Source code for torch.distributed.tensor.parallel.multihead_attention_tp
# Copyright (c) Meta Platforms, Inc. and affiliates
# pyre-ignore-all-errors[6]
import math
from typing import Optional, Union
import torch
from torch.distributed._tensor import DTensor as DT
from torch.distributed._tensor.placement_types import Shard
from torch.distributed.tensor.parallel._view_with_dim_change import (
_view_with_sharding_dim_change,
)
__all__ = ["TensorParallelMultiheadAttention"]
# TODO: Add a test to test equivalence between our Multihead Attention
# with other mainstream ones (Megatron-LM or PyTorch).
def _stride_same_as_shard(
tensor: torch.Tensor, tp_size: int, chunk_dim: int, cat_dim: int
) -> torch.Tensor:
"""
Adjust local tensor's stride same as the sharded situation.
So that view result will keeps the same.
"""
if isinstance(tensor, DT):
return tensor
view_size = list(tensor.size())
view_size[chunk_dim] //= tp_size
return torch.cat(
[t.view(*view_size) for t in tensor.chunk(tp_size, dim=chunk_dim)],
dim=cat_dim,
).contiguous()
[docs]class TensorParallelMultiheadAttention(torch.nn.Module):
"""
Multi-head Attention block from Transformer models.
Since we need some customizations for the attention layer,
we are writing a customized but mathematically equivalent
attention module as defined in torch.nn.
Note that:
We now only support the case when it's self attention with
limited input args and we also assume that the input tensor
has a dimension of three. Although we do implement the logic
for multihead attention, it was not fully tested.
"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
bias: bool = True,
add_bias_kv: bool = False,
add_zero_attn: bool = False,
kdim: Optional[int] = None,
vdim: Optional[int] = None,
batch_first: bool = False,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
tp_size: int = 1,
self_attention: bool = True,
) -> None:
super().__init__()
self.device: torch.device = (
torch.device("cuda" if torch.cuda.is_available() else "cpu")
if device is None
else device
)
self.num_heads = num_heads
self.hidden_size = embed_dim
self.hidden_size_per_attention_head: int = self.hidden_size // num_heads
self.scale: float = self.hidden_size_per_attention_head**-0.5
if self_attention:
self.qkv: torch.nn.Module = torch.nn.Linear(
embed_dim, embed_dim * 3, bias=add_bias_kv, device=self.device
)
torch.nn.init.xavier_uniform_(self.qkv.weight)
if add_bias_kv:
torch.nn.init.zeros_(self.qkv.bias)
else:
self.query: torch.nn.Module = torch.nn.Linear(
embed_dim, embed_dim, bias=add_bias_kv, device=self.device
)
self.key: torch.nn.Module = torch.nn.Linear(
embed_dim, embed_dim, bias=add_bias_kv, device=self.device
)
self.value: torch.nn.Module = torch.nn.Linear(
embed_dim, embed_dim, bias=add_bias_kv, device=self.device
)
torch.nn.init.xavier_uniform_(self.query.weight)
torch.nn.init.xavier_uniform_(self.key.weight)
torch.nn.init.xavier_uniform_(self.value.weight)
if add_bias_kv:
torch.nn.init.zeros_(self.query.bias)
torch.nn.init.zeros_(self.key.bias)
torch.nn.init.zeros_(self.value.bias)
self.proj: torch.nn.Module = torch.nn.Linear(
embed_dim, embed_dim, bias=bias, device=self.device
)
torch.nn.init.kaiming_uniform_(self.proj.weight, a=math.sqrt(5))
if bias:
torch.nn.init.zeros_(self.proj.bias)
self.tp_size = tp_size
self.hidden_size = embed_dim
self.norm_factor: float = math.sqrt(self.hidden_size_per_attention_head)
self.self_attention = self_attention
def forward(
self,
query: Union[torch.Tensor, DT],
key: Union[torch.Tensor, DT],
value: Union[torch.Tensor, DT],
key_padding_mask: Optional[torch.Tensor] = None,
need_weights: bool = True,
attn_mask: Optional[torch.Tensor] = None,
average_attn_weights: bool = True,
) -> Union[torch.Tensor, DT]:
b, sq, h = query.shape
sk = key.size(1)
nh = self.num_heads
hn = self.hidden_size_per_attention_head
# x: [b, sq/sk/sv, h]
# ===================
# Permute. [sq/sk/sv, b, h]
# ===================
if not self.self_attention:
# =====================
# Query, Key, and Value
# =====================
query = query.permute(1, 0, 2).contiguous()
key = key.permute(1, 0, 2).contiguous()
value = value.permute(1, 0, 2).contiguous()
# Attention heads [sq/sk/sv, b, h] --> [sq/sk/sv * b, (nh * hn)]
query = query.view(-1, h)
key = key.view(-1, h)
value = value.view(-1, h)
query_layer = _view_with_sharding_dim_change(
self.query(query), 1, (sq, b * nh, hn)
)
key_layer = _view_with_sharding_dim_change(
self.key(key), 1, (sk, b * nh, hn)
)
value_layer = _view_with_sharding_dim_change(
self.value(value), 1, (sk, b * nh, hn)
)
else:
assert torch.equal(query, key) and torch.equal(
query, value
), "inputs are different for self-attention."
