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574 lines
21 KiB
574 lines
21 KiB
# 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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import unittest
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import numpy as np
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import random
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import itertools
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import paddle.fluid.core as core
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import collections
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from paddle.fluid.backward import append_backward
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from paddle.fluid.op import Operator
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from paddle.fluid.executor import Executor
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from paddle.fluid.framework import Program, OpProtoHolder
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def randomize_probability(batch_size, class_num, dtype='float32'):
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prob = np.random.uniform(
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0.1, 1.0, size=(batch_size, class_num)).astype(dtype)
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prob_sum = prob.sum(axis=1)
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for i in xrange(len(prob)):
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prob[i] /= prob_sum[i]
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return prob
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def create_op(scope, op_type, inputs, outputs, attrs):
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kwargs = dict()
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def __create_var__(name, var_name):
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scope.var(var_name).get_tensor()
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kwargs[name].append(var_name)
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for in_name, in_dup in Operator.get_op_inputs(op_type):
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if in_name in inputs:
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kwargs[in_name] = []
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if in_dup:
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sub_in = inputs[in_name]
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for item in sub_in:
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sub_in_name, _ = item[0], item[1]
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__create_var__(in_name, sub_in_name)
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else:
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__create_var__(in_name, in_name)
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for out_name, out_dup in Operator.get_op_outputs(op_type):
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if out_name in outputs:
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kwargs[out_name] = []
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if out_dup:
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sub_out = outputs[out_name]
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for item in sub_out:
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sub_out_name, _ = item[0], item[1]
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__create_var__(out_name, sub_out_name)
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else:
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__create_var__(out_name, out_name)
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for attr_name in Operator.get_op_attr_names(op_type):
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if attr_name in attrs:
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kwargs[attr_name] = attrs[attr_name]
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return Operator(op_type, **kwargs)
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def set_input(scope, op, inputs, place):
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def __set_input__(var_name, var):
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if isinstance(var, tuple) or isinstance(var, np.ndarray):
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tensor = scope.find_var(var_name).get_tensor()
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if isinstance(var, tuple):
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tensor.set_lod(var[1])
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var = var[0]
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tensor.set_dims(var.shape)
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tensor.set(var, place)
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elif isinstance(var, float):
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scope.find_var(var_name).set_float(var)
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elif isinstance(var, int):
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scope.find_var(var_name).set_int(var)
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for in_name, in_dup in Operator.get_op_inputs(op.type()):
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if in_name in inputs:
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if in_dup:
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sub_in = inputs[in_name]
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for item in sub_in:
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sub_in_name, sub_in_val = item[0], item[1]
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__set_input__(sub_in_name, sub_in_val)
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else:
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__set_input__(in_name, inputs[in_name])
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def get_numeric_gradient(place,
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scope,
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op,
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inputs,
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input_to_check,
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output_names,
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delta=0.005,
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in_place=False):
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# FIXME: change this method by compile time concepts
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set_input(scope, op, inputs, place)
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def product(dim):
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return reduce(lambda a, b: a * b, dim, 1)
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def get_output():
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sum = []
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for output_name in output_names:
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op.run(scope, place)
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sum.append(
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np.array(scope.find_var(output_name).get_tensor()).mean())
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return np.array(sum).mean()
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tensor_to_check = scope.find_var(input_to_check).get_tensor()
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tensor_size = product(tensor_to_check.get_dims())
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tensor_to_check_dtype = tensor_to_check.dtype()
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if tensor_to_check_dtype == core.VarDesc.VarType.FP32:
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tensor_to_check_dtype = np.float32
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elif tensor_to_check_dtype == core.VarDesc.VarType.FP64:
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tensor_to_check_dtype = np.float64
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else:
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raise ValueError("Not supported data type " + str(
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tensor_to_check_dtype))
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gradient_flat = np.zeros(shape=(tensor_size, ), dtype=tensor_to_check_dtype)
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def __get_elem__(tensor, i):
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if tensor_to_check_dtype == np.float32:
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return tensor.get_float_element(i)
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else:
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return tensor.get_double_element(i)
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def __set_elem__(tensor, i, e):
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if tensor_to_check_dtype == np.float32:
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tensor.set_float_element(i, e)
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else:
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tensor.set_double_element(i, e)
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# we only compute gradient of one element each time.
