diff --git a/README.md b/README.md index 07970a39d29bfc3f976358f0bb9083f2d9354063..50cf95d28e142a255378ef5047c54e72f4e7346d 100644 --- a/README.md +++ b/README.md @@ -123,6 +123,18 @@ Downloads views use the live Hub ordering, which can change after this snapshot. | Downloads | 80 | 6 | 12 | 2 | 100 | | Unique models | 119 | 15 | 18 | 12 | 164 | +All 33 failed checkpoints were rerun after access was granted to the two gated +repositories. Their outcome categories are unchanged, but the current errors and +measurements are from these fresh runs. `litert-community/embeddinggemma-300m` +contains TFLite exports rather than a native Transformers checkpoint and fails +model loading; `google/embeddinggemma-300m` already passes. `nvidia/NV-Embed-v1` +now reaches its required custom code (`trust_remote_code=True`), which imports +`MISTRAL_INPUTS_DOCSTRING`, unavailable in the tested Transformers 5.22.1 stack. +Neither current failure is an access error. The comparison is recorded in +`validation/sentence-transformers/2026-11-03-top100/failed-rerun-1.json`; +previous scripts, logs, results, and captured memory samples are preserved under +`prior_runs/failed-rerun-1/` in the same directory. + SentenceTransformer uses `encode_query`, `encode_document`, and `similarity`; CrossEncoder uses `predict` on query/document pairs; MultiVectorEncoder uses query/document encoding followed by native MaxSim. Saved retrieval prompts are @@ -144,7 +156,7 @@ CPU score agreement at the same precision (`atol=rtol=0.001`), and a stable repe with the same tolerance. `FAIL(output)` means execution completed but a ranking, parity, or repeat check failed. A ranking failure can also occur on CPU; it does not by itself indicate a Neuron defect. Loading and forward failures include -CPU-side incompatibilities, gated checkpoint access failures, and native-device +CPU-side incompatibilities, unsupported checkpoint formats, and native-device errors; the recorded stage and traceback distinguish them. Scores are in original document order and are not comparable between models. Results describe this configuration and example, not universal model support or retrieval quality. diff --git a/data/trn2/neuronxcc-2.28.4568.0+0163fa9e/sentence-transformers-6.3.0.dev0.parquet b/data/trn2/neuronxcc-2.28.4568.0+0163fa9e/sentence-transformers-6.3.0.dev0.parquet index 5f1c6c29446133ae0bcc2d64a272fc99b6a3d485..b4d787f306253eab74dbd991d21e6eaeb0aa1fad 100644 --- a/data/trn2/neuronxcc-2.28.4568.0+0163fa9e/sentence-transformers-6.3.0.dev0.parquet +++ b/data/trn2/neuronxcc-2.28.4568.0+0163fa9e/sentence-transformers-6.3.0.dev0.parquet @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:cc7ddaa661ba9c9d5f5ecd646afaf26a3fa6cbd9192b0fd8d33abc8e787dbd2c -size 165506 +oid sha256:cfa069ce6fb04d18c4758dae01829bc5bdf2ab122fbf0b50323004a4b9659931 +size 164325 diff --git a/validation/sentence-transformers/2026-11-03-top100/failed-rerun-1.json b/validation/sentence-transformers/2026-11-03-top100/failed-rerun-1.json new file mode 100644 index 0000000000000000000000000000000000000000..8f0237d3023bcc68543c47a3305453db2752951a --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/failed-rerun-1.json @@ -0,0 +1,407 @@ +{ + "count": 33, + "results": { + "FAIL(output)": 15, + "FAIL(load)": 9, + "FAIL(forward)": 9 + }, + "source_commit": "6be0abf38d00d70bb3c732131df59ad073ff3768", + "reruns": [ + { + "model": "nvidia/Nemotron-3.5-Rerank-8B-BF16", + "index": 7, + "previous_result": "FAIL(output)", + "result": "FAIL(output)", + "previous_started_at": "2026-11-03T15:28:39.934071+00:00", + "started_at": "2026-11-03T16:26:48.318127+00:00", + "previous_stage": null, + "stage": null, + "previous_error": null, + "error": null + }, + { + "model": "Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2", + "index": 21, + "previous_result": "FAIL(output)", + "result": "FAIL(output)", + "previous_started_at": "2026-11-03T15:31:28.531464+00:00", + "started_at": "2026-11-03T16:26:48.318264+00:00", + "previous_stage": null, + "stage": null, + "previous_error": null, + "error": null + }, + { + "model": "litert-community/embeddinggemma-300m", + "index": 23, + "previous_result": "FAIL(load)", + "result": "FAIL(load)", + "previous_started_at": "2026-11-03T15:31:58.747378+00:00", + "started_at": "2026-11-03T16:26:48.324742+00:00", + "previous_stage": "load_cpu", + "stage": "load_cpu", + "previous_error": "403 Client Error. (Request ID: Root=1-6ae9fe77-5b99b3f12170976f33af1862;24fb7211-ac04-4557-9d7c-d58c3b8e6212)\n\nCannot access gated repo for url https://huggingface.co/litert-community/embeddinggemma-300m/resolve/459f1d37fec9635eb1730ebdbc219bfc3226c8e7/modules.json.\nAccess to model litert-community/embeddinggemma-300m is restricted and you are not in the authorized list. Visit https://huggingface.co/litert-community/embeddinggemma-300m to ask for access.", + "error": "Unrecognized model in litert-community/embeddinggemma-300m. Should have a `model_type` key in its config.json." + }, + { + "model": "Qwen/Qwen3-VL-Embedding-8B", + "index": 24, + "previous_result": "FAIL(output)", + "result": "FAIL(output)", + "previous_started_at": "2026-11-03T15:32:09.263638+00:00", + "started_at": "2026-11-03T16:26:48.323954+00:00", + "previous_stage": null, + "stage": null, + "previous_error": null, + "error": null + }, + { + "model": "BrunoHays/embeddinggemma-2-nullspace-audio", + "index": 29, + "previous_result": "FAIL(load)", + "result": "FAIL(load)", + "previous_started_at": "2026-11-03T15:34:11.569070+00:00", + "started_at": "2026-11-03T16:27:22.235996+00:00", + "previous_stage": "load_cpu", + "stage": "load_cpu", + "previous_error": "Unrecognized model in BrunoHays/embeddinggemma-2-nullspace-audio. Should have a `model_type` key in its config.json.", + "error": "Unrecognized model in BrunoHays/embeddinggemma-2-nullspace-audio. Should have a `model_type` key in its config.json." + }, + { + "model": "BAAI/bge-reranker-v2-gemma", + "index": 31, + "previous_result": "FAIL(output)", + "result": "FAIL(output)", + "previous_started_at": "2026-11-03T15:34:37.563999+00:00", + "started_at": "2026-11-03T16:27:37.150732+00:00", + "previous_stage": null, + "stage": null, + "previous_error": null, + "error": null + }, + { + "model": "Snowflake/snowflake-arctic-embed-m-v2.0", + "index": 33, + "previous_result": "FAIL(forward)", + "result": "FAIL(forward)", + "previous_started_at": "2026-11-03T16:00:36.977122+00:00", + "started_at": "2026-11-03T16:27:40.804993+00:00", + "previous_stage": "cpu_inference", + "stage": "cpu_inference", + "previous_error": "index 140493294592752 is out of bounds for dimension 0 with size 14", + "error": "index 140685528539888 is out of bounds for dimension 0 with size 14" + }, + { + "model": "Qwen/Qwen3-Reranker-8B", + "index": 34, + "previous_result": "FAIL(output)", + "result": "FAIL(output)", + "previous_started_at": "2026-11-03T15:36:33.649395+00:00", + "started_at": "2026-11-03T16:27:41.821223+00:00", + "previous_stage": null, + "stage": null, + "previous_error": null, + "error": null + }, + { + "model": "HukukcuApp/tr-legal-embedding-gemma-2", + "index": 54, + "previous_result": "FAIL(load)", + "result": "FAIL(load)", + "previous_started_at": "2026-11-03T15:42:02.918547+00:00", + "started_at": "2026-11-03T16:27:42.275977+00:00", + "previous_stage": "load_cpu", + "stage": "load_cpu", + "previous_error": "Can't load feature extractor for 'HukukcuApp/tr-legal-embedding-gemma-2'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'HukukcuApp/tr-legal-embedding-gemma-2' is the correct path to a directory containing a preprocessor_config.json file", + "error": "Can't load feature extractor for 'HukukcuApp/tr-legal-embedding-gemma-2'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'HukukcuApp/tr-legal-embedding-gemma-2' is the correct path to a directory containing a preprocessor_config.json file" + }, + { + "model": "moka-ai/m3e-base", + "index": 67, + "previous_result": "FAIL(output)", + "result": "FAIL(output)", + "previous_started_at": "2026-11-03T15:45:11.803493+00:00", + "started_at": "2026-11-03T16:27:56.535897+00:00", + "previous_stage": null, + "stage": null, + "previous_error": null, + "error": null + }, + { + "model": "jinaai/jina-embeddings-v2-base-en", + "index": 70, + "previous_result": "FAIL(load)", + "result": "FAIL(load)", + "previous_started_at": "2026-11-03T15:45:55.385742+00:00", + "started_at": "2026-11-03T16:27:57.819913+00:00", + "previous_stage": "load_cpu", + "stage": "load_cpu", + "previous_error": "No module named 'transformers.onnx'", + "error": "No module named 'transformers.onnx'" + }, + { + "model": "Alibaba-NLP/gte-large-en-v1.5", + "index": 73, + "previous_result": "FAIL(forward)", + "result": "FAIL(forward)", + "previous_started_at": "2026-11-03T15:46:09.987170+00:00", + "started_at": "2026-11-03T16:28:05.050764+00:00", + "previous_stage": "cpu_inference", + "stage": "cpu_inference", + "previous_error": "index 239254133933015924 is out of bounds for dimension 0 with size 11", + "error": "index 755665224 is out of bounds for dimension 0 with size 11" + }, + { + "model": "nvidia/NV-Embed-v1", + "index": 75, + "previous_result": "FAIL(load)", + "result": "FAIL(load)", + "previous_started_at": "2026-11-03T15:46:17.899843+00:00", + "started_at": "2026-11-03T16:30:04.363338+00:00", + "previous_stage": "load_cpu", + "stage": "load_cpu", + "previous_error": "403 Client Error. (Request ID: Root=1-6aea01d2-3206be3e053d1684092fc8e7;1a302a3a-37c9-46e0-94a8-99f64b2e4802)\n\nCannot access gated repo for url https://huggingface.co/nvidia/NV-Embed-v1/resolve/2e8b322e2b738a383dae0dde5fa19e0b28a5bb11/modules.json.\nAccess to model nvidia/NV-Embed-v1 is restricted and you are not in the authorized list. Visit https://huggingface.co/nvidia/NV-Embed-v1 to ask for access.", + "error": "cannot import name 'MISTRAL_INPUTS_DOCSTRING' from 'transformers.models.mistral.modeling_mistral' (/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/mistral/modeling_mistral.py)" + }, + { + "model": "Jaume/gemma-2b-embeddings", + "index": 77, + "previous_result": "FAIL(output)", + "result": "FAIL(output)", + "previous_started_at": "2026-11-03T15:46:28.024988+00:00", + "started_at": "2026-11-03T16:28:11.198209+00:00", + "previous_stage": null, + "stage": null, + "previous_error": null, + "error": null + }, + { + "model": "Alibaba-NLP/gte-multilingual-reranker-base", + "index": 78, + "previous_result": "FAIL(forward)", + "result": "FAIL(forward)", + "previous_started_at": "2026-11-03T15:46:48.108932+00:00", + "started_at": "2026-11-03T16:28:16.434610+00:00", + "previous_stage": "cpu_inference", + "stage": "cpu_inference", + "previous_error": "index 139625091826798 is out of bounds for dimension 0 with size 32", + "error": "index 90194313471 is out of bounds for dimension 0 with size 32" + }, + { + "model": "akhooli/Arabic-SBERT-100K", + "index": 79, + "previous_result": "FAIL(output)", + "result": "FAIL(output)", + "previous_started_at": "2026-11-03T15:46:59.382279+00:00", + "started_at": "2026-11-03T16:28:19.317175+00:00", + "previous_stage": null, + "stage": null, + "previous_error": null, + "error": null + }, + { + "model": "jinaai/jina-embeddings-v3", + "index": 81, + "previous_result": "FAIL(load)", + "result": "FAIL(load)", + "previous_started_at": "2026-11-03T15:47:35.211638+00:00", + "started_at": "2026-11-03T16:28:19.331849+00:00", + "previous_stage": "load_cpu", + "stage": "load_cpu", + "previous_error": "'XLMRobertaLoRA' object has no attribute 'all_tied_weights_keys'", + "error": "'XLMRobertaLoRA' object has no attribute 'all_tied_weights_keys'" + }, + { + "model": "meandyou200175/phobert-finetune", + "index": 83, + "previous_result": "FAIL(output)", + "result": "FAIL(output)", + "previous_started_at": "2026-11-03T15:47:38.793972+00:00", + "started_at": "2026-11-03T16:28:28.113973+00:00", + "previous_stage": null, + "stage": null, + "previous_error": null, + "error": null + }, + { + "model": "vidore/colqwen2.5-v0.2", + "index": 89, + "previous_result": "FAIL(output)", + "result": "FAIL(output)", + "previous_started_at": "2026-11-03T15:49:04.100500+00:00", + "started_at": "2026-11-03T16:28:31.417399+00:00", + "previous_stage": null, + "stage": null, + "previous_error": null, + "error": null + }, + { + "model": "jinaai/jina-reranker-v2-base-multilingual", + "index": 108, + "previous_result": "FAIL(load)", + "result": "FAIL(load)", + "previous_started_at": "2026-11-03T15:52:03.168120+00:00", + "started_at": "2026-11-03T16:28:32.287840+00:00", + "previous_stage": "load_cpu", + "stage": "load_cpu", + "previous_error": "cannot import name 'create_position_ids_from_input_ids' from 'transformers.models.xlm_roberta.modeling_xlm_roberta' (/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/xlm_roberta/modeling_xlm_roberta.py)", + "error": "cannot import name 'create_position_ids_from_input_ids' from 'transformers.models.xlm_roberta.modeling_xlm_roberta' (/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/xlm_roberta/modeling_xlm_roberta.py)" + }, + { + "model": "sentence-transformers/multi-qa-mpnet-base-dot-v1", + "index": 112, + "previous_result": "FAIL(forward)", + "result": "FAIL(forward)", + "previous_started_at": "2026-11-03T15:52:37.157163+00:00", + "started_at": "2026-11-03T16:28:36.861567+00:00", + "previous_stage": "neuron_inference", + "stage": "neuron_inference", + "previous_error": "Inference tensors do not track version counter.", + "error": "Inference tensors do not track version counter." + }, + { + "model": "sentence-transformers/paraphrase-MiniLM-L6-v2", + "index": 113, + "previous_result": "FAIL(output)", + "result": "FAIL(output)", + "previous_started_at": "2026-11-03T15:52:29.729330+00:00", + "started_at": "2026-11-03T16:28:41.932312+00:00", + "previous_stage": null, + "stage": null, + "previous_error": null, + "error": null + }, + { + "model": "jinaai/jina-embeddings-v2-small-en", + "index": 114, + "previous_result": "FAIL(load)", + "result": "FAIL(load)", + "previous_started_at": "2026-11-03T15:52:45.768912+00:00", + "started_at": "2026-11-03T16:28:45.116156+00:00", + "previous_stage": "load_cpu", + "stage": "load_cpu", + "previous_error": "No module named 'transformers.onnx'", + "error": "No module named 'transformers.onnx'" + }, + { + "model": "jhgan/ko-sroberta-multitask", + "index": 116, + "previous_result": "FAIL(output)", + "result": "FAIL(output)", + "previous_started_at": "2026-11-03T15:52:52.178170+00:00", + "started_at": "2026-11-03T16:28:53.794159+00:00", + "previous_stage": null, + "stage": null, + "previous_error": null, + "error": null + }, + { + "model": "sentence-transformers-testing/stsb-bert-tiny-safetensors", + "index": 122, + "previous_result": "FAIL(output)", + "result": "FAIL(output)", + "previous_started_at": "2026-11-03T15:53:27.210620+00:00", + "started_at": "2026-11-03T16:28:54.750978+00:00", + "previous_stage": null, + "stage": null, + "previous_error": null, + "error": null + }, + { + "model": "Alibaba-NLP/gte-multilingual-base", + "index": 123, + "previous_result": "FAIL(forward)", + "result": "FAIL(forward)", + "previous_started_at": "2026-11-03T15:53:34.948327+00:00", + "started_at": "2026-11-03T16:28:57.681382+00:00", + "previous_stage": "cpu_inference", + "stage": "cpu_inference", + "previous_error": "index 670466312 is out of bounds for dimension 0 with size 11", + "error": "index 1193 is out of bounds for dimension 0 with size 11" + }, + { + "model": "nomic-ai/nomic-embed-text-v2-moe", + "index": 129, + "previous_result": "FAIL(forward)", + "result": "FAIL(forward)", + "previous_started_at": "2026-11-03T15:54:25.528812+00:00", + "started_at": "2026-11-03T16:28:59.211576+00:00", + "previous_stage": "cpu_inference", + "stage": "cpu_inference", + "previous_error": "'NomicBertModel' object has no attribute 'get_extended_attention_mask'", + "error": "'NomicBertModel' object has no attribute 'get_extended_attention_mask'" + }, + { + "model": "sentence-transformers/msmarco-bert-base-dot-v5", + "index": 137, + "previous_result": "FAIL(forward)", + "result": "FAIL(forward)", + "previous_started_at": "2026-11-03T15:55:09.237654+00:00", + "started_at": "2026-11-03T16:29:10.989662+00:00", + "previous_stage": "neuron_inference", + "stage": "neuron_inference", + "previous_error": "Inference tensors do not track version counter.", + "error": "Inference tensors do not track version counter." + }, + { + "model": "Alibaba-NLP/gte-base-en-v1.5", + "index": 140, + "previous_result": "FAIL(forward)", + "result": "FAIL(forward)", + "previous_started_at": "2026-11-03T15:55:35.076933+00:00", + "started_at": "2026-11-03T16:29:12.754293+00:00", + "previous_stage": "cpu_inference", + "stage": "cpu_inference", + "previous_error": "index 140471200382993 is out of bounds for dimension 0 with size 11", + "error": "index 23048696 is out of bounds for dimension 0 with size 11" + }, + { + "model": "minishlab/potion-base-8M", + "index": 144, + "previous_result": "FAIL(output)", + "result": "FAIL(output)", + "previous_started_at": "2026-11-03T15:56:13.809543+00:00", + "started_at": "2026-11-03T16:29:13.720726+00:00", + "previous_stage": null, + "stage": null, + "previous_error": null, + "error": null + }, + { + "model": "TimKond/S-PubMedBert-MedQuAD", + "index": 146, + "previous_result": "FAIL(output)", + "result": "FAIL(output)", + "previous_started_at": "2026-11-03T15:56:33.415911+00:00", + "started_at": "2026-11-03T16:29:15.091834+00:00", + "previous_stage": null, + "stage": null, + "previous_error": null, + "error": null + }, + { + "model": "nvidia/NV-Embed-v2", + "index": 158, + "previous_result": "FAIL(load)", + "result": "FAIL(load)", + "previous_started_at": "2026-11-03T15:58:12.973659+00:00", + "started_at": "2026-11-03T16:29:22.582373+00:00", + "previous_stage": "load_cpu", + "stage": "load_cpu", + "previous_error": "'NVEmbedModel' object has no attribute 'all_tied_weights_keys'", + "error": "'NVEmbedModel' object has no attribute 'all_tied_weights_keys'" + }, + { + "model": "sentence-transformers/multi-qa-MiniLM-L6-dot-v1", + "index": 164, + "previous_result": "FAIL(forward)", + "result": "FAIL(forward)", + "previous_started_at": "2026-11-03T15:59:45.659267+00:00", + "started_at": "2026-11-03T16:29:29.177391+00:00", + "previous_stage": "neuron_inference", + "stage": "neuron_inference", + "previous_error": "Inference tensors do not track version counter.", + "error": "Inference tensors do not track version counter." + } + ] +} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/007.log b/validation/sentence-transformers/2026-11-03-top100/logs/007.log index 030125b4ab04ae6028ee9fa1f4ba9c25d5036032..7804439a872a8ef08411962ced1fd6ed8702163c 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/007.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/007.log @@ -7,7 +7,7 @@ language_model.score.weight | UNEXPECTED | | Notes: - UNEXPECTED: can be ignored when loading from different task/architecture; not ok if you expect identical arch. CPU [-2.699960470199585, -1.0288699865341187, -2.2972545623779297, -2.0584821701049805] -2026-Nov-03 15:28:51.541569 2133586:2133586 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +2026-Nov-03 16:27:23.181404 2938473:2938473 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) /home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. _warn_trace_cache_once() -{"index": 7, "model": "nvidia/Nemotron-3.5-Rerank-8B-BF16", "revision": "f88f8d9104726747791283ab156d2fcdb29e90a7", "trending_rank": 7, "downloads_rank": null, "class": "CrossEncoder", "dtype": "bfloat16", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:28:39.934071+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 1.2689191922545433, "prompts": {}, "parameter_dtypes": ["torch.bfloat16"], "cpu_scores": [-2.699960470199585, -1.0288699865341187, -2.2972545623779297, -2.0584821701049805], "cpu_seconds": 1.485507046803832, "cpu_top_document": 2, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 0.4639179026708007, "neuron_scores": [-2.700716495513916, -1.0203332901000977, -2.303680419921875, -2.0575268268585205], "max_abs_error": 0.008536696434020996, "neuron_top_document": 2, "mars_first": true, "cpu_parity": false, "neuron_repeat_seconds": 0.16087585594505072, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 23, "sample_interval_seconds": 0.1, "peak_bytes": 16937250251, "peak_categories": {"code": 8118208, "tensors": 16848523264, "constants": 1605643, "scratchpad": 67108864, "misc": 10510848, "dma_rings": 1383424}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 16791547648, "allocator_peak_reserved_bytes": 16848519168, "total_seconds": 20.877234225161374} +{"index": 7, "model": "nvidia/Nemotron-3.5-Rerank-8B-BF16", "revision": "f88f8d9104726747791283ab156d2fcdb29e90a7", "trending_rank": 7, "downloads_rank": null, "class": "CrossEncoder", "dtype": "bfloat16", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:26:48.318127+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 1.3913734536617994, "prompts": {}, "parameter_dtypes": ["torch.bfloat16"], "cpu_scores": [-2.699960470199585, -1.0288699865341187, -2.2972545623779297, -2.0584821701049805], "cpu_seconds": 1.4555524634197354, "cpu_top_document": 2, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 0.46388536784797907, "neuron_scores": [-2.700716495513916, -1.0203332901000977, -2.303680419921875, -2.0575268268585205], "max_abs_error": 0.008536696434020996, "neuron_top_document": 2, "mars_first": true, "cpu_parity": false, "neuron_repeat_seconds": 0.15859147626906633, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 31, "sample_interval_seconds": 0.1, "peak_bytes": 16937250251, "peak_categories": {"code": 8118208, "tensors": 16848523264, "constants": 1605643, "scratchpad": 67108864, "misc": 10510848, "dma_rings": 1383424}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 16792596224, "allocator_peak_reserved_bytes": 16848519168, "total_seconds": 50.44789768289775} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/021.log b/validation/sentence-transformers/2026-11-03-top100/logs/021.log index 36e4619e414f0afc2f96078ecc006b345e6f4ac6..9be7da006cc24e8e6935bb8f4cafcf2e846d9249 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/021.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/021.log @@ -1,5 +1,5 @@ CPU [0.4586111605167389, 0.6143702864646912, 0.5348622798919678, 0.615755021572113] -2026-Nov-03 15:31:46.722403 2188264:2188264 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +2026-Nov-03 16:27:20.904398 2938474:2938474 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) /home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. _warn_trace_cache_once() -{"index": 21, "model": "Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2", "revision": "17b87422aa4bfb3b478369299e701070b308314f", "trending_rank": 21, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:31:28.531464+00:00", "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 6.291727272793651, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.4586111605167389, 0.6143702864646912, 0.5348622798919678, 0.615755021572113], "cpu_seconds": 0.3750715795904398, "cpu_top_document": 4, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 105.76572554465383, "neuron_scores": [0.4586114287376404, 0.6143702268600464, 0.534862220287323, 0.6157552003860474], "max_abs_error": 2.682209014892578e-07, "neuron_top_document": 4, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.09435997158288956, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 1059, "sample_interval_seconds": 0.1, "peak_bytes": 695672385, "peak_categories": {"code": 6654512, "tensors": 606081024, "constants": 3473425, "scratchpad": 67108864, "misc": 9832960, "dma_rings": 2521600}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 588299648, "allocator_peak_reserved_bytes": 606076928, "total_seconds": 131.14824269991368} +{"index": 21, "model": "Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2", "revision": "17b87422aa4bfb3b478369299e701070b308314f", "trending_rank": 21, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:26:48.318264+00:00", "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 0.4274421576410532, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.4586111605167389, 0.6143702864646912, 0.5348622798919678, 0.615755021572113], "cpu_seconds": 0.1396558079868555, "cpu_top_document": 4, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 0.4122119527310133, "neuron_scores": [0.4586114287376404, 0.6143702268600464, 0.534862220287323, 0.6157552003860474], "max_abs_error": 2.682209014892578e-07, "neuron_top_document": 4, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.04447338543832302, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 7, "sample_interval_seconds": 0.1, "peak_bytes": 695674433, "peak_categories": {"code": 6654512, "tensors": 606081024, "constants": 3473425, "scratchpad": 67108864, "misc": 9832960, "dma_rings": 2523648}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 587197440, "allocator_peak_reserved_bytes": 606076928, "total_seconds": 47.0560898212716} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/023.log b/validation/sentence-transformers/2026-11-03-top100/logs/023.log index 83b696da3fc476f73844080c577fb8bfd892f90e..718e33033f3413cf245ef2a7f4e8df7a38ac0fd0 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/023.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/023.log @@ -1,11 +1,6 @@ -httpx.HTTPStatusError: Client error '403 Forbidden' for url 'https://huggingface.co/litert-community/embeddinggemma-300m/resolve/459f1d37fec9635eb1730ebdbc219bfc3226c8e7/modules.json' -For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/403 - -The above exception was the direct cause of the following exception: - Traceback (most recent call last): - File "/tmp/neuron-top100-sweep/runs/023/run.py", line 50, in - model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + File "/tmp/neuron-top100-rerun/runs/023/run.py", line 51, in + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})}) File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper return func(*args, **kwargs) File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 193, in __init__ @@ -28,35 +23,26 @@ Traceback (most recent call last): ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ) ^ - File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 1190, in _load_modules - modules_json_path = load_file_path( + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 1200, in _load_modules + return self._load_default_modules(model_name_or_path, **load_kwargs) + ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 1272, in _load_default_modules + transformer_model = Transformer( model_name_or_path, ...<4 lines>... - local_files_only=local_files_only, + backend=self.backend, ) - File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/file_io.py", line 208, in load_file_path - return hf_hub_download( - model_name_or_path, - ...<6 lines>... - local_files_only=local_files_only, - ) - File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_validators.py", line 89, in _inner_fn - return fn(*args, **kwargs) - File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_http.py", line 266, in _inner - return fn(*args, **kwargs) - File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py", line 1047, in hf_hub_download - return _hf_hub_download_to_cache_dir( - # Destination - ...<15 lines>... - dry_run=dry_run, + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 92, in wrapper + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py", line 839, in __init__ + config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs) + ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py", line 2259, in _load_config + return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False + ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py", line 450, in from_pretrained + raise ValueError( + ...<2 lines>... ) - File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py", line 1194, in _hf_hub_download_to_cache_dir - _raise_on_head_call_error(head_call_error, force_download, local_files_only) - ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py", line 1953, in _raise_on_head_call_error - raise head_call_error -huggingface_hub.errors.GatedRepoError: 403 Client Error. (Request ID: Root=1-6ae9fe77-5b99b3f12170976f33af1862;24fb7211-ac04-4557-9d7c-d58c3b8e6212) - -Cannot access gated repo for url https://huggingface.co/litert-community/embeddinggemma-300m/resolve/459f1d37fec9635eb1730ebdbc219bfc3226c8e7/modules.json. -Access to model litert-community/embeddinggemma-300m is restricted and you are not in the authorized list. Visit https://huggingface.co/litert-community/embeddinggemma-300m to ask for access. -{"index": 23, "model": "litert-community/embeddinggemma-300m", "revision": "459f1d37fec9635eb1730ebdbc219bfc3226c8e7", "trending_rank": 23, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:31:58.747378+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "status": "error", "stage": "load_cpu", "error_type": "GatedRepoError", "error": "403 Client Error. (Request ID: Root=1-6ae9fe77-5b99b3f12170976f33af1862;24fb7211-ac04-4557-9d7c-d58c3b8e6212)\n\nCannot access gated repo for url https://huggingface.co/litert-community/embeddinggemma-300m/resolve/459f1d37fec9635eb1730ebdbc219bfc3226c8e7/modules.json.\nAccess to model litert-community/embeddinggemma-300m is restricted and you are not in the authorized list. Visit https://huggingface.co/litert-community/embeddinggemma-300m to ask for access.", "traceback": "httpx.HTTPStatusError: Client error '403 Forbidden' for url 'https://huggingface.co/litert-community/embeddinggemma-300m/resolve/459f1d37fec9635eb1730ebdbc219bfc3226c8e7/modules.json'\nFor more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/403\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/023/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1190, in _load_modules\n modules_json_path = load_file_path(\n model_name_or_path,\n ...<4 lines>...\n local_files_only=local_files_only,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/file_io.py\", line 208, in load_file_path\n return hf_hub_download(\n model_name_or_path,\n ...<6 lines>...\n local_files_only=local_files_only,\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_validators.py\", line 89, in _inner_fn\n return fn(*args, **kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_http.py\", line 266, in _inner\n return fn(*args, **kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1047, in hf_hub_download\n return _hf_hub_download_to_cache_dir(\n # Destination\n ...<15 lines>...\n dry_run=dry_run,\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1194, in _hf_hub_download_to_cache_dir\n _raise_on_head_call_error(head_call_error, force_download, local_files_only)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1953, in _raise_on_head_call_error\n raise head_call_error\nhuggingface_hub.errors.GatedRepoError: 403 Client Error. (Request ID: Root=1-6ae9fe77-5b99b3f12170976f33af1862;24fb7211-ac04-4557-9d7c-d58c3b8e6212)\n\nCannot access gated repo for url https://huggingface.co/litert-community/embeddinggemma-300m/resolve/459f1d37fec9635eb1730ebdbc219bfc3226c8e7/modules.json.\nAccess to model litert-community/embeddinggemma-300m is restricted and you are not in the authorized list. Visit https://huggingface.co/litert-community/embeddinggemma-300m to ask for access.\n", "result": "FAIL(load)", "total_seconds": 8.972315741702914} +ValueError: Unrecognized model in litert-community/embeddinggemma-300m. Should have a `model_type` key in its config.json. +{"index": 23, "model": "litert-community/embeddinggemma-300m", "revision": "459f1d37fec9635eb1730ebdbc219bfc3226c8e7", "trending_rank": 23, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:26:48.324742+00:00", "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "status": "error", "stage": "load_cpu", "error_type": "ValueError", "error": "Unrecognized model in litert-community/embeddinggemma-300m. Should have a `model_type` key in its config.json.", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/023/run.py\", line 51, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1200, in _load_modules\n return self._load_default_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1272, in _load_default_modules\n transformer_model = Transformer(\n model_name_or_path,\n ...<4 lines>...\n backend=self.backend,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 839, in __init__\n config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2259, in _load_config\n return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py\", line 450, in from_pretrained\n raise ValueError(\n ...<2 lines>...\n )\nValueError: Unrecognized model in litert-community/embeddinggemma-300m. Should have a `model_type` key in its config.json.\n", "result": "FAIL(load)", "total_seconds": 32.182727108709514} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/024.log b/validation/sentence-transformers/2026-11-03-top100/logs/024.log index 1a3528235119fb28c98c8b1240674d233cb72486..81f918ca8e9de9450936c01302a4b8edad3369ee 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/024.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/024.log @@ -1,6 +1,6 @@ Default prompt name is set to 'default'. This prompt applies unless overridden by `prompt`, `prompt_name`, or a method-specific prompt, including an empty prompt. CPU [0.5104122161865234, 0.8333847522735596, 0.5737250447273254, 0.6311128735542297] -2026-Nov-03 15:33:26.885580 2200020:2200020 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +2026-Nov-03 16:27:23.382160 2938476:2938476 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) /home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. _warn_trace_cache_once() -{"index": 24, "model": "Qwen/Qwen3-VL-Embedding-8B", "revision": "2c4565515e0f265c6511776e7193b22c0968ddc7", "trending_rank": 24, "downloads_rank": 54, "class": "SentenceTransformer", "dtype": "bfloat16", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:32:09.263638+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 45.57155937794596, "prompts": {"query": "", "document": "", "default": "Represent the user's input."}, "parameter_dtypes": ["torch.bfloat16"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.5104122161865234, 0.8333847522735596, 0.5737250447273254, 0.6311128735542297], "cpu_seconds": 11.08764569554478, "cpu_top_document": 2, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 30.12761875614524, "neuron_scores": [0.5109585523605347, 0.8327626585960388, 0.5716332197189331, 0.6295062899589539], "max_abs_error": 0.002091825008392334, "neuron_top_document": 2, "mars_first": true, "cpu_parity": false, "neuron_repeat_seconds": 0.4174205930903554, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 330, "sample_interval_seconds": 0.1, "peak_bytes": 16529538997, "peak_categories": {"code": 14566248, "tensors": 16414412800, "constants": 4571149, "scratchpad": 67108864, "misc": 24082496, "dma_rings": 4797440}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 16390182848, "allocator_peak_reserved_bytes": 16414408704, "total_seconds": 119.78512970823795} +{"index": 24, "model": "Qwen/Qwen3-VL-Embedding-8B", "revision": "2c4565515e0f265c6511776e7193b22c0968ddc7", "trending_rank": 24, "downloads_rank": 54, "class": "SentenceTransformer", "dtype": "bfloat16", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:26:48.323954+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 0.8875982435420156, "prompts": {"query": "", "document": "", "default": "Represent the user's input."}, "parameter_dtypes": ["torch.bfloat16"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.5104122161865234, 0.8333847522735596, 0.5737250447273254, 0.6311128735542297], "cpu_seconds": 2.1594849564135075, "cpu_top_document": 2, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 1.226473362185061, "neuron_scores": [0.5109585523605347, 0.8327626585960388, 0.5716332197189331, 0.6295062899589539], "max_abs_error": 0.002091825008392334, "neuron_top_document": 2, "mars_first": true, "cpu_parity": false, "neuron_repeat_seconds": 0.4109700918197632, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 40, "sample_interval_seconds": 0.1, "peak_bytes": 16529538997, "peak_categories": {"code": 14566248, "tensors": 16414412800, "constants": 4571149, "scratchpad": 67108864, "misc": 24082496, "dma_rings": 4797440}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 16383648960, "allocator_peak_reserved_bytes": 16414408704, "total_seconds": 51.54317019227892} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/029.log b/validation/sentence-transformers/2026-11-03-top100/logs/029.log index 74c62274c98747dad18003d6f134789fcd99888c..2f48451337c05eea5e8d0432e7e0a9e238acb593 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/029.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/029.log @@ -1,6 +1,6 @@ Traceback (most recent call last): - File "/tmp/neuron-top100-sweep/runs/029/run.py", line 50, in - model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + File "/tmp/neuron-top100-rerun/runs/029/run.py", line 51, in + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})}) File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper return func(*args, **kwargs) File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 193, in __init__ @@ -45,4 +45,4 @@ Traceback (most recent call last): ...<2 lines>... ) ValueError: Unrecognized model in BrunoHays/embeddinggemma-2-nullspace-audio. Should have a `model_type` key in its config.json. -{"index": 29, "model": "BrunoHays/embeddinggemma-2-nullspace-audio", "revision": "4e54c6816e4ba368be62885788974458f2748adf", "trending_rank": 29, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:34:11.569070+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "status": "error", "stage": "load_cpu", "error_type": "ValueError", "error": "Unrecognized model in BrunoHays/embeddinggemma-2-nullspace-audio. Should have a `model_type` key in its config.json.", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/029/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1200, in _load_modules\n return self._load_default_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1272, in _load_default_modules\n transformer_model = Transformer(\n model_name_or_path,\n ...<4 lines>...\n backend=self.backend,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 839, in __init__\n config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2259, in _load_config\n return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py\", line 450, in from_pretrained\n raise ValueError(\n ...<2 lines>...\n )\nValueError: Unrecognized model in BrunoHays/embeddinggemma-2-nullspace-audio. Should have a `model_type` key in its config.json.\n", "result": "FAIL(load)", "total_seconds": 24.265914521180093} +{"index": 29, "model": "BrunoHays/embeddinggemma-2-nullspace-audio", "revision": "4e54c6816e4ba368be62885788974458f2748adf", "trending_rank": 29, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:27:22.235996+00:00", "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "status": "error", "stage": "load_cpu", "error_type": "ValueError", "error": "Unrecognized model in BrunoHays/embeddinggemma-2-nullspace-audio. Should have a `model_type` key in its config.json.", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/029/run.py\", line 51, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1200, in _load_modules\n return self._load_default_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1272, in _load_default_modules\n transformer_model = Transformer(\n model_name_or_path,\n ...<4 lines>...\n backend=self.backend,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 839, in __init__\n config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2259, in _load_config\n return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py\", line 450, in from_pretrained\n raise ValueError(\n ...<2 lines>...\n )\nValueError: Unrecognized model in BrunoHays/embeddinggemma-2-nullspace-audio. Should have a `model_type` key in its config.json.\n", "result": "FAIL(load)", "total_seconds": 18.733008542098105} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/031.log b/validation/sentence-transformers/2026-11-03-top100/logs/031.log index 8e9dc82b0f9f6486beb3b197bde960f6055cc9ee..5f17651506b9b31f36d3b6a77316a857290a0af2 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/031.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/031.log @@ -1,6 +1,6 @@ [transformers] We found `hidden_act="gelu"` in this Gemma config. This is a legacy value of the official releases but it is meant to target the tanh approximation. Setting `hidden_act="gelu_pytorch_tanh"` instead. CPU [0.9997572302818298, 0.9997515082359314, 0.9998114705085754, 0.9997087121009827] -2026-Nov-03 15:35:46.446477 2247012:2247012 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +2026-Nov-03 16:27:53.346466 2939929:2939929 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) /home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. _warn_trace_cache_once() -{"index": 31, "model": "BAAI/bge-reranker-v2-gemma", "revision": "1787044f8b6fb740a9de4557c3a12377f84d9e17", "trending_rank": 31, "downloads_rank": null, "class": "CrossEncoder", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:34:37.563999+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 6.2028824258595705, "prompts": {}, "parameter_dtypes": ["torch.float32"], "cpu_scores": [0.9997572302818298, 0.9997515082359314, 0.9998114705085754, 0.9997087121009827], "cpu_seconds": 32.531744067557156, "cpu_top_document": 3, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 45.031333486549556, "neuron_scores": [0.9997572898864746, 0.9997514486312866, 0.9998114109039307, 0.9997087717056274], "max_abs_error": 5.960464477539063e-08, "neuron_top_document": 3, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.09113490767776966, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 460, "sample_interval_seconds": 0.1, "peak_bytes": 10210461645, "peak_categories": {"code": 20083904, "tensors": 10112471040, "constants": 1474573, "scratchpad": 67108864, "misc": 8413952, "dma_rings": 909312}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 10061462720, "allocator_peak_reserved_bytes": 10112466944, "total_seconds": 123.40695661306381} +{"index": 31, "model": "BAAI/bge-reranker-v2-gemma", "revision": "1787044f8b6fb740a9de4557c3a12377f84d9e17", "trending_rank": 31, "downloads_rank": null, "class": "CrossEncoder", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:27:37.150732+00:00", "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 2.0308772353455424, "prompts": {}, "parameter_dtypes": ["torch.float32"], "cpu_scores": [0.9997572302818298, 0.9997515082359314, 0.9998114705085754, 0.9997087121009827], "cpu_seconds": 2.098253570497036, "cpu_top_document": 3, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 0.4742284342646599, "neuron_scores": [0.9997572898864746, 0.9997514486312866, 0.9998114109039307, 0.9997087717056274], "max_abs_error": 5.960464477539063e-08, "neuron_top_document": 3, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.08166952524334192, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 15, "sample_interval_seconds": 0.1, "peak_bytes": 10210461645, "peak_categories": {"code": 20083904, "tensors": 10112471040, "constants": 1474573, "scratchpad": 67108864, "misc": 8413952, "dma_rings": 909312}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 10061462912, "allocator_peak_reserved_bytes": 10112466944, "total_seconds": 26.05086704529822} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/033.log b/validation/sentence-transformers/2026-11-03-top100/logs/033.log index e09b9acebfc91ca08ec8a20d9a210536c7ad9a6b..393a6ac0bed2a34c1e4b563219401e4a2490f070 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/033.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/033.log @@ -1,10 +1,10 @@ /tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/configuration_utils.py:723: FutureWarning: `rope_scaling` is deprecated and will be removed in a future version. Set `rope_parameters` instead, with `rope_type` and `rope_theta` included. warnings.warn( Traceback (most recent call last): - File "/tmp/neuron-top100-sweep/runs/033/run.py", line 64, in + File "/tmp/neuron-top100-rerun/runs/033/run.py", line 64, in stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) ~~~~~^^ - File "/tmp/neuron-top100-sweep/runs/033/run.py", line 59, in score + File "/tmp/neuron-top100-rerun/runs/033/run.py", line 59, in score q=(model.encode([query],batch_size=4,show_progress_bar=False,**kw,**SPEC['query_kwargs']) if SPEC.get('query_kwargs') else model.encode_query([query],batch_size=4,show_progress_bar=False,**kw)) ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper @@ -61,5 +61,5 @@ Traceback (most recent call last): File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Snowflake/snowflake_hyphen_arctic_hyphen_embed_hyphen_m_hyphen_v2_dot_0/95c2741480856aa9666782eb4afe11959938017f/modeling_hf_alibaba_nlp_gte.py", line 391, in forward rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim] ~~~~~~~~^^^^^^^^^^^^^^ -IndexError: index 140493294592752 is out of bounds for dimension 0 with size 14 -{"index": 33, "model": "Snowflake/snowflake-arctic-embed-m-v2.0", "revision": "95c2741480856aa9666782eb4afe11959938017f", "trending_rank": 33, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {"use_memory_efficient_attention": false, "unpad_inputs": false}, "excluded": null, "exclusion": null, "reason": "Checkpoint custom code supports standard SDPA when xformers attention and input unpadding are disabled.", "started_at": "2026-11-03T16:00:36.977122+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 3.8345429748296738, "prompts": {"query": "query: ", "document": ""}, "parameter_dtypes": ["torch.float32"], "status": "error", "stage": "cpu_inference", "error_type": "IndexError", "error": "index 140493294592752 is out of bounds for dimension 0 with size 14", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/033/run.py\", line 64, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/033/run.py\", line 59, in score\n q=(model.encode([query],batch_size=4,show_progress_bar=False,**kw,**SPEC['query_kwargs']) if SPEC.get('query_kwargs') else model.encode_query([query],batch_size=4,show_progress_bar=False,**kw))\n ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Snowflake/snowflake_hyphen_arctic_hyphen_embed_hyphen_m_hyphen_v2_dot_0/95c2741480856aa9666782eb4afe11959938017f/modeling_hf_alibaba_nlp_gte.py\", line 900, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Snowflake/snowflake_hyphen_arctic_hyphen_embed_hyphen_m_hyphen_v2_dot_0/95c2741480856aa9666782eb4afe11959938017f/modeling_hf_alibaba_nlp_gte.py\", line 391, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 140493294592752 is out of bounds for dimension 0 with size 14\n", "result": "FAIL(forward)", "total_seconds": 12.881709131412208} +IndexError: index 140685528539888 is out of bounds for dimension 0 with size 14 +{"index": 33, "model": "Snowflake/snowflake-arctic-embed-m-v2.0", "revision": "95c2741480856aa9666782eb4afe11959938017f", "trending_rank": 33, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {"use_memory_efficient_attention": false, "unpad_inputs": false}, "excluded": null, "exclusion": null, "reason": "Checkpoint custom code supports standard SDPA when xformers attention and input unpadding are disabled.", "started_at": "2026-11-03T16:27:40.804993+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 1.9271560944616795, "prompts": {"query": "query: ", "document": ""}, "parameter_dtypes": ["torch.float32"], "status": "error", "stage": "cpu_inference", "error_type": "IndexError", "error": "index 140685528539888 is out of bounds for dimension 0 with size 14", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/033/run.py\", line 64, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-rerun/runs/033/run.py\", line 59, in score\n q=(model.encode([query],batch_size=4,show_progress_bar=False,**kw,**SPEC['query_kwargs']) if SPEC.get('query_kwargs') else model.encode_query([query],batch_size=4,show_progress_bar=False,**kw))\n ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Snowflake/snowflake_hyphen_arctic_hyphen_embed_hyphen_m_hyphen_v2_dot_0/95c2741480856aa9666782eb4afe11959938017f/modeling_hf_alibaba_nlp_gte.py\", line 900, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Snowflake/snowflake_hyphen_arctic_hyphen_embed_hyphen_m_hyphen_v2_dot_0/95c2741480856aa9666782eb4afe11959938017f/modeling_hf_alibaba_nlp_gte.py\", line 391, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 140685528539888 is out of bounds for dimension 0 with size 14\n", "result": "FAIL(forward)", "total_seconds": 14.150352603755891} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/034.log b/validation/sentence-transformers/2026-11-03-top100/logs/034.log index e73d12c57102f2a7996c649e7acb304e1425d2c8..ce1043785402e0b99b27e43ab3d8127ce4ca18e0 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/034.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/034.log @@ -1,6 +1,6 @@ Default prompt name is set to 'query'. This prompt applies unless overridden by `prompt`, `prompt_name`, or a method-specific prompt, including an empty prompt. CPU [-8.125, 4.0625, -5.34375, -4.5625] -2026-Nov-03 15:37:25.459653 2272047:2272047 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +2026-Nov-03 16:27:58.358244 2940842:2940842 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) /home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. _warn_trace_cache_once() -{"index": 34, "model": "Qwen/Qwen3-Reranker-8B", "revision": "77d193c791ed757ca307ee72715aa132723da912", "trending_rank": 34, "downloads_rank": null, "class": "CrossEncoder", "dtype": "bfloat16", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:36:33.649395+00:00", "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 8.139716992154717, "prompts": {"query": "Given a web search query, retrieve relevant passages that answer the query"}, "parameter_dtypes": ["torch.bfloat16"], "cpu_scores": [-8.125, 4.0625, -5.34375, -4.5625], "cpu_seconds": 24.723026665858924, "cpu_top_document": 2, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 59.1664319653064, "neuron_scores": [-8.0625, 4.0625, -5.375, -4.5625], "max_abs_error": 0.0625, "neuron_top_document": 2, "mars_first": true, "cpu_parity": false, "neuron_repeat_seconds": 0.24539023637771606, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 621, "sample_interval_seconds": 0.1, "peak_bytes": 19178892667, "peak_categories": {"code": 25923376, "tensors": 16521367552, "constants": 1425419, "scratchpad": 2617245696, "misc": 11091520, "dma_rings": 1839104}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 16468168832, "allocator_peak_reserved_bytes": 16521363456, "total_seconds": 126.22209540009499} +{"index": 34, "model": "Qwen/Qwen3-Reranker-8B", "revision": "77d193c791ed757ca307ee72715aa132723da912", "trending_rank": 34, "downloads_rank": null, "class": "CrossEncoder", "dtype": "bfloat16", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:27:41.821223+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 0.6515219714492559, "prompts": {"query": "Given a web search query, retrieve relevant passages that answer the query"}, "parameter_dtypes": ["torch.bfloat16"], "cpu_scores": [-8.125, 4.0625, -5.34375, -4.5625], "cpu_seconds": 2.2711389483883977, "cpu_top_document": 2, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 0.8582923850044608, "neuron_scores": [-8.0625, 4.0625, -5.375, -4.5625], "max_abs_error": 0.0625, "neuron_top_document": 2, "mars_first": true, "cpu_parity": false, "neuron_repeat_seconds": 0.24533799476921558, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 26, "sample_interval_seconds": 0.1, "peak_bytes": 19178892667, "peak_categories": {"code": 25923376, "tensors": 16521367552, "constants": 1425419, "scratchpad": 2617245696, "misc": 11091520, "dma_rings": 1839104}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 16467906688, "allocator_peak_reserved_bytes": 16521363456, "total_seconds": 25.253529236651957} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/054.log b/validation/sentence-transformers/2026-11-03-top100/logs/054.log index 67e36c72d24bc3d34680a0fd90486a44c95629f8..d0e0fade84ac0479d2fa9ab2f79f1430955a7ab8 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/054.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/054.log @@ -1,6 +1,6 @@ Traceback (most recent call last): - File "/tmp/neuron-top100-sweep/runs/054/run.py", line 50, in - model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + File "/tmp/neuron-top100-rerun/runs/054/run.py", line 51, in + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})}) File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper return func(*args, **kwargs) File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 193, in __init__ @@ -66,4 +66,4 @@ Traceback (most recent call last): ...<4 lines>... ) OSError: Can't load feature extractor for 'HukukcuApp/tr-legal-embedding-gemma-2'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'HukukcuApp/tr-legal-embedding-gemma-2' is the correct path to a directory containing a preprocessor_config.json file -{"index": 54, "model": "HukukcuApp/tr-legal-embedding-gemma-2", "revision": "a33e497f3f123019d3b03fe40082992fffeac728", "trending_rank": 54, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:42:02.918547+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "status": "error", "stage": "load_cpu", "error_type": "OSError", "error": "Can't load feature extractor for 'HukukcuApp/tr-legal-embedding-gemma-2'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'HukukcuApp/tr-legal-embedding-gemma-2' is the correct path to a directory containing a preprocessor_config.json file", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/054/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 870, in __init__\n self.processor = AutoProcessor.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n tokenizer_name_or_path if tokenizer_name_or_path is not None else model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n **processor_kwargs,\n ^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/processing_auto.py\", line 350, in from_pretrained\n return processor_class.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/processing_utils.py\", line 1749, in from_pretrained\n args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, processor_dict, **kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/processing_utils.py\", line 1889, in _get_arguments_from_pretrained\n sub_processor = auto_processor_class.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/feature_extraction_auto.py\", line 329, in from_pretrained\n config_dict, _ = FeatureExtractionMixin.get_feature_extractor_dict(pretrained_model_name_or_path, **kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/feature_extraction_utils.py\", line 545, in get_feature_extractor_dict\n raise OSError(\n ...<4 lines>...\n )\nOSError: Can't load feature extractor for 'HukukcuApp/tr-legal-embedding-gemma-2'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'HukukcuApp/tr-legal-embedding-gemma-2' is the correct path to a directory containing a preprocessor_config.json file\n", "result": "FAIL(load)", "total_seconds": 14.706041608005762} +{"index": 54, "model": "HukukcuApp/tr-legal-embedding-gemma-2", "revision": "a33e497f3f123019d3b03fe40082992fffeac728", "trending_rank": 54, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:27:42.275977+00:00", "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "status": "error", "stage": "load_cpu", "error_type": "OSError", "error": "Can't load feature extractor for 'HukukcuApp/tr-legal-embedding-gemma-2'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'HukukcuApp/tr-legal-embedding-gemma-2' is the correct path to a directory containing a preprocessor_config.json file", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/054/run.py\", line 51, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 870, in __init__\n self.processor = AutoProcessor.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n tokenizer_name_or_path if tokenizer_name_or_path is not None else model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n **processor_kwargs,\n ^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/processing_auto.py\", line 350, in from_pretrained\n return processor_class.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/processing_utils.py\", line 1749, in from_pretrained\n args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, processor_dict, **kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/processing_utils.py\", line 1889, in _get_arguments_from_pretrained\n sub_processor = auto_processor_class.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/feature_extraction_auto.py\", line 329, in from_pretrained\n config_dict, _ = FeatureExtractionMixin.get_feature_extractor_dict(pretrained_model_name_or_path, **kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/feature_extraction_utils.py\", line 545, in get_feature_extractor_dict\n raise OSError(\n ...<4 lines>...\n )\nOSError: Can't load feature extractor for 'HukukcuApp/tr-legal-embedding-gemma-2'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'HukukcuApp/tr-legal-embedding-gemma-2' is the correct path to a directory containing a preprocessor_config.json file\n", "result": "FAIL(load)", "total_seconds": 13.635371790267527} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/067.log b/validation/sentence-transformers/2026-11-03-top100/logs/067.log index c7726d132cf190f3eb519980a6a5026279616883..6bb19ba754819acf58b19e96fda177c06342555a 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/067.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/067.log @@ -1,5 +1,5 @@ CPU [0.6934455037117004, 0.8071481585502625, 0.8093117475509644, 0.8064126968383789] -2026-Nov-03 15:45:21.230829 2446525:2446525 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +2026-Nov-03 16:28:10.265957 2941585:2941585 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) /home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. _warn_trace_cache_once() -{"index": 67, "model": "moka-ai/m3e-base", "revision": "764b537a0e50e5c7d64db883f2d2e051cbe3c64c", "trending_rank": 67, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:45:11.803493+00:00", "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 1.331957183778286, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.6934455037117004, 0.8071481585502625, 0.8093117475509644, 0.8064126968383789], "cpu_seconds": 0.14178986009210348, "cpu_top_document": 3, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 39.688529022969306, "neuron_scores": [0.6934453845024109, 0.807148277759552, 0.8093113899230957, 0.8064124584197998], "max_abs_error": 3.5762786865234375e-07, "neuron_top_document": 3, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.1130013084039092, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 400, "sample_interval_seconds": 0.1, "peak_bytes": 560925521, "peak_categories": {"code": 6555520, "tensors": 471863296, "constants": 3178513, "scratchpad": 67108864, "misc": 9490112, "dma_rings": 2729216}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 452711936, "allocator_peak_reserved_bytes": 471859200, "total_seconds": 56.262652300298214} +{"index": 67, "model": "moka-ai/m3e-base", "revision": "764b537a0e50e5c7d64db883f2d2e051cbe3c64c", "trending_rank": 67, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:27:56.535897+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 0.4231596179306507, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.6934455037117004, 0.8071481585502625, 0.8093117475509644, 0.8064126968383789], "cpu_seconds": 0.11570570152252913, "cpu_top_document": 3, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 0.3781044092029333, "neuron_scores": [0.6934453845024109, 0.807148277759552, 0.8093113899230957, 0.8064124584197998], "max_abs_error": 3.5762786865234375e-07, "neuron_top_document": 3, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.0854030791670084, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 7, "sample_interval_seconds": 0.1, "peak_bytes": 560932689, "peak_categories": {"code": 6555520, "tensors": 471863296, "constants": 3178513, "scratchpad": 67108864, "misc": 9490112, "dma_rings": 2736384}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 452711936, "allocator_peak_reserved_bytes": 471859200, "total_seconds": 20.959871355444193} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/070.log b/validation/sentence-transformers/2026-11-03-top100/logs/070.log index dfed8394bf2f76aa0982b864fe3aa3faa3c89197..03ab369922987c25b3ecfa24f3dd2f89ba3902f2 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/070.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/070.log @@ -1,9 +1,6 @@ -[transformers] A new version of the following files was downloaded from https://huggingface.co/jinaai/jina-bert-implementation: -- configuration_bert.py -. Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision. Traceback (most recent call last): - File "/tmp/neuron-top100-sweep/runs/070/run.py", line 50, in - model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + File "/tmp/neuron-top100-rerun/runs/070/run.py", line 51, in + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})}) File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper return func(*args, **kwargs) File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 193, in __init__ @@ -59,4 +56,4 @@ Traceback (most recent call last): File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_bert_hyphen_implementation/f3ec4cf7de7e561007f27c9efc7148b0bd713f81/configuration_bert.py", line 22, in from transformers.onnx import OnnxConfig ModuleNotFoundError: No module named 'transformers.onnx' -{"index": 70, "model": "jinaai/jina-embeddings-v2-base-en", "revision": "322d4d7e2f35e84137961a65af894fda0385eb7a", "trending_rank": 70, "downloads_rank": 88, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:45:55.385742+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "status": "error", "stage": "load_cpu", "error_type": "ModuleNotFoundError", "error": "No module named 'transformers.onnx'", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/070/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 839, in __init__\n config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2259, in _load_config\n return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py\", line 422, in from_pretrained\n config_class = get_class_from_dynamic_module(\n class_ref, pretrained_model_name_or_path, code_revision=code_revision, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 625, in get_class_from_dynamic_module\n return get_class_in_module(class_name, final_module, force_reload=force_download)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 309, in get_class_in_module\n module_spec.loader.exec_module(module)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^\n File \"\", line 759, in exec_module\n File \"\", line 491, in _call_with_frames_removed\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_bert_hyphen_implementation/f3ec4cf7de7e561007f27c9efc7148b0bd713f81/configuration_bert.py\", line 22, in \n from transformers.onnx import OnnxConfig\nModuleNotFoundError: No module named 'transformers.onnx'\n", "result": "FAIL(load)", "total_seconds": 8.849647373892367} +{"index": 70, "model": "jinaai/jina-embeddings-v2-base-en", "revision": "322d4d7e2f35e84137961a65af894fda0385eb7a", "trending_rank": 70, "downloads_rank": 88, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:27:57.819913+00:00", "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "status": "error", "stage": "load_cpu", "error_type": "ModuleNotFoundError", "error": "No module named 'transformers.onnx'", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/070/run.py\", line 51, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 839, in __init__\n config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2259, in _load_config\n return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py\", line 422, in from_pretrained\n config_class = get_class_from_dynamic_module(\n class_ref, pretrained_model_name_or_path, code_revision=code_revision, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 625, in get_class_from_dynamic_module\n return get_class_in_module(class_name, final_module, force_reload=force_download)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 309, in get_class_in_module\n module_spec.loader.exec_module(module)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^\n File \"\", line 759, in exec_module\n File \"\", line 491, in _call_with_frames_removed\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_bert_hyphen_implementation/f3ec4cf7de7e561007f27c9efc7148b0bd713f81/configuration_bert.py\", line 22, in \n from transformers.onnx import OnnxConfig\nModuleNotFoundError: No module named 'transformers.onnx'\n", "result": "FAIL(load)", "total_seconds": 12.02744938340038} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/073.log b/validation/sentence-transformers/2026-11-03-top100/logs/073.log index f0e3cf1e5e1c7b67eac2df31cab0c82c24d017eb..ed35318c560a0273198fee7120e73338cdc50e25 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/073.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/073.log @@ -1,17 +1,12 @@ -[transformers] A new version of the following files was downloaded from https://huggingface.co/Alibaba-NLP/new-impl: -- configuration.py -. Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision. /tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/configuration_utils.py:723: FutureWarning: `rope_scaling` is deprecated and will be removed in a future version. Set `rope_parameters` instead, with `rope_type` and `rope_theta` included. warnings.warn( -[transformers] A new version of the following files was downloaded from https://huggingface.co/Alibaba-NLP/new-impl: -- modeling.py -. Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision. Traceback (most recent call last): - File "/tmp/neuron-top100-sweep/runs/073/run.py", line 63, in + File "/tmp/neuron-top100-rerun/runs/073/run.py", line 64, in stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) ~~~~~^^ - File "/tmp/neuron-top100-sweep/runs/073/run.py", line 58, in score - q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + File "/tmp/neuron-top100-rerun/runs/073/run.py", line 59, in score + q=(model.encode([query],batch_size=4,show_progress_bar=False,**kw,**SPEC['query_kwargs']) if SPEC.get('query_kwargs') else model.encode_query([query],batch_size=4,show_progress_bar=False,**kw)) + ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper return func(*args, **kwargs) File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 370, in encode_query @@ -66,5 +61,5 @@ Traceback (most recent call last): File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py", line 392, in forward rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim] ~~~~~~~~^^^^^^^^^^^^^^ -IndexError: index 239254133933015924 is out of bounds for dimension 0 with size 11 -{"index": 73, "model": "Alibaba-NLP/gte-large-en-v1.5", "revision": "104333d6af6f97649377c2afbde10a7704870c7b", "trending_rank": 73, "downloads_rank": 27, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:46:09.987170+00:00", "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 3.194855031557381, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "status": "error", "stage": "cpu_inference", "error_type": "IndexError", "error": "index 239254133933015924 is out of bounds for dimension 0 with size 11", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/073/run.py\", line 63, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/073/run.py\", line 58, in score\n q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 239254133933015924 is out of bounds for dimension 0 with size 11\n", "result": "FAIL(forward)", "total_seconds": 11.818632579408586} +IndexError: index 755665224 is out of bounds for dimension 0 with size 11 +{"index": 73, "model": "Alibaba-NLP/gte-large-en-v1.5", "revision": "104333d6af6f97649377c2afbde10a7704870c7b", "trending_rank": 73, "downloads_rank": 27, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:28:05.050764+00:00", "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 0.6149875931441784, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "status": "error", "stage": "cpu_inference", "error_type": "IndexError", "error": "index 755665224 is out of bounds for dimension 0 with size 11", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/073/run.py\", line 64, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-rerun/runs/073/run.py\", line 59, in score\n q=(model.encode([query],batch_size=4,show_progress_bar=False,**kw,**SPEC['query_kwargs']) if SPEC.get('query_kwargs') else model.encode_query([query],batch_size=4,show_progress_bar=False,**kw))\n ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 755665224 is out of bounds for dimension 0 with size 11\n", "result": "FAIL(forward)", "total_seconds": 10.126153020188212} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/075.log b/validation/sentence-transformers/2026-11-03-top100/logs/075.log index d6cdf6a7a42eede98a2724ed5d4a03217a4115aa..84cd10f53ace13103e0d2c3fe6467572ff24a021 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/075.