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未验证 提交 d4451cb0 编写于 作者: zhouweiwei2014's avatar zhouweiwei2014 提交者： GitHub
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[Zero-Dim] OpTest support shape check and fix previous case problem (#54117)

上级 6e54adb4
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Showing with 366 addition and 462 deletion
paddle/fluid/operators/l1_norm_op.cc
浏览文件 @ d4451cb0
......@@ -27,7 +27,7 @@ class L1NormOp : public framework::OperatorWithKernel {
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "L1NormOp");
OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "L1NormOp");
ctx->SetOutputDim("Out", {1});
ctx->SetOutputDim("Out", phi::make_ddim({}));
}
};
......
paddle/fluid/operators/mean_iou_op.cc
浏览文件 @ d4451cb0
......@@ -34,7 +34,7 @@ class MeanIoUOp : public framework::OperatorWithKernel {
int64_t num_classes =
static_cast<int64_t>(ctx->Attrs().Get<int>("num_classes"));
ctx->SetOutputDim("OutMeanIou", {1});
ctx->SetOutputDim("OutMeanIou", phi::make_ddim({}));
ctx->SetOutputDim("OutWrong", {num_classes});
ctx->SetOutputDim("OutCorrect", {num_classes});
}
......@@ -78,7 +78,7 @@ class MeanIoUOpMaker : public framework::OpProtoAndCheckerMaker {
.AsDispensable();
AddOutput("OutMeanIou",
"(vector<Tensor>), A Tensor representing the"
" mean intersection-over-union with shape [1].");
" mean intersection-over-union with shape [].");
AddOutput("OutWrong", "(Tensor), A Tensor with shape [num_classes]. ");
AddOutput("OutCorrect", "(Tensor), A Tensor with shape [num_classes]. ");
AddAttr<int>("num_classes", "(int), The possible number of labels.");
......
paddle/fluid/operators/mean_iou_op.cu
浏览文件 @ d4451cb0
......@@ -111,7 +111,7 @@ class MeanIoUCUDAOpKernel : public framework::OpKernel<T> {
out_mean_iou->mutable_data<float>(ctx.GetPlace());
// Get Eigen tensor
auto out_mean_iou_t = EigenTensor<float, 1>::From(*out_mean_iou);
auto out_mean_iou_t = EigenScalar<float>::From(*out_mean_iou);
auto out_wrong_t = EigenTensor<int, 1>::From(*out_wrong);
auto out_correct_t = EigenTensor<int, 1>::From(*out_correct);
......@@ -131,7 +131,7 @@ class MeanIoUCUDAOpKernel : public framework::OpKernel<T> {
auto in_mean_ious = ctx.MultiInput<phi::DenseTensor>("InMeanIou");
for (int i = 0; i < in_mean_ious.size(); ++i) {
out_mean_iou_t.device(place) +=
EigenTensor<float, 1>::From(*in_mean_ious[i]);
EigenScalar<float>::From(*in_mean_ious[i]);
}
auto in_wrongs = ctx.MultiInput<phi::DenseTensor>("InWrongs");
for (int i = 0; i < in_wrongs.size(); ++i) {
......
paddle/fluid/operators/mean_iou_op.h
浏览文件 @ d4451cb0
......@@ -27,6 +27,11 @@ template <typename T,
typename IndexType = Eigen::DenseIndex>
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
template <typename T,
int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenScalar = framework::EigenScalar<T, MajorType, IndexType>;
template <typename T, typename DeviceContext>
class MeanIoUKernel : public framework::OpKernel<T> {
public:
......@@ -50,7 +55,7 @@ class MeanIoUKernel : public framework::OpKernel<T> {
int* out_correct_data = out_correct->mutable_data<int>(ctx.GetPlace());
// get eigen tensor
auto out_mean_iou_t = EigenTensor<float, 1>::From(*out_mean_iou);
auto out_mean_iou_t = EigenScalar<float>::From(*out_mean_iou);
auto out_wrong_t = EigenTensor<int, 1>::From(*out_wrong);
auto out_correct_t = EigenTensor<int, 1>::From(*out_correct);
......@@ -79,8 +84,9 @@ class MeanIoUKernel : public framework::OpKernel<T> {
auto in_mean_ious = ctx.MultiInput<phi::DenseTensor>("InMeanIou");
for (size_t i = 0; i < in_mean_ious.size(); ++i) {
out_mean_iou_t.device(place) +=
EigenTensor<float, 1>::From(*in_mean_ious[i]);
EigenScalar<float>::From(*in_mean_ious[i]);
}
auto in_wrongs = ctx.MultiInput<phi::DenseTensor>("InWrongs");
for (size_t i = 0; i < in_wrongs.size(); ++i) {
out_wrong_t.device(place) += EigenTensor<int, 1>::From(*in_wrongs[i]);
......
paddle/fluid/operators/reduce_ops/reduce_mean_op.cc
浏览文件 @ d4451cb0
......@@ -98,9 +98,10 @@ class __reduce_meanMaker__ : public ops::ReduceBaseOpMaker {
virtual std::string GetOpType() const { return "Reduce reduce_mean"; }
};
DECLARE_INFER_SHAPE_FUNCTOR(reduce_mean,
ReduceMeanInferShapeFunctor,
PD_INFER_META(phi::OriginReduceInferMetaBase));
DECLARE_INFER_SHAPE_FUNCTOR(
reduce_mean,
ReduceMeanInferShapeFunctor,
PD_INFER_META(phi::ReduceIntArrayAxisInferMetaBase));
REGISTER_OPERATOR(reduce_mean,
ops::ReduceBaseOp,
......
paddle/phi/api/yaml/legacy_ops.yaml
浏览文件 @ d4451cb0
......@@ -633,7 +633,7 @@
args : (Tensor x, IntArray axis={}, bool keepdim=false)
output : Tensor(out)
infer_meta :
func : OriginReduceInferMeta
func : ReduceIntArrayAxisInferMeta
kernel :
func : mean
backward : mean_grad
......
paddle/phi/infermeta/binary.cc
浏览文件 @ d4451cb0
......@@ -130,7 +130,7 @@ void KLDivInferMeta(const MetaTensor& x,
if ("none" == reduction) {
out->set_dims(dim_x);
} else {
out->set_dims({1});
out->set_dims(phi::make_ddim({}));
}
out->set_dtype(x.dtype());
}
......
paddle/phi/infermeta/unary.cc
浏览文件 @ d4451cb0
......@@ -1659,7 +1659,7 @@ void IdentityLossInferMeta(const MetaTensor& x,
out->set_dtype(x.dtype());
out->set_dims(x.dims());
} else {
out->set_dims(phi::make_ddim({1}));
out->set_dims(phi::make_ddim({}));
out->set_dtype(x.dtype());
}
}
......@@ -3069,6 +3069,28 @@ DDim ReduceInferDim(const MetaTensor& x,
return out_dim;
}
void ReduceInferMetaBase(const MetaTensor& x,
const std::vector<int64_t>& axis,
bool keep_dim,
bool reduce_all,
MetaTensor* out) {
DDim out_dim = ReduceInferDim(x, axis, keep_dim, reduce_all);
out->set_dims(out_dim);
out->set_dtype(x.dtype());
out->set_layout(x.layout());
}
void ReduceInferMeta(const MetaTensor& x,
const std::vector<int64_t>& axis,
bool keep_dim,
MetaTensor* out) {
bool reduce_all = false;
if (axis.size() == 0) {
reduce_all = true;
}
ReduceInferMetaBase(x, axis, keep_dim, reduce_all, out);
}
DDim ReduceInferDimForIntArrayAxis(const MetaTensor& x,
const IntArray& axis,
bool keep_dim,
......@@ -3096,23 +3118,18 @@ DDim ReduceInferDimForIntArrayAxis(const MetaTensor& x,
return phi::make_ddim(vec_dim);
}
void ReduceInferMeta(const MetaTensor& x,
const std::vector<int64_t>& axis,
bool keep_dim,
MetaTensor* out) {
bool reduce_all = false;
if (axis.size() == 0) {
reduce_all = true;
void ReduceIntArrayAxisInferMetaBase(const MetaTensor& x,
const IntArray& axis,
bool keep_dim,
bool reduce_all,
MetaTensor* out,
MetaConfig config) {
DDim out_dim;
if (config.is_runtime || !axis.FromTensor()) {
out_dim = ReduceInferDim(x, axis.GetData(), keep_dim, reduce_all);
} else {
out_dim = ReduceInferDimForIntArrayAxis(x, axis, keep_dim, reduce_all);
}
ReduceInferMetaBase(x, axis, keep_dim, reduce_all, out);
}
void ReduceInferMetaBase(const MetaTensor& x,
const std::vector<int64_t>& axis,
bool keep_dim,
bool reduce_all,
MetaTensor* out) {
DDim out_dim = ReduceInferDim(x, axis, keep_dim, reduce_all);
out->set_dims(out_dim);
out->set_dtype(x.dtype());
out->set_layout(x.layout());
......@@ -3130,23 +3147,6 @@ void ReduceIntArrayAxisInferMeta(const MetaTensor& x,
ReduceIntArrayAxisInferMetaBase(x, axis, keep_dim, reduce_all, out, config);
}
void ReduceIntArrayAxisInferMetaBase(const MetaTensor& x,
const IntArray& axis,
bool keep_dim,
bool reduce_all,
MetaTensor* out,
MetaConfig config) {
DDim out_dim;
if (config.is_runtime || !axis.FromTensor()) {
out_dim = ReduceInferDim(x, axis.GetData(), keep_dim, reduce_all);
