forked from mindspore-Ecosystem/mindspore
remove custom akg test
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huangxinjing
parent
fc7346ffad
commit
0969fd6240
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# Copyright 2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import platform
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import pytest
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import numpy as np
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from mindspore import context, Tensor
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from mindspore.nn import Cell
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import mindspore.ops as ops
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from mindspore.ops import kernel
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@kernel
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def dtype_and_cast_example(a, b):
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"""
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test function for dtype and cast in Hybrid DSL
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"""
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d = allocate(a.shape, "float16")
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c = output_tensor(a.shape, "float16")
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for i0 in range(a.shape[0]):
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for i1 in range(a.shape[1]):
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d[i0, i1] = float16(1.0)
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c[i0, i1] = d[i0, i1] + float16(a[i0, i1])
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c[i0, i1] = c[i0, i1] * float16(b[i0, i1])
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return c
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@kernel
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def allocate_and_math_intrin_example(a, b):
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"""
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test function for allocate and math function in Hybrid DSL
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"""
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d = allocate(a.shape, a.dtype)
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c = output_tensor(a.shape, a.dtype)
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for i0 in range(a.shape[0]):
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for i1 in range(b.shape[1]):
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d[i0, i1] = abs(a[i0, i1])
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c[i0, i1] = d[i0, i1] + b[i0, i1]
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return c
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@kernel
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def grid_example(a, b):
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"""
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test function for grid in Hybrid DSL
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"""
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c = output_tensor(a.shape, a.dtype)
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for arg in grid(a.shape):
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c[arg] = a[arg] + b[arg[0], arg[1]]
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return c
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class TestMsHybridDSL(Cell):
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"""Net definition"""
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def __init__(self, func, func_type, out_shape=None, out_dtype=None):
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super(TestMsHybridDSL, self).__init__()
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self.program = ops.Custom(func, out_shape=out_shape, out_dtype=out_dtype, func_type=func_type)
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def construct(self, x, y):
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return self.program(x, y)
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def ms_kernel_cast_with_infer():
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"""
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test case Custom Op with functions written in Hybrid DSL and infer functions
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"""
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np.random.seed(10)
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input_x = np.random.normal(0, 1, [4, 4]).astype(np.float16)
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input_y = np.random.normal(0, 1, [4, 4]).astype(np.float16)
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test = TestMsHybridDSL(dtype_and_cast_example, "hybrid", lambda x, _: x, lambda x, _: x)
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output = test(Tensor(input_x), Tensor(input_y))
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expect = dtype_and_cast_example(input_x, input_y)
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compare_res = np.allclose(expect, output.asnumpy(), 0.001, 0.001)
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if not compare_res:
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raise ValueError("Precision error, compare result: {}".format(compare_res))
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def ms_kernel_cast_without_infer():
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"""
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test case Custom Op with functions written in Hybrid DSL and without infer functions
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"""
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np.random.seed(10)
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input_x = np.random.normal(0, 1, [4, 4]).astype(np.float16)
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input_y = np.random.normal(0, 1, [4, 4]).astype(np.float16)
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test = TestMsHybridDSL(dtype_and_cast_example, "hybrid")
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output = test(Tensor(input_x), Tensor(input_y))
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expect = dtype_and_cast_example(input_x, input_y)
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compare_res = np.allclose(expect, output.asnumpy(), 0.001, 0.001)
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if not compare_res:
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raise ValueError("Precision error, compare result: {}".format(compare_res))
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def ms_kernel_cast_pyfunc():
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"""
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test case Custom Op with functions written in Hybrid DSL and func_type pyfunc
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"""
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np.random.seed(10)
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input_x = np.random.normal(0, 1, [4, 4]).astype(np.float16)
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input_y = np.random.normal(0, 1, [4, 4]).astype(np.float16)
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test = TestMsHybridDSL(dtype_and_cast_example, "pyfunc", lambda x, _: x, lambda x, _: x)
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output = test(Tensor(input_x), Tensor(input_y))
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expect = dtype_and_cast_example(input_x, input_y)
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compare_res = np.allclose(expect, output.asnumpy(), 0.001, 0.001)
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if not compare_res:
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raise ValueError("Precision error, compare result: {}".format(compare_res))
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def ms_kernel_allocate():
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"""
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test case Custom Op with functions written in Hybrid DSL about math functions and allocate
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"""
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np.random.seed(10)
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input_x = np.random.normal(0, 1, [4, 4]).astype(np.float16)
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input_y = np.random.normal(0, 1, [4, 4]).astype(np.float16)
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test = TestMsHybridDSL(allocate_and_math_intrin_example, "hybrid", lambda x, _: x, lambda x, _: x)
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output = test(Tensor(input_x), Tensor(input_y))
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expect = allocate_and_math_intrin_example(input_x, input_y)
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compare_res = np.allclose(expect, output.asnumpy(), 0.001, 0.001)
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if not compare_res:
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raise ValueError("Precision error, compare result: {}".format(compare_res))
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def ms_kernel_allocate_cpu():
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"""
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test case Custom Op with functions written in Hybrid DSL about math functions and allocate
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for cpu, we test fp32 to avoid env diff in support of data types.
