# Copyright (c) 2020 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 copy from ..common import is_optimizer_op, OP_ROLE_KEY, OpRole, is_update_op from paddle.fluid import core, unique_name from .shard import Shard __all__ = [] class OffloadHelper(object): cpu_place_type = 0 cuda_place_type = 1 cuda_pinned_place_type = 2 def __init__(self, ring_id=None): self.ring_id = ring_id def _insert_cast_op(self, block, idx, src_name, dst_name): src_var = block.var(src_name) if not block.has_var(dst_name): block.create_var( name=dst_name, shape=src_var.shape, dtype=core.VarDesc.VarType.FP16, persistable=True) dst_var = block.var(dst_name) assert dst_var.dtype == core.VarDesc.VarType.FP16 block._insert_op_without_sync( idx, type='cast', inputs={'X': src_var}, outputs={'Out': dst_var}, attrs={ 'in_dtype': src_var.dtype, 'out_dtype': dst_var.dtype, OP_ROLE_KEY: OpRole.Optimize }) def _insert_broadcast_op(self, block, idx, param): if self.ring_id is None: return block._insert_op_without_sync( idx, type="c_broadcast", inputs={'X': param}, outputs={'Out': param}, attrs={ 'ring_id': self.ring_id, 'root': 0, 'use_calc_stream': True, OP_ROLE_KEY: OpRole.Forward, }) def _insert_memcpy_op(self, block, idx, src_name, dst_name, dst_place_type): src_var = block.var(src_name) dst_var = block.var(dst_name) block._insert_op_without_sync( idx, type='memcpy', inputs={'X': src_var}, outputs={'Out': dst_var}, attrs={ 'dst_place_type': dst_place_type, OP_ROLE_KEY: OpRole.Optimize, }) def _insert_fetch_op(self, block, idx, src_name, dst_name): self._insert_memcpy_op(block, idx, src_name, dst_name, OffloadHelper.cuda_place_type) def _insert_offload_op(self, block, idx, src_name, dst_name): self._insert_memcpy_op(block, idx, src_name, dst_name, OffloadHelper.cuda_pinned_place_type) def _get_offload_var_name(self, name): return unique_name.generate(name + '@offload') def _create_offload_var(self, var_name, offload_var_name, blocks): for block in blocks: var = block.var(var_name) var.persistable = False offload_var = block.create_var( name=offload_var_name, shape=var.shape, dtype=var.dtype, persistable=True) def offload_fp32param(self, block, startup_block, offload=True): """ (p_fp16) = cast(p) (p_fp16_recompute) = cast(p) (pout,) = adam(p) ===========================> rename(p_fp16_recompute, p_fp16) (p,) = prefetch(p@offload) (pout,) = adam(p) (p_fp16) = cast(p) (p@offload) = memcpy(p) """ param_to_idx = dict() param_to_fp16 = dict() # recompute_var which need rename to fp16_param fp16_param_to_recompute = dict() recompute_to_fp16 = dict() def remove_param(input_name): param_to_idx.pop(input_name) if input_name in param_to_fp16: fp16_param = param_to_fp16.pop(input_name) if fp16_param in fp16_param_to_recompute: recompute = fp16_param_to_recompute.pop(fp16_param) recompute_to_fp16.pop(recompute) # step1: record param for idx, op in reversed(list(enumerate(block.ops))): if is_update_op(op): param = op.desc.input("Param")[0] param_to_idx[param] = idx # step2: remove param which can't offload and # record param->fp16param, fp16param->recompute_var for idx, op in enumerate(block.ops): if is_optimizer_op(op): break # TODO (Yuang Liu): tmp solution for fuse_grad_merge + optimize_cast if not offload and op.type == 'coalesce_tensor': continue for input_name in op.desc.input_arg_names(): if input_name not in param_to_idx: continue # param which will be used by fp32 op if op.type != 'cast': remove_param(input_name) continue # param is only used by cast op, # which to cast fp32_param to fp16_param output_name = op.output_arg_names[0] if 'cast_fp16' not in output_name: remove_param(input_name) continue if 'subprog' not in output_name: assert output_name == input_name + '.cast_fp16' assert input_name not in param_to_fp16, \ "There must be only one cast op from fp32 param to fp16 param." param_to_fp16[input_name] = output_name else: # fp16-->recompute_var assert input_name in param_to_fp16, \ "param must first be cast to fp16" fp16_param = param_to_fp16[input_name] fp16_param_to_recompute[fp16_param] = output_name recompute_to_fp16[output_name] = fp16_param param_name_to_offload_name = dict() # step3: main_block add offload, cast op # change recompute to fp16, remove cast(param) to fp16 for idx, op in reversed(list(enumerate(block.ops))): if is_update_op(op): param = op.desc.input("Param")[0] if param not in param_to_idx: continue # step3.1: create offload_var offload_var_name = self._get_offload_var_name(param) param_name_to_offload_name[param] = offload_var_name if offload: self._create_offload_var(param, offload_var_name, [block, startup_block]) # step3.2: insert cast op and offload op self._insert_offload_op(block, idx + 1, param, offload_var_name) assert param in param_to_fp16 fp16_param_name = param_to_fp16[param] fp16_param_var = block.var(fp16_param_name) fp16_param_var.persistable = True self._insert_cast_op(block, idx + 1, param, param_to_fp16[param]) if offload: # step3.3: insert fetch op self._insert_fetch_op(block, idx, offload_var_name, param) continue # step3.4: remove cast op if op.type == 'cast': input_name = op.desc.input_arg_names()[0] if input_name in param_to_idx: block._remove_op(idx, sync=False) continue # step3.5: change recompute_param to fp16_param for input_name in op.desc.input_arg_names(): if input_name in recompute_to_fp16: op._rename_input(input_name, recompute_to_fp16[input_name]) for output_name in op.desc.output_arg_names(): if output_name in recompute_to_fp16: op._rename_output(output_name, recompute_to_fp16[output_name]) # step4: remove recompute_param for name in recompute_to_fp16.keys(): block._remove_var(name, sync=False) # step5: startup_block add offload visited_vars = set() # FIXME(wangxi): should insert in idx, need move comm init to the head. insert_idx = len(startup_block.ops) for idx, op in reversed(list(enumerate(startup_block.ops))): for out_name in op.output_arg_names: if out_name in visited_vars: continue if out_name in param_name_to_offload_name: var_name = out_name if offload: offload_var_name = param_name_to_offload_name[var_name] self._insert_offload_op(startup_block, insert_idx, var_name, offload_var_name) self._insert_cast_op(startup_block, insert_idx, var_name, param_to_fp16[var_name]) # NOTE(wangxi): cast and offload should insert after broadcast param. # the insert op order is: broadcast, cast, offload self._insert_broadcast_op(startup_block, insert_idx, var_name) visited_vars.add(out_name) block._sync_with_cpp() startup_block._sync_with_cpp() def cast_fp32param_in_optimize(self, block, startup_block): """ (p_fp16) = cast(p) (p_fp16_recompute) = cast(p) (pout,) = adam(p) ===========================> rename(p_fp16_recompute, p_fp16) (pout,) = adam(p) (p_fp16) = cast(p) """ self.offload_fp32param(block, startup_block, offload=False) def offload(self, block, startup_block): """ (m1, m2) = prefetch(m1@offload, m2@offload) (m1out, m2out, pout) = adam(m1, m2, p) (m1@offload, m2@offload) = memcpy(m1, m2) """ vars_name_to_offload_name = dict() # main_block add offload for idx, op in reversed(list(enumerate(block.ops))): if not is_optimizer_op(op): break vars_name = [] if op.type == "adam": # {Moment1Out = [''], Moment2Out = [''], ParamOut = ['']} = # adam(inputs={Moment1 = [''], Moment2 = [''], Param = ['']}) vars_name.append(op.desc.input("Moment1")[0]) vars_name.append(op.desc.input("Moment2")[0]) elif op.type == 'momentum': pass elif op.type == 'lars': pass elif op.type == 'lamb': pass # step1: create and init offload_var for var_name in vars_name: assert var_name not in vars_name_to_offload_name offload_var_name = self._get_offload_var_name(var_name) vars_name_to_offload_name[var_name] = offload_var_name self._create_offload_var(var_name, offload_var_name, [block, startup_block]) # step2: insert offload op for var_name in vars_name: offload_var_name = vars_name_to_offload_name[var_name] self._insert_offload_op(block, idx + 1, var_name, offload_var_name) # step3: insert fetch op for var_name in vars_name: offload_var_name = vars_name_to_offload_name[var_name] self._insert_fetch_op(block, idx, offload_var_name, var_name) # startup_block add offload visited_vars = set() for idx, op in reversed(list(enumerate(startup_block.ops))): for out_name in op.output_arg_names: if out_name in visited_vars: continue if out_name in vars_name_to_offload_name: var_name = out_name offload_var_name = vars_name_to_offload_name[var_name] # insert offload op after var is generated self._insert_offload_op(startup_block, idx + 1, var_name, offload_var_name) visited_vars.add(out_name) block._sync_with_cpp() startup_block._sync_with_cpp() def opt_sharding_cast_fp32param(self, block, startup_block, params, offload=False): """ (p_fp16) = cast(p) (p_fp16_recompute) = cast(p) (pout,) = adam(p) ===========================> rename(p_fp16_recompute, p_fp16) (pout,) = adam(p) (p_fp16) = cast(p) broadcast(p_fp16) """ global_params = set() local_params = set() param_to_fp16 = dict() # recompute_var which need rename to fp16_param fp16_param_to_recompute = dict() recompute_to_fp16 = dict() def remove_param(input_name): global_params.remove(input_name) if input_name in local_params: local_params.remove(input_name) if input_name in param_to_fp16: fp16_param = param_to_fp16.pop(input_name) if fp16_param in fp16_param_to_recompute: recompute = fp16_param_to_recompute.pop(fp16_param) recompute_to_fp16.pop(recompute) # step1: record param global_params = set(params) for idx, op in reversed(list(enumerate(block.ops))): if is_update_op(op): param = op.desc.input("Param")[0] local_params.add(param) # step2: remove param which can't offload and # record param->fp16param, fp16param->recompute_var for idx, op in enumerate(block.ops): if is_optimizer_op(op): break # TODO (Yuang Liu): tmp solution for fuse_grad_merge + optimize_cast if op.type == 'coalesce_tensor': continue for input_name in op.desc.input_arg_names(): if input_name not in global_params: continue # param which will be used by fp32 op if op.type != 'cast': remove_param(input_name) continue # param is only used by cast op, # which to cast fp32_param to fp16_param output_name = op.output_arg_names[0] if 'cast_fp16' not in output_name: remove_param(input_name) continue if 'subprog' not in output_name: assert output_name == input_name + '.cast_fp16' assert input_name not in param_to_fp16, \ "There must be only one cast op from fp32 param to fp16 param." param_to_fp16[input_name] = output_name else: # fp16-->recompute_var assert input_name in param_to_fp16, \ "param must first be cast to fp16" fp16_param = param_to_fp16[input_name] fp16_param_to_recompute[fp16_param] = output_name recompute_to_fp16[output_name] = fp16_param param_name_to_offload_name = dict() # step3: main_block add offload, cast op # change recompute to fp16, remove cast(param) to fp16 for idx, op in reversed(list(enumerate(block.ops))): if is_update_op(op): param = op.desc.input("Param")[0] if param not in global_params: continue # step3.1: create offload_var offload_var_name = self._get_offload_var_name(param) param_name_to_offload_name[param] = offload_var_name if offload: self._create_offload_var(param, offload_var_name, [block, startup_block]) # step3.2: insert cast op and offload op self._insert_offload_op(block, idx + 1, param, offload_var_name) assert param in param_to_fp16 fp16_param_name = param_to_fp16[param] fp16_param_var = block.var(fp16_param_name) fp16_param_var.persistable = True self._insert_cast_op(block, idx + 1, param, param_to_fp16[param]) if offload: # step3.3: insert fetch op self._insert_fetch_op(block, idx, offload_var_name, param) continue # step3.4: remove cast op if op.type == 'cast': input_name = op.desc.input_arg_names()[0] if input_name in global_params: block._remove_op(idx, sync=False) continue # step3.5: change recompute_param to fp16_param for input_name in op.desc.input_arg_names(): if input_name in recompute_to_fp16: op._rename_input(input_name, recompute_to_fp16[input_name]) for output_name in op.desc.output_arg_names(): if output_name in recompute_to_fp16: op._rename_output(output_name, recompute_to_fp16[output_name]) # step4: remove recompute_param for name in recompute_to_fp16.keys(): block._remove_var(name, sync=False) # step5: remove fp32 param which not need for idx, op in enumerate(block.ops): if op.type not in ['coalesce_tensor', 'c_broadcast']: continue for input_name in op.desc.input_arg_names(): if input_name in param_to_fp16: op._rename_input(input_name, param_to_fp16[input_name]) for output_name in op.desc.output_arg_names(): if output_name in param_to_fp16: op._rename_output(output_name, param_to_fp16[output_name]) for param in global_params: assert param in param_to_fp16 fp16_param_name = param_to_fp16[param] fp16_param_var = block.var(fp16_param_name) fp16_param_var.persistable = True if param not in local_params: block._remove_var(param, sync=False) # step6: startup_block add offload visited_vars = set() insert_idx = len(startup_block.ops) for idx, op in reversed(list(enumerate(startup_block.ops))): for out_name in op.output_arg_names: if out_name in visited_vars: continue if out_name in param_to_fp16: var_name = out_name if offload: self._insert_offload_op( startup_block, idx + 1, var_name, param_name_to_offload_name[var_name]) self._insert_cast_op(startup_block, insert_idx, var_name, param_to_fp16[var_name]) self._insert_broadcast_op(startup_block, insert_idx, var_name) if var_name not in local_params: param = startup_block.var(out_name) param.persistable = False visited_vars.add(out_name) block._sync_with_cpp() startup_block._sync_with_cpp()