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Commit 30f5009a authored by Tom Birch's avatar Tom Birch Committed by Mandeep Singh Baines
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[feat] Model parallel (#3)

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.isort.cfg
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[settings]
known_third_party =pytest,setuptools,torch,torchtext
known_third_party =numpy,pytest,setuptools,torch,torchtext
NOTICE
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......@@ -203,3 +203,272 @@ torchgpipe's Apache License 2.0
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README.md
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......@@ -3,6 +3,7 @@ fairscale is a PyTorch extension library for high performance and large scale tr
fairscale supports:
* pipeline parallelism (fairscale.nn.Pipe)
* tensor parallelism (fairscale.nn.model_parallel)
* optimizer state sharding (fairscale.optim.oss)
## Examples
......
fairscale/nn/model_parallel/__init__.py 0 → 100644
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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
from .cross_entropy import vocab_parallel_cross_entropy
from .initialize import (
get_data_parallel_group,
get_data_parallel_rank,
get_data_parallel_world_size,
get_model_parallel_group,
get_model_parallel_rank,
get_model_parallel_src_rank,
get_model_parallel_world_size,
initialize_model_parallel,
)
from .layers import ColumnParallelLinear, RowParallelLinear, VocabParallelEmbedding
from .mappings import copy_to_model_parallel_region, gather_from_model_parallel_region
from .random import get_cuda_rng_tracker, model_parallel_cuda_manual_seed
fairscale/nn/model_parallel/cross_entropy.py 0 → 100644
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# coding=utf-8
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from .initialize import get_model_parallel_group, get_model_parallel_rank, get_model_parallel_world_size
from .utils import VocabUtility
class _VocabParallelCrossEntropy(torch.autograd.Function):
@staticmethod
def forward(ctx, vocab_parallel_logits, target): # type: ignore
# Maximum value along vocab dimension across all GPUs.
logits_max = torch.max(vocab_parallel_logits, dim=-1)[0]
torch.distributed.all_reduce(logits_max, op=torch.distributed.ReduceOp.MAX, group=get_model_parallel_group())
# Subtract the maximum value.
vocab_parallel_logits.sub_(logits_max.unsqueeze(dim=-1))
# Get the partition's vocab indecies
get_vocab_range = VocabUtility.vocab_range_from_per_partition_vocab_size
partition_vocab_size = vocab_parallel_logits.size()[-1]
rank = get_model_parallel_rank()
world_size = get_model_parallel_world_size()
vocab_start_index, vocab_end_index = get_vocab_range(partition_vocab_size, rank, world_size)
# Create a mask of valid vocab ids (1 means it needs to be masked).
target_mask = (target < vocab_start_index) | (target >= vocab_end_index)
masked_target = target.clone() - vocab_start_index
masked_target[target_mask] = 0
# Get predicted-logits = logits[target].
# For Simplicity, we convert logits to a 2-D tensor with size
# [*, partition-vocab-size] and target to a 1-D tensor of size [*].
logits_2d = vocab_parallel_logits.view(-1, partition_vocab_size)
masked_target_1d = masked_target.view(-1)
arange_1d = torch.arange(start=0, end=logits_2d.size()[0], device=logits_2d.device)
predicted_logits_1d = logits_2d[arange_1d, masked_target_1d]
predicted_logits_1d = predicted_logits_1d.clone().contiguous()
predicted_logits = predicted_logits_1d.view_as(target)
predicted_logits[target_mask] = 0.0
# All reduce is needed to get the chunks from other GPUs.
torch.distributed.all_reduce(
predicted_logits, op=torch.distributed.ReduceOp.SUM, group=get_model_parallel_group()
)
# Sum of exponential of logits along vocab dimension across all GPUs.
exp_logits = vocab_parallel_logits
torch.exp(vocab_parallel_logits, out=exp_logits)
sum_exp_logits = exp_logits.sum(dim=-1)
torch.distributed.all_reduce(
sum_exp_logits, op=torch.distributed.ReduceOp.SUM, group=get_model_parallel_group()
)
# Loss = log(sum(exp(logits))) - predicted-logit.
loss = torch.log(sum_exp_logits) - predicted_logits
# Store softmax, target-mask and masked-target for backward pass.
exp_logits.div_(sum_exp_logits.unsqueeze(dim=-1))
ctx.save_for_backward(exp_logits, target_mask, masked_target_1d)
return loss
@staticmethod
def backward(ctx, grad_output): # type: ignore
# Retreive tensors from the forward path.
softmax, target_mask, masked_target_1d = ctx.saved_tensors
# All the inputs have softmax as thier gradient.
grad_input = softmax
# For simplicity, work with the 2D gradient.
partition_vocab_size = softmax.size()[-1]
grad_2d = grad_input.view(-1, partition_vocab_size)
# Add the gradient from matching classes.
arange_1d = torch.arange(start=0, end=grad_2d.size()[0], device=grad_2d.device)
grad_2d[arange_1d, masked_target_1d] -= 1.0 - target_mask.view(-1).float()
# Finally elementwise multiplication with the output gradients.
grad_input.mul_(grad_output.unsqueeze(dim=-1))
return grad_input, None
def vocab_parallel_cross_entropy(vocab_parallel_logits: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
"""Helper function for the cross entropy."""
