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vllm/cacheflow/parallel_utils/tensor_parallel/layers.py

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Support tensor parallel (#2)
2023-03-22 04:45:42 +08:00
# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
# Parts of the code here are adapted from PyTorch
# repo: https://github.com/pytorch/pytorch
import math
import os
from typing import Optional
import warnings
import torch
import torch.nn.functional as F
import torch.nn.init as init
from torch.nn.parameter import Parameter
from cacheflow.parallel_utils.parallel_state import (
get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size,
get_tensor_model_parallel_group,
get_global_memory_buffer,
)
from .mappings import (
copy_to_tensor_model_parallel_region,
gather_from_tensor_model_parallel_region,
gather_from_sequence_parallel_region,
reduce_from_tensor_model_parallel_region,
scatter_to_tensor_model_parallel_region,
reduce_scatter_to_sequence_parallel_region,
)
from .random import get_cuda_rng_tracker
from .utils import (
divide,
split_tensor_along_last_dim,
VocabUtility,
)
_grad_accum_fusion_available = True
try:
import fused_weight_gradient_mlp_cuda
except ImportError:
_grad_accum_fusion_available = False
_MODEL_PARALLEL_ATTRIBUTE_DEFAULTS = {'tensor_model_parallel': False,
'partition_dim': -1,
'partition_stride': 1}
def param_is_not_tensor_parallel_duplicate(param):
return (hasattr(param, 'tensor_model_parallel') and
param.tensor_model_parallel) or (
get_tensor_model_parallel_rank() == 0)
def set_tensor_model_parallel_attributes(tensor, is_parallel, dim, stride):
# Make sure the attributes are not set.
for attribute in _MODEL_PARALLEL_ATTRIBUTE_DEFAULTS:
assert not hasattr(tensor, attribute)
# Set the attributes.
setattr(tensor, 'tensor_model_parallel', is_parallel)
setattr(tensor, 'partition_dim', dim)
setattr(tensor, 'partition_stride', stride)
def set_defaults_if_not_set_tensor_model_parallel_attributes(tensor):
def maybe_set(attribute, value):
if not hasattr(tensor, attribute):
setattr(tensor, attribute, value)
for attribute in _MODEL_PARALLEL_ATTRIBUTE_DEFAULTS:
maybe_set(attribute, _MODEL_PARALLEL_ATTRIBUTE_DEFAULTS[attribute])
def copy_tensor_model_parallel_attributes(destination_tensor, source_tensor):
def maybe_copy(attribute):
if hasattr(source_tensor, attribute):
setattr(destination_tensor, attribute,
getattr(source_tensor, attribute))
for attribute in _MODEL_PARALLEL_ATTRIBUTE_DEFAULTS:
maybe_copy(attribute)
def _initialize_affine_weight_gpu(weight, init_method,
partition_dim, stride=1):
"""Initialize affine weight for model parallel on GPU."""
set_tensor_model_parallel_attributes(tensor=weight,
is_parallel=True,
dim=partition_dim,
stride=stride)
with get_cuda_rng_tracker().fork():
init_method(weight)
def _initialize_affine_weight_cpu(weight, output_size, input_size,
per_partition_size, partition_dim,
init_method, stride=1,
return_master_weight=False,
*, params_dtype=None):
"""Initialize affine weight for model parallel.
Build the master weight on all processes and scatter
the relevant chunk."""
set_tensor_model_parallel_attributes(tensor=weight,
is_parallel=True,
dim=partition_dim,
stride=stride)
if params_dtype is None:
params_dtype = torch.get_default_dtype()
# Initialize master weight
master_weight = torch.empty(output_size, input_size,
dtype=torch.float,
requires_grad=False)
init_method(master_weight)
master_weight = master_weight.to(dtype=params_dtype)
# Split and copy
per_partition_per_stride_size = divide(per_partition_size, stride)
weight_list = torch.split(master_weight, per_partition_per_stride_size,
dim=partition_dim)
rank = get_tensor_model_parallel_rank()
world_size = get_tensor_model_parallel_world_size()
my_weight_list = weight_list[rank::world_size]
with torch.no_grad():
torch.cat(my_weight_list, dim=partition_dim, out=weight)
if return_master_weight:
return master_weight
return None
class VocabParallelEmbedding(torch.nn.Module):
"""Embedding parallelized in the vocabulary dimension.