} else {
out_dim = ReduceInferDimForIntArrayAxis(x, axis, keep_dim, reduce_all);
}
out->set_dims(out_dim);
out->set_dtype(x.dtype());
out->set_layout(x.layout());
}
void ReduceScatterInferMeta(const MetaTensor& x, int nranks, MetaTensor* out) {
auto dim = x.dims();
if (dim[0] > 0 || dim[0] < -1) {
......@@ -3945,119 +3945,6 @@ void StridedSliceInferMeta(const MetaTensor& x,
x, axes, starts, ends, strides, infer_flags, decrease_axis, out, config);
}
// TODO(zhouwei): OriginReduceInferDim doesn't support 0D, remove in future
DDim OriginReduceInferDim(const MetaTensor& x,
const std::vector<int64_t>& axis,
bool keep_dim,
bool reduce_all) {
auto x_rank = x.dims().size();
std::vector<int64_t> formated_axis = axis;
for (size_t i = 0; i < axis.size(); ++i) {
if (x_rank == 0) {
PADDLE_ENFORCE_EQ(
axis[i] == 0 || axis[i] == -1,
true,
phi::errors::InvalidArgument(
"When input 0D Tensor, the axis can only be -1, 0, None or []"));
} else {
PADDLE_ENFORCE_LT(axis[i],
x_rank,
errors::InvalidArgument(
"The reduce dim index %d should be in the "
"range [ -dimension(X), dimension(X) ) "
"which dimesion = %d. But received dim index = %d.",
i,
x_rank,
axis[i]));
PADDLE_ENFORCE_GE(axis[i],
-x_rank,
errors::InvalidArgument(
"The reduce dim index %d should be in the "
"range [ -dimension(X), dimension(X) ) "
"which dimesion = %d. But received dim index = %d.",
i,
x_rank,
axis[i]));
}
if (axis[i] < 0) {
formated_axis[i] = axis[i] + x_rank;
}
}
bool full_dim = true;
std::set<int64_t> dims_set(formated_axis.begin(), formated_axis.end());
for (int64_t i = 0; i < x_rank; ++i) {
if (dims_set.find(i) == dims_set.end()) {
full_dim = false;
break;
}
}
reduce_all = reduce_all || full_dim;
std::vector<int64_t> out_dim_vector;
for (int64_t i = 0; i < x_rank; ++i) {
if (reduce_all || dims_set.find(i) != dims_set.end()) {
if (keep_dim) {
out_dim_vector.push_back(1);
} else {
continue;
}
} else {
out_dim_vector.push_back(x.dims().at(i));
}
}
DDim out_dim = phi::make_ddim(out_dim_vector);
return out_dim;
}
// TODO(zhouwei): OriginReduceInferDim doesn't support 0D, remove in future
DDim OriginReduceInferDimForIntArrayAxis(const MetaTensor& x,
const IntArray& axis,
bool keep_dim,
bool reduce_all) {
std::vector<int64_t> vec_axis = axis.GetData();
std::vector<int64_t> vec_dim;
if (reduce_all) {
if (keep_dim) {
vec_dim = std::vector<int64_t>(x.dims().size(), 1);
} else {
vec_dim = {};
}
} else {
if (keep_dim) {
vec_dim = std::vector<int64_t>(x.dims().size(), -1);
} else {
auto x_rank = static_cast<size_t>(x.dims().size());
if (vec_axis.size() > x_rank) {
vec_dim = {-1};
} else {
vec_dim = std::vector<int64_t>(x.dims().size() - vec_axis.size(), -1);
}
}
}
return phi::make_ddim(vec_dim);
}
/* Why not use SumRawInferMeta directly?
Because we need make InferMetaFunction's args follow the design of
ops.yaml
*/
void SumInferMeta(const MetaTensor& x,
const IntArray& axis,
DataType dtype,
bool keep_dim,
MetaTensor* out,
MetaConfig config) {
bool reduce_all = false;
if (axis.size() == 0) {
reduce_all = true;
}
SumRawInferMeta(x, axis, keep_dim, reduce_all, dtype, out, config);
}
void SumRawInferMeta(const MetaTensor& x,
const IntArray& axis,
bool keep_dim,
......@@ -4067,10 +3954,9 @@ void SumRawInferMeta(const MetaTensor& x,
MetaConfig config) {
DDim out_dim;
if (config.is_runtime || !axis.FromTensor()) {
out_dim = OriginReduceInferDim(x, axis.GetData(), keep_dim, reduce_all);
out_dim = ReduceInferDim(x, axis.GetData(), keep_dim, reduce_all);
} else {
out_dim =
OriginReduceInferDimForIntArrayAxis(x, axis, keep_dim, reduce_all);
out_dim = ReduceInferDimForIntArrayAxis(x, axis, keep_dim, reduce_all);
}
DataType out_dtype;
......@@ -4089,36 +3975,21 @@ void SumRawInferMeta(const MetaTensor& x,
out->set_layout(x.layout());
}
// TODO(zhouwei): OriginReduce doesn't support 0D, remove in future
void OriginReduceInferMeta(const MetaTensor& x,
const IntArray& axis,
bool keep_dim,
MetaTensor* out,
MetaConfig config) {
/* Why not use SumRawInferMeta directly?
Because we need make InferMetaFunction's args follow the design of
ops.yaml
*/
void SumInferMeta(const MetaTensor& x,
const IntArray& axis,
DataType dtype,
bool keep_dim,
MetaTensor* out,
MetaConfig config) {
bool reduce_all = false;
if (axis.size() == 0) {
reduce_all = true;
}
OriginReduceInferMetaBase(x, axis, keep_dim, reduce_all, out, config);
}
// TODO(zhouwei): OriginReduce doesn't support 0D, remove in future
void OriginReduceInferMetaBase(const MetaTensor& x,
const IntArray& axis,
bool keep_dim,
bool reduce_all,
MetaTensor* out,
MetaConfig config) {
DDim out_dim;
if (config.is_runtime || !axis.FromTensor()) {
out_dim = OriginReduceInferDim(x, axis.GetData(), keep_dim, reduce_all);
} else {
out_dim =
OriginReduceInferDimForIntArrayAxis(x, axis, keep_dim, reduce_all);
}
out->set_dims(out_dim);
out->set_dtype(x.dtype());
out->set_layout(x.layout());
SumRawInferMeta(x, axis, keep_dim, reduce_all, dtype, out, config);
}
void SvdInferMeta(const MetaTensor& x,
......
paddle/phi/infermeta/unary.h
浏览文件 @ d4451cb0
......@@ -572,19 +572,6 @@ void SumRawInferMeta(const MetaTensor& x,
MetaTensor* out,
MetaConfig config = MetaConfig());
void OriginReduceInferMeta(const MetaTensor& x,
const IntArray& axis,
bool keep_dim,
MetaTensor* out,
MetaConfig config = MetaConfig());
void OriginReduceInferMetaBase(const MetaTensor& x,
const IntArray& axis,
bool keep_dim,
bool reduce_all,
MetaTensor* out,
MetaConfig config = MetaConfig());
void SvdInferMeta(const MetaTensor& x,
bool full_matrices,
MetaTensor* u,
......
paddle/phi/kernels/impl/multi_dot_kernel_impl.h
浏览文件 @ d4451cb0
......@@ -446,9 +446,14 @@ void MultiDotGradKernel(const Context& ctx,
} else {
MultiDotGradMatChainOrder<Context, T>(
ctx, dout, ins, dout_dim, ins_dims, &dx);
// if x's shape is: [3] [3, 4] [4]
// dx's shape will be: [1, 3] [3, 4] [4, 1]
if (ins[n - 1]->dims().size() == 1) {
dx[n - 1]->Resize({dx[n - 1]->dims()[0]});
}
if (ins[0]->dims().size() == 1) {
dx[0]->Resize({dx[0]->dims()[1]});
}
}
}
......
paddle/phi/kernels/selected_rows/elementwise_multiply_kernel.cc
浏览文件 @ d4451cb0
......@@ -31,12 +31,11 @@ void MultiplyRawKernel(const Context& dev_ctx,
const DenseTensor& y,
int axis,
SelectedRows* out) {
PADDLE_ENFORCE_EQ(y.dims().size() == 1 && y.dims()[0] == 1,
true,
phi::errors::InvalidArgument(
"For MultiplyKernel, if X is Sparse, Y must be "
"scalar. But reveived the size of Y = %s.",
y.dims().size()));
PADDLE_ENFORCE_EQ(
phi::product(y.dims()),
1,
phi::errors::InvalidArgument("For MultiplyKernel, if X is Sparse, Y must "
"contain only one element."));
out->set_rows(x.rows());
out->set_height(x.height());
auto z = out->mutable_value();
......
test/collective/collective_global_gather_dygraph.py
浏览文件 @ d4451cb0
......@@ -60,7 +60,7 @@ class TestCollectiveGlobalGatherAPI(TestCollectiveAPIRunnerBase):
c = output * output
c.stop_gradient = False
c.backward()
return [output.numpy(), local_input_buf.grad.numpy()]
return [output.numpy(False), local_input_buf.grad.numpy(False)]
if __name__ == "__main__":
......
test/collective/collective_global_scatter_dygraph.py
浏览文件 @ d4451cb0
......@@ -58,7 +58,7 @@ class TestCollectiveGlobalScatterAPI(TestCollectiveAPIRunnerBase):
output.stop_gradient = False
c = output * output
c.backward()
return [output.numpy(), local_input_buf.grad.numpy()]
return [output.numpy(False), local_input_buf.grad.numpy(False)]
if __name__ == "__main__":
......
