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未验证 提交 fa97e5ba 编写于 作者: W wuhuachaocoding 提交者： GitHub
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refactor mp. (#45803) 

* refactor mp.

* update setup.py.

* update mp_layers.py for compatibility.

* add documents for mp_layers.py

* update init.py

* update collective.py.

* update.

* update mp_ops.py

* update.

* update code style.

* update code style.
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python/paddle/distributed/collective.py
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......@@ -40,46 +40,23 @@ import paddle.fluid.core as core
from paddle import _C_ops, _legacy_C_ops
import paddle.fluid.dygraph_utils as dygraph_utils
import contextlib
from .fleet.layers.mpu.mp_ops import split
from .fleet.layers.mpu.mp_ops import _c_identity
from .fleet.layers.mpu.mp_ops import _c_concat
from .fleet.layers.mpu.mp_ops import _c_split
from .fleet.layers.mpu.mp_ops import _mp_allreduce
from .fleet.layers.mpu.mp_ops import _c_lookup_table
from .fleet.layers.mpu.mp_ops import _Linear
from .fleet.layers.mpu.mp_ops import _set_var_distributed
from .fleet.layers.mpu.mp_ops import _c_softmax_with_cross_entropy
from .fleet.layers.mpu.mp_ops import _linear
from .fleet.layers.mpu.mp_ops import _parallel_linear
from .fleet.layers.mpu.mp_ops import _parallel_embedding
from .communication.comm_utils import ReduceOp
__all__ = []
class ReduceOp:
"""
Specify the type of operation used for element-wise reductions.
It should be one of the following values:
ReduceOp.SUM
ReduceOp.MAX
ReduceOp.MIN
ReduceOp.PROD
Examples:
.. code-block:: python
# required: distributed
import paddle
import paddle.distributed as dist
dist.init_parallel_env()
if dist.get_rank() == 0:
data = paddle.to_tensor([[4, 5, 6], [4, 5, 6]])
else:
data = paddle.to_tensor([[1, 2, 3], [1, 2, 3]])
dist.all_reduce(data, op=dist.ReduceOp.SUM)
print(data)
# [[5, 7, 9], [5, 7, 9]] (2 GPUs)
"""
SUM = 0
MAX = 1
MIN = 2
PROD = 3
AVG = 4
class Group():
"""
The abstract representation of group.
......@@ -1259,747 +1236,6 @@ def scatter(tensor, tensor_list=None, src=0, group=None, use_calc_stream=True):
})
def _c_identity(tensor, group=None):
"""
Return a copy of the tensor, mainly used with model parallel.
Args:
tensor (Tensor): The input Tensor. Its data type
should be float16, float32, float64, int32 or int64.
group (int): The id of the process group to work on.
Returns:
Tensor.
"""
if group is not None and not group.is_member():
return
ring_id = 0 if group is None else group.id
if _non_static_mode():
return _legacy_C_ops.c_identity(tensor, 'use_calc_stream', True,
'ring_id', ring_id,
'use_model_parallel', True)
op_type = 'c_identity'
helper = LayerHelper(op_type, **locals())
out = helper.create_variable_for_type_inference(dtype=tensor.dtype)
check_variable_and_dtype(
tensor, 'tensor', ['float16', 'float32', 'float64', 'int32', 'int64'],
'_c_identity')
helper.append_op(type=op_type,
inputs={'X': tensor},
outputs={'Out': out},
attrs={
'ring_id': ring_id,
'use_calc_stream': True,
'use_model_parallel': True,
})
return out
def _c_concat(tensor, group=None):
"""
Return allgather of the tensor, mainly used with model parallel.
Args:
tensor (Tensor): The input Tensor. Its data type
should be float16, float32, float64, int32 or int64.
group (int): The id of the process group to work on.
Returns:
Tensor.
"""
if group is not None and not group.is_member():
return
group = _get_default_group() if group is None else group
ring_id = group.id
global_rank = _get_global_env().rank
rank = group.rank
nranks = group.nranks
if _non_static_mode():
return _legacy_C_ops.c_concat(tensor, 'ring_id', ring_id,
'use_calc_stream', True, 'rank', rank,
'nranks', nranks, 'use_model_parallel',
True)
op_type = 'c_concat'
helper = LayerHelper(op_type, **locals())
out = helper.create_variable_for_type_inference(dtype=tensor.dtype)
check_variable_and_dtype(
tensor, 'tensor', ['float16', 'float32', 'float64', 'int32', 'int64'],
'_c_concat')
helper.append_op(type=op_type,
inputs={'X': tensor},
outputs={'Out': out},
attrs={
'ring_id': ring_id,
'use_calc_stream': True,
'use_model_parallel': True,
'nranks': nranks,
'rank': rank
})
return out
def _c_split(tensor, group=None):
"""
Split tensor evenly among all members, mainly used with model parallel.
Args:
tensor (Tensor): The input Tensor. Its data type
should be float16, float32, float64, int32 or int64.
rank (int): The rank of the current process.
group (int): The id of the process group to work on.
Returns:
Tensor.
"""
if group is not None and not group.is_member():
return
ring_id = 0 if group is None else group.id
global_rank = _get_global_env().rank
rank = global_rank if group is None else group.get_group_rank(global_rank)
nranks = _get_global_env().world_size if group is None else group.nranks
if _non_static_mode():
return _legacy_C_ops.c_split(tensor, 'use_calc_stream', True, 'ring_id',
ring_id, 'rank', rank, 'nranks', nranks,
'use_model_parallel', True)
op_type = 'c_split'
helper = LayerHelper(op_type, **locals())
out = helper.create_variable_for_type_inference(dtype=tensor.dtype)
check_variable_and_dtype(
tensor, 'tensor', ['float16', 'float32', 'float64', 'int32', 'int64'],
'_c_split')
helper.append_op(type=op_type,
inputs={'X': tensor},
outputs={'Out': out},
attrs={
'ring_id': ring_id,
'use_calc_stream': True,
'rank': rank,
'nranks': nranks,
'use_model_parallel': True,
})
return out
def _mp_allreduce(tensor,
op=ReduceOp.SUM,
group=None,
use_calc_stream=True,
use_model_parallel=True):
"""[it is same as allreduce above, but it supports model parallel. And it support inplace startegy]
"""
if group is not None and not group.is_member():
return
if in_dygraph_mode():
group = _get_default_group() if group is None else group
assert op == ReduceOp.SUM, "Unknown parameter: {}.".format(op)
from paddle.autograd import PyLayer
class mp_allreduce_eager(PyLayer):
@staticmethod
def forward(ctx, tensor, group, use_calc_stream,
use_model_parallel):
ctx.ring_id = group.id
if use_calc_stream:
op_type = _get_reduce_op(op, "_mp_allreduce")
group.process_group.allreduce_on_calc_stream(
tensor, op_type)
return tensor
else:
return _legacy_C_ops.c_allreduce_sum_(
tensor, 'use_calc_stream', use_calc_stream, 'ring_id',
ring_id, "use_model_parallel", use_model_parallel)
@staticmethod
def backward(ctx, dy):
return _legacy_C_ops.c_identity(dy, 'use_calc_stream', True,
'ring_id', ctx.ring_id,
'use_model_parallel', True)
return mp_allreduce_eager.apply(tensor, group, use_calc_stream,
use_model_parallel)
ring_id = 0 if group is None else group.id
if _in_legacy_dygraph():
if op == ReduceOp.SUM:
return _legacy_C_ops.c_allreduce_sum_(tensor, 'use_calc_stream',
use_calc_stream, 'ring_id',
ring_id, "use_model_parallel",
use_model_parallel)
else:
raise ValueError("Unknown parameter: {}.".format(op))
op_type = 'c_allreduce_sum'
helper = LayerHelper(op_type, **locals())
out = helper.create_variable_for_type_inference(dtype=tensor.dtype)
check_variable_and_dtype(
tensor, 'tensor', ['float16', 'float32', 'float64', 'int32', 'int64'],
op_type)
helper.append_op(type=op_type,
inputs={'X': tensor},
outputs={'Out': out},
attrs={
'ring_id': ring_id,
'use_calc_stream': use_calc_stream,
'use_model_parallel': use_model_parallel,
})
return out
def _c_lookup_table(table, index, start_index=0, name=None):
"""
Lookup table according to index.
