drbh

Migrated from kernels-community/mamba-ssm

8e0389f unverified about 1 month ago

12 kB

	# Copyright (c) 2024, Tri Dao.
	# The TensorParallel linear modules are inspired by https://github.com/NVIDIA/apex/blob/master/apex/transformer/tensor_parallel/layers.py
	from typing import Optional

	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	from torch import Tensor
	from torch.distributed import ProcessGroup
	from ..utils.torch import custom_bwd, custom_fwd

	from einops import rearrange

	from ..distributed.distributed_utils import (
	all_gather_raw,
	all_reduce,
	all_reduce_raw,
	reduce_scatter,
	reduce_scatter_raw,
	)


	class ParallelLinearFunc(torch.autograd.Function):
	@staticmethod
	@custom_fwd
	def forward(ctx, x, weight, bias, process_group=None, sequence_parallel=True):
	"""
	If process_group is not None and sequence_parallel=True, we're doing Tensor Parallel
	with sequence parallelism: we do an all_gather_raw of x before doing the matmul.
	"""
	ctx.compute_weight_gradient = weight.requires_grad
	ctx.process_group = process_group
	ctx.sequence_parallel = sequence_parallel

	if torch.is_autocast_enabled():
	x = x.to(dtype=torch.get_autocast_gpu_dtype())
	x = x.contiguous()
	if process_group is not None and sequence_parallel:
	# We want to kick off the all_gather early, before weight dtype conversion
	total_x, handle_x = all_gather_raw(x, process_group, async_op=True)
	else:
	total_x = x

	if torch.is_autocast_enabled():
	weight = weight.to(dtype=torch.get_autocast_gpu_dtype())
	bias = bias.to(dtype=torch.get_autocast_gpu_dtype()) if bias is not None else None
	weight = weight.contiguous()
	if process_group is not None and sequence_parallel:
	handle_x.wait()
	batch_shape, n = total_x.shape[:-1], total_x.shape[-1]
	batch_dim = batch_shape.numel()
	# https://github.com/pytorch/pytorch/blob/5b51849b48a7dbccd297286cc0110def4706f9e7/aten/src/ATen/native/cuda/Blas.cpp#L174
	output = F.linear(total_x, weight, bias)
	if ctx.compute_weight_gradient:
	ctx.save_for_backward(x, weight)
	else:
	ctx.save_for_backward(weight)
	return output

	@staticmethod
	@custom_bwd
	def backward(ctx, grad_output):
	grad_output = grad_output.contiguous()
	process_group = ctx.process_group
	sequence_parallel = ctx.sequence_parallel
	if ctx.compute_weight_gradient:
	x, weight = ctx.saved_tensors
	if process_group is not None and sequence_parallel:
	total_x, handle_x = all_gather_raw(x, process_group, async_op=True)
	else:
	total_x = x
	else:
	(weight,) = ctx.saved_tensors
	total_x = None
	batch_shape = grad_output.shape[:-1]
	batch_dim = batch_shape.numel()
	grad_output = grad_output.reshape(batch_dim, grad_output.shape[-1])
	if ctx.needs_input_grad[0]:
	grad_input = F.linear(grad_output, weight.t())
	grad_input = grad_input.reshape(*batch_shape, grad_input.shape[-1])
	if process_group is not None:
	reduce_fn = reduce_scatter_raw if sequence_parallel else all_reduce_raw
	grad_input, handle_grad_input = reduce_fn(grad_input, process_group, async_op=True)
	else:
	grad_input = None
	if ctx.needs_input_grad[1]:
	assert ctx.compute_weight_gradient
	if process_group is not None and sequence_parallel:
	handle_x.wait()
	grad_weight = torch.einsum(
	"bo,bi->oi", grad_output, total_x.reshape(batch_dim, total_x.shape[-1])
	)
	else:
	grad_weight = None
	grad_bias = grad_output.sum(dim=0) if ctx.needs_input_grad[2] else None
	if process_group is not None and ctx.needs_input_grad[0]:
	handle_grad_input.wait()
	return grad_input, grad_weight, grad_bias, None, None


	def parallel_linear_func(
	x: Tensor,
	weight: Tensor,
	bias: Optional[Tensor] = None,
	process_group: Optional[ProcessGroup] = None,
	sequence_parallel: bool = True,
	):
	return ParallelLinearFunc.apply(x, weight, bias, process_group, sequence_parallel)


	class ColumnParallelLinear(nn.Linear):
	def __init__(
	self,
	in_features: int,
	out_features: int,
	process_group: ProcessGroup,
	bias: bool = True,
	sequence_parallel=True,
	multiple_of=1,
	device=None,
	dtype=None,
	) -> None:
	world_size = torch.distributed.get_world_size(process_group)
	if out_features % multiple_of:
	raise ValueError(f"out_features ({out_features}) must be a multiple of {multiple_of}")
	multiple = out_features // multiple_of
	# We want to split @multiple across world_size, but it could be an uneven split
	div = multiple // world_size
	mod = multiple % world_size
	# The first @mod ranks get @div + 1 copies, the rest get @div copies
	local_multiple = div + int(torch.distributed.get_rank(process_group) < mod)
	super().__init__(
	in_features, local_multiple * multiple_of, bias=bias, device=device, dtype=dtype
	)
	self.process_group = process_group
	self.sequence_parallel = sequence_parallel

	def forward(self, x):
	# If self.sequence_parallel is True, we're doing Tensor Parallel with sequence parallelism:
	# we do an all_gather of x before doing the matmul.
	# If not, then the input is already gathered.
	return parallel_linear_func(
	x,
	self.weight,
	self.bias,
	process_group=self.process_group,
	sequence_parallel=self.sequence_parallel,
	)


