Merge c67156597b into 9e2e2198b0
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# Vulkan Chunked Gated Delta Net (GDN) — Performance & Development Notes
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PR #20377 — First chunked parallel GDN implementation on any GPU shader backend.
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## Architecture
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Three-stage chunked parallel decomposition (matches FLA/NVlabs reference implementations):
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1. **Intra-chunk** (`gated_delta_net_chunk_intra.comp`) — Builds attention matrix A, computes W/U via WY representation. Outputs g_cumsum and total chunk decay.
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2. **Inter-chunk** (`gated_delta_net_chunk_inter.comp`) — Sequential across chunks, parallel across state columns. State update: `S_next = exp(g_total) * S + K_gated^T @ v_corrected`.
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3. **Output** (`gated_delta_net_chunk_output_cm1.comp`) — Coopmat GEMM kernel. Computes `A_decayed[64x64] @ vnew[64x128]` using VK_KHR_cooperative_matrix (f16 inputs, f32 accumulation).
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Chunk size: C=64 tokens. State dimensions: S_K=S_V=128. Pipeline: d128 non-KDA configs only.
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## Development History
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### Phase 1: Infrastructure (PR #20334, merged)
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- Autoregressive GDN Vulkan shader — single-token sequential processing
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- PP-512: 165 t/s, TG-128: 21.2 t/s on 890M (16 CU)
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- 13/13 backend-ops tests
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### Phase 2: Graph-level chunked ops (PR #20340, merged)
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- Chunked op decomposition at the GGML graph level
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- Feeds autoregressive shader more efficiently
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- PP-512: 165 → 220 t/s (+30.3%) — this gain is already in master
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### Phase 3: Vulkan chunked shaders (PR #20377, this PR)
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- Three new compute shaders for intra/inter/output stages
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- Initial scalar output kernel — functional but dispatch overhead made it slower than autoregressive on 16 CU
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- Threshold gating: chunked path activates only when beneficial
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### Phase 4: Coopmat output kernel
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- Replaced scalar output with VK_KHR_cooperative_matrix GEMM
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- f16 shared memory for A_decayed and vnew, f32 accumulation via coopmat
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- 4-phase architecture: QK^T via coopmat → decay mask → vnew staging → A_decayed @ vnew GEMM
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- Numerically stable: direct `exp(g_i - g_j)` per element (no factorization — factorized approach caused PPL regression to 20.06)
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- 16/16 backend-ops tests pass
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### Abandoned Approaches
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- **Factorized exp with g_max**: `exp(g_max - gcum[j])` amplified vnew, caused catastrophic cancellation. PPL 20.06 vs 13.46 baseline.
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- **Scoped register split**: Attempted to reduce VGPR pressure via scope boundaries. RADV compiler ignores scope for register allocation — no measurable difference.
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## Current Performance
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Hardware: AMD Radeon 890M (RDNA3.5, 16 CU, 64KB LDS/CU, warp 64, KHR_coopmat)
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Model: Qwen3-Coder-Next-REAM Q4_K_M (60.33B params, 34.21 GiB)
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### Throughput (chunked coopmat, GDN_CHUNK_THRESHOLD=2)
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| Test | t/s |
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|------|-----|
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| PP-512 | 217.55 ± 1.41 |
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| PP-1024 | 219.84 ± 4.00 |
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| PP-2048 | 216.89 ± 1.94 |
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| TG-128 | 21.76 ± 0.06 |
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### Autoregressive vs Chunked Comparison
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| Test | Autoregressive | Chunked coopmat | Delta |
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|------|---------------|-----------------|-------|
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| PP-512 | 225.68 ± 3.00 | 217.55 ± 1.41 | -3.6% |
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| PP-1024 | 229.63 ± 4.39 | 219.84 ± 4.00 | -4.3% |
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| PP-2048 | 230.88 ± 1.44 | 216.89 ± 1.94 | -6.1% |
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| TG-128 | 21.29 ± 0.03 | 21.76 ± 0.06 | +2.2% |
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On 16 CU, autoregressive is 3.6-6.1% faster for PP due to lower dispatch overhead. Note autoregressive PP improves from 512→2048 while chunked stays flat — the gap widens on small hardware but the scaling characteristics favor chunked on wider hardware.
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GDN kernel time comparison (PP-512):
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- Autoregressive: 36 × 1,150 us = 41 ms (1.8% of total)
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- Chunked (3 dispatches): 36 × 5,173 us = 186 ms (7.9% of total)
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The chunked path's 3-dispatch overhead (intra + inter + output) accounts for the per-kernel cost difference, but end-to-end impact is only 3.6-6.1% since GDN is a small fraction of total wall time on this MoE model.
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### Perplexity Validation (WikiText-2, 299K tokens)
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| Context | Chunked coopmat | f32 baseline | Delta |
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|---------|----------------|--------------|-------|
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| 512 (584 chunks) | 13.52 ± 0.11 | 13.46 | +0.06 |
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| 4096 (73 chunks) | 10.18 ± 0.08 | 10.15 | +0.03 |
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Both within error bars. Chunked coopmat path is numerically lossless.
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### Per-Kernel Timing (GGML_VK_PERF_LOGGER, PP-512)
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```
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GATED_DELTA_NET: 36 × 5173 us = 186 ms (7.9% of 2.35s total)
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FLASH_ATTN_EXT: 12 × 783 us = 9.4 ms (0.4% of 2.35s total)
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```
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GDN is 7.9% of PP-512 wall time on this MoE-heavy model. MUL_MAT and MoE routing dominate the remaining 92%.
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## Scaling Analysis
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### Why flat PP scaling matters
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PP-512/1024/2048 all within ±2 t/s. The chunked architecture processes fixed-size 64-token chunks — adding more tokens adds more chunks at constant cost each. Autoregressive dispatches scale linearly with token count (36 layers × N tokens = 36N sequential dispatches).
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### Why 16 CU doesn't show the crossover
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- Chunked output kernel dispatches 3 shaders (intra + inter + output) vs 1 for autoregressive
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- Each shader has launch overhead (~10-20 us) that dominates on small hardware
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- The 64×64 @ 64×128 coopmat GEMM in the output kernel can't saturate 16 CUs
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- On 40+ CU hardware (e.g., Strix Halo 8060S, discrete GPUs), the matmul-heavy chunked path has more headroom
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### GDN share grows with model density
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On Qwen3-Next (384-expert MoE), GDN is only 8% of wall time. On GDN-dense architectures with fewer/no MoE layers, GDN's share would be 30-40%+, making the chunked optimization proportionally more impactful.