Args:
table (Tensor): The input Tensor. Its data type
should be float16, float32, float64.
index (Tensor): The index to lookup table.
start_index (int): The initial index for table range.
name (string): The name of the api
Returns:
Tensor.
"""
if _non_static_mode():
return _legacy_C_ops.c_embedding(table, index, "start_index",
start_index)
op_type = 'c_embedding'
helper = LayerHelper(op_type, **locals())
dtype = helper.input_dtype(input_param_name='table')
check_variable_and_dtype(index, 'input', ['int32', 'int64'], op_type)
tmp = helper.create_variable_for_type_inference(dtype)
helper.append_op(type='c_embedding',
inputs={
'Ids': index,
'W': table
},
outputs={'Out': tmp},
attrs={"start_index": start_index})
return tmp
class _Linear(layers.Layer):
"""
Linear
"""
def __init__(self,
in_features,
out_features,
weight_attr=None,
bias_attr=None,
name=None):
super(_Linear, self).__init__()
self._dtype = self._helper.get_default_dtype()
self._weight_attr = weight_attr
self._bias_attr = bias_attr
self.weight = self.create_parameter(shape=[in_features, out_features],
attr=self._weight_attr,
dtype=self._dtype,
is_bias=False)
self.bias = self.create_parameter(shape=[out_features],
attr=self._bias_attr,
dtype=self._dtype,
is_bias=True)
self.name = name
def forward(self, input):
out = _linear(x=input,
weight=self.weight,
bias=self.bias,
name=self.name)
return out
def extra_repr(self):
name_str = ', name={}'.format(self.name) if self.name else ''
return 'in_features={}, out_features={}, dtype={}{}'.format(
self.weight.shape[0], self.weight.shape[1], self._dtype, name_str)
def _c_softmax_with_cross_entropy(logits,
label,
group=None,
return_softmax=False):
if group is not None and not group.is_member():
return
ring_id = 0 if group is None else group.id
global_rank = _get_global_env().rank
rank = global_rank if group is None else group.get_group_rank(global_rank)
nranks = _get_global_env().world_size if group is None else group.nranks
input_dims = len(list(logits.shape))
label_dims = len(list(label.shape))
if input_dims - 1 != label_dims and input_dims != label_dims:
raise ValueError(
'Expected nput_dims - 1 = label_dims or input_dims == label_dims\
(got nput_dims{}, label_dims{})'.format(input_dims, label_dims))
if input_dims - 1 == label_dims:
label = paddle.unsqueeze(label, axis=-1)
if _non_static_mode():
softmax, loss = _legacy_C_ops.c_softmax_with_cross_entropy(
logits, label, 'ring_id', ring_id, 'rank', rank, 'nranks', nranks)
if not return_softmax:
return loss
else:
return loss, softmax
attrs = {
'ring_id': ring_id,
'rank': rank,
'nranks': nranks,
}
helper = LayerHelper('c_softmax_with_cross_entropy', **locals())
softmax = helper.create_variable_for_type_inference(dtype=logits.dtype)
loss = helper.create_variable_for_type_inference(dtype=logits.dtype)
helper.append_op(type='c_softmax_with_cross_entropy',
inputs={
'Logits': logits,
'Label': label
},
outputs={
'Softmax': softmax,
'Loss': loss
},
attrs=attrs)
if return_softmax:
return loss, softmax
return loss
def _linear(x, weight, bias=None, name=None):
"""
Fuction Linear
"""
if _non_static_mode():
pre_bias = _varbase_creator(dtype=x.dtype)
_legacy_C_ops.matmul(x, weight, pre_bias, 'transpose_X', False,
'transpose_Y', False, "alpha", 1)
return dygraph_utils._append_bias_in_dygraph(pre_bias,
bias,
axis=len(x.shape) - 1)
else:
helper = LayerHelper('linear', **locals())
dtype = x.dtype
assert len(
x.shape) < 4, "X latitude is not supported greater than 3 now."
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
'linear')
check_dtype(dtype, 'dtype', ['float16', 'float32', 'float64'], 'linear')
inputs = {'X': [x], 'Y': [weight]}
attrs = {
'transpose_X': False,
'transpose_Y': False,
'alpha': 1,
}
tmp = helper.create_variable_for_type_inference(dtype)
helper.append_op(type='matmul_v2',
inputs=inputs,
outputs={'Out': tmp},
attrs=attrs)
if bias is not None:
res = helper.create_variable_for_type_inference(dtype)
helper.append_op(type='elementwise_add',
inputs={
'X': [tmp],
'Y': [bias]
},
outputs={'Out': [res]},
attrs={'axis': len(x.shape) - 1})
else:
res = tmp
return res
def _set_var_distributed(var):
if var is None:
return
var.is_distributed = True
# NOTE: use current_block and find_var_recursive to support while_loop
startup_block = paddle.static.default_startup_program().current_block()
main_block = paddle.static.default_main_program().current_block()
startup_block._find_var_recursive(var.name).is_distributed = True
main_block._find_var_recursive(var.name).is_distributed = True
def _parallel_linear(x,
num_rows,
num_cols,
axis,
param_attr,
bias_attr,
gather_out,
inner_rank,
nranks,
split_tensor,
name,
group=None):
"""
Parallel Linear
axis the dimension of the parameter of linear layer.
axis = 0: the row dimension
axis = 1: the col dimension
"""
if group is not None and not group.is_member():
return
ring_id = 0 if group is None else group.id
if axis == 0:
if split_tensor:
x = _c_split(x, group=group)
else:
x = _c_identity(x, group=group)
linear = paddle.nn.Linear(num_rows,
num_cols,
weight_attr=param_attr,
bias_attr=bias_attr,
name=name)
# NOTE: npu linear function use matmul_v2 but linear use matmul
linear_function = _linear if core.is_compiled_with_npu()\
else paddle.nn.functional.linear
linear_out = linear_function(
x,
linear.weight,
# NOTE(wangxi): row split, bias need add after allreduce
None if axis == 0 else linear.bias,
linear.name)
_set_var_distributed(linear.weight)
# set is_distributed for splited bias
# if a linear layer is splited by row, each rank would hold a complete bias and they should be the same in each rank.
# if a linear layer is splited by col, the bias would also be split into each rank as its weight
if axis == 1 and linear._bias_attr != False:
_set_var_distributed(linear.bias)
if not gather_out: return linear_out
out_shape = list(linear_out.shape)
out_shape[0] *= 1 if axis == 0 else nranks
main_block = paddle.static.default_main_program().current_block()
out = main_block.create_var(
shape=out_shape,
dtype=linear_out.dtype,
type=linear_out.type,
lod_level=linear_out.lod_level,
persistable=False,
is_data=False,
need_check_feed=linear_out.desc.need_check_feed())
if axis == 0:
main_block.append_op(type='c_allreduce_sum',
inputs={'X': linear_out},
outputs={'Out': out},
attrs={
'ring_id': ring_id,
'use_calc_stream': True,
'use_model_parallel': True
})
if linear.bias is not None:
out = out + linear.bias
else:
main_block.append_op(type='c_concat',
inputs={'X': linear_out},
outputs={'Out': out},
attrs={
'rank': inner_rank,
'ring_id': ring_id,
'nranks': nranks,
'use_calc_stream': True,
'use_model_parallel': True
})
return out
def _parallel_embedding(x,
per_part_embeddings,
origin_size,
param_attr,
inner_rank,
num_partitions,
name,
group=None):
"""
Parallel Embedding
"""
if group is not None and not group.is_member():
return
ring_id = 0 if group is None else group.id
helper = LayerHelper("_parallel_embedding", **locals())
per_part_size = per_part_embeddings
rank = inner_rank
vocab_start_index = rank * per_part_size
dtype = helper.get_default_dtype()
size = [per_part_size, origin_size[1]]
weight = helper.create_parameter(attr=param_attr,
shape=size,
dtype=dtype,
is_bias=False)
if num_partitions == 1:
return paddle.nn.functional.embedding(x,
weight=weight,
padding_idx=None,
sparse=False,
name=name)
startup_block = paddle.static.default_startup_program().global_block()
main_block = paddle.static.default_main_program().global_block()
startup_block.vars[weight.name].is_distributed = True
main_block.vars[weight.name].is_distributed = True
output_parallel = paddle.distributed.collective._c_lookup_table(
weight, x, start_index=vocab_start_index, name=name)
out = paddle.distributed.collective._mp_allreduce(output_parallel,
group=group,
use_calc_stream=True,
use_model_parallel=True)
return out
def split(x,
size,
operation,
axis=0,
num_partitions=1,
gather_out=True,
weight_attr=None,
bias_attr=None,
name=None):
"""
Split the weight of the specified operation into multiple devices
and do the computation in parallel.
