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未验证 提交 bd2a4e23 编写于 作者: R ronnywang 提交者： GitHub
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操作

[ROCM] fix some unittests (#32129) 

* [ROCM] fix test_gru_rnn_op

* [ROCM] fix test_expand_op

* [ROCM] fix test_cross_entropy_loss

* [ROCM] fix test_conv_nn_grad

* [ROCM] fix test_bilinear_tensor_product_op

* [ROCM] fix elementwise_op_function

* [ROCM] fix test_lstm_cudnn_op

* [ROCM] fix test_gpu_package_without_gpu_device

* [ROCM] fix test_gru_unit_op

* [ROCM] fix test_imperative_optimizer

* [ROCM] fix rnn

* [ROCM] fix group_norm_op

* [ROCM] fix test_pool3d_api

* [ROCM] fix test_pool3d_op
上级 d8afe407
隐藏空白更改
内联 并排
Showing with 239 addition and 134 deletion
paddle/fluid/operators/elementwise/elementwise_op_function.h
浏览文件 @ bd2a4e23
......@@ -39,7 +39,11 @@ limitations under the License. */
#include "paddle/fluid/platform/cuda_device_function.h"
#include "paddle/fluid/platform/cuda_primitives.h"
#ifdef __HIPCC__
constexpr int ELEMWISE_MAX_BLOCK_DIM = 256;
#else
constexpr int ELEMWISE_MAX_BLOCK_DIM = 1024;
#endif
#define BLOCK_X 32
#define BLOCK_Y 32
#endif
......
paddle/fluid/operators/group_norm_op.cu
浏览文件 @ bd2a4e23
......@@ -174,7 +174,11 @@ class GroupNormKernel<platform::CUDADeviceContext, T>
int imsize = (data_layout == DataLayout::kNCHW ? x_dims[2] * x_dims[3]
: x_dims[1] * x_dims[2]);
#ifdef __HIPCC__
int block_size = std::max(std::min(256, imsize), 64);
#else
int block_size = std::min(1024, imsize);
#endif
dim3 grid(group_size, groups, x_dims[0]);
dim3 threads(block_size, 1, 1);
GroupNormForwardGetMeanAndVar<T><<<grid, threads, 0, dev_ctx.stream()>>>(
......@@ -348,7 +352,11 @@ class GroupNormGradKernel<platform::CUDADeviceContext, T>
int imsize = (data_layout == DataLayout::kNCHW ? x_dims[2] * x_dims[3]
: x_dims[1] * x_dims[2]);
#ifdef __HIPCC__
int block_size = std::max(std::min(256, imsize), 64);
#else
int block_size = std::min(1024, imsize);
#endif
dim3 grid(group_size, groups, x_dims[0]);
dim3 threads(block_size, 1, 1);
int flags =
......
paddle/fluid/operators/miopen_lstm_cache.h
浏览文件 @ bd2a4e23
......@@ -75,10 +75,11 @@ class ScopedRNNBase {
dropout_state, seed_, state_size);
// ------------------- miopen rnn descriptors ---------------------
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::miopenSetRNNDescriptor(
rnn_desc_.desc(), hidden_size_, num_layers_, miopenRNNlinear,
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::miopenSetRNNDescriptor_V2(
rnn_desc_.desc(), hidden_size_, num_layers_, dropout_desc_.desc(),
miopenRNNlinear,
is_bidirec_ ? miopenRNNbidirection : miopenRNNunidirection, miopenLSTM,
miopenRNNNoBias, miopenRNNdefault, miopen_type));
miopenRNNwithBias, miopenRNNdefault, miopen_type));
// ------------------- miopen weights_size ---------------------
size_t weights_size_;
......
paddle/fluid/platform/miopen_helper.h
浏览文件 @ bd2a4e23
......@@ -434,9 +434,10 @@ class ScopedPoolingDescriptor {
"The size of kernel and strides should be equal. But "
"received size of kernel is %d, size of strides is %d.",
kernel.size(), strides.size()));
PADDLE_ENFORCE_CUDA_SUCCESS(dynload::miopenSet2dPoolingDescriptor(
desc_, GetPoolingMode(mode), kernel[0], kernel[1], pads[0], pads[1],
strides[0], strides[1]));
PADDLE_ENFORCE_CUDA_SUCCESS(dynload::miopenSetNdPoolingDescriptor(
desc_, GetPoolingMode(mode), kernel.size(),
const_cast<int*>(kernel.data()), const_cast<int*>(pads.data()),
const_cast<int*>(strides.data())));
return desc_;
}
......
python/paddle/fluid/tests/unittests/test_bilinear_tensor_product_op.py
浏览文件 @ bd2a4e23
......@@ -42,11 +42,12 @@ class TestBilinearTensorProductOp(OpTest):
size0 = 5
size1 = 4
size2 = 5
a = np.random.random((batch_size, size0)).astype("float64")
b = np.random.random((batch_size, size1)).astype("float64")
w = np.random.random((size2, size0, size1)).astype("float64")
bias = np.random.random((1, size2)).astype("float64")
output = np.zeros((batch_size, size2)).astype("float64")
dtype = "float32" if fluid.core.is_compiled_with_rocm() else "float64"
a = np.random.random((batch_size, size0)).astype(dtype)
b = np.random.random((batch_size, size1)).astype(dtype)
w = np.random.random((size2, size0, size1)).astype(dtype)
bias = np.random.random((1, size2)).astype(dtype)
output = np.zeros((batch_size, size2)).astype(dtype)
for i in range(size2):
w_i = w[i, :, :]
output[:, i] = np.sum(np.matmul(a, w_i) * b, axis=1)
......
python/paddle/fluid/tests/unittests/test_conv_nn_grad.py
浏览文件 @ bd2a4e23
......@@ -30,7 +30,7 @@ class TestConvDoubleGradCheck(unittest.TestCase):
def func(self, place):
shape = [2, 4, 3, 3]
eps = 0.005
dtype = np.float64
dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv2d(x, 2, 1, groups=1, bias_attr=False)
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
......@@ -57,7 +57,7 @@ class TestConvDoubleGradCheck(unittest.TestCase):
def func(self, place):
shape = [2, 4, 3, 3]
eps = 0.005
dtype = np.float64
dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv2d(x, 2, 1, bias_attr=False)
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
......@@ -82,7 +82,7 @@ class TestConvDoubleGradCheckTest1(unittest.TestCase):
def func(self, place):
shape = [2, 3, 3, 3]
eps = 0.005
dtype = np.float64
dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv2d(x, 2, 1, padding=1, bias_attr=False)
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
......@@ -107,7 +107,7 @@ class TestConv3DDoubleGradCheck(unittest.TestCase):
def func(self, place):
shape = [2, 4, 3, 4, 2]
eps = 0.005
dtype = np.float64
dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv3d(x, 2, 1, bias_attr=False)
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
......@@ -132,7 +132,7 @@ class TestConv3DDoubleGradCheckTest1(unittest.TestCase):
def func(self, place):
shape = [2, 4, 5, 3, 2]
eps = 0.005
dtype = np.float64
dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv3d(x, 2, 1, padding=1, bias_attr=False)
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
......@@ -157,7 +157,7 @@ class TestConv2DoubleGradCheck_AsyPadding(unittest.TestCase):
def func(self, place):
shape = [2, 2, 3, 3]
eps = 0.005
dtype = np.float64
dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv2d(
input=x,
......@@ -188,7 +188,7 @@ class TestConv2DoubleGradCheck_PaddingSAME(unittest.TestCase):
def func(self, place):
shape = [2, 2, 3, 3]
eps = 0.005
dtype = np.float64
dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv2d(
input=x,
......@@ -219,7 +219,7 @@ class TestConv2DoubleGradCheck_PaddingVALID(unittest.TestCase):
def func(self, place):
shape = [2, 2, 3, 3]
eps = 0.005
dtype = np.float64
dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv2d(
input=x,
......@@ -250,7 +250,7 @@ class TestConv2DoubleGradCheck_ChannelLast(unittest.TestCase):
def func(self, place):
shape = [2, 2, 3, 3]
eps = 0.005
dtype = np.float64
dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv2d(
input=x,
......@@ -283,7 +283,7 @@ class TestConv2DoubleGradCheck_ChannelLast_AsyPadding(unittest.TestCase):
def func(self, place):
shape = [2, 2, 3, 3]
eps = 0.005
dtype = np.float64
dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv2d(
input=x,
......@@ -316,7 +316,7 @@ class TestConv3DDoubleGradCheck_AsyPadding(unittest.TestCase):
def func(self, place):
shape = [2, 2, 2, 2, 2]
eps = 0.005
dtype = np.float64
dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv3d(
input=x,
......@@ -347,7 +347,7 @@ class TestConv3DoubleGradCheck_PaddingSAME(unittest.TestCase):
def func(self, place):
shape = [2, 2, 2, 2, 2]
eps = 0.005
dtype = np.float64
dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv3d(
input=x,
......@@ -379,7 +379,7 @@ class TestConv3DoubleGradCheck_PaddingVALID(unittest.TestCase):
def func(self, place):
shape = [2, 2, 3, 3, 2]
eps = 0.005
dtype = np.float64
dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv3d(
input=x,
......@@ -410,7 +410,7 @@ class TestConv3DDoubleGradCheck_ChannelLast(unittest.TestCase):
def func(self, place):
shape = [2, 2, 2, 2, 3]
eps = 0.005
dtype = np.float64
dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv3d(
input=x,
......@@ -443,7 +443,7 @@ class TestConv3DDoubleGradCheck_ChannelLast_AsyPadding(unittest.TestCase):
def func(self, place):
shape = [2, 2, 2, 2, 3]
eps = 0.005
dtype = np.float64
dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv3d(
input=x,
......@@ -476,7 +476,7 @@ class TestDepthWiseConvDoubleGradCheck(unittest.TestCase):
def func(self, place):
shape = [2, 4, 3, 3]
eps = 0.005
dtype = np.float64
dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64
x = layers.data('x', shape, False, dtype)
# condition of depthwise conv:
......
python/paddle/fluid/tests/unittests/test_cross_entropy_loss.py
浏览文件 @ bd2a4e23
......@@ -191,12 +191,16 @@ def cross_entropy_soft_2d(softmax,
class CrossEntropyLoss(unittest.TestCase):
def setUp(self):
self.dtype = 'float32' if fluid.core.is_compiled_with_rocm(
) else 'float64'
###test for deprecated softmax_with_cross_entropy
def test_softmax_with_cross_entropy(self):
self.numeric_stable_mode = False
self.soft_label = True
self.dtype = np.float64
self.dtype = 'float32' if fluid.core.is_compiled_with_rocm(
) else 'float64'
self.axis = -1
self.ignore_index = -100 #should not be changed
self.N = 4
......@@ -248,7 +252,8 @@ class CrossEntropyLoss(unittest.TestCase):
def test_cross_entropy_loss_soft_1d(self):
self.numeric_stable_mode = False
self.soft_label = True
self.dtype = np.float64
self.dtype = 'float32' if fluid.core.is_compiled_with_rocm(
) else 'float64'
self.axis = -1
self.ignore_index = -100 #should not be changed
self.N = 4
......@@ -296,9 +301,9 @@ class CrossEntropyLoss(unittest.TestCase):
) else fluid.CPUPlace()
with fluid.program_guard(prog, startup_prog):
input = fluid.data(
name='input', shape=[self.N, self.C], dtype='float64')
name='input', shape=[self.N, self.C], dtype=self.dtype)
label = fluid.data(
name='label', shape=[self.N, self.C], dtype='float64')
name='label', shape=[self.N, self.C], dtype=self.dtype)
cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
reduction=self.reduction, soft_label=True)
......@@ -321,7 +326,8 @@ class CrossEntropyLoss(unittest.TestCase):
def test_cross_entropy_loss_soft_1d_weight(self):
self.numeric_stable_mode = False
self.soft_label = True
self.dtype = np.float64
self.dtype = 'float32' if fluid.core.is_compiled_with_rocm(
) else 'float64'
self.axis = -1
self.ignore_index = -100 #should not be changed
self.N = 4
......@@ -376,10 +382,10 @@ class CrossEntropyLoss(unittest.TestCase):
