add mkldnn matmulv2 ut (#38749)

python/paddle/fluid/tests/unittests/ir/inference/test_mkldnn_matmulv2_op.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 MkldnnAutoScanTest, 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 TestMkldnnMatmulv2Op(MkldnnAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        if len(program_config.inputs["input_data2"].shape) == 4:
+            if program_config.inputs["input_data1"].shape[
+                    -4] != 1 and program_config.inputs["input_data2"].shape[
+                        -4] != 1:
+                if program_config.inputs["input_data1"].shape[
+                        -4] != program_config.inputs["input_data2"].shape[-4]:
+                    return False
+
+        if program_config.inputs["input_data1"].shape[
+                -3] != 1 and program_config.inputs["input_data2"].shape[
+                    -3] != 1:
+            if program_config.inputs["input_data1"].shape[
+                    -3] != program_config.inputs["input_data2"].shape[-3]:
+                return False
+        return True
+
+    def sample_program_configs(self, *args, **kwargs):
+        def generate_input(type, *args, **kwargs):
+            transpose_X = kwargs["transpose_X"]
+            transpose_Y = kwargs["transpose_Y"]
+            batch_size1 = kwargs["batch_size1"]
+            batch_size2 = kwargs["batch_size2"]
+            channel1 = kwargs["channel1"]
+            channel2 = kwargs["channel2"]
+            input_dim = kwargs["input_dim"]
+            y_dim_len = kwargs["y_dim_len"]
+            if transpose_X and transpose_Y:
+                shape_x = [batch_size1, channel1, input_dim, 32]
+                if y_dim_len == 4:
+                    shape_y = [batch_size2, channel2, 64, input_dim]
+                elif y_dim_len == 3:
+                    shape_y = [channel2, 64, input_dim]
+            elif transpose_X:
+                shape_x = [batch_size1, channel1, input_dim, 32]
+                if y_dim_len == 4:
+                    shape_y = [batch_size2, channel2, input_dim, 64]
+                elif y_dim_len == 3:
+                    shape_y = [channel2, input_dim, 64]
+            elif transpose_Y:
+                shape_x = [batch_size1, channel1, 32, input_dim]
+                if y_dim_len == 4:
+                    shape_y = [batch_size2, channel2, 8, input_dim]
+                elif y_dim_len == 3:
+                    shape_y = [channel2, 8, input_dim]
+            else:
+                shape_x = [batch_size1, channel1, 32, input_dim]
+                if y_dim_len == 4:
+                    shape_y = [batch_size2, channel2, input_dim, 16]
+                elif y_dim_len == 3:
+                    shape_y = [channel2, input_dim, 16]
+
+            if type == "x":
+                return np.random.random(shape_x).astype(np.float32)
+            else:
+                return np.random.random(shape_y).astype(np.float32)
+
+        matmul_op = OpConfig(
+            type="matmul_v2",
+            inputs={"X": ["input_data1"],
+                    "Y": ["input_data2"]},
+            outputs={"Out": ["matmul_output"]},
+            attrs={
+                "trans_x": kwargs["transpose_X"],
+                "trans_y": kwargs["transpose_Y"],
+                "fused_reshape_X": [],
+                "fused_reshape_Y": [],
+                "fused_transpose_X": [],
+                "fused_transpose_Y": [],
+                "fused_reshape_Out": [],
+                "fused_transpose_Out": []
+            })
+
+        program_config = ProgramConfig(
+            ops=[matmul_op],
+            weights={},
+            inputs={
+                "input_data1": TensorConfig(data_gen=partial(
+                    generate_input, "x", *args, **kwargs)),
+                "input_data2": TensorConfig(data_gen=partial(
+                    generate_input, "y", *args, **kwargs))
+            },
+            outputs=["matmul_output"])
+
+        yield program_config
+
+    def sample_predictor_configs(self, program_config):
+        config = self.create_inference_config(use_mkldnn=True)
+        yield config, (1e-5, 1e-5)
+
+    @given(
+        transpose_X=st.booleans(),
+        transpose_Y=st.booleans(),
+        y_dim_len=st.sampled_from([3, 4]),
+        batch_size1=st.integers(
+            min_value=1, max_value=4),
+        batch_size2=st.integers(
+            min_value=1, max_value=4),
+        channel1=st.sampled_from([1, 16, 32, 64]),
+        channel2=st.sampled_from([1, 16, 32, 64]),
+        input_dim=st.sampled_from([16, 32, 64]))
+    def test(self, *args, **kwargs):
+        self.run_test(*args, **kwargs)
+
+
+if __name__ == "__main__":
+    unittest.main()