Now the following three cases are supported.
Case 1: Parallel Embedding
The weight of the embedding operation is a NxM matrix with N rows and M columns.
With parallel embedding, the weight is split into num_partitions partitions, each
of which is a matrix with (N/num_partitions + 1) rows and M column where the last
row as the padding idx.
Suppose we split the NxM weight into two partitons on device_0 and device_1
respectively. Then, one each device, the final weight has (N/2 + 1) rows with the
index range from 0 to N/2. On device_0, all values in the input within [0, N/2 -1]
keep unchanged and all other values are changed to N/2 which is the padding index and
are mapped to all zeros after embedding. In the same way, on device_1, the value V in the
input within [N/2, N-1] will be changed to (V - N/2), and all other values are changed
to N/2 and are mapped to all zeros after embedding. Finally, the results on the two
devices are sum-reduced.
The Embedding put on single card is as shown below:
.. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/split_embedding_single.png
:width: 800
:height: 350
:alt: single_embedding
:align: center
Parallel Embedding is shown as below:
.. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/split_embedding_split.png
:width: 800
:alt: split_embedding
:align: center
Case 2: Row Parallel Linear
The weight of the linear operation is a NxM matrix with N rows and M columns.
With row parallel linear, the weight is split into num_partitions partitions, each
of which is a matrix with N/num_partitions rows and M column.
The linear layer put on single card is shown as below, the input variable is represented by X,
the weight matrix is represented by W and the output vaiable is O. The linear layer on single card is
simple matrix multiplication operation, O = X * W.
.. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/split_single.png
:width: 800
:alt: single_linear
:align: center
Row Parallel Linear is shown as below. As the name suggests, Row Parallel Linear splits the weight matrix W into
[[W_row1], [W_row2]] along the row. And accordingly the input is splitted along the column into [X_col1, X_col2] and multiply their
respective weight matrices. Finally apply AllReduce on the output from each card to get the final output.
.. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/split_row.png
:width: 800
:alt: split_row
:align: center
Case 3: Column Parallel Linear
The weight of the linear operation is a NxM matrix with N rows and M columns.
With column parallel linear, the weight is split into num_paratitions partitions, each
of which is a matrix with N rows and M/num_partitions column.
The linear layer put on single card has been illustrated on case 2 and Column Parallel Linear
is shown as below. The Column Parallel Linear splits the weight matrix W into [W_col1, W_col2] along the column and
these splitted matrices respectively multiply the input. Finally apply AllGather on the output from each card to get the final output.
.. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/split_col.png
:width: 800
:alt: split_col
:align: center
As observed, the column parallel linear and row parallel linear can be combined to skip one ALLGATHER communication
operator. Furthermore the Attention and MLP can be combined to imporve the performance as shown below.
.. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/split_col_row.png
:width: 800
:alt: split_col_row
:align: center
Args:
x (Tensor): Input tensor. It's data type should be float16, float32, float64, int32 or int64.
size (list|tuple): A list or tuple with two elements indicating the shape of the weight.
operation (str): The name of the operation. The supported operations are 'linear' and 'embedding'.
axis (int, Optional): Indicate along which axis to split the weight. Default: 0.
num_partitions (int, Optional): How many parts the weight is partitioned. Default: 1.
gather_out (bool, Optional): Whether to gather the output after computation. By default, the output
on each partitions will be gathered after computation. Default: True.
weight_attr (ParamAttr, Optional): The parameter attribute for the learnable
weights(Parameter) of the specified operation. Default: None.
bias_attr (ParamAttr, Optional): The parameter attribute for the bias
of the specified operation. Default: None.
name (str, Optional): The default value is None. Normally there is no need for user to set this
property. Default: None. For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor.
Examples:
.. code-block:: python
# required: distributed
import paddle
import paddle.distributed.fleet as fleet
paddle.enable_static()
paddle.set_device('gpu:%d'%paddle.distributed.ParallelEnv().dev_id)
fleet.init(is_collective=True)
data = paddle.randint(0, 8, shape=[10,4])
emb_out = paddle.distributed.split(
data,
(8, 8),
operation="embedding",
num_partitions=2)
"""
assert isinstance(
size,
(list, tuple)), ("The type of size for "
"paddle.distributed.split must be list or tuple.")
assert len(size) == 2, ("Number of elements in size of "
"paddle.distributed.split must be two.")
assert isinstance(operation, str), ("The type of operation for "
"paddle.distributed.split must be str.")
supported_operations = [
'linear',
'embedding',
]
assert operation in supported_operations, (
"The operation for "
"paddle.distributed.split must be one of {}.".format(
supported_operations))
if _non_static_mode():
raise ValueError(
"paddle.distributed.split cannot be used in dynamic "
"graph mode, plese use ParallelEmbedding, ParallelRowLinear, "
"ParallelColumnLinear instead.")
else:
from .fleet import fleet
assert fleet._role_maker, ("To use paddle.distributed.split, "
"you must call fleet.init() firstly.")
rank = fleet.worker_index()
nranks = fleet.worker_num()
# rank within a model parallel group
inner_rank = rank % num_partitions
if operation == "embedding":
assert axis == 0, ("We only support to split the weight of embedding "
"along the first axis now.")
assert size[0] % num_partitions == 0, \
"The length of the vocabulary must be divisible by num_partitions " \
"but received vocabulary={} num_partitions={}".format(size[0], num_partitions)
per_part_size = size[0] // num_partitions
emb_out = _parallel_embedding(x,
per_part_size,
size,
weight_attr,
inner_rank,
num_partitions,
name,
group=None)
return emb_out
else:
should_split = False
if axis == 0:
assert size[0] % num_partitions == 0, (
"Number of rows of the weight for linear ({}) must be"
" divisible by num_partitions ({})".format(
size[0], num_partitions))
per_part_size = size[0] // num_partitions
linear_size = (per_part_size, size[1])
if x.shape[-1] == size[0]: should_split = True
elif axis == 1:
assert size[1] % num_partitions == 0, (
"Number of column of the weight for linear ({}) must be"
" divisible by num_partitions ({})".format(
size[1], num_partitions))
per_part_size = size[1] // num_partitions
linear_size = (size[0], per_part_size)
else:
raise ValueError("The value of axis must be 0 or 1, but the value "
"given is {}.".format(axis))
linear_out = _parallel_linear(x,
linear_size[0],
linear_size[1],
axis,
weight_attr,
bias_attr,
gather_out,
inner_rank,
num_partitions,
should_split,
name=name,
group=None)
return linear_out
def alltoall(in_tensor_list, out_tensor_list, group=None, use_calc_stream=True):
"""
Scatter tensors in in_tensor_list to all participators averagely and gather the result tensors in out_tensor_list.
