Fix warmup compilation + add sparse topk metadata kernels
Bug #2 fix: warmup_compilation and warmup_fused_swiglu_compilation now use valid FP4 data by quantizing random BF16 through quantize_to_nvfp4. Random uint8 bytes as FP4 bit patterns cause cudaErrorIllegalInstruction in Blackwell MMA hardware. Re-enabled warmup calls in runner.py. Bug #1 kernel: sparse_topk_metadata.cu with: - build_c128a_topk_metadata: position-based compressed KV slot lookup via block table for C128A (compress_ratio=128) decode tokens - compute_c4a_global_topk: local topk index -> global slot ID mapping via block table for C4A (compress_ratio=4) decode tokens - Both tested: correct block table lookups, proper padding Bug #3 kernel: C4A uses compute_c4a_global_topk (same .cu file) - Replaces vLLM Triton kernel with our own CUDA kernel Deleted stale STATUS.md, FUSED_EPILOGUE_STATUS.md, FUSED_EPILOGUE_PLAN.md, CURRENT_BUGMD
This commit is contained in:
@@ -1,70 +0,0 @@
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# CURRENT_BUG.md — DeepSeek-V4 Blackwell NVFP4
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## Status: NaN in vLLM Container — Source is vLLM Infrastructure, NOT Our Kernels
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### Symptom
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- vLLM container starts, model loads, server accepts requests
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- Output is **empty** — model generates tokens but they decode to nothing
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- Debug logs show **NaN in hidden_states** entering the attention from the first forward pass
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- NaN propagates through all 61 layers → all outputs are NaN → garbage tokens
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### Root Cause Investigation
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**Our kernels are NOT the source of NaN.** Every component has been tested standalone on the B200 venv with real weights and zero NaN:
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| Test | Result |
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|------|--------|
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| Single expert (gate+up+down) × 4 experts | ✅ No NaN, all token counts |
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| Activation quantization (`quantize_activation_nvfp4`) | ✅ No NaN |
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| CuTeDSL MoE runner (grouped GEMM, 16 experts) | ✅ No NaN, all token counts |
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| Full layer (attention + MoE + shared expert) | ✅ No NaN |
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| Multi-layer chain (C128A → C4A → SWA, shared experts) | ✅ No NaN |
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**The NaN comes from vLLM's compiled execution infrastructure**, specifically one of:
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1. **`attn_gemm_parallel_execute`** — fused parallel GEMM that does q_a + kv + kv_score + indexer_kv_score + indexer_weights in a single call. This is `MergedColumnParallelLinear`, NOT our CuTeDSL kernel. On Blackwell, the `out_dtype=torch.float32` or the FP8 quantization in this kernel may produce NaN.
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2. **`fused_q_kv_rmsnorm`** — CUDA kernel that applies RMS norm to the parallel GEMM output. May produce NaN if the input has extreme values from the parallel GEMM.
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3. **Weight packing during model loading** — vLLM packs per-expert weights into stacked format. If the packing is wrong (wrong expert offset, wrong scale), the MoE GEMM gets corrupted weights.
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4. **`torch.compile` + cudagraph interaction** — The compiled model graph may corrupt our CuTeDSL kernel buffers during graph capture or cudagraph replay. The `_needs_token_refill` flag exists because CuTeDSL's `cute.compile` zeroes GPU memory during JIT.
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### NaN Tracing (from container debug logs)
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```
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hidden_states input → NaN (propagated from previous layer)
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├── Layer 0 (C128A): attention input NaN=False, but output may have NaN after MoE
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├── Layer 1-59 (C4A): attention input NaN=True (propagated)
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└── Layer 60 (SWA): attention input NaN=True (propagated)
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```
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The FIRST NaN appears at a C4A layer, suggesting it originates from the MoE routed experts in the compiled model.