test/collective/fleet/hybrid_parallel_mp_layers.py
浏览文件 @ d4451cb0
......@@ -343,7 +343,9 @@ class TestDistTraning(unittest.TestCase):
integral_grad = paddle.concat(integral_grad, axis=-1)
np.testing.assert_allclose(
integral_data.grad.numpy(), integral_grad.numpy(), rtol=1e-6
integral_data.grad.numpy(False),
integral_grad.numpy(False),
rtol=1e-6,
)
......
test/collective/fleet/parallel_dygraph_no_sync_gradient_check.py
浏览文件 @ d4451cb0
......@@ -115,8 +115,8 @@ class TestDistTraning(unittest.TestCase):
out_b.sum().backward()
def check_acc(self, grad, acc_grad):
grad = grad.numpy() if grad is not None else None
acc_grad = acc_grad.numpy() if acc_grad is not None else None
grad = grad.numpy(False) if grad is not None else None
acc_grad = acc_grad.numpy(False) if acc_grad is not None else None
return np.testing.assert_allclose(grad, acc_grad, rtol=1e-6)
def print_trainer_0(self, *args):
......@@ -134,7 +134,9 @@ class TestDistTraning(unittest.TestCase):
grad = param._grad_ivar()
other_grad = self.broadcast_param(grad.clone(), root=1)
if self.trainer_id == 0:
np.testing.assert_allclose(other_grad.numpy(), grad.numpy())
np.testing.assert_allclose(
other_grad.numpy(False), grad.numpy(False)
)
if __name__ == '__main__':
......
test/collective/fleet/parallel_margin_cross_entropy.py
浏览文件 @ d4451cb0
......@@ -208,8 +208,8 @@ class TestParallelMarginSoftmaxCrossEntropyOp(unittest.TestCase):
)
np.testing.assert_allclose(
integral_data.grad.numpy(),
integral_grad.numpy(),
integral_data.grad.numpy(False),
integral_grad.numpy(False),
rtol=1e-5,
atol=1e-7,
)
......
test/legacy_test/eager_op_test.py
浏览文件 @ d4451cb0
......@@ -964,6 +964,11 @@ class OpTest(unittest.TestCase):
for name in api_outs:
np_api = np.array(api_outs[name])
np_dyg = np.array(dygraph_outs[name])
assert (
np_api.shape == np_dyg.shape
), "Operator ({}) : Output ({}) shape mismatch, expect shape is {}, but actual shape is {}".format(
self.op_type, name, np_dyg.shape, np_api.shape
)
np.testing.assert_allclose(
np_api,
np_dyg,
......@@ -1230,6 +1235,11 @@ class OpTest(unittest.TestCase):
# to check inplace result instead of numpy.array_equal.
expect_out = np.array(expect_outs[i])
actual_out = np.array(actual_outs[i])
assert (
actual_out.shape == expect_out.shape
), "Operator ({}) : Output ({}) shape mismatch, expect shape is {}, but actual shape is {}".format(
self.op_type, name, expect_out.shape, actual_out.shape
)
if inplace_atol is not None:
np.testing.assert_allclose(
expect_out,
......@@ -1720,41 +1730,28 @@ class OpTest(unittest.TestCase):
raise NotImplementedError("base class, not implement!")
def _compare_numpy(self, name, actual_np, expect_np):
if actual_np.shape == expect_np.shape:
np.testing.assert_allclose(
actual_np,
expect_np,
atol=self.atol if hasattr(self, 'atol') else atol,
rtol=self.rtol if hasattr(self, 'rtol') else rtol,
equal_nan=equal_nan,
err_msg=(
"Operator ("
+ self.op_type
+ ") Output ("
+ name
+ ") has diff at "
+ str(place)
+ " in "
+ self.checker_name
),
)
return
self.op_test.assertTrue(
np.allclose(
actual_np,
expect_np,
atol=self.atol if hasattr(self, 'atol') else atol,
rtol=self.rtol if hasattr(self, 'rtol') else rtol,
equal_nan=equal_nan,
expect_np = np.array(expect_np)
assert (
actual_np.shape == expect_np.shape
), "Operator ({}) : Output ({}) shape mismatch, expect shape is {}, but actual shape is {}".format(
self.op_type, name, expect_np.shape, actual_np.shape
)
np.testing.assert_allclose(
actual_np,
expect_np,
atol=self.atol if hasattr(self, 'atol') else atol,
rtol=self.rtol if hasattr(self, 'rtol') else rtol,
equal_nan=equal_nan,
err_msg=(
"Operator ("
+ self.op_type
+ ") Output ("
+ name
+ ") has diff at "
+ str(place)
+ " in "
+ self.checker_name
),
"Operator ("
+ self.op_type
+ ") Output ("
+ name
+ ") has diff at "
+ str(place)
+ " in "
+ self.checker_name,
)
def _compare_list(self, name, actual, expect):
......@@ -1775,10 +1772,6 @@ class OpTest(unittest.TestCase):
)
# modify there for fp32 check
# 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._compare_numpy(name, actual_np, expect_np)
if isinstance(expect, tuple):
self._compare_list(name, actual, expect)
......@@ -1905,41 +1898,19 @@ class OpTest(unittest.TestCase):
self.op_test.disable_cal_ref_output()
def _compare_numpy(self, name, actual_np, expect_np):
if (
functools.reduce(lambda x, y: x * y, actual_np.shape, 1)
== 0
and functools.reduce(lambda x, y: x * y, expect_np.shape, 1)
== 0
):
pass
else:
if actual_np.shape == expect_np.shape:
np.testing.assert_allclose(
actual_np,
expect_np,
atol=atol,
rtol=self.rtol if hasattr(self, 'rtol') else rtol,
equal_nan=equal_nan,
err_msg=(
"Operator ("
+ self.op_type
+ ") Output ("
+ name
+ ") has diff at "
+ str(place)
+ " in "
+ self.checker_name
),
)
return
self.op_test.assertTrue(
np.allclose(
actual_np,
expect_np,
atol=atol,
rtol=self.rtol if hasattr(self, 'rtol') else rtol,
equal_nan=equal_nan,
),
expect_np = np.array(expect_np)
assert (
actual_np.shape == expect_np.shape
), "Operator ({}) : Output ({}) shape mismatch, expect shape is {}, but actual shape is {}".format(
self.op_type, name, expect_np.shape, actual_np.shape
)
np.testing.assert_allclose(
actual_np,
expect_np,
atol=atol,
rtol=self.rtol if hasattr(self, 'rtol') else rtol,
equal_nan=equal_nan,
err_msg=(
"Operator ("
+ self.op_type
+ ") Output ("
......@@ -1947,8 +1918,9 @@ class OpTest(unittest.TestCase):
+ ") has diff at "
+ str(place)
+ " in "
+ self.checker_name,
)
+ self.checker_name
),
)
def convert_uint16_to_float_ifneed(self, actual_np, expect_np):
if actual_np.dtype == np.uint16:
......@@ -2240,6 +2212,11 @@ class OpTest(unittest.TestCase):
atol=1e-5,
):
for a, b, name in zip(numeric_grads, analytic_grads, names):
assert tuple(a.shape) == tuple(
b.shape
), "Operator ({}) : Output ({}) gradient shape mismatch, expect shape is {}, but actual shape is {}".format(
self.op_type, name, a.shape, b.shape
)
# Used by bfloat16 for now to solve precision problem
if self.is_bfloat16_op():
if a.size == 0:
......@@ -2721,7 +2698,7 @@ class OpTest(unittest.TestCase):
inputs=paddle.utils.flatten(inputs),
grad_outputs=grad_outputs,
)
return [grad.numpy() for grad in grad_inputs]
return [grad.numpy(False) for grad in grad_inputs]
@staticmethod
def _numpy_to_lod_tensor(np_value, lod, place):
......
test/legacy_test/parallel_dygraph_dataparallel_with_pylayer.py
浏览文件 @ d4451cb0
......@@ -100,8 +100,8 @@ class TestDistTraning(unittest.TestCase):
model_b.clear_gradients()
def check_acc(self, grad, acc_grad):
grad = grad.numpy() if grad is not None else None
acc_grad = acc_grad.numpy() if acc_grad is not None else None
grad = grad.numpy(False) if grad is not None else None
acc_grad = acc_grad.numpy(False) if acc_grad is not None else None
return np.testing.assert_allclose(grad, acc_grad, rtol=1e-6)
def broadcast_param(self, param, root):
......@@ -115,7 +115,9 @@ class TestDistTraning(unittest.TestCase):
grad = param._grad_ivar()
other_grad = self.broadcast_param(grad.clone(), root=1)
if self.trainer_id == 0:
np.testing.assert_allclose(other_grad.numpy(), grad.numpy())
np.testing.assert_allclose(
other_grad.numpy(False), grad.numpy(False)
)
if __name__ == '__main__':
......
test/legacy_test/parallel_dygraph_gradient_check.py
浏览文件 @ d4451cb0
......@@ -113,8 +113,8 @@ class TestDistTraning(unittest.TestCase):
def check_acc(self, grad, grad_sum, acc_grad):
if grad is not None:
grad_sum = grad_sum + grad.numpy()
acc_grad = acc_grad.numpy() if acc_grad is not None else None
grad_sum = grad_sum + grad.numpy(False)
acc_grad = acc_grad.numpy(False) if acc_grad is not None else None
np.testing.assert_allclose(grad_sum, acc_grad, rtol=1e-6)
return grad_sum
......@@ -133,7 +133,9 @@ class TestDistTraning(unittest.TestCase):
grad = param._grad_ivar()
other_grad = self.broadcast_param(grad.clone(), root=1)
if self.trainer_id == 0:
np.testing.assert_allclose(other_grad.numpy(), grad.numpy())
np.testing.assert_allclose(
other_grad.numpy(False), grad.numpy(False)
)
if __name__ == '__main__':
......