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/075.log @@ -1,11 +1,6 @@ -httpx.HTTPStatusError: Client error '403 Forbidden' for url 'https://huggingface.co/nvidia/NV-Embed-v1/resolve/2e8b322e2b738a383dae0dde5fa19e0b28a5bb11/modules.json' -For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/403 - -The above exception was the direct cause of the following exception: - Traceback (most recent call last): - File "/tmp/neuron-top100-sweep/runs/075/run.py", line 50, in - model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + File "/tmp/neuron-nv-embed-v1-rerun/runs/075/run.py", line 51, in + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})}) File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper return func(*args, **kwargs) File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 193, in __init__ @@ -28,35 +23,41 @@ Traceback (most recent call last): ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ) ^ - File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 1190, in _load_modules - modules_json_path = load_file_path( + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 1211, in _load_modules + return self._load_config_modules(model_name_or_path, **load_kwargs) + ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 1431, in _load_config_modules + module = module_class.load( model_name_or_path, - ...<4 lines>... - local_files_only=local_files_only, + ...<12 lines>... + **extra_load_kwargs, ) - File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/file_io.py", line 208, in load_file_path - return hf_hub_download( - model_name_or_path, - ...<6 lines>... - local_files_only=local_files_only, + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py", line 2641, in load + return cls(model_name_or_path=model_name_or_path, **init_kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 92, in wrapper + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py", line 855, in __init__ + self.model = self._load_model( + ~~~~~~~~~~~~~~~~^ + model_name_or_path, transformer_task, config, backend, is_peft_model, **model_kwargs + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ) - File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_validators.py", line 89, in _inner_fn - return fn(*args, **kwargs) - File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_http.py", line 266, in _inner - return fn(*args, **kwargs) - File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py", line 1047, in hf_hub_download - return _hf_hub_download_to_cache_dir( - # Destination - ...<15 lines>... - dry_run=dry_run, + ^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py", line 2343, in _load_model + return model_cls.from_pretrained(model_name_or_path, config=config, **model_kwargs) + ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py", line 381, in from_pretrained + model_class = get_class_from_dynamic_module( + class_ref, pretrained_model_name_or_path, code_revision=code_revision, **hub_kwargs, **kwargs ) - File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py", line 1194, in _hf_hub_download_to_cache_dir - _raise_on_head_call_error(head_call_error, force_download, local_files_only) - ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py", line 1953, in _raise_on_head_call_error - raise head_call_error -huggingface_hub.errors.GatedRepoError: 403 Client Error. (Request ID: Root=1-6aea01d2-3206be3e053d1684092fc8e7;1a302a3a-37c9-46e0-94a8-99f64b2e4802) - -Cannot access gated repo for url https://huggingface.co/nvidia/NV-Embed-v1/resolve/2e8b322e2b738a383dae0dde5fa19e0b28a5bb11/modules.json. -Access to model nvidia/NV-Embed-v1 is restricted and you are not in the authorized list. Visit https://huggingface.co/nvidia/NV-Embed-v1 to ask for access. -{"index": 75, "model": "nvidia/NV-Embed-v1", "revision": "2e8b322e2b738a383dae0dde5fa19e0b28a5bb11", "trending_rank": 75, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "bfloat16", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:46:17.899843+00:00", "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "status": "error", "stage": "load_cpu", "error_type": "GatedRepoError", "error": "403 Client Error. (Request ID: Root=1-6aea01d2-3206be3e053d1684092fc8e7;1a302a3a-37c9-46e0-94a8-99f64b2e4802)\n\nCannot access gated repo for url https://huggingface.co/nvidia/NV-Embed-v1/resolve/2e8b322e2b738a383dae0dde5fa19e0b28a5bb11/modules.json.\nAccess to model nvidia/NV-Embed-v1 is restricted and you are not in the authorized list. Visit https://huggingface.co/nvidia/NV-Embed-v1 to ask for access.", "traceback": "httpx.HTTPStatusError: Client error '403 Forbidden' for url 'https://huggingface.co/nvidia/NV-Embed-v1/resolve/2e8b322e2b738a383dae0dde5fa19e0b28a5bb11/modules.json'\nFor more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/403\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/075/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1190, in _load_modules\n modules_json_path = load_file_path(\n model_name_or_path,\n ...<4 lines>...\n local_files_only=local_files_only,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/file_io.py\", line 208, in load_file_path\n return hf_hub_download(\n model_name_or_path,\n ...<6 lines>...\n local_files_only=local_files_only,\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_validators.py\", line 89, in _inner_fn\n return fn(*args, **kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_http.py\", line 266, in _inner\n return fn(*args, **kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1047, in hf_hub_download\n return _hf_hub_download_to_cache_dir(\n # Destination\n ...<15 lines>...\n dry_run=dry_run,\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1194, in _hf_hub_download_to_cache_dir\n _raise_on_head_call_error(head_call_error, force_download, local_files_only)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1953, in _raise_on_head_call_error\n raise head_call_error\nhuggingface_hub.errors.GatedRepoError: 403 Client Error. (Request ID: Root=1-6aea01d2-3206be3e053d1684092fc8e7;1a302a3a-37c9-46e0-94a8-99f64b2e4802)\n\nCannot access gated repo for url https://huggingface.co/nvidia/NV-Embed-v1/resolve/2e8b322e2b738a383dae0dde5fa19e0b28a5bb11/modules.json.\nAccess to model nvidia/NV-Embed-v1 is restricted and you are not in the authorized list. Visit https://huggingface.co/nvidia/NV-Embed-v1 to ask for access.\n", "result": "FAIL(load)", "total_seconds": 8.890233975835145} + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py", line 625, in get_class_from_dynamic_module + return get_class_in_module(class_name, final_module, force_reload=force_download) + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py", line 309, in get_class_in_module + module_spec.loader.exec_module(module) + ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^ + File "", line 759, in exec_module + File "", line 491, in _call_with_frames_removed + File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/nvidia/NV_hyphen_Embed_hyphen_v1/2e8b322e2b738a383dae0dde5fa19e0b28a5bb11/modeling_nvembed.py", line 11, in + from transformers.models.mistral.modeling_mistral import MISTRAL_INPUTS_DOCSTRING +ImportError: cannot import name 'MISTRAL_INPUTS_DOCSTRING' from 'transformers.models.mistral.modeling_mistral' (/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/mistral/modeling_mistral.py) +{"index": 75, "model": "nvidia/NV-Embed-v1", "revision": "2e8b322e2b738a383dae0dde5fa19e0b28a5bb11", "trending_rank": 75, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "bfloat16", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "reason": "Hub access granted; enable the custom model code required by NV-Embed-v1.", "started_at": "2026-11-03T16:30:04.363338+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "status": "error", "stage": "load_cpu", "error_type": "ImportError", "error": "cannot import name 'MISTRAL_INPUTS_DOCSTRING' from 'transformers.models.mistral.modeling_mistral' (/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/mistral/modeling_mistral.py)", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-nv-embed-v1-rerun/runs/075/run.py\", line 51, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 855, in __init__\n self.model = self._load_model(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path, transformer_task, config, backend, is_peft_model, **model_kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2343, in _load_model\n return model_cls.from_pretrained(model_name_or_path, config=config, **model_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py\", line 381, in from_pretrained\n model_class = get_class_from_dynamic_module(\n class_ref, pretrained_model_name_or_path, code_revision=code_revision, **hub_kwargs, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 625, in get_class_from_dynamic_module\n return get_class_in_module(class_name, final_module, force_reload=force_download)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 309, in get_class_in_module\n module_spec.loader.exec_module(module)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^\n File \"\", line 759, in exec_module\n File \"\", line 491, in _call_with_frames_removed\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/nvidia/NV_hyphen_Embed_hyphen_v1/2e8b322e2b738a383dae0dde5fa19e0b28a5bb11/modeling_nvembed.py\", line 11, in \n from transformers.models.mistral.modeling_mistral import MISTRAL_INPUTS_DOCSTRING\nImportError: cannot import name 'MISTRAL_INPUTS_DOCSTRING' from 'transformers.models.mistral.modeling_mistral' (/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/mistral/modeling_mistral.py)\n", "result": "FAIL(load)", "total_seconds": 13.725843532010913} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/077.log b/validation/sentence-transformers/2026-11-03-top100/logs/077.log index b2569142b92d1c76de868705f2bbdf9d5dbc6e9e..af3855b3ae052968c36603dc2db4e1b6614cf9c0 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/077.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/077.log @@ -1,6 +1,6 @@ [transformers] We found `hidden_act="gelu"` in this Gemma config. This is a legacy value of the official releases but it is meant to target the tanh approximation. Setting `hidden_act="gelu_pytorch_tanh"` instead. CPU [0.9210729002952576, 0.9032096862792969, 0.9318515062332153, 0.9134941101074219] -2026-Nov-03 15:46:48.420935 2475124:2475124 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +2026-Nov-03 16:28:21.988818 2942474:2942474 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) /home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. _warn_trace_cache_once() -{"index": 77, "model": "Jaume/gemma-2b-embeddings", "revision": "86431f65d7c3f66b2af096c61e614a2958f191f1", "trending_rank": 77, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:46:28.024988+00:00", "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 4.274750509299338, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.9210729002952576, 0.9032096862792969, 0.9318515062332153, 0.9134941101074219], "cpu_seconds": 6.217862844467163, "cpu_top_document": 3, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 38.68630393128842, "neuron_scores": [0.921073317527771, 0.9032103419303894, 0.9318520426750183, 0.9134945273399353], "max_abs_error": 6.556510925292969e-07, "neuron_top_document": 3, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.15275748260319233, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 410, "sample_interval_seconds": 0.1, "peak_bytes": 10209788025, "peak_categories": {"code": 11395752, "tensors": 10108276736, "constants": 4882449, "scratchpad": 67108864, "misc": 15922624, "dma_rings": 2201600}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 10050966144, "allocator_peak_reserved_bytes": 10108272640, "total_seconds": 68.6340330131352} +{"index": 77, "model": "Jaume/gemma-2b-embeddings", "revision": "86431f65d7c3f66b2af096c61e614a2958f191f1", "trending_rank": 77, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:28:11.198209+00:00", "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 1.1411347407847643, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.9210729002952576, 0.9032096862792969, 0.9318515062332153, 0.9134941101074219], "cpu_seconds": 1.887984692119062, "cpu_top_document": 3, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 0.6383720831945539, "neuron_scores": [0.921073317527771, 0.9032103419303894, 0.9318520426750183, 0.9134945273399353], "max_abs_error": 6.556510925292969e-07, "neuron_top_document": 3, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.14952720422297716, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 18, "sample_interval_seconds": 0.1, "peak_bytes": 10209796217, "peak_categories": {"code": 11395752, "tensors": 10108276736, "constants": 4882449, "scratchpad": 67108864, "misc": 15922624, "dma_rings": 2209792}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 10050966144, "allocator_peak_reserved_bytes": 10108272640, "total_seconds": 19.1534237684682} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/078.log b/validation/sentence-transformers/2026-11-03-top100/logs/078.log index 9efa9872604eff15123722e723f680d22ff66f1b..dd28f4fe7dd477560c53b313f1d51522456cbe5c 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/078.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/078.log @@ -1,10 +1,10 @@ /tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/configuration_utils.py:723: FutureWarning: `rope_scaling` is deprecated and will be removed in a future version. Set `rope_parameters` instead, with `rope_type` and `rope_theta` included. warnings.warn( Traceback (most recent call last): - File "/tmp/neuron-top100-sweep/runs/078/run.py", line 63, in + File "/tmp/neuron-top100-rerun/runs/078/run.py", line 64, in stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) ~~~~~^^ - File "/tmp/neuron-top100-sweep/runs/078/run.py", line 55, in score + File "/tmp/neuron-top100-rerun/runs/078/run.py", line 56, in score values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context return func(*args, **kwargs) @@ -61,5 +61,5 @@ Traceback (most recent call last): File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py", line 392, in forward rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim] ~~~~~~~~^^^^^^^^^^^^^^ -IndexError: index 139625091826798 is out of bounds for dimension 0 with size 32 -{"index": 78, "model": "Alibaba-NLP/gte-multilingual-reranker-base", "revision": "8215cf04918ba6f7b6a62bb44238ce2953d8831c", "trending_rank": 78, "downloads_rank": null, "class": "CrossEncoder", "dtype": "float32", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:46:48.108932+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 2.7446684883907437, "prompts": {}, "parameter_dtypes": ["torch.float32"], "status": "error", "stage": "cpu_inference", "error_type": "IndexError", "error": "index 139625091826798 is out of bounds for dimension 0 with size 32", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/078/run.py\", line 63, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/078/run.py\", line 55, in score\n values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 209, in wrapper\n return func(self, *args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 645, in predict\n pred_scores = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 689, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 1117, in forward\n outputs = self.new(\n input_ids,\n ...<8 lines>...\n unpad_inputs=unpad_inputs,\n )\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 139625091826798 is out of bounds for dimension 0 with size 32\n", "result": "FAIL(forward)", "total_seconds": 15.83839768357575} +IndexError: index 90194313471 is out of bounds for dimension 0 with size 32 +{"index": 78, "model": "Alibaba-NLP/gte-multilingual-reranker-base", "revision": "8215cf04918ba6f7b6a62bb44238ce2953d8831c", "trending_rank": 78, "downloads_rank": null, "class": "CrossEncoder", "dtype": "float32", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:28:16.434610+00:00", "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 1.973351868800819, "prompts": {}, "parameter_dtypes": ["torch.float32"], "status": "error", "stage": "cpu_inference", "error_type": "IndexError", "error": "index 90194313471 is out of bounds for dimension 0 with size 32", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/078/run.py\", line 64, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-rerun/runs/078/run.py\", line 56, in score\n values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 209, in wrapper\n return func(self, *args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 645, in predict\n pred_scores = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 689, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 1117, in forward\n outputs = self.new(\n input_ids,\n ...<8 lines>...\n unpad_inputs=unpad_inputs,\n )\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 90194313471 is out of bounds for dimension 0 with size 32\n", "result": "FAIL(forward)", "total_seconds": 9.826986877247691} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/079.log b/validation/sentence-transformers/2026-11-03-top100/logs/079.log index 7a15412385ea70fe040564fc14b2bef4f3012090..c9ccc3ec1ba326deda1a3b022b693a17abf1ba45 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/079.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/079.log @@ -1,5 +1,5 @@ CPU [0.45112812519073486, 0.5921509265899658, 0.3681429922580719, 0.6955137848854065] -2026-Nov-03 15:47:10.573216 2480704:2480704 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +2026-Nov-03 16:28:27.482344 2943037:2943037 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) /home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. _warn_trace_cache_once() -{"index": 79, "model": "akhooli/Arabic-SBERT-100K", "revision": "67199ee95d81bb10047c946b1b4268c8703b83db", "trending_rank": 79, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:46:59.382279+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 1.3999376483261585, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.45112812519073486, 0.5921509265899658, 0.3681429922580719, 0.6955137848854065], "cpu_seconds": 0.4114110991358757, "cpu_top_document": 4, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 0.4038874404504895, "neuron_scores": [0.45112812519073486, 0.5921508073806763, 0.3681427240371704, 0.6955137252807617], "max_abs_error": 2.682209014892578e-07, "neuron_top_document": 4, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.06409406755119562, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 7, "sample_interval_seconds": 0.1, "peak_bytes": 695672385, "peak_categories": {"code": 6654512, "tensors": 606081024, "constants": 3473425, "scratchpad": 67108864, "misc": 9832960, "dma_rings": 2521600}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 587197440, "allocator_peak_reserved_bytes": 606076928, "total_seconds": 21.84079162403941} +{"index": 79, "model": "akhooli/Arabic-SBERT-100K", "revision": "67199ee95d81bb10047c946b1b4268c8703b83db", "trending_rank": 79, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:28:19.317175+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 0.5274149598553777, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.45112812519073486, 0.5921509265899658, 0.3681429922580719, 0.6955137848854065], "cpu_seconds": 0.13939552847296, "cpu_top_document": 4, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 0.35299276281148195, "neuron_scores": [0.45112812519073486, 0.5921508073806763, 0.3681427240371704, 0.6955137252807617], "max_abs_error": 2.682209014892578e-07, "neuron_top_document": 4, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.04365590400993824, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 6, "sample_interval_seconds": 0.1, "peak_bytes": 695672385, "peak_categories": {"code": 6654512, "tensors": 606081024, "constants": 3473425, "scratchpad": 67108864, "misc": 9832960, "dma_rings": 2521600}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 587197440, "allocator_peak_reserved_bytes": 606076928, "total_seconds": 15.680451227352023} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/081.log b/validation/sentence-transformers/2026-11-03-top100/logs/081.log index 67edb7cee20c351cd3f0b971498db782e7e521d2..adb73858ea87ac1acbb94d6d22c66db340b08c1a 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/081.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/081.log @@ -1,7 +1,7 @@ [transformers] `torch_dtype` is deprecated! Use `dtype` instead! Traceback (most recent call last): - File "/tmp/neuron-top100-sweep/runs/081/run.py", line 50, in - model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + File "/tmp/neuron-top100-rerun/runs/081/run.py", line 51, in + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})}) File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper return func(*args, **kwargs) File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 193, in __init__ @@ -72,4 +72,4 @@ Traceback (most recent call last): f"'{type(self).__name__}' object has no attribute '{name}'" ) AttributeError: 'XLMRobertaLoRA' object has no attribute 'all_tied_weights_keys'. Did you mean: '_tied_weights_keys'? -{"index": 81, "model": "jinaai/jina-embeddings-v3", "revision": "ab036b023d30b4d1138c4c3bfa9f0c445ab455d6", "trending_rank": 81, "downloads_rank": 32, "class": "SentenceTransformer", "dtype": "float32", "attention": "eager", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:47:35.211638+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "status": "error", "stage": "load_cpu", "error_type": "AttributeError", "error": "'XLMRobertaLoRA' object has no attribute 'all_tied_weights_keys'", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/081/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1404, in _load_config_modules\n module = module_class(model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_embeddings_hyphen_v3/ab036b023d30b4d1138c4c3bfa9f0c445ab455d6/custom_st.py\", line 87, in __init__\n self.auto_model = AutoModel.from_pretrained(model_name_or_path, config=self.config, **model_args)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py\", line 389, in from_pretrained\n return model_class.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/xlm_hyphen_roberta_hyphen_flash_hyphen_implementation/bd55a5ec8e6c0fb1d6c26efb4b6a4a74ce8a88d3/modeling_lora.py\", line 338, in from_pretrained\n return super().from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<9 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/xlm_hyphen_roberta_hyphen_flash_hyphen_implementation/bd55a5ec8e6c0fb1d6c26efb4b6a4a74ce8a88d3/modeling_xlm_roberta.py\", line 442, in from_pretrained\n return super().from_pretrained(*args, **kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4356, in from_pretrained\n loading_info = cls._finalize_model_loading(model, load_config, loading_info)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4524, in _finalize_model_loading\n model._move_missing_keys_from_meta_to_device(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n loading_info.missing_and_mismatched(),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<2 lines>...\n load_config.hf_quantizer,\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4781, in _move_missing_keys_from_meta_to_device\n for key in missing_keys - self.all_tied_weights_keys.keys():\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1972, in __getattr__\n raise AttributeError(\n f\"'{type(self).__name__}' object has no attribute '{name}'\"\n )\nAttributeError: 'XLMRobertaLoRA' object has no attribute 'all_tied_weights_keys'. Did you mean: '_tied_weights_keys'?\n", "result": "FAIL(load)", "total_seconds": 19.574221355840564} +{"index": 81, "model": "jinaai/jina-embeddings-v3", "revision": "ab036b023d30b4d1138c4c3bfa9f0c445ab455d6", "trending_rank": 81, "downloads_rank": 32, "class": "SentenceTransformer", "dtype": "float32", "attention": "eager", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:28:19.331849+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "status": "error", "stage": "load_cpu", "error_type": "AttributeError", "error": "'XLMRobertaLoRA' object has no attribute 'all_tied_weights_keys'", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/081/run.py\", line 51, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1404, in _load_config_modules\n module = module_class(model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_embeddings_hyphen_v3/ab036b023d30b4d1138c4c3bfa9f0c445ab455d6/custom_st.py\", line 87, in __init__\n self.auto_model = AutoModel.from_pretrained(model_name_or_path, config=self.config, **model_args)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py\", line 389, in from_pretrained\n return model_class.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/xlm_hyphen_roberta_hyphen_flash_hyphen_implementation/bd55a5ec8e6c0fb1d6c26efb4b6a4a74ce8a88d3/modeling_lora.py\", line 338, in from_pretrained\n return super().from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<9 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/xlm_hyphen_roberta_hyphen_flash_hyphen_implementation/bd55a5ec8e6c0fb1d6c26efb4b6a4a74ce8a88d3/modeling_xlm_roberta.py\", line 442, in from_pretrained\n return super().from_pretrained(*args, **kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4356, in from_pretrained\n loading_info = cls._finalize_model_loading(model, load_config, loading_info)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4524, in _finalize_model_loading\n model._move_missing_keys_from_meta_to_device(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n loading_info.missing_and_mismatched(),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<2 lines>...\n load_config.hf_quantizer,\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4781, in _move_missing_keys_from_meta_to_device\n for key in missing_keys - self.all_tied_weights_keys.keys():\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1972, in __getattr__\n raise AttributeError(\n f\"'{type(self).__name__}' object has no attribute '{name}'\"\n )\nAttributeError: 'XLMRobertaLoRA' object has no attribute 'all_tied_weights_keys'. Did you mean: '_tied_weights_keys'?\n", "result": "FAIL(load)", "total_seconds": 10.509030694141984} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/083.log b/validation/sentence-transformers/2026-11-03-top100/logs/083.log index dc9869d8aaa61098e637b67eaf31d43e5fe01295..c9c0c8b227409971b1195a9e2069b141bccc698d 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/083.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/083.log @@ -1,5 +1,5 @@ CPU [0.20963680744171143, 0.5245946049690247, 0.4401358366012573, 0.585011899471283] -2026-Nov-03 15:47:52.897933 2508243:2508243 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +2026-Nov-03 16:28:36.613425 2943706:2943706 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) /home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. _warn_trace_cache_once() -{"index": 83, "model": "meandyou200175/phobert-finetune", "revision": "26e5f91ad1eb94b0af813021a2b7097ae4ba228e", "trending_rank": 83, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:47:38.793972+00:00", "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 1.9071867559105158, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.20963680744171143, 0.5245946049690247, 0.4401358366012573, 0.585011899471283], "cpu_seconds": 0.2299742652103305, "cpu_top_document": 4, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 43.493039570748806, "neuron_scores": [0.20963667333126068, 0.5245950222015381, 0.44013622403144836, 0.5850120782852173], "max_abs_error": 4.172325134277344e-07, "neuron_top_document": 4, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.05636020377278328, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 437, "sample_interval_seconds": 0.1, "peak_bytes": 701178529, "peak_categories": {"code": 7318992, "tensors": 608178176, "constants": 3440657, "scratchpad": 67108864, "misc": 11850688, "dma_rings": 3281152}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 586056448, "allocator_peak_reserved_bytes": 608174080, "total_seconds": 64.42325356975198} +{"index": 83, "model": "meandyou200175/phobert-finetune", "revision": "26e5f91ad1eb94b0af813021a2b7097ae4ba228e", "trending_rank": 83, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:28:28.113973+00:00", "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 0.3918236820027232, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.20963680744171143, 0.5245946049690247, 0.4401358366012573, 0.585011899471283], "cpu_seconds": 0.11149388272315264, "cpu_top_document": 4, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 0.44567768834531307, "neuron_scores": [0.20963667333126068, 0.5245950222015381, 0.44013622403144836, 0.5850120782852173], "max_abs_error": 4.172325134277344e-07, "neuron_top_document": 4, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.054856122471392155, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 7, "sample_interval_seconds": 0.1, "peak_bytes": 699050657, "peak_categories": {"code": 7318992, "tensors": 606081024, "constants": 3440657, "scratchpad": 67108864, "misc": 11850688, "dma_rings": 3250432}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 584563200, "allocator_peak_reserved_bytes": 606076928, "total_seconds": 15.188867619261146} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/089.log b/validation/sentence-transformers/2026-11-03-top100/logs/089.log index 8b55c1cea9c7d1b4c33676173e0412f3a1024891..5a6199b4d31da780cdce8af3b09da865911da8dd 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/089.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/089.log @@ -1,8 +1,8 @@ [transformers] Qwen2_5_VLModel LOAD REPORT from: vidore/colqwen2.5-base Key | Status | | ------------------------+------------+--+- -custom_text_proj.weight | UNEXPECTED | | custom_text_proj.bias | UNEXPECTED | | +custom_text_proj.weight | UNEXPECTED | | Notes: - UNEXPECTED: can be ignored when loading from different task/architecture; not ok if you expect identical arch. @@ -15,7 +15,7 @@ custom_text_proj.lora_B.default.weight | UNEXPECTED | | Notes: - UNEXPECTED: can be ignored when loading from different task/architecture; not ok if you expect identical arch. CPU [10.587596893310547, 15.282479286193848, 11.481404304504395, 15.91786003112793] -2026-Nov-03 15:49:49.360524 2553666:2553666 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +2026-Nov-03 16:28:43.778744 2944185:2944185 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) /home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. _warn_trace_cache_once() -{"index": 89, "model": "vidore/colqwen2.5-v0.2", "revision": "dcbe8d9cede518bce830488364ba0e40c873645b", "trending_rank": 89, "downloads_rank": null, "class": "MultiVectorEncoder", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:49:04.100500+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 30.20843593403697, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [10.587596893310547, 15.282479286193848, 11.481404304504395, 15.91786003112793], "cpu_seconds": 2.5352238938212395, "cpu_top_document": 4, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 78.56518525443971, "neuron_scores": [10.587594032287598, 15.282479286193848, 11.481398582458496, 15.917861938476562], "max_abs_error": 5.7220458984375e-06, "neuron_top_document": 4, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.9287767671048641, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 811, "sample_interval_seconds": 0.1, "peak_bytes": 15556525689, "peak_categories": {"code": 13058680, "tensors": 15437139968, "constants": 6002945, "scratchpad": 67108864, "misc": 22477824, "dma_rings": 10737408}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 15333365888, "allocator_peak_reserved_bytes": 15437135872, "total_seconds": 134.7353895958513} +{"index": 89, "model": "vidore/colqwen2.5-v0.2", "revision": "dcbe8d9cede518bce830488364ba0e40c873645b", "trending_rank": 89, "downloads_rank": null, "class": "MultiVectorEncoder", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:28:31.417399+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 2.2792115388438106, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [10.587596893310547, 15.282479286193848, 11.481404304504395, 15.91786003112793], "cpu_seconds": 2.1973909735679626, "cpu_top_document": 4, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 2.5120828673243523, "neuron_scores": [10.587594032287598, 15.282479286193848, 11.481398582458496, 15.917861938476562], "max_abs_error": 5.7220458984375e-06, "neuron_top_document": 4, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.8568719290196896, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 51, "sample_interval_seconds": 0.1, "peak_bytes": 15556522617, "peak_categories": {"code": 13058680, "tensors": 15437139968, "constants": 6002945, "scratchpad": 67108864, "misc": 22477824, "dma_rings": 10734336}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 15333555840, "allocator_peak_reserved_bytes": 15437135872, "total_seconds": 24.30755981616676} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/108.log b/validation/sentence-transformers/2026-11-03-top100/logs/108.log index e379e146dd1e7eb3ea4905e8416a1a87aec864ae..9131e2e120f8c9655589ac7082d3fc4238df5515 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/108.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/108.log @@ -1,7 +1,7 @@ [transformers] `torch_dtype` is deprecated! Use `dtype` instead! Traceback (most recent call last): - File "/tmp/neuron-top100-sweep/runs/108/run.py", line 50, in - model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + File "/tmp/neuron-top100-rerun/runs/108/run.py", line 51, in + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})}) File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 163, in wrapper return func(self, *args, **kwargs) File "/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py", line 200, in __init__ @@ -61,4 +61,4 @@ Traceback (most recent call last): File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_reranker_hyphen_v2_hyphen_base_hyphen_multilingual/9cfeff2df7d40d1b78e75e5e9cebec92a99813c9/embedding.py", line 11, in from transformers.models.xlm_roberta.modeling_xlm_roberta import create_position_ids_from_input_ids ImportError: cannot import name 'create_position_ids_from_input_ids' from 'transformers.models.xlm_roberta.modeling_xlm_roberta' (/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/xlm_roberta/modeling_xlm_roberta.py) -{"index": 108, "model": "jinaai/jina-reranker-v2-base-multilingual", "revision": "9cfeff2df7d40d1b78e75e5e9cebec92a99813c9", "trending_rank": null, "downloads_rank": 29, "class": "CrossEncoder", "dtype": "float32", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:52:03.168120+00:00", "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "status": "error", "stage": "load_cpu", "error_type": "ImportError", "error": "cannot import name 'create_position_ids_from_input_ids' from 'transformers.models.xlm_roberta.modeling_xlm_roberta' (/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/xlm_roberta/modeling_xlm_roberta.py)", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/108/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 163, in wrapper\n return func(self, *args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 200, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1200, in _load_modules\n return self._load_default_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 337, in _load_default_modules\n transformer_model = Transformer(\n model_name_or_path,\n ...<5 lines>...\n backend=self.backend,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 855, in __init__\n self.model = self._load_model(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path, transformer_task, config, backend, is_peft_model, **model_kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2343, in _load_model\n return model_cls.from_pretrained(model_name_or_path, config=config, **model_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py\", line 381, in from_pretrained\n model_class = get_class_from_dynamic_module(\n class_ref, pretrained_model_name_or_path, code_revision=code_revision, **hub_kwargs, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 625, in get_class_from_dynamic_module\n return get_class_in_module(class_name, final_module, force_reload=force_download)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 309, in get_class_in_module\n module_spec.loader.exec_module(module)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^\n File \"\", line 759, in exec_module\n File \"\", line 491, in _call_with_frames_removed\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_reranker_hyphen_v2_hyphen_base_hyphen_multilingual/9cfeff2df7d40d1b78e75e5e9cebec92a99813c9/modeling_xlm_roberta.py\", line 44, in \n from .embedding import XLMRobertaEmbeddings\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_reranker_hyphen_v2_hyphen_base_hyphen_multilingual/9cfeff2df7d40d1b78e75e5e9cebec92a99813c9/embedding.py\", line 11, in \n from transformers.models.xlm_roberta.modeling_xlm_roberta import create_position_ids_from_input_ids\nImportError: cannot import name 'create_position_ids_from_input_ids' from 'transformers.models.xlm_roberta.modeling_xlm_roberta' (/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/xlm_roberta/modeling_xlm_roberta.py)\n", "result": "FAIL(load)", "total_seconds": 10.194885298609734} +{"index": 108, "model": "jinaai/jina-reranker-v2-base-multilingual", "revision": "9cfeff2df7d40d1b78e75e5e9cebec92a99813c9", "trending_rank": null, "downloads_rank": 29, "class": "CrossEncoder", "dtype": "float32", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:28:32.287840+00:00", "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "status": "error", "stage": "load_cpu", "error_type": "ImportError", "error": "cannot import name 'create_position_ids_from_input_ids' from 'transformers.models.xlm_roberta.modeling_xlm_roberta' (/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/xlm_roberta/modeling_xlm_roberta.py)", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/108/run.py\", line 51, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 163, in wrapper\n return func(self, *args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 200, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1200, in _load_modules\n return self._load_default_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 337, in _load_default_modules\n transformer_model = Transformer(\n model_name_or_path,\n ...<5 lines>...\n backend=self.backend,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 855, in __init__\n self.model = self._load_model(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path, transformer_task, config, backend, is_peft_model, **model_kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2343, in _load_model\n return model_cls.from_pretrained(model_name_or_path, config=config, **model_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py\", line 381, in from_pretrained\n model_class = get_class_from_dynamic_module(\n class_ref, pretrained_model_name_or_path, code_revision=code_revision, **hub_kwargs, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 625, in get_class_from_dynamic_module\n return get_class_in_module(class_name, final_module, force_reload=force_download)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 309, in get_class_in_module\n module_spec.loader.exec_module(module)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^\n File \"\", line 759, in exec_module\n File \"\", line 491, in _call_with_frames_removed\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_reranker_hyphen_v2_hyphen_base_hyphen_multilingual/9cfeff2df7d40d1b78e75e5e9cebec92a99813c9/modeling_xlm_roberta.py\", line 44, in \n from .embedding import XLMRobertaEmbeddings\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_reranker_hyphen_v2_hyphen_base_hyphen_multilingual/9cfeff2df7d40d1b78e75e5e9cebec92a99813c9/embedding.py\", line 11, in \n from transformers.models.xlm_roberta.modeling_xlm_roberta import create_position_ids_from_input_ids\nImportError: cannot import name 'create_position_ids_from_input_ids' from 'transformers.models.xlm_roberta.modeling_xlm_roberta' (/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/xlm_roberta/modeling_xlm_roberta.py)\n", "result": "FAIL(load)", "total_seconds": 8.39421737473458} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/112.log b/validation/sentence-transformers/2026-11-03-top100/logs/112.log index 61bdd6d7c8876657bc786cdd8de54464f494b99d..dffdc44a33915587e471ab953e8cf063fd87a8ae 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/112.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/112.log @@ -1,12 +1,12 @@ CPU [18.622793197631836, 25.37649154663086, 24.276288986206055, 23.726768493652344] -2026-Nov-03 15:52:45.685760 2659064:2659064 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +2026-Nov-03 16:28:44.832058 2944506:2944506 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) /home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. _warn_trace_cache_once() Traceback (most recent call last): - File "/tmp/neuron-top100-sweep/runs/112/run.py", line 69, in + File "/tmp/neuron-top100-rerun/runs/112/run.py", line 70, in stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) ~~~~~^^ - File "/tmp/neuron-top100-sweep/runs/112/run.py", line 61, in score + File "/tmp/neuron-top100-rerun/runs/112/run.py", line 62, in score values=model.similarity(q,d) File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 1072, in similarity return self._similarity(embeddings1, embeddings2) @@ -15,4 +15,4 @@ Traceback (most recent call last): return torch.mm(a, b.transpose(0, 1)).to_dense() ~~~~~~~~~~~^^^^^^ RuntimeError: Inference tensors do not track version counter. -{"index": 112, "model": "sentence-transformers/multi-qa-mpnet-base-dot-v1", "revision": "17997f24dca0df1a4fed68894fb0e1e133e60482", "trending_rank": null, "downloads_rank": 34, "class": "SentenceTransformer", "dtype": "float32", "attention": "eager", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:52:37.157163+00:00", "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 0.9057708652690053, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [18.622793197631836, 25.37649154663086, 24.276288986206055, 23.726768493652344], "cpu_seconds": 0.13503286056220531, "cpu_top_document": 2, "model_device": "neuron:0", "status": "error", "stage": "neuron_inference", "error_type": "RuntimeError", "error": "Inference tensors do not track version counter.", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/112/run.py\", line 69, in \n stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/112/run.py\", line 61, in score\n values=model.similarity(q,d)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1072, in similarity\n return self._similarity(embeddings1, embeddings2)\n ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/similarity.py\", line 115, in dot_score\n return torch.mm(a, b.transpose(0, 1)).to_dense()\n ~~~~~~~~~~~^^^^^^\nRuntimeError: Inference tensors do not track version counter.\n", "result": "FAIL(forward)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 9, "sample_interval_seconds": 0.1, "peak_bytes": 602256101, "peak_categories": {"code": 8931864, "tensors": 501223424, "constants": 3440653, "scratchpad": 67108864, "misc": 17067456, "dma_rings": 4483840}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 481360448, "allocator_peak_reserved_bytes": 501219328, "total_seconds": 16.84018260613084} +{"index": 112, "model": "sentence-transformers/multi-qa-mpnet-base-dot-v1", "revision": "17997f24dca0df1a4fed68894fb0e1e133e60482", "trending_rank": null, "downloads_rank": 34, "class": "SentenceTransformer", "dtype": "float32", "attention": "eager", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:28:36.861567+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 0.3251633159816265, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [18.622793197631836, 25.37649154663086, 24.276288986206055, 23.726768493652344], "cpu_seconds": 0.0869925981387496, "cpu_top_document": 2, "model_device": "neuron:0", "status": "error", "stage": "neuron_inference", "error_type": "RuntimeError", "error": "Inference tensors do not track version counter.", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/112/run.py\", line 70, in \n stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device)\n ~~~~~^^\n File \"/tmp/neuron-top100-rerun/runs/112/run.py\", line 62, in score\n values=model.similarity(q,d)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1072, in similarity\n return self._similarity(embeddings1, embeddings2)\n ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/similarity.py\", line 115, in dot_score\n return torch.mm(a, b.transpose(0, 1)).to_dense()\n ~~~~~~~~~~~^^^^^^\nRuntimeError: Inference tensors do not track version counter.\n", "result": "FAIL(forward)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 9, "sample_interval_seconds": 0.1, "peak_bytes": 602256101, "peak_categories": {"code": 8931864, "tensors": 501223424, "constants": 3440653, "scratchpad": 67108864, "misc": 17067456, "dma_rings": 4483840}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 481622592, "allocator_peak_reserved_bytes": 501219328, "total_seconds": 16.085152969695628} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/113.log b/validation/sentence-transformers/2026-11-03-top100/logs/113.log index fcc669387deb7402dce5b5234893efec55c38397..373fa8f52950664054da4c176dd93fb1b3e579d1 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/113.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/113.log @@ -1,5 +1,5 @@ CPU [0.4128049910068512, 0.6425286531448364, 0.671911358833313, 0.6263707876205444] -2026-Nov-03 15:52:38.469930 2658598:2658598 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +2026-Nov-03 16:28:49.652791 2944770:2944770 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) /home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. _warn_trace_cache_once() -{"index": 113, "model": "sentence-transformers/paraphrase-MiniLM-L6-v2", "revision": "c9a2bfebc254878aee8c3aca9e6844d5bbb102d1", "trending_rank": null, "downloads_rank": 35, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:52:29.729330+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 0.8102488135918975, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.4128049910068512, 0.6425286531448364, 0.671911358833313, 0.6263707876205444], "cpu_seconds": 0.053661770187318325, "cpu_top_document": 3, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 0.3346906406804919, "neuron_scores": [0.4128049612045288, 0.6425285339355469, 0.671911358833313, 0.6263706684112549], "max_abs_error": 1.1920928955078125e-07, "neuron_top_document": 3, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.03071187622845173, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 5, "sample_interval_seconds": 0.1, "peak_bytes": 224453665, "peak_categories": {"code": 6359632, "tensors": 136318976, "constants": 3047441, "scratchpad": 67108864, "misc": 8984256, "dma_rings": 2634496}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 97223040, "allocator_peak_reserved_bytes": 136314880, "total_seconds": 16.178581200540066} +{"index": 113, "model": "sentence-transformers/paraphrase-MiniLM-L6-v2", "revision": "c9a2bfebc254878aee8c3aca9e6844d5bbb102d1", "trending_rank": null, "downloads_rank": 35, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:28:41.932312+00:00", "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 0.28461221139878035, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.4128049910068512, 0.6425286531448364, 0.671911358833313, 0.6263707876205444], "cpu_seconds": 0.0305560240522027, "cpu_top_document": 3, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 0.3316585561260581, "neuron_scores": [0.4128049612045288, 0.6425285339355469, 0.671911358833313, 0.6263706684112549], "max_abs_error": 1.1920928955078125e-07, "neuron_top_document": 3, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.028371094726026058, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 5, "sample_interval_seconds": 0.1, "peak_bytes": 224453665, "peak_categories": {"code": 6359632, "tensors": 136318976, "constants": 3047441, "scratchpad": 67108864, "misc": 8984256, "dma_rings": 2634496}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 97223040, "allocator_peak_reserved_bytes": 136314880, "total_seconds": 15.499813811853528} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/114.log b/validation/sentence-transformers/2026-11-03-top100/logs/114.log index 974a0b53148f296d107ce3306a6ab15987d94e62..1b874857575650d1f9f32722b06f1c148c2f5ec4 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/114.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/114.log @@ -1,6 +1,6 @@ Traceback (most recent call last): - File "/tmp/neuron-top100-sweep/runs/114/run.py", line 50, in - model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + File "/tmp/neuron-top100-rerun/runs/114/run.py", line 51, in + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})}) File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper return func(*args, **kwargs) File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 193, in __init__ @@ -56,4 +56,4 @@ Traceback (most recent call last): File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_bert_hyphen_implementation/f3ec4cf7de7e561007f27c9efc7148b0bd713f81/configuration_bert.py", line 22, in from transformers.onnx import OnnxConfig ModuleNotFoundError: No module named 'transformers.onnx' -{"index": 114, "model": "jinaai/jina-embeddings-v2-small-en", "revision": "44e7d1d6caec8c883c2d4b207588504d519788d0", "trending_rank": null, "downloads_rank": 38, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:52:45.768912+00:00", "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "status": "error", "stage": "load_cpu", "error_type": "ModuleNotFoundError", "error": "No module named 'transformers.onnx'", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/114/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 839, in __init__\n config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2259, in _load_config\n return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py\", line 422, in from_pretrained\n config_class = get_class_from_dynamic_module(\n class_ref, pretrained_model_name_or_path, code_revision=code_revision, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 625, in get_class_from_dynamic_module\n return get_class_in_module(class_name, final_module, force_reload=force_download)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 309, in get_class_in_module\n module_spec.loader.exec_module(module)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^\n File \"\", line 759, in exec_module\n File \"\", line 491, in _call_with_frames_removed\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_bert_hyphen_implementation/f3ec4cf7de7e561007f27c9efc7148b0bd713f81/configuration_bert.py\", line 22, in \n from transformers.onnx import OnnxConfig\nModuleNotFoundError: No module named 'transformers.onnx'\n", "result": "FAIL(load)", "total_seconds": 8.320869617164135} +{"index": 114, "model": "jinaai/jina-embeddings-v2-small-en", "revision": "44e7d1d6caec8c883c2d4b207588504d519788d0", "trending_rank": null, "downloads_rank": 38, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:28:45.116156+00:00", "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "status": "error", "stage": "load_cpu", "error_type": "ModuleNotFoundError", "error": "No module named 'transformers.onnx'", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/114/run.py\", line 51, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 839, in __init__\n config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2259, in _load_config\n return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py\", line 422, in from_pretrained\n config_class = get_class_from_dynamic_module(\n class_ref, pretrained_model_name_or_path, code_revision=code_revision, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 625, in get_class_from_dynamic_module\n return get_class_in_module(class_name, final_module, force_reload=force_download)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 309, in get_class_in_module\n module_spec.loader.exec_module(module)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^\n File \"\", line 759, in exec_module\n File \"\", line 491, in _call_with_frames_removed\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_bert_hyphen_implementation/f3ec4cf7de7e561007f27c9efc7148b0bd713f81/configuration_bert.py\", line 22, in \n from transformers.onnx import OnnxConfig\nModuleNotFoundError: No module named 'transformers.onnx'\n", "result": "FAIL(load)", "total_seconds": 7.416379694826901} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/116.log b/validation/sentence-transformers/2026-11-03-top100/logs/116.log index ea9b99c62ca873b06e7c7d3781d38ecb0ba66f1e..ae6e8a24cde4e1e95b3bc2f721e48b759ad4a659 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/116.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/116.log @@ -1,5 +1,5 @@ CPU [0.3725859224796295, 0.5596363544464111, 0.475629597902298, 0.6078733205795288] -2026-Nov-03 15:53:01.715051 2661367:2661367 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +2026-Nov-03 16:29:01.995093 2946484:2946484 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) /home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. _warn_trace_cache_once() -{"index": 116, "model": "jhgan/ko-sroberta-multitask", "revision": "8fca7c9c98c26599be0e14b9916b11a756a26f19", "trending_rank": null, "downloads_rank": 42, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:52:52.178170+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 1.2254862170666456, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.3725859224796295, 0.5596363544464111, 0.475629597902298, 0.6078733205795288], "cpu_seconds": 0.2339512938633561, "cpu_top_document": 4, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 46.99652501568198, "neuron_scores": [0.3725859224796295, 0.5596362352371216, 0.47562968730926514, 0.6078732013702393], "max_abs_error": 1.1920928955078125e-07, "neuron_top_document": 4, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.04155152477324009, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 472, "sample_interval_seconds": 0.1, "peak_bytes": 604724833, "peak_categories": {"code": 7286160, "tensors": 511709184, "constants": 3866641, "scratchpad": 67108864, "misc": 12000704, "dma_rings": 2753280}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 492647808, "allocator_peak_reserved_bytes": 511705088, "total_seconds": 62.91153274290264} +{"index": 116, "model": "jhgan/ko-sroberta-multitask", "revision": "8fca7c9c98c26599be0e14b9916b11a756a26f19", "trending_rank": null, "downloads_rank": 42, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:28:53.794159+00:00", "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 0.3335246341302991, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.3725859224796295, 0.5596363544464111, 0.475629597902298, 0.6078733205795288], "cpu_seconds": 0.13313346728682518, "cpu_top_document": 4, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 2.8663115464150906, "neuron_scores": [0.3725859224796295, 0.5596362352371216, 0.47562968730926514, 0.6078732013702393], "max_abs_error": 1.1920928955078125e-07, "neuron_top_document": 4, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.043972715735435486, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 31, "sample_interval_seconds": 0.1, "peak_bytes": 602627681, "peak_categories": {"code": 7286160, "tensors": 509612032, "constants": 3866641, "scratchpad": 67108864, "misc": 12000704, "dma_rings": 2753280}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 492353024, "allocator_peak_reserved_bytes": 509607936, "total_seconds": 18.043465487658978} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/122.log b/validation/sentence-transformers/2026-11-03-top100/logs/122.log index 4afede4233d4d6dac4f6b3235ca9a7eec9b6e837..a0e6d9ff77cd4b5d3f2d8c34e2c7e11b53a198ed 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/122.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/122.log @@ -1,5 +1,5 @@ CPU [0.5895186066627502, 0.6596499085426331, 0.6842697262763977, 0.7085862755775452] -2026-Nov-03 15:53:40.215912 2667583:2667583 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +2026-Nov-03 16:29:02.548514 2946508:2946508 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) /home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. _warn_trace_cache_once() -{"index": 122, "model": "sentence-transformers-testing/stsb-bert-tiny-safetensors", "revision": "f3cb857cba53019a20df283396bcca179cf051a4", "trending_rank": null, "downloads_rank": 49, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:53:27.210620+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 0.339894263073802, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.5895186066627502, 0.6596499085426331, 0.6842697262763977, 0.7085862755775452], "cpu_seconds": 0.01665687095373869, "cpu_top_document": 4, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 35.90269974432886, "neuron_scores": [0.5895187258720398, 0.6596502065658569, 0.6842698454856873, 0.7085865139961243], "max_abs_error": 2.980232238769531e-07, "neuron_top_document": 4, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.01644056662917137, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 360, "sample_interval_seconds": 0.1, "peak_bytes": 108670225, "peak_categories": {"code": 6199232, "tensors": 20975616, "constants": 2523153, "scratchpad": 67108864, "misc": 9350720, "dma_rings": 2512640}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 19238400, "allocator_peak_reserved_bytes": 20971520, "total_seconds": 57.112566504627466} +{"index": 122, "model": "sentence-transformers-testing/stsb-bert-tiny-safetensors", "revision": "f3cb857cba53019a20df283396bcca179cf051a4", "trending_rank": null, "downloads_rank": 49, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:28:54.750978+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 0.25583696737885475, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.5895186066627502, 0.6596499085426331, 0.6842697262763977, 0.7085862755775452], "cpu_seconds": 0.016696914099156857, "cpu_top_document": 4, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 0.31738284416496754, "neuron_scores": [0.5895187258720398, 0.6596502065658569, 0.6842698454856873, 0.7085865139961243], "max_abs_error": 2.980232238769531e-07, "neuron_top_document": 4, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.015191987156867981, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 5, "sample_interval_seconds": 0.1, "peak_bytes": 108670225, "peak_categories": {"code": 6199232, "tensors": 20975616, "constants": 2523153, "scratchpad": 67108864, "misc": 9350720, "dma_rings": 2512640}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 19238400, "allocator_peak_reserved_bytes": 20971520, "total_seconds": 14.501598661765456} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/123.log b/validation/sentence-transformers/2026-11-03-top100/logs/123.log index 24121a84ee4c92a11f35286894ee876e7d9d8568..0b665d38b07714221481de16b10194484cfc1805 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/123.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/123.log @@ -9,11 +9,12 @@ classifier.bias | UNEXPECTED | | Notes: - UNEXPECTED: can be ignored when loading from different task/architecture; not ok if you expect identical arch. Traceback (most recent call last): - File "/tmp/neuron-top100-sweep/runs/123/run.py", line 63, in + File "/tmp/neuron-top100-rerun/runs/123/run.py", line 64, in stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) ~~~~~^^ - File "/tmp/neuron-top100-sweep/runs/123/run.py", line 58, in score - q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + File "/tmp/neuron-top100-rerun/runs/123/run.py", line 59, in score + q=(model.encode([query],batch_size=4,show_progress_bar=False,**kw,**SPEC['query_kwargs']) if SPEC.get('query_kwargs') else model.encode_query([query],batch_size=4,show_progress_bar=False,**kw)) + ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper return func(*args, **kwargs) File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 370, in encode_query @@ -68,5 +69,5 @@ Traceback (most recent call last): File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py", line 392, in forward rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim] ~~~~~~~~^^^^^^^^^^^^^^ -IndexError: index 670466312 is out of bounds for dimension 0 with size 11 -{"index": 123, "model": "Alibaba-NLP/gte-multilingual-base", "revision": "9bbca17d9273fd0d03d5725c7a4b0f6b45142062", "trending_rank": null, "downloads_rank": 50, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:53:34.948327+00:00", "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 2.7565125236287713, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "status": "error", "stage": "cpu_inference", "error_type": "IndexError", "error": "index 670466312 is out of bounds for dimension 0 with size 11", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/123/run.py\", line 63, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/123/run.py\", line 58, in score\n q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 670466312 is out of bounds for dimension 0 with size 11\n", "result": "FAIL(forward)", "total_seconds": 10.929977044463158} +IndexError: index 1193 is out of bounds for dimension 0 with size 11 +{"index": 123, "model": "Alibaba-NLP/gte-multilingual-base", "revision": "9bbca17d9273fd0d03d5725c7a4b0f6b45142062", "trending_rank": null, "downloads_rank": 50, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:28:57.681382+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 1.974479383789003, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "status": "error", "stage": "cpu_inference", "error_type": "IndexError", "error": "index 1193 is out of bounds for dimension 0 with size 11", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/123/run.py\", line 64, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-rerun/runs/123/run.py\", line 59, in score\n q=(model.encode([query],batch_size=4,show_progress_bar=False,**kw,**SPEC['query_kwargs']) if SPEC.get('query_kwargs') else model.encode_query([query],batch_size=4,show_progress_bar=False,**kw))\n ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 1193 is out of bounds for dimension 0 with size 11\n", "result": "FAIL(forward)", "total_seconds": 13.360865868628025} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/129.log b/validation/sentence-transformers/2026-11-03-top100/logs/129.log index 74add8e99cc434b17a6430b2d3eae72681c6f095..f209b22d15e6728dcb72c70a6d1f4aa0d6497f04 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/129.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/129.log @@ -1,11 +1,12 @@ /home/jingyahuang/.cache/huggingface/modules/transformers_modules/nomic_hyphen_ai/nomic_hyphen_bert_hyphen_2048/7710840340a098cfb869c4f65e87cf2b1b70caca/modeling_hf_nomic_bert.py:1634: UserWarning: Install Nomic's megablocks fork for better speed: `pip install git+https://github.com/nomic-ai/megablocks.git` warnings.warn("Install Nomic's megablocks fork for better speed: " + Traceback (most recent call last): - File "/tmp/neuron-top100-sweep/runs/129/run.py", line 63, in + File "/tmp/neuron-top100-rerun/runs/129/run.py", line 64, in stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) ~~~~~^^ - File "/tmp/neuron-top100-sweep/runs/129/run.py", line 58, in score - q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + File "/tmp/neuron-top100-rerun/runs/129/run.py", line 59, in score + q=(model.encode([query],batch_size=4,show_progress_bar=False,**kw,**SPEC['query_kwargs']) if SPEC.get('query_kwargs') else model.encode_query([query],batch_size=4,show_progress_bar=False,**kw)) + ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper return func(*args, **kwargs) File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 370, in encode_query @@ -50,4 +51,4 @@ Traceback (most recent call last): f"'{type(self).__name__}' object has no attribute '{name}'" ) AttributeError: 'NomicBertModel' object has no attribute 'get_extended_attention_mask' -{"index": 129, "model": "nomic-ai/nomic-embed-text-v2-moe", "revision": "1066b6599d099fbb93dfcb64f9c37a7c9e503e85", "trending_rank": null, "downloads_rank": 58, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:54:25.528812+00:00", "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 6.00257970392704, "prompts": {"query": "search_query: ", "document": "", "passage": "search_document: ", "Classification": "classification: ", "MultilabelClassification": "classification: ", "Clustering": "clustering: ", "PairClassification": "classification: ", "STS": "classification: ", "Summarization": "classification: ", "Speed": "search_document: "}, "parameter_dtypes": ["torch.float32"], "status": "error", "stage": "cpu_inference", "error_type": "AttributeError", "error": "'NomicBertModel' object has no attribute 'get_extended_attention_mask'", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/129/run.py\", line 63, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/129/run.py\", line 58, in score\n q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/nomic_hyphen_ai/nomic_hyphen_bert_hyphen_2048/7710840340a098cfb869c4f65e87cf2b1b70caca/modeling_hf_nomic_bert.py\", line 1914, in forward\n attention_mask = self.get_extended_attention_mask(attention_mask, hidden_states.shape[:-1])\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1972, in __getattr__\n raise AttributeError(\n f\"'{type(self).__name__}' object has no attribute '{name}'\"\n )\nAttributeError: 'NomicBertModel' object has no attribute 'get_extended_attention_mask'\n", "result": "FAIL(forward)", "total_seconds": 13.943258575163782} +{"index": 129, "model": "nomic-ai/nomic-embed-text-v2-moe", "revision": "1066b6599d099fbb93dfcb64f9c37a7c9e503e85", "trending_rank": null, "downloads_rank": 58, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:28:59.211576+00:00", "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 4.217651919461787, "prompts": {"query": "search_query: ", "document": "", "passage": "search_document: ", "Classification": "classification: ", "MultilabelClassification": "classification: ", "Clustering": "clustering: ", "PairClassification": "classification: ", "STS": "classification: ", "Summarization": "classification: ", "Speed": "search_document: "}, "parameter_dtypes": ["torch.float32"], "status": "error", "stage": "cpu_inference", "error_type": "AttributeError", "error": "'NomicBertModel' object has no attribute 'get_extended_attention_mask'", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/129/run.py\", line 64, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-rerun/runs/129/run.py\", line 59, in score\n q=(model.encode([query],batch_size=4,show_progress_bar=False,**kw,**SPEC['query_kwargs']) if SPEC.get('query_kwargs') else model.encode_query([query],batch_size=4,show_progress_bar=False,**kw))\n ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/nomic_hyphen_ai/nomic_hyphen_bert_hyphen_2048/7710840340a098cfb869c4f65e87cf2b1b70caca/modeling_hf_nomic_bert.py\", line 1914, in forward\n attention_mask = self.get_extended_attention_mask(attention_mask, hidden_states.shape[:-1])\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1972, in __getattr__\n raise AttributeError(\n f\"'{type(self).