text: # 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 TestSqueeze2MatmulFusePass(PassAutoScanTest):
      """
          x_var  
            |          
         squeeze2 
            \
      squeeze2_out_var    y_var
               \           /
                   matmul      bias_var
                      \          /
                     elementwise_add  
      """
  
      def sample_predictor_configs(self, program_config):
          # cpu
          config = self.create_inference_config(use_gpu=False)
          yield config, ["mul", "elementwise_add"], (1e-5, 1e-5)
  
          # for gpu
          config = self.create_inference_config(use_gpu=True)
          yield config, ["mul", "elementwise_add"], (1e-5, 1e-5)
  
          # TRT
          # config = self.create_trt_inference_config()
          # config.enable_tensorrt_engine(
          #     max_batch_size=10,
          #     workspace_size=10240,
          #     min_subgraph_size=0,
          #     precision_mode=paddle_infer.PrecisionType.Float32,
          #     use_static=False,
          #     use_calib_mode=False)
          # yield config, ['mul', 'elementwise_add'], (1e-5, 1e-5)
  
      def add_ignore_pass_case(self):
          # Here we put some skip rules to avoid known bugs
          def teller1(program_config, predictor_config):
              if predictor_config.tensorrt_engine_enabled():
                  # On 3080, the results of MatMul and Mul are different 
                  # When the input Y is weight
                  return True
  
                  # On TRT when the input Y is weight, Mul is converted to FC
                  predictor_config.exp_disable_tensorrt_ops(["elementwise_add"])
                  if "matmul_y" not in program_config.weights \
                         or "bias" not in program_config.weights:
                      return True
  
                  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 of input:X of squeeze2
          x_shape = draw(
              st.lists(
                  st.integers(
                      min_value=1, max_value=8), min_size=2, max_size=2))
          # axes of squeeze2 == [2, 3]
          x_shape += [1, 1]
          axes = [2, 3]
  
          # 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 or axis == -1:
              bias_shape = [x_shape[0], y_shape[1]]
          else:
              bias_shape = [y_shape[1], ]
          if draw(st.booleans()):
              bias_shape[-1] = 1
              if len(bias_shape) == 2 and draw(st.booleans()):
                  bias_shape[0] = 1
  
          axis = 0
          bias_shape = [2, ]
          x_shape = [2, 1, 1, 1]
          y_shape = [1, 2]
  
          squeeze2_op = OpConfig(
              "squeeze2",
              inputs={"X": ["squeeze2_x"], },
              axes=axes,
              outputs={"Out": ["squeeze2_out"],
                       "XShape": ["xshape"]}, )
          matmul_op = OpConfig(
              "matmul",
              inputs={"X": ["squeeze2_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 = [squeeze2_op, matmul_op, add_op]
  
          if draw(st.integers(min_value=1, max_value=10)) <= 8:
              program_config = ProgramConfig(
                  ops=ops,
                  weights={
                      "matmul_y": TensorConfig(shape=y_shape),
                      "bias": TensorConfig(shape=bias_shape),
                  },
                  inputs={"squeeze2_x": TensorConfig(shape=x_shape), },
                  outputs=ops[-1].outputs["Out"], )
          else:
              program_config = ProgramConfig(
                  ops=ops,
                  weights={},
                  inputs={
                      "squeeze2_x": TensorConfig(shape=x_shape),
                      "matmul_y": TensorConfig(shape=y_shape),
                      "bias": TensorConfig(shape=bias_shape),
                  },
                  outputs=ops[-1].outputs["Out"], )
          return program_config
  
      def test(self):
          self.run_and_statis(
              quant=False,
              max_examples=50,
              max_duration=1000,
              passes=["gpu_cpu_squeeze2_matmul_fuse_pass"])
  
  
  if __name__ == "__main__":
      unittest.main()
  