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## Key Files
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| File | Purpose |
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|------|---------|
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| `vulkan-shaders/gated_delta_net.comp` | Autoregressive kernel |
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| `vulkan-shaders/gated_delta_net_chunk_intra.comp` | Intra-chunk (A matrix, WY) |
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| `vulkan-shaders/gated_delta_net_chunk_inter.comp` | Inter-chunk (state update) |
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| `vulkan-shaders/gated_delta_net_chunk_output.comp` | Original scalar output |
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| `vulkan-shaders/gated_delta_net_chunk_output_cm1.comp` | Coopmat GEMM output |
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| `ggml-vulkan.cpp:10409` | GDN_CHUNK_THRESHOLD (dispatch gating) |
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## Test Commands
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```bash
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# Backend ops tests
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./build/bin/test-backend-ops -b Vulkan0 -o GATED_DELTA_NET
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# Benchmark
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./build/bin/llama-bench -m <model> -ngl 99 -fa 1 -n 128 -p 512 --output md
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# Perf logger
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GGML_VK_PERF_LOGGER=1 ./build/bin/llama-bench -m <model> -ngl 99 -fa 1 -n 128 -p 512 -r 3 --output md
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# Perplexity
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./build/bin/llama-perplexity -m <model> -ngl 99 -fa 1 --ctx-size 4096 -f data/wikitext-2-raw/wiki.test.raw
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```
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@ -827,6 +827,10 @@ struct vk_device_struct {
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vk_pipeline pipeline_rwkv_wkv7_f32;
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// [size_idx][kda] where size_idx: 0=d32, 1=d64, 2=d128
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vk_pipeline pipeline_gated_delta_net[3][2];
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vk_pipeline pipeline_gated_delta_net_chunk_intra;
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vk_pipeline pipeline_gated_delta_net_chunk_inter;
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vk_pipeline pipeline_gated_delta_net_chunk_output;
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vk_pipeline pipeline_gated_delta_net_chunk_output_cm;
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vk_pipeline pipeline_ssm_scan_f32_d128;
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vk_pipeline pipeline_ssm_scan_f32_d256;
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vk_pipeline pipeline_ssm_conv_f32;
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@ -1468,6 +1472,18 @@ struct vk_op_gated_delta_net_push_constants {
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float scale;
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};
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struct vk_op_gated_delta_net_chunk_push_constants {
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uint32_t H;
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uint32_t n_tokens;
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uint32_t n_seqs;
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uint32_t sq1, sq2, sq3;
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uint32_t sv1, sv2, sv3;
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uint32_t sb1, sb2, sb3;
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uint32_t neq1, rq3;
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uint32_t n_chunks;
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uint32_t s_off;
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};
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struct vk_op_ssm_scan_push_constants {
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uint32_t nb02, nb03, nb12, nb13;
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uint32_t nb21, nb22, nb31;
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@ -4599,6 +4615,22 @@ static void ggml_vk_load_shaders(vk_device& device) {
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}
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}
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ggml_vk_create_pipeline(device, device->pipeline_gated_delta_net_chunk_intra, "gated_delta_net_chunk_intra_f32_d128",
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gated_delta_net_chunk_intra_f32_len, gated_delta_net_chunk_intra_f32_data, "main",
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8, sizeof(vk_op_gated_delta_net_chunk_push_constants), {1, 1, 1}, {128, 64}, 1);
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ggml_vk_create_pipeline(device, device->pipeline_gated_delta_net_chunk_inter, "gated_delta_net_chunk_inter_f32_d128",
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gated_delta_net_chunk_inter_f32_len, gated_delta_net_chunk_inter_f32_data, "main",
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9, sizeof(vk_op_gated_delta_net_chunk_push_constants), {1, 1, 1}, {128, 64}, 1);
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ggml_vk_create_pipeline(device, device->pipeline_gated_delta_net_chunk_output, "gated_delta_net_chunk_output_f32_d128",
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gated_delta_net_chunk_output_f32_len, gated_delta_net_chunk_output_f32_data, "main",
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6, sizeof(vk_op_gated_delta_net_chunk_push_constants), {1, 1, 1}, {128, 64}, 1);
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if (device->coopmat_support && device->coopmat_acc_f32_support) {
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ggml_vk_create_pipeline(device, device->pipeline_gated_delta_net_chunk_output_cm, "gated_delta_net_chunk_output_cm1_f32_d128",
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gated_delta_net_chunk_output_cm1_f32_len, gated_delta_net_chunk_output_cm1_f32_data, "main",
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6, sizeof(vk_op_gated_delta_net_chunk_push_constants), {1, 1, 1}, {256, 64, 128}, 1, true);
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}
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if (device->subgroup_arithmetic && device->subgroup_require_full_support) {
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ggml_vk_create_pipeline(device, device->pipeline_ssm_scan_f32_d128, "ssm_scan_128_f32", ssm_scan_subgroup_f32_len, ssm_scan_subgroup_f32_data, "main", 8, sizeof(vk_op_ssm_scan_push_constants), {1, 1, 1}, {128, device->subgroup_size}, 1, true, true);
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ggml_vk_create_pipeline(device, device->pipeline_ssm_scan_f32_d256, "ssm_scan_256_f32", ssm_scan_subgroup_f32_len, ssm_scan_subgroup_f32_data, "main", 8, sizeof(vk_op_ssm_scan_push_constants), {1, 1, 1}, {256, device->subgroup_size}, 1, true, true);
