# 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 warnings 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) return op 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.dygraph.base.to_variable(value=data) v._ivar.value().get_tensor().set_recursive_sequence_lengths(lod) return v else: return fluid.dygraph.base.to_variable(value) def _calc_dygraph_output(self, place, parallel=False, no_check_set=None): with fluid.dygraph.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, loss=None, enable_inplace=None, for_inplace_test=None): program = Program() block = program.global_block() op = self._append_ops(block) inputs = self._get_inputs(block) outputs = self._get_outputs(block) feed_map = self.feed_var(inputs, place) if for_inplace_test: # Some variables' tensors hold no buffer (tensor's _holder is NULL), like XShape in reshape2 op, # and the shapes of those variables contain 0 (eg. Xshape.shape = [0, 2, 5]). # Set persistable for those variables in order to get them from global_scope for inplace grad test directly other than feed them, # since feed op calls check_memory_size() which fails when tensor's holder_ is NULL. for out_name in op.output_arg_names: var = block.var(out_name) if 0 in var.shape: var.persistable = True original_program = program if parallel: use_cuda = False if isinstance(place, fluid.CUDAPlace): use_cuda = True compiled_prog = fluid.CompiledProgram(program).with_data_parallel( loss_name=loss.name if loss else None, places=place) program = compiled_prog 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.name) else: fetch_list.append(var.name) # 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)) if enable_inplace is not None: build_strategy = fluid.BuildStrategy() build_strategy.enable_inplace = enable_inplace compiled_prog = fluid.CompiledProgram(program).with_data_parallel( build_strategy=build_strategy, places=place) program = compiled_prog executor = Executor(place) outs = executor.run(program, feed=feed_map, fetch_list=fetch_list, return_numpy=False) if for_inplace_test: return outs, fetch_list, feed_map, original_program, op.desc else: return outs, fetch_list def _compare_expect_and_actual_outputs(self, place, fetch_list, expect_outs, actual_outs, inplace_atol=None): """Compare expect outs and actual outs of an tested op. Args: place (CPUPlace | CUDAPlace): The place where the op runs. fetch_list (list): The outputs of tested op. expect_outs (list): The expect outs of tested op. actual_outs (list): The actual outs of tested op. inplace_atol (float): The tolerable error, only set when tested op doesn't ensure computational consistency, like group_norm op. Returns: None. """ # compare expect_outs and actual_outs for i, name in enumerate(fetch_list): if inplace_atol is not None: self.assertTrue( np.allclose( np.array(expect_outs[i]), np.array(actual_outs[i]), atol=inplace_atol), "Output (" + name + ") has diff at " + str(place) + " when using and not using inplace" + "\nExpect " + str(expect_outs[i]) + "\n" + "But Got" + str(actual_outs[i]) + " in class " + self.__class__.__name__) else: self.assertTrue( np.array_equal( np.array(expect_outs[i]), np.array(actual_outs[i])), "Output (" + name + ") has diff at " + str(place) + " when using and not using inplace" + "\nExpect " + str(expect_outs[i]) + "\n" + "But Got" + str(actual_outs[i]) + " in class " + self.__class__.__name__ + '\n') def _construct_grad_program_from_forward(self, fwd_program, grad_op_desc, op_grad_to_var): """Generate grad_program which contains the grad_op. Args: fwd_program (tuple): The program that contains grad_op_desc's corresponding forward op. grad_op_desc (OpDesc): The OpDesc of grad op. op_grad_to_var (dict): The relation of variables in grad op and its forward op. Returns: grad_program (program): The program which contains the grad_op. """ grad_program = Program() grad_block = grad_program.global_block() new_op_desc = grad_block.desc.append_op() new_op_desc.copy_from(grad_op_desc) grad_program._sync_with_cpp() # Create grad vars based on fwd vars (shape and dtype) for arg in grad_op_desc.input_arg_names( ) + grad_op_desc.output_arg_names(): fwd_var_name = op_grad_to_var.get(arg, None) if fwd_var_name is None: fwd_var_name = arg fwd_var = fwd_program.global_block().vars.get(fwd_var_name) assert fwd_var is not None, "{} cannot be found".format( fwd_var_name) grad_var = grad_block.create_var( name=arg, dtype=fwd_var.dtype, shape=fwd_var.shape, type=fwd_var.type, persistable=False) # Some variables' tensors hold no buffer (tensor's _holder is NULL), like XShape in reshape2 op, # and the shapes of those variables contain 0 (eg. Xshape.shape = [0, 2, 5]). # Set persistable for those variables in order to get them from global_scope for inplace grad test directly other than feed them, # since feed op calls check_memory_size() which fails when tensor's holder_ is NULL. if 0 in grad_var.shape: grad_var.persistable = True grad_program._sync_with_cpp() return grad_program def _construct_grad_feed_map_from_forward(self, place, fwd_res, grad_op_desc, op_grad_to_var): """Generate grad_feed_map for grad_program. since we don`t really check gradient accuracy, but check the consistency when using and not using inplace, we use fwd outs (also inputs sometimes) to construct grad inputs. Args: place (CPUPlace | CUDAPlace): The place where the op runs. fwd_res (tuple): The outputs of its forward op, in the same form as returns of _calc_outputs() when for_inplace_test is True. i.e., tuple(fwd_outs, fwd_fetch_list, fwd_feed_map, fwd_program, fwd_op_desc) grad_op_desc (OpDesc): The OpDesc of grad op. op_grad_to_var (dict): The relation of variables in grad op and its fwd_op. Returns: grad_feed_map (dict): The feed_map of grad_op. """ fwd_outs, fwd_fetch_list, fwd_feed_map, fwd_program, fwd_op_desc = fwd_res p = core.Place() p.set_place(place) grad_feed_map = {} for arg in grad_op_desc.input_arg_names(): if arg in fwd_feed_map.keys(): grad_feed_map[arg] = fwd_feed_map[arg]._copy(p) else: fwd_var_name = op_grad_to_var.get(arg, None) if fwd_var_name is None: fwd_var_name = arg for i, out_name in enumerate(fwd_fetch_list): if out_name == fwd_var_name: # don't feed variables whose tensors hold no buffer (shape contains 0 like shape = [0,2,5] and holder_ is NULL), like XShape in reshape2 op. # get them from global_scope directly since we have set them persistable in fwd execution if 0 in fwd_program.global_block().var(out_name).shape: continue else: grad_feed_map[arg] = fwd_outs[i]._copy(p) return grad_feed_map def _get_need_run_ops(self, op_desc, fwd_op_desc=None): """Postorder traversal of the 'grad' tree to get all ops that need to run during inplace test. An op needs to run druing inplace check if, (1) it has infer_inplace, (2) it has infer_inplace in its grad descendants. (since we need its outputs as to construct its grad's inputs) Args: op_desc (OpDesc): The op_desc of current op. fwd_op_desc (OpDesc): The op_desc of current op's forward op, None if current op has no forward op. Eg. relu's fwd_op is None, relu_grad's fwd_op is relu, relu_grad_grad's fwd_op is relu_grad, etc. Returns: need_run_ops (list[(op_desc, fwd_op_desc)]): The ops that need to run during inplace test. """ need_run_ops = [] visited_ops = [] def _dfs_grad_op(op_desc, fwd_op_desc=None): visited_ops.append(op_desc.type()) has_infer_inplace = fluid.core.has_infer_inplace(op_desc.type()) has_grad_op_maker = fluid.core.has_grad_op_maker(op_desc.type()) has_infer_inplace_in_grad_descendants = False if not has_grad_op_maker: has_infer_inplace_in_descendants = False else: # get grad_op_desc grad_op_desc_list, op_grad_to_var = core.get_grad_op_desc( op_desc, set(), []) if not grad_op_desc_list: has_infer_inplace_in_grad_descendants = False else: for i, grad_op_desc in enumerate(grad_op_desc_list): if grad_op_desc.type( ) not in visited_ops and _dfs_grad_op( grad_op_desc, fwd_op_desc=op_desc): has_infer_inplace_in_grad_descendants = True if has_infer_inplace or has_infer_inplace_in_grad_descendants: need_run_ops.append((op_desc, fwd_op_desc)) return True else: return False _dfs_grad_op(op_desc, fwd_op_desc=fwd_op_desc) return need_run_ops def _check_forward_inplace(self, place, no_check_set=None, inplace_atol=None): """Chech the inplace correctness of given op (self.op_type). Run the op twice with