forked from mindspore-Ecosystem/mindspore
remove custom akg test
This commit is contained in:
huangxinjing
parent
fc7346ffad
commit
0969fd6240
|
|
@ -1,295 +0,0 @@
|
||||||
# Copyright 2022 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 platform
|
|
||||||
import pytest
|
|
||||||
import numpy as np
|
|
||||||
from mindspore import context, Tensor
|
|
||||||
from mindspore.nn import Cell
|
|
||||||
import mindspore.ops as ops
|
|
||||||
from mindspore.ops import kernel
|
|
||||||
|
|
||||||
|
|
||||||
@kernel
|
|
||||||
def dtype_and_cast_example(a, b):
|
|
||||||
"""
|
|
||||||
test function for dtype and cast in Hybrid DSL
|
|
||||||
"""
|
|
||||||
d = allocate(a.shape, "float16")
|
|
||||||
c = output_tensor(a.shape, "float16")
|
|
||||||
|
|
||||||
for i0 in range(a.shape[0]):
|
|
||||||
for i1 in range(a.shape[1]):
|
|
||||||
d[i0, i1] = float16(1.0)
|
|
||||||
c[i0, i1] = d[i0, i1] + float16(a[i0, i1])
|
|
||||||
c[i0, i1] = c[i0, i1] * float16(b[i0, i1])
|
|
||||||
return c
|
|
||||||
|
|
||||||
|
|
||||||
@kernel
|
|
||||||
def allocate_and_math_intrin_example(a, b):
|
|
||||||
"""
|
|
||||||
test function for allocate and math function in Hybrid DSL
|
|
||||||
"""
|
|
||||||
d = allocate(a.shape, a.dtype)
|
|
||||||
c = output_tensor(a.shape, a.dtype)
|
|
||||||
|
|
||||||
for i0 in range(a.shape[0]):
|
|
||||||
for i1 in range(b.shape[1]):
|
|
||||||
d[i0, i1] = abs(a[i0, i1])
|
|
||||||
c[i0, i1] = d[i0, i1] + b[i0, i1]
|
|
||||||
return c
|
|
||||||
|
|
||||||
|
|
||||||
@kernel
|
|
||||||
def grid_example(a, b):
|
|
||||||
"""
|
|
||||||
test function for grid in Hybrid DSL
|
|
||||||
"""
|
|
||||||
c = output_tensor(a.shape, a.dtype)
|
|
||||||
|
|
||||||
for arg in grid(a.shape):
|
|
||||||
c[arg] = a[arg] + b[arg[0], arg[1]]
|
|
||||||
return c
|
|
||||||
|
|
||||||
|
|
||||||
class TestMsHybridDSL(Cell):
|
|
||||||
"""Net definition"""
|
|
||||||
|
|
||||||
def __init__(self, func, func_type, out_shape=None, out_dtype=None):
|
|
||||||
super(TestMsHybridDSL, self).__init__()
|
|
||||||
|
|
||||||
self.program = ops.Custom(func, out_shape=out_shape, out_dtype=out_dtype, func_type=func_type)
|
|
||||||
|
|
||||||
def construct(self, x, y):
|
|
||||||
return self.program(x, y)
|
|
||||||
|
|
||||||
|
|
||||||
def ms_kernel_cast_with_infer():
|
|
||||||
"""
|
|
||||||
test case Custom Op with functions written in Hybrid DSL and infer functions
|
|
||||||
"""
|
|
||||||
np.random.seed(10)
|
|
||||||
input_x = np.random.normal(0, 1, [4, 4]).astype(np.float16)
|
|
||||||
input_y = np.random.normal(0, 1, [4, 4]).astype(np.float16)
|
|
||||||
|
|
||||||
test = TestMsHybridDSL(dtype_and_cast_example, "hybrid", lambda x, _: x, lambda x, _: x)
|
|
||||||
output = test(Tensor(input_x), Tensor(input_y))
|
|
||||||
expect = dtype_and_cast_example(input_x, input_y)