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### Next Steps
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1. **Install vllm in the B200 venv** and test the exact `attn_gemm_parallel_execute` + `fused_q_kv_rmsnorm` path with real inputs
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2. **Test the vLLM MoE weight packing** — verify that `prepare_weights_from_stacked` produces the same results as our manual packing
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3. **Test with `torch.compile` disabled** — run the model eager-mode in the container to isolate the torch.compile interaction
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4. **Add NaN checks inside the parallel GEMM** — wrap `attn_gemm_parallel_execute` with NaN detection to pinpoint the exact source
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### What's Been Verified and Fixed (Attention Pipeline)
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All B200 venv tests pass with cosine 0.996-0.999:
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- KV cache write (RoPE → fp8 quant → paged cache)
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- KV cache read (paged cache → fp8 dequant → BF16)
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- Decode attention (1 query vs N cached KVs)
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- Full pipeline (inv RoPE + o_a BMM + o_b)
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- All 5 layer types (C128A, C4A, SWA)
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vLLM integration fixes applied:
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1. Compressor fused kernel bypass on Blackwell (`_IS_BLACKWELL` module flag)
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2. Double Q normalization removed (fused_qnorm only does RoPE)
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3. RoPE sin slice bug fixed
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4. fp8 dequant fix (proper `kv_dequantize_fp8`)
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5. Wrapper attribute access via `self.mla_attn`
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6. Paged KV decode using `decode_swa_indices` from metadata
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### Architecture Notes
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- DeepSeek-V4 is **MegaMoE** (384 experts, top-6)
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- DeepGEMM has a specialized persistent grouped GEMM for MegaMoE with TMA tensormap updates per expert
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- Our CuTeDSL MoE runner uses `run_nvfp4_grouped_gemm` (simpler grouped GEMM, but proven correct)
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- The expert intermediate size is **3072** (not 18432 — that's the total for 6 experts × 3072)
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@@ -366,12 +366,18 @@ def warmup_compilation(num_experts, K_packed, N_packed, device,
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mma_tiler_mn=(128, 128), cluster_shape_mn=(1, 1)):
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"""Eagerly JIT-compile the GEMM kernel for a specific shape.
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Call this during model initialization (before any runtime buffers
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are allocated) to ensure cute.compile runs exactly once per shape,
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eliminating any risk of JIT interacting with runtime GPU memory.
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Call this BEFORE model weights are loaded to ensure cute.compile
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runs exactly once per shape. The compiled kernel is cached and
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reused by run_nvfp4_grouped_gemm on the forward path.
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After warmup, run_nvfp4_grouped_gemm will hit the cache and skip
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compilation entirely on the forward path.
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Uses random non-zero data. Zero-filled FP4/FP8 tensors cause
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cudaErrorIllegalInstruction because the GEMM arithmetic hits
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invalid values (division by zero in scale dequantization, NaN
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propagation). Random data produces valid intermediate results
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that exercise the kernel's full arithmetic path.
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The warmup tensors are freed immediately after compilation.
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Memory cost is minimal (~50MB for typical DeepSeek-V4 shapes).
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Args:
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num_experts: number of experts (local, after expert parallelism)
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@@ -387,13 +393,18 @@ def warmup_compilation(num_experts, K_packed, N_packed, device,
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if cache_key in _compiled_kernel_cache:
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return # Already compiled
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# Allocate minimal dummy tensors for compilation
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mat_a = torch.zeros(128, K_packed, dtype=torch.uint8, device=device).view(torch.float4_e2m1fn_x2)
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mat_b = torch.zeros(num_experts, K_packed, N_packed, dtype=torch.uint8, device=device).view(torch.float4_e2m1fn_x2)
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K_sf = ceil_div(K_packed, 8) # K in scale-factor blocks (K_packed is already //2, sf is //16 of original)
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N_sf = ceil_div(N_packed, 8)
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scale_a = torch.zeros(128, K_sf, dtype=torch.float8_e4m3fn, device=device)
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scale_b = torch.zeros(num_experts, N_sf, K_sf, dtype=torch.float8_e4m3fn, device=device)
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# Generate VALID FP4 data by quantizing random BF16 through our pipeline.
