mlp_cuda.cu

// New MLP with denorm mitigation only for backprop

#include <ATen/ATen.h>
#include <ATen/cuda/CUDAContext.h>
#include <assert.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <torch/torch.h>

/* Includes, cuda */
#include <cublas_v2.h>
#include <cuda_runtime.h>
#include "utils.h"

#if defined(CUBLAS_VERSION) && CUBLAS_VERSION >= 11000
// includes cublaslt
#include <cublasLt.h>
#endif
// constants for fused bias+relu kernel
#define BIAS_RELU_FW_NTHREADS 128 // forward number of thread per block
#define BIAS_RELU_BW_NTHREADS_X 32 // backward number of thread in feature dim
#define BIAS_RELU_BW_NTHREADS_Y 16 // backward number of thread in batch dim
#define BIAS_RELU_RED_PER_THREAD 16 // backward minimal reduction length per thread



// move to a header later on
#define ILP 4
template<typename T>
__host__ __device__ __forceinline__ bool is_aligned(T* p){
  return ((uint64_t)p) % (ILP*sizeof(T)) == 0;
}

template<typename T>
__device__ __forceinline__ void load_store(T* dst, T* src, int dst_offset, int src_offset){
  typedef typename std::aligned_storage<ILP*sizeof(T), ILP*alignof(T)>::type LT;
  ((LT*)dst)[dst_offset] = ((LT*)src)[src_offset];
}
template<typename T>
__device__ __forceinline__ void load_store(T* dst, volatile T* src, int dst_offset, int src_offset){
  typedef typename std::aligned_storage<ILP*sizeof(T), ILP*alignof(T)>::type LT;
  ((LT*)dst)[dst_offset] = ((LT*)src)[src_offset];
}
template<typename T>
__device__ __forceinline__ void load_store(volatile T* dst, T* src, int dst_offset, int src_offset){
  typedef typename std::aligned_storage<ILP*sizeof(T), ILP*alignof(T)>::type LT;
  ((LT*)dst)[dst_offset] = ((LT*)src)[src_offset];
}

// Keep ReLU in float only. When using half, cast to float before calling.
__device__ __inline__ float relu(float a) {
  float retf = max(a, 0.f);
  return (retf);
}

// Keep Sigmoid in float only. When using half, cast to float before calling.
__device__ __inline__ float sigmoid(float a) {
  float retf = 1.f / (1.f + expf(-a));
  return (retf);
}

// FP64 Wrapper around cublas GEMMEx
cublasStatus_t mlp_gemm(
    cublasHandle_t handle,
    cublasOperation_t transa,
    cublasOperation_t transb,
    int m,
    int n,
    int k,
    float* alpha,
    const double* A,
    int lda,
    const double* B,
    int ldb,
    const float* beta,
    double* C,
    int ldc,
    int flag) {
#ifdef __HIP_PLATFORM_HCC__
  return rocblas_gemm_ex(
      handle,
      transa,
      transb,
      m,
      n,
      k,
      alpha,
      A,
      rocblas_datatype_f64_r,
      lda,
      B,
      rocblas_datatype_f64_r,
      ldb,
      beta,
      C,
      rocblas_datatype_f64_r,
      ldc,
      C,
      rocblas_datatype_f64_r,
      ldc,
      rocblas_datatype_f64_r,
      rocblas_gemm_algo_standard,
      0,
      flag);  
#else
  return cublasGemmEx(
      handle,
      transa,
      transb,
      m,
      n,
      k,
      alpha,
      A,
      CUDA_R_64F,
      lda,
      B,
      CUDA_R_64F,
      ldb,
      beta,
      C,
      CUDA_R_64F,
      ldc,
      CUDA_R_64F,
      CUBLAS_GEMM_DEFAULT);
#endif
}

// FP32 Wrapper around cublas GEMMEx
cublasStatus_t mlp_gemm(
    cublasHandle_t handle,
    cublasOperation_t transa,
    cublasOperation_t transb,
    int m,
    int n,
    int k,
    float* alpha,
    const float* A,
    int lda,
    const float* B,
    int ldb,
    const float* beta,
    float* C,
    int ldc,
    int flag) {
#ifdef __HIP_PLATFORM_HCC__
  return rocblas_gemm_ex(
      handle,
      transa,
      transb,
      m,
      n,
      k,
      alpha,
      A,
      rocblas_datatype_f32_r,
      lda,
      B,
      rocblas_datatype_f32_r,
      ldb,
      beta,
      C,
      rocblas_datatype_f32_r,
      ldc,
      C,
      rocblas_datatype_f32_r,
      ldc,
      rocblas_datatype_f32_r,
      rocblas_gemm_algo_standard,
      0,
      flag);

#else
  return cublasGemmEx(
      handle,
      transa,
      transb,
      m,
      n,
      k,
      alpha,
      A,
      CUDA_R_32F,
      lda,
      B,
      CUDA_R_32F,
      ldb,
      beta,
      C,
      CUDA_R_32F,
      ldc,
      CUDA_R_32F,
      CUBLAS_GEMM_DEFAULT);
#endif
}

// FP16 Tensor core wrapper around cublas GEMMEx
cublasStatus_t mlp_gemm(
    cublasHandle_t handle,
    cublasOperation_t transa,
    cublasOperation_t transb,
    int m,
    int n,
    int k,
    float* alpha,
    const at::Half* A,
    int lda,
    const at::Half* B,
    int ldb,
    float* beta,
    at::Half* C,
    int ldc,
    int flag) {
#ifdef __HIP_PLATFORM_HCC__
  if (parseEnvVarFlag("APEX_ROCBLAS_GEMM_ALLOW_HALF")) {
    half h_alpha = __float2half(*alpha);
    half h_beta = __float2half(*beta);
    return rocblas_gemm_ex(
        handle,
        transa,
        transb,
        m,
        n,
        k,
        /* alpha */ &h_alpha,
        A,
        rocblas_datatype_f16_r,
        lda,
        B,
        rocblas_datatype_f16_r,
        ldb,
        /* beta */ &h_beta,
        C,
        rocblas_datatype_f16_r,
        ldc,
        C,
        rocblas_datatype_f16_r,
        ldc,
        /* rocblas_datatype_f32_r */ rocblas_datatype_f16_r,
        rocblas_gemm_algo_standard,
        0,
        flag);
  } else {
    return rocblas_gemm_ex(
        handle,
        transa,
        transb,
        m,
        n,
        k,
        alpha,
        A,
        rocblas_datatype_f16_r,
        lda,
        B,
        rocblas_datatype_f16_r,
        ldb,
        beta,
        C,
        rocblas_datatype_f16_r,
        ldc,
        C,
        rocblas_datatype_f16_r,
        ldc,
        rocblas_datatype_f32_r,
        rocblas_gemm_algo_standard,
        0,
        flag);
  }
#else
  return cublasGemmEx(
      handle,
      transa,
      transb,
      m,
      n,
      k,
      alpha,
      A,
      CUDA_R_16F,
      lda,
      B,
      CUDA_R_16F,
      ldb,
      beta,
      C,
      CUDA_R_16F,
      ldc,
      CUDA_R_32F,
      CUBLAS_GEMM_DEFAULT_TENSOR_OP);
#endif
}
#if defined(CUBLAS_VERSION) && CUBLAS_VERSION >= 11000
int mlp_gemm_lt(
    cublasLtHandle_t ltHandle,
    cublasOperation_t transa,
    cublasOperation_t transb,
    int m,
    int n,
    int k,
    float *alpha, /* host pointer */
    const at::Half* A,
    int lda,
    const at::Half* B,
    int ldb,
    float *beta, /* host pointer */
    at::Half* C,
    int ldc,
    void *workspace,
    size_t workspaceSize,
    cudaStream_t stream,
    bool use_bias,
    bool use_relu,
    const void* bias) {
  cublasStatus_t status = CUBLAS_STATUS_SUCCESS;

  cublasLtMatmulDescOpaque_t operationDesc = {};
  cublasLtMatrixLayoutOpaque_t Adesc = {}, Bdesc = {}, Cdesc = {};
  cublasLtMatmulPreferenceOpaque_t preference = {};

  int returnedResults                             = 0;
  cublasLtMatmulHeuristicResult_t heuristicResult = {};
  cublasLtEpilogue_t epilogue = CUBLASLT_EPILOGUE_DEFAULT;

