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support Pad(18) #14219

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merged 8 commits into from
Jan 23, 2023
Merged

support Pad(18) #14219

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21de73d
support Pad(18)
liqunfu Jan 11, 2023
f979eb2
refactor, update op.md
liqunfu Jan 11, 2023
c9eba74
cast size_t
liqunfu Jan 12, 2023
99704d8
merge main
liqunfu Jan 17, 2023
4827b3d
update with reviewer's comments
liqunfu Jan 17, 2023
f599e47
fix ci failure
liqunfu Jan 18, 2023
cffe796
merge main, update with reviewer's comments
liqunfu Jan 20, 2023
ecd1638
narrow axis
liqunfu Jan 20, 2023
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14 changes: 0 additions & 14 deletions csharp/test/Microsoft.ML.OnnxRuntime.Tests.NetCoreApp/InferenceTest.netcore.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -397,25 +397,11 @@ private static Dictionary<string, string> GetSkippedModels(DirectoryInfo modelsD
{ "test_bitwise_xor_i32_2d", "pending opset 18 support"},
{ "test_bitwise_xor_ui8_bcast_4v3d", "pending opset 18 support"},
{ "test_bitwise_xor_ui64_bcast_3v1d", "pending opset 18 support"},
{ "test_center_crop_pad_crop", "pending opset 18 support"},
{ "test_center_crop_pad_crop_and_pad", "pending opset 18 support"},
{ "test_center_crop_pad_crop_and_pad_expanded", "pending opset 18 support"},
{ "test_center_crop_pad_crop_axes_chw", "pending opset 18 support"},
{ "test_center_crop_pad_crop_axes_chw_expanded", "pending opset 18 support"},
{ "test_center_crop_pad_crop_axes_hwc", "pending opset 18 support"},
{ "test_center_crop_pad_crop_axes_hwc_expanded", "pending opset 18 support"},
{ "test_center_crop_pad_crop_expanded", "pending opset 18 support"},
{ "test_center_crop_pad_pad", "pending opset 18 support"},
{ "test_center_crop_pad_pad_expanded", "pending opset 18 support"},
{ "test_col2im", "pending opset 18 support"},
{ "test_col2im_5d", "pending opset 18 support"},
{ "test_col2im_dilations", "pending opset 18 support"},
{ "test_col2im_pads", "pending opset 18 support"},
{ "test_col2im_strides", "pending opset 18 support"},
{ "test_constant_pad", "pending opset 18 support"},
{ "test_constant_pad_axes", "pending opset 18 support"},
{ "test_edge_pad", "pending opset 18 support"},
{ "test_reflect_pad", "pending opset 18 support"},
{ "test_scatter_elements_with_axis", "pending opset 18 support"},
{ "test_scatter_elements_without_axis", "pending opset 18 support"},
{ "test_scatter_elements_with_duplicate_indices", "pending opset 18 support"},
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3 changes: 2 additions & 1 deletion docs/OperatorKernels.md
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Expand Up @@ -219,7 +219,8 @@ Do not modify directly.*
|PRelu|*in* X:**T**<br> *in* slope:**T**<br> *out* Y:**T**|16+|**T** = tensor(float)|
|||[9, 15]|**T** = tensor(float)|
|||[7, 8]|**T** = tensor(float)|
|Pad|*in* data:**T**<br> *in* pads:**tensor(int64)**<br> *in* constant_value:**T**<br> *in* axes:**Tind**<br> *out* output:**T**<br><br>or<br><br>*in* data:**T**<br> *in* pads:**tensor(int64)**<br> *in* constant_value:**T**<br> *out* output:**T**<br><br>or<br><br>*in* data:**T**<br> *out* output:**T**|13+|**T** = tensor(bool), tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint32), tensor(uint64), tensor(uint8)|
|Pad|*in* data:**T**<br> *in* pads:**tensor(int64)**<br> *in* constant_value:**T**<br> *in* axes:**Tind**<br> *out* output:**T**<br><br>or<br><br>*in* data:**T**<br> *in* pads:**tensor(int64)**<br> *in* constant_value:**T**<br> *out* output:**T**<br><br>or<br><br>*in* data:**T**<br> *out* output:**T**|18+|**T** = tensor(bool), tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[13, 17]|**T** = tensor(bool), tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[11, 12]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[2, 10]|**T** = tensor(double), tensor(float)|
|ParametricSoftplus|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
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10 changes: 6 additions & 4 deletions onnxruntime/core/providers/cpu/cpu_execution_provider.cc
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Expand Up @@ -665,7 +665,7 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain,
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, int64_t, NonZero);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, uint8_t, NonZero);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, GatherND);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Pad);
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 17, Pad);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 17, float, ReduceL1);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 17, int32_t, ReduceL1);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 17, float, ReduceL2);
Expand Down Expand Up @@ -830,6 +830,7 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain,
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, float, ReduceSumSquare);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, double, ReduceSumSquare);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, int32_t, ReduceSumSquare);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, Pad);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, Split);
#if !defined(DISABLE_OPTIONAL_TYPE)
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, OptionalHasElement);
Expand Down Expand Up @@ -1853,7 +1854,7 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, uint8_t,
NonZero)>,
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, GatherND)>,
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Pad)>,
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 17, Pad)>,
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 17, float,
ReduceL1)>,
BuildKernelCreateInfo<ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 17, int32_t,
Expand Down Expand Up @@ -2128,11 +2129,12 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
ReduceSumSquare)>,
BuildKernelCreateInfo<ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, double,
ReduceSumSquare)>,
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, Split)>,
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, Pad)>,
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, Split)>,
#if !defined(DISABLE_OPTIONAL_TYPE)
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, OptionalHasElement)>,
BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 18, OptionalGetElement)>,
#endif
#endif
};

