# 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 os import unittest import numpy as np import random import six import time import itertools import collections from collections import defaultdict import paddle.fluid as fluid import paddle.fluid.core as core from paddle.fluid.backward import append_backward from paddle.fluid.op import Operator from paddle.fluid.executor import Executor from paddle.fluid.framework import Program, OpProtoHolder, Variable from testsuite import create_op, set_input, append_input_output, append_loss_ops def randomize_probability(batch_size, class_num, dtype='float32'): prob = np.random.uniform( 0.1, 1.0, size=(batch_size, class_num)).astype(dtype) prob_sum = prob.sum(axis=1) for i in six.moves.xrange(len(prob)): prob[i] /= prob_sum[i] return prob def get_numeric_gradient(place, scope, op, inputs, input_to_check, output_names, delta=0.005, in_place=False): # FIXME: change this method by compile time concepts set_input(scope, op, inputs, place) def product(dim): return six.moves.reduce(lambda a, b: a * b, dim, 1) tensor_to_check = scope.find_var(input_to_check).get_tensor() tensor_size = product(tensor_to_check.shape()) tensor_to_check_dtype = tensor_to_check._dtype() if tensor_to_check_dtype == core.VarDesc.VarType.FP32: tensor_to_check_dtype = np.float32 elif tensor_to_check_dtype == core.VarDesc.VarType.FP64: tensor_to_check_dtype = np.float64 elif tensor_to_check_dtype == core.VarDesc.VarType.FP16: tensor_to_check_dtype = np.float16 # set delta as np.float16, will automatic convert to float32, float64 delta = np.array(delta).astype(np.float16) else: raise ValueError("Not supported data type " + str( tensor_to_check_dtype)) def get_output(): sum = [] op.run(scope, place) for output_name in output_names: sum.append( np.array(scope.find_var(output_name).get_tensor()).astype( tensor_to_check_dtype).mean()) return tensor_to_check_dtype(np.array(sum).sum() / len(output_names)) gradient_flat = np.zeros(shape=(tensor_size, ), dtype=tensor_to_check_dtype) def __get_elem__(tensor, i): if tensor_to_check_dtype == np.float16: numpy_tensor = np.array(tensor).astype(np.float16) numpy_tensor = numpy_tensor.flatten() return numpy_tensor[i] elif tensor_to_check_dtype == np.float32: return tensor._get_float_element(i) else: return tensor._get_double_element(i) def __set_elem__(tensor, i, e): if tensor_to_check_dtype == np.float16: numpy_tensor = np.array(tensor).astype(np.float16) shape = numpy_tensor.shape numpy_tensor = numpy_tensor.flatten() numpy_tensor[i] = e numpy_tensor = numpy_tensor.reshape(shape).view(np.uint16) tensor.set(numpy_tensor, place) elif tensor_to_check_dtype == np.float32: tensor._set_float_element(i, e) else: tensor._set_double_element(i, e) # we only compute gradient of one element each time. # we use a for loop to compute the gradient of every element. for i in six.moves.xrange(tensor_size): if in_place: set_input(scope, op, inputs, place) # get one input element throw it's index i. origin = __get_elem__(tensor_to_check, i) # add delta to it, run op and then get the sum of the result tensor. x_pos = origin + delta __set_elem__(tensor_to_check, i, x_pos) y_pos = get_output() if in_place: set_input(scope, op, inputs, place) x_neg = origin - delta __set_elem__(tensor_to_check, i, x_neg) y_neg = get_output() __set_elem__(tensor_to_check, i, origin) gradient_flat[i] = (y_pos - y_neg) / delta / 2 return gradient_flat.reshape(tensor_to_check.shape()) class OpTest(unittest.TestCase): @classmethod def setUpClass(cls): '''Fix random seeds to remove randomness from tests''' cls._np_rand_state = np.random.get_state() cls._py_rand_state = random.getstate() cls.call_once = False cls.dtype = "float32" cls.outputs = {} np.random.seed(123) random.seed(124) @classmethod def tearDownClass(cls): """Restore random seeds""" np.random.set_state(cls._np_rand_state) random.setstate(cls._py_rand_state) def try_call_once(self, data_type): if not self.call_once: self.call_once = True self.dtype = data_type # See the comment of np_dtype_to_fluid_dtype # If the input type is uint16, we assume use float16 # for lodtensor dtype. if self.dtype == np.uint16: self.dtype == np.float16 def infer_dtype_from_inputs_outputs(self, inputs, outputs): def infer_dtype(numpy_dict): assert isinstance( numpy_dict, dict), "self.inputs, self.outputs must be numpy_dict" for var_name, var_value in six.iteritems(numpy_dict): if