PaddlePaddle
diff --git a/‎paddle/fluid/operators/fused/attn_gemm_int8.h
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+34-34Lines changed: 34 additions & 34 deletions b/‎paddle/fluid/operators/fused/attn_gemm_int8.h
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+34-34Lines changed: 34 additions & 34 deletions
diff --git a/‎paddle/fluid/operators/fused/quant_dequant_kernel.h
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+40-40Lines changed: 40 additions & 40 deletions b/‎paddle/fluid/operators/fused/quant_dequant_kernel.h
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+40-40Lines changed: 40 additions & 40 deletions
diff --git a/‎paddle/phi/api/yaml/legacy_ops.yaml
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+8Lines changed: 8 additions & 0 deletions b/‎paddle/phi/api/yaml/legacy_ops.yaml
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+8Lines changed: 8 additions & 0 deletions
diff --git a/‎paddle/phi/infermeta/binary.cc
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+70Lines changed: 70 additions & 0 deletions b/‎paddle/phi/infermeta/binary.cc
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+70Lines changed: 70 additions & 0 deletions
diff --git a/‎paddle/phi/infermeta/binary.h
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+6Lines changed: 6 additions & 0 deletions b/‎paddle/phi/infermeta/binary.h
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+6Lines changed: 6 additions & 0 deletions
diff --git a/‎paddle/phi/kernels/funcs/cublaslt.h
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+10-19Lines changed: 10 additions & 19 deletions b/‎paddle/phi/kernels/funcs/cublaslt.h
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+10-19Lines changed: 10 additions & 19 deletions
@@ -57,29 +57,29 @@ class AttnMatmulINT8 {
                       const int quant_round_type = 1,
                       const float quant_max_bound = 127.0,
                       const float quant_min_bound = -127.0) {
-    quantize_kernel_launcher<T>(input->data<T>(),
-                                input_tmp->data<int8_t>(),
-                                quant_in_scale,
-                                m_,
-                                k_,
-                                quant_round_type,
-                                quant_max_bound,
-                                quant_min_bound,
-                                dev_ctx_.stream());
+    LaunchQuantKernel<T>(input->data<T>(),
+                         input_tmp->data<int8_t>(),
+                         quant_in_scale,
+                         m_,
+                         k_,
+                         quant_round_type,
+                         quant_max_bound,
+                         quant_min_bound,
+                         dev_ctx_.stream());
 
     helpers_[0]->GEMM(input_tmp->data<int8_t>(),
                       weight->data<int8_t>(),
                       output_tmp->data<int32_t>(),
                       dev_ctx_.stream());
 
-    dequantize_kernel_launcher<T>(output_tmp->data<int32_t>(),
-                                  output->data<T>(),
-                                  m_,
-                                  n_,
-                                  dev_ctx_.stream(),
-                                  gpu_config_.get(),
-                                  quant_in_scale,
-                                  dequant_out_scale->data<float>());
+    LaunchDequantKernel<T>(output_tmp->data<int32_t>(),
+                           output->data<T>(),
+                           m_,
+                           n_,
+                           dev_ctx_.stream(),
+                           gpu_config_.get(),
+                           quant_in_scale,
+                           dequant_out_scale->data<float>());
 
     if (compute_bias_) {
       // bias_out = output + bias
@@ -126,14 +126,14 @@ class AttnMatmulINT8 {
                       output_tmp->data<int32_t>(),
                       dev_ctx_.stream());
 
-    dequantize_kernel_launcher<T>(output_tmp->data<int32_t>(),
-                                  output->data<T>(),
-                                  m_,
-                                  n_,
-                                  dev_ctx_.stream(),
-                                  gpu_config_.get(),
-                                  quant_in_scale,
-                                  dequant_out_scale->data<float>());
+    LaunchDequantKernel<T>(output_tmp->data<int32_t>(),
+                           output->data<T>(),
+                           m_,
+                           n_,
+                           dev_ctx_.stream(),
+                           gpu_config_.get(),
+                           quant_in_scale,
+                           dequant_out_scale->data<float>());
 
     if (compute_bias_) {
       // bias_out = output + bias
@@ -162,15 +162,15 @@ class AttnMatmulINT8 {
                              const int quant_round_type = 1,
                              const float quant_max_bound = 127.0,
                              const float quant_min_bound = -127.0) {
-    quantize_kernel_launcher<T>(input->data<T>(),
-                                input_tmp->data<int8_t>(),
-                                quant_in_scale,
-                                m_,
-                                k_,
-                                quant_round_type,
-                                quant_max_bound,
-                                quant_min_bound,
-                                dev_ctx_.stream());
+    LaunchQuantKernel<T>(input->data<T>(),
+                         input_tmp->data<int8_t>(),
+                         quant_in_scale,
+                         m_,
+                         k_,
+                         quant_round_type,
+                         quant_max_bound,
+                         quant_min_bound,
+                         dev_ctx_.stream());
 
     helpers_[0]->GEMM(input_tmp->data<int8_t>(),
                       weight->data<int8_t>(),
 
