# Copyright (c) 2019 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 __future__ import print_function from ... import core from ... import layers from ... import global_scope from ...log_helper import get_logger import logging import numpy as np __all__ = ["cast_model_to_fp16", "cast_parameters_to_fp16"] _logger = get_logger( __name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s') def _rename_arg(op, old_name, new_name): """ If an op has old_name input and output, rename these input args new_name. Args: op (Operator): Current operator. old_name (str): The old name of input args. new_name (str): The new name of input args. """ op_desc = op.desc if isinstance(op_desc, tuple): op_desc = op_desc[0] op_desc._rename_input(old_name, new_name) op_desc._rename_output(old_name, new_name) def _dtype_to_str(dtype): """ Convert specific variable type to its corresponding string. Args: dtype (VarType): Variable type. """ if dtype == core.VarDesc.VarType.FP16: return 'fp16' else: return 'fp32' def _insert_cast_op(block, op, idx, src_dtype, dest_dtype): """ Insert cast op and rename args of input and output. Args: block (Program): The block in which the operator is. op (Operator): The operator to insert cast op. idx (int): The index of current operator. src_dtype (VarType): The input variable dtype of cast op. dest_dtype (VarType): The output variable dtype of cast op. Returns: num_cast_op (int): The number of cast ops that have been inserted. """ num_cast_ops = 0 valid_types = [ core.VarDesc.VarType.LOD_TENSOR, core.VarDesc.VarType.SELECTED_ROWS, core.VarDesc.VarType.LOD_TENSOR_ARRAY ] for in_name in op.input_names: if src_dtype == core.VarDesc.VarType.FP32 and op.type in [ 'batch_norm', 'fused_bn_add_activation', 'layer_norm' ]: if in_name not in {'X', 'Z'}: continue for in_var_name in op.input(in_name): in_var = block.var(in_var_name) if in_var.type not in valid_types or in_var.dtype == dest_dtype: continue if in_var.dtype == src_dtype: cast_name = in_var.name + '.cast_' + _dtype_to_str(dest_dtype) out_var = block.vars.get(cast_name) if out_var is None or out_var.dtype != dest_dtype: out_var = block.create_var( name=cast_name, dtype=dest_dtype, persistable=False, stop_gradient=in_var.stop_gradient) block._insert_op( idx, type="cast", inputs={"X": in_var}, outputs={"Out": out_var}, attrs={ "in_dtype": in_var.dtype, "out_dtype": out_var.dtype }) num_cast_ops += 1 _rename_arg(op, in_var.name, out_var.name) else: if op.has_attr('in_dtype'): op._set_attr('in_dtype', dest_dtype) if src_dtype == core.VarDesc.VarType.FP32 and dest_dtype == core.VarDesc.VarType.FP16: for out_name in op.output_names: if op.type in [ 'batch_norm', 'fused_bn_add_activation', 'layer_norm' ] and out_name != 'Y': continue for out_var_name in op.output(out_name): out_var = block.var(out_var_name) if out_var.type not in valid_types: continue if out_var.dtype == core.VarDesc.VarType.FP32: out_var.desc.set_dtype(core.VarDesc.VarType.FP16) if op.has_attr('out_dtype'): op._set_attr('out_dtype', core.VarDesc.VarType.FP16) return num_cast_ops def find_true_prev_op(ops, cur_op, var_name): """ Find the true prev op that outputs var_name variable. Args: ops (list): A list of ops. cur_op (Operator): Current operator which has var_name variable. var_name (string): Variable name. """ prev_op = [] for op in ops: if op == cur_op: break for out_name in op.output_names: for out_var_name in op.output(out_name): if out_var_name == var_name: prev_op.append(op) if prev_op: if not len(prev_op) == 1: raise ValueError("There must be only one previous op " "that outputs {0} variable".format(var_name)) else: return prev_op[0] return None def find_true_post_op(ops, cur_op, var_name): """ if there are post ops, return them, if there is no post op, return None instead. Args: ops (list): A list of ops. cur_op (Operator): Current operator which has var_name variable. var_name (string): Variable name. """ post_op = [] for idx, op in enumerate(ops): if op == cur_op: break for i in range(idx + 1, len(ops)): op = ops[i] for in_name in op.input_names: for in_var_name in op.input(in_name): if in_var_name == var_name: post_op.append(op) if post_op != []: return post_op