......
python/paddle/distributed/communication/comm_utils.py 0 → 100644
浏览文件 @ fa97e5ba
# Copyright (c) 2022 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.
class ReduceOp:
"""
Specify the type of operation used for element-wise reductions.
It should be one of the following values:
ReduceOp.SUM
ReduceOp.MAX
ReduceOp.MIN
ReduceOp.PROD
Examples:
.. code-block:: python
# required: distributed
import paddle
import paddle.distributed as dist
dist.init_parallel_env()
if dist.get_rank() == 0:
data = paddle.to_tensor([[4, 5, 6], [4, 5, 6]])
else:
data = paddle.to_tensor([[1, 2, 3], [1, 2, 3]])
dist.all_reduce(data, op=dist.ReduceOp.SUM)
print(data)
# [[5, 7, 9], [5, 7, 9]] (2 GPUs)
"""
SUM = 0
MAX = 1
MIN = 2
PROD = 3
AVG = 4
python/paddle/distributed/fleet/layers/mpu/__init__.py 0 → 100644
浏览文件 @ fa97e5ba
# Copyright (c) 2022 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.
from .mp_layers import VocabParallelEmbedding
from .mp_layers import ColumnParallelLinear
from .mp_layers import RowParallelLinear
from .mp_layers import ParallelCrossEntropy
from .random import RNGStatesTracker
from .random import get_rng_state_tracker
from .random import model_parallel_random_seed
from .random import determinate_seed
from .random import dropout
python/paddle/distributed/fleet/layers/mpu/mp_layers.py 0 → 100644
浏览文件 @ fa97e5ba
# 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
python/paddle/distributed/fleet/layers/mpu/mp_ops.py 0 → 100644
浏览文件 @ fa97e5ba
# Copyright (c) 2022 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 paddle import _C_ops, _legacy_C_ops
from paddle.fluid import core
from paddle.fluid.framework import _non_static_mode
from paddle.fluid.framework import _in_legacy_dygraph
from paddle.fluid.framework import in_dygraph_mode
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.data_feeder import check_variable_and_dtype
from paddle.fluid.dygraph import layers
from paddle.distributed import collective
from ....communication.comm_utils import ReduceOp
from paddle.fluid.data_feeder import check_dtype
import paddle.fluid.dygraph_utils as dygraph_utils
def _c_identity(tensor, group=None):
"""
Return a copy of the tensor, mainly used with model parallel.
Args:
tensor (Tensor): The input Tensor. Its data type
should be float16, float32, float64, int32 or int64.
group (int): The id of the process group to work on.
Returns:
Tensor.
"""
if group is not None and not group.is_member():
return
ring_id = 0 if group is None else group.id
if _non_static_mode():
return _legacy_C_ops.c_identity(tensor, 'use_calc_stream', True,
'ring_id', ring_id,
'use_model_parallel', True)
op_type = 'c_identity'
helper = LayerHelper(op_type, **locals())
out = helper.create_variable_for_type_inference(dtype=tensor.dtype)
check_variable_and_dtype(
tensor, 'tensor', ['float16', 'float32', 'float64', 'int32', 'int64'],
'_c_identity')
helper.append_op(type=op_type,
inputs={'X': tensor},
outputs={'Out': out},
attrs={
'ring_id': ring_id,
'use_calc_stream': True,
'use_model_parallel': True,
})
return out
def _c_concat(tensor, group=None):
"""
Return allgather of the tensor, mainly used with model parallel.
Args:
tensor (Tensor): The input Tensor. Its data type
should be float16, float32, float64, int32 or int64.
group (int): The id of the process group to work on.
Returns:
Tensor.
"""
if group is not None and not group.is_member():
return
group = collective._get_default_group() if group is None else group
ring_id = group.id
global_rank = collective._get_global_env().rank
rank = group.rank
nranks = group.nranks
if _non_static_mode():
return _legacy_C_ops.c_concat(tensor, 'ring_id', ring_id,
'use_calc_stream', True, 'rank', rank,
'nranks', nranks, 'use_model_parallel',
True)
op_type = 'c_concat'
helper = LayerHelper(op_type, **locals())
out = helper.create_variable_for_type_inference(dtype=tensor.dtype)
check_variable_and_dtype(
tensor, 'tensor', ['float16', 'float32', 'float64', 'int32', 'int64'],
'_c_concat')
helper.append_op(type=op_type,
inputs={'X': tensor},
outputs={'Out': out},
attrs={
'ring_id': ring_id,
'use_calc_stream': True,
'use_model_parallel': True,
'nranks': nranks,
'rank': rank
})
return out
def _c_split(tensor, group=None):
"""
Split tensor evenly among all members, mainly used with model parallel.
Args:
tensor (Tensor): The input Tensor. Its data type
should be float16, float32, float64, int32 or int64.
rank (int): The rank of the current process.
group (int): The id of the process group to work on.
Returns:
Tensor.
"""
if group is not None and not group.is_member():
return
ring_id = 0 if group is None else group.id
global_rank = collective._get_global_env().rank
rank = global_rank if group is None else group.get_group_rank(global_rank)
nranks = collective._get_global_env(
).world_size if group is None else group.nranks
if _non_static_mode():
return _legacy_C_ops.c_split(tensor, 'use_calc_stream', True, 'ring_id',
ring_id, 'rank', rank, 'nranks', nranks,
'use_model_parallel', True)
op_type = 'c_split'
helper = LayerHelper(op_type, **locals())
out = helper.create_variable_for_type_inference(dtype=tensor.dtype)
check_variable_and_dtype(
tensor, 'tensor', ['float16', 'float32', 'float64', 'int32', 'int64'],
'_c_split')
helper.append_op(type=op_type,
inputs={'X': tensor},
outputs={'Out': out},
attrs={
'ring_id': ring_id,
'use_calc_stream': True,
'rank': rank,
'nranks': nranks,
'use_model_parallel': True,
})
return out
def _mp_allreduce(tensor,
op=ReduceOp.SUM,
group=None,
use_calc_stream=True,
use_model_parallel=True):
"""[it is same as allreduce above, but it supports model parallel. And it support inplace startegy]
"""
if group is not None and not group.is_member():
return
if in_dygraph_mode():
group = collective._get_default_group() if group is None else group
assert op == ReduceOp.SUM, "Unknown parameter: {}.".format(op)
from paddle.autograd import PyLayer
class mp_allreduce_eager(PyLayer):
@staticmethod
def forward(ctx, tensor, group, use_calc_stream,
use_model_parallel):
ctx.ring_id = group.id
if use_calc_stream:
op_type = collective._get_reduce_op(op, "_mp_allreduce")
group.process_group.allreduce_on_calc_stream(
tensor, op_type)
return tensor
else:
return _legacy_C_ops.c_allreduce_sum_(
tensor, 'use_calc_stream', use_calc_stream, 'ring_id',
ring_id, "use_model_parallel", use_model_parallel)
@staticmethod
def backward(ctx, dy):
return _legacy_C_ops.c_identity(dy, 'use_calc_stream', True,
'ring_id', ctx.ring_id,
'use_model_parallel', True)
return mp_allreduce_eager.apply(tensor, group, use_calc_stream,
use_model_parallel)
ring_id = 0 if group is None else group.id
if _in_legacy_dygraph():
if op == ReduceOp.SUM:
return _legacy_C_ops.c_allreduce_sum_(tensor, 'use_calc_stream',
use_calc_stream, 'ring_id',
ring_id, "use_model_parallel",
use_model_parallel)
else:
raise ValueError("Unknown parameter: {}.".format(op))
op_type = 'c_allreduce_sum'
helper = LayerHelper(op_type, **locals())
out = helper.create_variable_for_type_inference(dtype=tensor.dtype)
check_variable_and_dtype(
tensor, 'tensor', ['float16', 'float32', 'float64', 'int32', 'int64'],
op_type)
helper.append_op(type=op_type,
inputs={'X': tensor},
outputs={'Out': out},
attrs={
'ring_id': ring_id,
'use_calc_stream': use_calc_stream,
'use_model_parallel': use_model_parallel,
})
return out
def _c_lookup_table(table, index, start_index=0, name=None):
"""
Lookup table according to index.