same inputs, one enable inplace and another disable, compare their outputs. Args: place (CPUPlace | CUDAPlace): The place where the op runs. no_check_set (list): The names of outputs that needn't check, like XShape of reshape op. inplace_atol (float): The tolerable error, only set when op doesn't ensure computational consistency, like group_norm op. Returns: expect_res (tuple(outs, fetch_list, feed_map, program, op_desc)): The results of given op. We return this to construct grad_program and grad_feed_map for grad inplace check. """ # _calc_output() returns in the form tuple(outs, fetch_list, feed_map, program, op_desc) when for_inplace_test=True. expect_res = self._calc_output( place, no_check_set=no_check_set, enable_inplace=False, for_inplace_test=True) actual_res = self._calc_output( place, no_check_set=no_check_set, enable_inplace=True, for_inplace_test=True) # compare expect_outs and actual_outs self._compare_expect_and_actual_outputs( place, expect_res[1], expect_res[0], actual_res[0], inplace_atol=inplace_atol) return expect_res def _calc_grad_output(self, place, fwd_res, grad_op_desc, enable_inplace=None): """Calculate grad_output for given grad_op_desc. since we don`t really check gradient accuracy, but check the consistency when using and not using inplace, we use fwd outs (also inputs sometimes) to construct grad inputs. Args: place (CPUPlace | CUDAPlace): The place where the op runs. fwd_res (tuple): The outputs of its forward op, in the same form as returns of _calc_outputs() when for_inplace_test is True. i.e., tuple(fwd_outs, fwd_fetch_list, fwd_feed_map, fwd_program, fwd_op_desc). grad_op_desc (OpDesc): The OpDesc of grad op. enable_inplace (bool): Enable inplace or not. Returns: res (tuple(outs, fetch_list, feed_map, program, op_desc)): The results of given grad_op_desc. """ fwd_outs, fwd_fetch_list, fwd_feed_map, fwd_program, fwd_op_desc = fwd_res grad_op_desc_list, op_grad_to_var = core.get_grad_op_desc(fwd_op_desc, set(), []) grad_program = self._construct_grad_program_from_forward( fwd_program, grad_op_desc, op_grad_to_var) grad_feed_map = self._construct_grad_feed_map_from_forward( place, fwd_res, grad_op_desc, op_grad_to_var) grad_fetch_list = grad_op_desc.output_arg_names() exe = Executor(place) program = grad_program if enable_inplace is not None: build_strategy = fluid.BuildStrategy() build_strategy.enable_inplace = enable_inplace compiled_program = fluid.CompiledProgram( grad_program).with_data_parallel( loss_name="", build_strategy=build_strategy, places=place) program = compiled_program outs = exe.run(program, feed=grad_feed_map, fetch_list=grad_fetch_list, return_numpy=False) return outs, grad_fetch_list, grad_feed_map, grad_program, grad_op_desc def _check_grad_inplace(self, place, fwd_res, grad_op_desc, inplace_atol=None): """Chech the inplace correctness of given grad_op_desc. Run the grad op twice with same inputs, one enable inplace and another disable, compare their outputs. It works like _check_forward_inplace, but the way to construct program and feed_map differs. So we define a new function for grad, grad_grad, etc. Args: place (CPUPlace | CUDAPlace): The place where the op runs. fwd_res (tuple): The outputs of its forward op, in the same form as returns of _calc_outputs() when for_inplace_test is True. i.e., tuple(fwd_outs, fwd_fetch_list, fwd_feed_map, fwd_program, fwd_op_desc). grad_op_desc (OpDesc): The OpDesc of grad op. inplace_atol (float): The tolerable error, only set when op doesn't ensure computational consistency, like group_norm op. Returns: expect_res (tuple(outs, fetch_list, feed_map, program, op_desc)): The results of given op. We return this to construct grad_program and grad_feed_map for grad inplace check. """ expect_res = self._calc_grad_output( place, fwd_res, grad_op_desc, enable_inplace=False) actual_res = self._calc_grad_output( place, fwd_res, grad_op_desc, enable_inplace=True) self._compare_expect_and_actual_outputs( place, expect_res[1], expect_res[0], actual_res[0], inplace_atol=inplace_atol) return expect_res def check_inplace_output_with_place(self, place, no_check_set=None, inplace_atol=None): """Chech the