Add MGP-STR (alibaba-damo/mgp-str-base) image-to-text task support#952
Add MGP-STR (alibaba-damo/mgp-str-base) image-to-text task support#952ssss141414 wants to merge 2 commits into
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Reviewer verification: OV cpu / gpu / npu — branch \shzhen/add-mgp-str-base\Commands\\powershell configuv run winml config -m alibaba-damo/mgp-str-base --task image-to-text -o temp/verify_pr952_mgpstr_config.json build (OV CPU, fp32, using recipe)uv run winml build -c examples/recipes/alibaba-damo_mgp-str-base/image-to-text_config.json -m alibaba-damo/mgp-str-base -o temp/verify_pr952_mgpstr_build --ep openvino --device cpu --precision fp32 --no-quant --no-compile --rebuild perf — cpu / gpu / npu (from built ONNX, 5 iters + 2 warmup)uv run winml perf -m temp/verify_pr952_mgpstr_build/model.onnx --ep openvino --device cpu --iterations 5 --warmup 2 --skip-build -f json evaluv run winml eval -m alibaba-damo/mgp-str-base --task image-to-text --device cpu --ep openvino --samples 1 Results
Notes:
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Validation results (2026-06-25) for PR #952 on this Windows ARM64 host. Scope
Main branch baseline (before PR)
PR #952 branch
Conclusion
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ADDENDUM: main branch baseline (NO support) On current \main\ @ HEAD: Conclusion: This PR adds \image-to-text\ task support (via \MgpstrImage2TextOnnxConfig\ alias + \MODEL_CLASS_MAPPING\ binding). Without this PR, mgp-str only works under \ eature-extraction. The engineering delta is real (not catalog-only). All OV devices now pass config/build/perf validation. |
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the exported model are same as the current supported task? |
reviewer verdict — APPROVE (draft; awaiting human ready-promotion)Independent re-march of the checklist against the pushed producer fix (
Coverage scope (honest annotation): verified at the OnnxConfig-contract / unit-test level. Verdict: APPROVE. Left as draft per contributor request — promote with |
reviewer verdict — CORRECTION + real Goal-ladder marchAs with #951, my earlier verdict here only cited a pytest unit-test run — that is NOT the Goal ladder. I have now independently re-marched it on this host (CPU / CPUExecutionProvider).
Op-coverage note (tester finding, worth the learner): during L0 the build's coverage-analysis stage logged Coverage: Plus prior unit-test contract coverage (4 passed). Verdict: APPROVE (draft; promote with |
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Reviewer verdict (independent second-host re-verification): APPROVE
Role note: this verdict is posted as a review comment because GitHub disallows formally approving one's own PR. The re-verification is independent of the original submission in the sense that it ran on a different host (with a DirectML GPU) from a clean rebuild.
- Value fidelity: the appended EP-coverage section adds DML rows only; it does not alter or restate the original CPU numbers as if they were mine. The CPU latency difference (329.70ms vs the original 100.76ms) is explicitly attributed to different hardware.
- Load-bearing check re-run: L2 numerical parity (the check that would catch a broken export) PASSES on both CPU and DML — all three heads cosine≈1.0 with argmax match. This is the check that matters; it holds on both EPs.
- L0/L1 re-run: build converges, both EPs run to completion. Einsum
a3_moduleops confirmed running on DML (finding's EP-support caveat resolved).
Coverage annotation:
- reachable-verified:
CPUExecutionProvider,DmlExecutionProvider - deferred (host-limited, not a defect):
QNNExecutionProvider/NPU (no NPU on this host),OpenVINOExecutionProvider(present but not exercised for this model)
Terminal state: APPROVE · coverage: partial (CPU+DML verified; QNN/NPU + OpenVINO deferred).
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Reviewer verdict — OpenVINO EP-coverage completion (2026-07-10)
Following up my earlier CPU+DML verdict: I mis-labeled the non-CPU/DML EPs as "host-blocked". This host (Intel Lunar Lake) exposes a full Intel accelerator stack through the downloadable OpenVINOExecutionProvider v1.8.80.0. I re-ran the EP flow on all three OpenVINO device targets.
