316 lines
9.4 KiB
Python
316 lines
9.4 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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from typing import (
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Any,
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Callable,
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Dict,
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Iterator,
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Mapping,
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Optional,
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Set,
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Tuple,
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TypeVar,
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Union,
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overload,
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)
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import torch
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import torch.nn.functional as F
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from torch import Tensor, device, dtype, nn
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from torch.nn.parameter import Parameter
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import bitsandbytes as bnb
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from bitsandbytes.optim import GlobalOptimManager
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T = TypeVar("T", bound="torch.nn.Module")
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class StableEmbedding(torch.nn.Embedding):
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def __init__(
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self,
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num_embeddings: int,
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embedding_dim: int,
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padding_idx: Optional[int] = None,
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max_norm: Optional[float] = None,
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norm_type: float = 2.0,
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scale_grad_by_freq: bool = False,
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sparse: bool = False,
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_weight: Optional[Tensor] = None,
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) -> None:
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super(StableEmbedding, self).__init__(
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num_embeddings,
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embedding_dim,
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padding_idx,
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max_norm,
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norm_type,
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scale_grad_by_freq,
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sparse,
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_weight,
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)
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self.norm = torch.nn.LayerNorm(embedding_dim)
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GlobalOptimManager.get_instance().register_module_override(
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self, "weight", {"optim_bits": 32}
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)
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def reset_parameters(self) -> None:
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torch.nn.init.xavier_uniform_(self.weight)
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self._fill_padding_idx_with_zero()
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""" !!! This is a redefinition of _fill_padding_idx_with_zero in torch.nn.Embedding
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to make the Layer compatible with Pytorch < 1.9.
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This means that if this changes in future PyTorch releases this need to change too
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which is cumbersome. However, with this we can ensure compatibility with previous
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PyTorch releases.
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"""
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def _fill_padding_idx_with_zero(self) -> None:
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if self.padding_idx is not None:
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with torch.no_grad():
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self.weight[self.padding_idx].fill_(0)
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def forward(self, input: Tensor) -> Tensor:
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emb = F.embedding(
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input,
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self.weight,
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self.padding_idx,
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self.max_norm,
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self.norm_type,
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self.scale_grad_by_freq,
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self.sparse,
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)
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return self.norm(emb)
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class Embedding(torch.nn.Embedding):
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def __init__(
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self,
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num_embeddings: int,
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embedding_dim: int,
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padding_idx: Optional[int] = None,
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max_norm: Optional[float] = None,
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norm_type: float = 2.0,
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scale_grad_by_freq: bool = False,
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sparse: bool = False,
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_weight: Optional[Tensor] = None,
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) -> None:
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super(Embedding, self).__init__(
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num_embeddings,
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embedding_dim,
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padding_idx,
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max_norm,
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norm_type,
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scale_grad_by_freq,
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sparse,
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_weight,
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)
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GlobalOptimManager.get_instance().register_module_override(
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self, "weight", {"optim_bits": 32}
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)
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def reset_parameters(self) -> None:
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torch.nn.init.xavier_uniform_(self.weight)
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self._fill_padding_idx_with_zero()
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""" !!! This is a redefinition of _fill_padding_idx_with_zero in torch.nn.Embedding
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to make the Layer compatible with Pytorch < 1.9.
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This means that if this changes in future PyTorch releases this need to change too
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which is cumbersome. However, with this we can ensure compatibility with previous
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PyTorch releases.
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"""
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def _fill_padding_idx_with_zero(self) -> None:
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if self.padding_idx is not None:
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with torch.no_grad():
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self.weight[self.padding_idx].fill_(0)
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def forward(self, input: Tensor) -> Tensor:
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emb = F.embedding(
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input,
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self.weight,
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self.padding_idx,
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self.max_norm,
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self.norm_type,
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self.scale_grad_by_freq,
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self.sparse,
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)
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return emb
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class Int8Params(torch.nn.Parameter):
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def __new__(
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cls,
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data=None,
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requires_grad=True,
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has_fp16_weights=False,
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CB=None,
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SCB=None,
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):
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cls.has_fp16_weights = has_fp16_weights
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cls.CB = None
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cls.SCB = None
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if data is None:
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data = torch.empty(0)
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return torch.Tensor._make_subclass(cls, data, requires_grad)
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def cuda(self, device):
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if self.has_fp16_weights:
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return super().cuda(device)
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else:
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# we store the 8-bit rows-major weight
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# we convert this weight to the turning/ampere weight during the first inference pass
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B = self.data.contiguous().half().cuda(device)
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CB, CBt, SCB, SCBt, coo_tensorB = bnb.functional.double_quant(B)
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del CBt
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del SCBt
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self.data = CB
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setattr(self, "CB", CB)
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setattr(self, "SCB", SCB)
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return self
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@overload
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def to(
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self: T,
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device: Optional[Union[int, device]] = ...,
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dtype: Optional[Union[dtype, str]] = ...,
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non_blocking: bool = ...,
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) -> T:
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...