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@ -10373,9 +10405,13 @@ static void ggml_vk_rwkv_wkv7(ggml_backend_vk_context * ctx, vk_context& subctx,
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);
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}
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static constexpr uint32_t GDN_CHUNK_SIZE = 64;
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static constexpr uint32_t GDN_CHUNK_THRESHOLD = GDN_CHUNK_SIZE;
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static void ggml_vk_gated_delta_net(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst) {
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const ggml_tensor * src_q = dst->src[0];
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const ggml_tensor * src_v = dst->src[2];
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const ggml_tensor * src_g = dst->src[3];
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const ggml_tensor * src_beta = dst->src[4];
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GGML_ASSERT(dst->buffer != nullptr);
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@ -10386,11 +10422,8 @@ static void ggml_vk_gated_delta_net(ggml_backend_vk_context * ctx, vk_context& s
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const uint32_t n_seqs = (uint32_t)src_v->ne[3];
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const uint32_t s_off = S_v * H * n_tokens * n_seqs;
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vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, dst->src[0], dst->src[1], dst->src[2], dst, dst->op);
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GGML_ASSERT(pipeline != nullptr);
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ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);
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const bool kda = (src_g->ne[0] == (int64_t)S_v);
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const bool use_chunked = !kda && S_v == 128 && n_tokens > GDN_CHUNK_THRESHOLD;
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vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst);
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vk_subbuffer src_buf[6] = {};
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@ -10411,19 +10444,116 @@ static void ggml_vk_gated_delta_net(ggml_backend_vk_context * ctx, vk_context& s
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const uint32_t neq1 = (uint32_t)src_q->ne[1];
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const uint32_t rq3 = (uint32_t)(src_v->ne[3] / src_q->ne[3]);
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const float scale = 1.0f / sqrtf((float)S_v);
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const vk_op_gated_delta_net_push_constants pc = {
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H, n_tokens, n_seqs, s_off,
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if (!use_chunked) {
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// Autoregressive path (optimal for TG / small n_tokens)
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vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, dst->src[0], dst->src[1], dst->src[2], dst, dst->op);
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GGML_ASSERT(pipeline != nullptr);
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ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);
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const float scale = 1.0f / sqrtf((float)S_v);
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const vk_op_gated_delta_net_push_constants pc = {
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H, n_tokens, n_seqs, s_off,
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sq1, sq2, sq3,
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sv1, sv2, sv3,
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sb1, sb2, sb3,
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neq1, rq3,
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scale
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};
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ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
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{src_buf[0], src_buf[1], src_buf[2], src_buf[3], src_buf[4], src_buf[5], dst_buf},
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pc, { H, n_seqs, 1u });
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return;
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}
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// Chunked parallel path (PP acceleration)
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const uint32_t n_chunks = (n_tokens + GDN_CHUNK_SIZE - 1) / GDN_CHUNK_SIZE;
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vk_pipeline pl_intra = ctx->device->pipeline_gated_delta_net_chunk_intra;
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vk_pipeline pl_inter = ctx->device->pipeline_gated_delta_net_chunk_inter;
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vk_pipeline pl_output = ctx->device->pipeline_gated_delta_net_chunk_output_cm
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? ctx->device->pipeline_gated_delta_net_chunk_output_cm
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: ctx->device->pipeline_gated_delta_net_chunk_output;
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ggml_pipeline_request_descriptor_sets(ctx, pl_intra, 1);
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ggml_pipeline_request_descriptor_sets(ctx, pl_inter, 1);
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ggml_pipeline_request_descriptor_sets(ctx, pl_output, 1);
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// Scratch buffer layout within prealloc_split_k
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const size_t wu_size = (size_t)n_seqs * n_chunks * H * GDN_CHUNK_SIZE * S_v * sizeof(float);
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const size_t d_size = (size_t)n_seqs * n_chunks * H * sizeof(float);
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const size_t g_size = (size_t)n_seqs * n_chunks * H * GDN_CHUNK_SIZE * sizeof(float);
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const size_t h_size = (size_t)n_seqs * n_chunks * H * S_v * S_v * sizeof(float);
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const size_t w_off = 0;
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const size_t u_off = wu_size;
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const size_t vn_off = 2 * wu_size;
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const size_t dec_off = 3 * wu_size;
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const size_t gcum_off = dec_off + d_size;
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const size_t h_off = gcum_off + g_size;
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const size_t total_scratch = h_off + h_size;
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if (total_scratch > ctx->device->properties.limits.maxStorageBufferRange) {
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// Fall back to autoregressive if scratch exceeds device limits
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vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, dst->src[0], dst->src[1], dst->src[2], dst, dst->op);
