HybridTensor/modules/references/block_dejavu.py

from typing import Optional
from functools import partial

import torch
import torch.nn as nn
from torch import Tensor

from torchvision.ops import StochasticDepth

from flash_attn.modules.mha import MHA
from flash_attn.modules.mlp import Mlp

try:
    from flash_attn.ops.layer_norm import dropout_add_layer_norm
except ImportError:
    dropout_add_layer_norm = None


class Block(nn.Module):
    def __init__(
        self,
        dim,
        mixer_cls=None,
        mlp_cls=None,
        norm_cls=nn.LayerNorm,
        dropout_cls=nn.Dropout,
        prenorm=True,
        resid_dropout1=0.0,
        resid_dropout2=0.0,
        drop_path1=0.0,
        drop_path2=0.0,
        fused_dropout_add_ln=False,
        return_residual=False,
        residual_in_fp32=False,
        sequence_parallel=False,
        mark_shared_params=False,
    ):
        """
        For prenorm=True, this Block has a slightly different structure compared to a regular
        prenorm Transformer block.
        The standard block is: LN -> MHA -> Dropout -> Add -> LN -> MLP -> Dropout -> Add.
        [Ref: https://arxiv.org/abs/2002.04745]
        Here we have: Dropout -> Add -> LN -> MHA -> Dropout -> Add -> LN -> MLP, returning both
        the hidden_states (output of the MLP) and the residual.
        This is for performance reasons, as we can fuse the dropout, add and LayerNorm.
        The residual needs to be provided (except for the very first block).

        For prenorm=False, this Block has the same structure as a regular postnorm Transformer
        block: MHA -> Dropout -> Add -> LN -> MLP -> Dropout -> Add -> LN.

        return_residual: whether each of the sub-layers (mixer and mlp) will return the residual.
        This is for performance reason: for post-norm architecture, returning the input allows us
        to fuse the backward of nn.Linear with the residual connection.
        """
        super().__init__()
        self.prenorm = prenorm
        self.fused_dropout_add_ln = fused_dropout_add_ln
        self.return_residual = return_residual
        self.residual_in_fp32 = residual_in_fp32
        if self.residual_in_fp32:
            assert self.prenorm, "residual_in_fp32 is only compatible with prenorm=True"
        if mixer_cls is None:
            mixer_cls = partial(MHA, num_heads=dim // 64)
        if mlp_cls is None:
            mlp_cls = partial(Mlp, hidden_features=4 * dim)
        self.mixer = mixer_cls(dim)
        self.dropout1 = dropout_cls(resid_dropout1)
        self.drop_path1 = StochasticDepth(drop_path1, mode="row")
        self.norm1 = norm_cls(dim)
        self.mlp = mlp_cls(dim)
        if not isinstance(self.mlp, nn.Identity):
            self.dropout2 = dropout_cls(resid_dropout2)
            self.drop_path2 = StochasticDepth(drop_path2, mode="row")
            self.norm2 = norm_cls(dim)

        if self.fused_dropout_add_ln:
            assert dropout_add_layer_norm is not None, "dropout_add_ln is not installed"
            assert isinstance(self.norm1, nn.LayerNorm) and isinstance(
                self.dropout1, nn.Dropout
            )

        # TD [2023-01-07]: TODO: During training, if sequence_parallel is False and dropout != 0.0,
        # then the input to each worker in the tensor parallel group will be different.
        # This would produce wrong outputs? Somehow we'd need to sync the RNG state across workers.
        # For now this is not an issue because we always use sequence_parallel=True during training
        # and only use sequence_parallel=False during inference.

        # Mark the norm parameters as "sequence_parallel" so that we run all-reduce on their grads.
        if sequence_parallel:
            for p in self.norm1.parameters():
                p._sequence_parallel = True
            if hasattr(self, "norm2"):
                for p in self.norm2.parameters():
                    p._sequence_parallel = True
        # Mark the norm parameters as "shared_params" so that we sync their values at init.
        if mark_shared_params:
            for p in self.norm1.parameters():
                p._shared_params = True
            if hasattr(self, "norm2"):
                for p in self.norm2.parameters():
                    p._shared_params = True

    def forward(
        self,
        hidden_states: Tensor,
        residual: Optional[Tensor] = None,
        mixer_subset=None,
        mixer_kwargs=None,
    ):
        r"""Pass the input through the encoder layer.

