusoidal_positional_embedding.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
def LayerNorm(normalized_shape, eps=1e-5, elementwise_affine=True, export=False):
if not export and torch.cuda.is_available():
try:
from apex.normalization import FusedLayerNorm
return FusedLayerNorm(normalized_shape, eps, elementwise_affine)
except ImportError:
pass
return torch.nn.LayerNorm(normalized_shape, eps, elementwise_affine)
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/layer_norm.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
def gen_forward():
kernels = [3, 5, 7, 15, 31, 63, 127, 255]
seqs = [32 * x for x in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]]
head = """
/**
* Copyright (c) Facebook, Inc. and its affiliates.
*
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.
*/
#include "lightconv_cuda.cuh"
std::vector<at::Tensor> lightconv_cuda_forward(at::Tensor input, at::Tensor filters, int padding_l) {
at::DeviceGuard g(input.device());
const auto minibatch = input.size(0);
const auto numFeatures = input.size(1);
const auto sequenceLength = input.size(2);
const auto numHeads = filters.size(0);
const auto filterSize = filters.size(1);
const auto numFiltersInBlock = numFeatures / numHeads;
const dim3 blocks(minibatch, numFeatures);
auto output = at::zeros_like(input);
auto stream = at::cuda::getCurrentCUDAStream();
"""
sequence_if = """
if (sequenceLength <= {seq}) {{
switch(filterSize) {{
"""
case_k = """
case {k}:
"""
main_block = """
if (padding_l == {pad}) {{
AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.scalar_type(), "lightconv_forward", ([&] {{
lightconv_forward_kernel<{k}, {b_size}, {pad}, scalar_t>
<<<blocks, {b_size}, 0, stream>>>(
input.data<scalar_t>(),
filters.data<scalar_t>(),
minibatch,
sequenceLength,
numFeatures,
numFiltersInBlock,
output.data<scalar_t>());
}}));
}} else
"""
bad_padding = """
{
std::cout << "WARNING: Unsupported padding size - skipping forward pass" << std::endl;
}
break;
"""
bad_filter = """
default:
std::cout << "WARNING: Unsupported filter length passed - skipping forward pass" << std::endl;
}
"""
con_else = """
} else
"""
final_else = """
{
switch(filterSize) {
"""
final_return = """
}
return {output};
}
"""
with open("lightconv_cuda_forward.cu", 'w') as forward:
forward.write(head)
for seq in seqs:
forward.write(sequence_if.format(seq=seq))
for k in kernels:
forward.write(case_k.format(k=k))
for pad in [k // 2, k - 1]:
forward.write(main_block.format(k=k, b_size=seq, pad=pad))
forward.write(bad_padding)
forward.write(bad_filter)
forward.write(con_else)
forward.write(final_else)
for k in kernels:
forward.write(case_k.format(k=k))
for pad in [k // 2, k - 1]:
forward.write(main_block.format(k=k, b_size=seq, pad=pad))
forward.write(bad_padding)
forward.write(bad_filter)
forward.write(final_return)
def gen_backward():
head = """
/**
* Copyright (c) Facebook, Inc. and its affiliates.
*
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.
*/
#include "lightconv_cuda.cuh"
std::vector<at::Tensor> lightconv_cuda_backward(
at::Tensor gradOutput,
int padding_l,
at::Tensor input,
at::Tensor filters) {
// gradWrtInput
const int minibatch = input.size(0);
const int numFeatures = input.size(1);
const int sequenceLength = input.size(2);
const int numHeads = filters.size(0);
const int filterSize = filters.size(1);
const dim3 gradBlocks(minibatch, numFeatures);
const dim3 weightGradFirstpassShortBlocks(minibatch, numHeads);
const dim3 weightGradSecondpassBlocks(numHeads, filterSize);
const int numFiltersInBlock = numFeatures / numHeads;
auto gradInput = at::zeros_like(input);
auto gradFilters = at::zeros_like(filters);
at::DeviceGuard g(input.device());
auto stream = at::cuda::getCurrentCUDAStream();
switch(filterSize) {
"""
sequence_if = """
if (sequenceLength <= {seq}) {{
"""
case_k = """
case {k}:
"""
main_block = """
if (padding_l == {p}) {{
AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.scalar_type(), "lightconv_backward", ([&] {{
lightconv_grad_wrt_input_kernel<{k}, {b_size}, {p}, scalar_t>
<<<gradBlocks, {b_size}, 0, stream>>>(
gradOutput.data<scalar_t>(),
filters.data<scalar_t>(),
minibatch,
sequenceLength,
numFeatures,
numFiltersInBlock,
gradInput.data<scalar_t>());
"""
weight_grad_short = """
at::Tensor tempSumGradFilters = at::zeros({{minibatch, numHeads, filterSize}}, input.options().dtype(at::kFloat));
lightconv_grad_wrt_weights_firstpass_short_kernel<{k}, {b_size}, {p}, scalar_t>
<<<weightGradFirstpassShortBlocks, {b_size}, 0, stream>>>(
input.data<scalar_t>(),
gradOutput.data<scalar_t>(),
minibatch,
sequenceLength,
numFeatures,
numFiltersInBlock,
numHeads,
tempSumGradFilters.data<float>()
);
lightconv_grad_wrt_weights_secondpass_short_kernel<{k}, {b_size}, scalar_t>
<<<weightGradSecondpassBlocks, {b_size}, 0, stream>>>(
tempSumGradFilters.data<float>(),
minibatch,
numFiltersInBlock,
gradFilters.data<scalar_t>()
);
}}));
}} else
"""
weight_grad = """
at::Tensor tempSumGradFilters = at::zeros({{minibatch, numFeatures, filterSize}}, input.options().dtype(at::kFloat));
lightconv_grad_wrt_weights_firstpass_kernel<{k}, {b_size}, {p}, scalar_t>
<<<gradBlocks, {b_size}, 0, stream>>>(
input.data<scalar_t>(),
gradOutput.data<scalar_t>(),
minibatch,
sequenceLength,
numFeatures,
numFiltersInBlock,
tempSumGradFilters.data<float>()
);
lightconv_grad_wrt_weights_secondpass_kernel<{k}, {b_size}, scalar_t>
<<<weightGradSecondpassBlocks, {b_size}, 0, stream>>>(
tempSumGradFilters.data<float>(),
minibatch,
numFiltersInBlock,
gradFilters.data<scalar_t>()
);
}}));
}} else
"""
bad_padding = """
{
std::cout << "WARNING: Unsupported padding size - skipping backward pass" << std::endl;
}
"""
breakout = """
break;
"""
bad_filter = """
default:
std::cout << "WARNING: Unsupported filter length passed - skipping backward pass" << std::endl;
"""
con_else = """
} else
"""
final_else = """
{
switch(filterSize) {
"""
last_return = """
}
return {gradInput, gradFilters};
}
"""
kernels = [3, 5, 7, 15, 31, 63, 127, 255]
seqs = [32 * x for x in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]]
thresh = [32, 32, 64, 128, 256, -1, -1, -1]
max_mem = [-1, -1, -1, -1, -1, 192, 96, 64]
with open("lightconv_cuda_backward.cu", 'w') as backward:
backward.write(head)
for (k, t, mem) in zip(kernels, thresh, max_mem):
backward.write(case_k.format(k=k))
for seq in seqs:
if (t == -1 or seq <= t) and (mem == -1 or seq < mem):
backward.write(sequence_if.format(seq=seq))
for p in [k // 2, k - 1]:
backward.write(main_block.format(k=k, b_size=seq, p=p))
backward.write(weight_grad_short.format(k=k, b_size=seq, p=p))
backward.write(bad_padding)
else:
for p in [k // 2, k - 1]:
backward.write(main_block.format(k=k, b_size=32, p=p))
backward.write(weight_grad.format(k=k, b_size=32, p=p))
backward.write(bad_padding)
backward.write(breakout)
break
backward.write(con_else)
backward.write(bad_filter)
backward.write(last_return)
if __name__ == "__main__":
gen_forward()
gen_backward()
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/lightconv_layer/cuda_function_gen.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .lightconv_layer import LightconvLayer # noqa
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/lightconv_layer/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import nn
from torch.autograd import Function
import torch.nn.functional as F
import lightconv_cuda
from fairseq import utils
class lightconvFunction(Function):
@staticmethod
def forward(ctx, x, weights, padding_l):
ctx.padding_l = padding_l
outputs = lightconv_cuda.forward(x, weights, padding_l)
variables = [x, weights]
ctx.save_for_backward(*variables)
return outputs[0]
@staticmethod
def backward(ctx, grad_output):
outputs = lightconv_cuda.backward(
grad_output.contiguous(),
ctx.padding_l,
*ctx.saved_variables)
grad_input, grad_weights = outputs
return grad_input, grad_weights, None
class LightconvLayer(nn.Module):
def __init__(
self,
input_size,
kernel_size=1,
padding_l=None,
weight_softmax=False,
num_heads=1,
weight_dropout=0.,
bias=False):
super(LightconvLayer, self).__init__()
self.input_size = input_size
self.kernel_size = kernel_size
self.padding_l = padding_l
self.num_heads = num_heads
self.weight_softmax = weight_softmax
self.weight_dropout = weight_dropout
self.weight = nn.Parameter(torch.Tensor(num_heads, kernel_size))
if bias:
self.bias = nn.Parameter(torch.Tensor(input_size))
else:
self.bias = None
self.reset_parameters()
def reset_parameters(self):
nn.init.xavier_uniform_(self.weight)
if self.bias is not None:
nn.init.constant_(self.bias, 0.)