test/legacy_test/parallel_dygraph_gradient_check_in_eager_mode.py
浏览文件 @ d4451cb0
......@@ -121,8 +121,8 @@ class TestDistTraning(unittest.TestCase):
def check_acc(self, grad, grad_sum, acc_grad):
if grad is not None:
grad_sum = grad_sum + grad.numpy()
acc_grad = acc_grad.numpy() if acc_grad is not None else None
grad_sum = grad_sum + grad.numpy(False)
acc_grad = acc_grad.numpy(False) if acc_grad is not None else None
np.testing.assert_allclose(grad_sum, acc_grad, rtol=1e-6)
return grad_sum
......@@ -141,7 +141,9 @@ class TestDistTraning(unittest.TestCase):
grad = param.grad
other_grad = self.broadcast_param(grad, root=1)
if self.trainer_id == 0:
np.testing.assert_allclose(other_grad.numpy(), grad.numpy())
np.testing.assert_allclose(
other_grad.numpy(False), grad.numpy(False)
)
if __name__ == '__main__':
......
test/legacy_test/test_accuracy_op.py
浏览文件 @ d4451cb0
......@@ -44,9 +44,9 @@ class TestAccuracyOp(OpTest):
num_correct += 1
break
self.outputs = {
'Accuracy': np.array([num_correct / float(n)]).astype(self.dtype),
'Correct': np.array([num_correct]).astype("int32"),
'Total': np.array([n]).astype("int32"),
'Accuracy': np.array(num_correct / float(n)).astype(self.dtype),
'Correct': np.array(num_correct).astype("int32"),
'Total': np.array(n).astype("int32"),
}
def init_dtype(self):
......@@ -91,10 +91,10 @@ class TestAccuracyOpBf16(OpTest):
break
self.outputs = {
'Accuracy': convert_float_to_uint16(
np.array([num_correct / float(n)]).astype(np.float32)
np.array(num_correct / float(n)).astype(np.float32)
),
'Correct': np.array([num_correct]).astype("int32"),
'Total': np.array([n]).astype("int32"),
'Correct': np.array(num_correct).astype("int32"),
'Total': np.array(n).astype("int32"),
}
def init_dtype(self):
......
test/legacy_test/test_adam_op.py
浏览文件 @ d4451cb0
......@@ -639,8 +639,8 @@ class TestAdamOpWithSkipUpdate(OpTest):
'Moment1Out': moment1,
'Moment2Out': moment2,
'ParamOut': param,
'Beta1PowOut': self.inputs['Beta1Pow'],
'Beta2PowOut': self.inputs['Beta2Pow'],
'Beta1PowOut': np.array([]),
'Beta2PowOut': np.array([]),
}
def test_check_output(self):
......
test/legacy_test/test_allclose_op.py
浏览文件 @ d4451cb0
......@@ -42,15 +42,13 @@ class TestAllcloseOp(OpTest):
self.attrs = {'equal_nan': self.equal_nan}
self.outputs = {
'Out': np.array(
[
np.allclose(
self.inputs['Input'],
self.inputs['Other'],
rtol=self.rtol,
atol=self.atol,
equal_nan=self.equal_nan,
)
]
np.allclose(
self.inputs['Input'],
self.inputs['Other'],
rtol=self.rtol,
atol=self.atol,
equal_nan=self.equal_nan,
)
)
}
......
test/legacy_test/test_arg_min_max_v2_op.py
浏览文件 @ d4451cb0
......@@ -117,7 +117,7 @@ def create_kernel_case(op_type, numpy_op_type):
self.dtype = "float64"
self.x = 1000 * np.random.random(self.dims).astype(self.dtype)
self.inputs = {'X': self.x}
self.attrs = {"axis": self.axis, "flatten": True, "keepdims": True}
self.attrs = {"axis": self.axis, "flatten": True, "keepdims": False}
self.numpy_op = eval("np.%s" % (numpy_op_type))
self.outputs = {
'Out': np.array(self.numpy_op(self.x.flatten(), axis=self.axis))
......
test/legacy_test/test_auc_op.py
浏览文件 @ d4451cb0
......@@ -99,8 +99,8 @@ class TestGlobalAucOp(OpTest):
neg = python_auc._stat_neg
self.outputs = {
'AUC': np.array(python_auc.accumulate()),
'StatPosOut': np.array(pos),
'StatNegOut': np.array(neg),
'StatPosOut': np.array([pos]),
'StatNegOut': np.array([neg]),
}
def test_check_output(self):
......@@ -132,8 +132,9 @@ class TestAucAPI(unittest.TestCase):
feed={"input": x, "label": y, "ins_tag_weight": z},
fetch_list=[result[0]],
)
auc_np = np.array([0.66666667]).astype("float32")
auc_np = np.array(0.66666667).astype("float32")
np.testing.assert_allclose(output, auc_np, rtol=1e-05)
assert auc_np.shape == auc_np.shape
class TestAucOpError(unittest.TestCase):
......
test/legacy_test/test_auc_single_pred_op.py
浏览文件 @ d4451cb0
......@@ -104,8 +104,8 @@ class TestAucGlobalSinglePredOp(OpTest):
neg = python_auc._stat_neg
self.outputs = {
'AUC': np.array(python_auc.accumulate()),
'StatPosOut': np.array(pos),
'StatNegOut': np.array(neg),
'StatPosOut': np.array([pos]),
'StatNegOut': np.array([neg]),
}
def test_check_output(self):
......
test/legacy_test/test_cross_entropy_op.py
浏览文件 @ d4451cb0
......@@ -58,7 +58,7 @@ class TestCrossEntropyOp(OpTest):
)
def get_cross_entropy(self):
self.cross_entropy = np.asmatrix(
self.cross_entropy = np.array(
[
[-np.log(self.x[i][self.label[i][0]])]
for i in range(self.x.shape[0])
......@@ -91,7 +91,7 @@ class TestCrossEntropyOpRemoveLastDim(TestCrossEntropyOp):
)
def get_cross_entropy(self):
self.cross_entropy = np.asmatrix(
self.cross_entropy = np.array(
[-np.log(self.x[i][self.label[i]]) for i in range(self.x.shape[0])],
dtype="float64",
)
......@@ -140,7 +140,7 @@ class TestCrossEntropyOp3(TestCrossEntropyOp):
self.label[np.arange(self.batch_size), self.label_index] = 1
def get_cross_entropy(self):
self.cross_entropy = np.asmatrix(
self.cross_entropy = np.array(
[
[-np.log(self.x[i][self.label_index[i]])]
for i in range(self.x.shape[0])
......@@ -181,7 +181,7 @@ class TestCrossEntropyOp4(TestCrossEntropyOp):
self.label = self.label_2d.reshape(self.shape + [1])
def get_cross_entropy(self):
cross_entropy_2d = np.asmatrix(
cross_entropy_2d = np.array(
[
[-np.log(self.X_2d[i][self.label_2d[i][0]])]
for i in range(self.X_2d.shape[0])
......@@ -211,7 +211,7 @@ class TestCrossEntropyOp4RemoveLastDim(TestCrossEntropyOp4):
self.label = self.label_2d.reshape(self.shape)
def get_cross_entropy(self):
cross_entropy_2d = np.asmatrix(
cross_entropy_2d = np.array(
[
[-np.log(self.X_2d[i][self.label_2d[i][0]])]
for i in range(self.X_2d.shape[0])
......@@ -285,7 +285,7 @@ class TestCrossEntropyOp6(TestCrossEntropyOp):
)
def get_cross_entropy(self):
cross_entropy_2d = np.asmatrix(
cross_entropy_2d = np.array(
[
[-np.log(self.X_2d[i][self.label_index_2d[i]])]
for i in range(self.X_2d.shape[0])
......@@ -321,7 +321,7 @@ class TestCrossEntropyOp7(TestCrossEntropyOp):
)
def get_cross_entropy(self):
self.cross_entropy = np.asmatrix(
self.cross_entropy = np.array(
[
[-np.log(self.x[i][self.label[i][0]])]
if self.label[i][0] != self.ignore_index
......@@ -351,7 +351,7 @@ class TestCrossEntropyOp7RemoveLastDim(TestCrossEntropyOp7):
)
def get_cross_entropy(self):
self.cross_entropy = np.asmatrix(
self.cross_entropy = np.array(
[
[-np.log(self.x[i][self.label[i]])]
if self.label[i] != self.ignore_index
......
test/legacy_test/test_ctc_align.py
浏览文件 @ d4451cb0
......@@ -37,7 +37,7 @@ def CTCAlign(input, lod, blank, merge_repeated, padding=0, input_length=None):
cur_offset += lod0[i]
result = np.array(result).reshape([len(result), 1]).astype("int32")
if len(result) == 0:
result = np.array([-1])
result = np.array([[-1]])
return result
else:
result = [[] for i in range(len(input))]
......
