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未验证 提交 05c2b9ba 编写于 作者: zhouweiwei2014's avatar zhouweiwei2014 提交者： GitHub
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[Zero-Dim] support input 0D Tensor for some unary api (#45992) 

* [Zero-Dim] support input 0D Tensor for unary api

* fix CI
上级 95768115
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Showing with 905 addition and 173 deletion
paddle/fluid/framework/details/fetch_async_op_handle.cc
浏览文件 @ 05c2b9ba
......@@ -164,6 +164,19 @@ void FetchAsyncOpHandle::FetchMergedLodTensor(
}
}
// check src type,layout,dim,lod consistence
for (size_t i = 1; i < src_lodtensors.size(); ++i) {
CheckTensorAttrs(
src_lodtensors[i], new_type, new_layout, check_dim, new_lod, offset_);
}
auto rank = src_lodtensors[0]->dims().size();
// for 0D tensor, can't concat eath tensor. So stack 0D and concat 1+D tensor
if (rank == 0) {
int src_lodtensor_size = src_lodtensors.size();
new_dim = phi::make_ddim(std::vector<int>({src_lodtensor_size}));
} else {
bool find_first_dims = false;
for (auto *t : src_lodtensors) {
if (t->numel() && t->IsInitialized()) {
......@@ -175,11 +188,6 @@ void FetchAsyncOpHandle::FetchMergedLodTensor(
}
}
}
// check src type,layout,dim,lod consistence
for (size_t i = 1; i < src_lodtensors.size(); ++i) {
CheckTensorAttrs(
src_lodtensors[i], new_type, new_layout, check_dim, new_lod, offset_);
}
// set dst tensor
......@@ -195,9 +203,17 @@ void FetchAsyncOpHandle::FetchMergedLodTensor(
}
// slice and memcpy
// for 0D tensor, can't concat eath tensor, stack them. for 1+D tensor, concat
// them
int begin = 0;
int end = 0;
for (auto *src : src_lodtensors) {
int end = begin + src->dims()[0];
if (rank == 0) {
end = begin + 1;
} else {
end = begin + src->dims()[0];
}
if (end == begin) {
continue;
}
......
paddle/fluid/framework/details/scale_loss_grad_op_handle.cc
浏览文件 @ 05c2b9ba
......@@ -16,6 +16,7 @@
#include <string>
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/platform/profiler/event_tracing.h"
namespace phi {
......@@ -101,6 +102,7 @@ std::string ScaleLossGradOpHandle::LossGradName() const {
void ScaleLossGradOpHandle::RunImpl() {
platform::RecordEvent record_event(
Name(), platform::TracerEventType::UserDefined, 2);
RunOnVar(local_exec_scopes_[0]->FindVar(LossGradName()), true);
}
......
paddle/fluid/framework/infershape_utils.cc
浏览文件 @ 05c2b9ba
......@@ -213,8 +213,9 @@ DDim CompatMetaTensor::dims() const {
} else {
auto* var = PADDLE_GET_CONST(VarDesc*, var_);
return var->GetShape().empty() ? phi::make_ddim({0UL})
: phi::make_ddim(var->GetShape());
return phi::make_ddim(var->GetShape());
// return var->GetShape().empty() ? phi::make_ddim({0UL}) :
// phi::make_ddim(var->GetShape());
}
}
......
paddle/fluid/framework/lod_tensor.cc
浏览文件 @ 05c2b9ba
......@@ -262,6 +262,16 @@ std::vector<LoDTensor> SplitLoDTensor(
platform::errors::InvalidArgument(
"Place number cannot be empty when splitting."));
src.check_memory_size();
auto rank = src.dims().size();
// if rank is 0, just return #places.size() copys of src
if (rank == 0) {
LoDTensor dst;
framework::TensorCopy(src, src.place(), &dst);
std::vector<LoDTensor> ret;
ret.emplace_back(std::move(dst));
return ret;
}
size_t batch_size = src.lod().empty() ? static_cast<size_t>(src.dims()[0])
: src.lod()[0].size() - 1;
......@@ -349,6 +359,7 @@ void MergeLoDTensor(LoDTensor *target,
}
LoD new_lod = lod_tensors[0]->lod();
auto rank = lod_tensors[0]->dims().size();
for (size_t i = 1; i < lod_tensors.size(); ++i) {
auto *t = lod_tensors[i];
......@@ -369,17 +380,25 @@ void MergeLoDTensor(LoDTensor *target,
"actual layout is %s.",
DataLayoutToString(new_layout),
DataLayoutToString(t->layout())));
auto tensor_dims = t->dims();
PADDLE_ENFORCE_EQ(tensor_dims.size(),
new_dim.size(),
platform::errors::InvalidArgument(
"dimensions of LoDTensor does not match"));
for (int j = 1; j < t->dims().size(); j++) {
PADDLE_ENFORCE_EQ(
phi::product(new_dim) / new_dim[0],
phi::product(t->dims()) / t->dims()[0],
tensor_dims[j],
new_dim[j],
platform::errors::InvalidArgument(
"LoDTensor dimension does not match, all dimensions except the "
"first dimension need to be equal,"
"but expected dimension is %s, actual dimension is %s.",
new_dim,
t->dims()));
"LoDTensor.ddim[%d] should eaqual to %d, but is %d",
j,
new_dim[j],
tensor_dims[j]));
}
if (rank > 0) {
new_dim[0] += t->dims()[0];
}
}
auto &lod = t->lod();
PADDLE_ENFORCE_EQ(new_lod.size(),
......
paddle/fluid/framework/op_desc.cc
浏览文件 @ 05c2b9ba
......@@ -362,7 +362,7 @@ class CompileTimeInferShapeContext : public InferShapeContext {
DDim res;
try {
auto shape = var->GetShape();
res = shape.empty() ? phi::make_ddim({0UL}) : phi::make_ddim(shape);
res = phi::make_ddim(shape);
} catch (...) {
VLOG(5) << "GetDim of variable " << name << " error";
std::rethrow_exception(std::current_exception());
......@@ -1258,7 +1258,7 @@ std::vector<DDim> CompileTimeInferShapeContext::GetRepeatedDims(
try {
auto shapes = var->GetShapes();
for (const auto &s : shapes) {
res.push_back(s.empty() ? phi::make_ddim({0UL}) : phi::make_ddim(s));
res.push_back(phi::make_ddim(s));
}
} catch (...) {
VLOG(5) << "GetRepeatedDim of variable " << name << " error.";
......
paddle/phi/core/utils/dim.h
浏览文件 @ 05c2b9ba
......@@ -72,10 +72,15 @@ HOSTDEVICE inline Dim<sizeof...(Args)> make_dim(Args... idxes) {
// Allows us to output a Dim
template <int D>
inline std::ostream& operator<<(std::ostream& os, const Dim<D>& d) {
if (D > 0) {
os << d[0];
for (int i = 1; i < D; ++i) {
os << ", " << d[i];
}
} else {
os << "";
}
return os;
}
......
paddle/phi/infermeta/multiary.cc
浏览文件 @ 05c2b9ba
......@@ -305,9 +305,14 @@ void AddNInferMeta(const std::vector<const MetaTensor*>& x,
if (x[i]->is_selected_rows() && x_dim.size() == 1) {
continue;
}
// for zero-sized tensor
if (phi::product(x_dim) == 0) {
continue;
}
// for 0D tensor
if (x_dim.size() == 0) {
continue;
}
if (phi::product(in_dim) == 0) {
in_dim = x_dim;
} else {
......@@ -2547,8 +2552,8 @@ void WarpctcInferMeta(const MetaTensor& logits,
const MetaTensor& labels_length,
int blank,
bool norm_by_times,
MetaTensor* warpctcgrad,
MetaTensor* loss) {
MetaTensor* loss,
MetaTensor* warpctcgrad) {
auto logits_dims = logits.dims();
int sequence_width = 0;
......
paddle/phi/infermeta/multiary.h
浏览文件 @ 05c2b9ba
......@@ -483,8 +483,8 @@ void WarpctcInferMeta(const MetaTensor& logits,
const MetaTensor& labels_length,
int blank,
bool norm_by_times,
MetaTensor* warpctcgrad,
MetaTensor* loss);
MetaTensor* loss,
MetaTensor* warpctcgrad);
void WhereInferMeta(const MetaTensor& condition,
const MetaTensor& x,
......
paddle/phi/infermeta/unary.cc
浏览文件 @ 05c2b9ba
......@@ -2668,7 +2668,7 @@ DDim ReduceInferDim(const MetaTensor& x,
x_rank,
errors::InvalidArgument(
"The reduce dim index %d should be in the "
"range [-dimension(X), dimension(X)] "
"range [ -dimension(X), dimension(X) ) "
"which dimesion = %d. But received dim index = %d.",
i,
x_rank,
......@@ -2677,7 +2677,7 @@ DDim ReduceInferDim(const MetaTensor& x,
-x_rank,
errors::InvalidArgument(
"The reduce dim index %d should be in the "
"range [-dimension(X), dimension(X)] "
"range [ -dimension(X), dimension(X) ) "
"which dimesion = %d. But received dim index = %d.",
i,
x_rank,
......