) else fluid.CPUPlace()
with fluid.program_guard(prog, startup_prog):
input = fluid.data(
name='input', shape=[self.N, self.C], dtype='float64')
name='input', shape=[self.N, self.C], dtype=self.dtype)
label = fluid.data(
name='label', shape=[self.N, self.C], dtype='float64')
weight = fluid.data(name='weight', shape=[self.C], dtype='float64')
name='label', shape=[self.N, self.C], dtype=self.dtype)
weight = fluid.data(name='weight', shape=[self.C], dtype=self.dtype)
cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
weight=weight, reduction=self.reduction, soft_label=True)
......@@ -403,7 +409,8 @@ class CrossEntropyLoss(unittest.TestCase):
def test_cross_entropy_loss_soft_1d_mean(self):
self.numeric_stable_mode = False
self.soft_label = True
self.dtype = np.float64
self.dtype = 'float32' if fluid.core.is_compiled_with_rocm(
) else 'float64'
self.axis = -1
self.ignore_index = -100 #should not be changed
self.N = 4
......@@ -451,9 +458,9 @@ class CrossEntropyLoss(unittest.TestCase):
) else fluid.CPUPlace()
with fluid.program_guard(prog, startup_prog):
input = fluid.data(
name='input', shape=[self.N, self.C], dtype='float64')
name='input', shape=[self.N, self.C], dtype=self.dtype)
label = fluid.data(
name='label', shape=[self.N, self.C], dtype='float64')
name='label', shape=[self.N, self.C], dtype=self.dtype)
cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
reduction=self.reduction, soft_label=True)
......@@ -475,7 +482,8 @@ class CrossEntropyLoss(unittest.TestCase):
def test_cross_entropy_loss_soft_1d_weight_mean(self):
self.numeric_stable_mode = False
self.soft_label = True
self.dtype = np.float64
self.dtype = 'float32' if fluid.core.is_compiled_with_rocm(
) else 'float64'
self.axis = -1
self.ignore_index = -100 #should not be changed
self.N = 4
......@@ -523,10 +531,10 @@ class CrossEntropyLoss(unittest.TestCase):
) else fluid.CPUPlace()
with fluid.program_guard(prog, startup_prog):
input = fluid.data(
name='input', shape=[self.N, self.C], dtype='float64')
name='input', shape=[self.N, self.C], dtype=self.dtype)
label = fluid.data(
name='label', shape=[self.N, self.C], dtype='float64')
weight = fluid.data(name='weight', shape=[self.C], dtype='float64')
name='label', shape=[self.N, self.C], dtype=self.dtype)
weight = fluid.data(name='weight', shape=[self.C], dtype=self.dtype)
cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
weight=weight, reduction=self.reduction, soft_label=True)
......@@ -549,7 +557,8 @@ class CrossEntropyLoss(unittest.TestCase):
def test_cross_entropy_loss_soft_2d(self):
self.numeric_stable_mode = False
self.soft_label = True
self.dtype = np.float64
self.dtype = 'float32' if fluid.core.is_compiled_with_rocm(
) else 'float64'
self.axis = -1
self.ignore_index = -100 #should not be changed
self.N = 3
......@@ -604,11 +613,11 @@ class CrossEntropyLoss(unittest.TestCase):
input = fluid.data(
name='input',
shape=[self.N, self.H, self.W, self.C],
dtype='float64')
dtype=self.dtype)
label = fluid.data(
name='label',
shape=[self.N, self.H, self.W, self.C],
dtype='float64')
dtype=self.dtype)
cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
reduction=self.reduction, soft_label=True)
......@@ -631,7 +640,8 @@ class CrossEntropyLoss(unittest.TestCase):
def test_cross_entropy_loss_soft_2d_weight_mean(self):
self.numeric_stable_mode = False
self.soft_label = True
self.dtype = np.float64
self.dtype = 'float32' if fluid.core.is_compiled_with_rocm(
) else 'float64'
self.axis = -1
self.ignore_index = -100 #should not be changed
self.N = 3
......@@ -685,12 +695,12 @@ class CrossEntropyLoss(unittest.TestCase):
input = fluid.data(
name='input',
shape=[self.N, self.H, self.W, self.C],
dtype='float64')
dtype=self.dtype)
label = fluid.data(
name='label',
shape=[self.N, self.H, self.W, self.C],
dtype='float64')
weight = fluid.data(name='weight', shape=[self.C], dtype='float64')
dtype=self.dtype)
weight = fluid.data(name='weight', shape=[self.C], dtype=self.dtype)
cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
weight=weight, reduction=self.reduction, soft_label=True)
......@@ -713,7 +723,7 @@ class CrossEntropyLoss(unittest.TestCase):
###soft_label test end
def test_cross_entropy_loss_1d_with_mean_ignore(self):
input_np = np.random.random([2, 4]).astype(np.float64)
input_np = np.random.random([2, 4]).astype(self.dtype)
label_np = np.random.randint(0, 4, size=(2)).astype(np.int64)
paddle.enable_static()
prog = fluid.Program()
......@@ -721,7 +731,7 @@ class CrossEntropyLoss(unittest.TestCase):
place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
) else fluid.CPUPlace()
with fluid.program_guard(prog, startup_prog):
input = fluid.data(name='input', shape=[2, 4], dtype='float64')
input = fluid.data(name='input', shape=[2, 4], dtype=self.dtype)
label = fluid.data(name='label', shape=[2], dtype='int64')
cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(ignore_index=0)
ret = cross_entropy_loss(input, label)
......@@ -752,20 +762,20 @@ class CrossEntropyLoss(unittest.TestCase):
def test_cross_entropy_loss_1d_with_weight_mean_ignore(self):
N = 100
C = 200
input_np = np.random.random([N, C]).astype(np.float64)
input_np = np.random.random([N, C]).astype(self.dtype)
