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files : remove old wkv6 (#0)

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ggml-ci
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Georgi Gerganov 2025-03-27 10:15:02 +02:00
parent ee5581633b
commit 1fbdfb1d36
4 changed files with 0 additions and 246 deletions
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89
ggml/src/ggml-cuda/wkv6.cu
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#include "common.cuh"
#include "wkv6.cuh"
static __global__ void rwkv_wkv_f32(const int B, const int T, const int C, const int H, const float * k, const float * v, const float * r, const float * tf, const float * td, const float * s, float * dst) {
const int tid = threadIdx.x;
const int bid = blockIdx.x;
const int head_size = CUDA_WKV_BLOCK_SIZE;
const int batch_i = bid / H;
const int head_i = bid % H;
const int state_size = C * head_size;
const int n_seq_tokens = T / B;
float state[head_size];
__shared__ float _k[head_size], _r[head_size], _tf[head_size], _td[head_size];
#pragma unroll
for (int i = 0; i < head_size; i++) {
state[i] = s[batch_i * state_size + head_i * head_size * head_size + i * head_size + tid];
}
__syncthreads();
_tf[tid] = tf[head_i * head_size + tid];
__syncthreads();
for (int t = batch_i * n_seq_tokens * C + head_i * head_size + tid; t < (batch_i + 1) * n_seq_tokens * C + head_i * head_size + tid; t += C) {
__syncthreads();
_k[tid] = k[t];
_r[tid] = r[t];
_td[tid] = td[t];
__syncthreads();
const float _v = v[t];
float y = 0;
for (int j = 0; j < head_size; j += 4) {
const float4& k = (float4&)(_k[j]);
const float4& r = (float4&)(_r[j]);
const float4& tf = (float4&)(_tf[j]);
const float4& td = (float4&)(_td[j]);
float4& s = (float4&)(state[j]);
float4 kv;
kv.x = k.x * _v;
kv.y = k.y * _v;
kv.z = k.z * _v;
kv.w = k.w * _v;
y += r.x * (tf.x * kv.x + s.x);
y += r.y * (tf.y * kv.y + s.y);
y += r.z * (tf.z * kv.z + s.z);
y += r.w * (tf.w * kv.w + s.w);
s.x = s.x * td.x + kv.x;
s.y = s.y * td.y + kv.y;
s.z = s.z * td.z + kv.z;
s.w = s.w * td.w + kv.w;
}
dst[t] = y;
}
#pragma unroll
for (int i = 0; i < head_size; i++) {
dst[T * C + batch_i * state_size + head_i * head_size * head_size + i * head_size + tid] = state[i];
}
}
void ggml_cuda_op_rwkv_wkv6(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const float * k_d = (const float *)dst->src[0]->data;
const float * v_d = (const float *)dst->src[1]->data;
const float * r_d = (const float *)dst->src[2]->data;
const float * tf_d = (const float *)dst->src[3]->data;
const float * td_d = (const float *)dst->src[4]->data;
const float * s_d = (const float *)dst->src[5]->data;
const int64_t B = dst->src[5]->ne[1];
const int64_t T = dst->src[0]->ne[2];
const int64_t C = dst->ne[0];
const int64_t H = dst->src[0]->ne[1];
float * dst_d = (float *)dst->data;
cudaStream_t stream = ctx.stream();
GGML_ASSERT(dst->src[5]->type == GGML_TYPE_F32);
GGML_ASSERT(C % H == 0);
GGML_ASSERT(C / H == CUDA_WKV_BLOCK_SIZE); // The current cuda kernel is designed for RWKV6, HEAD_SIZE == 64
rwkv_wkv_f32<<<B * H, C / H, 0, stream>>>(B, T, C, H, k_d, v_d, r_d, tf_d, td_d, s_d, dst_d);
}

5
ggml/src/ggml-cuda/wkv6.cuh
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#include "common.cuh"
#define CUDA_WKV_BLOCK_SIZE 64
void ggml_cuda_op_rwkv_wkv6(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

