# Copyright (c) 2018 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 import six from collections import defaultdict from paddle.fluid import core from paddle.fluid import framework from paddle import _C_ops final_state_name_mapping = { "matmul_v2": { "final_op_name": "final_state_matmul", "transpose_x": "trans_x", "transpose_y": "trans_y", "x": "X", "y": "Y", "out": "Out", }, # "elementwise_add": { # "final_op_name": "final_state_add", # "x": "X", # "y": "Y", # }, "trunc": { "final_op_name": "final_state_trunc", "x": "X", "out": "Out", }, # "pool2d": { # "final_op_name": "final_state_pool2d", # "x": "X", # "kernel_size": "ksize", # "out": "Out", # }, "abs": { "final_op_name": "final_state_abs", "x": "X", "out": "Out", }, "digamma": { "final_op_name": "final_state_digamma", "x": "X", "out": "Out", }, "diagonal": { "final_op_name": "final_state_diagonal", "x": "Input", "offset": "offset", "axis1": "axis1", "axis2": "axis2", "out": "Out", }, # "one_hot": { # "final_op_name": "final_state_one_hot", # "x": "X", # "num_class": "depth", # "out": "Out", # } } class Tracer(core.Tracer): """ :api_attr: imperative Tracer is used to execute and record the operators executed, to construct the computation graph in dygraph model. Tracer has two mode, :code:`train_mode` and :code:`eval_mode`. In :code:`train_mode`, Tracer would add backward network automatically and perform AutoGrad by method :code:`loss.backward()`. In :code:`eval_mode`, Tracer would not add backward network. This is a low level API, users don't need to use it directly. """ def __init__(self): super(Tracer, self).__init__() self._train_mode = True def eager_trace_op(self, type, inputs, outputs, attrs, stop_gradient=False, inplace_map=None): function_ptr = _C_ops.__dict__[type] core_ops_args_info = _C_ops.get_core_ops_args_info() core_ops_args_type_info = _C_ops.get_core_ops_args_type_info() core_ops_returns_info = _C_ops.get_core_ops_returns_info() op_args = core_ops_args_info[type] op_args_type = core_ops_args_type_info[type] op_returns = core_ops_returns_info[type] arg_list = [] for i in range(len(op_args)): # initialized with None arg_to_append = None arg_name = op_args[i] arg_type = op_args_type[i] if arg_name in inputs.keys(): arg_to_append = inputs[arg_name] elif arg_name in outputs.keys(): arg_to_append = outputs[arg_name] else: if "Num" in arg_name[-3:]: # Remove "Num" suffix to get out_name out_name = arg_name[:-3] assert out_name in outputs.keys() num_outs = len(outputs[out_name]) arg_to_append = num_outs # NOTE(dev): For MasterParam/MasterParamOut in optimzer op elif "Var" in arg_name[-3:]: out_name = arg_name[:-3] print(out_name) if out_name in outputs.keys(): arg_to_append = outputs[out_name] elif out_name in inputs.keys(): arg_to_append = inputs[out_name] if arg_to_append is None: arg_list.append(arg_to_append) elif arg_type == "tensor": if isinstance(arg_to_append, list): arg_list.append(arg_to_append[0]) else: arg_list.append(arg_to_append) elif arg_type == "list": assert isinstance(arg_to_append, list) arg_list.append(arg_to_append) else: assert arg_type == "int" assert isinstance(arg_to_append, int) arg_list.append(arg_to_append) attrs_list = [] for k, v in attrs.items(): attrs_list.append(k) attrs_list.append(v) returns = function_ptr(*arg_list, *attrs_list) if type == 'load_combine': assert len(outputs.keys()) == 1 key = list(outputs.keys())[0] for j in range(len(returns)): returns[j]._share_underline_tensor_to(outputs[key][j]) return if isinstance(returns, tuple): for i in range(len(op_returns)): retname = op_returns[i] if retname in outputs.keys(): # Replaced outputs by function returns if isinstance(returns[i], list): for j in