test/legacy_test/test_data_norm_op.py
浏览文件 @ d4451cb0
......@@ -252,8 +252,8 @@ class TestDataNormOp(OpTest):
y = np.array(x_val)
mean = np.zeros(x_shape).astype(tp)
scale = np.ones(x_shape).astype(tp)
mean = np.zeros(x_shape[1]).astype(tp)
scale = np.ones(x_shape[1]).astype(tp)
self.inputs = {
"X": x_val,
......@@ -309,8 +309,8 @@ class TestDataNormOpWithEnableScaleAndShift(OpTest):
y = np.array(x_val)
mean = np.zeros(x_shape).astype(tp)
scale = np.ones(x_shape).astype(tp)
mean = np.zeros(x_shape[1]).astype(tp)
scale = np.ones(x_shape[1]).astype(tp)
self.inputs = {
"X": x_val,
......@@ -373,8 +373,8 @@ class TestDataNormOpWithoutEnableScaleAndShift(OpTest):
y = np.array(x_val)
mean = np.zeros(x_shape).astype(tp)
scale = np.ones(x_shape).astype(tp)
mean = np.zeros(x_shape[1]).astype(tp)
scale = np.ones(x_shape[1]).astype(tp)
self.inputs = {
"X": x_val,
......@@ -432,8 +432,8 @@ class TestDataNormOpWithEnableScaleAndShift_1(OpTest):
y = np.array(x_val)
mean = np.zeros(x_shape).astype(tp)
scale = np.ones(x_shape).astype(tp)
mean = np.zeros(x_shape[1]).astype(tp)
scale = np.ones(x_shape[1]).astype(tp)
self.inputs = {
"X": x_val,
......@@ -493,8 +493,8 @@ class TestDataNormOpWithSlotDim(OpTest):
y = np.array(x_val)
mean = np.zeros(x_shape).astype(tp)
scale = np.ones(x_shape).astype(tp)
mean = np.zeros(x_shape[1]).astype(tp)
scale = np.ones(x_shape[1]).astype(tp)
self.inputs = {
"X": x_val,
......
test/legacy_test/test_edit_distance_op.py
浏览文件 @ d4451cb0
......@@ -83,7 +83,7 @@ class TestEditDistanceOp(OpTest):
num_strs = len(self.x1_lod)
distance = np.zeros((num_strs, 1)).astype("float32")
sequence_num = np.array(2).astype("int64")
sequence_num = np.array([2]).astype("int64")
x1_offset = 0
x2_offset = 0
......@@ -128,7 +128,7 @@ class TestEditDistanceOpNormalizedCase0(OpTest):
num_strs = len(self.x1_lod)
distance = np.zeros((num_strs, 1)).astype("float32")
sequence_num = np.array(num_strs).astype("int64")
sequence_num = np.array([num_strs]).astype("int64")
x1_offset = 0
x2_offset = 0
......@@ -184,7 +184,7 @@ class TestEditDistanceOpNormalizedTensor(OpTest):
num_strs = len(self.x1_lod)
distance = np.zeros((num_strs, 1)).astype("float32")
sequence_num = np.array(num_strs).astype("int64")
sequence_num = np.array([num_strs]).astype("int64")
for i in range(0, num_strs):
distance[i] = Levenshtein(
......
test/legacy_test/test_elementwise_div_op.py
浏览文件 @ d4451cb0
......@@ -503,7 +503,7 @@ class TestDivideOp(unittest.TestCase):
x = paddle.to_tensor(np_x)
y = paddle.to_tensor(np_y)
z = paddle.divide(x, y)
np_z = z.numpy()
np_z = z.numpy(False)
z_expected = np.array([2.0, 0.6, 2.0])
self.assertEqual((np_z == z_expected).all(), True)
......
test/legacy_test/test_elementwise_sub_op.py
浏览文件 @ d4451cb0
......@@ -889,7 +889,7 @@ class TestSubtractApi(unittest.TestCase):
x = fluid.dygraph.to_variable(np_x)
y = fluid.dygraph.to_variable(np_y)
z = self._executed_api(x, y)
np_z = z.numpy()
np_z = z.numpy(False)
z_expected = np.array([1.0, -2.0, 2.0])
self.assertEqual((np_z == z_expected).all(), True)
......
test/legacy_test/test_fake_quantize_op.py
浏览文件 @ d4451cb0
......@@ -48,7 +48,7 @@ class TestFakeQuantizeAbsMaxOp(OpTest):
):
input_data = distribution(input_shape).astype(dtype)
compute_type = get_compute_type(dtype)
scale = np.max(np.abs(input_data))
scale = np.max(np.abs(input_data)).flatten()
bnt = (1 << (self.attrs['bit_length'] - 1)) - 1
inv_scale = 1.0 / (scale + 1e-6) if scale < 1e-30 else 1.0 / scale
if round_type == 'TiesToEven':
......@@ -195,8 +195,8 @@ class TestFakeQuantizeRangeAbsMaxOp(OpTest):
}
self.outputs = {
'Out': output_data,
'OutScale': out_scale[0],
'OutScales': out_scale,
'OutScale': np.array([], dtype) if is_test else out_scale,
'OutScales': np.array([], dtype) if is_test else out_scale,
}
self.dtype = dtype
self.attrs['is_test'] = is_test
......@@ -231,10 +231,10 @@ class TestMovingAverageAbsMaxScaleOp(OpTest):
input_data = distribution(input_shape).astype(dtype)
in_accum = np.ones(1).astype(dtype)
in_state = np.ones(1).astype(dtype)
out_accum = self.attrs['moving_rate'] * in_accum[0] + np.max(
out_accum = self.attrs['moving_rate'] * in_accum + np.max(
np.abs(input_data)
)
out_state = self.attrs['moving_rate'] * in_state[0] + 1.0
out_state = self.attrs['moving_rate'] * in_state + 1.0
out_scale = out_accum / out_state
self.inputs = {
'X': input_data,
......@@ -276,13 +276,10 @@ class TestFakeQuantizeMovingAverageAbsMaxOp(OpTest):
in_accum = np.ones(1).astype(dtype)
in_state = np.ones(1).astype(dtype)
in_scale = np.array([0.001]).astype(dtype)
out_accum = np.zeros(1).astype(dtype)
out_state = np.zeros(1).astype(dtype)
out_scale = np.zeros(1).astype(dtype)
out_accum[0] = self.attrs['moving_rate'] * in_accum[0] + np.max(
out_accum = self.attrs['moving_rate'] * in_accum + np.max(
np.abs(input_data)
)
out_state[0] = self.attrs['moving_rate'] * in_state[0] + 1.0
out_state = self.attrs['moving_rate'] * in_state + 1.0
out_scale = out_accum / out_state
if round_type == 'TiesToEven':
round_out = np.round(
......@@ -354,7 +351,7 @@ class TestFakeQuantizeDequantizeAbsMaxOp(OpTest):
self, dtype, input_shape, distribution, round_type='TiesAwayFromZero'
):
input_data = distribution(input_shape).astype(dtype)
scale = np.max(np.abs(input_data)).astype(dtype)
scale = np.max(np.abs(input_data)).flatten().astype(dtype)
bnt = (1 << (self.attrs['bit_length'] - 1)) - 1
if round_type == 'TiesToEven':
round_out = np.round(input_data / scale * bnt)
......@@ -592,12 +589,8 @@ class TestquantizeOpTrain(TestquantizeOp):
zero_point = np.zeros(scale.shape, dtype="int32")
in_accum = np.ones(1).astype(self.data_type)
in_state = np.ones(1).astype(self.data_type)
out_accum = np.zeros(1).astype(self.data_type)
out_state = np.zeros(1).astype(self.data_type)
out_accum[0] = self.attrs['moving_rate'] * in_accum[0] + np.max(
np.abs(x)
)
out_state[0] = self.attrs['moving_rate'] * in_state[0] + 1.0
out_accum = self.attrs['moving_rate'] * in_accum + np.max(np.abs(x))
out_state = self.attrs['moving_rate'] * in_state + 1.0
out_scale = out_accum / out_state
round_out = np.round(x / out_scale * self.max_range)
......
test/legacy_test/test_fused_adam_op.py
浏览文件 @ d4451cb0
......@@ -64,12 +64,8 @@ def fused_adam_step(inputs, attributes, num):
)
for i in range(num):
beta1_pows_out.append(
np.array([beta1_pows[i][1]]).astype("float32") * beta1
)
beta2_pows_out.append(
np.array([beta2_pows[i][1]]).astype("float32") * beta2
)
beta1_pows_out.append(beta1_pows[i][1] * beta1)
beta2_pows_out.append(beta2_pows[i][1] * beta2)
return (
params_out,
......
test/legacy_test/test_gather_nd_op.py
浏览文件 @ d4451cb0
......@@ -93,7 +93,7 @@ class TestGatherNdOpWithIndex1(OpTest):
target_dtype = "float32"
xnp = np.random.random((5, 20)).astype(target_dtype)
index = np.array([1]).astype("int32")
output = xnp[index]
output = xnp[index[-1]]
if self.dtype == np.uint16:
xnp = convert_float_to_uint16(xnp)
output = convert_float_to_uint16(output)
......@@ -150,7 +150,7 @@ class TestGatherNdOpWithLowIndex(OpTest):
target_dtype = "float32"
xnp = np.random.uniform(0, 100, (10, 10)).astype(target_dtype)
index = np.array([[1], [2]]).astype("int64")
output = xnp[tuple(index.T)] # [[14, 25, 1], [76, 22, 3]]
output = xnp[tuple(index.T)] # shape is [2, 10]
if self.dtype == np.uint16:
xnp = convert_float_to_uint16(xnp)
......
test/legacy_test/test_generate_proposal_labels_op.py
浏览文件 @ d4451cb0
......@@ -420,7 +420,7 @@ class TestGenerateProposalLabelsOp(OpTest):
self.bbox_targets = np.vstack(self.bbox_targets)
self.bbox_inside_weights = np.vstack(self.bbox_inside_weights)
self.bbox_outside_weights = np.vstack(self.bbox_outside_weights)
self.max_overlap_with_gt = np.vstack(self.max_overlap_with_gt)
self.max_overlap_with_gt = np.concatenate(self.max_overlap_with_gt)
class TestCascade(TestGenerateProposalLabelsOp):
......
