wehub-resource-sync
608 lines
22 KiB
Python
608 lines
22 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||
#
|
||
# 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 unittest
|
||
|
||
import numpy as np
|
||
from op_test import (
|
||
OpTest,
|
||
convert_float_to_uint16,
|
||
get_device_place,
|
||
is_custom_device,
|
||
)
|
||
|
||
import paddle
|
||
from paddle.base import core
|
||
|
||
|
||
def Heaviside_grad(x, y, dout, astype="float16", is_bfloat16=False):
|
||
tmp = np.zeros(x.shape).astype(astype)
|
||
dx = np.multiply(tmp, dout)
|
||
dy = np.multiply(np.equal(x, 0), dout).astype(astype)
|
||
if is_bfloat16:
|
||
dx = convert_float_to_uint16(dx)
|
||
dy = convert_float_to_uint16(dy)
|
||
return dx, dy
|
||
|
||
|
||
class TestElementwiseOp(OpTest):
|
||
def setUp(self):
|
||
self.op_type = "elementwise_heaviside"
|
||
x = np.random.random((13, 17)).astype("float64")
|
||
y = np.random.random((13, 17)).astype("float64")
|
||
self.python_api = paddle.heaviside
|
||
self.prim_op_type = "comp"
|
||
self.public_python_api = paddle.heaviside
|
||
self.inputs = {'X': x, 'Y': y}
|
||
self.outputs = {'Out': np.heaviside(self.inputs['X'], self.inputs['Y'])}
|
||
|
||
def test_check_output(self):
|
||
self.check_output(
|
||
check_pir=True, check_prim_pir=True, check_symbol_infer=False
|
||
)
|
||
|
||
def test_check_grad_normal(self):
|
||
self.check_grad(['X', 'Y'], 'Out', check_pir=True, check_prim_pir=True)
|
||
|
||
def test_check_grad_ignore_x(self):
|
||
self.check_grad(
|
||
['Y'],
|
||
'Out',
|
||
no_grad_set=set("X"),
|
||
check_pir=True,
|
||
check_prim_pir=True,
|
||
)
|
||
|
||
def test_check_grad_ignore_y(self):
|
||
self.check_grad(
|
||
['X'],
|
||
'Out',
|
||
no_grad_set=set('Y'),
|
||
check_pir=True,
|
||
check_prim_pir=True,
|
||
)
|
||
|
||
|
||
class TestHeavisideBroadcast(unittest.TestCase):
|
||
def setUp(self):
|
||
self.input_1 = np.random.rand(2, 100, 13, 17).astype("float32")
|
||
self.input_2 = np.random.rand(100, 13, 17).astype("float32")
|
||
self.input_3 = np.random.rand(100, 13, 1).astype("float32")
|
||
self.input_4 = np.random.rand(13, 17).astype("float32")
|
||
self.input_5 = np.random.rand(1).astype("float32")
|
||
|
||
self.np_expected1 = np.heaviside(self.input_1, self.input_2)
|
||
self.np_expected2 = np.heaviside(self.input_2, self.input_3)
|
||
self.np_expected3 = np.heaviside(self.input_2, self.input_4)
|
||
self.np_expected4 = np.heaviside(self.input_4, self.input_5)
|
||
|
||
def test_broadcast(self):
|
||
paddle.disable_static()
|
||
self.tensor_1 = paddle.to_tensor(self.input_1)
|
||
self.tensor_2 = paddle.to_tensor(self.input_2)
|
||
self.tensor_3 = paddle.to_tensor(self.input_3)
|
||
self.tensor_4 = paddle.to_tensor(self.input_4)
|
||
self.tensor_5 = paddle.to_tensor(self.input_5)
|
||
|
||
res = paddle.heaviside(self.tensor_1, self.tensor_2)
|
||
res = res.numpy()
|
||
np.testing.assert_allclose(res, self.np_expected1, rtol=1e-05)
|
||
|
||
res = paddle.heaviside(self.tensor_2, self.tensor_3)
|
||
res = res.numpy()
|
||
np.testing.assert_allclose(res, self.np_expected2, rtol=1e-05)
|
||
|
||