__name__}' object has no attribute '{name}'\"\n )\nAttributeError: 'NomicBertModel' object has no attribute 'get_extended_attention_mask'\n", "result": "FAIL(forward)", "total_seconds": 14.102670108899474} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/137.log b/validation/sentence-transformers/2026-11-03-top100/logs/137.log index 43c0713bca72c540a47b0585dd01afd5c65f0f31..88a03eb31b807cf8cdb6b02e2b58a2f51a765695 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/137.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/137.log @@ -1,12 +1,12 @@ CPU [163.48626708984375, 172.62042236328125, 169.48434448242188, 169.65316772460938] -2026-Nov-03 15:55:20.166982 2695042:2695042 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +2026-Nov-03 16:29:20.481939 2947849:2947849 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) /home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. _warn_trace_cache_once() Traceback (most recent call last): - File "/tmp/neuron-top100-sweep/runs/137/run.py", line 69, in + File "/tmp/neuron-top100-rerun/runs/137/run.py", line 70, in stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) ~~~~~^^ - File "/tmp/neuron-top100-sweep/runs/137/run.py", line 61, in score + File "/tmp/neuron-top100-rerun/runs/137/run.py", line 62, in score values=model.similarity(q,d) File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 1072, in similarity return self._similarity(embeddings1, embeddings2) @@ -15,4 +15,4 @@ Traceback (most recent call last): return torch.mm(a, b.transpose(0, 1)).to_dense() ~~~~~~~~~~~^^^^^^ RuntimeError: Inference tensors do not track version counter. -{"index": 137, "model": "sentence-transformers/msmarco-bert-base-dot-v5", "revision": "dbf04e3911e5b11c887fe563d3f2479f81c4415f", "trending_rank": null, "downloads_rank": 68, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:55:09.237654+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 1.3280827356502414, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [163.48626708984375, 172.62042236328125, 169.48434448242188, 169.65316772460938], "cpu_seconds": 0.12728324253112078, "cpu_top_document": 2, "model_device": "neuron:0", "status": "error", "stage": "neuron_inference", "error_type": "RuntimeError", "error": "Inference tensors do not track version counter.", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/137/run.py\", line 69, in \n stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/137/run.py\", line 61, in score\n values=model.similarity(q,d)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1072, in similarity\n return self._similarity(embeddings1, embeddings2)\n ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/similarity.py\", line 115, in dot_score\n return torch.mm(a, b.transpose(0, 1)).to_dense()\n ~~~~~~~~~~~^^^^^^\nRuntimeError: Inference tensors do not track version counter.\n", "result": "FAIL(forward)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 6, "sample_interval_seconds": 0.1, "peak_bytes": 588585921, "peak_categories": {"code": 6350192, "tensors": 501223424, "constants": 2850833, "scratchpad": 67108864, "misc": 8545088, "dma_rings": 2507520}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 481544320, "allocator_peak_reserved_bytes": 501219328, "total_seconds": 20.18780482839793} +{"index": 137, "model": "sentence-transformers/msmarco-bert-base-dot-v5", "revision": "dbf04e3911e5b11c887fe563d3f2479f81c4415f", "trending_rank": null, "downloads_rank": 68, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:29:10.989662+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 0.3936761999502778, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [163.48626708984375, 172.62042236328125, 169.48434448242188, 169.65316772460938], "cpu_seconds": 0.10769253689795732, "cpu_top_document": 2, "model_device": "neuron:0", "status": "error", "stage": "neuron_inference", "error_type": "RuntimeError", "error": "Inference tensors do not track version counter.", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/137/run.py\", line 70, in \n stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device)\n ~~~~~^^\n File \"/tmp/neuron-top100-rerun/runs/137/run.py\", line 62, in score\n values=model.similarity(q,d)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1072, in similarity\n return self._similarity(embeddings1, embeddings2)\n ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/similarity.py\", line 115, in dot_score\n return torch.mm(a, b.transpose(0, 1)).to_dense()\n ~~~~~~~~~~~^^^^^^\nRuntimeError: Inference tensors do not track version counter.\n", "result": "FAIL(forward)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 5, "sample_interval_seconds": 0.1, "peak_bytes": 588585921, "peak_categories": {"code": 6350192, "tensors": 501223424, "constants": 2850833, "scratchpad": 67108864, "misc": 8545088, "dma_rings": 2507520}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 481544192, "allocator_peak_reserved_bytes": 501219328, "total_seconds": 16.502824168652296} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/140.log b/validation/sentence-transformers/2026-11-03-top100/logs/140.log index 5da0c4f4975269d9a067855c991b52b450a445de..58d777e91c3d8fbd26199d637bf9d5f529abbdfa 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/140.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/140.log @@ -1,11 +1,12 @@ /tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/configuration_utils.py:723: FutureWarning: `rope_scaling` is deprecated and will be removed in a future version. Set `rope_parameters` instead, with `rope_type` and `rope_theta` included. warnings.warn( Traceback (most recent call last): - File "/tmp/neuron-top100-sweep/runs/140/run.py", line 63, in + File "/tmp/neuron-top100-rerun/runs/140/run.py", line 64, in stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) ~~~~~^^ - File "/tmp/neuron-top100-sweep/runs/140/run.py", line 58, in score - q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + File "/tmp/neuron-top100-rerun/runs/140/run.py", line 59, in score + q=(model.encode([query],batch_size=4,show_progress_bar=False,**kw,**SPEC['query_kwargs']) if SPEC.get('query_kwargs') else model.encode_query([query],batch_size=4,show_progress_bar=False,**kw)) + ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper return func(*args, **kwargs) File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 370, in encode_query @@ -60,5 +61,5 @@ Traceback (most recent call last): File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py", line 392, in forward rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim] ~~~~~~~~^^^^^^^^^^^^^^ -IndexError: index 140471200382993 is out of bounds for dimension 0 with size 11 -{"index": 140, "model": "Alibaba-NLP/gte-base-en-v1.5", "revision": "a829fd0e060bb84554da0dfd354d0de0f7712b7f", "trending_rank": null, "downloads_rank": 71, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:55:35.076933+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 1.5855842670425773, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "status": "error", "stage": "cpu_inference", "error_type": "IndexError", "error": "index 140471200382993 is out of bounds for dimension 0 with size 11", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/140/run.py\", line 63, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/140/run.py\", line 58, in score\n q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 140471200382993 is out of bounds for dimension 0 with size 11\n", "result": "FAIL(forward)", "total_seconds": 12.225869985297322} +IndexError: index 23048696 is out of bounds for dimension 0 with size 11 +{"index": 140, "model": "Alibaba-NLP/gte-base-en-v1.5", "revision": "a829fd0e060bb84554da0dfd354d0de0f7712b7f", "trending_rank": null, "downloads_rank": 71, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:29:12.754293+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 0.5557530513033271, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "status": "error", "stage": "cpu_inference", "error_type": "IndexError", "error": "index 23048696 is out of bounds for dimension 0 with size 11", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/140/run.py\", line 64, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-rerun/runs/140/run.py\", line 59, in score\n q=(model.encode([query],batch_size=4,show_progress_bar=False,**kw,**SPEC['query_kwargs']) if SPEC.get('query_kwargs') else model.encode_query([query],batch_size=4,show_progress_bar=False,**kw))\n ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 23048696 is out of bounds for dimension 0 with size 11\n", "result": "FAIL(forward)", "total_seconds": 8.462658165954053} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/144.log b/validation/sentence-transformers/2026-11-03-top100/logs/144.log index 84e847524620ac9cbac218a12c7325ae745b966a..26ff503ca38e0685a6a5619c5dfee1129836efff 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/144.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/144.log @@ -1,5 +1,5 @@ CPU [0.4463503956794739, 0.692706823348999, 0.6985108852386475, 0.6134428977966309] -2026-Nov-03 15:56:23.036324 2721788:2721788 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +2026-Nov-03 16:29:21.667891 2948406:2948406 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) /home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. _warn_trace_cache_once() -{"index": 144, "model": "minishlab/potion-base-8M", "revision": "bf8b056651a2c21b8d2565580b8569da283cab23", "trending_rank": null, "downloads_rank": 75, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:56:13.809543+00:00", "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 0.3890756834298372, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.4463503956794739, 0.692706823348999, 0.6985108852386475, 0.6134428977966309], "cpu_seconds": 0.06325524486601353, "cpu_top_document": 3, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 6.991000535897911, "neuron_scores": [0.4463503956794739, 0.6927067637443542, 0.6985103487968445, 0.6134430170059204], "max_abs_error": 5.364418029785156e-07, "neuron_top_document": 3, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.007872754707932472, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 72, "sample_interval_seconds": 0.1, "peak_bytes": 40488680, "peak_categories": {"code": 4221032, "tensors": 33558528, "constants": 327680, "misc": 2111104, "dma_rings": 270336}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 30251264, "allocator_peak_reserved_bytes": 33554432, "total_seconds": 25.33328312728554} +{"index": 144, "model": "minishlab/potion-base-8M", "revision": "bf8b056651a2c21b8d2565580b8569da283cab23", "trending_rank": null, "downloads_rank": 75, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:29:13.720726+00:00", "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 0.17954427190124989, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.4463503956794739, 0.692706823348999, 0.6985108852386475, 0.6134428977966309], "cpu_seconds": 0.016356867738068104, "cpu_top_document": 3, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 0.06938606780022383, "neuron_scores": [0.4463503956794739, 0.6927067637443542, 0.6985103487968445, 0.6134430170059204], "max_abs_error": 5.364418029785156e-07, "neuron_top_document": 3, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.007342119701206684, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 2, "sample_interval_seconds": 0.1, "peak_bytes": 40488680, "peak_categories": {"code": 4221032, "tensors": 33558528, "constants": 327680, "misc": 2111104, "dma_rings": 270336}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 30251008, "allocator_peak_reserved_bytes": 33554432, "total_seconds": 15.43065809365362} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/146.log b/validation/sentence-transformers/2026-11-03-top100/logs/146.log index 314ce88404cba7271d4bb73fc4f009c6b69eed71..1140e12a85f6321307ce931c3706b824c42b2b56 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/146.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/146.log @@ -1,5 +1,5 @@ CPU [0.46113887429237366, 0.7457321882247925, 0.8234667181968689, 0.7024552822113037] -2026-Nov-03 15:56:47.229090 2726999:2726999 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +2026-Nov-03 16:29:23.457873 2948419:2948419 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) /home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. _warn_trace_cache_once() -{"index": 146, "model": "TimKond/S-PubMedBert-MedQuAD", "revision": "b73d84decb1da6bd4c1cf084a1b6c6602157d267", "trending_rank": null, "downloads_rank": 77, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:56:33.415911+00:00", "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 1.2769848257303238, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.46113887429237366, 0.7457321882247925, 0.8234667181968689, 0.7024552822113037], "cpu_seconds": 0.15781457629054785, "cpu_top_document": 3, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 0.31044141575694084, "neuron_scores": [0.46113866567611694, 0.745732307434082, 0.823466420173645, 0.7024555206298828], "max_abs_error": 2.980232238769531e-07, "neuron_top_document": 3, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.03502273000776768, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 6, "sample_interval_seconds": 0.1, "peak_bytes": 589734025, "peak_categories": {"code": 6608056, "tensors": 501223424, "constants": 3309585, "scratchpad": 67108864, "misc": 9499584, "dma_rings": 1984512}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 482003968, "allocator_peak_reserved_bytes": 501219328, "total_seconds": 21.299358570948243} +{"index": 146, "model": "TimKond/S-PubMedBert-MedQuAD", "revision": "b73d84decb1da6bd4c1cf084a1b6c6602157d267", "trending_rank": null, "downloads_rank": 77, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:29:15.091834+00:00", "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 0.5070198401808739, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.46113887429237366, 0.7457321882247925, 0.8234667181968689, 0.7024552822113037], "cpu_seconds": 0.1015954865142703, "cpu_top_document": 3, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 0.32567631360143423, "neuron_scores": [0.46113866567611694, 0.745732307434082, 0.823466420173645, 0.7024555206298828], "max_abs_error": 2.980232238769531e-07, "neuron_top_document": 3, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.035069906152784824, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 6, "sample_interval_seconds": 0.1, "peak_bytes": 589734025, "peak_categories": {"code": 6608056, "tensors": 501223424, "constants": 3309585, "scratchpad": 67108864, "misc": 9499584, "dma_rings": 1984512}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 482003968, "allocator_peak_reserved_bytes": 501219328, "total_seconds": 14.539557542651892} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/158.log b/validation/sentence-transformers/2026-11-03-top100/logs/158.log index 3597c525d0ac42de027a8a9182c0fbda148ddb2d..b25f9d50fd890924df32e48516580d45f7e64adf 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/158.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/158.log @@ -1,6 +1,6 @@ Traceback (most recent call last): - File "/tmp/neuron-top100-sweep/runs/158/run.py", line 50, in - model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + File "/tmp/neuron-top100-rerun/runs/158/run.py", line 51, in + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})}) File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper return func(*args, **kwargs) File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 193, in __init__ @@ -73,4 +73,4 @@ Traceback (most recent call last): f"'{type(self).__name__}' object has no attribute '{name}'" ) AttributeError: 'NVEmbedModel' object has no attribute 'all_tied_weights_keys'. Did you mean: '_tied_weights_keys'? -{"index": 158, "model": "nvidia/NV-Embed-v2", "revision": "3fa59658547db50a1e8e3346cf057fd0c77ed6ef", "trending_rank": null, "downloads_rank": 93, "class": "SentenceTransformer", "dtype": "bfloat16", "attention": "eager", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:58:12.973659+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "status": "error", "stage": "load_cpu", "error_type": "AttributeError", "error": "'NVEmbedModel' object has no attribute 'all_tied_weights_keys'", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/158/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 855, in __init__\n self.model = self._load_model(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path, transformer_task, config, backend, is_peft_model, **model_kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2343, in _load_model\n return model_cls.from_pretrained(model_name_or_path, config=config, **model_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py\", line 389, in from_pretrained\n return model_class.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4356, in from_pretrained\n loading_info = cls._finalize_model_loading(model, load_config, loading_info)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4524, in _finalize_model_loading\n model._move_missing_keys_from_meta_to_device(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n loading_info.missing_and_mismatched(),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<2 lines>...\n load_config.hf_quantizer,\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4781, in _move_missing_keys_from_meta_to_device\n for key in missing_keys - self.all_tied_weights_keys.keys():\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1972, in __getattr__\n raise AttributeError(\n f\"'{type(self).__name__}' object has no attribute '{name}'\"\n )\nAttributeError: 'NVEmbedModel' object has no attribute 'all_tied_weights_keys'. Did you mean: '_tied_weights_keys'?\n", "result": "FAIL(load)", "total_seconds": 29.60385298728943} +{"index": 158, "model": "nvidia/NV-Embed-v2", "revision": "3fa59658547db50a1e8e3346cf057fd0c77ed6ef", "trending_rank": null, "downloads_rank": 93, "class": "SentenceTransformer", "dtype": "bfloat16", "attention": "eager", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:29:22.582373+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "status": "error", "stage": "load_cpu", "error_type": "AttributeError", "error": "'NVEmbedModel' object has no attribute 'all_tied_weights_keys'", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/158/run.py\", line 51, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 855, in __init__\n self.model = self._load_model(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path, transformer_task, config, backend, is_peft_model, **model_kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2343, in _load_model\n return model_cls.from_pretrained(model_name_or_path, config=config, **model_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py\", line 389, in from_pretrained\n return model_class.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4356, in from_pretrained\n loading_info = cls._finalize_model_loading(model, load_config, loading_info)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4524, in _finalize_model_loading\n model._move_missing_keys_from_meta_to_device(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n loading_info.missing_and_mismatched(),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<2 lines>...\n load_config.hf_quantizer,\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4781, in _move_missing_keys_from_meta_to_device\n for key in missing_keys - self.all_tied_weights_keys.keys():\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1972, in __getattr__\n raise AttributeError(\n f\"'{type(self).__name__}' object has no attribute '{name}'\"\n )\nAttributeError: 'NVEmbedModel' object has no attribute 'all_tied_weights_keys'. Did you mean: '_tied_weights_keys'?\n", "result": "FAIL(load)", "total_seconds": 10.885062549263239} diff --git a/validation/sentence-transformers/2026-11-03-top100/logs/164.log b/validation/sentence-transformers/2026-11-03-top100/logs/164.log index 592b03a04223233c6e5f07b5092ee98efbc9b1cd..180526faa3309ace38c28bae2a5be5948be4f8aa 100644 --- a/validation/sentence-transformers/2026-11-03-top100/logs/164.log +++ b/validation/sentence-transformers/2026-11-03-top100/logs/164.log @@ -1,12 +1,12 @@ CPU [37.162418365478516, 43.29322814941406, 43.17018127441406, 42.00007629394531] -2026-Nov-03 16:00:02.462563 2793780:2793780 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +2026-Nov-03 16:29:37.249477 2949738:2949738 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) /home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. _warn_trace_cache_once() Traceback (most recent call last): - File "/tmp/neuron-top100-sweep/runs/164/run.py", line 69, in + File "/tmp/neuron-top100-rerun/runs/164/run.py", line 70, in stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) ~~~~~^^ - File "/tmp/neuron-top100-sweep/runs/164/run.py", line 61, in score + File "/tmp/neuron-top100-rerun/runs/164/run.py", line 62, in score values=model.similarity(q,d) File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 1072, in similarity return self._similarity(embeddings1, embeddings2) @@ -15,4 +15,4 @@ Traceback (most recent call last): return torch.mm(a, b.transpose(0, 1)).to_dense() ~~~~~~~~~~~^^^^^^ RuntimeError: Inference tensors do not track version counter. -{"index": 164, "model": "sentence-transformers/multi-qa-MiniLM-L6-dot-v1", "revision": "4151c507ffb0f2fcd311cf431f54b5fc7d097851", "trending_rank": null, "downloads_rank": 100, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:59:45.659267+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 1.0640968950465322, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [37.162418365478516, 43.29322814941406, 43.17018127441406, 42.00007629394531], "cpu_seconds": 0.06058734655380249, "cpu_top_document": 2, "model_device": "neuron:0", "status": "error", "stage": "neuron_inference", "error_type": "RuntimeError", "error": "Inference tensors do not track version counter.", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/164/run.py\", line 69, in \n stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/164/run.py\", line 61, in score\n values=model.similarity(q,d)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1072, in similarity\n return self._similarity(embeddings1, embeddings2)\n ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/similarity.py\", line 115, in dot_score\n return torch.mm(a, b.transpose(0, 1)).to_dense()\n ~~~~~~~~~~~^^^^^^\nRuntimeError: Inference tensors do not track version counter.\n", "result": "FAIL(forward)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 5, "sample_interval_seconds": 0.1, "peak_bytes": 223731977, "peak_categories": {"code": 6030712, "tensors": 136318976, "constants": 2785297, "scratchpad": 67108864, "misc": 8847488, "dma_rings": 2640640}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 97223168, "allocator_peak_reserved_bytes": 136314880, "total_seconds": 24.259669181890786} +{"index": 164, "model": "sentence-transformers/multi-qa-MiniLM-L6-dot-v1", "revision": "4151c507ffb0f2fcd311cf431f54b5fc7d097851", "trending_rank": null, "downloads_rank": 100, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T16:29:29.177391+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 0.3136888975277543, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [37.162418365478516, 43.29322814941406, 43.17018127441406, 42.00007629394531], "cpu_seconds": 0.028249465860426426, "cpu_top_document": 2, "model_device": "neuron:0", "status": "error", "stage": "neuron_inference", "error_type": "RuntimeError", "error": "Inference tensors do not track version counter.", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/164/run.py\", line 70, in \n stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device)\n ~~~~~^^\n File \"/tmp/neuron-top100-rerun/runs/164/run.py\", line 62, in score\n values=model.similarity(q,d)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1072, in similarity\n return self._similarity(embeddings1, embeddings2)\n ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/similarity.py\", line 115, in dot_score\n return torch.mm(a, b.transpose(0, 1)).to_dense()\n ~~~~~~~~~~~^^^^^^\nRuntimeError: Inference tensors do not track version counter.\n", "result": "FAIL(forward)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 5, "sample_interval_seconds": 0.1, "peak_bytes": 223731977, "peak_categories": {"code": 6030712, "tensors": 136318976, "constants": 2785297, "scratchpad": 67108864, "misc": 8847488, "dma_rings": 2640640}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 97223168, "allocator_peak_reserved_bytes": 136314880, "total_seconds": 16.736036530695856} diff --git a/validation/sentence-transformers/2026-11-03-top100/memory_samples/007.json b/validation/sentence-transformers/2026-11-03-top100/memory_samples/007.json index 66774b4fa0bd36ba623676ad80fc5f97c93b9efe..08969cd6e7bdbfd34f8a42f6bcd40e712874dce2 100644 --- a/validation/sentence-transformers/2026-11-03-top100/memory_samples/007.json +++ b/validation/sentence-transformers/2026-11-03-top100/memory_samples/007.json @@ -1 +1 @@ -[{"elapsed_seconds": 2.182554453611374e-05, "bytes": 7141760, "categories": {"code": 3842944, "tensors": 2097152, "misc": 1144320, "dma_rings": 57344}}, {"elapsed_seconds": 0.10033575538545847, "bytes": 1953403664, "categories": {"code": 3880080, "tensors": 1948254208, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.20041451789438725, "bytes": 2590937872, "categories": {"code": 3880080, "tensors": 2585788416, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.30048870481550694, "bytes": 4033778448, "categories": {"code": 3880080, "tensors": 4028628992, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.40055954083800316, "bytes": 5325624080, "categories": {"code": 3880080, "tensors": 5320474624, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.5006627449765801, "bytes": 6768464656, "categories": {"code": 3880080, "tensors": 6763315200, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.6007413947954774, "bytes": 8060310288, "categories": {"code": 3880080, "tensors": 8055160832, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.7008123304694891, "bytes": 9419264784, "categories": {"code": 3880080, "tensors": 9414115328, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.8511335793882608, "bytes": 9570259728, "categories": {"code": 3880080, "tensors": 9565110272, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.9512062203139067, "bytes": 11013100304, "categories": {"code": 3880080, "tensors": 11007950848, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 1.051294500939548, "bytes": 12304945936, "categories": {"code": 3880080, "tensors": 12299796480, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 1.1513691693544388, "bytes": 13747786512, "categories": {"code": 3880080, "tensors": 13742637056, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 1.2514410335570574, "bytes": 15039632144, "categories": {"code": 3880080, "tensors": 15034482688, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 1.3851883672177792, "bytes": 15677166352, "categories": {"code": 3880080, "tensors": 15672016896, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 1.4853369127959013, "bytes": 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"categories": {"code": 3880080, "tensors": 10286530560, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 1.5429472429677844, "bytes": 11432530704, "categories": {"code": 3880080, "tensors": 11427381248, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 1.6430310783907771, "bytes": 12640490256, "categories": {"code": 3880080, "tensors": 12635340800, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 1.743112189695239, "bytes": 13294801680, "categories": {"code": 3880080, "tensors": 13289652224, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 1.8431895980611444, "bytes": 13932335888, "categories": {"code": 3880080, "tensors": 13927186432, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 1.9432630445808172, "bytes": 14620201744, "categories": {"code": 3880080, "tensors": 14615052288, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 2.043334954418242, "bytes": 15257735952, "categories": {"code": 3880080, "tensors": 15252586496, 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a/validation/sentence-transformers/2026-11-03-top100/memory_samples/021.json b/validation/sentence-transformers/2026-11-03-top100/memory_samples/021.json index 9946222317d4f09b402e128dd1d88ee54087ff79..6adf1877181467ae6cfa2beaf61f95edb2b18b2b 100644 --- a/validation/sentence-transformers/2026-11-03-top100/memory_samples/021.json +++ b/validation/sentence-transformers/2026-11-03-top100/memory_samples/021.json @@ -1 +1 @@ -[{"elapsed_seconds": 1.7882324755191803e-05, "bytes": 7141760, "categories": {"code": 3842944, "tensors": 2097152, "misc": 1144320, "dma_rings": 57344}}, {"elapsed_seconds": 0.10146998800337315, "bytes": 607032080, "categories": {"code": 3880080, "tensors": 601882624, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.20156665984541178, "bytes": 607032080, "categories": {"code": 3880080, "tensors": 601882624, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.3016632879152894, "bytes": 607032080, "categories": {"code": 3880080, "tensors": 601882624, 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--git a/validation/sentence-transformers/2026-11-03-top100/memory_samples/067.json b/validation/sentence-transformers/2026-11-03-top100/memory_samples/067.json index 31a2c61475e9f6edeabbff794a0b803bb22f06e5..89bcf0d01a4c1eb44e534a7aa75bb1b295818bb3 100644 --- a/validation/sentence-transformers/2026-11-03-top100/memory_samples/067.json +++ b/validation/sentence-transformers/2026-11-03-top100/memory_samples/067.json @@ -1 +1 @@ -[{"elapsed_seconds": 2.180691808462143e-05, "bytes": 7141760, "categories": {"code": 3842944, "tensors": 2097152, "misc": 1144320, "dma_rings": 57344}}, {"elapsed_seconds": 0.10035528894513845, "bytes": 474911504, "categories": {"code": 3880080, "tensors": 469762048, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.20045102946460247, "bytes": 474911504, "categories": {"code": 3880080, "tensors": 469762048, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.30053481459617615, "bytes": 474911504, "categories": {"code": 3880080, "tensors": 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b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/007/result.json @@ -0,0 +1,77 @@ +{ + "index": 7, + "model": "nvidia/Nemotron-3.5-Rerank-8B-BF16", + "revision": "f88f8d9104726747791283ab156d2fcdb29e90a7", + "trending_rank": 7, + "downloads_rank": null, + "class": "CrossEncoder", + "dtype": "bfloat16", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:28:39.934071+00:00", + "physical_visible_core": "0", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 1.2689191922545433, + "prompts": {}, + "parameter_dtypes": [ + "torch.bfloat16" + ], + "cpu_scores": [ + -2.699960470199585, + -1.0288699865341187, + -2.2972545623779297, + -2.0584821701049805 + ], + "cpu_seconds": 1.485507046803832, + "cpu_top_document": 2, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 0.4639179026708007, + "neuron_scores": [ + -2.700716495513916, + -1.0203332901000977, + -2.303680419921875, + -2.0575268268585205 + ], + "max_abs_error": 0.008536696434020996, + "neuron_top_document": 2, + "mars_first": true, + "cpu_parity": false, + "neuron_repeat_seconds": 0.16087585594505072, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 23, + "sample_interval_seconds": 0.1, + "peak_bytes": 16937250251, + "peak_categories": { + "code": 8118208, + "tensors": 16848523264, + "constants": 1605643, + "scratchpad": 67108864, + "misc": 10510848, + "dma_rings": 1383424 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 16791547648, + "allocator_peak_reserved_bytes": 16848519168, + "total_seconds": 20.877234225161374 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/007/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/007/run.log new file mode 100644 index 0000000000000000000000000000000000000000..030125b4ab04ae6028ee9fa1f4ba9c25d5036032 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/007/run.log @@ -0,0 +1,13 @@ +[transformers] Unrecognized keys in `rope_parameters` for 'rope_type'='yarn': {'apply_yarn_scaling'} +[transformers] Mistral3Model LOAD REPORT from: nvidia/Nemotron-3.5-Rerank-8B-BF16 +Key | Status | | +----------------------------+------------+--+- +language_model.score.weight | UNEXPECTED | | + +Notes: +- UNEXPECTED: can be ignored when loading from different task/architecture; not ok if you expect identical arch. +CPU [-2.699960470199585, -1.0288699865341187, -2.2972545623779297, -2.0584821701049805] +2026-Nov-03 15:28:51.541569 2133586:2133586 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. + _warn_trace_cache_once() +{"index": 7, "model": "nvidia/Nemotron-3.5-Rerank-8B-BF16", "revision": "f88f8d9104726747791283ab156d2fcdb29e90a7", "trending_rank": 7, "downloads_rank": null, "class": "CrossEncoder", "dtype": "bfloat16", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:28:39.934071+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 1.2689191922545433, "prompts": {}, "parameter_dtypes": ["torch.bfloat16"], "cpu_scores": [-2.699960470199585, -1.0288699865341187, -2.2972545623779297, -2.0584821701049805], "cpu_seconds": 1.485507046803832, "cpu_top_document": 2, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 0.4639179026708007, "neuron_scores": [-2.700716495513916, -1.0203332901000977, -2.303680419921875, -2.0575268268585205], "max_abs_error": 0.008536696434020996, "neuron_top_document": 2, "mars_first": true, "cpu_parity": false, "neuron_repeat_seconds": 0.16087585594505072, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 23, "sample_interval_seconds": 0.1, "peak_bytes": 16937250251, "peak_categories": {"code": 8118208, "tensors": 16848523264, "constants": 1605643, "scratchpad": 67108864, "misc": 10510848, "dma_rings": 1383424}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 16791547648, "allocator_peak_reserved_bytes": 16848519168, "total_seconds": 20.877234225161374} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/007/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/007/run.py new file mode 100644 index 0000000000000000000000000000000000000000..984921e6e864392726f074b47e6c4d52e919899d --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/007/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 7, 'model': 'nvidia/Nemotron-3.5-Rerank-8B-BF16', 'revision': 'f88f8d9104726747791283ab156d2fcdb29e90a7', 'trending_rank': 7, 'downloads_rank': None, 'class': 'CrossEncoder', 'dtype': 'bfloat16', 'attention': 'sdpa', 'trust_remote_code': False, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/021/memory_samples.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/021/memory_samples.json new file mode 100644 index 0000000000000000000000000000000000000000..9946222317d4f09b402e128dd1d88ee54087ff79 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/021/memory_samples.json @@ -0,0 +1 @@ +[{"elapsed_seconds": 1.7882324755191803e-05, "bytes": 7141760, "categories": {"code": 3842944, "tensors": 2097152, "misc": 1144320, "dma_rings": 57344}}, {"elapsed_seconds": 0.10146998800337315, "bytes": 607032080, "categories": {"code": 3880080, "tensors": 601882624, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.20156665984541178, "bytes": 607032080, "categories": {"code": 3880080, "tensors": 601882624, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.3016632879152894, "bytes": 607032080, "categories": {"code": 3880080, "tensors": 601882624, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.4017549157142639, "bytes": 607032080, "categories": {"code": 3880080, "tensors": 601882624, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.5018517281860113, "bytes": 607032080, "categories": {"code": 3880080, "tensors": 601882624, 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6098776, "tensors": 606081024, "constants": 2621457, "scratchpad": 67108864, "misc": 7943808, "dma_rings": 2046464}}, {"elapsed_seconds": 104.77900900971144, "bytes": 691900393, "categories": {"code": 6098776, "tensors": 606081024, "constants": 2621457, "scratchpad": 67108864, "misc": 7943808, "dma_rings": 2046464}}, {"elapsed_seconds": 104.87909600418061, "bytes": 691900393, "categories": {"code": 6098776, "tensors": 606081024, "constants": 2621457, "scratchpad": 67108864, "misc": 7943808, "dma_rings": 2046464}}, {"elapsed_seconds": 104.9791742246598, "bytes": 691900393, "categories": {"code": 6098776, "tensors": 606081024, "constants": 2621457, "scratchpad": 67108864, "misc": 7943808, "dma_rings": 2046464}}, {"elapsed_seconds": 105.0792482662946, "bytes": 691900393, "categories": {"code": 6098776, "tensors": 606081024, "constants": 2621457, "scratchpad": 67108864, "misc": 7943808, "dma_rings": 2046464}}, {"elapsed_seconds": 105.17933266051114, "bytes": 691900393, "categories": {"code": 6098776, "tensors": 606081024, "constants": 2621457, "scratchpad": 67108864, "misc": 7943808, "dma_rings": 2046464}}, {"elapsed_seconds": 105.27942359354347, "bytes": 691900393, "categories": {"code": 6098776, "tensors": 606081024, "constants": 2621457, "scratchpad": 67108864, "misc": 7943808, "dma_rings": 2046464}}, {"elapsed_seconds": 105.37951993197203, "bytes": 691900393, "categories": {"code": 6098776, "tensors": 606081024, "constants": 2621457, "scratchpad": 67108864, "misc": 7943808, "dma_rings": 2046464}}, {"elapsed_seconds": 105.47962897736579, "bytes": 691900393, "categories": {"code": 6098776, "tensors": 606081024, "constants": 2621457, "scratchpad": 67108864, "misc": 7943808, "dma_rings": 2046464}}, {"elapsed_seconds": 105.57977224700153, "bytes": 691900393, "categories": {"code": 6098776, "tensors": 606081024, "constants": 2621457, "scratchpad": 67108864, "misc": 7943808, "dma_rings": 2046464}}, {"elapsed_seconds": 105.67989267688245, "bytes": 691900393, "categories": {"code": 6098776, "tensors": 606081024, "constants": 2621457, "scratchpad": 67108864, "misc": 7943808, "dma_rings": 2046464}}, {"elapsed_seconds": 105.77997673954815, "bytes": 691900393, "categories": {"code": 6098776, "tensors": 606081024, "constants": 2621457, "scratchpad": 67108864, "misc": 7943808, "dma_rings": 2046464}}, {"elapsed_seconds": 105.88008269947022, "bytes": 695672385, "categories": {"code": 6654512, "tensors": 606081024, "constants": 3473425, "scratchpad": 67108864, "misc": 9832960, "dma_rings": 2521600}}, {"elapsed_seconds": 105.95583753753453, "bytes": 695672385, "categories": {"code": 6654512, "tensors": 606081024, "constants": 3473425, "scratchpad": 67108864, "misc": 9832960, "dma_rings": 2521600}}] \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/021/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/021/result.json new file mode 100644 index 0000000000000000000000000000000000000000..04ff634ab34bc1479201a4984fbd02cc01add8bd --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/021/result.json @@ -0,0 +1,84 @@ +{ + "index": 21, + "model": "Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2", + "revision": "17b87422aa4bfb3b478369299e701070b308314f", + "trending_rank": 21, + "downloads_rank": null, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:31:28.531464+00:00", + "physical_visible_core": "8", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 6.291727272793651, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 0.4586111605167389, + 0.6143702864646912, + 0.5348622798919678, + 0.615755021572113 + ], + "cpu_seconds": 0.3750715795904398, + "cpu_top_document": 4, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 105.76572554465383, + "neuron_scores": [ + 0.4586114287376404, + 0.6143702268600464, + 0.534862220287323, + 0.6157552003860474 + ], + "max_abs_error": 2.682209014892578e-07, + "neuron_top_document": 4, + "mars_first": false, + "cpu_parity": true, + "neuron_repeat_seconds": 0.09435997158288956, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 1059, + "sample_interval_seconds": 0.1, + "peak_bytes": 695672385, + "peak_categories": { + "code": 6654512, + "tensors": 606081024, + "constants": 3473425, + "scratchpad": 67108864, + "misc": 9832960, + "dma_rings": 2521600 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 588299648, + "allocator_peak_reserved_bytes": 606076928, + "total_seconds": 131.14824269991368 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/021/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/021/run.log new file mode 100644 index 0000000000000000000000000000000000000000..36e4619e414f0afc2f96078ecc006b345e6f4ac6 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/021/run.log @@ -0,0 +1,5 @@ +CPU [0.4586111605167389, 0.6143702864646912, 0.5348622798919678, 0.615755021572113] +2026-Nov-03 15:31:46.722403 2188264:2188264 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. + _warn_trace_cache_once() +{"index": 21, "model": "Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2", "revision": "17b87422aa4bfb3b478369299e701070b308314f", "trending_rank": 21, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:31:28.531464+00:00", "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 6.291727272793651, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.4586111605167389, 0.6143702864646912, 0.5348622798919678, 0.615755021572113], "cpu_seconds": 0.3750715795904398, "cpu_top_document": 4, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 105.76572554465383, "neuron_scores": [0.4586114287376404, 0.6143702268600464, 0.534862220287323, 0.6157552003860474], "max_abs_error": 2.682209014892578e-07, "neuron_top_document": 4, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.09435997158288956, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 1059, "sample_interval_seconds": 0.1, "peak_bytes": 695672385, "peak_categories": {"code": 6654512, "tensors": 606081024, "constants": 3473425, "scratchpad": 67108864, "misc": 9832960, "dma_rings": 2521600}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 588299648, "allocator_peak_reserved_bytes": 606076928, "total_seconds": 131.14824269991368} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/021/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/021/run.py new file mode 100644 index 0000000000000000000000000000000000000000..02adaaafcdc2be10f555ad1b585080356d4f76da --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/021/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 21, 'model': 'Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2', 'revision': '17b87422aa4bfb3b478369299e701070b308314f', 'trending_rank': 21, 'downloads_rank': None, 'class': 'SentenceTransformer', 'dtype': 'float32', 'attention': 'sdpa', 'trust_remote_code': False, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/023/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/023/result.json new file mode 100644 index 0000000000000000000000000000000000000000..cb5c73d20dd4b660bbd74b54d1002c53910239eb --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/023/result.json @@ -0,0 +1,35 @@ +{ + "index": 23, + "model": "litert-community/embeddinggemma-300m", + "revision": "459f1d37fec9635eb1730ebdbc219bfc3226c8e7", + "trending_rank": 23, + "downloads_rank": null, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:31:58.747378+00:00", + "physical_visible_core": "12", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "status": "error", + "stage": "load_cpu", + "error_type": "GatedRepoError", + "error": "403 Client Error. (Request ID: Root=1-6ae9fe77-5b99b3f12170976f33af1862;24fb7211-ac04-4557-9d7c-d58c3b8e6212)\n\nCannot access gated repo for url https://huggingface.co/litert-community/embeddinggemma-300m/resolve/459f1d37fec9635eb1730ebdbc219bfc3226c8e7/modules.json.\nAccess to model litert-community/embeddinggemma-300m is restricted and you are not in the authorized list. Visit https://huggingface.co/litert-community/embeddinggemma-300m to ask for access.", + "traceback": "httpx.HTTPStatusError: Client error '403 Forbidden' for url 'https://huggingface.co/litert-community/embeddinggemma-300m/resolve/459f1d37fec9635eb1730ebdbc219bfc3226c8e7/modules.json'\nFor more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/403\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/023/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1190, in _load_modules\n modules_json_path = load_file_path(\n model_name_or_path,\n ...<4 lines>...\n local_files_only=local_files_only,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/file_io.py\", line 208, in load_file_path\n return hf_hub_download(\n model_name_or_path,\n ...<6 lines>...\n local_files_only=local_files_only,\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_validators.py\", line 89, in _inner_fn\n return fn(*args, **kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_http.py\", line 266, in _inner\n return fn(*args, **kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1047, in hf_hub_download\n return _hf_hub_download_to_cache_dir(\n # Destination\n ...<15 lines>...\n dry_run=dry_run,\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1194, in _hf_hub_download_to_cache_dir\n _raise_on_head_call_error(head_call_error, force_download, local_files_only)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1953, in _raise_on_head_call_error\n raise head_call_error\nhuggingface_hub.errors.GatedRepoError: 403 Client Error. (Request ID: Root=1-6ae9fe77-5b99b3f12170976f33af1862;24fb7211-ac04-4557-9d7c-d58c3b8e6212)\n\nCannot access gated repo for url https://huggingface.co/litert-community/embeddinggemma-300m/resolve/459f1d37fec9635eb1730ebdbc219bfc3226c8e7/modules.json.\nAccess to model litert-community/embeddinggemma-300m is restricted and you are not in the authorized list. Visit https://huggingface.co/litert-community/embeddinggemma-300m to ask for access.\n", + "result": "FAIL(load)", + "total_seconds": 8.972315741702914 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/023/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/023/run.log new file mode 100644 index 0000000000000000000000000000000000000000..83b696da3fc476f73844080c577fb8bfd892f90e --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/023/run.log @@ -0,0 +1,62 @@ +httpx.HTTPStatusError: Client error '403 Forbidden' for url 'https://huggingface.co/litert-community/embeddinggemma-300m/resolve/459f1d37fec9635eb1730ebdbc219bfc3226c8e7/modules.json' +For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/403 + +The above exception was the direct cause of the following exception: + +Traceback (most recent call last): + File "/tmp/neuron-top100-sweep/runs/023/run.py", line 50, in + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 193, in __init__ + super().__init__( + ~~~~~~~~~~~~~~~~^ + model_name_or_path=model_name_or_path, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ...<13 lines>... + default_prompt_name=default_prompt_name, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 256, in __init__ + modules, self.module_kwargs = self._load_modules( + ~~~~~~~~~~~~~~~~~~^ + model_name_or_path, + ^^^^^^^^^^^^^^^^^^^ + ...<7 lines>... + config_kwargs=config_kwargs, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 1190, in _load_modules + modules_json_path = load_file_path( + model_name_or_path, + ...<4 lines>... + local_files_only=local_files_only, + ) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/file_io.py", line 208, in load_file_path + return hf_hub_download( + model_name_or_path, + ...<6 lines>... + local_files_only=local_files_only, + ) + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_validators.py", line 89, in _inner_fn + return fn(*args, **kwargs) + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_http.py", line 266, in _inner + return fn(*args, **kwargs) + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py", line 1047, in hf_hub_download + return _hf_hub_download_to_cache_dir( + # Destination + ...<15 lines>... + dry_run=dry_run, + ) + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py", line 1194, in _hf_hub_download_to_cache_dir + _raise_on_head_call_error(head_call_error, force_download, local_files_only) + ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py", line 1953, in _raise_on_head_call_error + raise head_call_error +huggingface_hub.errors.GatedRepoError: 403 Client Error. (Request ID: Root=1-6ae9fe77-5b99b3f12170976f33af1862;24fb7211-ac04-4557-9d7c-d58c3b8e6212) + +Cannot access gated repo for url https://huggingface.co/litert-community/embeddinggemma-300m/resolve/459f1d37fec9635eb1730ebdbc219bfc3226c8e7/modules.json. +Access to model litert-community/embeddinggemma-300m is restricted and you are not in the authorized list. Visit https://huggingface.co/litert-community/embeddinggemma-300m to ask for access. +{"index": 23, "model": "litert-community/embeddinggemma-300m", "revision": "459f1d37fec9635eb1730ebdbc219bfc3226c8e7", "trending_rank": 23, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:31:58.747378+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "status": "error", "stage": "load_cpu", "error_type": "GatedRepoError", "error": "403 Client Error. (Request ID: Root=1-6ae9fe77-5b99b3f12170976f33af1862;24fb7211-ac04-4557-9d7c-d58c3b8e6212)\n\nCannot access gated repo for url https://huggingface.co/litert-community/embeddinggemma-300m/resolve/459f1d37fec9635eb1730ebdbc219bfc3226c8e7/modules.json.\nAccess to model litert-community/embeddinggemma-300m is restricted and you are not in the authorized list. Visit https://huggingface.co/litert-community/embeddinggemma-300m to ask for access.", "traceback": "httpx.HTTPStatusError: Client error '403 Forbidden' for url 'https://huggingface.co/litert-community/embeddinggemma-300m/resolve/459f1d37fec9635eb1730ebdbc219bfc3226c8e7/modules.json'\nFor more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/403\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/023/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1190, in _load_modules\n modules_json_path = load_file_path(\n model_name_or_path,\n ...<4 lines>...\n local_files_only=local_files_only,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/file_io.py\", line 208, in load_file_path\n return hf_hub_download(\n model_name_or_path,\n ...<6 lines>...\n local_files_only=local_files_only,\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_validators.py\", line 89, in _inner_fn\n return fn(*args, **kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_http.py\", line 266, in _inner\n return fn(*args, **kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1047, in hf_hub_download\n return _hf_hub_download_to_cache_dir(\n # Destination\n ...<15 lines>...\n dry_run=dry_run,\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1194, in _hf_hub_download_to_cache_dir\n _raise_on_head_call_error(head_call_error, force_download, local_files_only)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1953, in _raise_on_head_call_error\n raise head_call_error\nhuggingface_hub.errors.GatedRepoError: 403 Client Error. (Request ID: Root=1-6ae9fe77-5b99b3f12170976f33af1862;24fb7211-ac04-4557-9d7c-d58c3b8e6212)\n\nCannot access gated repo for url https://huggingface.co/litert-community/embeddinggemma-300m/resolve/459f1d37fec9635eb1730ebdbc219bfc3226c8e7/modules.json.\nAccess to model litert-community/embeddinggemma-300m is restricted and you are not in the authorized list. Visit https://huggingface.co/litert-community/embeddinggemma-300m to ask for access.\n", "result": "FAIL(load)", "total_seconds": 8.972315741702914} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/023/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/023/run.py new file mode 100644 index 0000000000000000000000000000000000000000..99eeff83681120df07aa8eb521a013aee2c98085 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/023/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 23, 'model': 'litert-community/embeddinggemma-300m', 'revision': '459f1d37fec9635eb1730ebdbc219bfc3226c8e7', 'trending_rank': 23, 'downloads_rank': None, 'class': 'SentenceTransformer', 'dtype': 'float32', 'attention': 'sdpa', 'trust_remote_code': False, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/024/memory_samples.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/024/memory_samples.json new file mode 100644 index 0000000000000000000000000000000000000000..27e267a1960fdfab167f4d4dd787d709e0e6be01 --- /dev/null +++ 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"bytes": 16525089813, "categories": {"code": 14249544, "tensors": 16412315648, "constants": 3981325, "scratchpad": 67108864, "misc": 22884800, "dma_rings": 4549632}}, {"elapsed_seconds": 32.90745550394058, "bytes": 16529536949, "categories": {"code": 14566248, "tensors": 16414412800, "constants": 4571149, "scratchpad": 67108864, "misc": 24082496, "dma_rings": 4795392}}, {"elapsed_seconds": 33.00754430051893, "bytes": 16529538997, "categories": {"code": 14566248, "tensors": 16414412800, "constants": 4571149, "scratchpad": 67108864, "misc": 24082496, "dma_rings": 4797440}}, {"elapsed_seconds": 33.10762688703835, "bytes": 16529534901, "categories": {"code": 14566248, "tensors": 16414412800, "constants": 4571149, "scratchpad": 67108864, "misc": 24082496, "dma_rings": 4793344}}, {"elapsed_seconds": 33.20771568547934, "bytes": 16529534901, "categories": {"code": 14566248, "tensors": 16414412800, "constants": 4571149, "scratchpad": 67108864, "misc": 24082496, "dma_rings": 4793344}}, {"elapsed_seconds": 33.2574276663363, "bytes": 16529534901, "categories": {"code": 14566248, "tensors": 16414412800, "constants": 4571149, "scratchpad": 67108864, "misc": 24082496, "dma_rings": 4793344}}] \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/024/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/024/result.json new file mode 100644 index 0000000000000000000000000000000000000000..3030b543f773f8f986751e655ef7d4e5e39bfdf1 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/024/result.json @@ -0,0 +1,85 @@ +{ + "index": 24, + "model": "Qwen/Qwen3-VL-Embedding-8B", + "revision": "2c4565515e0f265c6511776e7193b22c0968ddc7", + "trending_rank": 24, + "downloads_rank": 54, + "class": "SentenceTransformer", + "dtype": "bfloat16", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:32:09.263638+00:00", + "physical_visible_core": "12", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 45.57155937794596, + "prompts": { + "query": "", + "document": "", + "default": "Represent the user's input." + }, + "parameter_dtypes": [ + "torch.bfloat16" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 0.5104122161865234, + 0.8333847522735596, + 0.5737250447273254, + 0.6311128735542297 + ], + "cpu_seconds": 11.08764569554478, + "cpu_top_document": 2, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 30.12761875614524, + "neuron_scores": [ + 0.5109585523605347, + 0.8327626585960388, + 0.5716332197189331, + 0.6295062899589539 + ], + "max_abs_error": 0.002091825008392334, + "neuron_top_document": 2, + "mars_first": true, + "cpu_parity": false, + "neuron_repeat_seconds": 0.4174205930903554, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 330, + "sample_interval_seconds": 0.1, + "peak_bytes": 16529538997, + "peak_categories": { + "code": 14566248, + "tensors": 16414412800, + "constants": 4571149, + "scratchpad": 67108864, + "misc": 24082496, + "dma_rings": 4797440 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 16390182848, + "allocator_peak_reserved_bytes": 16414408704, + "total_seconds": 119.78512970823795 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/024/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/024/run.log new file mode 100644 index 0000000000000000000000000000000000000000..1a3528235119fb28c98c8b1240674d233cb72486 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/024/run.log @@ -0,0 +1,6 @@ +Default prompt name is set to 'default'. This prompt applies unless overridden by `prompt`, `prompt_name`, or a method-specific prompt, including an empty prompt. +CPU [0.5104122161865234, 0.8333847522735596, 0.5737250447273254, 0.6311128735542297] +2026-Nov-03 15:33:26.885580 2200020:2200020 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. + _warn_trace_cache_once() +{"index": 24, "model": "Qwen/Qwen3-VL-Embedding-8B", "revision": "2c4565515e0f265c6511776e7193b22c0968ddc7", "trending_rank": 24, "downloads_rank": 54, "class": "SentenceTransformer", "dtype": "bfloat16", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:32:09.263638+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 45.57155937794596, "prompts": {"query": "", "document": "", "default": "Represent the user's input."}, "parameter_dtypes": ["torch.bfloat16"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.5104122161865234, 0.8333847522735596, 0.5737250447273254, 0.6311128735542297], "cpu_seconds": 11.08764569554478, "cpu_top_document": 2, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 30.12761875614524, "neuron_scores": [0.5109585523605347, 0.8327626585960388, 0.5716332197189331, 0.6295062899589539], "max_abs_error": 0.002091825008392334, "neuron_top_document": 2, "mars_first": true, "cpu_parity": false, "neuron_repeat_seconds": 0.4174205930903554, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 330, "sample_interval_seconds": 0.1, "peak_bytes": 16529538997, "peak_categories": {"code": 14566248, "tensors": 16414412800, "constants": 4571149, "scratchpad": 67108864, "misc": 24082496, "dma_rings": 4797440}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 16390182848, "allocator_peak_reserved_bytes": 16414408704, "total_seconds": 119.78512970823795} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/024/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/024/run.py new file mode 100644 index 0000000000000000000000000000000000000000..3f2136f22acb475105208476a71f3de639c67e8e --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/024/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 24, 'model': 'Qwen/Qwen3-VL-Embedding-8B', 'revision': '2c4565515e0f265c6511776e7193b22c0968ddc7', 'trending_rank': 24, 'downloads_rank': 54, 'class': 'SentenceTransformer', 'dtype': 'bfloat16', 'attention': 'sdpa', 'trust_remote_code': False, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/029/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/029/result.json new file mode 100644 index 0000000000000000000000000000000000000000..c44ba88fa4dc3781e87050150ea7d7a4cead3019 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/029/result.json @@ -0,0 +1,35 @@ +{ + "index": 29, + "model": "BrunoHays/embeddinggemma-2-nullspace-audio", + "revision": "4e54c6816e4ba368be62885788974458f2748adf", + "trending_rank": 29, + "downloads_rank": null, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:34:11.569070+00:00", + "physical_visible_core": "12", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "status": "error", + "stage": "load_cpu", + "error_type": "ValueError", + "error": "Unrecognized model in BrunoHays/embeddinggemma-2-nullspace-audio. Should have a `model_type` key in its config.json.", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/029/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1200, in _load_modules\n return self._load_default_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1272, in _load_default_modules\n transformer_model = Transformer(\n model_name_or_path,\n ...<4 lines>...\n backend=self.backend,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 839, in __init__\n config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2259, in _load_config\n return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py\", line 450, in from_pretrained\n raise ValueError(\n ...<2 lines>...\n )\nValueError: Unrecognized model in BrunoHays/embeddinggemma-2-nullspace-audio. Should have a `model_type` key in its config.json.\n", + "result": "FAIL(load)", + "total_seconds": 24.265914521180093 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/029/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/029/run.log new file mode 100644 index 0000000000000000000000000000000000000000..74c62274c98747dad18003d6f134789fcd99888c --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/029/run.log @@ -0,0 +1,48 @@ +Traceback (most recent call last): + File "/tmp/neuron-top100-sweep/runs/029/run.py", line 50, in + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 193, in __init__ + super().__init__( + ~~~~~~~~~~~~~~~~^ + model_name_or_path=model_name_or_path, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ...<13 lines>... + default_prompt_name=default_prompt_name, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 256, in __init__ + modules, self.module_kwargs = self._load_modules( + ~~~~~~~~~~~~~~~~~~^ + model_name_or_path, + ^^^^^^^^^^^^^^^^^^^ + ...<7 lines>... + config_kwargs=config_kwargs, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 1200, in _load_modules + return self._load_default_modules(model_name_or_path, **load_kwargs) + ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 1272, in _load_default_modules + transformer_model = Transformer( + model_name_or_path, + ...<4 lines>... + backend=self.backend, + ) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 92, in wrapper + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py", line 839, in __init__ + config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs) + ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py", line 2259, in _load_config + return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False + ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py", line 450, in from_pretrained + raise ValueError( + ...<2 lines>... + ) +ValueError: Unrecognized model in BrunoHays/embeddinggemma-2-nullspace-audio. Should have a `model_type` key in its config.json. +{"index": 29, "model": "BrunoHays/embeddinggemma-2-nullspace-audio", "revision": "4e54c6816e4ba368be62885788974458f2748adf", "trending_rank": 29, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:34:11.569070+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "status": "error", "stage": "load_cpu", "error_type": "ValueError", "error": "Unrecognized model in BrunoHays/embeddinggemma-2-nullspace-audio. Should have a `model_type` key in its config.json.", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/029/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1200, in _load_modules\n return self._load_default_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1272, in _load_default_modules\n transformer_model = Transformer(\n model_name_or_path,\n ...<4 lines>...\n backend=self.backend,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 839, in __init__\n config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2259, in _load_config\n return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py\", line 450, in from_pretrained\n raise ValueError(\n ...<2 lines>...\n )\nValueError: Unrecognized model in BrunoHays/embeddinggemma-2-nullspace-audio. Should have a `model_type` key in its config.json.\n", "result": "FAIL(load)", "total_seconds": 24.265914521180093} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/029/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/029/run.py new file mode 100644 index 0000000000000000000000000000000000000000..ef51a1d6331892b5296da5d4909317075cb29374 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/029/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 29, 'model': 'BrunoHays/embeddinggemma-2-nullspace-audio', 'revision': '4e54c6816e4ba368be62885788974458f2748adf', 'trending_rank': 29, 'downloads_rank': None, 'class': 'SentenceTransformer', 'dtype': 'float32', 'attention': 'sdpa', 'trust_remote_code': False, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/031/memory_samples.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/031/memory_samples.json new file mode 100644 index 0000000000000000000000000000000000000000..2c3189828ee385e26405a045408c34c62d307bf6 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/031/memory_samples.json @@ -0,0 +1 @@ +[{"elapsed_seconds": 3.147311508655548e-05, "bytes": 7141760, "categories": {"code": 3842944, "tensors": 2097152, "misc": 1144320, "dma_rings": 57344}}, {"elapsed_seconds": 0.10044058691710234, "bytes": 2104398608, "categories": {"code": 3880080, "tensors": 2099249152, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.20059097278863192, "bytes": 2725155600, "categories": {"code": 3880080, "tensors": 2720006144, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.30073264986276627, "bytes": 4084110096, "categories": {"code": 3880080, "tensors": 4078960640, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.4900190392509103, "bytes": 4922970896, "categories": {"code": 3880080, "tensors": 4917821440, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.5901362830772996, "bytes": 6382588688, "categories": {"code": 3880080, "tensors": 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"excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:34:37.563999+00:00", + "physical_visible_core": "12", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 6.2028824258595705, + "prompts": {}, + "parameter_dtypes": [ + "torch.float32" + ], + "cpu_scores": [ + 0.9997572302818298, + 0.9997515082359314, + 0.9998114705085754, + 0.9997087121009827 + ], + "cpu_seconds": 32.531744067557156, + "cpu_top_document": 3, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 45.031333486549556, + "neuron_scores": [ + 0.9997572898864746, + 0.9997514486312866, + 0.9998114109039307, + 0.9997087717056274 + ], + "max_abs_error": 5.960464477539063e-08, + "neuron_top_document": 3, + "mars_first": false, + "cpu_parity": true, + "neuron_repeat_seconds": 0.09113490767776966, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 460, + "sample_interval_seconds": 0.1, + "peak_bytes": 10210461645, + "peak_categories": { + "code": 20083904, + "tensors": 10112471040, + "constants": 1474573, + "scratchpad": 67108864, + "misc": 8413952, + "dma_rings": 909312 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 10061462720, + "allocator_peak_reserved_bytes": 10112466944, + "total_seconds": 123.40695661306381 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/031/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/031/run.log new file mode 100644 index 0000000000000000000000000000000000000000..8e9dc82b0f9f6486beb3b197bde960f6055cc9ee --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/031/run.log @@ -0,0 +1,6 @@ +[transformers] We found `hidden_act="gelu"` in this Gemma config. This is a legacy value of the official releases but it is meant to target the tanh approximation. Setting `hidden_act="gelu_pytorch_tanh"` instead. +CPU [0.9997572302818298, 0.9997515082359314, 0.9998114705085754, 0.9997087121009827] +2026-Nov-03 15:35:46.446477 2247012:2247012 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. + _warn_trace_cache_once() +{"index": 31, "model": "BAAI/bge-reranker-v2-gemma", "revision": "1787044f8b6fb740a9de4557c3a12377f84d9e17", "trending_rank": 31, "downloads_rank": null, "class": "CrossEncoder", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:34:37.563999+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 6.2028824258595705, "prompts": {}, "parameter_dtypes": ["torch.float32"], "cpu_scores": [0.9997572302818298, 0.9997515082359314, 0.9998114705085754, 0.9997087121009827], "cpu_seconds": 32.531744067557156, "cpu_top_document": 3, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 45.031333486549556, "neuron_scores": [0.9997572898864746, 0.9997514486312866, 0.9998114109039307, 0.9997087717056274], "max_abs_error": 5.960464477539063e-08, "neuron_top_document": 3, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.09113490767776966, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 460, "sample_interval_seconds": 0.1, "peak_bytes": 10210461645, "peak_categories": {"code": 20083904, "tensors": 10112471040, "constants": 1474573, "scratchpad": 67108864, "misc": 8413952, "dma_rings": 909312}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 10061462720, "allocator_peak_reserved_bytes": 10112466944, "total_seconds": 123.40695661306381} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/031/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/031/run.py new file mode 100644 index 0000000000000000000000000000000000000000..8704cd6d27d31e57baa4eadd3162f61c84f2880e --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/031/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 31, 'model': 'BAAI/bge-reranker-v2-gemma', 'revision': '1787044f8b6fb740a9de4557c3a12377f84d9e17', 'trending_rank': 31, 'downloads_rank': None, 'class': 'CrossEncoder', 'dtype': 'float32', 'attention': 'sdpa', 'trust_remote_code': False, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/033/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/033/result.json new file mode 100644 index 0000000000000000000000000000000000000000..632004609112193dabe0967bb7d2d24b84ed768b --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/033/result.json @@ -0,0 +1,47 @@ +{ + "index": 33, + "model": "Snowflake/snowflake-arctic-embed-m-v2.0", + "revision": "95c2741480856aa9666782eb4afe11959938017f", + "trending_rank": 33, + "downloads_rank": null, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": true, + "config_kwargs": { + "use_memory_efficient_attention": false, + "unpad_inputs": false + }, + "excluded": null, + "exclusion": null, + "reason": "Checkpoint custom code supports standard SDPA when xformers attention and input unpadding are disabled.", + "started_at": "2026-11-03T16:00:36.977122+00:00", + "physical_visible_core": "0", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 3.8345429748296738, + "prompts": { + "query": "query: ", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "status": "error", + "stage": "cpu_inference", + "error_type": "IndexError", + "error": "index 140493294592752 is out of bounds for dimension 0 with size 14", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/033/run.py\", line 64, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/033/run.py\", line 59, in score\n q=(model.encode([query],batch_size=4,show_progress_bar=False,**kw,**SPEC['query_kwargs']) if SPEC.get('query_kwargs') else model.encode_query([query],batch_size=4,show_progress_bar=False,**kw))\n ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Snowflake/snowflake_hyphen_arctic_hyphen_embed_hyphen_m_hyphen_v2_dot_0/95c2741480856aa9666782eb4afe11959938017f/modeling_hf_alibaba_nlp_gte.py\", line 900, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Snowflake/snowflake_hyphen_arctic_hyphen_embed_hyphen_m_hyphen_v2_dot_0/95c2741480856aa9666782eb4afe11959938017f/modeling_hf_alibaba_nlp_gte.py\", line 391, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 140493294592752 is out of bounds for dimension 0 with size 14\n", + "result": "FAIL(forward)", + "total_seconds": 12.881709131412208 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/033/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/033/run.log new file mode 100644 index 0000000000000000000000000000000000000000..e09b9acebfc91ca08ec8a20d9a210536c7ad9a6b --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/033/run.log @@ -0,0 +1,65 @@ +/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/configuration_utils.py:723: FutureWarning: `rope_scaling` is deprecated and will be removed in a future version. Set `rope_parameters` instead, with `rope_type` and `rope_theta` included. + warnings.warn( +Traceback (most recent call last): + File "/tmp/neuron-top100-sweep/runs/033/run.py", line 64, in + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + ~~~~~^^ + File "/tmp/neuron-top100-sweep/runs/033/run.py", line 59, in score + q=(model.encode([query],batch_size=4,show_progress_bar=False,**kw,**SPEC['query_kwargs']) if SPEC.get('query_kwargs') else model.encode_query([query],batch_size=4,show_progress_bar=False,**kw)) + ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 370, in encode_query + return self.encode( + ~~~~~~~~~~~^ + inputs=inputs, + ^^^^^^^^^^^^^^ + ...