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# we use a for loop to compute the gradient of every element.
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for i in xrange(tensor_size):
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if in_place:
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set_input(scope, op, inputs, place)
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# get one input element throw it's index i.
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origin = __get_elem__(tensor_to_check, i)
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# add delta to it, run op and then get the sum of the result tensor.
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x_pos = origin + delta
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__set_elem__(tensor_to_check, i, x_pos)
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y_pos = get_output()
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if in_place:
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set_input(scope, op, inputs, place)
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x_neg = origin - delta
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__set_elem__(tensor_to_check, i, x_neg)
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y_neg = get_output()
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__set_elem__(tensor_to_check, i, origin)
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gradient_flat[i] = (y_pos - y_neg) / delta / 2
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return gradient_flat.reshape(tensor_to_check.get_dims())
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def append_input_output(block, op_proto, np_list, is_input):
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'''Insert VarDesc and generate Python variable instance'''
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proto_list = op_proto.inputs if is_input else op_proto.outputs
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def create_var(block, name, np_list, var_proto):
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if name not in np_list:
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assert var_proto.intermediate, "{} not found".format(name)
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shape = None
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lod_level = None
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else:
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np_value = np_list[name]
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if isinstance(np_value, tuple):
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shape = list(np_value[0].shape)
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lod_level = len(np_value[1])
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else:
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shape = list(np_value.shape)
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lod_level = 0
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return block.create_var(
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dtype="float32", shape=shape, lod_level=lod_level, name=name)
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var_dict = {}
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for var_proto in proto_list:
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var_name = str(var_proto.name)
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if is_input:
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if (var_name not in np_list) and var_proto.dispensable:
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continue
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assert (var_name in np_list) or (var_proto.dispensable), \
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"Missing {} as input".format(var_name)
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if var_proto.duplicable:
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assert isinstance(np_list[var_name], list), \
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"Duplicable {} should be set as list".format(var_name)
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var_list = []
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for (name, np_value) in np_list[var_name]:
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var_list.append(
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create_var(block, name, {name: np_value}, var_proto))
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var_dict[var_name] = var_list
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else:
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var_dict[var_name] = create_var(block, var_name, np_list, var_proto)
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return var_dict
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class OpTest(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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'''Fix random seeds to remove randomness from tests'''
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cls._np_rand_state = np.random.get_state()
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cls._py_rand_state = random.getstate()
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np.random.seed(123)
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random.seed(124)
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@classmethod
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def tearDownClass(cls):
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'''Restore random seeds'''
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np.random.set_state(cls._np_rand_state)
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random.setstate(cls._py_rand_state)
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def feed_var(self, input_vars, place):
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feed_map = {}
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for var_name in input_vars:
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if isinstance(input_vars[var_name], list):
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for name, np_value in self.inputs[var_name]:
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tensor = core.LoDTensor()
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if isinstance(np_value, tuple):
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tensor.set(np_value[0], place)
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tensor.set_lod(np_value[1])
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else:
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tensor.set(np_value, place)
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feed_map[name] = tensor
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else:
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tensor = core.LoDTensor()
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if isinstance(self.inputs[var_name], tuple):
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tensor.set(self.inputs[var_name][0], place)
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tensor.set_lod(self.inputs[var_name][1])
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else:
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tensor.set(self.inputs[var_name], place)
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feed_map[var_name] = tensor
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return feed_map
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def calc_output(self, place):
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outs, _ = self._calc_output(place)
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return outs
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def _calc_output(self, place):
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op_proto = OpProtoHolder.instance().get_op_proto(self.op_type)
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program = Program()
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block = program.global_block()
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inputs = append_input_output(block, op_proto, self.inputs, True)