res = paddle.heaviside(self.tensor_2, self.tensor_4)
|
||
res = res.numpy()
|
||
np.testing.assert_allclose(res, self.np_expected3, rtol=1e-05)
|
||
|
||
res = paddle.heaviside(self.tensor_4, self.tensor_5)
|
||
res = res.numpy()
|
||
np.testing.assert_allclose(res, self.np_expected4, rtol=1e-05)
|
||
|
||
|
||
class TestHeavisideAPI_float64(unittest.TestCase):
|
||
def setUp(self):
|
||
self.x_np = np.random.random((13, 17)).astype("float64")
|
||
self.y_np = np.random.random((13, 17)).astype("float64")
|
||
self.out_np = np.heaviside(self.x_np, self.y_np)
|
||
self.dtype = "float64"
|
||
|
||
def test_static(self):
|
||
for use_cuda in (
|
||
[False, True]
|
||
if (paddle.device.is_compiled_with_cuda() or is_custom_device())
|
||
else [False]
|
||
):
|
||
place = get_device_place() if use_cuda else paddle.CPUPlace()
|
||
|
||
paddle.enable_static()
|
||
prog = paddle.static.Program()
|
||
with paddle.static.program_guard(prog):
|
||
x = paddle.static.data(
|
||
name=f"x_{self.dtype}", shape=[13, 17], dtype=self.dtype
|
||
)
|
||
y = paddle.static.data(
|
||
name=f"y_{self.dtype}", shape=[13, 17], dtype=self.dtype
|
||
)
|
||
out = paddle.heaviside(x, y)
|
||
|
||
exe = paddle.static.Executor(place=place)
|
||
(res,) = exe.run(
|
||
prog,
|
||
feed={
|
||
f"x_{self.dtype}": self.x_np,
|
||
f"y_{self.dtype}": self.y_np,
|
||
},
|
||
fetch_list=out,
|
||
use_prune=True,
|
||
)
|
||
|
||
np.testing.assert_allclose(res, self.out_np, rtol=1e-05)
|
||
|
||
def test_dygraph(self):
|
||
for use_cuda in (
|
||
[False, True]
|
||
if (paddle.device.is_compiled_with_cuda() or is_custom_device())
|
||
else [False]
|
||
):
|
||
place = get_device_place() if use_cuda else paddle.CPUPlace()
|
||
paddle.disable_static(place=place)
|
||
result = paddle.heaviside(
|
||
paddle.to_tensor(self.x_np), paddle.to_tensor(self.y_np)
|
||
)
|
||
|
||
np.testing.assert_allclose(result.numpy(), self.out_np, rtol=1e-05)
|
||
|
||
|
||
class TestHeavisideAPI_float32(TestHeavisideAPI_float64):
|
||
def setUp(self):
|
||
self.x_np = np.random.random((13, 17)).astype("float32")
|
||
self.y_np = np.random.random((13, 17)).astype("float32")
|
||
self.out_np = np.heaviside(self.x_np, self.y_np)
|
||
self.dtype = "float32"
|
||
|
||
|
||
class TestHeavisideAPI_int64(TestHeavisideAPI_float64):
|
||
def setUp(self):
|
||
self.x_np = np.random.random((13, 17)).astype("int64")
|
||
self.y_np = np.random.random((13, 17)).astype("int64")
|
||
self.out_np = np.heaviside(self.x_np, self.y_np)
|
||
self.dtype = "int64"
|
||
|
||
|
||
class TestHeavisideAPI_int32(TestHeavisideAPI_float64):
|
||
def setUp(self):
|
||
self.x_np = np.random.random((13, 17)).astype("int32")
|
||
self.y_np = np.random.random((13, 17)).astype("int32")
|
||
self.out_np = np.heaviside(self.x_np, self.y_np)
|
||
self.dtype = "int32"
|
||
|
||
|
||
class TestElementwiseOp1(TestElementwiseOp):
|
||
def setUp(self):
|
||
self.op_type = "elementwise_heaviside"
|
||
x = np.random.random(100).astype("float64")
|
||
y = np.random.random((13, 100)).astype("float64")
|
||
self.python_api = paddle.heaviside
|
||
self.prim_op_type = "comp"
|
||