|
|
||||||
compare_res = np.allclose(expect, output.asnumpy(), 0.001, 0.001)
|
|
||||||
if not compare_res:
|
|
||||||
raise ValueError("Precision error, compare result: {}".format(compare_res))
|
|
||||||
|
|
||||||
|
|
||||||
def ms_kernel_cast_without_infer():
|
|
||||||
"""
|
|
||||||
test case Custom Op with functions written in Hybrid DSL and without infer functions
|
|
||||||
"""
|
|
||||||
np.random.seed(10)
|
|
||||||
input_x = np.random.normal(0, 1, [4, 4]).astype(np.float16)
|
|
||||||
input_y = np.random.normal(0, 1, [4, 4]).astype(np.float16)
|
|
||||||
|
|
||||||
test = TestMsHybridDSL(dtype_and_cast_example, "hybrid")
|
|
||||||
output = test(Tensor(input_x), Tensor(input_y))
|
|
||||||
expect = dtype_and_cast_example(input_x, input_y)
|
|
||||||
compare_res = np.allclose(expect, output.asnumpy(), 0.001, 0.001)
|
|
||||||
if not compare_res:
|
|
||||||
raise ValueError("Precision error, compare result: {}".format(compare_res))
|
|
||||||
|
|
||||||
|
|
||||||
def ms_kernel_cast_pyfunc():
|
|
||||||
"""
|
|
||||||
test case Custom Op with functions written in Hybrid DSL and func_type pyfunc
|
|
||||||
"""
|
|
||||||
np.random.seed(10)
|
|
||||||
input_x = np.random.normal(0, 1, [4, 4]).astype(np.float16)
|
|
||||||
input_y = np.random.normal(0, 1, [4, 4]).astype(np.float16)
|
|
||||||
|
|
||||||
test = TestMsHybridDSL(dtype_and_cast_example, "pyfunc", lambda x, _: x, lambda x, _: x)
|
|
||||||
output = test(Tensor(input_x), Tensor(input_y))
|
|
||||||
expect = dtype_and_cast_example(input_x, input_y)
|
|
||||||
compare_res = np.allclose(expect, output.asnumpy(), 0.001, 0.001)
|
|
||||||
if not compare_res:
|
|
||||||
raise ValueError("Precision error, compare result: {}".format(compare_res))
|
|
||||||
|
|
||||||
|
|
||||||
def ms_kernel_allocate():
|
|
||||||
"""
|
|
||||||
test case Custom Op with functions written in Hybrid DSL about math functions and allocate
|
|
||||||
"""
|
|
||||||
np.random.seed(10)
|
|
||||||
input_x = np.random.normal(0, 1, [4, 4]).astype(np.float16)
|
|
||||||
input_y = np.random.normal(0, 1, [4, 4]).astype(np.float16)
|
|
||||||
|
|
||||||
test = TestMsHybridDSL(allocate_and_math_intrin_example, "hybrid", lambda x, _: x, lambda x, _: x)
|
|
||||||
output = test(Tensor(input_x), Tensor(input_y))
|
|
||||||
expect = allocate_and_math_intrin_example(input_x, input_y)
|
|
||||||
compare_res = np.allclose(expect, output.asnumpy(), 0.001, 0.001)
|
|
||||||
if not compare_res:
|
|
||||||
raise ValueError("Precision error, compare result: {}".format(compare_res))
|
|
||||||
|
|
||||||
|
|
||||||
def ms_kernel_allocate_cpu():
|
|
||||||
"""
|
|
||||||
test case Custom Op with functions written in Hybrid DSL about math functions and allocate
|
|
||||||
for cpu, we test fp32 to avoid env diff in support of data types.
