# 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 collections import defaultdict import paddle from paddle.framework import core from paddle.fluid import unique_name from .pass_base import register_pass from paddle.distributed.fleet.meta_optimizers.common import OpRole from paddle.fluid.data_feeder import check_variable_and_dtype, check_type from paddle.distributed.auto_parallel.utils import set_var_dist_attr, naive_set_dist_op_attr_for_program_by_mesh_and_mapping from paddle.distributed.auto_parallel.process_group import get_world_process_group from paddle.fluid.contrib.mixed_precision.fp16_utils import AutoMixedPrecisionLists from paddle.fluid.contrib.mixed_precision.fp16_utils import _keep_layer_norm_scale_bias_to_fp32, _need_keep_fp32, _valid_types, _dtype_to_str from paddle.distributed.auto_parallel.dist_attribute import OperatorDistributedAttribute from paddle.distributed.auto_parallel.utils import is_forward_op, is_backward_op from .auto_parallel_amp import AMPPass world_process_group = get_world_process_group() # if user use python "+, -, * /" for network, there might be cast in vanilla program __amp_skip_ops__ = [ 'create_py_reader', 'create_double_buffer_reader', 'while', 'cast', ] def set_op_dtype_to_fp16(op): if op.has_attr('in_dtype') and op.attr( 'in_dtype') == core.VarDesc.VarType.FP32: op._set_attr('in_dtype', core.VarDesc.VarType.FP16) if op.has_attr('out_dtype') and op.attr( 'out_dtype') == core.VarDesc.VarType.FP32: op._set_attr('out_dtype', core.VarDesc.VarType.FP16) if op.has_attr('dtype') and op.attr('dtype') == core.VarDesc.VarType.FP32: op._set_attr('dtype', core.VarDesc.VarType.FP16) # adapot for backward op def _keep_fp32_input(op, in_name): op_type = op.type if op_type == 'batch_norm': # Scale, Bias, Mean, Variance should be float32. return in_name != 'X' if op_type == 'layer_norm' and _keep_layer_norm_scale_bias_to_fp32(): return in_name != 'X' if op_type == 'fused_bn_add_activation': return in_name not in {'X', 'Z'} if op_type == 'resnet_unit': return in_name not in {'X', 'FilterX', 'Z', 'FilterZ'} if op_type in ['fused_attention', 'fused_feedforward']: return in_name in { 'LnScale', 'LnBias', 'Ln2Scale', 'Ln2Bias', "Ln1Scale", "Ln1Bias" } # backward if op_type in ['batch_norm_grad']: return in_name not in {'X', 'Y@GRAD'} if op_type in ['layer_norm_grad']: return in_name not in {'X', 'Y@GRAD'} return False def _keep_fp32_output(op, out_name): op_type = op.type if op_type in ['batch_norm', 'fused_bn_add_activation']: return out_name != 'Y' if op_type == 'layer_norm' and _keep_layer_norm_scale_bias_to_fp32(): return out_name != 'Y' if op_type == 'resnet_unit': return out_name not in {'Y', 'ConvX', 'ConvZ'} if op_type in ['fused_attention', 'fused_feedforward']: return out_name in { 'LnMean', 'LnVariance', 'Ln2Mean', 'Ln2Variance', 'Ln1Mean', 'Ln1Variance' } # backward if op_type in ['layer_norm_grad']: return out_name != 'X@GRAD' if op_type in ['batch_norm_grad']: return out_name != 'X@GRAD' return False class FP16State(object): def __init__(self, program, amp_list, dist_context, use_fp16_guard): self.program = program self.amp_list = amp_list self.use_fp16_guard = use_fp16_guard self.dist_context = dist_context self.grad_op_to_op_map = self.dist_context.dist_op_context.grad_op_id_to_op_id self._op_fp16_dict = { } # op_id --> True/False. 