Source code for torch.ao.nn.quantized.modules.embedding_ops
import torch
import torch.nn as nn
from torch import Tensor # noqa: F401
from torch._jit_internal import Optional, List # noqa: F401
from .utils import hide_packed_params_repr
from .utils import _quantize_weight
__all__ = ['EmbeddingPackedParams', 'Embedding', 'EmbeddingBag']
class EmbeddingPackedParams(torch.nn.Module):
_version = 1
def __init__(self, num_embeddings, embedding_dim, dtype=torch.quint8):
super(EmbeddingPackedParams, self).__init__()
self.dtype = dtype
if self.dtype in [torch.quint8, torch.quint4x2]:
scales = torch.ones(num_embeddings, dtype=torch.float)
zero_points = torch.zeros(num_embeddings, dtype=torch.float)
wq = torch._empty_per_channel_affine_quantized([num_embeddings, embedding_dim], scales=scales,
zero_points=zero_points,
axis=0, dtype=self.dtype)
self.set_weight(wq)
else:
raise NotImplementedError(f'Unsupported dtype on quantized embedding! Supports quint8 and quint4x2. Got dtype: {dtype}')
@torch.jit.export
def set_weight(self, weight: torch.Tensor) -> None:
if self.dtype in [torch.quint8, torch.quint4x2]:
self._packed_weight = torch.ops.quantized.embedding_bag_prepack(weight)
else:
raise NotImplementedError('Unsupported dtype for quantized embedding prepack! Supports quint8 and quint4x2.')
@torch.jit.export
def _weight(self):
if self.dtype in [torch.quint8, torch.quint4x2]:
return torch.ops.quantized.embedding_bag_unpack(self._packed_weight)
else:
raise NotImplementedError('Unsupported dtype for quantized embedding unpack! Supports quint8 and quint4x2.')
def forward(self, x):
return x
# Version 1
# self
# |--- _packed_weight : Tensor representing weight of EmbeddingPackedParamsBase
# |--- dtype : torch.dtype
def _save_to_state_dict(self, destination, prefix, keep_vars):
super(EmbeddingPackedParams, self)._save_to_state_dict(destination, prefix, keep_vars)
destination[prefix + 'dtype'] = self.dtype
destination[prefix + '_packed_weight'] = self._weight()
def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
missing_keys, unexpected_keys, error_msgs):
self.dtype = state_dict[prefix + 'dtype']
state_dict.pop(prefix + 'dtype')
weight = state_dict[prefix + '_packed_weight']
state_dict.pop(prefix + '_packed_weight')
self.set_weight(weight)
super(EmbeddingPackedParams, self)._load_from_state_dict(state_dict, prefix, local_metadata, False,
missing_keys, unexpected_keys, error_msgs)
def __repr__(self):
return self._weight().__repr__()
[docs]class Embedding(torch.nn.Module):
r"""
A quantized Embedding module with quantized packed weights as inputs.
We adopt the same interface as `torch.nn.Embedding`, please see
https://pytorch.org/docs/stable/nn.html#torch.nn.Embedding for documentation.
Similar to :class:`~torch.nn.Embedding`, attributes will be randomly
initialized at module creation time and will be overwritten later
Attributes:
weight (Tensor): the non-learnable quantized weights of the module of
shape :math:`(\text{num\_embeddings}, \text{embedding\_dim})`.
