[Bugfix][Model] Fix DeepSeek-V4.1 microbatch positions and Engram histories

[0z5a]

Purpose

Fix DeepSeek-V4.1's inputs when a batch is divided into microbatches. A request can start before the current microbatch, so slicing request rows alone does not provide the history preceding that microbatch's first token. Reconstruct that history, pass it through UBatchWrapper, preserve absolute positions when splitting attention metadata, and normalize the final token boundary to a Python int.

Graph replay refreshes the captured lookback tensor in place and clears padding; each microbatch receives its own synchronous Engram staging buffer, passed explicitly to the decoder. The contribution is microbatch input and history correctness. Full-model DBO support and performance optimization are outside this PR's delivered scope.

Searching open Engram/lookback/microbatch PRs found no duplicate of this request-boundary reconstruction and graph-input refresh. #56220 covers asynchronous lookup scheduling, while #56219 covers SP exchange; neither supplies the input corrections here.

Based on #56214 (dsv41-feat, c9d909e802a39292f54101bff8a36096761ea605). The PR targets that feature branch so the model implementation is not repeated in this diff.

Rebased on 2026-09-11 after the feature branch was rewritten. Changed-file Python parsing and all applicable pre-commit hooks passed. The microbatch history suite passed (7 CPU tests; 2 CUDA cases skipped). GPU/full-model measurements below belong to the prior revision based on e47aa780bccf59f59dfa2cbb18e17a10b4fe69ba; they were not rerun on this rebased head.

AI assistance: OpenAI Codex assisted with implementation, review, test execution and preparation of this PR. This draft does not claim that a human has completed a line-by-line review.

Test Plan

Fresh isolated-source check, using the standalone loader below to avoid CUDA registry imports on macOS:

DSV41_SPLIT_REPO="$PWD" .venv/bin/python run_cpu_checks.py tests/v1/worker/test_ubatch_inputs.py

Result: 7 CPU tests passed; 2 CUDA cases skipped. All applicable pre-commit hooks and changed-file Python parsing passed. Earlier H100 integration separately ran the attention-metadata and actual UBatchWrapper thread/graph regressions. CUDA/full-model execution was not repeated after isolating this PR.

Test Result

Corrections implemented and checked

Case	Corrected behavior	Evidence
A request crosses a microbatch boundary	Rebuild history immediately before the microbatch's first actual token, including tokens already present in this batch	Boundary tests and independent reconstruction in real model workers
Attention metadata is split	Preserve the corresponding absolute positions slice	Attention-metadata regression suite
Final token boundary originates from NumPy	Convert the boundary to Python int before downstream indexing/kernel use	The two added type-regression cases failed before the fix and passed after it
Captured graph receives new request lengths/history	Refresh the captured lookback storage in place and clear padded rows	Actual UBatchWrapper graph replay tests and four-worker refresh probes
Two microbatches prepare Engram inputs	Keep synchronous staging storage separate and pass each prepared tensor to its decoder call	Per-microbatch input implementation exercised by the integrated wrapper path

Validation results

Validation	Result	Scope
Fresh lookback CPU tests	7 PASS; 2 CUDA cases skipped	This isolated PR, actual leaf-module code
Attention metadata and real UBatchWrapper thread/graph regressions	32 PASS	Earlier H100 integration after the positions and integer-boundary corrections
Independent lookback comparison in real model workers	4 workers × 182 checked slices; all matched	Covered a request crossing a microbatch boundary, decode and graph input refresh
Decode and captured-input refresh coverage	180 decode slices and 180 replay refreshes per worker	Earlier full-model diagnostic; establishes exercised paths, not full-model correctness
Applicable pre-commit hooks and changed Python parsing	PASS; 8 changed files	Fresh isolated PR source

The full-model probe used official deepseek-ai/DeepSeek-V4.1-Flash, revision df42c109f1defefcbfcedbe7d905718a12266e40, on 4×SXM H100 80 GiB. It ran the earlier combined integration based on #56214, including companion changes. These are input-correctness and serving diagnostics, not a standard model-accuracy benchmark or isolated-branch full-model proof. No throughput or GPU-memory saving is attributed to this fix.