Args:
table (Tensor): The input Tensor. Its data type
should be float16, float32, float64.
index (Tensor): The index to lookup table.
start_index (int): The initial index for table range.
name (string): The name of the api
Returns:
Tensor.
"""
if _non_static_mode():
return _legacy_C_ops.c_embedding(table, index, "start_index",
start_index)
op_type = 'c_embedding'
helper = LayerHelper(op_type, **locals())
dtype = helper.input_dtype(input_param_name='table')
check_variable_and_dtype(index, 'input', ['int32', 'int64'], op_type)
tmp = helper.create_variable_for_type_inference(dtype)
helper.append_op(type='c_embedding',
inputs={
'Ids': index,
'W': table
},
outputs={'Out': tmp},
attrs={"start_index": start_index})
return tmp
class _Linear(layers.Layer):
"""
Linear
"""
def __init__(self,
in_features,
out_features,
weight_attr=None,
bias_attr=None,
name=None):
super(_Linear, self).__init__()
self._dtype = self._helper.get_default_dtype()
self._weight_attr = weight_attr
self._bias_attr = bias_attr
self.weight = self.create_parameter(shape=[in_features, out_features],
attr=self._weight_attr,
dtype=self._dtype,
is_bias=False)
self.bias = self.create_parameter(shape=[out_features],
attr=self._bias_attr,
dtype=self._dtype,
is_bias=True)
self.name = name
def forward(self, input):
out = _linear(x=input,
weight=self.weight,
bias=self.bias,
name=self.name)
return out
def extra_repr(self):
name_str = ', name={}'.format(self.name) if self.name else ''
return 'in_features={}, out_features={}, dtype={}{}'.format(
self.weight.shape[0], self.weight.shape[1], self._dtype, name_str)
def _c_softmax_with_cross_entropy(logits,
label,
group=None,
return_softmax=False):
if group is not None and not group.is_member():
return
ring_id = 0 if group is None else group.id
global_rank = collective._get_global_env().rank
rank = global_rank if group is None else group.get_group_rank(global_rank)
nranks = collective._get_global_env(
).world_size if group is None else group.nranks
input_dims = len(list(logits.shape))
label_dims = len(list(label.shape))
if input_dims - 1 != label_dims and input_dims != label_dims:
raise ValueError(
'Expected nput_dims - 1 = label_dims or input_dims == label_dims\
(got nput_dims{}, label_dims{})'.format(input_dims, label_dims))
if input_dims - 1 == label_dims:
label = paddle.unsqueeze(label, axis=-1)
if _non_static_mode():
softmax, loss = _legacy_C_ops.c_softmax_with_cross_entropy(
logits, label, 'ring_id', ring_id, 'rank', rank, 'nranks', nranks)
if not return_softmax:
return loss
else:
return loss, softmax
attrs = {
'ring_id': ring_id,
'rank': rank,
'nranks': nranks,
}
helper = LayerHelper('c_softmax_with_cross_entropy', **locals())
softmax = helper.create_variable_for_type_inference(dtype=logits.dtype)
loss = helper.create_variable_for_type_inference(dtype=logits.dtype)
helper.append_op(type='c_softmax_with_cross_entropy',
inputs={
'Logits': logits,
'Label': label
},
outputs={
'Softmax': softmax,
'Loss': loss
},
attrs=attrs)
if return_softmax:
return loss, softmax
return loss
def _linear(x, weight, bias=None, name=None):
"""
Fuction Linear
"""
if _non_static_mode():
pre_bias = _varbase_creator(dtype=x.dtype)
_legacy_C_ops.matmul(x, weight, pre_bias, 'transpose_X', False,
'transpose_Y', False, "alpha", 1)
return dygraph_utils._append_bias_in_dygraph(pre_bias,
bias,
axis=len(x.shape) - 1)
else:
helper = LayerHelper('linear', **locals())
dtype = x.dtype
assert len(
x.shape) < 4, "X latitude is not supported greater than 3 now."
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
'linear')
check_dtype(dtype, 'dtype', ['float16', 'float32', 'float64'], 'linear')
inputs = {'X': [x], 'Y': [weight]}
attrs = {
'transpose_X': False,
'transpose_Y': False,
'alpha': 1,
}
tmp = helper.create_variable_for_type_inference(dtype)
helper.append_op(type='matmul_v2',
inputs=inputs,
outputs={'Out': tmp},
attrs=attrs)
if bias is not None:
res = helper.create_variable_for_type_inference(dtype)
helper.append_op(type='elementwise_add',
inputs={
'X': [tmp],
'Y': [bias]
},
outputs={'Out': [res]},
attrs={'axis': len(x.shape) - 1})
else:
res = tmp
return res
def _set_var_distributed(var):
if var is None:
return
var.is_distributed = True
# NOTE: use current_block and find_var_recursive to support while_loop
startup_block = paddle.static.default_startup_program().current_block()
main_block = paddle.static.default_main_program().current_block()
startup_block._find_var_recursive(var.name).is_distributed = True
main_block._find_var_recursive(var.name).is_distributed = True
def _parallel_linear(x,
num_rows,
num_cols,
axis,
param_attr,
bias_attr,
gather_out,
inner_rank,
nranks,
split_tensor,
name,
group=None):
"""
Parallel Linear
axis the dimension of the parameter of linear layer.
axis = 0: the row dimension
axis = 1: the col dimension
"""
if group is not None and not group.is_member():
return
ring_id = 0 if group is None else group.id
if axis == 0:
if split_tensor:
x = _c_split(x, group=group)
else:
x = _c_identity(x, group=group)
linear = paddle.nn.Linear(num_rows,
num_cols,
weight_attr=param_attr,
bias_attr=bias_attr,
name=name)
# NOTE: npu linear function use matmul_v2 but linear use matmul
linear_function = _linear if core.is_compiled_with_npu()\
else paddle.nn.functional.linear
linear_out = linear_function(
x,
linear.weight,
# NOTE(wangxi): row split, bias need add after allreduce
None if axis == 0 else linear.bias,
linear.name)
_set_var_distributed(linear.weight)
# set is_distributed for splited bias
# if a linear layer is splited by row, each rank would hold a complete bias and they should be the same in each rank.
# if a linear layer is splited by col, the bias would also be split into each rank as its weight
if axis == 1 and linear._bias_attr != False:
_set_var_distributed(linear.bias)
if not gather_out: return linear_out
out_shape = list(linear_out.shape)
out_shape[0] *= 1 if axis == 0 else nranks
main_block = paddle.static.default_main_program().current_block()
out = main_block.create_var(
shape=out_shape,
dtype=linear_out.dtype,
type=linear_out.type,
lod_level=linear_out.lod_level,
persistable=False,
is_data=False,
need_check_feed=linear_out.desc.need_check_feed())
if axis == 0:
main_block.append_op(type='c_allreduce_sum',
inputs={'X': linear_out},
outputs={'Out': out},
attrs={
'ring_id': ring_id,
'use_calc_stream': True,
'use_model_parallel': True
})
if linear.bias is not None:
out = out + linear.bias
else:
main_block.append_op(type='c_concat',
inputs={'X': linear_out},
outputs={'Out': out},
attrs={
'rank': inner_rank,
'ring_id': ring_id,
'nranks': nranks,
'use_calc_stream': True,
'use_model_parallel': True
})
return out
def _parallel_embedding(x,
per_part_embeddings,
origin_size,
param_attr,
inner_rank,
num_partitions,
name,
group=None):
"""
Parallel Embedding
"""
if group is not None and not group.is_member():
return
ring_id = 0 if group is None else group.id
helper = LayerHelper("_parallel_embedding", **locals())
per_part_size = per_part_embeddings
rank = inner_rank
vocab_start_index = rank * per_part_size
dtype = helper.get_default_dtype()
size = [per_part_size, origin_size[1]]
weight = helper.create_parameter(attr=param_attr,
shape=size,
dtype=dtype,
is_bias=False)
if num_partitions == 1:
return paddle.nn.functional.embedding(x,
weight=weight,
padding_idx=None,
sparse=False,
name=name)
startup_block = paddle.static.default_startup_program().global_block()
main_block = paddle.static.default_main_program().global_block()
startup_block.vars[weight.name].is_distributed = True
main_block.vars[weight.name].is_distributed = True
output_parallel = _c_lookup_table(weight,
x,
start_index=vocab_start_index,
name=name)
out = _mp_allreduce(output_parallel,
group=group,
use_calc_stream=True,
use_model_parallel=True)
return out
def split(x,
size,
operation,
axis=0,
num_partitions=1,
gather_out=True,
weight_attr=None,
bias_attr=None,
name=None):
"""
Split the weight of the specified operation into multiple devices
and do the computation in parallel.
