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# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
import torch
from deepspeed import comm as dist
from torch import nn
from torch.nn import functional as F
from torch.nn.parameter import Parameter
from deepspeed.accelerator import get_accelerator
from deepspeed.module_inject.tp_shard import get_shard_size, get_shard_size_list
class LinearAllreduce(nn.Module):
def __init__(self, weight, bias=None, mp_group=None):
super(LinearAllreduce, self).__init__()
self.weight = weight
self.bias = bias
self.mp_group = mp_group
def forward(self, input):
output = torch.matmul(input, self.weight.transpose(-1, -2))
if self.mp_group is not None:
dist.inference_all_reduce(output, group=self.mp_group)
if self.bias is not None:
output += self.bias
return output
class LmHeadLinearAllreduce(nn.Module):
def __init__(
self,
weight,
rank,
world_size,
bias=None,
mp_group=None,
):
super(LmHeadLinearAllreduce, self).__init__()
self.weight = weight
self.bias = bias
self.mp_group = mp_group
self.rank = rank
self.world_size = world_size
def forward(self, input):
input_shard_size = get_shard_size(input.shape[-1], self.world_size, "lm_head")
input_shard_offset = sum(get_shard_size_list(input.shape[-1], self.world_size, "lm_head")[0:self.rank])
output = torch.matmul(input[:, :, input_shard_offset:input_shard_offset + input_shard_size],
self.weight.transpose(-1, -2))
if self.mp_group is not None:
dist.inference_all_reduce(output, group=self.mp_group)
if self.bias is not None:
output += self.bias
return output
class LinearLayer(nn.Module):
def __init__(self, weight_shape=None, dtype=torch.half, weight=None, bias=None):
super(LinearLayer, self).__init__()
if weight is not None:
self.weight = weight
self.bias = bias
else:
self.weight = Parameter(
torch.empty(weight_shape, dtype=dtype, device=get_accelerator().current_device_name()))
self.bias = Parameter(
torch.empty(weight_shape[0],
dtype=dtype,
device=get_accelerator().current_device_name())) \
if bias is not None else None
def forward(self, input):
output = torch.matmul(input, self.weight.transpose(-1, -2))
if self.bias is not None:
output += self.bias
return output
class Normalize(nn.Module):
def __init__(self, dim=None, dtype=torch.float, eps=1e-5, weight=None, bias=None):
super(Normalize, self).__init__()
if weight is not None:
self.weight = weight
self.bias = bias
else:
self.norm = nn.LayerNorm(dim, eps=eps).to(dtype).to(get_accelerator().current_device_name())
self.weight = self.norm.weight
self.bias = self.norm.bias
self.eps = eps
def forward(self, input):
return nn.functional.layer_norm(input, input.shape[-1:], self.weight, self.bias, eps=self.eps)
class EmbeddingLayer(nn.Module):
def __init__(self, weight_shape=None, dtype=torch.half, weight=None, bias=None):
super(EmbeddingLayer, self).__init__()
if weight is None:
self.weight = Parameter(
torch.empty(weight_shape[0],
weight_shape[1],
dtype=dtype,
device=get_accelerator().current_device_name()))
else:
self.weight = weight
def forward(self, input):
return F.embedding(input, self.weight)
class OPTEmbedding(EmbeddingLayer):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, weight_shape=None, weight=None, bias=None):
# OPT is set up so that if padding_idx is specified then offset the embedding ids by 2
# and adjust num_embeddings appropriately. Other models don't have this hack
self.offset = 2
super().__init__(weight_shape, weight=weight)
def forward(self, attention_mask: torch.LongTensor, past_key_values_length: int = 0):
"""`input_ids_shape` is expected to be [bsz x seqlen]."""
attention_mask = attention_mask.long()
# create positions depending on attention_mask
positions = (torch.cumsum(attention_mask, dim=1).type_as(attention_mask) * attention_mask).long() - 1
# cut positions if `past_key_values_length` is > 0
positions = positions[:, past_key_values_length:]
return super().forward(positions + self.offset)
class RMSNormalize(nn.Module):
def __init__(self, dim=None, dtype=torch.float, eps=1e-5, weight=None):
super(RMSNormalize, self).__init__()
if weight is not None:
self.weight = weight
else:
self.weight = nn.Parameter(torch.ones(dim, dtype=dtype, device=get_accelerator().current_device_name()))
self.eps = eps
def forward(self, hidden_states):
variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
if self.weight.dtype in [torch.float16, torch.bfloat16]:
hidden_states = hidden_states.to(self.weight.dtype)
return hidden_states * self.weight
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