paddle/phi/kernels/funcs/unsqueeze.h
浏览文件 @ 05c2b9ba
......@@ -36,6 +36,24 @@ inline DDim GetOutputSqueezeShape(const std::vector<int> squeeze_dims,
}
} else {
for (size_t i = 0; i < num_squeeze_dims; ++i) {
if (in_dims.size() == 0) {
PADDLE_ENFORCE_GE(
squeeze_dims[i],
-1,
phi::errors::InvalidArgument(
"For 0D Tensor, Each axis in Attr(axes) should be in the range "
"of [-1, 0]"
"But current axis is:%d, input tensor's shape = [%s]."));
PADDLE_ENFORCE_LE(
squeeze_dims[i],
0,
phi::errors::InvalidArgument(
"For 0D Tensor, Each axis in Attr(axes) should be in the range "
"of [-1, 0]"
"But current axis is:%d, input tensor's shape = [%s]."));
continue;
}
int current = squeeze_dims[i] < 0 ? squeeze_dims[i] + in_dims.size()
: squeeze_dims[i];
......
paddle/phi/kernels/onednn/reduce_kernel_impl.h
浏览文件 @ 05c2b9ba
......@@ -25,8 +25,7 @@ inline std::vector<int64_t> CalculateReducedDims(
bool keep_dim) {
if (keep_dim) return vectorize(output->dims());
if (reduce_all && reduce_dims.size() > 0)
return std::vector<int64_t>(input->dims().size(), 1);
if (reduce_all) return std::vector<int64_t>(input->dims().size(), 1);
std::vector<int64_t> output_dims(vectorize(input->dims()));
for (size_t i = 0; i < reduce_dims.size(); ++i) {
......
paddle/phi/tests/core/test_ddim.cc
浏览文件 @ 05c2b9ba
......@@ -21,18 +21,43 @@ namespace phi {
namespace tests {
TEST(DDim, Equality) {
// default construct ddim
phi::DDim default_ddim;
EXPECT_EQ(arity(default_ddim), 1);
EXPECT_EQ(default_ddim[0], 0);
// construct a zero-DDim
phi::DDim zero_ddim = phi::make_ddim({});
EXPECT_EQ(arity(zero_ddim), 0);
EXPECT_EQ(zero_ddim.size(), 0);
EXPECT_EQ(phi::product(zero_ddim), 1);
std::vector<int64_t> zero_vec;
phi::DDim zero_ddim1 = phi::make_ddim(zero_vec);
EXPECT_EQ(arity(zero_ddim1), 0);
EXPECT_EQ(zero_ddim1.size(), 0);
EXPECT_EQ(phi::product(zero_ddim1), 1);
// zero-DDim to vector
std::vector<int64_t> zero_ddim_vec = phi::vectorize(zero_ddim);
EXPECT_EQ(zero_ddim_vec.size(), size_t(0));
// reshape zero-DDim
std::vector<int> reshape_vec = {1};
phi::DDim reshape_ddim = zero_ddim.reshape(reshape_vec);
EXPECT_EQ(arity(reshape_ddim), 1);
EXPECT_EQ(reshape_ddim.size(), 1);
EXPECT_EQ(phi::product(reshape_ddim), 1);
// construct a DDim from an initialization list
phi::DDim ddim = phi::make_ddim({9, 1, 5});
EXPECT_EQ(ddim[0], 9);
EXPECT_EQ(ddim[1], 1);
EXPECT_EQ(ddim[2], 5);
// construct a DDim from a vector
std::vector<int64_t> vec({9, 1, 5});
phi::DDim vddim = phi::make_ddim(vec);
EXPECT_EQ(ddim[0], 9);
EXPECT_EQ(ddim[1], 1);
EXPECT_EQ(ddim[2], 5);
// arity of a DDim
EXPECT_EQ(phi::arity(ddim), 3);
EXPECT_EQ(ddim.size(), 3);
// mutate a DDim
ddim[1] = 2;
......@@ -40,6 +65,13 @@ TEST(DDim, Equality) {
ddim[0] = 6;
EXPECT_EQ(ddim[0], 6);
// construct a DDim from a vector
std::vector<int64_t> vec({9, 1, 5});
phi::DDim vddim = phi::make_ddim(vec);
EXPECT_EQ(vddim[0], 9);
EXPECT_EQ(vddim[1], 1);
EXPECT_EQ(vddim[2], 5);
// vectorize a DDim
std::vector<int64_t> res_vec = phi::vectorize(vddim);
EXPECT_EQ(res_vec[0], 9);
......@@ -51,37 +83,45 @@ TEST(DDim, Equality) {
EXPECT_EQ(res_vec[1], 2);
EXPECT_EQ(res_vec[2], 1);
// arity of a DDim
EXPECT_EQ(phi::arity(ddim), 3);
EXPECT_EQ(ddim.size(), 3);
// product of a DDim
EXPECT_EQ(phi::product(vddim), 45);
EXPECT_EQ(phi::product(phi::make_ddim({3, 2, 5, 3})), 90);
// slice a DDim
phi::DDim ddim2 = phi::make_ddim({1, 2, 3, 4, 5, 6});
phi::DDim ss = phi::slice_ddim(ddim2, 2, 5);
EXPECT_EQ(arity(ss), 3);
EXPECT_EQ(ss[0], 3);
EXPECT_EQ(ss[1], 4);
EXPECT_EQ(ss[2], 5);
phi::DDim ss2 = phi::slice_ddim(ddim2, 0, 6);
EXPECT_EQ(arity(ss2), 6);
EXPECT_EQ(ss2[0], 1);
EXPECT_EQ(ss2[1], 2);
EXPECT_EQ(ss2[2], 3);
EXPECT_EQ(ss2[3], 4);
EXPECT_EQ(ss2[4], 5);
EXPECT_EQ(ss2[5], 6);
phi::DDim slice_dim1 = phi::slice_ddim(ddim2, 2, 5);
EXPECT_EQ(arity(slice_dim1), 3);
EXPECT_EQ(slice_dim1[0], 3);
EXPECT_EQ(slice_dim1[1], 4);
EXPECT_EQ(slice_dim1[2], 5);
phi::DDim slice_dim2 = phi::slice_ddim(ddim2, 0, 6);
EXPECT_EQ(arity(slice_dim2), 6);
EXPECT_EQ(slice_dim2[0], 1);
EXPECT_EQ(slice_dim2[1], 2);
EXPECT_EQ(slice_dim2[2], 3);
EXPECT_EQ(slice_dim2[3], 4);
EXPECT_EQ(slice_dim2[4], 5);
EXPECT_EQ(slice_dim2[5], 6);
phi::DDim slice_dim3 = phi::slice_ddim(ddim2, 1, 1);
EXPECT_EQ(arity(slice_dim3), 0);
EXPECT_EQ(slice_dim3.size(), 0);
EXPECT_EQ(phi::product(slice_dim3), 1);
}
TEST(DDim, Print) {
// print a DDim
std::stringstream ss;
std::stringstream ss1;
phi::DDim ddim = phi::make_ddim({2, 3, 4});
ss << ddim;
EXPECT_EQ("2, 3, 4", ss.str());
ss1 << ddim;
EXPECT_EQ("2, 3, 4", ss1.str());
// print a zero-DDim
std::stringstream ss2;
phi::DDim zero_ddim = phi::make_ddim({});
ss2 << zero_ddim;
EXPECT_EQ("", ss2.str());
}
} // namespace tests
......
python/paddle/distributed/fleet/utils/hybrid_parallel_inference.py
浏览文件 @ 05c2b9ba
......@@ -688,6 +688,8 @@ class HybridParallelInferenceHelper(object):
})
else:
var_shape = list(var.shape)
print(var_name)
if len(var.shape) > 0:
var_shape[0] = self.micro_batch_size if var_shape[
0] < 0 else var_shape[0]
block._insert_op_without_sync(
......
python/paddle/fluid/dygraph/dygraph_to_static/convert_operators.py
浏览文件 @ 05c2b9ba
......@@ -462,6 +462,7 @@ def convert_len(var):
`shape_op` in var.block.
