/*************************************************************************
 * Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 *
 * See LICENSE for license information.
 ************************************************************************/

#ifndef __HIP_PLATFORM_AMD__
#include <cublasLt.h>
#include <cublas_v2.h>
#include <cuda.h>
#else
#include <iostream>
#include "hipblas_gemm.h"
#include "rocm_gemm.hip"
#endif // #ifndef __HIP_PLATFORM_AMD__
#include <transformer_engine/gemm.h>
#include <transformer_engine/multi_stream.h>
#include <transformer_engine/recipe.h>
#include <transformer_engine/transformer_engine.h>

#include <algorithm>
#include <cstdint>
#include <mutex>
#include <vector>

#include "../common.h"
#include "../util/cuda_runtime.h"
#include "../util/handle_manager.h"
#include "../util/logging.h"
#include "../util/multi_stream.h"
#include "./config.h"
#ifndef __HIP_PLATFORM_AMD__
#include "./cutlass_grouped_gemm.cuh"
#endif

#ifndef __HIP_PLATFORM_AMD__
namespace {

/* Use CUDA const memory to store scalar 1 and 0 for cublas usage
*/
__device__ __constant__ float one_device;
__device__ __constant__ float zero_device;

inline float *GetScalarOne() {
  static std::once_flag init_flag;
  std::call_once(init_flag, []() {
    float one = 1.0f;
    NVTE_CHECK_CUDA(cudaMemcpyToSymbol(one_device, &one, sizeof(float)));
  });
  // return address by cudaGetSymbolAddress
  float *dev_ptr;
  NVTE_CHECK_CUDA(cudaGetSymbolAddress(reinterpret_cast<void **>(&dev_ptr), one_device));
  return dev_ptr;
}

inline float *GetScalarZero() {
  static std::once_flag init_flag;
  std::call_once(init_flag, []() {
    float zero = 0.0f;
    NVTE_CHECK_CUDA(cudaMemcpyToSymbol(zero_device, &zero, sizeof(float)));
  });
  // return address by cudaGetSymbolAddress
  float *dev_ptr;
  NVTE_CHECK_CUDA(cudaGetSymbolAddress(reinterpret_cast<void **>(&dev_ptr), zero_device));
  return dev_ptr;
}

__global__ __launch_bounds__(1) void set_float_kernel(float *ptr, float val) { *ptr = val; }

uint32_t _getAlignment(uintptr_t address) {
  // alignment are in bytes
  uint32_t alignment = 256;
  for (;; alignment /= 2) {
    if (address % alignment == 0) {
      return alignment;
    }
  }
}

inline void CreateCublasHandle(cublasLtHandle_t *handle) {
  NVTE_CHECK_CUBLAS(cublasLtCreate(handle));
}

/* Parameters for cuBLAS GEMM
 *
 * cuBLAS follows the BLAS convention of column-major ordering. This
 * is different than the row-major that is typically used in
 * Transformer Engine.
 *
 */
struct GemmParam {
  void *A = nullptr;
  void *B = nullptr;
  cublasOperation_t transA = CUBLAS_OP_N;
  cublasOperation_t transB = CUBLAS_OP_N;
  transformer_engine::DType Atype = transformer_engine::DType::kNumTypes;
  transformer_engine::DType Btype = transformer_engine::DType::kNumTypes;
  void *A_scale_inv = nullptr;
  void *B_scale_inv = nullptr;
  int lda = 0;  // A column strides
  int ldb = 0;  // B column strides
};

/* Populate parameters for cuBLAS GEMM
 *
 * cuBLAS follows the BLAS convention of column-major ordering. This
 * is different than the row-major that is typically used in
 * Transformer Engine.
 *
 */
GemmParam CanonicalizeGemmInput(const transformer_engine::Tensor &A, const cublasOperation_t transA,
                                const transformer_engine::Tensor &B, const cublasOperation_t transB,
                                int m, int n, int k) {
  using namespace transformer_engine;
  NVTE_CHECK(
      A.scaling_mode == B.scaling_mode ||
          (A.scaling_mode == NVTE_BLOCK_SCALING_1D && B.scaling_mode == NVTE_BLOCK_SCALING_2D) ||
          (A.scaling_mode == NVTE_BLOCK_SCALING_2D && B.scaling_mode == NVTE_BLOCK_SCALING_1D),
      "Inputs A and B to GEMM need to have compatible scaling modes, but got A.scaling_mode = " +
          to_string(A.scaling_mode) + ", B.scaling_mode = " + to_string(B.scaling_mode));
  NVTE_CHECK(A.has_data() || A.has_columnwise_data(), "Input A does not hold any data!");
  NVTE_CHECK(B.has_data() || B.has_columnwise_data(), "Input B does not hold any data!");
  GemmParam ret;

  // Transpose mode with column-major ordering
  bool is_A_transposed = transA == CUBLAS_OP_T;
  bool is_B_transposed = transB == CUBLAS_OP_T;

  // Set conditions for MXFP8 and NVFP4 gemm execution.
  const auto nvfp4 = is_nvfp_scaling(A.scaling_mode) && is_nvfp_scaling(B.scaling_mode);
  const auto mxfp8 = !nvfp4 && is_mxfp_scaling(A.scaling_mode) && is_mxfp_scaling(B.scaling_mode);

  // Configure A matrix
  if (is_tensor_scaling(A.scaling_mode)) {
    // Unscaled or FP8 tensor scaling
    ret.A = A.data.dptr;
    ret.transA = transA;
    ret.Atype = A.data.dtype;
    ret.A_scale_inv = A.scale_inv.dptr;
    ret.lda = is_A_transposed ? k : m;
    if (!nvte_is_non_tn_fp8_gemm_supported() && !is_A_transposed) {
      // Hopper only supports TN GEMMs for FP8. "Column-wise data" is transpose of data.
      if (A.has_columnwise_data() && is_fp8_dtype(A.columnwise_data.dtype)) {
        ret.A = A.columnwise_data.dptr;
        ret.transA = CUBLAS_OP_T;
        ret.Atype = A.columnwise_data.dtype;
        ret.A_scale_inv = A.columnwise_scale_inv.dptr;
        ret.lda = k;
      } else {
        NVTE_CHECK(!is_fp8_dtype(ret.Atype), "Input A is missing column-wise usage");
      }
    } else if (nvte_is_non_tn_fp8_gemm_supported() && !A.has_data()) {
      // Blackwell supports any GEMM layout for FP8, so we can use column-wise/transposed
      // data  with the mirrored transpose-flag if we don't have row-wise data.
      NVTE_CHECK(A.has_columnwise_data() && is_fp8_dtype(A.columnwise_data.dtype),
                 "Input A is missing column-wise usage");
      ret.A = A.columnwise_data.dptr;
      ret.transA = is_A_transposed ? CUBLAS_OP_N : CUBLAS_OP_T;
      ret.Atype = A.columnwise_data.dtype;
      ret.A_scale_inv = A.columnwise_scale_inv.dptr;
      ret.lda = is_A_transposed ? m : k;
    }

    if (is_fp8_dtype(ret.Atype)) {
      // Requirements from https://docs.nvidia.com/cuda/cublas/#tensor-core-usage
      NVTE_CHECK(ret.lda % 16 == 0,
                 "Leading dimension requirement on A for FP8 GEMM. Caller must pad.");
    }
  } else if (nvfp4) {
    // NVFP4 GEMM. Either the pure NVFP4 recipe or the FWD pass of the Hybrid NVFP4/MXFP8 recipe.

    if (is_A_transposed) {
      NVTE_CHECK(A.has_data(), "Input A is missing row-wise usage");
    } else {
      NVTE_CHECK(is_nvfp4_scaling(A.scaling_mode),
                 "Input A has unsupported combination of recipe and layout");
      NVTE_CHECK(A.has_columnwise_data(), "Input A is missing column-wise usage");
    }
    ret.A = is_A_transposed ? A.data.dptr : A.columnwise_data.dptr;
    ret.transA = CUBLAS_OP_T;  // NVFP4 gemm is only supported in TN layout.
    ret.Atype = is_A_transposed ? A.data.dtype : A.columnwise_data.dtype;
    ret.A_scale_inv = is_A_transposed ? A.scale_inv.dptr : A.columnwise_scale_inv.dptr;
    ret.lda = k;
  } else if (mxfp8) {
    // MXFP8 GEMM. Either for pure MXFP8 recipe or backward of Hybrid NVFP4 recipe.
    // Note: Row-wise and column-wise data are scaled along different
    // dimensions (with matrix interpreted in row-major order).

    if (is_A_transposed) {
      NVTE_CHECK(A.has_data(), "Input A is missing row-wise usage");
    } else {
      NVTE_CHECK(A.has_columnwise_data(), "Input A is missing column-wise usage");
    }
    ret.A = is_A_transposed ? A.data.dptr : A.columnwise_data.dptr;
    ret.transA = transA;
    ret.Atype = is_A_transposed ? A.data.dtype : A.columnwise_data.dtype;
    ret.A_scale_inv = is_A_transposed ? A.scale_inv.dptr : A.columnwise_scale_inv.dptr;
    ret.lda = is_A_transposed ? k : m;
  } else if (A.scaling_mode == NVTE_BLOCK_SCALING_1D || A.scaling_mode == NVTE_BLOCK_SCALING_2D) {
    // FP8 block scaling
    // Note: Hopper only supports TN GEMMs for FP8. "Column-wise data" is transpose of data.
    if (is_A_transposed) {
      NVTE_CHECK(A.has_data(), "Input A is missing row-wise usage");
    } else {
      NVTE_CHECK(A.has_columnwise_data(), "Input A is missing column-wise usage");
    }
    ret.A = is_A_transposed ? A.data.dptr : A.columnwise_data.dptr;
    ret.transA = CUBLAS_OP_T;
    ret.Atype = is_A_transposed ? A.data.dtype : A.columnwise_data.dtype;
    ret.A_scale_inv = is_A_transposed ? A.scale_inv.dptr : A.columnwise_scale_inv.dptr;
    ret.lda = k;

    // Requirements from https://docs.nvidia.com/cuda/cublas/#tensor-core-usage
    NVTE_CHECK((ret.lda % 16) == 0,
               "Leading dimension requirement on NVTE_BLOCK_SCALING GEMM. Caller must pad.");
    // Divisibility of 8 derived from FP8 (m * CTypeSize) % 16 == 0 requirement.
    // Smallest supported CType is 2 bytes in this scaling mode.
    NVTE_CHECK((m % 8) == 0,
               "Outer dimension requirement on A for NVTE_BLOCK_SCALING GEMM. Caller must pad.");
  } else {
    NVTE_ERROR("A has unsupported scaling mode");
  }