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GGML_ASSERT(pipeline != nullptr);
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ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);
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const float scale = 1.0f / sqrtf((float)S_v);
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const vk_op_gated_delta_net_push_constants pc_ar = {
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H, n_tokens, n_seqs, s_off,
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sq1, sq2, sq3, sv1, sv2, sv3, sb1, sb2, sb3, neq1, rq3, scale
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};
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ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
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{src_buf[0], src_buf[1], src_buf[2], src_buf[3], src_buf[4], src_buf[5], dst_buf},
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pc_ar, { H, n_seqs, 1u });
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return;
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}
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if (ctx->prealloc_size_split_k < total_scratch) {
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ctx->prealloc_size_split_k = total_scratch;
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ggml_vk_preallocate_buffers(ctx, subctx);
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}
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if (ctx->prealloc_split_k_need_sync) {
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ggml_vk_sync_buffers(ctx, subctx);
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}
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vk_subbuffer scratch_w = { ctx->prealloc_split_k, w_off, wu_size };
|
||||
vk_subbuffer scratch_u = { ctx->prealloc_split_k, u_off, wu_size };
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vk_subbuffer scratch_vnew = { ctx->prealloc_split_k, vn_off, wu_size };
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vk_subbuffer scratch_dec = { ctx->prealloc_split_k, dec_off, d_size };
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vk_subbuffer scratch_gcum = { ctx->prealloc_split_k, gcum_off, g_size };
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vk_subbuffer scratch_h = { ctx->prealloc_split_k, h_off, h_size };
|
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const vk_op_gated_delta_net_chunk_push_constants pc = {
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H, n_tokens, n_seqs,
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sq1, sq2, sq3,
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sv1, sv2, sv3,
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sb1, sb2, sb3,
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neq1, rq3,
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scale
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n_chunks, s_off
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};
|
||||
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||||
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
|
||||
{src_buf[0], src_buf[1], src_buf[2], src_buf[3], src_buf[4], src_buf[5], dst_buf},
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||||
ggml_vk_dispatch_pipeline(ctx, subctx, pl_intra,
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||||
{src_buf[1], src_buf[2], src_buf[3], src_buf[4],
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||||
scratch_w, scratch_u, scratch_dec, scratch_gcum},
|
||||
pc, { n_chunks * H, n_seqs, 1u });
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||||
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ggml_vk_sync_buffers(ctx, subctx);
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ggml_vk_dispatch_pipeline(ctx, subctx, pl_inter,
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||||
{src_buf[1], scratch_w, scratch_u, scratch_dec, scratch_gcum,
|
||||
src_buf[5], scratch_h, scratch_vnew, dst_buf},
|
||||
pc, { H, n_seqs, 1u });
|
||||
|
||||
ggml_vk_sync_buffers(ctx, subctx);
|
||||
|
||||
ggml_vk_dispatch_pipeline(ctx, subctx, pl_output,
|
||||
{src_buf[0], src_buf[1], scratch_h, scratch_vnew, scratch_gcum, dst_buf},
|
||||
pc, { n_chunks * H, n_seqs, 1u });
|
||||
|
||||
ctx->prealloc_split_k_need_sync = true;
|
||||
}
|
||||
|
||||
static void ggml_vk_ssm_scan(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst) {
|
||||
|
|
|
|||
|
|
@ -0,0 +1,111 @@
|
|||
#version 450
|
||||
|
||||
#extension GL_EXT_control_flow_attributes : require
|
||||
|
||||
// Inter-chunk state propagation for chunked gated delta net
|
||||
|
||||
layout(constant_id = 0) const uint S_V = 128;
|
||||
layout(constant_id = 1) const uint CHUNK_SIZE = 64;
|
||||
|
||||
layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
|
||||
|
||||
layout(push_constant) uniform Parameters {
|
||||
uint H;
|
||||
uint n_tokens;
|
||||
uint n_seqs;
|
||||
uint sq1, sq2, sq3;
|
||||
uint sv1, sv2, sv3;
|
||||
uint sb1, sb2, sb3;
|
||||
uint neq1, rq3;
|
||||
uint n_chunks;
|
||||
uint s_off;
|
||||
};
|
||||
|
||||
layout(binding = 0) readonly buffer KBuf { float k_in[]; };
|
||||
layout(binding = 1) readonly buffer WBuf { float w_in[]; };
|
||||
layout(binding = 2) readonly buffer UBuf { float u_in[]; };
|
||||
layout(binding = 3) readonly buffer DecayBuf { float decay_in[]; };
|
||||
layout(binding = 4) readonly buffer GCumBuf { float gcum_in[]; };
|
||||
layout(binding = 5) readonly buffer StateBuf { float state_in[]; };
|
||||
layout(binding = 6) writeonly buffer HBuf { float h_out[]; };
|
||||
layout(binding = 7) writeonly buffer VNewBuf { float vnew_out[]; };
|
||||
layout(binding = 8) buffer FinalBuf { float final_out[]; };
|
||||
|
||||
shared float s_w[S_V];
|
||||
shared float s_kg[S_V];
|
||||
|
||||
void main() {
|
||||
const uint head_id = gl_WorkGroupID.x;
|
||||
const uint seq_id = gl_WorkGroupID.y;
|
||||
const uint col = gl_LocalInvocationID.x;
|
||||
|
||||
if (col >= S_V) return;
|
||||
|
||||
const uint iq1 = head_id % neq1;
|
||||
const uint iq3 = seq_id / rq3;
|
||||
|
||||
const uint state_size = S_V * S_V;
|
||||
const uint state_base = (seq_id * H + head_id) * state_size;
|
||||
|
||||
float state[S_V];
|
||||
[[unroll]] for (uint i = 0; i < S_V; i++) {
|
||||
state[i] = state_in[state_base + col * S_V + i];
|
||||
}
|
||||
|
||||
for (uint c = 0; c < n_chunks; c++) {
|
||||
const uint chunk_start = c * CHUNK_SIZE;
|
||||
const uint chunk_len = min(CHUNK_SIZE, n_tokens - chunk_start);
|
||||
|
||||
const uint h_base = ((seq_id * n_chunks + c) * H + head_id) * state_size;
|
||||
[[unroll]] for (uint i = 0; i < S_V; i++) {
|
||||
h_out[h_base + i * S_V + col] = state[i];
|
||||
}
|
||||
|
||||
const uint wu_base = ((seq_id * n_chunks + c) * H + head_id) * CHUNK_SIZE * S_V;
|
||||
const uint gcum_base = ((seq_id * n_chunks + c) * H + head_id) * CHUNK_SIZE;
|
||||
|
||||
const uint decay_idx = (seq_id * n_chunks + c) * H + head_id;
|
||||
const float g_total = decay_in[decay_idx];
|
||||
|
||||
float delta[S_V];
|
||||
[[unroll]] for (uint i = 0; i < S_V; i++) {
|
||||
delta[i] = 0.0;
|
||||
}
|
||||
|
||||
for (uint t = 0; t < chunk_len; t++) {
|
||||
s_w[col] = w_in[wu_base + t * S_V + col];
|
||||
|
||||
const float g_cumsum_t = gcum_in[gcum_base + t];
|
||||
