add mkldnn reshape_transpose_matmul fuse pass ut and op version check (#37468)

* add mkldnn reshape_transpose_matmul fuse pass ut and op version check

* update reshape_transpose_matmul_mkldnn_fuse_pass ut

* update ut

paddle/fluid/framework/ir/mkldnn/reshape_transpose_matmul_mkldnn_fuse_pass.cc

@@ -16,6 +16,7 @@
 #include <string>
 #include <unordered_set>
 #include <vector>
+#include "paddle/fluid/framework/op_version_registry.h"
 #include "paddle/fluid/platform/enforce.h"
 #include "paddle/fluid/string/pretty_log.h"
 
@@ -178,3 +179,8 @@ void ReshapeTransposeMatmulMkldnnFusePass::ApplyImpl(ir::Graph *graph) const {
 
 REGISTER_PASS(reshape_transpose_matmul_mkldnn_fuse_pass,
               paddle::framework::ir::ReshapeTransposeMatmulMkldnnFusePass);
+
+REGISTER_PASS_CAPABILITY(reshape_transpose_matmul_mkldnn_fuse_pass)
+    .AddCombination(
+        paddle::framework::compatible::OpVersionComparatorCombination().EQ(
+            "matmul", 1));

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

@@ -90,6 +90,7 @@ endif()
 
 if (WITH_MKLDNN)
   set_tests_properties(test_mkldnn_depthwise_conv_pass PROPERTIES TIMEOUT 120)
+  set_tests_properties(test_mkldnn_reshape_transpose_matmul_fuse_pass PROPERTIES TIMEOUT 100)
   set_tests_properties(test_mkldnn_prelu_op PROPERTIES TIMEOUT 300)
   set_tests_properties(test_conv_act_mkldnn_fuse_pass PROPERTIES TIMEOUT 120)
   set_tests_properties(test_conv_transpose_eltwiseadd_bn_fuse_pass PROPERTIES TIMEOUT 250)

python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_reshape_transpose_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, SkipReasons
+from program_config import TensorConfig, ProgramConfig
+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
+from functools import reduce
+
+num = 32 * 64
+
+
+class TestReshapeTransposeMatmulMkldnnFusePass(PassAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        return True
+
+    def sample_program_config(self, draw):
+        transpose_X = draw(st.booleans())
+        transpose_Y = draw(st.booleans())
+        alpha = draw(st.floats(min_value=0.01, max_value=2))
+        axis = draw(st.sampled_from([[0, 2, 1, 3]]))
+        shape = draw(
+            st.sampled_from([[0, 64, -1, 32], [0, 32, -1, 64], [-1, 32, 1, 64]
+                             ]))
+        batch_size = draw(st.integers(min_value=1, max_value=4))
+        channel = draw(st.integers(min_value=1, max_value=64))
+        input_dim = draw(st.sampled_from([32, 64]))
+
+        def generate_input1(attrs):
+            shape_x = [attrs[3]['batch_size'], attrs[3]['channel'], num]
+            return np.random.random(shape_x).astype(np.float32)
+
+        def generate_input2(attrs):
+            shape_x = [attrs[3]['batch_size'], attrs[3]['channel'], num]
+            input_volume = reduce(lambda x, y: x * y, shape_x)
+            matmul_shape = [i for i in attrs[0]['shape']]
+            if 0 in matmul_shape:
+                for i in range(len(matmul_shape)):
+                    if matmul_shape[i] == 0:
+                        matmul_shape[i] = shape_x[i]
+            shape_volume = reduce(lambda x, y: x * y, matmul_shape)
+
+            if -1 in matmul_shape:
+                for i in range(len(matmul_shape)):
+                    if matmul_shape[i] == -1:
+                        matmul_shape[i] = int(abs(input_volume / shape_volume))
+
+            # Only for transpose axis [0, 2, 1, 3]     
+            matmul_shape[1], matmul_shape[2] = matmul_shape[2], matmul_shape[1]
+
+            if attrs[2]['transpose_X'] and attrs[2]['transpose_Y']:
+                shape_y = [
+                    matmul_shape[0], matmul_shape[1], matmul_shape[-1],
+                    int(num / matmul_shape[-1])
+                ]
+            elif attrs[2]['transpose_X']:
+                shape_y = matmul_shape
+            elif attrs[2]['transpose_Y']:
+                shape_y = matmul_shape
+            else:
+                shape_y = [
+                    matmul_shape[0], matmul_shape[1], matmul_shape[-1],
+                    int(num / matmul_shape[-1])
+                ]
+            return np.random.random(shape_y).astype(np.float32)
+
+        attrs = [{
+            "shape": shape
+        }, {
+            "axis": axis
+        }, {
+            "transpose_X": transpose_X,
+            "transpose_Y": transpose_Y,
+            "alpha": alpha
+        }, {
+            'batch_size': batch_size,
+            'channel': channel,
+            'input_dim': input_dim
+        }]
+
+        ops_config = [{
+            "op_type": "reshape2",
+            "op_inputs": {
+                "X": ["input_data1"]
+            },
+            "op_outputs": {
+                "Out": ["reshape2_output"],
+                "XShape": ["reshape2_xshape"]
+            },
+            "op_attrs": {
+                'shape': attrs[0]['shape']
+            },
+        }, {
+            "op_type": "transpose2",
+            "op_inputs": {
+                "X": ["reshape2_output"]
+            },
+            "op_outputs": {
+                "Out": ["transpose2_output"],
+                "XShape": ["transpose2_xshape"]
+            },
+            "op_attrs": {
+                'axis': attrs[1]['axis']
+            },
+        }, {
+            "op_type": "matmul",
+            "op_inputs": {
+                "X": ["transpose2_output"],
+                "Y": ["input_data2"]
+            },
+            "op_outputs": {
+                "Out": ["matmul_output"]
+            },
+            "op_attrs": {
+                'transpose_X': attrs[2]['transpose_X'],
+                'transpose_Y': attrs[2]['transpose_Y'],
+                'alpha': attrs[2]['alpha'],
+                "fused_reshape_X": [],
+                "fused_reshape_Y": [],
+                "fused_transpose_X": [],
+                "fused_transpose_Y": [],
+                "fused_reshape_Out": [],
+                "fused_transpose_Out": []
+            }
+        }]
+
+        ops = self.generate_op_config(ops_config)
+
+        program_config = ProgramConfig(
+            ops=ops,
+            weights={},
+            inputs={
+                "input_data1":
+                TensorConfig(data_gen=partial(generate_input1, attrs)),
+                "input_data2":
+                TensorConfig(data_gen=partial(generate_input2, attrs))
+            },
+            outputs=["matmul_output"])
+
+        return program_config
+
+    def sample_predictor_configs(self, program_config):
+        config = self.create_inference_config(use_mkldnn=True)
+        yield config, ["matmul"], (1e-5, 1e-5)
+
+    def test(self):
+        self.run_and_statis(
+            quant=False, passes=["reshape_transpose_matmul_mkldnn_fuse_pass"])
+
+
+if __name__ == "__main__":
+    unittest.main()