self.public_python_api = paddle.heaviside
|
||
self.inputs = {'X': x, 'Y': y}
|
||
self.outputs = {'Out': np.heaviside(self.inputs['X'], self.inputs['Y'])}
|
||
|
||
|
||
class TestElementwiseOp2(TestElementwiseOp):
|
||
def setUp(self):
|
||
self.op_type = "elementwise_heaviside"
|
||
x = np.random.random((13, 100)).astype("float64")
|
||
y = np.random.random(100).astype("float64")
|
||
self.python_api = paddle.heaviside
|
||
self.prim_op_type = "comp"
|
||
self.public_python_api = paddle.heaviside
|
||
self.inputs = {'X': x, 'Y': y}
|
||
self.outputs = {'Out': np.heaviside(self.inputs['X'], self.inputs['Y'])}
|
||
|
||
|
||
class TestElementwiseOp3(TestElementwiseOp):
|
||
def setUp(self):
|
||
self.op_type = "elementwise_heaviside"
|
||
x = np.random.uniform(-10, 10, [100]).astype("float64")
|
||
y = np.random.uniform(-10, 10, [3, 100]).astype("float64")
|
||
self.python_api = paddle.heaviside
|
||
self.prim_op_type = "comp"
|
||
self.public_python_api = paddle.heaviside
|
||
self.inputs = {'X': x, 'Y': y}
|
||
self.outputs = {'Out': np.heaviside(self.inputs['X'], self.inputs['Y'])}
|
||
|
||
|
||
class TestElementwiseOp4(TestElementwiseOp):
|
||
def setUp(self):
|
||
self.op_type = "elementwise_heaviside"
|
||
x = np.random.uniform(0, 10, []).astype("float64")
|
||
y = np.random.uniform(-10, 0, [2, 3, 20]).astype("float64")
|
||
self.python_api = paddle.heaviside
|
||
self.prim_op_type = "comp"
|
||
self.public_python_api = paddle.heaviside
|
||
self.inputs = {'X': x, 'Y': y}
|
||
self.outputs = {'Out': np.heaviside(self.inputs['X'], self.inputs['Y'])}
|
||
|
||
|
||
class TestHeavisideFP16Op(OpTest):
|
||
def setUp(self):
|
||
self.dtype = np.float16
|
||
self.op_type = "elementwise_heaviside"
|
||
self.python_api = paddle.heaviside
|
||
self.prim_op_type = "comp"
|
||
self.public_python_api = paddle.heaviside
|
||
self.inputs = {
|
||
'X': np.random.uniform(1, 2, [20, 5]).astype("float16"),
|
||
'Y': np.random.uniform(1, 2, [20, 5]).astype("float16"),
|
||
}
|
||
self.outputs = {'Out': np.heaviside(self.inputs['X'], self.inputs['Y'])}
|
||
|
||
def test_check_output(self):
|
||
self.check_output(
|
||
check_pir=True, check_prim_pir=True, check_symbol_infer=False
|
||
)
|
||
|
||
def test_check_grad(self):
|
||
self.check_grad(
|
||
['X', 'Y'],
|
||
'Out',
|
||
user_defined_grads=Heaviside_grad(
|
||
self.inputs['X'], self.inputs['Y'], 1 / self.inputs['X'].size
|
||
),
|
||
check_pir=True,
|
||
check_prim_pir=True,
|
||
)
|
||
|
||
|
||
@unittest.skipIf(
|
||
not (core.is_compiled_with_cuda() or is_custom_device())
|
||
or not core.is_bfloat16_supported(get_device_place()),
|
||
"core is not compiled with CUDA or not support bfloat16",
|
||
)
|
||
class TestHeavisideBF16Op(OpTest):
|
||
def setUp(self):
|
||
self.dtype = np.uint16
|
||
self.np_dtype = np.float32
|
||
self.op_type = "elementwise_heaviside"
|
||
self.python_api = paddle.heaviside
|
||
self.prim_op_type = "comp"
|
||
self.public_python_api = paddle.heaviside
|
||
self.inputs = {
|
||
'X': np.random.uniform(1, 2, [20, 5]).astype(self.np_dtype),
|
||
'Y': np.random.uniform(1, 2, [20, 5]).astype(self.np_dtype),
|
||
