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# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
from typing import Any, Tuple
import torch
import torch.distributed as dist
from torch import Tensor
from .parallel_states import nccl_info
def broadcast(input_: torch.Tensor):
src = nccl_info.group_id * nccl_info.sp_size
dist.broadcast(input_, src=src, group=nccl_info.group)
def _all_to_all_4D(input: torch.tensor,
scatter_idx: int = 2,
gather_idx: int = 1,
group=None) -> torch.tensor:
"""
all-to-all for QKV
Args:
input (torch.tensor): a tensor sharded along dim scatter dim
scatter_idx (int): default 1
gather_idx (int): default 2
group : torch process group
Returns:
torch.tensor: resharded tensor (bs, seqlen/P, hc, hs)
"""
assert (
input.dim() == 4
), f"input must be 4D tensor, got {input.dim()} and shape {input.shape}"
seq_world_size = dist.get_world_size(group)
if scatter_idx == 2 and gather_idx == 1:
# input (torch.tensor): a tensor sharded along dim 1 (bs, seqlen/P, hc, hs) output: (bs, seqlen, hc/P, hs)
bs, shard_seqlen, hc, hs = input.shape
seqlen = shard_seqlen * seq_world_size
shard_hc = hc // seq_world_size
# transpose groups of heads with the seq-len parallel dimension, so that we can scatter them!
# (bs, seqlen/P, hc, hs) -reshape-> (bs, seq_len/P, P, hc/P, hs) -transpose(0,2)-> (P, seq_len/P, bs, hc/P, hs)
input_t = (input.reshape(bs, shard_seqlen, seq_world_size, shard_hc,
hs).transpose(0, 2).contiguous())
output = torch.empty_like(input_t)
# https://pytorch.org/docs/stable/distributed.html#torch.distributed.all_to_all_single
# (P, seq_len/P, bs, hc/P, hs) scatter seqlen -all2all-> (P, seq_len/P, bs, hc/P, hs) scatter head
if seq_world_size > 1:
dist.all_to_all_single(output, input_t, group=group)
torch.cuda.synchronize()
else:
output = input_t
# if scattering the seq-dim, transpose the heads back to the original dimension
output = output.reshape(seqlen, bs, shard_hc, hs)
# (seq_len, bs, hc/P, hs) -reshape-> (bs, seq_len, hc/P, hs)
output = output.transpose(0, 1).contiguous().reshape(
bs, seqlen, shard_hc, hs)
return output
elif scatter_idx == 1 and gather_idx == 2:
# input (torch.tensor): a tensor sharded along dim 1 (bs, seqlen, hc/P, hs) output: (bs, seqlen/P, hc, hs)
bs, seqlen, shard_hc, hs = input.shape
hc = shard_hc * seq_world_size
shard_seqlen = seqlen // seq_world_size
seq_world_size = dist.get_world_size(group)
# transpose groups of heads with the seq-len parallel dimension, so that we can scatter them!
# (bs, seqlen, hc/P, hs) -reshape-> (bs, P, seq_len/P, hc/P, hs) -transpose(0, 3)-> (hc/P, P, seqlen/P, bs, hs) -transpose(0, 1) -> (P, hc/P, seqlen/P, bs, hs)
input_t = (input.reshape(
bs, seq_world_size, shard_seqlen, shard_hc,
hs).transpose(0, 3).transpose(0, 1).contiguous().reshape(
seq_world_size, shard_hc, shard_seqlen, bs, hs))
output = torch.empty_like(input_t)
# https://pytorch.org/docs/stable/distributed.html#torch.distributed.all_to_all_single
# (P, bs x hc/P, seqlen/P, hs) scatter seqlen -all2all-> (P, bs x seq_len/P, hc/P, hs) scatter head
if seq_world_size > 1:
dist.all_to_all_single(output, input_t, group=group)
torch.cuda.synchronize()
else:
output = input_t
# if scattering the seq-dim, transpose the heads back to the original dimension
output = output.reshape(hc, shard_seqlen, bs, hs)
# (hc, seqlen/N, bs, hs) -tranpose(0,2)-> (bs, seqlen/N, hc, hs)
output = output.transpose(0, 2).contiguous().reshape(
bs, shard_seqlen, hc, hs)
return output
else:
raise RuntimeError(
"scatter_idx must be 1 or 2 and gather_idx must be 1 or 2")
class SeqAllToAll4D(torch.autograd.Function):
@staticmethod
def forward(
ctx: Any,
group: dist.ProcessGroup,
input: Tensor,
scatter_idx: int,
gather_idx: int,
) -> Tensor:
ctx.group = group
ctx.scatter_idx = scatter_idx
ctx.gather_idx = gather_idx
return _all_to_all_4D(input, scatter_idx, gather_idx, group=group)
@staticmethod
def backward(ctx: Any,
*grad_output: Tensor) -> Tuple[None, Tensor, None, None]:
return (
None,
SeqAllToAll4D.apply(ctx.group, *grad_output, ctx.gather_idx,
ctx.scatter_idx),
None,
None,
)
def all_to_all_4D(
input_: torch.Tensor,
scatter_dim: int = 2,
gather_dim: int = 1,
):
return SeqAllToAll4D.apply(nccl_info.group, input_, scatter_dim,
gather_dim)
def _all_to_all(
input_: torch.Tensor,
world_size: int,
group: dist.ProcessGroup,
scatter_dim: int,
gather_dim: int,
):
input_list = [
t.contiguous()
for t in torch.tensor_split(input_, world_size, scatter_dim)
]
output_list = [torch.empty_like(input_list[0]) for _ in range(world_size)]
dist.all_to_all(output_list, input_list, group=group)
return torch.cat(output_list, dim=gather_dim).contiguous()
class _AllToAll(torch.autograd.Function):
"""All-to-all communication.
