simplify_with_basic_ops_pass UT (#37704)

* first commit

* more uts

* file name duplicated

* timeout

* Update CMakeLists.txt

change TIMEOUT from 120 to 240

* Update CMakeLists.txt

more time

* Update CMakeLists.txt

timeout

* Update CMakeLists.txt

60s

paddle/fluid/framework/ir/simplify_with_basic_ops_pass.cc

@@ -17,6 +17,7 @@ limitations under the License. */
 #include "glog/logging.h"
 #include "paddle/fluid/framework/ir/graph_pattern_detector.h"
 #include "paddle/fluid/framework/ir/node.h"
+#include "paddle/fluid/framework/op_version_registry.h"
 #include "paddle/fluid/platform/enforce.h"
 
 namespace paddle {
@@ -231,3 +232,7 @@ void SimplifyWithBasicOpsPass::ReplaceOutputVar(Node* op, Node* old_var,
 
 REGISTER_PASS(simplify_with_basic_ops_pass,
               paddle::framework::ir::SimplifyWithBasicOpsPass);
+REGISTER_PASS_CAPABILITY(simplify_with_basic_ops_pass)
+    .AddCombination(
+        paddle::framework::compatible::OpVersionComparatorCombination().EQ(
+            "scale", 0));

python/paddle/fluid/tests/unittests/ir/inference/CMakeLists.txt

@@ -71,6 +71,7 @@ set_tests_properties(test_trt_matmul_quant_dequant PROPERTIES TIMEOUT 100)
 set_tests_properties(test_trt_conv3d_op PROPERTIES TIMEOUT 60)
 set_tests_properties(test_trt_conv3d_transpose_op PROPERTIES TIMEOUT 60)
 set_tests_properties(test_trt_nearest_interp_v2_op PROPERTIES TIMEOUT 30)
+set_tests_properties(test_simplify_with_basic_ops_pass_autoscan PROPERTIES TIMEOUT 60)
 
 if (WITH_MKLDNN AND TENSORRT_FOUND AND WITH_GPU)
 set_tests_properties(test_emb_eltwise_layernorm_fuse_pass PROPERTIES TIMEOUT 120)

python/paddle/fluid/tests/unittests/ir/inference/test_simplify_with_basic_ops_pass_autoscan.py

+# Copyright (c) 2021 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 auto_scan_test import PassAutoScanTest, SkipReasons
+from program_config import TensorConfig, ProgramConfig, OpConfig
+import numpy as np
+import paddle.inference as paddle_infer
+from functools import partial
+from typing import Optional, List, Callable, Dict, Any, Set
+import unittest
+
+import hypothesis
+from hypothesis import given, settings, seed, example, assume
+import hypothesis.strategies as st
+
+
+class TestSimplifyWithBasicOpsPassUpscale(PassAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_config(self, draw):
+        #scale = draw(st.floats(min_value=0.01, max_value=1.0))
+        #bias = draw(st.floats(min_value=0.01, max_value=2.0))
+        #bias_after_scale = draw(st.booleans())
+        fix_seed = draw(st.booleans())
+        dropout_implementation = "upscale_in_train"
+        dropout_prob = draw(st.floats(min_value=0.0, max_value=1.0))
+        seed = draw(st.integers(min_value=0, max_value=512))
+        x_shape = draw(
+            st.lists(
+                st.integers(
+                    min_value=1, max_value=4), min_size=2, max_size=4))
+        is_test = True
+
+        dropout_op = OpConfig(
+            "dropout",
+            inputs={"X": ["input_data"]},
+            outputs={"Out": ["dropout_output"]},
+            fix_seed=fix_seed,
+            dropout_implementation=dropout_implementation,
+            dropout_prob=dropout_prob,
+            seed=seed,
+            is_test=is_test)
+        relu_op = OpConfig(
+            "relu",
+            inputs={"X": ["dropout_output"]},
+            outputs={"Out": ["relu_out"]})
+        ops = [dropout_op, relu_op]
+
+        program_config = ProgramConfig(
+            ops=ops,
+            weights={},
+            inputs={"input_data": TensorConfig(shape=x_shape), },
+            outputs=["relu_out"])
+
+        return program_config
+
+    def sample_predictor_configs(self, program_config):
+        config = self.create_inference_config(use_gpu=True)
+        yield config, ['relu'], (1e-5, 1e-5)
+        config = self.create_inference_config(use_gpu=False)
+        yield config, ['relu'], (1e-5, 1e-5)
+        config = self.create_trt_inference_config()
+        config.enable_tensorrt_engine(
+            max_batch_size=4,
+            workspace_size=102400,
+            min_subgraph_size=0,
+            precision_mode=paddle_infer.PrecisionType.Float32,
+            use_static=False,
+            use_calib_mode=False)
+        yield config, ['relu'], (1e-5, 1e-5)
+
+    def test(self):
+        self.run_and_statis(
+            quant=False,
+            max_examples=30,
+            passes=["simplify_with_basic_ops_pass"],
+            min_success_num=30)
+
+
+class TestSimplifyWithBasicOpsPassDowngrade(PassAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_config(self, draw):
+        fix_seed = draw(st.booleans())
+        dropout_implementation = "downgrade_in_infer"
+        dropout_prob = draw(st.floats(min_value=0.0, max_value=1.0))
+        seed = draw(st.integers(min_value=0, max_value=512))
+        x_shape = draw(
+            st.lists(
+                st.integers(
+                    min_value=1, max_value=4), min_size=2, max_size=4))
+        is_test = True
+
+        dropout_op = OpConfig(
+            "dropout",
+            inputs={"X": ["input_data"]},
+            outputs={"Out": ["dropout_output"]},
+            fix_seed=fix_seed,
+            dropout_implementation=dropout_implementation,
+            dropout_prob=dropout_prob,
+            seed=seed,
+            is_test=is_test)
+        relu_op = OpConfig(
+            "relu",
+            inputs={"X": ["dropout_output"]},
+            outputs={"Out": ["relu_out"]})
+        ops = [dropout_op, relu_op]
+
+        program_config = ProgramConfig(
+            ops=ops,
+            weights={},
+            inputs={"input_data": TensorConfig(shape=x_shape), },
+            outputs=["relu_out"])
+
+        return program_config
+
+    def sample_predictor_configs(self, program_config):
+        config = self.create_inference_config(use_gpu=True)
+        yield config, ['scale', 'relu'], (1e-5, 1e-5)
+        config = self.create_inference_config(use_gpu=False)
+        yield config, ['scale', 'relu'], (1e-5, 1e-5)
+        config = self.create_trt_inference_config()
+        config.enable_tensorrt_engine(
+            max_batch_size=4,
+            workspace_size=102400,
+            min_subgraph_size=0,
+            precision_mode=paddle_infer.PrecisionType.Float32,
+            use_static=False,
+            use_calib_mode=False)
+        yield config, ['scale', 'relu'], (1e-5, 1e-5)
+
+    def test(self):
+        self.run_and_statis(
+            quant=False,
+            max_examples=30,
+            passes=["simplify_with_basic_ops_pass"],
+            min_success_num=30)
+
+
+if __name__ == "__main__":
+    unittest.main()