Examples::
>>> m = nn.quantized.Embedding(num_embeddings=10, embedding_dim=12)
>>> indices = torch.tensor([9, 6, 5, 7, 8, 8, 9, 2, 8])
>>> output = m(indices)
>>> print(output.size())
torch.Size([9, 12])
"""
_version = 1
def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None,
max_norm: Optional[float] = None, norm_type: float = 2., scale_grad_by_freq: bool = False,
sparse: bool = False, _weight: Optional[Tensor] = None, dtype=torch.quint8) -> None:
super(Embedding, self).__init__()
self.num_embeddings = num_embeddings
self.embedding_dim = embedding_dim
self.dtype = dtype
if _weight is None:
scales = torch.ones(num_embeddings, dtype=torch.float)
zero_points = torch.zeros(num_embeddings, dtype=torch.float)
qweight = torch._empty_per_channel_affine_quantized([num_embeddings, embedding_dim],
scales=scales, zero_points=zero_points,
axis=0, dtype=torch.quint8)
else:
assert list(_weight.shape) == [num_embeddings, embedding_dim], \
'Shape of weight does not match num_embeddings and embedding_dim'
qweight = _weight
self._packed_params = EmbeddingPackedParams(num_embeddings, embedding_dim, dtype)
self._packed_params.set_weight(qweight)
def forward(self, indices: Tensor) -> Tensor:
if self.dtype == torch.quint4x2:
return torch.ops.quantized.embedding_4bit(self._packed_params._packed_weight, indices)
else:
return torch.ops.quantized.embedding_byte(self._packed_params._packed_weight, indices)
def _get_name(self):
return 'QuantizedEmbedding'
def __repr__(self):
return hide_packed_params_repr(self, EmbeddingPackedParams)
def extra_repr(self):
extra_repr_str = 'num_embeddings={}, embedding_dim={}, dtype={}, qscheme={}'.format(
self.num_embeddings, self.embedding_dim, self._packed_params.dtype, self.weight().qscheme()
)
return extra_repr_str
def set_weight(self, w: torch.Tensor) -> None:
self._packed_params.set_weight(w)
def weight(self):
return self._packed_params._weight()
[docs] @classmethod
def from_float(cls, mod):
r"""Create a quantized embedding module from a float module
Args:
mod (Module): a float module, either produced by torch.ao.quantization
utilities or provided by user
"""
if hasattr(mod, 'weight_fake_quant'):
assert type(mod) == torch.ao.nn.qat.Embedding, 'nnq.' + cls.__name__ + '.from_float ' + \
'with fake quant only works for ' + torch.ao.nn.qat.Embedding.__name__
weight_observer = mod.weight_fake_quant
activation_post_process = mod.activation_post_process
else:
assert type(mod) == nn.Embedding, 'nnq.' + cls.__name__ + '.from_float only works for ' + \
nn.Embedding.__name__
assert hasattr(mod, 'qconfig'), 'Embedding input float module must have qconfig defined'
from torch.ao.quantization import float_qparams_weight_only_qconfig
if mod.qconfig is not None and mod.qconfig.weight is not None: # type: ignore[union-attr]
weight_observer = mod.qconfig.weight() # type: ignore[union-attr, operator]
else:
weight_observer = float_qparams_weight_only_qconfig.weight()
dtype = weight_observer.dtype
is_float_qparams_qconfig = weight_observer.qscheme == torch.per_channel_affine_float_qparams
assert is_float_qparams_qconfig, \
'Embedding quantization is only supported with float_qparams_weight_only_qconfig.'
assert dtype == torch.quint8 or dtype == torch.quint4x2, \
f'The only supported dtype for nnq.Embedding is torch.quint8 and torch.quint4x2, got {dtype}'
# Run the observer to calculate qparams.
weight_observer(mod.weight)
qweight = _quantize_weight(mod.weight.float(), weight_observer)
# Create quantized Embedding module and pass in the quantized weight
qembedding = Embedding(mod.num_embeddings, mod.embedding_dim)
qembedding.set_weight(qweight)
return qembedding
@classmethod
def from_reference(cls, ref_embedding):
qembedding = cls(
ref_embedding.num_embeddings,
ref_embedding.embedding_dim,
ref_embedding.padding_idx,
ref_embedding.max_norm,
ref_embedding.norm_type,
ref_embedding.scale_grad_by_freq,
ref_embedding.sparse,
ref_embedding.get_quantized_weight(),
ref_embedding.weight_dtype,
)
return qembedding
[docs]class EmbeddingBag(Embedding):
r"""
A quantized EmbeddingBag module with quantized packed weights as inputs.
We adopt the same interface as `torch.nn.EmbeddingBag`, please see
https://pytorch.org/docs/stable/nn.html#torch.nn.EmbeddingBag for documentation.
Similar to :class:`~torch.nn.EmbeddingBag`, attributes will be randomly
initialized at module creation time and will be overwritten later
Attributes:
weight (Tensor): the non-learnable quantized weights of the module of
shape :math:`(\text{num\_embeddings}, \text{embedding\_dim})`.
