modules.py

# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Optional, TypeVar, Union, overload

import torch
import torch.nn.functional as F
from torch import Tensor, device, dtype, nn

import bitsandbytes as bnb
import bitsandbytes.functional
from bitsandbytes.autograd._functions import get_inverse_transform_indices, undo_layout
from bitsandbytes.optim import GlobalOptimManager
from bitsandbytes.utils import OutlierTracer, find_outlier_dims

T = TypeVar("T", bound="torch.nn.Module")


class StableEmbedding(torch.nn.Embedding):
    def __init__(
        self,
        num_embeddings: int,
        embedding_dim: int,
        padding_idx: Optional[int] = None,
        max_norm: Optional[float] = None,
        norm_type: float = 2.0,
        scale_grad_by_freq: bool = False,
        sparse: bool = False,
        _weight: Optional[Tensor] = None,
        device=None,
        dtype=None,
    ) -> None:
        super().__init__(
            num_embeddings,
            embedding_dim,
            padding_idx,
            max_norm,
            norm_type,
            scale_grad_by_freq,
            sparse,
            _weight,
            device,
            dtype,
        )
        self.norm = torch.nn.LayerNorm(embedding_dim, device=device)
        GlobalOptimManager.get_instance().register_module_override(
            self, "weight", {"optim_bits": 32}
        )

    def reset_parameters(self) -> None:
        torch.nn.init.xavier_uniform_(self.weight)
        self._fill_padding_idx_with_zero()

    """ !!! This is a redefinition of _fill_padding_idx_with_zero in torch.nn.Embedding
        to make the Layer compatible with Pytorch < 1.9.
        This means that if this changes in future PyTorch releases this need to change too
        which is cumbersome. However, with this we can ensure compatibility with previous
        PyTorch releases.
    """

    def _fill_padding_idx_with_zero(self) -> None:
        if self.padding_idx is not None:
            with torch.no_grad():
                self.weight[self.padding_idx].fill_(0)

    def forward(self, input: Tensor) -> Tensor:
        emb = F.embedding(
            input,
            self.weight,
            self.padding_idx,
            self.max_norm,
            self.norm_type,
            self.scale_grad_by_freq,
            self.sparse,
        )

        # always apply layer norm in full precision
        emb = emb.to(torch.get_default_dtype())

        return self.norm(emb).to(self.weight.dtype)


class Embedding(torch.nn.Embedding):
    def __init__(
        self,
        num_embeddings: int,
        embedding_dim: int,
        padding_idx: Optional[int] = None,
        max_norm: Optional[float] = None,
        norm_type: float = 2.0,
        scale_grad_by_freq: bool = False,
        sparse: bool = False,
        _weight: Optional[Tensor] = None,
        device: Optional[device] = None,
    ) -> None:
        super().__init__(
            num_embeddings,
            embedding_dim,
            padding_idx,
            max_norm,
            norm_type,
            scale_grad_by_freq,
            sparse,
            _weight,
            device=device
        )
        GlobalOptimManager.get_instance().register_module_override(
            self, "weight", {"optim_bits": 32}
        )

    def reset_parameters(self) -> None:
        torch.nn.init.xavier_uniform_(self.weight)
        self._fill_padding_idx_with_zero()

    """ !!! This is a redefinition of _fill_padding_idx_with_zero in torch.nn.Embedding
        to make the Layer compatible with Pytorch < 1.9.
        This means that if this changes in future PyTorch releases this need to change too
        which is cumbersome. However, with this we can ensure compatibility with previous
        PyTorch releases.
    """

    def _fill_padding_idx_with_zero(self) -> None:
        if self.padding_idx is not None:
            with torch.no_grad():
                self.weight[self.padding_idx].fill_(0)

    def forward(self, input: Tensor) -> Tensor:
        emb = F.embedding(
            input,
            self.weight,
            self.padding_idx,
            self.max_norm,
            self.norm_type,
            self.scale_grad_by_freq,
            self.sparse,
        )