<14 lines>... + **kwargs, + ^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 895, in encode + all_embeddings = self._inference(inputs, device=device, **inference_kwargs) + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 963, in _inference + out_features = self(features, **kwargs) + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1783, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1794, in _call_impl + return forward_call(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 600, in forward + input = module(input, **module_kwargs) + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1783, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1794, in _call_impl + return forward_call(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py", line 1647, in forward + model_output = model_method(**filtered_kwargs) + File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Snowflake/snowflake_hyphen_arctic_hyphen_embed_hyphen_m_hyphen_v2_dot_0/95c2741480856aa9666782eb4afe11959938017f/modeling_hf_alibaba_nlp_gte.py", line 900, in forward + (embedding_output, attention_mask, rope_embeds, length) = self.embeddings( + ~~~~~~~~~~~~~~~^ + unpad_inputs, + ^^^^^^^^^^^^^ + ...<5 lines>... + inputs_embeds=inputs_embeds + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1783, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1794, in _call_impl + return forward_call(*args, **kwargs) + File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Snowflake/snowflake_hyphen_arctic_hyphen_embed_hyphen_m_hyphen_v2_dot_0/95c2741480856aa9666782eb4afe11959938017f/modeling_hf_alibaba_nlp_gte.py", line 391, in forward + rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim] + ~~~~~~~~^^^^^^^^^^^^^^ +IndexError: index 140493294592752 is out of bounds for dimension 0 with size 14 +{"index": 33, "model": "Snowflake/snowflake-arctic-embed-m-v2.0", "revision": "95c2741480856aa9666782eb4afe11959938017f", "trending_rank": 33, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {"use_memory_efficient_attention": false, "unpad_inputs": false}, "excluded": null, "exclusion": null, "reason": "Checkpoint custom code supports standard SDPA when xformers attention and input unpadding are disabled.", "started_at": "2026-11-03T16:00:36.977122+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 3.8345429748296738, "prompts": {"query": "query: ", "document": ""}, "parameter_dtypes": ["torch.float32"], "status": "error", "stage": "cpu_inference", "error_type": "IndexError", "error": "index 140493294592752 is out of bounds for dimension 0 with size 14", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/033/run.py\", line 64, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/033/run.py\", line 59, in score\n q=(model.encode([query],batch_size=4,show_progress_bar=False,**kw,**SPEC['query_kwargs']) if SPEC.get('query_kwargs') else model.encode_query([query],batch_size=4,show_progress_bar=False,**kw))\n ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Snowflake/snowflake_hyphen_arctic_hyphen_embed_hyphen_m_hyphen_v2_dot_0/95c2741480856aa9666782eb4afe11959938017f/modeling_hf_alibaba_nlp_gte.py\", line 900, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Snowflake/snowflake_hyphen_arctic_hyphen_embed_hyphen_m_hyphen_v2_dot_0/95c2741480856aa9666782eb4afe11959938017f/modeling_hf_alibaba_nlp_gte.py\", line 391, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 140493294592752 is out of bounds for dimension 0 with size 14\n", "result": "FAIL(forward)", "total_seconds": 12.881709131412208} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/033/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/033/run.py new file mode 100644 index 0000000000000000000000000000000000000000..c362a9f14f2daeda785179f9173754bd030d6262 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/033/run.py @@ -0,0 +1,95 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Additional dependency for Tencent WeMM: qwen-vl-utils==0.0.14. +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 33, 'model': 'Snowflake/snowflake-arctic-embed-m-v2.0', 'revision': '95c2741480856aa9666782eb4afe11959938017f', 'trending_rank': 33, 'downloads_rank': None, 'class': 'SentenceTransformer', 'dtype': 'float32', 'attention': 'sdpa', 'trust_remote_code': True, 'config_kwargs': {'use_memory_efficient_attention': False, 'unpad_inputs': False}, 'excluded': None, 'exclusion': None, 'reason': 'Checkpoint custom code supports standard SDPA when xformers attention and input unpadding are disabled.'} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=(model.encode([query],batch_size=4,show_progress_bar=False,**kw,**SPEC['query_kwargs']) if SPEC.get('query_kwargs') else model.encode_query([query],batch_size=4,show_progress_bar=False,**kw)) + d=(model.encode(docs,batch_size=4,show_progress_bar=False,**kw,**SPEC['document_kwargs']) if SPEC.get('document_kwargs') else model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw)) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/034/memory_samples.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/034/memory_samples.json new file mode 100644 index 0000000000000000000000000000000000000000..14128901b221a37c00f56a9fb4abb1f3cec5d1e5 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/034/memory_samples.json @@ -0,0 +1 @@ +[{"elapsed_seconds": 2.5550834834575653e-05, "bytes": 7141760, "categories": {"code": 3842944, "tensors": 2097152, "misc": 1144320, "dma_rings": 57344}}, {"elapsed_seconds": 0.10035884752869606, "bytes": 1250857744, "categories": {"code": 3880080, "tensors": 1245708288, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.20046252384781837, "bytes": 1250857744, "categories": {"code": 3880080, "tensors": 1245708288, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.30054773204028606, "bytes": 1250857744, "categories": 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a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/034/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/034/result.json new file mode 100644 index 0000000000000000000000000000000000000000..d7e59a96ab3178ed94ab68a74770552ec2fef26e --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/034/result.json @@ -0,0 +1,79 @@ +{ + "index": 34, + "model": "Qwen/Qwen3-Reranker-8B", + "revision": "77d193c791ed757ca307ee72715aa132723da912", + "trending_rank": 34, + "downloads_rank": null, + "class": "CrossEncoder", + "dtype": "bfloat16", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:36:33.649395+00:00", + "physical_visible_core": "8", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 8.139716992154717, + "prompts": { + "query": "Given a web search query, retrieve relevant passages that answer the query" + }, + "parameter_dtypes": [ + "torch.bfloat16" + ], + "cpu_scores": [ + -8.125, + 4.0625, + -5.34375, + -4.5625 + ], + "cpu_seconds": 24.723026665858924, + "cpu_top_document": 2, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 59.1664319653064, + "neuron_scores": [ + -8.0625, + 4.0625, + -5.375, + -4.5625 + ], + "max_abs_error": 0.0625, + "neuron_top_document": 2, + "mars_first": true, + "cpu_parity": false, + "neuron_repeat_seconds": 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b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/034/run.log @@ -0,0 +1,6 @@ +Default prompt name is set to 'query'. This prompt applies unless overridden by `prompt`, `prompt_name`, or a method-specific prompt, including an empty prompt. +CPU [-8.125, 4.0625, -5.34375, -4.5625] +2026-Nov-03 15:37:25.459653 2272047:2272047 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. + _warn_trace_cache_once() +{"index": 34, "model": "Qwen/Qwen3-Reranker-8B", "revision": "77d193c791ed757ca307ee72715aa132723da912", "trending_rank": 34, "downloads_rank": null, "class": "CrossEncoder", "dtype": "bfloat16", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:36:33.649395+00:00", "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 8.139716992154717, "prompts": {"query": "Given a web search query, retrieve relevant passages that answer the query"}, "parameter_dtypes": ["torch.bfloat16"], "cpu_scores": [-8.125, 4.0625, -5.34375, -4.5625], "cpu_seconds": 24.723026665858924, "cpu_top_document": 2, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 59.1664319653064, "neuron_scores": [-8.0625, 4.0625, -5.375, -4.5625], "max_abs_error": 0.0625, "neuron_top_document": 2, "mars_first": true, "cpu_parity": false, "neuron_repeat_seconds": 0.24539023637771606, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 621, "sample_interval_seconds": 0.1, "peak_bytes": 19178892667, "peak_categories": {"code": 25923376, "tensors": 16521367552, "constants": 1425419, "scratchpad": 2617245696, "misc": 11091520, "dma_rings": 1839104}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 16468168832, "allocator_peak_reserved_bytes": 16521363456, "total_seconds": 126.22209540009499} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/034/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/034/run.py new file mode 100644 index 0000000000000000000000000000000000000000..3b3c07179ca40bb28de75ef9b583e4877ad35cbd --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/034/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 34, 'model': 'Qwen/Qwen3-Reranker-8B', 'revision': '77d193c791ed757ca307ee72715aa132723da912', 'trending_rank': 34, 'downloads_rank': None, 'class': 'CrossEncoder', 'dtype': 'bfloat16', 'attention': 'sdpa', 'trust_remote_code': False, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/054/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/054/result.json new file mode 100644 index 0000000000000000000000000000000000000000..2798f0d923ec215ba7eb4f62117429cdf9bbe234 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/054/result.json @@ -0,0 +1,35 @@ +{ + "index": 54, + "model": "HukukcuApp/tr-legal-embedding-gemma-2", + "revision": "a33e497f3f123019d3b03fe40082992fffeac728", + "trending_rank": 54, + "downloads_rank": null, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:42:02.918547+00:00", + "physical_visible_core": "0", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "status": "error", + "stage": "load_cpu", + "error_type": "OSError", + "error": "Can't load feature extractor for 'HukukcuApp/tr-legal-embedding-gemma-2'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'HukukcuApp/tr-legal-embedding-gemma-2' is the correct path to a directory containing a preprocessor_config.json file", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/054/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 870, in __init__\n self.processor = AutoProcessor.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n tokenizer_name_or_path if tokenizer_name_or_path is not None else model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n **processor_kwargs,\n ^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/processing_auto.py\", line 350, in from_pretrained\n return processor_class.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/processing_utils.py\", line 1749, in from_pretrained\n args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, processor_dict, **kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/processing_utils.py\", line 1889, in _get_arguments_from_pretrained\n sub_processor = auto_processor_class.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/feature_extraction_auto.py\", line 329, in from_pretrained\n config_dict, _ = FeatureExtractionMixin.get_feature_extractor_dict(pretrained_model_name_or_path, **kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/feature_extraction_utils.py\", line 545, in get_feature_extractor_dict\n raise OSError(\n ...<4 lines>...\n )\nOSError: Can't load feature extractor for 'HukukcuApp/tr-legal-embedding-gemma-2'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'HukukcuApp/tr-legal-embedding-gemma-2' is the correct path to a directory containing a preprocessor_config.json file\n", + "result": "FAIL(load)", + "total_seconds": 14.706041608005762 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/054/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/054/run.log new file mode 100644 index 0000000000000000000000000000000000000000..67e36c72d24bc3d34680a0fd90486a44c95629f8 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/054/run.log @@ -0,0 +1,69 @@ +Traceback (most recent call last): + File "/tmp/neuron-top100-sweep/runs/054/run.py", line 50, in + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 193, in __init__ + super().__init__( + ~~~~~~~~~~~~~~~~^ + model_name_or_path=model_name_or_path, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ...<13 lines>... + default_prompt_name=default_prompt_name, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 256, in __init__ + modules, self.module_kwargs = self._load_modules( + ~~~~~~~~~~~~~~~~~~^ + model_name_or_path, + ^^^^^^^^^^^^^^^^^^^ + ...<7 lines>... + config_kwargs=config_kwargs, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 1211, in _load_modules + return self._load_config_modules(model_name_or_path, **load_kwargs) + ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 1431, in _load_config_modules + module = module_class.load( + model_name_or_path, + ...<12 lines>... + **extra_load_kwargs, + ) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py", line 2641, in load + return cls(model_name_or_path=model_name_or_path, **init_kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 92, in wrapper + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py", line 870, in __init__ + self.processor = AutoProcessor.from_pretrained( + ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^ + tokenizer_name_or_path if tokenizer_name_or_path is not None else model_name_or_path, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + **processor_kwargs, + ^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/processing_auto.py", line 350, in from_pretrained + return processor_class.from_pretrained( + ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^ + pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/processing_utils.py", line 1749, in from_pretrained + args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, processor_dict, **kwargs) + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/processing_utils.py", line 1889, in _get_arguments_from_pretrained + sub_processor = auto_processor_class.from_pretrained( + pretrained_model_name_or_path, subfolder=subfolder, **kwargs + ) + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/feature_extraction_auto.py", line 329, in from_pretrained + config_dict, _ = FeatureExtractionMixin.get_feature_extractor_dict(pretrained_model_name_or_path, **kwargs) + ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/feature_extraction_utils.py", line 545, in get_feature_extractor_dict + raise OSError( + ...<4 lines>... + ) +OSError: Can't load feature extractor for 'HukukcuApp/tr-legal-embedding-gemma-2'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'HukukcuApp/tr-legal-embedding-gemma-2' is the correct path to a directory containing a preprocessor_config.json file +{"index": 54, "model": "HukukcuApp/tr-legal-embedding-gemma-2", "revision": "a33e497f3f123019d3b03fe40082992fffeac728", "trending_rank": 54, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:42:02.918547+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "status": "error", "stage": "load_cpu", "error_type": "OSError", "error": "Can't load feature extractor for 'HukukcuApp/tr-legal-embedding-gemma-2'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'HukukcuApp/tr-legal-embedding-gemma-2' is the correct path to a directory containing a preprocessor_config.json file", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/054/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 870, in __init__\n self.processor = AutoProcessor.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n tokenizer_name_or_path if tokenizer_name_or_path is not None else model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n **processor_kwargs,\n ^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/processing_auto.py\", line 350, in from_pretrained\n return processor_class.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/processing_utils.py\", line 1749, in from_pretrained\n args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, processor_dict, **kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/processing_utils.py\", line 1889, in _get_arguments_from_pretrained\n sub_processor = auto_processor_class.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/feature_extraction_auto.py\", line 329, in from_pretrained\n config_dict, _ = FeatureExtractionMixin.get_feature_extractor_dict(pretrained_model_name_or_path, **kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/feature_extraction_utils.py\", line 545, in get_feature_extractor_dict\n raise OSError(\n ...<4 lines>...\n )\nOSError: Can't load feature extractor for 'HukukcuApp/tr-legal-embedding-gemma-2'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'HukukcuApp/tr-legal-embedding-gemma-2' is the correct path to a directory containing a preprocessor_config.json file\n", "result": "FAIL(load)", "total_seconds": 14.706041608005762} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/054/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/054/run.py new file mode 100644 index 0000000000000000000000000000000000000000..82984d4a9ec33c20c1e24b4e7afa8b9fdb3da79f --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/054/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 54, 'model': 'HukukcuApp/tr-legal-embedding-gemma-2', 'revision': 'a33e497f3f123019d3b03fe40082992fffeac728', 'trending_rank': 54, 'downloads_rank': None, 'class': 'SentenceTransformer', 'dtype': 'float32', 'attention': 'sdpa', 'trust_remote_code': False, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/067/memory_samples.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/067/memory_samples.json new file mode 100644 index 0000000000000000000000000000000000000000..31a2c61475e9f6edeabbff794a0b803bb22f06e5 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/067/memory_samples.json @@ -0,0 +1 @@ +[{"elapsed_seconds": 2.180691808462143e-05, "bytes": 7141760, "categories": {"code": 3842944, "tensors": 2097152, "misc": 1144320, "dma_rings": 57344}}, {"elapsed_seconds": 0.10035528894513845, "bytes": 474911504, "categories": {"code": 3880080, "tensors": 469762048, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.20045102946460247, "bytes": 474911504, "categories": {"code": 3880080, "tensors": 469762048, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.30053481459617615, "bytes": 474911504, "categories": {"code": 3880080, "tensors": 469762048, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.40062682144343853, "bytes": 474911504, "categories": {"code": 3880080, "tensors": 469762048, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.5007306365296245, "bytes": 474911504, "categories": {"code": 3880080, "tensors": 469762048, 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67108864, "misc": 7831744, "dma_rings": 2323712}}, {"elapsed_seconds": 39.74575906340033, "bytes": 559381137, "categories": {"code": 6342976, "tensors": 471863296, "constants": 2785297, "scratchpad": 67108864, "misc": 8750144, "dma_rings": 2530560}}, {"elapsed_seconds": 39.84587494377047, "bytes": 560925521, "categories": {"code": 6555520, "tensors": 471863296, "constants": 3178513, "scratchpad": 67108864, "misc": 9490112, "dma_rings": 2729216}}, {"elapsed_seconds": 39.88388103619218, "bytes": 560921425, "categories": {"code": 6555520, "tensors": 471863296, "constants": 3178513, "scratchpad": 67108864, "misc": 9490112, "dma_rings": 2725120}}] \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/067/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/067/result.json new file mode 100644 index 0000000000000000000000000000000000000000..466badec447330637c3ea0a008b6b5d05c4624ce --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/067/result.json @@ -0,0 +1,84 @@ +{ + "index": 67, + "model": "moka-ai/m3e-base", + "revision": "764b537a0e50e5c7d64db883f2d2e051cbe3c64c", + "trending_rank": 67, + "downloads_rank": null, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:45:11.803493+00:00", + "physical_visible_core": "8", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 1.331957183778286, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 0.6934455037117004, + 0.8071481585502625, + 0.8093117475509644, + 0.8064126968383789 + ], + "cpu_seconds": 0.14178986009210348, + "cpu_top_document": 3, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 39.688529022969306, + "neuron_scores": [ + 0.6934453845024109, + 0.807148277759552, + 0.8093113899230957, + 0.8064124584197998 + ], + "max_abs_error": 3.5762786865234375e-07, + "neuron_top_document": 3, + "mars_first": false, + "cpu_parity": true, + "neuron_repeat_seconds": 0.1130013084039092, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 400, + "sample_interval_seconds": 0.1, + "peak_bytes": 560925521, + "peak_categories": { + "code": 6555520, + "tensors": 471863296, + "constants": 3178513, + "scratchpad": 67108864, + "misc": 9490112, + "dma_rings": 2729216 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 452711936, + "allocator_peak_reserved_bytes": 471859200, + "total_seconds": 56.262652300298214 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/067/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/067/run.log new file mode 100644 index 0000000000000000000000000000000000000000..c7726d132cf190f3eb519980a6a5026279616883 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/067/run.log @@ -0,0 +1,5 @@ +CPU [0.6934455037117004, 0.8071481585502625, 0.8093117475509644, 0.8064126968383789] +2026-Nov-03 15:45:21.230829 2446525:2446525 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. + _warn_trace_cache_once() +{"index": 67, "model": "moka-ai/m3e-base", "revision": "764b537a0e50e5c7d64db883f2d2e051cbe3c64c", "trending_rank": 67, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:45:11.803493+00:00", "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 1.331957183778286, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.6934455037117004, 0.8071481585502625, 0.8093117475509644, 0.8064126968383789], "cpu_seconds": 0.14178986009210348, "cpu_top_document": 3, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 39.688529022969306, "neuron_scores": [0.6934453845024109, 0.807148277759552, 0.8093113899230957, 0.8064124584197998], "max_abs_error": 3.5762786865234375e-07, "neuron_top_document": 3, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.1130013084039092, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 400, "sample_interval_seconds": 0.1, "peak_bytes": 560925521, "peak_categories": {"code": 6555520, "tensors": 471863296, "constants": 3178513, "scratchpad": 67108864, "misc": 9490112, "dma_rings": 2729216}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 452711936, "allocator_peak_reserved_bytes": 471859200, "total_seconds": 56.262652300298214} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/067/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/067/run.py new file mode 100644 index 0000000000000000000000000000000000000000..c34eabe2b95c2222eaf0841c7768dc0add5f6d17 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/067/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 67, 'model': 'moka-ai/m3e-base', 'revision': '764b537a0e50e5c7d64db883f2d2e051cbe3c64c', 'trending_rank': 67, 'downloads_rank': None, 'class': 'SentenceTransformer', 'dtype': 'float32', 'attention': 'sdpa', 'trust_remote_code': False, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/070/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/070/result.json new file mode 100644 index 0000000000000000000000000000000000000000..0fa111c042f83f5818a92985009a9d87209ac77d --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/070/result.json @@ -0,0 +1,35 @@ +{ + "index": 70, + "model": "jinaai/jina-embeddings-v2-base-en", + "revision": "322d4d7e2f35e84137961a65af894fda0385eb7a", + "trending_rank": 70, + "downloads_rank": 88, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": true, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:45:55.385742+00:00", + "physical_visible_core": "12", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "status": "error", + "stage": "load_cpu", + "error_type": "ModuleNotFoundError", + "error": "No module named 'transformers.onnx'", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/070/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 839, in __init__\n config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2259, in _load_config\n return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py\", line 422, in from_pretrained\n config_class = get_class_from_dynamic_module(\n class_ref, pretrained_model_name_or_path, code_revision=code_revision, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 625, in get_class_from_dynamic_module\n return get_class_in_module(class_name, final_module, force_reload=force_download)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 309, in get_class_in_module\n module_spec.loader.exec_module(module)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^\n File \"\", line 759, in exec_module\n File \"\", line 491, in _call_with_frames_removed\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_bert_hyphen_implementation/f3ec4cf7de7e561007f27c9efc7148b0bd713f81/configuration_bert.py\", line 22, in \n from transformers.onnx import OnnxConfig\nModuleNotFoundError: No module named 'transformers.onnx'\n", + "result": "FAIL(load)", + "total_seconds": 8.849647373892367 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/070/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/070/run.log new file mode 100644 index 0000000000000000000000000000000000000000..dfed8394bf2f76aa0982b864fe3aa3faa3c89197 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/070/run.log @@ -0,0 +1,62 @@ +[transformers] A new version of the following files was downloaded from https://huggingface.co/jinaai/jina-bert-implementation: +- configuration_bert.py +. Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision. +Traceback (most recent call last): + File "/tmp/neuron-top100-sweep/runs/070/run.py", line 50, in + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 193, in __init__ + super().__init__( + ~~~~~~~~~~~~~~~~^ + model_name_or_path=model_name_or_path, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ...<13 lines>... + default_prompt_name=default_prompt_name, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 256, in __init__ + modules, self.module_kwargs = self._load_modules( + ~~~~~~~~~~~~~~~~~~^ + model_name_or_path, + ^^^^^^^^^^^^^^^^^^^ + ...<7 lines>... + config_kwargs=config_kwargs, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 1211, in _load_modules + return self._load_config_modules(model_name_or_path, **load_kwargs) + ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 1431, in _load_config_modules + module = module_class.load( + model_name_or_path, + ...<12 lines>... + **extra_load_kwargs, + ) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py", line 2641, in load + return cls(model_name_or_path=model_name_or_path, **init_kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 92, in wrapper + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py", line 839, in __init__ + config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs) + ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py", line 2259, in _load_config + return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False + ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py", line 422, in from_pretrained + config_class = get_class_from_dynamic_module( + class_ref, pretrained_model_name_or_path, code_revision=code_revision, **kwargs + ) + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py", line 625, in get_class_from_dynamic_module + return get_class_in_module(class_name, final_module, force_reload=force_download) + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py", line 309, in get_class_in_module + module_spec.loader.exec_module(module) + ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^ + File "", line 759, in exec_module + File "", line 491, in _call_with_frames_removed + File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_bert_hyphen_implementation/f3ec4cf7de7e561007f27c9efc7148b0bd713f81/configuration_bert.py", line 22, in + from transformers.onnx import OnnxConfig +ModuleNotFoundError: No module named 'transformers.onnx' +{"index": 70, "model": "jinaai/jina-embeddings-v2-base-en", "revision": "322d4d7e2f35e84137961a65af894fda0385eb7a", "trending_rank": 70, "downloads_rank": 88, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:45:55.385742+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "status": "error", "stage": "load_cpu", "error_type": "ModuleNotFoundError", "error": "No module named 'transformers.onnx'", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/070/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 839, in __init__\n config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2259, in _load_config\n return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py\", line 422, in from_pretrained\n config_class = get_class_from_dynamic_module(\n class_ref, pretrained_model_name_or_path, code_revision=code_revision, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 625, in get_class_from_dynamic_module\n return get_class_in_module(class_name, final_module, force_reload=force_download)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 309, in get_class_in_module\n module_spec.loader.exec_module(module)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^\n File \"\", line 759, in exec_module\n File \"\", line 491, in _call_with_frames_removed\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_bert_hyphen_implementation/f3ec4cf7de7e561007f27c9efc7148b0bd713f81/configuration_bert.py\", line 22, in \n from transformers.onnx import OnnxConfig\nModuleNotFoundError: No module named 'transformers.onnx'\n", "result": "FAIL(load)", "total_seconds": 8.849647373892367} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/070/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/070/run.py new file mode 100644 index 0000000000000000000000000000000000000000..6f31b006b2856b9c6de6bea879cdfbb8444a19ed --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/070/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 70, 'model': 'jinaai/jina-embeddings-v2-base-en', 'revision': '322d4d7e2f35e84137961a65af894fda0385eb7a', 'trending_rank': 70, 'downloads_rank': 88, 'class': 'SentenceTransformer', 'dtype': 'float32', 'attention': 'sdpa', 'trust_remote_code': True, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/073/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/073/result.json new file mode 100644 index 0000000000000000000000000000000000000000..afa008e53608f77d554e7c125aed1e73c7336094 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/073/result.json @@ -0,0 +1,43 @@ +{ + "index": 73, + "model": "Alibaba-NLP/gte-large-en-v1.5", + "revision": "104333d6af6f97649377c2afbde10a7704870c7b", + "trending_rank": 73, + "downloads_rank": 27, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": true, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:46:09.987170+00:00", + "physical_visible_core": "8", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 3.194855031557381, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "status": "error", + "stage": "cpu_inference", + "error_type": "IndexError", + "error": "index 239254133933015924 is out of bounds for dimension 0 with size 11", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/073/run.py\", line 63, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/073/run.py\", line 58, in score\n q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 239254133933015924 is out of bounds for dimension 0 with size 11\n", + "result": "FAIL(forward)", + "total_seconds": 11.818632579408586 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/073/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/073/run.log new file mode 100644 index 0000000000000000000000000000000000000000..f0e3cf1e5e1c7b67eac2df31cab0c82c24d017eb --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/073/run.log @@ -0,0 +1,70 @@ +[transformers] A new version of the following files was downloaded from https://huggingface.co/Alibaba-NLP/new-impl: +- configuration.py +. Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision. +/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/configuration_utils.py:723: FutureWarning: `rope_scaling` is deprecated and will be removed in a future version. Set `rope_parameters` instead, with `rope_type` and `rope_theta` included. + warnings.warn( +[transformers] A new version of the following files was downloaded from https://huggingface.co/Alibaba-NLP/new-impl: +- modeling.py +. Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision. +Traceback (most recent call last): + File "/tmp/neuron-top100-sweep/runs/073/run.py", line 63, in + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + ~~~~~^^ + File "/tmp/neuron-top100-sweep/runs/073/run.py", line 58, in score + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 370, in encode_query + return self.encode( + ~~~~~~~~~~~^ + inputs=inputs, + ^^^^^^^^^^^^^^ + ...<14 lines>... + **kwargs, + ^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 895, in encode + all_embeddings = self._inference(inputs, device=device, **inference_kwargs) + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 963, in _inference + out_features = self(features, **kwargs) + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1783, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1794, in _call_impl + return forward_call(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 600, in forward + input = module(input, **module_kwargs) + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1783, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1794, in _call_impl + return forward_call(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py", line 1647, in forward + model_output = model_method(**filtered_kwargs) + File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py", line 901, in forward + (embedding_output, attention_mask, rope_embeds, length) = self.embeddings( + ~~~~~~~~~~~~~~~^ + unpad_inputs, + ^^^^^^^^^^^^^ + ...<5 lines>... + inputs_embeds=inputs_embeds + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1783, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1794, in _call_impl + return forward_call(*args, **kwargs) + File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py", line 392, in forward + rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim] + ~~~~~~~~^^^^^^^^^^^^^^ +IndexError: index 239254133933015924 is out of bounds for dimension 0 with size 11 +{"index": 73, "model": "Alibaba-NLP/gte-large-en-v1.5", "revision": "104333d6af6f97649377c2afbde10a7704870c7b", "trending_rank": 73, "downloads_rank": 27, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:46:09.987170+00:00", "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 3.194855031557381, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "status": "error", "stage": "cpu_inference", "error_type": "IndexError", "error": "index 239254133933015924 is out of bounds for dimension 0 with size 11", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/073/run.py\", line 63, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/073/run.py\", line 58, in score\n q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 239254133933015924 is out of bounds for dimension 0 with size 11\n", "result": "FAIL(forward)", "total_seconds": 11.818632579408586} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/073/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/073/run.py new file mode 100644 index 0000000000000000000000000000000000000000..b2a8d3d797d675861c56ff1590bb8043fc80c8e1 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/073/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 73, 'model': 'Alibaba-NLP/gte-large-en-v1.5', 'revision': '104333d6af6f97649377c2afbde10a7704870c7b', 'trending_rank': 73, 'downloads_rank': 27, 'class': 'SentenceTransformer', 'dtype': 'float32', 'attention': 'sdpa', 'trust_remote_code': True, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/075/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/075/result.json new file mode 100644 index 0000000000000000000000000000000000000000..c9c0c89120bf41160690a78b671fa7bcf58872f2 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/075/result.json @@ -0,0 +1,35 @@ +{ + "index": 75, + "model": "nvidia/NV-Embed-v1", + "revision": "2e8b322e2b738a383dae0dde5fa19e0b28a5bb11", + "trending_rank": 75, + "downloads_rank": null, + "class": "SentenceTransformer", + "dtype": "bfloat16", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:46:17.899843+00:00", + "physical_visible_core": "4", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "status": "error", + "stage": "load_cpu", + "error_type": "GatedRepoError", + "error": "403 Client Error. (Request ID: Root=1-6aea01d2-3206be3e053d1684092fc8e7;1a302a3a-37c9-46e0-94a8-99f64b2e4802)\n\nCannot access gated repo for url https://huggingface.co/nvidia/NV-Embed-v1/resolve/2e8b322e2b738a383dae0dde5fa19e0b28a5bb11/modules.json.\nAccess to model nvidia/NV-Embed-v1 is restricted and you are not in the authorized list. Visit https://huggingface.co/nvidia/NV-Embed-v1 to ask for access.", + "traceback": "httpx.HTTPStatusError: Client error '403 Forbidden' for url 'https://huggingface.co/nvidia/NV-Embed-v1/resolve/2e8b322e2b738a383dae0dde5fa19e0b28a5bb11/modules.json'\nFor more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/403\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/075/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1190, in _load_modules\n modules_json_path = load_file_path(\n model_name_or_path,\n ...<4 lines>...\n local_files_only=local_files_only,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/file_io.py\", line 208, in load_file_path\n return hf_hub_download(\n model_name_or_path,\n ...<6 lines>...\n local_files_only=local_files_only,\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_validators.py\", line 89, in _inner_fn\n return fn(*args, **kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_http.py\", line 266, in _inner\n return fn(*args, **kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1047, in hf_hub_download\n return _hf_hub_download_to_cache_dir(\n # Destination\n ...<15 lines>...\n dry_run=dry_run,\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1194, in _hf_hub_download_to_cache_dir\n _raise_on_head_call_error(head_call_error, force_download, local_files_only)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1953, in _raise_on_head_call_error\n raise head_call_error\nhuggingface_hub.errors.GatedRepoError: 403 Client Error. (Request ID: Root=1-6aea01d2-3206be3e053d1684092fc8e7;1a302a3a-37c9-46e0-94a8-99f64b2e4802)\n\nCannot access gated repo for url https://huggingface.co/nvidia/NV-Embed-v1/resolve/2e8b322e2b738a383dae0dde5fa19e0b28a5bb11/modules.json.\nAccess to model nvidia/NV-Embed-v1 is restricted and you are not in the authorized list. Visit https://huggingface.co/nvidia/NV-Embed-v1 to ask for access.\n", + "result": "FAIL(load)", + "total_seconds": 8.890233975835145 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/075/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/075/run.log new file mode 100644 index 0000000000000000000000000000000000000000..d6cdf6a7a42eede98a2724ed5d4a03217a4115aa --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/075/run.log @@ -0,0 +1,62 @@ +httpx.HTTPStatusError: Client error '403 Forbidden' for url 'https://huggingface.co/nvidia/NV-Embed-v1/resolve/2e8b322e2b738a383dae0dde5fa19e0b28a5bb11/modules.json' +For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/403 + +The above exception was the direct cause of the following exception: + +Traceback (most recent call last): + File "/tmp/neuron-top100-sweep/runs/075/run.py", line 50, in + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 193, in __init__ + super().__init__( + ~~~~~~~~~~~~~~~~^ + model_name_or_path=model_name_or_path, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ...<13 lines>... + default_prompt_name=default_prompt_name, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 256, in __init__ + modules, self.module_kwargs = self._load_modules( + ~~~~~~~~~~~~~~~~~~^ + model_name_or_path, + ^^^^^^^^^^^^^^^^^^^ + ...<7 lines>... + config_kwargs=config_kwargs, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 1190, in _load_modules + modules_json_path = load_file_path( + model_name_or_path, + ...<4 lines>... + local_files_only=local_files_only, + ) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/file_io.py", line 208, in load_file_path + return hf_hub_download( + model_name_or_path, + ...<6 lines>... + local_files_only=local_files_only, + ) + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_validators.py", line 89, in _inner_fn + return fn(*args, **kwargs) + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_http.py", line 266, in _inner + return fn(*args, **kwargs) + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py", line 1047, in hf_hub_download + return _hf_hub_download_to_cache_dir( + # Destination + ...<15 lines>... + dry_run=dry_run, + ) + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py", line 1194, in _hf_hub_download_to_cache_dir + _raise_on_head_call_error(head_call_error, force_download, local_files_only) + ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py", line 1953, in _raise_on_head_call_error + raise head_call_error +huggingface_hub.errors.GatedRepoError: 403 Client Error. (Request ID: Root=1-6aea01d2-3206be3e053d1684092fc8e7;1a302a3a-37c9-46e0-94a8-99f64b2e4802) + +Cannot access gated repo for url https://huggingface.co/nvidia/NV-Embed-v1/resolve/2e8b322e2b738a383dae0dde5fa19e0b28a5bb11/modules.json. +Access to model nvidia/NV-Embed-v1 is restricted and you are not in the authorized list. Visit https://huggingface.co/nvidia/NV-Embed-v1 to ask for access. +{"index": 75, "model": "nvidia/NV-Embed-v1", "revision": "2e8b322e2b738a383dae0dde5fa19e0b28a5bb11", "trending_rank": 75, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "bfloat16", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:46:17.899843+00:00", "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "status": "error", "stage": "load_cpu", "error_type": "GatedRepoError", "error": "403 Client Error. (Request ID: Root=1-6aea01d2-3206be3e053d1684092fc8e7;1a302a3a-37c9-46e0-94a8-99f64b2e4802)\n\nCannot access gated repo for url https://huggingface.co/nvidia/NV-Embed-v1/resolve/2e8b322e2b738a383dae0dde5fa19e0b28a5bb11/modules.json.\nAccess to model nvidia/NV-Embed-v1 is restricted and you are not in the authorized list. Visit https://huggingface.co/nvidia/NV-Embed-v1 to ask for access.", "traceback": "httpx.HTTPStatusError: Client error '403 Forbidden' for url 'https://huggingface.co/nvidia/NV-Embed-v1/resolve/2e8b322e2b738a383dae0dde5fa19e0b28a5bb11/modules.json'\nFor more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/403\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/075/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1190, in _load_modules\n modules_json_path = load_file_path(\n model_name_or_path,\n ...<4 lines>...\n local_files_only=local_files_only,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/file_io.py\", line 208, in load_file_path\n return hf_hub_download(\n model_name_or_path,\n ...<6 lines>...\n local_files_only=local_files_only,\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_validators.py\", line 89, in _inner_fn\n return fn(*args, **kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_http.py\", line 266, in _inner\n return fn(*args, **kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1047, in hf_hub_download\n return _hf_hub_download_to_cache_dir(\n # Destination\n ...<15 lines>...\n dry_run=dry_run,\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1194, in _hf_hub_download_to_cache_dir\n _raise_on_head_call_error(head_call_error, force_download, local_files_only)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1953, in _raise_on_head_call_error\n raise head_call_error\nhuggingface_hub.errors.GatedRepoError: 403 Client Error. (Request ID: Root=1-6aea01d2-3206be3e053d1684092fc8e7;1a302a3a-37c9-46e0-94a8-99f64b2e4802)\n\nCannot access gated repo for url https://huggingface.co/nvidia/NV-Embed-v1/resolve/2e8b322e2b738a383dae0dde5fa19e0b28a5bb11/modules.json.\nAccess to model nvidia/NV-Embed-v1 is restricted and you are not in the authorized list. Visit https://huggingface.co/nvidia/NV-Embed-v1 to ask for access.\n", "result": "FAIL(load)", "total_seconds": 8.890233975835145} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/075/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/075/run.py new file mode 100644 index 0000000000000000000000000000000000000000..05fd7a1c865e24cab2855de265b274f4e9ea74b2 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/075/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 75, 'model': 'nvidia/NV-Embed-v1', 'revision': '2e8b322e2b738a383dae0dde5fa19e0b28a5bb11', 'trending_rank': 75, 'downloads_rank': None, 'class': 'SentenceTransformer', 'dtype': 'bfloat16', 'attention': 'sdpa', 'trust_remote_code': False, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/077/memory_samples.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/077/memory_samples.json new file mode 100644 index 0000000000000000000000000000000000000000..605ecb400e25e6f8cc9dbf3b40acd02da9929aac --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/077/memory_samples.json @@ -0,0 +1 @@ +[{"elapsed_seconds": 2.162996679544449e-05, "bytes": 7141760, "categories": {"code": 3842944, "tensors": 2097152, "misc": 1144320, "dma_rings": 57344}}, {"elapsed_seconds": 0.10034063551574945, "bytes": 2104398608, "categories": {"code": 3880080, "tensors": 2099249152, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.20046478044241667, "bytes": 2104398608, "categories": {"code": 3880080, "tensors": 2099249152, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.3005949342623353, "bytes": 2104398608, "categories": {"code": 3880080, "tensors": 2099249152, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.40069175232201815, "bytes": 2104398608, "categories": {"code": 3880080, "tensors": 2099249152, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.5007757460698485, "bytes": 2104398608, "categories": {"code": 3880080, "tensors": 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4030481, "scratchpad": 67108864, "misc": 14095744, "dma_rings": 1796096}}, {"elapsed_seconds": 40.982729566283524, "bytes": 10209788025, "categories": {"code": 11395752, "tensors": 10108276736, "constants": 4882449, "scratchpad": 67108864, "misc": 15922624, "dma_rings": 2201600}}, {"elapsed_seconds": 41.08280893508345, "bytes": 10209788025, "categories": {"code": 11395752, "tensors": 10108276736, "constants": 4882449, "scratchpad": 67108864, "misc": 15922624, "dma_rings": 2201600}}, {"elapsed_seconds": 41.107950064353645, "bytes": 10209788025, "categories": {"code": 11395752, "tensors": 10108276736, "constants": 4882449, "scratchpad": 67108864, "misc": 15922624, "dma_rings": 2201600}}] \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/077/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/077/result.json new file mode 100644 index 0000000000000000000000000000000000000000..b6757d53b0a91326201b732c88bfe1bdc465f7dd --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/077/result.json @@ -0,0 +1,84 @@ +{ + "index": 77, + "model": "Jaume/gemma-2b-embeddings", + "revision": "86431f65d7c3f66b2af096c61e614a2958f191f1", + "trending_rank": 77, + "downloads_rank": null, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:46:28.024988+00:00", + "physical_visible_core": "4", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 4.274750509299338, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 0.9210729002952576, + 0.9032096862792969, + 0.9318515062332153, + 0.9134941101074219 + ], + "cpu_seconds": 6.217862844467163, + "cpu_top_document": 3, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 38.68630393128842, + "neuron_scores": [ + 0.921073317527771, + 0.9032103419303894, + 0.9318520426750183, + 0.9134945273399353 + ], + "max_abs_error": 6.556510925292969e-07, + "neuron_top_document": 3, + "mars_first": false, + "cpu_parity": true, + "neuron_repeat_seconds": 0.15275748260319233, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 410, + "sample_interval_seconds": 0.1, + "peak_bytes": 10209788025, + "peak_categories": { + "code": 11395752, + "tensors": 10108276736, + "constants": 4882449, + "scratchpad": 67108864, + "misc": 15922624, + "dma_rings": 2201600 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 10050966144, + "allocator_peak_reserved_bytes": 10108272640, + "total_seconds": 68.6340330131352 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/077/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/077/run.log new file mode 100644 index 0000000000000000000000000000000000000000..b2569142b92d1c76de868705f2bbdf9d5dbc6e9e --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/077/run.log @@ -0,0 +1,6 @@ +[transformers] We found `hidden_act="gelu"` in this Gemma config. This is a legacy value of the official releases but it is meant to target the tanh approximation. Setting `hidden_act="gelu_pytorch_tanh"` instead. +CPU [0.9210729002952576, 0.9032096862792969, 0.9318515062332153, 0.9134941101074219] +2026-Nov-03 15:46:48.420935 2475124:2475124 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. + _warn_trace_cache_once() +{"index": 77, "model": "Jaume/gemma-2b-embeddings", "revision": "86431f65d7c3f66b2af096c61e614a2958f191f1", "trending_rank": 77, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:46:28.024988+00:00", "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 4.274750509299338, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.9210729002952576, 0.9032096862792969, 0.9318515062332153, 0.9134941101074219], "cpu_seconds": 6.217862844467163, "cpu_top_document": 3, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 38.68630393128842, "neuron_scores": [0.921073317527771, 0.9032103419303894, 0.9318520426750183, 0.9134945273399353], "max_abs_error": 6.556510925292969e-07, "neuron_top_document": 3, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.15275748260319233, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 410, "sample_interval_seconds": 0.1, "peak_bytes": 10209788025, "peak_categories": {"code": 11395752, "tensors": 10108276736, "constants": 4882449, "scratchpad": 67108864, "misc": 15922624, "dma_rings": 2201600}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 10050966144, "allocator_peak_reserved_bytes": 10108272640, "total_seconds": 68.6340330131352} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/077/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/077/run.py new file mode 100644 index 0000000000000000000000000000000000000000..080636cec89cc46e95d9c128d181467616deecba --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/077/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 77, 'model': 'Jaume/gemma-2b-embeddings', 'revision': '86431f65d7c3f66b2af096c61e614a2958f191f1', 'trending_rank': 77, 'downloads_rank': None, 'class': 'SentenceTransformer', 'dtype': 'float32', 'attention': 'sdpa', 'trust_remote_code': False, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/078/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/078/result.json new file mode 100644 index 0000000000000000000000000000000000000000..6f66261ab73036bfc963512d97b3a0669351a0bd --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/078/result.json @@ -0,0 +1,40 @@ +{ + "index": 78, + "model": "Alibaba-NLP/gte-multilingual-reranker-base", + "revision": "8215cf04918ba6f7b6a62bb44238ce2953d8831c", + "trending_rank": 78, + "downloads_rank": null, + "class": "CrossEncoder", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": true, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:46:48.108932+00:00", + "physical_visible_core": "0", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 2.7446684883907437, + "prompts": {}, + "parameter_dtypes": [ + "torch.float32" + ], + "status": "error", + "stage": "cpu_inference", + "error_type": "IndexError", + "error": "index 139625091826798 is out of bounds for dimension 0 with size 32", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/078/run.py\", line 63, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/078/run.py\", line 55, in score\n values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 209, in wrapper\n return func(self, *args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 645, in predict\n pred_scores = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 689, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 1117, in forward\n outputs = self.new(\n input_ids,\n ...<8 lines>...\n unpad_inputs=unpad_inputs,\n )\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 139625091826798 is out of bounds for dimension 0 with size 32\n", + "result": "FAIL(forward)", + "total_seconds": 15.83839768357575 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/078/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/078/run.log new file mode 100644 index 0000000000000000000000000000000000000000..9efa9872604eff15123722e723f680d22ff66f1b --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/078/run.log @@ -0,0 +1,65 @@ +/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/configuration_utils.py:723: FutureWarning: `rope_scaling` is deprecated and will be removed in a future version. Set `rope_parameters` instead, with `rope_type` and `rope_theta` included. + warnings.warn( +Traceback (most recent call last): + File "/tmp/neuron-top100-sweep/runs/078/run.py", line 63, in + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + ~~~~~^^ + File "/tmp/neuron-top100-sweep/runs/078/run.py", line 55, in score + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 209, in wrapper + return func(self, *args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py", line 645, in predict + pred_scores = self._inference(inputs, device=device, **inference_kwargs) + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py", line 689, in _inference + out_features = self(features, **kwargs) + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1783, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1794, in _call_impl + return forward_call(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 600, in forward + input = module(input, **module_kwargs) + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1783, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1794, in _call_impl + return forward_call(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py", line 1647, in forward + model_output = model_method(**filtered_kwargs) + File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py", line 1117, in forward + outputs = self.new( + input_ids, + ...<8 lines>... + unpad_inputs=unpad_inputs, + ) + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1783, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1794, in _call_impl + return forward_call(*args, **kwargs) + File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py", line 901, in forward + (embedding_output, attention_mask, rope_embeds, length) = self.embeddings( + ~~~~~~~~~~~~~~~^ + unpad_inputs, + ^^^^^^^^^^^^^ + ...<5 lines>... + inputs_embeds=inputs_embeds + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1783, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1794, in _call_impl + return forward_call(*args, **kwargs) + File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py", line 392, in forward + rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim] + ~~~~~~~~^^^^^^^^^^^^^^ +IndexError: index 139625091826798 is out of bounds for dimension 0 with size 32 +{"index": 78, "model": "Alibaba-NLP/gte-multilingual-reranker-base", "revision": "8215cf04918ba6f7b6a62bb44238ce2953d8831c", "trending_rank": 78, "downloads_rank": null, "class": "CrossEncoder", "dtype": "float32", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:46:48.108932+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 2.7446684883907437, "prompts": {}, "parameter_dtypes": ["torch.float32"], "status": "error", "stage": "cpu_inference", "error_type": "IndexError", "error": "index 139625091826798 is out of bounds for dimension 0 with size 32", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/078/run.py\", line 63, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/078/run.py\", line 55, in score\n values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 209, in wrapper\n return func(self, *args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 645, in predict\n pred_scores = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 689, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 1117, in forward\n outputs = self.new(\n input_ids,\n ...<8 lines>...\n unpad_inputs=unpad_inputs,\n )\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 139625091826798 is out of bounds for dimension 0 with size 32\n", "result": "FAIL(forward)", "total_seconds": 15.83839768357575} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/078/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/078/run.py new file mode 100644 index 0000000000000000000000000000000000000000..d31654e36311387254cb0caf84730e9739a237dd --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/078/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 78, 'model': 'Alibaba-NLP/gte-multilingual-reranker-base', 'revision': '8215cf04918ba6f7b6a62bb44238ce2953d8831c', 'trending_rank': 78, 'downloads_rank': None, 'class': 'CrossEncoder', 'dtype': 'float32', 'attention': 'sdpa', 'trust_remote_code': True, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/079/memory_samples.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/079/memory_samples.json new file mode 100644 index 0000000000000000000000000000000000000000..c17b11bac32d9f06c3d4d3cb7e059dfcb4b3077b --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/079/memory_samples.json @@ -0,0 +1 @@ +[{"elapsed_seconds": 2.373475581407547e-05, "bytes": 7141760, "categories": {"code": 3842944, "tensors": 2097152, "misc": 1144320, "dma_rings": 57344}}, {"elapsed_seconds": 0.10080441925674677, "bytes": 607135776, "categories": {"code": 3916320, "tensors": 601882624, "misc": 1271296, "dma_rings": 65536}}, {"elapsed_seconds": 0.20089501701295376, "bytes": 613539340, "categories": {"code": 4593224, "tensors": 603979776, "constants": 1048580, "misc": 3377088, "dma_rings": 540672}}, {"elapsed_seconds": 0.30101258493959904, "bytes": 685539917, "categories": {"code": 5466752, "tensors": 603983872, "constants": 1671181, "scratchpad": 67108864, "misc": 5861312, "dma_rings": 1447936}}, {"elapsed_seconds": 0.4011523490771651, "bytes": 690949593, "categories": {"code": 5995464, "tensors": 606081024, "constants": 2359313, "scratchpad": 67108864, "misc": 7482368, "dma_rings": 1922560}}, {"elapsed_seconds": 0.5012772148475051, "bytes": 695672385, "categories": {"code": 6654512, "tensors": 606081024, "constants": 3473425, "scratchpad": 67108864, "misc": 9832960, "dma_rings": 2521600}}, {"elapsed_seconds": 0.5625708736479282, "bytes": 695672385, "categories": {"code": 6654512, "tensors": 606081024, "constants": 3473425, "scratchpad": 67108864, "misc": 9832960, "dma_rings": 2521600}}] \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/079/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/079/result.json new file mode 100644 index 0000000000000000000000000000000000000000..55400e6bd6a28875a7a2960cb227704ac5875415 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/079/result.json @@ -0,0 +1,84 @@ +{ + "index": 79, + "model": "akhooli/Arabic-SBERT-100K", + "revision": "67199ee95d81bb10047c946b1b4268c8703b83db", + "trending_rank": 79, + "downloads_rank": null, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:46:59.382279+00:00", + "physical_visible_core": "12", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 1.3999376483261585, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 0.45112812519073486, + 0.5921509265899658, + 0.3681429922580719, + 0.6955137848854065 + ], + "cpu_seconds": 0.4114110991358757, + "cpu_top_document": 4, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 0.4038874404504895, + "neuron_scores": [ + 0.45112812519073486, + 0.5921508073806763, + 0.3681427240371704, + 0.6955137252807617 + ], + "max_abs_error": 2.682209014892578e-07, + "neuron_top_document": 4, + "mars_first": false, + "cpu_parity": true, + "neuron_repeat_seconds": 0.06409406755119562, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 7, + "sample_interval_seconds": 0.1, + "peak_bytes": 695672385, + "peak_categories": { + "code": 6654512, + "tensors": 606081024, + "constants": 3473425, + "scratchpad": 67108864, + "misc": 9832960, + "dma_rings": 2521600 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 587197440, + "allocator_peak_reserved_bytes": 606076928, + "total_seconds": 21.84079162403941 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/079/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/079/run.log new file mode 100644 index 0000000000000000000000000000000000000000..7a15412385ea70fe040564fc14b2bef4f3012090 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/079/run.log @@ -0,0 +1,5 @@ +CPU [0.45112812519073486, 0.5921509265899658, 0.3681429922580719, 0.6955137848854065] +2026-Nov-03 15:47:10.573216 2480704:2480704 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. + _warn_trace_cache_once() +{"index": 79, "model": "akhooli/Arabic-SBERT-100K", "revision": "67199ee95d81bb10047c946b1b4268c8703b83db", "trending_rank": 79, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:46:59.382279+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 1.3999376483261585, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.45112812519073486, 0.5921509265899658, 0.3681429922580719, 0.6955137848854065], "cpu_seconds": 0.4114110991358757, "cpu_top_document": 4, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 0.4038874404504895, "neuron_scores": [0.45112812519073486, 0.5921508073806763, 0.3681427240371704, 0.6955137252807617], "max_abs_error": 2.682209014892578e-07, "neuron_top_document": 4, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.06409406755119562, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 7, "sample_interval_seconds": 0.1, "peak_bytes": 695672385, "peak_categories": {"code": 6654512, "tensors": 606081024, "constants": 3473425, "scratchpad": 67108864, "misc": 9832960, "dma_rings": 2521600}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 587197440, "allocator_peak_reserved_bytes": 606076928, "total_seconds": 21.84079162403941} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/079/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/079/run.py new file mode 100644 index 0000000000000000000000000000000000000000..70e9e227292a97363878f8b69063b2bfb2a0d741 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/079/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 79, 'model': 'akhooli/Arabic-SBERT-100K', 'revision': '67199ee95d81bb10047c946b1b4268c8703b83db', 'trending_rank': 79, 'downloads_rank': None, 'class': 'SentenceTransformer', 'dtype': 'float32', 'attention': 'sdpa', 'trust_remote_code': False, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/081/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/081/result.json new file mode 100644 index 0000000000000000000000000000000000000000..3d21513ddc989cb84c0cccc4091cbfa3b2283ed1 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/081/result.json @@ -0,0 +1,35 @@ +{ + "index": 81, + "model": "jinaai/jina-embeddings-v3", + "revision": "ab036b023d30b4d1138c4c3bfa9f0c445ab455d6", + "trending_rank": 81, + "downloads_rank": 32, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "eager", + "trust_remote_code": true, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:47:35.211638+00:00", + "physical_visible_core": "12", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "status": "error", + "stage": "load_cpu", + "error_type": "AttributeError", + "error": "'XLMRobertaLoRA' object has no attribute 'all_tied_weights_keys'", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/081/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1404, in _load_config_modules\n module = module_class(model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_embeddings_hyphen_v3/ab036b023d30b4d1138c4c3bfa9f0c445ab455d6/custom_st.py\", line 87, in __init__\n self.auto_model = AutoModel.from_pretrained(model_name_or_path, config=self.config, **model_args)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py\", line 389, in from_pretrained\n return model_class.