        Args:
            hidden_states: the sequence to the encoder layer (required).
            residual: if postnorm, residual=None, If prenorm, hidden_states = Attn/MLP(LN(residual))
            mixer_subset: for cross-attention only. If not None, will take a subset of x
                before applying the query projection. Useful for e.g., ViT where we only care
                about the CLS token in the last layer.
        """

        if self.prenorm:
            if not self.fused_dropout_add_ln:
                dropped = self.drop_path1(self.dropout1(hidden_states))
                residual = (dropped + residual) if residual is not None else dropped
                hidden_states = self.norm1(residual.to(dtype=self.norm1.weight.dtype))
                if self.residual_in_fp32:
                    residual = residual.to(torch.float32)
            else:
                if self.drop_path1.p == 0 or not self.training:
                    rowscale1 = None
                else:
                    rowscale1 = self.drop_path1(
                        torch.ones(
                            hidden_states.shape[:-1],
                            device=hidden_states.device,
                            dtype=hidden_states.dtype,
                        )
                    )
                hidden_states, residual = dropout_add_layer_norm(
                    hidden_states,
                    residual,
                    self.norm1.weight,
                    self.norm1.bias,
                    self.dropout1.p if self.training else 0.0,
                    self.norm1.eps,
                    rowscale=rowscale1,
                    prenorm=True,
                    residual_in_fp32=self.residual_in_fp32,
                )

            if mixer_kwargs is None:
                mixer_kwargs = {}
            if mixer_subset is not None:
                mixer_kwargs["mixer_subset"] = mixer_subset

            hidden_states = self.mixer(hidden_states, **mixer_kwargs)

            if mixer_subset is not None:
                residual = residual[:, mixer_subset]

            if not isinstance(self.mlp, nn.Identity):
                if not self.fused_dropout_add_ln:
                    dropped = self.drop_path2(self.dropout2(hidden_states))
                    residual = (dropped + residual) if residual is not None else dropped
                    hidden_states = self.norm2(
                        residual.to(dtype=self.norm2.weight.dtype)
                    )
                    if self.residual_in_fp32:
                        residual = residual.to(torch.float32)
                else:
                    if self.drop_path2.p == 0 or not self.training:
                        rowscale2 = None
                    else:
                        rowscale2 = self.drop_path2(
                            torch.ones(
                                hidden_states.shape[:-1],
                                device=hidden_states.device,
                                dtype=hidden_states.dtype,
                            )
                        )
                    hidden_states, residual = dropout_add_layer_norm(
                        hidden_states,
                        residual,
                        self.norm2.weight,
                        self.norm2.bias,
                        self.dropout2.p if self.training else 0.0,
                        self.norm2.eps,
                        rowscale=rowscale2,
                        prenorm=True,
                        residual_in_fp32=self.residual_in_fp32,
                    )

