chore: import upstream snapshot with attribution
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wehub-resource-sync
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from paddle import _C_ops, _legacy_C_ops
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from paddle.base import core, framework
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name_mapping = {
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"graph_send_recv": {
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"final_op_name": "graph_send_recv",
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"x": "X",
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"src_index": "Src_index",
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"dst_index": "Dst_index",
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"out": "Out",
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"dst_count": "Dst_count",
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},
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"matmul_v2": {
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"final_op_name": "matmul",
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"transpose_x": "trans_x",
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"transpose_y": "trans_y",
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"x": "X",
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"y": "Y",
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"out": "Out",
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},
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# "elementwise_add": {
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# "final_op_name": "add",
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# "x": "X",
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# "y": "Y",
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# },
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"trunc": {
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"final_op_name": "trunc",
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"x": "X",
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"out": "Out",
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},
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# "pool2d": {
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# "final_op_name": "pool2d",
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# "x": "X",
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# "kernel_size": "ksize",
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# "out": "Out",
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# },
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"abs": {
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"final_op_name": "abs",
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"x": "X",
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"out": "Out",
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},
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"digamma": {
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"final_op_name": "digamma",
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"x": "X",
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"out": "Out",
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},
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"diagonal": {
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"final_op_name": "diagonal",
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"x": "Input",
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"offset": "offset",
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"axis1": "axis1",
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"axis2": "axis2",
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"out": "Out",
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},
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"roi_align": {
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"final_op_name": "roi_align",
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"x": "X",
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"boxes": "ROIs",
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"boxes_num": "RoisNum",
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"pooled_height": "pooled_height",
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"pooled_width": "pooled_width",
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"spatial_scale": "spatial_scale",
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"sampling_ratio": "sampling_ratio",
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"aligned": "aligned",
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},
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# "one_hot": {
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# "final_op_name": "one_hot",
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# "x": "X",
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# "num_class": "depth",
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# "out": "Out",
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# }
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}
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core_ops_args_info = _legacy_C_ops.get_core_ops_args_info()
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core_ops_args_type_info = _legacy_C_ops.get_core_ops_args_type_info()
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core_ops_returns_info = _legacy_C_ops.get_core_ops_returns_info()
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class Tracer(core.Tracer):
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"""
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:api_attr: imperative
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Tracer is used to execute and record the operators executed, to construct the
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computation graph in dygraph model. Tracer has two mode, :code:`train_mode`
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and :code:`eval_mode`. In :code:`train_mode`, Tracer would add backward network
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automatically and perform AutoGrad by method :code:`loss.backward()`.
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In :code:`eval_mode`, Tracer would not add backward network.
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This is a low level API, users don't need to use it directly.
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"""
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def __init__(self):
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super().__init__()
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self._train_mode = True
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def eager_legacy_trace_op(
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self,
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op_type,
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inputs,
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outputs,
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attrs,
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stop_gradient=False,
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inplace_map=None,
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):
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function_ptr = _legacy_C_ops.__dict__[op_type]
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op_args = core_ops_args_info[op_type]
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op_args_type = core_ops_args_type_info[op_type]
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op_returns = core_ops_returns_info[op_type]
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arg_list = []
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for i in range(len(op_args)):
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# initialized with None
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arg_to_append = None
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arg_name = op_args[i]
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arg_type = op_args_type[i]
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if arg_name in inputs.keys():
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arg_to_append = inputs[arg_name]
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elif arg_name in outputs.keys():
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arg_to_append = outputs[arg_name]
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else:
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if "Num" in arg_name[-3:]:
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# Remove "Num" suffix to get out_name
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out_name = arg_name[:-3]
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assert out_name in outputs.keys()
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num_outs = len(outputs[out_name])
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arg_to_append = num_outs
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# NOTE(dev): For MasterParam/MasterParamOut in optimizer op
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elif "Var" in arg_name[-3:]:
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out_name = arg_name[:-3]
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print(out_name)
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if out_name in outputs.keys():
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arg_to_append = outputs[out_name]
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elif out_name in inputs.keys():
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arg_to_append = inputs[out_name]
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if arg_to_append is None:
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arg_list.append(arg_to_append)
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elif arg_type == "tensor":
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if isinstance(arg_to_append, list):
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arg_list.append(arg_to_append[0])
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else:
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arg_list.append(arg_to_append)
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elif arg_type == "list":
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assert isinstance(arg_to_append, list)
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arg_list.append(arg_to_append)
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else:
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assert arg_type == "int"
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assert isinstance(arg_to_append, int)
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arg_list.append(arg_to_append)
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attrs_list = []
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for k, v in attrs.items():
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attrs_list.append(k)
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attrs_list.append(v)
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returns = function_ptr(*arg_list, *attrs_list)