def forward(self, x, incremental_state=None):
# during inference time, incremental BMM is faster
if incremental_state is not None:
T, B, C = x.size()
K, H = self.kernel_size, self.num_heads
R = C // H
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is None:
input_buffer = x.new()
x_unfold = torch.cat([input_buffer, x.unsqueeze(3)], dim=3)
if self.kernel_size > 1:
self._set_input_buffer(incremental_state, x_unfold[:, :, :, -self.kernel_size+1:])
x_unfold = x_unfold.view(T*B*H, R, -1)
weight = self.weight
if self.weight_softmax:
weight = F.softmax(weight.float(), dim=1).type_as(weight)
weight = weight[:, -x_unfold.size(2):]
K = weight.size(1)
weight = weight.view(1, H, K).expand(T*B, H, K).contiguous().view(T*B*H, K, 1)
weight = F.dropout(weight, self.weight_dropout, training=self.training)
output = torch.bmm(x_unfold, weight) # T*B*H x R x 1
output = output.view(T, B, C)
return output
# during training time, use CUDA kernel
else:
x = x.permute(1, 2, 0).contiguous()
weight = self.weight
if self.weight_softmax:
weight = F.softmax(self.weight, -1)
if self.weight_dropout:
weight = F.dropout(weight, self.weight_dropout, training=self.training)
return lightconvFunction.apply(x, weight, self.padding_l).permute(2, 0, 1)
def reorder_incremental_state(self, incremental_state, new_order):
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is not None:
input_buffer = input_buffer.index_select(1, new_order)
self._set_input_buffer(incremental_state, input_buffer)
def _get_input_buffer(self, incremental_state):
return utils.get_incremental_state(self, incremental_state, 'input_buffer')
def _set_input_buffer(self, incremental_state, new_buffer):
return utils.set_incremental_state(self, incremental_state, 'input_buffer', new_buffer)
def half(self):
print("HALF")
return self._apply(lambda t: t.half() if t.is_floating_point() else t)
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/lightconv_layer/lightconv_layer.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from setuptools import setup
from torch.utils.cpp_extension import CUDAExtension, BuildExtension
setup(
name='lightconv_layer',
ext_modules=[
CUDAExtension('lightconv_cuda', [
'lightconv_cuda.cpp',
'lightconv_cuda_kernel.cu',
]),
],
cmdclass={
'build_ext': BuildExtension
})
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/lightconv_layer/setup.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
def gen_forward():
kernels = [3, 5, 7, 15, 31, 63, 127, 255]
blocks = [32, 64, 128, 256]
head = """
/**
* Copyright (c) Facebook, Inc. and its affiliates.
*
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.
*/
#include "dynamicconv_cuda.cuh"
std::vector<at::Tensor> dynamicconv_cuda_forward(at::Tensor input, at::Tensor weight, int padding_l) {
at::DeviceGuard g(input.device());
const auto minibatch = input.size(0);
const auto numFeatures = input.size(1);
const auto sequenceLength = input.size(2);
const auto numHeads = weight.size(1);
const auto filterSize = weight.size(2);
const auto numFiltersInBlock = numFeatures / numHeads;
const dim3 blocks(minibatch, numFeatures);
auto output = at::zeros_like(input);
auto stream = at::cuda::getCurrentCUDAStream();
"""
switch = """
switch(filterSize) {
"""
case_k = """
case {k}:
"""
main_block = """
if (padding_l == {pad}) {{
AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.scalar_type(), "dynamicconv_forward", ([&] {{
dynamicconv_forward_kernel<{k}, {b_size}, {pad}, scalar_t>
<<<blocks, {b_size}, 0, stream>>>(
input.data<scalar_t>(),
weight.data<scalar_t>(),
minibatch,
sequenceLength,
numFeatures,
numFiltersInBlock,
numHeads,
output.data<scalar_t>());
}}));
}} else
"""
bad_padding = """
{
std::cout << "WARNING: Unsupported padding size - skipping forward pass" << std::endl;
}
break;\n
"""
end = """
default:
std::cout << "WARNING: Unsupported filter length passed - skipping forward pass" << std::endl;
}
return {output};
}
"""
with open("dynamicconv_cuda_forward.cu", 'w') as forward:
forward.write(head)
forward.write(switch)
for k in kernels:
b_size = 32
for b in blocks:
if b > k:
b_size = b
break
forward.write(case_k.format(k=k))
for pad in [k // 2, k - 1]:
forward.write(main_block.format(k=k, b_size=b_size, pad=pad))
forward.write(bad_padding)
forward.write(end)
def gen_backward():
kernels = [3, 5, 7, 15, 31, 63, 127, 255]
thresh = [512, 512, 512, 512, 512, 380, 256, 256]
min_block = [64, 64, 64, 64, 64, 64, 128, 256]
seqs = [32 * x for x in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]]
head = """
/**
* Copyright (c) Facebook, Inc. and its affiliates.
*
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.