  // Configure B matrix
  if (is_tensor_scaling(B.scaling_mode)) {
    // Unscaled or FP8 tensor scaling
    ret.B = B.data.dptr;
    ret.transB = transB;
    ret.Btype = B.data.dtype;
    ret.B_scale_inv = B.scale_inv.dptr;
    ret.ldb = is_B_transposed ? n : k;
    if (!nvte_is_non_tn_fp8_gemm_supported() && is_B_transposed) {
      // Hopper only supports TN GEMMs for FP8. "Column-wise data" is transpose of data.
      if (B.has_columnwise_data() && is_fp8_dtype(B.columnwise_data.dtype)) {
        ret.B = B.columnwise_data.dptr;
        ret.transB = CUBLAS_OP_N;
        ret.Btype = B.columnwise_data.dtype;
        ret.B_scale_inv = B.columnwise_scale_inv.dptr;
        ret.ldb = k;
      } else {
        NVTE_CHECK(!is_fp8_dtype(ret.Btype), "Input B is missing column-wise usage");
      }
    } else if (nvte_is_non_tn_fp8_gemm_supported() && !B.has_data()) {
      // Blackwell supports any GEMM layout for FP8, so we can use column-wise/transposed
      // data with the mirrored transpose-flag if we don't have row-wise data.
      NVTE_CHECK(B.has_columnwise_data() && is_fp8_dtype(B.columnwise_data.dtype),
                 "Input B is missing column-wise usage");
      ret.B = B.columnwise_data.dptr;
      ret.transB = is_B_transposed ? CUBLAS_OP_N : CUBLAS_OP_T;
      ret.Btype = B.columnwise_data.dtype;
      ret.B_scale_inv = B.columnwise_scale_inv.dptr;
      ret.ldb = is_B_transposed ? k : n;
    }

    if (is_fp8_dtype(ret.Atype)) {
      // Requirements from https://docs.nvidia.com/cuda/cublas/#tensor-core-usage
      NVTE_CHECK(ret.ldb % 16 == 0,
                 "Leading dimension requirement on B for FP8 GEMM. Caller must pad.");
    }
  } else if (nvfp4) {
    if (is_B_transposed) {
      NVTE_CHECK(is_nvfp4_scaling(B.scaling_mode),
                 "Input B has unsupported combination of recipe and layout");
      NVTE_CHECK(B.has_columnwise_data(), "Input B is missing column-wise usage");
    } else {
      NVTE_CHECK(B.has_data(), "Input B is missing row-wise usage");
    }
    ret.B = is_B_transposed ? B.columnwise_data.dptr : B.data.dptr;
    ret.transB = CUBLAS_OP_N;  // NVFP4 gemm is only supported in TN layout.
    ret.Btype = is_B_transposed ? B.columnwise_data.dtype : B.data.dtype;
    ret.B_scale_inv = is_B_transposed ? B.columnwise_scale_inv.dptr : B.scale_inv.dptr;
    ret.ldb = k;
  } else if (mxfp8) {
    if (is_B_transposed) {
      NVTE_CHECK(B.has_columnwise_data(), "Input B is missing column-wise usage");
    } else {
      NVTE_CHECK(B.has_data(), "Input B is missing row-wise usage");
    }
    ret.B = is_B_transposed ? B.columnwise_data.dptr : B.data.dptr;
    ret.transB = transB;
    ret.Btype = is_B_transposed ? B.columnwise_data.dtype : B.data.dtype;
    ret.B_scale_inv = is_B_transposed ? B.columnwise_scale_inv.dptr : B.scale_inv.dptr;
    ret.ldb = is_B_transposed ? n : k;
  } else if (B.scaling_mode == NVTE_BLOCK_SCALING_1D || B.scaling_mode == NVTE_BLOCK_SCALING_2D) {
    // FP8 block scaling
    // Note: Hopper only supports TN GEMMs for FP8. "Column-wise data" is transpose of data.
    if (is_B_transposed) {
      NVTE_CHECK(B.has_columnwise_data(), "Input B is missing column-wise usage");
    } else {
      NVTE_CHECK(B.has_data(), "Input B is missing row-wise usage");
    }
    ret.B = is_B_transposed ? B.columnwise_data.dptr : B.data.dptr;
    ret.transB = CUBLAS_OP_N;
    ret.Btype = is_B_transposed ? B.columnwise_data.dtype : B.data.dtype;
    ret.B_scale_inv = is_B_transposed ? B.columnwise_scale_inv.dptr : B.scale_inv.dptr;
    ret.ldb = k;

    // Requirements from
    // https://docs.nvidia.com/cuda/cublas/#tensor-core-usage
    NVTE_CHECK((ret.ldb % 16) == 0,
               "B tensor stride requirement on NVTE_BLOCK_SCALING GEMM. Caller must pad.");
    if (B.scaling_mode == NVTE_BLOCK_SCALING_1D) {
      // Observed this requirement only present for B tensor is 1D quantized.
      NVTE_CHECK((n % 8) == 0,
                 "Outer dimension requirement on B for NVTE_BLOCK_SCALING GEMM. Caller must pad.");
    }
  } else {
    NVTE_ERROR("B has unsupported scaling mode");
  }

  return ret;
}

/* cuBLAS version number at run-time */
size_t cublas_version() {
  // Cache version to avoid cuBLAS logging overhead
  static size_t version = cublasLtGetVersion();
  return version;
}

}  // namespace
#endif // __HIP_PLATFORM_AMD__

namespace transformer_engine {
#ifdef __HIP_PLATFORM_AMD__
//Forward declaration. The implementation is in rocm_gemm.cu
void cublas_gemm(const Tensor *inputA, const Tensor *inputB, Tensor *outputD,
                 const Tensor *inputBias, Tensor *outputPreGelu, int m, int n, int k, int lda,
                 int ldb, int ldd, bool transa, bool transb, bool grad,
                 void* workspace, size_t workspaceSize, bool accumulate, bool use_split_accumulator,
                 int math_sm_count, int m_split, int n_split, bool gemm_producer,
                 const Tensor *inputCounter, hipStream_t stream, bool nvte_use_hipblaslt, bool nvte_use_rocblas, int compute_stream_offset);
#else // Use cublasLt
using cublasHandleManager = detail::HandleManager<cublasLtHandle_t, CreateCublasHandle>;
void cublas_gemm(const Tensor *inputA, const Tensor *inputB, Tensor *outputD,
                 const Tensor *inputBias, Tensor *outputPreGelu, cublasOperation_t transa,
                 cublasOperation_t transb, bool grad, void *workspace, size_t workspaceSize,
                 const void *alpha, const void *beta, bool use_split_accumulator, int math_sm_count,
                 int m_split, int n_split, bool gemm_producer, const Tensor *inputCounter,
                 cudaStream_t stream) {
  // Tensor dims in row-major order
  const int A0 = inputA->flat_first_dim();
  const int A1 = inputA->flat_last_dim();
  const int B0 = inputB->flat_first_dim();
  const int B1 = inputB->flat_last_dim();

  // GEMM dims in column-major order
  const int m = transa == CUBLAS_OP_T ? A0 : A1;
  const int n = transb == CUBLAS_OP_T ? B1 : B0;
  const int k = transa == CUBLAS_OP_T ? A1 : A0;
  NVTE_CHECK((transb == CUBLAS_OP_T ? B0 : B1) == k,
             "GEMM inputs have incompatible dimensions (A is ", A0, "x", A1, ", B is ", B0, "x", B1,
             ")");
  const int ldd = m;

  // Return immediately if GEMM is trivial
  if (m <= 0 || n <= 0) {
    return;
  }
  NVTE_CHECK(k > 0);

  const GemmParam param = CanonicalizeGemmInput(*inputA, transa, *inputB, transb, m, n, k);

  void *C = outputD->data.dptr;
  void *D = outputD->data.dptr;
  void *D_scale = outputD->scale.dptr;
  void *D_amax = outputD->amax.dptr;
  void *bias_ptr = inputBias->data.dptr;
  const bool bias = bias_ptr != nullptr;
  void *pre_gelu_out = outputPreGelu->data.dptr;
  void *counter = nullptr;
  if (inputCounter != nullptr) {
    counter = inputCounter->data.dptr;
  }
  const bool gelu = pre_gelu_out != nullptr;
  const bool use_fp8 = is_fp8_dtype(param.Atype) || is_fp8_dtype(param.Btype);
  const bool use_fp4 = is_fp4_dtype(param.Atype) || is_fp4_dtype(param.Btype);

  // Update scaling factors with NVFP4 tensor scales
  // TODO: Check whether scales are on CPU/GPU or add API to control.
  // Currently scales are assumed to be on CPU when amax is provided
  // and on GPU when not provided, but this is brittle.
  if (use_fp4 && (inputA->amax.dptr != nullptr || inputB->amax.dptr != nullptr)) {
    // Reserve some workspace for alpha scale
    NVTE_CHECK(workspaceSize >= 4,
               "NVFP4 GEMM requires at least 4 byte workspace for alpha scale, but only has ",
               workspaceSize, " bytes remaining.");
    workspaceSize = (workspaceSize / 4) * 4 - 4;  // Remove last 4 aligned bytes
    uint8_t *workspace_ptr = reinterpret_cast<uint8_t *>(workspace);
    float *new_alpha_ptr = reinterpret_cast<float *>(&workspace_ptr[workspaceSize]);

    // Update alpha scale on device
    // Note: Compute NVFP4 tensor scales based on amaxes and then
    // divide from alpha scale. This way we only need to apply NVFP4
    // tensor scales in matmul output, instead of in matmul inputs.
    float old_alpha = *reinterpret_cast<const float *>(alpha);  // Assumed to be on CPU
    TensorWrapper new_alpha_tensor(new_alpha_ptr, std::vector<size_t>{1}, DType::kFloat32);
    nvte_nvfp4_compute_per_tensor_scale(inputA->nvte_tensor, transa, inputB->nvte_tensor, !transb,
                                        old_alpha, new_alpha_tensor.data(), stream);
    alpha = new_alpha_ptr;

    // Make sure beta scale is on device
    float old_beta = *reinterpret_cast<const float *>(beta);  // Assumed to be on CPU
    if (old_beta == 0) {
      beta = GetScalarZero();  // Device constant memory
    } else if (old_beta == 1) {
      beta = GetScalarOne();  // Device constant memory
    } else {
      // Move beta to workspace
      NVTE_CHECK(workspaceSize >= 4,
                 "NVFP4 GEMM requires at least 4 byte workspace for beta scale, but only has ",
                 workspaceSize, " bytes remaining.");
      workspaceSize = (workspaceSize / 4) * 4 - 4;  // Remove last 4 aligned bytes
      float *new_beta_ptr = reinterpret_cast<float *>(&workspace_ptr[workspaceSize]);
      set_float_kernel<<<1, 1, 0, stream>>>(new_beta_ptr, old_beta);
      NVTE_CHECK_CUDA(cudaGetLastError());
      beta = new_beta_ptr;
    }
  }

  const cudaDataType_t A_type = get_cuda_dtype(param.Atype);
  const cudaDataType_t B_type = get_cuda_dtype(param.Btype);
  const cudaDataType_t D_type = get_cuda_dtype(outputD->data.dtype);
  const cudaDataType_t bias_type = get_cuda_dtype(inputBias->data.dtype);

  NVTE_CHECK(!is_fp8_dtype(param.Atype) || param.A_scale_inv != nullptr,
             "FP8 input to GEMM requires inverse of scale!");
  NVTE_CHECK(!is_fp8_dtype(param.Btype) || param.B_scale_inv != nullptr,
             "FP8 input to GEMM requires inverse of scale!");
  NVTE_CHECK(!is_fp4_dtype(param.Atype) || param.A_scale_inv != nullptr,
             "FP4 input to GEMM requires inverse of scale!");
  NVTE_CHECK(!is_fp4_dtype(param.Btype) || param.B_scale_inv != nullptr,
             "FP4 input to GEMM requires inverse of scale!");

