94 lines
3.4 KiB
Plaintext
94 lines
3.4 KiB
Plaintext
// Batched scaled-dot-product attention helpers (Phase T10).
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//
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// The QKᵀ and PV matmuls run as cublasSgemmStridedBatched in Rust; the only
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// kernel attention needs of its own is a CAUSAL row-wise softmax over the score
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// rows. Scores are [B*nh, S, S] flattened to rows of length S; for a flat row r
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// the query position within its sequence is `r % S`, so columns j > r%S are
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// future positions and get probability 0 (no additive -1e9 mask tensor needed).
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//
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// The forward also folds in the 1/sqrt(head_dim) scale (applied to logits before
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// the max/exp) so we don't need a separate scale pass. Backward is the ordinary
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// softmax Jacobian (csrc/ops/nn.cu launch_softmax_dx_f32): masked entries have
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// y=0, so their contribution vanishes — no causal-specific backward needed.
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//
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// All F32, row-major, contiguous. Reduction helpers mirror nn.cu (inlined so the
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// file is self-contained, matching the csrc/ layout).
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#include <math.h>
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extern "C" {
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__device__ __forceinline__ float att_warp_sum(float v) {
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#pragma unroll
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for (int off = 16; off > 0; off >>= 1)
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v += __shfl_down_sync(0xffffffff, v, off);
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return v;
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}
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__device__ __forceinline__ float att_warp_max(float v) {
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#pragma unroll
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for (int off = 16; off > 0; off >>= 1)
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v = fmaxf(v, __shfl_down_sync(0xffffffff, v, off));
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return v;
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}
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__device__ __forceinline__ float att_block_sum(float v) {
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__shared__ float sh[32];
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int lane = threadIdx.x & 31, warp = threadIdx.x >> 5;
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int nwarps = (blockDim.x + 31) >> 5;
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v = att_warp_sum(v);
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if (lane == 0) sh[warp] = v;
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__syncthreads();
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v = (threadIdx.x < nwarps) ? sh[threadIdx.x] : 0.0f;
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if (warp == 0) v = att_warp_sum(v);
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__shared__ float bc;
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if (threadIdx.x == 0) bc = v;
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__syncthreads();
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return bc;
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}
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__device__ __forceinline__ float att_block_max(float v) {
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__shared__ float sh[32];
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int lane = threadIdx.x & 31, warp = threadIdx.x >> 5;
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int nwarps = (blockDim.x + 31) >> 5;
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v = att_warp_max(v);
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if (lane == 0) sh[warp] = v;
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__syncthreads();
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v = (threadIdx.x < nwarps) ? sh[threadIdx.x] : -INFINITY;
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if (warp == 0) v = att_warp_max(v);
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__shared__ float bc;
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if (threadIdx.x == 0) bc = v;
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__syncthreads();
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return bc;
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}
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// One block per score row. rows = B*nh*S total; the query position within its
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// sequence is (blockIdx.x % seq). Logits are scaled by `scale` (= 1/sqrt(hd))
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// before softmax; columns j > qpos are masked to probability 0.
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__global__ void softmax_causal_k(const float* x, float* y, int seq, float scale) {
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int r = blockIdx.x;
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int qpos = r % seq;
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const float* xr = x + (size_t)r * seq;
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float* yr = y + (size_t)r * seq;
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int valid = qpos + 1; // attend to columns [0, qpos]
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float m = -INFINITY;
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for (int c = threadIdx.x; c < valid; c += blockDim.x)
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m = fmaxf(m, xr[c] * scale);
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m = att_block_max(m);
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float sum = 0.0f;
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for (int c = threadIdx.x; c < valid; c += blockDim.x) {
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float e = expf(xr[c] * scale - m);
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yr[c] = e;
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sum += e;
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}
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sum = att_block_sum(sum);
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float inv = 1.0f / sum;
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for (int c = threadIdx.x; c < seq; c += blockDim.x)
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yr[c] = (c < valid) ? yr[c] * inv : 0.0f;
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}
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void launch_softmax_causal_f32(const float* x, float* y, int rows, int seq,
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float scale, void* s) {
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int blk = seq < 1024 ? seq : 1024;
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if (blk < 32) blk = 32;
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softmax_causal_k<<<rows, blk, 0, (cudaStream_t)s>>>(x, y, seq, scale);
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}
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} // extern "C"
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