update mkldnn scale_matmul fuse pass ut (#37210)

* update mkldnn scale_matmul fuse pass ut

* update mkldnn scale_matmul_fuse_pass ut

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

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+# 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.
@@ -12,61 +12,147 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from __future__ import print_function
-
-import unittest
+from auto_scan_test import PassAutoScanTest, SkipReasons
+from program_config import TensorConfig, ProgramConfig
 import numpy as np
-from inference_pass_test import InferencePassTest
-import paddle.fluid as fluid
-import paddle.fluid.core as core
-from paddle.fluid.core import AnalysisConfig
-from paddle.fluid.core import PassVersionChecker
-
-
-class ScaleMatmulMkldnnFusePassTest(InferencePassTest):
-    def setUp(self):
-        self.set_params()
-        with fluid.program_guard(self.main_program, self.startup_program):
-            data = fluid.data(
-                name="data", shape=[1, 3, 100, 100], dtype="float32")
-            weight = fluid.layers.create_parameter(
-                shape=[1, 3, 100, 100], dtype="float32")
-            scale = fluid.layers.scale(data, scale=self.scale_scale)
-            matmul = fluid.layers.matmul(
-                scale,
-                weight,
-                transpose_x=self.transpose_x,
-                transpose_y=self.transpose_y)
-
-        self.fetch_list = [matmul]
-        self.enable_mkldnn = True
-
-    def set_params(self):
-        self.feeds = {
-            "data": np.random.random((1, 3, 100, 100)).astype("float32")
-        }
-        self.scale_scale = 2.0
-        self.transpose_x = False
-        self.transpose_y = False
-        self.pass_name = "scale_matmul_fuse_pass"
-
-    def test_check_output(self):
-        use_gpu = False
-        self.check_output_with_option(use_gpu)
-
-    def test_pass_compatible(self):
-        self.assertTrue(PassVersionChecker.IsCompatible(self.pass_name))
-
-
-class ScaleMatmulMkldnnFusePassTest_1(ScaleMatmulMkldnnFusePassTest):
-    def set_params(self):
-        self.feeds = {
-            "data": np.random.random((1, 3, 100, 100)).astype("float32")
-        }
-        self.scale_scale = 5.0
-        self.transpose_x = True
-        self.transpose_y = True
-        self.pass_name = "scale_matmul_fuse_pass"
+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 TestScaleMatmulMkldnnFusePass(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=2))
+        bias = 0.0
+        bias_after_scale = draw(st.booleans())
+        transpose_X = draw(st.booleans())
+        transpose_Y = draw(st.booleans())
+        alpha = draw(st.floats(min_value=0.01, max_value=2))
+        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([1, 32, 64]))
+
+        def generate_input(attrs, type):
+            if attrs[1]['transpose_X'] and attrs[1]['transpose_Y']:
+                shape_x = [
+                    attrs[2]['batch_size'], attrs[2]['channel'],
+                    attrs[2]['input_dim'], 32
+                ]
+                shape_y = [
+                    attrs[2]['batch_size'], attrs[2]['channel'], 64,
+                    attrs[2]['input_dim']
+                ]
+            elif attrs[1]['transpose_X']:
+                shape_x = [
+                    attrs[2]['batch_size'], attrs[2]['channel'],
+                    attrs[2]['input_dim'], 32
+                ]
+                shape_y = [
+                    attrs[2]['batch_size'], attrs[2]['channel'],
+                    attrs[2]['input_dim'], 64
+                ]
+            elif attrs[1]['transpose_Y']:
+                shape_x = [
+                    attrs[2]['batch_size'], attrs[2]['channel'], 32,
+                    attrs[2]['input_dim']
+                ]
+                shape_y = [
+                    attrs[2]['batch_size'], attrs[2]['channel'], 8,
+                    attrs[2]['input_dim']
+                ]
+            else:
+                shape_x = [
+                    attrs[2]['batch_size'], attrs[2]['channel'], 32,
+                    attrs[2]['input_dim']
+                ]
+                shape_y = [
+                    attrs[2]['batch_size'], attrs[2]['channel'],
+                    attrs[2]['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)
+
+        attrs = [{
+            "scale": scale,
+            "bias": bias,
+            "bias_after_scale": bias_after_scale
+        }, {
+            "transpose_X": transpose_X,
+            "transpose_Y": transpose_Y,
+            "alpha": alpha
+        }, {
+            'batch_size': batch_size,
+            'channel': channel,
+            'input_dim': input_dim
+        }]
+
+        ops_config = [{
+            "op_type": "scale",
+            "op_inputs": {
+                "X": ["input_data1"]
+            },
+            "op_outputs": {
+                "Out": ["scale_output"]
+            },
+            "op_attrs": {
+                "scale": attrs[0]['scale'],
+                "bias": attrs[0]['bias'],
+                "bias_after_scale": attrs[0]['bias_after_scale']
+            },
+        }, {
+            "op_type": "matmul",
+            "op_inputs": {
+                "X": ["scale_output"],
+                "Y": ["input_data2"]
+            },
+            "op_outputs": {
+                "Out": ["matmul_output"]
+            },
+            "op_attrs": {
+                'transpose_X': attrs[1]['transpose_X'],
+                'transpose_Y': attrs[1]['transpose_Y'],
+                'alpha': attrs[1]['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_input, attrs, "x")),
+                "input_data2":
+                TensorConfig(data_gen=partial(generate_input, attrs, "y"))
+            },
+            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=["scale_matmul_fuse_pass"])
 
 
 if __name__ == "__main__":