add check for check_grad in Op unittests (#21383)

python/paddle/fluid/tests/unittests/op_test.py

@@ -33,7 +33,7 @@ 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
 from paddle.fluid import unique_name
-from white_list import op_accuracy_white_list
+from white_list import op_accuracy_white_list, op_check_grad_white_list
 
 
 def _set_use_system_allocator(value=None):
@@ -1362,9 +1362,9 @@ class OpTestFp16(OpTestBase):
         np.random.set_state(cls._np_rand_state)
         random.setstate(cls._py_rand_state)
 
-        if cls.__name__ not in op_accuracy_white_list.NO_NEED_FP16_CHECK_GRAD_CASES \
-            and not hasattr(cls, "exist_check_grad") \
-            and cls.op_type not in op_accuracy_white_list.NO_FP16_CHECK_GRAD_OP_LIST:
+        if not hasattr(cls, "exist_check_grad") \
+            and cls.__name__ not in op_check_grad_white_list.NO_NEED_CHECK_GRAD_CASES \
+            and cls.op_type not in op_check_grad_white_list.EMPTY_GRAD_OP_LIST:
             raise AssertionError("This test of %s op needs check_grad." %
                                  cls.op_type)
 
@@ -1438,8 +1438,12 @@ class OpTest(OpTestBase):
         np.random.set_state(cls._np_rand_state)
         random.setstate(cls._py_rand_state)
 
-        if cls.__name__ not in op_accuracy_white_list.NO_NEED_FP64_CHECK_GRAD_CASES \
+        # only for pass ci, but cases in NO_FP64_CHECK_GRAD_CASES 
+        # and op in NO_FP64_CHECK_GRAD_OP_LIST should be fixed
+        if cls.__name__ not in op_accuracy_white_list.NO_FP64_CHECK_GRAD_CASES \
             and not hasattr(cls, 'exist_fp64_check_grad') \
+            and cls.__name__ not in op_check_grad_white_list.NO_NEED_CHECK_GRAD_CASES \
+            and cls.op_type not in op_check_grad_white_list.EMPTY_GRAD_OP_LIST \
             and cls.op_type not in op_accuracy_white_list.NO_FP64_CHECK_GRAD_OP_LIST:
             raise AssertionError("This test of %s op needs fp64 check_grad." %
                                  cls.op_type)

