Add fp16xq4 matmul sm80 cuda kernel to ORT operator

And to use graph transformer as prepack

cmake/onnxruntime_optimizer.cmake

@@ -111,6 +111,10 @@ onnxruntime_add_static_library(onnxruntime_optimizer ${onnxruntime_optimizer_src
 
 onnxruntime_add_include_to_target(onnxruntime_optimizer onnxruntime_common onnxruntime_framework onnx onnx_proto ${PROTOBUF_LIB} flatbuffers::flatbuffers Boost::mp11 safeint_interface)
 target_include_directories(onnxruntime_optimizer PRIVATE ${ONNXRUNTIME_ROOT})
+
+# using optimizer as cuda prepacking, so extra headers are needed
+target_include_directories(onnxruntime_optimizer PRIVATE ${ONNXRUNTIME_ROOT}/core/mickey)
+
 if (onnxruntime_ENABLE_TRAINING)
   target_include_directories(onnxruntime_optimizer PRIVATE ${ORTTRAINING_ROOT})
   onnxruntime_add_include_to_target(onnxruntime_optimizer nlohmann_json::nlohmann_json)

cmake/onnxruntime_providers_cuda.cmake

@@ -217,6 +217,7 @@
 
     include(cutlass)
     target_include_directories(${target} PRIVATE ${cutlass_SOURCE_DIR}/include ${cutlass_SOURCE_DIR}/examples ${cutlass_SOURCE_DIR}/tools/util/include)
+    target_include_directories(${target} PRIVATE ${ONNXRUNTIME_ROOT}/core/mickey)
 
     target_include_directories(${target} PRIVATE ${ONNXRUNTIME_ROOT} ${CMAKE_CURRENT_BINARY_DIR}  ${eigen_INCLUDE_DIRS} ${TVM_INCLUDES}
      PUBLIC ${CUDAToolkit_INCLUDE_DIRS})

cmake/onnxruntime_unittests.cmake

@@ -272,6 +272,13 @@ if(NOT onnxruntime_MINIMAL_BUILD AND NOT onnxruntime_REDUCED_OPS_BUILD)
     "${TEST_SRC_DIR}/optimizer/*.h"
     )
 
+  if (MSVC AND ((onnxruntime_target_platform STREQUAL "ARM64") OR (onnxruntime_target_platform STREQUAL "ARM64EC")))
+    set_source_files_properties("${TEST_SRC_DIR}/optimizer/graph_transform_test.cc" PROPERTIES COMPILE_FLAGS "/bigobj")
+    list(REMOVE_ITEM onnxruntime_test_optimizer_src
+        "${TEST_SRC_DIR}/optimizer/gpu_op_prepack_test.cc"
+    )
+  endif()
+
   set(onnxruntime_test_framework_src_patterns
     "${TEST_SRC_DIR}/framework/*.cc"
     "${TEST_SRC_DIR}/framework/*.h"

docs/ContribOperators.md

@@ -2915,6 +2915,14 @@ This version of the operator has been available since version 1 of the 'com.micr
 <dd>number of bits used for weight quantization (default 4)</dd>
 <dt><tt>block_size</tt> : int (required)</dt>
 <dd>number of groupsize used for weight quantization,(default 128). It needs to be a power of 2 and not smaller than 16.</dd>
+<dt><tt>column_wise_blocking</tt> : int</dt>
+<dd>whether to quantize weight columnwise (value 1, default), or rowwise (value 0)</dd>
+<dt><tt>prepacked</tt> : int</dt>
+<dd>
+Indicates whether the weight matrix is prepacked (value 1), or not (value 0, default).
+This property should NEVER be set by user. It is set by ONNX Runtime internally during
+model loading time.
+</dd>
 </dl>
 
 #### Inputs (3 - 6)

include/onnxruntime/core/optimizer/graph_transformer_utils.h

@@ -22,6 +22,7 @@
 
 namespace onnxruntime {
 class IExecutionProvider;
+class ExecutionProviders;
 
 namespace optimizer_utils {
 
@@ -48,7 +49,7 @@ std::unique_ptr<RuleBasedGraphTransformer> GenerateRuleBasedGraphTransformer(
 InlinedVector<std::unique_ptr<GraphTransformer>> GenerateTransformers(
     TransformerLevel level,
     const SessionOptions& session_options,
-    const IExecutionProvider& execution_provider /*required by constant folding*/,
+    const ExecutionProviders& execution_providers /* cpu ep required by constant folding*/,
     const InlinedHashSet<std::string>& rules_and_transformers_to_disable = {});
 