  // check consistency of arguments:
  // if fp8 is desired, context cannot be null
  // fp8 + gelu fusion + fp8 aux is unavailable right now.
  if ((use_fp8 || use_fp4) && gelu) {
    NVTE_CHECK(!is_fp8_dtype(outputPreGelu->data.dtype),
               "fp8 Aux output for gemm + gelu fusion not supported!");
  }
  if (is_fp4_dtype(outputD->data.dtype)) {
    NVTE_ERROR("FP4 GEMM output is not supported!");
  }
  if (use_fp4 && (D_type == CUDA_R_16F)) {
    NVTE_ERROR("FP4 GEMM does not support FP16 output!");
  }

  cublasLtHandle_t handle = cublasHandleManager::Instance().GetHandle();

  cublasLtMatmulDesc_t operationDesc = nullptr;
  cublasLtMatrixLayout_t Adesc = nullptr, Bdesc = nullptr, Cdesc = nullptr, Ddesc = nullptr;
  cublasLtMatmulPreference_t preference = nullptr;
  int returnedResults = 0;
  cublasLtMatmulHeuristicResult_t heuristicResult = {};
  cublasLtEpilogue_t epilogue = CUBLASLT_EPILOGUE_DEFAULT;

  int64_t ld_gelumat = (int64_t)ldd;

  // Use TF32 only for pure FP32 GEMM.
  cublasComputeType_t gemm_compute_type = CUBLAS_COMPUTE_32F;
  if (A_type == CUDA_R_32F && B_type == CUDA_R_32F && D_type == CUDA_R_32F) {
    gemm_compute_type = CUBLAS_COMPUTE_32F_FAST_TF32;
  }

  // Create matrix descriptors. Not setting any extra attributes.
  NVTE_CHECK_CUBLAS(cublasLtMatrixLayoutCreate(&Adesc, A_type, param.transA == CUBLAS_OP_N ? m : k,
                                               param.transA == CUBLAS_OP_N ? k : m, param.lda));
  NVTE_CHECK_CUBLAS(cublasLtMatrixLayoutCreate(&Bdesc, B_type, param.transB == CUBLAS_OP_N ? k : n,
                                               param.transB == CUBLAS_OP_N ? n : k, param.ldb));

  NVTE_CHECK_CUBLAS(cublasLtMatrixLayoutCreate(&Ddesc, D_type, m, n, ldd));

  NVTE_CHECK_CUBLAS(cublasLtMatmulDescCreate(&operationDesc, gemm_compute_type, CUDA_R_32F));
  NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSA,
                                                   &param.transA, sizeof(param.transA)));
  NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSB,
                                                   &param.transB, sizeof(param.transB)));
  // Set math SM count
  if (math_sm_count != 0) {
    NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(operationDesc,
                                                     CUBLASLT_MATMUL_DESC_SM_COUNT_TARGET,
                                                     &math_sm_count, sizeof(math_sm_count)));
  }

  // set fp8/fp4 attributes -- input and output types should already be set to fp8/fp4
  // as appropriate. Note: gelu fusion isn't available right now, and we don't need
  // amax(D) either (next op is high precision).
  const bool mxfp8_gemm = !use_fp4 && is_mxfp8_scaling(inputA->scaling_mode);

  if (use_fp8 || use_fp4) {
    // Fast accumulation is only supported for FP8.
    const int8_t fastAccuMode = (use_split_accumulator) ? 0 : use_fp8;
    NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_FAST_ACCUM,
                                                     &fastAccuMode, sizeof(fastAccuMode)));

    // Scaling factors.
#if CUBLAS_VERSION >= 120800
    cublasLtMatmulMatrixScale_t scaling_mode_a;
    cublasLtMatmulMatrixScale_t scaling_mode_b;
#endif  // CUBLAS_VERSION >= 120800
    if (is_tensor_scaling(inputA->scaling_mode) && is_tensor_scaling(inputB->scaling_mode)) {
      void *A_scale_inverse = param.A_scale_inv;
      void *B_scale_inverse = param.B_scale_inv;
      NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(operationDesc,
                                                       CUBLASLT_MATMUL_DESC_A_SCALE_POINTER,
                                                       &A_scale_inverse, sizeof(A_scale_inverse)));
      NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(operationDesc,
                                                       CUBLASLT_MATMUL_DESC_B_SCALE_POINTER,
                                                       &B_scale_inverse, sizeof(B_scale_inverse)));
#if CUBLAS_VERSION >= 120800
      scaling_mode_a = CUBLASLT_MATMUL_MATRIX_SCALE_SCALAR_32F;
      scaling_mode_b = CUBLASLT_MATMUL_MATRIX_SCALE_SCALAR_32F;
#endif  // CUBLAS_VERSION >= 120800
    } else if (mxfp8_gemm) {
#if CUBLAS_VERSION >= 120800
      NVTE_CHECK(cublas_version() >= 120800,
                 "MXFP8 requires cuBLAS 12.8+, but run-time cuBLAS version is ", cublas_version());
      fp8e8m0 *A_scale_inverse = reinterpret_cast<fp8e8m0 *>(param.A_scale_inv);
      fp8e8m0 *B_scale_inverse = reinterpret_cast<fp8e8m0 *>(param.B_scale_inv);
      NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(operationDesc,
                                                       CUBLASLT_MATMUL_DESC_A_SCALE_POINTER,
                                                       &A_scale_inverse, sizeof(A_scale_inverse)));
      NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(operationDesc,
                                                       CUBLASLT_MATMUL_DESC_B_SCALE_POINTER,
                                                       &B_scale_inverse, sizeof(B_scale_inverse)));
      scaling_mode_a = CUBLASLT_MATMUL_MATRIX_SCALE_VEC32_UE8M0;
      scaling_mode_b = CUBLASLT_MATMUL_MATRIX_SCALE_VEC32_UE8M0;
      // Workaround for heuristic cache bug in cublasLt. This separates the MXFP8 cache key from non-block scaling.
      // CUBLASLT_MATMUL_DESC_ALPHA_VECTOR_BATCH_STRIDE is unused for block scaling so it's safe to set.
      if (cublas_version() <= 120803) {
        const int64_t dummy_a_vec_stride = 1;
        NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(
            operationDesc, CUBLASLT_MATMUL_DESC_ALPHA_VECTOR_BATCH_STRIDE, &dummy_a_vec_stride,
            sizeof(dummy_a_vec_stride)));
      }
#else
      NVTE_ERROR("MXFP8 requires cuBLAS 12.8+, but compile-time cuBLAS version is ",
                 CUBLAS_VERSION);
#endif                     // CUBLAS_VERSION >= 120800
    } else if (use_fp4) {  // NVFP4 GEMM
#if CUBLAS_VERSION >= 120800
      NVTE_CHECK(cublas_version() >= 120800,
                 "FP4 requires cuBLAS 12.8+, but run-time cuBLAS version is ", cublas_version());
      // make sure alpha beta computation dtype remains fp32 by CUBLASLT_MATMUL_DESC_SCALE_TYPE
      cublasDataType_t scale_type = CUDA_R_32F;
      NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(
          operationDesc, CUBLASLT_MATMUL_DESC_SCALE_TYPE, &scale_type, sizeof(scale_type)));

      // Set pointer mode: alpha and beta are both device pointers
      // https://docs.nvidia.com/cuda/cublas/#cublasltpointermode-t
      cublasLtPointerMode_t pointer_mode = CUBLASLT_POINTER_MODE_DEVICE;
      NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(
          operationDesc, CUBLASLT_MATMUL_DESC_POINTER_MODE, &pointer_mode, sizeof(pointer_mode)));

      fp8e4m3 *A_scale_inverse = reinterpret_cast<fp8e4m3 *>(param.A_scale_inv);
      fp8e4m3 *B_scale_inverse = reinterpret_cast<fp8e4m3 *>(param.B_scale_inv);
      NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(operationDesc,
                                                       CUBLASLT_MATMUL_DESC_A_SCALE_POINTER,
                                                       &A_scale_inverse, sizeof(A_scale_inverse)));
      NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(operationDesc,
                                                       CUBLASLT_MATMUL_DESC_B_SCALE_POINTER,
                                                       &B_scale_inverse, sizeof(B_scale_inverse)));
      scaling_mode_a = CUBLASLT_MATMUL_MATRIX_SCALE_VEC16_UE4M3;
      scaling_mode_b = CUBLASLT_MATMUL_MATRIX_SCALE_VEC16_UE4M3;
#else
      NVTE_ERROR("FP4 requires cuBLAS 12.8+, but compile-time cuBLAS version is ", CUBLAS_VERSION);
#endif  // CUBLAS_VERSION >= 120800
    } else if ((inputA->scaling_mode == NVTE_BLOCK_SCALING_1D ||
                inputA->scaling_mode == NVTE_BLOCK_SCALING_2D) &&
               (inputB->scaling_mode == NVTE_BLOCK_SCALING_1D ||
                inputB->scaling_mode == NVTE_BLOCK_SCALING_2D)) {
#if CUBLAS_VERSION >= 120900
      NVTE_CHECK(cublas_version() >= 120900,
                 "FP8 block scaling requires cuBLAS 12.9+, but run-time cuBLAS version is ",
                 cublas_version());
      float *A_scale_inverse = reinterpret_cast<float *>(param.A_scale_inv);
      float *B_scale_inverse = reinterpret_cast<float *>(param.B_scale_inv);
      NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(operationDesc,
                                                       CUBLASLT_MATMUL_DESC_A_SCALE_POINTER,
                                                       &A_scale_inverse, sizeof(A_scale_inverse)));
      NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(operationDesc,
                                                       CUBLASLT_MATMUL_DESC_B_SCALE_POINTER,
                                                       &B_scale_inverse, sizeof(B_scale_inverse)));
      NVTE_CHECK((!(inputA->scaling_mode == NVTE_BLOCK_SCALING_2D &&
                    inputB->scaling_mode == NVTE_BLOCK_SCALING_2D)),
                 "Only 1D by 1D, 1D by 2D, and 2D by 1D block scaling supported, but got 2D by 2D");
      scaling_mode_a = inputA->scaling_mode == NVTE_BLOCK_SCALING_1D
                           ? CUBLASLT_MATMUL_MATRIX_SCALE_VEC128_32F
                           : CUBLASLT_MATMUL_MATRIX_SCALE_BLK128x128_32F;
      scaling_mode_b = inputB->scaling_mode == NVTE_BLOCK_SCALING_1D
                           ? CUBLASLT_MATMUL_MATRIX_SCALE_VEC128_32F
                           : CUBLASLT_MATMUL_MATRIX_SCALE_BLK128x128_32F;
#else
      NVTE_ERROR("FP8 block scaling requires cuBLAS 12.9+, but compile-time cuBLAS version is ",
                 CUBLAS_VERSION);
#endif  // CUBLAS_VERSION >= 120900
    } else {
      NVTE_ERROR("Not implemented scaling modes: " + to_string(inputA->scaling_mode) + " and  " +
                 to_string(inputB->scaling_mode) + ".");
    }

