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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.
# 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], torch.Tensor],
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], torch.Tensor] = 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], torch.Tensor] = 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], torch.Tensor] = 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 get_master_weight(self) -> torch.Tensor:
return gather_from_model_parallel_region(self.weight.data.transpose(0, 1)).transpose_(0, 1)
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], torch.Tensor] = 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 get_master_weight(self) -> torch.Tensor:
return gather_from_model_parallel_region(self.weight.data)
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
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