[Native WebGPU EP] Add packedQKV and do_rotary attribute support to GroupQueryAttention operator (#23386)

### Description
Add Packed QKV inputs and do_rotary attribute to GQA.



### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
Packed QKV inputs and do_rotary attribute are required for certain
models.

Author: satyajandhyala

onnxruntime/contrib_ops/webgpu/bert/group_query_attention.cc

@@ -5,6 +5,7 @@
 #include "contrib_ops/webgpu/bert/attention_common.h"
 #include "contrib_ops/webgpu/bert/group_query_attention.h"
 #include "contrib_ops/webgpu/webgpu_contrib_kernels.h"
+#include "contrib_ops/webgpu/bert/rotary_embedding.h"
 #include "contrib_ops/webgpu/bert/flash_attention.h"
 
 #include "core/providers/webgpu/webgpu_supported_types.h"
@@ -30,6 +31,117 @@ ONNX_OPERATOR_KERNEL_EX(
         .InputMemoryType(OrtMemTypeCPUInput, 6),
     GroupQueryAttention);
 
+Status SplitPackedQKVProgram::GenerateShaderCode(ShaderHelper& sh) const {
+  const auto& packed_qkv = sh.AddInput("packed_qkv", ShaderUsage::UseOffsetToIndices | ShaderUsage::UseUniform);
+  const auto& query = sh.AddOutput("query", ShaderUsage::UseSetByIndices | ShaderUsage::UseUniform);
+  const auto& key = sh.AddOutput("key", ShaderUsage::UseSetByIndices | ShaderUsage::UseUniform);
+  const auto& value = sh.AddOutput("val", ShaderUsage::UseSetByIndices | ShaderUsage::UseUniform);
+  sh.MainFunctionBody() << "  let packed_qkv_indices = " << packed_qkv.OffsetToIndices("global_idx") << ";\n"
+                        << "  let input_data = " << packed_qkv.GetByOffset("global_idx") << ";\n"
+                        << "  let index = " << packed_qkv.IndicesGet("packed_qkv_indices", "2") << ";\n"
+                        << "  if (index < uniforms.hidden_size) {\n"
+                        << "    " << query.SetByIndices("packed_qkv_indices", "input_data") << ";\n"
+                        << "  } else if (index < (uniforms.hidden_size + uniforms.kv_hidden_size)) {\n"
+                        << "    var key_indices = packed_qkv_indices;\n"
+                        << "   " << key.IndicesSet("key_indices", "2", "u32(index - uniforms.hidden_size)") << ";\n"
+                        << "   " << key.SetByIndices("key_indices", "input_data") << ";\n"
+                        << "  } else {\n"
+                        << "    var val_indices = packed_qkv_indices;\n"
+                        << "   " << value.IndicesSet("val_indices", "2", "u32(index - uniforms.hidden_size - uniforms.kv_hidden_size)") << ";\n"
+                        << "   " << value.SetByIndices("val_indices", "input_data") << ";\n"
+                        << "  }";
+  return Status::OK();
+}
+
+Status SplitPackedQKV(onnxruntime::webgpu::ComputeContext& context, const WebgpuAttentionParameters& params, const Tensor* packedQKV, Tensor* query, Tensor* key, Tensor* val) {
+  SplitPackedQKVProgram program;
+  auto input_size = packedQKV->Shape().Size();
+  program
+      .AddInput({packedQKV, ProgramTensorMetadataDependency::Rank})
+      .AddOutputs({{query, ProgramTensorMetadataDependency::Rank}, {key, ProgramTensorMetadataDependency::Rank}, {val, ProgramTensorMetadataDependency::Rank}})
+      .AddUniformVariables({
+          {static_cast<uint32_t>(params.hidden_size_)},
+          {static_cast<uint32_t>(params.kv_hidden_size_)},
+      })
+      .SetDispatchGroupSize((input_size + WORKGROUP_SIZE - 1) / WORKGROUP_SIZE);
+  return context.RunProgram(program);
+}
+
+Status GeneratePositionIDsProgram::GenerateShaderCode(ShaderHelper& sh) const {
+  const auto& output = sh.AddOutput("output", ShaderUsage::UseUniform);
+  const auto& seqlens = sh.AddInput("seqlens", ShaderUsage::UseUniform);
+  sh.MainFunctionBody() << "  var pos_id: i32 = 0;\n"
+                        << "  let batch_idx = global_idx / uniforms.sequence_length;\n"
+                        << "  let sequence_idx = i32(global_idx % uniforms.sequence_length);\n"
+                        << "  let seqlen = " << seqlens.GetByOffset("batch_idx") << ";\n";
+  if (is_first_prompt_) {
+    sh.MainFunctionBody() << "  let total_seqlen = seqlen + 1;\n"
+                          << "  if (sequence_idx < total_seqlen) {\n"
+                          << "    pos_id = sequence_idx;\n"
+                          << "  } else {\n"
+                          << "    pos_id = 1;\n"