"""
if isinstance(var, Variable):
assert var.ndim > 0, "len() of a 0D tensor is wrong"
if var.type in [
core.VarDesc.VarType.LOD_TENSOR,
core.VarDesc.VarType.SELECTED_ROWS
......
python/paddle/fluid/dygraph/math_op_patch.py
浏览文件 @ 05c2b9ba
......@@ -144,6 +144,7 @@ def monkey_patch_math_varbase():
return int(var.numpy().flatten()[0])
def _len_(var):
assert var.ndim > 0, "len() of a 0D tensor is wrong"
if var.type == core.VarDesc.VarType.VOCAB:
return len(var.value().get_map_tensor())
elif var.type == core.VarDesc.VarType.STRINGS:
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 05c2b9ba
......@@ -208,6 +208,30 @@ OP_NAMEMAPPING = {
}
def _get_reduce_dim(dim, input):
"""
Internal function for reduce_sum, reduce_mean, reduce_max, reduce_min, reduce_prod.
It computes the attribute reduce_all value based on axis.
"""
if dim is not None and not isinstance(dim, list):
if isinstance(dim, (tuple, range)):
dim = list(dim)
elif isinstance(dim, int):
dim = [dim]
else:
raise TypeError(
"The type of dim must be int, list, tuple or range, but received {}"
.format(type(axis)))
if dim is None:
dim = []
if dim == [] or len(dim) == len(input.shape):
reduce_all = True
else:
reduce_all = False
return reduce_all, dim
@dygraph_only
def _elementwise_op_in_dygraph(x,
y,
......@@ -4689,29 +4713,14 @@ def reduce_sum(input, dim=None, keep_dim=False, name=None):
if dim is not None and not isinstance(dim, list):
dim = [dim]
reduce_all, dim = _get_reduce_dim(dim, input)
if in_dygraph_mode():
reduce_all = True if dim == None or dim == [] or len(dim) == len(
input.shape) else False
dim = dim if dim != None and dim != [] else [0]
if reduce_all:
return _C_ops.sum(input, [], None, keep_dim)
else:
return _C_ops.sum(input, dim, None, keep_dim)
elif _in_legacy_dygraph():
reduce_all = True if dim == None or dim == [] or len(dim) == len(
input.shape) else False
dim = dim if dim != None and dim != [] else [0]
return _legacy_C_ops.reduce_sum(input, 'dim', dim, 'keep_dim', keep_dim,
'reduce_all', reduce_all)
attrs = {
'dim':
dim if dim != None and dim != [] else [0],
'keep_dim':
keep_dim,
'reduce_all':
True
if dim == None or dim == [] or len(dim) == len(input.shape) else False
}
attrs = {'dim': dim, 'keep_dim': keep_dim, 'reduce_all': reduce_all}
check_variable_and_dtype(
input, 'input', ['float16', 'float32', 'float64', 'int32', 'int64'],
'reduce_sum')
......
python/paddle/fluid/layers/utils.py
浏览文件 @ 05c2b9ba
......@@ -363,9 +363,6 @@ def get_shape_tensor_inputs(inputs, attrs, shape, op_type):
shape = cast(shape, 'int32')
inputs["ShapeTensor"] = shape
elif isinstance(shape, (list, tuple)):
assert len(shape) > 0, ("The size of 'shape' in" + op_type +
" can't be zero, "
"but received %s." % len(shape))
attrs["shape"] = _get_attr_shape(shape)
if _contain_var(shape):
inputs['ShapeTensorList'] = _get_shape_tensor(shape)
......
python/paddle/fluid/tests/unittests/collective/fleet/hybrid_parallel_inference_helper.py
浏览文件 @ 05c2b9ba
......@@ -82,6 +82,7 @@ class TestHybridParallelInferenceHelperClass(unittest.TestCase):
value=0,
force_cpu=False,
name="cond_int")
print(cond_int.shape)
cond = layers.less_than(x=step_idx, y=max_len)
while_op = layers.While(cond, is_test=True)
......
python/paddle/fluid/tests/unittests/dygraph_to_static/test_len.py
浏览文件 @ 05c2b9ba
......@@ -82,6 +82,7 @@ def len_with_selected_rows(place):
# create selected_rows variable
var = block.create_var(name="X",
dtype="float32",
shape=[-1],
persistable=True,
type=fluid.core.VarDesc.VarType.SELECTED_ROWS)
# y is Variable(SelectedRows)
......
python/paddle/fluid/tests/unittests/op_test.py
浏览文件 @ 05c2b9ba
......@@ -505,6 +505,7 @@ class OpTest(unittest.TestCase):
else:
tensor.set(self.inputs[var_name], place)
feed_map[var_name] = tensor
return feed_map
def _append_ops(self, block):
......@@ -1136,6 +1137,7 @@ class OpTest(unittest.TestCase):
continue
else:
grad_feed_map[arg] = fwd_outs[i]._copy(p)
return grad_feed_map
def _get_need_run_ops(self, op_desc, fwd_op_desc=None):
......@@ -1254,6 +1256,7 @@ class OpTest(unittest.TestCase):
build_strategy=build_strategy,
places=place)
program = compiled_program
outs = exe.run(program,
feed=grad_feed_map,
fetch_list=grad_fetch_list,
......@@ -1290,6 +1293,7 @@ class OpTest(unittest.TestCase):
fwd_res,
grad_op_desc,
enable_inplace=True)
self._compare_expect_and_actual_outputs(place,
expect_res[1],
expect_res[0],
......@@ -1457,7 +1461,7 @@ class OpTest(unittest.TestCase):
# NOTE(zhiqiu): np.allclose([], [1.]) returns True
# see details: https://stackoverflow.com/questions/38331703/why-does-numpys-broadcasting-sometimes-allow-comparing-arrays-of-different-leng
if expect_np.size == 0:
self.op_test.assertTrue(actual_np.size == 0) # }}}
self.op_test.assertTrue(actual_np.size == 0)
self._compare_numpy(name, actual_np, expect_np)
if isinstance(expect, tuple):
self._compare_list(name, actual, expect)
......@@ -1663,7 +1667,6 @@ class OpTest(unittest.TestCase):
if check_dygraph:
# always enable legacy dygraph
g_enable_legacy_dygraph()
dygraph_checker = DygraphChecker(self, self.outputs)
dygraph_checker.check()
dygraph_outs = dygraph_checker.outputs
......@@ -1830,6 +1833,7 @@ class OpTest(unittest.TestCase):
# Therefore, it asserts np.abs(a - b) / (np.abs(a)*1e4) < max_relative_error,
# which is the same as np.abs(a - b) / np.abs(a) < max_relative_error*1e4.
abs_a = np.abs(a)
if abs_a.ndim > 0:
if self.dtype == np.float64 and \
self.op_type not in op_threshold_white_list.NEED_FIX_FP64_CHECK_GRAD_THRESHOLD_OP_LIST:
abs_a[abs_a < 1e-10] = 1e-3
......@@ -1839,6 +1843,19 @@ class OpTest(unittest.TestCase):
abs_a[abs_a < 1e-2] = 1
else:
abs_a[abs_a < 1e-3] = 1
elif abs_a.ndim == 0:
if self.dtype == np.float64 and \
self.op_type not in op_threshold_white_list.NEED_FIX_FP64_CHECK_GRAD_THRESHOLD_OP_LIST:
if abs_a < 1e-10:
abs_a = 1e-3
elif abs_a > 1e-10 and abs_a <= 1e-8:
abs_a = abs_a * 1e4
elif abs_a > 1e-8 and abs_a <= 1e-6:
abs_a = abs_a * 1e2
elif self.is_bfloat16_op():
abs_a = 1 if abs_a < 1e-2 else abs_a
else:
abs_a = 1 if abs_a < 1e-3 else abs_a
diff_mat = np.abs(a - b) / abs_a
max_diff = np.max(diff_mat)
......@@ -1958,7 +1975,9 @@ class OpTest(unittest.TestCase):
tensor_to_check = self.scope.find_var(input_to_check).get_tensor()
tensor_size = six.moves.reduce(lambda a, b: a * b,
tensor_to_check.shape(), 1)
if tensor_size < 100:
tensor_ndim = len(tensor_to_check.shape())
# for 0D Tensor, it's additional case for OP, so not raise error
if tensor_ndim > 0 and tensor_size < 100:
self.__class__.input_shape_is_large = False
if not type(output_names) is list:
......