const float decay_factor = exp(g_total - g_cumsum_t);
|
||||
const uint t_global = chunk_start + t;
|
||||
const uint k_off = iq3 * sq3 + t_global * sq2 + iq1 * sq1;
|
||||
s_kg[col] = k_in[k_off + col] * decay_factor;
|
||||
barrier();
|
||||
|
||||
float ws = 0.0;
|
||||
[[unroll]] for (uint i = 0; i < S_V; i += 4) {
|
||||
ws += dot(
|
||||
vec4(s_w[i], s_w[i+1], s_w[i+2], s_w[i+3]),
|
||||
vec4(state[i], state[i+1], state[i+2], state[i+3])
|
||||
);
|
||||
}
|
||||
|
||||
float vnew = u_in[wu_base + t * S_V + col] - ws;
|
||||
vnew_out[wu_base + t * S_V + col] = vnew;
|
||||
|
||||
[[unroll]] for (uint i = 0; i < S_V; i++) {
|
||||
delta[i] += s_kg[i] * vnew;
|
||||
}
|
||||
barrier();
|
||||
}
|
||||
|
||||
float total_decay = exp(g_total);
|
||||
[[unroll]] for (uint i = 0; i < S_V; i++) {
|
||||
state[i] = total_decay * state[i] + delta[i];
|
||||
}
|
||||
}
|
||||
|
||||
[[unroll]] for (uint i = 0; i < S_V; i++) {
|
||||
final_out[s_off + state_base + col * S_V + i] = state[i];
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,182 @@
|
|||
#version 450
|
||||
|
||||
#extension GL_EXT_control_flow_attributes : require
|
||||
|
||||
layout(constant_id = 0) const uint S_V = 128;
|
||||
layout(constant_id = 1) const uint CHUNK_SIZE = 64;
|
||||
|
||||
layout(local_size_x_id = 1, local_size_y = 1, local_size_z = 1) in;
|
||||
|
||||
layout(push_constant) uniform Parameters {
|
||||
uint H;
|
||||
uint n_tokens;
|
||||
uint n_seqs;
|
||||
uint sq1, sq2, sq3;
|
||||
uint sv1, sv2, sv3;
|
||||
uint sb1, sb2, sb3;
|
||||
uint neq1, rq3;
|
||||
uint n_chunks;
|
||||
uint s_off;
|
||||
};
|
||||
|
||||
layout(binding = 0) readonly buffer KBuf { float k_in[]; };
|
||||
layout(binding = 1) readonly buffer VBuf { float v_in[]; };
|
||||
layout(binding = 2) readonly buffer GBuf { float g_in[]; };
|
||||
layout(binding = 3) readonly buffer BetaBuf { float beta_in[]; };
|
||||
layout(binding = 4) writeonly buffer WBuf { float w_out[]; };
|
||||
layout(binding = 5) writeonly buffer UBuf { float u_out[]; };
|
||||
layout(binding = 6) writeonly buffer DecayBuf { float decay_out[]; };
|
||||
layout(binding = 7) writeonly buffer GCumBuf { float gcum_out[]; };
|
||||
|
||||
const uint A_STRIDE = CHUNK_SIZE + 1;
|
||||
shared float s_A[CHUNK_SIZE * A_STRIDE];
|
||||
shared float s_decay[CHUNK_SIZE];
|
||||
shared float s_beta[CHUNK_SIZE];
|
||||
shared float s_k_broadcast[S_V];
|
||||
shared float s_v_broadcast[S_V];
|
||||
|
||||
#define A(i,j) s_A[(i) * A_STRIDE + (j)]
|
||||
|
||||
void main() {
|
||||
const uint chunk_head = gl_WorkGroupID.x;
|
||||
const uint seq_id = gl_WorkGroupID.y;
|
||||
const uint tid = gl_LocalInvocationID.x;
|
||||
|
||||
const uint head_id = chunk_head % H;
|
||||
const uint chunk_id = chunk_head / H;
|
||||
const uint iq1 = head_id % neq1;
|
||||
const uint iq3 = seq_id / rq3;
|
||||
|
||||
const uint chunk_start = chunk_id * CHUNK_SIZE;
|
||||
const uint chunk_len = min(CHUNK_SIZE, n_tokens - chunk_start);
|
||||
const uint global_t = chunk_start + tid;
|
||||
const bool valid = tid < chunk_len;
|
||||
|
||||
if (valid) {
|
||||
const uint gb_off = seq_id * sb3 + global_t * sb2 + head_id * sb1;
|
||||
s_beta[tid] = beta_in[gb_off];
|
||||
s_decay[tid] = g_in[gb_off];
|
||||
} else {
|
||||
s_beta[tid] = 0.0;
|
||||
s_decay[tid] = 0.0;
|
||||
}
|
||||
barrier();
|
||||
|
||||
if (tid == 0) {
|
||||
for (uint i = 1; i < chunk_len; i++) {
|
||||
s_decay[i] += s_decay[i - 1];
|
||||
}
|
||||
}
|
||||
barrier();
|
||||
|
||||
if (valid) {
|
||||
const uint gcum_base = ((seq_id * n_chunks + chunk_id) * H + head_id) * CHUNK_SIZE;
|
||||
gcum_out[gcum_base + tid] = s_decay[tid];
|
||||
}
|
||||
|
||||
float my_k[S_V];
|
||||
if (valid) {
|
||||
const uint k_off = iq3 * sq3 + global_t * sq2 + iq1 * sq1;
|
||||
[[unroll]] for (uint d = 0; d < S_V; d++) {
|
||||
my_k[d] = k_in[k_off + d];
|
||||
}
|
||||
} else {
|
||||
[[unroll]] for (uint d = 0; d < S_V; d++) {
|
||||
my_k[d] = 0.0;
|
||||
}
|
||||
}
|
||||
|
||||
[[unroll]] for (uint j = 0; j < CHUNK_SIZE; j++) {
|
||||
A(tid, j) = 0.0;
|
||||
}
|
||||
barrier();
|
||||
|
||||
for (uint j = 0; j < chunk_len; j++) {
|
||||
{
|
||||
const uint j_global = chunk_start + j;
|
||||
const uint kj_off = iq3 * sq3 + j_global * sq2 + iq1 * sq1;
|
||||
for (uint d = tid; d < S_V; d += CHUNK_SIZE) {
|
||||
s_k_broadcast[d] = k_in[kj_off + d];
|
||||
}
|
||||
}
|
||||
barrier();
|
||||
|
||||
if (valid && tid > j) {
|
||||
float dot_kk = 0.0;
|
||||
[[unroll]] for (uint d = 0; d < S_V; d += 4) {
|
||||
dot_kk += dot(
|
||||
vec4(my_k[d], my_k[d+1], my_k[d+2], my_k[d+3]),
|
||||
vec4(s_k_broadcast[d], s_k_broadcast[d+1],
|
||||
s_k_broadcast[d+2], s_k_broadcast[d+3])
|
||||
);
|
||||
}
|
||||
float decay_factor = exp(s_decay[tid] - s_decay[j]);
|
||||
A(tid, j) = -s_beta[tid] * dot_kk * decay_factor;
|
||||
}
|
||||
}
|
||||
barrier();
|
||||
|
||||
for (uint i = 1; i < chunk_len; i++) {
|
||||
if (tid < i) {
|
||||
float sum = 0.0;
|
||||
for (uint m = tid; m < i; m++) {
|
||||
sum += A(i, m) * A(m, tid);
|
||||
}
|
||||
A(i, tid) += sum;
|
||||
}
|
||||
barrier();
|
||||
}
|
||||
|
||||
if (valid) {
|
||||
A(tid, tid) = 1.0;
|
||||
}
|
||||
barrier();
|
||||
|
||||
const uint out_base = ((seq_id * n_chunks + chunk_id) * H + head_id) * CHUNK_SIZE * S_V;
|
||||
const uint TILE_D = 32;
|
||||
|
||||
for (uint d_start = 0; d_start < S_V; d_start += TILE_D) {
|
||||
float my_w[TILE_D];
|
||||
float my_u[TILE_D];
|
||||
[[unroll]] for (uint d = 0; d < TILE_D; d++) {
|
||||
my_w[d] = 0.0;
|
||||
my_u[d] = 0.0;
|
||||
}
|
||||
|
||||
for (uint j = 0; j < chunk_len; j++) {
|
||||
const uint j_global = chunk_start + j;
|
||||
const uint kj_off = iq3 * sq3 + j_global * sq2 + iq1 * sq1;
|
||||
const uint vj_off = seq_id * sv3 + j_global * sv2 + head_id * sv1;
|
||||
float eg = exp(s_decay[j]);
|
||||
|
||||
for (uint d = tid; d < S_V; d += CHUNK_SIZE) {
|
||||
if (d >= d_start && d < d_start + TILE_D) {
|
||||
s_k_broadcast[d] = k_in[kj_off + d] * eg;
|
||||
s_v_broadcast[d] = v_in[vj_off + d];
|
||||
}
|
||||
}
|
||||
barrier();
|
||||
|
||||
if (valid && j <= tid) {
|
||||
float t_beta = A(tid, j) * s_beta[j];
|
||||
[[unroll]] for (uint d = 0; d < TILE_D; d++) {
|
||||
my_w[d] += t_beta * s_k_broadcast[d_start + d];
|
||||
my_u[d] += t_beta * s_v_broadcast[d_start + d];
|
||||
}
|
||||
}
|
||||
barrier();
|
||||
}
|
||||
|
||||
if (valid) {
|
||||
[[unroll]] for (uint d = 0; d < TILE_D; d++) {
|
||||
w_out[out_base + tid * S_V + d_start + d] = my_w[d];
|
||||
u_out[out_base + tid * S_V + d_start + d] = my_u[d];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (tid == 0 && chunk_len > 0) {
|
||||
const uint decay_idx = (seq_id * n_chunks + chunk_id) * H + head_id;
|
||||
decay_out[decay_idx] = s_decay[chunk_len - 1];
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,124 @@
|
|||
#version 450
|
||||
|
||||
#extension GL_EXT_control_flow_attributes : require
|
||||
|
||||
// Output kernel for chunked gated delta net
|
||||
//
|
||||
// For each chunk, combines inter-chunk and intra-chunk contributions:
|
||||
// o[t] = q[t]^T @ (exp(g_cumsum[t]) * S_chunk) + causal_attn(q, k, v_corrected)
|
||||
//
|
||||
// Grid: (n_chunks * H, n_seqs, 1)
|
||||
// Workgroup: S_V threads (one per output column)
|
||||
|
||||
layout(constant_id = 0) const uint S_V = 128;
|
||||