  // Create operation descriptor; see cublasLtMatmulDescAttributes_t
  // for details about defaults; here we just set the transforms for
  // A and B.
  status = cublasLtMatmulDescInit(&operationDesc, CUBLAS_COMPUTE_32F, CUDA_R_32F);
  if (status != CUBLAS_STATUS_SUCCESS) goto CLEANUP;
  status = cublasLtMatmulDescSetAttribute(&operationDesc, CUBLASLT_MATMUL_DESC_TRANSA, &transa, sizeof(transa));
  if (status != CUBLAS_STATUS_SUCCESS) goto CLEANUP;
  status = cublasLtMatmulDescSetAttribute(&operationDesc, CUBLASLT_MATMUL_DESC_TRANSB, &transb, sizeof(transa));
  if (status != CUBLAS_STATUS_SUCCESS) goto CLEANUP;

  if (use_bias) {
    status = cublasLtMatmulDescSetAttribute(&operationDesc, CUBLASLT_MATMUL_DESC_BIAS_POINTER, &bias, sizeof(bias));
    if (status != CUBLAS_STATUS_SUCCESS) {
      goto CLEANUP;
    }
    if (use_relu) {
      epilogue = CUBLASLT_EPILOGUE_RELU_BIAS;
    } else {
      epilogue = CUBLASLT_EPILOGUE_BIAS;
    }
  } else {
    if (use_relu) {
      epilogue = CUBLASLT_EPILOGUE_RELU;
    }
  }

  status = cublasLtMatmulDescSetAttribute(&operationDesc, CUBLASLT_MATMUL_DESC_EPILOGUE, &epilogue, sizeof(epilogue));
  if (status != CUBLAS_STATUS_SUCCESS) {
    goto CLEANUP;
  }

  // Create matrix descriptors. Not setting any extra attributes.
  status = cublasLtMatrixLayoutInit(
    &Adesc, CUDA_R_16F, transa == CUBLAS_OP_N ? m : k, transa == CUBLAS_OP_N ? k : m, lda);
  if (status != CUBLAS_STATUS_SUCCESS) goto CLEANUP;
  status = cublasLtMatrixLayoutInit(
    &Bdesc, CUDA_R_16F, transb == CUBLAS_OP_N ? k : n, transb == CUBLAS_OP_N ? n : k, ldb);
  if (status != CUBLAS_STATUS_SUCCESS) goto CLEANUP;
  status = cublasLtMatrixLayoutInit(&Cdesc, CUDA_R_16F, m, n, ldc);
  if (status != CUBLAS_STATUS_SUCCESS) goto CLEANUP;

  // Create preference handle; In general, extra attributes can be
  // used here to disable tensor ops or to make sure algo selected
  // will work with badly aligned A, B, C. However, for simplicity
  // here we assume A,B,C are always well aligned (e.g., directly
  // come from cudaMalloc)
  status = cublasLtMatmulPreferenceInit(&preference);
  if (status != CUBLAS_STATUS_SUCCESS) goto CLEANUP;
  status = cublasLtMatmulPreferenceSetAttribute(
    &preference, CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES, &workspaceSize, sizeof(workspaceSize));
  if (status != CUBLAS_STATUS_SUCCESS) goto CLEANUP;

  // We just need the best available heuristic to try and run matmul.
  // There is no guarantee that this will work. For example, if A is
  // badly aligned, you can request more (e.g. 32) algos and try to
  // run them one by one until something works.
  status = cublasLtMatmulAlgoGetHeuristic(
    ltHandle, &operationDesc, &Adesc, &Bdesc, &Cdesc, &Cdesc, &preference, 1, &heuristicResult, &returnedResults);
  if (status != CUBLAS_STATUS_SUCCESS) goto CLEANUP;

  if (returnedResults == 0) {
    status = CUBLAS_STATUS_NOT_SUPPORTED;
    goto CLEANUP;
  }
  status = cublasLtMatmul(ltHandle,
                          &operationDesc,
                          alpha,
                          A,
                          &Adesc,
                          B,
                          &Bdesc,
                          beta,
                          C,
                          &Cdesc,
                          C,
                          &Cdesc,
                          &heuristicResult.algo,
                          workspace,
                          workspaceSize,
                          stream);

CLEANUP:
  // Descriptors are no longer needed as all GPU work was already
  // enqueued.
  return status == CUBLAS_STATUS_SUCCESS ? 0 : 1;
}

int mlp_gemm_lt(
    cublasLtHandle_t ltHandle,
    cublasOperation_t transa,
    cublasOperation_t transb,
    int m,
    int n,
    int k,
    float *alpha, /* host pointer */
    const double* A,
    int lda,
    const double* B,
    int ldb,
    float *beta, /* host pointer */
    double* C,
    int ldc,
    void *workspace,
    size_t workspaceSize,
    cudaStream_t stream,
    bool use_bias,
    bool use_relu,
    const void* bias) {
  return 1;
}

int mlp_gemm_lt(
    cublasLtHandle_t ltHandle,
    cublasOperation_t transa,
    cublasOperation_t transb,
    int m,
    int n,
    int k,
    float *alpha, /* host pointer */
    const float *A,
    int lda,
    const float *B,
    int ldb,
    float *beta, /* host pointer */
    float *C,
    int ldc,
    void *workspace,
    size_t workspaceSize,
    cudaStream_t stream,
    bool use_bias,
    bool use_relu,
    const void* bias) {
  cublasStatus_t status = CUBLAS_STATUS_SUCCESS;

  cublasLtMatmulDescOpaque_t operationDesc = {};
  cublasLtMatrixLayoutOpaque_t Adesc = {}, Bdesc = {}, Cdesc = {};
  cublasLtMatmulPreferenceOpaque_t preference = {};

  int returnedResults                             = 0;
  cublasLtMatmulHeuristicResult_t heuristicResult = {};
  cublasLtEpilogue_t epilogue = CUBLASLT_EPILOGUE_DEFAULT;

  // Create operation descriptor; see cublasLtMatmulDescAttributes_t
  // for details about defaults; here we just set the transforms for
  // A and B.
  status = cublasLtMatmulDescInit(&operationDesc, CUBLAS_COMPUTE_32F, CUDA_R_32F);
  if (status != CUBLAS_STATUS_SUCCESS) goto CLEANUP;
  status = cublasLtMatmulDescSetAttribute(&operationDesc, CUBLASLT_MATMUL_DESC_TRANSA, &transa, sizeof(transa));
  if (status != CUBLAS_STATUS_SUCCESS) goto CLEANUP;
  status = cublasLtMatmulDescSetAttribute(&operationDesc, CUBLASLT_MATMUL_DESC_TRANSB, &transb, sizeof(transa));
  if (status != CUBLAS_STATUS_SUCCESS) goto CLEANUP;

  if (use_bias) {
    status = cublasLtMatmulDescSetAttribute(&operationDesc, CUBLASLT_MATMUL_DESC_BIAS_POINTER, &bias, sizeof(bias));
    if (status != CUBLAS_STATUS_SUCCESS) {
      goto CLEANUP;
    }
    if (use_relu) {
      epilogue = CUBLASLT_EPILOGUE_RELU_BIAS;
    } else {
      epilogue = CUBLASLT_EPILOGUE_BIAS;
    }
  } else {
    if (use_relu) {
      epilogue = CUBLASLT_EPILOGUE_RELU;
    }
  }

  status = cublasLtMatmulDescSetAttribute(&operationDesc, CUBLASLT_MATMUL_DESC_EPILOGUE, &epilogue, sizeof(epilogue));
  if (status != CUBLAS_STATUS_SUCCESS) {
    goto CLEANUP;
  }

  // Create matrix descriptors. Not setting any extra attributes.
  status = cublasLtMatrixLayoutInit(
    &Adesc, CUDA_R_32F, transa == CUBLAS_OP_N ? m : k, transa == CUBLAS_OP_N ? k : m, lda);
  if (status != CUBLAS_STATUS_SUCCESS) goto CLEANUP;
  status = cublasLtMatrixLayoutInit(
    &Bdesc, CUDA_R_32F, transb == CUBLAS_OP_N ? k : n, transb == CUBLAS_OP_N ? n : k, ldb);
  if (status != CUBLAS_STATUS_SUCCESS) goto CLEANUP;
  status = cublasLtMatrixLayoutInit(&Cdesc, CUDA_R_32F, m, n, ldc);
  if (status != CUBLAS_STATUS_SUCCESS) goto CLEANUP;