isinstance(var_value, (np.ndarray, np.generic)): self.try_call_once(var_value.dtype) elif isinstance(var_value, (list, tuple)): # the case of self.inputs = {"X": [("x0", x0), ("x1", x1), ("x2", x2)]} if len(var_value) > 1 and isinstance(var_value[1], ( np.ndarray, np.generic)): instance = var_value[1] self.try_call_once(instance[1].dtype) else: self.try_call_once("float32") infer_dtype(inputs) infer_dtype(outputs) def feed_var(self, input_vars, place): feed_map = {} for var_name in input_vars: if isinstance(input_vars[var_name], list): for name, np_value in self.inputs[var_name]: tensor = core.LoDTensor() if isinstance(np_value, tuple): tensor.set( OpTest.np_value_to_fluid_value(np_value[0]), place) tensor.set_recursive_sequence_lengths(np_value[1]) else: tensor.set( OpTest.np_value_to_fluid_value(np_value), place) feed_map[name] = tensor else: tensor = core.LoDTensor() if isinstance(self.inputs[var_name], tuple): tensor.set( OpTest.np_value_to_fluid_value(self.inputs[var_name][ 0]), place) tensor.set_recursive_sequence_lengths(self.inputs[var_name][ 1]) else: tensor.set( OpTest.np_value_to_fluid_value(self.inputs[var_name]), place) feed_map[var_name] = tensor return feed_map def _append_ops(self, block): op_proto = OpProtoHolder.instance().get_op_proto(self.op_type) "infer datatype from inputs and outputs for this test case" self.infer_dtype_from_inputs_outputs(self.inputs, self.outputs) inputs = append_input_output(block, op_proto, self.inputs, True, self.dtype) outputs = append_input_output(block, op_proto, self.outputs, False, self.dtype) if hasattr(self, "cache_name_list"): for name in self.cache_name_list: inputs[name] = block.create_var( name=name, persistable=True, type=core.VarDesc.VarType.RAW, stop_gradient=True) op = block.append_op( type=self.op_type, inputs=inputs, outputs=outputs, attrs=self.attrs if hasattr(self, "attrs") else dict()) # infer variable type and infer shape in compile-time op.desc.infer_var_type(block.desc) op.desc.infer_shape(block.desc) def _get_io_vars(self, block, numpy_inputs): inputs = {} for name, value in six.iteritems(numpy_inputs): if isinstance(value, list): var_list = [ block.var(sub_name) for sub_name, sub_value in value ] inputs[name] = var_list else: inputs[name] = block.var(name) return inputs def _get_inputs(self, block): return self._get_io_vars(block, self.inputs) def _get_outputs(self, block): return self._get_io_vars(block, self.outputs) def calc_output(self, place): outs, _ = self._calc_output(place) return outs def _create_var_from_numpy(self, value): if isinstance(value, tuple): data = value[0] lod = value[1] v = fluid.imperative.base.to_variable(value=data) v._ivar.value().get_tensor().set_recursive_sequence_lengths(lod) return v else: return fluid.imperative.base.to_variable(value) def _calc_imperative_output(self, place, parallel=False, no_check_set=None): with fluid.imperative.base.guard(place=place): block = fluid.default_main_program().global_block() # prepare input variable inputs = defaultdict(list) for name, np_value in six.iteritems(self.inputs): if not isinstance(np_value, list): np_value = [np_value] for i in range(len(np_value)): inputs[name].append( self._create_var_from_numpy(np_value[i])) # prepare output variable outputs = defaultdict(list) for name, np_value in six.iteritems(self.outputs): if not isinstance(np_value, list): np_value = [np_value] for i in range(len(np_value)): value = np_value[i] if isinstance(value, tuple): v = block.create_var( name="%s_out%d" % (name, i), dtype=value[0].dtype, type=core.VarDesc.VarType.LOD_TENSOR, persistable=False, stop_gradient=False) v._ivar.value().get_tensor( ).set_recursive_sequence_lengths(value[1]) else: v = block.create_var( name="%s_out%d" % (name, i), dtype=value.dtype, type=core.VarDesc.VarType.LOD_TENSOR, persistable=False, stop_gradient=False) outputs[name].append(v) block.append_op( type=self.op_type, inputs=inputs, outputs=outputs, attrs=self.attrs) return outputs def _calc_output(self, place, parallel=False, no_check_set=None): program = Program() block = program.global_block() self._append_ops(block) inputs = self._get_inputs(block) outputs = self._get_outputs(block) feed_map = self.feed_var(inputs, place) if parallel: use_cuda = False if isinstance(place, fluid.CUDAPlace(0)): use_cuda = True executor = fluid.ParallelExecutor( use_cuda=use_cuda, loss_name=loss.name, main_program=program) else: executor = Executor(place) fetch_list = getattr(self, "fetch_list", []) # if the fetch_list is customized by user, we use it directly. # if not, fill the fetch_list by the user configured outputs in test. if len(fetch_list) == 0: for var_name, var in six.iteritems(outputs): if no_check_set is not None and var_name in no_check_set: continue if isinstance(var, list): for v in var: fetch_list.append(v) else: fetch_list.append(var) # if the fetch_list still empty, fill the fetch_list by the operator output. if len(fetch_list) == 0: for out_name, out_dup in Operator.get_op_outputs(self.op_type): fetch_list.append(str(out_name)) # fetch_list = map(block.var, fetch_list) if not isinstance(fetch_list[0], fluid.framework.Variable): fetch_list = list(map(block.var, fetch_list)) outs = executor.run(program, feed=feed_map, fetch_list=fetch_list, return_numpy=False) return outs, fetch_list def check_output_with_place(self, place, atol, no_check_set=None, equal_nan=False, check_imperative=False): if check_imperative: imperative_outs = self._calc_imperative_output( place, no_check_set=no_check_set) outs, fetch_list = self._calc_output(place, no_check_set=no_check_set) for out_name, out_dup in Operator.get_op_outputs(self.op_type): if out_name not in self.outputs: continue if no_check_set is not None and out_name in no_check_set: continue def find_actual(target_name, fetch_list): found = [ i for i, var in enumerate(fetch_list) if var.name == target_name ] self.assertTrue( len(found) == 1, "Found {} {}".format( len(found), target_name)) return found[0] if out_dup: sub_out = self.outputs[out_name] if not isinstance(sub_out, list): raise AssertionError("sub_out type %s is not list", type(sub_out)) for item in sub_out: sub_out_name, expect = item[0], item[1] if check_imperative: imperative_actual = imperative_outs[sub_out_name][0] imperative_actual_t = np.array( imperative_actual._ivar.value().get_tensor()) idx = find_actual(sub_out_name, fetch_list) actual = outs[idx] actual_t = np.array(actual) expect_t = expect[0] \ if isinstance(expect, tuple) else expect self.assertTrue( np.allclose( actual_t, expect_t, atol=atol, equal_nan=equal_nan), "Output (" + sub_out_name + ") has diff at " + str(place)) if check_imperative: self.assertTrue( np.allclose( imperative_actual_t, expect_t, atol=atol, equal_nan=equal_nan), "Output (" + sub_out_name + ") has diff at " + str(place) + " in imperative mode") if isinstance(expect, tuple): self.assertListEqual( actual.recursive_sequence_lengths(), expect[1], "Output (" + sub_out_name + ") has different lod at " + str(place)) if check_imperative: self.assertListEqual( imperative_actual._ivar.value().get_tensor() .recursive_sequence_lengths(), expect[1], "Output (" + out_name + ") has different lod at " + str(place) + " in imperative mode") else: if check_imperative: imperative_actual = imperative_outs[out_name][0] imperative_actual_t = np.array( imperative_actual._ivar.value().get_tensor()) idx = find_actual(out_name, fetch_list) actual = outs[idx] actual_t = np.array(actual) expect = self.outputs[out_name] expect_t = expect[0] if isinstance(expect, tuple) else expect self.assertTrue( np.allclose( actual_t, expect_t, atol=atol, equal_nan=equal_nan), "Output (" + out_name + ") has diff at " + str(place) + "\nExpect " + str(expect_t) + "\n" + "But Got" + str(actual_t) + " in class " + self.__class__.__name__) if check_imperative: self.assertTrue( np.allclose( imperative_actual_t, expect_t, atol=atol, equal_nan=equal_nan), "Output (" + out_name + ") has diff at " + str(place) + "\nExpect " + str(expect_t) + "\n" + "But Got" + str(imperative_actual_t) + " in class " + self.__class__.__name__) if isinstance(expect, tuple): self.assertListEqual(actual.recursive_sequence_lengths(), expect[1], "Output (" + out_name + ") has different lod at " + str(place)) if check_imperative: self.assertListEqual( imperative_actual._ivar.value().get_tensor() .recursive_sequence_lengths(), expect[1], "Output (" + out_name + ") has different lod at " + str(place) + " in imperative mode") def _get_places(self): if self.dtype == np.float16: if core.is_compiled_with_cuda() and core.op_support_gpu( self.op_type): place = core.CUDAPlace(0) if core.is_float16_supported(place): return [place] else: return [] else: return [] places = [fluid.CPUPlace()] cpu_only = self._cpu_only if hasattr(self, '_cpu_only') else False use_ngraph = bool(os.getenv("FLAGS_use_ngraph", False)) if use_ngraph: cpu_only = True if core.is_compiled_with_cuda() and core.op_support_gpu(self.op_type)\ and not cpu_only: places.append(core.CUDAPlace(0)) return places def check_output(self, atol=1e-5, no_check_set=None, equal_nan=False, check_imperative=False): places = self._get_places() for place in places: self.check_output_with_place(place, atol, no_check_set, equal_nan, check_imperative) def check_output_customized(self, checker): places = self._get_places() for place in places: outs = self.calc_output(place) outs = [np.array(out) for out in outs] outs.sort(key=len) checker(outs) def _assert_is_close(self, numeric_grads, analytic_grads, names, max_relative_error, msg_prefix): for a, b, name in six.moves.zip(numeric_grads, analytic_grads, names): abs_a = np.abs(a) abs_a[abs_a < 1e-3] = 1 diff_mat = np.abs(a - b) / abs_a max_diff = np.max(diff_mat) def err_msg(): offset = np.argmax(diff_mat > max_relative_error) return ("%s Variable %s max gradient diff %f over limit %f, " "the first error element is %d, expected %f, but got %f" ) % (msg_prefix, name, max_diff, max_relative_error, offset, a.flatten()[offset], b.flatten()[offset]) self.assertLessEqual(max_diff, max_relative_error, err_msg()) def check_grad(self, inputs_to_check, output_names, no_grad_set=None, numeric_grad_delta=0.005, in_place=False, max_relative_error=0.005, user_defined_grads=None): places = self._get_places() for place in places: self.check_grad_with_place(place, inputs_to_check, output_names, no_grad_set, numeric_grad_delta, in_place, max_relative_error, user_defined_grads) def check_grad_with_place(self, place, inputs_to_check, output_names, no_grad_set=None, numeric_grad_delta=0.005, in_place=False, max_relative_error=0.005, user_defined_grads=None): self.scope = core.Scope() op_inputs = self.inputs if hasattr(self, "inputs") else dict() op_outputs = self.outputs if hasattr(self, "outputs") else dict() op_attrs = self.attrs if hasattr(self, "attrs") else dict() cache_list = None if hasattr(self, "cache_name_list"): cache_list = self.cache_name_list self.op = create_op( self.scope, self.op_type, op_inputs, op_outputs, op_attrs, cache_list=cache_list) if no_grad_set is None: no_grad_set = set() if not type(output_names) is list: output_names = [output_names] numeric_grads = user_defined_grads or [ get_numeric_gradient( place, self.scope, self.op, self.inputs, input_to_check, output_names, delta=numeric_grad_delta, in_place=in_place) for input_to_check in inputs_to_check ] analytic_grads = self._get_gradient(inputs_to_check, place, output_names, no_grad_set) self._assert_is_close(numeric_grads, analytic_grads, inputs_to_check, max_relative_error, "Gradient Check On %s" % str(place)) @staticmethod def _numpy_to_lod_tensor(np_value, lod, place): tensor = core.LoDTensor() tensor.set(np_value, place) if lod is not None: tensor.set_recursive_sequence_lengths(lod) return tensor @staticmethod def np_dtype_to_fluid_dtype(input): """Change the dtype of float16 numpy array numpy float16 is binded to paddle::platform::float16 in tensor_py.h via the help of uint16 data type since the internal memory representation of float16 is uint16_t in paddle and np.uint16 in numpy, which are themselves binded together by pybind. Args: input: input numpy array Returns: 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 @staticmethod def fluid_dtype_to_np_dtype(self, dtype): """ See above, convert the dtype to normal type. """ if dtype == np.uint16: dtype = np.float16 return dtype @staticmethod def np_value_to_fluid_value(input): if input.dtype == np.float16: input = input.view(np.uint16) return input def _get_gradient(self, input_to_check, place, output_names, no_grad_set, parallel=False): prog = Program() block = prog.global_block() self._append_ops(block) loss = append_loss_ops(block, output_names) param_grad_list = append_backward( loss=loss, parameter_list=input_to_check, no_grad_set=no_grad_set) inputs = self._get_inputs(block) feed_dict = self.feed_var(inputs, place) fetch_list = [g for p, g in param_grad_list] if parallel: use_cuda = False if isinstance(place, fluid.CUDAPlace(0)): use_cuda = True executor = fluid.ParallelExecutor( use_cuda=use_cuda, loss_name=loss.name, main_program=prog) else: executor = Executor(place) return list( map(np.array, executor.run(prog, feed_dict, fetch_list, return_numpy=False)))