429 lines
16 KiB
Plaintext
429 lines
16 KiB
Plaintext
/*
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* Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
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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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// Adapted from
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// https://github.com/NVIDIA/TensorRT-LLM/blob/main/cpp/tensorrt_llm/kernels/fusedQKNormRopeKernel.cu
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#include <ATen/cuda/Exceptions.h>
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#include <c10/cuda/CUDAGuard.h>
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#include <c10/cuda/CUDAStream.h>
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#include <cuda_bf16.h>
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#include <cuda_fp16.h>
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#include <cuda_fp8.h>
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#include <cuda_runtime.h>
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#include <torch/all.h>
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#include <torch/cuda.h>
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#include <cmath>
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#define CHECK_TYPE(x, st) \
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TORCH_CHECK(x.scalar_type() == st, #x " dtype is ", x.scalar_type(), ", while ", st, " is expected")
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#define CHECK_TH_CUDA(x) TORCH_CHECK(x.is_cuda(), #x " must be a CUDA tensor")
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#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
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#define CHECK_INPUT(x, st) \
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CHECK_TH_CUDA(x); \
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CHECK_CONTIGUOUS(x); \
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CHECK_TYPE(x, st)
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#define FINAL_MASK 0xffffffff
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namespace tensorrt_llm::common {
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template <typename T, int num>
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struct packed_as;
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// Specialization for packed_as used in this kernel.
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template <>
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struct packed_as<uint, 1> {
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using type = uint;
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};
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template <>
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struct packed_as<uint, 2> {
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using type = uint2;
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};
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template <>
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struct packed_as<uint, 4> {
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using type = uint4;
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};
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template <typename T>
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__inline__ __device__ T warpReduceSum(T val) {
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#pragma unroll
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for (int mask = 16; mask > 0; mask >>= 1)
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val += __shfl_xor_sync(FINAL_MASK, val, mask,
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32); //__shfl_sync bf16 return float when sm < 80
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return val;
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}
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template <typename T>
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inline __device__ __host__ T divUp(T m, T n) {
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return (m + n - 1) / n;
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}
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} // namespace tensorrt_llm::common
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namespace tensorrt_llm::kernels {
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__device__ inline float compute_freq_yarn(float base, int head_dim, int half_dim, float factor, float low, float high) {
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float freq = powf(base, -2.0f * half_dim / static_cast<float>(head_dim));
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if (factor != 1.0f) {
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float inv_freq_extrapolation = freq;
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float inv_freq_interpolation = freq / factor;
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float high_adj = high;
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if (fabsf(low - high_adj) <= 1e-6f) {
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high_adj += 0.001f;
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}
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float linear_func = (static_cast<float>(half_dim) - low) / (high_adj - low);
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float ramp_func = fminf(fmaxf(linear_func, 0.0f), 1.0f);
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float inv_freq_extrapolation_factor = 1.0f - ramp_func;
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freq = inv_freq_interpolation * (1.0f - inv_freq_extrapolation_factor) +
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inv_freq_extrapolation * inv_freq_extrapolation_factor;
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}
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return freq;
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////
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// Perform per-head QK Norm and RoPE in a single kernel.
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// head_dim: the dimension of each head
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// interleave: interleave=!is_neox.
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template <int head_dim, bool interleave>
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__global__ void fusedQKNormRopeKernel(
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__nv_bfloat16* qkv, // Combined QKV tensor [num_tokens, (num_heads_q+num_heads_k+num_heads_v)*head_dim]
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int const num_heads_q, // Number of query heads
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int const num_heads_k, // Number of key heads
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int const num_heads_v, // Number of value heads
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float const eps, // Epsilon for RMS normalization
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__nv_bfloat16 const* q_weight, // RMSNorm weights for query
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__nv_bfloat16 const* k_weight, // RMSNorm weights for key
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float const base, // Base for RoPE computation
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int const* position_ids, // Position IDs for RoPE
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int const num_tokens, // Number of tokens
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// parameters for yarn
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float factor, // factor in rope_scaling in config.json. When it is not 1.0, it means the model is using yarn.