}
|
||
self.outputs = {'Out': np.heaviside(self.inputs['X'], self.inputs['Y'])}
|
||
|
||
self.place = get_device_place()
|
||
self.inputs['X'] = convert_float_to_uint16(self.inputs['X'])
|
||
self.inputs['Y'] = convert_float_to_uint16(self.inputs['Y'])
|
||
self.outputs['Out'] = convert_float_to_uint16(self.outputs['Out'])
|
||
|
||
def test_check_output(self):
|
||
self.check_output_with_place(
|
||
self.place,
|
||
check_pir=True,
|
||
check_prim_pir=True,
|
||
check_symbol_infer=False,
|
||
)
|
||
|
||
def test_check_grad(self):
|
||
self.check_grad_with_place(
|
||
self.place,
|
||
['X', 'Y'],
|
||
'Out',
|
||
user_defined_grads=Heaviside_grad(
|
||
self.inputs['X'],
|
||
self.inputs['Y'],
|
||
1 / self.inputs['X'].size,
|
||
self.np_dtype,
|
||
True,
|
||
),
|
||
check_pir=True,
|
||
check_prim_pir=True,
|
||
)
|
||
|
||
|
||
class TestHeavisideError(unittest.TestCase):
|
||
def test_input(self):
|
||
paddle.disable_static()
|
||
|
||
def test_input_x():
|
||
paddle.heaviside(1, paddle.randn([100]))
|
||
|
||
self.assertRaises(ValueError, test_input_x)
|
||
|
||
def test_input_y():
|
||
paddle.heaviside(paddle.randn([100]), 1)
|
||
|
||
self.assertRaises(ValueError, test_input_y)
|
||
|
||
def test_input_xy():
|
||
paddle.heaviside(
|
||
paddle.randn([100], 'float32'), paddle.randn([100], 'float64')
|
||
)
|
||
|
||
self.assertRaises(ValueError, test_input_xy)
|
||
|
||
|
||
@unittest.skipIf(
|
||
not (core.is_compiled_with_cuda() or is_custom_device()),
|
||
"core is not compiled with CUDA",
|
||
)
|
||
class TestElementwiseHeavisideOp_Stride(OpTest):
|
||
no_need_check_grad = True
|
||
|
||
def setUp(self):
|
||
self.op_type = "elementwise_heaviside"
|
||
self.python_api = paddle.heaviside
|
||
self.public_python_api = paddle.heaviside
|
||
self.transpose_api = paddle.transpose
|
||
self.as_stride_api = paddle.as_strided
|
||
self.init_dtype()
|
||
self.init_input_output()
|
||
|
||
self.inputs_stride = {
|
||
'X': OpTest.np_dtype_to_base_dtype(self.x),
|
||
'Y': OpTest.np_dtype_to_base_dtype(self.y_trans),
|
||
}
|
||
|
||
self.inputs = {
|
||
'X': OpTest.np_dtype_to_base_dtype(self.x),
|
||
'Y': OpTest.np_dtype_to_base_dtype(self.y),
|
||
}
|
||
|
||
self.outputs = {'Out': self.out}
|
||
|
||
def init_dtype(self):
|
||
self.dtype = np.float64
|
||
self.val_dtype = np.float64
|
||
|
||
def test_check_output(self):
|
||
place = get_device_place()
|
||
self.check_strided_forward = True
|
||
self.check_output(
|
||
place,
|
||
)
|
||
|
||
def init_input_output(self):
|
||
self.strided_input_type = "transpose"
|
||
self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
|
||
self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
|
||
self.out = np.heaviside(self.x, self.y)
|
||
self.perm = [1, 0]
|
||
self.y_trans = np.transpose(self.y, self.perm)
|
||
|
||
def test_check_gradient(self):
|
||
pass
|
||
|
||
|
||
class TestElementwiseHeavisideOp_Stride1(TestElementwiseHeavisideOp_Stride):
|
||
def init_input_output(self):
|
||
self.strided_input_type = "transpose"
|
||
self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
|
||
