# 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 from .common import DistributedOperatorImplContainer from .common import DistributedOperatorImpl from .common import register_distributed_operator_impl_container from .common import register_distributed_operator_impl, is_parameter_related from ..utils import is_dim_shard from ..utils import is_dim_replicate from ..utils import is_valid_list_index from ..utils import compute_compatible_dim_mapping from ..utils import compute_compatible_dims_mapping from ..utils import compute_compatible_and_update_dim_mapping from ..utils import set_dist_op_desc_original_id from ..dist_attribute import OperatorDistributedAttribute from paddle.fluid import core, unique_name from paddle.fluid.framework import in_dygraph_mode from paddle.fluid.framework import Program, Parameter, Variable, program_guard from paddle.fluid.data_feeder import check_variable_and_dtype, check_dtype from paddle.distributed.fleet.meta_optimizers.common import OpRole, OP_ROLE_KEY, OP_ROLE_VAR_KEY from ..process_group import new_process_group from ..utils import _get_comm_group, _get_corresponding_rank __op_not_need_param_init__ = ["while", "cond"] class DistributedDefault(DistributedOperatorImplContainer): def __init__(self, op_type): super(DistributedDefault, self).__init__(op_type) register_distributed_operator_impl_container(DistributedDefault("default")) # Replicated Default class DistributedDefaultImpl0(DistributedOperatorImpl): def __init__(self, name): super(DistributedDefaultImpl0, self).__init__(name) self._forward_implemented = True self._backward_implemented = True def is_input_compatible(self, dist_op): op_desc = dist_op.serial_op.desc op_dist_attr = dist_op.dist_attr for arg_name in op_desc.input_arg_names(): serial_tensor = dist_op.get_serial_input(arg_name) dims_mapping = op_dist_attr.get_input_dims_mapping(arg_name) if serial_tensor.is_parameter: for mapping in dims_mapping: if mapping != -1: return False # continue # if len(dims_mapping) < 1: # continue if len(dims_mapping) > 1: for mapping in dims_mapping[1:]: if mapping != -1: return False return True def is_output_compatible(self, dist_op): op_desc = dist_op.serial_op.desc op_dist_attr = dist_op.dist_attr output_names = op_desc.output_names() xshape_arg_names = [] if "XShape" in output_names: xshape_arg_names = op_desc.output("XShape") for arg_name in op_desc.output_arg_names(): serial_tensor = dist_op.get_serial_output(arg_name) dims_mapping = op_dist_attr.get_output_dims_mapping(arg_name) if serial_tensor.is_parameter: for mapping in dims_mapping: if mapping != -1: return False # continue # if len(dims_mapping) < 1: # continue if arg_name not in xshape_arg_names: if len(dims_mapping) > 1: for mapping in dims_mapping[1:]: if mapping != -1: return False else: if dims_mapping[0] != -1: return False if len(dims_mapping) > 2: for mapping in dims_mapping[2:]: if mapping != -1: return False return True def is_auto_compatible(self, dist_op): op_desc = dist_op.serial_op.desc op_dist_attr = dist_op.dist_attr batch_dim_mappings = [] # Check input compatibility for arg_name in op_desc.input_arg_names(): serial_tensor = dist_op.get_serial_input(arg_name) if serial_tensor.is_parameter: continue dims_mapping = op_dist_attr.get_input_dims_mapping(arg_name) if len(dims_mapping) > 1: for mapping in dims_mapping[1:]: if mapping != -1: return False if len(dims_mapping) >= 1: batch_dim_mappings.append(dims_mapping[0]) # Check output compatibility output_names = op_desc.output_names() xshape_arg_names = [] if "XShape" in output_names: xshape_arg_names = op_desc.output("XShape") for arg_name in op_desc.output_arg_names(): serial_tensor = dist_op.get_serial_output(arg_name) if serial_tensor.is_parameter: continue dims_mapping = op_dist_attr.get_output_dims_mapping(arg_name) if arg_name not in xshape_arg_names: if len(dims_mapping) > 1: for mapping in dims_mapping[1:]: if mapping != -1: return False if len(dims_mapping) >= 1: batch_dim_mappings.append(dims_mapping[0]) else: if dims_mapping[0] != -1: return False if len(dims_mapping) > 2: for mapping in dims_mapping[2:]: if mapping != -1: return False if len(dims_mapping) >= 2: batch_dim_mappings.append(dims_mapping[1]) # Check batch dim mapping compatibility if not all(batch_dim_mappings[0] == dim_mapping for dim_mapping in batch_dim_mappings): return False return True def update_dims_mapping(self, dist_op): changed = False op_desc = dist_op.serial_op.desc op_dist_attr = dist_op.dist_attr # The following statement will be replaced by a more elegent way if op_desc.type() == "shape" \ or op_desc.type() == "slice" \ or