  // Create preference handle; In general, extra attributes can be
  // used here to disable tensor ops or to make sure algo selected
  // will work with badly aligned A, B, C. However, for simplicity
  // here we assume A,B,C are always well aligned (e.g., directly
  // come from cudaMalloc)
  status = cublasLtMatmulPreferenceInit(&preference);
  if (status != CUBLAS_STATUS_SUCCESS) goto CLEANUP;
  status = cublasLtMatmulPreferenceSetAttribute(
    &preference, CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES, &workspaceSize, sizeof(workspaceSize));
  if (status != CUBLAS_STATUS_SUCCESS) goto CLEANUP;

  // We just need the best available heuristic to try and run matmul.
  // There is no guarantee that this will work. For example, if A is
  // badly aligned, you can request more (e.g. 32) algos and try to
  // run them one by one until something works.
  status = cublasLtMatmulAlgoGetHeuristic(
    ltHandle, &operationDesc, &Adesc, &Bdesc, &Cdesc, &Cdesc, &preference, 1, &heuristicResult, &returnedResults);
  if (status != CUBLAS_STATUS_SUCCESS) goto CLEANUP;

  if (returnedResults == 0) {
    status = CUBLAS_STATUS_NOT_SUPPORTED;
    goto CLEANUP;
  }

  status = cublasLtMatmul(ltHandle,
                          &operationDesc,
                          alpha,
                          A,
                          &Adesc,
                          B,
                          &Bdesc,
                          beta,
                          C,
                          &Cdesc,
                          C,
                          &Cdesc,
                          &heuristicResult.algo,
                          workspace,
                          workspaceSize,
                          stream);

CLEANUP:
  // Descriptors are no longer needed as all GPU work was already
  // enqueued.
  return status == CUBLAS_STATUS_SUCCESS ? 0 : 1;
}
#endif

// Bias ADD. Assume input X is [features x batch size], column major.
// Bias is one 'features' long vector, with implicit broadcast.
template <typename T>
__global__ void biasAdd_fprop(T *X, T *b, uint batch_size, uint features) {
  T r_x[ILP];
  T r_b[ILP];
  if(is_aligned(X) && is_aligned(b) && features % ILP ==0) {
    int tid = blockIdx.x * blockDim.x + threadIdx.x;
    for (; tid*ILP < features * batch_size; tid += blockDim.x * gridDim.x) {
      int row = tid % (features / ILP);
      load_store(r_x, X, 0 , tid);
      load_store(r_b, b, 0 , row);
#pragma unroll
      for(int ii = 0; ii < ILP; ii++) {
        float bias_sum = static_cast<float>(r_x[ii]) + static_cast<float>(r_b[ii]);
        r_x[ii] = bias_sum;
      }
      load_store(X, r_x, tid , 0);
    }
  } else {
    int tid = blockIdx.x * blockDim.x + threadIdx.x;
    for (; tid < features * batch_size; tid += ILP * blockDim.x * gridDim.x) {
#pragma unroll
      for(int ii = 0; ii < ILP; ii++) {
        int idx = tid + ii * blockDim.x * gridDim.x;
        if(idx < features * batch_size) {
          int row = tid % features;
          r_x[ii] = X[idx];
          r_b[ii] = b[row];
        }
      }
#pragma unroll
      for(int ii = 0; ii < ILP; ii++) {
        float bias_sum = static_cast<float>(r_x[ii]) + static_cast<float>(r_b[ii]);
        r_x[ii] = bias_sum;
      }
#pragma unroll
      for(int ii = 0; ii < ILP; ii++) {
        int idx = tid + ii * blockDim.x * gridDim.x;
        if(idx < features * batch_size) {
          X[idx] = r_x[ii];
        }
      }
    }
  }
}

// Bias ADD + ReLU. Assume input X is [features x batch size], column major.
// Activation support fuesed ReLU. Safe to call in-place.
template <typename T>
__global__ void biasAddRelu_fprop(T *X, T *b, uint batch_size, uint features) {
  T r_x[ILP];
  T r_b[ILP];
  if(is_aligned(X) && is_aligned(b) && features % ILP ==0) {
    int tid = blockIdx.x * blockDim.x + threadIdx.x;
    for (; tid*ILP < features * batch_size; tid += blockDim.x * gridDim.x) {
      int row = tid % (features / ILP);
      load_store(r_x, X, 0 , tid);
      load_store(r_b, b, 0 , row);
#pragma unroll
      for(int ii = 0; ii < ILP; ii++) {
        float bias_sum = static_cast<float>(r_x[ii]) + static_cast<float>(r_b[ii]);
        r_x[ii] = relu(bias_sum);
      }
      load_store(X, r_x, tid , 0);
    }
  } else {
    int tid = blockIdx.x * blockDim.x + threadIdx.x;
    for (; tid < features * batch_size; tid += ILP * blockDim.x * gridDim.x) {
#pragma unroll
      for(int ii = 0; ii < ILP; ii++) {
        int idx = tid + ii * blockDim.x * gridDim.x;
        if(idx < features * batch_size) {
          int row = tid % features;
          r_x[ii] = X[idx];
          r_b[ii] = b[row];
        }
      }
#pragma unroll
      for(int ii = 0; ii < ILP; ii++) {
        float bias_sum = static_cast<float>(r_x[ii]) + static_cast<float>(r_b[ii]);
        r_x[ii] = relu(bias_sum);
      }
#pragma unroll
      for(int ii = 0; ii < ILP; ii++) {
        int idx = tid + ii * blockDim.x * gridDim.x;
        if(idx < features * batch_size) {
          X[idx] = r_x[ii];
        }
      }
    }
  }
}

// ReLU. Assume input X is [features x batch size], column major.
// Safe to call in-place.
template <typename T>
__global__ void Relu_fprop(T *X, uint batch_size, uint features) {
  T r_x[ILP];
  if(is_aligned(X) && features % ILP ==0) {
    int tid = blockIdx.x * blockDim.x + threadIdx.x;
    for (; tid*ILP < features * batch_size; tid += blockDim.x * gridDim.x) {
      load_store(r_x, X, 0 , tid);
#pragma unroll
      for(int ii = 0; ii < ILP; ii++) {
        r_x[ii] = relu(static_cast<float>(r_x[ii]));
      }
      load_store(X, r_x, tid , 0);
    }
  } else {
    int tid = blockIdx.x * blockDim.x + threadIdx.x;
    for (; tid < features * batch_size; tid += ILP * blockDim.x * gridDim.x) {
#pragma unroll
      for(int ii = 0; ii < ILP; ii++) {
        int idx = tid + ii * blockDim.x * gridDim.x;
        if(idx < features * batch_size) {
          r_x[ii] = X[idx];
        }
      }
#pragma unroll
      for(int ii = 0; ii < ILP; ii++) {
        r_x[ii] = relu(static_cast<float>(r_x[ii]));
      }
#pragma unroll
      for(int ii = 0; ii < ILP; ii++) {
        int idx = tid + ii * blockDim.x * gridDim.x;
        if(idx < features * batch_size) {
          X[idx] = r_x[ii];
        }
      }
    }
  }
}

// Sigmoid. Assume input X is [features x batch size], column major.
// Safe to call in-place.
template <typename T>
__global__ void Sigmoid_fprop(T *X, uint batch_size, uint features) {
  T r_x[ILP];
  if(is_aligned(X) && features % ILP ==0) {
    int tid = blockIdx.x * blockDim.x + threadIdx.x;
    for (; tid*ILP < features * batch_size; tid += blockDim.x * gridDim.x) {
      load_store(r_x, X, 0 , tid);
#pragma unroll
      for(int ii = 0; ii < ILP; ii++) {
        r_x[ii] = sigmoid(static_cast<float>(r_x[ii]));
      }
      load_store(X, r_x, tid , 0);
    }
  } else {
    int tid = blockIdx.x * blockDim.x + threadIdx.x;
    for (; tid < features * batch_size; tid += ILP * blockDim.x * gridDim.x) {
#pragma unroll
      for(int ii = 0; ii < ILP; ii++) {
        int idx = tid + ii * blockDim.x * gridDim.x;
        if(idx < features * batch_size) {
          r_x[ii] = X[idx];
        }
      }
#pragma unroll
      for(int ii = 0; ii < ILP; ii++) {
        r_x[ii] = sigmoid(static_cast<float>(r_x[ii]));
      }
#pragma unroll
      for(int ii = 0; ii < ILP; ii++) {
        int idx = tid + ii * blockDim.x * gridDim.x;
        if(idx < features * batch_size) {
          X[idx] = r_x[ii];
        }
      }
    }
  }
}