python/paddle/fluid/tests/unittests/test_activation_op.py
浏览文件 @ 05c2b9ba
......@@ -52,12 +52,13 @@ class TestActivation(OpTest):
def setUp(self):
self.op_type = "exp"
self.init_dtype()
self.init_shape()
self.init_kernel_type()
self.check_eager = True
self.python_api = paddle.exp
np.random.seed(2049)
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
x = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
out = np.exp(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
......@@ -80,19 +81,29 @@ class TestActivation(OpTest):
def init_dtype(self):
self.dtype = np.float64
def init_shape(self):
self.shape = [11, 17]
def init_kernel_type(self):
pass
class TestActivation_ZeroDim(TestActivation):
def init_shape(self):
self.shape = []
class TestExpm1(TestActivation):
def setUp(self):
self.op_type = "expm1"
self.python_api = paddle.expm1
self.init_dtype()
self.init_shape()
np.random.seed(2049)
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
x = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
out = np.expm1(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
......@@ -105,6 +116,12 @@ class TestExpm1(TestActivation):
self.check_output(check_eager=True)
class TestExpm1_ZeroDim(TestExpm1):
def init_shape(self):
self.shape = []
class TestExpm1API(unittest.TestCase):
def init_dtype(self):
......@@ -182,9 +199,10 @@ class TestSigmoid(TestActivation):
def setUp(self):
self.op_type = "sigmoid"
self.init_dtype()
self.init_shape()
np.random.seed(1024)
x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
out = 1 / (1 + np.exp(-x))
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
......@@ -199,6 +217,12 @@ class TestSigmoid(TestActivation):
self.check_grad(['X'], 'Out', max_relative_error=0.01)
class TestSigmoid_ZeroDim(TestSigmoid):
def init_shape(self):
self.shape = []
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestSigmoidBF16(OpTest):
......@@ -206,9 +230,10 @@ class TestSigmoidBF16(OpTest):
def setUp(self):
self.op_type = "sigmoid"
self.init_dtype()
self.init_shape()
np.random.seed(1024)
x = np.random.uniform(-1, 1, [11, 17]).astype(np.float32)
x = np.random.uniform(-1, 1, self.shape).astype(np.float32)
out = 1 / (1 + np.exp(-x))
self.inputs = {
......@@ -219,6 +244,9 @@ class TestSigmoidBF16(OpTest):
def init_dtype(self):
self.dtype = np.uint16
def init_shape(self):
self.shape = [11, 17]
def test_check_output(self):
place = core.CUDAPlace(0)
self.check_output_with_place(place)
......@@ -228,14 +256,23 @@ class TestSigmoidBF16(OpTest):
self.check_grad_with_place(place, ['X'], 'Out')
'''
class TestSigmoidBF16_ZeroDim(TestSigmoidBF16):
def init_shape(self):
self.shape = []
'''
class TestSilu(TestActivation):
def setUp(self):
self.op_type = "silu"
self.init_dtype()
self.init_shape()
np.random.seed(1024)
x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
out = x / (np.exp(-x) + 1)
self.inputs = {'X': x}
......@@ -250,6 +287,12 @@ class TestSilu(TestActivation):
self.check_grad(['X'], 'Out')
class TestSilu_ZeroDim(TestSilu):
def init_shape(self):
self.shape = []
class TestSiluAPI(unittest.TestCase):
# test paddle.nn.Silu, paddle.nn.functional.silu
def setUp(self):
......@@ -302,9 +345,10 @@ class TestLogSigmoid(TestActivation):
def setUp(self):
self.op_type = "logsigmoid"
self.init_dtype()
self.init_shape()
np.random.seed(2048)
x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
out = np.log(1 / (1 + np.exp(-x)))
self.inputs = {'X': x}
......@@ -316,6 +360,12 @@ class TestLogSigmoid(TestActivation):
self.check_grad(['X'], 'Out', max_relative_error=0.008)
class TestLogSigmoid_ZeroDim(TestLogSigmoid):
def init_shape(self):
self.shape = []
class TestLogSigmoidAPI(unittest.TestCase):
# test paddle.nn.LogSigmoid, paddle.nn.functional.log_sigmoid
def setUp(self):
......@@ -380,8 +430,10 @@ class TestTanh(TestActivation, TestParameter):
def setUp(self):
self.op_type = "tanh"
self.init_dtype()
self.init_shape()
np.random.seed(1024)
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
x = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
out = np.tanh(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
......@@ -399,6 +451,12 @@ class TestTanh(TestActivation, TestParameter):
self.dtype = np.float32
class TestTanh_ZeroDim(TestTanh):
def init_shape(self):
self.shape = []
class TestTanhAPI(unittest.TestCase):
# test paddle.tanh, paddle.nn.tanh, paddle.nn.functional.tanh
def setUp(self):
......@@ -475,9 +533,10 @@ class TestAtan(TestActivation, TestParameter):
def setUp(self):
self.op_type = "atan"
self.init_dtype()
self.init_shape()
np.random.seed(1024)
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
x = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
out = np.arctan(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
......@@ -508,14 +567,21 @@ class TestAtan(TestActivation, TestParameter):
self.assertEqual(z, z_expected)
class TestAtan_ZeroDim(TestTanh):
def init_shape(self):
self.shape = []
class TestSinh(TestActivation):
def setUp(self):
self.op_type = "sinh"
self.init_dtype()
self.init_shape()
np.random.seed(1024)
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
x = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
out = np.sinh(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
......@@ -526,6 +592,15 @@ class TestSinh(TestActivation):
return
self.check_grad(['X'], 'Out')
class TestSinh_ZeroDim(TestSinh):
def init_shape(self):
self.shape = []
class TestSinhAPI(unittest.TestCase):
def test_dygraph(self):
with fluid.dygraph.guard():
np_x = np.array([0.1])
......@@ -586,9 +661,10 @@ class TestCosh(TestActivation):
def setUp(self):
self.op_type = "cosh"
self.init_dtype()
self.init_shape()
np.random.seed(1024)
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
x = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
out = np.cosh(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
......@@ -599,6 +675,15 @@ class TestCosh(TestActivation):
return
self.check_grad(['X'], 'Out')
class TestCosh_ZeroDim(TestCosh):
def init_shape(self):
self.shape = []
class TestCoshAPI(unittest.TestCase):
def test_dygraph(self):
with fluid.dygraph.guard():
np_x = np.array([0.1])
......@@ -664,9 +749,10 @@ class TestTanhshrink(TestActivation):
def setUp(self):
self.op_type = "tanh_shrink"
self.init_dtype()
self.init_shape()
np.random.seed(1024)
x = np.random.uniform(10, 20, [10, 17]).astype(self.dtype)
x = np.random.uniform(10, 20, self.shape).astype(self.dtype)
out = ref_tanhshrink(x)
self.inputs = {'X': x}
......@@ -678,6 +764,12 @@ class TestTanhshrink(TestActivation):
self.check_grad(['X'], 'Out')
class TestTanhshrink_ZeroDim(TestTanhshrink):
def init_shape(self):
self.shape = []
class TestTanhshrinkAPI(unittest.TestCase):
# test paddle.nn.Tanhshrink, paddle.nn.functional.tanhshrink
def setUp(self):
......@@ -748,17 +840,21 @@ class TestHardShrink(TestActivation):
def setUp(self):
self.op_type = "hard_shrink"
self.init_dtype()
self.init_shape()
self.threshold = 0.5
self.set_attrs()
np.random.seed(1024)
x = np.random.uniform(-1, 1, [10, 12]).astype(self.dtype) * 10
x = np.random.uniform(-1, 1, self.shape).astype(self.dtype) * 10
out = ref_hardshrink(x, self.threshold)
self.attrs = {'threshold': self.threshold}
self.inputs = {'X': x}
self.outputs = {'Out': out}
def init_shape(self):
self.shape = [10, 12]
def set_attrs(self):
pass
......@@ -774,6 +870,14 @@ class TestHardShrink_threshold_negative(TestHardShrink):
self.threshold = -0.1
'''
class TestHardShrink_ZeroDim(TestHardShrink):
def init_shape(self):
self.shape = []
'''
class TestHardShrinkAPI(unittest.TestCase):
# test paddle.nn.Hardshrink, paddle.nn.functional.hardshrink
def setUp(self):
......@@ -918,11 +1022,12 @@ class TestSoftshrink(TestActivation):
self.check_eager = True
self.python_api = paddle.nn.functional.softshrink
self.init_dtype()
self.init_shape()
threshold = 0.8
np.random.seed(1023)
x = np.random.uniform(0.25, 10, [10, 12]).astype(self.dtype)
x = np.random.uniform(0.25, 10, self.shape).astype(self.dtype)
out = ref_softshrink(x, threshold)
self.inputs = {'X': x}
self.attrs = {"lambda": threshold}
......@@ -934,6 +1039,12 @@ class TestSoftshrink(TestActivation):
self.check_grad(['X'], 'Out', check_eager=True)
class TestSoftshrink_ZeroDim(TestSoftshrink):
def init_shape(self):
self.shape = []
class TestSoftshrinkAPI(unittest.TestCase):
# test paddle.nn.Softshrink, paddle.nn.functional.softshrink
def setUp(self):
......@@ -1005,9 +1116,10 @@ class TestSqrt(TestActivation, TestParameter):
self.op_type = "sqrt"
self.python_api = paddle.sqrt
self.init_dtype()
self.init_shape()
np.random.seed(1023)
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
x = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
out = np.sqrt(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
......@@ -1022,6 +1134,12 @@ class TestSqrt(TestActivation, TestParameter):
self.check_output(check_eager=True)
class TestSqrt_ZeroDim(TestSqrt):
def init_shape(self):
self.shape = []
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestSqrtBF16(OpTest):
......@@ -1030,9 +1148,10 @@ class TestSqrtBF16(OpTest):
self.op_type = "sqrt"
self.python_api = paddle.sqrt
self.init_dtype()
self.init_shape()
np.random.seed(1023)
x = np.random.uniform(0.1, 1, [11, 17]).astype(np.float32)
x = np.random.uniform(0.1, 1, self.shape).astype(np.float32)
out = np.sqrt(x)
self.inputs = {
......@@ -1043,6 +1162,9 @@ class TestSqrtBF16(OpTest):
def init_dtype(self):
self.dtype = np.uint16
def init_shape(self):
self.shape = [11, 17]
def test_check_output(self):
place = core.CUDAPlace(0)
self.check_output_with_place(place, check_eager=True)
......@@ -1058,14 +1180,18 @@ class TestRsqrt(TestActivation):
self.op_type = "rsqrt"
self.python_api = paddle.rsqrt
self.init_dtype()
self.init_shape()
np.random.seed(1024)
x = np.random.uniform(0.1, 1, [10, 12]).astype(self.dtype) * 10
x = np.random.uniform(0.1, 1, self.shape).astype(self.dtype) * 10
out = 1.0 / np.sqrt(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
def init_shape(self):
self.shape = [10, 12]
def test_check_grad(self):
if self.dtype == np.float16:
return
......@@ -1075,14 +1201,23 @@ class TestRsqrt(TestActivation):
check_eager=True)
'''
class TestRsqrt_ZeroDim(TestRsqrt):
def init_shape(self):
self.shape = []
'''
class TestAbs(TestActivation):
def setUp(self):
self.op_type = "abs"
self.init_dtype()
self.init_shape()
np.random.seed(1024)
x = np.random.uniform(-1, 1, [4, 25]).astype(self.dtype)
