[GLM4.1V and GLM4.5V] Add vision transformer num_dummy_head support: max tp=4 -> max tp=8 (#9059)
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@@ -141,9 +141,13 @@ def eval_mmmu(args):
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print(f"response: {response}")
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process_result(response, sample, answer_dict, out_samples)
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args.output_path = f"{args.model_path}_val_hf.json"
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args.output_path = f"{args.model_path}_answer_hf.json"
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save_json(args.output_path, out_samples)
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eval_result(model_answer_path=args.output_path, answer_dict=answer_dict)
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eval_result(
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model_answer_path=args.output_path,
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answer_dict=answer_dict,
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eval_output_path=f"{args.model_path}_val_hf.json",
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)
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if __name__ == "__main__":
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@@ -187,9 +187,13 @@ async def eval_mmmu(args) -> None:
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print("Profiler stopped")
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print(f"Benchmark time: {time.perf_counter() - start}")
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args.output_path = f"./val_sglang.json"
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args.output_path = "./answer_sglang.json"
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save_json(args.output_path, out_samples)
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eval_result(model_answer_path=args.output_path, answer_dict=answer_dict)
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eval_result(
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model_answer_path=args.output_path,
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answer_dict=answer_dict,
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eval_output_path="./val_sglang.json",
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)
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def parse_args():
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@@ -544,7 +544,9 @@ def process_result(response, sample, answer_dict, out_samples):
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}
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def eval_result(model_answer_path, answer_dict):
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def eval_result(model_answer_path, answer_dict, eval_output_path=None):
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if eval_output_path is None:
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eval_output_path = model_answer_path
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print("Evaluating...")
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output_dict = json.load(open(model_answer_path))
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# answer_dict = json.load(open(answer_path))
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@@ -639,7 +641,7 @@ def eval_result(model_answer_path, answer_dict):
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"acc": overall_acc,
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}
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pprint.pprint(printable_results)
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out = model_answer_path
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out = eval_output_path
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with open(out, "w", encoding="utf-8") as outfile:
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json.dump(printable_results, outfile)
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print(f"eval out saved to {out}")
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