@@ -47,14 +47,14 @@ __forceinline__ __device__ int8_t quant_helper(const T input,
 }
 
 template <typename T>
-__global__ void quantize_kernel(const T* input,
-                                char4* output,
-                                const float scale,
-                                const int m,
-                                const int n,
-                                const int round_type,
-                                const float max_bound,
-                                const float min_bound) {
+__global__ void QuantKernel(const T* input,
+                            char4* output,
+                            const float scale,
+                            const int m,
+                            const int n,
+                            const int round_type,
+                            const float max_bound,
+                            const float min_bound) {
   int n_id = (blockIdx.x * blockDim.x + threadIdx.x) << 2;
   int m_id = blockIdx.y * blockDim.y + threadIdx.y;
 
@@ -74,36 +74,36 @@ __global__ void quantize_kernel(const T* input,
 }
 
 template <typename T>
-void quantize_kernel_launcher(const T* input,
-                              int8_t* output,
-                              const float scale,
-                              const int m,
-                              const int n,
-                              const int round_type,
-                              const float max_bound,
-                              const float min_bound,
-                              gpuStream_t stream) {
+void LaunchQuantKernel(const T* input,
+                       int8_t* output,
+                       const float scale,
+                       const int m,
+                       const int n,
+                       const int round_type,
+                       const float max_bound,
+                       const float min_bound,
+                       gpuStream_t stream) {
   // TODO(minghaoBD): optimize the kennel launch times when m==1 or n==1
   dim3 grid((n >> 2 + 31) / 32, (m + 31) / 32);
   dim3 block(32, 32);
 
-  quantize_kernel<<<grid, block, 0, stream>>>(input,
-                                              (char4*)output,  // NOLINT
-                                              scale,
-                                              m,
-                                              n,
-                                              round_type,
-                                              max_bound,
-                                              min_bound);
+  QuantKernel<<<grid, block, 0, stream>>>(input,
+                                          (char4*)output,  // NOLINT
+                                          scale,
+                                          m,
+                                          n,
+                                          round_type,
+                                          max_bound,
+                                          min_bound);
 }
 
 template <typename T, int VecSize>
-__global__ void dequantize_kernel(T* output,
-                                  const int32_t* input,
-                                  const int m,  // batch size
-                                  const int n,  // hidden
-                                  const float quant_in_scale,
-                                  const float* dequant_out_scale_data) {
+__global__ void DequantKernel(T* output,
+                              const int32_t* input,
+                              const int m,  // batch size
+                              const int n,  // hidden
+                              const float quant_in_scale,
+                              const float* dequant_out_scale_data) {
   int numel = m * n;
   int stride = blockDim.x * gridDim.x * VecSize;
   int idx = (blockIdx.x * blockDim.x + threadIdx.x) * VecSize;
@@ -128,15 +128,15 @@ __global__ void dequantize_kernel(T* output,
 }
 
 template <typename T>
-void dequantize_kernel_launcher(const int32_t* input,
-                                T* output,
-                                const int m,  // m
-                                const int n,  // n
-                                gpuStream_t stream,
-                                GpuLaunchConfig* gpu_config,
-                                const float quant_in_scale,
-                                const float* dequant_out_scale_data) {
-  dequantize_kernel<T, DequantKernelVecSize>
+void LaunchDequantKernel(const int32_t* input,
+                         T* output,
+                         const int m,  // m
+                         const int n,  // n
+                         gpuStream_t stream,
+                         GpuLaunchConfig* gpu_config,
+                         const float quant_in_scale,
+                         const float* dequant_out_scale_data) {
+  DequantKernel<T, DequantKernelVecSize>
       <<<gpu_config->block_per_grid, gpu_config->thread_per_block, 0, stream>>>(
           output, input, m, n, quant_in_scale, dequant_out_scale_data);
 }
 
@@ -523,6 +523,14 @@
     func : matmul
   backward : matmul_grad
 
+- op : matmul_int8
+  args : (Tensor x, Tensor y, bool transpose_x = false, bool transpose_y = false)
+  output : Tensor
+  infer_meta :
+    func : MatmulInt8InferMeta
+  kernel :
+    func : matmul_int8
+
 - op : matrix_rank
   args : (Tensor x, float tol, bool use_default_tol=true, bool hermitian=false)
   output : Tensor(out)
 
@@ -2096,6 +2096,76 @@ void MatmulInferMeta(const MetaTensor& x,
   out->set_layout(x.layout());
 }
 