// ReLU. Assume input X is [features x batch size], column major.
// Safe to call in-place.
template <typename T>
__global__ void Relu_bprop(T *dY, T *Y, uint batch_size, uint features, T *dX) {
  T r_dy[ILP];
  T r_y[ILP];
  if(is_aligned(dY) &&
     is_aligned(Y) &&
     is_aligned(dX) &&
     features % ILP ==0) {
    int tid = blockIdx.x * blockDim.x + threadIdx.x;
    for (; tid*ILP < features * batch_size; tid += blockDim.x * gridDim.x) {
      load_store(r_dy, dY, 0 , tid);
      load_store(r_y, Y, 0 , tid);
#pragma unroll
      for(int ii=0;ii<ILP;ii++){
        if ((float)r_y[ii] <= 0.f)
          r_dy[ii] = 0;
      }
      load_store(dX, r_dy, tid, 0);
    }
  } else {
    int tid = blockIdx.x * blockDim.x + threadIdx.x;
    for (; tid < features * batch_size; tid += ILP * blockDim.x * gridDim.x) {
#pragma unroll
      for(int ii = 0; ii < ILP; ii++) {
        int idx = tid + ii * blockDim.x * gridDim.x;
        if(idx < features * batch_size) {
          r_dy[ii] = dY[idx];
          r_y[ii] = Y[idx];
        }
      }
#pragma unroll
      for(int ii = 0; ii < ILP; ii++) {
        if ((float)r_y[ii] <= 0.f)
          r_dy[ii] = 0;
      }
#pragma unroll
      for(int ii = 0; ii < ILP; ii++) {
        int idx = tid + ii * blockDim.x * gridDim.x;
        if(idx < features * batch_size) {
          dX[idx] = r_dy[ii];
        }
      }
    }
  }
}

// Sigmoid. Assume input X is [features x batch size], column major.
// Safe to call in-place.
template <typename T>
__global__ void Sigmoid_bprop(T *dY, T *Y, uint batch_size, uint features, T *dX) {
  T r_dy[ILP];
  T r_y[ILP];
  if(is_aligned(dY) &&
     is_aligned(Y) &&
     is_aligned(dX) &&
     features % ILP ==0) {
    int tid = blockIdx.x * blockDim.x + threadIdx.x;
    for (; tid*ILP < features * batch_size; tid += blockDim.x * gridDim.x) {
      load_store(r_dy, dY, 0 , tid);
      load_store(r_y, Y, 0 , tid);
#pragma unroll
      for(int ii=0;ii<ILP;ii++){
        float grad_out = r_dy[ii];
        float out = r_y[ii];
        float grad_i = out * ( 1.f - out) * grad_out;
        r_dy[ii] = grad_i;
      }
      load_store(dX, r_dy, tid, 0);
    }
  } else {
    int tid = blockIdx.x * blockDim.x + threadIdx.x;
    for (; tid < features * batch_size; tid += ILP * blockDim.x * gridDim.x) {
#pragma unroll
      for(int ii = 0; ii < ILP; ii++) {
        int idx = tid + ii * blockDim.x * gridDim.x;
        if(idx < features * batch_size) {
          r_dy[ii] = dY[idx];
          r_y[ii] = Y[idx];
        }
      }
#pragma unroll
      for(int ii = 0; ii < ILP; ii++) {
        float grad_out = r_dy[ii];
        float out = r_y[ii];
        float grad_i = out * ( 1.f - out) * grad_out;
        r_dy[ii] = grad_i;
      }
#pragma unroll
      for(int ii = 0; ii < ILP; ii++) {
        int idx = tid + ii * blockDim.x * gridDim.x;
        if(idx < features * batch_size) {
          dX[idx] = r_dy[ii];
        }
      }
    }
  }
}

// Compute grid size for pointwise backward kernel.
// block_x/y is total elment being handled per block, not number of threads
void get_biasAddRelu_bprop_grid_size(
    int yfeat,
    int batch_size,
    int block_x,
    int block_y,
    int* grid_x,
    int* grid_y) {

  *grid_x = (yfeat + block_x - 1) / block_x;
  // Get number of SMs for efficient reduction.
  int num_SMs = at::cuda::getCurrentDeviceProperties()->multiProcessorCount;
  // can switch to occupancy calculation. use 4 below now for sm_70
  int max_blocks_y = (num_SMs * 4+(*grid_x)-1) / (*grid_x);
  // block_y should be from minimal work per thread
  int nRedSplits = (batch_size + block_y - 1) / block_y;
  // increase number of elem per thread redcution to not launch more than enough
  // kernel adjust work, so here we just launch max block
  *grid_y = std::min(nRedSplits, max_blocks_y);
  return;
}

// Addition done deterministically via a 2-pass approach. Each CTA writes out partial
// sum, and the last CTA in grid Y dimension accumulates partials serially and writes to result.
template <typename T, int UNROLL_FACTOR>
__global__ void biasAdd_bprop(
    T* dY,
    int features,
    int batch_size,
    volatile float* intermediate,
    int* semaphores,
    T* db) {
  // The feature that this thread is responsible for
  int f = blockIdx.x * blockDim.x + threadIdx.x;

  // Compute the span this thread is responsible for
  // For this block
  int b_chunkSize = (batch_size + gridDim.y - 1) / gridDim.y;
  int b_nStart = blockIdx.y * b_chunkSize;
  int b_nSpan = min(batch_size, b_nStart + b_chunkSize) - b_nStart;
  // For this thread
  int chunkSize = (b_chunkSize + blockDim.y - 1) / blockDim.y;
  int nStart = threadIdx.y * chunkSize + b_nStart;
  int nSpan = min(b_nStart + b_nSpan, nStart + chunkSize) - nStart;

  volatile float* out = intermediate + blockIdx.y * features;

  // Flag to trigger last reduction.
  __shared__ bool isLastBlock;
  // we know block size for now
  __shared__ float smem[BIAS_RELU_BW_NTHREADS_X*BIAS_RELU_BW_NTHREADS_Y];

  // Accumulate db in FP32 always
  float db_local = 0;
  if (f < features) {
    int nidx = 0;
    // Handle non-multiple of UNROLL_FACTOR residue
    for (; nidx < nSpan % UNROLL_FACTOR; nidx++) {
      int64_t row, col, flat_idx;
      row = f;
      col = nStart + nidx;
      flat_idx = col * features + row;
      db_local += (float)dY[flat_idx];
    }

    // Handle meat of work
    for (; (nidx + UNROLL_FACTOR - 1) < nSpan; nidx += UNROLL_FACTOR) {
      int64_t row, col, flat_idx;
      row = f;
      col = nStart + nidx;
      flat_idx = col * features + row;
#pragma unroll 4
      for (int u = 0; u < UNROLL_FACTOR; u++) {
        db_local += (float)dY[flat_idx];
        flat_idx += features;
      }
    }

    // naive block reduction on y-dim
    int linear_idx = threadIdx.y * blockDim.x + threadIdx.x;
    smem[linear_idx] = db_local;
  }
  __syncthreads();
  if (f < features) {
    if(threadIdx.y == 0) {
      for(int yidx = 1; yidx < blockDim.y; yidx++){
        db_local += smem[yidx * blockDim.x + threadIdx.x];
      }

      // block result is in db_local now for all threadIdx.y == 0
      // Write out partial result
      out[f] = db_local;
    }
  }
  __threadfence();
  __syncthreads();