self.y = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
|
||
self.out = np.heaviside(self.x, self.y)
|
||
self.perm = [0, 1, 3, 2]
|
||
self.y_trans = np.transpose(self.y, self.perm)
|
||
|
||
|
||
class TestElementwiseHeavisideOp_Stride2(TestElementwiseHeavisideOp_Stride):
|
||
def init_input_output(self):
|
||
self.strided_input_type = "transpose"
|
||
self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
|
||
self.y = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
|
||
self.out = np.heaviside(self.x, self.y)
|
||
self.perm = [0, 2, 1, 3]
|
||
self.y_trans = np.transpose(self.y, self.perm)
|
||
|
||
|
||
class TestElementwiseHeavisideOp_Stride3(TestElementwiseHeavisideOp_Stride):
|
||
def init_input_output(self):
|
||
self.strided_input_type = "transpose"
|
||
self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
|
||
self.y = np.random.uniform(0.1, 1, [20, 2, 13, 1]).astype(self.dtype)
|
||
self.out = np.heaviside(self.x, self.y)
|
||
self.perm = [0, 1, 3, 2]
|
||
self.y_trans = np.transpose(self.y, self.perm)
|
||
|
||
|
||
class TestElementwiseHeavisideOp_Stride4(TestElementwiseHeavisideOp_Stride):
|
||
def init_input_output(self):
|
||
self.strided_input_type = "transpose"
|
||
self.x = np.random.uniform(0.1, 1, [1, 2, 13, 17]).astype(self.dtype)
|
||
self.y = np.random.uniform(0.1, 1, [20, 2, 13, 1]).astype(self.dtype)
|
||
self.out = np.heaviside(self.x, self.y)
|
||
self.perm = [1, 0, 2, 3]
|
||
self.y_trans = np.transpose(self.y, self.perm)
|
||
|
||
|
||
class TestElementwiseHeavisideOp_Stride5(TestElementwiseHeavisideOp_Stride):
|
||
def init_input_output(self):
|
||
self.strided_input_type = "as_stride"
|
||
self.x = np.random.uniform(0.1, 1, [23, 10, 1, 17]).astype(self.dtype)
|
||
self.y = np.random.uniform(0.1, 1, [23, 2, 13, 20]).astype(self.dtype)
|
||
self.y_trans = self.y
|
||
self.y = self.y[:, 0:1, :, 0:1]
|
||
self.out = np.heaviside(self.x, self.y)
|
||
self.shape_param = [23, 1, 13, 1]
|
||
self.stride_param = [520, 260, 20, 1]
|
||
|
||
|
||
class TestElementwiseHeavisideOp_Stride_ZeroDim1(
|
||
TestElementwiseHeavisideOp_Stride
|
||
):
|
||
def init_input_output(self):
|
||
self.strided_input_type = "transpose"
|
||
self.x = np.random.uniform(0.1, 1, []).astype(self.dtype)
|
||
self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
|
||
self.out = np.heaviside(self.x, self.y)
|
||
self.perm = [1, 0]
|
||
self.y_trans = np.transpose(self.y, self.perm)
|
||
|
||
|
||
class TestElementwiseHeavisideOp_Stride_ZeroSize1(
|
||
TestElementwiseHeavisideOp_Stride
|
||
):
|
||
def init_data(self):
|
||
self.strided_input_type = "transpose"
|
||
self.x = np.random.rand(1, 0, 2).astype('float32')
|
||
self.y = np.random.rand(3, 0, 1).astype('float32')
|
||
self.out = np.heaviside(self.x, self.y)
|
||
self.perm = [2, 1, 0]
|
||
self.y_trans = np.transpose(self.y, self.perm)
|
||
|
||
|
||
@unittest.skipIf(
|
||
not (core.is_compiled_with_cuda() or is_custom_device()),
|
||
"core is not compiled with CUDA",
|
||
)
|
||
class TestHeavisideZeroSizeTensor(unittest.TestCase):
|
||
"""Regression test for 0-size tensor in paddle.heaviside forward and backward.