for (auto& function_table_entry : function_table) {
Expand Down
92 changes: 80 additions & 12 deletions onnxruntime/core/providers/cpu/tensor/pad.cc
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Expand Up @@ -66,10 +66,24 @@ ORT_SPECIFY_OP_KERNEL_ARG_DEFAULT_TYPES(
uint8_t,
bool);

ORT_SPECIFY_OP_KERNEL_ARG_DEFAULT_TYPES(
kCpuExecutionProvider, kOnnxDomain, Pad, 18, Input, 0,
float,
double,
int32_t,
int64_t,
uint32_t,
uint64_t,
int8_t,
uint8_t,
bool);

ORT_SPECIFY_OP_KERNEL_ARG_REQUIRED_TYPES(
kCpuExecutionProvider, kOnnxDomain, Pad, 11, Input, 0, int32_t, int64_t);
ORT_SPECIFY_OP_KERNEL_ARG_REQUIRED_TYPES(
kCpuExecutionProvider, kOnnxDomain, Pad, 13, Input, 0, int32_t, int64_t);
ORT_SPECIFY_OP_KERNEL_ARG_REQUIRED_TYPES(
kCpuExecutionProvider, kOnnxDomain, Pad, 18, Input, 0, int32_t, int64_t);
} // namespace op_kernel_type_control

using EnabledPad2Types = ORT_OP_KERNEL_ARG_ENABLED_TYPE_LIST(
Expand All @@ -78,6 +92,9 @@ using EnabledPad11Types = ORT_OP_KERNEL_ARG_ENABLED_TYPE_LIST(
kCpuExecutionProvider, kOnnxDomain, Pad, 11, Input, 0);
using EnabledPad13Types = ORT_OP_KERNEL_ARG_ENABLED_TYPE_LIST(
kCpuExecutionProvider, kOnnxDomain, Pad, 13, Input, 0);
using EnabledPad18Types = ORT_OP_KERNEL_ARG_ENABLED_TYPE_LIST(
kCpuExecutionProvider, kOnnxDomain, Pad, 18, Input, 0);


using AllEnabledPadTypes =
utils::TypeSetUnion<
Expand Down Expand Up @@ -106,13 +123,21 @@ ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
BuildKernelDefConstraintsFromTypeList<EnabledPad11Types>()),
Pad);

ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
Pad,
13, 17,
KernelDefBuilder().TypeConstraint(
"T",
BuildKernelDefConstraintsFromTypeList<EnabledPad13Types>()),
Pad);

ONNX_CPU_OPERATOR_KERNEL(
Pad,
13,
18,
KernelDefBuilder()
.TypeConstraint(
"T",
BuildKernelDefConstraintsFromTypeList<EnabledPad13Types>()),
BuildKernelDefConstraintsFromTypeList<EnabledPad18Types>()),
Pad);


Expand Down Expand Up @@ -463,6 +488,24 @@ static PadValue PadValueFromFloat(float value, MLDataType data_type) {
return result;
}

template <class T>
void ComputePadWithAxes(
gsl::span<const int64_t> pads_tensor_raw_data,
gsl::span<const T> axes_tensor_raw_data,
size_t data_rank,
PadsVector& pads) {
size_t axes_size = axes_tensor_raw_data.size();
for (size_t i = 0; i < axes_size; ++i) {
T axis = axes_tensor_raw_data[i];
if (axis < 0) {
axis = (T)data_rank + axis; // -1 as data_rank - 1
}
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ORT_ENFORCE(axis >= 0 && axis < (int64_t)data_rank, "values in Axes should be within data_rank.");
pads[(unsigned int)axis] = pads_tensor_raw_data[i]; // xi_begin
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pads[data_rank + (unsigned int)axis] = pads_tensor_raw_data[axes_size + i]; // xi_end
}
}

Status Pad::Compute(OpKernelContext* ctx) const {
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const Tensor& input_tensor = *ctx->Input<Tensor>(0);
MLDataType data_type = input_tensor.DataType();
Expand All @@ -479,20 +522,44 @@ Status Pad::Compute(OpKernelContext* ctx) const {

const Tensor& pads_tensor = *ctx->Input<Tensor>(1);
auto pads_tensor_dims = pads_tensor.Shape().GetDims();
ORT_ENFORCE(pads_tensor.IsDataType<int64_t>(),
"Pads tensor should be an INT64 tensor");
ORT_ENFORCE(pads_tensor.IsDataType<int64_t>(), "Pads tensor should be an INT64 tensor");
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ORT_ENFORCE(pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1),
"Pads tensor should be a 1D tensor of shape [2 * input_rank] "
"or a 2D tensor of shape [1, 2 * input_rank]");

