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https://github.com/PaddlePaddle/FastDeploy.git
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[CI] Adjust model-specific diff threshold and include iluvatar XPU paths in coverage (#6663)
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@@ -33,6 +33,8 @@ omit =
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*/fastdeploy/model_executor/ops/gpu/fastdeploy_ops.py
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*/fastdeploy/model_executor/ops/gpu/fastdeploy_ops.py
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*/fastdeploy/model_executor/ops/gpu/fastdeploy_ops/__init__.py
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*/fastdeploy/model_executor/ops/gpu/fastdeploy_ops/__init__.py
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*/fastdeploy/model_executor/ops/gpu/deep_gemm/utils.py
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*/fastdeploy/model_executor/ops/gpu/deep_gemm/utils.py
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*/fastdeploy/model_executor/layers/attention/iluvatar_attn_backend.py
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*/fastdeploy/model_executor/xpu_pre_and_post_process.py
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*/fastdeploy/**/dcu/*
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*/fastdeploy/**/dcu/*
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*/fastdeploy/worker/dcu*.py
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*/fastdeploy/worker/dcu*.py
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*/fastdeploy/**/gcu/*
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*/fastdeploy/**/gcu/*
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@@ -85,6 +85,12 @@ hugging_face_model_param_map = {
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},
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},
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}
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}
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# Model-specific diff threshold (default: 0.05)
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model_threshold_map = {
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"Qwen2.5-7B-Instruct": 0.2, # dynamic quantization may introduce higher variance
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"Qwen3-30B-A3B": 0.05,
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}
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hf_params = []
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hf_params = []
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for model, cfg in hugging_face_model_param_map.items():
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for model, cfg in hugging_face_model_param_map.items():
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for q in cfg["quantizations"]:
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for q in cfg["quantizations"]:
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@@ -148,5 +154,12 @@ def test_model_against_baseline(
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else:
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else:
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baseline_file = baseline_filename
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baseline_file = baseline_filename
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# Use model-specific threshold
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threshold = model_threshold_map.get(model_name_or_path, 0.05)
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# Compare against baseline file
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# Compare against baseline file
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check_result_against_baseline(hf_outputs, baseline_file, threshold=0.05)
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check_result_against_baseline(
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hf_outputs,
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baseline_file,
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threshold=threshold,
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)
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