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# Copyright (c) 2021 PaddlePaddle Authors. 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 paddle
from . import mp_ops
from paddle.fluid import core
from paddle.fluid.dygraph.layers import Layer
from .random import get_rng_state_tracker
from paddle.nn import functional as F
from paddle import framework
from paddle.autograd import PyLayer
from ...base import topology as tp
__all__ = []
# Follow this paper to achieve the file:
# Shoeybi M, Patwary M, Puri R, et al. Megatron-lm: Training multi-billion parameter
# language models using model parallelism[J]. arXiv preprint arXiv:1909.08053, 2019. (https://arxiv.org/abs/1909.08053)
def is_fused_matmul_bias_supported():
if paddle.is_compiled_with_cuda() and not paddle.is_compiled_with_rocm():
return hasattr(core.ops, 'fused_gemm_epilogue')
else:
return False
class VocabParallelEmbedding(Layer):
"""Embedding mp parallelized in the vocabulary dimension.
this class is used for splitting embedding in mp group.
Args:
num_embeddings(int): One element which indicate the size of the dictionary of embeddings.
embedding_dim(int): One element which indicate the size of each embedding vector respectively.
weight_attr(ParamAttr|None): To specify the weight parameter property. Default: None, which means the
default weight parameter property is used. See usage for details in :ref:`api_ParamAttr` . In addition,
user-defined or pre-trained word vectors can be loaded with the :attr:`param_attr` parameter.
The local word vector needs to be transformed into numpy format, and the shape of local word
vector should be consistent with :attr:`num_embeddings` . Then :ref:`api_initializer_NumpyArrayInitializer`
is used to load custom or pre-trained word vectors. See code example for details.
mp_group(Group): The tensor parallel group.
name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
None by default.
Examples:
.. code-block:: python
import paddle
from paddle.distributed import fleet
class SimpleMPNet(paddle.nn.Layer):
def __init__(self, vocab_size, hidden_size, inner_size, output_size):
super(SimpleMPNet, self).__init__()
self.linear1 = fleet.meta_parallel.ColumnParallelLinear(
hidden_size,
inner_size,
gather_output=False,
has_bias=True)
self.linear2 = fleet.meta_parallel.RowParallelLinear(
inner_size,
hidden_size,
input_is_parallel=True,
has_bias=True)
self.linear3 = paddle.nn.Linear(hidden_size, output_size)
self.embedding = fleet.meta_parallel.VocabParallelEmbedding(
vocab_size,
hidden_size)
def forward(self, x):
x = self.embedding(x)
x = self.linear1(x)
x = self.linear2(x)
x = self.linear3(x)
return x
"""
def __init__(self,
num_embeddings,
embedding_dim,
weight_attr=None,
mp_group=None,
name=None):
super(VocabParallelEmbedding, self).__init__()
self.model_parallel_group = tp._HYBRID_PARALLEL_GROUP.get_model_parallel_group(
) if mp_group is None else mp_group
self.world_size = tp._HYBRID_PARALLEL_GROUP.get_model_parallel_world_size(
) if mp_group is None else mp_group.nranks
self.rank = tp._HYBRID_PARALLEL_GROUP.get_model_parallel_rank(
) if mp_group is None else mp_group.rank
self.origin_num_embeddings = num_embeddings
self.is_mp = (self.world_size > 1)
assert num_embeddings % self.world_size == 0, (
"The length of the vocabulary must be divisible by the parallelism degree of MP"
)
per_part_size = num_embeddings // self.world_size
self.vocab_start_index = self.rank * per_part_size
self._dtype = self._helper.get_default_dtype()
self._size = [per_part_size, embedding_dim]
self._weight_attr = weight_attr
self._name = name
if self.is_mp and paddle.in_dynamic_mode():
with get_rng_state_tracker().rng_state():
self.weight = self.create_parameter(attr=self._weight_attr,
shape=self._size,
dtype=self._dtype,
is_bias=False)
else:
self.weight = self.create_parameter(attr=self._weight_attr,
shape=self._size,
dtype=self._dtype,
is_bias=False)
self.weight.is_distributed = True if self.is_mp else False
def forward(self, x):
if self.is_mp:
output_parallel = mp_ops._c_lookup_table(
self.weight,
x,
start_index=self.vocab_start_index,
name=self._name)
output = mp_ops._mp_allreduce(output_parallel,
group=self.model_parallel_group,
use_calc_stream=True,
use_model_parallel=True)
else:
output = F.embedding(x,
weight=self.weight,
padding_idx=None,
sparse=False,
name=self._name)
return output
class ColumnParallelLinear(Layer):
"""Linear layer with mp parallelized(column).
this class is used for splitting Linear Layer in mp group, column split the weight of the Linear layer.