inplace correctness of given op, its grad op, its grad_grad op, etc. (1) Get all ops need to run. (see conditions in _get_need_run_ops()) (2) Run op in need_run_ops, and do inplace check if it has infer_inplace. Args: place (CPUPlace | CUDAPlace): The place where the op runs. no_check_set (list): The names of outputs that needn't check, like XShape of reshape op. inplace_atol (float): The tolerable error, only set when op doesn't ensure computational consistency, like group_norm op. Returns: None """ has_infer_inplace = fluid.core.has_infer_inplace(self.op_type) has_grad_op_maker = fluid.core.has_grad_op_maker(self.op_type) fwd_res = self._calc_output( place, no_check_set=no_check_set, for_inplace_test=True) op_desc = fwd_res[4] need_run_ops = self._get_need_run_ops(op_desc) res = {} for op_desc, father_op_desc in reversed(need_run_ops): # The first one is the forward op has_infer_inplace = fluid.core.has_infer_inplace(op_desc.type()) if op_desc.type() == self.op_type: if has_infer_inplace: res[op_desc] = self._check_forward_inplace( place, no_check_set=no_check_set, inplace_atol=inplace_atol) else: res[op_desc] = self._calc_output( place, no_check_set=no_check_set, for_inplace_test=True) else: # TODO(zhiqiu): enhance inplace_grad test for ops (sum and activation) using mkldnn/ngraph # skip op that use_mkldnn and use_ngraph currently flags_use_mkldnn = fluid.core.get_flags_use_mkldnn() attrs_use_mkldnn = hasattr( self, 'attrs') and bool(self.attrs.get('use_mkldnn', False)) if flags_use_mkldnn or attrs_use_mkldnn: warnings.warn( "check inplace_grad for ops using mkldnn is not supported" ) continue use_ngraph = fluid.core.is_compiled_with_ngraph( ) and fluid.core.get_flags_use_ngraph() if use_ngraph: warnings.warn( "check inplace_grad for ops using ngraph is not supported" ) continue if has_infer_inplace: fwd_res = res[father_op_desc] res[op_desc] = self._check_grad_inplace( place, fwd_res, op_desc, inplace_atol=inplace_atol) else: res[op_desc] = self._calc_grad_output(place, fwd_res, op_desc) def check_output_with_place(self, place, atol, no_check_set=None, equal_nan=False, check_dygraph=False, inplace_atol=None): if check_dygraph: dygraph_outs = self._calc_dygraph_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_name 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_dygraph: imperative_actual = dygraph_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_dygraph: 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 dygraph 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_dygraph: self.assertListEqual( imperative_actual._ivar.value().get_tensor() .recursive_sequence_lengths(), expect[1], "Output (" + out_name + ") has different lod at " + str(place) + " in dygraph mode") else: if check_dygraph: imperative_actual = dygraph_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_dygraph: 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_dygraph: self.assertListEqual( imperative_actual._ivar.value().get_tensor() .recursive_sequence_lengths(), expect[1], "Output (" + out_name + ") has different lod at " + str(place) + " in dygraph mode") # inplace_atol only used when op doesn't ensure computational consistency if inplace_atol is not None: warnings.warn( "By default, inplace_atol should not be set, please check it") # Check inplace for given op, its grad op, its grad_grad op, etc. # No effect on original OpTest self.check_inplace_output_with_place( place, no_check_set=no_check_set, inplace_atol=inplace_atol) 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 = fluid.core.is_compiled_with_ngraph( ) and fluid.core.get_flags_use_ngraph() 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_dygraph=False, inplace_atol=None): places = self._get_places() for place in places: self.check_output_with_place(place, atol, no_check_set, equal_nan, check_dygraph) 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): use_cuda = True compiled_prog = fluid.CompiledProgram(prog).with_data_parallel( loss_name=loss.name, places=place) prog = compiled_prog executor = fluid.Executor(place) return list( map(np.array, executor.run(prog, feed_dict, fetch_list, return_numpy=False)))