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# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
import torch
from torch import nn
from torch import Tensor
from torch.nn import functional as F
from .utils import Quantizer, DeQuantizer, concat_to_compat_param
from typing import Tuple, Callable, Dict
from deepspeed.runtime.zero import register_external_parameter
quantized_weight_registry = {}
is_zero3_enabled = False
# deal with weight sharing
def get_quantized_weight_wrapper(model, pre_quant_weight: nn.Parameter, quantize_weight_fn: Callable) -> nn.Parameter:
if id(pre_quant_weight) in quantized_weight_registry:
compat_tensor = quantized_weight_registry[id(pre_quant_weight)]
if is_zero3_enabled:
register_external_parameter(model, compat_tensor)
return quantized_weight_registry[id(pre_quant_weight)]
else:
quantized_weights, quant_scale, quant_min = quantize_weight_fn()
quantized_weight_registry[id(pre_quant_weight)] = concat_to_compat_param(quantized_weights, quant_scale,
quant_min)
return quantized_weight_registry[id(pre_quant_weight)]
def get_quantize_weight_fn(quantizer: Quantizer, pre_quant_weight: nn.Parameter) -> Callable:
def func() -> Tuple[nn.Parameter, Tensor, Tensor]:
quantized_weights, quant_scale, quant_min = quantizer.quantize(pre_quant_weight.data)
# A temporary hack as zero Zero3 assume all model weights has the same type. in all_gather_coalesced.get_only_unique_item
quantized_weights = quantized_weights.view(pre_quant_weight.dtype)
quant_scale = quant_scale.type(pre_quant_weight.dtype)
quant_min = quant_min.type(pre_quant_weight.dtype)
return quantized_weights, quant_scale, quant_min
return func
class QuantizedLinear(nn.Linear):
def __init__(self, config: Dict, pre_quant_layer: nn.Linear) -> None:
super(QuantizedLinear, self).__init__(in_features=pre_quant_layer.in_features,
out_features=pre_quant_layer.out_features,
bias=pre_quant_layer.bias is not None,
device=pre_quant_layer.weight.device,
dtype=pre_quant_layer.weight.dtype)
self.config = config
self.quantizer = Quantizer(config=config)
self.bias = pre_quant_layer.bias
self.weight = get_quantized_weight_wrapper(self, pre_quant_layer.weight,
get_quantize_weight_fn(self.quantizer, pre_quant_layer.weight))
self.weight.dequantizer = DeQuantizer(config, pre_quant_layer.weight.dtype)
def forward(self, input: Tensor) -> Tensor:
quantized_weight, quant_scale, quant_min = self.weight.deconcat(self.weight)
temp_dequantized_weight = self.weight.dequantizer.dequantize(quantized_weight.view(torch.uint8), quant_scale,
quant_min)
# !!! Do not use torch.functional.linear(input, temp_dequantized_weight, self.bias) here as in zero3 torch.functional.linear is
# replaced by LinearFunctionForZeroStage3. Which assume weight is non-temporary.
# If weight is temp buffer there will be memory leak.
return torch._C._nn.linear(input, temp_dequantized_weight, self.bias)
class QuantizedEmbedding(nn.Embedding):
def __init__(self, config: Dict, pre_quant_layer: nn.Embedding) -> None:
super(QuantizedEmbedding, self).__init__(num_embeddings=pre_quant_layer.num_embeddings,
embedding_dim=pre_quant_layer.embedding_dim,
padding_idx=pre_quant_layer.padding_idx,
max_norm=pre_quant_layer.max_norm,
norm_type=pre_quant_layer.norm_type,
scale_grad_by_freq=pre_quant_layer.scale_grad_by_freq,
sparse=pre_quant_layer.sparse,
_weight=pre_quant_layer.weight,
device=pre_quant_layer.weight.device,
dtype=pre_quant_layer.weight.dtype)
assert pre_quant_layer.max_norm is None, 'Not supported'
assert pre_quant_layer.norm_type == 2, 'Not supported'
assert pre_quant_layer.scale_grad_by_freq == False, 'Not supported'
assert pre_quant_layer.sparse == False, 'Not supported'
self.config = config
quantizer = Quantizer(config=config)
self.weight = get_quantized_weight_wrapper(self, pre_quant_layer.weight,
get_quantize_weight_fn(quantizer, pre_quant_layer.weight))
self.weight.dequantizer = DeQuantizer(config, pre_quant_layer.weight.dtype)
def forward(self, input: Tensor) -> Tensor:
quantized_weight, quant_scale, quant_min = self.weight.deconcat(self.weight)
temp_dequantized_weight = self.weight.dequantizer.dequantize(quantized_weight.view(torch.uint8), quant_scale,
quant_min)
return F.embedding(input, temp_dequantized_weight, self.padding_idx, self.max_norm, self.norm_type,
self.scale_grad_by_freq, self.sparse)
QUANTIZATION_LAYER_MAPPINGS = {
nn.Linear: QuantizedLinear,
nn.Embedding: QuantizedEmbedding,
}
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