python/paddle/fluid/tests/unittests/white_list/op_accuracy_white_list.py

@@ -24,81 +24,43 @@ FP16_CHECK_OP_LIST = [
 
 # For op in NO_FP64_CHECK_GRAD_OP_LIST, the op test requires check_grad with fp64 precision
 NO_FP64_CHECK_GRAD_OP_LIST = [
-    'abs', 'accuracy', 'acos', 'adadelta', 'adagrad', 'adam', 'adamax',
-    'add_position_encoding', 'affine_grid', 'anchor_generator', 'arg_max',
-    'arg_min', 'argsort', 'asin', 'assign_value', 'atan', 'attention_lstm',
-    'auc', 'bilinear_interp', 'bilinear_tensor_product', 'bipartite_match',
-    'box_clip', 'box_coder', 'box_decoder_and_assign', 'brelu', 'cast', 'ceil',
-    'center_loss', 'chunk_eval', 'clip', 'clip_by_norm', 'coalesce_tensor',
-    'collect_fpn_proposals', 'concat', 'conv2d', 'conv2d_fusion',
-    'conv2d_transpose', 'conv3d', 'conv3d_transpose', 'conv_shift', 'cos',
-    'cos_sim', 'crf_decoding', 'crop', 'crop_tensor', 'cross_entropy',
-    'cross_entropy2', 'ctc_align', 'cudnn_lstm', 'cvm', 'data_norm',
-    'decayed_adagrad', 'deformable_conv', 'deformable_conv_v1',
-    'deformable_psroi_pooling', 'density_prior_box', 'depthwise_conv2d',
-    'depthwise_conv2d_transpose', 'dequantize', 'dequantize_abs_max',
-    'detection_map', 'diag', 'distribute_fpn_proposals', 'dpsgd', 'dropout',
-    'edit_distance', 'elementwise_add', 'elementwise_div',
-    'elementwise_floordiv', 'elementwise_max', 'elementwise_min',
-    'elementwise_mod', 'elementwise_mul', 'elementwise_pow', 'elementwise_sub',
-    'elu', 'equal', 'exp', 'expand', 'eye',
-    'fake_channel_wise_dequantize_max_abs',
-    'fake_channel_wise_quantize_abs_max', 'fake_dequantize_max_abs',
-    'fake_quantize_abs_max', 'fake_quantize_dequantize_moving_average_abs_max',
-    'fake_quantize_moving_average_abs_max', 'fake_quantize_range_abs_max', 'fc',
-    'fill', 'fill_any_like', 'fill_constant', 'fill_constant_batch_size_like',
-    'fill_zeros_like', 'fill_zeros_like2', 'flatten', 'flatten2', 'floor',
-    'ftrl', 'fused_elemwise_activation', 'fused_embedding_fc_lstm',
-    'fused_embedding_seq_pool', 'fused_fc_elementwise_layernorm', 'fusion_gru',
-    'fusion_lstm', 'fusion_repeated_fc_relu', 'fusion_seqconv_eltadd_relu',
-    'fusion_seqexpand_concat_fc', 'fusion_seqpool_concat',
-    'fusion_seqpool_cvm_concat', 'fusion_squared_mat_sub',
-    'fusion_transpose_flatten_concat', 'gather', 'gather_nd', 'gather_tree',
-    'gaussian_random_batch_size_like', 'gelu', 'generate_mask_labels',
-    'generate_proposal_labels', 'generate_proposals', 'greater_equal',
-    'greater_than', 'grid_sampler', 'group_norm', 'hard_shrink', 'hard_sigmoid',
-    'hard_swish', 'hash', 'hierarchical_sigmoid', 'hinge_loss', 'huber_loss',
-    'im2sequence', 'increment', 'iou_similarity', 'is_empty', 'isfinite',
-    'isinf', 'isnan', 'kldiv_loss', 'l1_norm', 'lamb', 'lars_momentum',
-    'leaky_relu', 'less_equal', 'less_than', 'linspace', 'locality_aware_nms',
-    'lod_reset', 'log', 'log_loss', 'logical_and', 'logical_not', 'logical_or',
-    'logical_xor', 'logsigmoid', 'lookup_table', 'lookup_table_v2', 'lrn',
+    'abs', 'acos', 'add_position_encoding', 'affine_grid', 'asin', 'atan',
+    'bilinear_interp', 'bilinear_tensor_product', 'brelu', 'center_loss',
+    'clip', 'concat', 'conv2d', 'conv2d_transpose', 'conv3d',
+    'conv3d_transpose', 'conv_shift', 'cos', 'cos_sim', 'crop', 'crop_tensor',
+    'cross_entropy', 'cross_entropy2', 'cudnn_lstm', 'cvm', 'data_norm',
+    'deformable_conv', 'deformable_conv_v1', 'deformable_psroi_pooling',
+    'depthwise_conv2d', 'depthwise_conv2d_transpose', 'dropout',
+    'elementwise_add', 'elementwise_div', 'elementwise_max', 'elementwise_min',
+    'elementwise_mul', 'elementwise_pow', 'elementwise_sub', 'elu', 'exp',
+    'expand', 'flatten', 'flatten2', 'fused_elemwise_activation',
+    'fused_embedding_seq_pool', 'gather', 'gather_nd', 'gelu', 'grid_sampler',
