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# This implementation was adapted from https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/block.py
# Commit id: abbc1311731867310635f9edc2a9ec18317c8c48
# Copyright (c) 2024, Tri Dao.
from functools import partial
from typing import Optional
import torch
import torch.fx
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from .stochastic_depth import StochasticDepth
from .mha import MHA
from .mlp import Mlp
try:
from flash_attn.ops.triton.layer_norm import layer_norm_fn, RMSNorm
except ImportError:
layer_norm_fn, RMSNorm = None, None
def stochastic_depth(
input: Tensor, p: float, mode: str, training: bool = True
) -> Tensor:
"""
Implements the Stochastic Depth from `"Deep Networks with Stochastic Depth"
<https://arxiv.org/abs/1603.09382>`_ used for randomly dropping residual
branches of residual architectures.
Args:
input (Tensor[N, ...]): The input tensor or arbitrary dimensions with the first one
being its batch i.e. a batch with ``N`` rows.
p (float): probability of the input to be zeroed.
mode (str): ``"batch"`` or ``"row"``.
``"batch"`` randomly zeroes the entire input, ``"row"`` zeroes
randomly selected rows from the batch.
training: apply stochastic depth if is ``True``. Default: ``True``
Returns:
Tensor[N, ...]: The randomly zeroed tensor.
"""
if p < 0.0 or p > 1.0:
raise ValueError(f"drop probability has to be between 0 and 1, but got {p}")
if mode not in ["batch", "row"]:
raise ValueError(f"mode has to be either 'batch' or 'row', but got {mode}")
if not training or p == 0.0:
return input
survival_rate = 1.0 - p
if mode == "row":
size = [input.shape[0]] + [1] * (input.ndim - 1)
else:
size = [1] * input.ndim
noise = torch.empty(size, dtype=input.dtype, device=input.device)
noise = noise.bernoulli_(survival_rate)
if survival_rate > 0.0:
noise.div_(survival_rate)
return input * noise
torch.fx.wrap("stochastic_depth")
class StochasticDepth(nn.Module):
"""
See :func:`stochastic_depth`.
"""
def __init__(self, p: float, mode: str) -> None:
super().__init__()
self.p = p
self.mode = mode
def forward(self, input: Tensor) -> Tensor:
return stochastic_depth(input, self.p, self.mode, self.training)
def __repr__(self) -> str:
s = f"{self.__class__.__name__}(p={self.p}, mode={self.mode})"
return s
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 layer_norm_fn is not None, "Triton is not installed"
assert isinstance(self.norm1, (nn.LayerNorm, RMSNorm)) 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 allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, **kwargs):
return self.mixer.allocate_inference_cache(
batch_size, max_seqlen, dtype=dtype, **kwargs
)
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 = layer_norm_fn(
hidden_states,
self.norm1.weight,
self.norm1.bias,
residual=residual,
eps=self.norm1.eps,
dropout_p=self.dropout1.p if self.training else 0.0,
rowscale=rowscale1,
prenorm=True,
residual_in_fp32=self.residual_in_fp32,
is_rms_norm=isinstance(self.norm1, RMSNorm),
)
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 = layer_norm_fn(
hidden_states,
self.norm2.weight,
self.norm2.bias,
residual=residual,
eps=self.norm2.eps,
dropout_p=self.dropout2.p if self.training else 0.0,
rowscale=rowscale2,
prenorm=True,
residual_in_fp32=self.residual_in_fp32,
is_rms_norm=isinstance(self.norm2, RMSNorm),
)
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 = layer_norm_fn(
mixer_out,
self.norm1.weight,
self.norm1.bias,
residual=hidden_states,
eps=self.norm1.eps,
dropout_p=self.dropout1.p if self.training else 0.0,
rowscale=rowscale1,
prenorm=False,
is_rms_norm=isinstance(self.norm1, RMSNorm),
)
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 = layer_norm_fn(
mlp_out,
self.norm2.weight,
self.norm2.bias,
residual=hidden_states,
eps=self.norm2.eps,
dropout_p=self.dropout2.p if self.training else 0.0,
rowscale=rowscale2,
prenorm=False,
is_rms_norm=isinstance(self.norm2, RMSNorm),
)
return hidden_states
class ParallelBlock(nn.Module):
"""The attention (mixer) and MLP blocks are done in parallel, similar to GPT-J, GPT-NeoX,
and PaLM.
