Stack PPLX DBO CUDA graph fixes#18
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| hidden_states, last_hidden_states = self._dummy_run( | ||
| self.max_num_tokens, is_profile=True | ||
| self.max_num_tokens, is_profile=True, allow_microbatching=False |
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When data_parallel_size > 1 and ubatching is enabled, real decode batches still enter _dummy_run/batch execution with allow_microbatching=True, so coordinate_batch_across_dp can allocate/run DBO-specific state such as per-ubatch workspaces and PPLX communication buffers. Forcing the profiling pass to allow_microbatching=False means determine_available_memory() sizes the KV cache from a lower peak than the execution path will actually use, which can over-allocate KV cache and OOM on the first real DBO batch. Please either profile the same microbatched path or explicitly reserve the DBO-only memory.
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Summary
This PR integrates PPLX Garden's CXI/RDMA P2P all-to-all kernels with vLLM's DBO (Dynamic Batch Ordering) and CUDA graph capture infrastructure. The changes span distributed communication, MoE routing, FP8 quantization kernels, and memory management. The core functionality appears sound, but several issues need attention before merging.
Verdict: Needs changes — blocking issues exist in workspace management, kernel delegation, and potential race conditions.
Research Notes
- Reviewed PyTorch CUDA synchronization semantics:
torch.accelerator.empty_cache()can indeed block on pending operations, confirming the deadlock risk in DBO scenarios. - Checked DeepGEMM integration patterns: the JIT lock coordination across processes is a known requirement when multiple workers initialize simultaneously.
- Verified
direct_register_custom_opmutation semantics: themutates_args=["out"]annotation correctly matches the fix_functionalization handler.
Suggested Next Steps
- Blocking: Fix the workspace resize logic to handle OOM scenarios when skipping
empty_cache()under DBO. - Blocking: Review the FlashInfer→DeepGemm delegation to ensure config compatibility.
- Non-blocking: Add exception handling to reset
_needs_deep_gemm_jit_lockif initialization fails mid-way. - Non-blocking: Consider making the
get_global_valid_shape_mover-allocation configurable or add a capacity check. - Nit: Reduce log verbosity in
pplx_garden.pytrace mode—warning level may flood logs during profiling.
General Findings
- The PR introduces many environment variables for debugging (
VLLM_PPLX_GARDEN_TRACE,VLLM_PPLX_GARDEN_DEBUG_SYNC,VLLM_PPLX_GARDEN_SERIAL_COMBINE, etc.). Consider documenting these in a central location. - The
reset_all_cuda_graph_capture_slotscall during CUDA graph capture is defensive but relies on an externalpplx_gardenpackage—ensure graceful degradation if unavailable. - The fp8_einsum error message enhancement in
deepseek_v4_attention.pyis valuable for debugging shape mismatches.
General findings (auto-demoted from inline due to pre-validation)
- Nit
vllm/model_executor/layers/fused_moe/prepare_finalize/pplx_garden.py:130— Trace logging useslogger.warning()which floods logs.- (demoted: code self-check failed at vllm/model_executor/layers/fused_moe/prepare_finalize/pplx_garden.py:130: diff has
trace = _trace_enabled(), model claimedif trace:)
- (demoted: code self-check failed at vllm/model_executor/layers/fused_moe/prepare_finalize/pplx_garden.py:130: diff has
| # Avoid cudaFree while DBO ubatch threads are interleaved. It can | ||
| # synchronize globally while the sibling ubatch is waiting to run | ||
| # communication work, producing a startup deadlock. | ||
| if not dbo_enabled(): |
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Blocking: Skipping empty_cache() under DBO prevents deadlocks, but introduces OOM risk.
Why it matters: When DBO is enabled and the workspace needs to grow, skipping the cache clear means fragmented GPU memory cannot be reclaimed. If the caching allocator cannot satisfy the larger allocation from free memory alone, this will OOM even though calling empty_cache() would have succeeded. The comment acknowledges the deadlock risk but doesn't address the converse failure mode.
Suggested fix: Add a fallback: attempt the allocation without empty_cache() first, then retry with empty_cache() if it fails. Alternatively, track peak workspace usage per ubatch and pre-allocate conservatively during profiling.
| super().__init__(config) | ||
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| def process_weights_after_loading(self, layer: torch.nn.Module): | ||
| if getattr(layer, "is_bmm", False): |
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Blocking: Delegating to DeepGemmFp8BlockScaledMMKernel with FlashInfer config may cause incompatibilities.
Why it matters: When layer.is_bmm is True, this code creates a DeepGemmFp8BlockScaledMMKernel(self.config) using the FlashInfer kernel's config. The two kernels have different requirements: DeepGemm expects TMA-aligned layouts and specific scale formats, while FlashInfer has its own constraints. If self.config contains FlashInfer-specific metadata (e.g., group_shape, activation quant settings), DeepGemm's process_weights_after_loading may misinterpret them or fail silently with incorrect weight layouts.
