83 lines
3.0 KiB
Plaintext
83 lines
3.0 KiB
Plaintext
/*
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* Copyright (c) 2024 by FlashInfer team.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include <flashinfer/activation.cuh>
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#include "pytorch_extension_utils.h"
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using namespace flashinfer;
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__device__ __forceinline__ float silu(const float& val) {
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return val / (1.0f + __expf(-val));
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}
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__device__ __forceinline__ float gelu(const float& val) {
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constexpr float kAlpha = M_SQRT1_2;
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return val * 0.5f * (1.0f + ::erf(val * kAlpha));
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}
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__device__ __forceinline__ float gelu_tanh(const float& val) {
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const float cdf = 0.5f * (1.0f + math::tanh((0.7978845608028654f * (val + 0.044715f * val * val * val))));
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return val * cdf;
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}
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void silu_and_mul(at::Tensor& out, at::Tensor& input, int64_t cuda_stream) {
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int d = input.size(-1) / 2;
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int64_t num_tokens = input.numel() / input.size(-1);
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dim3 grid(num_tokens);
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cudaStream_t stream = reinterpret_cast<cudaStream_t>(cuda_stream);
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DISPATCH_PYTORCH_DTYPE_TO_CTYPE_FP16(input.scalar_type(), c_type, [&] {
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uint32_t vec_size = 16 / sizeof(c_type);
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dim3 block(std::min(d / vec_size, 1024U));
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flashinfer::activation::act_and_mul_kernel<c_type, silu>
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<<<grid, block, 0, stream>>>(static_cast<c_type*>(out.data_ptr()), static_cast<c_type*>(input.data_ptr()), d);
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return true;
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});
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}
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void gelu_tanh_and_mul(at::Tensor& out, at::Tensor& input, int64_t cuda_stream) {
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int d = input.size(-1) / 2;
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int64_t num_tokens = input.numel() / input.size(-1);
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dim3 grid(num_tokens);
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cudaStream_t stream = reinterpret_cast<cudaStream_t>(cuda_stream);
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DISPATCH_PYTORCH_DTYPE_TO_CTYPE_FP16(input.scalar_type(), c_type, [&] {
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uint32_t vec_size = 16 / sizeof(c_type);
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dim3 block(std::min(d / vec_size, 1024U));
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flashinfer::activation::act_and_mul_kernel<c_type, gelu_tanh>
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<<<grid, block, 0, stream>>>(static_cast<c_type*>(out.data_ptr()), static_cast<c_type*>(input.data_ptr()), d);
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return true;
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});
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}
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void gelu_and_mul(at::Tensor& out, at::Tensor& input, int64_t cuda_stream) {
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int d = input.size(-1) / 2;
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int64_t num_tokens = input.numel() / input.size(-1);
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dim3 grid(num_tokens);
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cudaStream_t stream = reinterpret_cast<cudaStream_t>(cuda_stream);
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DISPATCH_PYTORCH_DTYPE_TO_CTYPE_FP16(input.scalar_type(), c_type, [&] {
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uint32_t vec_size = 16 / sizeof(c_type);
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dim3 block(std::min(d / vec_size, 1024U));
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flashinfer::activation::act_and_mul_kernel<c_type, gelu>
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<<<grid, block, 0, stream>>>(static_cast<c_type*>(out.data_ptr()), static_cast<c_type*>(input.data_ptr()), d);
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return true;
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});
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}
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