# 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 struct import time import itertools import collections from collections import defaultdict from copy import copy import paddle import paddle.fluid as fluid from paddle.fluid.framework import _dygraph_tracer import paddle.fluid.core as core from paddle.fluid.framework import _in_eager_mode from paddle.fluid.framework import _test_eager_guard 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, _current_expected_place from paddle.fluid.tests.unittests.testsuite import ( create_op, set_input, append_input_output, append_loss_ops, ) from paddle.fluid import unique_name from paddle.fluid.tests.unittests.white_list import ( op_accuracy_white_list, check_shape_white_list, compile_vs_runtime_white_list, no_check_set_white_list, op_threshold_white_list, no_grad_set_white_list, ) from paddle.fluid.dygraph.dygraph_to_static.utils import parse_arg_and_kwargs def check_out_dtype(api_fn, in_specs, expect_dtypes, target_index=0, **configs): """ Determines whether dtype of output tensor is as expected. Args: api_fn(callable): paddle api function in_specs(list[tuple]): list of shape and dtype information for constructing input tensor of api_fn, such as [(shape, dtype), (shape, dtype)]. expected_dtype(list[str]): expected dtype of output tensor. target_index(int): indicate which one from in_specs to infer the dtype of output. config(dict): other arguments of paddle api function Example: check_out_dtype(fluid.layers.pad_constant_like, [([2,3,2,3], 'float64'), ([1, 3, 1,3], )], ['float32', 'float64', 'int64'], target_index=1, pad_value=0.) """ paddle.enable_static() for i, expect_dtype in enumerate(expect_dtypes): with paddle.static.program_guard(paddle.static.Program()): input_t = [] for index, spec in enumerate(in_specs): if len(spec) == 1: shape = spec[0] dtype = expect_dtype if target_index == index else 'float32' elif len(spec) == 2: shape, dtype = spec else: raise ValueError( "Value of in_specs[{}] should contains two elements: [shape, dtype]". format(index)) input_t.append( paddle.static.data( name='data_%s' % index, shape=shape, dtype=dtype)) out = api_fn(*input_t, **configs) out_dtype = fluid.data_feeder.convert_dtype(out.dtype) if out_dtype != expect_dtype: raise ValueError( "Expected out.dtype is {}, but got {} from {}.".format( expect_dtype, out_dtype, api_fn.__name__)) def _set_use_system_allocator(value=None): USE_SYSTEM_ALLOCATOR_FLAG = "FLAGS_use_system_allocator" old_value = core.globals()[USE_SYSTEM_ALLOCATOR_FLAG] value = old_value if value is None else value core.globals()[USE_SYSTEM_ALLOCATOR_FLAG] = value return old_value 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) elif tensor_to_check_dtype == core.VarDesc.VarType.BF16: tensor_to_check_dtype = np.float32 elif tensor_to_check_dtype == core.VarDesc.VarType.COMPLEX64: tensor_to_check_dtype = np.complex64 elif tensor_to_check_dtype == core.VarDesc.VarType.COMPLEX128: tensor_tp_check_dtype = np.complex128 else: raise ValueError("Not supported data type " + str(tensor_to_check_dtype) + ", tensor name : " + str(input_to_check)) def get_output(): sum = [] op.run(scope, place) for output_name in output_names: output_numpy = np.array(scope.find_var(output_name).get_tensor()) # numpy.dtype does not have bfloat16, thus we use numpy.uint16 to # store bfloat16 data, and need to be converted to float to check # the floating precision. if tensor_to_check._dtype() == core.VarDesc.VarType.BF16: output_numpy = convert_uint16_to_float(output_numpy) sum.append(output_numpy.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() == core.VarDesc.VarType.BF16: numpy_tensor = np.array(tensor).astype(np.uint16) numpy_tensor = numpy_tensor.flatten() return struct.unpack('> 16 def convert_float_to_uint16(float_list, data_format="NCHW"): if data_format == "NHWC": float_list = np.transpose(float_list, [0, 3, 1, 2]) new_output = [] for x in np.nditer(float_list): new_output.append(np.uint16(copy_bits_from_float_to_uint16(x))) new_output = np.reshape(new_output, float_list.shape).view(np.uint16) if data_format == "NHWC": new_output = np.transpose(new_output, [0, 2, 3, 1]) return new_output def convert_uint16_to_float(in_list): in_list = np.asarray(in_list) out = np.vectorize( lambda x: struct.unpack(' 1 and is_np_data( sub_val_value[1]): # case 3 dtype_set.add(sub_val_value[1].dtype) elif len(sub_val_value) > 1 and isinstance(sub_val_value[1], (list, tuple)) \ and is_np_data(sub_val_value[1][0]): # case 4 dtype_set.add(sub_val_value[1][0].dtype) # infer dtype from inputs, and dtype means the precision of the test # collect dtype of all inputs input_dtype_set = set() infer_dtype(inputs, input_dtype_set) dtype_list = [ np.dtype(np.float64), np.dtype(np.float32), np.dtype(np.float16), np.dtype(np.int64), np.dtype(np.int32), np.dtype(np.uint16), np.dtype(np.int16), np.dtype(np.int8), np.dtype(np.uint8), np.dtype(np.bool) ] # check the dtype in dtype_list in order, select the first dtype that in dtype_set for dtype in dtype_list: if dtype in input_dtype_set: self.dtype = dtype break # save input dtype in class attr self.