        return emb

class Params4bit(torch.nn.Parameter):
    def __new__(cls, data=None, requires_grad=True, quant_state=None, blocksize=64, compress_statistics=True, quant_type='fp4'):
        if data is None:
            data = torch.empty(0)

        self = torch.Tensor._make_subclass(cls, data, requires_grad)
        self.blocksize = blocksize
        self.compress_statistics = compress_statistics
        self.quant_type = quant_type
        self.quant_state = quant_state
        self.data = data
        return self

    def cuda(self, device):
        w = self.data.contiguous().half().cuda(device)
        w_4bit, quant_state = bnb.functional.quantize_4bit(w, blocksize=self.blocksize, compress_statistics=self.compress_statistics, quant_type=self.quant_type)
        self.data = w_4bit
        self.quant_state = quant_state

        return self

    @overload
    def to(self: T, device: Optional[Union[int, device]] = ..., dtype: Optional[Union[dtype, str]] = ..., non_blocking: bool = ...,) -> T:
        ...

    @overload
    def to(self: T, dtype: Union[dtype, str], non_blocking: bool = ...) -> T:
        ...

    @overload
    def to(self: T, tensor: Tensor, non_blocking: bool = ...) -> T:
        ...

    def to(self, *args, **kwargs):
        device, dtype, non_blocking, convert_to_format = torch._C._nn._parse_to(*args, **kwargs)

        if (device is not None and device.type == "cuda" and self.data.device.type == "cpu"):
            return self.cuda(device)
        else:
            s = self.quant_state
            if s is not None:
                # make sure the quantization state is on the right device
                s[0] = s[0].to(device)
                if self.compress_statistics:
                    # TODO: refactor this. This is a nightmare
                    # for 4-bit: 
                    # state = [qabsmax, input_shape, A.dtype, blocksize, [offset, state2], quant_type]
                    # state2 = [absmax, input_shape, A.dtype, blocksize, None, quant_type]
                    #s[-2][0] = s[-2][0].to(device) # offset
                    #s[-2][1][0] = s[-2][1][0].to(device) # nested absmax

                    # for 8-bit
                    s[-2][0] = s[-2][0].to(device) # offset
                    s[-2][1][0] = s[-2][1][0].to(device) # nested quantiation state statitics
                    s[-2][1][1] = s[-2][1][1].to(device) # nested quantiation codebook
            new_param = Params4bit(super().to(device=device, dtype=dtype, non_blocking=non_blocking),
                                  requires_grad=self.requires_grad, quant_state=self.quant_state,
                                   blocksize=self.blocksize, compress_statistics=self.compress_statistics,
                                   quant_type=self.quant_type)

            return new_param

class Linear4bit(nn.Linear):
    def __init__(self, input_features, output_features, bias=True, compute_dtype=None, compress_statistics=True, quant_type='fp4',device=None):
        super().__init__(input_features, output_features, bias, device)
        self.weight = Params4bit(self.weight.data, requires_grad=False, compress_statistics=compress_statistics, quant_type=quant_type)
        self.compute_dtype = compute_dtype

    def forward(self, x: torch.Tensor):
        # weights are cast automatically as Int8Params, but the bias has to be cast manually
        if self.bias is not None and self.bias.dtype != x.dtype:
            self.bias.data = self.bias.data.to(x.dtype)

        if getattr(self.weight, 'quant_state', None) is None:
            print('FP4 quantization state not initialized. Please call .cuda() or .to(device) on the LinearFP4 layer first.')
        inp_dtype = x.dtype
        if self.compute_dtype is not None:
            x = x.to(self.compute_dtype)

        bias = None if self.bias is None else self.bias.to(self.compute_dtype)
        out = bnb.matmul_4bit(x, self.weight.t(), bias=bias, quant_state=self.weight.quant_state)

        out = out.to(inp_dtype)

        return out

class LinearFP4(Linear4bit):
    def __init__(self, input_features, output_features, bias=True, compute_dtype=None, compress_statistics=True,device=None):
        super().__init__(input_features, output_features, bias, compute_dtype, compress_statistics, 'fp4', device)