  // Increment semaphore and check if this is the last CTA in the grid_y dimension.
  // Only thread (0,0) calls this
  if (threadIdx.x == 0 && threadIdx.y == 0 && f < features) {
    unsigned int sum_idx;
    sum_idx = atomicAdd(&(semaphores[blockIdx.x]), 1);
    isLastBlock = (sum_idx == (gridDim.y - 1));
  }
  __syncthreads();

  db_local = 0;
  // No block reduction for now, only thread (*,0) do grid reduction
  if (isLastBlock && f < features) {
    if(threadIdx.y == 0) {
      for (int n = 0; n < gridDim.y; n++) {
        int row, col;
        row = f;
        col = n;
        db_local += (float)(intermediate[col * features + row]);
      }
      db[f] = (T)db_local;
    }
  }
}

// Addition done deterministically via a 2-pass approach. Each CTA writes out partial
// sum, and the last CTA in grid Y dimension accumulates partials serially and writes to result.
template <typename T, int UNROLL_FACTOR>
__global__ void biasAddRelu_bprop(
    T* Y,
    T* dY,
    int features,
    int batch_size,
    T* dX,
    volatile float* intermediate,
    int* semaphores,
    T* db) {
  // The feature that this thread is responsible for
  int f = blockIdx.x * blockDim.x + threadIdx.x;

  // Compute the span this thread is responsible for
  // For this block
  int b_chunkSize = (batch_size + gridDim.y - 1) / gridDim.y;
  int b_nStart = blockIdx.y * b_chunkSize;
  int b_nSpan = min(batch_size, b_nStart + b_chunkSize) - b_nStart;
  // For this thread
  int chunkSize = (b_chunkSize + blockDim.y - 1) / blockDim.y;
  int nStart = threadIdx.y * chunkSize + b_nStart;
  int nSpan = min(b_nStart + b_nSpan, nStart + chunkSize) - nStart;

  volatile float* out = intermediate + blockIdx.y * features;

  // Flag to trigger last reduction.
  __shared__ bool isLastBlock;
  // we know block size for now
  __shared__ float smem[BIAS_RELU_BW_NTHREADS_X*BIAS_RELU_BW_NTHREADS_Y];

  // Accumulate db in FP32 always
  float db_local = 0;
  if (f < features) {
    int nidx = 0;
    // Handle non-multiple of UNROLL_FACTOR residue
    for (; nidx < nSpan % UNROLL_FACTOR; nidx++) {
      int row, col, flat_idx;
      row = f;
      col = nStart + nidx;
      flat_idx = col * features + row;
      T y_val = Y[flat_idx];
      T dy_val = dY[flat_idx];
      T dx_val;
      if ((float)y_val > 0.f)
        dx_val = dy_val;
      else
        dx_val = 0;
      dX[flat_idx] = dx_val;
      db_local += (float)dx_val;
    }

    // Handle meat of work
    for (; (nidx + UNROLL_FACTOR - 1) < nSpan; nidx += UNROLL_FACTOR) {
      int row, col, flat_idx;
      row = f;
      col = nStart + nidx;
      flat_idx = col * features + row;
#pragma unroll 4
      for (int u = 0; u < UNROLL_FACTOR; u++) {
        T y_val = Y[flat_idx];
        T dy_val = dY[flat_idx];
        T dx_val;
        if ((float)y_val > 0.f)
          dx_val = dy_val;
        else
          dx_val = 0;
        dX[flat_idx] = dx_val;
        db_local += (float)dx_val;
        flat_idx += features;
      }
    }

    // naive block reduction on y-dim
    int linear_idx = threadIdx.y * blockDim.x + threadIdx.x;
    smem[linear_idx] = db_local;
  }
  __syncthreads();
  if (f < features) {
    if(threadIdx.y == 0) {
      for(int yidx = 1; yidx < blockDim.y; yidx++){
        db_local += smem[yidx * blockDim.x + threadIdx.x];
      }

      // block result is in db_local now for all threadIdx.y == 0
      // Write out partial result
      out[f] = db_local;
    }
  }
  __threadfence();
  __syncthreads();

  // Increment semaphore and check if this is the last CTA in the grid_y dimension.
  // Only thread (0,0) calls this
  if (threadIdx.x == 0 && threadIdx.y == 0 && f < features) {
    unsigned int sum_idx;
    sum_idx = atomicAdd(&(semaphores[blockIdx.x]), 1);
    isLastBlock = (sum_idx == (gridDim.y - 1));
  }
  __syncthreads();

  db_local = 0;
  // No block reduction for now, only thread (*,0) do grid reduction
  if (isLastBlock && f < features) {
    if(threadIdx.y == 0) {
      for (int n = 0; n < gridDim.y; n++) {
        int row, col;
        row = f;
        col = n;
        db_local += (float)(intermediate[col * features + row]);
      }
      db[f] = (T)db_local;
    }
  }
}

// Addition done deterministically via a 2-pass approach. Each CTA writes out partial
// sum, and the last CTA in grid Y dimension accumulates partials serially and writes to result.
template <typename T, int UNROLL_FACTOR>
__global__ void biasAddRelu_bprop_aligned(
    T* Y,
    T* dY,
    int features,
    int batch_size,
    T* dX,
    volatile float* intermediate,
    int* semaphores,
    T* db) {
  // The feature that this thread is responsible for
  int f = blockIdx.x * blockDim.x + threadIdx.x;

  // Compute the span this thread is responsible for
  // For this block
  int b_chunkSize = (batch_size + gridDim.y - 1) / gridDim.y;
  int b_nStart = blockIdx.y * b_chunkSize;
  int b_nSpan = min(batch_size, b_nStart + b_chunkSize) - b_nStart;
  // For this thread
  int chunkSize = (b_chunkSize + blockDim.y - 1) / blockDim.y;
  int nStart = threadIdx.y * chunkSize + b_nStart;
  int nSpan = min(b_nStart + b_nSpan, nStart + chunkSize) - nStart;

  volatile float* out = intermediate + blockIdx.y * features;

  // Flag to trigger last reduction.
  __shared__ bool isLastBlock;

  // Accumulate db in FP32 always
  float db_local[ILP];
  T r_y[ILP];
  T r_dy[ILP];
#pragma unroll
  for(int ii=0;ii<ILP;ii++){
    db_local[ii] = 0.f;
  }

  // f always <= features in this case
  //if (f < features) {
  int nidx = 0;

  // Handle non-multiple of UNROLL_FACTOR residue
  for (; nidx < nSpan % UNROLL_FACTOR; nidx++) {
    int row, col, flat_idx;
    row = f;
    col = nStart + nidx;
    flat_idx = col * features / ILP + row;

    load_store(r_y, Y, 0, flat_idx);
    load_store(r_dy, dY, 0, flat_idx);
#pragma unroll
    for(int ii=0;ii<ILP;ii++){
      if ((float)r_y[ii] <= 0.f)
        r_dy[ii] = 0;
      db_local[ii] += (float)r_dy[ii];
    }
    load_store(dX, r_dy, flat_idx, 0);
  }

  // Handle meat of work
  for (; (nidx + UNROLL_FACTOR - 1) < nSpan; nidx += UNROLL_FACTOR) {
    int row, col, flat_idx;
    row = f;
    col = nStart + nidx;
    flat_idx = col * features / ILP + row; // total threads in x == features/ILP
#pragma unroll
    for (int u = 0; u < UNROLL_FACTOR; u++) {
      load_store(r_y, Y, 0, flat_idx);
      load_store(r_dy, dY, 0, flat_idx);
#pragma unroll
      for(int ii=0;ii<ILP;ii++){
        if ((float)r_y[ii] <= 0.f)
          r_dy[ii] = 0;
        db_local[ii] += (float)r_dy[ii];
      }
      load_store(dX, r_dy, flat_idx, 0);
      flat_idx += features/ILP;
    }
  }

  // we know block size for now
  __shared__ float smem[BIAS_RELU_BW_NTHREADS_X*BIAS_RELU_BW_NTHREADS_Y*ILP];
  // naive block reduction on y-dim
  int linear_idx = threadIdx.y * blockDim.x + threadIdx.x;
  float* smem_out = smem + ILP * linear_idx;
#pragma unroll
  for(int ii=0;ii<ILP;ii++){
    smem_out[ii] = db_local[ii]; // reuse local dy buffer
  }
  __syncthreads();
  if(threadIdx.y == 0) {
    for(int yidx = 1; yidx < blockDim.y; yidx++){
      float* smem_in = smem + ILP * (yidx * blockDim.x + threadIdx.x);
#pragma unroll
      for(int ii=0;ii<ILP;ii++){
        db_local[ii] += smem_in[ii]; // reuse local dy buffer
      }
    }