#if CUBLAS_VERSION >= 120800
    if (cublas_version() >= 120800) {
      NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(operationDesc,
                                                       CUBLASLT_MATMUL_DESC_A_SCALE_MODE,
                                                       &scaling_mode_a, sizeof(scaling_mode_a)));
      NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(operationDesc,
                                                       CUBLASLT_MATMUL_DESC_B_SCALE_MODE,
                                                       &scaling_mode_b, sizeof(scaling_mode_b)));
    }
#endif  // CUBLAS_VERSION >= 120800
    if (is_fp8_dtype(outputD->data.dtype)) {
      // Accumulation mode not supported for FP8 output
      C = nullptr;
      NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(
          operationDesc, CUBLASLT_MATMUL_DESC_D_SCALE_POINTER, &D_scale, sizeof(D_scale)));
      NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(
          operationDesc, CUBLASLT_MATMUL_DESC_AMAX_D_POINTER, &D_amax, sizeof(D_amax)));
#if CUBLAS_VERSION >= 120800
      if (cublas_version() >= 120800) {
        // NOTE: In all current cases where FP8 output is supported, the input is
        // scaled identically to the output.
        NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(operationDesc,
                                                         CUBLASLT_MATMUL_DESC_D_SCALE_MODE,
                                                         &scaling_mode_a, sizeof(scaling_mode_a)));
      }
#endif  // CUBLAS_VERSION >= 120800
      // For FP8 output, cuBLAS requires C_type to match bias_type and
      // be FP16/BF16
      const cudaDataType_t C_type = bias ? bias_type : CUDA_R_16BF;
      NVTE_CHECK_CUBLAS(cublasLtMatrixLayoutCreate(&Cdesc, C_type, m, n, ldd));
    } else {
      NVTE_CHECK_CUBLAS(cublasLtMatrixLayoutCreate(&Cdesc, D_type, m, n, ldd));
    }
    if (bias) {
      NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(
          operationDesc, CUBLASLT_MATMUL_DESC_BIAS_DATA_TYPE, &bias_type, sizeof(bias_type)));
    }
  } else {
    NVTE_CHECK_CUBLAS(cublasLtMatrixLayoutCreate(&Cdesc, D_type, m, n, ldd));
  }

  if (bias && gelu) {
    if (grad) {
      epilogue = CUBLASLT_EPILOGUE_DGELU_BGRAD;
    } else {
      epilogue = CUBLASLT_EPILOGUE_GELU_AUX_BIAS;
    }
    NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(
        operationDesc, CUBLASLT_MATMUL_DESC_BIAS_POINTER, &bias_ptr, sizeof(bias_ptr)));
    NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(operationDesc,
                                                     CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER,
                                                     &pre_gelu_out, sizeof(pre_gelu_out)));
    NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(
        operationDesc, CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_LD, &ld_gelumat, sizeof(ld_gelumat)));
    const cudaDataType_t aux_type = get_cuda_dtype(outputPreGelu->data.dtype);
    NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(
        operationDesc, CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_DATA_TYPE, &aux_type, sizeof(aux_type)));
  } else if (bias) {
    if (grad) {
      // grad output is always input B
      epilogue = CUBLASLT_EPILOGUE_BGRADB;
    } else {
      epilogue = CUBLASLT_EPILOGUE_BIAS;
    }
    NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(
        operationDesc, CUBLASLT_MATMUL_DESC_BIAS_POINTER, &bias_ptr, sizeof(bias_ptr)));
  } else if (gelu) {
    if (grad) {
      epilogue = CUBLASLT_EPILOGUE_DGELU;
    } else {
      epilogue = CUBLASLT_EPILOGUE_GELU_AUX;
    }
    NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(operationDesc,
                                                     CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_POINTER,
                                                     &pre_gelu_out, sizeof(pre_gelu_out)));
    NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(
        operationDesc, CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_LD, &ld_gelumat, sizeof(ld_gelumat)));
    const cudaDataType_t aux_type = get_cuda_dtype(outputPreGelu->data.dtype);
    NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(
        operationDesc, CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_DATA_TYPE, &aux_type, sizeof(aux_type)));
  }

  if ((inputA->scaling_mode == NVTE_BLOCK_SCALING_1D) ||
      (inputA->scaling_mode == NVTE_BLOCK_SCALING_2D)) {
    NVTE_CHECK((epilogue == CUBLASLT_EPILOGUE_DEFAULT || epilogue == CUBLASLT_EPILOGUE_BIAS ||
                epilogue == CUBLASLT_EPILOGUE_DGELU),
               "Epilogue requested outside of the available and tested cuBLAS functionality for "
               "float8 block scaled GEMM");
  }

  NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_EPILOGUE,
                                                   &epilogue, sizeof(epilogue)));

  if (counter != nullptr) {
#if !(CUDA_VERSION >= 12020 && CUDA_VERSION < 13000)
    NVTE_ERROR("Atomic GEMM requires CUDA >=12.2.0 and <13.0.0, but compile-time CUDA version is ",
               CUDA_VERSION);
#elif !(CUBLAS_VERSION >= 120205 && CUBLAS_VERSION < 130000)
    NVTE_ERROR(
        "Atomic GEMM requires cuBLAS >=12.2.5 and <13.0.0, but compile-time cuBLAS version is ",
        CUBLAS_VERSION);
#else
    NVTE_CHECK(cuda::cudart_version() >= 12020 && cuda::cudart_version() < 13000,
               "Atomic GEMM requires CUDA >=12.2.0 and <13.0.0, but run-time CUDA version is ",
               cuda::cudart_version());
    NVTE_CHECK(cublas_version() >= 120205 && cublas_version() < 130000,
               "Atomic GEMM requires cuBLAS >=12.2.5 and <13.0.0, but run-time cuBLAS version is ",
               cublas_version());
    if (m_split == 0) m_split = 1;
    if (n_split == 0) n_split = 1;
    NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(
        operationDesc, CUBLASLT_MATMUL_DESC_ATOMIC_SYNC_NUM_CHUNKS_D_ROWS, &m_split,
        sizeof(m_split)));
    NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(
        operationDesc, CUBLASLT_MATMUL_DESC_ATOMIC_SYNC_NUM_CHUNKS_D_COLS, &n_split,
        sizeof(n_split)));
    if (gemm_producer) {
      NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(
          operationDesc, CUBLASLT_MATMUL_DESC_ATOMIC_SYNC_OUT_COUNTERS_POINTER, &counter,
          sizeof(counter)));
    } else {
      NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(
          operationDesc, CUBLASLT_MATMUL_DESC_ATOMIC_SYNC_IN_COUNTERS_POINTER, &counter,
          sizeof(counter)));
    }
#endif
  }

  // align the workspace to 256 B
  const int required_alignment = 256;
  const auto original_workspace_alignment = _getAlignment(reinterpret_cast<uintptr_t>(workspace));
  uint8_t *aligned_workspace_ptr =
      reinterpret_cast<uint8_t *>(workspace) + required_alignment - original_workspace_alignment;
  workspaceSize = workspaceSize - required_alignment + original_workspace_alignment;
  const auto new_workspace_alignment =
      _getAlignment(reinterpret_cast<uintptr_t>(aligned_workspace_ptr));
  NVTE_CHECK_CUBLAS(cublasLtMatmulPreferenceCreate(&preference));
  NVTE_CHECK_CUBLAS(cublasLtMatmulPreferenceSetAttribute(
      preference, CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES, &workspaceSize, sizeof(workspaceSize)));
  const auto A_alignment = _getAlignment(reinterpret_cast<uintptr_t>(param.A));
  const auto B_alignment = _getAlignment(reinterpret_cast<uintptr_t>(param.B));
  const auto C_alignment = _getAlignment(reinterpret_cast<uintptr_t>(C));
  const auto D_alignment = _getAlignment(reinterpret_cast<uintptr_t>(D));
  NVTE_CHECK_CUBLAS(cublasLtMatmulPreferenceSetAttribute(
      preference, CUBLASLT_MATMUL_PREF_MIN_ALIGNMENT_A_BYTES, &A_alignment, sizeof(A_alignment)));
  NVTE_CHECK_CUBLAS(cublasLtMatmulPreferenceSetAttribute(
      preference, CUBLASLT_MATMUL_PREF_MIN_ALIGNMENT_B_BYTES, &B_alignment, sizeof(B_alignment)));
  NVTE_CHECK_CUBLAS(cublasLtMatmulPreferenceSetAttribute(
      preference, CUBLASLT_MATMUL_PREF_MIN_ALIGNMENT_C_BYTES, &C_alignment, sizeof(C_alignment)));
  NVTE_CHECK_CUBLAS(cublasLtMatmulPreferenceSetAttribute(
      preference, CUBLASLT_MATMUL_PREF_MIN_ALIGNMENT_D_BYTES, &D_alignment, sizeof(D_alignment)));
  NVTE_CHECK(new_workspace_alignment % 256 == 0,
             "cuBLAS workspace pointer must be aligned to 256 bytes, got ",
             new_workspace_alignment);

  const auto status =
      cublasLtMatmulAlgoGetHeuristic(handle, operationDesc, Adesc, Bdesc, Cdesc, Ddesc, preference,
                                     1, &heuristicResult, &returnedResults);
  NVTE_CHECK(status != CUBLAS_STATUS_NOT_SUPPORTED,
             "Unable to find suitable cuBLAS GEMM algorithm");
  NVTE_CHECK_CUBLAS(status);
  if (returnedResults == 0) NVTE_ERROR("Unable to find any suitable algorithms");

  // D = alpha * (A * B) + beta * C
  NVTE_CHECK_CUBLAS(cublasLtMatmul(handle, operationDesc, alpha, /* alpha */
                                   param.A,                      /* A */
                                   Adesc, param.B,               /* B */
                                   Bdesc, beta,                  /* beta */
                                   C,                            /* C */
                                   Cdesc, D,                     /* D */
                                   Ddesc, &heuristicResult.algo, /* algo */
                                   aligned_workspace_ptr,        /* workspace */
                                   workspaceSize, stream));      /* stream */

  // Update FP8 scale-inv in output tensor
  // Note: This is a WAR for the case when we have fp8 output but D->scale_inv is not allocated.
  // TODO: Changing gemm interface so that D->scale_inv is allocated and the scale_inv can be
  // calculated here.
  if (is_fp8_dtype(outputD->data.dtype) && outputD->scale_inv.dptr) {
    update_tensor_scale_inv(outputD, stream);
  }

  NVTE_CHECK_CUBLAS(cublasLtMatmulPreferenceDestroy(preference));
  NVTE_CHECK_CUBLAS(cublasLtMatrixLayoutDestroy(Ddesc));
  NVTE_CHECK_CUBLAS(cublasLtMatrixLayoutDestroy(Cdesc));
  NVTE_CHECK_CUBLAS(cublasLtMatrixLayoutDestroy(Bdesc));
  NVTE_CHECK_CUBLAS(cublasLtMatrixLayoutDestroy(Adesc));
  NVTE_CHECK_CUBLAS(cublasLtMatmulDescDestroy(operationDesc));
}
#endif // __HIP_PLATFORM_AMD__

// Add for batchgemm
static std::once_flag init_flag_batchgemm;
static cudaStream_t compute_streams_batchgemm[num_batchgemm_streams];
static cudaEvent_t cublas_event_batchgemm[num_batchgemm_streams];