This is mainly adapted from torch.nn.Embedding and all the default
values are kept.
Arguments:
num_embeddings: vocabulary size.
embedding_dim: size of hidden state.
Keyword Arguments:
init_method: method to initialize weights.
params_dtype
use_cpu_initialization
perform_initialization
"""
def __init__(self, num_embeddings: int, embedding_dim: int, *,
init_method=init.xavier_normal_,
params_dtype: torch.dtype=None,
use_cpu_initialization: bool=False,
perform_initialization: bool=True):
super(VocabParallelEmbedding, self).__init__()
# Keep the input dimensions.
self.num_embeddings = num_embeddings
self.embedding_dim = embedding_dim
if params_dtype is None:
params_dtype = torch.get_default_dtype()
# Set the defaults for compatibility.
self.padding_idx = None
self.max_norm = None
self.norm_type = 2.
self.scale_grad_by_freq = False
self.sparse = False
self._weight = None
self.tensor_model_parallel_size = get_tensor_model_parallel_world_size()
# Divide the weight matrix along the vocaburaly dimension.
self.vocab_start_index, self.vocab_end_index = \
VocabUtility.vocab_range_from_global_vocab_size(
self.num_embeddings, get_tensor_model_parallel_rank(),
self.tensor_model_parallel_size)
self.num_embeddings_per_partition = self.vocab_end_index - \
self.vocab_start_index
# Allocate weights and initialize.
if use_cpu_initialization:
self.weight = Parameter(torch.empty(
self.num_embeddings_per_partition, self.embedding_dim,
dtype=params_dtype))
if perform_initialization:
_initialize_affine_weight_cpu(
self.weight, self.num_embeddings, self.embedding_dim,
self.num_embeddings_per_partition, 0, init_method,
params_dtype=params_dtype)
else:
self.weight = Parameter(torch.empty(
self.num_embeddings_per_partition, self.embedding_dim,
device=torch.cuda.current_device(), dtype=params_dtype))
if perform_initialization:
_initialize_affine_weight_gpu(self.weight, init_method,
partition_dim=0, stride=1)
def forward(self, input_):
if self.tensor_model_parallel_size > 1:
# Build the mask.
input_mask = (input_ < self.vocab_start_index) | \
(input_ >= self.vocab_end_index)
# Mask the input.
masked_input = input_.clone() - self.vocab_start_index
masked_input[input_mask] = 0
else:
masked_input = input_
# Get the embeddings.
output_parallel = F.embedding(masked_input, self.weight,
self.padding_idx, self.max_norm,
self.norm_type, self.scale_grad_by_freq,
self.sparse)
# Mask the output embedding.
if self.tensor_model_parallel_size > 1:
output_parallel[input_mask, :] = 0.0
# Reduce across all the model parallel GPUs.
output = reduce_from_tensor_model_parallel_region(output_parallel)
return output
class LinearWithGradAccumulationAndAsyncCommunication(torch.autograd.Function):
"""See linear_with_grad_accumulation_and_async_allreduce"""
@staticmethod
def forward(ctx, input, weight, bias, gradient_accumulation_fusion,
async_grad_allreduce, sequence_parallel):
ctx.save_for_backward(input, weight)
ctx.use_bias = bias is not None
ctx.gradient_accumulation_fusion = gradient_accumulation_fusion
ctx.async_grad_allreduce = async_grad_allreduce
ctx.sequence_parallel = sequence_parallel
if sequence_parallel:
world_size = get_tensor_model_parallel_world_size()
dim_size = list(input.size())
dim_size[0] = dim_size[0] * world_size
all_gather_buffer = \
get_global_memory_buffer().get_tensor(dim_size, input.dtype, "mpu")
torch.distributed._all_gather_base(
all_gather_buffer,
input,
group=get_tensor_model_parallel_group())
total_input = all_gather_buffer
else:
total_input = input
output = torch.matmul(total_input, weight.t())
if bias is not None:
output = output + bias
return output
@staticmethod
def backward(ctx, grad_output):
input, weight = ctx.saved_tensors
use_bias = ctx.use_bias
if ctx.sequence_parallel:
world_size = get_tensor_model_parallel_world_size()
dim_size = list(input.size())
dim_size[0] = dim_size[0] * world_size
all_gather_buffer = \
get_global_memory_buffer().get_tensor(dim_size, input.dtype, "mpu")
handle = torch.distributed._all_gather_base(
all_gather_buffer,
input,
group=get_tensor_model_parallel_group(), async_op=True)
# Here we rely on CUDA_DEVICE_MAX_CONNECTIONS=1 to ensure that the
# gather is scheduled before the input gradient computation
total_input = all_gather_buffer
else:
total_input = input
grad_input = grad_output.matmul(weight)
if ctx.sequence_parallel:
handle.wait()
# Convert the tensor shapes to 2D for execution compatibility
grad_output = grad_output.view(grad_output.shape[0] * grad_output.shape[1],
grad_output.shape[2])
total_input = total_input.view(total_input.shape[0] * total_input.shape[1],
total_input.shape[2])
if ctx.async_grad_allreduce:
# Asynchronous all-reduce
handle = torch.distributed.all_reduce(
grad_input, group=get_tensor_model_parallel_group(), async_op=True)
# Here we rely on CUDA_DEVICE_MAX_CONNECTIONS=1 to ensure that the
# all-reduce is scheduled before the weight gradient computation
if ctx.sequence_parallel:
assert not ctx.async_grad_allreduce
dim_size = list(input.size())
sub_grad_input = torch.empty(dim_size, dtype=input.dtype,
device=torch.cuda.current_device(),
requires_grad=False)
# reduce_scatter
handle = torch.distributed._reduce_scatter_base(sub_grad_input, grad_input,
group=get_tensor_model_parallel_group(),
async_op=True)
# Here we rely on CUDA_DEVICE_MAX_CONNECTIONS=1 to ensure that the
# reduce scatter is scheduled before the weight gradient computation
if ctx.gradient_accumulation_fusion:
if weight.main_grad.dtype == torch.float32:
fused_weight_gradient_mlp_cuda.wgrad_gemm_accum_fp32(total_input, grad_output, weight.main_grad)
elif weight.main_grad.dtype == torch.float16:
fused_weight_gradient_mlp_cuda.wgrad_gemm_accum_fp16(total_input, grad_output, weight.main_grad)
else:
raise RuntimeError("Unsupported gradient type for gradient accumulation fusion")
grad_weight = None
else:
grad_weight = grad_output.t().matmul(total_input)
grad_bias = grad_output.sum(dim=0) if use_bias else None
if ctx.sequence_parallel:
handle.wait()
return sub_grad_input, grad_weight, grad_bias, None, None, None
if ctx.async_grad_allreduce:
handle.wait()
return grad_input, grad_weight, grad_bias, None, None, None
def linear_with_grad_accumulation_and_async_allreduce(
input: torch.Tensor,
weight: torch.Tensor,
bias: Optional[torch.Tensor],
gradient_accumulation_fusion: bool,
async_grad_allreduce: bool,
sequence_parallel_enabled: bool,
) -> torch.Tensor:
"""Linear layer execution with asynchronous communication and
gradient accumulation fusion in backprop.
This has the option to accumulate the result of backprop
calculation into an existing gradient buffer, preventing the need
to do an additional addition kernel after the gradient
calculation.
Additionally, the tensor parallel all reduce of the input
gradients can be done asynchronously with the calculation of
the weight gradients.
In the case of sequence parallelism, the reduce scatter of the
input gradients is done asynchronously with the calcluation of the
weight gradients.
Use of this module requires that the environment variable
CUDA_DEVICE_MAX_CONNECTIONS=1. There are a few collective
operations, noted in the code, that should be scheduled before
compute kernels to overlap the communication with the computation,
which is necessary for a speedup but not for correctness so that
ordering isn't imposed by the scheduler. Setting
CUDA_DEVICE_MAX_CONNECTIONS=1 forces the kernels to be scheduled
in the order they are called.