layout(constant_id = 1) const uint CHUNK_SIZE = 64;
|
||||
|
||||
layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
|
||||
|
||||
layout(push_constant) uniform Parameters {
|
||||
uint H;
|
||||
uint n_tokens;
|
||||
uint n_seqs;
|
||||
uint sq1, sq2, sq3;
|
||||
uint sv1, sv2, sv3;
|
||||
uint sb1, sb2, sb3;
|
||||
uint neq1, rq3;
|
||||
uint n_chunks;
|
||||
uint s_off;
|
||||
};
|
||||
|
||||
layout(binding = 0) readonly buffer QBuf { float q_in[]; };
|
||||
layout(binding = 1) readonly buffer KBuf { float k_in[]; };
|
||||
layout(binding = 2) readonly buffer HBuf { float h_in[]; };
|
||||
layout(binding = 3) readonly buffer VNewBuf { float vnew_in[]; };
|
||||
layout(binding = 4) readonly buffer GCumBuf { float gcum_in[]; };
|
||||
layout(binding = 5) buffer DstBuf { float dst[]; };
|
||||
|
||||
shared float s_q[S_V];
|
||||
shared float s_k[S_V];
|
||||
shared float s_gcum[CHUNK_SIZE];
|
||||
|
||||
void main() {
|
||||
const uint chunk_head = gl_WorkGroupID.x;
|
||||
const uint seq_id = gl_WorkGroupID.y;
|
||||
const uint col = gl_LocalInvocationID.x;
|
||||
|
||||
if (col >= S_V) return;
|
||||
|
||||
const uint head_id = chunk_head % H;
|
||||
const uint chunk_id = chunk_head / H;
|
||||
|
||||
const uint iq1 = head_id % neq1;
|
||||
const uint iq3 = seq_id / rq3;
|
||||
|
||||
const uint chunk_start = chunk_id * CHUNK_SIZE;
|
||||
const uint chunk_len = min(CHUNK_SIZE, n_tokens - chunk_start);
|
||||
|
||||
const float scale = 1.0 / sqrt(float(S_V));
|
||||
|
||||
const uint state_size = S_V * S_V;
|
||||
const uint h_base = ((seq_id * n_chunks + chunk_id) * H + head_id) * state_size;
|
||||
|
||||
float state_col[S_V];
|
||||
[[unroll]] for (uint i = 0; i < S_V; i++) {
|
||||
state_col[i] = h_in[h_base + i * S_V + col];
|
||||
}
|
||||
|
||||
const uint wu_base = ((seq_id * n_chunks + chunk_id) * H + head_id) * CHUNK_SIZE * S_V;
|
||||
|
||||
const uint gcum_base = ((seq_id * n_chunks + chunk_id) * H + head_id) * CHUNK_SIZE;
|
||||
if (col < CHUNK_SIZE) {
|
||||
s_gcum[col] = (col < chunk_len) ? gcum_in[gcum_base + col] : 0.0;
|
||||
}
|
||||
|
||||
// Preload vnew[j][col] into registers
|
||||
float my_vnew[CHUNK_SIZE];
|
||||
for (uint j = 0; j < chunk_len; j++) {
|
||||
my_vnew[j] = vnew_in[wu_base + j * S_V + col];
|
||||
}
|
||||
barrier();
|
||||
|
||||
uint attn_off = (seq_id * n_tokens * H + head_id) * S_V;
|
||||
|
||||
for (uint t = 0; t < chunk_len; t++) {
|
||||
const uint t_global = chunk_start + t;
|
||||
|
||||
const uint q_off = iq3 * sq3 + t_global * sq2 + iq1 * sq1;
|
||||
s_q[col] = q_in[q_off + col];
|
||||
barrier();
|
||||
|
||||
// Inter-chunk: o_inter = q^T @ (exp(g_cumsum[t]) * S)
|
||||
float decay_t = exp(s_gcum[t]);
|
||||
float o_inter = 0.0;
|
||||
[[unroll]] for (uint i = 0; i < S_V; i += 4) {
|
||||
o_inter += dot(
|
||||
vec4(s_q[i], s_q[i+1], s_q[i+2], s_q[i+3]),
|
||||
decay_t * vec4(state_col[i], state_col[i+1], state_col[i+2], state_col[i+3])
|
||||
);
|
||||
}
|
||||
|
||||
// Intra-chunk: o_intra = sum_{j<=t} dot(q[t], k[j]) * decay_mask * vnew[j][col]
|
||||
float o_intra = 0.0;
|
||||
for (uint j = 0; j <= t; j++) {
|
||||
const uint j_global = chunk_start + j;
|
||||
const uint kj_off = iq3 * sq3 + j_global * sq2 + iq1 * sq1;
|
||||
s_k[col] = k_in[kj_off + col];
|
||||
barrier();
|
||||
|
||||
float qk = 0.0;
|
||||
[[unroll]] for (uint i = 0; i < S_V; i += 4) {
|
||||
qk += dot(
|
||||
vec4(s_q[i], s_q[i+1], s_q[i+2], s_q[i+3]),
|
||||
vec4(s_k[i], s_k[i+1], s_k[i+2], s_k[i+3])
|
||||
);
|
||||
}
|
||||
|
||||
float mask = exp(s_gcum[t] - s_gcum[j]);
|
||||
o_intra += qk * mask * my_vnew[j];
|
||||
|
||||
barrier();
|
||||
}
|
||||
|
||||
dst[attn_off + t_global * S_V * H + col] = (o_inter + o_intra) * scale;
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,243 @@
|
|||
#version 450
|
||||
|
||||
#extension GL_EXT_control_flow_attributes : enable
|
||||
#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require
|
||||
#extension GL_KHR_shader_subgroup_basic : enable
|
||||
#extension GL_KHR_memory_scope_semantics : enable
|
||||
#extension GL_KHR_cooperative_matrix : enable
|
||||
|
||||
#include "types.glsl"
|
||||
|
||||
layout(constant_id = 0) const uint WG_SIZE = 256;
|
||||
layout(constant_id = 1) const uint CHUNK_SIZE = 64;
|
||||
layout(constant_id = 2) const uint S_V = 128;
|
||||
|
||||
layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
|
||||
|
||||
layout(push_constant) uniform Parameters {
|
||||
uint H;
|
||||
uint n_tokens;
|
||||
uint n_seqs;
|
||||
uint sq1, sq2, sq3;
|
||||
uint sv1, sv2, sv3;
|
||||
uint sb1, sb2, sb3;
|
||||
uint neq1, rq3;
|
||||
uint n_chunks;
|
||||
uint s_off;
|
||||
};
|
||||
|
||||
layout(binding = 0) readonly buffer QBuf { float q_in[]; };
|
||||
layout(binding = 1) readonly buffer KBuf { float k_in[]; };
|
||||
layout(binding = 2) readonly buffer HBuf { float h_in[]; };
|
||||
layout(binding = 3) readonly buffer VNewBuf { float vnew_in[]; };
|
||||
layout(binding = 4) readonly buffer GCumBuf { float gcum_in[]; };
|
||||
layout(binding = 5) buffer DstBuf { float dst[]; };
|
||||
|
||||
const uint TM = 16;
|
||||
const uint TN = 16;
|
||||
const uint TK = 16;
|
||||
|
||||
const uint C_TILES = CHUNK_SIZE / TM;
|
||||
const uint D_TILES = S_V / TN;
|
||||
|
||||
// Padded strides for bank conflict avoidance
|
||||
const uint QK_STRIDE = S_V / 4 + 2;
|
||||
const uint ATTN_V4_STRIDE = CHUNK_SIZE / 4 + 2;
|
||||
|
||||
shared f16vec4 sh_q[CHUNK_SIZE * QK_STRIDE];
|
||||
shared f16vec4 sh_kv[CHUNK_SIZE * QK_STRIDE];
|
||||
shared vec4 sh_attn[CHUNK_SIZE * ATTN_V4_STRIDE];
|
||||
shared f16vec4 sh_adecay[CHUNK_SIZE * ATTN_V4_STRIDE];
|
||||
shared float sh_gcum[CHUNK_SIZE];
|
||||
|
||||
void main() {
|
||||
const uint tid = gl_LocalInvocationIndex;
|
||||
const uint sg_id = gl_SubgroupID;
|
||||
|
||||
const uint chunk_head = gl_WorkGroupID.x;
|
||||
const uint seq_id = gl_WorkGroupID.y;
|
||||
|
||||
const uint head_id = chunk_head % H;
|
||||
const uint chunk_id = chunk_head / H;
|
||||
const uint iq1 = head_id % neq1;
|
||||
const uint iq3 = seq_id / rq3;
|
||||
|
||||
const uint chunk_start = chunk_id * CHUNK_SIZE;
|
||||
const uint chunk_len = min(CHUNK_SIZE, n_tokens - chunk_start);
|
||||
const float scale = 1.0 / sqrt(float(S_V));
|
||||
|
||||
if (tid < CHUNK_SIZE) {
|
||||
const uint gcum_base = ((seq_id * n_chunks + chunk_id) * H + head_id) * CHUNK_SIZE;
|
||||
sh_gcum[tid] = (tid < chunk_len) ? gcum_in[gcum_base + tid] : 0.0;
|
||||
}
|
||||
|
||||
for (uint idx = tid; idx < CHUNK_SIZE * (S_V / 4); idx += WG_SIZE) {
|
||||
const uint row = idx / (S_V / 4);
|
||||
const uint col4 = idx % (S_V / 4);
|
||||
f16vec4 q_val = f16vec4(0.0);
|
||||
f16vec4 k_val = f16vec4(0.0);
|
||||
if (row < chunk_len) {
|
||||
const uint off = iq3 * sq3 + (chunk_start + row) * sq2 + iq1 * sq1 + col4 * 4;
|
||||
q_val = f16vec4(q_in[off], q_in[off + 1], q_in[off + 2], q_in[off + 3]);
|
||||
k_val = f16vec4(k_in[off], k_in[off + 1], k_in[off + 2], k_in[off + 3]);
|
||||
}
|
||||
sh_q[row * QK_STRIDE + col4] = q_val;
|
||||
sh_kv[row * QK_STRIDE + col4] = k_val;
|
||||
}
|
||||
|
||||
barrier();
|
||||
|
||||
// A = Q @ K^T (coopmat)
|
||||
coopmat<float, gl_ScopeSubgroup, TM, TN, gl_MatrixUseAccumulator> A_acc[C_TILES];
|
||||
[[unroll]] for (uint tj = 0; tj < C_TILES; tj++) {
|
||||