Full-model integration limitation

The broader DeepEP LL diagnostic used DP2×TP2+EP+SP, CPU offload 32 GiB, fixed KV cache 1 GiB, NVSHMEM_QP_DEPTH=2048, graph sizes [2,4,8], and DBO decode/prefill thresholds 1/16.

Integration diagnostic	Short questions	Continuous generations	Result
Non-DBO control	6/6 expected answers	8/8 completed, no HTTP errors	PASS, including long/prefix and concurrent checks
DBO enabled	6/6 expected answers	5/8 completed; 3/8 HTTP 400	FAIL: runtime NaN; full-model support is not established

The error was Out of range float values are not JSON compliant: nan. A separate probe observed other microbatches overwriting a shared top-k buffer between source layer 2 and consumer layer 3 on all four workers. This PR does not repair that buffer, and its causal relationship to the NaN remains unproven. HT startup profiling also encountered illegal memory access. These failures remain unresolved; passing input/regression checks must not be read as full-model DBO support.

---

PR description checklist

• Purpose and related work described.
• Test plan and actual results stated.
• Model evaluation limitations stated.
• AI assistance disclosed.

Standalone CPU validation loader

Saved as run_cpu_checks.py; set DSV41_SPLIT_REPO to the checked-out branch and pass the test paths listed above. This avoids CUDA model-registry imports on macOS while executing the actual leaf-module implementation, tests and tensor/collective operations. It is not a CUDA or full-model test.

"""Run unchanged CPU test functions against split source without CUDA imports.

Preload only dependency-light leaf modules, as in the integration validation
driver. CUDA-only tests keep their own skip conditions. No tensor operations or
collectives are mocked by this loader.
"""
import importlib.util
import os
from pathlib import Path
import sys

import pytest

ROOT = Path(os.environ["DSV41_SPLIT_REPO"])
for name in (
    "vllm.models.deepseek_v4_1.common.pipeline",
    "vllm.models.deepseek_v4_1.common.pipeline_transfer",
    "vllm.v1.worker.ubatch_inputs",
):
    path = ROOT / (name.replace(".", "/") + ".py")
    if not path.is_file():
        continue
    spec = importlib.util.spec_from_file_location(name, path)
    module = importlib.util.module_from_spec(spec)
    sys.modules[name] = module
    spec.loader.exec_module(module)

if __name__ == "__main__":
    raise SystemExit(pytest.main(["-q", "--noconftest", "-o", "addopts=", *sys.argv[1:]]))

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[mergify]

Documentation preview: https://vllm--56224.org.readthedocs.build/en/56224/

[claude]

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[0z5a]

Validation follow-up for ee48bd5c5d1756eb8bb32a65f040a0d00beec30f: the current isolated-source run has 7 passing CPU history tests and 2 skipped CUDA cases. The recent rental session intentionally deferred this PR, so it adds no new GPU validation. Earlier integration input checks are useful, but the broader DBO NaN/HTTP failures and shared top-k-buffer observations remain unresolved and are not proof that this input fix caused or resolves them.

For this PR's narrow input-correctness scope, the useful contribution is current-head CUDA coverage of request-crossing microbatch histories, absolute positions, changing captured graph inputs, padding refresh, and independent per-microbatch staging through the actual UBatchWrapper. No throughput gain is required to justify a demonstrated input fix. If investigating full-model DBO, please keep any separate shared-buffer/lifetime fix isolated and provide a causal reproducer; full DBO support should not be claimed from the input tests alone.

Contributions from anyone with access to sufficient GPU capacity are very welcome: reproducible GPU E2E benchmarks, targeted correctness checks, and focused fixes. Please include source/model revisions, GPU topology, offload/KV settings, exact commands, and raw results; repeat timing runs when making performance claims. I am happy to review and cherry-pick fixes while preserving the original authorship and clear attribution.

[0z5a]

Resubmitted as #56440 against main, since dsv41-feat was merged into main in #56214.