diff --git a/onnxruntime/core/providers/webnn/builders/impl/builder_utils.cc b/onnxruntime/core/providers/webnn/builders/impl/builder_utils.cc index 113cc3df5438d..594e75042f2ae 100644 --- a/onnxruntime/core/providers/webnn/builders/impl/builder_utils.cc +++ b/onnxruntime/core/providers/webnn/builders/impl/builder_utils.cc @@ -19,10 +19,9 @@ common::Status ComputeConvPads(const std::vector input_shape, const std::vector& onnx_strides, const std::vector& onnx_dilations, AutoPadType auto_pad_type, - std::vector& 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& 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]; @@ -54,16 +53,15 @@ common::Status HandleAutoPad(const std::vector input_shape, const std::vector& onnx_strides, const std::vector& onnx_dilations, AutoPadType auto_pad_type, - std::vector& pads_out, - bool use_nchw) { + std::vector& 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(); } @@ -111,10 +109,9 @@ common::Status ComputeConvTransposePadsAndOutputShape(const std::vector const std::vector& onnx_output_padding, AutoPadType auto_pad_type, std::vector& pads_out, - std::vector& 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& 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]; diff --git a/onnxruntime/core/providers/webnn/builders/impl/builder_utils.h b/onnxruntime/core/providers/webnn/builders/impl/builder_utils.h index 5a156c96c4852..f9f9746d6ed83 100644 --- a/onnxruntime/core/providers/webnn/builders/impl/builder_utils.h +++ b/onnxruntime/core/providers/webnn/builders/impl/builder_utils.h @@ -21,8 +21,7 @@ common::Status HandleAutoPad(const std::vector input_shape, const std::vector& onnx_strides, const std::vector& onnx_dilations, AutoPadType auto_pad_type, - std::vector& pads_out, - bool use_nchw) ORT_MUST_USE_RESULT; + std::vector& pads_out) ORT_MUST_USE_RESULT; // Compute pads and output shape for ConvTranspose. common::Status ComputeConvTransposePadsAndOutputShape(const std::vector input_shape, @@ -34,8 +33,7 @@ common::Status ComputeConvTransposePadsAndOutputShape(const std::vector const std::vector& onnx_output_padding, AutoPadType auto_pad_type, std::vector& pads_out, - std::vector& output_shape_out, - bool use_nchw) ORT_MUST_USE_RESULT; + std::vector& output_shape_out) ORT_MUST_USE_RESULT; } // namespace webnn } // namespace onnxruntime diff --git a/onnxruntime/core/providers/webnn/builders/impl/conv_op_builder.cc b/onnxruntime/core/providers/webnn/builders/impl/conv_op_builder.cc index 22049d2519712..9285c9ad684f3 100644 --- a/onnxruntime/core/providers/webnn/builders/impl/conv_op_builder.cc +++ b/onnxruntime/core/providers/webnn/builders/impl/conv_op_builder.cc @@ -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; @@ -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, @@ -48,7 +38,6 @@ common::Status SetConvBaseOptions(ModelBuilder& model_builder, const std::vector& strides, const std::vector& dilations, std::vector& pads, - const bool is_nhwc, const bool is_conv1d, const logging::Logger& logger) { NodeAttrHelper helper(node); @@ -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") { @@ -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))); @@ -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 dims = GetVecUint32FromVecInt64(std::vector(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 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 num_elements = SafeInt(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 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, @@ -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 input_shape; ORT_RETURN_IF_NOT(GetShape(*input_defs[0], input_shape, logger), "Cannot get input shape"); @@ -216,19 +121,11 @@ Status ConvOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, const N auto dilations = helper.Get("dilations", std::vector{1, 1}); auto pads = helper.Get("pads", std::vector{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 new_shape = GetVecUint32FromVecInt64(input_shape); input = model_builder.GetBuilder().call("reshape", input, emscripten::val::array(new_shape)); @@ -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(); - 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 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("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 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("transpose", filter, transpose_options); + // The