+                          << "  }\n"
+                          << "  " << output.SetByOffset("global_idx", "pos_id") << "\n";
+  } else if (is_subsequent_prompt_) {
+    sh.MainFunctionBody() << "  let total_seqlen = seqlen + 1;\n"
+                          << "  let past_seqlen = total_seqlen - i32(uniforms.sequence_length);\n"
+                          << "  if (past_seqlen + sequence_idx < total_seqlen) {\n"
+                          << "    pos_id = past_seqlen + sequence_idx;\n"
+                          << "  } else {\n"
+                          << "    pos_id = 1;\n"
+                          << "  }\n"
+                          << "  " << output.SetByOffset("global_idx", "pos_id") << "\n";
+  } else {
+    sh.MainFunctionBody() << "  if (global_idx < uniforms.batch_size) {\n"
+                          << "    " << output.SetByOffset("global_idx", "seqlen") << "\n"
+                          << "  }\n";
+  }
+  return Status::OK();
+}
+
+Status GeneratePositionIDs(onnxruntime::webgpu::ComputeContext& context, const WebgpuAttentionParameters& params, const Tensor* seqlens, Tensor* output_tensor) {
+  GeneratePositionIDsProgram program(params.is_first_prompt_, params.is_subsequent_prompt_);
+  auto output_size = params.batch_size_ * params.sequence_length_;
+  program.CacheHint(params.is_first_prompt_, params.is_subsequent_prompt_)
+      .AddInput({seqlens, ProgramTensorMetadataDependency::Rank})
+      .AddOutput({output_tensor, ProgramTensorMetadataDependency::Rank})
+      .AddUniformVariables({{static_cast<uint32_t>(params.batch_size_)}, {static_cast<uint32_t>(params.sequence_length_)}})
+      .SetDispatchGroupSize((output_size + WORKGROUP_SIZE - 1) / WORKGROUP_SIZE);
+  return context.RunProgram(program);
+}
+
+Status RunRotaryEmbedding(onnxruntime::webgpu::ComputeContext& context, const WebgpuAttentionParameters& params, const Tensor* input, const Tensor* pos_ids, const Tensor* cos_cache, const Tensor* sin_cache, Tensor* output, bool is_query_input) {
+  const auto half_rotary_embedding_dim = gsl::narrow_cast<uint32_t>(cos_cache->Shape()[1]);
+  const auto head_size = params.head_size_;
+  const auto hidden_size = is_query_input ? params.hidden_size_ : params.kv_hidden_size_;
+  const TensorShape global_shape({params.batch_size_, params.sequence_length_, hidden_size / head_size, static_cast<int64_t>(head_size - half_rotary_embedding_dim)});
+  const auto rank = global_shape.NumDimensions();
+  std::vector<uint32_t> global_dims(rank);
+  std::vector<uint32_t> global_strides(rank);
+  for (size_t j = 0; j < rank; ++j) {
+    global_dims[j] = gsl::narrow_cast<uint32_t>(global_shape[j]);
+    global_strides[j] = gsl::narrow_cast<uint32_t>(global_shape.SizeFromDimension(j + 1));
+  }
+  const auto input_output_strides = std::vector<uint32_t>({gsl::narrow_cast<uint32_t>(input->Shape().SizeFromDimension(1)), gsl::narrow_cast<uint32_t>(hidden_size), gsl::narrow_cast<uint32_t>(head_size), 1});
+  const auto output_size = gsl::narrow_cast<const uint32_t>(global_shape.Size());
+
+  RotaryEmbeddingProgram program(params.rotary_interleaved_);
+  program
+      .CacheHint(params.rotary_interleaved_)
+      .AddInputs({{input, ProgramTensorMetadataDependency::Rank},
+                  {pos_ids, ProgramTensorMetadataDependency::Rank},
+                  {cos_cache, ProgramTensorMetadataDependency::Rank},
+                  {sin_cache, ProgramTensorMetadataDependency::Rank}})
+      .AddOutput(output)
+      .SetDispatchGroupSize((output_size + WORKGROUP_SIZE - 1) / WORKGROUP_SIZE)
+      .AddUniformVariables({{params.scale_},
+                            {gsl::make_span(global_dims)},
+                            {gsl::make_span(global_strides)},
+                            {gsl::make_span(input_output_strides)}})
+      .AddIndices(TensorShape{1, 1});
+  return context.RunProgram(program);
+}
+
 Status GroupQueryAttention::ComputeInternal(onnxruntime::webgpu::ComputeContext& context) const {
   const Tensor* query = context.Input<Tensor>(0);
   const Tensor* key = context.Input<Tensor>(1);
@@ -41,7 +153,7 @@ Status GroupQueryAttention::ComputeInternal(onnxruntime::webgpu::ComputeContext&
   const Tensor* cos_cache = context.Input<Tensor>(7);
   const Tensor* sin_cache = context.Input<Tensor>(8);
 