x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
# Because we set delta = 0.005 in calculating numeric gradient,
# if x is too small, such as 0.002, x_neg will be -0.003
# x_pos will be 0.007, so the numeric gradient is inaccurate.
......@@ -1093,12 +1228,21 @@ class TestAbs(TestActivation):
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
def init_shape(self):
self.shape = [4, 25]
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', check_eager=False)
class TestAbs_ZeroDim(TestAbs):
def init_shape(self):
self.shape = []
class TestCeil(TestActivation):
def setUp(self):
......@@ -1106,19 +1250,29 @@ class TestCeil(TestActivation):
self.check_eager = True
self.python_api = paddle.ceil
self.init_dtype()
self.init_shape()
np.random.seed(1024)
x = np.random.uniform(-1, 1, [10, 12]).astype(self.dtype)
x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
out = np.ceil(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
def init_shape(self):
self.shape = [10, 12]
# The same reason with TestFloor
def test_check_grad(self):
pass
class TestCeil_ZeroDim(TestCeil):
def init_shape(self):
self.shape = []
class TestFloor(TestActivation):
def setUp(self):
......@@ -1126,14 +1280,18 @@ class TestFloor(TestActivation):
self.check_eager = True
self.python_api = paddle.floor
self.init_dtype()
self.init_shape()
np.random.seed(1024)
x = np.random.uniform(-1, 1, [10, 12]).astype(self.dtype)
x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
out = np.floor(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
def init_shape(self):
self.shape = [10, 12]
# the gradient on floor, ceil, round is undefined.
# we return zero as gradient, but the numpy return nan
# The same reason with TestFloor
......@@ -1141,33 +1299,51 @@ class TestFloor(TestActivation):
pass
class TestFloor_ZeroDim(TestFloor):
def init_shape(self):
self.shape = []
class TestCos(TestActivation):
def setUp(self):
self.op_type = "cos"
self.init_dtype()
self.init_shape()
np.random.seed(1024)
x = np.random.uniform(-1, 1, [10, 12]).astype(self.dtype)
x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
out = np.cos(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
def init_shape(self):
self.shape = [10, 12]
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out')
class TestCos_ZeroDim(TestCos):
def init_shape(self):
self.shape = []
class TestTan(TestActivation):
def setUp(self):
np.random.seed(1024)
self.op_type = "tan"
self.init_dtype()
self.init_shape()
self.dtype = 'float32'
self.x_np = np.random.uniform(-1, 1, [10, 12]).astype(self.dtype)
self.x_np = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
self.place = paddle.CUDAPlace(0) if paddle.is_compiled_with_cuda() \
else paddle.CPUPlace()
......@@ -1176,11 +1352,30 @@ class TestTan(TestActivation):
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(self.x_np)}
self.outputs = {'Out': out}
def init_shape(self):
self.shape = [10, 12]
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out')
class TestTan_ZeroDim(TestTan):
def init_shape(self):
self.shape = []
class TestTanAPI(unittest.TestCase):
def setUp(self):
np.random.seed(1024)
self.dtype = 'float32'
self.x_np = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
self.place = paddle.CUDAPlace(0) if paddle.is_compiled_with_cuda() \
else paddle.CPUPlace()
def test_dygraph_api(self):
paddle.disable_static(self.place)
x = paddle.to_tensor(self.x_np)
......@@ -1192,7 +1387,7 @@ class TestTan(TestActivation):
def test_static_api(self):
paddle.enable_static()
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.static.data('X', [10, 12], self.dtype)
x = paddle.static.data('X', [11, 17], self.dtype)
out = paddle.tan(x)
exe = paddle.static.Executor(self.place)
res = exe.run(feed={'X': self.x_np}, fetch_list=[out])
......@@ -1217,115 +1412,175 @@ class TestAcos(TestActivation):
def setUp(self):
self.op_type = "acos"
self.init_dtype()
self.init_shape()
np.random.seed(1024)
x = np.random.uniform(-0.95, 0.95, [10, 12]).astype(self.dtype)
x = np.random.uniform(-0.95, 0.95, self.shape).astype(self.dtype)
out = np.arccos(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
def init_shape(self):
self.shape = [10, 12]
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out')
class TestAcos_ZeroDim(TestAcos):
def init_shape(self):
self.shape = []
class TestSin(TestActivation, TestParameter):
def setUp(self):
self.op_type = "sin"
self.init_dtype()
self.init_shape()
np.random.seed(1024)
x = np.random.uniform(-1, 1, [10, 12]).astype(self.dtype)
x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
out = np.sin(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
def init_shape(self):
self.shape = [10, 12]
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out')
class TestSin_ZeroDim(TestSin):
def init_shape(self):
self.shape = []
class TestAsin(TestActivation):
def setUp(self):
self.op_type = "asin"
self.init_dtype()
self.init_shape()
np.random.seed(2048)
x = np.random.uniform(-0.95, 0.95, [10, 12]).astype(self.dtype)
x = np.random.uniform(-0.95, 0.95, self.shape).astype(self.dtype)
out = np.arcsin(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
def init_shape(self):
self.shape = [10, 12]
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out')
class TestAsin_ZeroDim(TestAsin):
def init_shape(self):
self.shape = []
class TestAcosh(TestActivation):
def setUp(self):
self.op_type = "acosh"
self.init_dtype()
self.init_shape()
np.random.seed(1024)
x = np.random.uniform(2, 3, [10, 12]).astype(self.dtype)
x = np.random.uniform(2, 3, self.shape).astype(self.dtype)
out = np.arccosh(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
def init_shape(self):
self.shape = [10, 12]
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out')
class TestAcosh_ZeroDim(TestAcosh):
def init_shape(self):
self.shape = []
class TestAsinh(TestActivation):
def setUp(self):
self.op_type = "asinh"
self.init_dtype()
self.init_shape()
np.random.seed(1024)
x = np.random.uniform(1, 2, [10, 12]).astype(self.dtype)
x = np.random.uniform(1, 2, self.shape).astype(self.dtype)
out = np.arcsinh(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
def init_shape(self):
self.shape = [10, 12]
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out')
class TestAsinh_ZeroDim(TestAsinh):
def init_shape(self):
self.shape = []
class TestAtanh(TestActivation):
def setUp(self):
self.op_type = "atanh"
self.init_dtype()
self.init_shape()
np.random.seed(400)
x = np.random.uniform(-0.9, 0.9, [10, 12]).astype(self.dtype)
x = np.random.uniform(-0.9, 0.9, self.shape).astype(self.dtype)
out = np.arctanh(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
def init_shape(self):
self.shape = [10, 12]
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out')
class TestAtanh_ZeroDim(TestAtanh):
def init_shape(self):
self.shape = []
class TestRound(TestActivation):
def setUp(self):
......@@ -1333,33 +1588,44 @@ class TestRound(TestActivation):
self.check_eager = True
self.python_api = paddle.round
self.init_dtype()
self.init_shape()
np.random.seed(1024)
x = np.random.uniform(-1, 1, [10, 12]).astype(self.dtype)
x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
out = np.round(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
def init_shape(self):
self.shape = [10, 12]
def test_check_grad(self):
pass
class TestRound_ZeroDim(TestRound):
def init_shape(self):
self.shape = []
class TestRelu(TestActivation):
def setUp(self):
self.op_type = "relu"
self.init_dtype()
self.init_shape()
np.random.seed(1024)
if self.dtype == np.uint16:
x = np.random.uniform(-1, 1, [11, 17]).astype(np.float32)
x = np.random.uniform(-1, 1, self.shape).astype(np.float32)
# The same reason with TestAbs
x[np.abs(x) < 0.005] = 0.02