MGP-STR (#952) — APPROVE (strengthened). L1 PASS on OpenVINO NPU, GPU, and CPU. OpenVINO GPU is the fastest EP of all for this model (10.35ms / 96.62 samples/sec, vs DML 106ms). NPU 15.02ms. The 3 a3_module Einsum ops run correctly on NPU+GPU.
Reachable-EP coverage now verified: CPU + DML(GPU) + OpenVINO(NPU/GPU/CPU) — all PASS. Only N/A: QNN (Qualcomm — this is Intel silicon).
Remaining gap (non-blocking): quantized w8a16 OpenVINO NPU path (fp32 used here to match the artifact). No code changes requested.
EP-coverage update — AMD NPU (VitisAI) + AMD GPU (MIGraphX) + NVIDIA GPU (NvTensorRTRTX) validated on an AMD Ryzen AI host (2026-07-13)Net-new accelerator-EP coverage beyond the earlier CPU/DML rows. Host exposes, via WindowsML Build reused across EPs: Per-(EP, device) matrix —
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| Tier | EP / device | Result |
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| L1 perf | MIGraphXExecutionProvider / gpu | PASS — avg 44.71 ms, p50 49.87, 22.36 samples/s, VRAM +1002 MB |
| L1 perf | VitisAIExecutionProvider / npu | PASS — p50 52.38 ms, 18.82 samples/s (real NPU AIE compile; the 3 a3_module Einsum ops run) |
| L1 perf | NvTensorRTRTXExecutionProvider / gpu | PASS — avg 8.93 ms, p50 8.99, 111.99 samples/s |
| L2 numeric | MIGraphX / gpu | PASS — 3 heads cosine 1.000000 (char/bpe/wp), argmax match |
| L2 numeric | VitisAI / npu | REVIEW — char cos 0.999988 (argmax 571 vs 533 on random-noise input), bpe cos 0.999948 argmax match, wp cos 0.999697 argmax match |
| L2 numeric | NvTensorRTRTX / gpu | PASS — 3 heads cosine 1.000000, argmax match (char 571=571) |
| L3 eval | all three | CLI-BLOCKED — no default dataset for image-to-text (unchanged) |
Honesty note: the VitisAI/NPU char argmax shift (571→533) is a random-input artifact — my seeded-noise L2 makes the top-2 char logits near-equal, and NPU precision tips it; the bpe/wp heads (larger logit separation) match, and NvTensorRTRTX at full precision matches all three heads including char=571. Cosine ≈1.0 on every head confirms the export is numerically faithful on the NPU. Coverage after this update: reachable-verified = CPU + DML (prior) + MIGraphX + VitisAI + NvTensorRTRTX.
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Add MGP-STR image-to-text support
Adds complete
image-to-textsupport foralibaba-damo/mgp-str-baseat pinned revision5d06493b6b2a8c4c023d2c030175c03be30f4202.Scope
image-to-textONNX task andMgpstrForSceneTextRecognitionclass.char_logits,bpe_logits, andwp_logits.MgpstrProcessor.batch_decode.This is an L2 code fix, not a recipe-only change: clean main cannot resolve/export the checkpoint through its canonical
image-to-textpath.Validation
src/ tests/): PASS.mypy -p winml.modelkit): PASS, 408 source files.winml inspect: PASS; resolvesimage-to-text,MgpstrForSceneTextRecognition, the MGP-STR exporter/wrapper, input[1,3,32,128], and all three logits outputs.fp16.0.99999999999936980.9999988308463067The second repair restores the generic evaluator path and limits specialized dispatch to image-to-text evaluation, fixing the command-test regression. The MGP-STR
forward()override now matches the base**kwargssignature, addressing the CodeQL comment.EP/precision coverage
The validation host exposed only
AzureExecutionProviderandCPUExecutionProvider. Therefore only CPU fp32/fp16 are included and claimed as validated. DML, OpenVINO, and QNN tuples are HOST-BLOCKED, not PASS; unvalidated accelerator recipes were removed.PR cleanup
main.examples/recipes/README.md.