label_np = np.random.randint(0, C, size=(N)).astype(np.int64)
weight_np = np.random.random([C]).astype(np.float64)
weight_np = np.random.random([C]).astype(self.dtype)
paddle.enable_static()
prog = fluid.Program()
startup_prog = fluid.Program()
place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
) else fluid.CPUPlace()
with fluid.program_guard(prog, startup_prog):
input = fluid.data(name='input', shape=[N, C], dtype='float64')
input = fluid.data(name='input', shape=[N, C], dtype=self.dtype)
label = fluid.data(name='label', shape=[N], dtype='int64')
weight = fluid.data(
name='weight', shape=[C],
dtype='float64') #weight for each class
dtype=self.dtype) #weight for each class
cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
weight=weight, ignore_index=0)
ret = cross_entropy_loss(input, label)
......@@ -798,20 +808,20 @@ class CrossEntropyLoss(unittest.TestCase):
self.assertTrue(np.allclose(dy_ret_value, expected))
def test_cross_entropy_loss_1d_with_weight_mean(self):
input_np = np.random.random([2, 4]).astype(np.float64)
input_np = np.random.random([2, 4]).astype(self.dtype)
label_np = np.random.randint(0, 4, size=(2)).astype(np.int64)
weight_np = np.random.random([4]).astype(np.float64) #shape:C
weight_np = np.random.random([4]).astype(self.dtype) #shape:C
paddle.enable_static()
prog = fluid.Program()
startup_prog = fluid.Program()
place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
) else fluid.CPUPlace()
with fluid.program_guard(prog, startup_prog):
input = fluid.data(name='input', shape=[2, 4], dtype='float64')
input = fluid.data(name='input', shape=[2, 4], dtype=self.dtype)
label = fluid.data(name='label', shape=[2], dtype='int64')
weight = fluid.data(
name='weight', shape=[4],
dtype='float64') #weight for each class
dtype=self.dtype) #weight for each class
cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(weight=weight)
ret = cross_entropy_loss(input, label)
......@@ -842,18 +852,18 @@ class CrossEntropyLoss(unittest.TestCase):
self.assertTrue(np.allclose(dy_ret_value, expected))
def test_cross_entropy_loss_1d_with_weight_sum(self):
input_np = np.random.random([100, 200]).astype(np.float64) #N,C
input_np = np.random.random([100, 200]).astype(self.dtype) #N,C
label_np = np.random.randint(0, 100, size=(100)).astype(np.int64) #N,1
weight_np = np.random.random([200]).astype(np.float64) #C
weight_np = np.random.random([200]).astype(self.dtype) #C
paddle.enable_static()
prog = fluid.Program()
startup_prog = fluid.Program()
place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
) else fluid.CPUPlace()
with fluid.program_guard(prog, startup_prog):
input = fluid.data(name='input', shape=[100, 200], dtype='float64')
input = fluid.data(name='input', shape=[100, 200], dtype=self.dtype)
label = fluid.data(name='label', shape=[100], dtype='int64')
weight = fluid.data(name='weight', shape=[200], dtype='float64')
weight = fluid.data(name='weight', shape=[200], dtype=self.dtype)
cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
weight=weight, reduction='sum')
ret = cross_entropy_loss(input, label)
......@@ -882,9 +892,9 @@ class CrossEntropyLoss(unittest.TestCase):
self.assertTrue(np.allclose(dy_ret_value, expected))
def test_cross_entropy_loss_1d_with_weight_none(self):
input_np = np.random.random([100, 200]).astype(np.float64) #N,C
input_np = np.random.random([100, 200]).astype(self.dtype) #N,C
label_np = np.random.randint(0, 100, size=(100)).astype(np.int64) #N,1
weight_np = np.random.random([200]).astype(np.float64) #C
weight_np = np.random.random([200]).astype(self.dtype) #C
paddle.enable_static()
prog = fluid.Program()
......@@ -892,9 +902,9 @@ class CrossEntropyLoss(unittest.TestCase):
place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
) else fluid.CPUPlace()
with fluid.program_guard(prog, startup_prog):
input = fluid.data(name='input', shape=[100, 200], dtype='float64')
input = fluid.data(name='input', shape=[100, 200], dtype=self.dtype)
label = fluid.data(name='label', shape=[100], dtype='int64')
weight = fluid.data(name='weight', shape=[200], dtype='float64')
weight = fluid.data(name='weight', shape=[200], dtype=self.dtype)
cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
weight=weight, reduction='none')
......@@ -926,18 +936,18 @@ class CrossEntropyLoss(unittest.TestCase):
self.assertTrue(np.allclose(dy_ret_value, expected))
def test_cross_entropy_loss_1d_with_weight_none_func(self):
input_np = np.random.random([100, 200]).astype(np.float64) #N,C
input_np = np.random.random([100, 200]).astype(self.dtype) #N,C
label_np = np.random.randint(0, 100, size=(100)).astype(np.int64) #N
weight_np = np.random.random([200]).astype(np.float64) #C
weight_np = np.random.random([200]).astype(self.dtype) #C
paddle.enable_static()
prog = fluid.Program()
startup_prog = fluid.Program()
place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
) else fluid.CPUPlace()
with fluid.program_guard(prog, startup_prog):
input = fluid.data(name='input', shape=[100, 200], dtype='float64')
input = fluid.data(name='input', shape=[100, 200], dtype=self.dtype)
label = fluid.data(name='label', shape=[100], dtype='int64')
weight = fluid.data(name='weight', shape=[200], dtype='float64')
weight = fluid.data(name='weight', shape=[200], dtype=self.dtype)