// Warning: only call once per device!
static void init_streams_and_events_batchgemm() {
  int comm_cu_nums = getIntEnv("TORCH_COMM_CU_NUMS", 8, 4);
  unsigned int cuMask[4];
  unsigned int cuMaskSize = 4;
  if (comm_cu_nums == 4) {
    cuMask[0] = 0xfffffff0;  
    cuMask[1] = 0xffffffff;
    cuMask[2] = 0xffffffff;
    cuMask[3] = 0xffffffff;
  } else if (comm_cu_nums == 8) {
    cuMask[0] = 0xffffff00;  
    cuMask[1] = 0xffffffff;
    cuMask[2] = 0xffffffff;
    cuMask[3] = 0xffffffff;
  } else if (comm_cu_nums == 16) {
    cuMask[0] = 0xffff0000;  
    cuMask[1] = 0xffffffff;
    cuMask[2] = 0xffffffff;
    cuMask[3] = 0xffffffff;
  } else if (comm_cu_nums == 32) {
    cuMask[0] = 0x00000000;  
    cuMask[1] = 0xffffffff;
    cuMask[2] = 0xffffffff;
    cuMask[3] = 0xffffffff;
  } else {
    NVTE_CHECK(false, "comm_cu_nums must be 4,8,16,32");
  }
  const char *TORCH_COMM_CU_NUMS = std::getenv("TORCH_COMM_CU_NUMS");
  for (int i = 0; i < num_batchgemm_streams; i++) {
#ifdef __HIP_PLATFORM_AMD__    
    if (TORCH_COMM_CU_NUMS != nullptr && TORCH_COMM_CU_NUMS[0] != '\0') {
      NVTE_CHECK_CUDA(hipExtStreamCreateWithCUMask(&compute_streams_batchgemm[i], cuMaskSize, cuMask));
    } else {
      NVTE_CHECK_CUDA(cudaStreamCreateWithPriority(&compute_streams_batchgemm[i], cudaStreamNonBlocking, -1));
    }
#else
    NVTE_CHECK_CUDA(cudaStreamCreateWithPriority(&compute_streams_batchgemm[i], cudaStreamNonBlocking, -1));
#endif
    NVTE_CHECK_CUDA(cudaEventCreate(&cublas_event_batchgemm[i]));
  }
}

}  // namespace transformer_engine

void nvte_cublas_gemm(const NVTETensor A, const NVTETensor B, NVTETensor D, const NVTETensor bias,
                      NVTETensor pre_gelu_out, bool transa, bool transb, bool grad,
                      NVTETensor workspace, bool accumulate, bool use_split_accumulator,
                      int math_sm_count, cudaStream_t stream, bool nvte_use_hipblaslt, bool nvte_use_rocblas, int compute_stream_offset) {
  NVTE_API_CALL(nvte_cublas_gemm);
  using namespace transformer_engine;

  // Tensors
  const Tensor *inputA = convertNVTETensorCheck(A);
  const Tensor *inputB = convertNVTETensorCheck(B);
  Tensor *outputD = convertNVTETensorCheck(D);
  const Tensor *biasTensor = convertNVTETensor(bias);
  Tensor *outputGelu = convertNVTETensor(pre_gelu_out);
  Tensor *wspace = convertNVTETensor(workspace);


  // Scales
  const float alpha = 1;
  const float beta = accumulate ? 1 : 0;

  // Check for NVFP4
  // TODO Remove once alpha scale logic is moved into cublas_gemm function
  if (is_nvfp_scaling(inputA->scaling_mode) || is_nvfp_scaling(inputB->scaling_mode)) {
    NVTE_ERROR("nvte_cublas_gemm does not support NVFP4 data. Use nvte_cublas_gemm_v2 instead.");
  }

#ifdef __HIP_PLATFORM_AMD__
  const size_t A0 = inputA->flat_first_dim();
  const size_t A1 = inputA->flat_last_dim();
  const size_t B0 = inputB->flat_first_dim();
  const size_t B1 = inputB->flat_last_dim();

  const int m = transa ? A0 : A1;
  const int k = transa ? A1 : A0;
  const int n = transb ? B1 : B0;
  int lda, ldb, ldd;
  if (transa && !transb) {  // TN
    lda = k;
    ldb = k;
    ldd = m;
  } else if (!transa && !transb) {  // NN
    lda = m;
    ldb = k;
    ldd = m;
  } else if (!transa && transb) {  // NT
    lda = m;
    ldb = n;
    ldd = m;
  } else {  // TT
    NVTE_ERROR("TT layout not allowed.");
  }

  const bool use_int8 = is_int8_dtype(inputA->data.dtype) ||
                        is_int8_dtype(inputB->data.dtype);

  const char *NVTE_FORCE_ROCM_GEMM = std::getenv("NVTE_FORCE_ROCM_GEMM");
  const bool use_fp8 = is_fp8_dtype(inputA->data.dtype) ||
                       is_fp8_dtype(inputB->data.dtype);
  const char *NVTE_INT8_SIM_FP8_TENSORWISE = std::getenv("NVTE_INT8_SIM_FP8_TENSORWISE");      
  if (NVTE_INT8_SIM_FP8_TENSORWISE != nullptr && NVTE_INT8_SIM_FP8_TENSORWISE[0] == '1' && use_int8 && use_split_accumulator) nvte_use_hipblaslt = 1;           
  if ((biasTensor->data.dptr != nullptr) || (outputGelu->data.dptr!=nullptr) || (use_fp8) || (NVTE_FORCE_ROCM_GEMM != nullptr && NVTE_FORCE_ROCM_GEMM[0] == '1') || (nvte_use_hipblaslt) || (nvte_use_rocblas)) {
    cublas_gemm(inputA, inputB, outputD, biasTensor, outputGelu, m, n, k, lda, ldb, ldd, transa, transb, grad,
                wspace->data.dptr, wspace->data.shape[0], accumulate, use_split_accumulator, math_sm_count, 0, 0, 
                false, nullptr, stream, nvte_use_hipblaslt, nvte_use_rocblas, compute_stream_offset);
  } else {
    hipblas_gemm(inputA,
                 inputB,
                 outputD,
                 biasTensor,
                 outputGelu,
                 m, n, k,
                 lda, ldb, ldd,
                 (transa) ? HIPBLAS_OP_T : HIPBLAS_OP_N,
                 (transb) ? HIPBLAS_OP_T : HIPBLAS_OP_N,
                 grad, wspace->data.dptr,
                 wspace->data.shape[0],
                 accumulate, use_split_accumulator,
                 math_sm_count,
                 0,
                 0,
                 false,
                 nullptr,
                 stream);
  }
#else 
  cublas_gemm(inputA, inputB, outputD, biasTensor, outputGelu, (transa) ? CUBLAS_OP_T : CUBLAS_OP_N,
              (transb) ? CUBLAS_OP_T : CUBLAS_OP_N, grad, wspace->data.dptr, wspace->data.shape[0],
              &alpha, &beta, use_split_accumulator, math_sm_count, 0, 0, false, nullptr, stream);
#endif
}

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) {
  NVTE_API_CALL(nvte_cublas_gemm_v2);
  using namespace transformer_engine;

  // Data tensors
  const Tensor *A_tensor = convertNVTETensorCheck(A);
  const Tensor *B_tensor = convertNVTETensorCheck(B);
  const Tensor *C_tensor = convertNVTETensorCheck(C);
  Tensor *D_tensor = convertNVTETensorCheck(D);
  NVTE_CHECK(C_tensor == D_tensor,
             "Currently nvte_cublas_gemm_v2 does not support different C and D tensors.");

  // Workspace
  void *workspace_ptr = nullptr;
  size_t workspace_size = 0;
  Tensor *workspace_tensor = convertNVTETensor(workspace);
  if (workspace_tensor != nullptr) {
    workspace_ptr = workspace_tensor->data.dptr;
    workspace_size =
        get_buffer_size_bytes(workspace_tensor->data.numel(), workspace_tensor->data.dtype);
  }

  // Additional config
  MatmulConfig config_;
  if (config != nullptr) {
    config_ = *reinterpret_cast<MatmulConfig *>(config);
  }

  // Configure GEMM epilogue
  const bool with_grad_epilogue = (config_.dbias_tensor != nullptr || config_.with_dgelu_epilogue);
  if (with_grad_epilogue) {
    NVTE_CHECK(config_.bias_tensor == nullptr && !config_.with_gelu_epilogue,
               "Invalid epilogue (bias=", config_.bias_tensor != nullptr,
               ", dbias=", config_.dbias_tensor != nullptr, ", gelu=", config_.with_gelu_epilogue,
               ", dgelu=", config_.with_dgelu_epilogue, ").");
  }
  Tensor dummy_tensor;
  Tensor *epilogue_bias_tensor = &dummy_tensor;
  if (!with_grad_epilogue && config_.bias_tensor != nullptr) {
    epilogue_bias_tensor = convertNVTETensorCheck(config_.bias_tensor);
  } else if (with_grad_epilogue && config_.dbias_tensor != nullptr) {
    epilogue_bias_tensor = convertNVTETensorCheck(config_.dbias_tensor);
  }
  Tensor *epilogue_aux_tensor = &dummy_tensor;
  if (config_.with_gelu_epilogue || config_.with_dgelu_epilogue) {
    NVTE_CHECK(config_.epilogue_aux_tensor != nullptr,
               "Requested epilogue (bias=", config_.bias_tensor != nullptr,
               ", dbias=", config_.dbias_tensor != nullptr, ", gelu=", config_.with_gelu_epilogue,
               ", dgelu=", config_.with_dgelu_epilogue, ") without providing aux tensor.");
    epilogue_aux_tensor = convertNVTETensor(config_.epilogue_aux_tensor);
  }

#ifdef __HIP_PLATFORM_AMD__
  NVTE_CHECK(*alpha == 1.0f, "alpha must be 1.0 for hip");
  NVTE_CHECK(*beta == 1.0f || *beta == 0.0f, "beta must be 1.0 or 0.0 for hip");
  bool accumulate = false;
  if (*alpha == 1.0f and *beta == 1.0f) {
    accumulate = true;
  }
  const size_t A0 = A_tensor->flat_first_dim();
  const size_t A1 = A_tensor->flat_last_dim();
  const size_t B0 = B_tensor->flat_first_dim();
  const size_t B1 = B_tensor->flat_last_dim();

  const int m = transa ? A0 : A1;
  const int k = transa ? A1 : A0;
  const int n = transb ? B1 : B0;
  int lda, ldb, ldd;
  if (transa && !transb) {  // TN
    lda = k;
    ldb = k;
    ldd = m;
  } else if (!transa && !transb) {  // NN
    lda = m;
    ldb = k;
    ldd = m;
  } else if (!transa && transb) {  // NT
    lda = m;
    ldb = n;
    ldd = m;
  } else {  // TT
    NVTE_ERROR("TT layout not allowed.");
  }

  const bool use_int8 = is_int8_dtype(A_tensor->data.dtype) ||
                        is_int8_dtype(B_tensor->data.dtype);

