Add INT8 support for fused_multi_transformer_op

paddle/fluid/inference/analysis/passes/ir_params_sync_among_devices_pass.cc

@@ -165,7 +165,8 @@ void IrParamsSyncAmongDevicesPass::CopyParamsToGpu(Argument *argument) {
         auto var_data_type = var_node->Var()->GetDataType();
         VLOG(5) << "var_name is " << var_name << ", data type is "
                 << var_data_type;
-        if (var_data_type == paddle::framework::proto::VarType::FP16) {
+        if (var_data_type == paddle::framework::proto::VarType::FP16 &&
+            t->dtype() != paddle::experimental::DataType::FLOAT16) {
           framework::Tensor half_tensor;
           half_tensor.set_type(paddle::experimental::DataType::FLOAT16);
           half_tensor.Resize(t->dims());

paddle/fluid/operators/fused/CMakeLists.txt

@@ -23,6 +23,7 @@ register_operators(
   fused_transformer_op
   fused_feedforward_op
   fused_multi_transformer_op
+  fused_multi_transformer_int8_op
   fused_bias_dropout_residual_layer_norm_op
   resnet_unit_op
   fused_gemm_epilogue_op
@@ -118,6 +119,7 @@ if(WITH_GPU OR WITH_ROCM)
     # fused_attention_op
     op_library(fused_attention_op)
     op_library(fused_multi_transformer_op)
+    op_library(fused_multi_transformer_int8_op)
     op_library(fused_bias_dropout_residual_layer_norm_op)
   endif()
   # resnet_unit needs cudnn 8.0 above

paddle/fluid/operators/fused/attention_layer_norm.h

@@ -19,7 +19,8 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
-template <typename T>
+// NOTE: T must be the same as OutType in ComputeBackward
+template <typename T, typename InType = T, typename OutType = T>
 class AttnLayerNorm {
  public:
   AttnLayerNorm(const phi::GPUContext& dev_ctx,
@@ -33,25 +34,36 @@ class AttnLayerNorm {
 
   ~AttnLayerNorm() {}
 
-  void ComputeForward(const T* x_data,
+  void ComputeForward(const InType* x_data,
                       const LayerNormParamType<T>* scale_data,
                       const LayerNormParamType<T>* bias_data,
-                      T* y_data,
+                      OutType* y_data,
                       LayerNormParamType<T>* mean_data,
-                      LayerNormParamType<T>* var_data) {
+                      LayerNormParamType<T>* var_data,
+                      const float* quant_out_scale_data = nullptr,
+                      const int quant_out_scale_offset = 0,
+                      const float quant_in_scale_data = 1.0) {
     auto stream = dev_ctx_.stream();
 
     switch (GetDesiredBlockDim(feature_size_)) {
       FIXED_BLOCK_DIM_CASE(
-          LayerNormForward<T, LayerNormParamType<T>, kBlockDim>
+          LayerNormForward<T,
+                           LayerNormParamType<T>,
+                           kBlockDim,
+                           false,
+                           InType,
+                           OutType>
           <<<batch_size_, kBlockDim, 0, stream>>>(x_data,
                                                   scale_data,
                                                   bias_data,
                                                   y_data,
                                                   mean_data,
                                                   var_data,
                                                   epsilon_,
-                                                  feature_size_));
+                                                  feature_size_,
+                                                  quant_out_scale_data,
+                                                  quant_out_scale_offset,
+                                                  quant_in_scale_data));
       default:
         PADDLE_THROW(platform::errors::InvalidArgument(
             "Feature_size must be larger than 1"));

