180 lines
6.4 KiB
Plaintext
180 lines
6.4 KiB
Plaintext
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#include "ATen/ATen.h"
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#include <cuda_fp16.h>
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#include <cuda_runtime.h>
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#include <torch/extension.h>
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#include "util.h"
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#include "element_wise.h"
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using torch::Tensor;
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void gemm_fp16_cublas(Tensor a, Tensor b, Tensor c);
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void gemm_fp16_cublas(const void *a, const void *b, void *c, int m,
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int n, int k, bool output_fp32);
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// based on `kernel_wkv_forward`, fusing more operations
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__global__ void kernel_wkv_forward_new(
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const int B, const int T, const int C, const float *__restrict__ const _w,
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const float *__restrict__ const _u, const float *__restrict__ const _k,
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const float *__restrict__ const _v, const half *__restrict__ const r,
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half *__restrict__ const _y, float *__restrict__ const _aa,
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float *__restrict__ const _bb, float *__restrict__ const _pp) {
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const int idx = blockIdx.x * blockDim.x + threadIdx.x;
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const int _b = idx / C;
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const int _c = idx % C;
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const int _offset = _b * T * C + _c;
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const int _state_offset = _b * C + _c;
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float u = _u[_c];
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float w = _w[_c];
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const float *__restrict__ const k = _k + _offset;
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const float *__restrict__ const v = _v + _offset;
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half *__restrict__ const y = _y + _offset;
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float aa = _aa[_state_offset];
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float bb = _bb[_state_offset];
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float pp = _pp[_state_offset];
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for (int i = 0; i < T; i++) {
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const int ii = i * C;
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const float kk = k[ii];
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const float vv = v[ii];
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float ww = u + kk;
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float p = max(pp, ww);
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float e1 = exp(pp - p);
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float e2 = exp(ww - p);
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y[ii] = __float2half((e1 * aa + e2 * vv) / (e1 * bb + e2));
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ww = w + pp;
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p = max(ww, kk);
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e1 = exp(ww - p);
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e2 = exp(kk - p);
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aa = e1 * aa + e2 * vv;
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bb = e1 * bb + e2;
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pp = p;
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}
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_aa[_state_offset] = aa;
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_bb[_state_offset] = bb;
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_pp[_state_offset] = pp;
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}
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void cuda_wkv_forward_new(int B, int T, int C, float *w, float *u, float *k,
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float *v, half *r, half *y, float *aa, float *bb,
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float *pp) {
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dim3 threadsPerBlock(min(C, 32));
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assert(B * C % threadsPerBlock.x == 0);
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dim3 numBlocks(B * C / threadsPerBlock.x);
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kernel_wkv_forward_new<<<numBlocks, threadsPerBlock>>>(B, T, C, w, u, k, v, r,
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y, aa, bb, pp);
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}
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__global__ void _att_mix(const half *xx, const half *sx, const half *k_mix,
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const half *v_mix, const half *r_mix,
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const int outer_size, const int inner_size, half *kx,
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half *vx, half *rx) {
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for (int idx2 = blockIdx.x * blockDim.x + threadIdx.x; idx2 < inner_size;
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idx2 += blockDim.x * gridDim.x) {
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half k_mix_ = k_mix[idx2];
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half v_mix_ = v_mix[idx2];
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half r_mix_ = r_mix[idx2];
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for (int row = 0; row < outer_size; ++row) {
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int idx1 = row * inner_size + idx2;
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half xx_ = xx[idx1];
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half sx_ = sx[idx1];
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kx[idx1] = __hadd(__hmul(xx_, k_mix_),
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__hmul(sx_, __hsub(__float2half(1), k_mix_)));
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vx[idx1] = __hadd(__hmul(xx_, v_mix_),
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__hmul(sx_, __hsub(__float2half(1), v_mix_)));
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rx[idx1] = __hadd(__hmul(xx_, r_mix_),
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__hmul(sx_, __hsub(__float2half(1), r_mix_)));
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}
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}
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}
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void att_mix(const half *xx, const half *sx, const half *k_mix,