  const char *NVTE_FORCE_ROCM_GEMM = std::getenv("NVTE_FORCE_ROCM_GEMM");
  const bool use_fp8 = is_fp8_dtype(A_tensor->data.dtype) ||
                       is_fp8_dtype(B_tensor->data.dtype);
  const char *NVTE_INT8_SIM_FP8_TENSORWISE = std::getenv("NVTE_INT8_SIM_FP8_TENSORWISE");      
  if (NVTE_INT8_SIM_FP8_TENSORWISE != nullptr && NVTE_INT8_SIM_FP8_TENSORWISE[0] == '1' && use_int8 && config_.use_split_accumulator) nvte_use_hipblaslt = 1;           
  if ((epilogue_bias_tensor->data.dptr != nullptr) || (epilogue_aux_tensor->data.dptr!=nullptr) || (use_fp8) || (NVTE_FORCE_ROCM_GEMM != nullptr && NVTE_FORCE_ROCM_GEMM[0] == '1') || (nvte_use_hipblaslt) || (nvte_use_rocblas)) {
    cublas_gemm(A_tensor, B_tensor, D_tensor, epilogue_bias_tensor, epilogue_aux_tensor, m, n, k, lda, ldb, ldd, transa, transb, with_grad_epilogue,
                workspace_ptr, workspace_size, accumulate, config_.use_split_accumulator, config_.sm_count, 0, 0, 
                false, nullptr, stream, nvte_use_hipblaslt, nvte_use_rocblas, compute_stream_offset);
  } else {
    hipblas_gemm(A_tensor,
                 B_tensor,
                 D_tensor,
                 epilogue_bias_tensor,
                 epilogue_aux_tensor,
                 m, n, k,
                 lda, ldb, ldd,
                 (transa) ? HIPBLAS_OP_T : HIPBLAS_OP_N,
                 (transb) ? HIPBLAS_OP_T : HIPBLAS_OP_N,
                 with_grad_epilogue, workspace_ptr,
                 workspace_size,
                 accumulate, config_.use_split_accumulator,
                 config_.sm_count,
                 0,
                 0,
                 false,
                 nullptr,
                 stream);
  }
#else
  // Launch GEMM
  cublas_gemm(A_tensor, B_tensor, D_tensor, epilogue_bias_tensor, epilogue_aux_tensor,
              transa ? CUBLAS_OP_T : CUBLAS_OP_N, transb ? CUBLAS_OP_T : CUBLAS_OP_N,
              with_grad_epilogue, workspace_ptr, workspace_size, alpha, beta,
              config_.use_split_accumulator, config_.sm_count, 0, 0, false, nullptr, stream);
#endif
}

void nvte_cublas_gemm_scaled(const NVTETensor A, const NVTETensor B, NVTETensor D,
                             const NVTETensor bias, NVTETensor pre_gelu_out, bool transa,
                             bool transb, bool grad, NVTETensor workspace, float alpha, float beta,
                             bool use_split_accumulator, int math_sm_count, cudaStream_t stream, bool nvte_use_hipblaslt, bool nvte_use_rocblas, int compute_stream_offset) {
  NVTE_API_CALL(nvte_cublas_gemm_scaled);
  using namespace transformer_engine;

  // Tensors
  const Tensor *inputA = convertNVTETensorCheck(A);
  const Tensor *inputB = convertNVTETensorCheck(B);
  Tensor *outputD = convertNVTETensorCheck(D);
  const Tensor *biasTensor = convertNVTETensor(bias);
  Tensor *outputGelu = convertNVTETensor(pre_gelu_out);
  Tensor *wspace = convertNVTETensor(workspace);

  // Check for NVFP4
  // TODO Remove once alpha scale logic is moved into cublas_gemm function
  if (is_nvfp_scaling(inputA->scaling_mode) || is_nvfp_scaling(inputB->scaling_mode)) {
    NVTE_ERROR("nvte_cublas_gemm does not support NVFP4 data. Use nvte_cublas_gemm_v2 instead.");
  }

#ifdef __HIP_PLATFORM_AMD__
  NVTE_CHECK(alpha == 1.0f, "alpha must be 1.0 for hip");
  NVTE_CHECK(beta == 1.0f || beta == 0.0f, "beta must be 1.0 or 0.0 for hip");
  bool accumulate = false;
  if (alpha == 1.0f and beta == 1.0f) {
    accumulate = true;
  }

  const size_t A0 = inputA->flat_first_dim();
  const size_t A1 = inputA->flat_last_dim();
  const size_t B0 = inputB->flat_first_dim();
  const size_t B1 = inputB->flat_last_dim();

  const int m = transa ? A0 : A1;
  const int k = transa ? A1 : A0;
  const int n = transb ? B1 : B0;
  int lda, ldb, ldd;
  if (transa && !transb) {  // TN
    lda = k;
    ldb = k;
    ldd = m;
  } else if (!transa && !transb) {  // NN
    lda = m;
    ldb = k;
    ldd = m;
  } else if (!transa && transb) {  // NT
    lda = m;
    ldb = n;
    ldd = m;
  } else {  // TT
    NVTE_ERROR("TT layout not allowed.");
  }

  const bool use_int8 = is_int8_dtype(inputA->data.dtype) ||
                        is_int8_dtype(inputB->data.dtype);

  const char *NVTE_FORCE_ROCM_GEMM = std::getenv("NVTE_FORCE_ROCM_GEMM");
  const bool use_fp8 = is_fp8_dtype(inputA->data.dtype) ||
                       is_fp8_dtype(inputB->data.dtype);
  const char *NVTE_INT8_SIM_FP8_TENSORWISE = std::getenv("NVTE_INT8_SIM_FP8_TENSORWISE");      
  if (NVTE_INT8_SIM_FP8_TENSORWISE != nullptr && NVTE_INT8_SIM_FP8_TENSORWISE[0] == '1' && use_int8 && use_split_accumulator) nvte_use_hipblaslt = 1;           
  if ((biasTensor->data.dptr != nullptr) || (outputGelu->data.dptr!=nullptr) || (use_fp8) || (NVTE_FORCE_ROCM_GEMM != nullptr && NVTE_FORCE_ROCM_GEMM[0] == '1') || (nvte_use_hipblaslt) || (nvte_use_rocblas)) {
    cublas_gemm(inputA, inputB, outputD, biasTensor, outputGelu, m, n, k, lda, ldb, ldd, transa, transb, grad,
                wspace->data.dptr, wspace->data.shape[0], accumulate, use_split_accumulator, math_sm_count, 0, 0, 
                false, nullptr, stream, nvte_use_hipblaslt, nvte_use_rocblas, compute_stream_offset);
  } else {
    hipblas_gemm(inputA,
                 inputB,
                 outputD,
                 biasTensor,
                 outputGelu,
                 m, n, k,
                 lda, ldb, ldd,
                 (transa) ? HIPBLAS_OP_T : HIPBLAS_OP_N,
                 (transb) ? HIPBLAS_OP_T : HIPBLAS_OP_N,
                 grad, wspace->data.dptr,
                 wspace->data.shape[0],
                 accumulate, use_split_accumulator,
                 math_sm_count,
                 0,
                 0,
                 false,
                 nullptr,
                 stream);
  }
#else 
  cublas_gemm(inputA, inputB, outputD, biasTensor, outputGelu, (transa) ? CUBLAS_OP_T : CUBLAS_OP_N,
              (transb) ? CUBLAS_OP_T : CUBLAS_OP_N, grad, wspace->data.dptr, wspace->data.shape[0],
              &alpha, &beta, use_split_accumulator, math_sm_count, 0, 0, false, nullptr, stream);
#endif
}

void nvte_cublas_atomic_gemm(const NVTETensor A, const NVTETensor B, NVTETensor D,
                             const NVTETensor bias, NVTETensor pre_gelu_out, bool transa,
                             bool transb, bool grad, NVTETensor workspace, bool accumulate,
                             bool use_split_accumulator, int math_sm_count, int m_split,
                             int n_split, bool gemm_producer, const NVTETensor counter,
                             cudaStream_t stream, bool nvte_use_hipblaslt, bool nvte_use_rocblas, int compute_stream_offset) {
  NVTE_API_CALL(nvte_cublas_atomic_gemm);
  using namespace transformer_engine;

#ifndef __HIP_PLATFORM_AMD__
  // Check CUDA and cuBLAS versions
#if !(CUDA_VERSION >= 12020 && CUDA_VERSION < 13000)
  NVTE_ERROR("Atomic GEMM requires CUDA >=12.2.0 and <13.0.0, but compile-time CUDA version is ",
             CUDA_VERSION);
#elif !(CUBLAS_VERSION >= 120205 && CUBLAS_VERSION < 130000)
  NVTE_ERROR(
      "Atomic GEMM requires cuBLAS >=12.2.5 and <13.0.0, but compile-time cuBLAS version is ",
      CUBLAS_VERSION);
#else
#define NVTE_CUBLAS_ATOMIC_GEMM_COMPILE 1
  NVTE_CHECK(
      transformer_engine::cuda::cudart_version() >= 12020 &&
          transformer_engine::cuda::cudart_version() < 13000,
      "Atomic GEMM requires CUDA version >=12.2.0 and <13.0.0, but run-time CUDA version is ",
      transformer_engine::cuda::cudart_version());
  NVTE_CHECK(
      cublas_version() >= 120205 && cublas_version() < 130000,
      "Atomic GEMM requires cuBLAS version >=12.2.5 and <13.0.0, but run-time cuBLAS version is ",
      cublas_version());
#endif
#else
#define NVTE_CUBLAS_ATOMIC_GEMM_COMPILE 1
#endif // __HIP_PLATFORM_AMD__

#ifdef NVTE_CUBLAS_ATOMIC_GEMM_COMPILE
  const Tensor *inputA = convertNVTETensorCheck(A);
  const Tensor *inputB = convertNVTETensorCheck(B);
  Tensor *outputD = convertNVTETensor(D);
  const Tensor *biasTensor = convertNVTETensor(bias);
  Tensor *outputGelu = convertNVTETensor(pre_gelu_out);
  const Tensor *inputCounter = convertNVTETensor(counter);
  Tensor *wspace = convertNVTETensor(workspace);

