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zhiyuan1izhiyuan1i
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rwkv6: support sycl
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4 files changed

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4 files changed

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‎ggml/src/ggml-sycl.cpp

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@@ -4803,6 +4803,9 @@ bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct ggml_tens
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case GGML_OP_TIMESTEP_EMBEDDING:
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func = ggml_sycl_op_timestep_embedding;
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break;
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case GGML_OP_RWKV_WKV6:
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func = ggml_sycl_op_rwkv_wkv6;
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break;
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default:
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return false;
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}
@@ -5251,6 +5254,7 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
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case GGML_OP_PAD:
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case GGML_OP_LEAKY_RELU:
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case GGML_OP_TIMESTEP_EMBEDDING:
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case GGML_OP_RWKV_WKV6:
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return true;
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default:
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return false;

‎ggml/src/ggml-sycl/backend.hpp

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@@ -26,5 +26,6 @@
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#include "softmax.hpp"
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#include "tsembd.hpp"
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#include "im2col.hpp"
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#include "wkv6.hpp"
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#endif // GGML_SYCL_BACKEND_HPP

‎ggml/src/ggml-sycl/wkv6.cpp

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#include <sycl/sycl.hpp>
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#include "wkv6.hpp"
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constexpr int WKV_BLOCK_SIZE = 64; // Matching CUDA_WKV_BLOCK_SIZE
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// Helper function for the main kernel
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static void rwkv_wkv_f32_kernel(
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const int B, const int T, const int C, const int H,
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const float* k, const float* v, const float* r,
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const float* tf, const float* td, const float* s,
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float* dst, const sycl::nd_item<3>& item_ct1, float* shared_mem) {
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const int tid = item_ct1.get_local_id(2);
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const int bid = item_ct1.get_group(2);
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const int head_size = WKV_BLOCK_SIZE;
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const int batch_i = bid / H;
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const int head_i = bid % H;
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const int state_size = C * head_size;
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const int n_seq_tokens = T / B;
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// Set up shared memory pointers
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float* _k = shared_mem;
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float* _r = _k + head_size;
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float* _tf = _r + head_size;
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float* _td = _tf + head_size;
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// Local state array
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float state[WKV_BLOCK_SIZE];
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// Load initial state
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#pragma unroll
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for (int i = 0; i < head_size; i++) {
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state[i] = s[batch_i * state_size + head_i * head_size * head_size + i * head_size + tid];
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}
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// Sync threads before shared memory operations
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item_ct1.barrier(sycl::access::fence_space::local_space);
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// Load time-mixing parameters
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_tf[tid] = tf[head_i * head_size + tid];
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item_ct1.barrier(sycl::access::fence_space::local_space);
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// Main sequence processing loop
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for (int t = batch_i * n_seq_tokens * C + head_i * head_size + tid;
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t < (batch_i + 1) * n_seq_tokens * C + head_i * head_size + tid;
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t += C) {
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item_ct1.barrier(sycl::access::fence_space::local_space);
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// Load current timestep data to shared memory
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_k[tid] = k[t];
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_r[tid] = r[t];
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_td[tid] = td[t];
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item_ct1.barrier(sycl::access::fence_space::local_space);
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const float _v = v[t];
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float y = 0;
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// Process in chunks of 4 for better vectorization
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#pragma unroll
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for (int j = 0; j < head_size; j += 4) {
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// Load data in vec4 chunks
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sycl::float4 k4(_k[j], _k[j+1], _k[j+2], _k[j+3]);
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sycl::float4 r4(_r[j], _r[j+1], _r[j+2], _r[j+3]);
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sycl::float4 tf4(_tf[j], _tf[j+1], _tf[j+2], _tf[j+3]);
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sycl::float4 td4(_td[j], _td[j+1], _td[j+2], _td[j+3]);
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sycl::float4 s4(state[j], state[j+1], state[j+2], state[j+3]);
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// Compute key-value product
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sycl::float4 kv4 = k4 * _v;
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// Accumulate weighted sum
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y += sycl::dot(r4, tf4 * kv4 + s4);
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// Update state
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s4 = s4 * td4 + kv4;
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// Store updated state
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state[j] = s4.x();
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state[j+1] = s4.y();
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state[j+2] = s4.z();
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state[j+3] = s4.w();
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}
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dst[t] = y;
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}
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// Save final state
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#pragma unroll
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for (int i = 0; i < head_size; i++) {
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dst[T * C + batch_i * state_size + head_i * head_size * head_size + i * head_size + tid] = state[i];
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}
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}
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void ggml_sycl_op_rwkv_wkv6(ggml_backend_sycl_context& ctx, const ggml_tensor* src0,
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const ggml_tensor* src1, ggml_tensor* dst) {
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const float* k_d = (const float*)dst->src[0]->data;
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const float* v_d = (const float*)dst->src[1]->data;
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const float* r_d = (const float*)dst->src[2]->data;
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const float* tf_d = (const float*)dst->src[3]->data;
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const float* td_d = (const float*)dst->src[4]->data;
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const float* s_d = (const float*)dst->src[5]->data;
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float* dst_d = (float*)dst->data;
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const int64_t B = dst->src[5]->ne[1];
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const int64_t T = dst->src[0]->ne[3];
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const int64_t C = dst->ne[0];
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const int64_t H = dst->src[0]->ne[2];
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GGML_ASSERT(dst->src[5]->type == GGML_TYPE_F32);
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GGML_ASSERT(C % H == 0);
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GGML_ASSERT(C / H == WKV_BLOCK_SIZE);
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dpct::queue_ptr stream = ctx.stream();
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// Calculate execution configuration
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const size_t shared_mem_size = WKV_BLOCK_SIZE * 4 * sizeof(float); // For k, r, tf, td
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sycl::range<3> block_dims(1, 1, C / H);
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sycl::range<3> grid_dims(1, 1, B * H);
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// Submit kernel
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stream->submit([&](sycl::handler& cgh) {
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sycl::local_accessor<float, 1> shared_mem_acc(shared_mem_size, cgh);
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cgh.parallel_for(
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sycl::nd_range<3>(grid_dims * block_dims, block_dims),
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[=](sycl::nd_item<3> item_ct1) {
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rwkv_wkv_f32_kernel(
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B, T, C, H, k_d, v_d, r_d, tf_d, td_d, s_d, dst_d,
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item_ct1, shared_mem_acc.get_pointer()
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);
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});
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});
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}

‎ggml/src/ggml-sycl/wkv6.hpp

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#ifndef GGML_SYCL_WKV6_HPP
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#define GGML_SYCL_WKV6_HPP
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#include "common.hpp"
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void ggml_sycl_op_rwkv_wkv6(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
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const ggml_tensor *src1, ggml_tensor * dst);
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#endif // GGML_SYCL_WKV6_HPP

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