/************************************************************************* * Copyright (c) 2022-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. * * See LICENSE for license information. ************************************************************************/ #include #include #include #include #include "../utils.cuh" #include "../common.h" #include #include <../util/vectorized_pointwise.h> namespace transformer_engine { namespace detail { struct GELUParam {}; __device__ inline fp32 gelu(fp32 value, const GELUParam &) { return value * (0.5F + 0.5F * tanhf(value * (0.79788456F + 0.03567741F * value * value))); } } void gelu_cast(const Tensor &input, Tensor *output, cudaStream_t stream) { CheckInputTensor(input, "gelu_input"); CheckOutputTensor(*output, "gelu_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 == output->data.shape, "Input and output shapes must match."); const size_t tot_elts = input.data.shape[1] * input.data.shape[0]; 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( reinterpret_cast(input.data.dptr), reinterpret_cast(output->data.dptr), reinterpret_cast(output->scale.dptr), reinterpret_cast(output->scale_inv.dptr), reinterpret_cast(output->amax.dptr), tot_elts, {}, stream); ); // NOLINT(*) ); // NOLINT(*) } } // namespace transformer_engine void nvte_gelu(const NVTETensor input, NVTETensor output, cudaStream_t stream) { using namespace transformer_engine; gelu_cast(*reinterpret_cast(input), reinterpret_cast(output), stream); }