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float low, // threshold for high frequency
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float high, // threshold for low frequency
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float attention_factor, // attention_factor applied on cos and sin
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int const rotary_dim) {
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int const warpsPerBlock = blockDim.x / 32;
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int const warpId = threadIdx.x / 32;
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int const laneId = threadIdx.x % 32;
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// Calculate global warp index to determine which head/token this warp processes
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int const globalWarpIdx = blockIdx.x * warpsPerBlock + warpId;
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// Total number of attention heads (Q and K)
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int const total_qk_heads = num_heads_q + num_heads_k;
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// Determine which token and head type (Q or K) this warp processes
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int const tokenIdx = globalWarpIdx / total_qk_heads;
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int const localHeadIdx = globalWarpIdx % total_qk_heads;
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// Skip if this warp is assigned beyond the number of tokens
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if (tokenIdx >= num_tokens) return;
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bool const isQ = localHeadIdx < num_heads_q;
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int const headIdx = isQ ? localHeadIdx : localHeadIdx - num_heads_q;
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int const num_heads = num_heads_q + num_heads_k + num_heads_v;
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static_assert(
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head_dim % (32 * 2) == 0,
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"head_dim must be divisible by 64 (each warp processes one head, and each thread gets even number of "
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"elements)");
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constexpr int numElemsPerThread = head_dim / 32;
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float elements[numElemsPerThread];
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constexpr int elemSizeBytes = numElemsPerThread * sizeof(__nv_bfloat16);
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static_assert(elemSizeBytes % 4 == 0, "numSizeBytes must be a multiple of 4");
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constexpr int vecSize = elemSizeBytes / 4; // Use packed_as<uint, vecSize> to perform loading/saving.
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using vec_T = typename tensorrt_llm::common::packed_as<uint, vecSize>::type;
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int offsetWarp; // Offset for the warp
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if (isQ) {
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// Q segment: token offset + head offset within Q segment
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offsetWarp = tokenIdx * num_heads * head_dim + headIdx * head_dim;
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} else {
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// K segment: token offset + entire Q segment + head offset within K segment
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offsetWarp = tokenIdx * num_heads * head_dim + num_heads_q * head_dim + headIdx * head_dim;
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}
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int offsetThread = offsetWarp + laneId * numElemsPerThread;
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// Sum of squares for RMSNorm
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float sumOfSquares = 0.0f;
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// Load.
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{
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vec_T vec = *reinterpret_cast<vec_T const*>(&qkv[offsetThread]);
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for (int i = 0; i < vecSize; i++) {
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float2 vals = __bfloat1622float2(*reinterpret_cast<__nv_bfloat162*>(reinterpret_cast<uint*>(&vec) + i));
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sumOfSquares += vals.x * vals.x;
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sumOfSquares += vals.y * vals.y;
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elements[2 * i] = vals.x;
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elements[2 * i + 1] = vals.y;
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}
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}
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// Reduce sum across warp using the utility function
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sumOfSquares = tensorrt_llm::common::warpReduceSum(sumOfSquares);
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// Compute RMS normalization factor
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float rms_rcp = rsqrtf(sumOfSquares / static_cast<float>(head_dim) + eps);
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// Normalize elements
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for (int i = 0; i < numElemsPerThread; i++) {
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int dim = laneId * numElemsPerThread + i;
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float weight = isQ ? __bfloat162float(q_weight[dim]) : __bfloat162float(k_weight[dim]);
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elements[i] *= rms_rcp * weight;
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}
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// Apply RoPE to normalized elements
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float elements2[numElemsPerThread]; // Additional buffer required for RoPE.
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float cos_vals[numElemsPerThread];
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float sin_vals[numElemsPerThread];
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float pos_id = static_cast<float>(position_ids[tokenIdx]);
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int const rotary_lanes = rotary_dim / numElemsPerThread; // rotary range
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bool const applyRotary = (laneId < rotary_lanes);
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if (applyRotary) {
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if constexpr (interleave) {
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// Perform interleaving. Fill cos_vals and sin_vals.
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for (int i = 0; i < numElemsPerThread; i++) {
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elements2[i] = (i % 2 == 0) ? -elements[i + 1] : elements[i - 1];
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int dim_idx = laneId * numElemsPerThread + i;
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int half_dim = dim_idx / 2;
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float freq = compute_freq_yarn(base, rotary_dim, half_dim, factor, low, high);
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float theta = pos_id * freq;
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__sincosf(theta, &sin_vals[i], &cos_vals[i]);
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}
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} else {
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// Neox style
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// Before data exchange with in warp, we need to sync.
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__syncwarp();
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int const half_rotary_lanes = rotary_lanes / 2;
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unsigned int active_mask = (1u << rotary_lanes) - 1;
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// Limitation: The operation below requires half_rotary_lanes to be a power of 2.
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// because it relies on __shfl_xor_sync to exchange data within a warp.