"Pads tensor should be a 1D tensor of shape [2 * num_axes] "
"or a 2D tensor of shape [1, 2 * num_axes]");
const int64_t* pads_tensor_raw_data = pads_tensor.Data<int64_t>();
size_t pads_size = static_cast<size_t>(pads_tensor.Shape().Size());
ORT_ENFORCE(pads_size == 2 * data_rank,
"Pads tensor size should be equal to twice the input dimension count ");

pads.reserve(2 * data_rank);
for (size_t i = 0; i < pads_size; ++i) {
pads.push_back(pads_tensor_raw_data[i]);

const Tensor* axes_tensor = ctx->Input<Tensor>(3);
if (axes_tensor) {
ORT_ENFORCE(axes_tensor->IsDataType<int32_t>() || axes_tensor->IsDataType<int64_t>(), "Axes tensor should be an INT32 or INT64 tensor");
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auto axes_tensor_dims = axes_tensor->Shape().GetDims();
ORT_ENFORCE(axes_tensor_dims.size() == 1, "Axes tensor should be a 1D tensor ");
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I know some of the code is old here and it has established a practice that it's ok to use ORT_ENFORCE in a Compute method. Since Compute already returns a Status we should try to return a Status with INVALID_ARGUMENT code.

int64_t axes_size = axes_tensor_dims[0];
ORT_ENFORCE((int64_t)pads_size == 2 * axes_size, "Pads tensor size should be [2 * num_axes] ");

pads.resize(2 * data_rank, 0);
if (axes_tensor->IsDataType<int32_t>()) {
const int32_t* axes_tensor_raw_data = axes_tensor->Data<int32_t>();
ComputePadWithAxes<int32_t>(
{pads_tensor_raw_data, onnxruntime::narrow<size_t>(2 * axes_size)},
{axes_tensor_raw_data, onnxruntime::narrow<size_t>(axes_size)},
data_rank,
pads);
} else if(axes_tensor->IsDataType<int64_t>()) {
const int64_t* axes_tensor_raw_data = axes_tensor->Data<int64_t>();
ComputePadWithAxes<int64_t>(
{pads_tensor_raw_data, onnxruntime::narrow<size_t>(2 * axes_size)},
{axes_tensor_raw_data, onnxruntime::narrow<size_t>(axes_size)},
data_rank,
pads);
}
} else {
ORT_ENFORCE(pads_size == 2 * data_rank,
"Pads tensor size should be equal to twice the input dimension count ");
for (size_t i = 0; i < pads_size; ++i) {
pads.push_back(pads_tensor_raw_data[i]);
}
}

// Separate out any negative pads into the slices array
Expand Down Expand Up @@ -525,6 +592,7 @@ Status Pad::Compute(OpKernelContext* ctx) const {
ORT_THROW("Unsupported input data type of ", data_type);
}
}

pads_to_use = &pads;
slices_to_use = &slices;
} else {
Expand Down
23 changes: 23 additions & 0 deletions onnxruntime/test/providers/cpu/tensor/pad_test.cc
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Expand Up @@ -805,5 +805,28 @@ TEST(PadOpTest, BoolType) {
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider});
}

TEST(PadOpTest, ConstantPadAxes) {
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Can we please add more tests?

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The feature added to Pad(18) is with pad axes input. One new test case was added in ONNX for this new feature. Here the similar unit test is added to test the new feature.

OpTester test("Pad", 18);
test.AddAttribute("mode", "constant");
test.AddInput<int32_t>("data", {1, 2, 2, 2},
{
1, 1,
1, 1,
1, 1,
1, 1});
test.AddInput<int64_t>("pads", {4}, {0, 1, 0, 1});
test.AddInput<int32_t>("value", {1}, {0});
test.AddInput<int32_t>("axes", {2}, {1, 3});
test.AddOutput<int32_t>("output", {1, 2, 2, 4},
{
0, 1, 1, 0,
0, 1, 1, 0,
0, 1, 1, 0,
0, 1, 1, 0
}
);
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kTensorrtExecutionProvider});
}

} // namespace test
} // namespace onnxruntime
5 changes: 0 additions & 5 deletions onnxruntime/test/testdata/onnx_backend_test_series_filters.jsonc
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Expand Up @@ -119,14 +119,9 @@
"^test_add_uint8_cuda",
"^test_roialign_aligned_*",
"^test_bitwise_*",
"^test_center_crop_pad_*",
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"^test_clip_default_int8_max_expanded_cpu",
"^test_clip_default_int8_min_expanded_cpu",
"^test_col2im_*",
"^test_constant_pad_axes_cpu",
"^test_constant_pad_cpu",
"^test_edge_pad_cpu",
"^test_reflect_pad_cpu",
"^test_scatter_elements_*",
"^test_softplus_example_expanded_cpu",
"^test_softplus_expanded_cpu",
Expand Down