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/xlm_hyphen_roberta_hyphen_flash_hyphen_implementation/bd55a5ec8e6c0fb1d6c26efb4b6a4a74ce8a88d3/modeling_lora.py\", line 338, in from_pretrained\n return super().from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<9 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/xlm_hyphen_roberta_hyphen_flash_hyphen_implementation/bd55a5ec8e6c0fb1d6c26efb4b6a4a74ce8a88d3/modeling_xlm_roberta.py\", line 442, in from_pretrained\n return super().from_pretrained(*args, **kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4356, in from_pretrained\n loading_info = cls._finalize_model_loading(model, load_config, loading_info)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4524, in _finalize_model_loading\n model._move_missing_keys_from_meta_to_device(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n loading_info.missing_and_mismatched(),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<2 lines>...\n load_config.hf_quantizer,\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4781, in _move_missing_keys_from_meta_to_device\n for key in missing_keys - self.all_tied_weights_keys.keys():\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1972, in __getattr__\n raise AttributeError(\n f\"'{type(self).__name__}' object has no attribute '{name}'\"\n )\nAttributeError: 'XLMRobertaLoRA' object has no attribute 'all_tied_weights_keys'. Did you mean: '_tied_weights_keys'?\n", + "result": "FAIL(load)", + "total_seconds": 19.574221355840564 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/081/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/081/run.log new file mode 100644 index 0000000000000000000000000000000000000000..67edb7cee20c351cd3f0b971498db782e7e521d2 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/081/run.log @@ -0,0 +1,75 @@ +[transformers] `torch_dtype` is deprecated! Use `dtype` instead! +Traceback (most recent call last): + File "/tmp/neuron-top100-sweep/runs/081/run.py", line 50, in + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 193, in __init__ + super().__init__( + ~~~~~~~~~~~~~~~~^ + model_name_or_path=model_name_or_path, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ...<13 lines>... + default_prompt_name=default_prompt_name, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 256, in __init__ + modules, self.module_kwargs = self._load_modules( + ~~~~~~~~~~~~~~~~~~^ + model_name_or_path, + ^^^^^^^^^^^^^^^^^^^ + ...<7 lines>... + config_kwargs=config_kwargs, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 1211, in _load_modules + return self._load_config_modules(model_name_or_path, **load_kwargs) + ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 1404, in _load_config_modules + module = module_class(model_name_or_path, **init_kwargs) + File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_embeddings_hyphen_v3/ab036b023d30b4d1138c4c3bfa9f0c445ab455d6/custom_st.py", line 87, in __init__ + self.auto_model = AutoModel.from_pretrained(model_name_or_path, config=self.config, **model_args) + ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py", line 389, in from_pretrained + return model_class.from_pretrained( + ~~~~~~~~~~~~~~~~~~~~~~~~~~~^ + pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/xlm_hyphen_roberta_hyphen_flash_hyphen_implementation/bd55a5ec8e6c0fb1d6c26efb4b6a4a74ce8a88d3/modeling_lora.py", line 338, in from_pretrained + return super().from_pretrained( + ~~~~~~~~~~~~~~~~~~~~~~~^ + pretrained_model_name_or_path, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ...<9 lines>... + **kwargs, + ^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/xlm_hyphen_roberta_hyphen_flash_hyphen_implementation/bd55a5ec8e6c0fb1d6c26efb4b6a4a74ce8a88d3/modeling_xlm_roberta.py", line 442, in from_pretrained + return super().from_pretrained(*args, **kwargs) + ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py", line 4356, in from_pretrained + loading_info = cls._finalize_model_loading(model, load_config, loading_info) + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py", line 4524, in _finalize_model_loading + model._move_missing_keys_from_meta_to_device( + ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^ + loading_info.missing_and_mismatched(), + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ...<2 lines>... + load_config.hf_quantizer, + ^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py", line 4781, in _move_missing_keys_from_meta_to_device + for key in missing_keys - self.all_tied_weights_keys.keys(): + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1972, in __getattr__ + raise AttributeError( + f"'{type(self).__name__}' object has no attribute '{name}'" + ) +AttributeError: 'XLMRobertaLoRA' object has no attribute 'all_tied_weights_keys'. Did you mean: '_tied_weights_keys'? +{"index": 81, "model": "jinaai/jina-embeddings-v3", "revision": "ab036b023d30b4d1138c4c3bfa9f0c445ab455d6", "trending_rank": 81, "downloads_rank": 32, "class": "SentenceTransformer", "dtype": "float32", "attention": "eager", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:47:35.211638+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "status": "error", "stage": "load_cpu", "error_type": "AttributeError", "error": "'XLMRobertaLoRA' object has no attribute 'all_tied_weights_keys'", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/081/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1404, in _load_config_modules\n module = module_class(model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_embeddings_hyphen_v3/ab036b023d30b4d1138c4c3bfa9f0c445ab455d6/custom_st.py\", line 87, in __init__\n self.auto_model = AutoModel.from_pretrained(model_name_or_path, config=self.config, **model_args)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py\", line 389, in from_pretrained\n return model_class.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/xlm_hyphen_roberta_hyphen_flash_hyphen_implementation/bd55a5ec8e6c0fb1d6c26efb4b6a4a74ce8a88d3/modeling_lora.py\", line 338, in from_pretrained\n return super().from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<9 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/xlm_hyphen_roberta_hyphen_flash_hyphen_implementation/bd55a5ec8e6c0fb1d6c26efb4b6a4a74ce8a88d3/modeling_xlm_roberta.py\", line 442, in from_pretrained\n return super().from_pretrained(*args, **kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4356, in from_pretrained\n loading_info = cls._finalize_model_loading(model, load_config, loading_info)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4524, in _finalize_model_loading\n model._move_missing_keys_from_meta_to_device(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n loading_info.missing_and_mismatched(),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<2 lines>...\n load_config.hf_quantizer,\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4781, in _move_missing_keys_from_meta_to_device\n for key in missing_keys - self.all_tied_weights_keys.keys():\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1972, in __getattr__\n raise AttributeError(\n f\"'{type(self).__name__}' object has no attribute '{name}'\"\n )\nAttributeError: 'XLMRobertaLoRA' object has no attribute 'all_tied_weights_keys'. Did you mean: '_tied_weights_keys'?\n", "result": "FAIL(load)", "total_seconds": 19.574221355840564} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/081/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/081/run.py new file mode 100644 index 0000000000000000000000000000000000000000..6da9dff077a0eeec7c85a03b2071324fbb272831 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/081/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 81, 'model': 'jinaai/jina-embeddings-v3', 'revision': 'ab036b023d30b4d1138c4c3bfa9f0c445ab455d6', 'trending_rank': 81, 'downloads_rank': 32, 'class': 'SentenceTransformer', 'dtype': 'float32', 'attention': 'eager', 'trust_remote_code': True, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/083/memory_samples.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/083/memory_samples.json new file mode 100644 index 0000000000000000000000000000000000000000..b9e68094bb1e422d7ef64a7cc7d193aea943254f --- /dev/null +++ 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6262320, "tensors": 606081024, "constants": 1933325, "scratchpad": 67108864, "misc": 8271872, "dma_rings": 2164992}}, {"elapsed_seconds": 43.51673884131014, "bytes": 697682649, "categories": {"code": 6766600, "tensors": 608178176, "constants": 2719761, "scratchpad": 67108864, "misc": 10129856, "dma_rings": 2779392}}, {"elapsed_seconds": 43.61682879552245, "bytes": 701178529, "categories": {"code": 7318992, "tensors": 608178176, "constants": 3440657, "scratchpad": 67108864, "misc": 11850688, "dma_rings": 3281152}}, {"elapsed_seconds": 43.63529870286584, "bytes": 701147809, "categories": {"code": 7318992, "tensors": 608178176, "constants": 3440657, "scratchpad": 67108864, "misc": 11850688, "dma_rings": 3250432}}] \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/083/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/083/result.json new file mode 100644 index 0000000000000000000000000000000000000000..9a47d05ab7cd061ed062171dfc820c14029cc57d --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/083/result.json @@ -0,0 +1,84 @@ +{ + "index": 83, + "model": "meandyou200175/phobert-finetune", + "revision": "26e5f91ad1eb94b0af813021a2b7097ae4ba228e", + "trending_rank": 83, + "downloads_rank": null, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:47:38.793972+00:00", + "physical_visible_core": "4", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 1.9071867559105158, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 0.20963680744171143, + 0.5245946049690247, + 0.4401358366012573, + 0.585011899471283 + ], + "cpu_seconds": 0.2299742652103305, + "cpu_top_document": 4, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 43.493039570748806, + "neuron_scores": [ + 0.20963667333126068, + 0.5245950222015381, + 0.44013622403144836, + 0.5850120782852173 + ], + "max_abs_error": 4.172325134277344e-07, + "neuron_top_document": 4, + "mars_first": false, + "cpu_parity": true, + "neuron_repeat_seconds": 0.05636020377278328, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 437, + "sample_interval_seconds": 0.1, + "peak_bytes": 701178529, + "peak_categories": { + "code": 7318992, + "tensors": 608178176, + "constants": 3440657, + "scratchpad": 67108864, + "misc": 11850688, + "dma_rings": 3281152 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 586056448, + "allocator_peak_reserved_bytes": 608174080, + "total_seconds": 64.42325356975198 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/083/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/083/run.log new file mode 100644 index 0000000000000000000000000000000000000000..dc9869d8aaa61098e637b67eaf31d43e5fe01295 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/083/run.log @@ -0,0 +1,5 @@ +CPU [0.20963680744171143, 0.5245946049690247, 0.4401358366012573, 0.585011899471283] +2026-Nov-03 15:47:52.897933 2508243:2508243 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. + _warn_trace_cache_once() +{"index": 83, "model": "meandyou200175/phobert-finetune", "revision": "26e5f91ad1eb94b0af813021a2b7097ae4ba228e", "trending_rank": 83, "downloads_rank": null, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:47:38.793972+00:00", "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 1.9071867559105158, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.20963680744171143, 0.5245946049690247, 0.4401358366012573, 0.585011899471283], "cpu_seconds": 0.2299742652103305, "cpu_top_document": 4, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 43.493039570748806, "neuron_scores": [0.20963667333126068, 0.5245950222015381, 0.44013622403144836, 0.5850120782852173], "max_abs_error": 4.172325134277344e-07, "neuron_top_document": 4, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.05636020377278328, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 437, "sample_interval_seconds": 0.1, "peak_bytes": 701178529, "peak_categories": {"code": 7318992, "tensors": 608178176, "constants": 3440657, "scratchpad": 67108864, "misc": 11850688, "dma_rings": 3281152}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 586056448, "allocator_peak_reserved_bytes": 608174080, "total_seconds": 64.42325356975198} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/083/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/083/run.py new file mode 100644 index 0000000000000000000000000000000000000000..8f7f662b1d5c455e5c855f44d5ea80cbc28da04f --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/083/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 83, 'model': 'meandyou200175/phobert-finetune', 'revision': '26e5f91ad1eb94b0af813021a2b7097ae4ba228e', 'trending_rank': 83, 'downloads_rank': None, 'class': 'SentenceTransformer', 'dtype': 'float32', 'attention': 'sdpa', 'trust_remote_code': False, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/089/memory_samples.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/089/memory_samples.json new file mode 100644 index 0000000000000000000000000000000000000000..b4f3d1bedd79c6ea4e502dbfd88e5c378c78caff --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/089/memory_samples.json @@ -0,0 +1 @@ +[{"elapsed_seconds": 2.012401819229126e-05, "bytes": 7141760, "categories": {"code": 3842944, "tensors": 2097152, "misc": 1144320, "dma_rings": 57344}}, {"elapsed_seconds": 0.10030788276344538, "bytes": 1345229584, "categories": {"code": 3880080, "tensors": 1340080128, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.20177461951971054, "bytes": 2754515728, "categories": {"code": 3880080, "tensors": 2749366272, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.3018535263836384, "bytes": 4216230672, "categories": {"code": 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"trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:49:04.100500+00:00", + "physical_visible_core": "0", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 30.20843593403697, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 10.587596893310547, + 15.282479286193848, + 11.481404304504395, + 15.91786003112793 + ], + "cpu_seconds": 2.5352238938212395, + "cpu_top_document": 4, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 78.56518525443971, + "neuron_scores": [ + 10.587594032287598, + 15.282479286193848, + 11.481398582458496, + 15.917861938476562 + ], + "max_abs_error": 5.7220458984375e-06, + "neuron_top_document": 4, + "mars_first": false, + "cpu_parity": true, + "neuron_repeat_seconds": 0.9287767671048641, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 811, + "sample_interval_seconds": 0.1, + "peak_bytes": 15556525689, + "peak_categories": { + "code": 13058680, + "tensors": 15437139968, + "constants": 6002945, + "scratchpad": 67108864, + "misc": 22477824, + "dma_rings": 10737408 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 15333365888, + "allocator_peak_reserved_bytes": 15437135872, + "total_seconds": 134.7353895958513 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/089/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/089/run.log new file mode 100644 index 0000000000000000000000000000000000000000..8b55c1cea9c7d1b4c33676173e0412f3a1024891 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/089/run.log @@ -0,0 +1,21 @@ +[transformers] Qwen2_5_VLModel LOAD REPORT from: vidore/colqwen2.5-base +Key | Status | | +------------------------+------------+--+- +custom_text_proj.weight | UNEXPECTED | | +custom_text_proj.bias | UNEXPECTED | | + +Notes: +- UNEXPECTED: can be ignored when loading from different task/architecture; not ok if you expect identical arch. +[transformers] Qwen2_5_VLModel LOAD REPORT from: vidore/colqwen2.5-v0.2 +Key | Status | | +---------------------------------------+------------+--+- +custom_text_proj.lora_A.default.weight | UNEXPECTED | | +custom_text_proj.lora_B.default.weight | UNEXPECTED | | + +Notes: +- UNEXPECTED: can be ignored when loading from different task/architecture; not ok if you expect identical arch. +CPU [10.587596893310547, 15.282479286193848, 11.481404304504395, 15.91786003112793] +2026-Nov-03 15:49:49.360524 2553666:2553666 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. + _warn_trace_cache_once() +{"index": 89, "model": "vidore/colqwen2.5-v0.2", "revision": "dcbe8d9cede518bce830488364ba0e40c873645b", "trending_rank": 89, "downloads_rank": null, "class": "MultiVectorEncoder", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:49:04.100500+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 30.20843593403697, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [10.587596893310547, 15.282479286193848, 11.481404304504395, 15.91786003112793], "cpu_seconds": 2.5352238938212395, "cpu_top_document": 4, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 78.56518525443971, "neuron_scores": [10.587594032287598, 15.282479286193848, 11.481398582458496, 15.917861938476562], "max_abs_error": 5.7220458984375e-06, "neuron_top_document": 4, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.9287767671048641, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 811, "sample_interval_seconds": 0.1, "peak_bytes": 15556525689, "peak_categories": {"code": 13058680, "tensors": 15437139968, "constants": 6002945, "scratchpad": 67108864, "misc": 22477824, "dma_rings": 10737408}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 15333365888, "allocator_peak_reserved_bytes": 15437135872, "total_seconds": 134.7353895958513} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/089/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/089/run.py new file mode 100644 index 0000000000000000000000000000000000000000..bea3df69c30cba4db5a7446110b60b8ff6a071d4 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/089/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 89, 'model': 'vidore/colqwen2.5-v0.2', 'revision': 'dcbe8d9cede518bce830488364ba0e40c873645b', 'trending_rank': 89, 'downloads_rank': None, 'class': 'MultiVectorEncoder', 'dtype': 'float32', 'attention': 'sdpa', 'trust_remote_code': False, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/108/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/108/result.json new file mode 100644 index 0000000000000000000000000000000000000000..9e406e489ce21539ff56d9fb5063165ee20fdf72 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/108/result.json @@ -0,0 +1,35 @@ +{ + "index": 108, + "model": "jinaai/jina-reranker-v2-base-multilingual", + "revision": "9cfeff2df7d40d1b78e75e5e9cebec92a99813c9", + "trending_rank": null, + "downloads_rank": 29, + "class": "CrossEncoder", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": true, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:52:03.168120+00:00", + "physical_visible_core": "4", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "status": "error", + "stage": "load_cpu", + "error_type": "ImportError", + "error": "cannot import name 'create_position_ids_from_input_ids' from 'transformers.models.xlm_roberta.modeling_xlm_roberta' (/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/xlm_roberta/modeling_xlm_roberta.py)", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/108/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 163, in wrapper\n return func(self, *args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 200, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1200, in _load_modules\n return self._load_default_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 337, in _load_default_modules\n transformer_model = Transformer(\n model_name_or_path,\n ...<5 lines>...\n backend=self.backend,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 855, in __init__\n self.model = self._load_model(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path, transformer_task, config, backend, is_peft_model, **model_kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2343, in _load_model\n return model_cls.from_pretrained(model_name_or_path, config=config, **model_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py\", line 381, in from_pretrained\n model_class = get_class_from_dynamic_module(\n class_ref, pretrained_model_name_or_path, code_revision=code_revision, **hub_kwargs, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 625, in get_class_from_dynamic_module\n return get_class_in_module(class_name, final_module, force_reload=force_download)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 309, in get_class_in_module\n module_spec.loader.exec_module(module)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^\n File \"\", line 759, in exec_module\n File \"\", line 491, in _call_with_frames_removed\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_reranker_hyphen_v2_hyphen_base_hyphen_multilingual/9cfeff2df7d40d1b78e75e5e9cebec92a99813c9/modeling_xlm_roberta.py\", line 44, in \n from .embedding import XLMRobertaEmbeddings\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_reranker_hyphen_v2_hyphen_base_hyphen_multilingual/9cfeff2df7d40d1b78e75e5e9cebec92a99813c9/embedding.py\", line 11, in \n from transformers.models.xlm_roberta.modeling_xlm_roberta import create_position_ids_from_input_ids\nImportError: cannot import name 'create_position_ids_from_input_ids' from 'transformers.models.xlm_roberta.modeling_xlm_roberta' (/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/xlm_roberta/modeling_xlm_roberta.py)\n", + "result": "FAIL(load)", + "total_seconds": 10.194885298609734 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/108/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/108/run.log new file mode 100644 index 0000000000000000000000000000000000000000..e379e146dd1e7eb3ea4905e8416a1a87aec864ae --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/108/run.log @@ -0,0 +1,64 @@ +[transformers] `torch_dtype` is deprecated! Use `dtype` instead! +Traceback (most recent call last): + File "/tmp/neuron-top100-sweep/runs/108/run.py", line 50, in + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 163, in wrapper + return func(self, *args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py", line 200, in __init__ + super().__init__( + ~~~~~~~~~~~~~~~~^ + model_name_or_path=model_name_or_path, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ...<13 lines>... + default_prompt_name=default_prompt_name, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 256, in __init__ + modules, self.module_kwargs = self._load_modules( + ~~~~~~~~~~~~~~~~~~^ + model_name_or_path, + ^^^^^^^^^^^^^^^^^^^ + ...<7 lines>... + config_kwargs=config_kwargs, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 1200, in _load_modules + return self._load_default_modules(model_name_or_path, **load_kwargs) + ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py", line 337, in _load_default_modules + transformer_model = Transformer( + model_name_or_path, + ...<5 lines>... + backend=self.backend, + ) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 92, in wrapper + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py", line 855, in __init__ + self.model = self._load_model( + ~~~~~~~~~~~~~~~~^ + model_name_or_path, transformer_task, config, backend, is_peft_model, **model_kwargs + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py", line 2343, in _load_model + return model_cls.from_pretrained(model_name_or_path, config=config, **model_kwargs) + ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py", line 381, in from_pretrained + model_class = get_class_from_dynamic_module( + class_ref, pretrained_model_name_or_path, code_revision=code_revision, **hub_kwargs, **kwargs + ) + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py", line 625, in get_class_from_dynamic_module + return get_class_in_module(class_name, final_module, force_reload=force_download) + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py", line 309, in get_class_in_module + module_spec.loader.exec_module(module) + ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^ + File "", line 759, in exec_module + File "", line 491, in _call_with_frames_removed + File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_reranker_hyphen_v2_hyphen_base_hyphen_multilingual/9cfeff2df7d40d1b78e75e5e9cebec92a99813c9/modeling_xlm_roberta.py", line 44, in + from .embedding import XLMRobertaEmbeddings + File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_reranker_hyphen_v2_hyphen_base_hyphen_multilingual/9cfeff2df7d40d1b78e75e5e9cebec92a99813c9/embedding.py", line 11, in + from transformers.models.xlm_roberta.modeling_xlm_roberta import create_position_ids_from_input_ids +ImportError: cannot import name 'create_position_ids_from_input_ids' from 'transformers.models.xlm_roberta.modeling_xlm_roberta' (/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/xlm_roberta/modeling_xlm_roberta.py) +{"index": 108, "model": "jinaai/jina-reranker-v2-base-multilingual", "revision": "9cfeff2df7d40d1b78e75e5e9cebec92a99813c9", "trending_rank": null, "downloads_rank": 29, "class": "CrossEncoder", "dtype": "float32", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:52:03.168120+00:00", "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "status": "error", "stage": "load_cpu", "error_type": "ImportError", "error": "cannot import name 'create_position_ids_from_input_ids' from 'transformers.models.xlm_roberta.modeling_xlm_roberta' (/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/xlm_roberta/modeling_xlm_roberta.py)", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/108/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 163, in wrapper\n return func(self, *args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 200, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1200, in _load_modules\n return self._load_default_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 337, in _load_default_modules\n transformer_model = Transformer(\n model_name_or_path,\n ...<5 lines>...\n backend=self.backend,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 855, in __init__\n self.model = self._load_model(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path, transformer_task, config, backend, is_peft_model, **model_kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2343, in _load_model\n return model_cls.from_pretrained(model_name_or_path, config=config, **model_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py\", line 381, in from_pretrained\n model_class = get_class_from_dynamic_module(\n class_ref, pretrained_model_name_or_path, code_revision=code_revision, **hub_kwargs, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 625, in get_class_from_dynamic_module\n return get_class_in_module(class_name, final_module, force_reload=force_download)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 309, in get_class_in_module\n module_spec.loader.exec_module(module)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^\n File \"\", line 759, in exec_module\n File \"\", line 491, in _call_with_frames_removed\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_reranker_hyphen_v2_hyphen_base_hyphen_multilingual/9cfeff2df7d40d1b78e75e5e9cebec92a99813c9/modeling_xlm_roberta.py\", line 44, in \n from .embedding import XLMRobertaEmbeddings\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_reranker_hyphen_v2_hyphen_base_hyphen_multilingual/9cfeff2df7d40d1b78e75e5e9cebec92a99813c9/embedding.py\", line 11, in \n from transformers.models.xlm_roberta.modeling_xlm_roberta import create_position_ids_from_input_ids\nImportError: cannot import name 'create_position_ids_from_input_ids' from 'transformers.models.xlm_roberta.modeling_xlm_roberta' (/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/xlm_roberta/modeling_xlm_roberta.py)\n", "result": "FAIL(load)", "total_seconds": 10.194885298609734} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/108/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/108/run.py new file mode 100644 index 0000000000000000000000000000000000000000..66fa36099d994d9641058304e4b93faeb620fcf3 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/108/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 108, 'model': 'jinaai/jina-reranker-v2-base-multilingual', 'revision': '9cfeff2df7d40d1b78e75e5e9cebec92a99813c9', 'trending_rank': None, 'downloads_rank': 29, 'class': 'CrossEncoder', 'dtype': 'float32', 'attention': 'sdpa', 'trust_remote_code': True, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/112/memory_samples.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/112/memory_samples.json new file mode 100644 index 0000000000000000000000000000000000000000..6f00af599d25594a2974aff85f9d9eaba7c9380b --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/112/memory_samples.json @@ -0,0 +1 @@ +[{"elapsed_seconds": 2.3632310330867767e-05, "bytes": 7141760, "categories": {"code": 3842944, "tensors": 2097152, "misc": 1144320, "dma_rings": 57344}}, {"elapsed_seconds": 0.10033226944506168, "bytes": 572061576, "categories": {"code": 4029192, "tensors": 499126272, "scratchpad": 67108864, "misc": 1713280, "dma_rings": 83968}}, {"elapsed_seconds": 0.20063955802470446, "bytes": 577249848, "categories": {"code": 5168440, "tensors": 499126272, "constants": 262144, "scratchpad": 67108864, "misc": 5207296, "dma_rings": 376832}}, {"elapsed_seconds": 0.30105345509946346, "bytes": 583002664, "categories": {"code": 6024296, "tensors": 499126272, "constants": 1572864, "scratchpad": 67108864, "misc": 8218048, "dma_rings": 952320}}, {"elapsed_seconds": 0.4011397510766983, "bytes": 588238485, "categories": {"code": 7014344, "tensors": 499126272, "constants": 1736717, "scratchpad": 67108864, "misc": 10974144, "dma_rings": 2278144}}, {"elapsed_seconds": 0.5012523103505373, "bytes": 593347357, "categories": {"code": 7859088, "tensors": 499126272, "constants": 1998861, "scratchpad": 67108864, "misc": 13965440, "dma_rings": 3288832}}, {"elapsed_seconds": 0.6013690494000912, "bytes": 599853421, "categories": {"code": 8630752, "tensors": 501223424, "constants": 2916365, "scratchpad": 67108864, "misc": 15864960, "dma_rings": 4109056}}, {"elapsed_seconds": 0.7014902606606483, "bytes": 601499581, "categories": {"code": 8816752, "tensors": 501223424, "constants": 3178509, "scratchpad": 67108864, "misc": 16704576, "dma_rings": 4467456}}, {"elapsed_seconds": 0.7076300121843815, "bytes": 602256101, "categories": {"code": 8931864, "tensors": 501223424, "constants": 3440653, "scratchpad": 67108864, "misc": 17067456, "dma_rings": 4483840}}] \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/112/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/112/result.json new file mode 100644 index 0000000000000000000000000000000000000000..43a099ba0eeaec0435c4173546b93069c7f428d2 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/112/result.json @@ -0,0 +1,73 @@ +{ + "index": 112, + "model": "sentence-transformers/multi-qa-mpnet-base-dot-v1", + "revision": "17997f24dca0df1a4fed68894fb0e1e133e60482", + "trending_rank": null, + "downloads_rank": 34, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "eager", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:52:37.157163+00:00", + "physical_visible_core": "8", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 0.9057708652690053, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 18.622793197631836, + 25.37649154663086, + 24.276288986206055, + 23.726768493652344 + ], + "cpu_seconds": 0.13503286056220531, + "cpu_top_document": 2, + "model_device": "neuron:0", + "status": "error", + "stage": "neuron_inference", + "error_type": "RuntimeError", + "error": "Inference tensors do not track version counter.", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/112/run.py\", line 69, in \n stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/112/run.py\", line 61, in score\n values=model.similarity(q,d)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1072, in similarity\n return self._similarity(embeddings1, embeddings2)\n ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/similarity.py\", line 115, in dot_score\n return torch.mm(a, b.transpose(0, 1)).to_dense()\n ~~~~~~~~~~~^^^^^^\nRuntimeError: Inference tensors do not track version counter.\n", + "result": "FAIL(forward)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 9, + "sample_interval_seconds": 0.1, + "peak_bytes": 602256101, + "peak_categories": { + "code": 8931864, + "tensors": 501223424, + "constants": 3440653, + "scratchpad": 67108864, + "misc": 17067456, + "dma_rings": 4483840 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 481360448, + "allocator_peak_reserved_bytes": 501219328, + "total_seconds": 16.84018260613084 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/112/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/112/run.log new file mode 100644 index 0000000000000000000000000000000000000000..61bdd6d7c8876657bc786cdd8de54464f494b99d --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/112/run.log @@ -0,0 +1,18 @@ +CPU [18.622793197631836, 25.37649154663086, 24.276288986206055, 23.726768493652344] +2026-Nov-03 15:52:45.685760 2659064:2659064 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. + _warn_trace_cache_once() +Traceback (most recent call last): + File "/tmp/neuron-top100-sweep/runs/112/run.py", line 69, in + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + ~~~~~^^ + File "/tmp/neuron-top100-sweep/runs/112/run.py", line 61, in score + values=model.similarity(q,d) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 1072, in similarity + return self._similarity(embeddings1, embeddings2) + ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/similarity.py", line 115, in dot_score + return torch.mm(a, b.transpose(0, 1)).to_dense() + ~~~~~~~~~~~^^^^^^ +RuntimeError: Inference tensors do not track version counter. +{"index": 112, "model": "sentence-transformers/multi-qa-mpnet-base-dot-v1", "revision": "17997f24dca0df1a4fed68894fb0e1e133e60482", "trending_rank": null, "downloads_rank": 34, "class": "SentenceTransformer", "dtype": "float32", "attention": "eager", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:52:37.157163+00:00", "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 0.9057708652690053, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [18.622793197631836, 25.37649154663086, 24.276288986206055, 23.726768493652344], "cpu_seconds": 0.13503286056220531, "cpu_top_document": 2, "model_device": "neuron:0", "status": "error", "stage": "neuron_inference", "error_type": "RuntimeError", "error": "Inference tensors do not track version counter.", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/112/run.py\", line 69, in \n stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/112/run.py\", line 61, in score\n values=model.similarity(q,d)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1072, in similarity\n return self._similarity(embeddings1, embeddings2)\n ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/similarity.py\", line 115, in dot_score\n return torch.mm(a, b.transpose(0, 1)).to_dense()\n ~~~~~~~~~~~^^^^^^\nRuntimeError: Inference tensors do not track version counter.\n", "result": "FAIL(forward)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 9, "sample_interval_seconds": 0.1, "peak_bytes": 602256101, "peak_categories": {"code": 8931864, "tensors": 501223424, "constants": 3440653, "scratchpad": 67108864, "misc": 17067456, "dma_rings": 4483840}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 481360448, "allocator_peak_reserved_bytes": 501219328, "total_seconds": 16.84018260613084} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/112/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/112/run.py new file mode 100644 index 0000000000000000000000000000000000000000..0ee3571fef0c7e9baaf7562ec1ac62c3bada2927 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/112/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 112, 'model': 'sentence-transformers/multi-qa-mpnet-base-dot-v1', 'revision': '17997f24dca0df1a4fed68894fb0e1e133e60482', 'trending_rank': None, 'downloads_rank': 34, 'class': 'SentenceTransformer', 'dtype': 'float32', 'attention': 'eager', 'trust_remote_code': False, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/113/memory_samples.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/113/memory_samples.json new file mode 100644 index 0000000000000000000000000000000000000000..3741114cc6f7c2fced049708306a18e9f86e5c92 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/113/memory_samples.json @@ -0,0 +1 @@ +[{"elapsed_seconds": 1.8637627363204956e-05, "bytes": 7141760, "categories": {"code": 3842944, "tensors": 2097152, "misc": 1144320, "dma_rings": 57344}}, {"elapsed_seconds": 0.10036514885723591, "bytes": 143136156, "categories": {"code": 4391192, "tensors": 134217728, "constants": 917508, "misc": 3079296, "dma_rings": 530432}}, {"elapsed_seconds": 0.20048574823886156, "bytes": 215232829, "categories": {"code": 5325616, "tensors": 134221824, "constants": 1376269, "scratchpad": 67108864, "misc": 5689600, "dma_rings": 1510656}}, {"elapsed_seconds": 0.30060875415802, "bytes": 221515545, "categories": {"code": 5818184, "tensors": 136318976, "constants": 2326545, "scratchpad": 67108864, "misc": 7767232, "dma_rings": 2175744}}, {"elapsed_seconds": 0.386721626855433, "bytes": 224453665, "categories": {"code": 6359632, "tensors": 136318976, "constants": 3047441, "scratchpad": 67108864, "misc": 8984256, "dma_rings": 2634496}}] \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/113/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/113/result.json new file mode 100644 index 0000000000000000000000000000000000000000..e10f85c068d199b3e2af26aaf5148d08233668d9 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/113/result.json @@ -0,0 +1,84 @@ +{ + "index": 113, + "model": "sentence-transformers/paraphrase-MiniLM-L6-v2", + "revision": "c9a2bfebc254878aee8c3aca9e6844d5bbb102d1", + "trending_rank": null, + "downloads_rank": 35, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:52:29.729330+00:00", + "physical_visible_core": "0", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 0.8102488135918975, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 0.4128049910068512, + 0.6425286531448364, + 0.671911358833313, + 0.6263707876205444 + ], + "cpu_seconds": 0.053661770187318325, + "cpu_top_document": 3, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 0.3346906406804919, + "neuron_scores": [ + 0.4128049612045288, + 0.6425285339355469, + 0.671911358833313, + 0.6263706684112549 + ], + "max_abs_error": 1.1920928955078125e-07, + "neuron_top_document": 3, + "mars_first": false, + "cpu_parity": true, + "neuron_repeat_seconds": 0.03071187622845173, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 5, + "sample_interval_seconds": 0.1, + "peak_bytes": 224453665, + "peak_categories": { + "code": 6359632, + "tensors": 136318976, + "constants": 3047441, + "scratchpad": 67108864, + "misc": 8984256, + "dma_rings": 2634496 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 97223040, + "allocator_peak_reserved_bytes": 136314880, + "total_seconds": 16.178581200540066 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/113/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/113/run.log new file mode 100644 index 0000000000000000000000000000000000000000..fcc669387deb7402dce5b5234893efec55c38397 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/113/run.log @@ -0,0 +1,5 @@ +CPU [0.4128049910068512, 0.6425286531448364, 0.671911358833313, 0.6263707876205444] +2026-Nov-03 15:52:38.469930 2658598:2658598 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. + _warn_trace_cache_once() +{"index": 113, "model": "sentence-transformers/paraphrase-MiniLM-L6-v2", "revision": "c9a2bfebc254878aee8c3aca9e6844d5bbb102d1", "trending_rank": null, "downloads_rank": 35, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:52:29.729330+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 0.8102488135918975, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.4128049910068512, 0.6425286531448364, 0.671911358833313, 0.6263707876205444], "cpu_seconds": 0.053661770187318325, "cpu_top_document": 3, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 0.3346906406804919, "neuron_scores": [0.4128049612045288, 0.6425285339355469, 0.671911358833313, 0.6263706684112549], "max_abs_error": 1.1920928955078125e-07, "neuron_top_document": 3, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.03071187622845173, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 5, "sample_interval_seconds": 0.1, "peak_bytes": 224453665, "peak_categories": {"code": 6359632, "tensors": 136318976, "constants": 3047441, "scratchpad": 67108864, "misc": 8984256, "dma_rings": 2634496}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 97223040, "allocator_peak_reserved_bytes": 136314880, "total_seconds": 16.178581200540066} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/113/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/113/run.py new file mode 100644 index 0000000000000000000000000000000000000000..70254175557a14d363551ee221a96265f7ba8db8 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/113/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 113, 'model': 'sentence-transformers/paraphrase-MiniLM-L6-v2', 'revision': 'c9a2bfebc254878aee8c3aca9e6844d5bbb102d1', 'trending_rank': None, 'downloads_rank': 35, 'class': 'SentenceTransformer', 'dtype': 'float32', 'attention': 'sdpa', 'trust_remote_code': False, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/114/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/114/result.json new file mode 100644 index 0000000000000000000000000000000000000000..59a386f20b7c1019cf40263ed643d72b7db1596f --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/114/result.json @@ -0,0 +1,35 @@ +{ + "index": 114, + "model": "jinaai/jina-embeddings-v2-small-en", + "revision": "44e7d1d6caec8c883c2d4b207588504d519788d0", + "trending_rank": null, + "downloads_rank": 38, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": true, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:52:45.768912+00:00", + "physical_visible_core": "4", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "status": "error", + "stage": "load_cpu", + "error_type": "ModuleNotFoundError", + "error": "No module named 'transformers.onnx'", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/114/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 839, in __init__\n config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2259, in _load_config\n return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py\", line 422, in from_pretrained\n config_class = get_class_from_dynamic_module(\n class_ref, pretrained_model_name_or_path, code_revision=code_revision, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 625, in get_class_from_dynamic_module\n return get_class_in_module(class_name, final_module, force_reload=force_download)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 309, in get_class_in_module\n module_spec.loader.exec_module(module)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^\n File \"\", line 759, in exec_module\n File \"\", line 491, in _call_with_frames_removed\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_bert_hyphen_implementation/f3ec4cf7de7e561007f27c9efc7148b0bd713f81/configuration_bert.py\", line 22, in \n from transformers.onnx import OnnxConfig\nModuleNotFoundError: No module named 'transformers.onnx'\n", + "result": "FAIL(load)", + "total_seconds": 8.320869617164135 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/114/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/114/run.log new file mode 100644 index 0000000000000000000000000000000000000000..974a0b53148f296d107ce3306a6ab15987d94e62 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/114/run.log @@ -0,0 +1,59 @@ +Traceback (most recent call last): + File "/tmp/neuron-top100-sweep/runs/114/run.py", line 50, in + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 193, in __init__ + super().__init__( + ~~~~~~~~~~~~~~~~^ + model_name_or_path=model_name_or_path, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ...<13 lines>... + default_prompt_name=default_prompt_name, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 256, in __init__ + modules, self.module_kwargs = self._load_modules( + ~~~~~~~~~~~~~~~~~~^ + model_name_or_path, + ^^^^^^^^^^^^^^^^^^^ + ...<7 lines>... + config_kwargs=config_kwargs, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 1211, in _load_modules + return self._load_config_modules(model_name_or_path, **load_kwargs) + ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 1431, in _load_config_modules + module = module_class.load( + model_name_or_path, + ...<12 lines>... + **extra_load_kwargs, + ) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py", line 2641, in load + return cls(model_name_or_path=model_name_or_path, **init_kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 92, in wrapper + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py", line 839, in __init__ + config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs) + ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py", line 2259, in _load_config + return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False + ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py", line 422, in from_pretrained + config_class = get_class_from_dynamic_module( + class_ref, pretrained_model_name_or_path, code_revision=code_revision, **kwargs + ) + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py", line 625, in get_class_from_dynamic_module + return get_class_in_module(class_name, final_module, force_reload=force_download) + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py", line 309, in get_class_in_module + module_spec.loader.exec_module(module) + ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^ + File "", line 759, in exec_module + File "", line 491, in _call_with_frames_removed + File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_bert_hyphen_implementation/f3ec4cf7de7e561007f27c9efc7148b0bd713f81/configuration_bert.py", line 22, in + from transformers.onnx import OnnxConfig +ModuleNotFoundError: No module named 'transformers.onnx' +{"index": 114, "model": "jinaai/jina-embeddings-v2-small-en", "revision": "44e7d1d6caec8c883c2d4b207588504d519788d0", "trending_rank": null, "downloads_rank": 38, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:52:45.768912+00:00", "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "status": "error", "stage": "load_cpu", "error_type": "ModuleNotFoundError", "error": "No module named 'transformers.onnx'", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/114/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 839, in __init__\n config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2259, in _load_config\n return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py\", line 422, in from_pretrained\n config_class = get_class_from_dynamic_module(\n class_ref, pretrained_model_name_or_path, code_revision=code_revision, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 625, in get_class_from_dynamic_module\n return get_class_in_module(class_name, final_module, force_reload=force_download)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 309, in get_class_in_module\n module_spec.loader.exec_module(module)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^\n File \"\", line 759, in exec_module\n File \"\", line 491, in _call_with_frames_removed\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_bert_hyphen_implementation/f3ec4cf7de7e561007f27c9efc7148b0bd713f81/configuration_bert.py\", line 22, in \n from transformers.onnx import OnnxConfig\nModuleNotFoundError: No module named 'transformers.onnx'\n", "result": "FAIL(load)", "total_seconds": 8.320869617164135} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/114/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/114/run.py new file mode 100644 index 0000000000000000000000000000000000000000..5126ccb201bbb2f64c7e046c9e9634b171b19929 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/114/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 114, 'model': 'jinaai/jina-embeddings-v2-small-en', 'revision': '44e7d1d6caec8c883c2d4b207588504d519788d0', 'trending_rank': None, 'downloads_rank': 38, 'class': 'SentenceTransformer', 'dtype': 'float32', 'attention': 'sdpa', 'trust_remote_code': True, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/116/memory_samples.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/116/memory_samples.json new file mode 100644 index 0000000000000000000000000000000000000000..20f48bcf75a633d8e4f4bac16e957e53710ccb05 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/116/memory_samples.json @@ -0,0 +1 @@ +[{"elapsed_seconds": 2.226419746875763e-05, "bytes": 7141760, "categories": {"code": 3842944, "tensors": 2097152, "misc": 1144320, "dma_rings": 57344}}, {"elapsed_seconds": 0.10030167549848557, "bytes": 508470032, "categories": {"code": 3880080, "tensors": 503320576, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.20042244251817465, "bytes": 508470032, "categories": {"code": 3880080, "tensors": 503320576, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.3006348442286253, "bytes": 508470032, "categories": {"code": 3880080, "tensors": 503320576, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.4007320934906602, "bytes": 508470032, "categories": {"code": 3880080, "tensors": 503320576, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.5008169403299689, "bytes": 508470032, "categories": {"code": 3880080, "tensors": 503320576, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.6008924059569836, "bytes": 508470032, "categories": {"code": 3880080, "tensors": 503320576, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.7010117648169398, "bytes": 508470032, "categories": {"code": 3880080, "tensors": 503320576, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.801149183884263, "bytes": 508470032, "categories": {"code": 3880080, "tensors": 503320576, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.90123953204602, "bytes": 508470032, "categories": {"code": 3880080, "tensors": 503320576, "misc": 1207936, "dma_rings": 61440}}, 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b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/116/result.json @@ -0,0 +1,84 @@ +{ + "index": 116, + "model": "jhgan/ko-sroberta-multitask", + "revision": "8fca7c9c98c26599be0e14b9916b11a756a26f19", + "trending_rank": null, + "downloads_rank": 42, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:52:52.178170+00:00", + "physical_visible_core": "12", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 1.2254862170666456, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 0.3725859224796295, + 0.5596363544464111, + 0.475629597902298, + 0.6078733205795288 + ], + "cpu_seconds": 0.2339512938633561, + "cpu_top_document": 4, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 46.99652501568198, + "neuron_scores": [ + 0.3725859224796295, + 0.5596362352371216, + 0.47562968730926514, + 0.6078732013702393 + ], + "max_abs_error": 1.1920928955078125e-07, + "neuron_top_document": 4, + "mars_first": false, + "cpu_parity": true, + "neuron_repeat_seconds": 0.04155152477324009, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 472, + "sample_interval_seconds": 0.1, + "peak_bytes": 604724833, + "peak_categories": { + "code": 7286160, + "tensors": 511709184, + "constants": 3866641, + "scratchpad": 67108864, + "misc": 12000704, + "dma_rings": 2753280 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 492647808, + "allocator_peak_reserved_bytes": 511705088, + "total_seconds": 62.91153274290264 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/116/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/116/run.log new file mode 100644 index 0000000000000000000000000000000000000000..ea9b99c62ca873b06e7c7d3781d38ecb0ba66f1e --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/116/run.log @@ -0,0 +1,5 @@ +CPU [0.3725859224796295, 0.5596363544464111, 0.475629597902298, 0.6078733205795288] +2026-Nov-03 15:53:01.715051 2661367:2661367 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. + _warn_trace_cache_once() +{"index": 116, "model": "jhgan/ko-sroberta-multitask", "revision": "8fca7c9c98c26599be0e14b9916b11a756a26f19", "trending_rank": null, "downloads_rank": 42, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:52:52.178170+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 1.2254862170666456, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.3725859224796295, 0.5596363544464111, 0.475629597902298, 0.6078733205795288], "cpu_seconds": 0.2339512938633561, "cpu_top_document": 4, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 46.99652501568198, "neuron_scores": [0.3725859224796295, 0.5596362352371216, 0.47562968730926514, 0.6078732013702393], "max_abs_error": 1.1920928955078125e-07, "neuron_top_document": 4, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.04155152477324009, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 472, "sample_interval_seconds": 0.1, "peak_bytes": 604724833, "peak_categories": {"code": 7286160, "tensors": 511709184, "constants": 3866641, "scratchpad": 67108864, "misc": 12000704, "dma_rings": 2753280}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 492647808, "allocator_peak_reserved_bytes": 511705088, "total_seconds": 62.91153274290264} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/116/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/116/run.py new file mode 100644 index 0000000000000000000000000000000000000000..2bfe8ac2eecfc82c22bb9d11d071f05d67b1ed79 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/116/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 116, 'model': 'jhgan/ko-sroberta-multitask', 'revision': '8fca7c9c98c26599be0e14b9916b11a756a26f19', 'trending_rank': None, 'downloads_rank': 42, 'class': 'SentenceTransformer', 'dtype': 'float32', 'attention': 'sdpa', 'trust_remote_code': False, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/122/memory_samples.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/122/memory_samples.json new file mode 100644 index 0000000000000000000000000000000000000000..2c637091ddf18e76394b9c2e78e5c0198e27c10b --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/122/memory_samples.json @@ -0,0 +1 @@ +[{"elapsed_seconds": 1.8709339201450348e-05, "bytes": 7141760, "categories": {"code": 3842944, "tensors": 2097152, "misc": 1144320, "dma_rings": 57344}}, {"elapsed_seconds": 0.10035741236060858, "bytes": 24023824, "categories": {"code": 3880080, "tensors": 18874368, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.20044377632439137, "bytes": 24023824, "categories": {"code": 3880080, "tensors": 18874368, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.3005241760984063, "bytes": 24023824, "categories": {"code": 3880080, 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b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/122/result.json @@ -0,0 +1,84 @@ +{ + "index": 122, + "model": "sentence-transformers-testing/stsb-bert-tiny-safetensors", + "revision": "f3cb857cba53019a20df283396bcca179cf051a4", + "trending_rank": null, + "downloads_rank": 49, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:53:27.210620+00:00", + "physical_visible_core": "0", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 0.339894263073802, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 0.5895186066627502, + 0.6596499085426331, + 0.6842697262763977, + 0.7085862755775452 + ], + "cpu_seconds": 0.01665687095373869, + "cpu_top_document": 4, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 35.90269974432886, + "neuron_scores": [ + 0.5895187258720398, + 0.6596502065658569, + 0.6842698454856873, + 0.7085865139961243 + ], + "max_abs_error": 2.980232238769531e-07, + "neuron_top_document": 4, + "mars_first": false, + "cpu_parity": true, + "neuron_repeat_seconds": 0.01644056662917137, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 360, + "sample_interval_seconds": 0.1, + "peak_bytes": 108670225, + "peak_categories": { + "code": 6199232, + "tensors": 20975616, + "constants": 2523153, + "scratchpad": 67108864, + "misc": 9350720, + "dma_rings": 2512640 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 19238400, + "allocator_peak_reserved_bytes": 20971520, + "total_seconds": 57.112566504627466 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/122/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/122/run.log new file mode 100644 index 0000000000000000000000000000000000000000..4afede4233d4d6dac4f6b3235ca9a7eec9b6e837 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/122/run.log @@ -0,0 +1,5 @@ +CPU [0.5895186066627502, 0.6596499085426331, 0.6842697262763977, 0.7085862755775452] +2026-Nov-03 15:53:40.215912 2667583:2667583 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. + _warn_trace_cache_once() +{"index": 122, "model": "sentence-transformers-testing/stsb-bert-tiny-safetensors", "revision": "f3cb857cba53019a20df283396bcca179cf051a4", "trending_rank": null, "downloads_rank": 49, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:53:27.210620+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 0.339894263073802, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.5895186066627502, 0.6596499085426331, 0.6842697262763977, 0.7085862755775452], "cpu_seconds": 0.01665687095373869, "cpu_top_document": 4, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 35.90269974432886, "neuron_scores": [0.5895187258720398, 0.6596502065658569, 0.6842698454856873, 0.7085865139961243], "max_abs_error": 2.980232238769531e-07, "neuron_top_document": 4, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.01644056662917137, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 360, "sample_interval_seconds": 0.1, "peak_bytes": 108670225, "peak_categories": {"code": 6199232, "tensors": 20975616, "constants": 2523153, "scratchpad": 67108864, "misc": 9350720, "dma_rings": 2512640}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 19238400, "allocator_peak_reserved_bytes": 20971520, "total_seconds": 57.112566504627466} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/122/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/122/run.py new file mode 100644 index 0000000000000000000000000000000000000000..576314a3b419f068bf76b19a8c1dbf66d6326ccb --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/122/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 122, 'model': 'sentence-transformers-testing/stsb-bert-tiny-safetensors', 'revision': 'f3cb857cba53019a20df283396bcca179cf051a4', 'trending_rank': None, 'downloads_rank': 49, 'class': 'SentenceTransformer', 'dtype': 'float32', 'attention': 'sdpa', 'trust_remote_code': False, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/123/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/123/result.json new file mode 100644 index 0000000000000000000000000000000000000000..f1b7bb6eeeb23b4f5d6ac9dd4727297b6ac89e07 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/123/result.json @@ -0,0 +1,43 @@ +{ + "index": 123, + "model": "Alibaba-NLP/gte-multilingual-base", + "revision": "9bbca17d9273fd0d03d5725c7a4b0f6b45142062", + "trending_rank": null, + "downloads_rank": 50, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": true, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:53:34.948327+00:00", + "physical_visible_core": "8", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 2.7565125236287713, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "status": "error", + "stage": "cpu_inference", + "error_type": "IndexError", + "error": "index 670466312 is out of bounds for dimension 0 with size 11", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/123/run.py\", line 63, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/123/run.py\", line 58, in score\n q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 670466312 is out of bounds for dimension 0 with size 11\n", + "result": "FAIL(forward)", + "total_seconds": 10.929977044463158 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/123/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/123/run.log new file mode 100644 index 0000000000000000000000000000000000000000..24121a84ee4c92a11f35286894ee876e7d9d8568 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/123/run.log @@ -0,0 +1,72 @@ +/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/configuration_utils.py:723: FutureWarning: `rope_scaling` is deprecated and will be removed in a future version. Set `rope_parameters` instead, with `rope_type` and `rope_theta` included. + warnings.warn( +[transformers] NewModel LOAD REPORT from: Alibaba-NLP/gte-multilingual-base +Key | Status | | +------------------+------------+--+- +classifier.weight | UNEXPECTED | | +classifier.bias | UNEXPECTED | | + +Notes: +- UNEXPECTED: can be ignored when loading from different task/architecture; not ok if you expect identical arch. +Traceback (most recent call last): + File "/tmp/neuron-top100-sweep/runs/123/run.py", line 63, in + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + ~~~~~^^ + File "/tmp/neuron-top100-sweep/runs/123/run.py", line 58, in score + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 370, in encode_query + return self.encode( + ~~~~~~~~~~~^ + inputs=inputs, + ^^^^^^^^^^^^^^ + ...<14 lines>... + **kwargs, + ^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 895, in encode + all_embeddings = self._inference(inputs, device=device, **inference_kwargs) + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 963, in _inference + out_features = self(features, **kwargs) + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1783, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1794, in _call_impl + return forward_call(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 600, in forward + input = module(input, **module_kwargs) + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1783, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1794, in _call_impl + return forward_call(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py", line 1647, in forward + model_output = model_method(**filtered_kwargs) + File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py", line 901, in forward + (embedding_output, attention_mask, rope_embeds, length) = self.embeddings( + ~~~~~~~~~~~~~~~^ + unpad_inputs, + ^^^^^^^^^^^^^ + ...<5 lines>... + inputs_embeds=inputs_embeds + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1783, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1794, in _call_impl + return forward_call(*args, **kwargs) + File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py", line 392, in forward + rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim] + ~~~~~~~~^^^^^^^^^^^^^^ +IndexError: index 670466312 is out of bounds for dimension 0 with size 11 +{"index": 123, "model": "Alibaba-NLP/gte-multilingual-base", "revision": "9bbca17d9273fd0d03d5725c7a4b0f6b45142062", "trending_rank": null, "downloads_rank": 50, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:53:34.948327+00:00", "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 2.7565125236287713, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "status": "error", "stage": "cpu_inference", "error_type": "IndexError", "error": "index 670466312 is out of bounds for dimension 0 with size 11", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/123/run.py\", line 63, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/123/run.py\", line 58, in score\n q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 670466312 is out of bounds for dimension 0 with size 11\n", "result": "FAIL(forward)", "total_seconds": 10.929977044463158} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/123/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/123/run.py new file mode 100644 index 0000000000000000000000000000000000000000..f37384e847e5f148f6ad9b7a09d92fa76b3b69a5 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/123/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 123, 'model': 'Alibaba-NLP/gte-multilingual-base', 'revision': '9bbca17d9273fd0d03d5725c7a4b0f6b45142062', 'trending_rank': None, 'downloads_rank': 50, 'class': 'SentenceTransformer', 'dtype': 'float32', 'attention': 'sdpa', 'trust_remote_code': True, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/129/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/129/result.json new file mode 100644 index 0000000000000000000000000000000000000000..4dc70a19d19248b7c02b21ba9eaa0c15cc88c6bf --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/129/result.json @@ -0,0 +1,51 @@ +{ + "index": 129, + "model": "nomic-ai/nomic-embed-text-v2-moe", + "revision": "1066b6599d099fbb93dfcb64f9c37a7c9e503e85", + "trending_rank": null, + "downloads_rank": 58, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": true, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:54:25.528812+00:00", + "physical_visible_core": "4", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 6.00257970392704, + "prompts": { + "query": "search_query: ", + "document": "", + "passage": "search_document: ", + "Classification": "classification: ", + "MultilabelClassification": "classification: ", + "Clustering": "clustering: ", + "PairClassification": "classification: ", + "STS": "classification: ", + "Summarization": "classification: ", + "Speed": "search_document: " + }, + "parameter_dtypes": [ + "torch.float32" + ], + "status": "error", + "stage": "cpu_inference", + "error_type": "AttributeError", + "error": "'NomicBertModel' object has no attribute 'get_extended_attention_mask'", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/129/run.py\", line 63, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/129/run.py\", line 58, in score\n q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/nomic_hyphen_ai/nomic_hyphen_bert_hyphen_2048/7710840340a098cfb869c4f65e87cf2b1b70caca/modeling_hf_nomic_bert.py\", line 1914, in forward\n attention_mask = self.get_extended_attention_mask(attention_mask, hidden_states.shape[:-1])\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1972, in __getattr__\n raise AttributeError(\n f\"'{type(self).__name__}' object has no attribute '{name}'\"\n )\nAttributeError: 'NomicBertModel' object has no attribute 'get_extended_attention_mask'\n", + "result": "FAIL(forward)", + "total_seconds": 13.943258575163782 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/129/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/129/run.log new file mode 100644 index 0000000000000000000000000000000000000000..74add8e99cc434b17a6430b2d3eae72681c6f095 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/129/run.log @@ -0,0 +1,53 @@ +/home/jingyahuang/.cache/huggingface/modules/transformers_modules/nomic_hyphen_ai/nomic_hyphen_bert_hyphen_2048/7710840340a098cfb869c4f65e87cf2b1b70caca/modeling_hf_nomic_bert.py:1634: UserWarning: Install Nomic's megablocks fork for better speed: `pip install git+https://github.com/nomic-ai/megablocks.git` + warnings.warn("Install Nomic's megablocks fork for better speed: " + +Traceback (most recent call last): + File "/tmp/neuron-top100-sweep/runs/129/run.py", line 63, in + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + ~~~~~^^ + File "/tmp/neuron-top100-sweep/runs/129/run.py", line 58, in score + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 370, in encode_query + return self.encode( + ~~~~~~~~~~~^ + inputs=inputs, + ^^^^^^^^^^^^^^ + ...<14 lines>... + **kwargs, + ^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 895, in encode + all_embeddings = self._inference(inputs, device=device, **inference_kwargs) + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 963, in _inference + out_features = self(features, **kwargs) + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1783, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1794, in _call_impl + return forward_call(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 600, in forward + input = module(input, **module_kwargs) + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1783, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1794, in _call_impl + return forward_call(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py", line 1647, in forward + model_output = model_method(**filtered_kwargs) + File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/nomic_hyphen_ai/nomic_hyphen_bert_hyphen_2048/7710840340a098cfb869c4f65e87cf2b1b70caca/modeling_hf_nomic_bert.py", line 1914, in forward + attention_mask = self.get_extended_attention_mask(attention_mask, hidden_states.shape[:-1]) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1972, in __getattr__ + raise AttributeError( + f"'{type(self).