remove trt_reshape2_matmul_fuse_pass (#46363)

paddle/fluid/inference/api/paddle_pass_builder.cc

@@ -110,7 +110,6 @@ const std::vector<std::string> kTRTSubgraphPasses({
       "conv_bn_fuse_pass",                           //
       "unsqueeze2_eltwise_fuse_pass",                //
       "trt_squeeze2_matmul_fuse_pass",               //
-      "trt_reshape2_matmul_fuse_pass",               //
       "trt_flatten2_matmul_fuse_pass",               //
       "trt_map_matmul_v2_to_mul_pass",               //
       "trt_map_matmul_v2_to_matmul_pass",            //

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

@@ -171,8 +171,6 @@ if(WITH_GPU AND TENSORRT_FOUND)
                                                                        240)
     set_tests_properties(test_trt_squeeze2_matmul_fuse_pass PROPERTIES TIMEOUT
                                                                        240)
-    set_tests_properties(test_trt_reshape2_matmul_fuse_pass PROPERTIES TIMEOUT
-                                                                       240)
     set_tests_properties(test_shuffle_channel_detect_pass PROPERTIES TIMEOUT
                                                                      120)
     if(WIN32)

python/paddle/fluid/tests/unittests/ir/inference/test_trt_reshape2_matmul_fuse_pass.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, IgnoreReasons
-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, reproduce_failure
-import hypothesis.strategies as st
-
-
-class TestReshape2MatmulFusePass(PassAutoScanTest):
-    """
-        x_var  
-          |          
-       reshape2 
-          \
-    reshape2_out_var    y_var
-             \           /
-                 matmul      bias_var
-                    \          /
-                   elementwise_add  
-    """
-
-    def sample_predictor_configs(self, program_config):
-        # TRT
-        config = self.create_trt_inference_config()
-        config.enable_tensorrt_engine(
-            max_batch_size=10,
-            workspace_size=102400,
-            min_subgraph_size=0,
-            precision_mode=paddle_infer.PrecisionType.Float32,
-            use_static=False,
-            use_calib_mode=False)
-        yield config, ['mul', 'elementwise_add'], (1e-4, 1e-1)
-
-    def add_ignore_pass_case(self):
-        # Here we put some skip rules to avoid known bugs
-        def teller1(program_config, predictor_config):
-            y_shape = list(program_config.weights["matmul_y"].shape)
-            bias_shape = program_config.weights["bias"].shape
-            axis = program_config.ops[2].attrs["axis"]
-            # bias should be [mul_y_shape[-1]]
-            if axis == 0 or bias_shape[0] != y_shape[1] or len(bias_shape) != 1:
-                return True
-            return False
-
-        self.add_ignore_check_case(
-            teller1,
-            IgnoreReasons.PASS_ACCURACY_ERROR,
-            "The pass error on TRT while shape of bias is not [out_size].",
-        )
-
-    def sample_program_config(self, draw):
-        # 1. Generate shape and attr of reshape2
-        reshape = draw(
-            st.lists(st.integers(min_value=1, max_value=10),
-                     min_size=2,
-                     max_size=2))
-        x_shape = reshape + [1, 1]
-
-        # 2. Generate attr:transpose_X/transpose_Y/alpha of matmul
-        alpha = 1.0
-        transpose_X = False
-        transpose_Y = False
-
-        # 3. Generate legal shape of input:Y of matmul
-        y_shape = draw(
-            st.lists(st.integers(min_value=1, max_value=8),
-                     min_size=2,
-                     max_size=2))
-        y_shape[0] = x_shape[1]
-
-        # 4. Generate legal attr:axis of elementwise_add
-        axis = draw(st.integers(min_value=-1, max_value=1))
-        if axis == 0:
-            axis = -1
-        bias_shape = [
-            y_shape[1],
-        ]
-        # if axis == -1:
-        #     if draw(st.booleans()):
-        #         bias_shape = [y_shape[1], ]
-        #     else:
-        #         bias_shape = [x_shape[0], y_shape[1]]
-
-        reshape2_op = OpConfig(
-            "reshape2",
-            inputs={
-                "X": ["reshape2_x"],
-            },
-            shape=reshape,
-            outputs={
-                "Out": ["reshape2_out"],
-                "XShape": ["xshape"]
-            },
-        )
-        matmul_op = OpConfig(
-            "matmul",
-            inputs={
-                "X": ["reshape2_out"],
-                "Y": ["matmul_y"]
-            },
-            outputs={"Out": ["matmul_out"]},
-            alpha=alpha,
-            transpose_X=transpose_X,
-            transpose_Y=transpose_Y,
-            fused_reshape_X=[],
-            fused_reshape_Y=[],
-            fused_transpose_X=[],
-            fused_transpose_Y=[],
-            fused_reshape_Out=[],
-            fused_transpose_Out=[],
-        )
-
-        add_op = OpConfig(
-            "elementwise_add",
-            inputs={
-                "X": ["matmul_out"],
-                "Y": ["bias"]
-            },
-            outputs={"Out": ["add_out"]},
-            axis=axis,
-        )
-
-        ops = [reshape2_op, matmul_op, add_op]
-
-        program_config = ProgramConfig(
-            ops=ops,
-            weights={
-                "matmul_y": TensorConfig(shape=y_shape),
-                "bias": TensorConfig(shape=bias_shape),
-            },
-            inputs={
-                "reshape2_x": TensorConfig(shape=x_shape),
-            },
-            outputs=ops[-1].outputs["Out"],
-        )
-
-        return program_config
-
-    def test(self):
-        self.run_and_statis(quant=False,
-                            max_examples=50,
-                            passes=["trt_reshape2_matmul_fuse_pass"])
-
-
-if __name__ == "__main__":
-    unittest.main()