+    'group_norm', 'hard_shrink', 'hard_sigmoid', 'hard_swish',
+    'hierarchical_sigmoid', 'hinge_loss', 'huber_loss', 'im2sequence',
+    'increment', 'kldiv_loss', 'l1_norm', 'leaky_relu', 'lod_reset', 'log',
+    'log_loss', 'logsigmoid', 'lookup_table', 'lookup_table_v2', 'lrn',
     'margin_rank_loss', 'match_matrix_tensor', 'matmul',
-    'max_pool2d_with_index', 'max_pool3d_with_index', 'maxout', 'mean',
-    'mean_iou', 'merge_ids', 'mine_hard_examples', 'minus',
-    'modified_huber_loss', 'momentum', 'moving_average_abs_max_scale', 'mul',
-    'multiclass_nms', 'multiclass_nms2', 'multihead_matmul', 'multiplex', 'nce',
-    'nearest_interp', 'not_equal', 'one_hot', 'one_hot_v2', 'pad', 'pad2d',
-    'pad_constant_like', 'pixel_shuffle', 'polygon_box_transform', 'pool2d',
-    'pool3d', 'positive_negative_pair', 'pow', 'precision_recall', 'prelu',
-    'prior_box', 'proximal_adagrad', 'proximal_gd', 'prroi_pool', 'psroi_pool',
-    'quantize', 'random_crop', 'range', 'rank_loss', 'reciprocal', 'reduce_all',
-    'reduce_any', 'reduce_max', 'reduce_min', 'ref_by_trainer_id', 'relu',
-    'relu6', 'requantize', 'reshape2', 'retinanet_detection_output',
-    'retinanet_target_assign', 'reverse', 'roi_align',
-    'roi_perspective_transform', 'roi_pool', 'round', 'row_conv',
-    'rpn_target_assign', 'rsqrt', 'sampling_id', 'scale', 'scatter',
-    'scatter_nd_add', 'seed', 'selu', 'sequence_concat', 'sequence_conv',
-    'sequence_enumerate', 'sequence_erase', 'sequence_expand',
-    'sequence_expand_as', 'sequence_mask', 'sequence_pad', 'sequence_pool',
-    'sequence_reshape', 'sequence_reverse', 'sequence_scatter',
+    'max_pool2d_with_index', 'max_pool3d_with_index', 'maxout', 'mean', 'minus',
+    'modified_huber_loss', 'mul', 'multiplex', 'nce', 'nearest_interp', 'pad',
+    'pad2d', 'pad_constant_like', 'pixel_shuffle', 'pool2d', 'pool3d', 'pow',
+    'prelu', 'prroi_pool', 'psroi_pool', 'rank_loss', 'reciprocal',
+    'reduce_max', 'reduce_min', 'relu', 'relu6', 'reshape2', 'reverse',
+    'roi_align', 'roi_perspective_transform', 'roi_pool', 'row_conv', 'rsqrt',
+    'scale', 'scatter', 'scatter_nd_add', 'seed', 'selu', 'sequence_concat',
+    'sequence_conv', 'sequence_expand', 'sequence_expand_as', 'sequence_pad',
+    'sequence_pool', 'sequence_reshape', 'sequence_reverse', 'sequence_scatter',
     'sequence_slice', 'sequence_softmax', 'sequence_topk_avg_pooling',
-    'sequence_unpad', 'sgd', 'shape', 'shard_index', 'shuffle_channel',
-    'sigmoid', 'sigmoid_cross_entropy_with_logits', 'sigmoid_focal_loss',
-    'sign', 'similarity_focus', 'sin', 'size', 'slice', 'smooth_l1_loss',
-    'soft_relu', 'softmax', 'softshrink', 'softsign', 'space_to_depth',
-    'spectral_norm', 'split', 'split_ids', 'spp', 'sqrt', 'square',
+    'sequence_unpad', 'shuffle_channel', 'sigmoid',
+    'sigmoid_cross_entropy_with_logits', 'sigmoid_focal_loss', 'sign', 'sin',
+    'slice', 'smooth_l1_loss', 'soft_relu', 'softmax', 'softshrink', 'softsign',
+    'space_to_depth', 'spectral_norm', 'split', 'spp', 'sqrt', 'square',
     'squared_l2_distance', 'squared_l2_norm', 'squeeze', 'squeeze2', 'stack',
-    'stanh', 'strided_slice', 'sum', 'swish', 'tanh', 'tanh_shrink',
-    'target_assign', 'teacher_student_sigmoid_loss', 'temporal_shift',
-    'thresholded_relu', 'top_k', 'transpose2', 'tree_conv', 'trilinear_interp',
-    'unfold', 'uniform_random', 'uniform_random_batch_size_like', 'unique',
-    'unique_with_counts', 'unpool', 'unsqueeze', 'unsqueeze2', 'unstack',
-    'var_conv_2d', 'warpctc', 'where', 'yolo_box', 'yolov3_loss'
+    'stanh', 'strided_slice', 'swish', 'tanh', 'tanh_shrink',
+    'teacher_student_sigmoid_loss', 'temporal_shift', 'thresholded_relu',
+    'transpose2', 'tree_conv', 'trilinear_interp', 'unfold', 'unpool',
+    'unsqueeze', 'unsqueeze2', 'unstack', 'var_conv_2d', 'warpctc',
+    'yolov3_loss'
 ]
 
