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Browse files- codesymbols_dataset.jsonl +0 -0
- dataset_details.md +109 -0
- eval.py +59 -0
codesymbols_dataset.jsonl
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dataset_details.md
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# HiRoPE: Length Extrapolation for Code Models
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## Code Symbol Understanding
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Note that we use the public repo to construct the evaluation dataset.
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> ddbourgin/numpy-ml
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>
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> gradio-app/gradio
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>
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> huggingface/accelerate
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>
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> huggingface/diffusers
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>
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> huggingface/optimum
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>
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> huggingface/peft
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>
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> huggingface/transformers
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>
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> langchain-ai/langchain/
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>
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> numpy/numpy
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>
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> tensorflow/tensorflow
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**During the experiment we fully complied with the license requirements of these projects.**
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Before conducting experiments, please confirm the license requirements in each project and mark them appropriately. We respect the efforts of every developer. If there is any possible violation, please contact us in time!
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## Metadata
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We list the file name in codesymbols_dataset.jsonl
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Here we list the file paths of each metadata. (The file path is simplified to only show the last two levels).
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| peft-main | | | |
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| ----------------- | ---------------------------------------------------------- | ---- | ---- |
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| | .../stable_diffusion/train_dreambooth.py | | |
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| langchain-master | | | |
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| | .../llms/openai.py | | |
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| | .../agents/agent.py | | |
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| numpy-main | | | |
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| | .../tests/test_defchararray.py | | |
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| | .../fcompiler/__init__.py | | |
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| | .../f2py/capi_maps.py | | |
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| | .../lib/_index_tricks_impl.py | | |
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| | .../tests/test_smoke.py | | |
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| | .../tools/refguide_check.py | | |
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| numpyml-master | | | |
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| | .../preprocessing/nlp.py | | |
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| | .../preprocessing/nlp.py | | |
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| gradio-main | | | |
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| | .../gradio/helpers.py | | |
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| tensorflow-master | | | |
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| | .../python/convert.py | | |
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| | .../ops/data_service_ops.py | | |
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| | .../cli/debugger_cli_common.py | | |
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| | .../cli/debugger_cli_common_test.py | | |
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| | .../lib/debug_events_reader.py | | |
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| | .../wrappers/framework.py | | |
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| | .../eager/forwardprop_test.py | | |
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| | .../framework/func_graph.py | | |
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| | .../framework/type_spec_test.py | | |
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| | .../initializers/initializers_v2.py | | |
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| | .../layers/merge.py | | |
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| | .../optimizer_v2/learning_rate_schedule.py | | |
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| | .../ops/init_ops_v2.py | | |
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| | .../ops/stateful_random_ops.py | | |
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| | .../trackable/data_structures.py | | |
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| | .../util/deprecation_test.py | | |
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| | .../compatibility/ast_edits.py | | |
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| accelerate-main | | | |
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| | .../commands/launch.py | | |
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| | .../accelerate/data_loader.py | | |
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| | .../accelerate/tracking.py | | |
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| | .../tests/test_big_modeling.py | | |
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| diffusers-main | | | |
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| | .../community/stable_diffusion_tensorrt_img2img.py | | |
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| | .../community/stable_diffusion_tensorrt_inpaint.py | | |
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| | .../community/stable_diffusion_tensorrt_txt2img.py | | |
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| | .../text_to_video_synthesis/pipeline_text_to_video_zero.py | | |
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| transformers-main | | | |
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| | .../bertabs/modeling_bertabs.py | | |
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| | .../emmental/modeling_bert_masked.py | | |
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| | .../benchmark/benchmark_utils.py | | |
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| | .../ctrl/modeling_tf_ctrl.py | | |
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| | .../mctct/modeling_mctct.py | | |
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| | .../ernie_m/modeling_ernie_m.py | | |
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| | .../openfold_utils/rigid_utils.py | | |
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| | .../gptj/modeling_gptj.py | | |
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| | .../gptj/modeling_tf_gptj.py | | |
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| | .../graphormer/modeling_graphormer.py | | |
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| | .../lilt/modeling_lilt.py | | |
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| | .../persimmon/modeling_persimmon.py | | |
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| | .../rwkv/modeling_rwkv.py | | |
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| | .../timesformer/modeling_timesformer.py | | |
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| | .../vit/modeling_vit.py | | |
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| | .../xglm/modeling_tf_xglm.py | | |
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| | .../blip_2/test_modeling_blip_2.py | | |
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| | .../bnb/test_mixed_int8.py | | |
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| optimum-main | | | |
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| | .../onnxruntime/configuration.py | | |
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eval.py
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import json
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# inp_path = "/Users/zkcpku/Documents/seke/mywork/分块ROPE/openai_src/gen_out/openai_default_codesymbols_outputless100.jsonl.renew"
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# inp_path is the parameter for script
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import sys
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# /Users/zkcpku/Documents/seke/pretrain/envAnalysis/
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# python extract_funcs.py
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# test_renew_dataset
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def eval_codesymbols(inp_path):
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with open(inp_path,'r') as f:
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lines = f.readlines()
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lines = [json.loads(x) for x in lines]
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print("len(lines):",len(lines))
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rst = {}
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for i,l in enumerate(lines):
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this_rst = {"true": [], "false": [], "acc": 0.0}
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ground_output = l['output']
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preds = l["out_str"]
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metadata = l['metadata']
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symbol_dict = metadata['symbol_dict']
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symbol2location = {e['symbol']: e['location'] for e in symbol_dict}
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for e in ground_output:
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if e in preds:
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this_rst["true"].append((e, symbol2location[e]))
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else:
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this_rst["false"].append((e, symbol2location[e]))
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acc = len(this_rst["true"]) / (len(this_rst["true"]) + len(this_rst["false"]))
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this_rst["acc"] = acc
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rst[i] = this_rst
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total_acc = sum([rst[i]["acc"] for i in rst]) / len(rst)
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print(f"total acc: {total_acc}")
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all_true = [rst[i]['true'] for i in range(len(rst))]
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all_true = [e for ee in all_true for e in ee]
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all_true_location = [e[1] for e in all_true]
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all_false = [rst[i]['false'] for i in range(len(rst))]
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all_false = [e for ee in all_false for e in ee]
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all_false_location = [e[1] for e in all_false]
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# print([len(rst[i]['false']) for i in range(len(rst))])
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# import ipdb; ipdb.set_trace()
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print(max(all_true_location), min(all_true_location), max(all_false_location), min(all_false_location))
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# each 1000 is a range, count acc
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# 0-1000 1000-2000 2000-3000 3000-4000 4000-5000 5000-6000 6000-7000 7000-8000 8000-9000 9000-10000
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for i in range(20):
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this_true = [e for e in all_true_location if e >= i*1000 and e < (i+1)*1000]
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this_false = [e for e in all_false_location if e >= i*1000 and e < (i+1)*1000]
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if len(this_true) + len(this_false) == 0:
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this_acc = 0
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else:
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this_acc = len(this_true) / (len(this_true) + len(this_false))
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print(f"range {i*1000}-{(i+1)*1000} acc: {this_acc} true: {len(this_true)} false: {len(this_false)}")
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if __name__ == "__main__":
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args = sys.argv
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inp_path = args[1]
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eval_codesymbols(inp_path)
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