update mkldnn matmul_transpose_reshape fuse pass ut

paddle/fluid/operators/matmul_op.cc

@@ -1,11 +1,8 @@
 /* Copyright (c) 2017 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.
@@ -695,9 +692,32 @@ class MatMulOp : public framework::OperatorWithKernel {
                                             "received %d",
                                             reshape_out_size));
 
-      auto it = std::find(reshape_out.begin(), reshape_out.end(), -1);
+      // int num_negative = std::count(reshape_out.begin(), reshape_out.end(),
+      // -1);
+      // PADDLE_ENFORCE_LE(num_negative, 1,
+      //                   platform::errors::InvalidArgument(
+      //                       "The max number of -1 in fused_reshape_Out is 1 "
+      //                       "but received %d.",
+      //                       num_negative));
+
+      // auto it_zero = std::find(reshape_out.begin(), reshape_out.end(), 0);
+      // if (it_zero != reshape_out.end()) {
+      //   for (uint64_t i = 0; i < reshape_out.size(); i++) {
+      //     if (reshape_out[i] == 0) {
+      //       PADDLE_ENFORCE_LT(
+      //           i, ddim_out.size(),
+      //           platform::errors::InvalidArgument(
+      //               "The index of 0 in fused_reshape_Out ",
+      //               "should be less than output dim size, ",
+      //               "but the index is %d and output dim size is %d", i,
+      //               ddim_out.size()));
+      //       reshape_out[i] = ddim_out.at(i);
+      //     }
+      //   }
+      // }
 
       // if "-1" is present then one of reshape dims must be infered
+      auto it = std::find(reshape_out.begin(), reshape_out.end(), -1);
       if (it != reshape_out.end()) {
         int index = std::distance(reshape_out.begin(), it);
 
@@ -840,28 +860,22 @@ class MatMulOpMaker : public framework::OpProtoAndCheckerMaker {
 #endif
     AddComment(R"DOC(
 MatMul Operator.
-
-
 This operator is used to perform (batched) matrix multiplication
 over the last two dimensions of the input tensors `X` and `Y`.
-
 If a transpose flag is specified, the last two dimensions of the
 tensor are transposed. If the tensor is rank-1 of shape [D], then
 for `X` it is treated as [1, D] in nontransposed form and as [D, 1]
 in transposed form, whereas for `Y` it is the opposite: It is treated
 as [D, 1] in nontransposed form and as [1, D] in transposed form.
-
 Examples without transpose:
 - X: [K], Y: [K] => Out: [1]
 - X: [K], Y: [K, N] => Out: [N]
 - X: [B, M, K], Y: [K] => Out: [B, M]
 - X: [M, K], Y: [B, K, N] => Out: [B, M, N]
 - X: [B, M, K], Y: [B, K, N] => Out: [B, M, N]
 - X: [B, ..., M, K], Y: [B, ..., K, N] => Out: [B, ..., M, N]
-
 Example of matrix multiplication with head_number of H
 - X: [B, M, K], Y: [B, K, N] => Out: [B, M, H * N]
-
 The behavior is designed to be similar to the `numpy.matmul` function.
 The differences are:
 - When the rank of the input data is less than or equal to 3, it
@@ -872,10 +886,8 @@ The differences are:
 - We add `head_number` attribute, which is used to multiple two matrixes head
   by head, and eventually concatenates the output of several (head_number)
   small matrixes multiplication.
-
 Both the input `X` and `Y` can carry the LoD (Level of Details) information,
 or not. But the output only shares the LoD information with input `X`.
-
 )DOC");
   }
 };

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

@@ -97,6 +97,7 @@ if (WITH_MKLDNN)
   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)
+  set_tests_properties(test_mkldnn_matmul_transpose_reshape_fuse_pass PROPERTIES TIMEOUT 100)
   set_tests_properties(test_conv_transpose_bn_fuse_pass PROPERTIES TIMEOUT 300)
   set_tests_properties(test_mkldnn_conv_hard_sigmoid_fuse_pass PROPERTIES TIMEOUT 300)
   set_tests_properties(test_mkldnn_conv_hard_swish_fuse_pass PROPERTIES TIMEOUT 300)