op_desc.type() == "while": return False output_names = op_desc.output_names() xshape_arg_names = [] if "XShape" in output_names: xshape_arg_names = op_desc.output("XShape") batch_dim_mappings = [] for arg_name in op_desc.input_arg_names(): serial_tensor = dist_op.get_serial_input(arg_name) if serial_tensor.is_parameter: continue dims_mapping = op_dist_attr.get_input_dims_mapping(arg_name) if len(dims_mapping) >= 1: batch_dim_mappings.append(dims_mapping[0]) for arg_name in op_desc.output_arg_names(): serial_tensor = dist_op.get_serial_output(arg_name) if serial_tensor.is_parameter: continue dims_mapping = op_dist_attr.get_output_dims_mapping(arg_name) if arg_name not in xshape_arg_names: if len(dims_mapping) >= 1: batch_dim_mappings.append(dims_mapping[0]) else: batch_dim_mappings.append(dims_mapping[1]) if not batch_dim_mappings: return changed compatible_dim_mapping = compute_compatible_dim_mapping( batch_dim_mappings) assert compatible_dim_mapping is not None, "There is no compatible dim mapping." for arg_name in op_desc.input_arg_names(): serial_tensor = dist_op.get_serial_input(arg_name) if serial_tensor.is_parameter: continue dims_mapping = op_dist_attr.get_input_dims_mapping(arg_name) if len(dims_mapping ) >= 1 and compatible_dim_mapping != dims_mapping[0]: dims_mapping[0] = compatible_dim_mapping changed = True for arg_name in op_desc.output_arg_names(): serial_tensor = dist_op.get_serial_output(arg_name) if serial_tensor.is_parameter: continue dims_mapping = op_dist_attr.get_output_dims_mapping(arg_name) if arg_name not in xshape_arg_names: if len(dims_mapping ) >= 1 and compatible_dim_mapping != dims_mapping[0]: dims_mapping[0] = compatible_dim_mapping changed = True else: if len(dims_mapping ) >= 2 and compatible_dim_mapping != dims_mapping[1]: dims_mapping[1] = compatible_dim_mapping changed = True return changed @staticmethod def forward(ctx, *args, **kwargs): dist_op_context = ctx.dist_op_context main_block = dist_op_context.work_block startup_block = dist_op_context.startup_block src_op = dist_op_context.cur_src_op rank_id = dist_op_context.rank_id # check validation of inputs / outputs for input_name in src_op.desc.input_names(): assert input_name in kwargs, "input [{}] is not given".format( input_name) assert len(kwargs[input_name]) == len( src_op.desc.input(input_name) ), "number of tensor for input [{}] is not match".format(input_name) for output_name in src_op.desc.output_names(): assert output_name in kwargs, "input [{}] is not given".format( output_name) assert len(kwargs[output_name]) == len( src_op.desc.output(output_name) ), "number of tensor for input [{}] is not match".format( output_name) # replicate op in dist program dist_op_desc = main_block.desc.append_op() dist_op_desc.copy_from(src_op.desc) set_dist_op_desc_original_id(dist_op_desc, src_op.desc, ctx) for input_name in src_op.desc.input_names(): dist_op_desc.set_input(input_name, kwargs[input_name]) for output_name in src_op.desc.output_names(): dist_op_desc.set_output(output_name, kwargs[output_name]) main_block._sync_with_cpp() # param initialization sync if src_op.type in __op_not_need_param_init__: return for varname in dist_op_desc.input_arg_names(): if startup_block.has_var(varname) and startup_block.var( varname ).is_parameter and varname not in dist_op_context.already_init_sync_vars: dist_op_context.already_init_sync_vars.add(varname) param = startup_block.var(varname) param_dist_attr = ctx.get_tensor_dist_attr_for_program(param) process_mesh = param_dist_attr.process_mesh dims_mapping = param_dist_attr.dims_mapping # FIXME (JZ-LIANG) Remove this hack to support any op mesh group for Pipeline Parallelism if rank_id not in process_mesh.processes: rank_id = _get_corresponding_rank(ctx, process_mesh, rank_id) # NOTE all not splited axis should be presented in mesh for axis, size in enumerate(process_mesh.topology): if size <= 1 or axis in dims_mapping: pass else: group_ranks = _get_comm_group(process_mesh.processes, process_mesh.topology, axis, rank_id) sync_group = new_process_group(group_ranks) new_op = startup_block.append_op( type='c_broadcast', inputs={'X': param}, outputs={'Out': param}, attrs={ 'ring_id': sync_group.id, 'root': 0, 'use_calc_stream': True, OP_ROLE_KEY: OpRole.Forward }) # set distributed attribute op_attr = OperatorDistributedAttribute() op_attr.process_mesh = process_mesh op_attr.set_output_dims_mapping(param.name, dims_mapping) op_attr.set_input_dims_mapping(param.name, dims_mapping) ctx.set_op_dist_attr_for_program(new_op, op_attr) startup_block._sync_with_cpp() @staticmethod def backward(ctx, *args, **kwargs): # by now the