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forgot some test cases
pull/11392/head
Peilin Wang 4 years ago
parent e214c69bc2
commit 945ef9ff89
2 changed files with 45 additions and 144 deletions
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3
mindspore/ops/_op_impl/akg/gpu/tile.py
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@ -22,8 +22,9 @@ tile_op_info = AkgGpuRegOp("Tile") \
.attr("multiples", "required", "listInt") \
.dtype_format(DataType.F16_Default, DataType.F16_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default) \
.dtype_format(DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.I16_Default, DataType.I16_Default) \
.dtype_format(DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default) \
.get_op_info()

186
tests/st/ops/gpu/test_tile_op.py
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@ -24,170 +24,70 @@ from mindspore.common.tensor import Tensor
from mindspore.nn import Cell
from mindspore.ops.operations import Tile
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
input_x0 = np.arange(2).reshape((2, 1, 1)).astype(np.float32)
mul0 = (8, 1, 1)
input_x1 = np.arange(32).reshape((2, 4, 4)).astype(np.float32)
mul1 = (2, 2, 2)
input_x2 = np.arange(1).reshape((1, 1, 1)).astype(np.float32)
mul2 = (1, 1, 1)
input_32_x0 = np.arange(2).reshape((2, 1, 1)).astype(np.int32)
mul_32_0 = (8, 1, 1)
input_32_x1 = np.arange(32).reshape((2, 4, 4)).astype(np.int32)
mul_32_1 = (2, 2, 2)
input_32_x2 = np.arange(1).reshape((1, 1, 1)).astype(np.int32)
mul_32_2 = (1, 1, 1)
input_16_x0 = np.arange(2).reshape((2, 1, 1)).astype(np.int16)
mul_16_0 = (8, 1, 1)
input_16_x1 = np.arange(32).reshape((2, 4, 4)).astype(np.int16)
mul_16_1 = (2, 2, 2)
input_16_x2 = np.arange(1).reshape((1, 1, 1)).astype(np.int16)
mul_16_2 = (1, 1, 1)
input_8_x0 = np.arange(2).reshape((2, 1, 1)).astype(np.uint8)
mul_8_0 = (8, 1, 1)
input_8_x1 = np.arange(32).reshape((2, 4, 4)).astype(np.int8)
mul_8_1 = (2, 2, 2)
input_8_x2 = np.arange(1).reshape((1, 1, 1)).astype(np.uint8)
mul_8_2 = (1, 1, 1)
class Net(Cell):
def __init__(self):
super(Net, self).__init__()
self.Tile = Tile()
self.input_x0 = Parameter(initializer(Tensor(input_x0), input_x0.shape), name='x0')
self.mul0 = mul0
self.input_x1 = Parameter(initializer(Tensor(input_x1), input_x1.shape), name='x1')
self.mul1 = mul1
self.input_x2 = Parameter(initializer(Tensor(input_x2), input_x2.shape), name='x2')
self.mul2 = mul2
@ms_function
def construct(self):
output = (self.Tile(self.input_x0, self.mul0),
self.Tile(self.input_x1, self.mul1),
self.Tile(self.input_x2, self.mul2))
return output
class Net32(Cell):
def __init__(self):
super(Net32, self).__init__()
class TileNet(Cell):
def __init__(self, numpy_input):
super(TileNet, self).__init__()
self.Tile = Tile()
self.input_32_x0 = Parameter(initializer(Tensor(input_32_x0), input_32_x0.shape), name='x0')
self.mul_32_0 = mul_32_0
self.input_32_x1 = Parameter(initializer(Tensor(input_32_x1), input_32_x1.shape), name='x1')
self.mul_32_1 = mul_32_1
self.input_32_x2 = Parameter(initializer(Tensor(input_32_x2), input_32_x2.shape), name='x2')
self.mul_32_2 = mul_32_2
self.input_parameter = Parameter(initializer(Tensor(numpy_input), numpy_input.shape), name='x')
@ms_function
def construct(self):
output = (self.Tile(self.input_32_x0, self.mul_32_0),
self.Tile(self.input_32_x1, self.mul_32_1),
self.Tile(self.input_32_x2, self.mul_32_2))
return output
def construct(self, mul):
return self.Tile(self.input_parameter, mul)
class Net16(Cell):
def __init__(self):
super(Net16, self).__init__()
self.Tile = Tile()
def ms_tile(nptype):
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