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

@@ -1,4 +1,4 @@
-# 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.
@@ -12,69 +12,118 @@
 # 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, OpConfig
 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 MatmulTransposeReshapeMkldnnFusePassTest(InferencePassTest):
-    def setUp(self):
-        self.set_params()
-        with fluid.program_guard(self.main_program, self.startup_program):
-            data = fluid.data(
-                name="data", shape=self.data_shape, dtype="float32")
-            weight = fluid.layers.create_parameter(
-                shape=self.weight_shape, dtype="float32")
-            matmul = fluid.layers.matmul(
-                data,
-                weight,
-                transpose_x=self.transpose_x,
-                transpose_y=self.transpose_y)
-            transpose = fluid.layers.transpose(matmul, self.tranpose_perm)
-            reshape = fluid.layers.reshape(transpose, shape=self.reshape_shape)
-
-        self.fetch_list = [reshape]
-        self.enable_mkldnn = True
-
-    def set_params(self):
-        self.data_shape = [-1, 3, 100, 110]
-        self.weight_shape = [1, 3, 110, 100]
-        self.feeds = {
-            "data": np.random.random((1, 3, 100, 110)).astype("float32")
-        }
-        self.transpose_x = False
-        self.transpose_y = False
-        self.tranpose_perm = [0, 2, 1, 3]
-        self.reshape_shape = [3, 100, 100]
-        self.pass_name = 'matmul_transpose_reshape_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 MatmulTransposeReshapeMkldnnFusePassTest_1(
-        MatmulTransposeReshapeMkldnnFusePassTest):
-    def set_params(self):
-        self.data_shape = [-1, 3, 100, 100]
-        self.weight_shape = [1, 3, 100, 100]
-        self.feeds = {
-            "data": np.random.random((1, 3, 100, 100)).astype("float32")
-        }
-        self.transpose_x = True
-        self.transpose_y = True
-        self.tranpose_perm = [0, 2, 1, 3]
-        self.reshape_shape = [6, 50, 100]
-        self.pass_name = 'matmul_transpose_reshape_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 TestMatmulTransposeReshapeMkldnnFusePass(PassAutoScanTest):
+    def is_program_valid(self, program_config: ProgramConfig) -> bool:
+        attrs = [
+            program_config.ops[i].attrs
+            for i in range(len(program_config.ops))
+        ]
+        # If the problem has been fixed, the judgment 
+        # needs to be deleted!!!
+        if 0 in attrs[2]['shape']:
+            return False
+
+        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, -1, 128], [-1, 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_input(type):
+            if transpose_X and transpose_Y:
+                shape_x = [batch_size, channel, input_dim, 32]
+                shape_y = [batch_size, channel, 64, input_dim]
+            elif transpose_X:
+                shape_x = [batch_size, channel, input_dim, 32]
+                shape_y = [batch_size, channel, input_dim, 64]
+            elif transpose_Y:
+                shape_x = [batch_size, channel, 32, input_dim]
+                shape_y = [batch_size, channel, 8, input_dim]
+            else:
+                shape_x = [batch_size, channel, 32, input_dim]
+                shape_y = [batch_size, channel, 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",
+            inputs={"X": ["input_data1"],
+                    "Y": ["input_data2"]},
+            outputs={"Out": ["matmul_output"]},
+            attrs={
+                "transpose_X": transpose_X,
+                "transpose_Y": transpose_Y,
+                "alpha": alpha,
+                "fused_reshape_X": [],
+                "fused_reshape_Y": [],
+                "fused_transpose_X": [],
+                "fused_transpose_Y": [],
+                "fused_reshape_Out": [],
+                "fused_transpose_Out": []
+            })
+
+        transpose2_op = OpConfig(
+            type="transpose2",
+            inputs={"X": ["matmul_output"]},
+            outputs={
+                "Out": ["transpose2_output"],
+                "XShape": ["transpose2_xshape"]
+            },
+            attrs={'axis': axis})
+
+        reshape2_op = OpConfig(
+            type="reshape2",
+            inputs={"X": ["transpose2_output"]},
+            outputs={
+                "Out": ["reshape2_output"],
+                "XShape": ["reshape2_xshape"]
+            },
+            attrs={'shape': shape})
+
+        model_net = [matmul_op, transpose2_op, reshape2_op]
+
+        program_config = ProgramConfig(
+            ops=model_net,
+            weights={},
+            inputs={
+                "input_data1":
+                TensorConfig(data_gen=partial(generate_input, "x")),
+                "input_data2":
+                TensorConfig(data_gen=partial(generate_input, "y"))
+            },
+            outputs=["reshape2_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=["matmul_transpose_reshape_fuse_pass"])
 
 
 if __name__ == "__main__":