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@overload
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def to(self: T, dtype: Union[dtype, str], non_blocking: bool = ...) -> T:
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...
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@overload
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def to(self: T, tensor: Tensor, non_blocking: bool = ...) -> T:
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...
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def to(self, *args, **kwargs):
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device, dtype, non_blocking, convert_to_format = torch._C._nn._parse_to(
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*args, **kwargs
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)
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if (
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device is not None
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and device.type == "cuda"
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and self.data.device.type == "cpu"
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):
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return self.cuda(device)
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else:
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new_param = Int8Params(
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super().to(
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device=device, dtype=dtype, non_blocking=non_blocking
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),
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requires_grad=self.requires_grad,
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has_fp16_weights=self.has_fp16_weights,
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)
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new_param.CB = self.CB
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new_param.SCB = self.SCB
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return new_param
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class Linear8bitLt(nn.Linear):
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def __init__(
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self,
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input_features,
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output_features,
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bias=True,
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has_fp16_weights=True,
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memory_efficient_backward=False,
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threshold=0.0,
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index=None,
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):
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super(Linear8bitLt, self).__init__(
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input_features, output_features, bias
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)
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self.state = bnb.MatmulLtState()
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self.index = index
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self.state.threshold = threshold
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self.state.has_fp16_weights = has_fp16_weights
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self.state.memory_efficient_backward = memory_efficient_backward
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if threshold > 0.0 and not has_fp16_weights:
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self.state.use_pool = True
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self.weight = Int8Params(self.weight.data, has_fp16_weights=has_fp16_weights)
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def init_8bit_state(self):
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self.state.CB = self.weight.CB
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self.state.SCB = self.weight.SCB
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self.weight.CB = None
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self.weight.SCB = None
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def forward(self, x):
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self.state.is_training = self.training
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if self.weight.CB is not None:
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self.init_8bit_state()
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# weights are cast automatically as Int8Params, but the bias has to be cast manually
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if self.bias is not None and self.bias.dtype != torch.float16:
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self.bias.data = self.bias.data.half()
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out = bnb.matmul(x, self.weight, bias=self.bias, state=self.state)
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if not self.state.has_fp16_weights:
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if not self.state.memory_efficient_backward and self.state.CB is not None:
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# we converted 8-bit row major to turing/ampere format in the first inference pass
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# we no longer need the row-major weight
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del self.state.CB
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self.weight.data = self.state.CxB
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elif self.state.memory_efficient_backward and self.state.CxB is not None:
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# For memory efficient backward, we convert 8-bit row major to turing/ampere format at each inference pass.
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# Thus, we delete CxB from the state.
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del self.state.CxB
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return out
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class Linear8bit(nn.Linear):
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def __init__(
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self,
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input_features,
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output_features,
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bias=True,
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quant_type="vector",
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index=None,
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args=None,
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sparse_decomp=False,
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):
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super(Linear8bit, self).__init__(input_features, output_features, bias)
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self.quant_type = quant_type
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self.index = index
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self.args = args
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self.iter = 0
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def forward(self, x):
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self.iter += 1
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if self.iter % self.args.clip_freq == 0:
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with torch.no_grad():
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maxval, maxidx = torch.topk(
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torch.abs(self.weight.flatten()), k=self.args.clip_idx
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)
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if not dist.is_initialized() or dist.get_rank() == 0:
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print("clip", maxval[-1].item())
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self.weight.clip_(-maxval[-1], maxval[-1])
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if self.args is not None:
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out = bnb.nn.functional.sparse_decomposed_linear8bit(
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x,
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self.weight,
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self.bias,
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qval=self.args.sparse_decomp_val,
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quant_type=self.args.quant_type,
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)
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else:
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out = bnb.nn.functional.linear8bit(
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x, self.weight, self.bias, quant_type=self.args.quant_type
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)
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return out
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