__name__}' object has no attribute '{name}'" + ) +AttributeError: 'NomicBertModel' object has no attribute 'get_extended_attention_mask' +{"index": 129, "model": "nomic-ai/nomic-embed-text-v2-moe", "revision": "1066b6599d099fbb93dfcb64f9c37a7c9e503e85", "trending_rank": null, "downloads_rank": 58, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:54:25.528812+00:00", "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 6.00257970392704, "prompts": {"query": "search_query: ", "document": "", "passage": "search_document: ", "Classification": "classification: ", "MultilabelClassification": "classification: ", "Clustering": "clustering: ", "PairClassification": "classification: ", "STS": "classification: ", "Summarization": "classification: ", "Speed": "search_document: "}, "parameter_dtypes": ["torch.float32"], "status": "error", "stage": "cpu_inference", "error_type": "AttributeError", "error": "'NomicBertModel' object has no attribute 'get_extended_attention_mask'", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/129/run.py\", line 63, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/129/run.py\", line 58, in score\n q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/nomic_hyphen_ai/nomic_hyphen_bert_hyphen_2048/7710840340a098cfb869c4f65e87cf2b1b70caca/modeling_hf_nomic_bert.py\", line 1914, in forward\n attention_mask = self.get_extended_attention_mask(attention_mask, hidden_states.shape[:-1])\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1972, in __getattr__\n raise AttributeError(\n f\"'{type(self).__name__}' object has no attribute '{name}'\"\n )\nAttributeError: 'NomicBertModel' object has no attribute 'get_extended_attention_mask'\n", "result": "FAIL(forward)", "total_seconds": 13.943258575163782} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/129/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/129/run.py new file mode 100644 index 0000000000000000000000000000000000000000..37acb1035d5198cab024485eeb9a22d341c9d684 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/129/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 129, 'model': 'nomic-ai/nomic-embed-text-v2-moe', 'revision': '1066b6599d099fbb93dfcb64f9c37a7c9e503e85', 'trending_rank': None, 'downloads_rank': 58, 'class': 'SentenceTransformer', 'dtype': 'float32', 'attention': 'sdpa', 'trust_remote_code': True, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/137/memory_samples.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/137/memory_samples.json new file mode 100644 index 0000000000000000000000000000000000000000..b46014f5edbf56c914b5e3f86c083128718a47d9 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/137/memory_samples.json @@ -0,0 +1 @@ +[{"elapsed_seconds": 6.434042006731033e-05, "bytes": 7141760, "categories": {"code": 3842944, "tensors": 2097152, "misc": 1144320, "dma_rings": 57344}}, {"elapsed_seconds": 0.10054141748696566, "bytes": 504271632, "categories": {"code": 3880080, "tensors": 499122176, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.20068938005715609, "bytes": 506572028, "categories": {"code": 4232312, "tensors": 499122176, "constants": 524292, "misc": 2386048, "dma_rings": 307200}}, {"elapsed_seconds": 0.30081549659371376, "bytes": 511260284, "categories": {"code": 5063736, "tensors": 499126272, "constants": 1507332, "misc": 4549184, "dma_rings": 1013760}}, {"elapsed_seconds": 0.4009874099865556, "bytes": 583298409, "categories": {"code": 5813912, "tensors": 499126272, "constants": 2195473, "scratchpad": 67108864, "misc": 7066560, "dma_rings": 1987328}}, {"elapsed_seconds": 0.4875427093356848, "bytes": 588585921, "categories": {"code": 6350192, "tensors": 501223424, "constants": 2850833, "scratchpad": 67108864, "misc": 8545088, "dma_rings": 2507520}}] \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/137/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/137/result.json new file mode 100644 index 0000000000000000000000000000000000000000..84afec267ea9c3cb19752e286af9a7daf23f43c1 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/137/result.json @@ -0,0 +1,73 @@ +{ + "index": 137, + "model": "sentence-transformers/msmarco-bert-base-dot-v5", + "revision": "dbf04e3911e5b11c887fe563d3f2479f81c4415f", + "trending_rank": null, + "downloads_rank": 68, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:55:09.237654+00:00", + "physical_visible_core": "0", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 1.3280827356502414, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 163.48626708984375, + 172.62042236328125, + 169.48434448242188, + 169.65316772460938 + ], + "cpu_seconds": 0.12728324253112078, + "cpu_top_document": 2, + "model_device": "neuron:0", + "status": "error", + "stage": "neuron_inference", + "error_type": "RuntimeError", + "error": "Inference tensors do not track version counter.", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/137/run.py\", line 69, in \n stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/137/run.py\", line 61, in score\n values=model.similarity(q,d)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1072, in similarity\n return self._similarity(embeddings1, embeddings2)\n ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/similarity.py\", line 115, in dot_score\n return torch.mm(a, b.transpose(0, 1)).to_dense()\n ~~~~~~~~~~~^^^^^^\nRuntimeError: Inference tensors do not track version counter.\n", + "result": "FAIL(forward)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 6, + "sample_interval_seconds": 0.1, + "peak_bytes": 588585921, + "peak_categories": { + "code": 6350192, + "tensors": 501223424, + "constants": 2850833, + "scratchpad": 67108864, + "misc": 8545088, + "dma_rings": 2507520 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 481544320, + "allocator_peak_reserved_bytes": 501219328, + "total_seconds": 20.18780482839793 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/137/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/137/run.log new file mode 100644 index 0000000000000000000000000000000000000000..43c0713bca72c540a47b0585dd01afd5c65f0f31 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/137/run.log @@ -0,0 +1,18 @@ +CPU [163.48626708984375, 172.62042236328125, 169.48434448242188, 169.65316772460938] +2026-Nov-03 15:55:20.166982 2695042:2695042 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. + _warn_trace_cache_once() +Traceback (most recent call last): + File "/tmp/neuron-top100-sweep/runs/137/run.py", line 69, in + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + ~~~~~^^ + File "/tmp/neuron-top100-sweep/runs/137/run.py", line 61, in score + values=model.similarity(q,d) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 1072, in similarity + return self._similarity(embeddings1, embeddings2) + ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/similarity.py", line 115, in dot_score + return torch.mm(a, b.transpose(0, 1)).to_dense() + ~~~~~~~~~~~^^^^^^ +RuntimeError: Inference tensors do not track version counter. +{"index": 137, "model": "sentence-transformers/msmarco-bert-base-dot-v5", "revision": "dbf04e3911e5b11c887fe563d3f2479f81c4415f", "trending_rank": null, "downloads_rank": 68, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:55:09.237654+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 1.3280827356502414, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [163.48626708984375, 172.62042236328125, 169.48434448242188, 169.65316772460938], "cpu_seconds": 0.12728324253112078, "cpu_top_document": 2, "model_device": "neuron:0", "status": "error", "stage": "neuron_inference", "error_type": "RuntimeError", "error": "Inference tensors do not track version counter.", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/137/run.py\", line 69, in \n stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/137/run.py\", line 61, in score\n values=model.similarity(q,d)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1072, in similarity\n return self._similarity(embeddings1, embeddings2)\n ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/similarity.py\", line 115, in dot_score\n return torch.mm(a, b.transpose(0, 1)).to_dense()\n ~~~~~~~~~~~^^^^^^\nRuntimeError: Inference tensors do not track version counter.\n", "result": "FAIL(forward)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 6, "sample_interval_seconds": 0.1, "peak_bytes": 588585921, "peak_categories": {"code": 6350192, "tensors": 501223424, "constants": 2850833, "scratchpad": 67108864, "misc": 8545088, "dma_rings": 2507520}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 481544320, "allocator_peak_reserved_bytes": 501219328, "total_seconds": 20.18780482839793} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/137/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/137/run.py new file mode 100644 index 0000000000000000000000000000000000000000..4e551052d26836adb1f3f9fdb947e384e92734e9 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/137/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 137, 'model': 'sentence-transformers/msmarco-bert-base-dot-v5', 'revision': 'dbf04e3911e5b11c887fe563d3f2479f81c4415f', 'trending_rank': None, 'downloads_rank': 68, 'class': 'SentenceTransformer', 'dtype': 'float32', 'attention': 'sdpa', 'trust_remote_code': False, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/140/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/140/result.json new file mode 100644 index 0000000000000000000000000000000000000000..2d0d8bd0631c46bfc915c8343747677c780c363b --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/140/result.json @@ -0,0 +1,43 @@ +{ + "index": 140, + "model": "Alibaba-NLP/gte-base-en-v1.5", + "revision": "a829fd0e060bb84554da0dfd354d0de0f7712b7f", + "trending_rank": null, + "downloads_rank": 71, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": true, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:55:35.076933+00:00", + "physical_visible_core": "12", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 1.5855842670425773, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "status": "error", + "stage": "cpu_inference", + "error_type": "IndexError", + "error": "index 140471200382993 is out of bounds for dimension 0 with size 11", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/140/run.py\", line 63, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/140/run.py\", line 58, in score\n q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 140471200382993 is out of bounds for dimension 0 with size 11\n", + "result": "FAIL(forward)", + "total_seconds": 12.225869985297322 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/140/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/140/run.log new file mode 100644 index 0000000000000000000000000000000000000000..5da0c4f4975269d9a067855c991b52b450a445de --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/140/run.log @@ -0,0 +1,64 @@ +/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/configuration_utils.py:723: FutureWarning: `rope_scaling` is deprecated and will be removed in a future version. Set `rope_parameters` instead, with `rope_type` and `rope_theta` included. + warnings.warn( +Traceback (most recent call last): + File "/tmp/neuron-top100-sweep/runs/140/run.py", line 63, in + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + ~~~~~^^ + File "/tmp/neuron-top100-sweep/runs/140/run.py", line 58, in score + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 370, in encode_query + return self.encode( + ~~~~~~~~~~~^ + inputs=inputs, + ^^^^^^^^^^^^^^ + ...<14 lines>... + **kwargs, + ^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 895, in encode + all_embeddings = self._inference(inputs, device=device, **inference_kwargs) + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 963, in _inference + out_features = self(features, **kwargs) + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1783, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1794, in _call_impl + return forward_call(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 600, in forward + input = module(input, **module_kwargs) + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1783, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1794, in _call_impl + return forward_call(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py", line 1647, in forward + model_output = model_method(**filtered_kwargs) + File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py", line 901, in forward + (embedding_output, attention_mask, rope_embeds, length) = self.embeddings( + ~~~~~~~~~~~~~~~^ + unpad_inputs, + ^^^^^^^^^^^^^ + ...<5 lines>... + inputs_embeds=inputs_embeds + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1783, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1794, in _call_impl + return forward_call(*args, **kwargs) + File "/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py", line 392, in forward + rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim] + ~~~~~~~~^^^^^^^^^^^^^^ +IndexError: index 140471200382993 is out of bounds for dimension 0 with size 11 +{"index": 140, "model": "Alibaba-NLP/gte-base-en-v1.5", "revision": "a829fd0e060bb84554da0dfd354d0de0f7712b7f", "trending_rank": null, "downloads_rank": 71, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:55:35.076933+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 1.5855842670425773, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "status": "error", "stage": "cpu_inference", "error_type": "IndexError", "error": "index 140471200382993 is out of bounds for dimension 0 with size 11", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/140/run.py\", line 63, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/140/run.py\", line 58, in score\n q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 140471200382993 is out of bounds for dimension 0 with size 11\n", "result": "FAIL(forward)", "total_seconds": 12.225869985297322} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/140/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/140/run.py new file mode 100644 index 0000000000000000000000000000000000000000..f0a6cec95d677fb2e8c009743171b2b82521e106 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/140/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 140, 'model': 'Alibaba-NLP/gte-base-en-v1.5', 'revision': 'a829fd0e060bb84554da0dfd354d0de0f7712b7f', 'trending_rank': None, 'downloads_rank': 71, 'class': 'SentenceTransformer', 'dtype': 'float32', 'attention': 'sdpa', 'trust_remote_code': True, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/144/memory_samples.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/144/memory_samples.json new file mode 100644 index 0000000000000000000000000000000000000000..7a691f4b0bf934fef2dc8b0a7de0b3716057bd2c --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/144/memory_samples.json @@ -0,0 +1 @@ +[{"elapsed_seconds": 1.7637386918067932e-05, "bytes": 7141760, "categories": {"code": 3842944, "tensors": 2097152, "misc": 1144320, "dma_rings": 57344}}, {"elapsed_seconds": 0.10030923783779144, "bytes": 38703888, "categories": {"code": 3880080, "tensors": 33554432, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.20038958173245192, "bytes": 38703888, "categories": {"code": 3880080, "tensors": 33554432, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.3005584329366684, "bytes": 38703888, "categories": {"code": 3880080, "tensors": 33554432, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.4006367204710841, "bytes": 38703888, "categories": {"code": 3880080, "tensors": 33554432, "misc": 1207936, "dma_rings": 61440}}, {"elapsed_seconds": 0.5007123192772269, "bytes": 38703888, "categories": {"code": 3880080, "tensors": 33554432, "misc": 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"bytes": 39274944, "categories": {"code": 4027072, "tensors": 33558528, "constants": 65536, "misc": 1543936, "dma_rings": 79872}}, {"elapsed_seconds": 6.80701176635921, "bytes": 39274944, "categories": {"code": 4027072, "tensors": 33558528, "constants": 65536, "misc": 1543936, "dma_rings": 79872}}, {"elapsed_seconds": 6.907096375711262, "bytes": 39274944, "categories": {"code": 4027072, "tensors": 33558528, "constants": 65536, "misc": 1543936, "dma_rings": 79872}}, {"elapsed_seconds": 7.00718130916357, "bytes": 39274944, "categories": {"code": 4027072, "tensors": 33558528, "constants": 65536, "misc": 1543936, "dma_rings": 79872}}, {"elapsed_seconds": 7.0349429827183485, "bytes": 40488680, "categories": {"code": 4221032, "tensors": 33558528, "constants": 327680, "misc": 2111104, "dma_rings": 270336}}] \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/144/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/144/result.json new file mode 100644 index 0000000000000000000000000000000000000000..8ee8ed018fc7e343413886d47719acf141718be0 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/144/result.json @@ -0,0 +1,83 @@ +{ + "index": 144, + "model": "minishlab/potion-base-8M", + "revision": "bf8b056651a2c21b8d2565580b8569da283cab23", + "trending_rank": null, + "downloads_rank": 75, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:56:13.809543+00:00", + "physical_visible_core": "8", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 0.3890756834298372, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 0.4463503956794739, + 0.692706823348999, + 0.6985108852386475, + 0.6134428977966309 + ], + "cpu_seconds": 0.06325524486601353, + "cpu_top_document": 3, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 6.991000535897911, + "neuron_scores": [ + 0.4463503956794739, + 0.6927067637443542, + 0.6985103487968445, + 0.6134430170059204 + ], + "max_abs_error": 5.364418029785156e-07, + "neuron_top_document": 3, + "mars_first": false, + "cpu_parity": true, + "neuron_repeat_seconds": 0.007872754707932472, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 72, + "sample_interval_seconds": 0.1, + "peak_bytes": 40488680, + "peak_categories": { + "code": 4221032, + "tensors": 33558528, + "constants": 327680, + "misc": 2111104, + "dma_rings": 270336 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 30251264, + "allocator_peak_reserved_bytes": 33554432, + "total_seconds": 25.33328312728554 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/144/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/144/run.log new file mode 100644 index 0000000000000000000000000000000000000000..84e847524620ac9cbac218a12c7325ae745b966a --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/144/run.log @@ -0,0 +1,5 @@ +CPU [0.4463503956794739, 0.692706823348999, 0.6985108852386475, 0.6134428977966309] +2026-Nov-03 15:56:23.036324 2721788:2721788 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. + _warn_trace_cache_once() +{"index": 144, "model": "minishlab/potion-base-8M", "revision": "bf8b056651a2c21b8d2565580b8569da283cab23", "trending_rank": null, "downloads_rank": 75, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:56:13.809543+00:00", "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 0.3890756834298372, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.4463503956794739, 0.692706823348999, 0.6985108852386475, 0.6134428977966309], "cpu_seconds": 0.06325524486601353, "cpu_top_document": 3, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 6.991000535897911, "neuron_scores": [0.4463503956794739, 0.6927067637443542, 0.6985103487968445, 0.6134430170059204], "max_abs_error": 5.364418029785156e-07, "neuron_top_document": 3, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.007872754707932472, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 72, "sample_interval_seconds": 0.1, "peak_bytes": 40488680, "peak_categories": {"code": 4221032, "tensors": 33558528, "constants": 327680, "misc": 2111104, "dma_rings": 270336}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 30251264, "allocator_peak_reserved_bytes": 33554432, "total_seconds": 25.33328312728554} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/144/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/144/run.py new file mode 100644 index 0000000000000000000000000000000000000000..cd4ef5441641c8841b1764ef48149c8ca54caf52 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/144/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 144, 'model': 'minishlab/potion-base-8M', 'revision': 'bf8b056651a2c21b8d2565580b8569da283cab23', 'trending_rank': None, 'downloads_rank': 75, 'class': 'SentenceTransformer', 'dtype': 'float32', 'attention': 'sdpa', 'trust_remote_code': False, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/146/memory_samples.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/146/memory_samples.json new file mode 100644 index 0000000000000000000000000000000000000000..ea426e49cc46707c35c73260c6602967c315496f --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/146/memory_samples.json @@ -0,0 +1 @@ +[{"elapsed_seconds": 2.0206905901432037e-05, "bytes": 7141760, "categories": {"code": 3842944, "tensors": 2097152, "misc": 1144320, "dma_rings": 57344}}, {"elapsed_seconds": 0.10033771768212318, "bytes": 505654424, "categories": {"code": 4122200, "tensors": 499122176, "constants": 262144, "misc": 1922624, "dma_rings": 225280}}, {"elapsed_seconds": 0.20043183770030737, "bytes": 510859980, "categories": {"code": 5010696, "tensors": 499126272, "constants": 1507332, "misc": 4488640, "dma_rings": 727040}}, {"elapsed_seconds": 0.3005128242075443, "bytes": 585361041, "categories": {"code": 5954496, "tensors": 501223424, "constants": 2326545, "scratchpad": 67108864, "misc": 7316160, "dma_rings": 1431552}}, {"elapsed_seconds": 0.40059990901499987, "bytes": 589734025, "categories": {"code": 6608056, "tensors": 501223424, "constants": 3309585, "scratchpad": 67108864, "misc": 9499584, "dma_rings": 1984512}}, {"elapsed_seconds": 0.41774189099669456, "bytes": 589734025, "categories": {"code": 6608056, "tensors": 501223424, "constants": 3309585, "scratchpad": 67108864, "misc": 9499584, "dma_rings": 1984512}}] \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/146/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/146/result.json new file mode 100644 index 0000000000000000000000000000000000000000..b6952d94ed67746b8d443a9ec1c9ae7f4b7a82c2 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/146/result.json @@ -0,0 +1,84 @@ +{ + "index": 146, + "model": "TimKond/S-PubMedBert-MedQuAD", + "revision": "b73d84decb1da6bd4c1cf084a1b6c6602157d267", + "trending_rank": null, + "downloads_rank": 77, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:56:33.415911+00:00", + "physical_visible_core": "4", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 1.2769848257303238, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 0.46113887429237366, + 0.7457321882247925, + 0.8234667181968689, + 0.7024552822113037 + ], + "cpu_seconds": 0.15781457629054785, + "cpu_top_document": 3, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 0.31044141575694084, + "neuron_scores": [ + 0.46113866567611694, + 0.745732307434082, + 0.823466420173645, + 0.7024555206298828 + ], + "max_abs_error": 2.980232238769531e-07, + "neuron_top_document": 3, + "mars_first": false, + "cpu_parity": true, + "neuron_repeat_seconds": 0.03502273000776768, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 6, + "sample_interval_seconds": 0.1, + "peak_bytes": 589734025, + "peak_categories": { + "code": 6608056, + "tensors": 501223424, + "constants": 3309585, + "scratchpad": 67108864, + "misc": 9499584, + "dma_rings": 1984512 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 482003968, + "allocator_peak_reserved_bytes": 501219328, + "total_seconds": 21.299358570948243 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/146/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/146/run.log new file mode 100644 index 0000000000000000000000000000000000000000..314ce88404cba7271d4bb73fc4f009c6b69eed71 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/146/run.log @@ -0,0 +1,5 @@ +CPU [0.46113887429237366, 0.7457321882247925, 0.8234667181968689, 0.7024552822113037] +2026-Nov-03 15:56:47.229090 2726999:2726999 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. + _warn_trace_cache_once() +{"index": 146, "model": "TimKond/S-PubMedBert-MedQuAD", "revision": "b73d84decb1da6bd4c1cf084a1b6c6602157d267", "trending_rank": null, "downloads_rank": 77, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:56:33.415911+00:00", "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 1.2769848257303238, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [0.46113887429237366, 0.7457321882247925, 0.8234667181968689, 0.7024552822113037], "cpu_seconds": 0.15781457629054785, "cpu_top_document": 3, "model_device": "neuron:0", "score_device": "neuron:0", "neuron_first_seconds": 0.31044141575694084, "neuron_scores": [0.46113866567611694, 0.745732307434082, 0.823466420173645, 0.7024555206298828], "max_abs_error": 2.980232238769531e-07, "neuron_top_document": 3, "mars_first": false, "cpu_parity": true, "neuron_repeat_seconds": 0.03502273000776768, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 6, "sample_interval_seconds": 0.1, "peak_bytes": 589734025, "peak_categories": {"code": 6608056, "tensors": 501223424, "constants": 3309585, "scratchpad": 67108864, "misc": 9499584, "dma_rings": 1984512}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 482003968, "allocator_peak_reserved_bytes": 501219328, "total_seconds": 21.299358570948243} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/146/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/146/run.py new file mode 100644 index 0000000000000000000000000000000000000000..914ca400f861a8d93b38cda246aa9aab627487ce --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/146/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 146, 'model': 'TimKond/S-PubMedBert-MedQuAD', 'revision': 'b73d84decb1da6bd4c1cf084a1b6c6602157d267', 'trending_rank': None, 'downloads_rank': 77, 'class': 'SentenceTransformer', 'dtype': 'float32', 'attention': 'sdpa', 'trust_remote_code': False, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/158/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/158/result.json new file mode 100644 index 0000000000000000000000000000000000000000..860d141516d565e89ca885e620c291c73defb2e6 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/158/result.json @@ -0,0 +1,35 @@ +{ + "index": 158, + "model": "nvidia/NV-Embed-v2", + "revision": "3fa59658547db50a1e8e3346cf057fd0c77ed6ef", + "trending_rank": null, + "downloads_rank": 93, + "class": "SentenceTransformer", + "dtype": "bfloat16", + "attention": "eager", + "trust_remote_code": true, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:58:12.973659+00:00", + "physical_visible_core": "0", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "status": "error", + "stage": "load_cpu", + "error_type": "AttributeError", + "error": "'NVEmbedModel' object has no attribute 'all_tied_weights_keys'", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/158/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 855, in __init__\n self.model = self._load_model(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path, transformer_task, config, backend, is_peft_model, **model_kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2343, in _load_model\n return model_cls.from_pretrained(model_name_or_path, config=config, **model_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py\", line 389, in from_pretrained\n return model_class.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4356, in from_pretrained\n loading_info = cls._finalize_model_loading(model, load_config, loading_info)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4524, in _finalize_model_loading\n model._move_missing_keys_from_meta_to_device(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n loading_info.missing_and_mismatched(),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<2 lines>...\n load_config.hf_quantizer,\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4781, in _move_missing_keys_from_meta_to_device\n for key in missing_keys - self.all_tied_weights_keys.keys():\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1972, in __getattr__\n raise AttributeError(\n f\"'{type(self).__name__}' object has no attribute '{name}'\"\n )\nAttributeError: 'NVEmbedModel' object has no attribute 'all_tied_weights_keys'. Did you mean: '_tied_weights_keys'?\n", + "result": "FAIL(load)", + "total_seconds": 29.60385298728943 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/158/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/158/run.log new file mode 100644 index 0000000000000000000000000000000000000000..3597c525d0ac42de027a8a9182c0fbda148ddb2d --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/158/run.log @@ -0,0 +1,76 @@ +Traceback (most recent call last): + File "/tmp/neuron-top100-sweep/runs/158/run.py", line 50, in + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 46, in wrapper + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 193, in __init__ + super().__init__( + ~~~~~~~~~~~~~~~~^ + model_name_or_path=model_name_or_path, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ...<13 lines>... + default_prompt_name=default_prompt_name, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 256, in __init__ + modules, self.module_kwargs = self._load_modules( + ~~~~~~~~~~~~~~~~~~^ + model_name_or_path, + ^^^^^^^^^^^^^^^^^^^ + ...<7 lines>... + config_kwargs=config_kwargs, + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 1211, in _load_modules + return self._load_config_modules(model_name_or_path, **load_kwargs) + ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py", line 1431, in _load_config_modules + module = module_class.load( + model_name_or_path, + ...<12 lines>... + **extra_load_kwargs, + ) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py", line 2641, in load + return cls(model_name_or_path=model_name_or_path, **init_kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py", line 92, in wrapper + return func(*args, **kwargs) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py", line 855, in __init__ + self.model = self._load_model( + ~~~~~~~~~~~~~~~~^ + model_name_or_path, transformer_task, config, backend, is_peft_model, **model_kwargs + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py", line 2343, in _load_model + return model_cls.from_pretrained(model_name_or_path, config=config, **model_kwargs) + ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py", line 389, in from_pretrained + return model_class.from_pretrained( + ~~~~~~~~~~~~~~~~~~~~~~~~~~~^ + pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py", line 4356, in from_pretrained + loading_info = cls._finalize_model_loading(model, load_config, loading_info) + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py", line 4524, in _finalize_model_loading + model._move_missing_keys_from_meta_to_device( + ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^ + loading_info.missing_and_mismatched(), + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + ...<2 lines>... + load_config.hf_quantizer, + ^^^^^^^^^^^^^^^^^^^^^^^^^ + ) + ^ + File "/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py", line 4781, in _move_missing_keys_from_meta_to_device + for key in missing_keys - self.all_tied_weights_keys.keys(): + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py", line 1972, in __getattr__ + raise AttributeError( + f"'{type(self).__name__}' object has no attribute '{name}'" + ) +AttributeError: 'NVEmbedModel' object has no attribute 'all_tied_weights_keys'. Did you mean: '_tied_weights_keys'? +{"index": 158, "model": "nvidia/NV-Embed-v2", "revision": "3fa59658547db50a1e8e3346cf057fd0c77ed6ef", "trending_rank": null, "downloads_rank": 93, "class": "SentenceTransformer", "dtype": "bfloat16", "attention": "eager", "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:58:12.973659+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "status": "error", "stage": "load_cpu", "error_type": "AttributeError", "error": "'NVEmbedModel' object has no attribute 'all_tied_weights_keys'", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/158/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 855, in __init__\n self.model = self._load_model(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path, transformer_task, config, backend, is_peft_model, **model_kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2343, in _load_model\n return model_cls.from_pretrained(model_name_or_path, config=config, **model_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py\", line 389, in from_pretrained\n return model_class.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4356, in from_pretrained\n loading_info = cls._finalize_model_loading(model, load_config, loading_info)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4524, in _finalize_model_loading\n model._move_missing_keys_from_meta_to_device(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n loading_info.missing_and_mismatched(),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<2 lines>...\n load_config.hf_quantizer,\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4781, in _move_missing_keys_from_meta_to_device\n for key in missing_keys - self.all_tied_weights_keys.keys():\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1972, in __getattr__\n raise AttributeError(\n f\"'{type(self).__name__}' object has no attribute '{name}'\"\n )\nAttributeError: 'NVEmbedModel' object has no attribute 'all_tied_weights_keys'. Did you mean: '_tied_weights_keys'?\n", "result": "FAIL(load)", "total_seconds": 29.60385298728943} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/158/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/158/run.py new file mode 100644 index 0000000000000000000000000000000000000000..6c97f140fb8ac477b3ddb7146d5b5b62de149c25 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/158/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 158, 'model': 'nvidia/NV-Embed-v2', 'revision': '3fa59658547db50a1e8e3346cf057fd0c77ed6ef', 'trending_rank': None, 'downloads_rank': 93, 'class': 'SentenceTransformer', 'dtype': 'bfloat16', 'attention': 'eager', 'trust_remote_code': True, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/164/memory_samples.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/164/memory_samples.json new file mode 100644 index 0000000000000000000000000000000000000000..9c797ca9f0f7c7faedb2285d779f77785656f91b --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/164/memory_samples.json @@ -0,0 +1 @@ +[{"elapsed_seconds": 1.809746026992798e-05, "bytes": 7141760, "categories": {"code": 3842944, "tensors": 2097152, "misc": 1144320, "dma_rings": 57344}}, {"elapsed_seconds": 0.10029691457748413, "bytes": 142073100, "categories": {"code": 4216072, "tensors": 134217728, "constants": 655364, "misc": 2607104, "dma_rings": 376832}}, {"elapsed_seconds": 0.2003889186307788, "bytes": 214733676, "categories": {"code": 5109856, "tensors": 134217728, "constants": 1572876, "scratchpad": 67108864, "misc": 5310720, "dma_rings": 1413632}}, {"elapsed_seconds": 0.300482427701354, "bytes": 221070273, "categories": {"code": 5699824, "tensors": 136318976, "constants": 2261009, "scratchpad": 67108864, "misc": 7583680, "dma_rings": 2097920}}, {"elapsed_seconds": 0.346355440095067, "bytes": 223731977, "categories": {"code": 6030712, "tensors": 136318976, "constants": 2785297, "scratchpad": 67108864, "misc": 8847488, "dma_rings": 2640640}}] \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/164/result.json b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/164/result.json new file mode 100644 index 0000000000000000000000000000000000000000..b8100e65e69c9a72d1585ce1a4766093fdc05cf6 --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/164/result.json @@ -0,0 +1,73 @@ +{ + "index": 164, + "model": "sentence-transformers/multi-qa-MiniLM-L6-dot-v1", + "revision": "4151c507ffb0f2fcd311cf431f54b5fc7d097851", + "trending_rank": null, + "downloads_rank": 100, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:59:45.659267+00:00", + "physical_visible_core": "0", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 1.0640968950465322, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 37.162418365478516, + 43.29322814941406, + 43.17018127441406, + 42.00007629394531 + ], + "cpu_seconds": 0.06058734655380249, + "cpu_top_document": 2, + "model_device": "neuron:0", + "status": "error", + "stage": "neuron_inference", + "error_type": "RuntimeError", + "error": "Inference tensors do not track version counter.", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/164/run.py\", line 69, in \n stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/164/run.py\", line 61, in score\n values=model.similarity(q,d)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1072, in similarity\n return self._similarity(embeddings1, embeddings2)\n ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/similarity.py\", line 115, in dot_score\n return torch.mm(a, b.transpose(0, 1)).to_dense()\n ~~~~~~~~~~~^^^^^^\nRuntimeError: Inference tensors do not track version counter.\n", + "result": "FAIL(forward)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 5, + "sample_interval_seconds": 0.1, + "peak_bytes": 223731977, + "peak_categories": { + "code": 6030712, + "tensors": 136318976, + "constants": 2785297, + "scratchpad": 67108864, + "misc": 8847488, + "dma_rings": 2640640 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 97223168, + "allocator_peak_reserved_bytes": 136314880, + "total_seconds": 24.259669181890786 +} \ No newline at end of file diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/164/run.log b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/164/run.log new file mode 100644 index 0000000000000000000000000000000000000000..592b03a04223233c6e5f07b5092ee98efbc9b1cd --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/164/run.log @@ -0,0 +1,18 @@ +CPU [37.162418365478516, 43.29322814941406, 43.17018127441406, 42.00007629394531] +2026-Nov-03 16:00:02.462563 2793780:2793780 WARN NRT:nrt_init dma-buf is not supported due to incompatible Libfabric. Falling back to p2p for efa memory registration. (this log repeats as INFO level) +/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch_neuronx/nki_kernel.py:85: UserWarning: NKI_ENABLE_TRACE_CACHE=1: the NKI kernel compile cache is persisted across processes. The caching process may not capture all kernel environment dependencies or non-argument values and can yield stale cache hits; see the torch-neuronx environment variables documentation for details. Set NKI_ENABLE_TRACE_CACHE=0 to disable. + _warn_trace_cache_once() +Traceback (most recent call last): + File "/tmp/neuron-top100-sweep/runs/164/run.py", line 69, in + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + ~~~~~^^ + File "/tmp/neuron-top100-sweep/runs/164/run.py", line 61, in score + values=model.similarity(q,d) + File "/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py", line 1072, in similarity + return self._similarity(embeddings1, embeddings2) + ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/jingyahuang/sentence-transformers/sentence_transformers/util/similarity.py", line 115, in dot_score + return torch.mm(a, b.transpose(0, 1)).to_dense() + ~~~~~~~~~~~^^^^^^ +RuntimeError: Inference tensors do not track version counter. +{"index": 164, "model": "sentence-transformers/multi-qa-MiniLM-L6-dot-v1", "revision": "4151c507ffb0f2fcd311cf431f54b5fc7d097851", "trending_rank": null, "downloads_rank": 100, "class": "SentenceTransformer", "dtype": "float32", "attention": "sdpa", "trust_remote_code": false, "config_kwargs": {}, "excluded": null, "exclusion": null, "started_at": "2026-11-03T15:59:45.659267+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", "python": "3.14.4", "query": "Which planet is known as the Red Planet?", "documents": ["Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet."], "batch_size": 4, "load_seconds": 1.0640968950465322, "prompts": {"query": "", "document": ""}, "parameter_dtypes": ["torch.float32"], "embedding_devices": ["neuron:0", "neuron:0"], "cpu_scores": [37.162418365478516, 43.29322814941406, 43.17018127441406, 42.00007629394531], "cpu_seconds": 0.06058734655380249, "cpu_top_document": 2, "model_device": "neuron:0", "status": "error", "stage": "neuron_inference", "error_type": "RuntimeError", "error": "Inference tensors do not track version counter.", "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/164/run.py\", line 69, in \n stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/164/run.py\", line 61, in score\n values=model.similarity(q,d)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1072, in similarity\n return self._similarity(embeddings1, embeddings2)\n ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/similarity.py\", line 115, in dot_score\n return torch.mm(a, b.transpose(0, 1)).to_dense()\n ~~~~~~~~~~~^^^^^^\nRuntimeError: Inference tensors do not track version counter.\n", "result": "FAIL(forward)", "memory_monitor": {"method": "Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count": 5, "sample_interval_seconds": 0.1, "peak_bytes": 223731977, "peak_categories": {"code": 6030712, "tensors": 136318976, "constants": 2785297, "scratchpad": 67108864, "misc": 8847488, "dma_rings": 2640640}, "capacity_bytes": 25769803776}, "allocator_peak_allocated_bytes": 97223168, "allocator_peak_reserved_bytes": 136314880, "total_seconds": 24.259669181890786} diff --git a/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/164/run.py b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/164/run.py new file mode 100644 index 0000000000000000000000000000000000000000..107e2c1f81a44cdeb53c4ab8d3e7e8a7d4f65ded --- /dev/null +++ b/validation/sentence-transformers/2026-11-03-top100/prior_runs/failed-rerun-1/164/run.py @@ -0,0 +1,94 @@ +#!/usr/bin/env python3 +# Native TorchNeuron text retrieval and device-memory validation. +# Stack: torch-neuronx 2.14.3.0.2115+149b6fca1; neuronx-cc 2.28.4568.0+0163fa9e; +# torch 2.14.0+cpu; nki 0.7.0+36523665040.g7b8cdcef; Python 3.14.4. +# pip install "git+https://github.com/JingyaHuang/sentence-transformers.git@6be0abf38d00d70bb3c732131df59ad073ff3768" "transformers==5.22.1" "huggingface-hub<2" +# Use a free logical device via NEURON_RT_VISIBLE_CORES before starting Python. +# Scores use the saved retrieval prompts and the original checkpoint revision. +# Samples are observed HBM peaks, not exact high-water marks. +import json,os,time,traceback,platform,datetime +from pathlib import Path +SPEC = {'index': 164, 'model': 'sentence-transformers/multi-qa-MiniLM-L6-dot-v1', 'revision': '4151c507ffb0f2fcd311cf431f54b5fc7d097851', 'trending_rank': None, 'downloads_rank': 100, 'class': 'SentenceTransformer', 'dtype': 'float32', 'attention': 'sdpa', 'trust_remote_code': False, 'config_kwargs': {}, 'excluded': None, 'exclusion': None} +STARTED_AT=datetime.datetime.now(datetime.timezone.utc).isoformat() +r={**SPEC,'started_at':STARTED_AT,'physical_visible_core':os.environ.get('NEURON_RT_VISIBLE_CORES')} +start=time.monotonic();stage='imports';memory_monitor=None +try: + import torch,transformers,sentence_transformers + from sentence_transformers import SentenceTransformer,CrossEncoder,MultiVectorEncoder + r.update(torch=torch.__version__,transformers=transformers.__version__,sentence_transformers=sentence_transformers.__version__,python=platform.python_version()) + import threading + class DeviceMemoryMonitor: + def __init__(self): + self.stop_event=threading.Event() + self.samples=[] + self.errors=[] + self.thread=threading.Thread(target=self.watch,daemon=True) + def sample(self): + from torch_neuronx import _C + categories=dict(_C._nrt_get_memory_usage_breakdown(0)) + if not categories:raise RuntimeError("No runtime HBM categories returned") + self.samples.append({"elapsed_seconds":time.monotonic()-self.started,"bytes":sum(categories.values()),"categories":categories}) + def watch(self): + while not self.stop_event.wait(.1): + try:self.sample() + except Exception as e:self.errors.append(str(e)) + def start(self): + self.started=time.monotonic();self.sample();self.thread.start() + def finish(self): + self.stop_event.set();self.thread.join();self.sample() + Path("memory_samples.json").write_text(json.dumps(self.samples)) + if self.errors:raise RuntimeError(str(self.errors)) + peak=max(self.samples,key=lambda sample:sample["bytes"]) + return {"method":"Neuron runtime HBM usage breakdown sampled every 100 ms", "sample_count":len(self.samples),"sample_interval_seconds":.1,"peak_bytes":peak["bytes"],"peak_categories":peak["categories"],"capacity_bytes":torch.neuron.get_device_properties(0).total_memory} + + cls={'SentenceTransformer':SentenceTransformer,'CrossEncoder':CrossEncoder,'MultiVectorEncoder':MultiVectorEncoder}[SPEC['class']] + dtype=getattr(torch,SPEC['dtype']) + query='Which planet is known as the Red Planet?' + docs=["Venus is often called Earth's twin because of its similar size and proximity.","Mars, known for its reddish appearance, is often referred to as the Red Planet.","Jupiter, the largest planet in our solar system, has a prominent red spot.","Saturn, famous for its rings, is sometimes mistaken for the Red Planet."] + r.update(query=query,documents=docs,batch_size=4) + stage='load_cpu';t=time.monotonic() + model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']}) + r['load_seconds']=time.monotonic()-t;r['prompts']=model.prompts + r['parameter_dtypes']=sorted({str(p.dtype) for p in model.parameters()}) + def score(): + if cls is CrossEncoder: + values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False) + else: + kw={} if cls is MultiVectorEncoder else {'convert_to_tensor':True} + q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw) + d=model.encode_document(docs,batch_size=4,show_progress_bar=False,**kw) + r['embedding_devices']=[str((q[0] if isinstance(q,list) else q).device),str((d[0] if isinstance(d,list) else d).device)] + values=model.similarity(q,d) + return values + stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1) + assert cpu.shape==(4,) and torch.isfinite(cpu).all(),f'Expected four finite CPU scores, got shape {cpu.shape}' + r['cpu_scores']=cpu.tolist();r['cpu_seconds']=time.monotonic()-t;r['cpu_top_document']=int(cpu.argmax())+1 + print('CPU',r['cpu_scores'],flush=True) + stage='move_to_neuron';torch.neuron.init();memory_monitor=DeviceMemoryMonitor();memory_monitor.start();model.to('neuron:0');r['model_device']=str(model.device) + assert model.device.type=='neuron',f'Model is on {model.device}' + stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device) + assert actual.device.type=='neuron',f'Scores are on {actual.device}' + actual=actual.detach().float().cpu().reshape(-1);r['neuron_first_seconds']=time.monotonic()-t + assert actual.shape==(4,) and torch.isfinite(actual).all(),f'Expected four finite Neuron scores, got shape {actual.shape}' + r.update(neuron_scores=actual.tolist(),max_abs_error=float((actual-cpu).abs().max()),neuron_top_document=int(actual.argmax())+1,mars_first=int(actual.argmax())==1) + r['cpu_parity']=torch.allclose(actual,cpu,atol=1e-3,rtol=1e-3) + stage='neuron_repeat';t=time.monotonic();repeat=score().detach().float().cpu().reshape(-1);r['neuron_repeat_seconds']=time.monotonic()-t + r['repeat_max_abs_error']=float((repeat-actual).abs().max());r['repeat_stable']=torch.allclose(repeat,actual,atol=1e-3,rtol=1e-3) + torch.neuron.synchronize();r['status']='executed' + r['result']='PASS' if r['mars_first'] and r['cpu_parity'] and r['repeat_stable'] else 'FAIL(output)' +except Exception as e: + r.update(status='error',stage=stage,error_type=type(e).__name__,error=str(e),traceback=traceback.format_exc()) + r['result']='FAIL(load)' if stage in ('imports','load_cpu','move_to_neuron') else 'FAIL(forward)' + traceback.print_exc() +finally: + if memory_monitor is not None: + try: + torch.neuron.synchronize();r['memory_monitor']=memory_monitor.finish() + r['allocator_peak_allocated_bytes']=torch.neuron.max_memory_allocated(0);r['allocator_peak_reserved_bytes']=torch.neuron.max_memory_reserved(0) + except Exception as e:r['memory_monitor_error']=str(e) + if r.get('result')=='PASS' and not r.get('memory_monitor'): + r.update(result='FAIL(other)',error='No device memory measurements captured') + r['total_seconds']=time.monotonic()-start + Path('result.json').write_text(json.dumps(r,indent=2)) + print(json.dumps(r),flush=True) +raise SystemExit(0 if r['result']=='PASS' else 1) diff --git a/validation/sentence-transformers/2026-11-03-top100/results.csv b/validation/sentence-transformers/2026-11-03-top100/results.csv index ba9f2574183b4ced471f11c97af96fcf07aa58e8..3d7e93365f0d1ae2c50670539c8b0efef94e0e85 100644 --- a/validation/sentence-transformers/2026-11-03-top100/results.csv +++ b/validation/sentence-transformers/2026-11-03-top100/results.csv @@ -19,7 +19,7 @@ cross-encoder/ms-marco-MiniLM-L6-v2,17,2,CrossEncoder,float32,sdpa,PASS,,2,2,Tru intfloat/multilingual-e5-base,18,16,SentenceTransformer,float32,sdpa,PASS,,2,2,True,True,8.940696716308594e-07,1.1819792250171304,24.0,d128750597153bb5987e10b1c3493a34e5a4502a BAAI/bge-small-en-v1.5,19,3,SentenceTransformer,float32,sdpa,PASS,,2,2,True,True,1.3113021850585938e-06,0.22558351699262857,24.0,5c38ec7c405ec4b44b94cc5a9bb96e735b38267a naver/splade-v3,20,,SentenceTransformer,float32,sdpa,EXCLUDED,,,,,,,,,fdfeceb91d7b9de7985b38addd3ba9f53a59a355 -Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2,21,,SentenceTransformer,float32,sdpa,FAIL(output),,4,4,False,True,2.682209014892578e-07,0.6478953966870904,24.0,17b87422aa4bfb3b478369299e701070b308314f +Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2,21,,SentenceTransformer,float32,sdpa,FAIL(output),,4,4,False,True,2.682209014892578e-07,0.6478973040357232,24.0,17b87422aa4bfb3b478369299e701070b308314f Qwen/Qwen3-Reranker-4B,22,22,CrossEncoder,float32,sdpa,PASS,,2,2,True,True,9.5367431640625e-06,18.65531821269542,24.0,22e683669bc0f0bd69640a1354a6d0aebcfeede5 litert-community/embeddinggemma-300m,23,,SentenceTransformer,float32,sdpa,FAIL(load),load_cpu,,,,,,,,459f1d37fec9635eb1730ebdbc219bfc3226c8e7 Qwen/Qwen3-VL-Embedding-8B,24,54,SentenceTransformer,bfloat16,sdpa,FAIL(output),,2,2,True,False,0.002091825008392334,15.394332815892994,24.0,2c4565515e0f265c6511776e7193b22c0968ddc7 @@ -65,7 +65,7 @@ sentence-transformers/clip-ViT-B-32-multilingual-v1,63,,SentenceTransformer,floa sentence-transformers/clip-ViT-B-32,64,,SentenceTransformer,float32,sdpa,PASS,,2,2,True,True,2.980232238769531e-07,0.7480063820257783,24.0,327ab6726d33c0e22f920c83f2ff9e4bd38ca37f sentence-transformers/msmarco-MiniLM-L6-v3,65,97,SentenceTransformer,float32,sdpa,PASS,,2,2,True,True,5.960464477539062e-07,0.20904544088989496,24.0,fea93b3df3924e5649a4e322c345f951239d2c13 cross-encoder/mmarco-mMiniLMv2-L12-H384-v1,66,39,CrossEncoder,float32,sdpa,PASS,,2,2,True,True,7.152557373046875e-06,0.5348287047818303,24.0,1427fd652930e4ba29e8149678df786c240d8825 -moka-ai/m3e-base,67,,SentenceTransformer,float32,sdpa,FAIL(output),,3,3,False,True,3.5762786865234375e-07,0.5224026003852487,24.0,764b537a0e50e5c7d64db883f2d2e051cbe3c64c +moka-ai/m3e-base,67,,SentenceTransformer,float32,sdpa,FAIL(output),,3,3,False,True,3.5762786865234375e-07,0.5224092761054635,24.0,764b537a0e50e5c7d64db883f2d2e051cbe3c64c BAAI/bge-reranker-base,68,21,CrossEncoder,float32,sdpa,PASS,,2,2,True,True,4.172325134277344e-07,1.1738110156729817,24.0,2cfc18c9415c912f9d8155881c133215df768a70 BAAI/bge-base-en-v1.5,69,13,SentenceTransformer,float32,sdpa,PASS,,2,2,True,True,1.2516975402832031e-06,0.550099560059607,24.0,a5beb1e3e68b9ab74eb54cfd186867f64f240e1a jinaai/jina-embeddings-v2-base-en,70,88,SentenceTransformer,float32,sdpa,FAIL(load),load_cpu,,,,,,,,322d4d7e2f35e84137961a65af894fda0385eb7a @@ -75,19 +75,19 @@ Alibaba-NLP/gte-large-en-v1.5,73,27,SentenceTransformer,float32,sdpa,FAIL(forwar udrearobert999/multi-qa-mpnet-base-cos-v1-contrastive-2e-1000samples,74,,SentenceTransformer,float32,eager,PASS,,2,2,True,True,1.1324882507324219e-06,0.5626998143270612,24.0,8a12b2b5886621830108824ffbb1382e66803888 nvidia/NV-Embed-v1,75,,SentenceTransformer,bfloat16,sdpa,FAIL(load),load_cpu,,,,,,,,2e8b322e2b738a383dae0dde5fa19e0b28a5bb11 deepvk/USER-base,76,,SentenceTransformer,float32,eager,PASS,,2,2,True,True,1.2516975402832031e-06,0.6232241913676262,24.0,e8446472f6024df155a04b2f0911b6044cabc51f -Jaume/gemma-2b-embeddings,77,,SentenceTransformer,float32,sdpa,FAIL(output),,3,3,False,True,6.556510925292969e-07,9.508606069721282,24.0,86431f65d7c3f66b2af096c61e614a2958f191f1 +Jaume/gemma-2b-embeddings,77,,SentenceTransformer,float32,sdpa,FAIL(output),,3,3,False,True,6.556510925292969e-07,9.508613699115813,24.0,86431f65d7c3f66b2af096c61e614a2958f191f1 Alibaba-NLP/gte-multilingual-reranker-base,78,,CrossEncoder,float32,sdpa,FAIL(forward),cpu_inference,,,,,,,,8215cf04918ba6f7b6a62bb44238ce2953d8831c akhooli/Arabic-SBERT-100K,79,,SentenceTransformer,float32,sdpa,FAIL(output),,4,4,False,True,2.682209014892578e-07,0.6478953966870904,24.0,67199ee95d81bb10047c946b1b4268c8703b83db jinaai/jina-colbert-v2,80,,MultiVectorEncoder,float32,eager,PASS,,2,2,True,True,1.1444091796875e-05,2.2476080134510994,24.0,a9dc5cd7293d4c71dbbba04829923ba4d0e4f6ea jinaai/jina-embeddings-v3,81,32,SentenceTransformer,float32,eager,FAIL(load),load_cpu,,,,,,,,ab036b023d30b4d1138c4c3bfa9f0c445ab455d6 dragonkue/bge-reranker-v2-m3-ko,82,,CrossEncoder,float32,sdpa,PASS,,2,2,True,True,1.7881393432617188e-07,2.226724443025887,24.0,2aca5884ecac490192af9ebd86836d9073d826cd -meandyou200175/phobert-finetune,83,,SentenceTransformer,float32,sdpa,FAIL(output),,4,4,False,True,4.172325134277344e-07,0.653023392893374,24.0,26e5f91ad1eb94b0af813021a2b7097ae4ba228e +meandyou200175/phobert-finetune,83,,SentenceTransformer,float32,sdpa,FAIL(output),,4,4,False,True,4.172325134277344e-07,0.6510416576638818,24.0,26e5f91ad1eb94b0af813021a2b7097ae4ba228e Snowflake/snowflake-arctic-embed-l-v2.0,84,19,SentenceTransformer,float32,sdpa,PASS,,2,2,True,True,4.172325134277344e-07,2.2685545096173882,24.0,ac6544c8a46e00af67e330e85a9028c66b8cfd9a perticarari/fine-tuned-sts-embedder,85,,SentenceTransformer,float32,sdpa,PASS,,2,2,True,True,5.960464477539062e-07,0.20903876516968012,24.0,ca5311f5764c05eb3dae4f5fb54056e614f90f00 ai-forever/FRIDA,86,62,SentenceTransformer,float32,sdpa,PASS,,2,2,True,True,8.344650268554688e-07,3.3286698097363114,24.0,850455b605544a944739b25f81ddf812b6e3d0d5 Alibaba-NLP/gte-modernbert-base,87,,SentenceTransformer,float32,sdpa,PASS,,2,2,True,True,2.384185791015625e-07,0.6767938891425729,24.0,e7f32e3c00f91d699e8c43b53106206bcc72bb22 Alibaba-NLP/gte-reranker-modernbert-base,88,36,CrossEncoder,float32,sdpa,PASS,,2,2,True,True,1.7881393432617188e-07,0.6695965128019452,24.0,f7481e6055501a30fb19d090657df9ec1f79ab2c -vidore/colqwen2.5-v0.2,89,,MultiVectorEncoder,float32,sdpa,FAIL(output),,4,4,False,True,5.7220458984375e-06,14.48814355675131,24.0,dcbe8d9cede518bce830488364ba0e40c873645b +vidore/colqwen2.5-v0.2,89,,MultiVectorEncoder,float32,sdpa,FAIL(output),,4,4,False,True,5.7220458984375e-06,14.488140695728362,24.0,dcbe8d9cede518bce830488364ba0e40c873645b ibm-granite/granite-embedding-30m-sparse,90,,SentenceTransformer,float32,sdpa,EXCLUDED,,,,,,,,,ad82b1fd09541c998c8d45045d601c51fdb8a9b7 deepvk/USER2-small,91,,SentenceTransformer,float32,sdpa,PASS,,2,2,True,True,1.7881393432617188e-07,0.2447022134438157,24.0,23f65b34cf7632032061f5cc66c14714e6d4cee4 mixedbread-ai/mxbai-rerank-base-v2,92,,CrossEncoder,float32,sdpa,PASS,,2,2,True,True,1.1920928955078125e-07,2.065345217473805,24.0,3ea9d4dffa7d12a4f366be8e275c349de9fc9865 @@ -114,7 +114,7 @@ sentence-transformers/multi-qa-mpnet-base-dot-v1,,34,SentenceTransformer,float32 sentence-transformers/paraphrase-MiniLM-L6-v2,,35,SentenceTransformer,float32,sdpa,FAIL(output),,3,3,False,True,1.1920928955078125e-07,0.20903876516968012,24.0,c9a2bfebc254878aee8c3aca9e6844d5bbb102d1 jinaai/jina-embeddings-v2-small-en,,38,SentenceTransformer,float32,sdpa,FAIL(load),load_cpu,,,,,,,,44e7d1d6caec8c883c2d4b207588504d519788d0 WhereIsAI/UAE-Large-V1,,41,SentenceTransformer,float32,sdpa,PASS,,2,2,True,True,4.76837158203125e-07,1.361337617971003,24.0,9c9b2c999b3350cfb3171ed429320668e39b00b8 -jhgan/ko-sroberta-multitask,,42,SentenceTransformer,float32,sdpa,FAIL(output),,4,4,False,True,1.1920928955078125e-07,0.5631938884034753,24.0,8fca7c9c98c26599be0e14b9916b11a756a26f19 +jhgan/ko-sroberta-multitask,,42,SentenceTransformer,float32,sdpa,FAIL(output),,4,4,False,True,1.1920928955078125e-07,0.5612407634034753,24.0,8fca7c9c98c26599be0e14b9916b11a756a26f19 cross-encoder/ms-marco-MiniLM-L12-v2,,43,CrossEncoder,float32,sdpa,PASS,,2,2,True,True,5.245208740234375e-06,0.21909079421311617,24.0,7b0235231ca2674cb8ca8f022859a6eba2b1c968 intfloat/e5-small-v2,,44,SentenceTransformer,float32,sdpa,PASS,,2,2,True,True,5.960464477539062e-07,0.22467234823852777,24.0,ffb93f3bd4047442299a41ebb6fa998a38507c52 intfloat/e5-base-v2,,45,SentenceTransformer,float32,sdpa,PASS,,2,2,True,True,1.5497207641601562e-06,0.549919799901545,24.0,f52bf8ec8c7124536f0efb74aca902b2995e5bcd diff --git a/validation/sentence-transformers/2026-11-03-top100/results.json b/validation/sentence-transformers/2026-11-03-top100/results.json index 44970da454b7de6ce813bb2d03eab8caf2803128..b6990dc8527b02169e9698987762f5e043249243 100644 --- a/validation/sentence-transformers/2026-11-03-top100/results.json +++ b/validation/sentence-transformers/2026-11-03-top100/results.json @@ -573,7 +573,7 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:28:39.934071+00:00", + "started_at": "2026-11-03T16:26:48.318127+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", @@ -587,7 +587,7 @@ "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ], "batch_size": 4, - "load_seconds": 1.2689191922545433, + "load_seconds": 1.3913734536617994, "prompts": {}, "parameter_dtypes": [ "torch.bfloat16" @@ -598,11 +598,11 @@ -2.2972545623779297, -2.0584821701049805 ], - "cpu_seconds": 1.485507046803832, + "cpu_seconds": 1.4555524634197354, "cpu_top_document": 2, "model_device": "neuron:0", "score_device": "neuron:0", - "neuron_first_seconds": 0.4639179026708007, + "neuron_first_seconds": 0.46388536784797907, "neuron_scores": [ -2.700716495513916, -1.0203332901000977, @@ -613,14 +613,14 @@ "neuron_top_document": 2, "mars_first": true, "cpu_parity": false, - "neuron_repeat_seconds": 0.16087585594505072, + "neuron_repeat_seconds": 0.15859147626906633, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": { "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", - "sample_count": 23, + "sample_count": 31, "sample_interval_seconds": 0.1, "peak_bytes": 16937250251, "peak_categories": { @@ -633,10 +633,88 @@ }, "capacity_bytes": 25769803776 }, - "allocator_peak_allocated_bytes": 16791547648, + "allocator_peak_allocated_bytes": 16792596224, "allocator_peak_reserved_bytes": 16848519168, - "total_seconds": 20.877234225161374, - "prior_attempts": [] + "total_seconds": 50.44789768289775, + "prior_attempts": [ + { + "index": 7, + "model": "nvidia/Nemotron-3.5-Rerank-8B-BF16", + "revision": "f88f8d9104726747791283ab156d2fcdb29e90a7", + "trending_rank": 7, + "downloads_rank": null, + "class": "CrossEncoder", + "dtype": "bfloat16", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:28:39.934071+00:00", + "physical_visible_core": "0", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 1.2689191922545433, + "prompts": {}, + "parameter_dtypes": [ + "torch.bfloat16" + ], + "cpu_scores": [ + -2.699960470199585, + -1.0288699865341187, + -2.2972545623779297, + -2.0584821701049805 + ], + "cpu_seconds": 1.485507046803832, + "cpu_top_document": 2, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 0.4639179026708007, + "neuron_scores": [ + -2.700716495513916, + -1.0203332901000977, + -2.303680419921875, + -2.0575268268585205 + ], + "max_abs_error": 0.008536696434020996, + "neuron_top_document": 2, + "mars_first": true, + "cpu_parity": false, + "neuron_repeat_seconds": 0.16087585594505072, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 23, + "sample_interval_seconds": 0.1, + "peak_bytes": 16937250251, + "peak_categories": { + "code": 8118208, + "tensors": 16848523264, + "constants": 1605643, + "scratchpad": 67108864, + "misc": 10510848, + "dma_rings": 1383424 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 16791547648, + "allocator_peak_reserved_bytes": 16848519168, + "total_seconds": 20.877234225161374 + } + ] }, { "index": 8, @@ -1669,8 +1747,8 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:31:28.531464+00:00", - "physical_visible_core": "8", + "started_at": "2026-11-03T16:26:48.318264+00:00", + "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", @@ -1683,7 +1761,7 @@ "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ], "batch_size": 4, - "load_seconds": 6.291727272793651, + "load_seconds": 0.4274421576410532, "prompts": { "query": "", "document": "" @@ -1701,11 +1779,11 @@ 0.5348622798919678, 0.615755021572113 ], - "cpu_seconds": 0.3750715795904398, + "cpu_seconds": 0.1396558079868555, "cpu_top_document": 4, "model_device": "neuron:0", "score_device": "neuron:0", - "neuron_first_seconds": 105.76572554465383, + "neuron_first_seconds": 0.4122119527310133, "neuron_scores": [ 0.4586114287376404, 0.6143702268600464, @@ -1716,30 +1794,115 @@ "neuron_top_document": 4, "mars_first": false, "cpu_parity": true, - "neuron_repeat_seconds": 0.09435997158288956, + "neuron_repeat_seconds": 0.04447338543832302, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": { "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", - "sample_count": 1059, + "sample_count": 7, "sample_interval_seconds": 0.1, - "peak_bytes": 695672385, + "peak_bytes": 695674433, "peak_categories": { "code": 6654512, "tensors": 606081024, "constants": 3473425, "scratchpad": 67108864, "misc": 9832960, - "dma_rings": 2521600 + "dma_rings": 2523648 }, "capacity_bytes": 25769803776 }, - "allocator_peak_allocated_bytes": 588299648, + "allocator_peak_allocated_bytes": 587197440, "allocator_peak_reserved_bytes": 606076928, - "total_seconds": 131.14824269991368, - "prior_attempts": [] + "total_seconds": 47.0560898212716, + "prior_attempts": [ + { + "index": 21, + "model": "Omartificial-Intelligence-Space/Arabic-Triplet-Matryoshka-V2", + "revision": "17b87422aa4bfb3b478369299e701070b308314f", + "trending_rank": 21, + "downloads_rank": null, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:31:28.531464+00:00", + "physical_visible_core": "8", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 6.291727272793651, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 0.4586111605167389, + 0.6143702864646912, + 0.5348622798919678, + 0.615755021572113 + ], + "cpu_seconds": 0.3750715795904398, + "cpu_top_document": 4, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 105.76572554465383, + "neuron_scores": [ + 0.4586114287376404, + 0.6143702268600464, + 0.534862220287323, + 0.6157552003860474 + ], + "max_abs_error": 2.682209014892578e-07, + "neuron_top_document": 4, + "mars_first": false, + "cpu_parity": true, + "neuron_repeat_seconds": 0.09435997158288956, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 1059, + "sample_interval_seconds": 0.1, + "peak_bytes": 695672385, + "peak_categories": { + "code": 6654512, + "tensors": 606081024, + "constants": 3473425, + "scratchpad": 67108864, + "misc": 9832960, + "dma_rings": 2521600 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 588299648, + "allocator_peak_reserved_bytes": 606076928, + "total_seconds": 131.14824269991368 + } + ] }, { "index": 22, @@ -1834,8 +1997,8 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:31:58.747378+00:00", - "physical_visible_core": "12", + "started_at": "2026-11-03T16:26:48.324742+00:00", + "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", @@ -1850,12 +2013,48 @@ "batch_size": 4, "status": "error", "stage": "load_cpu", - "error_type": "GatedRepoError", - "error": "403 Client Error. (Request ID: Root=1-6ae9fe77-5b99b3f12170976f33af1862;24fb7211-ac04-4557-9d7c-d58c3b8e6212)\n\nCannot access gated repo for url https://huggingface.co/litert-community/embeddinggemma-300m/resolve/459f1d37fec9635eb1730ebdbc219bfc3226c8e7/modules.json.\nAccess to model litert-community/embeddinggemma-300m is restricted and you are not in the authorized list. Visit https://huggingface.co/litert-community/embeddinggemma-300m to ask for access.", - "traceback": "httpx.HTTPStatusError: Client error '403 Forbidden' for url 'https://huggingface.co/litert-community/embeddinggemma-300m/resolve/459f1d37fec9635eb1730ebdbc219bfc3226c8e7/modules.json'\nFor more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/403\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/023/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1190, in _load_modules\n modules_json_path = load_file_path(\n model_name_or_path,\n ...<4 lines>...\n local_files_only=local_files_only,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/file_io.py\", line 208, in load_file_path\n return hf_hub_download(\n model_name_or_path,\n ...<6 lines>...\n local_files_only=local_files_only,\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_validators.py\", line 89, in _inner_fn\n return fn(*args, **kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_http.py\", line 266, in _inner\n return fn(*args, **kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1047, in hf_hub_download\n return _hf_hub_download_to_cache_dir(\n # Destination\n ...<15 lines>...\n dry_run=dry_run,\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1194, in _hf_hub_download_to_cache_dir\n _raise_on_head_call_error(head_call_error, force_download, local_files_only)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1953, in _raise_on_head_call_error\n raise head_call_error\nhuggingface_hub.errors.GatedRepoError: 403 Client Error. (Request ID: Root=1-6ae9fe77-5b99b3f12170976f33af1862;24fb7211-ac04-4557-9d7c-d58c3b8e6212)\n\nCannot access gated repo for url https://huggingface.co/litert-community/embeddinggemma-300m/resolve/459f1d37fec9635eb1730ebdbc219bfc3226c8e7/modules.json.\nAccess to model litert-community/embeddinggemma-300m is restricted and you are not in the authorized list. Visit https://huggingface.co/litert-community/embeddinggemma-300m to ask for access.\n", + "error_type": "ValueError", + "error": "Unrecognized model in litert-community/embeddinggemma-300m. Should have a `model_type` key in its config.json.", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/023/run.py\", line 51, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1200, in _load_modules\n return self._load_default_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1272, in _load_default_modules\n transformer_model = Transformer(\n model_name_or_path,\n ...<4 lines>...\n backend=self.backend,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 839, in __init__\n config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2259, in _load_config\n return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py\", line 450, in from_pretrained\n raise ValueError(\n ...<2 lines>...\n )\nValueError: Unrecognized model in litert-community/embeddinggemma-300m. Should have a `model_type` key in its config.json.\n", "result": "FAIL(load)", - "total_seconds": 8.972315741702914, - "prior_attempts": [] + "total_seconds": 32.182727108709514, + "prior_attempts": [ + { + "index": 23, + "model": "litert-community/embeddinggemma-300m", + "revision": "459f1d37fec9635eb1730ebdbc219bfc3226c8e7", + "trending_rank": 23, + "downloads_rank": null, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:31:58.747378+00:00", + "physical_visible_core": "12", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "status": "error", + "stage": "load_cpu", + "error_type": "GatedRepoError", + "error": "403 Client Error. (Request ID: Root=1-6ae9fe77-5b99b3f12170976f33af1862;24fb7211-ac04-4557-9d7c-d58c3b8e6212)\n\nCannot access gated repo for url https://huggingface.co/litert-community/embeddinggemma-300m/resolve/459f1d37fec9635eb1730ebdbc219bfc3226c8e7/modules.json.\nAccess to model litert-community/embeddinggemma-300m is restricted and you are not in the authorized list. Visit https://huggingface.co/litert-community/embeddinggemma-300m to ask for access.", + "traceback": "httpx.HTTPStatusError: Client error '403 Forbidden' for url 'https://huggingface.co/litert-community/embeddinggemma-300m/resolve/459f1d37fec9635eb1730ebdbc219bfc3226c8e7/modules.json'\nFor more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/403\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/023/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1190, in _load_modules\n modules_json_path = load_file_path(\n model_name_or_path,\n ...<4 lines>...