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for (int i = 0; i < numElemsPerThread; i++) {
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elements2[i] = __shfl_xor_sync(active_mask, elements[i], half_rotary_lanes);
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if (laneId < half_rotary_lanes) {
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elements2[i] = -elements2[i];
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}
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int dim_idx = laneId * numElemsPerThread + i;
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dim_idx = (dim_idx * 2) % rotary_dim;
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int half_dim = dim_idx / 2;
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float freq = compute_freq_yarn(base, rotary_dim, half_dim, factor, low, high);
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float theta = pos_id * freq;
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__sincosf(theta, &sin_vals[i], &cos_vals[i]);
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}
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// __shfl_xor_sync does not provide memfence. Need to sync again.
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__syncwarp();
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}
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for (int i = 0; i < numElemsPerThread; i++) {
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elements[i] = (elements[i] * cos_vals[i] + elements2[i] * sin_vals[i]) * attention_factor;
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}
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}
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// Store.
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{
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vec_T vec;
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for (int i = 0; i < vecSize; i++) {
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__nv_bfloat162 vals = __float22bfloat162_rn(make_float2(elements[2 * i], elements[2 * i + 1]));
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reinterpret_cast<__nv_bfloat162&>(*(reinterpret_cast<uint*>(&vec) + i)) = vals;
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}
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vec_T* outputPtr = reinterpret_cast<vec_T*>(&qkv[offsetThread]);
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*outputPtr = vec;
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}
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}
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// Borrowed from
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// https://github.com/flashinfer-ai/flashinfer/blob/8125d079a43e9a0ba463a4ed1b639cefd084cec9/include/flashinfer/pos_enc.cuh#L568
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#define DISPATCH_INTERLEAVE(interleave, INTERLEAVE, ...) \
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if (interleave) { \
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const bool INTERLEAVE = true; \
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__VA_ARGS__ \
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} else { \
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const bool INTERLEAVE = false; \
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__VA_ARGS__ \
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}
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void launchFusedQKNormRope(
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void* qkv,
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int const num_tokens,
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int const num_heads_q,
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int const num_heads_k,
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int const num_heads_v,
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int const head_dim,
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float const eps,
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void const* q_weight,
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void const* k_weight,
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float const base,
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bool const interleave,
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int const* position_ids,
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float factor,
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float low,
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float high,
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float attention_factor,
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int const rotary_dim,
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cudaStream_t stream) {
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constexpr int blockSize = 256;
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int const warpsPerBlock = blockSize / 32;
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int const totalQKHeads = num_heads_q + num_heads_k;
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int const totalWarps = num_tokens * totalQKHeads;
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int const gridSize = common::divUp(totalWarps, warpsPerBlock);
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dim3 gridDim(gridSize);
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dim3 blockDim(blockSize);
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// Head dimensions should be a multiple of 64
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// Add more cases as needed
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switch (head_dim) {
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case 64:
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DISPATCH_INTERLEAVE(interleave, INTERLEAVE, {
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fusedQKNormRopeKernel<64, INTERLEAVE><<<gridDim, blockDim, 0, stream>>>(
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reinterpret_cast<__nv_bfloat16*>(qkv),
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num_heads_q,
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num_heads_k,
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num_heads_v,
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eps,
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reinterpret_cast<__nv_bfloat16 const*>(q_weight),
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reinterpret_cast<__nv_bfloat16 const*>(k_weight),
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base,
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position_ids,
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num_tokens,
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factor,
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low,
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high,
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attention_factor,
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rotary_dim);
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});
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break;
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case 128:
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DISPATCH_INTERLEAVE(interleave, INTERLEAVE, {
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fusedQKNormRopeKernel<128, INTERLEAVE><<<gridDim, blockDim, 0, stream>>>(
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reinterpret_cast<__nv_bfloat16*>(qkv),
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num_heads_q,
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num_heads_k,
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num_heads_v,
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eps,
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reinterpret_cast<__nv_bfloat16 const*>(q_weight),
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reinterpret_cast<__nv_bfloat16 const*>(k_weight),
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base,
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position_ids,
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num_tokens,
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factor,
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low,
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high,
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attention_factor,
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rotary_dim);
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});
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break;
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case 256:
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DISPATCH_INTERLEAVE(interleave, INTERLEAVE, {
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fusedQKNormRopeKernel<256, INTERLEAVE><<<gridDim, blockDim, 0, stream>>>(
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reinterpret_cast<__nv_bfloat16*>(qkv),
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num_heads_q,
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num_heads_k,
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num_heads_v,
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eps,
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reinterpret_cast<__nv_bfloat16 const*>(q_weight),
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reinterpret_cast<__nv_bfloat16 const*>(k_weight),
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base,
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position_ids,
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num_tokens,
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factor,
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low,
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high,
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attention_factor,
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rotary_dim);
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});
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break;
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default:
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TORCH_CHECK(false, "Unsupported head dimension for fusedQKNormRope: ", head_dim);
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}
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}
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} // namespace tensorrt_llm::kernels
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// Function for fused QK Norm and RoPE
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// This operator applies RMS normalization and RoPE to Q and K tensors in a single CUDA kernel.