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"""
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np.random.seed(10)
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input_x = np.ones((4, 4)).astype(np.float32)
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input_y = np.ones((4, 4)).astype(np.float32)
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test = TestMsHybridDSL(allocate_and_math_intrin_example, "hybrid", lambda x, _: x, lambda x, _: x)
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output = test(Tensor(input_x), Tensor(input_y))
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expect = allocate_and_math_intrin_example(input_x, input_y)
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compare_res = np.allclose(expect, output.asnumpy(), 0.001, 0.001)
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if not compare_res:
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raise ValueError("Precision error, compare result: {}".format(compare_res))
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def ms_kernel_grid():
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"""
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test case Custom Op with functions written in Hybrid DSL about grid
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"""
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np.random.seed(10)
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input_x = np.random.normal(0, 1, [4, 4]).astype(np.float16)
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input_y = np.random.normal(0, 1, [4, 4]).astype(np.float16)
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test = TestMsHybridDSL(grid_example, "hybrid", lambda x, _: x, lambda x, _: x)
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output = test(Tensor(input_x), Tensor(input_y))
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expect = grid_example(input_x, input_y)
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compare_res = np.allclose(expect, output.asnumpy(), 0.001, 0.001)
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if not compare_res:
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raise ValueError("Precision error, compare result: {}".format(compare_res))
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def ms_kernel_grid_cpu():
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"""
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test case Custom Op with functions written in Hybrid DSL about grid
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"""
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np.random.seed(10)
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input_x = np.random.normal(0, 1, [4, 4]).astype(np.float32)
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input_y = np.random.normal(0, 1, [4, 4]).astype(np.float32)
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test = TestMsHybridDSL(grid_example, "hybrid", lambda x, _: x, lambda x, _: x)
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output = test(Tensor(input_x), Tensor(input_y))
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expect = grid_example(input_x, input_y)
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compare_res = np.allclose(expect, output.asnumpy(), 0.001, 0.001)
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if not compare_res:
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raise ValueError("Precision error, compare result: {}".format(compare_res))
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_ms_kernel_ascend_graph_mode():
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"""
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Feature: test case for Custom op with func_type="kernel"
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Description: ascend test case, Python DSL with kernel decorator in GRAPH_MODE.
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Expectation: the result match with numpy result
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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ms_kernel_cast_pyfunc()
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ms_kernel_cast_with_infer()
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ms_kernel_cast_without_infer()
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ms_kernel_allocate()
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ms_kernel_grid()
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_ms_kernel_ascend_pynative_mode():
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"""
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Feature: test case for Custom op with func_type="kernel"
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Description: ascend test case, Python DSL with kernel decorator in PYNATIVE_MODE.
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Expectation: the result match with numpy result
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"""
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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ms_kernel_cast_pyfunc()
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ms_kernel_cast_with_infer()
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ms_kernel_cast_without_infer()
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ms_kernel_allocate()
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ms_kernel_grid()
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_ms_kernel_gpu_graph_mode():
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"""
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Feature: test case for Custom op with func_type="kernel"
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Description: gpu test case, Python DSL with kernel decorator in GRAPH_MODE.
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Expectation: the result match with numpy result
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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ms_kernel_cast_pyfunc()
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ms_kernel_cast_with_infer()
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ms_kernel_cast_without_infer()
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ms_kernel_allocate()
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ms_kernel_grid()
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_ms_kernel_gpu_pynative_mode():
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"""
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Feature: test case for Custom op with func_type="kernel"
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Description: gpu test case, Python DSL with kernel decorator in PYNATIVE_MODE.
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Expectation: the result match with numpy result
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"""
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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ms_kernel_cast_pyfunc()
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ms_kernel_cast_with_infer()
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ms_kernel_cast_without_infer()
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ms_kernel_allocate()
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ms_kernel_grid()
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_ms_kernel_cpu_graph_mode():
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"""
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Feature: test case for Custom op with func_type="kernel"
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Description: cpu test case, Python DSL with kernel decorator in GRAPH_MODE.
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Expectation: the result match with numpy result
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"""
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if platform.system().lower() in {"windows", "darwin"}:
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# skip window and mac, same for pynative below
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pass
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else:
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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ms_kernel_allocate_cpu()
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ms_kernel_grid_cpu()
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_ms_kernel_cpu_pynative_mode():
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"""
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Feature: test case for Custom op with func_type="kernel"
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Description: cpu test case, Python DSL with kernel decorator in PYNATIVE_MODE.
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Expectation: the result match with numpy result
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"""
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if platform.system().lower() in {"windows", "darwin"}:
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pass
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else:
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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ms_kernel_allocate_cpu()
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ms_kernel_grid_cpu()
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