return _VocabParallelCrossEntropy.apply(vocab_parallel_logits, target)
fairscale/nn/model_parallel/initialize.py 0 → 100644
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# coding=utf-8
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Model and data parallel groups."""
from typing import List
import numpy as np # type: ignore
import torch
from .utils import ensure_divisibility
# Model parallel group that the current rank belongs to.
_MODEL_PARALLEL_GROUP = None
# Data parallel group that the current rank belongs to.
_DATA_PARALLEL_GROUP = None
# Pipeline parallel group that the current rank belongs to.
_PIPELINE_PARALLEL_GROUP = None
def initialize_model_parallel(model_parallel_size_: int, pipeline_length: int = 1) -> None:
"""
Initialize model data parallel groups.
Arguments:
model_parallel_size: number of GPUs used to parallelize model.
Let's say we have a total of 8 GPUs denoted by g0 ... g7 and we
use 2 GPUs to parallelize the model. The present function will
create 4 model parallel groups and 2 data parallel grous as:
4 model parallel groups:
[g0, g1], [g2, g3], [g4, g5], [g6, g7]
2 data parallel groups:
[g0, g2, g4, g6], [g1, g3, g5, g7]
Note that for efficiency, the caller should make sure adjacent ranks
are on the same DGX box. For example if we are using 2 DGX-1 boxes
with a total of 16 GPUs, rank 0 to 7 belong to the first box and
ranks 8 to 15 belong to the second box.
"""
if torch.distributed.get_rank() == 0:
print("> initializing model parallel with size {}".format(model_parallel_size_))
# Get world size and rank. Ensure some consistencies.
assert torch.distributed.is_initialized()
world_size = torch.distributed.get_world_size()
model_parallel_size = int(min(model_parallel_size_, world_size))
ensure_divisibility(world_size, model_parallel_size)
ensure_divisibility(world_size, model_parallel_size * pipeline_length)
rank = torch.distributed.get_rank()
data_parallel_size = int(world_size / (model_parallel_size * pipeline_length))
groups = (
torch.LongTensor(range(world_size)).reshape(data_parallel_size, pipeline_length, model_parallel_size).numpy()
)
found = np.where(groups == rank)
assert all(len(x) == 1 for x in found)
found = [x[0] for x in found]
# Build the data parallel groups.
global _DATA_PARALLEL_GROUP
assert _DATA_PARALLEL_GROUP is None, "data parallel group is already initialized"
for j in range(pipeline_length):
for k in range(model_parallel_size):
group = torch.distributed.new_group(groups[:, j, k].tolist())
if j == found[1] and k == found[2]:
_DATA_PARALLEL_GROUP = group
# Build the model parallel groups.
global _MODEL_PARALLEL_GROUP
assert _MODEL_PARALLEL_GROUP is None, "model parallel group is already initialized"
for i in range(data_parallel_size):
for j in range(pipeline_length):
group = torch.distributed.new_group(groups[i, j, :].tolist())
if i == found[0] and j == found[1]:
_MODEL_PARALLEL_GROUP = group
global _PIPELINE_PARALLEL_GROUP
assert _PIPELINE_PARALLEL_GROUP is None, "model parallel group is already initialized"
_PIPELINE_PARALLEL_GROUP = groups[found[0], :, found[2]].tolist()
def model_parallel_is_initialized() -> bool:
"""Check if model and data parallel groups are initialized."""
if _MODEL_PARALLEL_GROUP is None or _DATA_PARALLEL_GROUP is None or _PIPELINE_PARALLEL_GROUP is None:
return False
return True
def get_model_parallel_group() -> torch.distributed.ProcessGroup:
"""Get the model parallel group the caller rank belongs to."""
assert _MODEL_PARALLEL_GROUP is not None, "model parallel group is not initialized"
return _MODEL_PARALLEL_GROUP
def get_data_parallel_group() -> torch.distributed.ProcessGroup:
"""Get the data parallel group the caller rank belongs to."""
assert _DATA_PARALLEL_GROUP is not None, "data parallel group is not initialized"
return _DATA_PARALLEL_GROUP
def get_pipeline_parallel_group() -> List[int]:
"""Get the pipeline parallel group the caller rank belongs to."""
assert _PIPELINE_PARALLEL_GROUP is not None, "pipeline parallel group is not initialized"
return _PIPELINE_PARALLEL_GROUP
def get_model_parallel_world_size() -> int:
"""Return world size for the model parallel group."""
return torch.distributed.get_world_size(group=get_model_parallel_group())
def get_model_parallel_rank() -> int:
"""Return my rank for the model parallel group."""
return torch.distributed.get_rank(group=get_model_parallel_group())
def get_model_parallel_src_rank() -> int:
"""Calculate the global rank corresponding to a local rank zero
in the model parallel group."""
