2aaa21c41d
* update ppl tests * use load_dataset api * add exception handling * add language argument * address comments
82 lines
No EOL
3.2 KiB
Python
82 lines
No EOL
3.2 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import numpy as np
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import torch
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from torch.nn import CrossEntropyLoss
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from tqdm import tqdm
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import gc
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from bigdl.llm.transformers import AutoModelForCausalLM, AutoModel
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class BigDLPPL:
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def __init__(self, model_path, device, **model_kwargs) -> None:
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model_kwargs['trust_remote_code'] = model_kwargs.get('trust_remote_code', True)
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model_kwargs['optimize_model'] = model_kwargs.get('optimize_model', True)
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self.device = device
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if 'chatglm' in model_path.lower():
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self.model = AutoModel.from_pretrained(model_path, **model_kwargs)
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else:
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self.model = AutoModelForCausalLM.from_pretrained(model_path, **model_kwargs)
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self.model.to(device)
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def perplexity_hf(self, encoded_texts):
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self.model.eval()
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loss_fct = CrossEntropyLoss(reduction="none")
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ppls = []
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try:
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pbar = tqdm(range(len(encoded_texts)))
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for bid in pbar:
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encoded_batch = encoded_texts[bid:bid+1]
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if type(encoded_batch) == dict:
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attn_mask = encoded_batch['attention_mask'] if 'attention_mask' in encoded_batch.keys() else None
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encoded_batch = encoded_batch['input_ids']
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elif type(encoded_batch) == list:
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encoded_batch = encoded_batch[0]
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encoded_batch = encoded_batch.to(self.device)
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attn_mask = torch.ones_like(encoded_batch)
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out_logits = self.model(encoded_batch).logits
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labels = encoded_batch
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shift_logits = out_logits[..., :-1, :].contiguous()
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shift_labels = labels[..., 1:].contiguous()
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shift_attention_mask_batch = attn_mask[..., 1:].contiguous()
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loss_ = loss_fct(shift_logits.transpose(1, 2), shift_labels).float()
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perplexity_batch = torch.exp2(
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(loss_ * shift_attention_mask_batch).sum(1)
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/ shift_attention_mask_batch.sum(1)
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)
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ppls += perplexity_batch.tolist()
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pbar.set_description(f"[{bid:<4}/{len(encoded_texts)}] avg_ppls: {np.mean(np.array(ppls)[~np.isnan(np.array(ppls))]):.4f}")
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del out_logits, encoded_batch, attn_mask, shift_logits, shift_labels, shift_attention_mask_batch, perplexity_batch
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ppl_mean = np.mean(np.array(ppls)[~np.isnan(np.array(ppls))])
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finally:
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torch.xpu.synchronize()
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torch.xpu.empty_cache()
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del self.model
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gc.collect()
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return ppl_mean |