    // block result is in db_local now for all threadIdx.y == 0
    if(gridDim.y == 1) {
#pragma unroll
      for(int ii=0;ii<ILP;ii++){
        r_dy[ii] = db_local[ii]; // reuse local dy buffer
      }
      load_store(db, r_dy, f, 0);
      return;
    }

    // Write out partial result
    load_store(out, db_local, f, 0);
  }
  __threadfence();
  __syncthreads();

  // Increment semaphore and check if this is the last CTA in the grid_y dimension.
  // Only thread (0,0) calls this
  if (threadIdx.x == 0 && threadIdx.y == 0) {
    unsigned int sum_idx;
    sum_idx = atomicAdd(&(semaphores[blockIdx.x]), 1);
    isLastBlock = (sum_idx == (gridDim.y - 1));
  }
  __syncthreads();

#pragma unroll
  for(int ii=0;ii<ILP;ii++){
    db_local[ii] = 0.f;
  }
  float r_db[ILP];

  // No block reduction for now, only thread (*,0) do grid reduction
  if (isLastBlock) {
    if(threadIdx.y == 0){
      for (int n = 0; n < gridDim.y; n++) {
        int row, col;
        row = f;
        col = n;
        load_store(r_db, intermediate, 0, col * features / ILP + row);
#pragma unroll
        for(int ii=0;ii<ILP;ii++){
          db_local[ii] += r_db[ii];
        }
      }
#pragma unroll
      for(int ii=0;ii<ILP;ii++){
        r_dy[ii] = db_local[ii]; // reuse local dy buffer
      }
      load_store(db, r_dy, f, 0);
    }
  }
}

// Lists where the num_layers-1 intermediate Y buffers start in reserved space on fprop, starting
// offset 0. The last Y value is, of course, stored in the user provided output buffer.
void get_y_offsets(
    int batch_size,
    int num_layers,
    const int* output_features,
    int* y_start_offsets) {
  y_start_offsets[0] = 0;
  for (int i = 1; i < num_layers; i++) {
    y_start_offsets[i] = y_start_offsets[i - 1] + batch_size * output_features[i - 1];
  }
}

// Returns the reserved space (in elements) needed for the MLP
size_t get_mlp_reserved_space(int64_t batch_size, int num_layers, const int* output_features) {
  size_t res_space = 0;
  // Need to store output of every intermediate MLP - size equal to output_features[i] * batch_size
  // for all 'i' in [0, num_layers-1)
  for (int l = 0; l < num_layers; l++) {
    res_space += output_features[l] * batch_size;
  }
  return res_space;
}

// Returns the size of all fprop activations combined
size_t get_all_activations_size(int64_t batch_size, int num_layers, const int* output_features) {
  size_t acts_size = 0;
  for (int l = 0; l < num_layers; l++) {
    acts_size += output_features[l] * batch_size;
  }
  return acts_size;
}

#if 0
// Returns the work space (in elements) needed for the MLP bprop.
size_t get_mlp_bp_workspace (int batch_size, int num_layers, const int* output_features) {
    /*
       Workspace is partitioned as
       DY_GEMMs : DX_GEMMs
    */
    size_t work_space = 0;

    // Store each intermediate dY explicitly. Need 2 dYs per MLP layer (one for o/p
    // of biasReLU_bp and one for o/p of dgrad GEMM).
    work_space += 2*get_all_activations_size(batch_size, num_layers, output_features);

    return work_space;
}
#endif

// Scratch space needed for reductions in number of elements
size_t get_reduction_scratch_space(int batch_size, int num_layers, const int* output_features) {
  size_t max_scratch_space = 0;
  // Loop over all layers to see which one needs the max scratch space
  for (int l = 0; l < num_layers; l++) {
    // need to find max(aligned, not_aligned)
    int tmp, res0, res1;

    int block_x = BIAS_RELU_BW_NTHREADS_X;
    int block_y = BIAS_RELU_RED_PER_THREAD * BIAS_RELU_BW_NTHREADS_Y;
    get_biasAddRelu_bprop_grid_size(
      output_features[l], batch_size, block_x, block_y, &tmp, &res0);

    block_x = ILP * BIAS_RELU_BW_NTHREADS_X;
    get_biasAddRelu_bprop_grid_size(
      output_features[l], batch_size, block_x, block_y, &tmp, &res1);

    max_scratch_space = std::max(max_scratch_space, (size_t)(output_features[l] * res0));
    max_scratch_space = std::max(max_scratch_space, (size_t)(output_features[l] * res1));
  }

  return max_scratch_space;
}

// Buffer for semaphores
size_t get_semaphores_size(int num_layers, const int* output_features) {
  // Upper bound on semaphores is one per feature for the layer
  // with the most features.
  int max_features = 0;
  for (int l = 0; l < num_layers; l++) {
    max_features = std::max(max_features, output_features[l]);
  }
  return (size_t)max_features;
}

// Returns the work space (in elements) needed for the MLP bprop.
template <typename T>
size_t get_mlp_bp_workspace_in_bytes(int batch_size, int num_layers, const int* output_features) {
  size_t work_space = 0;

  // Store each intermediate dY explicitly. Need 2 dYs per MLP layer (one for o/p
  // of biasReLU_bp and one for o/p of dgrad GEMM).
  work_space += 2 * get_all_activations_size(batch_size, num_layers, output_features) * sizeof(T);
  work_space +=
      get_reduction_scratch_space(batch_size, num_layers, output_features) * sizeof(float);
  work_space += get_semaphores_size(num_layers, output_features) * sizeof(int);

  return work_space;
}

// Returns pointers to each segment of the workspace
template <typename T>
void partition_mlp_bp_workspace(
    int batch_size,
    int num_layers,
    const int* output_features,
    void* work_space,
    T** dy_gemms,
    T** dx_gemms,
    float** db_scratch,
    int** semaphores) {
  /*
     Workspace is partitioned as
     DY_GEMMs : DX_GEMMs : DB_SCRATCH : SEMAPHORES
  */
  // Start address where dy_gemm tensors are stored
  *dy_gemms = reinterpret_cast<T*>(work_space);
  // Start address where dx_gemm tensors are stored
  *dx_gemms = *dy_gemms + get_all_activations_size(batch_size, num_layers, output_features);
  // Start address where db intermediate tensors are stored
  *db_scratch = reinterpret_cast<float*>(
      *dx_gemms + get_all_activations_size(batch_size, num_layers, output_features));
  // Start address of semaphores
  *semaphores = reinterpret_cast<int*>(
      *db_scratch + get_reduction_scratch_space(batch_size, num_layers, output_features));

  return;
}

// Does a simple MLP fprop (GEMM+bias+ReLU).
// Can handle num_layers number of layers, each with its own shape. Output of layer i is assumed
// to be input of layer i+1. output_features, WPtr and BPtr are arrays of length num_layers, and
// must be in the same order i.e. WPtr[i] and BPtr[i] are respectively the weight and bias of layer
// 'i'.
template <typename T>
int mlp_fp(
    T* X,
    int input_features,
    int batch_size,
    T** WPtr,
    int num_layers,
    int* output_features,
    T** BPtr,
    T* Y,
    T* reserved_space,
    int use_bias,
    int activation,
    void* lt_workspace) {
  T *weight, *input, *output, *bias;
  T *reserved_space_x, *reserved_space_y;
  reserved_space_x = NULL;
  reserved_space_y = reserved_space;

  // Get cublas handle from Pytorch
  cublasHandle_t handle = at::cuda::getCurrentCUDABlasHandle();
  // Get the stream from cublas handle to reuse for biasReLU kernel.
  cudaStream_t stream;
  cublasGetStream(handle, &stream);

  for (int layer = 0; layer < num_layers; layer++) {
    weight = WPtr[layer];
    input = (layer == 0) ? X : reserved_space_x;
    output = (layer == num_layers - 1) ? Y : reserved_space_y;
    if (use_bias) {
      bias = BPtr[layer];
    }
    int ifeat = (layer == 0) ? input_features : output_features[layer - 1];
    int ofeat = output_features[layer];

    float one = 1.f;
    float zero = 0.f;