                hidden_states = self.mlp(hidden_states)
            return hidden_states, residual
        else:
            assert residual is None
            mixer_out = self.mixer(
                hidden_states, **(mixer_kwargs if mixer_kwargs is not None else {})
            )
            if self.return_residual:  # mixer out is actually a pair here
                mixer_out, hidden_states = mixer_out
            if not self.fused_dropout_add_ln:
                hidden_states = self.norm1(
                    (self.drop_path1(self.dropout1(mixer_out)) + hidden_states).to(
                        dtype=self.norm1.weight.dtype
                    )
                )
            else:
                if self.drop_path1.p == 0 or not self.training:
                    rowscale1 = None
                else:
                    rowscale1 = self.drop_path1(
                        torch.ones(
                            mixer_out.shape[:-1],
                            device=mixer_out.device,
                            dtype=mixer_out.dtype,
                        )
                    )
                hidden_states = dropout_add_layer_norm(
                    mixer_out,
                    hidden_states,
                    self.norm1.weight,
                    self.norm1.bias,
                    self.dropout1.p if self.training else 0.0,
                    self.norm1.eps,
                    rowscale=rowscale1,
                    prenorm=False,
                )
            if not isinstance(self.mlp, nn.Identity):
                mlp_out = self.mlp(hidden_states)
                if self.return_residual:  # mlp out is actually a pair here
                    mlp_out, hidden_states = mlp_out
                if not self.fused_dropout_add_ln:
                    hidden_states = self.norm2(
                        (self.drop_path2(self.dropout2(mlp_out)) + hidden_states).to(
                            dtype=self.norm2.weight.dtype
                        )
                    )
                else:
                    if self.drop_path2.p == 0 or not self.training:
                        rowscale2 = None
                    else:
                        rowscale2 = self.drop_path2(
                            torch.ones(
                                mlp_out.shape[:-1],
                                device=mlp_out.device,
                                dtype=mlp_out.dtype,
                            )
                        )
                    hidden_states = dropout_add_layer_norm(
                        mlp_out,
                        hidden_states,
                        self.norm2.weight,
                        self.norm2.bias,
                        self.dropout2.p if self.training else 0.0,
                        self.norm2.eps,
                        rowscale=rowscale2,
                        prenorm=False,
                    )
            return hidden_states


class BlockDejavu(nn.Module):
    def __init__(
        self,
        dim,
        mixer_cls=None,
        mlp_cls=None,
        norm_cls=nn.LayerNorm,
        dropout_cls=nn.Dropout,
        prenorm=True,
        resid_dropout1=0.0,
        resid_dropout2=0.0,
        drop_path1=0.0,
        drop_path2=0.0,
        fused_dropout_add_ln=False,
        return_residual=False,
        residual_in_fp32=False,
        sequence_parallel=False,
        mark_shared_params=False,
    ):
        """
        For prenorm=True, this Block has a slightly different structure compared to a regular
        prenorm Transformer block.
        The standard block is: LN -> MHA -> Dropout -> Add -> LN -> MLP -> Dropout -> Add.
        [Ref: https://arxiv.org/abs/2002.04745]
        Here we have: Dropout -> Add -> LN -> MHA -> Dropout -> Add -> LN -> MLP, returning both
        the hidden_states (output of the MLP) and the residual (input of the MLP).
        This is for performance reasons, as we can fuse the dropout, add and LayerNorm.
        The residual needs to be provided (except for the very first block).

        For prenorm=False, this Block has the same structure as a regular postnorm Transformer
        block: MHA -> Dropout -> Add -> LN -> MLP -> Dropout -> Add -> LN.

        return_residual: whether each of the sub-layers (mixer and mlp) will return the residual.
        This is for performance reason: for post-norm architecture, returning the input allows us
        to fuse the backward of nn.Linear with the residual connection.
        """
        super().__init__()
        assert prenorm == True, "Dejavu only support prenorm for now"
        self.prenorm = prenorm
        self.fused_dropout_add_ln = fused_dropout_add_ln
        self.return_residual = return_residual
        self.residual_in_fp32 = residual_in_fp32
        if self.residual_in_fp32:
            assert self.prenorm, "residual_in_fp32 is only compatible with prenorm=True"
        if mixer_cls is None:
            mixer_cls = partial(MHA, num_heads=dim // 64)
        if mlp_cls is None:
            mlp_cls = partial(Mlp, hidden_features=4 * dim)
        self.mixer = mixer_cls(dim)
        self.dropout1 = dropout_cls(resid_dropout1)
        self.drop_path1 = StochasticDepth(drop_path1, mode="row")
        self.norm1 = norm_cls(dim)
        self.mlp = mlp_cls(dim)
        if not isinstance(self.mlp, nn.Identity):
            self.dropout2 = dropout_cls(resid_dropout2)
            self.drop_path2 = StochasticDepth(drop_path2, mode="row")
            self.norm2 = norm_cls(dim)

        if self.fused_dropout_add_ln:
            assert dropout_add_layer_norm is not None, "dropout_add_ln is not installed"
            assert isinstance(self.norm1, nn.LayerNorm) and isinstance(
                self.dropout1, nn.Dropout
            )

        # TD [2023-01-07]: TODO: During training, if sequence_parallel is False and dropout != 0.0,
        # then the input to each worker in the tensor parallel group will be different.
        # This would produce wrong outputs? Somehow we'd need to sync the RNG state across workers.
        # For now this is not an issue because we always use sequence_parallel=True during training
        # and only use sequence_parallel=False during inference.

