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[fix] existing bugs

pull/8/head
jingyaogong 2026-05-07 19:07:26 +08:00
parent 2607758417
commit 0b85f814ed
2 changed files with 4 additions and 11 deletions
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4
model/model_minimind.py
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@ -267,9 +267,7 @@ class MiniMindForCausalLM(PreTrainedModel, GenerationMixin):
logits = outputs.logits[:, -1, :] / temperature logits = outputs.logits[:, -1, :] / temperature
if repetition_penalty != 1.0: if repetition_penalty != 1.0:
for i in range(input_ids.shape[0]): for i in range(input_ids.shape[0]):
seen = torch.unique(input_ids[i]) seen = torch.unique(input_ids[i]); score = logits[i, seen]; logits[i, seen] = torch.where(score > 0, score / repetition_penalty, score * repetition_penalty)
score = logits[i, seen]
logits[i, seen] = torch.where(score > 0, score / repetition_penalty, score * repetition_penalty)
if top_k > 0: if top_k > 0:
logits[logits < torch.topk(logits, top_k)[0][..., -1, None]] = -float('inf') logits[logits < torch.topk(logits, top_k)[0][..., -1, None]] = -float('inf')
if top_p < 1.0: if top_p < 1.0:

11
model/model_omni.py
View File

@ -339,7 +339,6 @@ class MiniMindOmni(MiniMindForCausalLM):
if spk_emb is not None and fill_start > 0: if spk_emb is not None and fill_start > 0:
audio_buffer[:, :, fill_start - 1] = self.audio_spk_token audio_buffer[:, :, fill_start - 1] = self.audio_spk_token
think_end_step, generated_tokens = None, ([] if args.get('open_thinking', False) else None) think_end_step, generated_tokens = None, ([] if args.get('open_thinking', False) else None)
seen_tokens = set(input_ids[0].tolist()) if rp != 1.0 else None
while input_ids.shape[1] < start_pos + max_new_tokens: while input_ids.shape[1] < start_pos + max_new_tokens:
if past_kvs is None or not use_cache: if past_kvs is None or not use_cache:
out = self.forward(torch.cat((audio_buffer, input_ids.unsqueeze(1)), dim=1), past_key_values=past_kvs, use_cache=use_cache, **args) out = self.forward(torch.cat((audio_buffer, input_ids.unsqueeze(1)), dim=1), past_key_values=past_kvs, use_cache=use_cache, **args)
@ -348,10 +347,8 @@ class MiniMindOmni(MiniMindForCausalLM):
past_kvs = out.past_key_values past_kvs = out.past_key_values
logits = out.logits[0, -1, :].clone() / (temperature + 1e-9) logits = out.logits[0, -1, :].clone() / (temperature + 1e-9)
if seen_tokens is not None: if rp != 1.0:
seen = list(seen_tokens) seen = list(set(input_ids[0].tolist())); score = logits[seen]; logits[seen] = torch.where(score > 0, score / rp, score * rp)
score = logits[seen]
logits[seen] = torch.where(score > 0, score / rp, score * rp)
if top_p and top_p < 1.0: if top_p and top_p < 1.0:
sorted_l, sorted_i = torch.sort(logits, descending=True) sorted_l, sorted_i = torch.sort(logits, descending=True)
mask = torch.cumsum(F.softmax(sorted_l, dim=-1), dim=-1) > top_p mask = torch.cumsum(F.softmax(sorted_l, dim=-1), dim=-1) > top_p
@ -362,8 +359,6 @@ class MiniMindOmni(MiniMindForCausalLM):
if text_finished: if text_finished:
text_token = args.get('enter_token_id', 201) if first_finished else args.get('pad_token_id', 0) text_token = args.get('enter_token_id', 201) if first_finished else args.get('pad_token_id', 0)
first_finished = False first_finished = False
if seen_tokens is not None:
seen_tokens.add(text_token)
step = input_ids.shape[1] - start_pos # 已生成token数（0=首次，此时模型处理prompt末尾token） step = input_ids.shape[1] - start_pos # 已生成token数（0=首次，此时模型处理prompt末尾token）
audio_step = step - 1 # 延迟1步：输出第1个text时无audio，输出第2个text时layer0开始 audio_step = step - 1 # 延迟1步：输出第1个text时无audio，输出第2个text时layer0开始
@ -376,7 +371,7 @@ class MiniMindOmni(MiniMindForCausalLM):
audio_codes[i].append(self.audio_pad_token) audio_codes[i].append(self.audio_pad_token)
else: else:
logits_i = al[0, -1, :].clone() / 0.2 logits_i = al[0, -1, :].clone() / 0.2
for prev_code in audio_codes[i][-3:]: logits_i[prev_code] /= 1.05 for prev_code in audio_codes[i][-3:]: score = logits_i[prev_code]; logits_i[prev_code] = torch.where(score > 0, score / 1.05, score * 1.05)
top_val, top_idx = logits_i.topk(50) top_val, top_idx = logits_i.topk(50)
code = top_idx[torch.multinomial(F.softmax(top_val, dim=-1), 1)].item() code = top_idx[torch.multinomial(F.softmax(top_val, dim=-1), 1)].item()
audio_codes[i].append(code) audio_codes[i].append(code)