|
||
|
||
When input has a dimension of 0, the broadcast backward kernels
|
||
(ElemwiseGradBroadcast1CUDA / ElemwiseGradBroadcast2CUDA) were incorrectly
|
||
launched with block_size=0 or grid_size=0, causing CUDA error(9)
|
||
(cudaErrorInvalidConfiguration).
|
||
|
||
Fix: add early-return guards in ElemwiseGradBroadcast1CUDA (h==0 || w==0)
|
||
and ElemwiseGradBroadcast2CUDA (pre==0 || n==0 || post==0).
|
||
"""
|
||
|
||
def setUp(self):
|
||
self.place = get_device_place()
|
||
paddle.disable_static(place=self.place)
|
||
|
||
def _check_forward_backward(self, x_shape, y_shape, dtype='float32'):
|
||
"""Run forward + backward and assert output shape and no CUDA error."""
|
||
x = paddle.zeros(x_shape, dtype=dtype)
|
||
y = paddle.ones(y_shape, dtype=dtype)
|
||
x.stop_gradient = False
|
||
y.stop_gradient = False
|
||
|
||
out = paddle.heaviside(x, y)
|
||
expected_shape = list(
|
||
np.broadcast_shapes(tuple(x_shape), tuple(y_shape))
|
||
)
|
||
self.assertEqual(list(out.shape), expected_shape)
|
||
|
||
out_grad = paddle.ones_like(out)
|
||
grads = paddle.grad(
|
||
[out],
|
||
[x, y],
|
||
grad_outputs=[out_grad],
|
||
allow_unused=True,
|
||
)
|
||
self.assertEqual(list(grads[0].shape), x_shape)
|
||
self.assertEqual(list(grads[1].shape), y_shape)
|
||
|
||
# Verify no sticky CUDA error was left by any kernel launch
|
||
core.eager._for_test_check_cuda_error()
|
||
return out
|
||
|
||
def _check_forward_only(self, x_shape, y_shape, dtype='int32'):
|
||
"""Run forward-only for non-float dtypes and assert shape + no CUDA error."""
|
||
x = paddle.zeros(x_shape, dtype=dtype)
|
||
y = paddle.ones(y_shape, dtype=dtype)
|
||
out = paddle.heaviside(x, y)
|
||
|
||
expected_shape = list(
|
||
np.broadcast_shapes(tuple(x_shape), tuple(y_shape))
|
||
)
|
||
self.assertEqual(list(out.shape), expected_shape)
|
||
core.eager._for_test_check_cuda_error()
|
||
return out
|
||
|
||
# ---------------------------------------------------------------
|
||
# Same-shape 0-size (no broadcast) — ElemwiseGradComputeNoBroadcast
|
||
# ---------------------------------------------------------------
|
||
|
||
def test_same_shape_zero_leading_dim_float32(self):
|
||
"""[0, 2048] x [0, 2048] – same shape, no broadcast."""
|
||
self._check_forward_backward([0, 2048], [0, 2048])
|
||
|
||
def test_same_shape_zero_leading_dim_float64(self):
|
||
"""[0, 17] x [0, 17]."""