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if op_type == 'load_combine':
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assert len(outputs.keys()) == 1
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key = next(iter(outputs.keys()))
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for j in range(len(returns)):
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returns[j]._share_underline_tensor_to(outputs[key][j])
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return
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if isinstance(returns, tuple):
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for i in range(len(op_returns)):
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retname = op_returns[i]
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if retname in outputs.keys():
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# Replaced outputs by function returns
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if isinstance(returns[i], list):
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for j in range(len(returns[i])):
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outputs[retname][j].reconstruct_from_(
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returns[i][j], False
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)
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else:
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if isinstance(outputs[retname], list):
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outputs[retname][0].reconstruct_from_(
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returns[i], False
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)
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else:
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outputs[retname].reconstruct_from_(
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returns[i], False
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)
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elif isinstance(returns, list):
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assert len(outputs.keys()) == 1
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key = next(iter(outputs.keys()))
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for j in range(len(returns)):
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outputs[key][j].reconstruct_from_(returns[j], False)
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else:
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assert len(outputs.keys()) == 1
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key = next(iter(outputs.keys()))
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if isinstance(outputs[key], list):
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outputs[key][0].reconstruct_from_(returns, False)
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else:
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outputs[key].reconstruct_from_(returns, False)
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def eager_trace_op(
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self,
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op_type,
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inputs,
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outputs,
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attrs,
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stop_gradient=False,
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inplace_map=None,
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):
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assert op_type in name_mapping.keys()
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op_type = name_mapping[op_type]["final_op_name"]
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function_ptr = _C_ops.__dict__[op_type]
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core_ops_args_info = _C_ops.get_core_ops_args_info()
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core_ops_args_type_info = _C_ops.get_core_ops_args_type_info()
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core_ops_returns_info = _C_ops.get_core_ops_returns_info()
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op_args = core_ops_args_info[op_type]
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op_args_type = core_ops_args_type_info[op_type]
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op_returns = core_ops_returns_info[op_type]
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arg_list = []
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for i in range(len(op_args)):
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eager_arg_name = op_args[i]
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arg_type = op_args_type[i]
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assert eager_arg_name in name_mapping[op_type].keys()
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arg_name = name_mapping[op_type][eager_arg_name]
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if arg_name in inputs.keys():
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arg_to_append = inputs[arg_name]
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elif arg_name in outputs.keys():
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arg_to_append = outputs[arg_name]
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elif arg_name in attrs.keys() and arg_type == "":
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arg_to_append = attrs[arg_name]
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else:
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# dispensable
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arg_to_append = None
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if arg_type == "":
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# attribute
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arg_list.append(arg_to_append)
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elif arg_type == "tensor":
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if isinstance(arg_to_append, list):
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arg_list.append(arg_to_append[0])
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else:
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arg_list.append(arg_to_append)
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elif arg_type == "list":
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assert isinstance(arg_to_append, list)
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arg_list.append(arg_to_append)
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else:
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assert arg_to_append is None
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arg_list.append(arg_to_append)
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returns = function_ptr(*arg_list)
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if isinstance(returns, tuple):
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for i in range(len(op_returns)):
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eager_retname = op_returns[i]
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assert eager_retname in name_mapping[op_type].keys()
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retname = name_mapping[op_type][eager_retname]
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if retname in outputs.keys():
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# Replaced outputs by function returns
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if isinstance(returns[i], list):
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for j in range(len(returns[i])):
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outputs[retname][j].reconstruct_from_(
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returns[i][j], False
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)
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else:
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outputs[retname][0].reconstruct_from_(returns[i], False)
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elif isinstance(returns, list):
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assert len(outputs.keys()) == 1
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key = next(iter(outputs.keys()))
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for j in range(len(returns)):
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outputs[key][j].reconstruct_from_(returns[j], False)
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else:
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assert len(outputs.keys()) == 1
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key = next(iter(outputs.keys()))
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if isinstance(outputs[key], list):
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outputs[key][0].reconstruct_from_(returns, False)
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else:
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outputs[key].reconstruct_from_(returns, False)
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def trace_op(
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self,
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type,
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inputs,
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outputs,
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attrs,
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stop_gradient=False,
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inplace_map=None,
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):
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if framework.in_dygraph_mode():
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# inputs : {"sum": [tensor], ...}
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# outputs : {"sum": [tensor], ...}
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if type in name_mapping.keys():
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type = name_mapping[type]["final_op_name"]
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assert type in _legacy_C_ops.__dict__
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self.eager_trace_op(
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type, inputs, outputs, attrs, stop_gradient, inplace_map
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)
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else:
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self.eager_legacy_trace_op(
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type, inputs, outputs, attrs, stop_gradient, inplace_map
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)
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else:
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raise ValueError("trace_op only work in dygraph mode")
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def train_mode(self):
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self._train_mode = True
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def eval_mode(self):
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self._train_mode = False
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