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outputs = append_input_output(block, op_proto, self.outputs, False)
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op = block.append_op(
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type=self.op_type,
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inputs=inputs,
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outputs=outputs,
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attrs=self.attrs if hasattr(self, "attrs") else dict())
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# infer variable type and infer shape in compile-time
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op.desc.infer_var_type(block.desc)
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op.desc.infer_shape(block.desc)
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fetch_list = []
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for var_name, var in outputs.iteritems():
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if var_name in self.outputs:
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if isinstance(var, list):
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for v in var:
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fetch_list.append(v)
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else:
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fetch_list.append(var)
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feed_map = self.feed_var(inputs, place)
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exe = Executor(place)
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outs = exe.run(program,
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feed=feed_map,
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fetch_list=fetch_list,
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return_numpy=False)
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return outs, fetch_list
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def check_output_with_place(self, place, atol):
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outs, fetch_list = self._calc_output(place)
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for out_name, out_dup in Operator.get_op_outputs(self.op_type):
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if out_name not in self.outputs:
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continue
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def find_actual(target_name, fetch_list):
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found = [
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i for i, var in enumerate(fetch_list)
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if var.name == target_name
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]
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self.assertTrue(
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len(found) == 1, "Found {} {}".format(
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len(found), target_name))
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return found[0]
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if out_dup:
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sub_out = self.outputs[out_name]
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if not isinstance(sub_out, list):
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raise AssertionError("sub_out type %s is not list",
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type(sub_out))
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for item in sub_out:
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sub_out_name, expect = item[0], item[1]
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idx = find_actual(sub_out_name, fetch_list)
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actual = outs[idx]
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actual_t = np.array(actual)
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expect_t = expect[0] \
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if isinstance(expect, tuple) else expect
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self.assertTrue(
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np.allclose(
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actual_t, expect_t, atol=atol),
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"Output (" + sub_out_name + ") has diff at " +
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str(place))
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if isinstance(expect, tuple):
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self.assertListEqual(
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actual.lod(), expect[1], "Output (" + sub_out_name +
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") has different lod at " + str(place))
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else:
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idx = find_actual(out_name, fetch_list)
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actual = outs[idx]
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actual_t = np.array(actual)
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expect = self.outputs[out_name]
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expect_t = expect[0] if isinstance(expect, tuple) else expect
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self.assertTrue(
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np.allclose(
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actual_t, expect_t, atol=atol),
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"Output (" + out_name + ") has diff at " + str(place) +
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str(actual_t) + "\n" + str(expect_t))
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if isinstance(expect, tuple):
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self.assertListEqual(actual.lod(), expect[1],
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"Output (" + out_name +
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") has different lod at " + str(place))
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def check_output(self, atol=1e-5):
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places = [core.CPUPlace()]
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if core.is_compiled_with_cuda() and core.op_support_gpu(self.op_type):
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places.append(core.CUDAPlace(0))
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for place in places:
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self.check_output_with_place(place, atol)
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def check_output_customized(self, checker):
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places = [core.CPUPlace()]
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if core.is_compiled_with_cuda() and core.op_support_gpu(self.op_type):
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places.append(core.CUDAPlace(0))
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for place in places:
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outs = self.calc_output(place)
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outs = [np.array(out) for out in outs]
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checker(outs)
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def __assert_is_close(self, numeric_grads, analytic_grads, names,
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max_relative_error, msg_prefix):
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for a, b, name in itertools.izip(numeric_grads, analytic_grads, names):
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abs_a = np.abs(a)
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abs_a[abs_a < 1e-3] = 1
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diff_mat = np.abs(a - b) / abs_a
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max_diff = np.max(diff_mat)
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def err_msg():
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offset = np.argmax(diff_mat > max_relative_error)