return None def find_op_index(block_desc, cur_op_desc): """ """ for idx in range(block_desc.op_size()): if cur_op_desc == block_desc.op(idx): return idx return -1 def _is_in_black_varnames(op, amp_lists): for in_name in op.input_arg_names: if in_name in amp_lists.black_varnames: return True for out_name in op.output_arg_names: if out_name in amp_lists.black_varnames: return True return False def cast_model_to_fp16(main_program): """ Traverse all ops in the whole model and set their inputs and outputs to the fp16 data type. This function will do some special process for the batch normalization, which keeps the computational process of batchnorms in FP32. Args: main_program (Program): The main program for training. """ valid_types = [ core.VarDesc.VarType.LOD_TENSOR, core.VarDesc.VarType.SELECTED_ROWS, core.VarDesc.VarType.LOD_TENSOR_ARRAY ] global_block = main_program.global_block() for block in main_program.blocks: ops = block.ops for op in ops: if op.type == 'create_py_reader' or op.type == 'read': continue for in_name in op.input_names: if op.type in { 'batch_norm', 'fused_bn_add_activation', 'layer_norm' } and in_name not in {'X', 'Z'}: continue for in_var_name in op.input(in_name): in_var = None try: in_var = block.var(in_var_name) except ValueError as e: _logger.debug( "-- {}, try to get it in the global block. --". format(e)) in_var = global_block.var(in_var_name) if in_var is not None: _logger.debug( "-- var {} is got in the global block. --". format(in_var_name)) if in_var is None or in_var.type not in valid_types: continue if in_var.dtype == core.VarDesc.VarType.FP32: in_var.desc.set_dtype(core.VarDesc.VarType.FP16) _logger.debug( "-- op type: {}, in var name: {}, in var dtype: {} --". format(op.type, in_var_name, in_var.dtype)) for out_name in op.output_names: if op.type in { 'batch_norm', 'fused_bn_add_activation', 'layer_norm' } and out_name != 'Y': continue for out_var_name in op.output(out_name): out_var = None try: out_var = block.var(out_var_name) except ValueError as e: _logger.debug( "-- {}, try to get it in the global block. --". format(e)) out_var = global_block.var(out_var_name) if out_var is not None: _logger.debug( "-- var {} is got in the global block. --". format(out_var_name)) if out_var is None or out_var.type not in valid_types: continue if out_var.dtype == core.VarDesc.VarType.FP32: out_var.desc.set_dtype(core.VarDesc.VarType.FP16) _logger.debug( "-- op type: {}, out var name: {}, out var dtype: {} --". format(op.type, out_var_name, out_var.dtype)) 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) def cast_parameters_to_fp16(place, main_program, scope=None): """ Traverse all parameters in the whole model and set them to the fp16 data type. Whereas, this function will keep parameters of batchnorms in FP32. Args: place(fluid.CPUPlace|fluid.CUDAPlace): place is used to restore the weight tensors. main_program (Program): The main program for training. scope(fluid.Scope, optional): scope is used to get the weight tensor values. Default is None. """ all_ops = [] for block in main_program.blocks: all_ops.extend(block.ops) bn_params = set() for op in all_ops: if op.type not in { 'batch_norm', 'fused_bn_add_activation', 'layer_norm' }: continue for in_name in op.input_names: if in_name not in {'X', 'Z'}: for in_var_name in op.input(in_name): bn_params.add(in_var_name) global_block = main_program.global_block() all_parameters = global_block.all_parameters() var_scope = scope if scope is not None else global_scope() for param in all_parameters: if param.name not in bn_params: param_t = var_scope.find_var(param.name).get_tensor() data = np.array(param_t) param_t.set(np.float16(data), place) def rewrite_program(main_prog, amp_lists): """ Traverse all ops in current block and insert cast op according to which set current op belongs to. 1. When an op belongs to the black list, add it to black set 2. When an op belongs to the white list, add it to white set 3. When an op belongs to the gray list. If one of its inputs is the output of black set op or black list op, add it to black set. If all of its previous ops are not black op and one of its inputs is the output of white set op or white