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- # 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 akg.ops.math import addn
- from akg.ms.cce import add
-
- def matmul_addn(x, y, adds, 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 = {}
- addn_res = addn.addn(adds)
- res, attr_map= add.TensorAdd(matmul_res, addn_res)
- return res, attrs
-
-
- def matmul_addn_compile(shape_x, shape_y, bias, add_n, 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)
- shape_dy = out_shape
- addn_shapes = []
- for i in range(add_n):
- addn_shapes.append(out_shape)
-
- input_shapes = [shape_xx, shape_yy, addn_shapes, bias_shape]
- input_types = [dtype, dtype, out_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, addn_shapes]
- input_types = [dtype, dtype, out_dtype]
- op_attrs = [None, out_dtype, left_format, right_format, output_format, adj_x, adj_y, attrs]
- return utils.op_build_test(matmul_addn, input_shapes, input_types, op_attrs, kernel_name, attrs=attrs, tuning=tuning)
-
- def matmul_addn_execute(shape_x, shape_y, bias , add_n, 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_addn_compile(shape_x, shape_y, bias, add_n, 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)
-
- inputs = []
- for i in range(add_n):
- input = random_gaussian(out_shape, miu=1, sigma=0.1).astype(out_dtype)
- inputs.append(input)
- bench_mark = np.add(np.sum(inputs, axis=0), bench_mark)
-
- # mod launch
- output = np.full(out_shape, np.nan, out_dtype)
-
- if bias == 0:
- inputs.insert(0, m_y)
- inputs.insert(0, m_x)
- inputs.append(output)
- output = utils.mod_launch(mod, inputs, expect=bench_mark)
- elif bias == 1:
- inputs.insert(0, m_y)
- inputs.insert(0, m_x)
- inputs.append(bias_data)
- inputs.append(output)
-
- output = utils.mod_launch(mod, inputs, 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
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