Args:
in_features(int): The number of input units.
out_features(int): The number of output units.
weight_attr(ParamAttr|None): The attribute for the learnable weight of this layer. The default value is None
and the weight will be initialized to zero. For detailed information, please refer to paddle.ParamAttr.
has_bias(bool): whether to add bias.
gather_output(bool): whether to do allgahter for the output of each rank.
fuse_matmul_bias(bool): whether to fuse matmul and bias.
mp_group(Group): The tensor parallel group.
name(str, optional): Normally there is no need for user to set this parameter.
For detailed information, please refer to :ref:`api_guide_Name` .
Examples:
.. code-block:: python
import paddle
from paddle.distributed import fleet
class SimpleMPNet(paddle.nn.Layer):
def __init__(self, vocab_size, hidden_size, inner_size, output_size):
super(SimpleMPNet, self).__init__()
self.linear1 = fleet.meta_parallel.ColumnParallelLinear(
hidden_size,
inner_size,
gather_output=False,
has_bias=True)
self.linear2 = fleet.meta_parallel.RowParallelLinear(
inner_size,
hidden_size,
input_is_parallel=True,
has_bias=True)
self.linear3 = paddle.nn.Linear(hidden_size, output_size)
self.embedding = fleet.meta_parallel.VocabParallelEmbedding(
vocab_size,
hidden_size)
def forward(self, x):
x = self.embedding(x)
x = self.linear1(x)
x = self.linear2(x)
x = self.linear3(x)
return x
"""
def __init__(self,
in_features,
out_features,
weight_attr=None,
has_bias=None,
gather_output=True,
fuse_matmul_bias=False,
mp_group=None,
name=None):
super(ColumnParallelLinear, self).__init__()
self.model_parallel_group = tp._HYBRID_PARALLEL_GROUP.get_model_parallel_group(
) if mp_group is None else mp_group
self.world_size = tp._HYBRID_PARALLEL_GROUP.get_model_parallel_world_size(
) if mp_group is None else mp_group.nranks
self._name = name
self.is_mp = (self.world_size > 1)
self.gather_output = gather_output
assert out_features % self.world_size == 0, (
"Number of column of the weight for linear ({}) must be"
" divisible by model parallel size ({})".format(
out_features, self.world_size))
self.output_size_per_partition = out_features // self.world_size
self._weight_attr = weight_attr
self._dtype = self._helper.get_default_dtype()
if self.is_mp and paddle.in_dynamic_mode():
with get_rng_state_tracker().rng_state():
self.weight = self.create_parameter(
shape=[in_features, self.output_size_per_partition],
attr=self._weight_attr,
dtype=self._dtype,
is_bias=False)
else:
self.weight = self.create_parameter(
shape=[in_features, self.output_size_per_partition],
attr=self._weight_attr,
dtype=self._dtype,
is_bias=False)
self.weight.is_distributed = True if self.is_mp else False
if has_bias:
# initialize bias to zero like Megatron
self.bias = self.create_parameter(
shape=[self.output_size_per_partition],
attr=paddle.nn.initializer.Constant(value=0.0),
dtype=self._dtype,
is_bias=True)
self.bias.is_distributed = True if self.is_mp else False
else:
self.bias = None
self.linear = F.linear
if fuse_matmul_bias:
if not is_fused_matmul_bias_supported():
raise NotImplementedError(
"You set fuse_matmul_bias=True in ColumnParallelLinear, "
"however, the paddle you are using not support this operation. "
"Please set fuse_matmul_bias=False or use paddle compiled "
"with cuda 11.6 or higher.")
from paddle.incubate.nn.functional import fused_linear
self.linear = fused_linear
def forward(self, x):
# use inner api to process identity
if self.is_mp:
input_parallel = mp_ops._c_identity(x,
group=self.model_parallel_group)
else:
input_parallel = x
output_parallel = self.linear(input_parallel,
self.weight,
self.bias,
name=self._name)
if self.gather_output and self.is_mp:
output = mp_ops._c_concat(output_parallel,
group=self.model_parallel_group)
else:
output = output_parallel
return output
class RowParallelLinear(Layer):
"""Linear layer with mp parallelized(row).
this class is used for splitting Linear Layer in mp group, row split the weight of the Linear layer.
Args:
in_features(int): The number of input units.
out_features(int): The number of output units.
weight_attr(ParamAttr|None): The attribute for the learnable weight of this layer. The default value is None
and the weight will be initialized to zero. For detailed information, please refer to paddle.ParamAttr.
has_bias(bool): whether to add bias.
input_is_parallel(bool): whether the input has alreadly been splitted across the mp group.
fuse_matmul_bias(bool): whether to fuse matmul and bias.
mp_group(Group): The tensor parallel group.
name(str, optional): Normally there is no need for user to set this parameter.
For detailed information, please refer to :ref:`api_guide_Name` .