list op, add it to white set. 4. When an op isn't in the lists, add it to black op set. 5. Add necessary cast ops to make sure that black set op will be computed in fp32 mode, while white set op will be computed in fp16 mode. Args: main_prog (Program): The main program for training. """ block = main_prog.global_block() ops = block.ops white_op_set = set() black_op_set = set() for op in ops: # NOTE(zhiqiu): 'create_py_reader' and 'read' is used in non-iterable DataLoder, # we don't need to handle reader op and the input of 'create_py_reader' is not # in block, which may result in errors. # See GeneratorLoader._init_non_iterable() for details. if op.type == 'create_py_reader' or op.type == 'read': continue if amp_lists.black_varnames is not None and _is_in_black_varnames( op, amp_lists): black_op_set.add(op) continue if op.type in amp_lists.black_list: black_op_set.add(op) elif op.type in amp_lists.white_list: white_op_set.add(op) elif op.type in amp_lists.gray_list: is_black_op = False is_white_op = False for in_name in op.input_names: # if this op has inputs if in_name: for in_var_name in op.input(in_name): in_var = block.var(in_var_name) # this in_var isn't the output of other op if in_var.op is None: continue elif in_var.op is op: prev_op = find_true_prev_op(ops, op, in_var_name) if prev_op is None: continue else: prev_op = in_var.op # if it's one of inputs if prev_op in black_op_set or \ prev_op.type in amp_lists.black_list: is_black_op = True elif prev_op in white_op_set or \ prev_op.type in amp_lists.white_list: is_white_op = True if is_black_op: black_op_set.add(op) elif is_white_op: white_op_set.add(op) else: pass else: # For numerical safe, we apply fp32 computation on ops that # are not determined which list they should stay. black_op_set.add(op) idx = 0 while idx < len(ops): op = ops[idx] num_cast_ops = 0 if op in black_op_set: num_cast_ops = _insert_cast_op(block, op, idx, core.VarDesc.VarType.FP16, core.VarDesc.VarType.FP32) elif op in white_op_set: num_cast_ops = _insert_cast_op(block, op, idx, core.VarDesc.VarType.FP32, core.VarDesc.VarType.FP16) else: pass idx += num_cast_ops + 1 def update_role_var_grad(main_prog, params_grads): """ Update op_role_var attr for some ops to make sure the gradients transferred across GPUs is FP16. 1. Check whether the op that outputs gradient is cast or not. 2. If op is cast and gradient is FP32, remove the op_role_var and find the prev op which outputs FP16 gradient 3. Update the op_role_var of the prev op. Args: main_prog (Program): The main program for training. params_grads (list): A list of params and grads. """ block = main_prog.global_block() BACKWARD = core.op_proto_and_checker_maker.OpRole.Backward OPTIMIZE = core.op_proto_and_checker_maker.OpRole.Optimize for p, g in params_grads: op = g.op if g.dtype == core.VarDesc.VarType.FP32 and op.type == 'cast': role = op.attr('op_role') if role & int(BACKWARD) and op.has_attr('op_role_var'): op.desc.remove_attr("op_role_var") else: raise ValueError("The cast op {0} must be in BACKWARD role " "and have op_role_var attr.".format(op)) fp16_grad_name = op.input(op.input_names[0])[0] op_for_fp16_grad = find_true_prev_op(block.ops, op, fp16_grad_name) op_role_var_attr_name = \ core.op_proto_and_checker_maker.kOpRoleVarAttrName() attr_val = [p.name, fp16_grad_name] if op_for_fp16_grad.has_attr(op_role_var_attr_name): attr_val.extend(op_for_fp16_grad.attr(op_role_var_attr_name)) op_for_fp16_grad._set_attr(op_role_var_attr_name, attr_val) # Maximize the all_reduce overlap, and perform the cast # operation after gradients transfer. op._set_attr('op_role', OPTIMIZE) # optimize op should stay behind forward and backward ops if op == block.ops[-1]: continue post_ops = find_true_post_op(block.ops, op, g.name) if post_ops is not None: raise ValueError("The cast op {0}'s output should not be" "used by a non-optimize op, however, it" "is used by {1}".format(op, post_ops[0])) new_op_desc = block.desc.append_op() new_op_desc.copy_from(op.desc) op_idx = find_op_index(block.desc, op.desc) if op_idx == -1: raise ValueError("The op {0} is not in program".format(op)) block.desc._remove_op(op_idx, op_idx + 1) block._sync_with_cpp()