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[WebNN EP] Remove NHWC preferred layout
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Currently WebNN CPU backend has supported NCHW layout in Chromium,
we can now drop NHWC preferred layout for CPU backend in WebNN EP
to simplify the code.
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@Honry
Honry committed Jul 31, 2024
1 parent 1d4b161 commit 5a09614
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Showing 10 changed files with 38 additions and 295 deletions.
21 changes: 9 additions & 12 deletions onnxruntime/core/providers/webnn/builders/impl/builder_utils.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -19,10 +19,9 @@ common::Status ComputeConvPads(const std::vector<int64_t> input_shape,
const std::vector<int64_t>& onnx_strides,
const std::vector<int64_t>& onnx_dilations,
AutoPadType auto_pad_type,
std::vector<int64_t>& pads_out,
bool use_nchw) {
const int64_t input_size_y = use_nchw ? input_shape[2] : input_shape[1];
const int64_t input_size_x = use_nchw ? input_shape[3] : input_shape[2];
std::vector<int64_t>& pads_out) {
const int64_t input_size_y = input_shape[2];
const int64_t input_size_x = input_shape[3];
const int64_t stride_y = onnx_strides[0];
const int64_t stride_x = onnx_strides[1];
const int64_t dilation_y = onnx_dilations[0];
Expand Down Expand Up @@ -54,16 +53,15 @@ common::Status HandleAutoPad(const std::vector<int64_t> input_shape,
const std::vector<int64_t>& onnx_strides,
const std::vector<int64_t>& onnx_dilations,
AutoPadType auto_pad_type,
std::vector<int64_t>& pads_out,
bool use_nchw) {
std::vector<int64_t>& pads_out) {
if (AutoPadType::SAME_UPPER == auto_pad_type) {
ORT_RETURN_IF_ERROR(ComputeConvPads(input_shape, weight_size_y, weight_size_x,
onnx_pads, onnx_strides, onnx_dilations,
AutoPadType::SAME_UPPER, pads_out, use_nchw));
AutoPadType::SAME_UPPER, pads_out));
} else {
ORT_RETURN_IF_ERROR(ComputeConvPads(input_shape, weight_size_y, weight_size_x,
onnx_pads, onnx_strides, onnx_dilations,
AutoPadType::SAME_LOWER, pads_out, use_nchw));
AutoPadType::SAME_LOWER, pads_out));
}
return Status::OK();
}
Expand Down Expand Up @@ -111,10 +109,9 @@ common::Status ComputeConvTransposePadsAndOutputShape(const std::vector<int64_t>
const std::vector<int64_t>& onnx_output_padding,
AutoPadType auto_pad_type,
std::vector<int64_t>& pads_out,
std::vector<int64_t>& output_shape_out,
bool use_nchw) {
const int64_t input_size_y = use_nchw ? input_shape[2] : input_shape[1];
const int64_t input_size_x = use_nchw ? input_shape[3] : input_shape[2];
std::vector<int64_t>& output_shape_out) {
const int64_t input_size_y = input_shape[2];
const int64_t input_size_x = input_shape[3];
const int64_t stride_y = onnx_strides[0];
const int64_t stride_x = onnx_strides[1];
const int64_t dilation_y = onnx_dilations[0];
Expand Down
6 changes: 2 additions & 4 deletions onnxruntime/core/providers/webnn/builders/impl/builder_utils.h
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Original file line number Diff line number Diff line change
Expand Up @@ -21,8 +21,7 @@ common::Status HandleAutoPad(const std::vector<int64_t> input_shape,
const std::vector<int64_t>& onnx_strides,
const std::vector<int64_t>& onnx_dilations,
AutoPadType auto_pad_type,
std::vector<int64_t>& pads_out,
bool use_nchw) ORT_MUST_USE_RESULT;
std::vector<int64_t>& pads_out) ORT_MUST_USE_RESULT;

// Compute pads and output shape for ConvTranspose.
common::Status ComputeConvTransposePadsAndOutputShape(const std::vector<int64_t> input_shape,
Expand All @@ -34,8 +33,7 @@ common::Status ComputeConvTransposePadsAndOutputShape(const std::vector<int64_t>
const std::vector<int64_t>& onnx_output_padding,
AutoPadType auto_pad_type,
std::vector<int64_t>& pads_out,
std::vector<int64_t>& output_shape_out,
bool use_nchw) ORT_MUST_USE_RESULT;
std::vector<int64_t>& output_shape_out) ORT_MUST_USE_RESULT;