class LinearNF4(Linear4bit):
    def __init__(self, input_features, output_features, bias=True, compute_dtype=None, compress_statistics=True,device=None):
        super().__init__(input_features, output_features, bias, compute_dtype, compress_statistics, 'nf4', device)



class Int8Params(torch.nn.Parameter):
    def __new__(
        cls,
        data=None,
        requires_grad=True,
        has_fp16_weights=False,
        CB=None,
        SCB=None,
    ):
        cls.has_fp16_weights = has_fp16_weights
        cls.CB = None
        cls.SCB = None
        if data is None:
            data = torch.empty(0)
        return torch.Tensor._make_subclass(cls, data, requires_grad)

    def cuda(self, device):
        if self.has_fp16_weights:
            return super().cuda(device)
        else:
            # we store the 8-bit rows-major weight
            # we convert this weight to the turning/ampere weight during the first inference pass
            B = self.data.contiguous().half().cuda(device)
            CB, CBt, SCB, SCBt, coo_tensorB = bnb.functional.double_quant(B)
            del CBt
            del SCBt
            self.data = CB
            setattr(self, "CB", CB)
            setattr(self, "SCB", SCB)

        return self

    @overload
    def to(
        self: T,
        device: Optional[Union[int, device]] = ...,
        dtype: Optional[Union[dtype, str]] = ...,
        non_blocking: bool = ...,
    ) -> T:
        ...

    @overload
    def to(self: T, dtype: Union[dtype, str], non_blocking: bool = ...) -> T:
        ...

    @overload
    def to(self: T, tensor: Tensor, non_blocking: bool = ...) -> T:
        ...

    def to(self, *args, **kwargs):
        device, dtype, non_blocking, convert_to_format = torch._C._nn._parse_to(
            *args, **kwargs
        )

        if (
            device is not None
            and device.type == "cuda"
            and self.data.device.type == "cpu"
        ):
            return self.cuda(device)
        else:
            new_param = Int8Params(
                super().to(
                    device=device, dtype=dtype, non_blocking=non_blocking
                ),
                requires_grad=self.requires_grad,
                has_fp16_weights=self.has_fp16_weights,
            )
            new_param.CB = self.CB
            new_param.SCB = self.SCB

            return new_param



class Linear8bitLt(nn.Linear):
    def __init__(self, input_features, output_features, bias=True, has_fp16_weights=True,
                       memory_efficient_backward=False, threshold=0.0, index=None, device=None):
        super().__init__(input_features, output_features, bias, device)
        assert not memory_efficient_backward, "memory_efficient_backward is no longer required and the argument is deprecated in 0.37.0 and will be removed in 0.39.0"
        self.state = bnb.MatmulLtState()
        self.index = index

        self.state.threshold = threshold
        self.state.has_fp16_weights = has_fp16_weights
        self.state.memory_efficient_backward = memory_efficient_backward
        if threshold > 0.0 and not has_fp16_weights:
            self.state.use_pool = True

        self.weight = Int8Params(self.weight.data, has_fp16_weights=has_fp16_weights, requires_grad=has_fp16_weights)

    def _save_to_state_dict(self, destination, prefix, keep_vars):
        if not self.state.has_fp16_weights and self.state.CB is None and self.state.CxB is not None:
            # reorder weight layout back from ampere/turing to row
            reorder_layout = True
            weight_clone = self.weight.data.clone()
        else:
            reorder_layout = False

        try:
            if reorder_layout:
                self.weight.data = undo_layout(self.state.CxB, self.state.tile_indices)

            super()._save_to_state_dict(destination, prefix, keep_vars)

            # we only need to save SCB as extra data, because CB for quantized weights is already stored in weight.data
            weight_name = "SCB"

            # case 1: .cuda was called, SCB is in self.weight
            param_from_weight = getattr(self.weight, weight_name)
            # case 2: self.init_8bit_state was called, SCB is in self.state
            param_from_state = getattr(self.state, weight_name)

            key_name = prefix + f"{weight_name}"
            if param_from_weight is not None:
                destination[key_name] = param_from_weight if keep_vars else param_from_weight.detach()
            elif not self.state.has_fp16_weights and param_from_state is not None:
                destination[key_name] = param_from_state if keep_vars else param_from_state.detach()
        finally:
            if reorder_layout:
                self.weight.data = weight_clone