Arguments:
input (torch.Tensor required): input like torch.nn.functional.linear
weight (torch.Tensor required): weight like torch.nn.functional.linear
bias (torch.Tensor optional): bias like torch.nn.functional.linear
gradient_accumulation_fusion (bool required): Perform the gradient
accumulation fusion, requires the custom CUDA extension
fused_weight_gradient_mlp_cuda module. To use
gradient_accumulation_fusion you must install APEX with
--cpp_ext and --cuda_ext. For example: "pip install
--global-option=\"--cpp_ext\" --global-option=\"--cuda_ext .\"
" Note that the extension requires CUDA>=11. Otherwise, you
must turn off gradient accumulation fusion."
async_grad_allreduce (bool required): Do the allreduce of input
gradients asyncronously with the computation of weight
gradients. If sequence_parallel_enabled is True, this must be
False, as no all reduce is performed.
sequence_parallel_enabled (bool required): Indicates that sequence
parallelism is used and thus in the forward pass the input is
all gathered, and the backward pass the input gradients are
reduce scattered.
"""
args = [
input,
weight,
bias,
gradient_accumulation_fusion,
async_grad_allreduce,
sequence_parallel_enabled,
]
if not linear_with_grad_accumulation_and_async_allreduce.warned:
if os.environ.get('CUDA_DEVICE_MAX_CONNECTIONS') != "1":
if sequence_parallel_enabled:
warnings.warn(
"When using sequence parallelism it is recommended to set the "
"environment variable CUDA_DEVICE_MAX_CONNECTIONS to 1 for "
"maximum speedup")
linear_with_grad_accumulation_and_async_allreduce.warned = True
if async_grad_allreduce:
warnings.warn(
"When using async grad allreduce it is recommended to set the "
"environment variable CUDA_DEVICE_MAX_CONNECTIONS to 1 for "
"maximum speedup")
linear_with_grad_accumulation_and_async_allreduce.warned = True
with torch.cuda.amp.autocast(enabled=False):
return LinearWithGradAccumulationAndAsyncCommunication.apply(*args)
linear_with_grad_accumulation_and_async_allreduce.warned = False
class ColumnParallelLinear(torch.nn.Module):
"""Linear layer with column parallelism.
The linear layer is defined as Y = XA + b. A is parallelized along
its second dimension as A = [A_1, ..., A_p].
Arguments:
input_size: first dimension of matrix A.
output_size: second dimension of matrix A.
Keyword Arguments
bias: If true, add bias
gather_output: If true, call all-gather on output and make Y available
to all GPUs, otherwise, every GPU will have its output
which is Y_i = XA_i
init_method: method to initialize weights. Note that bias is always set
to zero.
stride: For the strided linear layers.
keep_master_weight_for_test: This was added for testing and should be
set to False. It returns the master weights
used for initialization.
skip_bias_add: This was added to enable performance optimations where bias
can be fused with other elementwise operations. we skip
adding bias but instead return it.
async_tensor_model_parallel_allreduce:
params_dtype:
use_cpu_initialization:
gradient_accumulation_fusion:
sequence_parallel_enabled:
"""
def __init__(self, input_size, output_size, *,
bias=True, gather_output=True,
init_method=init.xavier_normal_, stride=1,
keep_master_weight_for_test=False,
skip_bias_add=False,
async_tensor_model_parallel_allreduce=True,
params_dtype=None,
use_cpu_initialization=False,
perform_initialization=True,
gradient_accumulation_fusion=False,
sequence_parallel_enabled: bool = False,
):
super(ColumnParallelLinear, self).__init__()
# Keep input parameters
self.input_size = input_size
self.output_size = output_size
self.gather_output = gather_output
# Divide the weight matrix along the last dimension.
world_size = get_tensor_model_parallel_world_size()
self.output_size_per_partition = divide(output_size, world_size)
self.skip_bias_add = skip_bias_add
if params_dtype is None:
params_dtype = torch.get_default_dtype()