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return ("%s Variable %s max gradient diff %f over limit %f, "
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"the first error element is %d, %f, %f") % (
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msg_prefix, name, max_diff, max_relative_error,
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offset, a.flatten()[offset], b.flatten()[offset])
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self.assertLessEqual(max_diff, max_relative_error, err_msg())
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def check_grad(self,
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inputs_to_check,
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output_names,
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no_grad_set=None,
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numeric_grad_delta=0.005,
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in_place=False,
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max_relative_error=0.005,
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user_defined_grads=None):
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places = [core.CPUPlace()]
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if core.is_compiled_with_cuda() and core.op_support_gpu(self.op_type):
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places.append(core.CUDAPlace(0))
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for place in places:
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self.check_grad_with_place(place, inputs_to_check, output_names,
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no_grad_set, numeric_grad_delta,
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in_place, max_relative_error,
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user_defined_grads)
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def check_grad_with_place(self,
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place,
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inputs_to_check,
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output_names,
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no_grad_set=None,
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numeric_grad_delta=0.005,
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in_place=False,
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max_relative_error=0.005,
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user_defined_grads=None):
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self.scope = core.Scope()
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op_inputs = self.inputs if hasattr(self, "inputs") else dict()
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op_outputs = self.outputs if hasattr(self, "outputs") else dict()
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op_attrs = self.attrs if hasattr(self, "attrs") else dict()
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self.op = create_op(self.scope, self.op_type, op_inputs, op_outputs,
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op_attrs)
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if no_grad_set is None:
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no_grad_set = set()
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if not type(output_names) is list:
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output_names = [output_names]
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numeric_grads = user_defined_grads or [
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get_numeric_gradient(
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place,
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self.scope,
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self.op,
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self.inputs,
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input_to_check,
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output_names,
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delta=numeric_grad_delta,
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in_place=in_place) for input_to_check in inputs_to_check
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]
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analytic_grads = self._get_gradient(inputs_to_check, place,
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output_names, no_grad_set)
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self.__assert_is_close(numeric_grads, analytic_grads, inputs_to_check,
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max_relative_error,
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"Gradient Check On %s" % str(place))
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@staticmethod
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def _create_var_descs_(block, var_dict):
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# FIXME: Try unify with `append_input_output`
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for param_name in var_dict:
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var = var_dict[param_name]
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if not isinstance(var, list) and not isinstance(var, tuple):
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var = [(param_name, var, None)]
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if not isinstance(var[0], list) and not isinstance(var[0], tuple):
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var = [(param_name, var[0], var[1])]
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for i, item in enumerate(var):
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if not isinstance(item[0], basestring):
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item = [[param_name] + list(item)]
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if len(item) == 2:
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if isinstance(item[1], tuple):
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var[i] = [item[0], item[1][0], item[1][1]]
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else:
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# only set var name and value, set lod to None
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var[i] = list(item) + [None]
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var_descs = [(block.create_var(
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name=name, shape=each.shape, dtype=each.dtype), each, lod)
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for name, each, lod in var]
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yield param_name, var_descs
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@staticmethod
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def _merge_list(iterable):
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return reduce(lambda a, b: list(a) + list(b), iterable, [])
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@staticmethod
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def _numpy_to_lod_tensor(np_value, lod, place):
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tensor = core.LoDTensor()
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tensor.set(np_value, place)
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if lod is not None:
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tensor.set_lod(lod)
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return tensor
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@staticmethod
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def np_dtype_to_fluid_dtype(input):
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"""Change the dtype of float16 numpy array
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numpy float16 is binded to paddle::platform::float16
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in tensor_py.h via the help of uint16 data type since
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the internal memory representation of float16 is
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uint16_t in paddle and np.uint16 in numpy, which are
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themselves binded together by pybind.