*/
#include "dynamicconv_cuda.cuh"
std::vector<at::Tensor> dynamicconv_cuda_backward(at::Tensor gradOutput, int padding_l, at::Tensor input, at::Tensor weight) {
at::DeviceGuard g(input.device());
const auto minibatch = input.size(0);
const auto numFeatures = input.size(1);
const auto sequenceLength = input.size(2);
const auto numHeads = weight.size(1);
const auto filterSize = weight.size(2);
const auto numFiltersInBlock = numFeatures / numHeads;
auto numChunks = 1;
auto gradInput = at::zeros_like(input);
auto gradWeight = at::zeros_like(weight);
auto stream = at::cuda::getCurrentCUDAStream();
dim3 blocks(minibatch, numHeads, numChunks);
"""
sequence_if = """
if (sequenceLength < {seq}) {{
switch(filterSize) {{
"""
case_k = """
case {k}:
"""
chunks_reset = """
numChunks = int(ceilf(sequenceLength/float({b_size})));
blocks = dim3(minibatch, numHeads, numChunks);
"""
main_block = """
if (padding_l == {p}) {{
AT_DISPATCH_FLOATING_TYPES_AND_HALF(gradOutput.scalar_type(), "dynamicconv_backward", ([&] {{
dynamicconv_backward_kernel<{k}, {b_size}, {p}, scalar_t>
<<<blocks, {b_size}, 0, stream>>>(
gradOutput.data<scalar_t>(),
input.data<scalar_t>(),
weight.data<scalar_t>(),
minibatch,
sequenceLength,
numFeatures,
numFiltersInBlock,
numHeads,
gradWeight.data<scalar_t>(),
gradInput.data<scalar_t>());
}}));
}} else
"""
bad_padding = """
{
std::cout << "WARNING: Unsupported padding size - skipping backward pass" << std::endl;
}
break;\n
"""
bad_filter = """
default:
std::cout << "WARNING: Unsupported filter length passed - skipping backward pass" << std::endl;
}
"""
con_else = """
} else
"""
final_else = """
{
switch(filterSize) {
"""
last_return = """
}
return {gradInput, gradWeight};
}
"""
with open("dynamicconv_cuda_backward.cu", 'w') as backward:
backward.write(head)
for seq in seqs:
backward.write(sequence_if.format(seq=seq))
for k, t, m in zip(kernels, thresh, min_block):
backward.write(case_k.format(k=k))
if seq <= t:
b_size = seq
else:
b_size = m
backward.write(chunks_reset.format(b_size=b_size))
for p in [k // 2, k - 1]:
backward.write(main_block.format(k=k, b_size=b_size, p=p))
backward.write(bad_padding)
backward.write(bad_filter)
backward.write(con_else)
backward.write(final_else)
for k, m in zip(kernels, min_block):
backward.write(case_k.format(k=k))
backward.write(chunks_reset.format(b_size=m))
for p in [k // 2, k - 1]:
backward.write(main_block.format(k=k, b_size=m, p=p))
backward.write(bad_padding)
backward.write(bad_filter)
backward.write(last_return)
if __name__ == "__main__":
gen_forward()
gen_backward()
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/dynamicconv_layer/cuda_function_gen.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .dynamicconv_layer import DynamicconvLayer # noqa
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/dynamicconv_layer/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from torch import nn
from torch.autograd import Function
import torch.nn.functional as F
import dynamicconv_cuda
from fairseq import utils
from fairseq.modules.unfold import unfold1d
class dynamicconvFunction(Function):
@staticmethod
def forward(ctx, x, weights, padding_l):
ctx.padding_l = padding_l
outputs = dynamicconv_cuda.forward(x, weights, padding_l)
variables = [x, weights]
ctx.save_for_backward(*variables)
return outputs[0]
@staticmethod
def backward(ctx, grad_output):
outputs = dynamicconv_cuda.backward(
grad_output.contiguous(),
ctx.padding_l,
*ctx.saved_variables)
grad_input, grad_weights = outputs
return grad_input, grad_weights, None
class DynamicconvLayer(nn.Module):
def __init__(
self,
input_size,
kernel_size=1,
padding_l=None,
weight_softmax=False,
num_heads=1,
weight_dropout=0.,
bias=False,
renorm_padding=False,
conv_bias=False,
query_size=None):
super(DynamicconvLayer, self).__init__()
self.input_size = input_size
self.query_size = input_size if query_size is None else query_size
self.kernel_size = kernel_size
self.padding_l = padding_l
self.num_heads = num_heads
self.weight_softmax = weight_softmax
self.weight_dropout = weight_dropout
self.renorm_padding = renorm_padding
self.bias = bias
self.weight_linear = nn.Linear(input_size, num_heads * kernel_size, bias)
if conv_bias:
self.conv_bias = nn.Parameter(torch.Tensor(input_size))
else:
self.conv_bias = None
self.reset_parameters()
def reset_parameters(self):
nn.init.xavier_uniform_(self.weight_linear.weight)
if self.conv_bias is not None:
nn.init.constant_(self.conv_bias, 0.)
nn.init.constant_(self.weight_linaer.bias, 0.)
def forward(self, x, incremental_state=None, query=None, unfold=None):
T, B, C = x.size()
K, H = self.kernel_size, self.num_heads
# R = C // H
# during inference time, incremental BMM is faster
if incremental_state is not None:
unfold = x.size(0) > 512 if unfold is None else unfold # use unfold mode as default for long sequence to save memory
unfold = unfold or (incremental_state is not None)
assert query is None
if query is None:
query = x
if unfold:
output = self._forward_unfolded(x, incremental_state, query)
else:
output = self._forward_expanded(x, incremental_state, query)
if self.conv_bias is not None:
output = output + self.conv_bias.view(1, 1, -1)
return output
# during training time, use CUDA kernel
else:
weight = self.weight_linear(x).view(T, B, H, K)
if self.weight_softmax:
weight = F.softmax(weight, dim=-1)
if self.weight_dropout:
weight = F.dropout(weight, self.weight_dropout, training=self.training)
weight = weight.permute(1, 2, 3, 0).contiguous()
self.filters = weight
x = x.permute(1, 2, 0).contiguous()
output = dynamicconvFunction.apply(x, weight, self.padding_l).permute(2, 0, 1)
if self.conv_bias is not None:
output = output + self.conv_bias.view(1, 1, -1)
return output
def reorder_incremental_state(self, incremental_state, new_order):
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is not None:
input_buffer = input_buffer.index_select(1, new_order)
self._set_input_buffer(incremental_state, input_buffer)
def _get_input_buffer(self, incremental_state):
return utils.get_incremental_state(self, incremental_state, 'input_buffer')
def _set_input_buffer(self, incremental_state, new_buffer):
return utils.set_incremental_state(self, incremental_state, 'input_buffer', new_buffer)
def _forward_unfolded(self, x, incremental_state, query):
'''The conventional implementation of convolutions.
Unfolding the input by having a window shifting to the right.'''
T, B, C = x.size()
K, H = self.kernel_size, self.num_heads
R = C // H
assert R * H == C == self.input_size
weight = self.weight_linear(query).view(T*B*H, -1)
# renorm_padding is only implemented in _forward_expanded
assert not self.renorm_padding or incremental_state is not None
if incremental_state is not None:
input_buffer = self._get_input_buffer(incremental_state)
if input_buffer is None:
input_buffer = x.new()
x_unfold = torch.cat([input_buffer, x.unsqueeze(3)], dim=3)
if self.kernel_size > 1:
self._set_input_buffer(incremental_state, x_unfold[:, :, :, -self.kernel_size+1:])
x_unfold = x_unfold.view(T*B*H, R, -1)
else:
padding_l = self.padding_l
if K > T and padding_l == K-1:
weight = weight.narrow(1, K-T, T)
K, padding_l = T, T-1
# unfold the input: T x B x C --> T' x B x C x K
x_unfold = unfold1d(x, K, padding_l, 0)
x_unfold = x_unfold.view(T*B*H, R, K)
if self.weight_softmax and not self.renorm_padding:
weight = F.softmax(weight, dim=1)
weight = weight.narrow(1, 0, K)
if incremental_state is not None:
weight = weight[:, -x_unfold.size(2):]
K = weight.size(1)
if self.weight_softmax and self.renorm_padding:
weight = F.softmax(weight, dim=1)
weight = F.dropout(weight, self.weight_dropout, training=self.training, inplace=False)
output = torch.bmm(x_unfold, weight.unsqueeze(2)) # T*B*H x R x 1
output = output.view(T, B, C)
return output
def _forward_expanded(self, x, incremental_stat, query):
'''Turn the convolution filters into band matrices and do matrix multiplication.
This is faster when the sequence is short, but less memory efficient.
This is not used in the decoder during inference.
'''
T, B, C = x.size()
K, H = self.kernel_size, self.num_heads
R = C // H
assert R * H == C == self.input_size
weight = self.weight_linear(query).view(T*B*H, -1)
if not self.renorm_padding:
if self.weight_softmax:
weight = F.softmax(weight, dim=1)
weight = F.dropout(weight, self.weight_dropout, training=self.training, inplace=False)
weight = weight.narrow(1, 0, K).contiguous()
weight = weight.view(T, B*H, K).transpose(0, 1)
x = x.view(T, B*H, R).transpose(0, 1)
if self.weight_softmax and self.renorm_padding:
# turn the convolution filters into band matrices
weight_expanded = weight.new(B*H, T, T+K-1).fill_(float('-inf'))
weight_expanded.as_strided((B*H, T, K), (T*(T+K-1), T+K, 1)).copy_(weight)
weight_expanded = weight_expanded.narrow(2, self.padding_l, T)
# normalize the weight over valid positions like self-attention
weight_expanded = F.softmax(weight_expanded, dim=2)
weight_expanded = F.dropout(weight_expanded, self.weight_dropout, training=self.training, inplace=False)
else:
P = self.padding_l
# For efficieny, we cut the kernel size and reduce the padding when the kernel is larger than the length
if K > T and P == K-1:
weight = weight.narrow(2, K-T, T)
K, P = T, T-1
# turn the convolution filters into band matrices
weight_expanded = weight.new_zeros(B*H, T, T+K-1, requires_grad=False)
weight_expanded.as_strided((B*H, T, K), (T*(T+K-1), T+K, 1)).copy_(weight)
weight_expanded = weight_expanded.narrow(2, P, T) # B*H x T x T
output = torch.bmm(weight_expanded, x)
output = output.transpose(0, 1).contiguous().view(T, B, C)
return output
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/dynamicconv_layer/dynamicconv_layer.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from setuptools import setup
from torch.utils.cpp_extension import CUDAExtension, BuildExtension
setup(
name='dynamicconv_layer',
ext_modules=[
CUDAExtension(
name='dynamicconv_cuda',
sources=[
'dynamicconv_cuda.cpp',
'dynamicconv_cuda_kernel.cu',
],
),
],
cmdclass={
'build_ext': BuildExtension
})
| data2vec_vision-main | infoxlm/fairseq/fairseq/modules/dynamicconv_layer/setup.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import BaseWrapperDataset
class OffsetTokensDataset(BaseWrapperDataset):
def __init__(self, dataset, offset):
super().__init__(dataset)
self.offset = offset
def __getitem__(self, idx):
return self.dataset[idx] + self.offset
| data2vec_vision-main | infoxlm/fairseq/fairseq/data/offset_tokens_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from collections import OrderedDict
import torch
from torch.utils.data.dataloader import default_collate
from . import FairseqDataset
def _flatten(dico, prefix=None):
"""Flatten a nested dictionary."""