  NVTE_CHECK(is_delayed_tensor_scaling(inputA->scaling_mode) &&
                 is_delayed_tensor_scaling(inputB->scaling_mode),
             "Atomic GEMM only supports delayed scaling.");
#ifdef __HIP_PLATFORM_AMD__
  const int m = transa ? inputA->data.shape[0] : inputA->data.shape[1];
  const int k = transa ? inputA->data.shape[1] : inputA->data.shape[0];
  const int n = transb ? inputB->data.shape[1] : inputB->data.shape[0];
  int lda, ldb, ldd;
  if (transa && !transb) {  // TN
    lda = k;
    ldb = k;
    ldd = m;
  } else if (!transa && !transb) {  // NN
    lda = m;
    ldb = k;
    ldd = m;
  } else if (!transa && transb) {  // NT
    lda = m;
    ldb = n;
    ldd = m;
  } else {  // TT
    NVTE_ERROR("TT layout not allowed.");
  }

  const char *NVTE_FORCE_ROCM_GEMM = std::getenv("NVTE_FORCE_ROCM_GEMM");
  const bool use_fp8 = is_fp8_dtype(inputA->data.dtype) ||
                       is_fp8_dtype(inputB->data.dtype);
  if ((biasTensor->data.dptr != nullptr) || (outputGelu->data.dptr!=nullptr) || (use_fp8) || (NVTE_FORCE_ROCM_GEMM != nullptr && NVTE_FORCE_ROCM_GEMM[0] == '1') || (nvte_use_hipblaslt) || (nvte_use_rocblas)) {
    cublas_gemm(inputA, inputB, outputD, biasTensor, outputGelu, m, n, k, lda, ldb, ldd, transa, transb, grad,
                wspace->data.dptr, wspace->data.shape[0], accumulate, use_split_accumulator, math_sm_count, 
                m_split, n_split, gemm_producer, inputCounter, stream, nvte_use_hipblaslt, nvte_use_rocblas, compute_stream_offset);
  } else {
    hipblas_gemm(inputA,
                 inputB,
                 outputD,
                 biasTensor,
                 outputGelu,
                 m, n, k,
                 lda, ldb, ldd,
                 (transa) ? HIPBLAS_OP_T : HIPBLAS_OP_N,
                 (transb) ? HIPBLAS_OP_T : HIPBLAS_OP_N,
                 grad, wspace->data.dptr,
                 wspace->data.shape[0],
                 accumulate, use_split_accumulator,
                 math_sm_count,
                 m_split,
                 n_split,
                 gemm_producer,
                 inputCounter,
                 stream);
  }
#else 
    const void *alpha_ptr = GetScalarOne();
    const void *beta_ptr = accumulate ? GetScalarOne() : GetScalarZero();
    cublas_gemm(inputA, inputB, outputD, biasTensor, outputGelu, (transa) ? CUBLAS_OP_T : CUBLAS_OP_N,
              (transb) ? CUBLAS_OP_T : CUBLAS_OP_N, grad, wspace->data.dptr, wspace->data.shape[0],
              alpha_ptr, beta_ptr, use_split_accumulator, math_sm_count, m_split, n_split,
              gemm_producer, inputCounter, stream);
#endif  //__HIP_PLATFORM_AMD__
#endif // NVTE_CUBLAS_ATOMIC_GEMM_COMPILE
}

void multi_stream_cublas_gemm(const NVTETensor *A, const NVTETensor *B, NVTETensor *D,
                              const NVTETensor *bias, NVTETensor *pre_gelu_out, const int num_gemms,
                              bool transa, bool transb, bool grad, NVTETensor *workspace,
                              bool accumulate, bool use_split_accumulator, int math_sm_count,
                              cudaStream_t stream) {
  using namespace transformer_engine;

  int num_streams = nvte_get_num_compute_streams();

  int num_stream_used = std::min(num_streams, num_gemms);
  // wait for current stream to finish
  NVTE_CHECK_CUDA(cudaEventRecord(detail::get_compute_stream_event(0), stream));
  for (int s = 0; s < num_stream_used; s++) {
    NVTE_CHECK_CUDA(
        cudaStreamWaitEvent(detail::get_compute_stream(s), detail::get_compute_stream_event(0)));
  }
  const char *NVTE_BLAS_MULSTREAM = std::getenv("NVTE_FORCE_BLAS_MULSTREAM");
  const char *NVTE_FORCE_ROCM_GEMM = std::getenv("NVTE_FORCE_ROCM_GEMM");
  bool NVTE_FORCE_BLAS_MULSTREAM;
  if(NVTE_BLAS_MULSTREAM != nullptr && NVTE_BLAS_MULSTREAM[0] == '1'){
    NVTE_FORCE_BLAS_MULSTREAM = true;
    if((NVTE_FORCE_ROCM_GEMM != nullptr && NVTE_FORCE_ROCM_GEMM[0] == '1') && (NVTE_BLAS_MULSTREAM != nullptr && NVTE_BLAS_MULSTREAM[0] == '1'))
      NVTE_ERROR("NVTE_FORCE_BLAS_MULSTREAM and NVTE_FORCE_ROCM_GEMM can't be set at the same time.");
  } else{
    NVTE_FORCE_BLAS_MULSTREAM = false;
  }
  if (NVTE_FORCE_BLAS_MULSTREAM) {
    for (int i = 0; i < num_gemms; i++) {
      // Check whether GELU or dGELU epilogue is requested
      Tensor *pre_gelu_tensor = convertNVTETensor(pre_gelu_out[i]);
      bool with_gelu_dgelu_epilogue =
          (pre_gelu_tensor != nullptr && pre_gelu_tensor->data.dptr != nullptr);

      // Construct config
      MatmulConfig config;
      if (grad) {
        config.dbias_tensor = bias[i];
        config.with_dgelu_epilogue = with_gelu_dgelu_epilogue;
      } else {
        config.bias_tensor = bias[i];
        config.with_gelu_epilogue = with_gelu_dgelu_epilogue;
      }
      config.epilogue_aux_tensor = pre_gelu_out[i];
      config.use_split_accumulator = use_split_accumulator;
      config.sm_count = math_sm_count;

      // Launch GEMM
      const float alpha = 1.f;
      const float beta = accumulate ? 1.f : 0.f;
      nvte_cublas_gemm_v2(transa, transb, &alpha, A[i], B[i], &beta, D[i], D[i],
                          workspace[i % num_streams], &config,
                          detail::get_compute_stream(i % num_streams));
    }
  } else {
    for (int i = 0; i < num_gemms; i++) {
      // Check whether GELU or dGELU epilogue is requested
      Tensor *pre_gelu_tensor = convertNVTETensor(pre_gelu_out[i]);
      bool with_gelu_dgelu_epilogue =
          (pre_gelu_tensor != nullptr && pre_gelu_tensor->data.dptr != nullptr);

      // Construct config
      MatmulConfig config;
      if (grad) {
        config.dbias_tensor = bias[i];
        config.with_dgelu_epilogue = with_gelu_dgelu_epilogue;
      } else {
        config.bias_tensor = bias[i];
        config.with_gelu_epilogue = with_gelu_dgelu_epilogue;
      }
      config.epilogue_aux_tensor = pre_gelu_out[i];
      config.use_split_accumulator = use_split_accumulator;
      config.sm_count = math_sm_count;

      // Launch GEMM
      const float alpha = 1.f;
      const float beta = accumulate ? 1.f : 0.f;
      nvte_cublas_gemm_v2(transa, transb, &alpha, A[i], B[i], &beta, D[i], D[i],
                          workspace[i % num_streams], &config,
                          detail::get_compute_stream(i % num_streams), 1, 0, i % num_streams);
    }
  }

  // record events on compute streams
  for (int s = 0; s < num_stream_used; s++) {
    NVTE_CHECK_CUDA(
        cudaEventRecord(detail::get_compute_stream_event(s), detail::get_compute_stream(s)));
  }
  // wait for all compute streams to finish
  for (int s = 0; s < num_stream_used; s++) {
    NVTE_CHECK_CUDA(cudaStreamWaitEvent(stream, detail::get_compute_stream_event(s)));
  }
}

void nvte_multi_stream_cublas_gemm(const NVTETensor *A, const NVTETensor *B, NVTETensor *D,
                                   const NVTETensor *bias, NVTETensor *pre_gelu_out,
                                   const int num_gemms, bool transa, bool transb, bool grad,
                                   NVTETensor *workspace, bool accumulate,
                                   bool use_split_accumulator, int math_sm_count,
                                   cudaStream_t stream) {
  NVTE_API_CALL(nvte_multi_stream_cublas_gemm);
  using namespace transformer_engine;

  // Deprecation warning
  NVTE_WARN(
      "nvte_multi_stream_cublas_gemm is deprecated and will be removed in a future release. "
      "Please migrate to nvte_multi_tensor_gemm (with CUTLASS Grouped GEMM support when "
      "applicable).");

  multi_stream_cublas_gemm(A, B, D, bias, pre_gelu_out, num_gemms, transa, transb, grad, workspace,
                           accumulate, use_split_accumulator, math_sm_count, stream);
}

#ifndef __HIP_PLATFORM_AMD__
namespace transformer_engine {

using cublasHandleManager = detail::HandleManager<cublasLtHandle_t, CreateCublasHandle>;

void nvte_cublas_handle_init() { auto _ = cublasHandleManager::Instance().GetHandle(); }

}  //  namespace transformer_engine
#endif

#ifdef __HIP_PLATFORM_AMD__
void nvte_grouped_gemm(const NVTETensor *A, const NVTETensor *B, NVTETensor *D,
                                   const NVTETensor *bias, NVTETensor *pre_gelu_out,
                                   const int num_gemms, bool transa, bool transb, bool grad,
                                   NVTETensor *workspace, bool accumulate,
                                   bool use_split_accumulator, int math_sm_count,
                                   cudaStream_t stream) {
  using namespace transformer_engine;
  if(num_gemms == 0) { return; }
  
  std::vector<const Tensor*> inputA;
  std::vector<const Tensor*> inputB;
  std::vector<Tensor*> outputD;
  std::vector<const Tensor*> biasTensor;
  std::vector<Tensor*> outputGelu;
  std::vector<int64_t> m;
  std::vector<int64_t> n;
  std::vector<int64_t> k;
  std::vector<int64_t> b;
  
  for (int i = 0; i < num_gemms; i++) {
    inputA.push_back(convertNVTETensorCheck(A[i]));
    inputB.push_back(convertNVTETensorCheck(B[i]));
    outputD.push_back(convertNVTETensorCheck(D[i]));
    biasTensor.push_back(convertNVTETensorCheck(bias[i]));
    outputGelu.push_back(convertNVTETensorCheck(pre_gelu_out[i]));
    b.push_back(1);

    size_t A0 = inputA[i]->flat_first_dim();
    size_t A1 = inputA[i]->flat_last_dim();
    size_t B0 = inputB[i]->flat_first_dim();
    size_t B1 = inputB[i]->flat_last_dim();
  
    if (transa) {
      m.push_back(A0);
      k.push_back(A1);
    } else {
      m.push_back(A1);
      k.push_back(A0);
    }
    if (transb) {
      n.push_back(B1);
    } else {
      n.push_back(B0);
    }
  }
  Tensor *wspace = convertNVTETensorCheck(workspace[0]);
  
  if ((biasTensor[0]->data.dptr != nullptr) || (outputGelu[0]->data.dptr != nullptr)) {
    NVTE_ERROR("MOE nvte_grouped_gemm not surpport bias or gelu.");
  }

  hipblaslt_groupedgemm(inputA, inputB, outputD, m, n, k, b,
                      (transa) ? HIPBLAS_OP_T : HIPBLAS_OP_N,
                      (transb) ? HIPBLAS_OP_T : HIPBLAS_OP_N, 
                      wspace->data.dptr, wspace->data.shape[0],
                      accumulate, use_split_accumulator, 
                      math_sm_count, stream);

  
}

void nvte_multi_stream_cublas_batchgemm(const NVTETensor *A, const NVTETensor *B, NVTETensor *D,
                                   const NVTETensor *bias, NVTETensor *pre_gelu_out,
                                   const int num_gemms, bool transa, bool transb, bool grad,
                                   NVTETensor *workspace, bool accumulate,
                                   bool use_split_accumulator, int math_sm_count,
                                   cudaStream_t stream) {
  NVTE_API_CALL(nvte_multi_stream_cublas_batchgemm);
  using namespace transformer_engine;
  int batch_count = getIntEnv("NVTE_MOE_BATCHCOUNT", 2, 1);
  // Inits streams and events (once, globally)
  std::call_once(init_flag_batchgemm, init_streams_and_events_batchgemm);

  int num_stream_used = std::min(num_batchgemm_streams, num_gemms);
  // wait for current stream to finish
  NVTE_CHECK_CUDA(cudaEventRecord(cublas_event_batchgemm[0], stream));
  for (int s = 0; s < num_stream_used; s++) {
    NVTE_CHECK_CUDA(cudaStreamWaitEvent(compute_streams_batchgemm[s], cublas_event_batchgemm[0]));
  }
  for (int i = 0; i < num_gemms; i++) {
    nvte_cublas_batchgemm(A[i], B[i], D[i], bias[i], pre_gelu_out[i], transa, transb, grad,
                     workspace[i % num_batchgemm_streams], accumulate, use_split_accumulator, math_sm_count,
                     batch_count, compute_streams_batchgemm[i % num_batchgemm_streams]);
  }
  // record events on compute streams
  for (int s = 0; s < num_stream_used; s++) {
    NVTE_CHECK_CUDA(cudaEventRecord(cublas_event_batchgemm[s], compute_streams_batchgemm[s]));
  }
  // wait for all compute streams to finish
  for (int s = 0; s < num_stream_used; s++) {
    NVTE_CHECK_CUDA(cudaStreamWaitEvent(stream, cublas_event_batchgemm[s]));
  }
}