    // try with cublaslt first for supported case with valid handle
    int cublaslt_status = 1;
#if defined(CUBLAS_VERSION) && CUBLAS_VERSION >= 11000
    if(activation < 1){
        cublaslt_status = mlp_gemm_lt(
          //ltHandle,
          (cublasLtHandle_t)handle,
          CUBLAS_OP_T,
          CUBLAS_OP_N,
          ofeat,
          batch_size,
          ifeat,
          &one,
          weight,
          ifeat,
          input,
          ifeat,
          &zero,
          output,
          ofeat,
          lt_workspace,
          1 << 22,
          stream,
          use_bias == 1,
          activation == 1,
          bias);
    }
#endif

    // if cublaslt failed or not executed, fallback to cublas
    if (cublaslt_status != 0) {
      cublasStatus_t cublas_status;
      // Call GEMM: fprop is Y = W'X
      cublas_status = mlp_gemm(
        handle,
        CUBLAS_OP_T,
        CUBLAS_OP_N,
        ofeat,
        batch_size,
        ifeat,
        &one,
        weight,
        ifeat,
        input,
        ifeat,
        &zero,
        output,
        ofeat,
        int(0)); // Do nothing for forward prop

      if (cublas_status != CUBLAS_STATUS_SUCCESS) {
        printf("GEMM fprop failed with %d\n", cublas_status);
        return 1;
      }

      const uint &input_size = ofeat;
      int num_blocks = 0;
      int num_SMs = at::cuda::getCurrentDeviceProperties()->multiProcessorCount;
      // Call biasReLU
      if(use_bias == 1) {
        if (activation == 0) { // no activation
          cudaOccupancyMaxActiveBlocksPerMultiprocessor(&num_blocks, biasAdd_fprop<T>, BIAS_RELU_FW_NTHREADS, 0);
          biasAdd_fprop<<<num_SMs*num_blocks, BIAS_RELU_FW_NTHREADS, 0, stream>>>(output, bias, batch_size, input_size);
        } else if (activation == 1) { // relu
          cudaOccupancyMaxActiveBlocksPerMultiprocessor(&num_blocks, biasAddRelu_fprop<T>, BIAS_RELU_FW_NTHREADS, 0);
          biasAddRelu_fprop<<<num_SMs*num_blocks, BIAS_RELU_FW_NTHREADS, 0, stream>>>(output, bias, batch_size, input_size);
        } else if (activation == 2) { // sigmoid
          cudaOccupancyMaxActiveBlocksPerMultiprocessor(&num_blocks, biasAdd_fprop<T>, BIAS_RELU_FW_NTHREADS, 0);
          biasAdd_fprop<<<num_SMs*num_blocks, BIAS_RELU_FW_NTHREADS, 0, stream>>>(output, bias, batch_size, input_size);
          cudaOccupancyMaxActiveBlocksPerMultiprocessor(&num_blocks, Sigmoid_fprop<T>, BIAS_RELU_FW_NTHREADS, 0);
          Sigmoid_fprop<<<num_SMs*num_blocks, BIAS_RELU_FW_NTHREADS, 0, stream>>>(output, batch_size, input_size);
        }
      } else {
        // don't need to do anything in case of no activation and no bias
        if (activation == 1) { // relu
          cudaOccupancyMaxActiveBlocksPerMultiprocessor(&num_blocks, Relu_fprop<T>, BIAS_RELU_FW_NTHREADS, 0);
          Relu_fprop<<<num_SMs*num_blocks, BIAS_RELU_FW_NTHREADS, 0, stream>>>(output, batch_size, input_size);
        } else if (activation == 2) { // sigmoid
          cudaOccupancyMaxActiveBlocksPerMultiprocessor(&num_blocks, Sigmoid_fprop<T>, BIAS_RELU_FW_NTHREADS, 0);
          Sigmoid_fprop<<<num_SMs*num_blocks, BIAS_RELU_FW_NTHREADS, 0, stream>>>(output, batch_size, input_size);
        }
      }
    }
    // Set current output as next layer input
    reserved_space_x = reserved_space_y;
    // Set next layer output
    reserved_space_y += ofeat * batch_size;
  }

  return 0;
}

// Does a simple MLP bprop (GEMM+bias+ReLU).
// Needs reserved space to come back exactly as it was populated in fprop.
// Does dgrad and wgrad sequentially.
template <typename T>
int mlp_bp(
    T* X,
    T* Y,
    int input_features,
    int batch_size,
    T** WPtr,
    int num_layers,
    int* output_features,
    T* dY,
    T* reserved_space,
    T* work_space,
    T* dX,
    T** dwPtr,
    T** dbPtr,
    bool requires_grad,
    int use_bias,
    int activation) {
  T* weight;
  T *dweight, *dx, *dy, *dbias;
  T *x, *y;

  // Where the dx of the biasReLU (== dy of gemm) is stored. Can be thrown away
  // after bp call.
  T* dy_gemm_base;
  // Where the dx after GEMM is stored.
  T* dx_gemm_base;
  // Where partial reduction results are stored.
  float* db_scratch;
  // Semaphores for reduction.
  int* semaphores;

  partition_mlp_bp_workspace<T>(
      batch_size,
      num_layers,
      output_features,
      work_space,
      &dy_gemm_base,
      &dx_gemm_base,
      &db_scratch,
      &semaphores);

  size_t semaphore_size = get_semaphores_size(num_layers, output_features) * sizeof(int);

  // Get cublas handle from Pytorch
  cublasHandle_t handle = at::cuda::getCurrentCUDABlasHandle();
  // Get the stream from cublas handle to reuse for biasReLU kernel.
  cudaStream_t stream;
  cublasGetStream(handle, &stream);
  int flag = 0;
  #ifdef __HIP_PLATFORM_HCC__
    #define PYTORCH_ROCBLAS_VERSION_DECIMAL (ROCBLAS_VERSION_MAJOR * 100 + ROCBLAS_VERSION_MINOR)
    #define USE_GEMM_FLAGS_FP16_ALT_IMPL (PYTORCH_ROCBLAS_VERSION_DECIMAL >= 242)
    #if USE_GEMM_FLAGS_FP16_ALT_IMPL
      #ifdef BACKWARD_PASS_GUARD
        flag = at::BACKWARD_PASS_GUARD_CLASS::is_backward_pass() ? rocblas_gemm_flags_fp16_alt_impl : 0;
      #endif
    #endif
  #endif
  
  int* y_offsets = (int*)malloc(num_layers * sizeof(int));
  get_y_offsets(batch_size, num_layers, output_features, y_offsets);

  for (int layer = num_layers - 1; layer >= 0; layer--) {
    weight = WPtr[layer];
    dweight = dwPtr[layer];

    // x is read from reserved space
    x = (layer == 0) ? X : reserved_space + y_offsets[layer - 1];
    // dx is written in workspace for all but layer==0
    dx = (layer == 0) ? dX : dx_gemm_base + y_offsets[layer - 1];