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Args:
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input: input numpy array
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Returns:
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input: The dtype of input will be changed to np.uint16 if
|
|
it is originally np.float16, such that the internal memory
|
|
of input will be reinterpreted as of dtype np.uint16.
|
|
"""
|
|
if input.dtype == np.float16:
|
|
input.dtype = np.uint16
|
|
return input
|
|
|
|
def _get_gradient(self, input_to_check, place, output_names, no_grad_set):
|
|
prog = Program()
|
|
block = prog.global_block()
|
|
inputs_with_np = {
|
|
key: value
|
|
for (key, value) in OpTest._create_var_descs_(
|
|
block, getattr(self, 'inputs', {}))
|
|
}
|
|
outputs_with_np = {
|
|
key: val
|
|
for (key, val) in OpTest._create_var_descs_(
|
|
block, getattr(self, 'outputs', {}))
|
|
}
|
|
inputs = {
|
|
k: [item[0] for item in inputs_with_np[k]]
|
|
for k in inputs_with_np
|
|
}
|
|
outputs = {
|
|
k: [item[0] for item in outputs_with_np[k]]
|
|
for k in outputs_with_np
|
|
}
|
|
|
|
op = block.append_op(
|
|
type=self.op_type,
|
|
inputs=inputs,
|
|
outputs=outputs,
|
|
attrs=getattr(self, 'attrs', {}))
|
|
|
|
# infer variable type and infer shape in compile-time
|
|
op.desc.infer_var_type(block.desc)
|
|
op.desc.infer_shape(block.desc)
|
|
|
|
mean_inputs = map(block.var, output_names)
|
|
|
|
if len(mean_inputs) == 1:
|
|
loss = block.create_var(dtype=mean_inputs[0].dtype, shape=[1])
|
|
op = block.append_op(
|
|
inputs={"X": mean_inputs}, outputs={"Out": loss}, type='mean')
|
|
op.desc.infer_var_type(block.desc)
|
|
op.desc.infer_shape(block.desc)
|
|
else:
|
|
avg_sum = []
|
|
for cur_loss in mean_inputs:
|
|
cur_avg_loss = block.create_var(dtype=cur_loss.dtype, shape=[1])
|
|
op = block.append_op(
|
|
inputs={"X": [cur_loss]},
|
|
outputs={"Out": [cur_avg_loss]},
|
|
type="mean")
|
|
op.desc.infer_var_type(block.desc)
|
|
op.desc.infer_shape(block.desc)
|
|
avg_sum.append(cur_avg_loss)
|
|
|
|
loss_sum = block.create_var(dtype=avg_sum[0].dtype, shape=[1])
|
|
op_sum = block.append_op(
|
|
inputs={"X": avg_sum}, outputs={"Out": loss_sum}, type='sum')
|
|
op_sum.desc.infer_var_type(block.desc)
|
|
op_sum.desc.infer_shape(block.desc)
|
|
|
|
loss = block.create_var(dtype=loss_sum.dtype, shape=[1])
|
|
op_loss = block.append_op(
|
|
inputs={"X": loss_sum},
|
|
outputs={"Out": loss},
|
|
type='scale',
|
|
attrs={'scale': 1.0 / float(len(avg_sum))})
|
|
op_loss.desc.infer_var_type(block.desc)
|
|
op_loss.desc.infer_shape(block.desc)
|
|
|
|
param_grad_list = append_backward(
|
|
loss=loss, parameter_list=input_to_check, no_grad_set=no_grad_set)
|
|
|
|
feed_dict = {
|
|
item[0].name: OpTest._numpy_to_lod_tensor(item[1], item[2], place)
|
|
for p_name in inputs_with_np for item in inputs_with_np[p_name]
|
|
}
|
|
|
|
fetch_list = [g for p, g in param_grad_list]
|
|
executor = Executor(place)
|
|
return map(np.array,
|
|
executor.run(prog, feed_dict, fetch_list,
|
|
return_numpy=False))
|