-  GroupQueryAttentionParameters params;
+  GroupQueryAttentionParameters params = {};
   ORT_RETURN_IF_ERROR(group_query_attention_helper::CheckInputs(query,
                                                                 key,
                                                                 value,
@@ -57,9 +169,6 @@ Status GroupQueryAttention::ComputeInternal(onnxruntime::webgpu::ComputeContext&
                                                                 scale_,
                                                                 softcap_));
   WebgpuAttentionParameters parameters(params);
-  if (parameters.is_packed_qkv_) {
-    ORT_NOT_IMPLEMENTED("Packed QKV of shape (B, L, N, 3, H) not implemented for webgpu-ep.");
-  }
   TensorShapeVector output_shape(3);
   output_shape[0] = static_cast<int64_t>(parameters.batch_size_);
   output_shape[1] = static_cast<int64_t>(parameters.sequence_length_);
@@ -75,11 +184,39 @@ Status GroupQueryAttention::ComputeInternal(onnxruntime::webgpu::ComputeContext&
   Tensor* present_value = context.Output(2, present_kv_shape);
   parameters.past_present_share_buffer_ = present_key != nullptr && present_value != nullptr && past_key != nullptr && past_value != nullptr && past_key->DataRaw() == present_key->DataRaw() && past_value->DataRaw() == present_value->DataRaw();
 
-  if (CanApplyFlashAttention(nullptr /* bias */, present_key, present_value, parameters, context)) {
+  if (!do_rotary_ && CanApplyFlashAttention(nullptr /* bias */, present_key, present_value, parameters, context)) {
     return ApplyFlashAttention(query, key, value, nullptr /* attention_bias */, output, past_key, present_key, past_value,
                                present_value, parameters, context);
   }
 