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const half *v_mix, const half *r_mix, const int outer_size,
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const int inner_size, half *kx, half *vx, half *rx) {
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// 256 is good enough on most GPUs
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const int32_t BLOCK_SIZE = 256;
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assert(inner_size % BLOCK_SIZE == 0);
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_att_mix<<<inner_size / BLOCK_SIZE, BLOCK_SIZE>>>(
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xx, sx, k_mix, v_mix, r_mix, outer_size, inner_size, kx, vx, rx);
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}
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struct InplaceSigmoid {
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__device__ __forceinline__ half operator()(int i) const {
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ptr[i] = __float2half(1.0 / (1.0 + exp(-__half2float(ptr[i]))));
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}
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half *ptr;
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};
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struct InplaceMul {
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__device__ __forceinline__ half operator()(int i) const {
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y[i] = __hmul(x[i], y[i]);
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}
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half *y;
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half *x;
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};
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/*
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Equivalent Python code:
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xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
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sx = torch.cat((sx.unsqueeze(0), xx[:-1,:]))
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kx = xx * k_mix + sx * (1 - k_mix)
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vx = xx * v_mix + sx * (1 - v_mix)
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rx = xx * r_mix + sx * (1 - r_mix)
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r = torch.sigmoid(gemm(rx, rw))
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k = gemm(kx, kw, output_dtype=torch.float32)
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v = gemm(vx, vw, output_dtype=torch.float32)
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T = x.shape[0]
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for t in range(T):
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kk = k[t]
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vv = v[t]
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ww = t_first + kk
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p = torch.maximum(pp, ww)
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e1 = torch.exp(pp - p)
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e2 = torch.exp(ww - p)
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sx[t] = ((e1 * aa + e2 * vv) / (e1 * bb + e2)).to(dtype=x.dtype)
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ww = t_decay + pp
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p = torch.maximum(ww, kk)
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e1 = torch.exp(ww - p)
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e2 = torch.exp(kk - p)
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aa = e1 * aa + e2 * vv
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bb = e1 * bb + e2
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pp = p
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out = gemm(r * sx, ow)
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return x + out, xx[-1,:], aa, bb, pp
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*/
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Tensor att_seq(Tensor x, Tensor sx, Tensor ln_w, Tensor ln_b, Tensor k_mix,
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Tensor v_mix, Tensor r_mix, Tensor kw, Tensor vw, Tensor rw,
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Tensor ow, Tensor t_first, Tensor pp, Tensor aa, Tensor bb,
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Tensor t_decay, /* imm */ Tensor buf, /* out */ Tensor x_plus_out) {
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Tensor xx = at::layer_norm(x, {x.size(-1)}, ln_w, ln_b);
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sx = at::cat({sx.unsqueeze(0), xx.slice(0, 0, -1)}, 0);
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char* buf_ptr = (char*)buf.data_ptr();
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half* kx = (half*)buf_ptr;
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half* vx = kx + x.numel();
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half* rx = vx + x.numel();
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half* wkv_y = rx + x.numel();
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att_mix(data_ptr<half>(xx), data_ptr<half>(sx), data_ptr<half>(k_mix),
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data_ptr<half>(v_mix), data_ptr<half>(r_mix), xx.size(0), xx.size(1),
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kx, vx, rx);
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float* k = reinterpret_cast<float*>(wkv_y + x.numel());
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float* v = k + x.size(0) * kw.size(1);
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half* r = reinterpret_cast<half*>(v + x.size(0) * vw.size(1));
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gemm_fp16_cublas(kx, kw.data_ptr(), k, x.size(0), kw.size(1), kw.size(0), true);
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gemm_fp16_cublas(vx, vw.data_ptr(), v, x.size(0), vw.size(1), vw.size(0), true);
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gemm_fp16_cublas(rx, rw.data_ptr(), r, x.size(0), rw.size(1), rw.size(0), false);
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element_wise(InplaceSigmoid{r}, x.size(0) * rw.size(1));
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cuda_wkv_forward_new(1, x.size(0), x.size(1), data_ptr<float>(t_decay),
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data_ptr<float>(t_first), k, v, r,
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wkv_y, data_ptr<float>(aa),
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data_ptr<float>(bb), data_ptr<float>(pp));
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element_wise(InplaceMul{wkv_y, r}, x.numel());
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gemm_fp16_cublas(wkv_y, ow.data_ptr(), x_plus_out.data_ptr(), x.size(0), ow.size(1), ow.size(0), false);
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x_plus_out += x;
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return xx;
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}
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