-NO_NEED_FP64_CHECK_GRAD_CASES = ['TestFSPOp']
-
-NO_FP16_CHECK_GRAD_OP_LIST = []
-
-NO_NEED_FP16_CHECK_GRAD_CASES = []
+# For cases in NO_FP64_CHECK_GRAD_CASES, the op test requires check_grad with fp64 precision
+NO_FP64_CHECK_GRAD_CASES = ['TestFSPOp']

python/paddle/fluid/tests/unittests/white_list/op_check_grad_white_list.py

+# Copyright (c) 2019 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.
+
+# Grad op is not registered for Ops in EMPTY_GRAD_OP_LIST, so check grad 
+# will not be required.
+EMPTY_GRAD_OP_LIST = [
+    'fill_zeros_like2', 'gaussian_random_batch_size_like',
+    'fill_constant_batch_size_like', 'iou_similarity', 'where',
+    'uniform_random_batch_size_like', 'box_coder', 'equal', 'greater_equal',
+    'greater_than', 'less_equal', 'sequence_enumerate', 'logical_and',
+    'logical_not', 'logical_or', 'logical_xor', 'unique',
+    'fusion_seqconv_eltadd_relu', 'prior_box', 'decayed_adagrad',
+    'crf_decoding', 'mine_hard_examples', 'fusion_seqpool_concat',
+    'fused_embedding_fc_lstm', 'top_k', 'uniform_random', 'multihead_matmul',
+    'edit_distance', 'shard_index', 'generate_proposals', 'density_prior_box',
+    'round', 'floor', 'ceil', 'precision_recall', 'proximal_adagrad', 'cast',
+    'isinf', 'isfinite', 'isnan', 'fill_constant', 'fusion_seqpool_cvm_concat',
+    'accuracy', 'fc', 'sgd', 'anchor_generator',
+    'fake_channel_wise_quantize_abs_max',
+    'fake_quantize_dequantize_moving_average_abs_max', 'fake_quantize_abs_max',
+    'fake_quantize_range_abs_max', 'moving_average_abs_max_scale',
+    'fake_quantize_moving_average_abs_max', 'fill_any_like', 'one_hot',
+    'gather_tree', 'lookup_sparse_table', 'lamb', 'fusion_squared_mat_sub',
+    'range', 'box_decoder_and_assign', 'one_hot_v2', 'shape',
+    'fusion_transpose_flatten_concat', 'lars_momentum', 'momentum',
+    'fusion_lstm', 'assign_value', 'polygon_box_transform',
+    'retinanet_detection_output', 'generate_proposal_labels', 'ctc_align',
+    'sequence_erase', 'fake_channel_wise_dequantize_max_abs',
+    'fake_dequantize_max_abs', 'generate_mask_labels', 'elementwise_floordiv',
+    'sum', 'ftrl', 'fusion_repeated_fc_relu', 'size', 'bipartite_match',
+    'elementwise_mod', 'multiclass_nms2', 'multiclass_nms', 'fill_zeros_like',
+    'adadelta', 'conv2d_fusion', 'adamax', 'sampling_id', 'dpsgd',
+    'target_assign', 'random_crop', 'mean_iou', 'reduce_all', 'reduce_any',
+    'attention_lstm', 'fusion_seqexpand_concat_fc', 'dequantize_abs_max',
+    'clip_by_norm', 'diag', 'yolo_box', 'adam', 'fusion_gru',
+    'locality_aware_nms', 'ref_by_trainer_id', 'linspace', 'box_clip',
+    'similarity_focus', 'detection_map', 'sequence_mask', 'coalesce_tensor',
+    'arg_min', 'arg_max', 'split_ids', 'adagrad', 'fill', 'argsort',
+    'dequantize', 'merge_ids', 'fused_fc_elementwise_layernorm',
+    'retinanet_target_assign', 'rpn_target_assign', 'requantize',
+    'distribute_fpn_proposals', 'auc', 'quantize', 'positive_negative_pair',
+    'hash', 'less_than', 'not_equal', 'eye', 'chunk_eval', 'is_empty',
+    'proximal_gd', 'collect_fpn_proposals', 'unique_with_counts'
+]
+
+# Special cases do not need to check grad
+NO_NEED_CHECK_GRAD_CASES = [
+    'TestLookupTableOpWithPadding',
+    'TestLookupTableOpWithTensorIdsAndPadding',
+    'TestLookupTableOpWithPadding',
+    'TestLookupTableOpWithTensorIdsAndPadding',
+    'TestSeqMaxPool2DInference',
+    'TestSeqMaxPool2DInferenceLen0',
+    'TestSeqMaxPool2DInferenceLen0LoDLevel2',
+    'TestDropoutOp4',
+    'TestDropoutOp5',
+    'TestDropoutOp8',
+    'TestDropoutOp9',
+    'TestFP16DropoutOp',
+    'TestFP16DropoutOp2',
+    'TestExpandOpBoolean',
+    'TestFusedEmbeddingSeqPoolOp',
+    'TestMKLDNNConcatOp',
+    'TestMKLDNNConcatOp',
+    'TestMKLDNNConcatOp3',
+    'TestElementwiseMulMKLDNNOp_Integrated_With_Convs',
+    'TestConv2dTransposeMKLDNNOp',
+    'TestMKLDNNFuseBias',
+    'TestMKLDNNWithPad',
+    'TestMKLDNNWithStride',
+    'TestMKLDNNWithAsymPad',
+    'TestMKLDNNWithSamePad',
+    'TestMKLDNNWithValidPad',
+    'TestMKLDNNWithValidPad_NHWC',
+]