MindSpore-AKG/tests/common/test_run/matmul_gelu_run.py

# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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.

import numpy as np
from akg.utils import kernel_exec as utils
from akg.ops.nn import matmul
from tests.common.test_run.matmul_run import *
from tests.common.test_op import gelu

def np_gelu(inputs):
    # result_numpy
    cdf = 0.5 * (1.0 + np.tanh((np.sqrt(2 / np.pi) * (inputs + 0.044715 * np.power(inputs, 3)))))
    exp_output = inputs * cdf
    return exp_output

def matmul_gelu(x, y, b, out_dtype, left_format="zZ", right_format="nZ", out_format="zN", transpose_x=False, transpose_y=False, attrs={}):
    matmul_res, attrs = matmul.matmul(x, y, b, out_dtype, left_format, right_format, out_format, transpose_x, transpose_y, attrs=None)
    attr = {}
    res = gelu.gelu(matmul_res)
    return res, attrs

def matmul_gelu_compile(shape_x, shape_y, bias, left_format, right_format, output_format, adj_x, adj_y, dtype, bias_dtype, out_dtype, kernel_name, attrs, tuning=False):
    batch_tuple, m, k, n = extract_dim(shape_x, shape_y, adj_x, adj_y)
    m = (m + 15) // 16 * 16
    n = (n + 15) // 16 * 16
    k = (k + 15) // 16 * 16
    shape_xx, shape_yy, bias_shape, out_shape, k = get_converted_shapes(m, n, k, batch_tuple, adj_x, adj_y, bias,
                                                                        left_format, right_format, output_format)
    input_shapes = [shape_xx, shape_yy, bias_shape]
    input_types = [dtype, dtype, bias_dtype]
    has_bias = False
    if bias == 1:
        has_bias = True
    op_attrs = [out_dtype, left_format, right_format, output_format, adj_x, adj_y, attrs]
    if has_bias == False:
        input_shapes = [shape_xx, shape_yy]
        input_types = [dtype, dtype]
        op_attrs = [None, out_dtype, left_format, right_format, output_format, adj_x, adj_y, attrs]
    return utils.op_build_test(matmul_gelu, input_shapes, input_types, op_attrs, kernel_name, attrs=attrs, tuning=tuning)

def matmul_gelu_execute(shape_x, shape_y, bias, left_format, right_format, out_format, adj_x, adj_y, dtype, bias_dtype, out_dtype, kernel_name, attrs={}):
    batch_tuple, m, k, n = extract_dim(shape_x, shape_y, adj_x, adj_y)
    m = (m + 15) // 16 * 16
    n = (n + 15) // 16 * 16
    k = (k + 15) // 16 * 16
    shape_xx, shape_yy, bias_shape, out_shape, k = get_converted_shapes(m, n, k, batch_tuple, adj_x, adj_y, bias, left_format, right_format, out_format)
    mod = matmul_gelu_compile(shape_x, shape_y, bias, left_format, right_format, out_format, adj_x, adj_y, dtype, bias_dtype, out_dtype, kernel_name, attrs={})
    # generate data
    m_x, m_y, bench_mark, bias_data = matmul_data(batch_tuple, m, k, n, dtype, bias_dtype, out_dtype, bias, adj_x, adj_y, left_format, right_format, out_format)
    bench_mark = np_gelu(bench_mark)

    # mod launch
    output = np.full(out_shape, np.nan, out_dtype)
    if bias == 0:
        output = utils.mod_launch(mod, (m_x, m_y, output), expect=bench_mark)
    elif bias == 1:
        output = utils.mod_launch(mod, (m_x, m_y, bias_data, output), expect=bench_mark)
    # compare result
    rtol, atol = get_rtol_atol("matmul", dtype)
    compare_result = compare_tensor(output, bench_mark, rtol=rtol, atol=atol, equal_nan=True)
    return (m_x, m_y), output, bench_mark, compare_result