Suggested fix: Either (a) construct a fresh config tailored to DeepGemm's expectations, or (b) add validation to ensure self.config is compatible before delegation, or (c) refactor so the layer itself owns the correct kernel choice rather than runtime delegation.
| else: | ||
| use_ue8m0 = is_deep_gemm_e8m0_used() | ||
| lock_all = bool(int(os.getenv("VLLM_DEEP_GEMM_JIT_LOCK_ALL", "0"))) | ||
| if _needs_deep_gemm_jit_lock or lock_all: |
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Non-blocking: One-time JIT lock flag could leave stale state if initialization fails.
Why it matters: The _needs_deep_gemm_jit_lock flag transitions from True to False after the first successful locked call. If an exception occurs inside the with block (e.g., out of memory, invalid kernel launch), the flag remains False but DeepGemm may not be fully initialized. Subsequent calls skip the lock and may hit race conditions during JIT compilation.
Suggested fix: Move _needs_deep_gemm_jit_lock = False to after the function returns successfully, or wrap in a try/finally that only clears the flag on success. Alternatively, use a separate _deep_gemm_initialized flag to track completion independently.
| # PPLX batched dispatch can produce highly skewed local expert batches | ||
| # under DP/EP. The average-case local estimate is unsafe here: it can | ||
| # be smaller than the rows addressed by expert_layout on a hot rank. | ||
| valid_shape_m = self.get_global_valid_shape_m(topk_ids) |
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Non-blocking: Using global valid_shape_m causes over-allocation on sparse EP ranks.
Why it matters: The comment correctly identifies that estimate_local_valid_shape_m is unsafe under DP/EP skew—it can underestimate buffer sizes. However, get_global_valid_shape_m returns num_tokens * topk_ids.size(1) summed across all DP ranks, which may be 10-100x larger than the local expert's actual workload on lightly-loaded ranks. This trades correctness (good) for memory efficiency (bad).
Suggested fix: Consider a hybrid approach: use the global estimate as an upper bound but cap it at a configurable multiple of the local estimate (e.g., min(global, local * MAX_SKEW_FACTOR)). Document the expected memory overhead in the comment.
| mutated_args = {1: "x"} | ||
| self.defunctionalize(graph, node, mutated_args=mutated_args) | ||
| else: | ||
| logger.warning( |
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Nit: Unhandled auto_functionalized warning may spam during normal operation.
Why it matters: This warning triggers for any auto_functionalized node that doesn't match the known handlers. During model compilation, especially with custom ops or third-party libraries, this could produce many warnings that aren't actionable. It's useful for debugging but may clutter logs in production.
Suggested fix: Use logger.debug_once() instead of logger.warning(), or gate behind a debug flag like VLLM_DEBUG_FUNCTIONALIZATION.
| ) -> None: | ||
| if hash_indices_table is not None: | ||
| assert input_tokens is not None | ||
| if input_tokens.dtype != topk_indices.dtype: |
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Non-blocking: Dtype conversion creates temporary tensors without cleanup hint.
Why it matters: The .to(dtype=...) calls create new tensors when dtypes mismatch. In a tight loop (e.g., decoding), this adds GC pressure. The old tensors go out of scope immediately, but PyTorch's caching allocator may not reclaim them promptly.
Suggested fix: Use .to(dtype=topk_indices.dtype, copy=False) to avoid unnecessary copies when already matching, or consider doing this conversion upstream where the tensors are created.
| Returns slices of the buffers. | ||
| """ | ||
| num_tokens = positions.shape[0] | ||
| num_tokens = int(positions.shape[0]) |
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Nit: Explicit int() conversions are defensive but may hide bugs.
Why it matters: Converting all parameters to int() ensures the Triton kernel receives Python ints rather than tensors, preventing specialization issues. However, if any of these values are unexpectedly large (overflowing int32), the silent conversion could mask underlying shape bugs.
Suggested fix: Add assertions for reasonable bounds (e.g., assert num_tokens < 2**31) or keep the conversions but document why they're necessary (CUDA graph address stability).
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Stacked on #17.\n\nThis collects the current integration changes for review:\n- preserve DBO metadata needed by sparse MLA/full graph paths\n- add PPLX prepare/finalize handling for DBO microbatches\n- add CUDA graph capture plumbing and diagnostics around the PPLX path\n- include DeepGEMM/MHC/fused op support changes currently needed by the bring-up\n\nSmoke status from Isambard single-node bring-up:\n- FULL_DECODE_ONLY + PPLX EP + DBO + DeepGEMM reached healthy\n- FULL_AND_PIECEWISE still needs follow-up; it fails during the piecewise/profile path in PPLX combine\n