Examples::
>>> m = nn.quantized.EmbeddingBag(num_embeddings=10, embedding_dim=12, include_last_offset=True, mode='sum')
>>> indices = torch.tensor([9, 6, 5, 7, 8, 8, 9, 2, 8, 6, 6, 9, 1, 6, 8, 8, 3, 2, 3, 6, 3, 6, 5, 7, 0, 8, 4, 6, 5, 8, 2, 3])
>>> offsets = torch.tensor([0, 19, 20, 28, 28, 32])
>>> output = m(indices, offsets)
>>> print(output.size())
torch.Size([5, 12])
"""
_version = 1
def __init__(self, num_embeddings: int, embedding_dim: int,
max_norm: Optional[float] = None, norm_type: float = 2., scale_grad_by_freq: bool = False,
mode: str = 'sum', sparse: bool = False, _weight: Optional[Tensor] = None,
include_last_offset: bool = False, dtype=torch.quint8) -> None:
super(EmbeddingBag, self).__init__(num_embeddings, embedding_dim, _weight=_weight, dtype=dtype)
self.mode = mode
self.pruned_weights = False
self.include_last_offset = include_last_offset
self.dtype = dtype
def forward(self, indices: Tensor, offsets: Optional[Tensor] = None, per_sample_weights: Optional[Tensor] = None,
compressed_indices_mapping: Optional[Tensor] = None) -> Tensor:
if self.dtype == torch.quint4x2:
return torch.ops.quantized.embedding_bag_4bit(self._packed_params._packed_weight, indices, offsets, False, 0,
self.pruned_weights, per_sample_weights, compressed_indices_mapping,
self.include_last_offset)
else:
return torch.ops.quantized.embedding_bag_byte(self._packed_params._packed_weight, indices, offsets, False, 0,
self.pruned_weights, per_sample_weights, compressed_indices_mapping,
self.include_last_offset)
def _get_name(self):
return 'QuantizedEmbeddingBag'
[docs] @classmethod
def from_float(cls, mod):
r"""Create a quantized embedding_bag module from a float module
Args:
mod (Module): a float module, either produced by torch.ao.quantization
utilities or provided by user
"""
if hasattr(mod, 'weight_fake_quant'):
weight_observer = mod.weight_fake_quant
else:
assert type(mod) == nn.EmbeddingBag, 'nnq.' + cls.__name__ + '.from_float only works for ' + \
nn.EmbeddingBag.__name__
assert hasattr(mod, 'qconfig'), 'EmbeddingBag input float module must have qconfig defined'
from torch.ao.quantization.qconfig import float_qparams_weight_only_qconfig
if mod.qconfig is not None and mod.qconfig.weight is not None: # type: ignore[union-attr]
weight_observer = mod.qconfig.weight() # type: ignore[union-attr, operator]
else:
weight_observer = float_qparams_weight_only_qconfig.weight()
dtype = weight_observer.dtype
is_float_qparams_qconfig = weight_observer.qscheme == torch.per_channel_affine_float_qparams
assert is_float_qparams_qconfig, \
'EmbeddingBag quantization is only supported with float_qparams_weight_only_qconfig.'
assert dtype == torch.quint8 or dtype == torch.quint4x2, \
f'The only supported dtype for nnq.EmbeddingBag is torch.quint8 and torch.quint4x2, got {dtype}'
# Run the observer to calculate qparams.
weight_observer(mod.weight)
qweight = _quantize_weight(mod.weight.float(), weight_observer)
# Create quantized EmbeddingBag module and pass in the quantized weight
qembedding_bag = EmbeddingBag(mod.num_embeddings, mod.embedding_dim, dtype=dtype)
qembedding_bag.set_weight(qweight)
return qembedding_bag
@classmethod
def from_reference(cls, ref_embedding_bag):
qembedding_bag = cls(
ref_embedding_bag.num_embeddings,
ref_embedding_bag.embedding_dim,
ref_embedding_bag.max_norm,
ref_embedding_bag.norm_type,
ref_embedding_bag.scale_grad_by_freq,
ref_embedding_bag.mode,
ref_embedding_bag.sparse,
ref_embedding_bag.get_quantized_weight(),
ref_embedding_bag.include_last_offset,
ref_embedding_bag.weight_dtype,
)
return qembedding_bag