global_rank = torch.distributed.get_rank()
local_world_size = get_model_parallel_world_size()
return (global_rank // local_world_size) * local_world_size
def get_data_parallel_world_size() -> int:
"""Return world size for the data parallel group."""
return torch.distributed.get_world_size(group=get_data_parallel_group())
def get_data_parallel_rank() -> int:
"""Return my rank for the data parallel group."""
return torch.distributed.get_rank(group=get_data_parallel_group())
def destroy_model_parallel() -> None:
"""Set the groups to none."""
global _MODEL_PARALLEL_GROUP
_MODEL_PARALLEL_GROUP = None
global _DATA_PARALLEL_GROUP
_DATA_PARALLEL_GROUP = None
global _PIPELINE_PARALLEL_GROUP
_PIPELINE_PARALLEL_GROUP = None
fairscale/nn/model_parallel/layers.py 0 → 100644
View file @ 30f5009a
# coding=utf-8
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Parts of the code here are adapted from PyTorch
# repo: https://github.com/pytorch/pytorch
from typing import Callable, Optional
import torch
import torch.nn.functional as F
import torch.nn.init as init
from torch.nn.parameter import Parameter
from .initialize import get_model_parallel_rank, get_model_parallel_world_size
from .mappings import (
copy_to_model_parallel_region,
gather_from_model_parallel_region,
reduce_from_model_parallel_region,
scatter_to_model_parallel_region,
)
from .utils import VocabUtility, divide_and_check_no_remainder
def _initialize_affine_weight(
weight: torch.Tensor,
out_features: int,
in_features: int,
per_partition_size: int,
partition_dim: int,
init_method: Callable[[torch.Tensor], None],
stride: int = 1,
return_master_weight: bool = False,
) -> Optional[torch.Tensor]:
"""Initialize affine weight for model parallel.
Build the master weight on all processes and scatter
the relevant chunk."""
# If we only use 1 process for model parallelism, bypass scatter.
world_size = get_model_parallel_world_size()
if world_size == 1:
init_method(weight)
if return_master_weight:
return weight
return None
# Initialize master weight
master_weight = torch.empty(out_features, in_features, dtype=weight.dtype, requires_grad=False)
init_method(master_weight)
# Split and copy
per_partition_per_stride_size = divide_and_check_no_remainder(per_partition_size, stride)
weight_list = torch.split(master_weight, per_partition_per_stride_size, dim=partition_dim)
rank = get_model_parallel_rank()
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.
init_method: method to initialize weights.
"""
def __init__(
self,
num_embeddings: int,
embedding_dim: int,
padding_idx: Optional[int] = None,
max_norm: Optional[float] = None,
norm_type: float = 2.0,
scale_grad_by_freq: bool = False,
sparse: bool = False,
init_method: Callable[[torch.Tensor], None] = init.xavier_normal_,
) -> None:
super(VocabParallelEmbedding, self).__init__()
# Keep the input dimensions.
self.num_embeddings = num_embeddings
self.embedding_dim = embedding_dim
self.padding_idx = padding_idx
self.max_norm = max_norm
self.norm_type = norm_type
self.scale_grad_by_freq = scale_grad_by_freq
self.sparse = sparse
self._weight = None
# 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_model_parallel_rank(), get_model_parallel_world_size()
)
self.num_embeddings_per_partition = self.vocab_end_index - self.vocab_start_index
# Allocate weights.
self.weight = Parameter(torch.Tensor(self.num_embeddings_per_partition, self.embedding_dim))
# And initialize.
_initialize_affine_weight(
self.weight, self.num_embeddings, self.embedding_dim, self.num_embeddings_per_partition, 0, init_method
)
def forward(self, input_: torch.Tensor) -> torch.Tensor: # type: ignore
# 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
# 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.
output_parallel[input_mask, :] = 0.0
# Reduce across all the model parallel GPUs.
output = reduce_from_model_parallel_region(output_parallel)
return output
class ParallelEmbedding(torch.nn.Module):
"""Embedding parallelized in the embedding 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.
init_method: method to initialize weights.
"""
def __init__(
self,
num_embeddings: int,
embedding_dim: int,
padding_idx: Optional[int] = None,
max_norm: Optional[float] = None,
norm_type: float = 2.0,
scale_grad_by_freq: bool = False,
sparse: bool = False,
init_method: Callable[[torch.Tensor], None] = init.xavier_normal_,
keep_master_weight_for_test: bool = False,
) -> None:
super(ParallelEmbedding, self).__init__()
# Keep the input dimensions.
self.num_embeddings = num_embeddings
self.embedding_dim = embedding_dim
self.padding_idx = padding_idx
self.max_norm = max_norm
self.norm_type = scale_grad_by_freq
self.scale_grad_by_freq = scale_grad_by_freq
self.sparse = sparse
self._weight = None
# Divide the weight matrix along the embedding dimension.
world_size = get_model_parallel_world_size()
self.embedding_dim_per_partition = divide_and_check_no_remainder(self.embedding_dim, world_size)
# Allocate weights.
self.weight = Parameter(torch.Tensor(self.num_embeddings, self.embedding_dim_per_partition))