new_dico = OrderedDict()
if isinstance(dico, dict):
prefix = prefix + '.' if prefix is not None else ''
for k, v in dico.items():
if v is None:
continue
new_dico.update(_flatten(v, prefix + k))
elif isinstance(dico, list):
for i, v in enumerate(dico):
new_dico.update(_flatten(v, prefix + '.[' + str(i) + ']'))
else:
new_dico = OrderedDict({prefix: dico})
return new_dico
def _unflatten(dico):
"""Unflatten a flattened dictionary into a nested dictionary."""
new_dico = OrderedDict()
for full_k, v in dico.items():
full_k = full_k.split('.')
node = new_dico
for k in full_k[:-1]:
if k.startswith('[') and k.endswith(']'):
k = int(k[1:-1])
if k not in node:
node[k] = OrderedDict()
node = node[k]
node[full_k[-1]] = v
return new_dico
class NestedDictionaryDataset(FairseqDataset):
def __init__(self, defn, sizes=None):
super().__init__()
self.defn = _flatten(defn)
self.sizes = [sizes] if not isinstance(sizes, (list, tuple)) else sizes
first = None
for v in self.defn.values():
if not isinstance(v, (FairseqDataset, torch.utils.data.Dataset, )):
raise ValueError('Expected Dataset but found: {}'.format(v.__class__))
first = first or v
if len(v) > 0:
assert len(v) == len(first), 'dataset lengths must match'
self._len = len(first)
def __getitem__(self, index):
return OrderedDict((k, ds[index]) for k, ds in self.defn.items())
def __len__(self):
return self._len
def collater(self, samples):
"""Merge a list of samples to form a mini-batch.
Args:
samples (List[dict]): samples to collate
Returns:
dict: a mini-batch suitable for forwarding with a Model
"""
if len(samples) == 0:
return {}
sample = OrderedDict()
for k, ds in self.defn.items():
try:
sample[k] = ds.collater([s[k] for s in samples])
except NotImplementedError:
sample[k] = default_collate([s[k] for s in samples])
return _unflatten(sample)
def num_tokens(self, index):
"""Return the number of tokens in a sample. This value is used to
enforce ``--max-tokens`` during batching."""
return max(s[index] for s in self.sizes)
def size(self, index):
"""Return an example's size as a float or tuple. This value is used when
filtering a dataset with ``--max-positions``."""
if len(self.sizes) == 1:
return self.sizes[0][index]
else:
return (s[index] for s in self.sizes)
@property
def supports_prefetch(self):
"""Whether this dataset supports prefetching."""
return any(ds.supports_prefetch for ds in self.defn.values())
def prefetch(self, indices):
"""Prefetch the data required for this epoch."""
for ds in self.defn.values():
if getattr(ds, 'supports_prefetch', False):
ds.prefetch(indices)
def set_epoch(self, epoch):
super().set_epoch(epoch)
for ds in self.defn.values():
ds.set_epoch(epoch)
| data2vec_vision-main | infoxlm/fairseq/fairseq/data/nested_dictionary_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
from . import BaseWrapperDataset
class TruncateDataset(BaseWrapperDataset):
def __init__(self, dataset, truncation_length):
super().__init__(dataset)
assert truncation_length is not None
self.truncation_length = truncation_length
self.dataset = dataset
def __getitem__(self, index):
item = self.dataset[index]
item_len = item.size(0)
if item_len > self.truncation_length:
item = item[:self.truncation_length]
return item
@property
def sizes(self):
return np.minimum(self.dataset.sizes, self.truncation_length)
def __len__(self):
return len(self.dataset)
| data2vec_vision-main | infoxlm/fairseq/fairseq/data/truncate_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import bisect
import numpy as np
from torch.utils.data.dataloader import default_collate
from . import FairseqDataset
class ConcatDataset(FairseqDataset):
@staticmethod
def cumsum(sequence, sample_ratios):
r, s = [], 0
for e, ratio in zip(sequence, sample_ratios):
curr_len = int(ratio * len(e))
r.append(curr_len + s)
s += curr_len
return r
def __init__(self, datasets, sample_ratios=1):
super(ConcatDataset, self).__init__()
assert len(datasets) > 0, "datasets should not be an empty iterable"
self.datasets = list(datasets)
if isinstance(sample_ratios, int):
sample_ratios = [sample_ratios] * len(self.datasets)
self.sample_ratios = sample_ratios
self.cumulative_sizes = self.cumsum(self.datasets, sample_ratios)
self.real_sizes = [len(d) for d in self.datasets]
def __len__(self):
return self.cumulative_sizes[-1]
def __getitem__(self, idx):
dataset_idx, sample_idx = self._get_dataset_and_sample_index(idx)
return self.datasets[dataset_idx][sample_idx]
def _get_dataset_and_sample_index(self, idx: int):
dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx)
if dataset_idx == 0:
sample_idx = idx
else:
sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]
sample_idx = sample_idx % self.real_sizes[dataset_idx]
return dataset_idx, sample_idx
def collater(self, samples):
# For now only supports datasets with same underlying collater implementations
if hasattr(self.datasets[0], 'collater'):
return self.datasets[0].collater(samples)
else:
return default_collate(samples)
def size(self, idx: int):
"""
Return an example's size as a float or tuple.
"""
dataset_idx, sample_idx = self._get_dataset_and_sample_index(idx)
return self.datasets[dataset_idx].size(sample_idx)
def num_tokens(self, index: int):
return np.max(self.size(index))
def attr(self, attr: str, index: int):
dataset_idx = bisect.bisect_right(self.cumulative_sizes, index)
return getattr(self.datasets[dataset_idx], attr, None)
@property
def sizes(self):
_dataset_sizes = []
for ds, sr in zip(self.datasets, self.sample_ratios):
if isinstance(ds.sizes, np.ndarray):
_dataset_sizes.append(np.tile(ds.sizes, sr))
else:
# Only support underlying dataset with single size array.
assert isinstance(ds.sizes, list)
_dataset_sizes.append(np.tile(ds.sizes[0], sr))
return np.concatenate(_dataset_sizes)
@property
def supports_prefetch(self):
return all(d.supports_prefetch for d in self.datasets)
def ordered_indices(self):
"""
Returns indices sorted by length. So less padding is needed.
"""
return np.argsort(self.sizes)
def prefetch(self, indices):
frm = 0
for to, ds in zip(self.cumulative_sizes, self.datasets):
real_size = len(ds)
if getattr(ds, 'supports_prefetch', False):
ds.prefetch([(i - frm) % real_size for i in indices if frm <= i < to])
frm = to
def set_epoch(self, epoch):
super().set_epoch(epoch)
for ds in self.datasets:
if hasattr(ds, 'set_epoch'):
ds.set_epoch(epoch)
| data2vec_vision-main | infoxlm/fairseq/fairseq/data/concat_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import BaseWrapperDataset
class ReplaceDataset(BaseWrapperDataset):
"""Replaces tokens found in the dataset by a specified replacement token
Args:
dataset (~torch.utils.data.Dataset): dataset to replace tokens in
replace_map(Dictionary[int,int]): map of token to replace -> replacement token
offsets (List[int]): do not replace tokens before (from left if pos, right if neg) this offset. should be
as many as the number of objects returned by the underlying dataset __getitem__ method.
"""
def __init__(self, dataset, replace_map, offsets):
super().__init__(dataset)
assert len(replace_map) > 0
self.replace_map = replace_map
self.offsets = offsets
def __getitem__(self, index):
item = self.dataset[index]
is_tuple = isinstance(item, tuple)
srcs = item if is_tuple else [item]
for offset, src in zip(self.offsets, srcs):
for k, v in self.replace_map.items():
src_off = src[offset:] if offset >= 0 else src[:offset]
src_off.masked_fill_(src_off == k, v)
item = srcs if is_tuple else srcs[0]
return item
| data2vec_vision-main | infoxlm/fairseq/fairseq/data/replace_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from fairseq import utils
from . import FairseqDataset
def backtranslate_samples(samples, collate_fn, generate_fn, cuda=True):
"""Backtranslate a list of samples.