test/legacy_test/test_is_empty_op.py
浏览文件 @ d4451cb0
......@@ -25,7 +25,7 @@ class TestEmpty(OpTest):
self.op_type = "is_empty"
self.python_api = paddle.is_empty
self.inputs = {'X': np.array([1, 2, 3])}
self.outputs = {'Out': np.array([False])}
self.outputs = {'Out': np.array(False)}
def test_check_output(self):
self.check_output()
......@@ -36,7 +36,7 @@ class TestNotEmpty(TestEmpty):
self.op_type = "is_empty"
self.python_api = paddle.is_empty
self.inputs = {'X': np.array([])}
self.outputs = {'Out': np.array([True])}
self.outputs = {'Out': np.array(True)}
class TestIsEmptyOpError(unittest.TestCase):
......
test/legacy_test/test_isclose_op.py
浏览文件 @ d4451cb0
......@@ -42,16 +42,12 @@ class TestIscloseOp(OpTest):
}
self.attrs = {'equal_nan': self.equal_nan}
self.outputs = {
'Out': np.array(
[
np.isclose(
self.inputs['Input'],
self.inputs['Other'],
rtol=self.rtol,
atol=self.atol,
equal_nan=self.equal_nan,
)
]
'Out': np.isclose(
self.inputs['Input'],
self.inputs['Other'],
rtol=self.rtol,
atol=self.atol,
equal_nan=self.equal_nan,
)
}
......
test/legacy_test/test_isfinite_op.py
浏览文件 @ d4451cb0
......@@ -31,7 +31,7 @@ class TestInf(OpTest):
x[-1] = np.inf
self.inputs = {'X': x}
self.outputs = {'Out': np.array(True).astype(self.dtype)}
self.outputs = {'Out': np.array([True]).astype(self.dtype)}
def init_dtype(self):
pass
......@@ -62,7 +62,7 @@ class TestInfBF16(OpTest):
x[0] = np.inf
x[-1] = np.inf
out = np.array(True)
out = np.array([True])
self.inputs = {'X': convert_float_to_uint16(x)}
self.outputs = {'Out': out}
......@@ -81,7 +81,7 @@ class TestNAN(OpTest):
x[-1] = np.nan
self.inputs = {'X': x}
self.outputs = {'Out': np.array(True).astype(self.dtype)}
self.outputs = {'Out': np.array([True]).astype(self.dtype)}
def init_dtype(self):
pass
......@@ -112,7 +112,7 @@ class TestNANBF16(OpTest):
x[0] = np.nan
x[-1] = np.nan
out = np.array(True)
out = np.array([True])
self.inputs = {'X': convert_float_to_uint16(x)}
self.outputs = {'Out': out}
......@@ -132,7 +132,7 @@ class TestIsfinite(OpTest):
out = np.isinf(x) | np.isnan(x)
self.inputs = {'X': x}
self.outputs = {'Out': np.array(False).astype(self.dtype)}
self.outputs = {'Out': np.array([False]).astype(self.dtype)}
def init_dtype(self):
pass
......@@ -163,7 +163,7 @@ class TestIsfiniteBF16(OpTest):
x[0] = np.inf
x[-1] = np.nan
out = np.array(False)
out = np.array([False])
self.inputs = {'X': convert_float_to_uint16(x)}
self.outputs = {'Out': out}
......
test/legacy_test/test_linspace.py
浏览文件 @ d4451cb0
......@@ -66,7 +66,7 @@ class TestLinspaceOpNumOneCase(TestLinspaceOpCommonCase):
'Stop': np.array([0]).astype(self.dtype),
'Num': np.array([1]).astype('int32'),
}
self.outputs = {'Out': np.array(10, dtype=self.dtype)}
self.outputs = {'Out': np.array([10], dtype=self.dtype)}
def test_check_output(self):
self.check_output()
......@@ -136,7 +136,7 @@ class TestLinspaceOpNumOneCaseBF16(TestLinspaceOpCommonCaseBF16):
'Num': np.array([1]).astype('int32'),
}
self.outputs = {
'Out': convert_float_to_uint16(np.array(10, dtype="float32"))
'Out': convert_float_to_uint16(np.array([10], dtype="float32"))
}
......
test/legacy_test/test_logspace.py
浏览文件 @ d4451cb0
......@@ -113,7 +113,7 @@ class TestLogspaceOpNumOneCase(TestLogspaceOpCommonCase):
'Base': np.array([2]).astype(dtype),
}
self.attrs = {'dtype': int(paddle.float32)}
self.outputs = {'Out': np.power(2, np.array(10)).astype(dtype)}
self.outputs = {'Out': np.power(2, np.array([10])).astype(dtype)}
class TestLogspaceOpMinusBaseCase(TestLogspaceOpCommonCase):
......
test/legacy_test/test_mean_iou.py
浏览文件 @ d4451cb0
......@@ -47,7 +47,7 @@ def compute_mean_iou(
mean_iou = (out_correct / denominator).sum() / valid_count
for _, in_mean_iou in in_mean_ious:
mean_iou += in_mean_iou
mean_iou += float(in_mean_iou)
return mean_iou, out_wrong, out_correct
......@@ -84,21 +84,12 @@ class TestMeanIOUOp(OpTest):
)
)
in_mean_ious = []
for i in range(self.in_mean_iou_num):
in_mean_ious.append(
(
"in_mean_iou_%d" % i,
np.random.uniform(0, 1, [1]).astype("float32"),
)
)
self.inputs = {
'Predictions': predictions,
'Labels': labels,
'InWrongs': in_wrongs,
'InCorrects': in_corrects,
'InMeanIou': in_mean_ious,
'InMeanIou': self.in_mean_ious,
}
self.attrs = {'num_classes': int(self.num_classes)}
mean_iou, out_wrong, out_correct = compute_mean_iou(
......@@ -107,7 +98,7 @@ class TestMeanIOUOp(OpTest):
self.num_classes,
in_wrongs,
in_corrects,
in_mean_ious,
self.in_mean_ious,
)
self.outputs = {
'OutMeanIou': mean_iou,
......@@ -120,7 +111,7 @@ class TestMeanIOUOp(OpTest):
self.image_size = [128, 128]
self.in_wrong_num = 0
self.in_correct_num = 0
self.in_mean_iou_num = 0
self.in_mean_ious = []
def test_check_output(self):
self.check_output()
......@@ -132,7 +123,14 @@ class TestCase1(TestMeanIOUOp):
self.image_size = [100, 128]
self.in_wrong_num = 2
self.in_correct_num = 2
self.in_mean_iou_num = 2
self.in_mean_ious = []
for i in range(2):
self.in_mean_ious.append(
(
"in_mean_iou_%d" % i,
np.random.uniform(0, 1, []).astype("float32"),
)
)
# NOTE(dev): Skip check_dygraph becuase Python API doesn't expose
# in_wrong_num/in_correct_num/in_mean_iou_num argument
......@@ -140,5 +138,21 @@ class TestCase1(TestMeanIOUOp):
self.check_output(check_dygraph=False)
class TestCase2(TestCase1):
def config(self):
self.num_classes = 5
self.image_size = [100, 128]
self.in_wrong_num = 2
self.in_correct_num = 2
self.in_mean_ious = []
for i in range(2):
self.in_mean_ious.append(
(
"in_mean_iou_%d" % i,
np.random.uniform(0, 1, [1]).astype("float32"),
)
)
if __name__ == '__main__':
unittest.main()
test/legacy_test/test_multiclass_nms_op.py
浏览文件 @ d4451cb0
......@@ -613,7 +613,9 @@ class TestMulticlassNMS2Op(TestMulticlassNMSOp):
else np.array([], dtype=np.float32).reshape([0, BOX_SIZE + 2])
)
index_outs = (
det_outs[:, -1:].astype('int') if len(det_outs) else det_outs
det_outs[:, -1:].astype('int')
if len(det_outs)
else np.array([], dtype='int').reshape([0, 1])
)
self.op_type = 'multiclass_nms2'
self.inputs = {'BBoxes': boxes, 'Scores': scores}
......@@ -685,7 +687,9 @@ class TestMulticlassNMS2LoDInput(TestMulticlassNMSLoDInput):
else np.array([], dtype=np.float32).reshape([0, BOX_SIZE + 2])
)
index_outs = (
det_outs[:, -1:].astype('int') if len(det_outs) else det_outs
det_outs[:, -1:].astype('int')
if len(det_outs)
else np.array([], dtype='int').reshape([0, 1])
)
self.op_type = 'multiclass_nms2'
self.inputs = {
......@@ -760,7 +764,9 @@ class TestMulticlassNMS3Op(TestMulticlassNMS2Op):
else np.array([], dtype=np.float32).reshape([0, BOX_SIZE + 2])
)
index_outs = (
det_outs[:, -1:].astype('int') if len(det_outs) else det_outs
det_outs[:, -1:].astype('int')
if len(det_outs)
else np.array([], dtype='int').reshape([0, 1])
)
self.op_type = 'multiclass_nms3'
self.inputs = {'BBoxes': boxes, 'Scores': scores}
......