weight_shape has been appended 1's, reshape weight operand. + std::vector 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("reshape", + filter, + emscripten::val::array(new_shape), + reshape_options); } if (op_type == "Conv") { diff --git a/onnxruntime/core/providers/webnn/builders/impl/normalization_op_builder.cc b/onnxruntime/core/providers/webnn/builders/impl/normalization_op_builder.cc index 4d068baf35e72..347cd11898d25 100644 --- a/onnxruntime/core/providers/webnn/builders/impl/normalization_op_builder.cc +++ b/onnxruntime/core/providers/webnn/builders/impl/normalization_op_builder.cc @@ -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("batchNormalization", input, mean, variance, options); } else if (op_type == "LayerNormalization") { @@ -104,9 +101,8 @@ Status NormalizationOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder std::back_inserter(new_shape), [](int64_t dim) -> uint32_t { return SafeInt(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); @@ -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("instanceNormalization", input, options); // Reshape back to the original output shape for 3D input. if (input_shape.size() != 4) { diff --git a/onnxruntime/core/providers/webnn/builders/impl/pool_op_builder.cc b/onnxruntime/core/providers/webnn/builders/impl/pool_op_builder.cc index 0af62dacedbd5..09eb8e79ce1d3 100644 --- a/onnxruntime/core/providers/webnn/builders/impl/pool_op_builder.cc +++ b/onnxruntime/core/providers/webnn/builders/impl/pool_op_builder.cc @@ -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{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. @@ -93,8 +89,7 @@ Status PoolOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, helper.Get("strides", std::vector{1, 1}), helper.Get("dilations", std::vector{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], diff --git a/onnxruntime/core/providers/webnn/builders/impl/resize_op_builder.cc b/onnxruntime/core/providers/webnn/builders/impl/resize_op_builder.cc index 2218c858951d3..0e211de5a3986 100644 --- a/onnxruntime/core/providers/webnn/builders/impl/resize_op_builder.cc +++ b/onnxruntime/core/providers/webnn/builders/impl/resize_op_builder.cc @@ -120,18 +120,10 @@ Status ResizeOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, std::vector scales; std::vector sizes; - std::vector scales_hw; - std::vector sizes_hw; - std::vector axes; std::string scales_name = GetTensorName(input_defs, 2); - const bool is_nhwc = model_builder.GetPreferredLayout() == DataLayout::NHWC; if (!scales_name.empty()) { // Use scales. ORT_RETURN_IF_NOT(GetResizeScales(initializers, node, scales, logger), "Error getting resize scales"); - if (is_nhwc) { - scales_hw = {scales[1], scales[2]}; - } else { - scales_hw = {scales[2], scales[3]}; - } + std::vector scales_hw = {scales[2], scales[3]}; options.set("scales", emscripten::val::array(scales_hw)); } else { // Use sizes, we already checked inputs in IsOpSupportedImpl. std::vector output_sizes; @@ -140,19 +132,11 @@ Status ResizeOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder, std::transform(output_sizes.cbegin(), output_sizes.cend(), std::back_inserter(sizes), [](int64_t dim) -> int32_t { return SafeInt(dim); }); - if (is_nhwc) { - sizes_hw = {sizes[1], sizes[2]}; - } else { - sizes_hw = {sizes[2], sizes[3]}; - } + std::vector sizes_hw = {sizes[2], sizes[3]}; options.set("sizes", emscripten::val::array(sizes_hw)); } - if (is_nhwc) { - axes = {1, 2}; - } else { - axes = {2, 3}; - } + std::vector axes = {2, 3}; options.set("axes", emscripten::val::array(axes)); emscripten::val input = model_builder.GetOperand(input_defs[0]->Name()); @@ -221,7 +205,6 @@ bool ResizeOpBuilder::IsOpSupportedImpl(const InitializedTensorSet& initializers return false; } - const bool is_nhwc = node.Domain() == kMSInternalNHWCDomain; // We want to check if the scales or sizes are not trying to resize on N/C channels here. if (has_scales) { // We are using scales. std::vector scales; @@ -229,7 +212,7 @@ bool ResizeOpBuilder::IsOpSupportedImpl(const InitializedTensorSet& initializers return false; float scale_n = scales[0]; - float scale_c = is_nhwc ? scales[3] : scales[1]; + float scale_c = scales[1]; if (scale_n != 1.0f || scale_c != 1.0f) { LOGS(logger, VERBOSE) << "Scales of N/C channel should be 1" << "Resize of N/C channels are not supported" @@ -239,8 +222,8 @@ bool ResizeOpBuilder::IsOpSupportedImpl(const InitializedTensorSet& initializers // For now we only support upscale, so the scale_h and scale_w should be an integer >= 1. // TODO support ResizeBilinear. - float scale_h = is_nhwc ? scales[1] : scales[2]; - float scale_w = is_nhwc ? scales[2] : scales[3]; + float scale_h = scales[2]; + float scale_w = scales[3]; // Onnx spec requires scale to be a positive float, so we are not checking that here. if (roundf(scale_h) != scale_h) { @@ -261,12 +244,11 @@ bool ResizeOpBuilder::IsOpSupportedImpl(const InitializedTensorSet& initializers return false; auto output_size_n = output_sizes[0]; - const int c_idx = is_nhwc ? 