# And initialize.
_initialize_affine_weight(
self.weight,
self.num_embeddings,
self.embedding_dim,
self.embedding_dim_per_partition,
1,
init_method,
stride=1,
return_master_weight=False,
)
def forward(self, input_: torch.Tensor) -> torch.Tensor: # type: ignore
input_parallel = copy_to_model_parallel_region(input_)
output_parallel = F.embedding(
input_parallel,
self.weight,
self.padding_idx,
self.max_norm,
self.norm_type,
self.scale_grad_by_freq,
self.sparse,
)
output = gather_from_model_parallel_region(output_parallel)
return output
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:
in_features: first dimension of matrix A.
out_features: second dimension of matrix A.
bias: If true, add bias
gather_output: If true, call all-gether on output and make Y avaiable
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.
"""
def __init__(
self,
in_features: int,
out_features: int,
bias: bool = True,
gather_output: bool = True,
init_method: Callable[[torch.Tensor], None] = init.xavier_normal_,
stride: int = 1,
keep_master_weight_for_test: bool = False,
) -> None:
super(ColumnParallelLinear, self).__init__()
# Keep input parameters
self.in_features = in_features
self.out_features = out_features
self.gather_output = gather_output
# Divide the weight matrix along the last dimension.
world_size = get_model_parallel_world_size()
self.output_size_per_partition = divide_and_check_no_remainder(out_features, world_size)
# Parameters.
# Note: torch.nn.functional.linear performs XA^T + b and as a result
# we allocate the transpose.
self.weight = Parameter(torch.Tensor(self.output_size_per_partition, self.in_features))
if bias:
self.bias = Parameter(torch.Tensor(self.output_size_per_partition))
# Always initialize bias to zero.
with torch.no_grad():
self.bias.zero_()
else:
self.register_parameter("bias", None)
# Initialize weight.
self.master_weight = _initialize_affine_weight(
self.weight,
self.out_features,
self.in_features,
self.output_size_per_partition,
0,
init_method,
stride=stride,
return_master_weight=keep_master_weight_for_test,
)
def forward(self, input_: torch.Tensor) -> torch.Tensor: # type: ignore
# Set up backprop all-reduce.
input_parallel = copy_to_model_parallel_region(input_)
# Matrix multiply.
output_parallel = F.linear(input_parallel, self.weight, self.bias)
if self.gather_output:
# All-gather across the partitions.
output = gather_from_model_parallel_region(output_parallel)
else:
output = output_parallel
return output
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:
in_features: first dimension of matrix A.
out_features: second dimension of matrix A.
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.
"""
def __init__(
self,
in_features: int,
out_features: int,
bias: bool = True,
input_is_parallel: bool = False,
init_method: Callable[[torch.Tensor], None] = init.xavier_normal_,
stride: int = 1,
keep_master_weight_for_test: bool = False,
):
super(RowParallelLinear, self).__init__()
# Keep input parameters
self.in_features = in_features
self.out_features = out_features
self.input_is_parallel = input_is_parallel
# Divide the weight matrix along the last dimension.
world_size = get_model_parallel_world_size()
self.input_size_per_partition = divide_and_check_no_remainder(in_features, world_size)
# Parameters.
# Note: torch.nn.functional.linear performs XA^T + b and as a result
# we allocate the transpose.
self.weight = Parameter(torch.Tensor(self.out_features, self.input_size_per_partition))
if bias:
self.bias = Parameter(torch.Tensor(self.out_features))
# Always initialize bias to zero.
with torch.no_grad():
self.bias.zero_()
else:
self.register_parameter("bias", None)
# Initialize weight.
self.master_weight = _initialize_affine_weight(
self.weight,
self.out_features,
self.in_features,
self.input_size_per_partition,
1,
init_method,
stride=stride,
return_master_weight=keep_master_weight_for_test,
)
def forward(self, input_: torch.Tensor) -> torch.Tensor: # type:ignore
# Set up backprop all-reduce.
if self.input_is_parallel:
input_parallel = input_
else:
input_parallel = scatter_to_model_parallel_region(input_)
# Matrix multiply.
output_parallel = F.linear(input_parallel, self.weight)
# All-reduce across all the partitions.
output_ = reduce_from_model_parallel_region(output_parallel)
if self.bias is not None:
output = output_ + self.bias
else:
output = output_
return output
fairscale/nn/model_parallel/mappings.py 0 → 100644
View file @ 30f5009a
# coding=utf-8
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from .initialize import get_model_parallel_group
from .utils import split_tensor_along_last_dim
def _reduce(input_: torch.Tensor) -> torch.Tensor:
"""All-reduce the the input tensor across model parallel group."""
group = get_model_parallel_group()
# Bypass the function if we are using only 1 GPU.
if torch.distributed.get_world_size(group=group) == 1:
return input_
# All-reduce.
torch.distributed.all_reduce(input_, group=group)
return input_
def _split(input_: torch.Tensor) -> torch.Tensor:
"""Split the tensor along its last dimension and keep the
corresponding slice."""