Now the following three cases are supported.
Case 1: Parallel Embedding
The weight of the embedding operation is a NxM matrix with N rows and M columns.
With parallel embedding, the weight is split into num_partitions partitions, each
of which is a matrix with (N/num_partitions + 1) rows and M column where the last
row as the padding idx.
Suppose we split the NxM weight into two partitons on device_0 and device_1
respectively. Then, one each device, the final weight has (N/2 + 1) rows with the
index range from 0 to N/2. On device_0, all values in the input within [0, N/2 -1]
keep unchanged and all other values are changed to N/2 which is the padding index and
are mapped to all zeros after embedding. In the same way, on device_1, the value V in the
input within [N/2, N-1] will be changed to (V - N/2), and all other values are changed
to N/2 and are mapped to all zeros after embedding. Finally, the results on the two
devices are sum-reduced.
The Embedding put on single card is as shown below:
.. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/split_embedding_single.png
:width: 800
:height: 350
:alt: single_embedding
:align: center
Parallel Embedding is shown as below:
.. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/split_embedding_split.png
:width: 800
:alt: split_embedding
:align: center
Case 2: Row Parallel Linear
The weight of the linear operation is a NxM matrix with N rows and M columns.
With row parallel linear, the weight is split into num_partitions partitions, each
of which is a matrix with N/num_partitions rows and M column.
The linear layer put on single card is shown as below, the input variable is represented by X,
the weight matrix is represented by W and the output vaiable is O. The linear layer on single card is
simple matrix multiplication operation, O = X * W.
.. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/split_single.png
:width: 800
:alt: single_linear
:align: center
Row Parallel Linear is shown as below. As the name suggests, Row Parallel Linear splits the weight matrix W into
[[W_row1], [W_row2]] along the row. And accordingly the input is splitted along the column into [X_col1, X_col2] and multiply their
respective weight matrices. Finally apply AllReduce on the output from each card to get the final output.
.. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/split_row.png
:width: 800
:alt: split_row
:align: center
Case 3: Column Parallel Linear
The weight of the linear operation is a NxM matrix with N rows and M columns.
With column parallel linear, the weight is split into num_paratitions partitions, each
of which is a matrix with N rows and M/num_partitions column.
The linear layer put on single card has been illustrated on case 2 and Column Parallel Linear
is shown as below. The Column Parallel Linear splits the weight matrix W into [W_col1, W_col2] along the column and
these splitted matrices respectively multiply the input. Finally apply AllGather on the output from each card to get the final output.
.. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/split_col.png
:width: 800
:alt: split_col
:align: center
As observed, the column parallel linear and row parallel linear can be combined to skip one ALLGATHER communication
operator. Furthermore the Attention and MLP can be combined to imporve the performance as shown below.
.. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/api/paddle/distributed/img/split_col_row.png
:width: 800
:alt: split_col_row
:align: center
Args:
x (Tensor): Input tensor. It's data type should be float16, float32, float64, int32 or int64.
size (list|tuple): A list or tuple with two elements indicating the shape of the weight.
operation (str): The name of the operation. The supported operations are 'linear' and 'embedding'.
axis (int, Optional): Indicate along which axis to split the weight. Default: 0.
num_partitions (int, Optional): How many parts the weight is partitioned. Default: 1.
gather_out (bool, Optional): Whether to gather the output after computation. By default, the output
on each partitions will be gathered after computation. Default: True.
weight_attr (ParamAttr, Optional): The parameter attribute for the learnable
weights(Parameter) of the specified operation. Default: None.
bias_attr (ParamAttr, Optional): The parameter attribute for the bias
of the specified operation. Default: None.
name (str, Optional): The default value is None. Normally there is no need for user to set this
property. Default: None. For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor.
Examples:
.. code-block:: python
# required: distributed
import paddle
import paddle.distributed.fleet as fleet
paddle.enable_static()
paddle.set_device('gpu:%d'%paddle.distributed.ParallelEnv().dev_id)
fleet.init(is_collective=True)
data = paddle.randint(0, 8, shape=[10,4])
emb_out = paddle.distributed.split(
data,
(8, 8),
operation="embedding",
num_partitions=2)
"""
assert isinstance(
size,
(list, tuple)), ("The type of size for "
"paddle.distributed.split must be list or tuple.")
assert len(size) == 2, ("Number of elements in size of "
"paddle.distributed.split must be two.")
assert isinstance(operation, str), ("The type of operation for "
"paddle.distributed.split must be str.")
supported_operations = [
'linear',
'embedding',
]
assert operation in supported_operations, (
"The operation for "
"paddle.distributed.split must be one of {}.".format(
supported_operations))
if _non_static_mode():
raise ValueError(
"paddle.distributed.split cannot be used in dynamic "
"graph mode, plese use ParallelEmbedding, ParallelRowLinear, "
"ParallelColumnLinear instead.")
else:
from paddle.distributed.fleet import fleet
assert fleet._role_maker, ("To use paddle.distributed.split, "
"you must call fleet.init() firstly.")
rank = fleet.worker_index()
nranks = fleet.worker_num()
# rank within a model parallel group
inner_rank = rank % num_partitions
if operation == "embedding":
assert axis == 0, ("We only support to split the weight of embedding "
"along the first axis now.")
assert size[0] % num_partitions == 0, \
"The length of the vocabulary must be divisible by num_partitions " \
"but received vocabulary={} num_partitions={}".format(size[0], num_partitions)
per_part_size = size[0] // num_partitions
emb_out = _parallel_embedding(x,
per_part_size,
size,
weight_attr,
inner_rank,
num_partitions,
name,
group=None)
return emb_out
else:
should_split = False
if axis == 0:
assert size[0] % num_partitions == 0, (
"Number of rows of the weight for linear ({}) must be"
" divisible by num_partitions ({})".format(
size[0], num_partitions))
per_part_size = size[0] // num_partitions
linear_size = (per_part_size, size[1])
if x.shape[-1] == size[0]: should_split = True
elif axis == 1:
assert size[1] % num_partitions == 0, (
"Number of column of the weight for linear ({}) must be"
" divisible by num_partitions ({})".format(
size[1], num_partitions))
per_part_size = size[1] // num_partitions
linear_size = (size[0], per_part_size)
else:
raise ValueError("The value of axis must be 0 or 1, but the value "
"given is {}.".format(axis))
linear_out = _parallel_linear(x,
linear_size[0],
linear_size[1],
axis,
weight_attr,
bias_attr,
gather_out,
inner_rank,
num_partitions,
should_split,
name=name,
group=None)
return linear_out
python/paddle/distributed/fleet/layers/mpu/random.py 0 → 100644
浏览文件 @ fa97e5ba
# Copyright (c) 2022 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
import numpy as np
import contextlib
from paddle import _C_ops, _legacy_C_ops
from paddle.fluid import core
from paddle.fluid.data_feeder import check_variable_and_dtype
from paddle.fluid.framework import _non_static_mode, default_main_program, Variable
from paddle.fluid.layer_helper import LayerHelper
__all__ = []
MODEL_PARALLEL_RNG = 'model_parallel_rng'
# This file is inspired by Megatron to control random states for MP:
# https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/mpu/random.py
class RNGStatesTracker:
"""
Tracker the RNG states.