out = convert_float_to_uint16(np.maximum(x, 0))
self.inputs = {'X': convert_float_to_uint16(x)}
else:
x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
# The same reason with TestAbs
x[np.abs(x) < 0.005] = 0.02
out = np.maximum(x, 0)
......@@ -1373,6 +1639,12 @@ class TestRelu(TestActivation):
self.check_grad(['X'], 'Out')
class TestRelu_ZeroDim(TestRelu):
def init_shape(self):
self.shape = []
class TestReluAPI(unittest.TestCase):
# test paddle.nn.ReLU, paddle.nn.functional.relu
def setUp(self):
......@@ -1446,10 +1718,11 @@ class TestLeakyRelu(TestActivation):
def setUp(self):
self.op_type = "leaky_relu"
self.init_dtype()
self.init_shape()
alpha = self.get_alpha()
np.random.seed(1024)
x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
# The same reason with TestAbs
x[np.abs(x) < 0.005] = 0.05
out = ref_leaky_relu(x, alpha)
......@@ -1482,6 +1755,12 @@ class TestLeakyReluAlpha3(TestLeakyRelu):
return -2.0
class TestLeakyRelu_ZeroDim(TestLeakyRelu):
def init_shape(self):
self.shape = []
class TestLeakyReluAPI(unittest.TestCase):
# test paddle.nn.LeakyReLU, paddle.nn.functional.leaky_relu,
# fluid.layers.leaky_relu
......@@ -1563,9 +1842,10 @@ class TestGeluApproximate(TestActivation):
def setUp(self):
self.op_type = "gelu"
self.init_dtype()
self.init_shape()
approximate = True
np.random.seed(1024)
x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
out = gelu(x, approximate)
self.inputs = {'X': x}
......@@ -1583,9 +1863,10 @@ class TestGelu(TestActivation):
def setUp(self):
self.op_type = "gelu"
self.init_dtype()
self.init_shape()
approximate = False
np.random.seed(2048)
x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
out = gelu(x, approximate)
self.inputs = {'X': x}
......@@ -1598,6 +1879,12 @@ class TestGelu(TestActivation):
self.check_grad(['X'], 'Out')
class TestGelu_ZeroDim(TestGelu):
def init_shape(self):
self.shape = []
class TestGELUAPI(unittest.TestCase):
# test paddle.nn.GELU, paddle.nn.functional.gelu
def setUp(self):
......@@ -1735,10 +2022,11 @@ class TestRelu6(TestActivation):
def setUp(self):
self.op_type = "relu6"
self.init_dtype()
self.init_shape()
self.python_api = paddle.nn.functional.relu6
np.random.seed(1024)
x = np.random.uniform(-1, 10, [10, 12]).astype(self.dtype)
x = np.random.uniform(-1, 10, self.shape).astype(self.dtype)
x[np.abs(x) < 0.005] = 0.02
out = ref_relu6(x)
......@@ -1746,12 +2034,21 @@ class TestRelu6(TestActivation):
self.attrs = {'threshold': 6.0}
self.outputs = {'Out': out}
def init_shape(self):
self.shape = [10, 12]
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', check_eager=True)
class TestRelu6_ZeroDim(TestRelu6):
def init_shape(self):
self.shape = []
class TestRelu6API(unittest.TestCase):
# test paddle.nn.ReLU6, paddle.nn.functional.relu6
def setUp(self):
......@@ -1826,10 +2123,11 @@ class TestHardSwish(TestActivation):
def setUp(self):
self.op_type = 'hard_swish'
self.init_dtype()
self.init_shape()
self.python_api = paddle.nn.functional.hardswish
np.random.seed(1024)
x = np.random.uniform(-6, 6, [10, 12]).astype(self.dtype)
x = np.random.uniform(-6, 6, self.shape).astype(self.dtype)
threshold = 6.0
scale = 6.0
offset = 3.0
......@@ -1842,6 +2140,9 @@ class TestHardSwish(TestActivation):
self.attrs = {'threshold': threshold, 'scale': scale, 'offset': offset}
self.outputs = {'Out': out}
def init_shape(self):
self.shape = [10, 12]
def test_check_grad(self):
self.check_grad(['X'], 'Out', check_eager=True)
......@@ -1849,6 +2150,12 @@ class TestHardSwish(TestActivation):
self.check_output(check_eager=True)
class TestHardSwish_ZeroDim(TestHardSwish):
def init_shape(self):
self.shape = []
class TestHardswishAPI(unittest.TestCase):
# test paddle.nn.Hardswish, paddle.nn.functional.hardswish
def setUp(self):
......@@ -1966,9 +2273,10 @@ class TestELU(TestActivation):
def setUp(self):
self.op_type = "elu"
self.init_dtype()
self.init_shape()
np.random.seed(1024)
x = np.random.uniform(-3, 3, [10, 12]).astype(self.dtype)
x = np.random.uniform(-3, 3, self.shape).astype(self.dtype)
alpha = self.get_alpha()
out = elu(x, alpha)
# Note: unlike other Relu extensions, point 0 on standard ELU function (i.e. alpha = 1)
......@@ -1977,6 +2285,9 @@ class TestELU(TestActivation):
self.attrs = {'alpha': alpha}
self.outputs = {'Out': out}
def init_shape(self):
self.shape = [10, 12]
def test_check_grad(self):
if self.dtype == np.float16:
return
......@@ -1992,6 +2303,12 @@ class TestELUAlpha(TestELU):
return -0.2
class TestELU_ZeroDim(TestELU):
def init_shape(self):
self.shape = []
class TestELUAPI(unittest.TestCase):
# test paddle.nn.ELU, paddle.nn.functional.elu
def setUp(self):
......@@ -2076,22 +2393,32 @@ class TestCELU(TestActivation):
def setUp(self):
self.op_type = "celu"
self.init_dtype()
self.init_shape()
self.python_api = paddle.nn.functional.celu
np.random.seed(1024)
x = np.random.uniform(-3, 3, [10, 12]).astype(self.dtype)
x = np.random.uniform(-3, 3, self.shape).astype(self.dtype)
alpha = 1.5
out = celu(x, alpha)
self.inputs = {'X': x}
self.attrs = {'alpha': alpha}
self.outputs = {'Out': out}
def init_shape(self):
self.shape = [10, 12]
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', check_eager=True)
class TestCELU_ZeroDim(TestCELU):
def init_shape(self):
self.shape = []
class TestCELUAPI(unittest.TestCase):
# test paddle.nn.CELU, paddle.nn.functional.celu
def setUp(self):
......@@ -2170,9 +2497,10 @@ class TestReciprocal(TestActivation):
self.op_type = "reciprocal"
self.python_api = paddle.reciprocal
self.init_dtype()
self.init_shape()
np.random.seed(1024)
x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype)
x = np.random.uniform(1, 2, self.shape).astype(self.dtype)
out = np.reciprocal(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
......@@ -2187,6 +2515,12 @@ class TestReciprocal(TestActivation):
self.check_output(check_eager=True)
class TestReciprocal_ZeroDim(TestReciprocal):
def init_shape(self):
self.shape = []
class TestLog(TestActivation):
def setUp(self):
......@@ -2194,9 +2528,10 @@ class TestLog(TestActivation):
self.check_eager = True
self.python_api = paddle.log
self.init_dtype()
self.init_shape()
np.random.seed(1024)
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
x = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
out = np.log(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
......@@ -2221,6 +2556,12 @@ class TestLog(TestActivation):
self.assertRaises(TypeError, fluid.layers.log, in2)
class TestLog_ZeroDim(TestLog):
def init_shape(self):
self.shape = []
class TestLog2(TestActivation):
def setUp(self):
......@@ -2228,8 +2569,9 @@ class TestLog2(TestActivation):
self.check_eager = True
self.python_api = paddle.log2
self.init_dtype()
self.init_shape()
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
x = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
out = np.log2(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
......@@ -2274,6 +2616,12 @@ class TestLog2(TestActivation):
np.testing.assert_allclose(np_z, z_expected, rtol=1e-05)
class TestLog2_ZeroDim(TestLog2):
def init_shape(self):
self.shape = []
class TestLog10(TestActivation):
def setUp(self):
......@@ -2281,8 +2629,9 @@ class TestLog10(TestActivation):
self.check_eager = True
self.python_api = paddle.log10
self.init_dtype()
self.init_shape()
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
x = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
out = np.log10(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
......@@ -2293,6 +2642,15 @@ class TestLog10(TestActivation):
return
self.check_grad(['X'], 'Out', check_eager=True)
class TestLog10_ZeroDim(TestLog10):
def init_shape(self):
self.shape = []
class TestLog10API(unittest.TestCase):