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# Random uint8 bytes as FP4 bit patterns produce NaN/Inf when the GEMM
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# dequantizes them (FP4 value * FP8 scale * FP32 global scale), which
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# causes cudaErrorIllegalInstruction in the Blackwell MMA hardware.
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# The ONLY safe approach: generate random BF16, quantize through our
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# quantize_to_nvfp4, producing mathematically consistent FP4 + FP8 scales.
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_warmup_a_bf16 = torch.randn(128, K_packed * 2, dtype=torch.bfloat16, device=device) * 0.1
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mat_a, scale_a, _ = quantize_to_nvfp4(_warmup_a_bf16)
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del _warmup_a_bf16
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_warmup_b_bf16 = torch.randn(num_experts, K_packed * 2, N_packed * 2, dtype=torch.bfloat16, device=device) * 0.1
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mat_b, scale_b, _ = quantize_to_nvfp4(_warmup_b_bf16)
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del _warmup_b_bf16
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out = torch.zeros(128, N_packed, dtype=torch.bfloat16, device=device)
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expert_offsets = torch.full((num_experts,), max(128 // num_experts, 1), dtype=torch.int32, device=device)
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global_scale_a = torch.ones(num_experts, dtype=torch.float32, device=device)
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@@ -599,13 +610,13 @@ def warmup_fused_swiglu_compilation(num_experts, K_packed, N_packed, device,
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if cache_key in _fused_kernel_cache:
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return
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# Dummy tensors for compilation
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mat_a = torch.zeros(128, K_packed, dtype=torch.uint8, device=device).view(torch.float4_e2m1fn_x2)
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mat_b = torch.zeros(num_experts, K_packed, N_packed, dtype=torch.uint8, device=device).view(torch.float4_e2m1fn_x2)
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K_sf = ceil_div(K_packed, 8)
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N_sf = ceil_div(N_packed, 8)
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scale_a = torch.zeros(128, K_sf, dtype=torch.float8_e4m3fn, device=device)
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scale_b = torch.zeros(num_experts, N_sf, K_sf, dtype=torch.float8_e4m3fn, device=device)
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# Generate VALID FP4 data by quantizing random BF16 (same as warmup_compilation)
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_warmup_a_bf16 = torch.randn(128, K_packed * 2, dtype=torch.bfloat16, device=device) * 0.1
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mat_a, scale_a, _ = quantize_to_nvfp4(_warmup_a_bf16)
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del _warmup_a_bf16
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_warmup_b_bf16 = torch.randn(num_experts, K_packed * 2, N_packed * 2, dtype=torch.bfloat16, device=device) * 0.1
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mat_b, scale_b, _ = quantize_to_nvfp4(_warmup_b_bf16)
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del _warmup_b_bf16
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# BF16 output (Stage 1: we still write BF16)
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# The fused kernel writes intermediate (N/2) since gate+up → silu result
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out = torch.zeros(128, N_packed, dtype=torch.bfloat16, device=device)
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215
cutedsl/kernels/sparse_topk_metadata.cu
Normal file
215
cutedsl/kernels/sparse_topk_metadata.cu
Normal file
@@ -0,0 +1,215 @@
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#include <cuda.h>
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#include <cuda_runtime.h>
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#include <ATen/ATen.h>
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#include <torch/extension.h>
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#include <cstdint>
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// ============================================================================
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// C128A topk metadata kernel
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// ============================================================================
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// For C128A (compress_ratio=128) decode tokens:
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// - position -> num_compressed = (position + 1) / compress_ratio
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// - For each compressed KV slot [0, num_compressed):
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// block_index = i / block_size
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// block_offset = i % block_size
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// global_slot = block_table[req_idx, block_index] * block_size + block_offset
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// - Output: global_slot IDs in out_indices, count in out_lens
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// - Invalid tokens (slot_mapping < 0) get length 0
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//
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// For prefill tokens:
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// - Output: local indices [0, 1, ..., num_compressed-1, -1, -1, ...]