group = get_model_parallel_group()
# Bypass the function if we are using only 1 GPU.
if torch.distributed.get_world_size(group=group) == 1:
return input_
# Split along last dimension.
world_size = torch.distributed.get_world_size(group=group)
input_list = split_tensor_along_last_dim(input_, world_size)
# Note: torch.split does not create contiguous tensors by default.
rank = torch.distributed.get_rank(group=group)
output = input_list[rank].contiguous()
return output
def _gather(input_: torch.Tensor) -> torch.Tensor:
"""Gather tensors and concatinate along the last dimension."""
group = get_model_parallel_group()
# Bypass the function if we are using only 1 GPU.
if torch.distributed.get_world_size(group=group) == 1:
return input_
# Size and dimension.
last_dim = input_.dim() - 1
rank = torch.distributed.get_rank(group=group)
world_size = torch.distributed.get_world_size(group=group)
tensor_list = [torch.empty_like(input_) for _ in range(world_size)]
tensor_list[rank] = input_
torch.distributed.all_gather(tensor_list, input_, group=group)
# Note: torch.cat already creates a contiguous tensor.
output = torch.cat(tensor_list, dim=last_dim).contiguous()
return output
class _CopyToModelParallelRegion(torch.autograd.Function):
"""Pass the input to the model parallel region."""
@staticmethod
def forward(ctx, input_): # type: ignore
return input_
@staticmethod
def backward(ctx, grad_output): # type: ignore
return _reduce(grad_output)
class _ReduceFromModelParallelRegion(torch.autograd.Function):
"""All-redcue the input from the model parallel region."""
@staticmethod
def forward(ctx, input_): # type: ignore
return _reduce(input_)
@staticmethod
def backward(ctx, grad_output): # type: ignore
return grad_output
class _ScatterToModelParallelRegion(torch.autograd.Function):
"""Split the input and keep only the corresponding chuck to the rank."""
@staticmethod
def forward(ctx, input_): # type: ignore
return _split(input_)
@staticmethod
def backward(ctx, grad_output): # type: ignore
return _gather(grad_output)
class _GatherFromModelParallelRegion(torch.autograd.Function):
"""Gather the input from model parallel region and concatinate."""
@staticmethod
def forward(ctx, input_): # type: ignore
return _gather(input_)
@staticmethod
def backward(ctx, grad_output): # type: ignore
return _split(grad_output)
# -----------------
# Helper functions.
# -----------------
def copy_to_model_parallel_region(input_: torch.Tensor) -> torch.Tensor:
return _CopyToModelParallelRegion.apply(input_)
def reduce_from_model_parallel_region(input_: torch.Tensor) -> torch.Tensor:
return _ReduceFromModelParallelRegion.apply(input_)
def scatter_to_model_parallel_region(input_: torch.Tensor) -> torch.Tensor:
return _ScatterToModelParallelRegion.apply(input_)
def gather_from_model_parallel_region(input_: torch.Tensor) -> torch.Tensor:
return _GatherFromModelParallelRegion.apply(input_)
fairscale/nn/model_parallel/random.py 0 → 100644
View file @ 30f5009a
# coding=utf-8
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Parts of the code here are adapted from PyTorch
# repo: https://github.com/pytorch/pytorch
import contextlib
from typing import Dict, Iterator, Set, Union
import torch
from torch.cuda import _lazy_call
from torch.utils.checkpoint import detach_variable
from .initialize import get_data_parallel_rank, get_model_parallel_rank
# Default name for the model parallel rng tracker.
_MODEL_PARALLEL_RNG_TRACKER_NAME = "model-parallel-rng"
def _set_cuda_rng_state(new_state: torch.ByteTensor, device: Union[int, str, torch.device] = -1) -> None:
"""Sets the random number generator state of the current GPU.
Arguments:
new_state (torch.ByteTensor): The desired state
This function is adapted from PyTorch repo (torch.cuda.set_rng_state)
with a single change: the input state is not cloned. Cloning caused
major performance issues for +4 GPU cases.
"""
if device == -1:
device = torch.device("cuda")
elif isinstance(device, str):
device = torch.device(device)
elif isinstance(device, int):
device = torch.device("cuda", device)
def cb() -> None:
idx = device.index # type: ignore
if idx is None:
idx = torch.cuda.current_device()
default_generator = torch.cuda.default_generators[idx] # type: ignore
default_generator.set_state(new_state)
_lazy_call(cb)
class CudaRNGStatesTracker:
"""Tracker for the cuda RNG states.
Using the `add` method, a cuda rng state is initialized based on
the input `seed` and is assigned to `name`. Later, by forking the
rng state, we can perform operations and return to our starting
cuda state.