\n local_files_only=local_files_only,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/file_io.py\", line 208, in load_file_path\n return hf_hub_download(\n model_name_or_path,\n ...<6 lines>...\n local_files_only=local_files_only,\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_validators.py\", line 89, in _inner_fn\n return fn(*args, **kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_http.py\", line 266, in _inner\n return fn(*args, **kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1047, in hf_hub_download\n return _hf_hub_download_to_cache_dir(\n # Destination\n ...<15 lines>...\n dry_run=dry_run,\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1194, in _hf_hub_download_to_cache_dir\n _raise_on_head_call_error(head_call_error, force_download, local_files_only)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1953, in _raise_on_head_call_error\n raise head_call_error\nhuggingface_hub.errors.GatedRepoError: 403 Client Error. (Request ID: Root=1-6ae9fe77-5b99b3f12170976f33af1862;24fb7211-ac04-4557-9d7c-d58c3b8e6212)\n\nCannot access gated repo for url https://huggingface.co/litert-community/embeddinggemma-300m/resolve/459f1d37fec9635eb1730ebdbc219bfc3226c8e7/modules.json.\nAccess to model litert-community/embeddinggemma-300m is restricted and you are not in the authorized list. Visit https://huggingface.co/litert-community/embeddinggemma-300m to ask for access.\n", + "result": "FAIL(load)", + "total_seconds": 8.972315741702914 + } + ] }, { "index": 24, @@ -1870,7 +2069,7 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:32:09.263638+00:00", + "started_at": "2026-11-03T16:26:48.323954+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", @@ -1884,7 +2083,7 @@ "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ], "batch_size": 4, - "load_seconds": 45.57155937794596, + "load_seconds": 0.8875982435420156, "prompts": { "query": "", "document": "", @@ -1903,11 +2102,11 @@ 0.5737250447273254, 0.6311128735542297 ], - "cpu_seconds": 11.08764569554478, + "cpu_seconds": 2.1594849564135075, "cpu_top_document": 2, "model_device": "neuron:0", "score_device": "neuron:0", - "neuron_first_seconds": 30.12761875614524, + "neuron_first_seconds": 1.226473362185061, "neuron_scores": [ 0.5109585523605347, 0.8327626585960388, @@ -1918,14 +2117,14 @@ "neuron_top_document": 2, "mars_first": true, "cpu_parity": false, - "neuron_repeat_seconds": 0.4174205930903554, + "neuron_repeat_seconds": 0.4109700918197632, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": { "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", - "sample_count": 330, + "sample_count": 40, "sample_interval_seconds": 0.1, "peak_bytes": 16529538997, "peak_categories": { @@ -1938,10 +2137,96 @@ }, "capacity_bytes": 25769803776 }, - "allocator_peak_allocated_bytes": 16390182848, + "allocator_peak_allocated_bytes": 16383648960, "allocator_peak_reserved_bytes": 16414408704, - "total_seconds": 119.78512970823795, - "prior_attempts": [] + "total_seconds": 51.54317019227892, + "prior_attempts": [ + { + "index": 24, + "model": "Qwen/Qwen3-VL-Embedding-8B", + "revision": "2c4565515e0f265c6511776e7193b22c0968ddc7", + "trending_rank": 24, + "downloads_rank": 54, + "class": "SentenceTransformer", + "dtype": "bfloat16", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:32:09.263638+00:00", + "physical_visible_core": "12", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 45.57155937794596, + "prompts": { + "query": "", + "document": "", + "default": "Represent the user's input." + }, + "parameter_dtypes": [ + "torch.bfloat16" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 0.5104122161865234, + 0.8333847522735596, + 0.5737250447273254, + 0.6311128735542297 + ], + "cpu_seconds": 11.08764569554478, + "cpu_top_document": 2, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 30.12761875614524, + "neuron_scores": [ + 0.5109585523605347, + 0.8327626585960388, + 0.5716332197189331, + 0.6295062899589539 + ], + "max_abs_error": 0.002091825008392334, + "neuron_top_document": 2, + "mars_first": true, + "cpu_parity": false, + "neuron_repeat_seconds": 0.4174205930903554, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 330, + "sample_interval_seconds": 0.1, + "peak_bytes": 16529538997, + "peak_categories": { + "code": 14566248, + "tensors": 16414412800, + "constants": 4571149, + "scratchpad": 67108864, + "misc": 24082496, + "dma_rings": 4797440 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 16390182848, + "allocator_peak_reserved_bytes": 16414408704, + "total_seconds": 119.78512970823795 + } + ] }, { "index": 25, @@ -2317,8 +2602,8 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:34:11.569070+00:00", - "physical_visible_core": "12", + "started_at": "2026-11-03T16:27:22.235996+00:00", + "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", @@ -2335,10 +2620,46 @@ "stage": "load_cpu", "error_type": "ValueError", "error": "Unrecognized model in BrunoHays/embeddinggemma-2-nullspace-audio. Should have a `model_type` key in its config.json.", - "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/029/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1200, in _load_modules\n return self._load_default_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1272, in _load_default_modules\n transformer_model = Transformer(\n model_name_or_path,\n ...<4 lines>...\n backend=self.backend,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 839, in __init__\n config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2259, in _load_config\n return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py\", line 450, in from_pretrained\n raise ValueError(\n ...<2 lines>...\n )\nValueError: Unrecognized model in BrunoHays/embeddinggemma-2-nullspace-audio. Should have a `model_type` key in its config.json.\n", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/029/run.py\", line 51, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1200, in _load_modules\n return self._load_default_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1272, in _load_default_modules\n transformer_model = Transformer(\n model_name_or_path,\n ...<4 lines>...\n backend=self.backend,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 839, in __init__\n config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2259, in _load_config\n return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py\", line 450, in from_pretrained\n raise ValueError(\n ...<2 lines>...\n )\nValueError: Unrecognized model in BrunoHays/embeddinggemma-2-nullspace-audio. Should have a `model_type` key in its config.json.\n", "result": "FAIL(load)", - "total_seconds": 24.265914521180093, - "prior_attempts": [] + "total_seconds": 18.733008542098105, + "prior_attempts": [ + { + "index": 29, + "model": "BrunoHays/embeddinggemma-2-nullspace-audio", + "revision": "4e54c6816e4ba368be62885788974458f2748adf", + "trending_rank": 29, + "downloads_rank": null, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:34:11.569070+00:00", + "physical_visible_core": "12", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "status": "error", + "stage": "load_cpu", + "error_type": "ValueError", + "error": "Unrecognized model in BrunoHays/embeddinggemma-2-nullspace-audio. Should have a `model_type` key in its config.json.", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/029/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1200, in _load_modules\n return self._load_default_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1272, in _load_default_modules\n transformer_model = Transformer(\n model_name_or_path,\n ...<4 lines>...\n backend=self.backend,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 839, in __init__\n config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2259, in _load_config\n return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py\", line 450, in from_pretrained\n raise ValueError(\n ...<2 lines>...\n )\nValueError: Unrecognized model in BrunoHays/embeddinggemma-2-nullspace-audio. Should have a `model_type` key in its config.json.\n", + "result": "FAIL(load)", + "total_seconds": 24.265914521180093 + } + ] }, { "index": 30, @@ -2438,8 +2759,8 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:34:37.563999+00:00", - "physical_visible_core": "12", + "started_at": "2026-11-03T16:27:37.150732+00:00", + "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", @@ -2452,7 +2773,7 @@ "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ], "batch_size": 4, - "load_seconds": 6.2028824258595705, + "load_seconds": 2.0308772353455424, "prompts": {}, "parameter_dtypes": [ "torch.float32" @@ -2463,11 +2784,11 @@ 0.9998114705085754, 0.9997087121009827 ], - "cpu_seconds": 32.531744067557156, + "cpu_seconds": 2.098253570497036, "cpu_top_document": 3, "model_device": "neuron:0", "score_device": "neuron:0", - "neuron_first_seconds": 45.031333486549556, + "neuron_first_seconds": 0.4742284342646599, "neuron_scores": [ 0.9997572898864746, 0.9997514486312866, @@ -2478,14 +2799,14 @@ "neuron_top_document": 3, "mars_first": false, "cpu_parity": true, - "neuron_repeat_seconds": 0.09113490767776966, + "neuron_repeat_seconds": 0.08166952524334192, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": { "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", - "sample_count": 460, + "sample_count": 15, "sample_interval_seconds": 0.1, "peak_bytes": 10210461645, "peak_categories": { @@ -2498,10 +2819,88 @@ }, "capacity_bytes": 25769803776 }, - "allocator_peak_allocated_bytes": 10061462720, + "allocator_peak_allocated_bytes": 10061462912, "allocator_peak_reserved_bytes": 10112466944, - "total_seconds": 123.40695661306381, - "prior_attempts": [] + "total_seconds": 26.05086704529822, + "prior_attempts": [ + { + "index": 31, + "model": "BAAI/bge-reranker-v2-gemma", + "revision": "1787044f8b6fb740a9de4557c3a12377f84d9e17", + "trending_rank": 31, + "downloads_rank": null, + "class": "CrossEncoder", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:34:37.563999+00:00", + "physical_visible_core": "12", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 6.2028824258595705, + "prompts": {}, + "parameter_dtypes": [ + "torch.float32" + ], + "cpu_scores": [ + 0.9997572302818298, + 0.9997515082359314, + 0.9998114705085754, + 0.9997087121009827 + ], + "cpu_seconds": 32.531744067557156, + "cpu_top_document": 3, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 45.031333486549556, + "neuron_scores": [ + 0.9997572898864746, + 0.9997514486312866, + 0.9998114109039307, + 0.9997087717056274 + ], + "max_abs_error": 5.960464477539063e-08, + "neuron_top_document": 3, + "mars_first": false, + "cpu_parity": true, + "neuron_repeat_seconds": 0.09113490767776966, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 460, + "sample_interval_seconds": 0.1, + "peak_bytes": 10210461645, + "peak_categories": { + "code": 20083904, + "tensors": 10112471040, + "constants": 1474573, + "scratchpad": 67108864, + "misc": 8413952, + "dma_rings": 909312 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 10061462720, + "allocator_peak_reserved_bytes": 10112466944, + "total_seconds": 123.40695661306381 + } + ] }, { "index": 32, @@ -2605,7 +3004,7 @@ "excluded": null, "exclusion": null, "reason": "Checkpoint custom code supports standard SDPA when xformers attention and input unpadding are disabled.", - "started_at": "2026-11-03T16:00:36.977122+00:00", + "started_at": "2026-11-03T16:27:40.804993+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", @@ -2619,7 +3018,7 @@ "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ], "batch_size": 4, - "load_seconds": 3.8345429748296738, + "load_seconds": 1.9271560944616795, "prompts": { "query": "query: ", "document": "" @@ -2630,10 +3029,10 @@ "status": "error", "stage": "cpu_inference", "error_type": "IndexError", - "error": "index 140493294592752 is out of bounds for dimension 0 with size 14", - "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/033/run.py\", line 64, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/033/run.py\", line 59, in score\n q=(model.encode([query],batch_size=4,show_progress_bar=False,**kw,**SPEC['query_kwargs']) if SPEC.get('query_kwargs') else model.encode_query([query],batch_size=4,show_progress_bar=False,**kw))\n ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Snowflake/snowflake_hyphen_arctic_hyphen_embed_hyphen_m_hyphen_v2_dot_0/95c2741480856aa9666782eb4afe11959938017f/modeling_hf_alibaba_nlp_gte.py\", line 900, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Snowflake/snowflake_hyphen_arctic_hyphen_embed_hyphen_m_hyphen_v2_dot_0/95c2741480856aa9666782eb4afe11959938017f/modeling_hf_alibaba_nlp_gte.py\", line 391, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 140493294592752 is out of bounds for dimension 0 with size 14\n", + "error": "index 140685528539888 is out of bounds for dimension 0 with size 14", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/033/run.py\", line 64, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-rerun/runs/033/run.py\", line 59, in score\n q=(model.encode([query],batch_size=4,show_progress_bar=False,**kw,**SPEC['query_kwargs']) if SPEC.get('query_kwargs') else model.encode_query([query],batch_size=4,show_progress_bar=False,**kw))\n ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Snowflake/snowflake_hyphen_arctic_hyphen_embed_hyphen_m_hyphen_v2_dot_0/95c2741480856aa9666782eb4afe11959938017f/modeling_hf_alibaba_nlp_gte.py\", line 900, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Snowflake/snowflake_hyphen_arctic_hyphen_embed_hyphen_m_hyphen_v2_dot_0/95c2741480856aa9666782eb4afe11959938017f/modeling_hf_alibaba_nlp_gte.py\", line 391, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 140685528539888 is out of bounds for dimension 0 with size 14\n", "result": "FAIL(forward)", - "total_seconds": 12.881709131412208, + "total_seconds": 14.150352603755891, "prior_attempts": [ { "index": 33, @@ -2669,24 +3068,71 @@ "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/033/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 855, in __init__\n self.model = self._load_model(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path, transformer_task, config, backend, is_peft_model, **model_kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2343, in _load_model\n return model_cls.from_pretrained(model_name_or_path, config=config, **model_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py\", line 389, in from_pretrained\n return model_class.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4300, in from_pretrained\n model = cls(config, *model_args, **model_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Snowflake/snowflake_hyphen_arctic_hyphen_embed_hyphen_m_hyphen_v2_dot_0/95c2741480856aa9666782eb4afe11959938017f/modeling_hf_alibaba_nlp_gte.py\", line 837, in __init__\n self.encoder = GteEncoder(config)\n ~~~~~~~~~~^^^^^^^^\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Snowflake/snowflake_hyphen_arctic_hyphen_embed_hyphen_m_hyphen_v2_dot_0/95c2741480856aa9666782eb4afe11959938017f/modeling_hf_alibaba_nlp_gte.py\", line 706, in __init__\n self.layer = nn.ModuleList([GteLayer(config) for _ in range(config.num_hidden_layers)])\n ~~~~~~~~^^^^^^^^\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Snowflake/snowflake_hyphen_arctic_hyphen_embed_hyphen_m_hyphen_v2_dot_0/95c2741480856aa9666782eb4afe11959938017f/modeling_hf_alibaba_nlp_gte.py\", line 640, in __init__\n self.attention = GTE_ATTENTION_CLASSES[attn_implementation](\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n config, pack_qkv=pack_qkv, use_memory_efficient_attention=use_memory_efficient_attention\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Snowflake/snowflake_hyphen_arctic_hyphen_embed_hyphen_m_hyphen_v2_dot_0/95c2741480856aa9666782eb4afe11959938017f/modeling_hf_alibaba_nlp_gte.py\", line 454, in __init__\n assert self.memory_efficient_attention is not None, 'please install xformers'\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nAssertionError: please install xformers\n", "result": "FAIL(load)", "total_seconds": 29.104860222898424 - } - ] - }, - { - "index": 34, - "model": "Qwen/Qwen3-Reranker-8B", - "revision": "77d193c791ed757ca307ee72715aa132723da912", - "trending_rank": 34, - "downloads_rank": null, - "class": "CrossEncoder", - "dtype": "bfloat16", - "attention": "sdpa", - "trust_remote_code": false, - "config_kwargs": {}, - "excluded": null, - "exclusion": null, - "started_at": "2026-11-03T15:36:33.649395+00:00", - "physical_visible_core": "8", + }, + { + "index": 33, + "model": "Snowflake/snowflake-arctic-embed-m-v2.0", + "revision": "95c2741480856aa9666782eb4afe11959938017f", + "trending_rank": 33, + "downloads_rank": null, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": true, + "config_kwargs": { + "use_memory_efficient_attention": false, + "unpad_inputs": false + }, + "excluded": null, + "exclusion": null, + "reason": "Checkpoint custom code supports standard SDPA when xformers attention and input unpadding are disabled.", + "started_at": "2026-11-03T16:00:36.977122+00:00", + "physical_visible_core": "0", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 3.8345429748296738, + "prompts": { + "query": "query: ", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "status": "error", + "stage": "cpu_inference", + "error_type": "IndexError", + "error": "index 140493294592752 is out of bounds for dimension 0 with size 14", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/033/run.py\", line 64, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/033/run.py\", line 59, in score\n q=(model.encode([query],batch_size=4,show_progress_bar=False,**kw,**SPEC['query_kwargs']) if SPEC.get('query_kwargs') else model.encode_query([query],batch_size=4,show_progress_bar=False,**kw))\n ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Snowflake/snowflake_hyphen_arctic_hyphen_embed_hyphen_m_hyphen_v2_dot_0/95c2741480856aa9666782eb4afe11959938017f/modeling_hf_alibaba_nlp_gte.py\", line 900, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Snowflake/snowflake_hyphen_arctic_hyphen_embed_hyphen_m_hyphen_v2_dot_0/95c2741480856aa9666782eb4afe11959938017f/modeling_hf_alibaba_nlp_gte.py\", line 391, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 140493294592752 is out of bounds for dimension 0 with size 14\n", + "result": "FAIL(forward)", + "total_seconds": 12.881709131412208 + } + ] + }, + { + "index": 34, + "model": "Qwen/Qwen3-Reranker-8B", + "revision": "77d193c791ed757ca307ee72715aa132723da912", + "trending_rank": 34, + "downloads_rank": null, + "class": "CrossEncoder", + "dtype": "bfloat16", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T16:27:41.821223+00:00", + "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", @@ -2699,7 +3145,7 @@ "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ], "batch_size": 4, - "load_seconds": 8.139716992154717, + "load_seconds": 0.6515219714492559, "prompts": { "query": "Given a web search query, retrieve relevant passages that answer the query" }, @@ -2712,11 +3158,11 @@ -5.34375, -4.5625 ], - "cpu_seconds": 24.723026665858924, + "cpu_seconds": 2.2711389483883977, "cpu_top_document": 2, "model_device": "neuron:0", "score_device": "neuron:0", - "neuron_first_seconds": 59.1664319653064, + "neuron_first_seconds": 0.8582923850044608, "neuron_scores": [ -8.0625, 4.0625, @@ -2727,14 +3173,14 @@ "neuron_top_document": 2, "mars_first": true, "cpu_parity": false, - "neuron_repeat_seconds": 0.24539023637771606, + "neuron_repeat_seconds": 0.24533799476921558, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": { "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", - "sample_count": 621, + "sample_count": 26, "sample_interval_seconds": 0.1, "peak_bytes": 19178892667, "peak_categories": { @@ -2747,10 +3193,90 @@ }, "capacity_bytes": 25769803776 }, - "allocator_peak_allocated_bytes": 16468168832, + "allocator_peak_allocated_bytes": 16467906688, "allocator_peak_reserved_bytes": 16521363456, - "total_seconds": 126.22209540009499, - "prior_attempts": [] + "total_seconds": 25.253529236651957, + "prior_attempts": [ + { + "index": 34, + "model": "Qwen/Qwen3-Reranker-8B", + "revision": "77d193c791ed757ca307ee72715aa132723da912", + "trending_rank": 34, + "downloads_rank": null, + "class": "CrossEncoder", + "dtype": "bfloat16", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:36:33.649395+00:00", + "physical_visible_core": "8", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 8.139716992154717, + "prompts": { + "query": "Given a web search query, retrieve relevant passages that answer the query" + }, + "parameter_dtypes": [ + "torch.bfloat16" + ], + "cpu_scores": [ + -8.125, + 4.0625, + -5.34375, + -4.5625 + ], + "cpu_seconds": 24.723026665858924, + "cpu_top_document": 2, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 59.1664319653064, + "neuron_scores": [ + -8.0625, + 4.0625, + -5.375, + -4.5625 + ], + "max_abs_error": 0.0625, + "neuron_top_document": 2, + "mars_first": true, + "cpu_parity": false, + "neuron_repeat_seconds": 0.24539023637771606, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 621, + "sample_interval_seconds": 0.1, + "peak_bytes": 19178892667, + "peak_categories": { + "code": 25923376, + "tensors": 16521367552, + "constants": 1425419, + "scratchpad": 2617245696, + "misc": 11091520, + "dma_rings": 1839104 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 16468168832, + "allocator_peak_reserved_bytes": 16521363456, + "total_seconds": 126.22209540009499 + } + ] }, { "index": 35, @@ -4278,8 +4804,8 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:42:02.918547+00:00", - "physical_visible_core": "0", + "started_at": "2026-11-03T16:27:42.275977+00:00", + "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", @@ -4296,10 +4822,46 @@ "stage": "load_cpu", "error_type": "OSError", "error": "Can't load feature extractor for 'HukukcuApp/tr-legal-embedding-gemma-2'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'HukukcuApp/tr-legal-embedding-gemma-2' is the correct path to a directory containing a preprocessor_config.json file", - "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/054/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 870, in __init__\n self.processor = AutoProcessor.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n tokenizer_name_or_path if tokenizer_name_or_path is not None else model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n **processor_kwargs,\n ^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/processing_auto.py\", line 350, in from_pretrained\n return processor_class.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/processing_utils.py\", line 1749, in from_pretrained\n args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, processor_dict, **kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/processing_utils.py\", line 1889, in _get_arguments_from_pretrained\n sub_processor = auto_processor_class.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/feature_extraction_auto.py\", line 329, in from_pretrained\n config_dict, _ = FeatureExtractionMixin.get_feature_extractor_dict(pretrained_model_name_or_path, **kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/feature_extraction_utils.py\", line 545, in get_feature_extractor_dict\n raise OSError(\n ...<4 lines>...\n )\nOSError: Can't load feature extractor for 'HukukcuApp/tr-legal-embedding-gemma-2'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'HukukcuApp/tr-legal-embedding-gemma-2' is the correct path to a directory containing a preprocessor_config.json file\n", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/054/run.py\", line 51, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 870, in __init__\n self.processor = AutoProcessor.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n tokenizer_name_or_path if tokenizer_name_or_path is not None else model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n **processor_kwargs,\n ^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/processing_auto.py\", line 350, in from_pretrained\n return processor_class.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/processing_utils.py\", line 1749, in from_pretrained\n args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, processor_dict, **kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/processing_utils.py\", line 1889, in _get_arguments_from_pretrained\n sub_processor = auto_processor_class.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/feature_extraction_auto.py\", line 329, in from_pretrained\n config_dict, _ = FeatureExtractionMixin.get_feature_extractor_dict(pretrained_model_name_or_path, **kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/feature_extraction_utils.py\", line 545, in get_feature_extractor_dict\n raise OSError(\n ...<4 lines>...\n )\nOSError: Can't load feature extractor for 'HukukcuApp/tr-legal-embedding-gemma-2'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'HukukcuApp/tr-legal-embedding-gemma-2' is the correct path to a directory containing a preprocessor_config.json file\n", "result": "FAIL(load)", - "total_seconds": 14.706041608005762, - "prior_attempts": [] + "total_seconds": 13.635371790267527, + "prior_attempts": [ + { + "index": 54, + "model": "HukukcuApp/tr-legal-embedding-gemma-2", + "revision": "a33e497f3f123019d3b03fe40082992fffeac728", + "trending_rank": 54, + "downloads_rank": null, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:42:02.918547+00:00", + "physical_visible_core": "0", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "status": "error", + "stage": "load_cpu", + "error_type": "OSError", + "error": "Can't load feature extractor for 'HukukcuApp/tr-legal-embedding-gemma-2'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'HukukcuApp/tr-legal-embedding-gemma-2' is the correct path to a directory containing a preprocessor_config.json file", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/054/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 870, in __init__\n self.processor = AutoProcessor.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n tokenizer_name_or_path if tokenizer_name_or_path is not None else model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n **processor_kwargs,\n ^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/processing_auto.py\", line 350, in from_pretrained\n return processor_class.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path, trust_remote_code=trust_remote_code, **kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/processing_utils.py\", line 1749, in from_pretrained\n args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, processor_dict, **kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/processing_utils.py\", line 1889, in _get_arguments_from_pretrained\n sub_processor = auto_processor_class.from_pretrained(\n pretrained_model_name_or_path, subfolder=subfolder, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/feature_extraction_auto.py\", line 329, in from_pretrained\n config_dict, _ = FeatureExtractionMixin.get_feature_extractor_dict(pretrained_model_name_or_path, **kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/feature_extraction_utils.py\", line 545, in get_feature_extractor_dict\n raise OSError(\n ...<4 lines>...\n )\nOSError: Can't load feature extractor for 'HukukcuApp/tr-legal-embedding-gemma-2'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'HukukcuApp/tr-legal-embedding-gemma-2' is the correct path to a directory containing a preprocessor_config.json file\n", + "result": "FAIL(load)", + "total_seconds": 14.706041608005762 + } + ] }, { "index": 55, @@ -5312,8 +5874,8 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:45:11.803493+00:00", - "physical_visible_core": "8", + "started_at": "2026-11-03T16:27:56.535897+00:00", + "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", @@ -5326,7 +5888,7 @@ "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ], "batch_size": 4, - "load_seconds": 1.331957183778286, + "load_seconds": 0.4231596179306507, "prompts": { "query": "", "document": "" @@ -5344,11 +5906,11 @@ 0.8093117475509644, 0.8064126968383789 ], - "cpu_seconds": 0.14178986009210348, + "cpu_seconds": 0.11570570152252913, "cpu_top_document": 3, "model_device": "neuron:0", "score_device": "neuron:0", - "neuron_first_seconds": 39.688529022969306, + "neuron_first_seconds": 0.3781044092029333, "neuron_scores": [ 0.6934453845024109, 0.807148277759552, @@ -5359,30 +5921,115 @@ "neuron_top_document": 3, "mars_first": false, "cpu_parity": true, - "neuron_repeat_seconds": 0.1130013084039092, + "neuron_repeat_seconds": 0.0854030791670084, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": { "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", - "sample_count": 400, + "sample_count": 7, "sample_interval_seconds": 0.1, - "peak_bytes": 560925521, + "peak_bytes": 560932689, "peak_categories": { "code": 6555520, "tensors": 471863296, "constants": 3178513, "scratchpad": 67108864, "misc": 9490112, - "dma_rings": 2729216 + "dma_rings": 2736384 }, "capacity_bytes": 25769803776 }, "allocator_peak_allocated_bytes": 452711936, "allocator_peak_reserved_bytes": 471859200, - "total_seconds": 56.262652300298214, - "prior_attempts": [] + "total_seconds": 20.959871355444193, + "prior_attempts": [ + { + "index": 67, + "model": "moka-ai/m3e-base", + "revision": "764b537a0e50e5c7d64db883f2d2e051cbe3c64c", + "trending_rank": 67, + "downloads_rank": null, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:45:11.803493+00:00", + "physical_visible_core": "8", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 1.331957183778286, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 0.6934455037117004, + 0.8071481585502625, + 0.8093117475509644, + 0.8064126968383789 + ], + "cpu_seconds": 0.14178986009210348, + "cpu_top_document": 3, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 39.688529022969306, + "neuron_scores": [ + 0.6934453845024109, + 0.807148277759552, + 0.8093113899230957, + 0.8064124584197998 + ], + "max_abs_error": 3.5762786865234375e-07, + "neuron_top_document": 3, + "mars_first": false, + "cpu_parity": true, + "neuron_repeat_seconds": 0.1130013084039092, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 400, + "sample_interval_seconds": 0.1, + "peak_bytes": 560925521, + "peak_categories": { + "code": 6555520, + "tensors": 471863296, + "constants": 3178513, + "scratchpad": 67108864, + "misc": 9490112, + "dma_rings": 2729216 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 452711936, + "allocator_peak_reserved_bytes": 471859200, + "total_seconds": 56.262652300298214 + } + ] }, { "index": 68, @@ -5560,8 +6207,8 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:45:55.385742+00:00", - "physical_visible_core": "12", + "started_at": "2026-11-03T16:27:57.819913+00:00", + "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", @@ -5578,10 +6225,46 @@ "stage": "load_cpu", "error_type": "ModuleNotFoundError", "error": "No module named 'transformers.onnx'", - "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/070/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 839, in __init__\n config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2259, in _load_config\n return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py\", line 422, in from_pretrained\n config_class = get_class_from_dynamic_module(\n class_ref, pretrained_model_name_or_path, code_revision=code_revision, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 625, in get_class_from_dynamic_module\n return get_class_in_module(class_name, final_module, force_reload=force_download)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 309, in get_class_in_module\n module_spec.loader.exec_module(module)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^\n File \"\", line 759, in exec_module\n File \"\", line 491, in _call_with_frames_removed\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_bert_hyphen_implementation/f3ec4cf7de7e561007f27c9efc7148b0bd713f81/configuration_bert.py\", line 22, in \n from transformers.onnx import OnnxConfig\nModuleNotFoundError: No module named 'transformers.onnx'\n", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/070/run.py\", line 51, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 839, in __init__\n config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2259, in _load_config\n return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py\", line 422, in from_pretrained\n config_class = get_class_from_dynamic_module(\n class_ref, pretrained_model_name_or_path, code_revision=code_revision, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 625, in get_class_from_dynamic_module\n return get_class_in_module(class_name, final_module, force_reload=force_download)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 309, in get_class_in_module\n module_spec.loader.exec_module(module)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^\n File \"\", line 759, in exec_module\n File \"\", line 491, in _call_with_frames_removed\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_bert_hyphen_implementation/f3ec4cf7de7e561007f27c9efc7148b0bd713f81/configuration_bert.py\", line 22, in \n from transformers.onnx import OnnxConfig\nModuleNotFoundError: No module named 'transformers.onnx'\n", "result": "FAIL(load)", - "total_seconds": 8.849647373892367, - "prior_attempts": [] + "total_seconds": 12.02744938340038, + "prior_attempts": [ + { + "index": 70, + "model": "jinaai/jina-embeddings-v2-base-en", + "revision": "322d4d7e2f35e84137961a65af894fda0385eb7a", + "trending_rank": 70, + "downloads_rank": 88, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": true, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:45:55.385742+00:00", + "physical_visible_core": "12", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "status": "error", + "stage": "load_cpu", + "error_type": "ModuleNotFoundError", + "error": "No module named 'transformers.onnx'", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/070/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 839, in __init__\n config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2259, in _load_config\n return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py\", line 422, in from_pretrained\n config_class = get_class_from_dynamic_module(\n class_ref, pretrained_model_name_or_path, code_revision=code_revision, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 625, in get_class_from_dynamic_module\n return get_class_in_module(class_name, final_module, force_reload=force_download)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 309, in get_class_in_module\n module_spec.loader.exec_module(module)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^\n File \"\", line 759, in exec_module\n File \"\", line 491, in _call_with_frames_removed\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_bert_hyphen_implementation/f3ec4cf7de7e561007f27c9efc7148b0bd713f81/configuration_bert.py\", line 22, in \n from transformers.onnx import OnnxConfig\nModuleNotFoundError: No module named 'transformers.onnx'\n", + "result": "FAIL(load)", + "total_seconds": 8.849647373892367 + } + ] }, { "index": 71, @@ -5811,8 +6494,8 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:46:09.987170+00:00", - "physical_visible_core": "8", + "started_at": "2026-11-03T16:28:05.050764+00:00", + "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", @@ -5825,7 +6508,7 @@ "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ], "batch_size": 4, - "load_seconds": 3.194855031557381, + "load_seconds": 0.6149875931441784, "prompts": { "query": "", "document": "" @@ -5836,11 +6519,55 @@ "status": "error", "stage": "cpu_inference", "error_type": "IndexError", - "error": "index 239254133933015924 is out of bounds for dimension 0 with size 11", - "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/073/run.py\", line 63, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/073/run.py\", line 58, in score\n q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 239254133933015924 is out of bounds for dimension 0 with size 11\n", + "error": "index 755665224 is out of bounds for dimension 0 with size 11", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/073/run.py\", line 64, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-rerun/runs/073/run.py\", line 59, in score\n q=(model.encode([query],batch_size=4,show_progress_bar=False,**kw,**SPEC['query_kwargs']) if SPEC.get('query_kwargs') else model.encode_query([query],batch_size=4,show_progress_bar=False,**kw))\n ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 755665224 is out of bounds for dimension 0 with size 11\n", "result": "FAIL(forward)", - "total_seconds": 11.818632579408586, - "prior_attempts": [] + "total_seconds": 10.126153020188212, + "prior_attempts": [ + { + "index": 73, + "model": "Alibaba-NLP/gte-large-en-v1.5", + "revision": "104333d6af6f97649377c2afbde10a7704870c7b", + "trending_rank": 73, + "downloads_rank": 27, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": true, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:46:09.987170+00:00", + "physical_visible_core": "8", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 3.194855031557381, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "status": "error", + "stage": "cpu_inference", + "error_type": "IndexError", + "error": "index 239254133933015924 is out of bounds for dimension 0 with size 11", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/073/run.py\", line 63, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/073/run.py\", line 58, in score\n q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 239254133933015924 is out of bounds for dimension 0 with size 11\n", + "result": "FAIL(forward)", + "total_seconds": 11.818632579408586 + } + ] }, { "index": 74, @@ -5972,12 +6699,13 @@ "class": "SentenceTransformer", "dtype": "bfloat16", "attention": "sdpa", - "trust_remote_code": false, + "trust_remote_code": true, "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:46:17.899843+00:00", - "physical_visible_core": "4", + "reason": "Hub access granted; enable the custom model code required by NV-Embed-v1.", + "started_at": "2026-11-03T16:30:04.363338+00:00", + "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", @@ -5992,12 +6720,83 @@ "batch_size": 4, "status": "error", "stage": "load_cpu", - "error_type": "GatedRepoError", - "error": "403 Client Error. (Request ID: Root=1-6aea01d2-3206be3e053d1684092fc8e7;1a302a3a-37c9-46e0-94a8-99f64b2e4802)\n\nCannot access gated repo for url https://huggingface.co/nvidia/NV-Embed-v1/resolve/2e8b322e2b738a383dae0dde5fa19e0b28a5bb11/modules.json.\nAccess to model nvidia/NV-Embed-v1 is restricted and you are not in the authorized list. Visit https://huggingface.co/nvidia/NV-Embed-v1 to ask for access.", - "traceback": "httpx.HTTPStatusError: Client error '403 Forbidden' for url 'https://huggingface.co/nvidia/NV-Embed-v1/resolve/2e8b322e2b738a383dae0dde5fa19e0b28a5bb11/modules.json'\nFor more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/403\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/075/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1190, in _load_modules\n modules_json_path = load_file_path(\n model_name_or_path,\n ...<4 lines>...\n local_files_only=local_files_only,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/file_io.py\", line 208, in load_file_path\n return hf_hub_download(\n model_name_or_path,\n ...<6 lines>...\n local_files_only=local_files_only,\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_validators.py\", line 89, in _inner_fn\n return fn(*args, **kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_http.py\", line 266, in _inner\n return fn(*args, **kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1047, in hf_hub_download\n return _hf_hub_download_to_cache_dir(\n # Destination\n ...<15 lines>...\n dry_run=dry_run,\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1194, in _hf_hub_download_to_cache_dir\n _raise_on_head_call_error(head_call_error, force_download, local_files_only)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1953, in _raise_on_head_call_error\n raise head_call_error\nhuggingface_hub.errors.GatedRepoError: 403 Client Error. (Request ID: Root=1-6aea01d2-3206be3e053d1684092fc8e7;1a302a3a-37c9-46e0-94a8-99f64b2e4802)\n\nCannot access gated repo for url https://huggingface.co/nvidia/NV-Embed-v1/resolve/2e8b322e2b738a383dae0dde5fa19e0b28a5bb11/modules.json.\nAccess to model nvidia/NV-Embed-v1 is restricted and you are not in the authorized list. Visit https://huggingface.co/nvidia/NV-Embed-v1 to ask for access.\n", + "error_type": "ImportError", + "error": "cannot import name 'MISTRAL_INPUTS_DOCSTRING' from 'transformers.models.mistral.modeling_mistral' (/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/mistral/modeling_mistral.py)", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-nv-embed-v1-rerun/runs/075/run.py\", line 51, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 855, in __init__\n self.model = self._load_model(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path, transformer_task, config, backend, is_peft_model, **model_kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2343, in _load_model\n return model_cls.from_pretrained(model_name_or_path, config=config, **model_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py\", line 381, in from_pretrained\n model_class = get_class_from_dynamic_module(\n class_ref, pretrained_model_name_or_path, code_revision=code_revision, **hub_kwargs, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 625, in get_class_from_dynamic_module\n return get_class_in_module(class_name, final_module, force_reload=force_download)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 309, in get_class_in_module\n module_spec.loader.exec_module(module)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^\n File \"\", line 759, in exec_module\n File \"\", line 491, in _call_with_frames_removed\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/nvidia/NV_hyphen_Embed_hyphen_v1/2e8b322e2b738a383dae0dde5fa19e0b28a5bb11/modeling_nvembed.py\", line 11, in \n from transformers.models.mistral.modeling_mistral import MISTRAL_INPUTS_DOCSTRING\nImportError: cannot import name 'MISTRAL_INPUTS_DOCSTRING' from 'transformers.models.mistral.modeling_mistral' (/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/mistral/modeling_mistral.py)\n", "result": "FAIL(load)", - "total_seconds": 8.890233975835145, - "prior_attempts": [] + "total_seconds": 13.725843532010913, + "prior_attempts": [ + { + "index": 75, + "model": "nvidia/NV-Embed-v1", + "revision": "2e8b322e2b738a383dae0dde5fa19e0b28a5bb11", + "trending_rank": 75, + "downloads_rank": null, + "class": "SentenceTransformer", + "dtype": "bfloat16", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:46:17.899843+00:00", + "physical_visible_core": "4", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "status": "error", + "stage": "load_cpu", + "error_type": "GatedRepoError", + "error": "403 Client Error. (Request ID: Root=1-6aea01d2-3206be3e053d1684092fc8e7;1a302a3a-37c9-46e0-94a8-99f64b2e4802)\n\nCannot access gated repo for url https://huggingface.co/nvidia/NV-Embed-v1/resolve/2e8b322e2b738a383dae0dde5fa19e0b28a5bb11/modules.json.\nAccess to model nvidia/NV-Embed-v1 is restricted and you are not in the authorized list. Visit https://huggingface.co/nvidia/NV-Embed-v1 to ask for access.", + "traceback": "httpx.HTTPStatusError: Client error '403 Forbidden' for url 'https://huggingface.co/nvidia/NV-Embed-v1/resolve/2e8b322e2b738a383dae0dde5fa19e0b28a5bb11/modules.json'\nFor more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/403\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/075/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1190, in _load_modules\n modules_json_path = load_file_path(\n model_name_or_path,\n ...<4 lines>...\n local_files_only=local_files_only,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/file_io.py\", line 208, in load_file_path\n return hf_hub_download(\n model_name_or_path,\n ...<6 lines>...\n local_files_only=local_files_only,\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_validators.py\", line 89, in _inner_fn\n return fn(*args, **kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/utils/_http.py\", line 266, in _inner\n return fn(*args, **kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1047, in hf_hub_download\n return _hf_hub_download_to_cache_dir(\n # Destination\n ...<15 lines>...\n dry_run=dry_run,\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1194, in _hf_hub_download_to_cache_dir\n _raise_on_head_call_error(head_call_error, force_download, local_files_only)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/huggingface_hub/file_download.py\", line 1953, in _raise_on_head_call_error\n raise head_call_error\nhuggingface_hub.errors.GatedRepoError: 403 Client Error. (Request ID: Root=1-6aea01d2-3206be3e053d1684092fc8e7;1a302a3a-37c9-46e0-94a8-99f64b2e4802)\n\nCannot access gated repo for url https://huggingface.co/nvidia/NV-Embed-v1/resolve/2e8b322e2b738a383dae0dde5fa19e0b28a5bb11/modules.json.\nAccess to model nvidia/NV-Embed-v1 is restricted and you are not in the authorized list. Visit https://huggingface.co/nvidia/NV-Embed-v1 to ask for access.\n", + "result": "FAIL(load)", + "total_seconds": 8.890233975835145 + }, + { + "index": 75, + "model": "nvidia/NV-Embed-v1", + "revision": "2e8b322e2b738a383dae0dde5fa19e0b28a5bb11", + "trending_rank": 75, + "downloads_rank": null, + "class": "SentenceTransformer", + "dtype": "bfloat16", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T16:28:08.859256+00:00", + "physical_visible_core": "12", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "status": "error", + "stage": "load_cpu", + "error_type": "ValueError", + "error": "The repository nvidia/NV-Embed-v1 contains custom code which must be executed to correctly load the model. You can inspect the repository content at https://hf.co/nvidia/NV-Embed-v1 .\n You can inspect the repository content at https://hf.co/nvidia/NV-Embed-v1.\nPlease pass the argument `trust_remote_code=True` to allow custom code to be run.", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/075/run.py\", line 51, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 839, in __init__\n config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2259, in _load_config\n return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py\", line 417, in from_pretrained\n trust_remote_code = resolve_trust_remote_code(\n trust_remote_code, pretrained_model_name_or_path, has_local_code, has_remote_code, upstream_repo\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 791, in resolve_trust_remote_code\n raise ValueError(\n ...<2 lines>...\n )\nValueError: The repository nvidia/NV-Embed-v1 contains custom code which must be executed to correctly load the model. You can inspect the repository content at https://hf.co/nvidia/NV-Embed-v1 .\n You can inspect the repository content at https://hf.co/nvidia/NV-Embed-v1.\nPlease pass the argument `trust_remote_code=True` to allow custom code to be run.\n", + "result": "FAIL(load)", + "total_seconds": 9.04073691368103 + } + ] }, { "index": 76, @@ -6134,8 +6933,8 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:46:28.024988+00:00", - "physical_visible_core": "4", + "started_at": "2026-11-03T16:28:11.198209+00:00", + "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", @@ -6148,7 +6947,7 @@ "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ], "batch_size": 4, - "load_seconds": 4.274750509299338, + "load_seconds": 1.1411347407847643, "prompts": { "query": "", "document": "" @@ -6166,11 +6965,11 @@ 0.9318515062332153, 0.9134941101074219 ], - "cpu_seconds": 6.217862844467163, + "cpu_seconds": 1.887984692119062, "cpu_top_document": 3, "model_device": "neuron:0", "score_device": "neuron:0", - "neuron_first_seconds": 38.68630393128842, + "neuron_first_seconds": 0.6383720831945539, "neuron_scores": [ 0.921073317527771, 0.9032103419303894, @@ -6181,30 +6980,115 @@ "neuron_top_document": 3, "mars_first": false, "cpu_parity": true, - "neuron_repeat_seconds": 0.15275748260319233, + "neuron_repeat_seconds": 0.14952720422297716, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": { "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", - "sample_count": 410, + "sample_count": 18, "sample_interval_seconds": 0.1, - "peak_bytes": 10209788025, + "peak_bytes": 10209796217, "peak_categories": { "code": 11395752, "tensors": 10108276736, "constants": 4882449, "scratchpad": 67108864, "misc": 15922624, - "dma_rings": 2201600 + "dma_rings": 2209792 }, "capacity_bytes": 25769803776 }, "allocator_peak_allocated_bytes": 10050966144, "allocator_peak_reserved_bytes": 10108272640, - "total_seconds": 68.6340330131352, - "prior_attempts": [] + "total_seconds": 19.1534237684682, + "prior_attempts": [ + { + "index": 77, + "model": "Jaume/gemma-2b-embeddings", + "revision": "86431f65d7c3f66b2af096c61e614a2958f191f1", + "trending_rank": 77, + "downloads_rank": null, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:46:28.024988+00:00", + "physical_visible_core": "4", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 4.274750509299338, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 0.9210729002952576, + 0.9032096862792969, + 0.9318515062332153, + 0.9134941101074219 + ], + "cpu_seconds": 6.217862844467163, + "cpu_top_document": 3, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 38.68630393128842, + "neuron_scores": [ + 0.921073317527771, + 0.9032103419303894, + 0.9318520426750183, + 0.9134945273399353 + ], + "max_abs_error": 6.556510925292969e-07, + "neuron_top_document": 3, + "mars_first": false, + "cpu_parity": true, + "neuron_repeat_seconds": 0.15275748260319233, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 410, + "sample_interval_seconds": 0.1, + "peak_bytes": 10209788025, + "peak_categories": { + "code": 11395752, + "tensors": 10108276736, + "constants": 4882449, + "scratchpad": 67108864, + "misc": 15922624, + "dma_rings": 2201600 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 10050966144, + "allocator_peak_reserved_bytes": 10108272640, + "total_seconds": 68.6340330131352 + } + ] }, { "index": 78, @@ -6219,8 +7103,8 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:46:48.108932+00:00", - "physical_visible_core": "0", + "started_at": "2026-11-03T16:28:16.434610+00:00", + "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", @@ -6233,7 +7117,7 @@ "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ], "batch_size": 4, - "load_seconds": 2.7446684883907437, + "load_seconds": 1.973351868800819, "prompts": {}, "parameter_dtypes": [ "torch.float32" @@ -6241,11 +7125,52 @@ "status": "error", "stage": "cpu_inference", "error_type": "IndexError", - "error": "index 139625091826798 is out of bounds for dimension 0 with size 32", - "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/078/run.py\", line 63, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/078/run.py\", line 55, in score\n values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 209, in wrapper\n return func(self, *args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 645, in predict\n pred_scores = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 689, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 1117, in forward\n outputs = self.new(\n input_ids,\n ...<8 lines>...\n unpad_inputs=unpad_inputs,\n )\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 139625091826798 is out of bounds for dimension 0 with size 32\n", + "error": "index 90194313471 is out of bounds for dimension 0 with size 32", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/078/run.py\", line 64, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-rerun/runs/078/run.py\", line 56, in score\n values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 209, in wrapper\n return func(self, *args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 645, in predict\n pred_scores = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 689, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 1117, in forward\n outputs = self.new(\n input_ids,\n ...<8 lines>...\n unpad_inputs=unpad_inputs,\n )\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 90194313471 is out of bounds for dimension 0 with size 32\n", "result": "FAIL(forward)", - "total_seconds": 15.83839768357575, - "prior_attempts": [] + "total_seconds": 9.826986877247691, + "prior_attempts": [ + { + "index": 78, + "model": "Alibaba-NLP/gte-multilingual-reranker-base", + "revision": "8215cf04918ba6f7b6a62bb44238ce2953d8831c", + "trending_rank": 78, + "downloads_rank": null, + "class": "CrossEncoder", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": true, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:46:48.108932+00:00", + "physical_visible_core": "0", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 2.7446684883907437, + "prompts": {}, + "parameter_dtypes": [ + "torch.float32" + ], + "status": "error", + "stage": "cpu_inference", + "error_type": "IndexError", + "error": "index 139625091826798 is out of bounds for dimension 0 with size 32", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/078/run.py\", line 63, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/078/run.py\", line 55, in score\n values=model.predict([(query,d) for d in docs],batch_size=4,convert_to_tensor=True,show_progress_bar=False)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 209, in wrapper\n return func(self, *args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 645, in predict\n pred_scores = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 689, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 1117, in forward\n outputs = self.new(\n input_ids,\n ...<8 lines>...\n unpad_inputs=unpad_inputs,\n )\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 139625091826798 is out of bounds for dimension 0 with size 32\n", + "result": "FAIL(forward)", + "total_seconds": 15.83839768357575 + } + ] }, { "index": 79, @@ -6260,8 +7185,8 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:46:59.382279+00:00", - "physical_visible_core": "12", + "started_at": "2026-11-03T16:28:19.317175+00:00", + "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", @@ -6274,7 +7199,7 @@ "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ], "batch_size": 4, - "load_seconds": 1.3999376483261585, + "load_seconds": 0.5274149598553777, "prompts": { "query": "", "document": "" @@ -6292,11 +7217,11 @@ 0.3681429922580719, 0.6955137848854065 ], - "cpu_seconds": 0.4114110991358757, + "cpu_seconds": 0.13939552847296, "cpu_top_document": 4, "model_device": "neuron:0", "score_device": "neuron:0", - "neuron_first_seconds": 0.4038874404504895, + "neuron_first_seconds": 0.35299276281148195, "neuron_scores": [ 0.45112812519073486, 0.5921508073806763, @@ -6307,14 +7232,14 @@ "neuron_top_document": 4, "mars_first": false, "cpu_parity": true, - "neuron_repeat_seconds": 0.06409406755119562, + "neuron_repeat_seconds": 0.04365590400993824, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": { "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", - "sample_count": 7, + "sample_count": 6, "sample_interval_seconds": 0.1, "peak_bytes": 695672385, "peak_categories": { @@ -6329,8 +7254,93 @@ }, "allocator_peak_allocated_bytes": 587197440, "allocator_peak_reserved_bytes": 606076928, - "total_seconds": 21.84079162403941, - "prior_attempts": [] + "total_seconds": 15.680451227352023, + "prior_attempts": [ + { + "index": 79, + "model": "akhooli/Arabic-SBERT-100K", + "revision": "67199ee95d81bb10047c946b1b4268c8703b83db", + "trending_rank": 79, + "downloads_rank": null, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:46:59.382279+00:00", + "physical_visible_core": "12", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 1.3999376483261585, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 0.45112812519073486, + 0.5921509265899658, + 0.3681429922580719, + 0.6955137848854065 + ], + "cpu_seconds": 0.4114110991358757, + "cpu_top_document": 4, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 0.4038874404504895, + "neuron_scores": [ + 0.45112812519073486, + 0.5921508073806763, + 0.3681427240371704, + 0.6955137252807617 + ], + "max_abs_error": 2.682209014892578e-07, + "neuron_top_document": 4, + "mars_first": false, + "cpu_parity": true, + "neuron_repeat_seconds": 0.06409406755119562, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 7, + "sample_interval_seconds": 0.1, + "peak_bytes": 695672385, + "peak_categories": { + "code": 6654512, + "tensors": 606081024, + "constants": 3473425, + "scratchpad": 67108864, + "misc": 9832960, + "dma_rings": 2521600 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 587197440, + "allocator_peak_reserved_bytes": 606076928, + "total_seconds": 21.84079162403941 + } + ] }, { "index": 80, @@ -6466,7 +7476,7 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:47:35.211638+00:00", + "started_at": "2026-11-03T16:28:19.331849+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", @@ -6484,9 +7494,9 @@ "stage": "load_cpu", "error_type": "AttributeError", "error": "'XLMRobertaLoRA' object has no attribute 'all_tied_weights_keys'", - "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/081/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1404, in _load_config_modules\n module = module_class(model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_embeddings_hyphen_v3/ab036b023d30b4d1138c4c3bfa9f0c445ab455d6/custom_st.py\", line 87, in __init__\n self.auto_model = AutoModel.from_pretrained(model_name_or_path, config=self.config, **model_args)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py\", line 389, in from_pretrained\n return model_class.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/xlm_hyphen_roberta_hyphen_flash_hyphen_implementation/bd55a5ec8e6c0fb1d6c26efb4b6a4a74ce8a88d3/modeling_lora.py\", line 338, in from_pretrained\n return super().from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<9 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/xlm_hyphen_roberta_hyphen_flash_hyphen_implementation/bd55a5ec8e6c0fb1d6c26efb4b6a4a74ce8a88d3/modeling_xlm_roberta.py\", line 442, in from_pretrained\n return super().from_pretrained(*args, **kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4356, in from_pretrained\n loading_info = cls._finalize_model_loading(model, load_config, loading_info)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4524, in _finalize_model_loading\n model._move_missing_keys_from_meta_to_device(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n loading_info.missing_and_mismatched(),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<2 lines>...\n load_config.hf_quantizer,\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4781, in _move_missing_keys_from_meta_to_device\n for key in missing_keys - self.all_tied_weights_keys.keys():\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1972, in __getattr__\n raise AttributeError(\n f\"'{type(self).__name__}' object has no attribute '{name}'\"\n )\nAttributeError: 'XLMRobertaLoRA' object has no attribute 'all_tied_weights_keys'. Did you mean: '_tied_weights_keys'?\n", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/081/run.py\", line 51, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1404, in _load_config_modules\n module = module_class(model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_embeddings_hyphen_v3/ab036b023d30b4d1138c4c3bfa9f0c445ab455d6/custom_st.py\", line 87, in __init__\n self.auto_model = AutoModel.from_pretrained(model_name_or_path, config=self.config, **model_args)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py\", line 389, in from_pretrained\n return model_class.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/xlm_hyphen_roberta_hyphen_flash_hyphen_implementation/bd55a5ec8e6c0fb1d6c26efb4b6a4a74ce8a88d3/modeling_lora.py\", line 338, in from_pretrained\n return super().from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<9 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/xlm_hyphen_roberta_hyphen_flash_hyphen_implementation/bd55a5ec8e6c0fb1d6c26efb4b6a4a74ce8a88d3/modeling_xlm_roberta.py\", line 442, in from_pretrained\n return super().from_pretrained(*args, **kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4356, in from_pretrained\n loading_info = cls._finalize_model_loading(model, load_config, loading_info)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4524, in _finalize_model_loading\n model._move_missing_keys_from_meta_to_device(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n loading_info.missing_and_mismatched(),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<2 lines>...\n load_config.hf_quantizer,\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4781, in _move_missing_keys_from_meta_to_device\n for key in missing_keys - self.all_tied_weights_keys.keys():\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1972, in __getattr__\n raise AttributeError(\n f\"'{type(self).__name__}' object has no attribute '{name}'\"\n )\nAttributeError: 'XLMRobertaLoRA' object has no attribute 'all_tied_weights_keys'. Did you mean: '_tied_weights_keys'?\n", "result": "FAIL(load)", - "total_seconds": 19.574221355840564, + "total_seconds": 10.509030694141984, "prior_attempts": [ { "index": 81, @@ -6522,6 +7532,41 @@ "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/081/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1404, in _load_config_modules\n module = module_class(model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_embeddings_hyphen_v3/ab036b023d30b4d1138c4c3bfa9f0c445ab455d6/custom_st.py\", line 87, in __init__\n self.auto_model = AutoModel.from_pretrained(model_name_or_path, config=self.config, **model_args)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py\", line 389, in from_pretrained\n return model_class.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/xlm_hyphen_roberta_hyphen_flash_hyphen_implementation/bd55a5ec8e6c0fb1d6c26efb4b6a4a74ce8a88d3/modeling_lora.py\", line 338, in from_pretrained\n return super().from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<9 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/xlm_hyphen_roberta_hyphen_flash_hyphen_implementation/bd55a5ec8e6c0fb1d6c26efb4b6a4a74ce8a88d3/modeling_xlm_roberta.py\", line 442, in from_pretrained\n return super().from_pretrained(*args, **kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4300, in from_pretrained\n model = cls(config, *model_args, **model_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/xlm_hyphen_roberta_hyphen_flash_hyphen_implementation/bd55a5ec8e6c0fb1d6c26efb4b6a4a74ce8a88d3/modeling_lora.py\", line 254, in __init__\n super().__init__(config)\n ~~~~~~~~~~~~~~~~^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 1258, in __init__\n self.config._attn_implementation_internal = self._check_and_adjust_attn_implementation(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n self.config._attn_implementation,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<2 lines>...\n allow_all_kernels=hub_kernels.ALLOW_ALL_KERNELS,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 1821, in _check_and_adjust_attn_implementation\n applicable_attn_implementation = self.get_correct_attn_implementation(\n applicable_attn_implementation, is_init_check\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 1877, in get_correct_attn_implementation\n raise e\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 1874, in get_correct_attn_implementation\n self._sdpa_can_dispatch(is_init_check)\n ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 1665, in _sdpa_can_dispatch\n raise ValueError(\n ...<3 lines>...\n )\nValueError: XLMRobertaLoRA does not support an attention implementation through torch.nn.functional.scaled_dot_product_attention yet. Please request the support for this architecture: https://github.com/huggingface/transformers/issues/28005. If you believe this error is a bug, please open an issue in Transformers GitHub repository and load your model with the argument `attn_implementation=\"eager\"` meanwhile. Example: `model = AutoModel.from_pretrained(\"openai/whisper-tiny\", attn_implementation=\"eager\")`\n", "result": "FAIL(load)", "total_seconds": 10.663371765054762 + }, + { + "index": 81, + "model": "jinaai/jina-embeddings-v3", + "revision": "ab036b023d30b4d1138c4c3bfa9f0c445ab455d6", + "trending_rank": 81, + "downloads_rank": 32, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "eager", + "trust_remote_code": true, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:47:35.211638+00:00", + "physical_visible_core": "12", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "status": "error", + "stage": "load_cpu", + "error_type": "AttributeError", + "error": "'XLMRobertaLoRA' object has no attribute 'all_tied_weights_keys'", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/081/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1404, in _load_config_modules\n module = module_class(model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_embeddings_hyphen_v3/ab036b023d30b4d1138c4c3bfa9f0c445ab455d6/custom_st.py\", line 87, in __init__\n self.auto_model = AutoModel.from_pretrained(model_name_or_path, config=self.config, **model_args)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py\", line 389, in from_pretrained\n return model_class.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/xlm_hyphen_roberta_hyphen_flash_hyphen_implementation/bd55a5ec8e6c0fb1d6c26efb4b6a4a74ce8a88d3/modeling_lora.py\", line 338, in from_pretrained\n return super().from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<9 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/xlm_hyphen_roberta_hyphen_flash_hyphen_implementation/bd55a5ec8e6c0fb1d6c26efb4b6a4a74ce8a88d3/modeling_xlm_roberta.py\", line 442, in from_pretrained\n return super().from_pretrained(*args, **kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4356, in from_pretrained\n loading_info = cls._finalize_model_loading(model, load_config, loading_info)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4524, in _finalize_model_loading\n model._move_missing_keys_from_meta_to_device(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n loading_info.missing_and_mismatched(),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<2 lines>...\n load_config.hf_quantizer,\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4781, in _move_missing_keys_from_meta_to_device\n for key in missing_keys - self.all_tied_weights_keys.keys():\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1972, in __getattr__\n raise AttributeError(\n f\"'{type(self).__name__}' object has no attribute '{name}'\"\n )\nAttributeError: 'XLMRobertaLoRA' object has no attribute 'all_tied_weights_keys'. Did you mean: '_tied_weights_keys'?