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|
# mypy: allow-untyped-defs
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
__all__ = ["Embedding", "EmbeddingBag"]
class Embedding(nn.Embedding):
r"""
An embedding bag module attached with FakeQuantize modules for weight,
used for quantization aware training.
We adopt the same interface as `torch.nn.Embedding`, please see
https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html#torch.nn.Embedding
for documentation.
Similar to `torch.nn.Embedding`, with FakeQuantize modules initialized to
default.
Attributes:
weight: fake quant module for weight
"""
_FLOAT_MODULE = nn.Embedding
def __init__(
self,
num_embeddings,
embedding_dim,
padding_idx=None,
max_norm=None,
norm_type=2.0,
scale_grad_by_freq=False,
sparse=False,
_weight=None,
device=None,
dtype=None,
qconfig=None,
) -> None:
factory_kwargs = {"device": device, "dtype": dtype}
super().__init__(
num_embeddings,
embedding_dim,
padding_idx,
max_norm,
norm_type,
scale_grad_by_freq,
sparse,
_weight,
**factory_kwargs,
)
assert qconfig, "qconfig must be provided for QAT module"
assert qconfig.weight().qscheme == torch.per_channel_affine_float_qparams, (
"Embedding weights requires a qscheme of torch.per_channel_affine_float_qparams Got "
+ str(qconfig.weight().qscheme)
)
self.qconfig = qconfig
self.weight_fake_quant = qconfig.weight(factory_kwargs=factory_kwargs)
def forward(self, input) -> Tensor:
return F.embedding(
input,
self.weight_fake_quant(self.weight),
self.padding_idx,
self.max_norm,
self.norm_type,
self.scale_grad_by_freq,
self.sparse,
)
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
r"""Create a qat module from a float module
Args: `mod` a float module, either produced by torch.ao.quantization utilities
or directly from user
"""
assert type(mod) == cls._FLOAT_MODULE, (
" qat."
+ cls.__name__
+ ".from_float only works for "
+ cls._FLOAT_MODULE.__name__
)
assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
assert mod.qconfig, "Input float module must have a valid qconfig"
weight_qscheme = mod.qconfig.weight().qscheme # type: ignore[union-attr, operator]
assert weight_qscheme == torch.per_channel_affine_float_qparams, (
"Embedding weights requires a qscheme of torch.per_channel_affine_float_qparams Got "
+ str(weight_qscheme)
)
qconfig = mod.qconfig
qat_embedding_bag = cls(
mod.num_embeddings,
mod.embedding_dim,
mod.padding_idx,
mod.max_norm,
mod.norm_type,
mod.scale_grad_by_freq,
mod.sparse,
mod.weight,
qconfig=qconfig,
)
return qat_embedding_bag
def to_float(self):
embedding_bag = torch.nn.Embedding(
self.num_embeddings,
self.embedding_dim,
self.padding_idx,
self.max_norm,
self.norm_type,
self.scale_grad_by_freq,
self.sparse,
None,
)
embedding_bag.weight = torch.nn.Parameter(self.weight.detach())
embedding_bag.train(self.training)
return embedding_bag
class EmbeddingBag(nn.EmbeddingBag):
r"""
An embedding bag module attached with FakeQuantize modules for weight,
used for quantization aware training.
We adopt the same interface as `torch.nn.EmbeddingBag`, please see
https://pytorch.org/docs/stable/generated/torch.nn.EmbeddingBag.html#torch.nn.EmbeddingBag
for documentation.
Similar to `torch.nn.EmbeddingBag`, with FakeQuantize modules initialized to
default.
Attributes:
weight: fake quant module for weight
"""
_FLOAT_MODULE = nn.EmbeddingBag
def __init__(
self,
num_embeddings,
embedding_dim,
max_norm=None,
norm_type=2.0,
scale_grad_by_freq=False,
mode="mean",
sparse=False,
_weight=None,
include_last_offset=False,
padding_idx=None,
qconfig=None,
device=None,
dtype=None,
) -> None:
factory_kwargs = {"device": device, "dtype": dtype}
super().__init__(
num_embeddings,
embedding_dim,
max_norm,
norm_type,
scale_grad_by_freq,
mode,
sparse,
_weight,
include_last_offset,
padding_idx,
**factory_kwargs,
)
assert qconfig, "qconfig must be provided for QAT module"
assert qconfig.weight().qscheme == torch.per_channel_affine_float_qparams, (
"Embedding Bag weights requires a qscheme of torch.per_channel_affine_float_qparams Got "
+ str(qconfig.weight().qscheme)
)
self.qconfig = qconfig
self.weight_fake_quant = qconfig.weight(factory_kwargs=factory_kwargs)
def forward(self, input, offsets=None, per_sample_weights=None) -> Tensor:
return F.embedding_bag(
input,
self.weight_fake_quant(self.weight),
offsets,
self.max_norm,
self.norm_type,
self.scale_grad_by_freq,
self.mode,
self.sparse,
per_sample_weights,
self.include_last_offset,
self.padding_idx,
)
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
r"""Create a qat module from a float module
Args: `mod` a float module, either produced by torch.ao.quantization utilities
or directly from user
"""
assert type(mod) == cls._FLOAT_MODULE, (
" qat."
+ cls.__name__
+ ".from_float only works for "
+ cls._FLOAT_MODULE.__name__
)
assert hasattr(mod, "qconfig"), "Input float module must have qconfig defined"
assert mod.qconfig, "Input float module must have a valid qconfig"
weight_qscheme = mod.qconfig.weight().qscheme # type: ignore[union-attr, operator]
assert weight_qscheme == torch.per_channel_affine_float_qparams, (
"Embedding Bag weights requires a qscheme of torch.per_channel_affine_float_qparams Got "
+ str(weight_qscheme)
)
qconfig = mod.qconfig
qat_embedding_bag = cls(
mod.num_embeddings,
mod.embedding_dim,
mod.max_norm,
mod.norm_type,
mod.scale_grad_by_freq,
mod.mode,
mod.sparse,
mod.weight,
mod.include_last_offset,
mod.padding_idx,
qconfig=qconfig,
)
return qat_embedding_bag
def to_float(self):
embedding_bag = torch.nn.EmbeddingBag(
self.num_embeddings,
self.embedding_dim,
self.max_norm,
self.norm_type,
self.scale_grad_by_freq,
self.mode,
self.sparse,
None,
self.include_last_offset,
self.padding_idx,
)
embedding_bag.weight = torch.nn.Parameter(self.weight.detach())
embedding_bag.train(self.training)
return embedding_bag
|