"""
def __init__(self) -> None:
# Map from a string name to the cuda rng state.
self.states_: Dict[str, torch.ByteTensor] = {}
# Seeds are just for book keeping and ensure no seed is set twice.
self.seeds_: Set[int] = set()
def reset(self) -> None:
"""Set to the initial state (no tracker)."""
self.states_ = {}
self.seeds_ = set()
def get_states(self) -> Dict[str, torch.ByteTensor]:
"""Get rng states. Copy the dictionary so we have direct
pointers to the states, not just a pointer to the dictionary."""
states = {}
for name in self.states_:
states[name] = self.states_[name]
return states
def set_states(self, states: Dict[str, torch.ByteTensor]) -> None:
"""Set the rng states. For efficiency purposes, we do not check
the size of seed for compatibility."""
self.states_ = states
def add(self, name: str, seed: int) -> None:
"""Track the rng state.
Arguments:
name (str): The name of the seed
seed (int): The seed value
"""
# Check seed is not already used.
if seed in self.seeds_:
raise Exception("seed {} already exists".format(seed))
self.seeds_.add(seed)
# Check that state is not already defined.
if name in self.states_:
raise Exception("cuda rng state {} already exists".format(name))
# Get the current rng state.
orig_rng_state = torch.cuda.get_rng_state()
# Set the new state and store it.
torch.cuda.manual_seed(seed)
self.states_[name] = torch.cuda.get_rng_state()
# Reset rng state to what it was.
_set_cuda_rng_state(orig_rng_state)
@contextlib.contextmanager
def fork(self, name: str = _MODEL_PARALLEL_RNG_TRACKER_NAME) -> Iterator[None]:
"""Fork the cuda rng state, perform operations, and exit with
the original state."""
# Check if we have added the state
if name not in self.states_:
raise Exception("cuda rng state {} is not added".format(name))
# Store current rng state.
orig_cuda_rng_state = torch.cuda.get_rng_state()
# Set rng state to the desired one
_set_cuda_rng_state(self.states_[name])
# Do the stuff we wanted to do.
try:
yield
finally:
# Update the current rng state for later use.
self.states_[name] = torch.cuda.get_rng_state()
# And set the state to the original state we started with.
_set_cuda_rng_state(orig_cuda_rng_state)
# RNG tracker object.
_CUDA_RNG_STATE_TRACKER = CudaRNGStatesTracker()
def get_cuda_rng_tracker() -> CudaRNGStatesTracker:
"""Get cuda rng tracker."""
return _CUDA_RNG_STATE_TRACKER
def model_parallel_cuda_manual_seed(seed: int) -> None:
"""Initialize model parallel cuda seed.
This function should be called after the model parallel is
initialized. Also, no torch.cuda.manual_seed should be called
after this function. Basically, this is replacement for that
function.
Two set of RNG states are tracked:
default state: This is for data parallelism and is the same among a
set of model parallel GPUs but different across
different model paralle groups. This is used for
example for dropout in the non-model-parallel regions.
model-parallel state: This state is different among a set of model
parallel GPUs, but the same across data parallel
groups. This is used for example for dropout in
model parallel regions.
"""
# 2718 is just for fun and any POSITIVE value will work.
offset = seed + 2718
model_parallel_seed = offset + get_model_parallel_rank()
# Data parallel gets the original sedd.
data_parallel_seed = seed
if torch.distributed.get_rank() == 0:
print(
"> initializing model parallel cuda seeds on global rank {}, "
"model parallel rank {}, and data parallel rank {} with "
"model parallel seed: {} and data parallel seed: {}".format(
torch.distributed.get_rank(),
get_model_parallel_rank(),
get_data_parallel_rank(),
model_parallel_seed,
data_parallel_seed,
),
flush=True,
)
_CUDA_RNG_STATE_TRACKER.reset()
# Set the default state.
torch.cuda.manual_seed(data_parallel_seed)
# and model parallel state.
_CUDA_RNG_STATE_TRACKER.add(_MODEL_PARALLEL_RNG_TRACKER_NAME, model_parallel_seed)
class CheckpointFunction(torch.autograd.Function):
"""This function is adapted from torch.utils.checkpoint with
two main changes:
1) torch.cuda.set_rng_state is replaced with `_set_cuda_rng_state`
2) the states in the model parallel tracker are also properly
tracked/set/reset.
"""
@staticmethod
def forward(ctx, run_function, *args): # type: ignore
ctx.run_function = run_function
# Copy the rng states.
ctx.fwd_cpu_rng_state = torch.get_rng_state()
ctx.fwd_cuda_rng_state = torch.cuda.get_rng_state()
ctx.fwd_cuda_rng_state_tracker = get_cuda_rng_tracker().get_states()
ctx.save_for_backward(*args)
with torch.no_grad():
outputs = run_function(*args)
return outputs
@staticmethod
def backward(ctx, *args): # type: ignore
if not torch.autograd._is_checkpoint_valid():
raise RuntimeError("Checkpointing is not compatible with .grad(), please use .backward() if possible")
inputs = ctx.saved_tensors
# Store the current states.
bwd_cpu_rng_state = torch.get_rng_state()
bwd_cuda_rng_state = torch.cuda.get_rng_state()
bwd_cuda_rng_state_tracker = get_cuda_rng_tracker().get_states()
