# 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 numpy as np from collections import OrderedDict from typing import List, Tuple, Dict, Any import paddle from paddle.framework import core from paddle.fluid.framework import program_guard, device_guard from paddle.fluid import unique_name, layers from paddle.fluid.clip import append_gradient_clip_ops from .pass_base import PassBase, PassType, register_pass from paddle.distributed.auto_parallel.utils import set_var_dist_attr from paddle.distributed.auto_parallel.utils import naive_set_dist_op_attr_for_program_by_mesh_and_mapping from paddle.distributed.auto_parallel.process_group import get_world_process_group world_process_group = get_world_process_group() def _is_the_backward_op(op): OP_ROLE_KEY = core.op_proto_and_checker_maker.kOpRoleAttrName() OpRole = core.op_proto_and_checker_maker.OpRole return OP_ROLE_KEY in op.attr_names and \ int(op.all_attrs()[OP_ROLE_KEY]) & int(OpRole.Backward) def _is_the_optimizer_op(op): OP_ROLE_KEY = core.op_proto_and_checker_maker.kOpRoleAttrName() OpRole = core.op_proto_and_checker_maker.OpRole return OP_ROLE_KEY in op.attr_names and \ int(op.all_attrs()[OP_ROLE_KEY]) & int(OpRole.Optimize) def _remove_and_get_optimizer_op(main_program, dist_context): # 1 create tmp block # 2 mv optimizer op from global program to tmp block # 3 del the op from dist_context from paddle.distributed.fleet.meta_optimizers.common import OpRole main_block = main_program.global_block() temp_block = main_program._create_block() removed_op_idx = [] optimize_ops_desc = [] for idx, op in enumerate(main_block.ops): if _is_the_optimizer_op(op): # append optimizer op to tmp block new_op_desc = temp_block.desc.append_op() new_op_desc.copy_from(op.desc) optimize_ops_desc.append(new_op_desc) removed_op_idx.append(idx) # del op from dist_context if dist_context: dist_context.del_dist_op_for_program(op) for idx in removed_op_idx[::-1]: main_block._remove_op(idx) return optimize_ops_desc def _remove_op_role_var(param, grad): op_maker = core.op_proto_and_checker_maker op = grad.op if op.has_attr(op_maker.kOpRoleVarAttrName()): op._remove_attr(op_maker.kOpRoleVarAttrName()) def _get_gm_cond_var(main_program, k_steps, dist_context): main_block = main_program.global_block() # Add const var k_step_var = layers.create_global_var( name="gradient_merge_k", shape=[1], value=int(k_steps), dtype='int32', persistable=True, force_cpu=True) set_var_dist_attr(dist_context, k_step_var, [-1], world_process_group.ranks) zero_var = layers.create_global_var( name="gradient_merge_zero", shape=[1], value=int(0), dtype='int32', persistable=True, force_cpu=True) set_var_dist_attr(dist_context, zero_var, [-1], world_process_group.ranks) # Add step var & cond var step_var = layers.create_global_var( name="gradient_merge_step", shape=[1], value=int(0), dtype='int32', persistable=True, force_cpu=True) set_var_dist_attr(dist_context, step_var, [-1], world_process_group.ranks) cond_var = layers.create_global_var( name="gradient_merge_cond", shape=[1], value=bool(0), dtype='bool', persistable=False, force_cpu=True) set_var_dist_attr(dist_context, cond_var, [-1], world_process_group.ranks) with device_guard("cpu"): # step_var = (step_var + 1) % k_step layers.increment(x=step_var, value=1.0, in_place=True) elementwise_mod_op = main_block.append_op( type='elementwise_mod', inputs={'X': step_var, 'Y': k_step_var}, outputs={'Out': step_var}, attrs={'axis': -1, 'use_mkldnn': False}) naive_set_dist_op_attr_for_program_by_mesh_and_mapping( elementwise_mod_op, world_process_group.ranks, [-1], dist_context) # cond_var = (step_var == 0) equal_op = main_block.append_op( type='equal', inputs={'X': step_var, 'Y': zero_var}, outputs={'Out': cond_var}) naive_set_dist_op_attr_for_program_by_mesh_and_mapping( equal_op, world_process_group.ranks, [-1], dist_context) return cond_var def _append_gradient_merge_backward_op( main_program, startup_program, params_grads: List[Tuple[Any, Any]], cond_var_name: str, dist_context) -> Tuple[List[Tuple[Any, Any]], Dict[str, Any]]: main_block = main_program.global_block() startup_block = startup_program.global_block() # step1: remove grad.op's op_role_var for param, grad in params_grads: assert ( param.type != core.VarDesc.VarType.SELECTED_ROWS ), "SELECTED_ROWS is not supported in GradientMergeOptimizer for now" _remove_op_role_var(param, grad) param_to_gradient_merge = {} new_params_to_grads = [] # step2: create gradient_merge var and init with 0 for param, grad in params_grads: param_name = param.name param_var = main_block.var(param_name) assert (param_var is not None) ref_dist_attr = dist_context.get_tensor_dist_attr_for_program(param_var) assert ref_dist_attr is not None gradient_merge_var = main_block.create_var( name=param_name + "@GRAD@GradientMerge", shape=param_var.shape, dtype=param_var.dtype, persistable=True) param_to_gradient_merge[param_name] = gradient_merge_var ref_process_mesh = ref_dist_attr.process_mesh ref_dims_mapping = ref_dist_attr.dims_mapping set_var_dist_attr(dist_context, gradient_merge_var, ref_dims_mapping, ref_process_mesh) startup_gradient_merge_var = startup_block.create_var( name=param_name + "@GRAD@GradientMerge", shape=param_var.shape, dtype=param_var.dtype, persistable=True) startup_block.append_op( type="fill_constant", outputs={"Out": startup_gradient_merge_var}, attrs={ "shape": param_var.shape, "dtype": param_var.dtype, "value": float(0), }) # grad_merge += grad new_grad_op = main_block.append_op( type="elementwise_add", inputs={'X': grad, 'Y': gradient_merge_var}, outputs={'Out': gradient_merge_var}, attrs={'axis': -1, 'use_mkldnn': False}) new_params_to_grads.append([param, gradient_merge_var]) naive_set_dist_op_attr_for_program_by_mesh_and_mapping( new_grad_op, ref_process_mesh, ref_dims_mapping, dist_context) return new_params_to_grads, param_to_gradient_merge def _create_cond_block_and_update_optimizer( main_program, cond_var, new_params_to_grads: List[Tuple[Any, Any]], param_to_gradient_merge: Dict[str, Any], optimize_ops_desc: List[Any], k_steps, avg): def true_apply_gradient(): cur_block_idx = main_program.current_block_idx cur_block = main_program.current_block() # cur_block's forward_block & backward_block is itself cur_block._set_forward_block_idx(cur_block_idx) op_maker = core.op_proto_and_checker_maker if avg: for param, new_grad in new_params_to_grads: # grad /= k_steps cur_block.append_op( type='scale', inputs={'X': new_grad}, outputs={'Out': new_grad}, attrs={ 'scale': 1.0 / k_steps, 'bias': 0.0, 'bias_after_scale': False }) new_grad.op._set_attr(op_maker.kOpRoleAttrName(), op_maker.OpRole.Optimize) # append optimizer ops for op_desc in optimize_ops_desc: new_op_desc = cur_block.desc.append_op() new_op_desc.copy_from(op_desc) #update input/output for input_name in new_op_desc.input_arg_names(): if input_name in new_params_to_grads: new_op_desc._rename_input(input_name, new_params_to_grads[input_name]) for output_name in new_op_desc.output_arg_names(): if output_name in new_params_to_grads: new_op_desc._rename_output(output_name, new_params_to_grads[output_name]) # remove op_role_var if new_op_desc.has_attr(op_maker.kOpRoleVarAttrName()): new_op_desc.remove_attr(op_maker.kOpRoleVarAttrName()) # op's update Grad if core.grad_var_suffix() in new_op_desc.input_arg_names(): grad_value = new_op_desc.input("Grad")[0] # TODO FIXME(xym) support fp16 grad_merge_value = grad_value + '@GradientMerge' new_op_desc.set_input("Grad", [grad_merge_value]) main_program.global_block()._sync_with_cpp() cur_block._sync_with_cpp() # clear gradient_merge_vars for param, new_grad in new_params_to_grads: layers.fill_constant( shape=new_grad.shape, dtype=new_grad.dtype, value=0.0, out=new_grad) new_grad.op._set_attr(op_maker.kOpRoleAttrName(), op_maker.OpRole.Optimize) layers.cond(cond_var, true_fn=true_apply_gradient, false_fn=None) def parse_program(main_program, startup_program, params_grads, k_steps, avg, dist_context): # 1 create gradient_merge_cond cond_var = _get_gm_cond_var(main_program, k_steps, dist_context) # 2 remove optimizer_op from main_program optimize_ops_desc = _remove_and_get_optimizer_op(main_program, dist_context) # back to block 0 main_program._rollback() # 3 append gradient merge backward op to main_program new_params_to_grads, param_to_gradient_merge = _append_gradient_merge_backward_op( main_program, startup_program, params_grads, cond_var.name, dist_context) # 4 create ConditionalBlock and append gradient merge optimizer ops _create_cond_block_and_update_optimizer( main_program, cond_var, new_params_to_grads, param_to_gradient_merge, optimize_ops_desc, k_steps, avg) @register_pass("auto_parallel_gradient_merge_pass") class GradientMergePass(PassBase): def __init__(self): super(GradientMergePass, self).__init__() self.set_attr("k_steps", -1) self.set_attr("avg", True) self.set_attr("inner_optimizer", None) def _check_self(self): if self.get_attr("k_steps") < 1: return False return True def _check_conflict(self, other_pass): return True def _type(self): return PassType.COMM_OPT def _apply_single_impl(self, main_program, startup_program, context): k_steps = self.get_attr("k_steps", -1) avg = self.get_attr("avg", False) dist_context = self.get_attr("dist_context") params_grads = self.get_attr("params_grads") with paddle.static.program_guard(main_program, startup_program): parse_program(main_program, startup_program, params_grads, k_steps, avg, dist_context) main_program._sync_with_cpp()