"""
def __init__(self):
# Map from name to the rng state.
self.states_ = {}
self.seeds_ = set()
def reset(self):
self.states_ = {}
self.seeds_ = set()
def add(self, name, seed):
if seed in self.seeds_:
raise ValueError('seed {} already exists'.format(seed))
self.seeds_.add(seed)
if name in self.states_:
raise ValueError('state {} already exists'.format(name))
orig_rng_state = paddle.get_cuda_rng_state()
paddle.seed(seed)
self.states_[name] = paddle.get_cuda_rng_state()
paddle.set_cuda_rng_state(orig_rng_state)
def get_states_tracker(self):
states = {}
for name in self.states_:
states[name] = self.states_[name]
return states
def set_states_tracker(self, states):
self.states_ = states
@contextlib.contextmanager
def rng_state(self, name=MODEL_PARALLEL_RNG):
if name not in self.states_:
raise ValueError('state {} does not exist'.format(name))
orig_cuda_rng_state = paddle.get_cuda_rng_state()
paddle.set_cuda_rng_state(self.states_[name])
try:
yield
finally:
self.states_[name] = paddle.get_cuda_rng_state()
paddle.set_cuda_rng_state(orig_cuda_rng_state)
RNG_STATE_TRACKER = RNGStatesTracker()
def get_rng_state_tracker():
return RNG_STATE_TRACKER
def model_parallel_random_seed(seed=None):
import paddle.distributed.fleet as fleet
hcg = fleet.get_hybrid_communicate_group()
rank = hcg.get_model_parallel_rank()
if seed:
global_seed = seed
local_seed = seed * 1024 + rank * 100
else:
global_seed = np.random.randint(0, 655350)
local_seed = np.random.randint(rank * 10000, (rank + 1) * 10000 - 1)
RNG_STATE_TRACKER.reset()
RNG_STATE_TRACKER.add(MODEL_PARALLEL_RNG, local_seed)
paddle.seed(global_seed)
def determinate_seed(rng_name):
assert rng_name is not None and rng_name != ""
helper = LayerHelper('seed', **locals())
out = helper.create_variable_for_type_inference(dtype=paddle.int32)
# set force_cpu to reduce sync copy from CPU->GPU->CPU, and reduce pipeline hang
helper.append_op(type='seed',
outputs={'Out': out},
attrs={
'deterministic': True,
'rng_name': rng_name,
'force_cpu': True
})
return out
def dropout(x,
p=0.5,
axis=None,
rng_name=None,
training=True,
mode="upscale_in_train",
name=None):
"""
Dropout is a regularization technique for reducing overfitting by preventing
neuron co-adaption during training. The dropout operator randomly sets the
outputs of some units to zero, while upscale others according to the given
dropout probability.
Args:
x (Tensor): The input tensor. The data type is float32 or float64.
p (float|int): Probability of setting units to zero. Default 0.5.
axis (int|list|tuple): The axis along which the dropout is performed. Default None.
rng_name (str): The random seed generator name, which used to obtain deterministic results.
training (bool): A flag indicating whether it is in train phrase or not. Default True.
mode(str): ['upscale_in_train'(default) | 'downscale_in_infer'].
1. upscale_in_train(default), upscale the output at training time
- train: out = input * mask / ( 1.0 - dropout_prob )
- inference: out = input
2. downscale_in_infer, downscale the output at inference
- train: out = input * mask
- inference: out = input * (1.0 - dropout_prob)
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Tensor representing the dropout, has same shape and data type as `x` .
Examples:
We use ``p=0.5`` in the following description for simplicity.
1. When ``axis=None`` , this is commonly used dropout, which dropout each element of x randomly.
.. code-block:: text
Let's see a simple case when x is a 2d tensor with shape 2*3:
[[1 2 3]
[4 5 6]]
we generate mask with the same shape as x, which is 2*3. The value of mask is
sampled from a Bernoulli distribution randomly. For example, we may get such mask:
[[0 1 0]
[1 0 1]]
So the output is obtained from elementwise multiply of x and mask:
[[0 2 0]
[4 0 6]]
Using default setting, i.e. ``mode='upscale_in_train'`` ,
if in training phase, the final upscale output is:
[[0 4 0 ]
[8 0 12]]
if in test phase, the output is the same as input:
[[1 2 3]
[4 5 6]]
we can also set ``mode='downscale_in_infer'`` , then
if in training phase, the final output is:
[[0 2 0]
[4 0 6]]
if in test phase, the scale output is:
[[0.5 1. 1.5]
[2. 2.5 3. ]]
"""
if rng_name is None:
return paddle.nn.functional.dropout(x, p, axis, training, mode, name)
if not isinstance(p, (float, int, Variable)):
raise TypeError("p argument should be a number(int|float) or Variable")
# fast return for p == 0
if isinstance(p, (int, float)) and p == 0: return x
assert 0 <= p <= 1, ValueError("p argument should between 0 and 1")
assert mode in ('downscale_in_infer', 'upscale_in_train'), \
ValueError(
"mode argument should be 'downscale_in_infer' or 'upscale_in_train'")
assert axis is None, \
TypeError("unsupport axis when using random seed generator")
mode = 'downgrade_in_infer' if mode == 'downscale_in_infer' else mode #semantic transfer
# dygraph using tracker, doesn't need determinate seed
if _non_static_mode():
out, mask = _legacy_C_ops.dropout(x, 'dropout_prob', p, 'is_test',
not training, 'fix_seed', False,
'seed', 0, 'dropout_implementation',
mode)
return out
seed = determinate_seed(rng_name)
if isinstance(p, Variable) and not p.shape != [1]:
raise TypeError(
"Required p.shape == [1] if type(p) is Variable, but received p.shape = {}"
.format(p.shape))
helper = LayerHelper('dropout', **locals())
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
'dropout')
out = helper.create_variable_for_type_inference(dtype=x.dtype)
mask = helper.create_variable_for_type_inference(
dtype=core.VarDesc.VarType.UINT8, stop_gradient=True)
helper.append_op(type='dropout',
inputs={
'X': [x],
'Seed': seed
},
outputs={
'Out': [out],
'Mask': [mask]
},
attrs={
'dropout_prob': p,
'is_test': not training,
'dropout_implementation': mode,
})
return out
python/paddle/distributed/fleet/meta_parallel/parallel_layers/mp_layers.py
浏览文件 @ fa97e5ba
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Copyright (c) 2022 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.
......@@ -12,298 +12,9 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle
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 ...base import topology as tp
from paddle.autograd import PyLayer
from ...layers.mpu.mp_layers import VocabParallelEmbedding # noqa: F401
from ...layers.mpu.mp_layers import ColumnParallelLinear # noqa: F401
from ...layers.mpu.mp_layers import RowParallelLinear # noqa: F401
from ...layers.mpu.mp_layers import ParallelCrossEntropy # noqa: F401
__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):
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 = paddle.distributed.collective._c_lookup_table(
self.weight,
x,
start_index=self.vocab_start_index,
name=self._name)
output = paddle.distributed.collective._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):
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 = paddle.distributed.collective._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 = paddle.distributed.collective._c_concat(
output_parallel, group=self.model_parallel_group)
else:
output = output_parallel
return output
class RowParallelLinear(Layer):
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 = paddle.distributed.collective._c_split(
x, group=self.model_parallel_group)
if self.is_mp:
output_parallel = self.linear(input_parallel,
self.weight,
name=self._name)
output_ = paddle.distributed.collective._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):
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 = paddle.distributed.collective._c_softmax_with_cross_entropy(
input, label, group=self.model_parallel_group)
return loss
python/paddle/distributed/fleet/meta_parallel/parallel_layers/random.py
浏览文件 @ fa97e5ba
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Copyright (c) 2022 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.