    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
                              missing_keys, unexpected_keys, error_msgs):
        super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys,
                                      error_msgs)
        for key in unexpected_keys:
            input_name = key[len(prefix):]
            if input_name == "SCB":
                if self.weight.SCB is None:
                    # buffers not yet initialized, can't call them directly without
                    raise RuntimeError("Loading a quantized checkpoint into non-quantized Linear8bitLt is "
                                       "not supported. Please call module.cuda() before module.load_state_dict()")

                input_param = state_dict[key]
                self.weight.SCB.copy_(input_param)
                unexpected_keys.remove(key)

    def init_8bit_state(self):
        self.state.CB = self.weight.CB
        self.state.SCB = self.weight.SCB
        self.weight.CB = None
        self.weight.SCB = None

    def forward(self, x: torch.Tensor):
        self.state.is_training = self.training
        if self.weight.CB is not None:
            self.init_8bit_state()

        # weights are cast automatically as Int8Params, but the bias has to be cast manually
        if self.bias is not None and self.bias.dtype != x.dtype:
            self.bias.data = self.bias.data.to(x.dtype)

        out = bnb.matmul(x, self.weight, bias=self.bias, state=self.state)

        if not self.state.has_fp16_weights:
            if self.state.CB is not None and self.state.CxB is not None:
                # we converted 8-bit row major to turing/ampere format in the first inference pass
                # we no longer need the row-major weight
                del self.state.CB
                self.weight.data = self.state.CxB
        return out


class OutlierAwareLinear(nn.Linear):
    def __init__(self, input_features, output_features, bias=True, device=None):
        super().__init__(input_features, output_features, bias, device)
        self.outlier_dim = None
        self.is_quantized = False

    def forward_with_outliers(self, x, outlier_idx):
        raise NotImplementedError('Please override the `forward_with_outliers(self, x, outlier_idx)` function')

    def quantize_weight(self, w, outlier_idx):
        raise NotImplementedError('Please override the `quantize_weights(self, w, outlier_idx)` function')

    def forward(self, x):
        if self.outlier_dim is None:
            tracer = OutlierTracer.get_instance()
            if not tracer.is_initialized():
                print('Please use OutlierTracer.initialize(model) before using the OutlierAwareLinear layer')
            outlier_idx = tracer.get_outliers(self.weight)
            #print(outlier_idx, tracer.get_hvalue(self.weight))
            self.outlier_dim = outlier_idx

        if not self.is_quantized:
            w = self.quantize_weight(self.weight, self.outlier_dim)
            self.weight.data.copy_(w)
            self.is_quantized = True

class SwitchBackLinearBnb(nn.Linear):
    def __init__(
        self,
        input_features,
        output_features,
        bias=True,
        has_fp16_weights=True,
        memory_efficient_backward=False,
        threshold=0.0,
        index=None,
        device=None
    ):
        super().__init__(
            input_features, output_features, bias, device
        )
        self.state = bnb.MatmulLtState()
        self.index = index

        self.state.threshold = threshold
        self.state.has_fp16_weights = has_fp16_weights
        self.state.memory_efficient_backward = memory_efficient_backward
        if threshold > 0.0 and not has_fp16_weights:
            self.state.use_pool = True

        self.weight = Int8Params(
            self.weight.data, has_fp16_weights=has_fp16_weights, requires_grad=has_fp16_weights
        )

    def init_8bit_state(self):
        self.state.CB = self.weight.CB
        self.state.SCB = self.weight.SCB
        self.weight.CB = None
        self.weight.SCB = None

    def forward(self, x):
        self.state.is_training = self.training

        if self.weight.CB is not None:
            self.init_8bit_state()

        out = bnb.matmul_mixed(x.half(), self.weight.half(), bias=None, state=self.state) + self.bias