test_trt_flatten2_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, 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 TestFlatten2MatmulFusePass(PassAutoScanTest):
    """
        x_var  
          |          
       flatten2 
          \
    flatten2_out_var    y_var
             \           /
                 matmul      bias_var
                    \          /
                   elementwise_add  
    """

    def sample_predictor_configs(self, program_config):
        # TRT
        config = self.create_trt_inference_config()
        config.enable_tensorrt_engine(
            max_batch_size=10,
            workspace_size=102400,
            min_subgraph_size=0,
            precision_mode=paddle_infer.PrecisionType.Float32,
            use_static=False,
            use_calib_mode=False)
        yield config, ['mul', 'elementwise_add'], (1e-4, 1e-1)

    def add_ignore_pass_case(self):
        # Here we put some skip rules to avoid known bugs
        def teller1(program_config, predictor_config):
            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 and attr of flatten2
        x_shape = draw(
            st.lists(st.integers(min_value=1, max_value=10),
                     min_size=4,
                     max_size=4))
        # [a, b, c, d] => [a, b*c*d]
        flatten_axis = 1
        flatten_shape = [x_shape[0], x_shape[1] * x_shape[2] * x_shape[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] = flatten_shape[1]

        # 4. Generate legal attr:axis of elementwise_add
        axis = draw(st.integers(min_value=-1, max_value=1))
        if axis == 0:
            axis = -1
        bias_shape = [
            y_shape[1],
        ]

        flatten2_op = OpConfig(
            "flatten2",
            inputs={
                "X": ["flatten2_x"],
            },
            axis=flatten_axis,
            outputs={
                "Out": ["flatten2_out"],
                "XShape": ["xshape"]
            },
        )
        matmul_op = OpConfig(
            "matmul",
            inputs={
                "X": ["flatten2_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 = [flatten2_op, matmul_op, add_op]

        program_config = ProgramConfig(
            ops=ops,
            weights={
                "matmul_y": TensorConfig(shape=y_shape),
                "bias": TensorConfig(shape=bias_shape),
            },
            inputs={
                "flatten2_x": TensorConfig(shape=x_shape),
            },
            outputs=ops[-1].outputs["Out"],
        )

        return program_config

    def test(self):
        self.run_and_statis(quant=False,
                            max_examples=25,
                            passes=["trt_flatten2_matmul_fuse_pass"])


if __name__ == "__main__":
    unittest.main()