|
||
self._check_forward_backward([0, 17], [0, 17], 'float64')
|
||
|
||
def test_same_shape_zero_trailing_dim_float64(self):
|
||
"""[13, 0] x [13, 0]."""
|
||
self._check_forward_backward([13, 0], [13, 0], 'float64')
|
||
|
||
def test_same_shape_zero_trailing_dim_int32(self):
|
||
"""[13, 0] x [13, 0] – int32, forward only."""
|
||
self._check_forward_only([13, 0], [13, 0], 'int32')
|
||
|
||
def test_same_shape_zero_trailing_dim_int64(self):
|
||
"""[13, 0] x [13, 0] – int64, forward only."""
|
||
self._check_forward_only([13, 0], [13, 0], 'int64')
|
||
|
||
def test_same_shape_zero_leading_dim_int32(self):
|
||
"""[0, 17] x [0, 17] – int32, forward only."""
|
||
self._check_forward_only([0, 17], [0, 17], 'int32')
|
||
|
||
def test_same_shape_zero_leading_dim_int64(self):
|
||
"""[0, 17] x [0, 17] – int64, forward only."""
|
||
self._check_forward_only([0, 17], [0, 17], 'int64')
|
||
|
||
# ---------------------------------------------------------------
|
||
# ElemwiseGradBroadcast1CUDA — h=0 (block_size would be 0)
|
||
# ---------------------------------------------------------------
|
||
|
||
def test_broadcast1_zero_trailing_dim_scalar(self):
|
||
"""[300, 0] x [1] → Broadcast1CUDA(h=pre=0, w=n=1), block_size=0."""
|
||
self._check_forward_backward([300, 0], [1])
|
||
|
||
def test_broadcast1_zero_leading_dim_scalar(self):
|
||
"""[0, 2048] x [1] → Broadcast1CUDA(h=pre=0, w=n=1), block_size=0."""
|
||
self._check_forward_backward([0, 2048], [1])
|
||
|
||
def test_broadcast1_zero_leading_dim_last_dim(self):
|
||
"""[0, 2048] x [2048] → Broadcast1CUDA(h=pre=0, w=n=2048), block_size=0."""
|
||
self._check_forward_backward([0, 2048], [2048])
|
||
|
||
def test_broadcast1_scalar_zero_trailing_dim(self):
|
||
"""[1] x [300, 0] – symmetric of test_broadcast1_zero_trailing_dim_scalar."""
|
||
self._check_forward_backward([1], [300, 0])
|
||
|
||
def test_broadcast1_scalar_zero_leading_dim(self):
|
||
"""[1] x [0, 2048] – symmetric of test_broadcast1_zero_leading_dim_scalar."""
|
||
self._check_forward_backward([1], [0, 2048])
|
||
|
||
def test_broadcast1_last_dim_zero_leading_dim(self):
|
||
"""[2048] x [0, 2048] – symmetric of test_broadcast1_zero_leading_dim_last_dim."""
|
||
self._check_forward_backward([2048], [0, 2048])
|
||
|
||
# ---------------------------------------------------------------
|
||
# ElemwiseGradBroadcast1CUDA — w=0 (grid_size would be 0)
|
||
# ---------------------------------------------------------------
|
||
|
||
def test_broadcast1_zero_mid_dim_w_zero(self):
|
||
"""[2, 0, 3] x [0, 3] → Broadcast1CUDA(h=2, w=0), grid_size=0."""
|
||
self._check_forward_backward([2, 0, 3], [0, 3])
|
||
|
||
# ---------------------------------------------------------------
|
||
# ElemwiseGradBroadcast2CUDA — post=0 (block_size would be 0)
|
||
# ---------------------------------------------------------------
|
||
|
||
def test_broadcast2_zero_post_dim(self):
|
||
"""[2, 3, 0] x [3, 1] → Broadcast2CUDA(pre=2, n=3, post=0), block_size=0."""
|
||
self._check_forward_backward([2, 3, 0], [3, 1])
|
||
|
||
|
||
if __name__ == '__main__':
|
||
unittest.main()
|