        # Mark the norm parameters as "sequence_parallel" so that we run all-reduce on their grads.
        if sequence_parallel:
            for p in self.norm1.parameters():
                p._sequence_parallel = True
            if hasattr(self, "norm2"):
                for p in self.norm2.parameters():
                    p._sequence_parallel = True
        # Mark the norm parameters as "shared_params" so that we sync their values at init.
        if mark_shared_params:
            for p in self.norm1.parameters():
                p._shared_params = True
            if hasattr(self, "norm2"):
                for p in self.norm2.parameters():
                    p._shared_params = True

        # sp related
        self.mixer.mlp_k = self.mlp.sp.K

    def forward(
        self,
        hidden_states: Tensor,
        residual: Optional[Tensor] = None,
        mixer_subset=None,
        mixer_kwargs=None,
        mlp_sp_logit=None,
    ):
        r"""Pass the input through the encoder layer.

        Args:
            hidden_states: the sequence to the encoder layer (required).
            residual: If prenorm, residual is MLP block input before layer norm.
            mixer_subset: for cross-attention only. If not None, will take a subset of x
                before applying the query projection. Useful for e.g., ViT where we only care
                about the CLS token in the last layer.
        """

        if self.prenorm:
            if not self.fused_dropout_add_ln:
                dropped = self.drop_path1(self.dropout1(hidden_states))
                residual = (
                    (dropped + residual) if residual is not None else dropped
                )  # residual is output of MLP and input of MLP -> input of attention block
                hidden_states = self.norm1(
                    residual.to(dtype=self.norm1.weight.dtype)
                )  # hidden states is layer norm of attention block input
                if self.residual_in_fp32:
                    residual = residual.to(torch.float32)
            else:
                if self.drop_path1.p == 0 or not self.training:
                    rowscale1 = None
                else:
                    rowscale1 = self.drop_path1(
                        torch.ones(
                            hidden_states.shape[:-1],
                            device=hidden_states.device,
                            dtype=hidden_states.dtype,
                        )
                    )
                hidden_states, residual = dropout_add_layer_norm(
                    hidden_states,
                    residual,
                    self.norm1.weight,
                    self.norm1.bias,
                    self.dropout1.p if self.training else 0.0,
                    self.norm1.eps,
                    rowscale=rowscale1,
                    prenorm=True,
                    residual_in_fp32=self.residual_in_fp32,
                )

            if mixer_kwargs is None:
                mixer_kwargs = {}
            if mixer_subset is not None:
                mixer_kwargs["mixer_subset"] = mixer_subset

            hidden_states, mlp_idx = self.mixer(
                hidden_states,  # hidden states is after layer norm
                mlp_sp_logit=mlp_sp_logit,
                **mixer_kwargs
            )

            if mixer_subset is not None:
                residual = residual[:, mixer_subset]

            if not isinstance(self.mlp, nn.Identity):
                if not self.fused_dropout_add_ln:
                    dropped = self.drop_path2(
                        self.dropout2(hidden_states)
                    )  # hidden states is output of MHA
                    residual = (
                        (dropped + residual) if residual is not None else dropped
                    )  # residual is output of MHA + input of MHA
                    hidden_states = self.norm2(
                        residual.to(dtype=self.norm2.weight.dtype)
                    )  # hidden_states is layer norm of MLP input
                    if self.residual_in_fp32:
                        residual = residual.to(torch.float32)
                else:
                    if self.drop_path2.p == 0 or not self.training:
                        rowscale2 = None
                    else:
                        rowscale2 = self.drop_path2(
                            torch.ones(
                                hidden_states.shape[:-1],
                                device=hidden_states.device,
                                dtype=hidden_states.dtype,
                            )
                        )
                    hidden_states, residual = dropout_add_layer_norm(
                        hidden_states,
                        residual,
                        self.norm2.weight,
                        self.norm2.bias,
                        self.dropout2.p if self.training else 0.0,
                        self.norm2.eps,
                        rowscale=rowscale2,
                        prenorm=True,
                        residual_in_fp32=self.residual_in_fp32,
                    )
                # hidden states is after layer norm, residual is before layer norm
                hidden_states, next_mlp_sp_logit = self.mlp(
                    hidden_states, residual, mlp_idx
                )
            return (
                hidden_states,
                residual,
                next_mlp_sp_logit,
            )