3 : 1; - if (output_size_n != input_shape[0] || output_sizes[c_idx] != input_shape[c_idx]) { + if (output_size_n != input_shape[0] || output_sizes[1] != input_shape[1]) { LOGS(logger, VERBOSE) << "Output sizes of N/C chanel should match the input sizes, " << "Resize of N/C channels are not supported" << ", input_size_n, " << input_shape[0] << ", output_size_n, " << output_size_n - << ". input_size_c, " << input_shape[c_idx] << ", output_size_c, " << output_sizes[c_idx]; + << ". input_size_c, " << input_shape[1] << ", output_size_c, " << output_sizes[1]; return false; } } diff --git a/onnxruntime/core/providers/webnn/builders/model_builder.cc b/onnxruntime/core/providers/webnn/builders/model_builder.cc index 6b0e1495f552d..9cc140483cfa1 100644 --- a/onnxruntime/core/providers/webnn/builders/model_builder.cc +++ b/onnxruntime/core/providers/webnn/builders/model_builder.cc @@ -21,12 +21,11 @@ namespace webnn { ModelBuilder::ModelBuilder(const GraphViewer& graph_viewer, const logging::Logger& logger, const emscripten::val& context, const emscripten::val& builder, - const DataLayout preferred_layout, const WebnnDeviceType wnn_device_type) + const WebnnDeviceType wnn_device_type) : graph_viewer_(graph_viewer), logger_(logger), wnn_context_(context), wnn_builder_(builder), - preferred_layout_(preferred_layout), wnn_device_type_(wnn_device_type) {} Status ModelBuilder::Initialize() { @@ -255,64 +254,6 @@ Status ModelBuilder::AddOperations() { return Status::OK(); } -Status ModelBuilder::AddOperandFromPersistMemoryBuffer( - const std::string& name, const void* buffer, const size_t size, - const std::vector shape, const int32_t data_type) { - auto persist_buffer = std::make_unique(size); - uint8_t* dest = persist_buffer.get(); - memcpy(dest, buffer, size); - emscripten::val view = emscripten::val::undefined(); - emscripten::val desc = emscripten::val::object(); - ORT_RETURN_IF_NOT(SetWebnnDataType(desc, data_type), "Unsupported data type"); - switch (data_type) { - case ONNX_NAMESPACE::TensorProto_DataType_BOOL: - case ONNX_NAMESPACE::TensorProto_DataType_UINT8: - view = emscripten::val{emscripten::typed_memory_view(size / sizeof(uint8_t), - reinterpret_cast(dest))}; - break; - case ONNX_NAMESPACE::TensorProto_DataType_INT8: - view = emscripten::val{emscripten::typed_memory_view(size / sizeof(int8_t), - reinterpret_cast(dest))}; - break; - case ONNX_NAMESPACE::TensorProto_DataType_FLOAT16: - view = emscripten::val{emscripten::typed_memory_view(size / sizeof(uint16_t), - reinterpret_cast(dest))}; - break; - case ONNX_NAMESPACE::TensorProto_DataType_FLOAT: - view = emscripten::val{emscripten::typed_memory_view(size / sizeof(float), - reinterpret_cast(dest))}; - break; - case ONNX_NAMESPACE::TensorProto_DataType_INT32: - view = emscripten::val{emscripten::typed_memory_view(size / sizeof(int32_t), - reinterpret_cast(dest))}; - break; - case ONNX_NAMESPACE::TensorProto_DataType_INT64: - view = emscripten::val{emscripten::typed_memory_view(size / sizeof(int64_t), - reinterpret_cast(dest))}; - break; - case ONNX_NAMESPACE::TensorProto_DataType_UINT32: - view = emscripten::val{emscripten::typed_memory_view(size / sizeof(uint32_t), - reinterpret_cast(dest))}; - break; - case ONNX_NAMESPACE::TensorProto_DataType_UINT64: - view = emscripten::val{emscripten::typed_memory_view(size / sizeof(uint64_t), - reinterpret_cast(dest))}; - break; - default: - break; - } - - desc.set("dimensions", emscripten::val::array(shape)); - emscripten::val operand = emscripten::val::object(); - // Wasm memory grow will cause all array buffers reallocation, which will be treated as detached - // buffers in JS side. Simply create a copy to fix it. - operand = wnn_builder_.call("constant", desc, view.call("slice")); - - AddOperand(name, operand); - mem_persist_buffers_.push_back(std::move(persist_buffer)); - return Status::OK(); -} - Status ModelBuilder::RegisterModelOutputs() { for (const auto* node_arg : graph_viewer_.GetOutputs()) { ORT_RETURN_IF_ERROR(RegisterModelInputOutput(*node_arg, false /* is_input */)); diff --git a/onnxruntime/core/providers/webnn/builders/model_builder.h b/onnxruntime/core/providers/webnn/builders/model_builder.h index 6a1688f16d2a6..297b505e4e2da 100644 --- a/onnxruntime/core/providers/webnn/builders/model_builder.h +++ b/onnxruntime/core/providers/webnn/builders/model_builder.h @@ -23,7 +23,7 @@ class ModelBuilder { public: ModelBuilder(const GraphViewer& graph_viewer, const logging::Logger& logger, const emscripten::val& context, const emscripten::val& builder, - const DataLayout preferred_layout, const WebnnDeviceType wnn_device_type); + const WebnnDeviceType wnn_device_type); ~ModelBuilder() = default; Status Compile(std::unique_ptr& model) ORT_MUST_USE_RESULT; @@ -37,15 +37,6 @@ class ModelBuilder { const emscripten::val& GetOperand(const std::string& name) const { return wnn_operands_.at(name); } void AddOperand(const std::string& name, const emscripten::val& operand); const emscripten::val& GetZeroConstant(const std::string& data_type); - // Use the buffers to persist WebNN allocated data like transposed weight. - // It ensures the validity during inference session. - std::vector> mem_persist_buffers_; - // Add a constant operand (allocate persist buffer and move the ownership to mem_persist_buffers_). - Status AddOperandFromPersistMemoryBuffer( - const std::string& name, const void* buffer, - const size_t size, const std::vector shape, const int32_t data_type); - - DataLayout GetPreferredLayout() const { return preferred_layout_; } WebnnDeviceType GetWebnnDeviceType() const { return wnn_device_type_; } @@ -64,7 +55,6 @@ class ModelBuilder { emscripten::val wnn_context_ = emscripten::val::object(); emscripten::val wnn_builder_ = emscripten::val::object(); - DataLayout preferred_layout_; WebnnDeviceType wnn_device_type_; InlinedHashMap wnn_operands_; std::vector input_names_; diff --git a/onnxruntime/core/providers/webnn/webnn_execution_provider.cc b/onnxruntime/core/providers/webnn/webnn_execution_provider.cc index 0da0dfc6dfb26..d8a7e88c72d33 100644 --- a/onnxruntime/core/providers/webnn/webnn_execution_provider.cc +++ b/onnxruntime/core/providers/webnn/webnn_execution_provider.cc @@ -19,12 +19,10 @@ namespace onnxruntime { WebNNExecutionProvider::WebNNExecutionProvider(const std::string& webnn_device_flags) : IExecutionProvider{onnxruntime::kWebNNExecutionProvider} { - // WebNN EP uses NHWC layout for CPU XNNPACK backend and NCHW for GPU DML backend. + // WebNN EP uses default NCHW layout for all backends. if (webnn_device_flags.compare("cpu") == 0) { - preferred_layout_ = DataLayout::NHWC; wnn_device_type_ = webnn::WebnnDeviceType::CPU; } else { - preferred_layout_ = DataLayout::NCHW; if (webnn_device_flags.compare("gpu") == 0) { wnn_device_type_ = webnn::WebnnDeviceType::GPU; } else if (webnn_device_flags.compare("npu") == 0) { @@ -217,8 +215,7 @@ common::Status WebNNExecutionProvider::Compile(const std::vector model; ORT_RETURN_IF_ERROR(builder.Compile(model)); // Build map from input name to its index in input definitions. diff --git a/onnxruntime/core/providers/webnn/webnn_execution_provider.h b/onnxruntime/core/providers/webnn/webnn_execution_provider.h index 83d530efed141..8cabdc3ec40f9 100644 --- a/onnxruntime/core/providers/webnn/webnn_execution_provider.h +++ b/onnxruntime/core/providers/webnn/webnn_execution_provider.h @@ -26,8 +26,6 @@ class WebNNExecutionProvider : public IExecutionProvider { GetCapability(const onnxruntime::GraphViewer& graph_viewer, const IKernelLookup& /*kernel_registries*/) const override; - DataLayout GetPreferredLayout() const override { return preferred_layout_; } - // We implement the Compile that takes FusedNodeAndGraph instances. FusionStyle GetFusionStyle() const override { return FusionStyle::FilteredGraphViewer; } @@ -45,7 +43,6 @@ class WebNNExecutionProvider : public IExecutionProvider { emscripten::val wnn_context_ = emscripten::val::undefined(); mutable emscripten::val wnn_builder_ = emscripten::val::undefined(); - DataLayout preferred_layout_; webnn::WebnnDeviceType wnn_device_type_; InlinedHashMap> models_; ModelMetadefIdGenerator metadef_id_generator_;