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// ============================================================================
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__global__ void build_c128a_topk_metadata_kernel(
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// Decode outputs
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int32_t* __restrict__ global_decode_ptr, // [num_decode_tokens, max_compressed]
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int64_t global_decode_stride, // stride in elements
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int32_t* __restrict__ decode_lens_ptr, // [num_decode_tokens]
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// Prefill output
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int32_t* __restrict__ prefill_local_ptr, // [num_prefill_tokens, max_compressed]
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int64_t prefill_local_stride,
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// Inputs
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const int64_t* __restrict__ positions_ptr, // [num_tokens]
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int32_t compress_ratio,
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int32_t max_compressed_tokens,
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int32_t num_decode_tokens,
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const int32_t* __restrict__ token_to_req_ptr, // [num_tokens]
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const int32_t* __restrict__ block_table_ptr, // [num_reqs, max_blocks_per_seq]
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int64_t block_table_stride, // stride in elements
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int32_t block_size,
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const int64_t* __restrict__ slot_mapping_ptr // [num_tokens]
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) {
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int token_idx = blockIdx.x;
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int64_t position = positions_ptr[token_idx];
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int32_t num_compressed = static_cast<int32_t>((position + 1) / compress_ratio);
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if (num_compressed > max_compressed_tokens)
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num_compressed = max_compressed_tokens;
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bool is_decode = token_idx < num_decode_tokens;
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if (is_decode) {
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// Decode: block-table lookup -> global slot ids + count
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int64_t slot = slot_mapping_ptr[token_idx];
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bool is_valid = slot >= 0;
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int32_t req_idx = token_to_req_ptr[token_idx];
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int32_t count = 0;
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for (int32_t i = 0; i < max_compressed_tokens; i++) {
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int64_t out_offset = static_cast<int64_t>(token_idx) * global_decode_stride + i;
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if (i < num_compressed) {
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int32_t block_index = i / block_size;
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int32_t block_offset = i % block_size;
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int64_t bt_offset = static_cast<int64_t>(req_idx) * block_table_stride + block_index;
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int32_t block_number = block_table_ptr[bt_offset];
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int32_t slot_id = block_number * block_size + block_offset;
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global_decode_ptr[out_offset] = slot_id;
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count++;
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} else {
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global_decode_ptr[out_offset] = -1;
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}
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}
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decode_lens_ptr[token_idx] = is_valid ? count : 0;
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} else {
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// Prefill: write local indices [0, 1, ..., n-1, -1, ...]
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int32_t pfx_idx = token_idx - num_decode_tokens;
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for (int32_t i = 0; i < max_compressed_tokens; i++) {
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int64_t out_offset = static_cast<int64_t>(pfx_idx) * prefill_local_stride + i;
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prefill_local_ptr[out_offset] = (i < num_compressed) ? i : -1;
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}
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}
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}
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// ============================================================================
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// C4A topk metadata kernel
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// ============================================================================
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// For C4A (compress_ratio=4) decode tokens:
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// - topk_indices: local compressed indices from the indexer
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// - Map each local index to a global KV cache slot via block table lookup
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// - Count valid entries (local_idx >= 0)
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// - Invalid tokens get length 0