ret = paddle.nn.functional.cross_entropy(
input, label, weight=weight, reduction='none')
......@@ -967,18 +977,18 @@ class CrossEntropyLoss(unittest.TestCase):
self.assertTrue(np.allclose(dy_ret_value, expected))
def test_cross_entropy_loss_1d_mean(self):
input_np = np.random.random([100, 200]).astype(np.float64) #N,C
input_np = np.random.random([100, 200]).astype(self.dtype) #N,C
label_np = np.random.randint(0, 100, size=(100)).astype(np.int64) #N,1
weight_np = np.random.random([200]).astype(np.float64) #C
weight_np = np.random.random([200]).astype(self.dtype) #C
paddle.enable_static()
prog = fluid.Program()
startup_prog = fluid.Program()
place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
) else fluid.CPUPlace()
with fluid.program_guard(prog, startup_prog):
input = fluid.data(name='input', shape=[100, 200], dtype='float64')
input = fluid.data(name='input', shape=[100, 200], dtype=self.dtype)
label = fluid.data(name='label', shape=[100], dtype='int64')
weight = fluid.data(name='weight', shape=[100], dtype='float64')
weight = fluid.data(name='weight', shape=[100], dtype=self.dtype)
cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss()
ret = cross_entropy_loss(input, label)
exe = fluid.Executor(place)
......@@ -1000,7 +1010,7 @@ class CrossEntropyLoss(unittest.TestCase):
self.assertTrue(np.allclose(dy_ret_value, expected))
def test_cross_entropy_loss_1d_sum(self):
input_np = np.random.random([100, 200]).astype(np.float64) #N,C
input_np = np.random.random([100, 200]).astype(self.dtype) #N,C
label_np = np.random.randint(0, 100, size=(100)).astype(np.int64) #N,1
paddle.enable_static()
prog = fluid.Program()
......@@ -1008,7 +1018,7 @@ class CrossEntropyLoss(unittest.TestCase):
place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
) else fluid.CPUPlace()
with fluid.program_guard(prog, startup_prog):
input = fluid.data(name='input', shape=[100, 200], dtype='float64')
input = fluid.data(name='input', shape=[100, 200], dtype=self.dtype)
label = fluid.data(name='label', shape=[100], dtype='int64')
cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
reduction='sum')
......@@ -1033,7 +1043,7 @@ class CrossEntropyLoss(unittest.TestCase):
self.assertTrue(np.allclose(dy_ret_value, expected))
def test_cross_entropy_loss_1d_none(self):
input_np = np.random.random([100, 200]).astype(np.float64) #N,C
input_np = np.random.random([100, 200]).astype(self.dtype) #N,C
label_np = np.random.randint(0, 100, size=(100)).astype(np.int64) #N,1
paddle.enable_static()
prog = fluid.Program()
......@@ -1041,7 +1051,7 @@ class CrossEntropyLoss(unittest.TestCase):
place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
) else fluid.CPUPlace()
with fluid.program_guard(prog, startup_prog):
input = fluid.data(name='input', shape=[100, 200], dtype='float64')
input = fluid.data(name='input', shape=[100, 200], dtype=self.dtype)
label = fluid.data(name='label', shape=[100], dtype='int64')
cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
reduction='none')
......@@ -1068,10 +1078,10 @@ class CrossEntropyLoss(unittest.TestCase):
self.assertTrue(np.allclose(dy_ret_value, expected))
def test_cross_entropy_loss_2d_with_weight_none(self):
input_np = np.random.random(size=(2, 2, 2, 3)).astype(np.float64) #NHWC
input_np = np.random.random(size=(2, 2, 2, 3)).astype(self.dtype) #NHWC
label_np = np.random.randint(
0, 3, size=(2, 2, 2)).astype(np.int64) #NHW1
weight_np = np.random.random(size=(3, )).astype(np.float64) #C
weight_np = np.random.random(size=(3, )).astype(self.dtype) #C
paddle.enable_static()
prog = fluid.Program()
......@@ -1080,9 +1090,9 @@ class CrossEntropyLoss(unittest.TestCase):
) else fluid.CPUPlace()
with fluid.program_guard(prog, startup_prog):
input = fluid.data(
name='input', shape=[2, 2, 2, 3], dtype='float64')
name='input', shape=[2, 2, 2, 3], dtype=self.dtype)
label = fluid.data(name='label', shape=[2, 2, 2], dtype='int64')
weight = fluid.data(name='weight', shape=[3], dtype='float64')
weight = fluid.data(name='weight', shape=[3], dtype=self.dtype)
cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
weight=weight, reduction='none')
ret = cross_entropy_loss(input, label)
......@@ -1113,10 +1123,10 @@ class CrossEntropyLoss(unittest.TestCase):
self.assertTrue(np.allclose(dy_ret_value, expected))
def test_cross_entropy_loss_2d_with_weight_mean(self):
input_np = np.random.random(size=(2, 2, 2, 3)).astype(np.float64) #NHWC
input_np = np.random.random(size=(2, 2, 2, 3)).astype(self.dtype) #NHWC
label_np = np.random.randint(
0, 3, size=(2, 2, 2)).astype(np.int64) #NHW
weight_np = np.random.random(size=(3, )).astype(np.float64) #C
weight_np = np.random.random(size=(3, )).astype(self.dtype) #C
paddle.enable_static()
prog = fluid.Program()
startup_prog = fluid.Program()
......@@ -1124,9 +1134,9 @@ class CrossEntropyLoss(unittest.TestCase):
) else fluid.CPUPlace()
with fluid.program_guard(prog, startup_prog):
input = fluid.data(
name='input', shape=[2, 2, 2, 3], dtype='float64')
name='input', shape=[2, 2, 2, 3], dtype=self.dtype)
label = fluid.data(name='label', shape=[2, 2, 2], dtype='int64')
weight = fluid.data(name='weight', shape=[3], dtype='float64')
weight = fluid.data(name='weight', shape=[3], dtype=self.dtype)
cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
weight=weight, reduction='mean')
ret = cross_entropy_loss(input, label)
......@@ -1155,10 +1165,10 @@ class CrossEntropyLoss(unittest.TestCase):
self.assertTrue(np.allclose(dy_ret_value, expected))
def test_cross_entropy_loss_2d_with_weight_sum(self):
input_np = np.random.random(size=(2, 2, 2, 3)).astype(np.float64) #NHWC
input_np = np.random.random(size=(2, 2, 2, 3)).astype(self.dtype) #NHWC
label_np = np.random.randint(
0, 3, size=(2, 2, 2)).astype(np.int64) #NHW
weight_np = np.random.random(size=(3, )).astype(np.float64) #C
weight_np = np.random.random(size=(3, )).astype(self.dtype) #C
paddle.enable_static()
prog = fluid.Program()
......@@ -1167,9 +1177,9 @@ class CrossEntropyLoss(unittest.TestCase):
) else fluid.CPUPlace()
with fluid.program_guard(prog, startup_prog):
input = fluid.data(
name='input', shape=[2, 2, 2, 3], dtype='float64')
name='input', shape=[2, 2, 2, 3], dtype=self.dtype)
label = fluid.data(name='label', shape=[2, 2, 2], dtype='int64')
weight = fluid.data(name='weight', shape=[3], dtype='float64')
weight = fluid.data(name='weight', shape=[3], dtype=self.dtype)
cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
weight=weight, reduction='sum')
ret = cross_entropy_loss(input, label)
......@@ -1198,7 +1208,7 @@ class CrossEntropyLoss(unittest.TestCase):
self.assertTrue(np.allclose(dy_ret_value, expected))
def test_cross_entropy_loss_2d_none(self):
input_np = np.random.random(size=(2, 2, 2, 3)).astype(np.float64) #NHWC
input_np = np.random.random(size=(2, 2, 2, 3)).astype(self.dtype) #NHWC
label_np = np.random.randint(
0, 3, size=(2, 2, 2)).astype(np.int64) #NHW
paddle.enable_static()
......@@ -1208,7 +1218,7 @@ class CrossEntropyLoss(unittest.TestCase):
) else fluid.CPUPlace()
with fluid.program_guard(prog, startup_prog):
input = fluid.data(
name='input', shape=[2, 2, 2, 3], dtype='float64')
name='input', shape=[2, 2, 2, 3], dtype=self.dtype)
label = fluid.data(name='label', shape=[2, 2, 2], dtype='int64')
cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
reduction='none')
......@@ -1237,7 +1247,7 @@ class CrossEntropyLoss(unittest.TestCase):
self.assertTrue(np.allclose(dy_ret_value, expected))
def test_cross_entropy_loss_2d_mean(self):
input_np = np.random.random(size=(2, 2, 2, 3)).astype(np.float64) #NHWC
input_np = np.random.random(size=(2, 2, 2, 3)).astype(self.dtype) #NHWC
label_np = np.random.randint(
0, 3, size=(2, 2, 2)).astype(np.int64) #NHW
paddle.enable_static()
......@@ -1247,7 +1257,7 @@ class CrossEntropyLoss(unittest.TestCase):
) else fluid.CPUPlace()
with fluid.program_guard(prog, startup_prog):
input = fluid.data(
name='input', shape=[2, 2, 2, 3], dtype='float64')
name='input', shape=[2, 2, 2, 3], dtype=self.dtype)
label = fluid.data(name='label', shape=[2, 2, 2], dtype='int64')
cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
reduction='mean')
......@@ -1276,7 +1286,7 @@ class CrossEntropyLoss(unittest.TestCase):
self.assertTrue(np.allclose(dy_ret_value, expected))
def test_cross_entropy_loss_2d_sum(self):
input_np = np.random.random(size=(2, 2, 2, 3)).astype(np.float64) #NHWC
input_np = np.random.random(size=(2, 2, 2, 3)).astype(self.dtype) #NHWC
label_np = np.random.randint(
0, 3, size=(2, 2, 2)).astype(np.int64) #NHW
paddle.enable_static()
......@@ -1286,7 +1296,7 @@ class CrossEntropyLoss(unittest.TestCase):
) else fluid.CPUPlace()
with fluid.program_guard(prog, startup_prog):
input = fluid.data(
name='input', shape=[2, 2, 2, 3], dtype='float64')
name='input', shape=[2, 2, 2, 3], dtype=self.dtype)
label = fluid.data(name='label', shape=[2, 2, 2], dtype='int64')
cross_entropy_loss = paddle.nn.loss.CrossEntropyLoss(
reduction='sum')
......
python/paddle/fluid/tests/unittests/test_expand_op.py
浏览文件 @ bd2a4e23
......@@ -27,8 +27,10 @@ class TestExpandOpRank1(OpTest):
def setUp(self):
self.op_type = "expand"
self.init_data()
self.dtype = "float32" if fluid.core.is_compiled_with_rocm(
) else "float64"
self.inputs = {'X': np.random.random(self.ori_shape).astype("float64")}
self.inputs = {'X': np.random.random(self.ori_shape).astype(self.dtype)}
self.attrs = {'expand_times': self.expand_times}
output = np.tile(self.inputs['X'], self.expand_times)
self.outputs = {'Out': output}
......@@ -79,13 +81,16 @@ class TestExpandOpRank1_tensor_attr(OpTest):
def setUp(self):
self.op_type = "expand"
self.init_data()
self.dtype = "float32" if fluid.core.is_compiled_with_rocm(
) else "float64"
expand_times_tensor = []
for index, ele in enumerate(self.expand_times):
expand_times_tensor.append(("x" + str(index), np.ones(
(1)).astype('int32') * ele))
self.inputs = {
'X': np.random.random(self.ori_shape).astype("float64"),
'X': np.random.random(self.ori_shape).astype(self.dtype),
'expand_times_tensor': expand_times_tensor,
}
self.attrs = {"expand_times": self.infer_expand_times}
......@@ -123,9 +128,11 @@ class TestExpandOpRank1_tensor(OpTest):
def setUp(self):
self.op_type = "expand"
self.init_data()
self.dtype = "float32" if fluid.core.is_compiled_with_rocm(
) else "float64"
self.inputs = {
'X': np.random.random(self.ori_shape).astype("float64"),
'X': np.random.random(self.ori_shape).astype(self.dtype),
'ExpandTimes': np.array(self.expand_times).astype("int32"),
}
self.attrs = {}