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// The OP performs operations in-place on the input qkv tensor.
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void fused_qk_norm_rope(
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torch::Tensor& qkv, // Combined QKV tensor [num_tokens, (num_heads_q+num_heads_k+num_heads_v)*head_dim]
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int64_t num_heads_q, // Number of query heads
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int64_t num_heads_k, // Number of key heads
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int64_t num_heads_v, // Number of value heads
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int64_t head_dim, // Dimension per head
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double eps, // Epsilon for RMS normalization
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torch::Tensor& q_weight, // RMSNorm weights for query [head_dim]
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torch::Tensor& k_weight, // RMSNorm weights for key [head_dim]
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double base, // Base for RoPE computation
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bool is_neox, // Whether RoPE is applied in Neox style
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torch::Tensor& position_ids, // Position IDs for RoPE [num_tokens]
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// parameters for yarn
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double factor, // factor in rope_scaling in config.json. When it is not 1.0, it means the model is using yarn.
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double low, // threshold for high frequency
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double high, // threshold for low frequency
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double attention_factor, // attention_factor applied on cos and sin
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int64_t rotary_dim) {
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// Input validation
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TORCH_CHECK(qkv.dim() == 2, "QKV tensor must be 2D: [num_tokens, (num_heads_q+num_heads_k+num_heads_v)*head_dim]");
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TORCH_CHECK(position_ids.dim() == 1, "Position IDs must be 1D: [num_tokens]");
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TORCH_CHECK(q_weight.dim() == 1, "Query weights must be 1D: [head_dim]");
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TORCH_CHECK(k_weight.dim() == 1, "Key weights must be 1D: [head_dim]");
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TORCH_CHECK(q_weight.size(0) == head_dim, "Query weights size must match head dimension");
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TORCH_CHECK(k_weight.size(0) == head_dim, "Key weights size must match head dimension");
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TORCH_CHECK(rotary_dim % (head_dim / 32) == 0, "rotary_dim must be divisible by numElemsPerThread");
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if (is_neox) {
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int64_t half_rotary_lanes = rotary_dim / (head_dim / 32) / 2;
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TORCH_CHECK(
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half_rotary_lanes >= 1 && (half_rotary_lanes & (half_rotary_lanes - 1)) == 0,
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"half_rotary_lanes must be a power of 2 for neox style, got ",
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half_rotary_lanes);
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}
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CHECK_INPUT(qkv, torch::kBFloat16);
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CHECK_INPUT(position_ids, torch::kInt32);
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CHECK_INPUT(q_weight, torch::kBFloat16);
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CHECK_INPUT(k_weight, torch::kBFloat16);
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int64_t num_tokens = qkv.size(0);
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TORCH_CHECK(position_ids.size(0) == num_tokens, "Number of tokens in position_ids must match QKV");
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int64_t total_heads = num_heads_q + num_heads_k + num_heads_v;
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TORCH_CHECK(
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qkv.size(1) == total_heads * head_dim, "QKV tensor size must match total number of heads and head dimension");
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auto stream = at::cuda::getCurrentCUDAStream(qkv.get_device());
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tensorrt_llm::kernels::launchFusedQKNormRope(
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reinterpret_cast<__nv_bfloat16*>(qkv.data_ptr()),
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static_cast<int>(num_tokens),
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static_cast<int>(num_heads_q),
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static_cast<int>(num_heads_k),
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static_cast<int>(num_heads_v),
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static_cast<int>(head_dim),
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static_cast<float>(eps),
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reinterpret_cast<__nv_bfloat16*>(q_weight.data_ptr()),
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reinterpret_cast<__nv_bfloat16*>(k_weight.data_ptr()),
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static_cast<float>(base),
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!is_neox, // interleave
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reinterpret_cast<int const*>(position_ids.data_ptr()),
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static_cast<float>(factor),
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static_cast<float>(low),
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static_cast<float>(high),
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static_cast<float>(attention_factor),
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static_cast<int>(rotary_dim),
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stream);
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}
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