"""
def __init__(
self,
dim,
mixer_cls=None,
mlp_cls=None,
norm_cls=nn.LayerNorm,
dropout_cls=nn.Dropout,
resid_dropout1=0.0,
resid_dropout2=0.0,
tied_norm=False,
fused_dropout_add_ln=False,
residual_in_fp32=False,
sequence_parallel=False,
mark_shared_params=False,
):
"""
This Block has a slightly different structure compared to a regular
prenorm Transformer block.
The standard block is: LN -> MHA / MLP -> Dropout -> Add.
[Ref: https://arxiv.org/abs/2002.04745]
Here we have: Dropout -> Add -> LN -> MHA / MLP, returning both
the hidden_states (output1 of the MHA / 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).
"""
super().__init__()
self.tied_norm = tied_norm
self.fused_dropout_add_ln = fused_dropout_add_ln
self.residual_in_fp32 = residual_in_fp32
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.norm1 = norm_cls(dim)
self.mlp = mlp_cls(dim)
self.dropout2 = dropout_cls(resid_dropout2)
if not self.tied_norm:
self.norm2 = norm_cls(dim)
if self.fused_dropout_add_ln:
assert layer_norm_fn is not None, "Triton is not installed"
assert isinstance(self.norm1, (nn.LayerNorm, RMSNorm)) 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 allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, **kwargs):
return self.mixer.allocate_inference_cache(
batch_size, max_seqlen, dtype=dtype, **kwargs
)
def forward(
self,
hidden_states1: Tensor,
hidden_states2: Optional[Tensor] = None,
residual: Optional[Tensor] = None,
mixer_kwargs=None,
):
r"""Pass the input through the encoder layer.
Args:
hidden_states1: the output of the previous attention (mixer) or embedding layer.
hidden_states2: the output of the previous MLP layer (if None, will use hidden_states1).
residual.
"""
# TODO: Ideally we should only do the allgather / allreduce once for
# the Linear to MLP & Attention
if not self.fused_dropout_add_ln:
dropped1 = self.dropout1(hidden_states1)
# For the very 1st block, we only want 1 dropout, not two different dropouts
if hidden_states2 is not None:
dropped2 = self.dropout2(hidden_states2)
residual = (
(residual + dropped1 + dropped2)
if residual is not None
else dropped1 + dropped2
)
else:
residual = (residual + dropped1) if residual is not None else dropped1
hidden_states1 = self.norm1(residual.to(dtype=self.norm1.weight.dtype))
hidden_states2 = (
self.norm2(residual.to(dtype=self.norm2.weight.dtype))
if not self.tied_norm
else hidden_states1
)
if self.residual_in_fp32:
residual = residual.to(torch.float32)
else:
weight2, bias2 = (
(self.norm2.weight, self.norm2.bias)
if not self.tied_norm
else (None, None)
)
hidden_states1, *rest, residual = layer_norm_fn(
hidden_states1,
self.norm1.weight,
self.norm1.bias,
residual=residual,
x1=hidden_states2,
weight1=weight2,
bias1=bias2,
eps=self.norm1.eps,
dropout_p=self.dropout1.p if self.training else 0.0,
prenorm=True,
residual_in_fp32=self.residual_in_fp32,
is_rms_norm=isinstance(self.norm1, RMSNorm),
)
if self.tied_norm:
hidden_states2 = hidden_states1
else:
(hidden_states2,) = rest
if mixer_kwargs is None:
mixer_kwargs = {}
hidden_states1 = self.mixer(hidden_states1, **mixer_kwargs)
hidden_states2 = self.mlp(hidden_states2)
return hidden_states1, hidden_states2, residual
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