|
|
||||||
"""
|
|
||||||
np.random.seed(10)
|
|
||||||
input_x = np.ones((4, 4)).astype(np.float32)
|
|
||||||
input_y = np.ones((4, 4)).astype(np.float32)
|
|
||||||
|
|
||||||
test = TestMsHybridDSL(allocate_and_math_intrin_example, "hybrid", lambda x, _: x, lambda x, _: x)
|
|
||||||
output = test(Tensor(input_x), Tensor(input_y))
|
|
||||||
expect = allocate_and_math_intrin_example(input_x, input_y)
|
|
||||||
compare_res = np.allclose(expect, output.asnumpy(), 0.001, 0.001)
|
|
||||||
if not compare_res:
|
|
||||||
raise ValueError("Precision error, compare result: {}".format(compare_res))
|
|
||||||
|
|
||||||
|
|
||||||
def ms_kernel_grid():
|
|
||||||
"""
|
|
||||||
test case Custom Op with functions written in Hybrid DSL about grid
|
|
||||||
"""
|
|
||||||
np.random.seed(10)
|
|
||||||
input_x = np.random.normal(0, 1, [4, 4]).astype(np.float16)
|
|
||||||
input_y = np.random.normal(0, 1, [4, 4]).astype(np.float16)
|
|
||||||
|
|
||||||
test = TestMsHybridDSL(grid_example, "hybrid", lambda x, _: x, lambda x, _: x)
|
|
||||||
output = test(Tensor(input_x), Tensor(input_y))
|
|
||||||
expect = grid_example(input_x, input_y)
|
|
||||||
compare_res = np.allclose(expect, output.asnumpy(), 0.001, 0.001)
|
|
||||||
if not compare_res:
|
|
||||||
raise ValueError("Precision error, compare result: {}".format(compare_res))
|
|
||||||
|
|
||||||
|
|
||||||
def ms_kernel_grid_cpu():
|
|
||||||
"""
|
|
||||||
test case Custom Op with functions written in Hybrid DSL about grid
|
|
||||||
"""
|
|
||||||
np.random.seed(10)
|
|
||||||
input_x = np.random.normal(0, 1, [4, 4]).astype(np.float32)
|
|
||||||
input_y = np.random.normal(0, 1, [4, 4]).astype(np.float32)
|
|
||||||
|
|
||||||
test = TestMsHybridDSL(grid_example, "hybrid", lambda x, _: x, lambda x, _: x)
|
|
||||||
output = test(Tensor(input_x), Tensor(input_y))
|
|
||||||
expect = grid_example(input_x, input_y)
|
|
||||||
compare_res = np.allclose(expect, output.asnumpy(), 0.001, 0.001)
|
|
||||||
if not compare_res:
|
|
||||||
raise ValueError("Precision error, compare result: {}".format(compare_res))
|
|
||||||
|
|
||||||
|
|
||||||
@pytest.mark.level0
|
|
||||||
@pytest.mark.platform_arm_ascend_training
|
|
||||||
@pytest.mark.platform_x86_ascend_training
|
|
||||||
@pytest.mark.env_onecard
|
|
||||||
def test_ms_kernel_ascend_graph_mode():
|
|
||||||
"""
|
|
||||||
Feature: test case for Custom op with func_type="kernel"
|
|
||||||
Description: ascend test case, Python DSL with kernel decorator in GRAPH_MODE.
|
|
||||||
Expectation: the result match with numpy result
|
|
||||||
"""
|
|
||||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
|
||||||
ms_kernel_cast_pyfunc()
|
|
||||||
ms_kernel_cast_with_infer()
|
|
||||||
ms_kernel_cast_without_infer()
|
|
||||||
ms_kernel_allocate()
|
|
||||||
ms_kernel_grid()
|
|
||||||
|
|
||||||
|
|
||||||
@pytest.mark.level0
|
|
||||||
@pytest.mark.platform_arm_ascend_training
|
|
||||||
@pytest.mark.platform_x86_ascend_training
|
|
||||||
@pytest.mark.env_onecard
|
|
||||||
def test_ms_kernel_ascend_pynative_mode():
|
|
||||||
"""
|
|
||||||
Feature: test case for Custom op with func_type="kernel"
|
|
||||||
Description: ascend test case, Python DSL with kernel decorator in PYNATIVE_MODE.