	class RowParallelLinear(nn.Linear):
	def __init__(
	self,
	in_features: int,
	out_features: int,
	process_group: ProcessGroup,
	bias: bool = True,
	sequence_parallel=True,
	multiple_of=1,
	device=None,
	dtype=None,
	) -> None:
	world_size = torch.distributed.get_world_size(process_group)
	rank = torch.distributed.get_rank(process_group)
	if in_features % multiple_of:
	raise ValueError(f"in_features ({in_features}) must be a multiple of {multiple_of}")
	multiple = in_features // multiple_of
	# We want to split @multiple across world_size, but it could be an uneven split
	div = multiple // world_size
	mod = multiple % world_size
	# The first @mod ranks get @div + 1 copies, the rest get @div copies
	local_multiple = div + int(torch.distributed.get_rank(process_group) < mod)
	# Only rank 0 will have bias
	super().__init__(
	local_multiple * multiple_of,
	out_features,
	bias=bias and rank == 0,
	device=device,
	dtype=dtype,
	)
	self.process_group = process_group
	self.sequence_parallel = sequence_parallel

	def forward(self, x):
	"""
	We're doing Tensor Parallel with sequence parallelism: we do the matmul and then
	a reduce_scatter of the result.
	"""
	out = parallel_linear_func(x, self.weight, self.bias)
	reduce_fn = reduce_scatter if self.sequence_parallel else all_reduce
	return reduce_fn(out, self.process_group)


	class VocabParallelEmbedding(nn.Embedding):
	def __init__(self, num_embeddings, args, process_group=None, padding_idx=None, *kwargs):
	self.process_group = process_group
	if process_group is not None:
	world_size = torch.distributed.get_world_size(process_group)
	if num_embeddings % world_size != 0:
	raise ValueError(
	f"num_embeddings ({num_embeddings}) must be divisible by "
	f"world_size ({world_size})"
	)
	if world_size > 1 and padding_idx is not None:
	raise RuntimeError("ParallelEmbedding does not support padding_idx")
	else:
	world_size = 1
	super().__init__(num_embeddings // world_size, args, padding_idx=padding_idx, *kwargs)

	def forward(self, input: Tensor) -> Tensor:
	if self.process_group is None:
	return super().forward(input)
	else:
	rank = torch.distributed.get_rank(self.process_group)
	vocab_size = self.num_embeddings
	vocab_start_index, vocab_end_index = rank * vocab_size, (rank + 1) * vocab_size
	# Create a mask of valid vocab ids (1 means it needs to be masked).
	input_ids_mask = (input < vocab_start_index) \| (input >= vocab_end_index)
	input = input - vocab_start_index
	input[input_ids_mask] = 0
	embeddings = super().forward(input)
	embeddings[input_ids_mask] = 0.0
	return embeddings


	class ColumnParallelEmbedding(nn.Embedding):
	def __init__(self, num_embeddings, embedding_dim, args, process_group=None, *kwargs):
	self.process_group = process_group
	if process_group is not None:
	world_size = torch.distributed.get_world_size(process_group)
	if embedding_dim % world_size != 0:
	raise ValueError(
	f"embedding_dim ({embedding_dim}) must be divisible by "
	f"world_size ({world_size})"
	)
	else:
	world_size = 1
	super().__init__(num_embeddings, embedding_dim // world_size, args, *kwargs)


	class ParallelEmbeddings(nn.Module):
	def __init__(
	self,
	embed_dim,
	vocab_size,
	max_position_embeddings,
	process_group,
	padding_idx=None,
	sequence_parallel=True,
	device=None,
	dtype=None,
	):
	"""
	If max_position_embeddings <= 0, there's no position embeddings
	"""
	factory_kwargs = {"device": device, "dtype": dtype}
	super().__init__()
	self.process_group = process_group
	self.sequence_parallel = sequence_parallel
	self.word_embeddings = VocabParallelEmbedding(
	vocab_size,
	embed_dim,
	padding_idx=padding_idx,
	process_group=process_group,
	**factory_kwargs,
	)
	self.max_position_embeddings = max_position_embeddings
	if self.max_position_embeddings > 0:
	self.position_embeddings = ColumnParallelEmbedding(
	max_position_embeddings, embed_dim, process_group=process_group, **factory_kwargs
	)

	def forward(self, input_ids, position_ids=None, combine_batch_seqlen_dim=False):
	"""
	input_ids: (batch, seqlen)
	position_ids: (batch, seqlen)
	"""
	batch_size, seqlen = input_ids.shape
	world_size = torch.distributed.get_world_size(self.process_group)
	embeddings = self.word_embeddings(input_ids)
	if self.max_position_embeddings > 0:
	if position_ids is None:
	position_ids = torch.arange(seqlen, dtype=torch.long, device=input_ids.device)
	position_embeddings = self.position_embeddings(position_ids)
	if world_size <= 1:
	embeddings = embeddings + position_embeddings
	else:
	partition_dim = self.position_embeddings.embedding_dim
	rank = torch.distributed.get_rank(self.process_group)
	embeddings[
	..., rank * partition_dim : (rank + 1) * partition_dim
	] += position_embeddings
	if combine_batch_seqlen_dim:
	embeddings = rearrange(embeddings, "b s d -> (b s) d")
	reduce_fn = reduce_scatter if self.sequence_parallel else all_reduce
	return embeddings if world_size <= 1 else reduce_fn(embeddings, self.process_group)