class BlockMlpAttSparse(nn.Module):
    def __init__(
        self,
        dim,
        mixer_cls=None,
        mlp_cls=None,
        norm_cls=nn.LayerNorm,
        dropout_cls=nn.Dropout,
        prenorm=True,
        resid_dropout1=0.0,
        resid_dropout2=0.0,
        drop_path1=0.0,
        drop_path2=0.0,
        fused_dropout_add_ln=False,
        return_residual=False,
        residual_in_fp32=False,
        sequence_parallel=False,
        mark_shared_params=False,
    ):
        """
        For prenorm=True, this Block has a slightly different structure compared to a regular
        prenorm Transformer block.
        The standard block is: LN -> MHA -> Dropout -> Add -> LN -> MLP -> Dropout -> Add.
        [Ref: https://arxiv.org/abs/2002.04745]
        Here we have: Dropout -> Add -> LN -> MHA -> Dropout -> Add -> LN -> MLP, returning both
        the hidden_states (output of the MLP) and the residual (input of the MLP).
        This is for performance reasons, as we can fuse the dropout, add and LayerNorm.
        The residual needs to be provided (except for the very first block).

        For prenorm=False, this Block has the same structure as a regular postnorm Transformer
        block: MHA -> Dropout -> Add -> LN -> MLP -> Dropout -> Add -> LN.

        return_residual: whether each of the sub-layers (mixer and mlp) will return the residual.
        This is for performance reason: for post-norm architecture, returning the input allows us
        to fuse the backward of nn.Linear with the residual connection.
        """
        super().__init__()
        assert prenorm == True, "Dejavu only support prenorm for now"
        self.prenorm = prenorm
        self.fused_dropout_add_ln = fused_dropout_add_ln
        self.return_residual = return_residual
        self.residual_in_fp32 = residual_in_fp32
        if self.residual_in_fp32:
            assert self.prenorm, "residual_in_fp32 is only compatible with prenorm=True"
        if mixer_cls is None:
            mixer_cls = partial(MHA, num_heads=dim // 64)
        if mlp_cls is None:
            mlp_cls = partial(Mlp, hidden_features=4 * dim)
        self.mixer = mixer_cls(dim)
        self.dropout1 = dropout_cls(resid_dropout1)
        self.drop_path1 = StochasticDepth(drop_path1, mode="row")
        self.norm1 = norm_cls(dim)
        self.mlp = mlp_cls(dim)
        if not isinstance(self.mlp, nn.Identity):
            self.dropout2 = dropout_cls(resid_dropout2)
            self.drop_path2 = StochasticDepth(drop_path2, mode="row")
            self.norm2 = norm_cls(dim)

        if self.fused_dropout_add_ln:
            assert dropout_add_layer_norm is not None, "dropout_add_ln is not installed"
            assert isinstance(self.norm1, nn.LayerNorm) and isinstance(
                self.dropout1, nn.Dropout
            )

        # TD [2023-01-07]: TODO: During training, if sequence_parallel is False and dropout != 0.0,
        # then the input to each worker in the tensor parallel group will be different.
        # This would produce wrong outputs? Somehow we'd need to sync the RNG state across workers.
        # For now this is not an issue because we always use sequence_parallel=True during training
        # and only use sequence_parallel=False during inference.