def test_error(self):
in1 = paddle.static.data(name="in1", shape=[11, 17], dtype="int32")
in2 = paddle.static.data(name="in2", shape=[11, 17], dtype="int64")
......@@ -2334,9 +2692,10 @@ class TestLog1p(TestActivation):
self.check_eager = True
self.python_api = paddle.log1p
self.init_dtype()
self.init_shape()
np.random.seed(1024)
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
x = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
out = np.log1p(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
......@@ -2347,6 +2706,15 @@ class TestLog1p(TestActivation):
return
self.check_grad(['X'], 'Out', check_eager=True)
class TestLog1p_ZeroDim(TestLog1p):
def init_shape(self):
self.shape = []
class TestLog1pAPI(unittest.TestCase):
def test_api(self):
with fluid.program_guard(fluid.Program(), fluid.Program()):
input_x = np.random.uniform(0.1, 1, [11, 17]).astype("float64")
......@@ -2380,9 +2748,10 @@ class TestSquare(TestActivation):
self.op_type = "square"
self.python_api = paddle.square
self.init_dtype()
self.init_shape()
np.random.seed(1024)
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
x = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
out = np.square(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
......@@ -2400,6 +2769,12 @@ class TestSquare(TestActivation):
self.check_output(check_eager=True)
class TestSquare_ZeroDim(TestSquare):
def init_shape(self):
self.shape = []
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestSquareBF16(OpTest):
......@@ -2440,9 +2815,10 @@ class TestPow(TestActivation):
self.python_api = paddle.pow
self.check_eager = True
self.init_dtype()
self.init_shape()
np.random.seed(1024)
x = np.random.uniform(1, 2, [11, 17]).astype(self.dtype)
x = np.random.uniform(1, 2, self.shape).astype(self.dtype)
out = np.power(x, 3)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
......@@ -2458,6 +2834,12 @@ class TestPow(TestActivation):
self.check_grad(['X'], 'Out', check_eager=self.check_eager)
class TestPow_ZeroDim(TestPow):
def init_shape(self):
self.shape = []
class TestPow_factor_tensor(TestActivation):
def setUp(self):
......@@ -2557,11 +2939,13 @@ class TestSTanh(TestActivation):
def setUp(self):
self.op_type = "stanh"
self.init_dtype()
self.init_shape()
scale_a = self.get_scale_a()
scale_b = self.get_scale_b()
np.random.seed(1024)
x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
x = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
# The same reason with TestAbs
out = ref_stanh(x, scale_a, scale_b)
......@@ -2587,6 +2971,12 @@ class TestSTanhScaleB(TestSTanh):
return 0.5
class TestSTanh_ZeroDim(TestSTanh):
def init_shape(self):
self.shape = []
class TestSTanhAPI(unittest.TestCase):
# test paddle.nn.stanh
def get_scale_a(self):
......@@ -2675,12 +3065,13 @@ class TestSoftplus(TestActivation):
self.op_type = "softplus"
self.python_api = paddle.nn.functional.softplus
self.init_dtype()
self.init_shape()
beta = 2
threshold = 15
np.random.seed(1024)
x = np.random.uniform(-1, 1, [10, 12]).astype(self.dtype)
x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
out = ref_softplus(x, beta, threshold)
self.inputs = {'X': x}
self.attrs = {'beta': beta, "threshold": threshold}
......@@ -2688,6 +3079,9 @@ class TestSoftplus(TestActivation):
self.check_eager = True
def init_shape(self):
self.shape = [10, 12]
def test_check_grad(self):
if self.dtype == np.float16:
return
......@@ -2696,6 +3090,12 @@ class TestSoftplus(TestActivation):
self.check_grad(['X'], 'Out', check_eager=check_eager)
class TestSoftplus_ZeroDim(TestSoftplus):
def init_shape(self):
self.shape = []
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestSoftplusBF16(OpTest):
......@@ -2797,20 +3197,31 @@ class TestSoftsign(TestActivation):
def setUp(self):
self.op_type = "softsign"
self.init_dtype()
self.init_shape()
self.python_api = paddle.nn.functional.softsign
np.random.seed(1024)
x = np.random.uniform(-1, 1, [10, 12]).astype(self.dtype)
x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
out = ref_softsign(x)
self.inputs = {'X': x}
self.outputs = {'Out': out}
def init_shape(self):
self.shape = [10, 12]
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', check_eager=True)
class TestSoftsign_ZeroDim(TestSoftsign):
def init_shape(self):
self.shape = []
class TestSoftsignAPI(unittest.TestCase):
# test paddle.nn.Softsign, paddle.nn.functional.softsign
def setUp(self):
......@@ -2880,23 +3291,33 @@ class TestThresholdedRelu(TestActivation):
def setUp(self):
self.op_type = "thresholded_relu"
self.init_dtype()
self.init_shape()
threshold = 15
np.random.seed(1024)
x = np.random.uniform(-20, 20, [10, 12]).astype(self.dtype)
x = np.random.uniform(-20, 20, self.shape).astype(self.dtype)
x[np.abs(x) < 0.005] = 0.02
out = ref_thresholded_relu(x, threshold)
self.inputs = {'X': x}
self.attrs = {"threshold": threshold}
self.outputs = {'Out': out}
def init_shape(self):
self.shape = [10, 12]
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out')
class TestThresholdedRelu_ZeroDim(TestThresholdedRelu):
def init_shape(self):
self.shape = []
class TestThresholdedReluAPI(unittest.TestCase):
# test paddle.nn.ThresholdedReLU, paddle.nn.functional.thresholded_relu
def setUp(self):
......@@ -2970,8 +3391,9 @@ class TestHardSigmoid(TestActivation):
self.slope = 0.166666666666667
self.offset = 0.5
self.set_attrs()
self.init_shape()
x = np.random.uniform(-5, 5, [10, 12]).astype(self.dtype)
x = np.random.uniform(-5, 5, self.shape).astype(self.dtype)
lower_threshold = -self.offset / self.slope
upper_threshold = (1. - self.offset) / self.slope
......@@ -2986,6 +3408,9 @@ class TestHardSigmoid(TestActivation):
self.inputs = {'X': x}
self.outputs = {'Out': out}
def init_shape(self):
self.shape = [10, 12]
def set_attrs(self):
pass
......@@ -3003,6 +3428,12 @@ class TestHardSigmoidSlopeOffset(TestHardSigmoid):
self.offset = 0.4
class TestHardSigmoid_ZeroDim(TestHardSigmoid):
def init_shape(self):
self.shape = []
class TestHardsigmoidAPI(unittest.TestCase):
# test paddle.nn.Hardsigmoid, paddle.nn.functional.hardsigmoid
def setUp(self):
......@@ -3075,15 +3506,20 @@ class TestSwish(TestActivation):
self.op_type = "swish"
self.python_api = paddle.nn.functional.swish
self.init_dtype()
self.init_shape()
self.check_eager = True
np.random.seed(1024)
x = np.random.uniform(-1, 1, [10, 12]).astype(self.dtype)
x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
out = ref_swish(x)
self.inputs = {'X': x}
self.attrs = {'beta': 1.0}
self.outputs = {'Out': out}
def init_shape(self):
self.shape = [10, 12]
def test_check_grad(self):
if self.dtype == np.float16:
return
......@@ -3093,6 +3529,12 @@ class TestSwish(TestActivation):
self.check_grad(['X'], 'Out', check_eager=check_eager)
class TestSwish_ZeroDim(TestSwish):
def init_shape(self):
self.shape = []
class TestSwishAPI(unittest.TestCase):
# test paddle.nn.Swish, paddle.nn.functional.swish
def setUp(self):
......@@ -3169,13 +3611,17 @@ class TestMish(TestActivation):
self.op_type = "mish"
self.python_api = paddle.fluid.layers.nn.mish
self.init_dtype()
self.init_shape()
np.random.seed(1024)
x = np.random.uniform(-1, 1, [10, 12]).astype(self.dtype)
x = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
out = ref_mish(x)
self.inputs = {'X': x}
self.outputs = {'Out': out}
def init_shape(self):
self.shape = [10, 12]
def test_check_output(self):
self.check_output(check_eager=True)
......@@ -3185,6 +3631,12 @@ class TestMish(TestActivation):
self.check_grad(['X'], 'Out', check_eager=True)
class TestMish_ZeroDim(TestMish):
def init_shape(self):
self.shape = []
class TestMishAPI(unittest.TestCase):
# test paddle.nn.Mish, paddle.nn.functional.mish
def setUp(self):
......