test/legacy_test/test_nll_loss.py
浏览文件 @ d4451cb0
......@@ -38,9 +38,9 @@ def nll_loss_1d(
total_weight += cur_weight
out[i] = -logs[i][cur_target] * cur_weight
if reduction == 'sum':
return np.sum(out), np.array([total_weight]).astype('float64')
return np.sum(out), np.array(total_weight).astype('float64')
elif reduction == 'mean':
return out.sum() / total_weight, np.array([total_weight]).astype(
return out.sum() / total_weight, np.array(total_weight).astype(
'float64'
)
elif reduction == 'none':
......@@ -67,9 +67,9 @@ def nll_loss_2d(
total_weight += cur_weight
out[i][h][w] = -logs[i][cur_target][h][w] * cur_weight
if reduction == 'sum':
return np.sum(out), np.array([total_weight]).astype('float64')
return np.sum(out), np.array(total_weight).astype('float64')
elif reduction == 'mean':
return out.sum() / total_weight, np.array([total_weight]).astype(
return out.sum() / total_weight, np.array(total_weight).astype(
'float64'
)
elif reduction == 'none':
......@@ -993,7 +993,7 @@ class TestNLLLossOp1DNoReduce(OpTest):
0, self.input_shape[1], self.label_shape
).astype("int64")
output_np = nll_loss_1d(input_np, label_np, reduction='none')
total_weight_np = np.array([0]).astype('float64')
total_weight_np = np.array(0).astype('float64')
self.inputs = {'X': input_np, 'Label': label_np}
if self.with_weight:
np.random.seed(200)
......@@ -1094,7 +1094,7 @@ class TestNLLLossOp2DNoReduce(OpTest):
0, self.input_shape[1], self.label_shape
).astype("int64")
output_np = nll_loss_2d(input_np, label_np, reduction='none')
total_weight_np = np.array([0]).astype('float64')
total_weight_np = np.array(0).astype('float64')
self.inputs = {'X': input_np, 'Label': label_np}
if self.with_weight:
np.random.seed(200)
......
test/legacy_test/test_numel_op.py
浏览文件 @ d4451cb0
......@@ -31,7 +31,7 @@ class TestNumelOp(OpTest):
self.inputs = {
'Input': x,
}
self.outputs = {'Out': np.array([np.size(x)])}
self.outputs = {'Out': np.array(np.size(x))}
def test_check_output(self):
self.check_output()
......@@ -84,7 +84,7 @@ class TestNumelOpBF16(OpTest):
self.init()
x = np.random.random(self.shape).astype(np.float32)
self.inputs = {'Input': convert_float_to_uint16(x)}
self.outputs = {'Out': np.array([np.size(x)])}
self.outputs = {'Out': np.array(np.size(x))}
def test_check_output(self):
place = paddle.CUDAPlace(0)
......
test/legacy_test/test_positive_negative_pair_op.py
浏览文件 @ d4451cb0
......@@ -47,9 +47,9 @@ def py_pnpair_op(score, label, query, column=-1, weight=None):
neg += w
return (
np.array(pos).astype('float32'),
np.array(neg).astype('float32'),
np.array(neu).astype('float32'),
np.array([pos]).astype('float32'),
np.array([neg]).astype('float32'),
np.array([neu]).astype('float32'),
)
......
test/legacy_test/test_reduce_op.py
浏览文件 @ d4451cb0
......@@ -73,7 +73,7 @@ class TestComplexSumOP(TestSumOp):
class TestSumOp_ZeroDim(TestSumOp):
def init_attrs(self):
self.attrs = {'dim': [], 'reduce_all': True}
self.attrs = {'dim': []}
def init_input(self):
self.x = np.random.random([]).astype(self.dtype)
......@@ -736,10 +736,14 @@ class TestProd8DBFP16OP(TestProd8DOp):
self.check_grad_with_place(paddle.CUDAPlace(0), ['X'], 'Out')
def reduce_all_wrapper(x, axis=None, keepdim=False, reduce_all=True, name=None):
return paddle.all(x, axis, keepdim, name)
class TestAllOp(OpTest):
def setUp(self):
self.op_type = "reduce_all"
self.python_api = paddle.all
self.python_api = reduce_all_wrapper
self.inputs = {'X': np.random.randint(0, 2, (5, 6, 10)).astype("bool")}
self.outputs = {'Out': self.inputs['X'].all()}
self.attrs = {'reduce_all': True}
......@@ -754,7 +758,7 @@ class TestAllOp_ZeroDim(OpTest):
self.op_type = "reduce_all"
self.inputs = {'X': np.random.randint(0, 2, []).astype("bool")}
self.outputs = {'Out': self.inputs['X'].all()}
self.attrs = {'dim': [], 'reduce_all': True}
self.attrs = {'dim': []}
def test_check_output(self):
self.check_output()
......@@ -769,7 +773,7 @@ class TestAll8DOp(OpTest):
"bool"
)
}
self.attrs = {'reduce_all': True, 'dim': (2, 3, 4)}
self.attrs = {'dim': (2, 3, 4)}
self.outputs = {'Out': self.inputs['X'].all(axis=self.attrs['dim'])}
def test_check_output(self):
......@@ -851,10 +855,14 @@ class TestAllOpError(unittest.TestCase):
self.assertRaises(TypeError, paddle.all, input2)
def reduce_any_wrapper(x, axis=None, keepdim=False, reduce_all=True, name=None):
return paddle.any(x, axis, keepdim, name)
class TestAnyOp(OpTest):
def setUp(self):
self.op_type = "reduce_any"
self.python_api = paddle.any
self.python_api = reduce_any_wrapper
self.inputs = {'X': np.random.randint(0, 2, (5, 6, 10)).astype("bool")}
self.outputs = {'Out': self.inputs['X'].any()}
self.attrs = {'reduce_all': True}
......@@ -869,7 +877,7 @@ class TestAnyOp_ZeroDim(OpTest):
self.op_type = "reduce_any"
self.inputs = {'X': np.random.randint(0, 2, []).astype("bool")}
self.outputs = {'Out': self.inputs['X'].any()}
self.attrs = {'dim': [], 'reduce_all': True}
self.attrs = {'dim': []}
def test_check_output(self):
self.check_output()
......@@ -884,7 +892,7 @@ class TestAny8DOp(OpTest):
"bool"
)
}
self.attrs = {'reduce_all': True, 'dim': (3, 5, 4)}
self.attrs = {'dim': (3, 5, 4)}
self.outputs = {'Out': self.inputs['X'].any(axis=self.attrs['dim'])}
def test_check_output(self):
......@@ -1291,11 +1299,17 @@ class TestReduceSumWithNumelOne(OpTest):
self.check_grad(['X'], 'Out', check_prim=False)
def reduce_sum_wrapper(
x, axis=None, keepdim=False, reduce_all=True, out_dtype=None, name=None
):
return paddle.sum(x, axis, out_dtype, keepdim, name)
class TestReduceAll(OpTest):
def setUp(self):
self.op_type = "reduce_sum"
self.python_api = paddle.sum
self.public_python_api = paddle.sum
self.python_api = reduce_sum_wrapper
self.public_python_api = reduce_sum_wrapper
self.prim_op_type = "prim"
self.inputs = {'X': np.random.random((100, 1, 1)).astype("float64")}
self.attrs = {'reduce_all': True, 'keep_dim': False}
......@@ -1312,8 +1326,8 @@ class TestReduceAll(OpTest):
class TestReduceAllFp32(OpTest):
def setUp(self):
self.op_type = "reduce_sum"
self.python_api = paddle.sum
self.public_python_api = paddle.sum
self.python_api = reduce_sum_wrapper
self.public_python_api = reduce_sum_wrapper
self.prim_op_type = "prim"
self.inputs = {'X': np.random.random((100, 1, 1)).astype("float32")}
self.attrs = {'reduce_all': True, 'keep_dim': False}
......@@ -1345,15 +1359,17 @@ class Test1DReduceWithAxes1(OpTest):
self.check_grad(['X'], 'Out', check_prim=True)
def reduce_sum_wrapper(x, axis=None, out_dtype=None, keepdim=False, name=None):
return paddle.sum(x, axis, "float64", keepdim, name)
def reduce_sum_wrapper_fp64(
x, axis=None, keepdim=False, reduce_all=True, out_dtype=None, name=None
):
return paddle.sum(x, axis, 'float64', keepdim, name)
class TestReduceWithDtype(OpTest):
def setUp(self):
self.op_type = "reduce_sum"
self.python_api = reduce_sum_wrapper
self.public_python_api = reduce_sum_wrapper
self.python_api = reduce_sum_wrapper_fp64
self.public_python_api = reduce_sum_wrapper_fp64
self.prim_op_type = "prim"
self.inputs = {'X': np.random.random((6, 2, 10)).astype("float64")}
self.outputs = {'Out': self.inputs['X'].sum().astype('float64')}
......@@ -1375,8 +1391,8 @@ class TestReduceWithDtype(OpTest):
class TestReduceWithDtype1(TestReduceWithDtype):
def setUp(self):
self.op_type = "reduce_sum"
self.python_api = reduce_sum_wrapper
self.public_python_api = reduce_sum_wrapper
self.python_api = paddle.sum
self.public_python_api = paddle.sum
self.prim_op_type = "prim"
self.inputs = {'X': np.random.random((6, 2, 10)).astype("float64")}
self.outputs = {'Out': self.inputs['X'].sum(axis=1)}
......@@ -1401,8 +1417,8 @@ class TestReduceWithDtype2(TestReduceWithDtype):
def setUp(self):
self.op_type = "reduce_sum"
self.prim_op_type = "prim"
self.python_api = reduce_sum_wrapper
self.public_python_api = reduce_sum_wrapper
self.python_api = paddle.sum
self.public_python_api = paddle.sum
self.inputs = {'X': np.random.random((6, 2, 10)).astype("float64")}
self.outputs = {'Out': self.inputs['X'].sum(axis=1, keepdims=True)}
self.attrs = {'dim': [1], 'keep_dim': True}
......
