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
Bug #1 fix: The _needs_token_refill workaround was a band-aid over a
misdiagnosis. cute.compile does NOT corrupt GPU memory (verified on B200).
The original corruption was from a different bug (likely OOB write or
weight loading issue).
Changes:
- bridge.py: Add warmup_compilation() for eager JIT before runtime buffers
exist. Pre-allocate workspace per cache entry (no torch.full in hot path).
Cache stores {compiled, workspace, workspace_size} instead of just compiled.
CuTe tensor wrappers re-created per call (cheap metadata, avoids stale refs).
- runner.py: Remove _needs_token_refill hack. Add eager warmup call in
_ensure_stacked() for both L1 and L2 GEMM shapes.
- nvfp4_linear.py: Add eager warmup in finalize_weights() for single GEMM.
The warmup approach ensures cute.compile runs exactly once per shape during
model init, before any forward pass. This is deterministic and eliminates
any possible interaction between JIT and runtime GPU memory.
Dynamo (torch.compile fullgraph) cannot trace through CuTeDSL internals
(cute.compile, JIT, etc.). The autograd.Function approach was unreliable
with fullgraph mode — Dynamo would still try to trace through it.
Fix: torch.library.custom_op makes Dynamo treat our GEMM as an opaque
black box. No reimplementing the kernel — just route through the existing
runner via a registry pattern:
- Runners registered in global dict with integer IDs
- Custom op takes (tensors, runner_id, shape_hint) -> tensor
- Dynamo calls fake impl for shape inference, never touches the runner
- At execution time, real impl looks up runner and calls _run_impl
Changes:
- New: cutedsl/custom_ops.py (custom op definitions + registry)
- New: tests/test_custom_op.py (local unit tests, no GPU needed)
- Removed: _Nvfp4LinearApply, _MoEApply (autograd.Function classes)
- Updated: nvfp4_linear.py, runner.py, cutedsl.py, nvfp4_cutedsl.py
to use custom ops instead of autograd.Function
- Updated: cutedsl_quant_method.py to use custom op + registry
torch.compile fullgraph mode can't handle @torch.compiler.disable (skips
the function and refuses to compile). Custom autograd Functions are treated
as opaque ops by torch.compile — they execute eagerly without the compiler
trying to trace into CuTeDSL internals (JIT, Path.cwd, etc).
CuTeDSL internals (Path.cwd, threading, JIT) are incompatible with
torch.dynamo tracing. Marking run() as compiler-disabled makes the
runners opaque to torch.compile — they execute eagerly while the
rest of the model gets compiled.
The _NVFP4_STEP_LUT_LOCK caused 'Unsupported context manager' under
torch.compile/cudagraph. LUT is now pre-populated during warmup so
the fast path (cache hit) never hits a lock.
Also removed all init/warmup debug prints from CuTeDSL kernels.
- CuTeDSLNvfp4Method: custom quant method that creates CuTeDSL runners
during process_weights_after_loading, then swaps to CuTeDSLNvfp4LinearMethod
for forward dispatch
- Attention projections (fused_wqa_wkv, wq_b, wo_b) now route through
CuTeDSLNvfp4Linear (cosine 0.992-0.996 vs BF16 reference)
- Shared expert now uses CuTeDSLSharedExpertRunner (cosine 0.992 vs BF16)
with monkey-patched forward for fused L1+SiLU+L2 pipeline
- Deleted all BF16 dequant code (_dequant_nvfp4_to_bf16, _post_quant_fix,
input_scale fixes)
- Deleted _post_quant_fix hook from utils.py
- Fixed SwiGLU clamp: gate clamped BEFORE SiLU (matching SiluAndMulWithClamp)
- Cleaned up all debug prints
- Updated Dockerfile with new kernel files
- CuTeDSLNvfp4Linear: generic single-GEMM runner for any NVFP4 projection
- test_attention.py: tests q_a_proj, q_b_proj, kv_proj, o_b_proj vs BF16
- Same pad+swizzle pattern as shared expert, but no SiLU/fusion