A_acc[tj] = coopmat<float, gl_ScopeSubgroup, TM, TN, gl_MatrixUseAccumulator>(0.0);
|
||||
}
|
||||
|
||||
{
|
||||
coopmat<float16_t, gl_ScopeSubgroup, TM, TK, gl_MatrixUseA> Q_mat;
|
||||
coopmat<float16_t, gl_ScopeSubgroup, TK, TN, gl_MatrixUseB> KT_mat;
|
||||
|
||||
[[unroll]] for (uint dk = 0; dk < D_TILES; dk++) {
|
||||
coopMatLoad(Q_mat, sh_q,
|
||||
sg_id * TM * QK_STRIDE + dk * (TK / 4),
|
||||
QK_STRIDE, gl_CooperativeMatrixLayoutRowMajor);
|
||||
|
||||
[[unroll]] for (uint tj = 0; tj < C_TILES; tj++) {
|
||||
coopMatLoad(KT_mat, sh_kv,
|
||||
tj * TN * QK_STRIDE + dk * (TK / 4),
|
||||
QK_STRIDE, gl_CooperativeMatrixLayoutColumnMajor);
|
||||
|
||||
A_acc[tj] = coopMatMulAdd(Q_mat, KT_mat, A_acc[tj]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
[[unroll]] for (uint tj = 0; tj < C_TILES; tj++) {
|
||||
coopMatStore(A_acc[tj], sh_attn,
|
||||
sg_id * TM * ATTN_V4_STRIDE + tj * (TN / 4),
|
||||
ATTN_V4_STRIDE, gl_CooperativeMatrixLayoutRowMajor);
|
||||
}
|
||||
|
||||
barrier();
|
||||
|
||||
const uint wu_base = ((seq_id * n_chunks + chunk_id) * H + head_id) * CHUNK_SIZE * S_V;
|
||||
const uint attn_off = (seq_id * n_tokens * H + head_id) * S_V;
|
||||
|
||||
// Causal decay mask (f16)
|
||||
for (uint idx = tid; idx < CHUNK_SIZE * (CHUNK_SIZE / 4); idx += WG_SIZE) {
|
||||
const uint t = idx / (CHUNK_SIZE / 4);
|
||||
const uint j4 = idx % (CHUNK_SIZE / 4);
|
||||
f16vec4 val = f16vec4(0.0);
|
||||
if (t < chunk_len) {
|
||||
const float g_t = sh_gcum[t];
|
||||
[[unroll]] for (uint k = 0; k < 4; k++) {
|
||||
const uint j = j4 * 4 + k;
|
||||
if (j <= t && j < chunk_len) {
|
||||
val[k] = float16_t(clamp(
|
||||
exp(g_t - sh_gcum[j]) * sh_attn[t * ATTN_V4_STRIDE + j4][k],
|
||||
-65504.0, 65504.0));
|
||||
}
|
||||
}
|
||||
}
|
||||
sh_adecay[t * ATTN_V4_STRIDE + j4] = val;
|
||||
}
|
||||
|
||||
// vnew to f16, pre-scaled
|
||||
for (uint idx = tid; idx < CHUNK_SIZE * (S_V / 4); idx += WG_SIZE) {
|
||||
const uint row = idx / (S_V / 4);
|
||||
const uint col4 = idx % (S_V / 4);
|
||||
f16vec4 val = f16vec4(0.0);
|
||||
if (row < chunk_len) {
|
||||
const uint off = wu_base + row * S_V + col4 * 4;
|
||||
val = f16vec4(
|
||||
float16_t(clamp(vnew_in[off ] * scale, -65504.0, 65504.0)),
|
||||
float16_t(clamp(vnew_in[off + 1] * scale, -65504.0, 65504.0)),
|
||||
float16_t(clamp(vnew_in[off + 2] * scale, -65504.0, 65504.0)),
|
||||
float16_t(clamp(vnew_in[off + 3] * scale, -65504.0, 65504.0)));
|
||||
}
|
||||
sh_kv[row * QK_STRIDE + col4] = val;
|
||||
}
|
||||
|
||||
// O_inter = Q @ state
|
||||
{
|
||||
const uint col = tid;
|
||||
const bool col_active = (col < S_V);
|
||||
const uint state_size = S_V * S_V;
|
||||
const uint h_base = ((seq_id * n_chunks + chunk_id) * H + head_id) * state_size;
|
||||
|
||||
float state_col[S_V];
|
||||
if (col_active) {
|
||||
[[unroll]] for (uint i = 0; i < S_V; i++) {
|
||||
state_col[i] = h_in[h_base + i * S_V + col];
|
||||
}
|
||||
}
|
||||
|
||||
if (col_active) {
|
||||
for (uint t = 0; t < chunk_len; t++) {
|
||||
float o_inter = 0.0;
|
||||
[[unroll]] for (uint i = 0; i < S_V / 4; i++) {
|
||||
vec4 q_f32 = vec4(sh_q[t * QK_STRIDE + i]);
|
||||
o_inter += dot(q_f32,
|
||||
vec4(state_col[i*4], state_col[i*4+1],
|
||||
state_col[i*4+2], state_col[i*4+3]));
|
||||
}
|
||||
dst[attn_off + (chunk_start + t) * S_V * H + col] = exp(sh_gcum[t]) * o_inter * scale;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
barrier();
|
||||
|
||||
// O_intra = A_decayed @ vnew (coopmat GEMM, scalar fallback for partial chunks)
|
||||
if (chunk_len == CHUNK_SIZE) {
|
||||
coopmat<float16_t, gl_ScopeSubgroup, TM, TK, gl_MatrixUseA> A_mat;
|
||||
coopmat<float16_t, gl_ScopeSubgroup, TK, TN, gl_MatrixUseB> V_mat;
|
||||
|
||||
[[unroll]] for (uint dn = 0; dn < D_TILES; dn++) {
|
||||
coopmat<float, gl_ScopeSubgroup, TM, TN, gl_MatrixUseAccumulator> O_acc;
|
||||
|
||||
coopMatLoad(O_acc, dst,
|
||||
attn_off + (chunk_start + sg_id * TM) * S_V * H + dn * TN,
|
||||
S_V * H, gl_CooperativeMatrixLayoutRowMajor);
|
||||
|
||||
[[unroll]] for (uint dk = 0; dk < C_TILES; dk++) {
|
||||
coopMatLoad(A_mat, sh_adecay,
|
||||
sg_id * TM * ATTN_V4_STRIDE + dk * (TK / 4),
|
||||
ATTN_V4_STRIDE, gl_CooperativeMatrixLayoutRowMajor);
|
||||
|
||||
coopMatLoad(V_mat, sh_kv,
|
||||
dk * TK * QK_STRIDE + dn * (TN / 4),
|
||||
QK_STRIDE, gl_CooperativeMatrixLayoutRowMajor);
|
||||
|
||||
O_acc = coopMatMulAdd(A_mat, V_mat, O_acc);
|
||||
}
|
||||
|
||||
coopMatStore(O_acc, dst,
|
||||
attn_off + (chunk_start + sg_id * TM) * S_V * H + dn * TN,
|
||||
S_V * H, gl_CooperativeMatrixLayoutRowMajor);
|
||||
}
|
||||
} else {
|
||||
const uint col = tid;
|
||||
if (col < S_V) {
|
||||
const uint col4 = col / 4;
|
||||
const uint comp = col % 4;
|
||||
|
||||
vec4 my_vnew_v4[CHUNK_SIZE / 4];
|
||||
[[unroll]] for (uint j4 = 0; j4 < CHUNK_SIZE / 4; j4++) {
|
||||
my_vnew_v4[j4] = vec4(
|
||||
float(sh_kv[(j4*4 ) * QK_STRIDE + col4][comp]),
|
||||
float(sh_kv[(j4*4+1) * QK_STRIDE + col4][comp]),
|
||||
float(sh_kv[(j4*4+2) * QK_STRIDE + col4][comp]),
|
||||
float(sh_kv[(j4*4+3) * QK_STRIDE + col4][comp]));
|
||||
}
|
||||
|
||||
for (uint t = 0; t < chunk_len; t++) {
|
||||
float o_intra = 0.0;
|
||||
[[unroll]] for (uint j4 = 0; j4 < CHUNK_SIZE / 4; j4++) {
|
||||
o_intra += dot(vec4(sh_adecay[t * ATTN_V4_STRIDE + j4]), my_vnew_v4[j4]);
|
||||
}
|
||||
dst[attn_off + (chunk_start + t) * S_V * H + col] += o_intra;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -988,6 +988,10 @@ void process_shaders() {
|
|||
string_to_spv("rwkv_wkv7_f32", "wkv7.comp", merge_maps(base_dict, {{"A_TYPE", "float"}}));
|
||||
|
||||
string_to_spv("gated_delta_net_f32", "gated_delta_net.comp", merge_maps(base_dict, {{"FLOAT_TYPE", "float"}}));
|
||||
string_to_spv("gated_delta_net_chunk_intra_f32", "gated_delta_net_chunk_intra.comp", merge_maps(base_dict, {{"A_TYPE", "float"}}));
|
||||
string_to_spv("gated_delta_net_chunk_inter_f32", "gated_delta_net_chunk_inter.comp", merge_maps(base_dict, {{"A_TYPE", "float"}}));
|
||||
string_to_spv("gated_delta_net_chunk_output_f32", "gated_delta_net_chunk_output.comp", merge_maps(base_dict, {{"A_TYPE", "float"}}));
|
||||
string_to_spv("gated_delta_net_chunk_output_cm1_f32", "gated_delta_net_chunk_output_cm1.comp", merge_maps(base_dict, {{"A_TYPE", "float"}}));
|
||||
|
||||
string_to_spv("opt_step_adamw_f32", "opt_step_adamw.comp", merge_maps(base_dict, {{"A_TYPE", "float"}}));
|
||||
string_to_spv("opt_step_sgd_f32", "opt_step_sgd.comp", merge_maps(base_dict, {{"A_TYPE", "float"}}));
|
||||
|
|
|
|||
|
|
@ -0,0 +1,119 @@
|
|||
#!/bin/bash
|
||||
# Chunked GDN coopmat benchmark
|
||||
# Usage: ./scripts/bench-gdn-chunked.sh <model.gguf> [output_file]
|
||||
|
||||
set -euo pipefail
|
||||
|
||||
MODEL="${1:?Usage: $0 <model.gguf> [output_file]}"
|
||||
OUT="${2:-gdn-chunked-results.md}"
|
||||
LOG="${OUT%.md}.log"
|
||||
BENCH="./build/bin/llama-bench"
|
||||
|
||||
if [ ! -f "$BENCH" ]; then
|
||||
echo "ERROR: llama-bench not found. Build first:"
|
||||
echo " cmake -B build -DGGML_VULKAN=ON -DCMAKE_BUILD_TYPE=Release"
|
||||
echo " cmake --build build --target llama-bench -j\$(nproc)"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if [ ! -f "$MODEL" ]; then
|
||||
echo "ERROR: Model not found: $MODEL"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
echo "Checking model + GPU..."