python/paddle/fluid/tests/unittests/test_fill_constant_op.py
浏览文件 @ 05c2b9ba
......@@ -437,12 +437,6 @@ class TestFillConstantOpError(unittest.TestCase):
self.assertRaises(TypeError, test_shape_type)
# The argument shape's size of fill_constant_op must not be 0.
def test_shape_size():
fluid.layers.fill_constant(shape=[], dtype="float32", value=1)
self.assertRaises(AssertionError, test_shape_size)
# The shape dtype of fill_constant_op must be int32 or int64.
def test_shape_tensor_dtype():
shape = fluid.data(name="shape_tensor",
......
python/paddle/fluid/tests/unittests/test_full_op.py
浏览文件 @ 05c2b9ba
......@@ -175,12 +175,6 @@ class TestFullOpError(unittest.TestCase):
self.assertRaises(TypeError, test_shape_type)
# The argument shape's size of full_op must not be 0.
def test_shape_size():
paddle.full(shape=[], dtype="float32", fill_value=1)
self.assertRaises(AssertionError, test_shape_size)
# The shape dtype of full op must be int32 or int64.
def test_shape_tensor_dtype():
shape = fluid.data(name="shape_tensor",
......
python/paddle/fluid/tests/unittests/test_mse_loss.py
浏览文件 @ 05c2b9ba
......@@ -30,8 +30,8 @@ class TestMseLoss(unittest.TestCase):
sub = input_val - label_val
np_result = np.mean(sub * sub)
input_var = layers.create_tensor(dtype="float32", name="input")
label_var = layers.create_tensor(dtype="float32", name="label")
input_var = fluid.data(name="input", shape=[-1, 3], dtype="float32")
label_var = fluid.data(name="label", shape=[-1, 3], dtype="float32")
output = layers.mse_loss(input=input_var, label=label_var)
for use_cuda in ([False, True]
......@@ -54,7 +54,7 @@ class TestMseInvalidInput(unittest.TestCase):
def test_invalid_input():
input = [256, 3]
label = fluid.data(name='label', shape=[None, 3], dtype='float32')
label = fluid.data(name='label1', shape=[None, 3], dtype='float32')
loss = fluid.layers.mse_loss(input, label)
self.assertRaises(TypeError, test_invalid_input)
......
python/paddle/fluid/tests/unittests/test_randn_op.py
浏览文件 @ 05c2b9ba
......@@ -75,9 +75,6 @@ class TestRandnOpError(unittest.TestCase):
def test_error(self):
with program_guard(Program(), Program()):
# The argument shape's size of randn_op should not be 0.
self.assertRaises(AssertionError, paddle.randn, [])
# The argument shape's type of randn_op should be list or tuple.
self.assertRaises(TypeError, paddle.randn, 1)
......
python/paddle/fluid/tests/unittests/test_select_input_output_op.py
浏览文件 @ 05c2b9ba
......@@ -23,6 +23,8 @@ from paddle.fluid.executor import Executor
from paddle.fluid.framework import Program, program_guard
from paddle.fluid.layers.control_flow import select_input, select_output
paddle.enable_static()
class TestSplitMergeSelectedVarOps(unittest.TestCase):
......@@ -37,7 +39,9 @@ class TestSplitMergeSelectedVarOps(unittest.TestCase):
outputs = []
for i in range(branch_num):
out = program.current_block().create_var(
dtype='float32', type=core.VarDesc.VarType.LOD_TENSOR)
dtype='float32',
shape=[2],
type=core.VarDesc.VarType.LOD_TENSOR)
outputs.append(out)
select_output(x, outputs, mask)
......
python/paddle/fluid/tests/unittests/test_set_value_op.py
浏览文件 @ 05c2b9ba
......@@ -23,7 +23,6 @@ from paddle.fluid.layer_helper import LayerHelper
from functools import reduce
from paddle.fluid.framework import _test_eager_guard
class TestSetValueBase(unittest.TestCase):
def setUp(self):
......@@ -1442,7 +1441,6 @@ class TestGradientTruncated(unittest.TestCase):
# When `input.stop_gradient = True` and `value.stop_gradient = False`,
# set_value_grad_op will not be run during backward.
y, value = op(x)
y2 = y + 1
loss = paddle.fluid.layers.reduce_sum(y2)
sgd = paddle.optimizer.Adam()
......
python/paddle/fluid/tests/unittests/test_zero_dim_shape.py 0 → 100644
浏览文件 @ 05c2b9ba
# Copyright (c) 2018 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 paddle
import paddle.fluid as fluid
import numpy as np
import unittest
unary_api_list = [
paddle.nn.functional.elu,
paddle.nn.functional.gelu,
paddle.nn.functional.hardsigmoid,
paddle.nn.functional.hardswish,
paddle.nn.functional.leaky_relu,
paddle.nn.functional.log_sigmoid,
paddle.nn.functional.relu,
paddle.nn.functional.relu6,
paddle.nn.functional.sigmoid,
paddle.nn.functional.softplus,
paddle.nn.functional.softshrink,
paddle.nn.functional.softsign,
paddle.nn.functional.swish,
paddle.nn.functional.tanhshrink,
paddle.nn.functional.thresholded_relu,
paddle.stanh,
paddle.nn.functional.celu,
paddle.nn.functional.mish,
paddle.nn.functional.silu,
paddle.nn.functional.tanh,
paddle.cosh,
paddle.sinh,
paddle.abs,
paddle.acos,
paddle.asin,
paddle.atan,
paddle.ceil,
paddle.cos,
paddle.exp,
paddle.floor,
paddle.log,
paddle.log1p,
paddle.reciprocal,
paddle.round,
paddle.sin,
paddle.sqrt,
paddle.square,
paddle.tanh,
paddle.acosh,
paddle.asinh,
paddle.atanh,
paddle.expm1,
paddle.log10,
paddle.log2,
paddle.tan,
]
# Use to test zero-dim in the whole API
class TestUnaryAPI(unittest.TestCase):
def test_dygraph_unary(self):
paddle.disable_static()
fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True})
for api in unary_api_list:
x = paddle.rand([])
x.stop_gradient = False
out = api(x)
out.backward()
self.assertEqual(x.shape, [])
self.assertEqual(out.shape, [])
self.assertEqual(x.grad.shape, [])
self.assertEqual(out.grad.shape, [])
paddle.enable_static()
def test_static_unary(self):
paddle.enable_static()
for api in unary_api_list:
main_prog = fluid.Program()
with fluid.program_guard(main_prog, fluid.Program()):
x = paddle.rand([])
x.stop_gradient = False
out = api(x)
fluid.backward.append_backward(out)
# ScaleLossGradOp / append_backward always set grad shape to [1]
prog = paddle.static.default_main_program()
block = prog.global_block()
x_grad = block.var(fluid.framework.grad_var_name(x.name))
out_grad = block.var(fluid.framework.grad_var_name(out.name))
# Test compile shape, grad is always [1]
self.assertEqual(x.shape, ())
self.assertEqual(out.shape, ())
exe = fluid.Executor()
result = exe.run(main_prog,
fetch_list=[x, out, x_grad, out_grad])
# Test runtime shape
self.assertEqual(result[0].shape, ())
self.assertEqual(result[1].shape, ())
self.assertEqual(result[3].shape, (1, ))
# 0D will be stacked when 1+ place, due to it cannot be concated
# for 1 place: [ x-place1 ]
# for 1+ place: [ paddle.stack([x-place1, x_place2...]) ]
if paddle.device.is_compiled_with_cuda():
places = [paddle.CUDAPlace(0)]
device_num = 1
expect_shape = ()
else:
places = [paddle.CPUPlace()] * 4
device_num = 4
expect_shape = (device_num, )
compiled_program = fluid.CompiledProgram(
main_prog).with_data_parallel(out.name, places=places)
result = exe.run(compiled_program,
fetch_list=[x, out, x_grad, out_grad],
return_merged=True)
# Test runtime parallel shape
self.assertEqual(result[0].shape, expect_shape)
self.assertEqual(result[1].shape, expect_shape)
self.assertEqual(result[3].shape, (device_num, ))
compiled_program = fluid.CompiledProgram(
main_prog).with_data_parallel(out.name, places=places)
result = exe.run(compiled_program,
fetch_list=[x, out, x_grad, out_grad],
return_merged=False)
# [[x-place1, x-place2, ...], [], [], ...]
self.assertEqual(np.array(result[0]).shape, (device_num, ))
self.assertEqual(np.array(result[1]).shape, (device_num, ))
self.assertEqual(np.array(result[3]).shape, (device_num, 1))
paddle.disable_static()
if __name__ == "__main__":
unittest.main()
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