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修复batch下的报错

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lcc 2025-11-03 15:23:23 +08:00
parent 94f157c8cf
commit 7338302ee4
1 changed files with 112 additions and 40 deletions
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152
GPT_SoVITS/AR/models/t2s_model.py
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@ -700,8 +700,8 @@ class Text2SemanticDecoder(nn.Module):
idx_list = [None] * y.shape[0]
bad_tokens_list = [809, 207,411,679,676,25,23,7]
bad_tokens = torch.tensor(bad_tokens_list, device=x.device, dtype=torch.long)
last_2_token = 0
repeat_count = 0
last_2_token = torch.full((bsz,), -1, dtype=torch.long, device=x.device)
repeat_count = torch.zeros(bsz, dtype=torch.float, device=x.device)
for idx in tqdm(range(1500)):
if idx == 0:
xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, attn_mask, None)
@ -718,26 +718,62 @@ class Text2SemanticDecoder(nn.Module):
last_token = y[:, -1]
# 2. 检查对于批次中的每个序列，其上一步生成的token是否已经重复，如果重复，降低选中概率
# is_last_token_bad 的形状是 [batch_size], dtype=torch.bool
is_repeated = (last_token == last_2_token)
repeat_count[is_repeated] += 1
# 3. 对没有重复的序列，计数器清零
repeat_count[~is_repeated] = 0 # ~是布尔 'not' 操作
if is_repeated.any():
# 获取重复序列的行索引
repeated_rows = torch.where(is_repeated)[0]
# 获取这些重复序列对应的 token ID
repeated_tokens = last_2_token[repeated_rows]
# 获取这些重复 token 当前的 logits 值
# 这是一种 "gather" 操作，精确选取 logits[row, token_id]
current_logits_values = logits[repeated_rows, repeated_tokens]
# 获取这些重复序列的重复次数
current_repeat_counts = repeat_count[repeated_rows]
# --- 创建掩码来模拟 if/elif/else ---
# 条件1: 0 < logit < 35
mask1 = (current_logits_values > 0) & (current_logits_values < 35)
# 条件2: logit < 0
mask2 = current_logits_values < 0
# 条件3: logit >= 35
mask3 = current_logits_values >= 35
# 4. --- 应用惩罚 ---
# 对满足条件1的 logits 应用惩罚
if mask1.any():
rows_to_update = repeated_rows[mask1]
tokens_to_update = repeated_tokens[mask1]
counts_to_use = current_repeat_counts[mask1]
logits[rows_to_update, tokens_to_update] *= (0.618 ** counts_to_use.unsqueeze(1)).squeeze(1)
# 对满足条件2的 logits 应用惩罚
if mask2.any():
rows_to_update = repeated_rows[mask2]
tokens_to_update = repeated_tokens[mask2]
counts_to_use = current_repeat_counts[mask2]
logits[rows_to_update, tokens_to_update] *= (1.414 ** counts_to_use.unsqueeze(1)).squeeze(1)
# 对满足条件3的 logits 应用惩罚
if mask3.any():
rows_to_update = repeated_rows[mask3]
tokens_to_update = repeated_tokens[mask3]
logits[rows_to_update, tokens_to_update] = -float('inf') # 你的逻辑是 *= 0，设为 -inf 更能禁止采样
# 5. 更新 last_2_token 以备下一次迭代
# 使用 .clone() 以免在下一次循环中意外修改 last_token
last_2_token = last_token.clone()
# 6. 处理 bad_tokens 列表
is_last_token_bad = torch.isin(last_token, bad_tokens)
if last_token == last_2_token:
repeat_count += 1
if 0 < logits[:, last_2_token] < 35:
logits[:, last_2_token] *= 0.618**repeat_count
elif logits[:, last_2_token] < 0:
logits[:, last_2_token] *= 1.414**repeat_count
elif logits[:, last_2_token] >= 35:
logits[:, last_2_token] *= 0
else:
logits[:, last_2_token] = -float('inf')
else:
repeat_count = 0
last_2_token = last_token
# 3. 如果某个序列的上一个token是恶性token，则在这一步的logits中，
# 将所有恶性token的概率都设为-inf，以禁止选择它们。
# 我们使用高级索引(advanced indexing)来只修改需要修改的行。
if is_last_token_bad.any():
logits[is_last_token_bad, bad_tokens] = -float('inf')
