relu.cu

/*************************************************************************
 * Copyright (c) 2022-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 *
 * See LICENSE for license information.
 ************************************************************************/

#include <transformer_engine/activation.h>
#include <cuda_runtime.h>
#include "../util/vectorized_pointwise.h"
#include "../util/math.h"
#include "../common.h"


namespace transformer_engine {

void relu(const Tensor &input,
          Tensor *output,
          cudaStream_t stream) {
  CheckInputTensor(input, "relu_input");
  CheckOutputTensor(*output, "relu_output");
  NVTE_CHECK(input.data.shape == output->data.shape, "Input and output shapes must match.");
  const size_t tot_elts = product(input.data.shape);

  TRANSFORMER_ENGINE_TYPE_SWITCH_INPUT(input.data.dtype, IType,
    TRANSFORMER_ENGINE_TYPE_SWITCH_OUTPUT(output->data.dtype, OType,
      constexpr int nvec = 32 / sizeof(IType);
      VectorizedUnaryKernelLauncher<nvec, Empty, relu<fp32, fp32>>(
        reinterpret_cast<const IType*>(input.data.dptr),
        reinterpret_cast<OType*>(output->data.dptr),
        reinterpret_cast<const fp32*>(output->scale.dptr),
        reinterpret_cast<fp32*>(output->amax.dptr),
        tot_elts,
        {},
        stream);
    );  // NOLINT(*)
  );  // NOLINT(*)
}

void drelu(const Tensor &grad,
           const Tensor &input,
           Tensor *output,
           cudaStream_t stream) {
  CheckInputTensor(input, "drelu_input");
  CheckInputTensor(grad, "drelu_input_grad");
  CheckOutputTensor(*output, "drelu_output");
  NVTE_CHECK(input.data.shape == output->data.shape, "Input and output shapes must match.");
  NVTE_CHECK(input.data.dtype == grad.data.dtype,
             "Input and incoming gradient types must match.");
  const size_t tot_elts = product(input.data.shape);

  TRANSFORMER_ENGINE_TYPE_SWITCH_INPUT(input.data.dtype, IType,
    TRANSFORMER_ENGINE_TYPE_SWITCH_OUTPUT(output->data.dtype, OType,
      constexpr int nvec = 32 / sizeof(IType);
      VectorizedUnaryGradKernelLauncher<nvec, Empty, drelu<fp32, fp32>>(
        reinterpret_cast<const IType*>(grad.data.dptr),
        reinterpret_cast<const IType*>(input.data.dptr),
        reinterpret_cast<OType*>(output->data.dptr),
        reinterpret_cast<const fp32*>(output->scale.dptr),
        reinterpret_cast<fp32*>(output->amax.dptr),
        tot_elts,
        {},
        stream);
    );  // NOLINT(*)
  );  // NOLINT(*)
}

void reglu(const Tensor &input,
           Tensor *output,
           cudaStream_t stream) {
  CheckInputTensor(input, "reglu_input");
  CheckOutputTensor(*output, "reglu_output");
  NVTE_CHECK(input.data.shape.size() == 2, "Input must have 2 dimensions.");
  NVTE_CHECK(output->data.shape.size() == 2, "Output must have 2 dimensions.");
  NVTE_CHECK(input.data.shape[0] == output->data.shape[0],
             "Input shape[0] must be equal to output shape[0].");
  NVTE_CHECK(input.data.shape[1] == output->data.shape[1] * 2,
             "Input shape[1] must be 2x larger than output shape[1].");

  TRANSFORMER_ENGINE_TYPE_SWITCH_INPUT(input.data.dtype, IType,
    TRANSFORMER_ENGINE_TYPE_SWITCH_OUTPUT(output->data.dtype, OType,
      constexpr int nvec = 32 / sizeof(IType);
      GatedActivationKernelLauncher<nvec, fp32, Empty, relu<fp32, fp32>>(
        reinterpret_cast<const IType*>(input.data.dptr),
        reinterpret_cast<OType*>(output->data.dptr),
        reinterpret_cast<const fp32*>(output->scale.dptr),
        reinterpret_cast<fp32*>(output->amax.dptr),
        output->data.shape[0],
        output->data.shape[1],
        {},
        stream);
    );  // NOLINT(*)
  );  // NOLINT(*)
}

void dreglu(const Tensor &grad,
            const Tensor &input,
            Tensor *output,
            cudaStream_t stream) {
  CheckInputTensor(grad, "dreglu_grad");
  CheckInputTensor(input, "dreglu_input");
  CheckOutputTensor(*output, "dreglu_output");
  NVTE_CHECK(grad.data.shape.size() == 2, "Grad must have 2 dimensions.");
  NVTE_CHECK(input.data.shape.size() == 2, "Input must have 2 dimensions.");
  NVTE_CHECK(output->data.shape.size() == 2, "Output must have 2 dimensions.");
  NVTE_CHECK(output->data.shape[0] == grad.data.shape[0],
             "Output shape[0] must be equal to grad shape[0].");
  NVTE_CHECK(output->data.shape[1] == grad.data.shape[1] * 2,
             "Output shape[1] must be 2x larger than grad shape[1].");
  NVTE_CHECK(input.data.shape == output->data.shape,
             "Input and output shapes must match.");

  TRANSFORMER_ENGINE_TYPE_SWITCH_INPUT(input.data.dtype, IType,
    TRANSFORMER_ENGINE_TYPE_SWITCH_OUTPUT(output->data.dtype, OType,
      constexpr int nvec = 32 / sizeof(IType);
      DGatedActivationKernelLauncher<nvec, fp32, Empty, relu<fp32, fp32>, drelu<fp32, fp32>>(
        reinterpret_cast<const IType*>(grad.data.dptr),
        reinterpret_cast<const IType*>(input.data.dptr),
        reinterpret_cast<OType*>(output->data.dptr),
        grad.data.shape[0],
        grad.data.shape[1],
        {},
        stream);
    );  // NOLINT(*)
  );  // NOLINT(*)
}

}  // namespace transformer_engine

void nvte_relu(const NVTETensor input,
               NVTETensor output,
               cudaStream_t stream) {
  NVTE_API_CALL(nvte_relu);
  using namespace transformer_engine;
  relu(*reinterpret_cast<const Tensor*>(input),
       reinterpret_cast<Tensor*>(output),
       stream);
}

void nvte_drelu(const NVTETensor grad,
                const NVTETensor input,
                NVTETensor output,
                cudaStream_t stream) {
  NVTE_API_CALL(nvte_drelu);
  using namespace transformer_engine;
  drelu(*reinterpret_cast<const Tensor*>(grad),
        *reinterpret_cast<const Tensor*>(input),
        reinterpret_cast<Tensor*>(output),
        stream);
}

void nvte_reglu(const NVTETensor input,
                NVTETensor output,
                cudaStream_t stream) {
  NVTE_API_CALL(nvte_reglu);
  using namespace transformer_engine;
  reglu(*reinterpret_cast<const Tensor*>(input),
        reinterpret_cast<Tensor*>(output),
        stream);
}

void nvte_dreglu(const NVTETensor grad,
                 const NVTETensor input,
                 NVTETensor output,
                 cudaStream_t stream) {
  NVTE_API_CALL(nvte_dreglu);
  using namespace transformer_engine;
  dreglu(*reinterpret_cast<const Tensor*>(grad),
         *reinterpret_cast<const Tensor*>(input),
         reinterpret_cast<Tensor*>(output),
         stream);
}