paddle/fluid/operators/fused/attn_gemm_int8.h

@@ -0,0 +1,176 @@
+/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include <iostream>
+#include <vector>
+#include "paddle/fluid/operators/fused/cublaslt.h"
+#include "paddle/fluid/operators/fused/quant_dequant_kernel.h"
+#include "paddle/fluid/platform/device/gpu/gpu_info.h"
+#include "paddle/fluid/platform/float16.h"
+#include "paddle/phi/kernels/funcs/broadcast_function.h"
+#include "paddle/phi/kernels/funcs/elementwise_functor.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename T>
+class AttnMatmulINT8 {
+ public:
+  AttnMatmulINT8(
+      const phi::GPUContext& dev_ctx, int m, int n, int k, bool compute_bias)
+      : dev_ctx_(dev_ctx), m_(m), n_(n), k_(k), compute_bias_(compute_bias) {
+    auto helper = std::make_shared<CublasLtHelper>(m, k, n);
+    helpers_.emplace_back(helper);
+  }
+  ~AttnMatmulINT8() {}
+
+  // This function is used to execute GEMM, with input and output's types are
+  // both T.
+  void ComputeForward(const framework::Tensor* weight,
+                      const framework::Tensor* input,
+                      framework::Tensor* input_tmp,
+                      const framework::Tensor* bias,
+                      framework::Tensor* output,
+                      framework::Tensor* output_tmp,
+                      framework::Tensor* bias_out,
+                      const float quant_in_scale_data,
+                      const framework::Tensor* quant_out_scale,
+                      const int quant_out_scale_offset) {
+    int m = m_, k = k_, n = n_;
+
+    quantize_kernel_launcher<T>(input->data<T>(),
+                                input_tmp->data<int8_t>(),
+                                quant_in_scale_data,
+                                m_,
+                                k_,
+                                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(),
+                                  quant_in_scale_data,
+                                  quant_out_scale->data<float>(),
+                                  quant_out_scale_offset);
+
+    if (compute_bias_) {
+      // bias_out = output + bias
+      std::vector<const framework::Tensor*> ins = {output, bias};
+      std::vector<framework::Tensor*> outs = {bias_out};
+      phi::funcs::BroadcastKernel<phi::ElementwiseType::kBinary, T, T>(
+          dev_ctx_, ins, &outs, -1, phi::funcs::AddFunctor<T>());
+      PADDLE_ENFORCE_EQ(
+          cudaGetLastError(), cudaSuccess, platform::errors::Fatal("Add"));
+    }
+  }
+
+  // This function is used to execute GEMM, with input and output's types are
+  // both INT8.
+  void ComputeForwardINT8ToINT8(const framework::Tensor* weight,
+                                framework::Tensor* input,
+                                const framework::Tensor* bias,
+                                framework::Tensor* output,
+                                framework::Tensor* bias_out) {
+    int m = m_, k = k_, n = n_;
+
+    helpers_[0]->GEMM(input->data<int8_t>(),
+                      weight->data<int8_t>(),
+                      output->data<int32_t>(),
+                      dev_ctx_.stream());
+  }
+
+  // This function is used to execute GEMM, with input and output's types are
+  // INT8 and T.
+  void ComputeForwardINT8ToT(const framework::Tensor* weight,
+                             const float quant_in_scale_data,
+                             framework::Tensor* input,
+                             const framework::Tensor* bias,
+                             framework::Tensor* output,
+                             framework::Tensor* output_tmp,
+                             framework::Tensor* bias_out,
+                             const framework::Tensor* quant_out_scale,
+                             const int quant_out_scale_offset) {
+    int m = m_, k = k_, n = n_;
+
+    helpers_[0]->GEMM(input->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(),
+                                  quant_in_scale_data,
+                                  quant_out_scale->data<float>(),
+                                  quant_out_scale_offset);
+
+    if (compute_bias_) {
+      // bias_out = output + bias
+      std::vector<const framework::Tensor*> ins = {output, bias};
+      std::vector<framework::Tensor*> outs = {bias_out};
+      phi::funcs::BroadcastKernel<phi::ElementwiseType::kBinary, T, T>(
+          dev_ctx_, ins, &outs, -1, phi::funcs::AddFunctor<T>());
+      PADDLE_ENFORCE_EQ(
+          cudaGetLastError(), cudaSuccess, platform::errors::Fatal("Add"));
+    }
+  }
+
+  // This function is used to execute GEMM, with input and output's types are T
+  // and INT8.
+  void ComputeForwardTToINT8(const framework::Tensor* weight,
+                             const float quant_in_scale_data,
+                             const framework::Tensor* input,
+                             framework::Tensor* input_tmp,
+                             const framework::Tensor* bias,
+                             framework::Tensor* output,
+                             framework::Tensor* bias_out) {
+    int m = m_, k = k_, n = n_;
+    quantize_kernel_launcher<T>(input->data<T>(),
+                                input_tmp->data<int8_t>(),
+                                quant_in_scale_data,
+                                m_,
+                                k_,
+                                dev_ctx_.stream());
+
+    helpers_[0]->GEMM(input_tmp->data<int8_t>(),
+                      weight->data<int8_t>(),
+                      output->data<int32_t>(),
+                      dev_ctx_.stream());
+  }
+
+ private:
+  const phi::GPUContext& dev_ctx_;
+
+  int m_;  // m
+  int n_;  // n
+  int k_;  // k
+
+  int compute_bias_;
+  std::vector<std::shared_ptr<CublasLtHelper>> helpers_;
+};
+
+}  // namespace operators
+}  // namespace paddle