# =====================
# Query
# =====================
query = query.permute(1, 0, 2).contiguous()
# Attention heads [sq, b, h] --> [sq * b, (nh * 3 * hn)]
query = query.view(-1, h)
mixed_x_layer = self.qkv(query)
# [sq * b, 3 * h] --> [sq, b, nh, 3 * hn]
mixed_x_layer = _view_with_sharding_dim_change(
mixed_x_layer, 2, (sq, b, nh, 3 * hn)
)
# [sq, b, nh, 3 * hn] --> 3 [sq, b, nh, hn]
last_dim = mixed_x_layer.dim() - 1
last_dim_size = mixed_x_layer.size(last_dim) // 3
(query_layer, key_layer, value_layer) = mixed_x_layer.split(
last_dim_size, dim=last_dim
)
query_layer = _stride_same_as_shard(query_layer, self.tp_size, 2, 1)
key_layer = _stride_same_as_shard(key_layer, self.tp_size, 2, 1)
value_layer = _stride_same_as_shard(value_layer, self.tp_size, 2, 1)
# [sq, b, nh, hn] -> [sq, b * nh, hn]
query_layer = _view_with_sharding_dim_change(
query_layer, 1, (sq, b * nh, -1)
)
key_layer = _view_with_sharding_dim_change(key_layer, 1, (sq, b * nh, -1))
value_layer = _view_with_sharding_dim_change(
value_layer, 1, (sq, b * nh, -1)
)
# ===================================
# Raw attention scores. [b, nh, s, s]
# ===================================
factor = self.tp_size if isinstance(query_layer, DT) else 1
# preallocating result tensor: [b * nh, sq, sk]
matmul_result = torch.empty(
b * nh // factor,
sq,
sk,
dtype=query_layer.dtype,
device=self.device,
)
if isinstance(query_layer, DT):
matmul_result = DT.from_local(
matmul_result,
query_layer.device_mesh,
[Shard(0)],
run_check=False,
)
# Raw attention scores. [b * nh, sq, sk]
attn = torch.baddbmm(
matmul_result,
query_layer.transpose(0, 1), # [b * nh, sq, hn]
key_layer.transpose(0, 1).transpose(1, 2), # [b * nh, hn, sk]
beta=0.0,
alpha=(1.0 / self.norm_factor),
)
# ===============
# Attention probs
# ===============
attn = attn.softmax(dim=-1)
# =========================
# Context layer. [sq * b, hidden]
# =========================
# bmm: [b * nh, sq, hn]
context_layer = torch.bmm(attn, value_layer.transpose(0, 1))
# change view [nh, b, sq, hn]
context_layer = context_layer.view(nh, b, sq, hn)
# [nh, b, sq, hn] --> [sq, b, nh, hn]
context_layer = context_layer.permute(2, 1, 0, 3).contiguous()
# [sq, b, nh, hn] --> [sq * b, hidden]
context_layer = _view_with_sharding_dim_change(
context_layer.contiguous(), 1, (-1, self.hidden_size)
)
# =================
# Projection. [sq, b, h]
# =================
output = self.proj(context_layer).view(sq, b, h)
# ===================
# Permute. [b, sq, h]
# ===================
output = output.permute(1, 0, 2)
return output
def copy(self, that: torch.nn.MultiheadAttention) -> None:
# TODO: current implementation assume `self` is a self attention module
assert (
self.hidden_size == that.embed_dim
), "embed_dim must be equal in TensorParallelMultiheadAttention.copy()!"
if that.in_proj_weight is not None:
self.qkv.register_parameter("weight", that.in_proj_weight)
if that.in_proj_bias is not None:
self.qkv.register_parameter("bias", that.in_proj_bias)
if that.out_proj.weight is not None:
# TODO: The use of Parameter is to avoid `mypy` issue caused
# by the `tensor` type annotation on Linear.weight to which
# a Parameter object is actually assigned
self.proj.register_parameter(
"weight", torch.nn.Parameter(that.out_proj.weight)
)
if that.out_proj.bias is not None:
self.proj.register_parameter("bias", that.out_proj.bias)