# Set the states to what it used to be before the forward pass.
torch.set_rng_state(ctx.fwd_cpu_rng_state)
_set_cuda_rng_state(ctx.fwd_cuda_rng_state)
get_cuda_rng_tracker().set_states(ctx.fwd_cuda_rng_state_tracker)
# Compute the forward pass.
detached_inputs = detach_variable(inputs)
with torch.enable_grad():
outputs = ctx.run_function(*detached_inputs)
# Set the states back to what it was at the start of this function.
torch.set_rng_state(bwd_cpu_rng_state)
_set_cuda_rng_state(bwd_cuda_rng_state)
get_cuda_rng_tracker().set_states(bwd_cuda_rng_state_tracker)
if isinstance(outputs, torch.Tensor):
outputs = (outputs,)
torch.autograd.backward(outputs, args)
return (None,) + tuple(inp.grad for inp in detached_inputs)
def checkpoint(function, *args): # type: ignore
"""Checkpoint a model or part of the model.
This has been directly copied from torch.utils.checkpoint."""
return CheckpointFunction.apply(function, *args)
fairscale/nn/model_parallel/utils.py 0 → 100644
View file @ 30f5009a
# coding=utf-8
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Tuple
import torch
def ensure_divisibility(numerator: int, denominator: int) -> None:
"""Ensure that numerator is divisible by the denominator."""
assert numerator % denominator == 0, "{} is not divisible by {}".format(numerator, denominator)
def divide_and_check_no_remainder(numerator: int, denominator: int) -> int:
"""Ensure that numerator is divisible by the denominator and return
the division value."""
ensure_divisibility(numerator, denominator)
return numerator // denominator
def split_tensor_along_last_dim(
tensor: torch.Tensor, num_partitions: int, contiguous_split_chunks: bool = False
) -> Tuple[torch.Tensor, ...]:
"""Split a tensor along its last dimension.
Arguments:
tensor: input tensor.
num_partitions: number of partitions to split the tensor
contiguous_split_chunks: If True, make each chunk contiguous
in memory.
"""
# Get the size and dimension.
last_dim = tensor.dim() - 1
last_dim_size = divide_and_check_no_remainder(tensor.size()[last_dim], num_partitions)
# Split.
tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
# Note: torch.split does not create contiguous tensors by default.
if contiguous_split_chunks:
return tuple(chunk.contiguous() for chunk in tensor_list)
return tensor_list
class VocabUtility:
"""Split the vocabulary into `world_size` chunks amd return the
first and last index of the vocabulary belonging to the `rank`
partition: Note that indices in [first, last)"""
@staticmethod
def vocab_range_from_per_partition_vocab_size(
per_partition_vocab_size: int, rank: int, world_size: int
) -> Tuple[int, int]:
index_f = rank * per_partition_vocab_size
index_l = index_f + per_partition_vocab_size
return index_f, index_l
@staticmethod
def vocab_range_from_global_vocab_size(global_vocab_size: int, rank: int, world_size: int) -> Tuple[int, int]:
per_partition_vocab_size = divide_and_check_no_remainder(global_vocab_size, world_size)
return VocabUtility.vocab_range_from_per_partition_vocab_size(per_partition_vocab_size, rank, world_size)
stubs/torch/__init__.pyi
View file @ 30f5009a
......@@ -6,7 +6,7 @@
# @generated from torch/__init__.pyi.in
from typing import List, Tuple, Optional, Union, Any, ContextManager, Callable, overload, Iterator
from typing import List, Tuple, Optional, Union, Any, ContextManager, Callable, overload, Iterator, Iterable
from torch._six import inf
import builtins
......@@ -111,6 +111,9 @@ class Tensor:
grad: Optional[Tensor] = ...
data: Tensor = ...
names: List[str] = ...
def __init__(self, *args, **kwargs) -> None: ...
@property
def dtype(self) -> _dtype: ...
@property
......@@ -913,7 +916,7 @@ class Tensor:
def norm(self, p="fro", dim=None, keepdim=False): ...
def stft(self, n_fft, hop_length=None, win_length=None, window=None,
center=True, pad_mode='reflect', normalized=False, onesided=True): ...
def split(self, split_size, dim=0): ...
def split(self, split_size, dim=0) -> Tuple[Tensor, ...]: ...
def unique(self, sorted=True, return_inverse=False, dim=None): ...
def unique_consecutive(self, sorted=True, return_inverse=False, return_counts=False, dim=None): ...
def lu(self, pivot=True, get_infos=False): ...
......
stubs/torch/autograd/__init__.pyi
View file @ 30f5009a
......@@ -51,3 +51,4 @@ class set_detect_anomaly:
_TensorOrTensors = Union[Tensor, Sequence[Tensor]]
def backward(tensors: _TensorOrTensors, grad_tensors: Optional[_TensorOrTensors]=..., retain_graph: Optional[bool]=..., create_graph: bool=...) -> None: ...
def grad(outputs: _TensorOrTensors, inputs: _TensorOrTensors, grad_outputs: Optional[_TensorOrTensors]=..., retain_graph: Optional[bool]=..., create_graph: bool=..., only_inputs: bool=..., allow_unused: bool=...) -> Tuple[Tensor, ...]: ...
def _is_checkpoint_valid() -> bool: ...
stubs/torch/backends/cudnn.pyi
View file @ 30f5009a
......@@ -3,3 +3,5 @@
#MODIFIED BY TORCHGPIPE
def version() -> int: ...