# Parameters.
# Note: torch.nn.functional.linear performs XA^T + b and as a result
# we allocate the transpose.
# Initialize weight.
if use_cpu_initialization:
self.weight = Parameter(torch.empty(self.output_size_per_partition,
self.input_size,
dtype=params_dtype))
if perform_initialization:
self.master_weight = _initialize_affine_weight_cpu(
self.weight, self.output_size, self.input_size,
self.output_size_per_partition, 0, init_method,
stride=stride, return_master_weight=keep_master_weight_for_test)
else:
self.weight = Parameter(torch.empty(
self.output_size_per_partition, self.input_size,
device=torch.cuda.current_device(), dtype=params_dtype))
if perform_initialization:
_initialize_affine_weight_gpu(self.weight, init_method,
partition_dim=0, stride=stride)
if bias:
if use_cpu_initialization:
self.bias = Parameter(torch.empty(
self.output_size_per_partition, dtype=params_dtype))
else:
self.bias = Parameter(torch.empty(
self.output_size_per_partition,
device=torch.cuda.current_device(),
dtype=params_dtype))
set_tensor_model_parallel_attributes(self.bias, True, 0, stride)
# Always initialize bias to zero.
with torch.no_grad():
self.bias.zero_()
else:
self.register_parameter('bias', None)
self.async_tensor_model_parallel_allreduce = (
async_tensor_model_parallel_allreduce and
world_size > 1)
if sequence_parallel_enabled:
if world_size <= 1:
warnings.warn(
f"`sequence_parallel_enabled` is set to `True`, but tensor model parallel size is {world_size}. "
f"Disabling sequence parallel."
)
sequence_parallel_enabled = False
self.sequence_parallel_enabled = sequence_parallel_enabled
if gradient_accumulation_fusion:
if not _grad_accum_fusion_available:
raise RuntimeError(
"ColumnParallelLinear was called with gradient_accumulation_fusion set "
"to True but the custom CUDA extension fused_weight_gradient_mlp_cuda "
"module is not found. To use gradient_accumulation_fusion you must "
"install APEX with --cpp_ext and --cuda_ext. For example: "
"pip install --global-option=\"--cpp_ext\" --global-option=\"--cuda_ext .\" "
"Note that the extension requires CUDA>=11. Otherwise, you must turn off "
"gradient accumulation fusion."
)
self.gradient_accumulation_fusion = gradient_accumulation_fusion
if self.async_tensor_model_parallel_allreduce and self.sequence_parallel_enabled:
raise RuntimeError(
"`async_tensor_model_parallel_allreduce` and `sequence_parallel_enabled` "
"cannot be enabled at the same time."
)
def forward(self, input_):
"""Forward of ColumnParallelLinear
Args:
input_: 3D tensor whose order of dimension is [sequence, batch, hidden]
Returns:
- output
- bias
"""
bias = self.bias if not self.skip_bias_add else None
if self.async_tensor_model_parallel_allreduce or \
self.sequence_parallel_enabled:
input_parallel = input_
else:
input_parallel = copy_to_tensor_model_parallel_region(input_)
# Matrix multiply.
output_parallel = linear_with_grad_accumulation_and_async_allreduce(
input=input_parallel,
weight=self.weight,
bias=bias,
gradient_accumulation_fusion=self.gradient_accumulation_fusion,
async_grad_allreduce=self.async_tensor_model_parallel_allreduce,
sequence_parallel_enabled=self.sequence_parallel_enabled,
)
if self.gather_output:
# All-gather across the partitions.
assert not self.sequence_parallel_enabled
output = gather_from_tensor_model_parallel_region(output_parallel)
else:
output = output_parallel
output_bias = self.bias if self.skip_bias_add else None
return output, output_bias
class RowParallelLinear(torch.nn.Module):
"""Linear layer with row parallelism.
The linear layer is defined as Y = XA + b. A is parallelized along
its first dimension and X along its second dimension as:
- -
| A_1 |
| . |
A = | . | X = [X_1, ..., X_p]
| . |
| A_p |
- -
Arguments:
input_size: first dimension of matrix A.
output_size: second dimension of matrix A.