// add for batchgemm
void nvte_cublas_batchgemm(const NVTETensor A, const NVTETensor B, NVTETensor D, const NVTETensor bias,
                      NVTETensor pre_gelu_out, bool transa, bool transb, bool grad,
                      NVTETensor workspace, bool accumulate, bool use_split_accumulator,
                      int math_sm_count, int batch_count, cudaStream_t stream) {
  NVTE_API_CALL(nvte_cublas_batchgemm);
  using namespace transformer_engine;
  const Tensor *inputA = convertNVTETensorCheck(A);
  const Tensor *inputB = convertNVTETensorCheck(B);
  Tensor *outputD = convertNVTETensor(D);
  const Tensor *biasTensor = convertNVTETensor(bias);
  Tensor *outputGelu = convertNVTETensor(pre_gelu_out);
  Tensor *wspace = convertNVTETensor(workspace);
  if ((biasTensor->data.dptr != nullptr) || (outputGelu->data.dptr != nullptr)) {
    NVTE_ERROR("MOE batchgemm not surpport bias or gelu.");
  }

  int m, n, k;
  if (!transa && transb) {
  // for NT
  m = transa ? inputA->data.shape[0]/batch_count : inputA->data.shape[1];
  k = transa ? inputA->data.shape[1] : inputA->data.shape[0]/batch_count;
  n = transb ? inputB->data.shape[1] : inputB->data.shape[0]/batch_count;
  }  else if(transa && !transb){
  // for TN
  m = transa ? inputA->data.shape[0]/batch_count: inputA->data.shape[1];
  k = transa ? inputA->data.shape[1] : inputA->data.shape[0]/batch_count;
  n = transb ? inputB->data.shape[1] : inputB->data.shape[0]/batch_count;
  } else if(!transa && !transb){
  // for NN
  m = transa ? inputA->data.shape[0]/batch_count : inputA->data.shape[1];
  k = transa ? inputA->data.shape[1] : inputA->data.shape[0]/batch_count;
  n = transb ? inputB->data.shape[1] : inputB->data.shape[0]/batch_count; }
  int lda, ldb, ldd;
  if (transa && !transb) {  // TN
    lda = k;
    ldb = k;
    ldd = m;
  } else if (!transa && !transb) {  // NN
    lda = m; 
    ldb = k;
    ldd = m;
  } else if (!transa && transb) {  // NT
    lda = m;
    ldb = n;
    ldd = m;
  } else {  // TT
    NVTE_ERROR("TT layout not allowed.");
  }
  hipblas_batchgemm(inputA,
            inputB,
            outputD,
            biasTensor,
            outputGelu,
            m, n, k,
            lda, ldb, ldd,
            (transa) ? HIPBLAS_OP_T : HIPBLAS_OP_N,
            (transb) ? HIPBLAS_OP_T : HIPBLAS_OP_N,
            grad, wspace->data.dptr,
            wspace->data.shape[0],
            accumulate, use_split_accumulator,
            math_sm_count,
            0,
            0,
            false,
            nullptr,
            batch_count,
            stream);
}


// add for batchgemm
void nvte_cublas_batchgemm_tensorwise_int8(const NVTETensor A, const NVTETensor B, const NVTETensor A_scales, const NVTETensor B_scales, NVTETensor D, const NVTETensor bias,
                      NVTETensor pre_gelu_out, bool transa, bool transb, bool grad,
                      NVTETensor workspace, bool accumulate, bool use_split_accumulator,
                      int math_sm_count, int batch_count, cudaStream_t stream) {
  NVTE_API_CALL(nvte_cublas_batchgemm_tensorwise_int8);
  using namespace transformer_engine;
  const Tensor *inputA = convertNVTETensorCheck(A);
  const Tensor *inputB = convertNVTETensorCheck(B);
  const Tensor *inputA_scales = convertNVTETensorCheck(A_scales);
  const Tensor *inputB_scales = convertNVTETensorCheck(B_scales);
  Tensor *outputD = convertNVTETensor(D);
  const Tensor *biasTensor = convertNVTETensor(bias);
  Tensor *outputGelu = convertNVTETensor(pre_gelu_out);
  Tensor *wspace = convertNVTETensor(workspace);
  if ((biasTensor->data.dptr != nullptr) || (outputGelu->data.dptr != nullptr)) {
    NVTE_ERROR("MOE batchgemm not surpport bias or gelu.");
  }

  int m, n, k;
  if (!transa && transb) {
  // for NT
  m = transa ? inputA->data.shape[0]/batch_count : inputA->data.shape[1];
  k = transa ? inputA->data.shape[1] : inputA->data.shape[0]/batch_count;
  n = transb ? inputB->data.shape[1] : inputB->data.shape[0]/batch_count;
  }  else if(transa && !transb){
  // for TN
  m = transa ? inputA->data.shape[0]/batch_count: inputA->data.shape[1];
  k = transa ? inputA->data.shape[1] : inputA->data.shape[0]/batch_count;
  n = transb ? inputB->data.shape[1] : inputB->data.shape[0]/batch_count;
  } else if(!transa && !transb){
  // for NN
  m = transa ? inputA->data.shape[0]/batch_count : inputA->data.shape[1];
  k = transa ? inputA->data.shape[1] : inputA->data.shape[0]/batch_count;
  n = transb ? inputB->data.shape[1] : inputB->data.shape[0]/batch_count; }
  int lda, ldb, ldd;
  if (transa && !transb) {  // TN
    lda = k;
    ldb = k;
    ldd = m;
  } else if (!transa && !transb) {  // NN
    lda = m; 
    ldb = k;
    ldd = m;
  } else if (!transa && transb) {  // NT
    lda = m;
    ldb = n;
    ldd = m;
  } else {  // TT
    NVTE_ERROR("TT layout not allowed.");
  }

  NVTE_ERROR("Remove nvte_cublas_batchgemm_tensorwise_int8 for now.");

}
#endif

void nvte_multi_tensor_gemm(const NVTETensor *A, const NVTETensor *B, NVTETensor *D,
                            const NVTETensor *bias, NVTETensor *pre_gelu_out, const int num_gemms,
                            bool transa, bool transb, bool grad, NVTETensor *workspace,
                            bool accumulate, bool use_split_accumulator, int math_sm_count,
                            cudaStream_t stream) {
  NVTE_API_CALL(nvte_multi_tensor_gemm);
#ifdef __HIP_PLATFORM_AMD__
  const char *NVTE_USE_HIPBLASLT_GROUPEDGEMM = std::getenv("NVTE_USE_HIPBLASLT_GROUPEDGEMM");
  if(NVTE_USE_HIPBLASLT_GROUPEDGEMM != nullptr && NVTE_USE_HIPBLASLT_GROUPEDGEMM[0] == '1'){
      nvte_grouped_gemm(A, B, D, bias, pre_gelu_out, num_gemms, transa, transb, grad,
                             workspace, accumulate, use_split_accumulator, math_sm_count, stream);
  } else {
      multi_stream_cublas_gemm(A, B, D, bias, pre_gelu_out, num_gemms, transa, transb, grad,
                             workspace, accumulate, use_split_accumulator, math_sm_count, stream);
  }
#else
  const int current_device = transformer_engine::cuda::current_device();
  const bool is_hopper = (transformer_engine::cuda::sm_arch(current_device) == 90);
  const bool use_cutlass = transformer_engine::getenv<bool>("NVTE_USE_CUTLASS_GROUPED_GEMM", false);
  const bool warn_fallback =
      transformer_engine::getenv<bool>("NVTE_CUTLASS_GROUPED_GEMM_WARN_FALLBACK", false);

  auto cublas_path = [&]() {
    multi_stream_cublas_gemm(A, B, D, bias, pre_gelu_out, num_gemms, transa, transb, grad,
                             workspace, accumulate, use_split_accumulator, math_sm_count, stream);
  };

  // Currently only support cutlass group gemm on Hopper Arch
  if (!(is_hopper && use_cutlass)) {
    cublas_path();
    return;
  }

  auto is_empty_arr = [&](const NVTETensor *p) -> bool {
    if (p == nullptr) return true;
    for (int i = 0; i < num_gemms; ++i) {
      if (transformer_engine::convertNVTETensor(p[i])->has_data()) return false;
    }
    return true;
  };

  auto all_groups_uniform_k128 = [&](const NVTETensor *p, bool trans) -> bool {
    int64_t ref_k = -1;
    for (size_t i = 0; i < num_gemms; i++) {
      const auto tensor = transformer_engine::convertNVTETensorCheck(p[i]);
      const int k = trans ? tensor->data.shape[0] : tensor->data.shape[1];

      if ((k & 127) != 0) return false;

      if (ref_k < 0)
        ref_k = k;
      else if (k != ref_k)
        return false;
    }

    return true;
  };

  auto is_supported_dtype = [&]() -> bool {
    auto *inputA = transformer_engine::convertNVTETensorCheck(A[0]);
    auto *inputB = transformer_engine::convertNVTETensorCheck(B[0]);
    auto *OutputD = transformer_engine::convertNVTETensorCheck(D[0]);
    auto A_type = get_cuda_dtype(inputA->data.dtype);
    auto B_type = get_cuda_dtype(inputB->data.dtype);
    auto D_type = get_cuda_dtype(OutputD->data.dtype);

    return (A_type == B_type) && (A_type == D_type) &&
           ((A_type == CUDA_R_16BF) || (A_type == CUDA_R_16F));
  };

  // CUTLASS Grouped GEMM fast path (SM90/TMA)
  // Conditions:
  //  - No fused epilogue: both bias and pre_gelu_out are empty.
  //  - Supported dtypes only: FP16/BF16 (FP32 accumulate).
  //  - Uniform K across groups and K % 128 == 0.
  //  - use_split_accumulator is ignored for FP16/BF16.
  //  - grad is irrelevant when bias/pre_gelu_out are empty.
  //
  // Otherwise, fall back to cuBLAS.
  if (is_empty_arr(bias) && is_empty_arr(pre_gelu_out) && is_supported_dtype() &&
      all_groups_uniform_k128(B, transb)) {
    cutlass_grouped_gemm(A, B, D, num_gemms, transa, transb, grad, workspace, accumulate,
                         current_device, math_sm_count, stream);
  } else {
    if (warn_fallback) {
      NVTE_WARN("Fallback to cuBLAS grouped GEMM.");
    }
    cublas_path();
  }
#endif
}