    // y is read from reserved space
    y = (layer == num_layers - 1) ? Y : reserved_space + y_offsets[layer];
    // dx from layer+1
    dy = (layer == num_layers - 1) ? dY : dx_gemm_base + y_offsets[layer];
    // dy_gemm is written to and read immediately
    T* dy_gemm = dy_gemm_base + y_offsets[layer];

    dbias = dbPtr[layer];
    int xfeat = (layer == 0) ? input_features : output_features[layer - 1];
    int yfeat = output_features[layer];

    float one = 1.f;
    float zero = 0.f;

    if (use_bias == 1) {
      if (activation == 0) { // no acitvation
        // bgrad
        dim3 block(BIAS_RELU_BW_NTHREADS_X, BIAS_RELU_BW_NTHREADS_Y);
        int grid_x, grid_y;
        cudaMemsetAsync(semaphores, 0, semaphore_size, stream);

        int block_x = BIAS_RELU_BW_NTHREADS_X;
        int block_y = BIAS_RELU_RED_PER_THREAD * BIAS_RELU_BW_NTHREADS_Y;
        get_biasAddRelu_bprop_grid_size(yfeat, batch_size, block_x, block_y, &grid_x, &grid_y);
        dim3 grid(grid_x, grid_y);
        biasAdd_bprop<T, 4><<<grid, block, 0, stream>>>(
          dy, yfeat, batch_size, db_scratch, semaphores, dbias);
        // bypass dgrad through reset pointer
        dy_gemm = dy;
      } else if (activation == 1) { // relu
        dim3 block(BIAS_RELU_BW_NTHREADS_X, BIAS_RELU_BW_NTHREADS_Y);
        int grid_x, grid_y;
        cudaMemsetAsync(semaphores, 0, semaphore_size, stream);

        if(yfeat % (ILP * BIAS_RELU_BW_NTHREADS_X) == 0 &&
           is_aligned(y) &&
           is_aligned(dy) &&
           is_aligned(dy_gemm) &&
           is_aligned(dbias)){
          int block_x = ILP * BIAS_RELU_BW_NTHREADS_X;
          int block_y = BIAS_RELU_RED_PER_THREAD * BIAS_RELU_BW_NTHREADS_Y;
          get_biasAddRelu_bprop_grid_size(yfeat, batch_size, block_x, block_y, &grid_x, &grid_y);
          dim3 grid(grid_x, grid_y);
          biasAddRelu_bprop_aligned<T, 4><<<grid, block, 0, stream>>>(
            y, dy, yfeat, batch_size, dy_gemm, db_scratch, semaphores, dbias);
        } else {
          int block_x = BIAS_RELU_BW_NTHREADS_X;
          int block_y = BIAS_RELU_RED_PER_THREAD * BIAS_RELU_BW_NTHREADS_Y;
          get_biasAddRelu_bprop_grid_size(yfeat, batch_size, block_x, block_y, &grid_x, &grid_y);
          dim3 grid(grid_x, grid_y);
          biasAddRelu_bprop<T, 4><<<grid, block, 0, stream>>>(
            y, dy, yfeat, batch_size, dy_gemm, db_scratch, semaphores, dbias);
        }
      } else if (activation == 2) { // sigmoid
        // activation backward
        int num_blocks = 0;
        int num_SMs = at::cuda::getCurrentDeviceProperties()->multiProcessorCount;
        cudaOccupancyMaxActiveBlocksPerMultiprocessor(&num_blocks, Sigmoid_bprop<T>, BIAS_RELU_FW_NTHREADS, 0);
        Sigmoid_bprop<<<num_SMs*num_blocks, BIAS_RELU_FW_NTHREADS, 0, stream>>>(dy, y, batch_size, yfeat, dy_gemm);

        // bgrad, from dy_gemm
        dim3 block(BIAS_RELU_BW_NTHREADS_X, BIAS_RELU_BW_NTHREADS_Y);
        int grid_x, grid_y;
        cudaMemsetAsync(semaphores, 0, semaphore_size, stream);

        int block_x = BIAS_RELU_BW_NTHREADS_X;
        int block_y = BIAS_RELU_RED_PER_THREAD * BIAS_RELU_BW_NTHREADS_Y;
        get_biasAddRelu_bprop_grid_size(yfeat, batch_size, block_x, block_y, &grid_x, &grid_y);
        dim3 grid(grid_x, grid_y);
        biasAdd_bprop<T, 4><<<grid, block, 0, stream>>>(
          dy_gemm, yfeat, batch_size, db_scratch, semaphores, dbias);
      }
    } else { // no bias below
      if (activation == 0) {
        // bypass dgrad through reset pointer
        dy_gemm = dy;
      } else if (activation == 1) { // relu
        int num_blocks = 0;
        int num_SMs = at::cuda::getCurrentDeviceProperties()->multiProcessorCount;
        cudaOccupancyMaxActiveBlocksPerMultiprocessor(&num_blocks, Relu_bprop<T>, BIAS_RELU_FW_NTHREADS, 0);
        Relu_bprop<<<num_SMs*num_blocks, BIAS_RELU_FW_NTHREADS, 0, stream>>>(dy, y, batch_size, yfeat, dy_gemm);
      } else if (activation == 2) { // sigmoid
        int num_blocks = 0;
        int num_SMs = at::cuda::getCurrentDeviceProperties()->multiProcessorCount;
        cudaOccupancyMaxActiveBlocksPerMultiprocessor(&num_blocks, Sigmoid_bprop<T>, BIAS_RELU_FW_NTHREADS, 0);
        Sigmoid_bprop<<<num_SMs*num_blocks, BIAS_RELU_FW_NTHREADS, 0, stream>>>(dy, y, batch_size, yfeat, dy_gemm);
      }
    }

    cublasStatus_t cublas_status;
    // Call GEMM dgrad
    if (layer > 0 || requires_grad == 1) {
      cublas_status = mlp_gemm(
        handle,
        CUBLAS_OP_N,
        CUBLAS_OP_N,
        xfeat,
        batch_size,
        yfeat,
        &one,
        weight,
        xfeat,
        dy_gemm,
        yfeat,
        &zero,
        dx,
        xfeat,
        flag); //

      if (cublas_status != CUBLAS_STATUS_SUCCESS) {
        printf("GEMM dgrad failed with %d\n", cublas_status);
        return 1;
      }
    }

    // Call GEMM wgrad
    cublas_status = mlp_gemm(
        handle,
        CUBLAS_OP_N,
        CUBLAS_OP_T,
        xfeat,
        yfeat,
        batch_size,
        &one,
        x,
        xfeat,
        dy_gemm,
        yfeat,
        &zero,
        dweight,
        xfeat,
        flag); //

    if (cublas_status != CUBLAS_STATUS_SUCCESS) {
      printf("GEMM wgrad failed with %d\n", cublas_status);
      return 1;
    }
  }

  return 0;
}

// Instantiate for floating point types
template int mlp_fp<float>(
    float* X,
    int input_features,
    int batch_size,
    float** WPtr,
    int num_layers,
    int* output_features,
    float** BPtr,
    float* Y,
    float* reserved_space,
    int use_bias,
    int activation,
    void* lt_workspace);

template int mlp_bp<float>(
    float* X,
    float* Y,
    int input_features,
    int batch_size,
    float** WPtr,
    int num_layers,
    int* output_features,
    float* dY,
    float* reserved_space,
    float* work_space,
    float* dX,
    float** dwPtr,
    float** dbPtr,
    bool requires_grad,
    int use_bias,
    int activation);

template int mlp_fp<at::Half>(
    at::Half* X,
    int input_features,
    int batch_size,
    at::Half** WPtr,
    int num_layers,
    int* output_features,
    at::Half** BPtr,
    at::Half* Y,
    at::Half* reserved_space,
    int use_bias,
    int activation,
    void* lt_workspace);

template int mlp_bp<at::Half>(
    at::Half* X,
    at::Half* Y,
    int input_features,
    int batch_size,
    at::Half** WPtr,
    int num_layers,
    int* output_features,
    at::Half* dY,
    at::Half* reserved_space,
    at::Half* work_space,
    at::Half* dX,
    at::Half** dwPtr,
    at::Half** dbPtr,
    bool requires_grad,
    int use_bias,
    int activation);

template int mlp_fp<double>(
    double* X,
    int input_features,
    int batch_size,
    double** WPtr,
    int num_layers,
    int* output_features,
    double** BPtr,
    double* Y,
    double* reserved_space,
    int use_bias,
    int activation,
    void* lt_workspace);

template int mlp_bp<double>(
    double* X,
    double* Y,
    int input_features,
    int batch_size,
    double** WPtr,
    int num_layers,
    int* output_features,
    double* dY,
    double* reserved_space,
    double* work_space,
    double* dX,
    double** dwPtr,
    double** dbPtr,
    bool requires_grad,
    int use_bias,
    int activation);

template size_t get_mlp_bp_workspace_in_bytes<float>(
    int batch_size,
    int num_layers,
    const int* output_features);
template size_t get_mlp_bp_workspace_in_bytes<at::Half>(
    int batch_size,
    int num_layers,
    const int* output_features);
template size_t get_mlp_bp_workspace_in_bytes<double>(
    int batch_size,
    int num_layers,
    const int* output_features);