\n", + "result": "FAIL(load)", + "total_seconds": 19.574221355840564 } ] }, @@ -6616,7 +7661,7 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:47:38.793972+00:00", + "started_at": "2026-11-03T16:28:28.113973+00:00", "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", @@ -6630,7 +7675,7 @@ "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ], "batch_size": 4, - "load_seconds": 1.9071867559105158, + "load_seconds": 0.3918236820027232, "prompts": { "query": "", "document": "" @@ -6648,11 +7693,11 @@ 0.4401358366012573, 0.585011899471283 ], - "cpu_seconds": 0.2299742652103305, + "cpu_seconds": 0.11149388272315264, "cpu_top_document": 4, "model_device": "neuron:0", "score_device": "neuron:0", - "neuron_first_seconds": 43.493039570748806, + "neuron_first_seconds": 0.44567768834531307, "neuron_scores": [ 0.20963667333126068, 0.5245950222015381, @@ -6663,30 +7708,115 @@ "neuron_top_document": 4, "mars_first": false, "cpu_parity": true, - "neuron_repeat_seconds": 0.05636020377278328, + "neuron_repeat_seconds": 0.054856122471392155, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": { "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", - "sample_count": 437, + "sample_count": 7, "sample_interval_seconds": 0.1, - "peak_bytes": 701178529, + "peak_bytes": 699050657, "peak_categories": { "code": 7318992, - "tensors": 608178176, + "tensors": 606081024, "constants": 3440657, "scratchpad": 67108864, "misc": 11850688, - "dma_rings": 3281152 + "dma_rings": 3250432 }, "capacity_bytes": 25769803776 }, - "allocator_peak_allocated_bytes": 586056448, - "allocator_peak_reserved_bytes": 608174080, - "total_seconds": 64.42325356975198, - "prior_attempts": [] + "allocator_peak_allocated_bytes": 584563200, + "allocator_peak_reserved_bytes": 606076928, + "total_seconds": 15.188867619261146, + "prior_attempts": [ + { + "index": 83, + "model": "meandyou200175/phobert-finetune", + "revision": "26e5f91ad1eb94b0af813021a2b7097ae4ba228e", + "trending_rank": 83, + "downloads_rank": null, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:47:38.793972+00:00", + "physical_visible_core": "4", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 1.9071867559105158, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 0.20963680744171143, + 0.5245946049690247, + 0.4401358366012573, + 0.585011899471283 + ], + "cpu_seconds": 0.2299742652103305, + "cpu_top_document": 4, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 43.493039570748806, + "neuron_scores": [ + 0.20963667333126068, + 0.5245950222015381, + 0.44013622403144836, + 0.5850120782852173 + ], + "max_abs_error": 4.172325134277344e-07, + "neuron_top_document": 4, + "mars_first": false, + "cpu_parity": true, + "neuron_repeat_seconds": 0.05636020377278328, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 437, + "sample_interval_seconds": 0.1, + "peak_bytes": 701178529, + "peak_categories": { + "code": 7318992, + "tensors": 608178176, + "constants": 3440657, + "scratchpad": 67108864, + "misc": 11850688, + "dma_rings": 3281152 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 586056448, + "allocator_peak_reserved_bytes": 608174080, + "total_seconds": 64.42325356975198 + } + ] }, { "index": 84, @@ -7126,8 +8256,8 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:49:04.100500+00:00", - "physical_visible_core": "0", + "started_at": "2026-11-03T16:28:31.417399+00:00", + "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", @@ -7140,7 +8270,7 @@ "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ], "batch_size": 4, - "load_seconds": 30.20843593403697, + "load_seconds": 2.2792115388438106, "prompts": { "query": "", "document": "" @@ -7158,11 +8288,11 @@ 11.481404304504395, 15.91786003112793 ], - "cpu_seconds": 2.5352238938212395, + "cpu_seconds": 2.1973909735679626, "cpu_top_document": 4, "model_device": "neuron:0", "score_device": "neuron:0", - "neuron_first_seconds": 78.56518525443971, + "neuron_first_seconds": 2.5120828673243523, "neuron_scores": [ 10.587594032287598, 15.282479286193848, @@ -7173,30 +8303,115 @@ "neuron_top_document": 4, "mars_first": false, "cpu_parity": true, - "neuron_repeat_seconds": 0.9287767671048641, + "neuron_repeat_seconds": 0.8568719290196896, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": { "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", - "sample_count": 811, + "sample_count": 51, "sample_interval_seconds": 0.1, - "peak_bytes": 15556525689, + "peak_bytes": 15556522617, "peak_categories": { "code": 13058680, "tensors": 15437139968, "constants": 6002945, "scratchpad": 67108864, "misc": 22477824, - "dma_rings": 10737408 + "dma_rings": 10734336 }, "capacity_bytes": 25769803776 }, - "allocator_peak_allocated_bytes": 15333365888, + "allocator_peak_allocated_bytes": 15333555840, "allocator_peak_reserved_bytes": 15437135872, - "total_seconds": 134.7353895958513, - "prior_attempts": [] + "total_seconds": 24.30755981616676, + "prior_attempts": [ + { + "index": 89, + "model": "vidore/colqwen2.5-v0.2", + "revision": "dcbe8d9cede518bce830488364ba0e40c873645b", + "trending_rank": 89, + "downloads_rank": null, + "class": "MultiVectorEncoder", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:49:04.100500+00:00", + "physical_visible_core": "0", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 30.20843593403697, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 10.587596893310547, + 15.282479286193848, + 11.481404304504395, + 15.91786003112793 + ], + "cpu_seconds": 2.5352238938212395, + "cpu_top_document": 4, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 78.56518525443971, + "neuron_scores": [ + 10.587594032287598, + 15.282479286193848, + 11.481398582458496, + 15.917861938476562 + ], + "max_abs_error": 5.7220458984375e-06, + "neuron_top_document": 4, + "mars_first": false, + "cpu_parity": true, + "neuron_repeat_seconds": 0.9287767671048641, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 811, + "sample_interval_seconds": 0.1, + "peak_bytes": 15556525689, + "peak_categories": { + "code": 13058680, + "tensors": 15437139968, + "constants": 6002945, + "scratchpad": 67108864, + "misc": 22477824, + "dma_rings": 10737408 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 15333365888, + "allocator_peak_reserved_bytes": 15437135872, + "total_seconds": 134.7353895958513 + } + ] }, { "index": 90, @@ -8460,8 +9675,8 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:52:03.168120+00:00", - "physical_visible_core": "4", + "started_at": "2026-11-03T16:28:32.287840+00:00", + "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", @@ -8478,10 +9693,46 @@ "stage": "load_cpu", "error_type": "ImportError", "error": "cannot import name 'create_position_ids_from_input_ids' from 'transformers.models.xlm_roberta.modeling_xlm_roberta' (/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/xlm_roberta/modeling_xlm_roberta.py)", - "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/108/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 163, in wrapper\n return func(self, *args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 200, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1200, in _load_modules\n return self._load_default_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 337, in _load_default_modules\n transformer_model = Transformer(\n model_name_or_path,\n ...<5 lines>...\n backend=self.backend,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 855, in __init__\n self.model = self._load_model(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path, transformer_task, config, backend, is_peft_model, **model_kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2343, in _load_model\n return model_cls.from_pretrained(model_name_or_path, config=config, **model_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py\", line 381, in from_pretrained\n model_class = get_class_from_dynamic_module(\n class_ref, pretrained_model_name_or_path, code_revision=code_revision, **hub_kwargs, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 625, in get_class_from_dynamic_module\n return get_class_in_module(class_name, final_module, force_reload=force_download)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 309, in get_class_in_module\n module_spec.loader.exec_module(module)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^\n File \"\", line 759, in exec_module\n File \"\", line 491, in _call_with_frames_removed\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_reranker_hyphen_v2_hyphen_base_hyphen_multilingual/9cfeff2df7d40d1b78e75e5e9cebec92a99813c9/modeling_xlm_roberta.py\", line 44, in \n from .embedding import XLMRobertaEmbeddings\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_reranker_hyphen_v2_hyphen_base_hyphen_multilingual/9cfeff2df7d40d1b78e75e5e9cebec92a99813c9/embedding.py\", line 11, in \n from transformers.models.xlm_roberta.modeling_xlm_roberta import create_position_ids_from_input_ids\nImportError: cannot import name 'create_position_ids_from_input_ids' from 'transformers.models.xlm_roberta.modeling_xlm_roberta' (/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/xlm_roberta/modeling_xlm_roberta.py)\n", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/108/run.py\", line 51, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 163, in wrapper\n return func(self, *args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 200, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1200, in _load_modules\n return self._load_default_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 337, in _load_default_modules\n transformer_model = Transformer(\n model_name_or_path,\n ...<5 lines>...\n backend=self.backend,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 855, in __init__\n self.model = self._load_model(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path, transformer_task, config, backend, is_peft_model, **model_kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2343, in _load_model\n return model_cls.from_pretrained(model_name_or_path, config=config, **model_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py\", line 381, in from_pretrained\n model_class = get_class_from_dynamic_module(\n class_ref, pretrained_model_name_or_path, code_revision=code_revision, **hub_kwargs, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 625, in get_class_from_dynamic_module\n return get_class_in_module(class_name, final_module, force_reload=force_download)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 309, in get_class_in_module\n module_spec.loader.exec_module(module)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^\n File \"\", line 759, in exec_module\n File \"\", line 491, in _call_with_frames_removed\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_reranker_hyphen_v2_hyphen_base_hyphen_multilingual/9cfeff2df7d40d1b78e75e5e9cebec92a99813c9/modeling_xlm_roberta.py\", line 44, in \n from .embedding import XLMRobertaEmbeddings\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_reranker_hyphen_v2_hyphen_base_hyphen_multilingual/9cfeff2df7d40d1b78e75e5e9cebec92a99813c9/embedding.py\", line 11, in \n from transformers.models.xlm_roberta.modeling_xlm_roberta import create_position_ids_from_input_ids\nImportError: cannot import name 'create_position_ids_from_input_ids' from 'transformers.models.xlm_roberta.modeling_xlm_roberta' (/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/xlm_roberta/modeling_xlm_roberta.py)\n", "result": "FAIL(load)", - "total_seconds": 10.194885298609734, - "prior_attempts": [] + "total_seconds": 8.39421737473458, + "prior_attempts": [ + { + "index": 108, + "model": "jinaai/jina-reranker-v2-base-multilingual", + "revision": "9cfeff2df7d40d1b78e75e5e9cebec92a99813c9", + "trending_rank": null, + "downloads_rank": 29, + "class": "CrossEncoder", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": true, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:52:03.168120+00:00", + "physical_visible_core": "4", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "status": "error", + "stage": "load_cpu", + "error_type": "ImportError", + "error": "cannot import name 'create_position_ids_from_input_ids' from 'transformers.models.xlm_roberta.modeling_xlm_roberta' (/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/xlm_roberta/modeling_xlm_roberta.py)", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/108/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 163, in wrapper\n return func(self, *args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 200, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1200, in _load_modules\n return self._load_default_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/cross_encoder/model.py\", line 337, in _load_default_modules\n transformer_model = Transformer(\n model_name_or_path,\n ...<5 lines>...\n backend=self.backend,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 855, in __init__\n self.model = self._load_model(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path, transformer_task, config, backend, is_peft_model, **model_kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2343, in _load_model\n return model_cls.from_pretrained(model_name_or_path, config=config, **model_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py\", line 381, in from_pretrained\n model_class = get_class_from_dynamic_module(\n class_ref, pretrained_model_name_or_path, code_revision=code_revision, **hub_kwargs, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 625, in get_class_from_dynamic_module\n return get_class_in_module(class_name, final_module, force_reload=force_download)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 309, in get_class_in_module\n module_spec.loader.exec_module(module)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^\n File \"\", line 759, in exec_module\n File \"\", line 491, in _call_with_frames_removed\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_reranker_hyphen_v2_hyphen_base_hyphen_multilingual/9cfeff2df7d40d1b78e75e5e9cebec92a99813c9/modeling_xlm_roberta.py\", line 44, in \n from .embedding import XLMRobertaEmbeddings\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_reranker_hyphen_v2_hyphen_base_hyphen_multilingual/9cfeff2df7d40d1b78e75e5e9cebec92a99813c9/embedding.py\", line 11, in \n from transformers.models.xlm_roberta.modeling_xlm_roberta import create_position_ids_from_input_ids\nImportError: cannot import name 'create_position_ids_from_input_ids' from 'transformers.models.xlm_roberta.modeling_xlm_roberta' (/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/xlm_roberta/modeling_xlm_roberta.py)\n", + "result": "FAIL(load)", + "total_seconds": 10.194885298609734 + } + ] }, { "index": 109, @@ -8787,8 +10038,8 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:52:37.157163+00:00", - "physical_visible_core": "8", + "started_at": "2026-11-03T16:28:36.861567+00:00", + "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", @@ -8801,7 +10052,7 @@ "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ], "batch_size": 4, - "load_seconds": 0.9057708652690053, + "load_seconds": 0.3251633159816265, "prompts": { "query": "", "document": "" @@ -8819,14 +10070,14 @@ 24.276288986206055, 23.726768493652344 ], - "cpu_seconds": 0.13503286056220531, + "cpu_seconds": 0.0869925981387496, "cpu_top_document": 2, "model_device": "neuron:0", "status": "error", "stage": "neuron_inference", "error_type": "RuntimeError", "error": "Inference tensors do not track version counter.", - "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/112/run.py\", line 69, in \n stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/112/run.py\", line 61, in score\n values=model.similarity(q,d)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1072, in similarity\n return self._similarity(embeddings1, embeddings2)\n ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/similarity.py\", line 115, in dot_score\n return torch.mm(a, b.transpose(0, 1)).to_dense()\n ~~~~~~~~~~~^^^^^^\nRuntimeError: Inference tensors do not track version counter.\n", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/112/run.py\", line 70, in \n stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device)\n ~~~~~^^\n File \"/tmp/neuron-top100-rerun/runs/112/run.py\", line 62, in score\n values=model.similarity(q,d)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1072, in similarity\n return self._similarity(embeddings1, embeddings2)\n ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/similarity.py\", line 115, in dot_score\n return torch.mm(a, b.transpose(0, 1)).to_dense()\n ~~~~~~~~~~~^^^^^^\nRuntimeError: Inference tensors do not track version counter.\n", "result": "FAIL(forward)", "memory_monitor": { "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", @@ -8843,9 +10094,9 @@ }, "capacity_bytes": 25769803776 }, - "allocator_peak_allocated_bytes": 481360448, + "allocator_peak_allocated_bytes": 481622592, "allocator_peak_reserved_bytes": 501219328, - "total_seconds": 16.84018260613084, + "total_seconds": 16.085152969695628, "prior_attempts": [ { "index": 112, @@ -8881,6 +10132,79 @@ "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/112/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 855, in __init__\n self.model = self._load_model(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path, transformer_task, config, backend, is_peft_model, **model_kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2343, in _load_model\n return model_cls.from_pretrained(model_name_or_path, config=config, **model_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py\", line 406, in from_pretrained\n return model_class.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4300, in from_pretrained\n model = cls(config, *model_args, **model_kwargs)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/mpnet/modeling_mpnet.py\", line 373, in __init__\n super().__init__(config)\n ~~~~~~~~~~~~~~~~^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 1258, in __init__\n self.config._attn_implementation_internal = self._check_and_adjust_attn_implementation(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n self.config._attn_implementation,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<2 lines>...\n allow_all_kernels=hub_kernels.ALLOW_ALL_KERNELS,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 1821, in _check_and_adjust_attn_implementation\n applicable_attn_implementation = self.get_correct_attn_implementation(\n applicable_attn_implementation, is_init_check\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 1877, in get_correct_attn_implementation\n raise e\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 1874, in get_correct_attn_implementation\n self._sdpa_can_dispatch(is_init_check)\n ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 1665, in _sdpa_can_dispatch\n raise ValueError(\n ...<3 lines>...\n )\nValueError: MPNetModel does not support an attention implementation through torch.nn.functional.scaled_dot_product_attention yet. Please request the support for this architecture: https://github.com/huggingface/transformers/issues/28005. If you believe this error is a bug, please open an issue in Transformers GitHub repository and load your model with the argument `attn_implementation=\"eager\"` meanwhile. Example: `model = AutoModel.from_pretrained(\"openai/whisper-tiny\", attn_implementation=\"eager\")`\n", "result": "FAIL(load)", "total_seconds": 8.82788738142699 + }, + { + "index": 112, + "model": "sentence-transformers/multi-qa-mpnet-base-dot-v1", + "revision": "17997f24dca0df1a4fed68894fb0e1e133e60482", + "trending_rank": null, + "downloads_rank": 34, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "eager", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:52:37.157163+00:00", + "physical_visible_core": "8", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 0.9057708652690053, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 18.622793197631836, + 25.37649154663086, + 24.276288986206055, + 23.726768493652344 + ], + "cpu_seconds": 0.13503286056220531, + "cpu_top_document": 2, + "model_device": "neuron:0", + "status": "error", + "stage": "neuron_inference", + "error_type": "RuntimeError", + "error": "Inference tensors do not track version counter.", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/112/run.py\", line 69, in \n stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/112/run.py\", line 61, in score\n values=model.similarity(q,d)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1072, in similarity\n return self._similarity(embeddings1, embeddings2)\n ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/similarity.py\", line 115, in dot_score\n return torch.mm(a, b.transpose(0, 1)).to_dense()\n ~~~~~~~~~~~^^^^^^\nRuntimeError: Inference tensors do not track version counter.\n", + "result": "FAIL(forward)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 9, + "sample_interval_seconds": 0.1, + "peak_bytes": 602256101, + "peak_categories": { + "code": 8931864, + "tensors": 501223424, + "constants": 3440653, + "scratchpad": 67108864, + "misc": 17067456, + "dma_rings": 4483840 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 481360448, + "allocator_peak_reserved_bytes": 501219328, + "total_seconds": 16.84018260613084 } ] }, @@ -8897,8 +10221,8 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:52:29.729330+00:00", - "physical_visible_core": "0", + "started_at": "2026-11-03T16:28:41.932312+00:00", + "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", @@ -8911,7 +10235,7 @@ "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ], "batch_size": 4, - "load_seconds": 0.8102488135918975, + "load_seconds": 0.28461221139878035, "prompts": { "query": "", "document": "" @@ -8929,11 +10253,11 @@ 0.671911358833313, 0.6263707876205444 ], - "cpu_seconds": 0.053661770187318325, + "cpu_seconds": 0.0305560240522027, "cpu_top_document": 3, "model_device": "neuron:0", "score_device": "neuron:0", - "neuron_first_seconds": 0.3346906406804919, + "neuron_first_seconds": 0.3316585561260581, "neuron_scores": [ 0.4128049612045288, 0.6425285339355469, @@ -8944,7 +10268,7 @@ "neuron_top_document": 3, "mars_first": false, "cpu_parity": true, - "neuron_repeat_seconds": 0.03071187622845173, + "neuron_repeat_seconds": 0.028371094726026058, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", @@ -8966,8 +10290,93 @@ }, "allocator_peak_allocated_bytes": 97223040, "allocator_peak_reserved_bytes": 136314880, - "total_seconds": 16.178581200540066, - "prior_attempts": [] + "total_seconds": 15.499813811853528, + "prior_attempts": [ + { + "index": 113, + "model": "sentence-transformers/paraphrase-MiniLM-L6-v2", + "revision": "c9a2bfebc254878aee8c3aca9e6844d5bbb102d1", + "trending_rank": null, + "downloads_rank": 35, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:52:29.729330+00:00", + "physical_visible_core": "0", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 0.8102488135918975, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 0.4128049910068512, + 0.6425286531448364, + 0.671911358833313, + 0.6263707876205444 + ], + "cpu_seconds": 0.053661770187318325, + "cpu_top_document": 3, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 0.3346906406804919, + "neuron_scores": [ + 0.4128049612045288, + 0.6425285339355469, + 0.671911358833313, + 0.6263706684112549 + ], + "max_abs_error": 1.1920928955078125e-07, + "neuron_top_document": 3, + "mars_first": false, + "cpu_parity": true, + "neuron_repeat_seconds": 0.03071187622845173, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 5, + "sample_interval_seconds": 0.1, + "peak_bytes": 224453665, + "peak_categories": { + "code": 6359632, + "tensors": 136318976, + "constants": 3047441, + "scratchpad": 67108864, + "misc": 8984256, + "dma_rings": 2634496 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 97223040, + "allocator_peak_reserved_bytes": 136314880, + "total_seconds": 16.178581200540066 + } + ] }, { "index": 114, @@ -8982,7 +10391,7 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:52:45.768912+00:00", + "started_at": "2026-11-03T16:28:45.116156+00:00", "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", @@ -9000,10 +10409,46 @@ "stage": "load_cpu", "error_type": "ModuleNotFoundError", "error": "No module named 'transformers.onnx'", - "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/114/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 839, in __init__\n config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2259, in _load_config\n return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py\", line 422, in from_pretrained\n config_class = get_class_from_dynamic_module(\n class_ref, pretrained_model_name_or_path, code_revision=code_revision, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 625, in get_class_from_dynamic_module\n return get_class_in_module(class_name, final_module, force_reload=force_download)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 309, in get_class_in_module\n module_spec.loader.exec_module(module)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^\n File \"\", line 759, in exec_module\n File \"\", line 491, in _call_with_frames_removed\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_bert_hyphen_implementation/f3ec4cf7de7e561007f27c9efc7148b0bd713f81/configuration_bert.py\", line 22, in \n from transformers.onnx import OnnxConfig\nModuleNotFoundError: No module named 'transformers.onnx'\n", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/114/run.py\", line 51, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 839, in __init__\n config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2259, in _load_config\n return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py\", line 422, in from_pretrained\n config_class = get_class_from_dynamic_module(\n class_ref, pretrained_model_name_or_path, code_revision=code_revision, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 625, in get_class_from_dynamic_module\n return get_class_in_module(class_name, final_module, force_reload=force_download)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 309, in get_class_in_module\n module_spec.loader.exec_module(module)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^\n File \"\", line 759, in exec_module\n File \"\", line 491, in _call_with_frames_removed\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_bert_hyphen_implementation/f3ec4cf7de7e561007f27c9efc7148b0bd713f81/configuration_bert.py\", line 22, in \n from transformers.onnx import OnnxConfig\nModuleNotFoundError: No module named 'transformers.onnx'\n", "result": "FAIL(load)", - "total_seconds": 8.320869617164135, - "prior_attempts": [] + "total_seconds": 7.416379694826901, + "prior_attempts": [ + { + "index": 114, + "model": "jinaai/jina-embeddings-v2-small-en", + "revision": "44e7d1d6caec8c883c2d4b207588504d519788d0", + "trending_rank": null, + "downloads_rank": 38, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": true, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:52:45.768912+00:00", + "physical_visible_core": "4", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "status": "error", + "stage": "load_cpu", + "error_type": "ModuleNotFoundError", + "error": "No module named 'transformers.onnx'", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/114/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 839, in __init__\n config, is_peft_model = self._load_config(model_name_or_path, backend, config_kwargs)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2259, in _load_config\n return AutoConfig.from_pretrained(model_name_or_path, **config_kwargs), False\n ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/configuration_auto.py\", line 422, in from_pretrained\n config_class = get_class_from_dynamic_module(\n class_ref, pretrained_model_name_or_path, code_revision=code_revision, **kwargs\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 625, in get_class_from_dynamic_module\n return get_class_in_module(class_name, final_module, force_reload=force_download)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/dynamic_module_utils.py\", line 309, in get_class_in_module\n module_spec.loader.exec_module(module)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^\n File \"\", line 759, in exec_module\n File \"\", line 491, in _call_with_frames_removed\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/jinaai/jina_hyphen_bert_hyphen_implementation/f3ec4cf7de7e561007f27c9efc7148b0bd713f81/configuration_bert.py\", line 22, in \n from transformers.onnx import OnnxConfig\nModuleNotFoundError: No module named 'transformers.onnx'\n", + "result": "FAIL(load)", + "total_seconds": 8.320869617164135 + } + ] }, { "index": 115, @@ -9103,8 +10548,8 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:52:52.178170+00:00", - "physical_visible_core": "12", + "started_at": "2026-11-03T16:28:53.794159+00:00", + "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", @@ -9117,7 +10562,7 @@ "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ], "batch_size": 4, - "load_seconds": 1.2254862170666456, + "load_seconds": 0.3335246341302991, "prompts": { "query": "", "document": "" @@ -9135,11 +10580,11 @@ 0.475629597902298, 0.6078733205795288 ], - "cpu_seconds": 0.2339512938633561, + "cpu_seconds": 0.13313346728682518, "cpu_top_document": 4, "model_device": "neuron:0", "score_device": "neuron:0", - "neuron_first_seconds": 46.99652501568198, + "neuron_first_seconds": 2.8663115464150906, "neuron_scores": [ 0.3725859224796295, 0.5596362352371216, @@ -9150,19 +10595,19 @@ "neuron_top_document": 4, "mars_first": false, "cpu_parity": true, - "neuron_repeat_seconds": 0.04155152477324009, + "neuron_repeat_seconds": 0.043972715735435486, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": { "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", - "sample_count": 472, + "sample_count": 31, "sample_interval_seconds": 0.1, - "peak_bytes": 604724833, + "peak_bytes": 602627681, "peak_categories": { "code": 7286160, - "tensors": 511709184, + "tensors": 509612032, "constants": 3866641, "scratchpad": 67108864, "misc": 12000704, @@ -9170,10 +10615,95 @@ }, "capacity_bytes": 25769803776 }, - "allocator_peak_allocated_bytes": 492647808, - "allocator_peak_reserved_bytes": 511705088, - "total_seconds": 62.91153274290264, - "prior_attempts": [] + "allocator_peak_allocated_bytes": 492353024, + "allocator_peak_reserved_bytes": 509607936, + "total_seconds": 18.043465487658978, + "prior_attempts": [ + { + "index": 116, + "model": "jhgan/ko-sroberta-multitask", + "revision": "8fca7c9c98c26599be0e14b9916b11a756a26f19", + "trending_rank": null, + "downloads_rank": 42, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:52:52.178170+00:00", + "physical_visible_core": "12", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 1.2254862170666456, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 0.3725859224796295, + 0.5596363544464111, + 0.475629597902298, + 0.6078733205795288 + ], + "cpu_seconds": 0.2339512938633561, + "cpu_top_document": 4, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 46.99652501568198, + "neuron_scores": [ + 0.3725859224796295, + 0.5596362352371216, + 0.47562968730926514, + 0.6078732013702393 + ], + "max_abs_error": 1.1920928955078125e-07, + "neuron_top_document": 4, + "mars_first": false, + "cpu_parity": true, + "neuron_repeat_seconds": 0.04155152477324009, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 472, + "sample_interval_seconds": 0.1, + "peak_bytes": 604724833, + "peak_categories": { + "code": 7286160, + "tensors": 511709184, + "constants": 3866641, + "scratchpad": 67108864, + "misc": 12000704, + "dma_rings": 2753280 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 492647808, + "allocator_peak_reserved_bytes": 511705088, + "total_seconds": 62.91153274290264 + } + ] }, { "index": 117, @@ -9610,7 +11140,7 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:53:27.210620+00:00", + "started_at": "2026-11-03T16:28:54.750978+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", @@ -9624,7 +11154,7 @@ "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ], "batch_size": 4, - "load_seconds": 0.339894263073802, + "load_seconds": 0.25583696737885475, "prompts": { "query": "", "document": "" @@ -9642,11 +11172,11 @@ 0.6842697262763977, 0.7085862755775452 ], - "cpu_seconds": 0.01665687095373869, + "cpu_seconds": 0.016696914099156857, "cpu_top_document": 4, "model_device": "neuron:0", "score_device": "neuron:0", - "neuron_first_seconds": 35.90269974432886, + "neuron_first_seconds": 0.31738284416496754, "neuron_scores": [ 0.5895187258720398, 0.6596502065658569, @@ -9657,14 +11187,14 @@ "neuron_top_document": 4, "mars_first": false, "cpu_parity": true, - "neuron_repeat_seconds": 0.01644056662917137, + "neuron_repeat_seconds": 0.015191987156867981, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": { "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", - "sample_count": 360, + "sample_count": 5, "sample_interval_seconds": 0.1, "peak_bytes": 108670225, "peak_categories": { @@ -9679,8 +11209,93 @@ }, "allocator_peak_allocated_bytes": 19238400, "allocator_peak_reserved_bytes": 20971520, - "total_seconds": 57.112566504627466, - "prior_attempts": [] + "total_seconds": 14.501598661765456, + "prior_attempts": [ + { + "index": 122, + "model": "sentence-transformers-testing/stsb-bert-tiny-safetensors", + "revision": "f3cb857cba53019a20df283396bcca179cf051a4", + "trending_rank": null, + "downloads_rank": 49, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:53:27.210620+00:00", + "physical_visible_core": "0", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 0.339894263073802, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 0.5895186066627502, + 0.6596499085426331, + 0.6842697262763977, + 0.7085862755775452 + ], + "cpu_seconds": 0.01665687095373869, + "cpu_top_document": 4, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 35.90269974432886, + "neuron_scores": [ + 0.5895187258720398, + 0.6596502065658569, + 0.6842698454856873, + 0.7085865139961243 + ], + "max_abs_error": 2.980232238769531e-07, + "neuron_top_document": 4, + "mars_first": false, + "cpu_parity": true, + "neuron_repeat_seconds": 0.01644056662917137, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 360, + "sample_interval_seconds": 0.1, + "peak_bytes": 108670225, + "peak_categories": { + "code": 6199232, + "tensors": 20975616, + "constants": 2523153, + "scratchpad": 67108864, + "misc": 9350720, + "dma_rings": 2512640 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 19238400, + "allocator_peak_reserved_bytes": 20971520, + "total_seconds": 57.112566504627466 + } + ] }, { "index": 123, @@ -9695,8 +11310,8 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:53:34.948327+00:00", - "physical_visible_core": "8", + "started_at": "2026-11-03T16:28:57.681382+00:00", + "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", @@ -9709,7 +11324,7 @@ "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ], "batch_size": 4, - "load_seconds": 2.7565125236287713, + "load_seconds": 1.974479383789003, "prompts": { "query": "", "document": "" @@ -9720,11 +11335,55 @@ "status": "error", "stage": "cpu_inference", "error_type": "IndexError", - "error": "index 670466312 is out of bounds for dimension 0 with size 11", - "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/123/run.py\", line 63, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/123/run.py\", line 58, in score\n q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 670466312 is out of bounds for dimension 0 with size 11\n", + "error": "index 1193 is out of bounds for dimension 0 with size 11", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/123/run.py\", line 64, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-rerun/runs/123/run.py\", line 59, in score\n q=(model.encode([query],batch_size=4,show_progress_bar=False,**kw,**SPEC['query_kwargs']) if SPEC.get('query_kwargs') else model.encode_query([query],batch_size=4,show_progress_bar=False,**kw))\n ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 1193 is out of bounds for dimension 0 with size 11\n", "result": "FAIL(forward)", - "total_seconds": 10.929977044463158, - "prior_attempts": [] + "total_seconds": 13.360865868628025, + "prior_attempts": [ + { + "index": 123, + "model": "Alibaba-NLP/gte-multilingual-base", + "revision": "9bbca17d9273fd0d03d5725c7a4b0f6b45142062", + "trending_rank": null, + "downloads_rank": 50, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": true, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:53:34.948327+00:00", + "physical_visible_core": "8", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 2.7565125236287713, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "status": "error", + "stage": "cpu_inference", + "error_type": "IndexError", + "error": "index 670466312 is out of bounds for dimension 0 with size 11", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/123/run.py\", line 63, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/123/run.py\", line 58, in score\n q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 670466312 is out of bounds for dimension 0 with size 11\n", + "result": "FAIL(forward)", + "total_seconds": 10.929977044463158 + } + ] }, { "index": 124, @@ -10209,8 +11868,8 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:54:25.528812+00:00", - "physical_visible_core": "4", + "started_at": "2026-11-03T16:28:59.211576+00:00", + "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", @@ -10223,7 +11882,7 @@ "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ], "batch_size": 4, - "load_seconds": 6.00257970392704, + "load_seconds": 4.217651919461787, "prompts": { "query": "search_query: ", "document": "", @@ -10243,10 +11902,62 @@ "stage": "cpu_inference", "error_type": "AttributeError", "error": "'NomicBertModel' object has no attribute 'get_extended_attention_mask'", - "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/129/run.py\", line 63, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/129/run.py\", line 58, in score\n q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/nomic_hyphen_ai/nomic_hyphen_bert_hyphen_2048/7710840340a098cfb869c4f65e87cf2b1b70caca/modeling_hf_nomic_bert.py\", line 1914, in forward\n attention_mask = self.get_extended_attention_mask(attention_mask, hidden_states.shape[:-1])\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1972, in __getattr__\n raise AttributeError(\n f\"'{type(self).__name__}' object has no attribute '{name}'\"\n )\nAttributeError: 'NomicBertModel' object has no attribute 'get_extended_attention_mask'\n", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/129/run.py\", line 64, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-rerun/runs/129/run.py\", line 59, in score\n q=(model.encode([query],batch_size=4,show_progress_bar=False,**kw,**SPEC['query_kwargs']) if SPEC.get('query_kwargs') else model.encode_query([query],batch_size=4,show_progress_bar=False,**kw))\n ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/nomic_hyphen_ai/nomic_hyphen_bert_hyphen_2048/7710840340a098cfb869c4f65e87cf2b1b70caca/modeling_hf_nomic_bert.py\", line 1914, in forward\n attention_mask = self.get_extended_attention_mask(attention_mask, hidden_states.shape[:-1])\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1972, in __getattr__\n raise AttributeError(\n f\"'{type(self).__name__}' object has no attribute '{name}'\"\n )\nAttributeError: 'NomicBertModel' object has no attribute 'get_extended_attention_mask'\n", "result": "FAIL(forward)", - "total_seconds": 13.943258575163782, - "prior_attempts": [] + "total_seconds": 14.102670108899474, + "prior_attempts": [ + { + "index": 129, + "model": "nomic-ai/nomic-embed-text-v2-moe", + "revision": "1066b6599d099fbb93dfcb64f9c37a7c9e503e85", + "trending_rank": null, + "downloads_rank": 58, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": true, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:54:25.528812+00:00", + "physical_visible_core": "4", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 6.00257970392704, + "prompts": { + "query": "search_query: ", + "document": "", + "passage": "search_document: ", + "Classification": "classification: ", + "MultilabelClassification": "classification: ", + "Clustering": "clustering: ", + "PairClassification": "classification: ", + "STS": "classification: ", + "Summarization": "classification: ", + "Speed": "search_document: " + }, + "parameter_dtypes": [ + "torch.float32" + ], + "status": "error", + "stage": "cpu_inference", + "error_type": "AttributeError", + "error": "'NomicBertModel' object has no attribute 'get_extended_attention_mask'", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/129/run.py\", line 63, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/129/run.py\", line 58, in score\n q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/nomic_hyphen_ai/nomic_hyphen_bert_hyphen_2048/7710840340a098cfb869c4f65e87cf2b1b70caca/modeling_hf_nomic_bert.py\", line 1914, in forward\n attention_mask = self.get_extended_attention_mask(attention_mask, hidden_states.shape[:-1])\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1972, in __getattr__\n raise AttributeError(\n f\"'{type(self).__name__}' object has no attribute '{name}'\"\n )\nAttributeError: 'NomicBertModel' object has no attribute 'get_extended_attention_mask'\n", + "result": "FAIL(forward)", + "total_seconds": 13.943258575163782 + } + ] }, { "index": 130, @@ -10885,7 +12596,7 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:55:09.237654+00:00", + "started_at": "2026-11-03T16:29:10.989662+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", @@ -10899,7 +12610,7 @@ "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ], "batch_size": 4, - "load_seconds": 1.3280827356502414, + "load_seconds": 0.3936761999502778, "prompts": { "query": "", "document": "" @@ -10917,18 +12628,18 @@ 169.48434448242188, 169.65316772460938 ], - "cpu_seconds": 0.12728324253112078, + "cpu_seconds": 0.10769253689795732, "cpu_top_document": 2, "model_device": "neuron:0", "status": "error", "stage": "neuron_inference", "error_type": "RuntimeError", "error": "Inference tensors do not track version counter.", - "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/137/run.py\", line 69, in \n stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/137/run.py\", line 61, in score\n values=model.similarity(q,d)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1072, in similarity\n return self._similarity(embeddings1, embeddings2)\n ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/similarity.py\", line 115, in dot_score\n return torch.mm(a, b.transpose(0, 1)).to_dense()\n ~~~~~~~~~~~^^^^^^\nRuntimeError: Inference tensors do not track version counter.\n", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/137/run.py\", line 70, in \n stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device)\n ~~~~~^^\n File \"/tmp/neuron-top100-rerun/runs/137/run.py\", line 62, in score\n values=model.similarity(q,d)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1072, in similarity\n return self._similarity(embeddings1, embeddings2)\n ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/similarity.py\", line 115, in dot_score\n return torch.mm(a, b.transpose(0, 1)).to_dense()\n ~~~~~~~~~~~^^^^^^\nRuntimeError: Inference tensors do not track version counter.\n", "result": "FAIL(forward)", "memory_monitor": { "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", - "sample_count": 6, + "sample_count": 5, "sample_interval_seconds": 0.1, "peak_bytes": 588585921, "peak_categories": { @@ -10941,10 +12652,84 @@ }, "capacity_bytes": 25769803776 }, - "allocator_peak_allocated_bytes": 481544320, + "allocator_peak_allocated_bytes": 481544192, "allocator_peak_reserved_bytes": 501219328, - "total_seconds": 20.18780482839793, - "prior_attempts": [] + "total_seconds": 16.502824168652296, + "prior_attempts": [ + { + "index": 137, + "model": "sentence-transformers/msmarco-bert-base-dot-v5", + "revision": "dbf04e3911e5b11c887fe563d3f2479f81c4415f", + "trending_rank": null, + "downloads_rank": 68, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:55:09.237654+00:00", + "physical_visible_core": "0", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 1.3280827356502414, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 163.48626708984375, + 172.62042236328125, + 169.48434448242188, + 169.65316772460938 + ], + "cpu_seconds": 0.12728324253112078, + "cpu_top_document": 2, + "model_device": "neuron:0", + "status": "error", + "stage": "neuron_inference", + "error_type": "RuntimeError", + "error": "Inference tensors do not track version counter.", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/137/run.py\", line 69, in \n stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/137/run.py\", line 61, in score\n values=model.similarity(q,d)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1072, in similarity\n return self._similarity(embeddings1, embeddings2)\n ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/similarity.py\", line 115, in dot_score\n return torch.mm(a, b.transpose(0, 1)).to_dense()\n ~~~~~~~~~~~^^^^^^\nRuntimeError: Inference tensors do not track version counter.\n", + "result": "FAIL(forward)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 6, + "sample_interval_seconds": 0.1, + "peak_bytes": 588585921, + "peak_categories": { + "code": 6350192, + "tensors": 501223424, + "constants": 2850833, + "scratchpad": 67108864, + "misc": 8545088, + "dma_rings": 2507520 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 481544320, + "allocator_peak_reserved_bytes": 501219328, + "total_seconds": 20.18780482839793 + } + ] }, { "index": 138, @@ -11122,7 +12907,7 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:55:35.076933+00:00", + "started_at": "2026-11-03T16:29:12.754293+00:00", "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", @@ -11136,7 +12921,7 @@ "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ], "batch_size": 4, - "load_seconds": 1.5855842670425773, + "load_seconds": 0.5557530513033271, "prompts": { "query": "", "document": "" @@ -11147,11 +12932,55 @@ "status": "error", "stage": "cpu_inference", "error_type": "IndexError", - "error": "index 140471200382993 is out of bounds for dimension 0 with size 11", - "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/140/run.py\", line 63, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/140/run.py\", line 58, in score\n q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 140471200382993 is out of bounds for dimension 0 with size 11\n", + "error": "index 23048696 is out of bounds for dimension 0 with size 11", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/140/run.py\", line 64, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-rerun/runs/140/run.py\", line 59, in score\n q=(model.encode([query],batch_size=4,show_progress_bar=False,**kw,**SPEC['query_kwargs']) if SPEC.get('query_kwargs') else model.encode_query([query],batch_size=4,show_progress_bar=False,**kw))\n ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 23048696 is out of bounds for dimension 0 with size 11\n", "result": "FAIL(forward)", - "total_seconds": 12.225869985297322, - "prior_attempts": [] + "total_seconds": 8.462658165954053, + "prior_attempts": [ + { + "index": 140, + "model": "Alibaba-NLP/gte-base-en-v1.5", + "revision": "a829fd0e060bb84554da0dfd354d0de0f7712b7f", + "trending_rank": null, + "downloads_rank": 71, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": true, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:55:35.076933+00:00", + "physical_visible_core": "12", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 1.5855842670425773, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "status": "error", + "stage": "cpu_inference", + "error_type": "IndexError", + "error": "index 140471200382993 is out of bounds for dimension 0 with size 11", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/140/run.py\", line 63, in \n stage='cpu_inference';t=time.monotonic();cpu=score().detach().float().cpu().reshape(-1)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/140/run.py\", line 58, in score\n q=model.encode_query([query],batch_size=4,show_progress_bar=False,**kw)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 370, in encode_query\n return self.encode(\n ~~~~~~~~~~~^\n inputs=inputs,\n ^^^^^^^^^^^^^^\n ...<14 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 895, in encode\n all_embeddings = self._inference(inputs, device=device, **inference_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/utils/_contextlib.py\", line 124, in decorate_context\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 963, in _inference\n out_features = self(features, **kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 600, in forward\n input = module(input, **module_kwargs)\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 1647, in forward\n model_output = model_method(**filtered_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 901, in forward\n (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(\n ~~~~~~~~~~~~~~~^\n unpad_inputs,\n ^^^^^^^^^^^^^\n ...<5 lines>...\n inputs_embeds=inputs_embeds\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1783, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1794, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/Alibaba_hyphen_NLP/new_hyphen_impl/40ced75c3017eb27626c9d4ea981bde21a2662f4/modeling.py\", line 392, in forward\n rope_cos = rope_cos[position_ids].unsqueeze(2) # [bs, seq_len, 1, dim]\n ~~~~~~~~^^^^^^^^^^^^^^\nIndexError: index 140471200382993 is out of bounds for dimension 0 with size 11\n", + "result": "FAIL(forward)", + "total_seconds": 12.225869985297322 + } + ] }, { "index": 141, @@ -11457,8 +13286,8 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:56:13.809543+00:00", - "physical_visible_core": "8", + "started_at": "2026-11-03T16:29:13.720726+00:00", + "physical_visible_core": "4", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", @@ -11471,7 +13300,7 @@ "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ], "batch_size": 4, - "load_seconds": 0.3890756834298372, + "load_seconds": 0.17954427190124989, "prompts": { "query": "", "document": "" @@ -11489,11 +13318,11 @@ 0.6985108852386475, 0.6134428977966309 ], - "cpu_seconds": 0.06325524486601353, + "cpu_seconds": 0.016356867738068104, "cpu_top_document": 3, "model_device": "neuron:0", "score_device": "neuron:0", - "neuron_first_seconds": 6.991000535897911, + "neuron_first_seconds": 0.06938606780022383, "neuron_scores": [ 0.4463503956794739, 0.6927067637443542, @@ -11504,14 +13333,14 @@ "neuron_top_document": 3, "mars_first": false, "cpu_parity": true, - "neuron_repeat_seconds": 0.007872754707932472, + "neuron_repeat_seconds": 0.007342119701206684, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", "result": "FAIL(output)", "memory_monitor": { "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", - "sample_count": 72, + "sample_count": 2, "sample_interval_seconds": 0.1, "peak_bytes": 40488680, "peak_categories": { @@ -11523,10 +13352,94 @@ }, "capacity_bytes": 25769803776 }, - "allocator_peak_allocated_bytes": 30251264, + "allocator_peak_allocated_bytes": 30251008, "allocator_peak_reserved_bytes": 33554432, - "total_seconds": 25.33328312728554, - "prior_attempts": [] + "total_seconds": 15.43065809365362, + "prior_attempts": [ + { + "index": 144, + "model": "minishlab/potion-base-8M", + "revision": "bf8b056651a2c21b8d2565580b8569da283cab23", + "trending_rank": null, + "downloads_rank": 75, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:56:13.809543+00:00", + "physical_visible_core": "8", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 0.3890756834298372, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 0.4463503956794739, + 0.692706823348999, + 0.6985108852386475, + 0.6134428977966309 + ], + "cpu_seconds": 0.06325524486601353, + "cpu_top_document": 3, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 6.991000535897911, + "neuron_scores": [ + 0.4463503956794739, + 0.6927067637443542, + 0.6985103487968445, + 0.6134430170059204 + ], + "max_abs_error": 5.364418029785156e-07, + "neuron_top_document": 3, + "mars_first": false, + "cpu_parity": true, + "neuron_repeat_seconds": 0.007872754707932472, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 72, + "sample_interval_seconds": 0.1, + "peak_bytes": 40488680, + "peak_categories": { + "code": 4221032, + "tensors": 33558528, + "constants": 327680, + "misc": 2111104, + "dma_rings": 270336 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 30251264, + "allocator_peak_reserved_bytes": 33554432, + "total_seconds": 25.33328312728554 + } + ] }, { "index": 145, @@ -11626,8 +13539,8 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:56:33.415911+00:00", - "physical_visible_core": "4", + "started_at": "2026-11-03T16:29:15.091834+00:00", + "physical_visible_core": "8", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", @@ -11640,7 +13553,7 @@ "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ], "batch_size": 4, - "load_seconds": 1.2769848257303238, + "load_seconds": 0.5070198401808739, "prompts": { "query": "", "document": "" @@ -11658,11 +13571,11 @@ 0.8234667181968689, 0.7024552822113037 ], - "cpu_seconds": 0.15781457629054785, + "cpu_seconds": 0.1015954865142703, "cpu_top_document": 3, "model_device": "neuron:0", "score_device": "neuron:0", - "neuron_first_seconds": 0.31044141575694084, + "neuron_first_seconds": 0.32567631360143423, "neuron_scores": [ 0.46113866567611694, 0.745732307434082, @@ -11673,7 +13586,7 @@ "neuron_top_document": 3, "mars_first": false, "cpu_parity": true, - "neuron_repeat_seconds": 0.03502273000776768, + "neuron_repeat_seconds": 0.035069906152784824, "repeat_max_abs_error": 0.0, "repeat_stable": true, "status": "executed", @@ -11695,8 +13608,93 @@ }, "allocator_peak_allocated_bytes": 482003968, "allocator_peak_reserved_bytes": 501219328, - "total_seconds": 21.299358570948243, - "prior_attempts": [] + "total_seconds": 14.539557542651892, + "prior_attempts": [ + { + "index": 146, + "model": "TimKond/S-PubMedBert-MedQuAD", + "revision": "b73d84decb1da6bd4c1cf084a1b6c6602157d267", + "trending_rank": null, + "downloads_rank": 77, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:56:33.415911+00:00", + "physical_visible_core": "4", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 1.2769848257303238, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 0.46113887429237366, + 0.7457321882247925, + 0.8234667181968689, + 0.7024552822113037 + ], + "cpu_seconds": 0.15781457629054785, + "cpu_top_document": 3, + "model_device": "neuron:0", + "score_device": "neuron:0", + "neuron_first_seconds": 0.31044141575694084, + "neuron_scores": [ + 0.46113866567611694, + 0.745732307434082, + 0.823466420173645, + 0.7024555206298828 + ], + "max_abs_error": 2.980232238769531e-07, + "neuron_top_document": 3, + "mars_first": false, + "cpu_parity": true, + "neuron_repeat_seconds": 0.03502273000776768, + "repeat_max_abs_error": 0.0, + "repeat_stable": true, + "status": "executed", + "result": "FAIL(output)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 6, + "sample_interval_seconds": 0.1, + "peak_bytes": 589734025, + "peak_categories": { + "code": 6608056, + "tensors": 501223424, + "constants": 3309585, + "scratchpad": 67108864, + "misc": 9499584, + "dma_rings": 1984512 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 482003968, + "allocator_peak_reserved_bytes": 501219328, + "total_seconds": 21.299358570948243 + } + ] }, { "index": 147, @@ -12618,8 +14616,8 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:58:12.973659+00:00", - "physical_visible_core": "0", + "started_at": "2026-11-03T16:29:22.582373+00:00", + "physical_visible_core": "12", "torch": "2.14.0+cpu", "transformers": "5.22.1", "sentence_transformers": "6.3.0.dev0", @@ -12636,9 +14634,9 @@ "stage": "load_cpu", "error_type": "AttributeError", "error": "'NVEmbedModel' object has no attribute 'all_tied_weights_keys'", - "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/158/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 855, in __init__\n self.model = self._load_model(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path, transformer_task, config, backend, is_peft_model, **model_kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2343, in _load_model\n return model_cls.from_pretrained(model_name_or_path, config=config, **model_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py\", line 389, in from_pretrained\n return model_class.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4356, in from_pretrained\n loading_info = cls._finalize_model_loading(model, load_config, loading_info)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4524, in _finalize_model_loading\n model._move_missing_keys_from_meta_to_device(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n loading_info.missing_and_mismatched(),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<2 lines>...\n load_config.hf_quantizer,\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4781, in _move_missing_keys_from_meta_to_device\n for key in missing_keys - self.all_tied_weights_keys.keys():\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1972, in __getattr__\n raise AttributeError(\n f\"'{type(self).__name__}' object has no attribute '{name}'\"\n )\nAttributeError: 'NVEmbedModel' object has no attribute 'all_tied_weights_keys'. Did you mean: '_tied_weights_keys'?\n", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/158/run.py\", line 51, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention'],**SPEC.get('extra_model_kwargs',{})})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 855, in __init__\n self.model = self._load_model(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path, transformer_task, config, backend, is_peft_model, **model_kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2343, in _load_model\n return model_cls.from_pretrained(model_name_or_path, config=config, **model_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py\", line 389, in from_pretrained\n return model_class.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4356, in from_pretrained\n loading_info = cls._finalize_model_loading(model, load_config, loading_info)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4524, in _finalize_model_loading\n model._move_missing_keys_from_meta_to_device(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n loading_info.missing_and_mismatched(),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<2 lines>...\n load_config.hf_quantizer,\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4781, in _move_missing_keys_from_meta_to_device\n for key in missing_keys - self.all_tied_weights_keys.keys():\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1972, in __getattr__\n raise AttributeError(\n f\"'{type(self).__name__}' object has no attribute '{name}'\"\n )\nAttributeError: 'NVEmbedModel' object has no attribute 'all_tied_weights_keys'. Did you mean: '_tied_weights_keys'?\n", "result": "FAIL(load)", - "total_seconds": 29.60385298728943, + "total_seconds": 10.885062549263239, "prior_attempts": [ { "index": 158, @@ -12674,6 +14672,41 @@ "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/158/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 855, in __init__\n self.model = self._load_model(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path, transformer_task, config, backend, is_peft_model, **model_kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2343, in _load_model\n return model_cls.from_pretrained(model_name_or_path, config=config, **model_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py\", line 389, in from_pretrained\n return model_class.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4300, in from_pretrained\n model = cls(config, *model_args, **model_kwargs)\n File \"/home/jingyahuang/.cache/huggingface/modules/transformers_modules/nvidia/NV_hyphen_Embed_hyphen_v2/3fa59658547db50a1e8e3346cf057fd0c77ed6ef/modeling_nvembed.py\", line 320, in __init__\n super().__init__(config)\n ~~~~~~~~~~~~~~~~^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 1258, in __init__\n self.config._attn_implementation_internal = self._check_and_adjust_attn_implementation(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n self.config._attn_implementation,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<2 lines>...\n allow_all_kernels=hub_kernels.ALLOW_ALL_KERNELS,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 1821, in _check_and_adjust_attn_implementation\n applicable_attn_implementation = self.get_correct_attn_implementation(\n applicable_attn_implementation, is_init_check\n )\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 1877, in get_correct_attn_implementation\n raise e\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 1874, in get_correct_attn_implementation\n self._sdpa_can_dispatch(is_init_check)\n ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 1665, in _sdpa_can_dispatch\n raise ValueError(\n ...<3 lines>...\n )\nValueError: NVEmbedModel does not support an attention implementation through torch.nn.functional.scaled_dot_product_attention yet. Please request the support for this architecture: https://github.com/huggingface/transformers/issues/28005. If you believe this error is a bug, please open an issue in Transformers GitHub repository and load your model with the argument `attn_implementation=\"eager\"` meanwhile. Example: `model = AutoModel.from_pretrained(\"openai/whisper-tiny\", attn_implementation=\"eager\")`\n", "result": "FAIL(load)", "total_seconds": 18.42478364240378 + }, + { + "index": 158, + "model": "nvidia/NV-Embed-v2", + "revision": "3fa59658547db50a1e8e3346cf057fd0c77ed6ef", + "trending_rank": null, + "downloads_rank": 93, + "class": "SentenceTransformer", + "dtype": "bfloat16", + "attention": "eager", + "trust_remote_code": true, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:58:12.973659+00:00", + "physical_visible_core": "0", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "status": "error", + "stage": "load_cpu", + "error_type": "AttributeError", + "error": "'NVEmbedModel' object has no attribute 'all_tied_weights_keys'", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/158/run.py\", line 50, in \n model=cls(SPEC['model'],revision=SPEC['revision'],device='cpu',trust_remote_code=SPEC['trust_remote_code'],config_kwargs=SPEC['config_kwargs'],model_kwargs={'torch_dtype':dtype,'attn_implementation':SPEC['attention']})\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 46, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 193, in __init__\n super().__init__(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path=model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<13 lines>...\n default_prompt_name=default_prompt_name,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 256, in __init__\n modules, self.module_kwargs = self._load_modules(\n ~~~~~~~~~~~~~~~~~~^\n model_name_or_path,\n ^^^^^^^^^^^^^^^^^^^\n ...<7 lines>...\n config_kwargs=config_kwargs,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1211, in _load_modules\n return self._load_config_modules(model_name_or_path, **load_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/model.py\", line 1431, in _load_config_modules\n module = module_class.load(\n model_name_or_path,\n ...<12 lines>...\n **extra_load_kwargs,\n )\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2641, in load\n return cls(model_name_or_path=model_name_or_path, **init_kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/decorators.py\", line 92, in wrapper\n return func(*args, **kwargs)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 855, in __init__\n self.model = self._load_model(\n ~~~~~~~~~~~~~~~~^\n model_name_or_path, transformer_task, config, backend, is_peft_model, **model_kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/base/modules/transformer.py\", line 2343, in _load_model\n return model_cls.from_pretrained(model_name_or_path, config=config, **model_kwargs)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py\", line 389, in from_pretrained\n return model_class.from_pretrained(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4356, in from_pretrained\n loading_info = cls._finalize_model_loading(model, load_config, loading_info)\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4524, in _finalize_model_loading\n model._move_missing_keys_from_meta_to_device(\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^\n loading_info.missing_and_mismatched(),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n ...<2 lines>...\n load_config.hf_quantizer,\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/tmp/st-neuron-sweep/lib/python3.14/site-packages/transformers/modeling_utils.py\", line 4781, in _move_missing_keys_from_meta_to_device\n for key in missing_keys - self.all_tied_weights_keys.keys():\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/.venv-oct09/lib/python3.14/site-packages/torch/nn/modules/module.py\", line 1972, in __getattr__\n raise AttributeError(\n f\"'{type(self).__name__}' object has no attribute '{name}'\"\n )\nAttributeError: 'NVEmbedModel' object has no attribute 'all_tied_weights_keys'. Did you mean: '_tied_weights_keys'?\n", + "result": "FAIL(load)", + "total_seconds": 29.60385298728943 } ] }, @@ -13114,7 +15147,7 @@ "config_kwargs": {}, "excluded": null, "exclusion": null, - "started_at": "2026-11-03T15:59:45.659267+00:00", + "started_at": "2026-11-03T16:29:29.177391+00:00", "physical_visible_core": "0", "torch": "2.14.0+cpu", "transformers": "5.22.1", @@ -13128,7 +15161,7 @@ "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ], "batch_size": 4, - "load_seconds": 1.0640968950465322, + "load_seconds": 0.3136888975277543, "prompts": { "query": "", "document": "" @@ -13146,14 +15179,14 @@ 43.17018127441406, 42.00007629394531 ], - "cpu_seconds": 0.06058734655380249, + "cpu_seconds": 0.028249465860426426, "cpu_top_document": 2, "model_device": "neuron:0", "status": "error", "stage": "neuron_inference", "error_type": "RuntimeError", "error": "Inference tensors do not track version counter.", - "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/164/run.py\", line 69, in \n stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/164/run.py\", line 61, in score\n values=model.similarity(q,d)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1072, in similarity\n return self._similarity(embeddings1, embeddings2)\n ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/similarity.py\", line 115, in dot_score\n return torch.mm(a, b.transpose(0, 1)).to_dense()\n ~~~~~~~~~~~^^^^^^\nRuntimeError: Inference tensors do not track version counter.\n", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-rerun/runs/164/run.py\", line 70, in \n stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device)\n ~~~~~^^\n File \"/tmp/neuron-top100-rerun/runs/164/run.py\", line 62, in score\n values=model.similarity(q,d)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1072, in similarity\n return self._similarity(embeddings1, embeddings2)\n ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/similarity.py\", line 115, in dot_score\n return torch.mm(a, b.transpose(0, 1)).to_dense()\n ~~~~~~~~~~~^^^^^^\nRuntimeError: Inference tensors do not track version counter.\n", "result": "FAIL(forward)", "memory_monitor": { "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", @@ -13172,8 +15205,82 @@ }, "allocator_peak_allocated_bytes": 97223168, "allocator_peak_reserved_bytes": 136314880, - "total_seconds": 24.259669181890786, - "prior_attempts": [] + "total_seconds": 16.736036530695856, + "prior_attempts": [ + { + "index": 164, + "model": "sentence-transformers/multi-qa-MiniLM-L6-dot-v1", + "revision": "4151c507ffb0f2fcd311cf431f54b5fc7d097851", + "trending_rank": null, + "downloads_rank": 100, + "class": "SentenceTransformer", + "dtype": "float32", + "attention": "sdpa", + "trust_remote_code": false, + "config_kwargs": {}, + "excluded": null, + "exclusion": null, + "started_at": "2026-11-03T15:59:45.659267+00:00", + "physical_visible_core": "0", + "torch": "2.14.0+cpu", + "transformers": "5.22.1", + "sentence_transformers": "6.3.0.dev0", + "python": "3.14.4", + "query": "Which planet is known as the Red Planet?", + "documents": [ + "Venus is often called Earth's twin because of its similar size and proximity.", + "Mars, known for its reddish appearance, is often referred to as the Red Planet.", + "Jupiter, the largest planet in our solar system, has a prominent red spot.", + "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." + ], + "batch_size": 4, + "load_seconds": 1.0640968950465322, + "prompts": { + "query": "", + "document": "" + }, + "parameter_dtypes": [ + "torch.float32" + ], + "embedding_devices": [ + "neuron:0", + "neuron:0" + ], + "cpu_scores": [ + 37.162418365478516, + 43.29322814941406, + 43.17018127441406, + 42.00007629394531 + ], + "cpu_seconds": 0.06058734655380249, + "cpu_top_document": 2, + "model_device": "neuron:0", + "status": "error", + "stage": "neuron_inference", + "error_type": "RuntimeError", + "error": "Inference tensors do not track version counter.", + "traceback": "Traceback (most recent call last):\n File \"/tmp/neuron-top100-sweep/runs/164/run.py\", line 69, in \n stage='neuron_inference';t=time.monotonic();actual=score();r['score_device']=str(actual.device)\n ~~~~~^^\n File \"/tmp/neuron-top100-sweep/runs/164/run.py\", line 61, in score\n values=model.similarity(q,d)\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/sentence_transformer/model.py\", line 1072, in similarity\n return self._similarity(embeddings1, embeddings2)\n ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jingyahuang/sentence-transformers/sentence_transformers/util/similarity.py\", line 115, in dot_score\n return torch.mm(a, b.transpose(0, 1)).to_dense()\n ~~~~~~~~~~~^^^^^^\nRuntimeError: Inference tensors do not track version counter.\n", + "result": "FAIL(forward)", + "memory_monitor": { + "method": "Neuron runtime HBM usage breakdown sampled every 100 ms", + "sample_count": 5, + "sample_interval_seconds": 0.1, + "peak_bytes": 223731977, + "peak_categories": { + "code": 6030712, + "tensors": 136318976, + "constants": 2785297, + "scratchpad": 67108864, + "misc": 8847488, + "dma_rings": 2640640 + }, + "capacity_bytes": 25769803776 + }, + "allocator_peak_allocated_bytes": 97223168, + "allocator_peak_reserved_bytes": 136314880, + "total_seconds": 24.259669181890786 + } + ] } ] }