'True' means that the op is should run in fp16 mode. # a trick to determine leaf tensor node in program {varname: generator_op_id} self.forward_non_leaf_tensors = {} # record the cast ops that are inserted for a forward self.forward_input_cast_ops = defaultdict( list ) # {forward_op_id: [(output_name, input_name, out_dtype, in_dtype, slot_name), ]} self.is_train = False def _is_fp16_op(self, op_id): return self._op_fp16_dict.get(op_id, None) def _build_state(self): """ mark the execution mode (fp16 or fp32) for ops in all blocks include forward ops & backward ops """ # mark op dtype # assume all backward block are behind forward blocks for block in self.program.blocks: for op in block.ops: self._mark_op(op) # set forward tensor dtype for block in self.program.blocks: self.resolute_tensor_dtype(block) # insert cast ops for block in self.program.blocks: self.cast_block(block) return self.is_train def _mark_op(self, op): if op.type in __amp_skip_ops__: return if is_forward_op(op): # ernie inference trick if op.type == "assign" and "array_" in op.input_arg_names[0]: self._op_fp16_dict[op.desc.id()] = False return if _need_keep_fp32(op, self.amp_list.unsupported_list, self.use_fp16_guard): self._op_fp16_dict[op.desc.id()] = False else: self._op_fp16_dict[op.desc.id()] = True for var_name in op.output_arg_names: # assert var_name not in self.forward_non_leaf_tensors, "{}".format(var_name) self.forward_non_leaf_tensors[var_name] = op.desc.id() elif is_backward_op(op) == int(OpRole.Backward): if op.desc.id() in self.grad_op_to_op_map: fwd_op_id = self.grad_op_to_op_map[op.desc.id()] assert fwd_op_id in self._op_fp16_dict, "{}".format(str(op)) self._op_fp16_dict[op.desc.id()] = self._op_fp16_dict[fwd_op_id] if int(op.attr('op_role')) == 257: self.is_train = True def set_var_to_fp16(self, var_name, block): var = None try: var = block.var(var_name) except ValueError as e: var = self.program.global_block().var(var_name) # NOTE(JZ-LIANG) "array_" is a hack to adopt for ernie3.0 inference, since there is # a trick which make the LOD_TENSOR_ARRAY to the float32 in while block to reset the LOD_TENSOR_ARRAY if var is None or var.type not in _valid_types or "array_" in var_name: return if var.dtype == core.VarDesc.VarType.FP32: var.desc.set_dtype(core.VarDesc.VarType.FP16) def resolute_tensor_dtype(self, block): for op in block.ops: op_id = op.desc.id() if is_forward_op(op): # NOTE (JZ-LIANG) un-expected cast op when user call "+, -, *, /" in python if self._is_fp16_op(op_id) == True or op.type == "cast": for in_name in op.input_names: if _keep_fp32_input(op, in_name): continue for in_var_name in op.input(in_name): if in_var_name not in self.forward_non_leaf_tensors: self.set_var_to_fp16(in_var_name, block) for out_name in op.output_names: if _keep_fp32_output(op, out_name): continue for out_var_name in op.output(out_name): self.set_var_to_fp16(out_var_name, block) set_op_dtype_to_fp16(op) # NOTE (JZ-LIANG) un-expected cast op when user call "+, -, *, /" in python elif self._is_fp16_op(op_id) == False: for out_var_name in op.output_arg_names: out_var = block.vars.get(out_var_name) if out_var is None or out_var.type not in _valid_types: continue if out_var.dtype == core.VarDesc.VarType.FP16: out_var.desc.set_dtype(core.VarDesc.VarType.FP32) elif is_backward_op(op): if self._is_fp16_op(op_id) == True: for out_name in op.output_names: if _keep_fp32_output(op, out_name): continue for out_var_name in op.output(out_name): self.set_var_to_fp16(out_var_name, block) set_op_dtype_to_fp16(op) # NOTE (JZ-LIANG) un-expected cast op when user call "+, -, *, /" in python elif self._is_fp16_op(op_id) == False: for out_var_name in op.output_arg_names: out_var = block.vars.get(out_var_name) if out_var is None or out_var.type not in _valid_types: continue if out_var.dtype == core.VarDesc.VarType.FP16: out_var.desc.set_dtype(core.VarDesc.VarType.FP32) def cast_block(self, block): dist_op_context = self.dist_context.dist_op_context idx = 0 