test/legacy_test/test_scatter_op.py
浏览文件 @ d4451cb0
......@@ -674,13 +674,13 @@ class TestScatterOpFp16(OpTest):
)
ref_grad_updates = self.compute_ref_grad_updates()
np.testing.assert_allclose(
ref_grad_updates.numpy(),
updates_tensor.grad.numpy(),
ref_grad_updates.numpy(False),
updates_tensor.grad.numpy(False),
rtol=1e-5,
atol=1e-5,
)
np.testing.assert_allclose(
self.ref_dx, x_tensor.grad.numpy(), rtol=1e-5, atol=1e-5
self.ref_dx, x_tensor.grad.numpy(False), rtol=1e-5, atol=1e-5
)
......
test/legacy_test/test_seed_op.py
浏览文件 @ d4451cb0
......@@ -28,7 +28,7 @@ class TestSeedOpFixSeed(OpTest):
self.op_type = "seed"
self.inputs = {}
self.attrs = {"seed": 123}
self.outputs = {"Out": np.asarray(123).astype('int')}
self.outputs = {"Out": np.array([123]).astype('int')}
def test_check_output(self):
self.check_output()
......@@ -39,7 +39,7 @@ class TestSeedOpDiffSeed(OpTest):
self.op_type = "seed"
self.inputs = {}
self.attrs = {"seed": 0}
self.outputs = {"Out": np.asarray(123).astype('int')}
self.outputs = {"Out": np.array([123]).astype('int')}
def test_check_output(self):
self.check_output(no_check_set=["Out"])
......
test/legacy_test/test_segment_ops.py
浏览文件 @ d4451cb0
......@@ -218,6 +218,14 @@ class TestSegmentMean(TestSegmentOps):
}
self.convert_bf16()
def test_check_output(self):
if core.is_compiled_with_cuda():
self.check_output_with_place(core.CUDAPlace(0))
# due to CPU kernel not implement calculate 'SummedIds'
# so cannot check 'SummedIds'
del self.outputs['SummedIds']
self.check_output_with_place(core.CPUPlace())
class TestSegmentMean2(TestSegmentMean):
def prepare(self):
......
test/legacy_test/test_slice_op.py
浏览文件 @ d4451cb0
......@@ -148,9 +148,9 @@ class TestSliceOp_decs_dim(OpTest):
self.starts = [1, 0, 2]
self.ends = [2, 3, 4]
self.axes = [0, 1, 2]
self.decrease_axis = [0]
self.decrease_axis = []
self.infer_flags = [1, 1, 1]
self.out = self.input[1, 0:3, 2:4, :]
self.out = self.input[1:2, 0:3, 2:4, :]
def test_check_output(self):
self.check_output()
......
test/legacy_test/test_squared_l2_norm_op.py
浏览文件 @ d4451cb0
......@@ -81,7 +81,7 @@ class TestL2LossOp(OpTest):
X = np.random.uniform(-1, 1, (13, 19)).astype("float32")
X[np.abs(X) < self.max_relative_error] = 0.1
self.inputs = {'X': X}
self.outputs = {'Out': np.square(LA.norm(X))}
self.outputs = {'Out': np.array([np.square(LA.norm(X))])}
def test_check_output(self):
self.check_output()
......
test/legacy_test/test_unbind_op.py
浏览文件 @ d4451cb0
......@@ -77,7 +77,7 @@ class TestUnbind(unittest.TestCase):
np_grad = np.ones(x.shape, np.float32)
out.backward()
np.testing.assert_array_equal(x.grad.numpy(), np_grad)
np.testing.assert_array_equal(x.grad.numpy(False), np_grad)
class TestLayersUnbind(unittest.TestCase):
......@@ -105,7 +105,9 @@ class TestUnbindOp(OpTest):
pass
def outReshape(self):
pass
self.out[0] = self.out[0].reshape((2, 2))
self.out[1] = self.out[1].reshape((2, 2))
self.out[2] = self.out[2].reshape((2, 2))
def setAxis(self):
pass
......@@ -209,6 +211,7 @@ class TestUnbindFP16Op(OpTest):
self.num = 3
x = np.arange(12).reshape(3, 2, 2).astype(self.dtype)
self.out = np.split(x, self.num, self.axis)
self.outReshape()
self.inputs = {'X': x}
self.attrs = {'axis': self.axis}
self.outputs = {
......@@ -216,6 +219,11 @@ class TestUnbindFP16Op(OpTest):
}
self.python_out_sig = ['out%d' % i for i in range(len(self.out))]
def outReshape(self):
self.out[0] = self.out[0].reshape((2, 2))
self.out[1] = self.out[1].reshape((2, 2))
self.out[2] = self.out[2].reshape((2, 2))
def get_dtype(self):
return np.float16
......@@ -233,6 +241,7 @@ class TestUnbindBF16Op(OpTest):
self.num = 3
x = np.arange(12).reshape(3, 2, 2).astype(self.dtype)
self.out = np.split(x, self.num, self.axis)
self.outReshape()
self.inputs = {'X': convert_float_to_uint16(x)}
self.attrs = {'axis': self.axis}
self.outputs = {
......@@ -243,6 +252,11 @@ class TestUnbindBF16Op(OpTest):
}
self.python_out_sig = ['out%d' % i for i in range(len(self.out))]
def outReshape(self):
self.out[0] = self.out[0].reshape((2, 2))
self.out[1] = self.out[1].reshape((2, 2))
self.out[2] = self.out[2].reshape((2, 2))
def get_dtype(self):
return np.uint16
......@@ -277,7 +291,7 @@ class TestUnbindBool(unittest.TestCase):
x = paddle.to_tensor([[True, True], [False, False]])
xs = paddle.unbind(x, axis=0)
self.assertEqual(len(xs), 2)
np.testing.assert_array_equal(xs[0].numpy(), [True, True])
np.testing.assert_array_equal(xs[0].numpy(False), [True, True])
class TestUnbindGradOptionalInput(unittest.TestCase):
......@@ -290,7 +304,7 @@ class TestUnbindGradOptionalInput(unittest.TestCase):
a_grad = a.detach()
a_grad[:, 0, :] = 1
np.testing.assert_array_equal(a.grad.numpy(), a_grad.numpy())
np.testing.assert_array_equal(a.grad.numpy(False), a_grad.numpy(False))
if __name__ == '__main__':
......
test/xpu/parallel_dygraph_dataparallel_with_pylayer.py
浏览文件 @ d4451cb0
......@@ -100,8 +100,8 @@ class TestDistTraning(unittest.TestCase):
model_b.clear_gradients()
def check_acc(self, grad, acc_grad):
grad = grad.numpy() if grad is not None else None
acc_grad = acc_grad.numpy() if acc_grad is not None else None
grad = grad.numpy(False) if grad is not None else None
acc_grad = acc_grad.numpy(False) if acc_grad is not None else None
return np.testing.assert_allclose(grad, acc_grad, rtol=1e-6)
def broadcast_param(self, param, root):
......@@ -115,7 +115,9 @@ class TestDistTraning(unittest.TestCase):
grad = param._grad_ivar()
other_grad = self.broadcast_param(grad.clone(), root=1)
if self.trainer_id == 0:
np.testing.assert_allclose(other_grad.numpy(), grad.numpy())
np.testing.assert_allclose(
other_grad.numpy(False), grad.numpy(False)
)
if __name__ == '__main__':
......
test/xpu/parallel_dygraph_gradient_check.py
浏览文件 @ d4451cb0
......@@ -113,8 +113,8 @@ class TestDistTraning(unittest.TestCase):
def check_acc(self, grad, grad_sum, acc_grad):
if grad is not None:
grad_sum = grad_sum + grad.numpy()
acc_grad = acc_grad.numpy() if acc_grad is not None else None
grad_sum = grad_sum + grad.numpy(False)
acc_grad = acc_grad.numpy(False) if acc_grad is not None else None
np.testing.assert_allclose(grad_sum, acc_grad, rtol=1e-6)
return grad_sum
......@@ -133,7 +133,9 @@ class TestDistTraning(unittest.TestCase):
grad = param._grad_ivar()
other_grad = self.broadcast_param(grad.clone(), root=1)
if self.trainer_id == 0:
np.testing.assert_allclose(other_grad.numpy(), grad.numpy())
np.testing.assert_allclose(
other_grad.numpy(False), grad.numpy(False)
)
if __name__ == '__main__':
......
test/xpu/parallel_dygraph_gradient_check_in_eager_mode.py
浏览文件 @ d4451cb0
......@@ -121,8 +121,8 @@ class TestDistTraning(unittest.TestCase):
def check_acc(self, grad, grad_sum, acc_grad):
if grad is not None:
grad_sum = grad_sum + grad.numpy()
acc_grad = acc_grad.numpy() if acc_grad is not None else None
grad_sum = grad_sum + grad.numpy(False)
acc_grad = acc_grad.numpy(False) if acc_grad is not None else None
np.testing.assert_allclose(grad_sum, acc_grad, rtol=1e-6)
return grad_sum
......@@ -141,7 +141,9 @@ class TestDistTraning(unittest.TestCase):
grad = param.grad
other_grad = self.broadcast_param(grad, root=1)
if self.trainer_id == 0:
np.testing.assert_allclose(other_grad.numpy(), grad.numpy())
np.testing.assert_allclose(
other_grad.numpy(False), grad.numpy(False)
)
if __name__ == '__main__':
......
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