Given an input (*samples*) of the form:
[{'id': 1, 'source': 'hallo welt'}]
this will return:
[{'id': 1, 'source': 'hello world', 'target': 'hallo welt'}]
Args:
samples (List[dict]): samples to backtranslate. Individual samples are
expected to have a 'source' key, which will become the 'target'
after backtranslation.
collate_fn (callable): function to collate samples into a mini-batch
generate_fn (callable): function to generate backtranslations
cuda (bool): use GPU for generation (default: ``True``)
Returns:
List[dict]: an updated list of samples with a backtranslated source
"""
collated_samples = collate_fn(samples)
s = utils.move_to_cuda(collated_samples) if cuda else collated_samples
generated_sources = generate_fn(s)
id_to_src = {
sample['id']: sample['source'] for sample in samples
}
# Go through each tgt sentence in batch and its corresponding best
# generated hypothesis and create a backtranslation data pair
# {id: id, source: generated backtranslation, target: original tgt}
return [
{'id': id.item(), 'target': id_to_src[id.item()], 'source': hypos[0]['tokens'].cpu()}
for id, hypos in zip(collated_samples['id'], generated_sources)
]
class BacktranslationDataset(FairseqDataset):
"""
Sets up a backtranslation dataset which takes a tgt batch, generates
a src using a tgt-src backtranslation function (*backtranslation_fn*),
and returns the corresponding `{generated src, input tgt}` batch.
Args:
tgt_dataset (~fairseq.data.FairseqDataset): the dataset to be
backtranslated. Only the source side of this dataset will be used.
After backtranslation, the source sentences in this dataset will be
returned as the targets.
src_dict (~fairseq.data.Dictionary): the dictionary of backtranslated
sentences.
tgt_dict (~fairseq.data.Dictionary, optional): the dictionary of
sentences to be backtranslated.
backtranslation_fn (callable, optional): function to call to generate
backtranslations. This is typically the `generate` method of a
:class:`~fairseq.sequence_generator.SequenceGenerator` object.
Pass in None when it is not available at initialization time, and
use set_backtranslation_fn function to set it when available.
output_collater (callable, optional): function to call on the
backtranslated samples to create the final batch
(default: ``tgt_dataset.collater``).
cuda: use GPU for generation
"""
def __init__(
self,
tgt_dataset,
src_dict,
tgt_dict=None,
backtranslation_fn=None,
output_collater=None,
cuda=True,
**kwargs
):
self.tgt_dataset = tgt_dataset
self.backtranslation_fn = backtranslation_fn
self.output_collater = output_collater if output_collater is not None \
else tgt_dataset.collater
self.cuda = cuda if torch.cuda.is_available() else False
self.src_dict = src_dict
self.tgt_dict = tgt_dict
def __getitem__(self, index):
"""
Returns a single sample from *tgt_dataset*. Note that backtranslation is
not applied in this step; use :func:`collater` instead to backtranslate
a batch of samples.
"""
return self.tgt_dataset[index]
def __len__(self):
return len(self.tgt_dataset)
def set_backtranslation_fn(self, backtranslation_fn):
self.backtranslation_fn = backtranslation_fn
def collater(self, samples):
"""Merge and backtranslate a list of samples to form a mini-batch.
Using the samples from *tgt_dataset*, load a collated target sample to
feed to the backtranslation model. Then take the backtranslation with
the best score as the source and the original input as the target.
Note: we expect *tgt_dataset* to provide a function `collater()` that
will collate samples into the format expected by *backtranslation_fn*.
After backtranslation, we will feed the new list of samples (i.e., the
`(backtranslated source, original source)` pairs) to *output_collater*
and return the result.
Args:
samples (List[dict]): samples to backtranslate and collate
Returns:
dict: a mini-batch with keys coming from *output_collater*
"""
if samples[0].get('is_dummy', False):
return samples
samples = backtranslate_samples(
samples=samples,
collate_fn=self.tgt_dataset.collater,
generate_fn=(
lambda net_input: self.backtranslation_fn(net_input)
),
cuda=self.cuda,
)
return self.output_collater(samples)
def num_tokens(self, index):
"""Just use the tgt dataset num_tokens"""
return self.tgt_dataset.num_tokens(index)
def ordered_indices(self):
"""Just use the tgt dataset ordered_indices"""
return self.tgt_dataset.ordered_indices()
def size(self, index):
"""Return an example's size as a float or tuple. This value is used
when filtering a dataset with ``--max-positions``.
Note: we use *tgt_dataset* to approximate the length of the source
sentence, since we do not know the actual length until after
backtranslation.
"""
tgt_size = self.tgt_dataset.size(index)[0]
return (tgt_size, tgt_size)
@property
def supports_prefetch(self):
return getattr(self.tgt_dataset, 'supports_prefetch', False)
def prefetch(self, indices):
return self.tgt_dataset.prefetch(indices)
| data2vec_vision-main | infoxlm/fairseq/fairseq/data/backtranslation_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from . import FairseqDataset
class IdDataset(FairseqDataset):
def __getitem__(self, index):
return index
def __len__(self):
return 0
def collater(self, samples):
return torch.tensor(samples)
| data2vec_vision-main | infoxlm/fairseq/fairseq/data/id_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from . import BaseWrapperDataset
class PrependDataset(BaseWrapperDataset):
def __init__(self, dataset, prepend_getter, ensure_first_token_is=None):
super().__init__(dataset)
self.prepend_getter = prepend_getter
self.ensure_first_token = ensure_first_token_is
def __getitem__(self, idx):
item = self.dataset[idx]
is_tuple = isinstance(item, tuple)
src = item[0] if is_tuple else item
assert self.ensure_first_token is None or src[0] == self.ensure_first_token
prepend_idx = self.prepend_getter(self.dataset, idx)
assert isinstance(prepend_idx, int)
src[0] = prepend_idx
item = tuple((src,) + item[1:]) if is_tuple else src
return item
| data2vec_vision-main | infoxlm/fairseq/fairseq/data/prepend_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from collections import OrderedDict
from typing import Callable, Dict, List
import numpy as np
from . import FairseqDataset
def uniform_sampler(x):
# Sample from uniform distribution
return np.random.choice(x, 1).item()
class MultiCorpusSampledDataset(FairseqDataset):
"""
Stores multiple instances of FairseqDataset together and in every iteration
creates a batch by first sampling a dataset according to a specified
probability distribution and then getting instances from that dataset.
Args:
datasets: an OrderedDict of FairseqDataset instances.
sampling_func: A function for sampling over list of dataset keys.
Default strategy is to sample uniformly.
"""
def __init__(
self,
datasets: Dict[str, FairseqDataset],
sampling_func: Callable[[List], int] = None,
):
super().__init__()
assert isinstance(datasets, OrderedDict)
self.datasets = datasets
if sampling_func is None:
sampling_func = uniform_sampler
self.sampling_func = sampling_func
self.total_num_instances = 0
for _, dataset in datasets.items():
assert isinstance(dataset, FairseqDataset)
self.total_num_instances += dataset.__len__()
self._ordered_indices = None
def __len__(self):
"""
Length of this dataset is the sum of individual datasets
"""
return self.total_num_instances
def ordered_indices(self):
"""
Ordered indices for batching. Here we call the underlying
dataset's ordered_indices() so that we get the same random ordering
as we would have from using the underlying dataset directly.
"""
if self._ordered_indices is None:
self._ordered_indices = OrderedDict(
[
(key, dataset.ordered_indices())
for key, dataset in self.datasets.items()
]
)
return np.arange(len(self))
def _map_index_to_dataset(self, key: int, index: int):
"""
Different underlying datasets have different lengths. In order to ensure
we are not accessing an index outside the range of the current dataset
size, we wrap around. This function should be called after we have
created an ordering for this and all underlying datasets.
"""
assert (
self._ordered_indices is not None
), "Must call MultiCorpusSampledDataset.ordered_indices() first"
mapped_index = index % len(self.datasets[key])
return self._ordered_indices[key][mapped_index]
def __getitem__(self, index: int):
"""
Get the item associated with index from each underlying dataset.
Since index is in the range of [0, TotalNumInstances], we need to
map the index to the dataset before retrieving the item.
