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[ColossalEval] Fix ColossalEval #5186

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Merged
chengeharrison merged 1 commit into from
Dec 15, 2023
Merged

[ColossalEval] Fix ColossalEval #5186

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28 changes: 13 additions & 15 deletions applications/ColossalEval/colossal_eval/evaluate/dataset_evaluator/dataset_evaluator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -58,12 +58,12 @@ def _calculate_label_metrics(self, metric: str, category: str):
[sample["output"] for sample in self.data[category]["data"]]

flag = False
softmaxs = []
logits = []
for i, sample in enumerate(self.data[category]["data"]):
if np.any(np.isnan(np.array(list(sample["softmax_over_choices"].values())))):
if np.any(np.isnan(np.array(list(sample["logits_over_choices"].values())))):
if not flag:
print(
f"NaN in the softmax, switch to exact match for category {category} in dataset {self.dataset_name} in model {self.model_name}."
f"NaN in the logits, switch to exact match for category {category} in dataset {self.dataset_name} in model {self.model_name}."
)
flag = True
score = 0
Expand All @@ -79,13 +79,13 @@ def _calculate_label_metrics(self, metric: str, category: str):
score,
metric_helper.accuracy_by_options(sample["input"], sample["output"], ref),
)
softmaxs.append(references[i] if score == 1 else -1)
logits.append(references[i] if score == 1 else -1)
else:
softmaxs.append(np.argmax(np.array(list(sample["softmax_over_choices"].values()))))
logits.append(np.argmax(np.array(list(sample["logits_over_choices"].values()))))

references = np.array(references)
softmaxs = np.array(softmaxs)
scores = np.sum(references == softmaxs) / len(self.data[category]["data"]) * 100
logits = np.array(logits)
scores = np.sum(references == logits) / len(self.data[category]["data"]) * 100

self.evaluation_results[metric][category] = (scores, len(self.data[category]["data"]))
self.evaluation_results[metric]["ALL"] += scores * weight
Expand All @@ -105,12 +105,12 @@ def _calculate_combined_metrics(self, metric: str, category: str):
predictions = [sample["output"] for sample in self.data[category]["data"]]

flag = False
softmaxs = []
logits = []
for i, sample in enumerate(self.data[category]["data"]):
if np.any(np.isnan(np.array(list(sample["softmax_over_choices"].values())))):
if np.any(np.isnan(np.array(list(sample["logits_over_choices"].values())))):
if not flag:
print(
f"NaN in the softmax, switch to exact match for category {category} in dataset {self.dataset_name} in model {self.model_name}."
f"NaN in the logits, switch to exact match for category {category} in dataset {self.dataset_name} in model {self.model_name}."
)
flag = True
score = 0
Expand All @@ -121,16 +121,14 @@ def _calculate_combined_metrics(self, metric: str, category: str):
sample["output"], ref, all_classes=self.data[category]["inference_kwargs"]["all_classes"]
),
)
softmaxs.append(references[i] if score == 1 else -1)
logits.append(references[i] if score == 1 else -1)
else:
softmaxs.append(np.argmax(np.array(list(sample["softmax_over_choices"].values()))))
logits.append(np.argmax(np.array(list(sample["logits_over_choices"].values()))))

metric_method = eval("metric_helper." + metric)

total_score = 0.0
for prediction, reference, references_label, softmax in zip(
predictions, references, references_labels, softmaxs
):
for prediction, reference, references_label, softmax in zip(predictions, references, references_labels, logits):
score = 0.0

for ref in reference:
Expand Down
9 changes: 4 additions & 5 deletions applications/ColossalEval/colossal_eval/models/huggingface.py
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Original file line number Diff line number Diff line change
Expand Up @@ -116,10 +116,10 @@ def _load_model(
shard_config: Shard config for tensor parallel.

"""
model_kwargs.setdefault("torch_dtype", torch.float16)

if "torch_dtype" in model_kwargs:
model_kwargs["torch_dtype"] = eval(model_kwargs["torch_dtype"])
else:
model_kwargs.setdefault("torch_dtype", torch.float16)

if "config" in model_kwargs:
model_kwargs["config"] = AutoConfig.from_pretrained(model_kwargs["config"])
Expand Down Expand Up @@ -586,11 +586,10 @@ def _load_model(
shard_config: Shard config for tensor parallel.

"""

model_kwargs.setdefault("torch_dtype", torch.float16)

if "torch_dtype" in model_kwargs:
model_kwargs["torch_dtype"] = eval(model_kwargs["torch_dtype"])
else:
model_kwargs.setdefault("torch_dtype", torch.float16)

if "config" in model_kwargs:
model_kwargs["config"] = AutoConfig.from_pretrained(model_kwargs["config"])
Expand Down