} // namespace webnn
} // namespace onnxruntime
171 changes: 12 additions & 159 deletions onnxruntime/core/providers/webnn/builders/impl/conv_op_builder.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -18,9 +18,6 @@ namespace webnn {

class ConvOpBuilder : public BaseOpBuilder {
// Add operator related.
public:
void AddInitializersToSkip(ModelBuilder& model_builder, const Node& node) const override;

private:
Status AddToModelBuilderImpl(ModelBuilder& model_builder, const Node& node,
const logging::Logger& logger) const override ORT_MUST_USE_RESULT;
Expand All @@ -33,13 +30,6 @@ class ConvOpBuilder : public BaseOpBuilder {
const logging::Logger& logger) const override;
};

void ConvOpBuilder::AddInitializersToSkip(ModelBuilder& model_builder, const Node& node) const {
// skip the weight for conv as we need to transpose for preferred layout NHWC.
if (model_builder.GetPreferredLayout() == DataLayout::NHWC) {
model_builder.AddInitializerToSkip(node.InputDefs()[1]->Name()); // W
}
}

// Helper functions
common::Status SetConvBaseOptions(ModelBuilder& model_builder,
const Node& node, emscripten::val& options,
Expand All @@ -48,7 +38,6 @@ common::Status SetConvBaseOptions(ModelBuilder& model_builder,
const std::vector<int64_t>& strides,
const std::vector<int64_t>& dilations,
std::vector<int64_t>& pads,
const bool is_nhwc,
const bool is_conv1d,
const logging::Logger& logger) {
NodeAttrHelper helper(node);
Expand All @@ -61,7 +50,7 @@ common::Status SetConvBaseOptions(ModelBuilder& model_builder,
// Calculate explicit padding for autoPad.
if (AutoPadType::SAME_UPPER == auto_pad_type || AutoPadType::SAME_LOWER == auto_pad_type) {
ORT_RETURN_IF_ERROR(HandleAutoPad(input_shape, weight_shape[2], weight_shape[3],
pads, strides, dilations, auto_pad_type, pads_out, !is_nhwc));
pads, strides, dilations, auto_pad_type, pads_out));
pads = pads_out;
}
} else if (node.OpType() == "ConvTranspose") {
Expand All @@ -82,7 +71,7 @@ common::Status SetConvBaseOptions(ModelBuilder& model_builder,
// Otherwise compute the output shape, as well as the pads if the auto_pad attribute is SAME_UPPER/SAME_LOWER.
ORT_RETURN_IF_ERROR(ComputeConvTransposePadsAndOutputShape(input_shape, weight_shape[2], weight_shape[3],
pads, strides, dilations, output_padding,
auto_pad_type, pads_out, output_shape, !is_nhwc));
auto_pad_type, pads_out, output_shape));

if (output_shape[0] != -1 && output_shape[1] != -1) {
options.set("outputSizes", emscripten::val::array(GetVecUint32FromVecInt64(output_shape)));
Expand Down Expand Up @@ -111,89 +100,6 @@ common::Status SetConvBaseOptions(ModelBuilder& model_builder,
return Status::OK();
}

// Both depthwise Conv and ConvTranspose share the same logic to add the layout.
Status AddInitializerInNewLayout(ModelBuilder& model_builder,
const std::string& name,
bool is_conv,
bool is_conv1d) {
const auto& tensor = *model_builder.GetInitializerTensors().at(name);
auto data_type = tensor.data_type();

const auto& shape = tensor.dims();
std::vector<uint32_t> dims = GetVecUint32FromVecInt64(std::vector<int64_t>(std::begin(shape), std::end(shape)));

if (is_conv1d) {
// Support conv1d by prepending a 1 size dimension.
dims.push_back(1);
}

const uint8_t* src = nullptr;
Initializer unpacked_tensor(tensor, model_builder.GetGraphViewer().ModelPath());
src = unpacked_tensor.DataAsByteSpan().data();
const auto out_t = dims[0], in_t = dims[1],
h_t = dims[2], w_t = dims[3];
std::vector<uint32_t> dest_shape;
if (is_conv == 1)
dest_shape = {out_t, h_t, w_t, in_t}; // L_0231
else
dest_shape = {in_t, h_t, w_t, out_t}; // L_1230 for depthwise conv and convTranspose weight