__class__.dtype = self.dtype # infer dtype of outputs output_dtype_set = set() infer_dtype(outputs, output_dtype_set) for dtype in dtype_list: if dtype in output_dtype_set: self.output_dtype = dtype break 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(np_value[0], place) tensor.set_recursive_sequence_lengths(np_value[1]) else: tensor.set(np_value, place) feed_map[name] = tensor else: tensor = core.LoDTensor() if isinstance(self.inputs[var_name], tuple): tensor.set(self.inputs[var_name][0], place) tensor.set_recursive_sequence_lengths(self.inputs[var_name][ 1]) else: tensor.set(self.inputs[var_name], place) feed_map[var_name] = tensor return feed_map def _append_ops(self, block): self.__class__.op_type = self.op_type # for ci check, please not delete it for now if self.is_mkldnn_op(): self.__class__.use_mkldnn = True if self.is_xpu_op(): self.__class__.use_xpu = True op_proto = OpProtoHolder.instance().get_op_proto(self.op_type) "infer datatype from inputs and outputs for this test case" if self.is_bfloat16_op(): self.dtype = np.uint16 self.__class__.dtype = self.dtype self.output_dtype = np.uint16 else: 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=copy(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.value().get_tensor().set_recursive_sequence_lengths(lod) return v else: return fluid.dygraph.base.to_variable(value) def get_sequence_batch_size_1_input(self, lod=None, shape=None): """Get LoD input data whose batch size is 1. All sequence related OP unittests should call this function to contain the case of batch size = 1. Args: lod (list[list of int], optional): Length-based LoD, length of lod[0] should be 1. Default: [[13]]. shape (list, optional): Shape of input, shape[0] should be equals to lod[0][0]. Default: [13, 23]. Returns: tuple (ndarray, lod) : LoD input data whose batch size is 1. """ if lod is None: lod = [[13]] if shape is None: shape = [13, 23] assert len(lod[0]) == 1 assert lod[0][0] == shape[0] x = np.random.uniform(0.1, 1, shape).astype('float32') return (x, lod) def lod_has_single_zero(self, lod): for i in range(len(lod) - 2): if lod[i] != 0 and lod[i + 1] == 0 and lod[i + 2] != 0: return True return False def lod_has_continuous_zero(self, lod): for i in range(len(lod) - 3): if lod[i] != 0 and lod[i + 1] == 0 and lod[i + 2] == 0 and lod[ i + 3] != 0: return True return False def get_sequence_instance_size_0_input(self, lod=None, shape=None): """Get LoD input data whose instance size is 0. All sequence related OP unittests should call this function to contain the case of instance size is 0. Args: lod (list[list of int], optional): Length-based LoD, lod[0]'s size must at least eight, lod[0] must at least two zeros at the beginning and at least two zeros at the end, the middle position of lod[0] contains a single zero and multiple zero. Default: [[0, 0, 4, 0, 3, 0, 0, 5, 0, 0]]. shape (list, optional): Shape of input, shape[0] should be equals to lod[0][0]. Default: [13, 23]. Returns: tuple (ndarray, lod): LoD input data whose instance size is 0. """ if lod is None: lod = [[0, 0, 4, 0, 3, 0, 0, 5, 0, 0]] if shape is None: shape = [12, 10] assert len(lod[0]) >= 8 assert lod[0][0] == 0 and lod[0][1] == 0 and lod[0][-1] == 0 and lod[0][ -2] == 0 assert self.lod_has_single_zero(lod[0]) is True assert self.lod_has_continuous_zero(lod[0]) is True assert sum(lod[0]) == shape[0] x = np.random.uniform(0.1, 1, shape).astype('float32') return (x, lod) def append_input_output_for_dygraph(self, op_proto, np_list, is_input, if_return_inputs_grad_dict, block): def create_var(np_value, name, is_input, if_return_inputs_grad_dict): np_value_temp = np_value has_lod = False lod_temp = None if isinstance(np_value, tuple): np_value_temp = np_value[0] has_lod = True lod_temp = np_value[1] if is_input: v = self._create_var_from_numpy(np_value_temp) if if_return_inputs_grad_dict: v.stop_gradient = False if _in_eager_mode(): v.retain_grads() if has_lod: v.value().get_tensor().set_recursive_sequence_lengths( lod_temp) else: v = block.create_var( name=name, dtype=np_value_temp.dtype, type=core.VarDesc.VarType.LOD_TENSOR, persistable=False, stop_gradient=False) return v # prepare variable for input or output var_dict = defaultdict(list) if if_return_inputs_grad_dict: inputs_grad_dict = defaultdict() proto_list = op_proto.inputs if is_input else op_proto.outputs for var_proto in proto_list: name = var_proto.name