# 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.reduce 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 in_dygraph_mode(): from paddle.autograd import PyLayer class c_identity_eager(PyLayer): @staticmethod def forward(ctx, tensor): return _legacy_C_ops.c_identity(tensor, 'use_calc_stream', True, 'ring_id', group.id, 'use_model_parallel', True) @staticmethod def backward(ctx, dy): op_type = collective._get_reduce_op(ReduceOp.SUM, "_c_identity") group.process_group.allreduce_on_calc_stream(dy, op_type) return dy return c_identity_eager.apply(tensor) elif _in_legacy_dygraph(): 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