self.input_16_x0 = Parameter(initializer(Tensor(input_16_x0), input_16_x0.shape), name='x0')
self.mul_16_0 = mul_16_0
self.input_16_x1 = Parameter(initializer(Tensor(input_16_x1), input_16_x1.shape), name='x1')
self.mul_16_1 = mul_16_1
self.input_16_x2 = Parameter(initializer(Tensor(input_16_x2), input_16_x2.shape), name='x2')
self.mul_16_2 = mul_16_2
input_0 = np.arange(2).reshape((2, 1, 1)).astype(nptype)
mul_0 = (8, 1, 1)
input_1 = np.arange(32).reshape((2, 4, 4)).astype(nptype)
mul_1 = (2, 2, 2)
input_2 = np.arange(1).reshape((1, 1, 1)).astype(nptype)
mul_2 = (1, 1, 1)
@ms_function
def construct(self):
output = (self.Tile(self.input_16_x0, self.mul_16_0),
self.Tile(self.input_16_x1, self.mul_16_1),
self.Tile(self.input_16_x2, self.mul_16_2))
return output
tile_net = TileNet(input_0)
np_expected = np.tile(input_0, mul_0)
ms_output = tile_net(mul_0).asnumpy()
np.testing.assert_array_equal(ms_output, np_expected)
tile_net = TileNet(input_1)
np_expected = np.tile(input_1, mul_1)
ms_output = tile_net(mul_1).asnumpy()
np.testing.assert_array_equal(ms_output, np_expected)
tile_net = TileNet(input_2)
np_expected = np.tile(input_2, mul_2)
ms_output = tile_net(mul_2).asnumpy()
np.testing.assert_array_equal(ms_output, np_expected)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_tile():
net = Net()
output = net()
expect0 = np.tile(input_x0, mul0)
diff0 = output[0].asnumpy() - expect0
error0 = np.ones(shape=expect0.shape) * 1.0e-5
assert np.all(diff0 < error0)
assert output[0].shape == expect0.shape
expect1 = np.tile(input_x1, mul1)
diff1 = output[1].asnumpy() - expect1
error1 = np.ones(shape=expect1.shape) * 1.0e-5
assert np.all(diff1 < error1)
assert output[1].shape == expect1.shape
expect2 = np.tile(input_x2, mul2)
diff2 = output[2].asnumpy() - expect2
error2 = np.ones(shape=expect2.shape) * 1.0e-5
assert np.all(diff2 < error2)
assert output[2].shape == expect2.shape
def test_tile_float16():
ms_tile(np.float16)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_tile_32():
net = Net32()
output = net()
expect0 = np.tile(input_32_x0, mul_32_0)
diff0 = output[0].asnumpy() - expect0
error0 = np.ones(shape=expect0.shape) * 1.0e-5
assert np.all(diff0 < error0)
assert output[0].shape == expect0.shape
def test_tile_float32():
ms_tile(np.float32)
expect1 = np.tile(input_32_x1, mul_32_1)
diff1 = output[1].asnumpy() - expect1
error1 = np.ones(shape=expect1.shape) * 1.0e-5
assert np.all(diff1 < error1)
assert output[1].shape == expect1.shape
expect2 = np.tile(input_32_x2, mul_32_2)
diff2 = output[2].asnumpy() - expect2
error2 = np.ones(shape=expect2.shape) * 1.0e-5
assert np.all(diff2 < error2)
assert output[2].shape == expect2.shape
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_tile_int16():
ms_tile(np.int16)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_tile_int32():
ms_tile(np.int32)
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_tile_16():
net = Net16()
output = net()
expect0 = np.tile(input_16_x0, mul_16_0)
diff0 = output[0].asnumpy() - expect0
error0 = np.ones(shape=expect0.shape) * 1.0e-5
assert np.all(diff0 < error0)
assert output[0].shape == expect0.shape
expect1 = np.tile(input_16_x1, mul_16_1)
diff1 = output[1].asnumpy() - expect1
error1 = np.ones(shape=expect1.shape) * 1.0e-5
assert np.all(diff1 < error1)
assert output[1].shape == expect1.shape
expect2 = np.tile(input_16_x2, mul_16_2)
diff2 = output[2].asnumpy() - expect2
error2 = np.ones(shape=expect2.shape) * 1.0e-5
assert np.all(diff2 < error2)
assert output[2].shape == expect2.shape
def test_tile_int64():
ms_tile(np.int64)

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