Keyword Arguments:
bias: If true, add bias. Note that bias is not parallelized.
input_is_parallel: If true, we assume that the input is already
split across the GPUs and we do not split
again.
init_method: method to initialize weights. Note that bias is always set
to zero.
stride: For the strided linear layers.
keep_master_weight_for_test: This was added for testing and should be
set to False. It returns the master weights
used for initialization.
skip_bias_add: This was added to enable performance optimization where bias
can be fused with other elementwise operations. We skip
adding bias but instead return it.
params_dtype:
use_cpu_initialization:
perform_initialization:
gradient_accumulation_fusion:
sequence_parallel_enabled:
"""
def __init__(self, input_size, output_size, *,
bias=True, input_is_parallel=False,
init_method=init.xavier_normal_, stride=1,
keep_master_weight_for_test=False,
skip_bias_add=False,
params_dtype=None,
use_cpu_initialization=False,
perform_initialization=True,
gradient_accumulation_fusion=False,
sequence_parallel_enabled: bool = False,
):
super(RowParallelLinear, self).__init__()
# Keep input parameters
self.input_size = input_size
self.output_size = output_size
self.input_is_parallel = input_is_parallel
if params_dtype is None:
params_dtype = torch.get_default_dtype()
# Divide the weight matrix along the last dimension.
world_size = get_tensor_model_parallel_world_size()
self.input_size_per_partition = divide(input_size, world_size)
self.skip_bias_add = skip_bias_add
self.gradient_accumulation_fusion = gradient_accumulation_fusion
self.sequence_parallel_enabled = sequence_parallel_enabled
if self.sequence_parallel_enabled and not self.input_is_parallel:
raise RuntimeError("To enable `sequence_parallel_enabled`, `input_is_parallel` must be `True`")
# Parameters.
# Note: torch.nn.functional.linear performs XA^T + b and as a result
# we allocate the transpose.
# Initialize weight.
if use_cpu_initialization:
self.weight = Parameter(torch.empty(self.output_size,
self.input_size_per_partition,
dtype=params_dtype))
if perform_initialization:
self.master_weight = _initialize_affine_weight_cpu(
self.weight, self.output_size, self.input_size,
self.input_size_per_partition, 1, init_method,
stride=stride, return_master_weight=keep_master_weight_for_test,
params_dtype=params_dtype)
else:
self.weight = Parameter(torch.empty(
self.output_size, self.input_size_per_partition,
device=torch.cuda.current_device(), dtype=params_dtype))
if perform_initialization:
_initialize_affine_weight_gpu(self.weight, init_method,
partition_dim=1, stride=stride)
if bias:
if use_cpu_initialization:
self.bias = Parameter(torch.empty(self.output_size,
dtype=params_dtype))
else:
self.bias = Parameter(torch.empty(
self.output_size, device=torch.cuda.current_device(),
dtype=params_dtype))
setattr(self.bias, 'sequence_parallel', sequence_parallel_enabled)
# Always initialize bias to zero.
with torch.no_grad():
self.bias.zero_()
else:
self.register_parameter('bias', None)
def forward(self, input_):
"""Forward of RowParallelLinear
Args:
input_: 3D tensor whose order of dimension is [sequence, batch, hidden]
Returns:
- output
- bias
"""
# Set up backprop all-reduce.
if self.input_is_parallel:
input_parallel = input_
else:
assert not self.sequence_parallel_enabled
input_parallel = scatter_to_tensor_model_parallel_region(input_)
# Matrix multiply.
output_parallel = linear_with_grad_accumulation_and_async_allreduce(
input=input_parallel,
weight=self.weight,
bias=None,
gradient_accumulation_fusion=self.gradient_accumulation_fusion,
async_grad_allreduce=False,
sequence_parallel_enabled=False,
)
# All-reduce across all the partitions.
if self.sequence_parallel_enabled:
output_ = reduce_scatter_to_sequence_parallel_region(output_parallel)
else:
output_ = reduce_from_tensor_model_parallel_region(output_parallel)
if not self.skip_bias_add:
output = output_ + self.bias if self.bias is not None else output_
output_bias = None
else:
output = output_
output_bias = self.bias
return output, output_bias
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