 #endif  // !defined(ORT_MINIMAL_BUILD)
@@ -77,7 +78,7 @@ InlinedVector<std::unique_ptr<GraphTransformer>> GenerateTransformersForMinimalB
     TransformerLevel level,
     const SessionOptions& session_options,
     const SatApplyContextVariant& apply_context,
-    const IExecutionProvider& cpu_execution_provider,
+    const ExecutionProviders& execution_providers,
     const InlinedHashSet<std::string>& rules_and_transformers_to_disable = {});
 
 #endif  // !defined(ORT_MINIMAL_BUILD) || defined(ORT_EXTENDED_MINIMAL_BUILD)

onnxruntime/contrib_ops/cuda/quantization/matmul_nbits.cc

@@ -14,24 +14,44 @@
 namespace onnxruntime {
 namespace contrib {
 namespace cuda {
-using namespace onnxruntime::cuda;
+
+#ifndef USE_ROCM
+template <>
+Status MatMulNBits<MLFloat16>::PrepackedGemm(
+    cudaStream_t stream,
+    int M,
+    const Tensor* a,
+    const Tensor* b,
+    const Tensor* scales,
+    const Tensor* zero_points,
+    Tensor* Y) const {
+  uint8_t const* zero_points_ptr = nullptr;
+  size_t zero_points_size = 0;
+  if (zero_points != nullptr) {
+    zero_points_ptr = zero_points->Data<uint8_t>();
+    zero_points_size = zero_points->Shape().Size();
+  }
+
+  return blkq4_fp16_gemm_sm80_dispatch<MLFloat16>(
+      int(block_size_), column_wise_quant_blk_, int(M), int(N_), int(K_), stream,
+      a->Data<MLFloat16>(), a->Shape().Size(),
+      b->Data<uint8_t>(), b->Shape().Size(),
+      scales->Data<MLFloat16>(), scales->Shape().Size(),
+      zero_points_ptr, zero_points_size,
+      Y->MutableData<MLFloat16>(), Y->Shape().Size());
+}
+#endif  // !USE_ROCM
 
 template <typename T>
 Status MatMulNBits<T>::ComputeInternal(OpKernelContext* ctx) const {
+  using CudaT = typename ToCudaType<T>::MappedType;
+
   const Tensor* a = ctx->Input<Tensor>(0);
   const Tensor* b = ctx->Input<Tensor>(1);
   const Tensor* scales = ctx->Input<Tensor>(2);
   const Tensor* zero_points = ctx->Input<Tensor>(3);
   const Tensor* reorder_idx = ctx->Input<Tensor>(4);
 
-  const auto* a_data = a->Data<T>();
-  const uint8_t* blob_data = b->Data<uint8_t>();
-  const auto* scales_data = scales->Data<T>();
-  const auto* zero_points_data = zero_points == nullptr ? nullptr : zero_points->DataRaw();
-  const auto* reorder_idx_data = reorder_idx == nullptr ? nullptr : reorder_idx->Data<int32_t>();
-
-  typedef typename ToCudaType<T>::MappedType CudaT;
-
   constexpr bool transa = false;
   constexpr bool transb = true;
   MatMulComputeHelper helper;
@@ -43,6 +63,26 @@ Status MatMulNBits<T>::ComputeInternal(OpKernelContext* ctx) const {
   // Bail out early if the output is going to be empty
   if (Y->Shape().Size() == 0) return Status::OK();
 
+  if (prepack_ > 0) {
+#ifndef USE_ROCM
+    ORT_RETURN_IF(reorder_idx != nullptr,
+                  "Internal Error: Prepacked gemm does not support reorder index. Fix the prepacking logic!");
+    ORT_RETURN_IF(zero_points != nullptr && zero_points->IsDataType<T>(),
+                  "Internal Error: Prepacked gemm does not support zero points of type T. Fix the prepacking logic!");
+    return PrepackedGemm(
+        static_cast<cudaStream_t>(ctx->GetComputeStream()->GetHandle()),
+        static_cast<int>(helper.M()), a, b, scales, zero_points, Y);
+#else
+    ORT_RETURN_IF(true, "Prepacked gemm is not supported for MatMulNBits op.");
+#endif  // !USE_ROCM
+  }
+
+  const auto* a_data = a->Data<T>();
+  const uint8_t* blob_data = b->Data<uint8_t>();
+  const auto* scales_data = scales->Data<T>();
+  const auto* zero_points_data = zero_points == nullptr ? nullptr : zero_points->DataRaw();
+  const auto* reorder_idx_data = reorder_idx == nullptr ? nullptr : reorder_idx->Data<int32_t>();
+
   bool is_4bit_done = (reorder_idx_data == nullptr) &&
                       (!zero_points || !zero_points->IsDataType<T>()) &&
                       TryMatMul4Bits(