range(len(returns[i])): outputs[retname][j].reconstruct_from_(returns[i][j], False) else: if isinstance(outputs[retname], list): outputs[retname][0].reconstruct_from_(returns[i], False) else: outputs[retname].reconstruct_from_(returns[i], False) elif isinstance(returns, list): assert len(outputs.keys()) == 1 key = list(outputs.keys())[0] for j in range(len(returns)): outputs[key][j].reconstruct_from_(returns[j], False) else: assert len(outputs.keys()) == 1 key = list(outputs.keys())[0] if isinstance(outputs[key], list): outputs[key][0].reconstruct_from_(returns, False) else: outputs[key].reconstruct_from_(returns, False) def eager_final_state_trace_op(self, type, inputs, outputs, attrs, stop_gradient=False, inplace_map=None): assert type in final_state_name_mapping.keys() final_state_type = final_state_name_mapping[type]["final_op_name"] function_ptr = _C_ops.__dict__[final_state_type] core_ops_args_info = _C_ops.get_final_state_core_ops_args_info() core_ops_args_type_info = _C_ops.get_final_state_core_ops_args_type_info( ) core_ops_returns_info = _C_ops.get_final_state_core_ops_returns_info() op_args = core_ops_args_info[final_state_type] op_args_type = core_ops_args_type_info[final_state_type] op_returns = core_ops_returns_info[final_state_type] arg_list = [] for i in range(len(op_args)): eager_arg_name = op_args[i] arg_type = op_args_type[i] assert eager_arg_name in final_state_name_mapping[type].keys() arg_name = final_state_name_mapping[type][eager_arg_name] if arg_name in inputs.keys(): arg_to_append = inputs[arg_name] elif arg_name in outputs.keys(): arg_to_append = outputs[arg_name] elif arg_name in attrs.keys() and arg_type == "": arg_to_append = attrs[arg_name] else: # dispensable arg_to_append = None if arg_type == "": # attribute arg_list.append(arg_to_append) elif arg_type == "tensor": if isinstance(arg_to_append, list): arg_list.append(arg_to_append[0]) else: arg_list.append(arg_to_append) elif arg_type == "list": assert isinstance(arg_to_append, list) arg_list.append(arg_to_append) else: assert arg_to_append is None arg_list.append(arg_to_append) returns = function_ptr(*arg_list) if isinstance(returns, tuple): for i in range(len(op_returns)): eager_retname = op_returns[i] assert eager_retname in final_state_name_mapping[type].keys() retname = final_state_name_mapping[type][eager_retname] if retname in outputs.keys(): # Replaced outputs by function returns if isinstance(returns[i], list): for j in range(len(returns[i])): outputs[retname][j].reconstruct_from_(returns[i][j], False) else: outputs[retname][0].reconstruct_from_(returns[i], False) elif isinstance(returns, list): assert len(outputs.keys()) == 1 key = list(outputs.keys())[0] for j in range(len(returns)): outputs[key][j].reconstruct_from_(returns[j], False) else: assert len(outputs.keys()) == 1 key = list(outputs.keys())[0] if isinstance(outputs[key], list): outputs[key][0].reconstruct_from_(returns, False) else: outputs[key].reconstruct_from_(returns, False) def trace_op(self, type, inputs, outputs, attrs, stop_gradient=False, inplace_map=None): if not framework._in_legacy_dygraph(): # inputs : {"sum": [tensor], ...} # outputs : {"sum": [tensor], ...} if type in final_state_name_mapping.keys(): final_state_type = final_state_name_mapping[type][ "final_op_name"] assert final_state_type in _C_ops.__dict__ self.eager_final_state_trace_op(type, inputs, outputs, attrs, stop_gradient, inplace_map) else: self.eager_trace_op(type, inputs, outputs, attrs, stop_gradient, inplace_map) else: self.trace(type, inputs, outputs, attrs, framework._current_expected_place(), self._has_grad and not stop_gradient, inplace_map if inplace_map else {}) def train_mode(self): self._train_mode = True def eval_mode(self): self._train_mode = False