SafeInt<size_t> num_elements = SafeInt<size_t>(Product(dest_shape));

size_t element_size{0};
switch (data_type) {
case ONNX_NAMESPACE::TensorProto_DataType_UINT8:
element_size = sizeof(uint8_t);
break;
case ONNX_NAMESPACE::TensorProto_DataType_INT8:
element_size = sizeof(int8_t);
break;
case ONNX_NAMESPACE::TensorProto_DataType_FLOAT16:
element_size = sizeof(uint16_t);
break;
case ONNX_NAMESPACE::TensorProto_DataType_FLOAT:
element_size = sizeof(float);
break;
default:
break;
}
std::unique_ptr<uint8_t[]> buffer_holder(new uint8_t[element_size * num_elements]);
uint8_t* buffer = buffer_holder.get();

for (uint32_t out = 0; out < out_t; out++) {
for (uint32_t in = 0; in < in_t; in++) {
for (uint32_t h = 0; h < h_t; h++) {
for (uint32_t w = 0; w < w_t; w++) {
auto onnx_idx = out * in_t * h_t * w_t +
in * h_t * w_t +
h * w_t +
w;

uint32_t nnapi_idx;
if (is_conv == 1) { // L_0231
nnapi_idx = out * h_t * w_t * in_t +
h * w_t * in_t +
w * in_t +
in;
} else { // L_1230 for depthwise conv weight
nnapi_idx = in * h_t * w_t * out_t +
h * w_t * out_t +
w * out_t +
out;
}

for (size_t i = 0; i < element_size; i++) {
buffer[element_size * nnapi_idx + i] = src[element_size * onnx_idx + i];
}
}
}
}
}
ORT_RETURN_IF_ERROR(model_builder.AddOperandFromPersistMemoryBuffer(name, buffer, num_elements * element_size,
dest_shape, data_type));
return Status::OK();
}

// Add operator related.

Status ConvOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const Node& node,
Expand All @@ -203,7 +109,6 @@ Status ConvOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const N
const auto& op_type = node.OpType();
emscripten::val input = model_builder.GetOperand(input_defs[0]->Name());
emscripten::val output = emscripten::val::object();
const auto& initializers(model_builder.GetInitializerTensors());

std::vector<int64_t> input_shape;
ORT_RETURN_IF_NOT(GetShape(*input_defs[0], input_shape, logger), "Cannot get input shape");
Expand All @@ -216,19 +121,11 @@ Status ConvOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const N
auto dilations = helper.Get("dilations", std::vector<int64_t>{1, 1});
auto pads = helper.Get("pads", std::vector<int64_t>{0, 0, 0, 0});

const bool is_nhwc = model_builder.GetPreferredLayout() == DataLayout::NHWC;
const bool is_conv1d = input_shape.size() == 3 && weight_shape.size() == 3;
const bool is_constant_weight = Contains(initializers, weight_name);
// Support conv1d by prepending a 1 or 2 size dimensions.
if (is_conv1d) {
// Reshape input.
if (is_nhwc) {
// For NHWC preferred layout, the input has been transposed.
// For conv1d it is NCD1 -> ND1C, so we need to prepend 1 to the index 2.
input_shape.insert(input_shape.begin() + 2, 1);
} else {
input_shape.push_back(1);
}
input_shape.push_back(1);
std::vector<uint32_t> new_shape = GetVecUint32FromVecInt64(input_shape);
input = model_builder.GetBuilder().call<emscripten::val>("reshape", input, emscripten::val::array(new_shape));

Expand All @@ -244,63 +141,19 @@ Status ConvOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const N
emscripten::val options = emscripten::val::object();
options.set("label", node.Name());
ORT_RETURN_IF_ERROR(SetConvBaseOptions(
model_builder, node, options, input_shape, weight_shape, strides, dilations, pads, is_nhwc, is_conv1d, logger));
bool depthwise = false;
if (op_type == "Conv" || op_type == "ConvInteger") {
int groups = options["groups"].as<int>();
if (is_nhwc) {
depthwise = (groups == input_shape[3] && groups != 1);
options.set("inputLayout", emscripten::val("nhwc"));
if (is_constant_weight) {
ORT_RETURN_IF_ERROR(AddInitializerInNewLayout(model_builder, weight_name, !depthwise, is_conv1d));
}
if (!depthwise) {
options.set("filterLayout", emscripten::val("ohwi"));
} else {
options.set("filterLayout", emscripten::val("ihwo"));
}
}
} else { // ConvTranspose
if (is_nhwc) {
options.set("inputLayout", emscripten::val("nhwc"));
options.set("filterLayout", emscripten::val("ohwi"));
if (is_constant_weight) {
ORT_RETURN_IF_ERROR(AddInitializerInNewLayout(model_builder, weight_name, true, is_conv1d));
}
}
}

model_builder, node, options, input_shape, weight_shape, strides, dilations, pads, is_conv1d, logger));
emscripten::val filter = model_builder.GetOperand(weight_name);

if (is_conv1d) {
// Reshape weight to 4D for conv1d.
if (!is_nhwc || !is_constant_weight) {
// The weight_shape has been appended 1's, reshape weight operand.
std::vector<uint32_t> new_shape = GetVecUint32FromVecInt64(weight_shape);
emscripten::val reshape_options = emscripten::val::object();
reshape_options.set("label", node.Name() + "_reshape_filter");
filter = model_builder.GetBuilder().call<emscripten::val>("reshape",
filter,
emscripten::val::array(new_shape),
reshape_options);
}
}