        # Mark the norm parameters as "sequence_parallel" so that we run all-reduce on their grads.
        if sequence_parallel:
            for p in self.norm1.parameters():
                p._sequence_parallel = True
            if hasattr(self, "norm2"):
                for p in self.norm2.parameters():
                    p._sequence_parallel = True
        # Mark the norm parameters as "shared_params" so that we sync their values at init.
        if mark_shared_params:
            for p in self.norm1.parameters():
                p._shared_params = True
            if hasattr(self, "norm2"):
                for p in self.norm2.parameters():
                    p._shared_params = True

        # sp related
        if hasattr(self.mlp, "sp"):
            self.mixer.mlp_k = self.mlp.sp.K
        if hasattr(self.mixer, "sp"):
            self.mixer.att_k = self.mixer.sp.K

    def forward(
        self,
        hidden_states: Tensor,
        residual: Optional[Tensor] = None,
        mixer_subset=None,
        mixer_kwargs=None,
        head_idx=None,
        mlp_sp_logit=None,
    ):
        r"""Pass the input through the encoder layer.

        Args:
            hidden_states: the sequence to the encoder layer (required).
            residual: If prenorm, residual is MLP block input before layer norm.
            mixer_subset: for cross-attention only. If not None, will take a subset of x
                before applying the query projection. Useful for e.g., ViT where we only care
                about the CLS token in the last layer.
        """

        if self.prenorm:
            if not self.fused_dropout_add_ln:
                dropped = self.drop_path1(self.dropout1(hidden_states))
                residual = (
                    (dropped + residual) if residual is not None else dropped
                )  # residual is output of MLP and input of MLP -> input of attention block
                hidden_states = self.norm1(
                    residual.to(dtype=self.norm1.weight.dtype)
                )  # hidden states is layer norm of attention block input
                if self.residual_in_fp32:
                    residual = residual.to(torch.float32)
            else:
                if self.drop_path1.p == 0 or not self.training:
                    rowscale1 = None
                else:
                    rowscale1 = self.drop_path1(
                        torch.ones(
                            hidden_states.shape[:-1],
                            device=hidden_states.device,
                            dtype=hidden_states.dtype,
                        )
                    )
                hidden_states, residual = dropout_add_layer_norm(
                    hidden_states,
                    residual,
                    self.norm1.weight,
                    self.norm1.bias,
                    self.dropout1.p if self.training else 0.0,
                    self.norm1.eps,
                    rowscale=rowscale1,
                    prenorm=True,
                    residual_in_fp32=self.residual_in_fp32,
                )

            if mixer_kwargs is None:
                mixer_kwargs = {}
            if mixer_subset is not None:
                mixer_kwargs["mixer_subset"] = mixer_subset

            hidden_states, mlp_idx, next_att_idx = self.mixer(
                hidden_states,  # hidden states is after layer norm
                residual=residual,
                head_idx=head_idx,
                mlp_sp_logit=mlp_sp_logit,
                **mixer_kwargs
            )

            if mixer_subset is not None:
                residual = residual[:, mixer_subset]

            if not isinstance(self.mlp, nn.Identity):
                if not self.fused_dropout_add_ln:
                    dropped = self.drop_path2(
                        self.dropout2(hidden_states)
                    )  # hidden states is output of MHA
                    residual = (
                        (dropped + residual) if residual is not None else dropped
                    )  # residual is output of MHA + input of MHA
                    hidden_states = self.norm2(
                        residual.to(dtype=self.norm2.weight.dtype)
                    )  # hidden_states is layer norm of MLP input
                    if self.residual_in_fp32:
                        residual = residual.to(torch.float32)
                else:
                    if self.drop_path2.p == 0 or not self.training:
                        rowscale2 = None
                    else:
                        rowscale2 = self.drop_path2(
                            torch.ones(
                                hidden_states.shape[:-1],
                                device=hidden_states.device,
                                dtype=hidden_states.dtype,
                            )
                        )
                    hidden_states, residual = dropout_add_layer_norm(
                        hidden_states,
                        residual,
                        self.norm2.weight,
                        self.norm2.bias,
                        self.dropout2.p if self.training else 0.0,
                        self.norm2.eps,
                        rowscale=rowscale2,
                        prenorm=True,
                        residual_in_fp32=self.residual_in_fp32,
                    )
                # hidden states is after layer norm, residual is before layer norm
                hidden_states, next_mlp_sp_logit = self.mlp(
                    hidden_states, residual, mlp_idx
                )
            return (hidden_states, residual, next_mlp_sp_logit, next_att_idx)