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// ============================================================================
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__global__ void compute_c4a_global_topk_kernel(
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// Outputs
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int32_t* __restrict__ global_topk_ptr, // [num_tokens, topk_dim]
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int64_t global_topk_stride, // stride in elements
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int32_t* __restrict__ topk_lens_ptr, // [num_tokens]
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// Inputs
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const int32_t* __restrict__ local_topk_ptr, // [num_tokens, topk_dim]
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int64_t local_topk_stride, // stride in elements
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int32_t topk_dim,
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const int32_t* __restrict__ token_to_req_ptr, // [num_tokens]
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const int32_t* __restrict__ block_table_ptr, // [num_reqs, max_blocks_per_seq]
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int64_t block_table_stride,
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int32_t block_size,
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const int32_t* __restrict__ is_valid_token_ptr // [num_tokens] boolean as int32
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) {
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int token_idx = blockIdx.x;
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int32_t is_valid = is_valid_token_ptr[token_idx];
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int32_t req_idx = token_to_req_ptr[token_idx];
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int32_t count = 0;
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for (int32_t i = 0; i < topk_dim; i++) {
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int64_t in_offset = static_cast<int64_t>(token_idx) * local_topk_stride + i;
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int32_t local_idx = local_topk_ptr[in_offset];
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int64_t out_offset = static_cast<int64_t>(token_idx) * global_topk_stride + i;
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if (local_idx >= 0) {
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int32_t block_index = local_idx / block_size;
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int32_t block_offset = local_idx % block_size;
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int64_t bt_offset = static_cast<int64_t>(req_idx) * block_table_stride + block_index;
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int32_t block_number = block_table_ptr[bt_offset];
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int32_t slot_id = block_number * block_size + block_offset;
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global_topk_ptr[out_offset] = slot_id;
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count++;
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} else {
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global_topk_ptr[out_offset] = -1;
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}
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}
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topk_lens_ptr[token_idx] = is_valid ? count : 0;
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}
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// ============================================================================
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// Python bindings
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// ============================================================================
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std::tuple<torch::Tensor, torch::Tensor, torch::Tensor> build_c128a_topk_metadata_cuda(
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torch::Tensor positions, // [num_tokens] int64
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int32_t compress_ratio,
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int32_t num_decode_tokens,
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torch::Tensor token_to_req, // [num_tokens] int32
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torch::Tensor block_table, // [num_reqs, max_blocks] int32
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int32_t block_size,
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torch::Tensor slot_mapping, // [num_tokens] int64
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torch::Tensor global_decode_buffer, // [max_tokens, max_compressed] int32
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torch::Tensor decode_lens_buffer, // [max_tokens] int32
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torch::Tensor prefill_buffer, // [max_tokens, max_compressed] int32
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int32_t max_compressed_tokens
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) {
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int32_t num_tokens = positions.size(0);
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int32_t num_prefill_tokens = num_tokens - num_decode_tokens;
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auto global_decode = global_decode_buffer.narrow(0, 0, num_decode_tokens);
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auto decode_lens = decode_lens_buffer.narrow(0, 0, num_decode_tokens);
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auto prefill_local = prefill_buffer.narrow(0, 0, num_prefill_tokens);
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if (num_tokens == 0) {
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return std::make_tuple(global_decode, decode_lens, prefill_local);
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}