|
||||
PROBE=$($BENCH -m "$MODEL" -ngl 99 -fa 1 -n 0 -p 1 -v 2>&1) || {
|
||||
echo "ERROR: llama-bench failed to load model. Full output:"
|
||||
echo "$PROBE"
|
||||
echo "$PROBE" > "$LOG"
|
||||
exit 1
|
||||
}
|
||||
|
||||
GPU_LINE=$(echo "$PROBE" | grep "ggml_vulkan: 0 =" | head -1 || echo "unknown")
|
||||
GPU_NAME=$(echo "$GPU_LINE" | sed 's/.*0 = //' || echo "unknown")
|
||||
BUILD=$(echo "$PROBE" | grep "^build:" || echo "unknown")
|
||||
COOPMAT="no"
|
||||
echo "$GPU_LINE" | grep -q "KHR_coopmat" && COOPMAT="yes (KHR_coopmat)"
|
||||
GDN_MODE="not detected"
|
||||
echo "$PROBE" | grep -q "chunked) enabled" && GDN_MODE="chunked (coopmat)"
|
||||
echo "$PROBE" | grep -q "autoregressive) enabled" && [ "$GDN_MODE" = "not detected" ] && GDN_MODE="autoregressive"
|
||||
echo "$PROBE" | grep -q "chunked) enabled" && echo "$PROBE" | grep -q "autoregressive) enabled" && GDN_MODE="both (auto + chunked)"
|
||||
|
||||
{
|
||||
echo "# Chunked GDN Coopmat Benchmark"
|
||||
echo ""
|
||||
echo "**GPU:** ${GPU_NAME}"
|
||||
echo "**Coopmat:** ${COOPMAT}"
|
||||
echo "**GDN mode:** ${GDN_MODE}"
|
||||
echo "**Model:** $(basename "$MODEL")"
|
||||
echo "**Date:** $(date -u +%Y-%m-%dT%H:%M:%SZ)"
|
||||
echo "**Build:** $BUILD"
|
||||
echo "**OS:** $(uname -srm)"
|
||||
echo "**RAM:** $(free -h | awk '/Mem:/{print $2}') total"
|
||||
echo ""
|
||||
} > "$OUT"
|
||||
|
||||
if [ "$GDN_MODE" = "not detected" ]; then
|
||||
echo "WARNING: GDN not detected for this model. Results may not show GDN profiling data."
|
||||
fi
|
||||
|
||||
echo "Running throughput benchmark (PP-512/1024/2048 + TG-128)..."
|
||||
if ! RESULT=$($BENCH -m "$MODEL" -ngl 99 -fa 1 -n 128 -p 512,1024,2048 --output md 2>&1); then
|
||||
echo "ERROR: Benchmark failed. See $LOG for details."
|
||||
echo "$RESULT" > "$LOG"
|
||||
echo "" >> "$OUT"
|
||||
echo "## ERROR: Benchmark failed" >> "$OUT"
|
||||
echo '```' >> "$OUT"
|
||||
echo "$RESULT" | tail -30 >> "$OUT"
|
||||
echo '```' >> "$OUT"
|
||||
cat "$OUT"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
{
|
||||
echo "## Throughput (default ubatch)"
|
||||
echo ""
|
||||
echo "$RESULT" | grep -E "^\|"
|
||||
echo ""
|
||||
} >> "$OUT"
|
||||
|
||||
echo "Running n_ubatch sweep (PP-2048)..."
|
||||
{
|
||||
echo "## Throughput by n_ubatch (PP-2048)"
|
||||
echo ""
|
||||
} >> "$OUT"
|
||||
|
||||
for UB in 256 512 1024 2048; do
|
||||
echo " ubatch=$UB..."
|
||||
UB_RESULT=$($BENCH -m "$MODEL" -ngl 99 -fa 1 -n 0 -p 2048 -ub $UB --output md 2>&1) || true
|
||||
UB_LINE=$(echo "$UB_RESULT" | grep "pp2048" | head -1)
|
||||
if [ -n "$UB_LINE" ]; then
|
||||
if [ "$UB" = "256" ]; then
|
||||
echo "$UB_RESULT" | grep -E "^\| (model|---)" | head -2 >> "$OUT"
|
||||
fi
|
||||
echo "$UB_LINE" >> "$OUT"
|
||||
fi
|
||||
done
|
||||
echo "" >> "$OUT"
|
||||
|
||||
echo "Running GDN kernel profiling (PP-512)..."
|
||||
PROF=$(GGML_VK_PERF_LOGGER=1 GGML_VK_PERF_LOGGER_FREQUENCY=9999 $BENCH -m "$MODEL" -ngl 99 -fa 1 -n 0 -p 512 2>&1 | grep "GATED_DELTA" | head -5)
|
||||
|
||||
if [ -n "$PROF" ]; then
|
||||
{
|
||||
echo "## GDN Kernel Timing (PP-512)"
|
||||
echo ""
|
||||
echo '```'
|
||||
echo "$PROF"
|
||||
echo '```'
|
||||
echo ""
|
||||
} >> "$OUT"
|
||||
else
|
||||
echo "*No GDN profiling data — model may not use GATED_DELTA_NET.*" >> "$OUT"
|
||||
echo "" >> "$OUT"
|
||||
fi
|
||||
|
||||
echo ""
|
||||
echo "Done. Results saved to: $OUT"
|
||||
echo "---------------------------------------"
|
||||
cat "$OUT"
|
||||
|
|
@ -3689,6 +3689,12 @@ struct test_gated_delta_net : public test_case {
|
|||
: type(type), head_count(head_count), head_size(head_size), n_seq_tokens(n_seq_tokens), n_seqs(n_seqs),
|
||||
v_repeat(v_repeat), permuted(permuted), kda(kda) {}
|
||||
|
||||
double max_nmse_err() override {
|
||||
// Chunked coopmat output kernel uses f16 intermediates for A_decayed @ vnew GEMM.
|
||||
// Random test data can push exp(gcum) values near f16 limits at longer sequences.
|
||||
return n_seq_tokens >= 64 ? 5e-3 : 1e-7;
|
||||
}
|
||||
|
||||
ggml_tensor * build_graph(ggml_context * ctx) override {
|
||||
ggml_tensor * q;
|
||||
ggml_tensor * k;
|
||||
|
|
@ -8684,6 +8690,9 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
|
|||
test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 32, 16, 1, 1));
|
||||
test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 32, 16, 1, 1, 1, true, true));
|
||||
test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 32, 16, 1, 1, 1, false, true));
|
||||
test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 128, 4, 1)); // chunked path: S_V=128, n_tokens=4
|
||||
test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 128, 64, 1)); // chunked path: full chunk
|
||||
test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 128, 128, 1)); // chunked path: 2 chunks
|
||||
test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 16, 64, 1, 2));
|
||||
test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64, 4, 1));
|
||||
test_cases.emplace_back(new test_gated_delta_net(GGML_TYPE_F32, 4, 64, 4, 2));
|
||||
|
|
|
|||
Loading…
Reference in New Issue