......
python/paddle/fluid/tests/unittests/test_gpu_package_without_gpu_device.py
浏览文件 @ bd2a4e23
......@@ -26,7 +26,10 @@ from paddle.fluid import core
class TestGPUPackagePaddle(unittest.TestCase):
def test_import_paddle(self):
if core.is_compiled_with_cuda():
os.environ['CUDA_VISIBLE_DEVICES'] = ''
if core.is_compiled_with_rocm():
os.environ['HIP_VISIBLE_DEVICES'] = ''
else:
os.environ['CUDA_VISIBLE_DEVICES'] = ''
test_file = 'test_no_gpu_run_rand.py'
with open(test_file, 'w') as wb:
cmd_test = """
......
python/paddle/fluid/tests/unittests/test_gru_rnn_op.py
浏览文件 @ bd2a4e23
......@@ -44,8 +44,9 @@ class TestGRUOp(OpTest):
def setUp(self):
self.op_type = "rnn"
self.dtype = "float64"
self.sequence_length = np.array(
self.dtype = "float32" if core.is_compiled_with_rocm() else "float64"
self.sequence_length = None if core.is_compiled_with_rocm(
) else np.array(
[12, 11, 10, 9, 8, 7, 6, 5], dtype=np.int32)
self.num_layers = 1
self.is_bidirec = False
......@@ -83,6 +84,24 @@ class TestGRUOp(OpTest):
output, last_hidden = rnn1(input, sequence_length=self.sequence_length)
if core.is_compiled_with_rocm():
def rocm_rnn_get_place():
places = [core.CUDAPlace(0)]
return places
self._get_places = rocm_rnn_get_place
if self.is_bidirec:
for i in range(0, len(flat_w), 4):
flat_w[i + 1], flat_w[i + 2] = flat_w[i + 2], flat_w[i + 1]
for i in range(len(flat_w)):
w = np.split(flat_w[i][1], 3, 0)
w = [w[1], w[0], w[2]]
w = np.concatenate(w)
flat_w[i] = (flat_w[i][0], w)
init_h = np.zeros((self.num_layers * self.direction_num, batch_size,
self.hidden_size)).astype(self.dtype)
......
python/paddle/fluid/tests/unittests/test_gru_unit_op.py
浏览文件 @ bd2a4e23
......@@ -121,12 +121,12 @@ class TestGRUUnitOp(OpTest):
self.op_type = 'gru_unit'
self.inputs = {
'Input': np.random.uniform(
-0.1, 0.1, (batch_size, frame_size * 3)).astype('float64'),
-0.1, 0.1, (batch_size, frame_size * 3)).astype(self.dtype),
'HiddenPrev': np.random.uniform(
-0.1, 0.1, (batch_size, frame_size)).astype('float64'),
-0.1, 0.1, (batch_size, frame_size)).astype(self.dtype),
'Weight': np.random.uniform(
-1. / math.sqrt(frame_size), 1. / math.sqrt(frame_size),
(frame_size, frame_size * 3)).astype('float64'),
(frame_size, frame_size * 3)).astype(self.dtype),
}
self.attrs = {
'activation': GRUActivationType.tanh,
......@@ -161,12 +161,14 @@ class TestGRUUnitOp(OpTest):
else:
h = u * c + (1 - u) * h_p
self.outputs = {
'Gate': g.astype('float64'),
'ResetHiddenPrev': r_h_p.astype('float64'),
'Hidden': h.astype('float64')
'Gate': g.astype(self.dtype),
'ResetHiddenPrev': r_h_p.astype(self.dtype),
'Hidden': h.astype(self.dtype)
}
def setUp(self):
self.dtype = 'float32' if fluid.core.is_compiled_with_rocm(
) else 'float64'
self.set_inputs()
self.set_outputs()
......@@ -179,6 +181,8 @@ class TestGRUUnitOp(OpTest):
class TestGRUUnitOpOriginMode(TestGRUUnitOp):
def setUp(self):
self.dtype = 'float32' if fluid.core.is_compiled_with_rocm(
) else 'float64'
self.set_inputs(origin_mode=True)
self.set_outputs(origin_mode=True)
......@@ -189,7 +193,7 @@ class TestGRUUnitOpWithBias(TestGRUUnitOp):
frame_size = self.frame_size
super(TestGRUUnitOpWithBias, self).set_inputs()
self.inputs['Bias'] = np.random.uniform(
-0.1, 0.1, (1, frame_size * 3)).astype('float64')
-0.1, 0.1, (1, frame_size * 3)).astype(self.dtype)
self.attrs = {
'activation': GRUActivationType.identity,
'gate_activation': GRUActivationType.sigmoid,
......@@ -207,6 +211,8 @@ class TestGRUUnitOpWithBias(TestGRUUnitOp):
class TestGRUUnitOpWithBiasOriginMode(TestGRUUnitOpWithBias):
def setUp(self):
self.dtype = 'float32' if fluid.core.is_compiled_with_rocm(
) else 'float64'
self.set_inputs(origin_mode=True)
self.set_outputs(origin_mode=True)
......
python/paddle/fluid/tests/unittests/test_imperative_optimizer.py
浏览文件 @ bd2a4e23
......@@ -190,10 +190,18 @@ class TestImperativeOptimizerBase(unittest.TestCase):
for key, value in six.iteritems(static_param_init_value):
self.assertTrue(np.allclose(value, dy_param_init_value[key]))
self.assertTrue(np.allclose(static_out, dy_out))
if core.is_compiled_with_rocm():
self.assertTrue(np.allclose(static_out, dy_out, atol=1e-3))
else:
self.assertTrue(np.allclose(static_out, dy_out))
for key, value in six.iteritems(static_param_value):
self.assertTrue(np.allclose(value, dy_param_value[key]))
if core.is_compiled_with_rocm():
self.assertTrue(
np.allclose(
value, dy_param_value[key], atol=1e-3))
else:
self.assertTrue(np.allclose(value, dy_param_value[key]))
class TestImperativeOptimizerPiecewiseDecay(TestImperativeOptimizerBase):
......
python/paddle/fluid/tests/unittests/test_imperative_optimizer_v2.py
浏览文件 @ bd2a4e23
......@@ -207,10 +207,18 @@ class TestImperativeOptimizerBase(unittest.TestCase):
for key, value in six.iteritems(static_param_init_value):
self.assertTrue(np.allclose(value, dy_param_init_value[key]))
self.assertTrue(np.allclose(static_out, dy_out))
if core.is_compiled_with_rocm():
self.assertTrue(np.allclose(static_out, dy_out, atol=1e-3))
else:
self.assertTrue(np.allclose(static_out, dy_out))
for key, value in six.iteritems(static_param_value):
self.assertTrue(np.allclose(value, dy_param_value[key]))
if core.is_compiled_with_rocm():
self.assertTrue(
np.allclose(
value, dy_param_value[key], atol=1e-3))