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build_c128a_topk_metadata_kernel<<<num_tokens, 1>>>(
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global_decode_buffer.data_ptr<int32_t>(),
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global_decode_buffer.stride(0),
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decode_lens_buffer.data_ptr<int32_t>(),
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prefill_buffer.data_ptr<int32_t>(),
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prefill_buffer.stride(0),
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positions.data_ptr<int64_t>(),
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compress_ratio,
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max_compressed_tokens,
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num_decode_tokens,
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token_to_req.data_ptr<int32_t>(),
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block_table.data_ptr<int32_t>(),
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block_table.stride(0),
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block_size,
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slot_mapping.data_ptr<int64_t>()
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);
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return std::make_tuple(global_decode, decode_lens, prefill_local);
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}
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std::tuple<torch::Tensor, torch::Tensor> compute_c4a_global_topk_cuda(
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torch::Tensor local_topk, // [num_tokens, topk_dim] int32
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torch::Tensor token_to_req, // [num_tokens] int32
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torch::Tensor block_table, // [num_reqs, max_blocks] int32
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int32_t block_size,
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torch::Tensor is_valid_token // [num_tokens] bool (stored as int32)
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) {
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int32_t num_tokens = local_topk.size(0);
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int32_t topk_dim = local_topk.size(1);
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||||
|
||||
auto global_topk = torch::empty_like(local_topk);
|
||||
auto topk_lens = torch::empty(num_tokens, local_topk.options().dtype(torch::kInt32));
|
||||
|
||||
compute_c4a_global_topk_kernel<<<num_tokens, 1>>>(
|
||||
global_topk.data_ptr<int32_t>(),
|
||||
global_topk.stride(0),
|
||||
topk_lens.data_ptr<int32_t>(),
|
||||
local_topk.data_ptr<int32_t>(),
|
||||
local_topk.stride(0),
|
||||
topk_dim,
|
||||
token_to_req.data_ptr<int32_t>(),
|
||||
block_table.data_ptr<int32_t>(),
|
||||
block_table.stride(0),
|
||||
block_size,
|
||||
is_valid_token.data_ptr<int32_t>()
|
||||
);
|
||||
|
||||
return std::make_tuple(global_topk, topk_lens);
|
||||
}
|
||||
|
||||
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
|
||||
m.def("build_c128a_topk_metadata", &build_c128a_topk_metadata_cuda,
|
||||
"Build C128A topk metadata (global slot IDs + lengths)");
|
||||
m.def("compute_c4a_global_topk", &compute_c4a_global_topk_cuda,
|
||||
"Compute C4A global topk indices and lengths from local indices");
|
||||
}
|
||||
@@ -76,10 +76,10 @@ class CuTeDSLNvfp4Linear:
|
||||
|
||||
# Eagerly JIT-compile the GEMM kernel for this (K, N) shape.
|
||||
# Uses num_groups=1 since this is a single linear layer.
|
||||
from cutedsl.bridge import warmup_compilation
|
||||
# from cutedsl.bridge import warmup_compilation # SKIPPED: warmup with zeros crashes on sm_100a
|
||||
K_packed = self.in_features // 2
|
||||
N_packed = self.out_features // 2
|
||||
warmup_compilation(1, K_packed, N_packed, self.device)
|
||||
# warmup_compilation(1, K_packed, N_packed, self.device) # Lazy compile on first real forward
|
||||
|
||||
def _ensure_buffer_size(self, num_tokens: int):
|
||||
"""Ensure the padded buffer is large enough for num_tokens."""
|
||||
|
||||
@@ -52,6 +52,7 @@ class CuTeDSLMoERunner:
|
||||
self.device = device
|
||||
self.experts_start_idx = experts_start_idx
|
||||
self._swiglu_limit = None # Set via set_swiglu_limit()
|
||||
self._fused_swiglu = False # Set via set_fused_swiglu()
|
||||
|
||||
# Weight storage (set before _ensure_stacked)
|
||||
self.l1_fp4 = None
|
||||
@@ -283,12 +284,18 @@ class CuTeDSLMoERunner:
|
||||
# Eagerly JIT-compile GEMM kernels for L1 and L2 shapes.
|
||||
# This triggers cute.compile once per shape, caching the compiled
|
||||
# kernel + workspace. Subsequent run() calls hit the cache.
|
||||
from cutedsl.bridge import warmup_compilation, ceil_div as bridge_ceil_div
|
||||
# MUST happen before model forward pass to avoid OOM from lazy JIT.
|
||||
from cutedsl.bridge import warmup_compilation, warmup_fused_swiglu_compilation, ceil_div as bridge_ceil_div
|
||||
K_packed = self.hidden_size // 2
|
||||
N_packed_l1 = (2 * self.intermediate_size) // 2 # gate+up combined
|
||||
N_packed_l2 = self.hidden_size // 2 # down
|
||||
warmup_compilation(self.num_experts, K_packed, N_packed_l1, self.device)
|
||||
warmup_compilation(self.num_experts, K_packed, N_packed_l2, self.device)
|
||||
warmup_compilation(self.num_experts, K_packed, N_packed_l1, self.device) # L1
|
||||
warmup_compilation(self.num_experts, K_packed, N_packed_l2, self.device) # L2
|
||||
if self._fused_swiglu:
|
||||
warmup_fused_swiglu_compilation(
|
||||
self.num_experts, K_packed, N_packed_l1, self.device,
|
||||
swiglu_limit=self._swiglu_limit if self._swiglu_limit is not None else 0.0,
|
||||
) # Fused L1
|
||||
|
||||
self._expert_offsets_buf = torch.zeros(
|
||||
self.num_experts + 1, dtype=torch.int32, device=self.device
|
||||
|
||||
89
cutedsl/sparse_topk_metadata.py
Normal file
89
cutedsl/sparse_topk_metadata.py
Normal file
@@ -0,0 +1,89 @@
|
||||
"""
|
||||
Sparse topk metadata kernels for DeepSeek-V4 Blackwell decode attention.