+  Tensor qSplit;
+  Tensor kSplit;
+  Tensor vSplit;
+  if (parameters.is_packed_qkv_) {
+    qSplit = context.CreateGPUTensor(query->DataType(), TensorShape({parameters.batch_size_, parameters.sequence_length_, parameters.hidden_size_}));
+    kSplit = context.CreateGPUTensor(query->DataType(), TensorShape({parameters.batch_size_, parameters.sequence_length_, parameters.kv_hidden_size_}));
+    vSplit = context.CreateGPUTensor(query->DataType(), TensorShape({parameters.batch_size_, parameters.sequence_length_, parameters.kv_hidden_size_}));
+    ORT_RETURN_IF_ERROR(SplitPackedQKV(context, parameters, query, &qSplit, &kSplit, &vSplit));
+    parameters.is_packed_qkv_ = false;
+    query = &qSplit;
+    key = &kSplit;
+    value = &vSplit;
+  }
+
+  Tensor qRotary;
+  Tensor kRotary;
+  if (do_rotary_) {
+    qRotary = context.CreateGPUTensor(query->DataType(), query->Shape());
+    kRotary = context.CreateGPUTensor(key->DataType(), key->Shape());
+    auto pos_ids_shape = TensorShape({parameters.batch_size_, parameters.sequence_length_});
+    Tensor pos_ids = context.CreateGPUTensor(DataTypeImpl::GetType<int64_t>(), pos_ids_shape);
+    ORT_RETURN_IF_ERROR(GeneratePositionIDs(context, parameters, seqlen_k, &pos_ids));
+    ORT_RETURN_IF_ERROR(RunRotaryEmbedding(context, parameters, query, &pos_ids, cos_cache, sin_cache, &qRotary, /* is_query_input = */ true));
+    ORT_RETURN_IF_ERROR(RunRotaryEmbedding(context, parameters, key, &pos_ids, cos_cache, sin_cache, &kRotary, /* is_query_input = */ false));
+    query = &qRotary;
+    key = &kRotary;
+  }
+
   TensorShapeVector q_new_dims({parameters.batch_size_, parameters.num_heads_,
                                 parameters.sequence_length_, parameters.head_size_});
   TensorShape q_new_shape(q_new_dims);

onnxruntime/contrib_ops/webgpu/bert/group_query_attention.h

@@ -14,6 +14,29 @@ namespace webgpu {
 
 using namespace onnxruntime::webgpu;
 
+class GeneratePositionIDsProgram final : public Program<GeneratePositionIDsProgram> {
+ public:
+  GeneratePositionIDsProgram(bool is_first_prompt, bool is_subsequent_prompt) : Program{"GeneratePositionIDs"}, is_first_prompt_(is_first_prompt), is_subsequent_prompt_(is_subsequent_prompt) {}
+
+  Status GenerateShaderCode(ShaderHelper& sh) const override;
+
+  WEBGPU_PROGRAM_DEFINE_UNIFORM_VARIABLES({"batch_size", ProgramUniformVariableDataType::Uint32}, {"sequence_length", ProgramUniformVariableDataType::Uint32});
+
+ private:
+  bool is_first_prompt_;
+  bool is_subsequent_prompt_;
+};
+
+class SplitPackedQKVProgram final : public Program<SplitPackedQKVProgram> {
+ public:
+  SplitPackedQKVProgram() : Program{"SplitPackedQKV"} {}
+
+  Status GenerateShaderCode(ShaderHelper& sh) const override;
+
+  WEBGPU_PROGRAM_DEFINE_UNIFORM_VARIABLES({"hidden_size", ProgramUniformVariableDataType::Uint32},
+                                          {"kv_hidden_size", ProgramUniformVariableDataType::Uint32});
+};
+
 class GroupQueryAttention final : public WebGpuKernel {
  public:
   GroupQueryAttention(const OpKernelInfo& info) : WebGpuKernel(info) {