"""
return OrderedDict(
[
(key, dataset[self._map_index_to_dataset(key, index)])
for key, dataset in self.datasets.items()
]
)
def collater(self, samples: List[Dict]):
"""
Generate a mini-batch for this dataset.
To convert this into a regular mini-batch we use the following
logic:
1. Select a dataset using the specified probability distribution.
2. Call the collater function of the selected dataset.
"""
if len(samples) == 0:
return None
selected_key = self.sampling_func(list(self.datasets.keys()))
selected_samples = [sample[selected_key] for sample in samples]
return self.datasets[selected_key].collater(selected_samples)
def num_tokens(self, index: int):
"""
Return an example's length (number of tokens), used for batching. Here
we return the max across all examples at index across all underlying
datasets.
"""
return max(
dataset.num_tokens(self._map_index_to_dataset(key, index))
for key, dataset in self.datasets.items()
)
def size(self, index: int):
"""
Return an example's size as a float or tuple. Here we return the max
across all underlying datasets. This value is used when filtering a
dataset with max-positions.
"""
return max(
dataset.size(self._map_index_to_dataset(key, index))
for key, dataset in self.datasets.items()
)
@property
def supports_prefetch(self):
return all(
getattr(dataset, "supports_prefetch", False)
for dataset in self.datasets.values()
)
def prefetch(self, indices):
for key, dataset in self.datasets.items():
dataset.prefetch(
[self._map_index_to_dataset(key, index) for index in indices]
)
| data2vec_vision-main | infoxlm/fairseq/fairseq/data/multi_corpus_sampled_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import FairseqDataset
class NumSamplesDataset(FairseqDataset):
def __getitem__(self, index):
return 1
def __len__(self):
return 0
def collater(self, samples):
return sum(samples)
| data2vec_vision-main | infoxlm/fairseq/fairseq/data/num_samples_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import numpy as np
from fairseq.data import data_utils
class WordNoising(object):
"""Generate a noisy version of a sentence, without changing words themselves."""
def __init__(self, dictionary, bpe_cont_marker="@@", bpe_end_marker=None):
self.dictionary = dictionary
self.bpe_end = None
if bpe_cont_marker:
self.bpe_end = np.array([
not self.dictionary[i].endswith(bpe_cont_marker)
for i in range(len(self.dictionary))
])
elif bpe_end_marker:
self.bpe_end = np.array([
self.dictionary[i].endswith(bpe_end_marker)
for i in range(len(self.dictionary))
])
self.get_word_idx = (
self._get_bpe_word_idx
if self.bpe_end is not None
else self._get_token_idx
)
def noising(self, x, lengths, noising_prob=0.0):
raise NotImplementedError()
def _get_bpe_word_idx(self, x):
"""
Given a list of BPE tokens, for every index in the tokens list,
return the index of the word grouping that it belongs to.
For example, for input x corresponding to ["how", "are", "y@@", "ou"],
return [[0], [1], [2], [2]].
"""
# x: (T x B)
bpe_end = self.bpe_end[x]
if (x.size(0) == 1 and x.size(1) == 1):
# Special case when we only have one word in x. If x = [[N]],
# bpe_end is a scalar (bool) instead of a 2-dim array of bools,
# which makes the sum operation below fail.
return np.array([[0]])
# do a reduce front sum to generate word ids
word_idx = bpe_end[::-1].cumsum(0)[::-1]
word_idx = word_idx.max(0)[None, :] - word_idx
return word_idx
def _get_token_idx(self, x):
"""
This is to extend noising functions to be able to apply to non-bpe
tokens, e.g. word or characters.
"""
x = torch.t(x)
word_idx = np.array([range(len(x_i)) for x_i in x])
return np.transpose(word_idx)
class WordDropout(WordNoising):
"""Randomly drop input words. If not passing blank_idx (default is None),
then dropped words will be removed. Otherwise, it will be replaced by the
blank_idx."""
def __init__(self, dictionary, default_dropout_prob=0.1, bpe_cont_marker="@@", bpe_end_marker=None):
super().__init__(dictionary, bpe_cont_marker, bpe_end_marker)
self.default_dropout_prob = default_dropout_prob
def noising(self, x, lengths, dropout_prob=None, blank_idx=None):
if dropout_prob is None:
dropout_prob = self.default_dropout_prob
# x: (T x B), lengths: B
if dropout_prob == 0:
return x, lengths
assert 0 < dropout_prob < 1
# be sure to drop entire words
word_idx = self.get_word_idx(x)
sentences = []
modified_lengths = []
for i in range(lengths.size(0)):
# Since dropout probabilities need to apply over non-pad tokens,
# it is not trivial to generate the keep mask without consider
# input lengths; otherwise, this could be done outside the loop
# We want to drop whole words based on word_idx grouping
num_words = max(word_idx[:, i]) + 1
# ith example: [x0, x1, ..., eos, pad, ..., pad]
# We should only generate keep probs for non-EOS tokens. Thus if the
# input sentence ends in EOS, the last word idx is not included in
# the dropout mask generation and we append True to always keep EOS.
# Otherwise, just generate the dropout mask for all word idx
# positions.
has_eos = x[lengths[i] - 1, i] == self.dictionary.eos()
if has_eos: # has eos?
keep = np.random.rand(num_words - 1) >= dropout_prob
keep = np.append(keep, [True]) # keep EOS symbol
else:
keep = np.random.rand(num_words) >= dropout_prob
words = x[:lengths[i], i].tolist()
# TODO: speed up the following loop
# drop words from the input according to keep
new_s = [
w if keep[word_idx[j, i]] else blank_idx
for j, w in enumerate(words)
]
new_s = [w for w in new_s if w is not None]
# we need to have at least one word in the sentence (more than the
# start / end sentence symbols)
if len(new_s) <= 1:
# insert at beginning in case the only token left is EOS
# EOS should be at end of list.
new_s.insert(0, words[np.random.randint(0, len(words))])
assert len(new_s) >= 1 and (
not has_eos # Either don't have EOS at end or last token is EOS
or (len(new_s) >= 2 and new_s[-1] == self.dictionary.eos())
), "New sentence is invalid."
sentences.append(new_s)
modified_lengths.append(len(new_s))
# re-construct input
modified_lengths = torch.LongTensor(modified_lengths)
modified_x = torch.LongTensor(
modified_lengths.max(),
modified_lengths.size(0)
).fill_(self.dictionary.pad())
for i in range(modified_lengths.size(0)):
modified_x[:modified_lengths[i], i].copy_(torch.LongTensor(sentences[i]))
return modified_x, modified_lengths
class WordShuffle(WordNoising):
"""Shuffle words by no more than k positions."""
def __init__(self, dictionary, default_max_shuffle_distance=3, bpe_cont_marker="@@", bpe_end_marker=None):
super().__init__(dictionary, bpe_cont_marker, bpe_end_marker)
self.default_max_shuffle_distance = 3
def noising(self, x, lengths, max_shuffle_distance=None):
if max_shuffle_distance is None:
max_shuffle_distance = self.default_max_shuffle_distance
# x: (T x B), lengths: B
if max_shuffle_distance == 0:
return x, lengths
# max_shuffle_distance < 1 will return the same sequence
assert max_shuffle_distance > 1
# define noise word scores
noise = np.random.uniform(
0,
max_shuffle_distance,
size=(x.size(0), x.size(1)),
)
noise[0] = -1 # do not move start sentence symbol
# be sure to shuffle entire words
word_idx = self.get_word_idx(x)
x2 = x.clone()
for i in range(lengths.size(0)):
length_no_eos = lengths[i]
if x[lengths[i] - 1, i] == self.dictionary.eos():
length_no_eos = lengths[i] - 1
# generate a random permutation
scores = word_idx[:length_no_eos, i] + noise[word_idx[:length_no_eos, i], i]
# ensure no reordering inside a word
scores += 1e-6 * np.arange(length_no_eos)
permutation = scores.argsort()
# shuffle words
x2[:length_no_eos, i].copy_(
x2[:length_no_eos, i][torch.from_numpy(permutation)]
)
return x2, lengths
class UnsupervisedMTNoising(WordNoising):
"""
Implements the default configuration for noising in UnsupervisedMT
(github.com/facebookresearch/UnsupervisedMT)
"""
def __init__(
self,
dictionary,
max_word_shuffle_distance,
word_dropout_prob,
word_blanking_prob,
bpe_cont_marker="@@",
bpe_end_marker=None,
):
super().__init__(dictionary)
self.max_word_shuffle_distance = max_word_shuffle_distance
self.word_dropout_prob = word_dropout_prob
self.word_blanking_prob = word_blanking_prob
self.word_dropout = WordDropout(
dictionary=dictionary,
bpe_cont_marker=bpe_cont_marker,
bpe_end_marker=bpe_end_marker,
)
self.word_shuffle = WordShuffle(
dictionary=dictionary,
bpe_cont_marker=bpe_cont_marker,
bpe_end_marker=bpe_end_marker,
)
def noising(self, x, lengths):
# 1. Word Shuffle
noisy_src_tokens, noisy_src_lengths = self.word_shuffle.noising(
x=x,
lengths=lengths,
max_shuffle_distance=self.max_word_shuffle_distance,
)
# 2. Word Dropout
noisy_src_tokens, noisy_src_lengths = self.word_dropout.noising(
x=noisy_src_tokens,
lengths=noisy_src_lengths,
dropout_prob=self.word_dropout_prob,
)
# 3. Word Blanking
noisy_src_tokens, noisy_src_lengths = self.word_dropout.noising(
x=noisy_src_tokens,
lengths=noisy_src_lengths,
dropout_prob=self.word_blanking_prob,
blank_idx=self.dictionary.unk(),
)
return noisy_src_tokens
class NoisingDataset(torch.utils.data.Dataset):
def __init__(
self,
src_dataset,
src_dict,
seed,
noiser=None,
noising_class=UnsupervisedMTNoising,
**kwargs
):
"""
Wrap a :class:`~torch.utils.data.Dataset` and apply noise to the
samples based on the supplied noising configuration.