if (name not in np_list) and var_proto.dispensable: continue if name not in np_list: assert var_proto.intermediate, "{} not found".format(name) v = block.create_var( dtype='float32', type=core.VarDesc.VarType.LOD_TENSOR) var_dict[name].append(v) if if_return_inputs_grad_dict: inputs_grad_dict[name] = v continue if var_proto.duplicable: assert isinstance( np_list[name], list), "Duplicable {} should be set as list".format(name) var_list = [] slot_name = name for (name, np_value) in np_list[name]: v = create_var(np_value, name, is_input, if_return_inputs_grad_dict) var_list.append(v) if if_return_inputs_grad_dict: inputs_grad_dict[name] = v var_dict[slot_name] = var_list else: nplist_value_temp = None name_temp = None if isinstance(np_list[name], list): nplist_value_temp = np_list[name][0] name_temp = name else: nplist_value_temp = np_list[name] name_temp = unique_name.generate("%s_out" % (name)) v = create_var(nplist_value_temp, name_temp, is_input, if_return_inputs_grad_dict) var_dict[name].append(v) if if_return_inputs_grad_dict: inputs_grad_dict[name] = v if if_return_inputs_grad_dict: return var_dict, inputs_grad_dict else: return var_dict def _check_api_outs_by_dygraph_outs(self, api_outs, dygraph_outs, place): """ for quick verify, here we take a simplest strategy: 1. we only check variable in api_outs. 2. we simply check the numpy (tensor) . 3. we set atol and rtol as 1e-5, because they are unrelated to dtype. """ for name in api_outs: np_api = np.array(api_outs[name]) np_dyg = np.array(dygraph_outs[name]) self.assertTrue( np.allclose( np_api, np_dyg, equal_nan=False), "Output (" + name + ") has diff at " + str(place) + "\nExpect " + str(np_dyg) + "\n" + "But Got" + str(np_api) + " in class " + self.__class__.__name__) def _calc_python_api_output(self, place): def prepare_python_api_arguments(api, op_proto_ins, op_proto_attrs, kernel_sig): """ map from `op proto inputs and attrs` to `api input list and api attrs dict` """ class Empty: pass def is_empty(a): return isinstance(a, Empty) def get_default(idx, all_params_number, defaults): related_idx = idx - all_params_number + len(defaults) assert related_idx >= 0, "%d-th arguments don't have default value" % idx return defaults[related_idx] def filter_by_name(x): names = set(['name', 'out', 'output']) if isinstance(x, list): return [i for i in x if i not in names] if isinstance(x, dict): return {k: v for k, v in x.items() if k not in names} assert False, "Only support list or dict." def to_defaults_list(params, defaults): return [defaults[p] for p in params if p in defaults] # NOTE(xiongkun): why don't use input arguments dicts ? # Because we don't know the python api name of each arguments. # using parse_arg_and_kwargs, we can get the all api information we need. api_params, api_defaults = [ filter_by_name(item) for item in parse_arg_and_kwargs(api) ] api_defaults = to_defaults_list(api_params, api_defaults) inputs_sig, attrs_sig, outputs_sig = kernel_sig inputs_and_attrs = inputs_sig + attrs_sig assert ( len(api_params) == len(inputs_and_attrs) ), "inputs and attrs length must equals to python api length. (May be output is in argument list?)" input_arguments = [op_proto_ins[name] for name in inputs_sig] + [ op_proto_attrs[name] if name in op_proto_attrs else Empty() for name in attrs_sig ] results = [] for idx, arg in enumerate(input_arguments): if is_empty(arg): results.append( get_default(idx, len(input_arguments), api_defaults)) else: results.append(arg) return results def construct_output_dict_by_kernel_sig(ret_tuple, output_sig): if not isinstance(ret_tuple, (tuple, list)): ret_tuple = [ret_tuple] assert len(output_sig) == len( ret_tuple), "expect %d outputs, but get %d outputs" % ( len(output_sig), len(ret_tuple)) return {a: b for a, b in zip(output_sig, ret_tuple)} def assumption_assert_and_transform(args, inp_num): """ transform inputs by the following rules: 1. [Tensor] -> Tensor 2. [Tensor, Tensor, ...] -> list of Tensors only support "X" is list of Tensor, currently don't support other structure like dict. """ for inp in args[:inp_num]: assert isinstance( inp, list ), "currently only support `X` is [Tensor], don't support other structure." args = [ inp[0] if len(inp) == 1 else inp for inp in args[:inp_num] ] + args[inp_num:] return args def cal_python_api(python_api, args, kernel_sig): inputs_sig, attrs_sig, outputs_sig = kernel_sig args = assumption_assert_and_transform(args, len(inputs_sig)) ret_tuple = python_api(*args) return construct_output_dict_by_kernel_sig(ret_tuple, outputs_sig) with fluid.dygraph.base.guard(place=place): block = fluid.default_main_program().global_block() op_proto = OpProtoHolder.instance().get_op_proto(self.op_type) # prepare input variable eager_tensor_inputs = self.append_input_output_for_dygraph( op_proto, self.inputs, True, False, block) # prepare output variable eager_tensor_outputs = self.append_input_output_for_dygraph( op_proto, self.outputs, False, False, block) # prepare attrbutes attrs_outputs = {} if hasattr(self, "attrs"): for attrs_name in self.attrs: if self.attrs[attrs_name] is not None: attrs_outputs[attrs_name] = self.attrs[attrs_name] kernel_sig = _dygraph_tracer()._get_kernel_signature( self.op_type, eager_tensor_inputs, eager_tensor_outputs, attrs_outputs) assert hasattr( self, "python_api" ), "Please set the `self.python_api` if you want to compare python api output." args = prepare_python_api_arguments( self.python_api, eager_tensor_inputs, attrs_outputs, kernel_sig) """ we directly return the cal_python_api value because the value is already tensor. """ return cal_python_api(self.python_api, args, kernel_sig) def _calc_dygraph_output(self, place, parallel=False, no_check_set=None): self.__class__.op_type = self.op_type # for ci check, please not delete it for now with fluid.dygraph.base.guard(place=place): block = fluid.default_main_program().global_block() op_proto = OpProtoHolder.instance().get_op_proto(self.op_type) # prepare input variable inputs = self.append_input_output_for_dygraph(op_proto, self.inputs, True, False, block) # prepare output variable outputs = self.append_input_output_for_dygraph( op_proto, self.outputs, False, False, block) # prepare attrbutes attrs_outputs = {} if hasattr(self, "attrs"): for attrs_name in self.attrs: if self.attrs[attrs_name] is not None: attrs_outputs[attrs_name] = self.attrs[attrs_name] block.append_op( type=self.op_type, inputs=inputs, outputs=outputs, attrs=attrs_outputs if hasattr(self, "attrs") else None) 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) self.op = op self.program = original_program 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): # Note(zhiqiu): inplace_atol should be only set when op doesn't ensure # computational consistency. # When inplace_atol is not None, the inplace check uses numpy.allclose # to check inplace result instead of numpy.array_equal. expect_out = np.array(expect_outs[i]) actual_out = np.array(actual_outs[i]) if inplace_atol is not None: self.assertTrue( np.allclose( expect_out, actual_out, atol=inplace_atol), "Output (" + name + ") has diff at " + str(place) + " when using and not using inplace" + "\nExpect " + str(expect_out) + "\n" + "But Got" + str(actual_out) + " in class " + self.__class__.__name__) else: self.assertTrue( np.array_equal(expect_out, actual_out), "Output (" + name + ") has diff at " + str(place) + " when using and not using inplace" + "\nExpect " + str(expect_out) + "\n" + "But Got" + str(actual_out) + " 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): """Check 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): """Check 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 = {} if hasattr(self, 'attrs') and bool(self.attrs.get('use_xpu', False)): return 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 # skip op that use_mkldnn currently flags_use_mkldnn = fluid.core.globals()["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 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=0, no_check_set=None, equal_nan=False, check_dygraph=True, inplace_atol=None, check_eager=False): self.infer_dtype_from_inputs_outputs(self.inputs, self.outputs) if self.dtype == np.float64 and \ self.op_type not in op_threshold_white_list.NEED_FIX_FP64_CHECK_OUTPUT_THRESHOLD_OP_LIST: atol = 0 if self.is_bfloat16_op(): if self.is_mkldnn_op(): check_dygraph = False check_eager = False if hasattr(self, 'force_fp32_output') and getattr( self, 'force_fp32_output'): atol = 1e-2 else: atol = 2 else: atol = 1e-1 if no_check_set is not None: if self.op_type not in no_check_set_white_list.no_check_set_white_list: raise AssertionError( "no_check_set of op %s must be set to None." % self.op_type) if check_dygraph: dygraph_outs = self._calc_dygraph_output( place, no_check_set=no_check_set) if check_eager: with _test_eager_guard(): eager_dygraph_outs = self._calc_dygraph_output( place, no_check_set=no_check_set) # we only check end2end api when check_eager=True if hasattr(self, "python_api"): api_outs = self._calc_python_api_output(place) self._check_api_outs_by_dygraph_outs(api_outs, dygraph_outs, place) 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_imperative_actual(target_name, dygraph_outs, place): with fluid.dygraph.base.guard(place=place): for name in dygraph_outs: if name == target_name: return dygraph_outs[name][0] var_list = dygraph_outs[name] for i, var in enumerate(var_list): if var.name == target_name: return dygraph_outs[name][i] self.assertTrue(False, "Found failed {} {}".format( dygraph_outs.keys(), target_name)) 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 = find_imperative_actual( sub_out_name, dygraph_outs, place) imperative_actual_t = np.array(imperative_actual.value() .get_tensor()) if check_eager: with _test_eager_guard(): eager_imperative_actual = find_imperative_actual( sub_out_name, eager_dygraph_outs, place) eager_imperative_actual_t = eager_imperative_actual.numpy( ) 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 check_eager: with _test_eager_guard(): self.assertTrue( np.allclose( eager_imperative_actual_t, expect_t, atol=atol, equal_nan=equal_nan), "Output (" + sub_out_name + ") has diff at " + str(place) + " in eager 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.value().get_tensor() .recursive_sequence_lengths(), expect[1], "Output (" + out_name + ") has different lod at " + str(place) + " in dygraph mode") if check_eager: with _test_eager_guard(): self.assertListEqual( eager_imperative_actual.value().get_tensor() .recursive_sequence_lengths(), expect[1], "Output (" + out_name + ") has different lod at " + str(place) + " in eager dygraph mode") else: if check_dygraph: imperative_actual = find_imperative_actual( out_name, dygraph_outs, place) imperative_actual_t = np.array(imperative_actual.value() .get_tensor()) if check_eager: with _test_eager_guard(): eager_imperative_actual = find_imperative_actual( out_name, eager_dygraph_outs, place) eager_imperative_actual_t = eager_imperative_actual.numpy( ) 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 # np.uint16 represents bfloat16 if actual_t.dtype == np.uint16 and expect_t.dtype in [ np.float32, np.float64 ]: actual_t = convert_uint16_to_float(actual_t) rtol = 1.e-2 else: rtol = 1.e-5 if expect_t.dtype == np.uint16 and actual_t.dtype == np.uint16: expect_t = convert_uint16_to_float(expect_t) actual_t = convert_uint16_to_float(actual_t) atol = max(atol, 0.03) # NOTE(zhiqiu): np.allclose([], [1.]) returns True # see details: https://stackoverflow.com/questions/38331703/why-does-numpys-broadcasting-sometimes-allow-comparing-arrays-of-different-leng if expect_t.size == 0: self.assertTrue(actual_t.size == 0) self.assertTrue( np.allclose( actual_t, expect_t, atol=atol, rtol=rtol, 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: if self.is_bfloat16_op(): if imperative_actual_t.dtype == np.uint16: imperative_actual_t = convert_uint16_to_float( imperative_actual_t) if expect_t.dtype == np.uint16: expect_t = convert_uint16_to_float(expect_t) if six.moves.reduce( lambda x, y: x * y, imperative_actual_t.shape, 1) == 0 and six.moves.reduce( lambda x, y: x * y, expect_t.shape, 1) == 0: pass else: self.assertTrue( np.allclose( imperative_actual_t, expect_t, atol=atol, rtol=rtol, 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 check_eager: with _test_eager_guard(): if self.is_bfloat16_op(): if eager_imperative_actual_t.dtype == np.uint16: eager_imperative_actual_t = convert_uint16_to_float( eager_imperative_actual_t) if expect_t.dtype == np.uint16: expect_t = convert_uint16_to_float(expect_t) if six.moves.reduce(lambda x, y: x * y, eager_imperative_actual_t.shape, 1) == 0 and six.moves.reduce( lambda x, y: x * y, expect_t.shape, 1) == 0: pass else: self.assertTrue( np.allclose( eager_imperative_actual_t, expect_t, atol=atol, rtol=rtol, equal_nan=equal_nan), "Output (" + out_name + ") has diff at " + str(place) + "\nExpect " + str(expect_t) + "\n" + "But Got" + str(eager_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.value().get_tensor() .recursive_sequence_lengths(), expect[1], "Output (" + out_name + ") has different lod at " + str(place) + " in eager dygraph mode") if check_eager: with _test_eager_guard(): self.assertListEqual( eager_imperative_actual.value().get_tensor() .recursive_sequence_lengths(), expect[1], "Output (" + out_name + ") has different lod at " + str(place) + " in eager dygraph mode") # Note(zhiqiu): inplace_atol should be only set when op doesn't ensure # computational consistency. # For example, group_norm uses AtomicAdd on CUDAPlace, which do not ensure # computation order when multiple threads write the same address. So the # result of group_norm is non-deterministic when datatype is float. # When inplace_atol is not None, the inplace check uses numpy.allclose # to check inplace result instead of numpy.array_equal. if inplace_atol is not None: warnings.warn( "inplace_atol should only be set when op doesn't ensure computational consistency, please check it!" ) # Check inplace for given op, its grad op, its grad_grad op, etc. # No effect on original OpTest # Currently not support ParallelExecutor on XPUPlace. if not paddle.is_compiled_with_xpu( ) and not paddle.is_compiled_with_npu( ) and not