#END
deterministic : bool
benchmark: bool
stubs/torch/cuda/__init__.pyi
View file @ 30f5009a
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
from typing import Optional, Tuple, Union
from typing import Optional, Tuple, Union, Dict, Any
import ctypes
from .. import device as _device
def is_available() -> bool: ...
def init() -> None: ...
def _lazy_call(callable) -> None: ...
class cudaStatus:
SUCCESS: int
......@@ -23,7 +24,7 @@ class _CudaDeviceProperties:
is_integrated: int
is_multi_gpu_board: int
_device_t = Union[_device, int]
_device_t = Union[_device, int, str]
def check_error(res: int) -> None: ...
def device_count() -> int: ...
......@@ -34,6 +35,7 @@ def get_device_capability(device: Optional[_device_t]=...) -> Tuple[int, int]: .
def get_device_name(device: Optional[_device_t]=...) -> str: ...
def get_device_properties(device: _device_t) -> _CudaDeviceProperties: ...
def current_device() -> int: ...
def manual_seed(seed: int) -> None: ...
def memory_allocated(device: Optional[_device_t]=...) -> int: ...
def max_memory_allocated(device: Optional[_device_t]=...) -> int: ...
def reset_max_memory_allocated(device: Optional[_device_t]=...) -> None: ...
......@@ -69,3 +71,5 @@ class stream:
def current_stream(device: Optional[_device_t]) -> Stream: ...
def default_stream(device: Optional[_device_t]) -> Stream: ...
#END
#
default_generators: Tuple[Any]
stubs/torch/distributed/__init__.pyi
View file @ 30f5009a
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
from typing import Any
from typing import Any, List, Union, Optional
from torch import Tensor
import datetime
def get_rank(group: Any) -> int: ...
class Backend: ...
class ProcessGroup: ...
def get_world_size(group: Any) -> int: ...
class ReduceOp:
SUM: ReduceOp
PRODUCT: ReduceOp
MIN: ReduceOp
MAX: ReduceOp
BAND: ReduceOp
BOR: ReduceOp
BXOR: ReduceOp
def get_rank(group: Any = None) -> int: ...
def get_world_size(group: Any = None) -> int: ...
def broadcast(tensor: Tensor, src: Any, group: Any, async_op: Any = False): ...
def is_initialized() -> bool: ...
def new_group(ranks: List[int], timeout: datetime.timedelta = datetime.timedelta(0, 1800), backend: Union[None, str, Backend] = None): ...
def all_reduce(tensor: Tensor, op: ReduceOp = ReduceOp.SUM, group:Optional[ProcessGroup] = None, async_op: bool = False): ...
def all_gather(tensor_list: List[Tensor], tensor: Tensor, group:Optional[ProcessGroup] = None, async_op: bool = False): ...
class group(object):
WORLD: Any
stubs/torch/functional.pyi 0 → 100644
View file @ 30f5009a
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
from . import Tensor
from typing import Tuple, List, Union
def split(tensor: Tensor, split_size_or_sections: Union[int, List[int]], dim: int=0) -> Tuple[Tensor,...]: ...
stubs/torch/nn/functional.pyi
View file @ 30f5009a
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
from .. import Tensor, _size
from typing import Any, Optional, Tuple, Dict, List, Callable
from typing import Any, Optional, Tuple, Dict, List, Callable, Union
from .common_types import _ratio_any_t
# 'TypedDict' is a new accepted type that represents a dictionary with a fixed set of allowed keys.
......
stubs/torch/nn/modules/module.pyi
View file @ 30f5009a
......@@ -25,9 +25,9 @@ class Module(Generic[T_co]):
def __call__(self, *input: Any, **kwargs: Any) -> T_co: ... # type: ignore
def register_buffer(self, name: str, tensor: Tensor) -> None: ...
def register_buffer(self, name: str, tensor: Optional[Tensor], persistent: bool = True) -> None: ...
def register_parameter(self, name: str, param: Parameter) -> None: ...
def register_parameter(self, name: str, param: Optional[Parameter]) -> None: ...
def add_module(self, name: str, module: 'Module') -> None: ...
......
stubs/torch/nn/parameter.pyi
View file @ 30f5009a
......@@ -4,6 +4,6 @@ from .. import Tensor
import builtins
class Parameter(Tensor):
def __init__(self, data: Tensor, requires_grad: builtins.bool): ...
def __init__(self, data: Tensor, requires_grad: builtins.bool = True): ...
...
stubs/torch/utils/__init__.pyi 0 → 100644
View file @ 30f5009a
from . import checkpoint
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