else:
self.assertTrue(np.allclose(value, dy_param_value[key]))
class TestImperativeOptimizerPiecewiseDecay(TestImperativeOptimizerBase):
......
python/paddle/fluid/tests/unittests/test_lstm_cudnn_op.py
浏览文件 @ bd2a4e23
......@@ -390,8 +390,10 @@ class TestCUDNNLstmOp(OpTest):
def setUp(self):
self.op_type = "cudnn_lstm"
self.dtype = np.float64
self.sequence_length = np.array([12, 11, 10, 9, 8], dtype=np.int32)
self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64
self.sequence_length = None if core.is_compiled_with_rocm(
) else np.array(
[12, 11, 10, 9, 8], dtype=np.int32)
self.num_layers = 1
self.set_attrs()
......@@ -447,6 +449,13 @@ class TestCUDNNLstmOp(OpTest):
hidden_size)).astype(self.dtype)
state_out = np.ndarray((300)).astype("uint8")
if core.is_compiled_with_rocm():
for i in range(len(flat_w)):
w = np.split(flat_w[i][1], 4, 0)
w = [w[0], w[1], w[3], w[2]]
w = np.concatenate(w)
flat_w[i] = (flat_w[i][0], w)
self.inputs = {
'Input': input,
'WeightList': flat_w,
......@@ -454,6 +463,13 @@ class TestCUDNNLstmOp(OpTest):
'InitC': init_c,
'SequenceLength': self.sequence_length
}
if self.sequence_length is None:
self.inputs = {
'Input': input,
'WeightList': flat_w,
'InitH': init_h,
'InitC': init_c,
}
self.attrs = {
'dropout_prob': 0.0,
'is_bidirec': False,
......@@ -474,8 +490,12 @@ class TestCUDNNLstmOp(OpTest):
def test_output_with_place(self):
place = core.CUDAPlace(0)
self.check_output_with_place(
place, no_check_set=['Reserve', 'StateOut'])
if core.is_compiled_with_rocm():
self.check_output_with_place(
place, atol=1e-5, no_check_set=['Reserve', 'StateOut'])
else:
self.check_output_with_place(
place, no_check_set=['Reserve', 'StateOut'])
def test_grad_with_place(self):
place = core.CUDAPlace(0)
......@@ -496,14 +516,13 @@ class TestCUDNNlstmAPI(unittest.TestCase):
hidden_size = 20
dropout_prob = 0.0
num_layers = 1
dtype = 'float32' if core.is_compiled_with_rocm() else 'float64'
input = fluid.data(
name='input',
shape=[seq_len, batch_size, hidden_size],
dtype='float64')
name='input', shape=[seq_len, batch_size, hidden_size], dtype=dtype)
init_h = layers.fill_constant([num_layers, batch_size, hidden_size],
'float64', 0.0)
dtype, 0.0)
init_c = layers.fill_constant([num_layers, batch_size, hidden_size],
'float64', 0.0)
dtype, 0.0)
rnn_out, last_h, last_c = layers.lstm(input, init_h, init_c, seq_len,
hidden_size, num_layers,
dropout_prob, False)
......@@ -526,14 +545,13 @@ class TestCUDNNlstmAPI(unittest.TestCase):
hidden_size = 20
dropout_prob = 0.0
num_layers = 2
dtype = 'float32' if core.is_compiled_with_rocm() else 'float64'
input = fluid.data(
name='input',
shape=[seq_len, batch_size, hidden_size],
dtype='float64')
name='input', shape=[seq_len, batch_size, hidden_size], dtype=dtype)
init_h = layers.fill_constant([num_layers, batch_size, hidden_size],
'float64', 0.0)
dtype, 0.0)
init_c = layers.fill_constant([num_layers, batch_size, hidden_size],
'float64', 0.0)
dtype, 0.0)
rnn_out, last_h, last_c = layers.lstm(input, init_h, init_c, seq_len,
hidden_size, num_layers,
dropout_prob, False, True)
......@@ -541,7 +559,7 @@ class TestCUDNNlstmAPI(unittest.TestCase):
exe.run(fluid.default_startup_program())
input_i = np.random.uniform(
low=-0.1, high=0.1, size=(seq_len, batch_size,
hidden_size)).astype("float64")
hidden_size)).astype(dtype)
out = exe.run(fluid.default_main_program(),
feed={'input': input_i},
fetch_list=[rnn_out, last_h, last_c, 'cudnn_lstm_0.w_0'])
......
python/paddle/fluid/tests/unittests/test_pool3d_op.py
浏览文件 @ bd2a4e23
......@@ -224,7 +224,7 @@ class TestPool3D_Op(OpTest):
def setUp(self):
self.op_type = "pool3d"
self.init_kernel_type()
self.dtype = np.float64
self.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64
self.init_test_case()
self.padding_algorithm = "EXPLICIT"
self.init_paddings()
......@@ -277,9 +277,16 @@ class TestPool3D_Op(OpTest):
return
if self.has_cudnn() and self.pool_type != "max":
place = core.CUDAPlace(0)
self.check_grad_with_place(place, set(['X']), 'Out')
if core.is_compiled_with_rocm():
self.check_grad_with_place(
place, set(['X']), 'Out', max_relative_error=1e-2)
else:
self.check_grad_with_place(place, set(['X']), 'Out')
elif self.pool_type != "max":
self.check_grad(set(['X']), 'Out')
if core.is_compiled_with_rocm():
self.check_grad(set(['X']), 'Out', max_relative_error=1e-2)
else:
self.check_grad(set(['X']), 'Out')
def init_data_format(self):
self.data_format = "NCDHW"
......@@ -400,7 +407,10 @@ def create_test_cudnn_fp16_class(parent):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=1e-3)
if core.is_compiled_with_rocm():
self.check_output_with_place(place, atol=1e-2)
else:
self.check_output_with_place(place, atol=1e-3)
cls_name = "{0}_{1}".format(parent.__name__, "CUDNNFp16Op")
TestCUDNNFp16Case.__name__ = cls_name
......
python/paddle/nn/layer/rnn.py
浏览文件 @ bd2a4e23
......@@ -1053,7 +1053,8 @@ class RNNBase(LayerList):
initial_states,
paddle.fluid.framework.Variable) else initial_states
if self.could_use_cudnn:
if self.could_use_cudnn and (not fluid.core.is_compiled_with_rocm() or
sequence_length is None):
# Add CPU kernel and dispatch in backend later
return self._cudnn_impl(inputs, initial_states, sequence_length)
......
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