paddle.is_compiled_with_mlu(): self.check_inplace_output_with_place( place, no_check_set=no_check_set, inplace_atol=inplace_atol) if check_eager: return outs, dygraph_outs, eager_dygraph_outs, fetch_list elif check_dygraph: return outs, dygraph_outs, fetch_list else: return outs, fetch_list def check_compile_vs_runtime(self, fetch_list, fetch_outs): def find_fetch_index(target_name, fetch_list): found = [ i for i, var_name in enumerate(fetch_list) if var_name == target_name ] if len(found) == 0: return -1 else: self.assertTrue( len(found) == 1, "Found {} {}".format(len(found), target_name)) return found[0] for name in self.op.desc.output_names(): var_names = self.op.desc.output(name) for var_name in var_names: i = find_fetch_index(var_name, fetch_list) if i == -1: # The output is dispensiable or intermediate. break out = fetch_outs[i] if isinstance(out, core.LoDTensor): lod_level_runtime = len(out.lod()) else: if isinstance(out, core.LoDTensorArray): warnings.warn( "The check of LoDTensorArray's lod_level is not implemented now!" ) lod_level_runtime = 0 var = self.program.global_block().var(var_name) if var.type == core.VarDesc.VarType.LOD_TENSOR: lod_level_compile = var.lod_level else: lod_level_compile = 0 self.assertEqual( lod_level_compile, lod_level_runtime, "The lod_level of Output (" + name + ") is different between compile-time and runtime (" + str(lod_level_compile) + " vs " + str(lod_level_runtime) + ")") 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 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=True, inplace_atol=None, check_eager=False): self.__class__.op_type = self.op_type if self.is_mkldnn_op(): self.__class__.use_mkldnn = True if self.is_xpu_op(): self.__class__.use_xpu = True places = self._get_places() for place in places: res = self.check_output_with_place( place, atol, no_check_set, equal_nan, check_dygraph, inplace_atol, check_eager=check_eager) if check_eager: assert check_dygraph == True outs, dygraph_outs, eager_dygraph_outs, fetch_list = res elif check_dygraph: outs, dygraph_outs, fetch_list = res else: outs, fetch_list = res if self.op_type not in compile_vs_runtime_white_list.COMPILE_RUN_OP_WHITE_LIST: self.check_compile_vs_runtime(fetch_list, outs) def check_output_customized(self, checker, custom_place=None): places = self._get_places() if custom_place: places.append(custom_place) for place in places: outs = self.calc_output(place) outs = [np.array(out) for out in outs] outs.sort(key=len) checker(outs) def check_output_with_place_customized(self, checker, place): 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): # It asserts np.abs(a - b) / np.abs(a) < max_relative_error, in which # max_relative_error is 1e-7. According to the value of np.abs(a), we # change np.abs(a) to achieve dynamic threshold. For example, if # the value of np.abs(a) is between 1e-10 and 1e-8, we set np.abs(a)*=1e4. # Therefore, it asserts np.abs(a - b) / (np.abs(a)*1e4) < max_relative_error, # which is the same as np.abs(a - b) / np.abs(a) < max_relative_error*1e4. abs_a = np.abs(a) if self.dtype == np.float64 and \ self.op_type not in op_threshold_white_list.NEED_FIX_FP64_CHECK_GRAD_THRESHOLD_OP_LIST: abs_a[abs_a < 1e-10] = 1e-3 abs_a[np.logical_and(abs_a > 1e-10, abs_a <= 1e-8)] *= 1e4 abs_a[np.logical_and(abs_a > 1e-8, abs_a <= 1e-6)] *= 1e2 elif self.is_bfloat16_op(): abs_a[abs_a < 1e-2] = 1 else: 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 ("Operator %s error, %s variable %s (shape: %s, dtype: %s) max gradient diff %e over limit %e, " "the first error element is %d, expected %e, but got %e.") \ % (self.op_type, msg_prefix, name, str(a.shape), self.dtype, max_diff, max_relative_error, offset, a.flatten()[offset], b.flatten()[offset]) self.assertLessEqual(max_diff, max_relative_error, err_msg()) def _check_grad_helper(self): self.infer_dtype_from_inputs_outputs(self.inputs, self.outputs) self.__class__.op_type = self.op_type self.__class__.exist_check_grad = True if self.dtype == np.float64: self.__class__.exist_fp64_check_grad = True 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, user_defined_grad_outputs=None, check_dygraph=True, check_eager=False): self._check_grad_helper() 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, user_defined_grad_outputs, check_dygraph, check_eager=check_eager) 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, user_defined_grad_outputs=None, check_dygraph=True, numeric_place=None, check_eager=False): 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() self._check_grad_helper() if self.is_bfloat16_op() and self.is_mkldnn_op(): check_dygraph = False check_eager = False if self.dtype == np.float64 and \ self.op_type not in op_threshold_white_list.NEED_FIX_FP64_CHECK_GRAD_THRESHOLD_OP_LIST: numeric_grad_delta = 1e-5 max_relative_error = 1e-7 cache_list = None if hasattr(self, "cache_name_list"): cache_list = self.cache_name_list # oneDNN numeric gradient should use CPU kernel use_onednn = False if "use_mkldnn" in op_attrs and op_attrs["use_mkldnn"] == True: op_attrs["use_mkldnn"] = False use_onednn = True self.op = create_op( self.scope, self.op_type, op_inputs, op_outputs, op_attrs, cache_list=cache_list) if use_onednn: op_attrs["use_mkldnn"] = True if no_grad_set is None: no_grad_set = set() else: if (self.op_type not in no_grad_set_white_list.NEED_TO_FIX_OP_LIST ) and ( self.op_type not in no_grad_set_white_list.NOT_CHECK_OP_LIST ) and (not self.is_bfloat16_op()): raise AssertionError("no_grad_set must be None, op_type is " + self.op_type + " Op.") for input_to_check in inputs_to_check: set_input(self.scope, self.op, self.inputs, place) tensor_to_check = self.scope.find_var(input_to_check).get_tensor() tensor_size = six.moves.reduce(lambda a, b: a * b, tensor_to_check.shape(), 1) if tensor_size < 100: self.__class__.input_shape_is_large = False if not type(output_names) is list: output_names = [output_names] if numeric_place is None: numeric_place = place numeric_grads = user_defined_grads or [ get_numeric_gradient( numeric_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, user_defined_grad_outputs) # comparison of bf16 results will happen as fp32 # loop over list of grads and convert bf16 to fp32 fp32_analytic_grads = [] for grad in analytic_grads: if grad.dtype == np.uint16: grad = convert_uint16_to_float(grad) max_relative_error = 0.04 if max_relative_error < 0.04 else max_relative_error fp32_analytic_grads.append(grad) analytic_grads = fp32_analytic_grads fp32_numeric_grads = [] for grad in numeric_grads: if grad.dtype == np.uint16: grad = convert_uint16_to_float(grad) max_relative_error = 0.04 if max_relative_error < 0.04 else max_relative_error fp32_numeric_grads.append(grad) numeric_grads = fp32_numeric_grads self._assert_is_close(numeric_grads, analytic_grads, inputs_to_check, max_relative_error, "Gradient Check On %s" % str(place)) if check_dygraph: dygraph_grad = self._get_dygraph_grad( inputs_to_check, place, output_names, user_defined_grad_outputs, no_grad_set) fp32_grads = [] for grad in dygraph_grad: if grad.dtype == np.uint16: grad = convert_uint16_to_float(grad) max_relative_error = 0.03 if max_relative_error < 0.03 else max_relative_error fp32_grads.append(grad) dygraph_grad = fp32_grads self._assert_is_close(numeric_grads, dygraph_grad, inputs_to_check, max_relative_error, "Gradient Check On %s" % str(place)) if check_eager: with _test_eager_guard(): eager_dygraph_grad = self._get_dygraph_grad( inputs_to_check, place, output_names, user_defined_grad_outputs, no_grad_set) fp32_grads = [] for grad in eager_dygraph_grad: if grad.dtype == np.uint16: grad = convert_uint16_to_float(grad) max_relative_error = 0.03 if max_relative_error < 0.03 else max_relative_error fp32_grads.append(grad) eager_dygraph_grad = fp32_grads self._assert_is_close(numeric_grads, eager_dygraph_grad, inputs_to_check, max_relative_error, "Gradient Check On %s" % str(place)) def _find_var_in_dygraph(self, output_vars, name): if name in output_vars: return output_vars[name] else: for output_vars_index in output_vars: for output_vars_selected in output_vars[output_vars_index]: if output_vars_selected.name == name: return output_vars_selected def _get_dygraph_grad(self, inputs_to_check, place, output_names, user_defined_grad_outputs=None, no_grad_set=None): with fluid.dygraph.base.guard(place=place): block = fluid.default_main_program().global_block() op_proto = OpProtoHolder.instance().get_op_proto(self.op_type) # prepare input variable inputs, inputs_grad_dict = self.append_input_output_for_dygraph( op_proto, self.inputs, True, True, block) # prepare output variable outputs = self.append_input_output_for_dygraph( op_proto, self.outputs, False, False, block) # prepare attrbutes attrs_outputs = {} if hasattr(self, "attrs"): for attrs_name in self.attrs: if self.attrs[attrs_name] is not None: attrs_outputs[attrs_name] = self.attrs[attrs_name] block.append_op( type=self.op_type, inputs=inputs, outputs=outputs, attrs=attrs_outputs if hasattr(self, "attrs") else None) if self.dtype == np.uint16: cast_inputs = self._find_var_in_dygraph(outputs, output_names[0]) cast_outputs = block.create_var( dtype="float32", shape=cast_inputs[0].shape) cast_op = block.append_op( inputs={"X": cast_inputs}, outputs={"Out": cast_outputs}, type="cast", attrs={ "in_dtype": core.VarDesc.VarType.BF16, "out_dtype": core.VarDesc.VarType.FP32 }) outputs = {output_names[0]: cast_outputs} outputs_valid = {} for output_name in output_names: outputs_valid[output_name] = self._find_var_in_dygraph( outputs, output_name) if