Args:
src_dataset (~torch.utils.data.Dataset): dataset to wrap.
to build self.src_dataset --
a LanguagePairDataset with src dataset as the source dataset and
None as the target dataset. Should NOT have padding so that
src_lengths are accurately calculated by language_pair_dataset
collate function.
We use language_pair_dataset here to encapsulate the tgt_dataset
so we can re-use the LanguagePairDataset collater to format the
batches in the structure that SequenceGenerator expects.
src_dict (~fairseq.data.Dictionary): source dictionary
seed (int): seed to use when generating random noise
noiser (WordNoising): a pre-initialized :class:`WordNoising`
instance. If this is None, a new instance will be created using
*noising_class* and *kwargs*.
noising_class (class, optional): class to use to initialize a
default :class:`WordNoising` instance.
kwargs (dict, optional): arguments to initialize the default
:class:`WordNoising` instance given by *noiser*.
"""
self.src_dataset = src_dataset
self.src_dict = src_dict
self.seed = seed
self.noiser = noiser if noiser is not None else noising_class(
dictionary=src_dict, **kwargs,
)
def __getitem__(self, index):
"""
Returns a single noisy sample. Multiple samples are fed to the collater
create a noising dataset batch.
"""
src_tokens = self.src_dataset[index]
src_lengths = torch.LongTensor([len(src_tokens)])
src_tokens = src_tokens.unsqueeze(0)
# Transpose src tokens to fit expected shape of x in noising function
# (batch size, sequence length) -> (sequence length, batch size)
src_tokens_t = torch.t(src_tokens)
with data_utils.numpy_seed(self.seed + index):
noisy_src_tokens = self.noiser.noising(src_tokens_t, src_lengths)
# Transpose back to expected src_tokens format
# (sequence length, 1) -> (1, sequence length)
noisy_src_tokens = torch.t(noisy_src_tokens)
return noisy_src_tokens[0]
def __len__(self):
"""
The length of the noising dataset is the length of src.
"""
return len(self.src_dataset)
@property
def supports_prefetch(self):
return self.src_dataset.supports_prefetch
def prefetch(self, indices):
if self.src_dataset.supports_prefetch:
self.src_dataset.prefetch(indices)
| data2vec_vision-main | infoxlm/fairseq/fairseq/data/noising.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
from . import BaseWrapperDataset
class SubsampleDataset(BaseWrapperDataset):
"""Subsamples a given dataset by a specified ratio. Subsampling is done on the number of examples
Args:
dataset (~torch.utils.data.Dataset): dataset to subsample
size_ratio(float): the ratio to subsample to. must be between 0 and 1 (exclusive)
"""
def __init__(self, dataset, size_ratio):
super().__init__(dataset)
assert size_ratio < 1
self.actual_size = np.ceil(len(dataset) * size_ratio).astype(int)
self.indices = np.random.choice(
list(range(len(self.dataset))), self.actual_size, replace=False
)
print(
"subsampled dataset from {} to {} (ratio={})".format(
len(self.dataset), self.actual_size, size_ratio
)
)
def __getitem__(self, index):
return self.dataset[self.indices[index]]
def __len__(self):
return self.actual_size
def collater(self, samples):
return self.dataset.collater(samples)
@property
def sizes(self):
return self.dataset.sizes[self.indices]
@property
def name(self):
return self.dataset.name
def num_tokens(self, index):
return self.dataset.num_tokens(self.indices[index])
def size(self, index):
return self.dataset.size(self.indices[index])
def ordered_indices(self):
"""Return an ordered list of indices. Batches will be constructed based
on this order."""
if self.shuffle:
order = [np.random.permutation(len(self))]
else:
order = [np.arange(len(self))]
order.append(self.sizes)
return np.lexsort(order)
def prefetch(self, indices):
self.dataset.prefetch(self.indices[indices])
| data2vec_vision-main | infoxlm/fairseq/fairseq/data/subsample_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
from . import BaseWrapperDataset
class SortDataset(BaseWrapperDataset):
def __init__(self, dataset, sort_order):
super().__init__(dataset)
if not isinstance(sort_order, (list, tuple)):
sort_order = [sort_order]
self.sort_order = sort_order
assert all(len(so) == len(dataset) for so in sort_order)
def ordered_indices(self):
return np.lexsort(self.sort_order)
| data2vec_vision-main | infoxlm/fairseq/fairseq/data/sort_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from . import data_utils, FairseqDataset
def collate(samples, pad_idx, eos_idx):
if len(samples) == 0:
return {}
def merge(key, is_list=False):
if is_list:
res = []
for i in range(len(samples[0][key])):
res.append(data_utils.collate_tokens(
[s[key][i] for s in samples], pad_idx, eos_idx, left_pad=False,
))
return res
else:
return data_utils.collate_tokens(
[s[key] for s in samples], pad_idx, eos_idx, left_pad=False,
)
src_tokens = merge('source')
if samples[0]['target'] is not None:
is_target_list = isinstance(samples[0]['target'], list)
target = merge('target', is_target_list)
else:
target = src_tokens
return {
'id': torch.LongTensor([s['id'] for s in samples]),
'nsentences': len(samples),
'ntokens': sum(len(s['source']) for s in samples),
'net_input': {
'src_tokens': src_tokens,
'src_lengths': torch.LongTensor([
s['source'].numel() for s in samples
]),
},
'target': target,
}
class MonolingualDataset(FairseqDataset):
"""
A wrapper around torch.utils.data.Dataset for monolingual data.
Args:
dataset (torch.utils.data.Dataset): dataset to wrap
sizes (List[int]): sentence lengths
vocab (~fairseq.data.Dictionary): vocabulary
shuffle (bool, optional): shuffle the elements before batching
(default: True).