while idx < len(block.ops): op = block.ops[idx] op_id = op.desc.id() num_cast_ops = 0 if op.type in __amp_skip_ops__: idx += 1 continue elif is_forward_op(op): if self._is_fp16_op(op_id) == False: num_cast_ops = self._insert_forward_cast_ops( op, idx, block, core.VarDesc.VarType.FP16, core.VarDesc.VarType.FP32, self.dist_context) elif self._is_fp16_op(op_id) == True: num_cast_ops = self._insert_forward_cast_ops( op, idx, block, core.VarDesc.VarType.FP32, core.VarDesc.VarType.FP16, self.dist_context) elif is_backward_op(op): if op_id in dist_op_context.grad_op_id_to_op_id: if self._is_fp16_op(op_id) == False: num_cast_ops = self._insert_backward_cast_ops( op, idx, block, core.VarDesc.VarType.FP16, core.VarDesc.VarType.FP32, self.dist_context) elif self._is_fp16_op(op_id) == True: num_cast_ops = self._insert_backward_cast_ops( op, idx, block, core.VarDesc.VarType.FP32, core.VarDesc.VarType.FP16, self.dist_context) elif op.type == "sum": # all inputs dtype of sum should be equal and output dtype should follow input out_var_name = op.output_arg_names[0] in_var_name = op.input_arg_names[0] out_var = block.var(out_var_name) in_var = block._find_var_recursive(in_var_name) for in_var_name in op.input_arg_names: assert in_var.dtype == block.var( in_var_name).dtype, "{}, {}, {}".format( in_var, block.var(in_var_name), str(op)) out_var.desc.set_dtype(in_var.dtype) idx += num_cast_ops + 1 block._sync_with_cpp() def _insert_forward_cast_ops(self, op, idx, block, src_dtype, dst_dtype, dist_context): num_cast_ops = 0 op_id = op.desc.id() for in_name in op.input_names: if src_dtype == core.VarDesc.VarType.FP32 and _keep_fp32_input( op, in_name): continue consume_op_attr = dist_context.get_op_dist_attr_for_program(op) assert consume_op_attr is not None for in_var_name in op.input(in_name): in_var = block._find_var_recursive(in_var_name) if in_var is None or in_var.type not in _valid_types or in_var.dtype == dst_dtype: continue if in_var.dtype == src_dtype: cast_name = in_var.name + '.cast_' + _dtype_to_str( dst_dtype) cast_var = block.vars.get(cast_name) self.forward_input_cast_ops[op_id] += [( cast_name, in_var.name, dst_dtype, src_dtype, in_name)] in_var_dist_attr = consume_op_attr.get_input_dist_attr( in_var.name) assert in_var_dist_attr is not None # truely insert cast op if cast_var is None or cast_var.dtype != dst_dtype: # NOTE we make the cast op and var's dist attr as the op that consume the # cast var instead of the op which generates the var # refine op's dist_attr ref_mesh = in_var_dist_attr.process_mesh ref_mapping = in_var_dist_attr.dims_mapping cast_var = block.create_var( name=cast_name, dtype=dst_dtype, persistable=False, stop_gradient=in_var.stop_gradient) set_var_dist_attr(dist_context, cast_var, ref_mapping, ref_mesh) cast_op = block._insert_op_without_sync( idx, type="cast", inputs={"X": in_var}, outputs={"Out": cast_var}, attrs={ "in_dtype": in_var.dtype, "out_dtype": cast_var.dtype, }) naive_set_dist_op_attr_for_program_by_mesh_and_mapping( cast_op, ref_mesh, ref_mapping, dist_context) num_cast_ops += 1 op._rename_input(in_var.name, cast_name) consume_op_attr.set_input_dist_attr(cast_name, in_var_dist_attr) if op.has_attr('out_dtype') and op.attr('out_dtype') != -1: assert op.attr('out_dtype') == dst_dtype return num_cast_ops def _insert_backward_cast_ops(self, op, idx, block, src_dtype, dst_dtype, dist_context): num_cast_ops = 0 op_id = op.desc.id() dist_op_context = dist_context.dist_op_context forward_op_id = dist_op_context.grad_op_id_to_op_id[op_id] grad_op_attr = dist_context.get_op_dist_attr_for_program(op) assert grad_op_attr is not None for out_var_name in op.output_arg_names: out_var = block.var(out_var_name) if _keep_fp32_output(op, out_var.name): continue assert out_var.dtype == dst_dtype, "{}, {}".format( str(out_var), dst_dtype) for cast_name, src_name, dst_dtype, src_dtype, slot_name in self.forward_input_cast_ops[ forward_op_id]: # rename input assert src_name in op.input( slot_name), "var: {} not in op's {}. {}".format(src_name, slot_name, str(op)) src_var_dist_attr = grad_op_attr.get_input_dist_attr(src_name) assert src_var_dist_attr is not None op._rename_input(src_name, cast_name) grad_op_attr.set_input_dist_attr(cast_name, src_var_dist_attr) # create cast grad grad_slot_name = slot_name + "@GRAD" assert grad_slot_name in op.output_names assert len(op.output(grad_slot_name)) == 1 grad_name = op.output(grad_slot_name)[0] grad = block.var(grad_name) grad_dist_attr = grad_op_attr.get_output_dist_attr(grad_name) assert grad_dist_attr is not None, "{}".format(grad_name) ref_mesh = grad_dist_attr.process_mesh ref_mapping = grad_dist_attr.dims_mapping cast_grad = block.create_var( name=unique_name.generate_with_ignorable_key("".join( [cast_name, '@GRAD'])), dtype=dst_dtype, shape=grad.shape, type=grad.type, persistable=grad.persistable, stop_gradient=grad.stop_gradient) dist_context.set_tensor_dist_attr_for_program(cast_grad, grad_dist_attr) op._rename_output(grad_name, cast_grad.name) grad_op_attr.set_output_dist_attr(cast_grad.name, grad_dist_attr) # add cast cast_op = block._insert_op_without_sync( idx + 1, type="cast", inputs={"X": [cast_grad.name]}, outputs={"Out": [grad.name]}, attrs={ "in_dtype": dst_dtype, "out_dtype": src_dtype, }) grad.desc.set_dtype(src_dtype) naive_set_dist_op_attr_for_program_by_mesh_and_mapping( cast_op, ref_mesh, ref_mapping, dist_context) num_cast_ops += 1 return num_cast_ops def _check_and_update_gradient(grads, loss_scaling, name, dist_context): main_block = paddle.static.default_main_program().global_block() main_block._sync_with_cpp() check_type(grads, 'x', (tuple, list), 'check_finite_and_unscale') for e in grads: check_variable_and_dtype(e, "x", ['float16', 'float32', 'float64'], 'check_finite_and_unscale') found_inf = main_block.create_var( name=unique_name.generate_with_ignorable_key(".".join( ['find_infinite_scale', name])), shape=[1], dtype='bool', type=core.VarDesc.VarType.LOD_TENSOR, persistable=False, stop_gradient=False) set_var_dist_attr(dist_context, found_inf, [-1], world_process_group.ranks) inputs = {'X': grads, 'Scale': loss_scaling} outputs = {'Out': grads, 'FoundInfinite': found_inf} attrs = {'op_role': OpRole.Backward} new_op = main_block.append_op( type='check_finite_and_unscale', inputs=inputs, outputs=outputs, attrs=attrs) new_op_dist_attr = OperatorDistributedAttribute() new_op_dist_attr.process_mesh = world_process_group.ranks new_op_dist_attr.impl_idx = 0 if len(world_process_group.ranks) > 1: new_op_dist_attr.impl_type = "check_finite_and_unscale" for g in grads: g_dist_attr = dist_context.get_tensor_dist_attr_for_program(g) assert g_dist_attr is not None new_op_dist_attr.set_input_dims_mapping(g.name, g_dist_attr.dims_mapping) new_op_dist_attr.set_output_dims_mapping(g.name, g_dist_attr.dims_mapping) dist_context.set_op_dist_attr_for_program(new_op, new_op_dist_attr) return grads, found_inf def _split_grads(params_grads): grads = [g for _, g in params_grads] fp32_grads = [g for g in grads if g.dtype == core.VarDesc.VarType.FP32] fp16_grads = [g for g in grads if g.dtype == core.VarDesc.VarType.FP16] assert len(fp32_grads) + len(fp16_grads) == len(grads), \ "Data types of all grads must be either fp16 or fp32." return grads, fp32_grads, fp16_grads def _set_op_dist_attr_with_ranks(new_op, ranks, block, dist_context): new_op_dist_attr = OperatorDistributedAttribute() new_op_dist_attr.process_mesh = ranks new_op_dist_attr.impl_idx = 0 for var_name in new_op.input_arg_names: var = block.var(var_name) var_dist_attr = dist_context.get_tensor_dist_attr_for_program(var) assert var_dist_attr is not None new_op_dist_attr.set_input_dims_mapping(var_name, var_dist_attr.dims_mapping) for var_name in new_op.output_arg_names: var = block.var(var_name) var_dist_attr = dist_context.get_tensor_dist_attr_for_program(var) assert var_dist_attr is not None new_op_dist_attr.set_output_dims_mapping(var_name, var_dist_attr.dims_mapping) dist_context.set_op_dist_attr_for_program(new_op, new_op_dist_attr) @register_pass("auto_parallel_fp16") class FP16Pass(AMPPass): def __init__(self): super(FP16Pass, self).__init__() # NOTE: why FP16Pass can override apply_single_impl instead of # apply_impl? AMP is an optimization pass for serial program, # in distributed scenario, all ranks should have the same modification. def _apply_single_impl(self, main_program, startup_program, context): self.dist_context = self.get_attr("dist_context") params_grads = self.get_attr("params_grads") amp_list = AutoMixedPrecisionLists( set(self.get_attr("custom_white_list")), set(self.get_attr("custom_black_list")), None) # TODO support multiple blocks with paddle.static.program_guard(main_program, startup_program): fp16_state = FP16State(main_program, amp_list, self.dist_context, self.get_attr("use_fp16_guard")) is_train = fp16_state._build_state() if is_train: with paddle.static.program_guard(main_program, startup_program): # TODO (JZ-LIANG)support cast forward program only when inference self._init_amp_var() self._scale_loss() grads, fp32_grads, fp16_grads = _split_grads(params_grads) if self.get_attr("use_dynamic_loss_scaling") or self.get_attr( "init_loss_scaling") != 1.0: found_infs = [] if fp32_grads: with main_program._backward_role_guard(): _, found_inf_fp32 = _check_and_update_gradient( fp32_grads, self._loss_scaling, "@fp32", self.dist_context) found_infs.append(found_inf_fp32) if fp16_grads: with main_program._backward_role_guard(): _, found_inf_fp16 = _check_and_update_gradient( fp16_grads, self._loss_scaling, "@fp16", self.dist_context) found_infs.append(found_inf_fp16) with main_program._backward_role_guard(): block = main_program.global_block() all_infs = paddle.fluid.layers.concat(found_infs) set_var_dist_attr(self.dist_context, all_infs, [-1], world_process_group.ranks) new_op = block.ops[-1] assert new_op.type == "concat" _set_op_dist_attr_with_ranks(new_op, world_process_group.ranks, block, self.dist_context) found_inf = paddle.fluid.layers.reduce_any(all_infs) set_var_dist_attr(self.dist_context, found_inf, [-1], world_process_group.ranks) new_op = block.ops[-1] assert new_op.type == "reduce_any" _set_op_dist_attr_with_ranks(new_op, world_process_group.ranks, block, self.dist_context) if self.get_attr("use_dynamic_loss_scaling"): with main_program._backward_role_guard(): if fp32_grads: self._update_loss_scaling(fp32_grads, found_inf) if fp16_grads: self._update_loss_scaling(fp16_grads, found_inf) # modify optimizer base_opt = self.get_attr("base_opt") base_opt._multi_precision = True if self.get_attr("use_optimizer_fp16"): base_opt._multi_precision = False if isinstance(base_opt, (paddle.fluid.optimizer.Adam, paddle.optimizer.AdamW)): # with main_program._optimized_guard([]): # found_inf = paddle.tensor.creation._memcpy( # found_inf, paddle.CPUPlace()) base_opt._set_auxiliary_var('found_inf', found_inf.name) elif hasattr(base_opt, "_set_auxiliary_var"): base_opt._set_auxiliary_var('found_inf', found_inf.name)