user_defined_grad_outputs is None: if len(outputs_valid) == 1: loss = block.create_var( dtype=self.dtype, type=core.VarDesc.VarType.LOD_TENSOR, persistable=False, stop_gradient=False, shape=[1]) for outputs_valid_key in outputs_valid: block.append_op( type="mean", inputs={"X": outputs_valid[outputs_valid_key]}, outputs={"Out": [loss]}, attrs=None) else: avg_sum = [] for cur_loss in outputs_valid: cur_avg_loss = block.create_var( dtype=self.dtype, type=core.VarDesc.VarType.LOD_TENSOR, persistable=False, stop_gradient=False) block.append_op( type="mean", inputs={"X": outputs_valid[cur_loss]}, outputs={"Out": [cur_avg_loss]}, attrs=None) avg_sum.append(cur_avg_loss) loss_sum = block.create_var( dtype=self.dtype, type=core.VarDesc.VarType.LOD_TENSOR, persistable=False, stop_gradient=False, shape=[1]) block.append_op( type='sum', inputs={"X": avg_sum}, outputs={"Out": loss_sum}, attrs=None) loss = block.create_var( dtype=self.dtype, type=core.VarDesc.VarType.LOD_TENSOR, persistable=False, stop_gradient=False, shape=[1]) block.append_op( type='scale', inputs={"X": loss_sum}, outputs={"Out": loss}, attrs={'scale': 1.0 / float(len(avg_sum))}) loss.backward() fetch_list_grad = [] for inputs_to_check_name in inputs_to_check: a = inputs_grad_dict[inputs_to_check_name].gradient() fetch_list_grad.append(a) return fetch_list_grad else: # user_defined_grad_outputs here are numpy arrays if not isinstance(user_defined_grad_outputs, list): user_defined_grad_outputs = [user_defined_grad_outputs] grad_outputs = [] for grad_out_value in user_defined_grad_outputs: grad_outputs.append(paddle.to_tensor(grad_out_value)) # delete the inputs which no need to calculate grad for no_grad_val in no_grad_set: del (inputs[no_grad_val]) if _in_eager_mode(): core.eager.run_backward( fluid.layers.utils.flatten(outputs), grad_outputs, False) grad_inputs = [] for inputs_list in inputs.values(): for inp in inputs_list: grad_inputs.append(inp.grad.numpy()) return grad_inputs else: grad_inputs = paddle.grad( outputs=fluid.layers.utils.flatten(outputs), inputs=fluid.layers.utils.flatten(inputs), grad_outputs=grad_outputs) return [grad.numpy() for grad in grad_inputs] @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): return input @staticmethod def fluid_dtype_to_np_dtype(self, dtype): return dtype @staticmethod def np_value_to_fluid_value(input): return input def _get_gradient(self, input_to_check, place, output_names, no_grad_set, user_defined_grad_outputs=None, parallel=False): prog = Program() scope = core.Scope() block = prog.global_block() self._append_ops(block) inputs = self._get_inputs(block) outputs = self._get_outputs(block) feed_dict = self.feed_var(inputs, place) if user_defined_grad_outputs is None: if self.dtype == np.uint16: cast_inputs = list(map(block.var, output_names)) cast_outputs = block.create_var( dtype="float32", shape=cast_inputs[0].shape) cast_op = block.append_op( inputs={"X": cast_inputs}, outputs={"Out": cast_outputs}, type="cast", attrs={ "in_dtype": core.VarDesc.VarType.BF16, "out_dtype": core.VarDesc.VarType.FP32 }) cast_op.desc.infer_var_type(block.desc) cast_op.desc.infer_shape(block.desc) output_names = [cast_outputs.name] 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) fetch_list = [g for p, g in param_grad_list] else: assert parallel is False, "unsupported parallel mode when giving custom grad outputs." # user_defined_grad_outputs here are numpy arrays if not isinstance(user_defined_grad_outputs, list): user_defined_grad_outputs = [user_defined_grad_outputs] grad_outputs = [] for grad_out_value in user_defined_grad_outputs: # `presistable` is used to avoid executor create new var in local scope var = block.create_var( shape=grad_out_value.shape, dtype=grad_out_value.dtype, persistable=True) true_var = scope.var(var.name) tensor = true_var.get_tensor() tensor.set(grad_out_value, place) grad_outputs.append(var) targets = [ outputs[name] for name in outputs if name in output_names ] inputs = [inputs[name] for name in input_to_check if name in inputs] grad_inputs = paddle.static.gradients(targets, inputs, grad_outputs, no_grad_set) fetch_list = grad_inputs 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, scope=scope, return_numpy=False))) class OpTestTool: @classmethod def skip_if(cls, condition: object, reason: str): return unittest.skipIf(condition, reason) @classmethod def skip_if_not_cpu_bf16(cls): return OpTestTool.skip_if( not (isinstance(_current_expected_place(), core.CPUPlace) and core.supports_bfloat16()), "Place does not support BF16 evaluation") @classmethod def skip_if_not_cpu(cls): return OpTestTool.skip_if( not isinstance(_current_expected_place(), core.CPUPlace), "OneDNN supports only CPU for now")