"""
def __init__(self, dataset, sizes, src_vocab, tgt_vocab, add_eos_for_other_targets, shuffle,
targets=None, add_bos_token=False):
self.dataset = dataset
self.sizes = np.array(sizes)
self.vocab = src_vocab
self.tgt_vocab = tgt_vocab
self.add_eos_for_other_targets = add_eos_for_other_targets
self.shuffle = shuffle
self.add_bos_token = add_bos_token
assert targets is None or all(t in {'self', 'future', 'past'} for t in targets), \
"targets must be none or one of 'self', 'future', 'past'"
if targets is not None and len(targets) == 0:
targets = None
self.targets = targets
def __getitem__(self, index):
if self.targets is not None:
# *future_target* is the original sentence
# *source* is shifted right by 1 (maybe left-padded with eos)
# *past_target* is shifted right by 2 (left-padded as needed)
#
# Left-to-right language models should condition on *source* and
# predict *future_target*.
# Right-to-left language models should condition on *source* and
# predict *past_target*.
source, future_target, past_target = self.dataset[index]
source, target = self._make_source_target(source, future_target, past_target)
else:
source = self.dataset[index]
target = None
source, target = self._maybe_add_bos(source, target)
return {'id': index, 'source': source, 'target': target}
def __len__(self):
return len(self.dataset)
def _make_source_target(self, source, future_target, past_target):
if self.targets is not None:
target = []
if self.add_eos_for_other_targets and (('self' in self.targets) or ('past' in self.targets)) \
and source[-1] != self.vocab.eos():
# append eos at the end of source
source = torch.cat([source, source.new([self.vocab.eos()])])
if 'future' in self.targets:
future_target = torch.cat([future_target, future_target.new([self.vocab.pad()])])
if 'past' in self.targets:
# first token is before the start of sentence which is only used in "none" break mode when
# add_eos_for_other_targets is False
past_target = torch.cat([past_target.new([self.vocab.pad()]), past_target[1:], source[-2, None]])
for t in self.targets:
if t == 'self':
target.append(source)
elif t == 'future':
target.append(future_target)
elif t == 'past':
target.append(past_target)
else:
raise Exception('invalid target ' + t)
if len(target) == 1:
target = target[0]
else:
target = future_target
return source, self._filter_vocab(target)
def _maybe_add_bos(self, source, target):
if self.add_bos_token:
source = torch.cat([source.new([self.vocab.bos()]), source])
if target is not None:
target = torch.cat([target.new([self.tgt_vocab.bos()]), target])
return source, target
def _filter_vocab(self, target):
if len(self.tgt_vocab) != len(self.vocab):
def _filter(target):
mask = target.ge(len(self.tgt_vocab))
if mask.any():
target[mask] = self.tgt_vocab.unk()
return target
if isinstance(target, list):
return [_filter(t) for t in target]
return _filter(target)
return target
def collater(self, samples):
"""Merge a list of samples to form a mini-batch.
Args:
samples (List[dict]): samples to collate
Returns:
dict: a mini-batch with the following keys:
- `id` (LongTensor): example IDs in the original input order
- `ntokens` (int): total number of tokens in the batch
- `net_input` (dict): the input to the Model, containing keys:
- `src_tokens` (LongTensor): a padded 2D Tensor of tokens in
the source sentence of shape `(bsz, src_len)`. Padding will
appear on the right.
- `target` (LongTensor): a padded 2D Tensor of tokens in the
target sentence of shape `(bsz, tgt_len)`. Padding will appear
on the right.
"""
return collate(samples, self.vocab.pad(), self.vocab.eos())
def num_tokens(self, index):
"""Return the number of tokens in a sample. This value is used to
enforce ``--max-tokens`` during batching."""
return self.sizes[index]
def size(self, index):
"""Return an example's size as a float or tuple. This value is used when
filtering a dataset with ``--max-positions``."""
return self.sizes[index]
def ordered_indices(self):
"""Return an ordered list of indices. Batches will be constructed based
on this order."""
if self.shuffle:
order = [np.random.permutation(len(self))]
else:
order = [np.arange(len(self))]
order.append(self.sizes)
return np.lexsort(order)
@property
def supports_prefetch(self):
return getattr(self.dataset, 'supports_prefetch', False)
def prefetch(self, indices):
self.dataset.prefetch(indices)
| data2vec_vision-main | infoxlm/fairseq/fairseq/data/monolingual_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from torch.utils.data.dataloader import default_collate
from . import FairseqDataset
class BaseWrapperDataset(FairseqDataset):
def __init__(self, dataset):
super().__init__()
self.dataset = dataset
def __getitem__(self, index):
return self.dataset[index]
def __len__(self):
return len(self.dataset)
def collater(self, samples):
if hasattr(self.dataset, 'collater'):
return self.dataset.collater(samples)
else:
return default_collate(samples)
@property
def sizes(self):
return self.dataset.sizes
def num_tokens(self, index):
return self.dataset.num_tokens(index)
def size(self, index):
return self.dataset.size(index)
def ordered_indices(self):
return self.dataset.ordered_indices()
@property
def supports_prefetch(self):
return getattr(self.dataset, 'supports_prefetch', False)
def prefetch(self, indices):
self.dataset.prefetch(indices)
def set_epoch(self, epoch):
super().set_epoch(epoch)
if hasattr(self.dataset, 'set_epoch'):
self.dataset.set_epoch(epoch)
| data2vec_vision-main | infoxlm/fairseq/fairseq/data/base_wrapper_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from . import BaseWrapperDataset
class NumelDataset(BaseWrapperDataset):
def __init__(self, dataset, reduce=False):
super().__init__(dataset)
self.reduce = reduce
def __getitem__(self, index):
item = self.dataset[index]
if torch.is_tensor(item):
return torch.numel(item)
else:
return np.size(item)
def __len__(self):
return len(self.dataset)
def collater(self, samples):
if self.reduce:
return sum(samples)
else:
return torch.tensor(samples)
| data2vec_vision-main | infoxlm/fairseq/fairseq/data/numel_dataset.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .dictionary import Dictionary, TruncatedDictionary
from .fairseq_dataset import FairseqDataset, FairseqIterableDataset
from .base_wrapper_dataset import BaseWrapperDataset
from .append_token_dataset import AppendTokenDataset
from .audio.raw_audio_dataset import FileAudioDataset
from .backtranslation_dataset import BacktranslationDataset
from .colorize_dataset import ColorizeDataset
from .concat_dataset import ConcatDataset
from .concat_sentences_dataset import ConcatSentencesDataset
from .denoising_dataset import DenoisingDataset
from .id_dataset import IdDataset
from .indexed_dataset import IndexedCachedDataset, IndexedDataset, IndexedRawTextDataset, MMapIndexedDataset
from .language_pair_dataset import LanguagePairDataset
from .list_dataset import ListDataset
from .lm_context_window_dataset import LMContextWindowDataset
from .lru_cache_dataset import LRUCacheDataset
from .mask_tokens_dataset import MaskTokensDataset
from .monolingual_dataset import MonolingualDataset
from .nested_dictionary_dataset import NestedDictionaryDataset
from .noising import NoisingDataset
from .numel_dataset import NumelDataset
from .num_samples_dataset import NumSamplesDataset
from .offset_tokens_dataset import OffsetTokensDataset
from .pad_dataset import LeftPadDataset, PadDataset, RightPadDataset
from .prepend_dataset import PrependDataset
from .prepend_token_dataset import PrependTokenDataset
from .raw_label_dataset import RawLabelDataset
from .replace_dataset import ReplaceDataset
from .resampling_dataset import ResamplingDataset
from .roll_dataset import RollDataset
from .round_robin_zip_datasets import RoundRobinZipDatasets
from .sharded_dataset import ShardedDataset
from .sort_dataset import SortDataset
from .strip_token_dataset import StripTokenDataset
from .subsample_dataset import SubsampleDataset
from .token_block_dataset import TokenBlockDataset
from .transform_eos_dataset import TransformEosDataset
from .transform_eos_lang_pair_dataset import TransformEosLangPairDataset
from .truncate_dataset import TruncateDataset
from .iterators import (
CountingIterator,
EpochBatchIterator,
GroupedIterator,
ShardedIterator,
)
__all__ = [
'AppendTokenDataset',
'BacktranslationDataset',
'BaseWrapperDataset',
'ColorizeDataset',
'ConcatDataset',
'ConcatSentencesDataset',
'CountingIterator',
'DenoisingDataset',
'Dictionary',
'EpochBatchIterator',
'FairseqDataset',
'FairseqIterableDataset',
'GroupedIterator',
'IdDataset',
'IndexedCachedDataset',
'IndexedDataset',
'IndexedRawTextDataset',
'LanguagePairDataset',
'LeftPadDataset',
'ListDataset',
'LMContextWindowDataset',
'LRUCacheDataset',
'MaskTokensDataset',
'MMapIndexedDataset',
'MonolingualDataset',
'NestedDictionaryDataset',
'NoisingDataset',
'NumelDataset',
'NumSamplesDataset',
'OffsetTokensDataset',
'PadDataset',
'PrependDataset',
'PrependTokenDataset',
'ReplaceDataset',
'RollDataset',
'FileAudioDataset',
'RawLabelDataset',
'ResamplingDataset',
'RightPadDataset',
'RoundRobinZipDatasets',
'ShardedDataset',
'ShardedIterator',
'SortDataset',
'StripTokenDataset',
'SubsampleDataset',
'TokenBlockDataset',
'TransformEosDataset',
'TransformEosLangPairDataset',
'TruncateDataset',
'TruncatedDictionary',
]
| data2vec_vision-main | infoxlm/fairseq/fairseq/data/__init__.py |
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