Examples:
.. code-block:: python
import paddle
from paddle.distributed import fleet
class SimpleMPNet(paddle.nn.Layer):
def __init__(self, vocab_size, hidden_size, inner_size, output_size):
super(SimpleMPNet, self).__init__()
self.linear1 = fleet.meta_parallel.ColumnParallelLinear(
hidden_size,
inner_size,
gather_output=False,
has_bias=True)
self.linear2 = fleet.meta_parallel.RowParallelLinear(
inner_size,
hidden_size,
input_is_parallel=True,
has_bias=True)
self.linear3 = paddle.nn.Linear(hidden_size, output_size)
self.embedding = fleet.meta_parallel.VocabParallelEmbedding(
vocab_size,
hidden_size)
def forward(self, x):
x = self.embedding(x)
x = self.linear1(x)
x = self.linear2(x)
x = self.linear3(x)
return x
"""
def __init__(self,
in_features,
out_features,
weight_attr=None,
has_bias=True,
input_is_parallel=False,
fuse_matmul_bias=False,
mp_group=None,
name=None):
super(RowParallelLinear, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.input_is_parallel = input_is_parallel
self._weight_attr = weight_attr
self._dtype = self._helper.get_default_dtype()
self._name = name
self.model_parallel_group = tp._HYBRID_PARALLEL_GROUP.get_model_parallel_group(
) if mp_group is None else mp_group
self.world_size = tp._HYBRID_PARALLEL_GROUP.get_model_parallel_world_size(
) if mp_group is None else mp_group.nranks
self.rank = tp._HYBRID_PARALLEL_GROUP.get_model_parallel_rank(
) if mp_group is None else mp_group.rank
self.is_mp = (self.world_size > 1)
assert in_features % self.world_size == 0, (
"Number of row of the weight for linear ({}) must be"
" divisible by model parallel size ({})".format(
in_features, self.world_size))
self.input_size_per_partition = in_features // self.world_size
if self.is_mp and paddle.in_dynamic_mode():
with get_rng_state_tracker().rng_state():
self.weight = self.create_parameter(
shape=[self.input_size_per_partition, self.out_features],
attr=self._weight_attr,
dtype=self._dtype,
is_bias=False)
else:
self.weight = self.create_parameter(
shape=[self.input_size_per_partition, self.out_features],
attr=self._weight_attr,
dtype=self._dtype,
is_bias=False)
self.weight.is_distributed = True if self.is_mp else False
if has_bias:
self.bias = self.create_parameter(
shape=[self.out_features],
attr=paddle.nn.initializer.Constant(value=0.0),
dtype=self._dtype,
is_bias=True)
else:
self.bias = None
self.linear = F.linear
if fuse_matmul_bias:
if not is_fused_matmul_bias_supported():
raise NotImplementedError(
"You set fuse_matmul_bias=True in RowParallelLinear, "
"however, the paddle you are using not support this operation. "
"Please set fuse_matmul_bias=False or use paddle compiled "
"with cuda 11.6 or higher.")
from paddle.incubate.nn.functional import fused_linear
self.linear = fused_linear
def forward(self, x):
if self.input_is_parallel or (not self.is_mp):
input_parallel = x
else:
# split last dim
input_parallel = mp_ops._c_split(x, group=self.model_parallel_group)
if self.is_mp:
output_parallel = self.linear(input_parallel,
self.weight,
name=self._name)
output_ = mp_ops._mp_allreduce(output_parallel,
group=self.model_parallel_group,
use_calc_stream=True,
use_model_parallel=True)
output = output_ + self.bias if self.bias is not None else output_
else:
output = self.linear(input_parallel,
self.weight,
self.bias,
name=self._name)
return output
class ParallelCrossEntropy(Layer):
"""CrossEntropy with mp parallelized.
this class is used for splitting softmax cross entropy in mp group.
Args:
mp_group(Group): The tensor parallel group.
name(str, optional): Normally there is no need for user to set this parameter.
For detailed information, please refer to :ref:`api_guide_Name` .
Examples:
.. code-block:: python
loss_func = ParallelCrossEntropy()
loss = loss_func(img, lable)
"""
def __init__(self, mp_group=None, name=None):
super(ParallelCrossEntropy, self).__init__()
self.name = name
self.model_parallel_group = tp._HYBRID_PARALLEL_GROUP.get_model_parallel_group(
) if mp_group is None else mp_group
self.world_size = tp._HYBRID_PARALLEL_GROUP.get_model_parallel_world_size(
) if mp_group is None else mp_group.nranks
self.rank = tp._HYBRID_PARALLEL_GROUP.get_model_parallel_rank(
) if mp_group is None else mp_group.rank
def forward(self, input, label):
loss = mp_ops._c_softmax_with_cross_entropy(
input, label, group=self.model_parallel_group)
return loss