bad_rows = is_last_token_bad.nonzero(as_tuple=True)[0]
logits[bad_rows.unsqueeze(1), bad_tokens] = -float('inf')
samples = sample(
logits, y, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature
)[0]
@ -903,8 +939,8 @@ class Text2SemanticDecoder(nn.Module):
)
bad_tokens_list = [809, 207,411,679,676,25,23,7]
bad_tokens = torch.tensor(bad_tokens_list, device=x.device, dtype=torch.long)
last_2_token = 0
repeat_count = 0
last_2_token = torch.full((bsz,), -1, dtype=torch.long, device=x.device)
repeat_count = torch.zeros(bsz, dtype=torch.float, device=x.device)
for idx in tqdm(range(1500)):
if xy_attn_mask is not None:
xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, xy_attn_mask, None)
@ -921,26 +957,62 @@ class Text2SemanticDecoder(nn.Module):
last_token = y[:, -1]
# 2. 检查对于批次中的每个序列，其上一步生成的token是否已经重复，如果重复，降低选中概率
# is_last_token_bad 的形状是 [batch_size], dtype=torch.bool
is_repeated = (last_token == last_2_token)
repeat_count[is_repeated] += 1
# 3. 对没有重复的序列，计数器清零
repeat_count[~is_repeated] = 0 # ~是布尔 'not' 操作
if is_repeated.any():
# 获取重复序列的行索引
repeated_rows = torch.where(is_repeated)[0]
# 获取这些重复序列对应的 token ID
repeated_tokens = last_2_token[repeated_rows]
# 获取这些重复 token 当前的 logits 值
# 这是一种 "gather" 操作，精确选取 logits[row, token_id]
current_logits_values = logits[repeated_rows, repeated_tokens]
# 获取这些重复序列的重复次数
current_repeat_counts = repeat_count[repeated_rows]
# --- 创建掩码来模拟 if/elif/else ---
# 条件1: 0 < logit < 35
mask1 = (current_logits_values > 0) & (current_logits_values < 35)
# 条件2: logit < 0
mask2 = current_logits_values < 0
# 条件3: logit >= 35
mask3 = current_logits_values >= 35
# 4. --- 应用惩罚 ---
# 对满足条件1的 logits 应用惩罚
if mask1.any():
rows_to_update = repeated_rows[mask1]
tokens_to_update = repeated_tokens[mask1]
counts_to_use = current_repeat_counts[mask1]
logits[rows_to_update, tokens_to_update] *= (0.618 ** counts_to_use.unsqueeze(1)).squeeze(1)
# 对满足条件2的 logits 应用惩罚
if mask2.any():
rows_to_update = repeated_rows[mask2]
tokens_to_update = repeated_tokens[mask2]
counts_to_use = current_repeat_counts[mask2]
logits[rows_to_update, tokens_to_update] *= (1.414 ** counts_to_use.unsqueeze(1)).squeeze(1)
# 对满足条件3的 logits 应用惩罚
if mask3.any():
rows_to_update = repeated_rows[mask3]
tokens_to_update = repeated_tokens[mask3]
logits[rows_to_update, tokens_to_update] = -float('inf') # 你的逻辑是 *= 0，设为 -inf 更能禁止采样
# 5. 更新 last_2_token 以备下一次迭代
# 使用 .clone() 以免在下一次循环中意外修改 last_token
last_2_token = last_token.clone()
# 6. 处理 bad_tokens 列表
is_last_token_bad = torch.isin(last_token, bad_tokens)
if last_token == last_2_token:
repeat_count += 1
if 0 < logits[:, last_2_token] < 35:
logits[:, last_2_token] *= 0.618**repeat_count
elif logits[:, last_2_token] < 0:
logits[:, last_2_token] *= 1.414**repeat_count
elif logits[:, last_2_token] >= 35:
logits[:, last_2_token] *= 0
else:
logits[:, last_2_token] = -float('inf')
else:
repeat_count = 0
last_2_token = last_token
# 3. 如果某个序列的上一个token是恶性token，则在这一步的logits中，
# 将所有恶性token的概率都设为-inf，以禁止选择它们。
# 我们使用高级索引(advanced indexing)来只修改需要修改的行。
if is_last_token_bad.any():
logits[is_last_token_bad, bad_tokens] = -float('inf')
bad_rows = is_last_token_bad.nonzero(as_tuple=True)[0]
logits[bad_rows.unsqueeze(1), bad_tokens] = -float('inf')
samples = sample(
logits, y, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature
)[0]