backward function only insert the gradient allreduce for dist op itself dist_op_context = ctx.dist_op_context main_block = dist_op_context.work_block backward_op = dist_op_context.cur_src_op dist_attr = ctx.get_op_dist_attr_for_program(backward_op) assert dist_attr is not None, "backward op [{}] don't have dist attribute !".format( str(backward_op)) rank_id = dist_op_context.rank_id # check validation of inputs / outputs for input_name in backward_op.desc.input_names(): assert input_name in kwargs, "input [{}] is not given".format( input_name) assert len(kwargs[input_name]) == len( backward_op.desc.input(input_name) ), "number of tensor for input [{}] is not match".format(input_name) for output_name in backward_op.desc.output_names(): assert output_name in kwargs, "input [{}] is not given".format( output_name) assert len(kwargs[output_name]) == len( backward_op.desc.output(output_name) ), "number of tensor for input [{}] is not match".format( output_name) # replicate op in dist program dist_op_desc = main_block.desc.append_op() dist_op_desc.copy_from(backward_op.desc) # Refer to the related dist op set_dist_op_desc_original_id(dist_op_desc, backward_op.desc, ctx) for input_name in backward_op.desc.input_names(): dist_op_desc.set_input(input_name, kwargs[input_name]) for output_name in backward_op.desc.output_names(): dist_op_desc.set_output(output_name, kwargs[output_name]) main_block._sync_with_cpp() # check if need gradient allreduce # if there is a non-gradient & non-parameter input and its batch dimension is splited, # we need insert gradient allreduce for the gradient of parameter in its output need_gradient_allreduce = False for input_name in backward_op.desc.input_names(): for varname in backward_op.desc.input(input_name): if "@GRAD" not in varname and not is_parameter_related( varname, main_block): # NOTE input var's dim_mapping of backward op should be the same with input var instead of corresponding varname of forward op process_mesh = dist_attr.process_mesh var_dim_mapping = dist_attr.get_input_dims_mapping(varname) # FIXME (JZ-LIANG) Remove this hack to support any op mesh group for Pipeline Parallelism if rank_id not in process_mesh.processes: rank_id = _get_corresponding_rank(ctx, process_mesh, rank_id) mesh_shape = process_mesh.topology batch_size_axis = var_dim_mapping[0] if batch_size_axis > -1 and mesh_shape[batch_size_axis] > 1: need_gradient_allreduce = True group_ranks = _get_comm_group(process_mesh.processes, process_mesh.topology, batch_size_axis, rank_id) dp_degree = len(group_ranks) dp_group = new_process_group(group_ranks) break if need_gradient_allreduce: allreduce_vars = [] for input_name in backward_op.desc.input_names(): for varname in backward_op.desc.input(input_name): if "@GRAD" not in varname and is_parameter_related( varname, main_block): # NOTE: When amp and recompute pass are effective at the same time, # if a parameter is casted and recomputed, the 'parameter@GARD' can not # be found in the grad_op's output. if "subprog_" in varname: varname = varname[:varname.index(".subprog_")] assert len( backward_op.desc.input(input_name) ) == 1, "parameter input to grad op should be length 1, but got [{}]".format( backward_op.desc.input(input_name)) assert varname + "@GRAD" in backward_op.desc.output_arg_names( ), "parameter's grad [{}] not found in the grad op's output".format( varname + "@GRAD") assert len( backward_op.desc.output(input_name + "@GRAD") ) == 1, "parameter grad of grad op should be length 1, but got [{}]".format( backward_op.desc.output(input_name + "@GRAD")) allreduce_vars.append( backward_op.desc.output(input_name + "@GRAD")[0]) if len(allreduce_vars) > 0: for varname in allreduce_vars: grad_var = main_block.var(varname) allreduce_op = main_block.append_op( type='c_allreduce_sum', inputs={'X': [grad_var]}, outputs={'Out': [grad_var]}, attrs={ 'ring_id': dp_group.id, 'use_calc_stream': True, OP_ROLE_KEY: OpRole.Backward }) scale_op = main_block.append_op( type='scale', inputs={'X': grad_var}, outputs={'Out': grad_var}, attrs={ 'scale': 1.0 / dp_degree, OP_ROLE_KEY: OpRole.Backward }) dims_mapping = ctx.get_tensor_dist_attr_for_program( grad_var).dims_mapping process_mesh = dist_attr.process_mesh for op in [allreduce_op, scale_op]: op_attr = OperatorDistributedAttribute() op_attr.process_mesh = process_mesh op_attr.set_output_dims_mapping(grad_var.name, dims_mapping) op_attr.set_input_dims_mapping(grad_var.name, dims_mapping) ctx.set_op_dist_attr_for_program(op, op_attr) main_block._sync_with_cpp() register_distributed_operator_impl( "default", DistributedDefaultImpl0("replicate_parallel"))