143
ggml/src/ggml-sycl/wkv6.cpp
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#include <sycl/sycl.hpp>
#include "wkv6.hpp"
constexpr int WKV_BLOCK_SIZE = 64; // Matching CUDA_WKV_BLOCK_SIZE
// Helper function for the main kernel
static void rwkv_wkv_f32_kernel(
const int B, const int T, const int C, const int H,
const float* k, const float* v, const float* r,
const float* tf, const float* td, const float* s,
float* dst, const sycl::nd_item<3>& item_ct1, float* shared_mem) {
const int tid = item_ct1.get_local_id(2);
const int bid = item_ct1.get_group(2);
const int head_size = WKV_BLOCK_SIZE;
const int batch_i = bid / H;
const int head_i = bid % H;
const int state_size = C * head_size;
const int n_seq_tokens = T / B;
// Set up shared memory pointers
float* _k = shared_mem;
float* _r = _k + head_size;
float* _tf = _r + head_size;
float* _td = _tf + head_size;
// Local state array
float state[WKV_BLOCK_SIZE];
// Load initial state
#pragma unroll
for (int i = 0; i < head_size; i++) {
state[i] = s[batch_i * state_size + head_i * head_size * head_size + i * head_size + tid];
}
// Sync threads before shared memory operations
item_ct1.barrier(sycl::access::fence_space::local_space);
// Load time-mixing parameters
_tf[tid] = tf[head_i * head_size + tid];
item_ct1.barrier(sycl::access::fence_space::local_space);
// Main sequence processing loop
for (int t = batch_i * n_seq_tokens * C + head_i * head_size + tid;
t < (batch_i + 1) * n_seq_tokens * C + head_i * head_size + tid;
t += C) {
item_ct1.barrier(sycl::access::fence_space::local_space);
// Load current timestep data to shared memory
_k[tid] = k[t];
_r[tid] = r[t];
_td[tid] = td[t];
item_ct1.barrier(sycl::access::fence_space::local_space);
const float _v = v[t];
float y = 0;
// Process in chunks of 4 for better vectorization
sycl::float4 k4, r4, tf4, td4, s4;
#pragma unroll
for (int j = 0; j < head_size; j += 4) {
// Load data in vec4 chunks
k4 = sycl::float4(_k[j], _k[j+1], _k[j+2], _k[j+3]);
r4 = sycl::float4(_r[j], _r[j+1], _r[j+2], _r[j+3]);
tf4 = sycl::float4(_tf[j], _tf[j+1], _tf[j+2], _tf[j+3]);
td4 = sycl::float4(_td[j], _td[j+1], _td[j+2], _td[j+3]);
s4 = sycl::float4(state[j], state[j+1], state[j+2], state[j+3]);
// Compute key-value product
sycl::float4 kv4 = k4 * _v;
// Accumulate weighted sum
y += sycl::dot(r4, tf4 * kv4 + s4);
// Update state
s4 = s4 * td4 + kv4;
// Store updated state
state[j] = s4.x();
state[j+1] = s4.y();
state[j+2] = s4.z();
state[j+3] = s4.w();
}
dst[t] = y;
}
// Save final state
#pragma unroll
for (int i = 0; i < head_size; i++) {
dst[T * C + batch_i * state_size + head_i * head_size * head_size + i * head_size + tid] = state[i];
}
}
void ggml_sycl_op_rwkv_wkv6(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
const ggml_tensor *src0 = dst->src[0];
const ggml_tensor *src1 = dst->src[1];
const float* k_d = (const float*)dst->src[0]->data;
const float* v_d = (const float*)dst->src[1]->data;
const float* r_d = (const float*)dst->src[2]->data;
const float* tf_d = (const float*)dst->src[3]->data;
const float* td_d = (const float*)dst->src[4]->data;
const float* s_d = (const float*)dst->src[5]->data;
float* dst_d = (float*)dst->data;
const int64_t B = dst->src[5]->ne[1];
const int64_t T = dst->src[0]->ne[2];
const int64_t C = dst->ne[0];
const int64_t H = dst->src[0]->ne[1];
GGML_ASSERT(dst->src[5]->type == GGML_TYPE_F32);
GGML_ASSERT(C % H == 0);
GGML_ASSERT(C / H == WKV_BLOCK_SIZE); // The current sycl kernel is designed for RWKV6, HEAD_SIZE == 64
dpct::queue_ptr stream = ctx.stream();
// Calculate execution configuration
const size_t shared_mem_size = WKV_BLOCK_SIZE * 4 * sizeof(float); // For k, r, tf, td
sycl::range<3> block_dims(1, 1, C / H);
sycl::range<3> grid_dims(1, 1, B * H);
// Submit kernel
stream->submit([&](sycl::handler& cgh) {
sycl::local_accessor<float, 1> shared_mem_acc(shared_mem_size, cgh);
cgh.parallel_for(
sycl::nd_range<3>(grid_dims * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
rwkv_wkv_f32_kernel(
B, T, C, H, k_d, v_d, r_d, tf_d, td_d, s_d, dst_d,
item_ct1, (float*)shared_mem_acc.get_multi_ptr<sycl::access::decorated::no>().get()
);
});
});
GGML_UNUSED(src0);
GGML_UNUSED(src1);
}

9
ggml/src/ggml-sycl/wkv6.hpp
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#ifndef GGML_SYCL_WKV6_HPP
#define GGML_SYCL_WKV6_HPP
#include "common.hpp"
void ggml_sycl_op_rwkv_wkv6(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
#endif // GGML_SYCL_WKV6_HPP