Args:
input_: input matrix
process_group: communication group
scatter_dim: scatter dimension
gather_dim: gather dimension
"""
@staticmethod
def forward(ctx, input_, process_group, scatter_dim, gather_dim):
ctx.process_group = process_group
ctx.scatter_dim = scatter_dim
ctx.gather_dim = gather_dim
ctx.world_size = dist.get_world_size(process_group)
output = _all_to_all(input_, ctx.world_size, process_group,
scatter_dim, gather_dim)
return output
@staticmethod
def backward(ctx, grad_output):
grad_output = _all_to_all(
grad_output,
ctx.world_size,
ctx.process_group,
ctx.gather_dim,
ctx.scatter_dim,
)
return (
grad_output,
None,
None,
None,
)
def all_to_all(
input_: torch.Tensor,
scatter_dim: int = 2,
gather_dim: int = 1,
):
return _AllToAll.apply(input_, nccl_info.group, scatter_dim, gather_dim)
class _AllGather(torch.autograd.Function):
"""All-gather communication with autograd support.
Args:
input_: input tensor
dim: dimension along which to concatenate
"""
@staticmethod
def forward(ctx, input_, dim):
ctx.dim = dim
world_size = nccl_info.sp_size
group = nccl_info.group
input_size = list(input_.size())
ctx.input_size = input_size[dim]
tensor_list = [torch.empty_like(input_) for _ in range(world_size)]
input_ = input_.contiguous()
dist.all_gather(tensor_list, input_, group=group)
output = torch.cat(tensor_list, dim=dim)
return output
@staticmethod
def backward(ctx, grad_output):
world_size = nccl_info.sp_size
rank = nccl_info.rank_within_group
dim = ctx.dim
input_size = ctx.input_size
sizes = [input_size] * world_size
grad_input_list = torch.split(grad_output, sizes, dim=dim)
grad_input = grad_input_list[rank]
return grad_input, None
def all_gather(input_: torch.Tensor, dim: int = 1):
"""Performs an all-gather operation on the input tensor along the specified dimension.
Args:
input_ (torch.Tensor): Input tensor of shape [B, H, S, D].
dim (int, optional): Dimension along which to concatenate. Defaults to 1.
Returns:
torch.Tensor: Output tensor after all-gather operation, concatenated along 'dim'.
"""
return _AllGather.apply(input_, dim)
def prepare_sequence_parallel_data(hidden_states, encoder_hidden_states,
attention_mask, encoder_attention_mask):
if nccl_info.sp_size == 1:
return (
hidden_states,
encoder_hidden_states,
attention_mask,
encoder_attention_mask,
)
def prepare(hidden_states, encoder_hidden_states, attention_mask,
encoder_attention_mask):
hidden_states = all_to_all(hidden_states, scatter_dim=2, gather_dim=0)
encoder_hidden_states = all_to_all(encoder_hidden_states,
scatter_dim=1,
gather_dim=0)
attention_mask = all_to_all(attention_mask,
scatter_dim=1,
gather_dim=0)
encoder_attention_mask = all_to_all(encoder_attention_mask,
scatter_dim=1,
gather_dim=0)
return (
hidden_states,
encoder_hidden_states,
attention_mask,
encoder_attention_mask,
)
sp_size = nccl_info.sp_size
frame = hidden_states.shape[2]
assert frame % sp_size == 0, "frame should be a multiple of sp_size"
(
hidden_states,
encoder_hidden_states,
attention_mask,
encoder_attention_mask,
) = prepare(
hidden_states,
encoder_hidden_states.repeat(1, sp_size, 1),
attention_mask.repeat(1, sp_size, 1, 1),
encoder_attention_mask.repeat(1, sp_size),
)
return hidden_states, encoder_hidden_states, attention_mask, encoder_attention_mask
def sp_parallel_dataloader_wrapper(dataloader, device, train_batch_size,
sp_size, train_sp_batch_size):
while True:
for data_item in dataloader:
latents, cond, attn_mask, cond_mask = data_item
latents = latents.to(device)
cond = cond.to(device)
attn_mask = attn_mask.to(device)
cond_mask = cond_mask.to(device)
frame = latents.shape[2]
if frame == 1:
yield latents, cond, attn_mask, cond_mask
else:
latents, cond, attn_mask, cond_mask = prepare_sequence_parallel_data(
latents, cond, attn_mask, cond_mask)
assert (
train_batch_size * sp_size >= train_sp_batch_size
), "train_batch_size * sp_size should be greater than train_sp_batch_size"
for iter in range(train_batch_size * sp_size //
train_sp_batch_size):
st_idx = iter * train_sp_batch_size
ed_idx = (iter + 1) * train_sp_batch_size
encoder_hidden_states = cond[st_idx:ed_idx]
attention_mask = attn_mask[st_idx:ed_idx]
encoder_attention_mask = cond_mask[st_idx:ed_idx]
yield (
latents[st_idx:ed_idx],
encoder_hidden_states,
attention_mask,
encoder_attention_mask,
)
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