emscripten::val transpose_options = emscripten::val::object();
if (is_nhwc && !is_constant_weight) {
// For NHWC preferred layout, if the weight is input:
// - Transpose it from iohw -> ohwi for convTranspose.
// - Transpose it from oihw -> ihwo for depthwise conv.
// - Transpose it from oihw -> ohwi for conv.
std::vector<uint32_t> perm(4);
if (op_type == "ConvTranspose" || depthwise) {
perm = {1, 2, 3, 0}; // L_1230 for depthwise conv and convTranspose weight
} else {
perm = {0, 2, 3, 1}; // L_0231
}
transpose_options.set("permutation", emscripten::val::array(perm));
transpose_options.set("label", node.Name() + "_transpose_filter");
filter = model_builder.GetBuilder().call<emscripten::val>("transpose", filter, transpose_options);
// The weight_shape has been appended 1's, reshape weight operand.
std::vector<uint32_t> new_shape = GetVecUint32FromVecInt64(weight_shape);
emscripten::val reshape_options = emscripten::val::object();
reshape_options.set("label", node.Name() + "_reshape_filter");
filter = model_builder.GetBuilder().call<emscripten::val>("reshape",
filter,
emscripten::val::array(new_shape),
reshape_options);
}

if (op_type == "Conv") {
Expand Down
9 changes: 1 addition & 8 deletions onnxruntime/core/providers/webnn/builders/impl/normalization_op_builder.cc
View file
Original file line number Diff line number Diff line change
Expand Up @@ -79,9 +79,6 @@ Status NormalizationOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder
ORT_RETURN_IF_NOT(input_defs.size() == 5, "BatchNormalization requires five inputs.");
emscripten::val mean = model_builder.GetOperand(input_defs[3]->Name());
emscripten::val variance = model_builder.GetOperand(input_defs[4]->Name());
if (model_builder.GetPreferredLayout() == DataLayout::NHWC) {
options.set("axis", rank - 1);
}

output = model_builder.GetBuilder().call<emscripten::val>("batchNormalization", input, mean, variance, options);
} else if (op_type == "LayerNormalization") {
Expand All @@ -104,9 +101,8 @@ Status NormalizationOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder
std::back_inserter(new_shape),
[](int64_t dim) -> uint32_t { return SafeInt<uint32_t>(dim); });

size_t insertion_offset = (model_builder.GetPreferredLayout() == DataLayout::NHWC) ? 2 : 3;
ptrdiff_t excess_rank = new_shape.size() - webnn_shape_rank;
auto insertion_point = new_shape.begin() + insertion_offset;
auto insertion_point = new_shape.begin() + 3;
if (input_shape.size() < webnn_shape_rank) {
// Pad the shape with extra 1's to satisfy WebNN v1's rank requirements.
new_shape.insert(insertion_point, -excess_rank, 1);
Expand All @@ -125,9 +121,6 @@ Status NormalizationOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder
reshape_input_options);
}

if (model_builder.GetPreferredLayout() == DataLayout::NHWC) {
options.set("layout", emscripten::val("nhwc"));
}
output = model_builder.GetBuilder().call<emscripten::val>("instanceNormalization", input, options);
// Reshape back to the original output shape for 3D input.
if (input_shape.size() != 4) {
Expand Down
9 changes: 2 additions & 7 deletions onnxruntime/core/providers/webnn/builders/impl/pool_op_builder.cc
View file
Original file line number Diff line number Diff line change
Expand Up @@ -70,11 +70,7 @@ Status PoolOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
options.set("strides", emscripten::val::array(strides));
const auto dilations = helper.Get("dilations", std::vector<int32_t>{1, 1});
options.set("dilations", emscripten::val::array(dilations));
if (model_builder.GetPreferredLayout() == DataLayout::NHWC) {
options.set("layout", emscripten::val("nhwc"));
} else {
options.set("layout", emscripten::val("nchw"));
}
options.set("layout", emscripten::val("nchw"));

// Add Padding.
// Usually using autopadding is more efficient than using explicit padding.
Expand All @@ -93,8 +89,7 @@ Status PoolOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
helper.Get("strides", std::vector<int64_t>{1, 1}),
helper.Get("dilations", std::vector<int64_t>{1, 1}),
auto_pad_type,
pads_out,
model_builder.GetPreferredLayout() == DataLayout::NCHW));
pads_out));
pads = GetVecUint32FromVecInt64(pads_out);
}
// Permute the ONNX's pads, which is [beginning_height, beginning_width, ending_height, ending_width],
Expand Down
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