Make release_v2.9 compile pass

transformer_engine/common/include/transformer_engine/gemm.h

@@ -130,7 +130,7 @@ void nvte_cublas_gemm(const NVTETensor A, const NVTETensor B, NVTETensor D, cons
  */
 void nvte_cublas_gemm_v2(int transa, int transb, const float *alpha, const NVTETensor A,
                          const NVTETensor B, const float *beta, const NVTETensor C, NVTETensor D,
-                         NVTETensor workspace, NVTEMatmulConfig config, cudaStream_t stream, bool nvte_use_hipblaslt, bool nvte_use_rocblas, int compute_stream_offset);
+                         NVTETensor workspace, NVTEMatmulConfig config, cudaStream_t stream, bool nvte_use_hipblaslt = 0, bool nvte_use_rocblas = 0, int compute_stream_offset = 0);
 
 /*! \brief Compute matrix multiplication of 2 matrices, potentially fused with other operations,
  * allowing for using a scaling factor for the GEMM result and the accumulation input (deprecated)

transformer_engine/common/swizzle/swizzle_block_scaling.cu

@@ -14,7 +14,7 @@
 #include "../util/logging.h"
 #include "transformer_engine/transformer_engine.h"
 
-#ifdef __HIP_PLATFORM_AMD__
+#ifndef __HIP_PLATFORM_AMD__
 namespace transformer_engine {
 namespace {
 constexpr uint32_t WARP_SIZE = 32;

transformer_engine/pytorch/csrc/util.cpp

@@ -228,7 +228,7 @@ at::Tensor convert_block_scaling_to_mxfp8_tensor(transformer_engine::TensorWrapp
   // Allocate memory for swizzled mxfp8 scaling factors
   const auto options = at::TensorOptions().dtype(torch::kByte).device(torch::kCUDA);
   at::Tensor swizzled_scale_inv = at::empty(
-      std::vector<int64_t>{swizzled_scale_inv_first_dim, swizzled_scale_inv_last_dim}, options);
+      std::vector<int64_t>{static_cast<int64_t>(swizzled_scale_inv_first_dim), static_cast<int64_t>(swizzled_scale_inv_last_dim)}, options);
   // Set rowwise scaling factors on output
   void* const swizzled_scale_inv_dptr = getDataPtr(swizzled_scale_inv, 0);
   NVTEShape swizzled_scale_inv_shape{};