......@@ -12,232 +12,10 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle
import contextlib
import numpy as np
from paddle import _C_ops, _legacy_C_ops
from paddle.fluid import core
from paddle.fluid.data_feeder import check_variable_and_dtype
from paddle.fluid.framework import _non_static_mode, default_main_program, Variable
from paddle.fluid.layer_helper import LayerHelper
from ...layers.mpu.random import RNGStatesTracker # noqa: F401
from ...layers.mpu.random import get_rng_state_tracker # noqa: F401
from ...layers.mpu.random import model_parallel_random_seed # noqa: F401
from ...layers.mpu.random import determinate_seed # noqa: F401
from ...layers.mpu.random import dropout # noqa: F401
__all__ = []
MODEL_PARALLEL_RNG = 'model_parallel_rng'
# This file is inspired by Megatron to control random states for MP:
# https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/mpu/random.py
class RNGStatesTracker:
"""
Tracker the RNG states.
"""
def __init__(self):
# Map from name to the rng state.
self.states_ = {}
self.seeds_ = set()
def reset(self):
self.states_ = {}
self.seeds_ = set()
def add(self, name, seed):
if seed in self.seeds_:
raise ValueError('seed {} already exists'.format(seed))
self.seeds_.add(seed)
if name in self.states_:
raise ValueError('state {} already exists'.format(name))
orig_rng_state = paddle.get_cuda_rng_state()
paddle.seed(seed)
self.states_[name] = paddle.get_cuda_rng_state()
paddle.set_cuda_rng_state(orig_rng_state)
def get_states_tracker(self):
states = {}
for name in self.states_:
states[name] = self.states_[name]
return states
def set_states_tracker(self, states):
self.states_ = states
@contextlib.contextmanager
def rng_state(self, name=MODEL_PARALLEL_RNG):
if name not in self.states_:
raise ValueError('state {} does not exist'.format(name))
orig_cuda_rng_state = paddle.get_cuda_rng_state()
paddle.set_cuda_rng_state(self.states_[name])
try:
yield
finally:
self.states_[name] = paddle.get_cuda_rng_state()
paddle.set_cuda_rng_state(orig_cuda_rng_state)
RNG_STATE_TRACKER = RNGStatesTracker()
def get_rng_state_tracker():
return RNG_STATE_TRACKER
def model_parallel_random_seed(seed=None):
import paddle.distributed.fleet as fleet
hcg = fleet.get_hybrid_communicate_group()
rank = hcg.get_model_parallel_rank()
if seed:
global_seed = seed
local_seed = seed * 1024 + rank * 100
else:
global_seed = np.random.randint(0, 655350)
local_seed = np.random.randint(rank * 10000, (rank + 1) * 10000 - 1)
RNG_STATE_TRACKER.reset()
RNG_STATE_TRACKER.add(MODEL_PARALLEL_RNG, local_seed)
paddle.seed(global_seed)
def determinate_seed(rng_name):
assert rng_name is not None and rng_name != ""
helper = LayerHelper('seed', **locals())
out = helper.create_variable_for_type_inference(dtype=paddle.int32)
# set force_cpu to reduce sync copy from CPU->GPU->CPU, and reduce pipeline hang
helper.append_op(type='seed',
outputs={'Out': out},
attrs={
'deterministic': True,
'rng_name': rng_name,
'force_cpu': True
})
return out
def dropout(x,
p=0.5,
axis=None,
rng_name=None,
training=True,
mode="upscale_in_train",
name=None):
"""
Dropout is a regularization technique for reducing overfitting by preventing
neuron co-adaption during training. The dropout operator randomly sets the
outputs of some units to zero, while upscale others according to the given
dropout probability.
Args:
x (Tensor): The input tensor. The data type is float32 or float64.
p (float|int): Probability of setting units to zero. Default 0.5.
axis (int|list|tuple): The axis along which the dropout is performed. Default None.
rng_name (str): The random seed generator name, which used to obtain deterministic results.
training (bool): A flag indicating whether it is in train phrase or not. Default True.
mode(str): ['upscale_in_train'(default) | 'downscale_in_infer'].
1. upscale_in_train(default), upscale the output at training time
- train: out = input * mask / ( 1.0 - dropout_prob )
- inference: out = input
2. downscale_in_infer, downscale the output at inference
- train: out = input * mask
- inference: out = input * (1.0 - dropout_prob)
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Tensor representing the dropout, has same shape and data type as `x` .
Examples:
We use ``p=0.5`` in the following description for simplicity.
1. When ``axis=None`` , this is commonly used dropout, which dropout each element of x randomly.
.. code-block:: text
Let's see a simple case when x is a 2d tensor with shape 2*3:
[[1 2 3]
[4 5 6]]
we generate mask with the same shape as x, which is 2*3. The value of mask is
sampled from a Bernoulli distribution randomly. For example, we may get such mask:
[[0 1 0]
[1 0 1]]
So the output is obtained from elementwise multiply of x and mask:
[[0 2 0]
[4 0 6]]
Using default setting, i.e. ``mode='upscale_in_train'`` ,
if in training phase, the final upscale output is:
[[0 4 0 ]
[8 0 12]]
if in test phase, the output is the same as input:
[[1 2 3]
[4 5 6]]
we can also set ``mode='downscale_in_infer'`` , then
if in training phase, the final output is:
[[0 2 0]
[4 0 6]]
if in test phase, the scale output is:
[[0.5 1. 1.5]
[2. 2.5 3. ]]
"""
if rng_name is None:
return paddle.nn.functional.dropout(x, p, axis, training, mode, name)
if not isinstance(p, (float, int, Variable)):
raise TypeError("p argument should be a number(int|float) or Variable")
# fast return for p == 0
if isinstance(p, (int, float)) and p == 0: return x
assert 0 <= p <= 1, ValueError("p argument should between 0 and 1")
assert mode in ('downscale_in_infer', 'upscale_in_train'), \
ValueError(
"mode argument should be 'downscale_in_infer' or 'upscale_in_train'")
assert axis is None, \
TypeError("unsupport axis when using random seed generator")
mode = 'downgrade_in_infer' if mode == 'downscale_in_infer' else mode #semantic transfer
# dygraph using tracker, doesn't need determinate seed
if _non_static_mode():
out, mask = _legacy_C_ops.dropout(x, 'dropout_prob', p, 'is_test',
not training, 'fix_seed', False,
'seed', 0, 'dropout_implementation',
mode)
return out
seed = determinate_seed(rng_name)
if isinstance(p, Variable) and not p.shape != [1]:
raise TypeError(
"Required p.shape == [1] if type(p) is Variable, but received p.shape = {}"
.format(p.shape))
helper = LayerHelper('dropout', **locals())
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
'dropout')
out = helper.create_variable_for_type_inference(dtype=x.dtype)
mask = helper.create_variable_for_type_inference(
dtype=core.VarDesc.VarType.UINT8, stop_gradient=True)
helper.append_op(type='dropout',
inputs={
'X': [x],
'Seed': seed
},
outputs={
'Out': [out],
'Mask': [mask]
},
attrs={
'dropout_prob': p,
'is_test': not training,
'dropout_implementation': mode,
})
return out
python/setup.py.in
浏览文件 @ fa97e5ba
......@@ -307,6 +307,8 @@ packages=['paddle',
'paddle.distributed.fleet.metrics',
'paddle.distributed.fleet.proto',
'paddle.distributed.fleet.utils',
'paddle.distributed.fleet.layers',
'paddle.distributed.fleet.layers.mpu',
'paddle.distributed.fleet.meta_parallel',
'paddle.distributed.fleet.meta_parallel.pp_utils',
'paddle.distributed.fleet.meta_parallel.sharding',
......
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