|
|
||||||
Expectation: the result match with numpy result
|
|
||||||
"""
|
|
||||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
|
||||||
ms_kernel_cast_pyfunc()
|
|
||||||
ms_kernel_cast_with_infer()
|
|
||||||
ms_kernel_cast_without_infer()
|
|
||||||
ms_kernel_allocate()
|
|
||||||
ms_kernel_grid()
|
|
||||||
|
|
||||||
|
|
||||||
@pytest.mark.level0
|
|
||||||
@pytest.mark.platform_x86_gpu_training
|
|
||||||
@pytest.mark.env_onecard
|
|
||||||
def test_ms_kernel_gpu_graph_mode():
|
|
||||||
"""
|
|
||||||
Feature: test case for Custom op with func_type="kernel"
|
|
||||||
Description: gpu test case, Python DSL with kernel decorator in GRAPH_MODE.
|
|
||||||
Expectation: the result match with numpy result
|
|
||||||
"""
|
|
||||||
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
|
||||||
ms_kernel_cast_pyfunc()
|
|
||||||
ms_kernel_cast_with_infer()
|
|
||||||
ms_kernel_cast_without_infer()
|
|
||||||
ms_kernel_allocate()
|
|
||||||
ms_kernel_grid()
|
|
||||||
|
|
||||||
|
|
||||||
@pytest.mark.level0
|
|
||||||
@pytest.mark.platform_x86_gpu_training
|
|
||||||
@pytest.mark.env_onecard
|
|
||||||
def test_ms_kernel_gpu_pynative_mode():
|
|
||||||
"""
|
|
||||||
Feature: test case for Custom op with func_type="kernel"
|
|
||||||
Description: gpu test case, Python DSL with kernel decorator in PYNATIVE_MODE.
|
|
||||||
Expectation: the result match with numpy result
|
|
||||||
"""
|
|
||||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
|
|
||||||
ms_kernel_cast_pyfunc()
|
|
||||||
ms_kernel_cast_with_infer()
|
|
||||||
ms_kernel_cast_without_infer()
|
|
||||||
ms_kernel_allocate()
|
|
||||||
ms_kernel_grid()
|
|
||||||
|
|
||||||
|
|
||||||
@pytest.mark.level0
|
|
||||||
@pytest.mark.platform_x86_cpu
|
|
||||||
@pytest.mark.env_onecard
|
|
||||||
def test_ms_kernel_cpu_graph_mode():
|
|
||||||
"""
|
|
||||||
Feature: test case for Custom op with func_type="kernel"
|
|
||||||
Description: cpu test case, Python DSL with kernel decorator in GRAPH_MODE.
|
|
||||||
Expectation: the result match with numpy result
|
|
||||||
"""
|
|
||||||
if platform.system().lower() in {"windows", "darwin"}:
|
|
||||||
# skip window and mac, same for pynative below
|
|
||||||
pass
|
|
||||||
else:
|
|
||||||
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
|
|
||||||
ms_kernel_allocate_cpu()
|
|
||||||
ms_kernel_grid_cpu()
|
|
||||||
|
|
||||||
|
|
||||||
@pytest.mark.level0
|
|
||||||
@pytest.mark.platform_x86_cpu
|
|
||||||
@pytest.mark.env_onecard
|
|
||||||
def test_ms_kernel_cpu_pynative_mode():
|
|
||||||
"""
|
|
||||||
Feature: test case for Custom op with func_type="kernel"
|
|
||||||
Description: cpu test case, Python DSL with kernel decorator in PYNATIVE_MODE.
|
|
||||||
Expectation: the result match with numpy result
|
|
||||||
"""
|
|
||||||
if platform.system().lower() in {"windows", "darwin"}:
|
|
||||||
pass
|
|
||||||
else:
|
|
||||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
|
|
||||||
ms_kernel_allocate_cpu()
|
|
||||||
ms_kernel_grid_cpu()
|
|
||||||
Loading…
Reference in New Issue