+void MatmulInt8InferMeta(const MetaTensor& x,
+                         const MetaTensor& y,
+                         bool trans_x,
+                         bool trans_y,
+                         MetaTensor* out) {
+  std::vector<int64_t> dims_x = phi::vectorize(x.dims());
+  std::vector<int64_t> dims_y = phi::vectorize(y.dims());
+  auto ndims_x = dims_x.size();
+  auto ndims_y = dims_y.size();
+  PADDLE_ENFORCE_GT(ndims_x,
+                    0UL,
+                    phi::errors::InvalidArgument(
+                        "The Input(x) dims size must be greater than 0,"
+                        " but reviced dims size is 0. "));
+  PADDLE_ENFORCE_GT(ndims_y,
+                    0UL,
+                    phi::errors::InvalidArgument(
+                        "The Input(y) dims size must be greater than 0,"
+                        " but reviced dims size is 0. "));
+
+  bool x_broadcasted = false, y_broadcasted = false;
+  if (ndims_x == 1) {
+    dims_x.insert(dims_x.begin(), 1);
+    ndims_x = 2;
+    x_broadcasted = true;
+  }
+
+  if (ndims_y == 1) {
+    dims_y.push_back(1);
+    ndims_y = 2;
+    y_broadcasted = true;
+  }
+
+  size_t M, N;
+  if (trans_x) {
+    M = dims_x[ndims_x - 1];
+  } else {
+    M = dims_x[ndims_x - 2];
+  }
+  if (trans_y) {
+    N = dims_y[ndims_y - 2];
+  } else {
+    N = dims_y[ndims_y - 1];
+  }
+
+  std::vector<int64_t> new_dims;
+  if (ndims_x > ndims_y) {
+    new_dims.assign(dims_x.begin(), dims_x.end() - 2);
+  } else if (ndims_x < ndims_y) {
+    new_dims.assign(dims_y.begin(), dims_y.end() - 2);
+  } else {
+    new_dims.reserve(ndims_x);
+    for (size_t i = 0; i < ndims_x - 2; ++i) {
+      new_dims.push_back(std::max(dims_x[i], dims_y[i]));
+    }
+  }
+  if (!x_broadcasted) {
+    new_dims.push_back(M);
+  }
+  if (!y_broadcasted) {
+    new_dims.push_back(N);
+  }
+
+  auto ddim_out = phi::make_ddim(new_dims);
+
+  out->set_dims(ddim_out);
+  out->set_dtype(phi::DataType::INT32);
+  out->set_layout(x.layout());
+}
+
 void MatmulWithFlattenInferMeta(const MetaTensor& x,
                                 const MetaTensor& y,
                                 int x_num_col_dims,
 
@@ -333,6 +333,12 @@ void MatmulInferMeta(const MetaTensor& x,
                      bool trans_y,
                      MetaTensor* out);
 
+void MatmulInt8InferMeta(const MetaTensor& x,
+                         const MetaTensor& y,
+                         bool trans_x,
+                         bool trans_y,
+                         MetaTensor* out);
+
 void MatmulWithFlattenInferMeta(const MetaTensor& x,
                                 const MetaTensor& y,
                                 int x_num_col_dims,
 
@@ -39,25 +39,16 @@ const std::map<std::tuple<int, int, int>, CublasLtAlgoParam> AlgoParamCache{};
 
 class CublasLtHelper {
  public:
-  CublasLtHelper(int m, int k, int n)
-      : alpha_(1), beta_(0), m_(m), k_(k), n_(n) {
+  CublasLtHelper(int m, int k, int n, cublasLtHandle_t handle)
+      : handle_(handle), alpha_(1), beta_(0), m_(m), k_(k), n_(n) {
     cublasStatus_t status;
-    // handle and matmul desc
-    status = dyl::cublasLtCreate(&handle_);
 #if CUBLAS_VER_MAJOR < 11
     cudaDataType_t cudaComputeType = CUDA_R_32I;
 #else
     cublasComputeType_t cudaComputeType = CUBLAS_COMPUTE_32I;
 #endif
 
-    PADDLE_ENFORCE_EQ(
-        status,
-        CUBLAS_STATUS_SUCCESS,
-        phi::errors::External(
-            "cublasLtMatrixLayoutCreate execution error"
-            "refer https://docs.nvidia.com/cuda/cublas/index.html to get more "
-            "information"));
-
+    // matmul desc
 #if CUBLAS_VER_MAJOR < 11
     status = dyl::cublasLtMatmulDescCreate(&matmul_desc_, cudaComputeType);
 #else
@@ -179,7 +170,7 @@ class CublasLtHelper {
   }
   ~CublasLtHelper() {}
 
-  void GEMM(int8_t* A_dev,
+  void GEMM(const int8_t* A_dev,
             const int8_t* B_dev,
             int32_t* C_dev,
             cudaStream_t stream,
@@ -226,14 +217,14 @@ class CublasLtHelper {
 
   cublasLtMatmulAlgo_t algo_;
 
-  int32_t alpha_;
-  int32_t beta_;
+  int32_t alpha_ = 1;
+  int32_t beta_ = 0;
 
-  int m_;
-  int k_;
-  int n_;
+  int m_ = 0;
+  int k_ = 0;
+  int n_ = 0;
 
-  size_t workspace_size_;
+  size_t workspace_size_ = 0;
 };
 
 }  // namespace phi