Add MGP-STR (alibaba-damo/mgp-str-base) image-to-text task support

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Summary

Adds Effort-L1-light registration so MGP-STR scene-text-recognition models resolve under the user-facing image-to-text task label. The vendor MgpstrOnnxConfig (Optimum) already exposes the 3-head outputs (char_logits, bpe_logits, wp_logits) correctly, but is registered ONLY under feature-extraction. This PR adds a task-label alias + MODEL_CLASS_MAPPING binding to MgpstrForSceneTextRecognition (the head-bearing class — MGP-STR is NOT a generic Vision2Seq).

Files changed (5)

• src/winml/modelkit/models/hf/mgp_str.py (NEW, 58 lines) — MgpstrImage2TextOnnxConfig(MgpstrOnnxConfig) subclass
• src/winml/modelkit/models/hf/__init__.py — 3-line wiring
• examples/recipes/alibaba-damo_mgp-str-base/image-to-text_config.json (NEW, 49 lines) — recipe
• examples/recipes/README.md — catalog row
• research/adding-model-support/model_knowledge/mgp_str.json — mgp_str-004 post-mortem finding

Goal-ladder verdict

alibaba-damo/mgp-str-base @ image-to-text @ fp32 @ cpu

Tier	Verdict	Evidence
L0 build	PASS	83.7s, 374 nodes, 564.5 MB optimized; autoconf converged in 2 iters
L1 perf	PASS	avg=100.76ms, P90=123.26ms, 9.92 samples/sec (20 iters CPU)
L2 numerical	PASS	cosine vs PT: char=0.99999999999992, bpe=0.99999999999974, wp=0.99999999999860; max-abs 5.7e-05 / 2.4e-04 / 2.1e-04
L3 eval	CLI-BLOCKED	image-to-text task has no default dataset (same as vit-gpt2)

Step 1b verification — real engineering vs catalog-only

• Gate 1 (auto-config-diff): identical to winml config --task image-to-text (recipe is autoconf-faithful)
• Gate 2 (baseline build on main): FAILS with mgp-str doesn't support task image-to-text for the onnx backend. → real engineering delta, NOT catalog-only.

Known gotchas

• HF model card declares legacy architectures: ['MGPSTRModel'] but current transformers exports MgpstrModel (CamelCase rename). Without --task image-to-text explicit, winml inspect/config/build fail with Cannot import MGPSTRModel from transformers. CLI robustness gap separate from this PR.
• 3 Einsum ops in a3_module heads are non-fatal on CPU.

Verification

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/mgp_build --ep cpu --device cpu --rebuild
uv run winml perf -m temp/mgp_build/model.onnx --ep cpu --device cpu --iterations 20

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Reviewer verification: OV cpu / gpu / npu — branch \shzhen/add-mgp-str-base\

Commands

\\powershell

config

uv 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
uv run winml perf -m temp/verify_pr952_mgpstr_build/model.onnx --ep openvino --device gpu --iterations 5 --warmup 2 --skip-build -f json
uv run winml perf -m temp/verify_pr952_mgpstr_build/model.onnx --ep openvino --device npu --iterations 5 --warmup 2 --skip-build -f json

eval

uv run winml eval -m alibaba-damo/mgp-str-base --task image-to-text --device cpu --ep openvino --samples 1
\\

Results

Command	cpu	gpu	npu
config	✅ PASS	—	—
build	✅ PASS (79s, 564.5 MB, autoconf converged in 2 iters)	—	—
perf mean	✅ 305 ms/iter	✅ 9.1 ms/iter	✅ 22 ms/iter
perf throughput	3.27 samples/s	109.38 samples/s	45.48 samples/s
eval	❌ CLI-BLOCKED	❌ CLI-BLOCKED	❌ CLI-BLOCKED

Notes:

• \config\ / \�uild\ / \perf\ pass on all three OV devices. OV sessions created successfully for cpu, gpu, and npu.
• Build emits 3 \Einsum\ op warnings (\OpUnsupportedError: Node Einsum is not supported\ for \char_a3_module, \�pe_a3_module, \wp_a3_module) — consistent with the 'non-fatal on CPU' note in the PR. OV EP handles these via fallback.
• \�val\ returns \No dataset provided and no default for task 'image-to-text'. Use --dataset.\ — same CLI-BLOCKED verdict as described in the PR (same as vit-gpt2). Not an OV EP limitation.
• ONNX artifact: 374 nodes (post-optimize), opset 17, fp32, input: \pixel_values[1,3,32,128], outputs: \char_logits[1,27,38], \�pe_logits[1,27,50257], \wp_logits[1,27,30522].

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Validation results (2026-06-25) for PR #952 on this Windows ARM64 host.

Scope

• Compare main vs PR branch behavior
• Verify winml config on QNN NPU/GPU

Main branch baseline (before PR)

• Command: uv run winml config -m alibaba-damo/mgp-str-base --task image-to-text --ep cpu --device cpu
• Result: FAIL
• Error: mgp-str doesn't support task image-to-text for the onnx backend. Supported tasks are: feature-extraction.

PR #952 branch

• CPU config: PASS
  • uv run winml config -m alibaba-damo/mgp-str-base --task image-to-text --ep cpu --device cpu
  • Resolved to Device=CPU, EP=CPUExecutionProvider
• QNN NPU config: PASS
  • uv run winml config -m alibaba-damo/mgp-str-base --task image-to-text --ep qnn --device npu
  • Resolved to Device=NPU, EP=QNNExecutionProvider
• QNN GPU config: PASS
  • uv run winml config -m alibaba-damo/mgp-str-base --task image-to-text --ep qnn --device gpu
  • Resolved to Device=GPU, EP=QNNExecutionProvider

Conclusion

• Confirmed: this PR adds real image-to-text task support for mgp-str (main fails, PR passes), including QNN NPU/GPU configuration resolution.

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ADDENDUM: main branch baseline (NO support)

On current \main\ @ HEAD:
\\powershell
uv run winml config -m alibaba-damo/mgp-str-base --task image-to-text
\
Returns:
\
Error: mgp-str doesn't support task image-to-text for the onnx backend. Supported tasks are: feature-extraction.
\\

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.

[xieofxie]

the exported model are same as the current supported task?