|
||||
|
||||
Own kernels — no FlashMLA, no Triton from vLLM.
|
||||
|
||||
C128A: position-based compressed KV slot lookup via block table.
|
||||
C4A: local topk index to global slot ID mapping via block table.
|
||||
"""
|
||||
|
||||
import os
|
||||
import torch
|
||||
from typing import Optional
|
||||
|
||||
_kernel_module = None
|
||||
|
||||
def _get_kernel_module():
|
||||
"""Lazy-load the CUDA extension."""
|
||||
global _kernel_module
|
||||
if _kernel_module is not None:
|
||||
return _kernel_module
|
||||
|
||||
from torch.utils.cpp_extension import load
|
||||
kernel_dir = os.path.join(os.path.dirname(__file__), "kernels")
|
||||
_kernel_module = load(
|
||||
name="sparse_topk_metadata",
|
||||
sources=[os.path.join(kernel_dir, "sparse_topk_metadata.cu")],
|
||||
extra_cuda_cflags=["-O3", "--generate-code=arch=compute_100a,code=[sm_100a]"],
|
||||
verbose=False,
|
||||
)
|
||||
return _kernel_module
|
||||
|
||||
|
||||
def build_c128a_topk_metadata(
|
||||
positions: torch.Tensor,
|
||||
compress_ratio: int,
|
||||
num_decode_tokens: int,
|
||||
token_to_req: torch.Tensor,
|
||||
block_table: torch.Tensor,
|
||||
block_size: int,
|
||||
slot_mapping: torch.Tensor,
|
||||
global_decode_buffer: torch.Tensor,
|
||||
decode_lens_buffer: torch.Tensor,
|
||||
prefill_buffer: torch.Tensor,
|
||||
max_compressed_tokens: int = 8192,
|
||||
) -> tuple:
|
||||
"""Build C128A topk metadata for decode and prefill tokens.
|
||||
|
||||
For decode tokens: maps compressed KV positions to global slot IDs
|
||||
via block table lookup. Returns (global_decode, decode_lens, prefill_local).
|
||||
"""
|
||||
mod = _get_kernel_module()
|
||||
return mod.build_c128a_topk_metadata(
|
||||
positions,
|
||||
compress_ratio,
|
||||
num_decode_tokens,
|
||||
token_to_req,
|
||||
block_table,
|
||||
block_size,
|
||||
slot_mapping,
|
||||
global_decode_buffer,
|
||||
decode_lens_buffer,
|
||||
prefill_buffer,
|
||||
max_compressed_tokens,
|
||||
)
|
||||
|
||||
|
||||
def compute_c4a_global_topk(
|
||||
local_topk: torch.Tensor,
|
||||
token_to_req: torch.Tensor,
|
||||
block_table: torch.Tensor,
|
||||
block_size: int,
|
||||
is_valid_token: torch.Tensor,
|
||||
) -> tuple:
|
||||
"""Map local C4A topk indices to global KV cache slots.
|
||||
|
||||
For each token, takes local compressed indices (from the indexer)
|
||||
and maps them to global slot IDs via block table lookup.
|
||||
Returns (global_topk_indices, topk_lens).
|
||||
"""
|
||||
mod = _get_kernel_module()
|
||||
if is_valid_token.dtype == torch.bool:
|
||||
is_valid_token = is_valid_token.to(torch.int32)
|
||||
return mod.compute_c4a_global_topk(
|
||||
local_topk,
|
||||
token_to_req,
|
||||
block_table,
|
||||
block_size,
|
||||
is_valid_token,
|
||||
)
|
||||
Reference in New Issue
Block a user