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preserve device type in a temporary variable

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Raf Gemmail
2023-05-13 14:33:43 +12:00
parent 8660b7d7bb
commit c84f71e187
1 changed files with 4 additions and 6 deletions
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10
bark/generation.py
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@@ -451,8 +451,7 @@ def generate_text_semantic(
) )
if top_p is not None: if top_p is not None:
# faster to convert to numpy # faster to convert to numpy
logits_device = relevant_logits.device original_device = relevant_logits.device
logits_dtype = relevant_logits.type()
relevant_logits = relevant_logits.detach().cpu().type(torch.float32).numpy() relevant_logits = relevant_logits.detach().cpu().type(torch.float32).numpy()
sorted_indices = np.argsort(relevant_logits)[::-1] sorted_indices = np.argsort(relevant_logits)[::-1]
sorted_logits = relevant_logits[sorted_indices] sorted_logits = relevant_logits[sorted_indices]
@@ -462,7 +461,7 @@ def generate_text_semantic(
sorted_indices_to_remove[0] = False sorted_indices_to_remove[0] = False
relevant_logits[sorted_indices[sorted_indices_to_remove]] = -np.inf relevant_logits[sorted_indices[sorted_indices_to_remove]] = -np.inf
relevant_logits = torch.from_numpy(relevant_logits) relevant_logits = torch.from_numpy(relevant_logits)
relevant_logits = relevant_logits.to(logits_device) relevant_logits = relevant_logits.to(original_device)
if top_k is not None: if top_k is not None:
v, _ = torch.topk(relevant_logits, min(top_k, relevant_logits.size(-1))) v, _ = torch.topk(relevant_logits, min(top_k, relevant_logits.size(-1)))
relevant_logits[relevant_logits < v[-1]] = -float("Inf") relevant_logits[relevant_logits < v[-1]] = -float("Inf")
@@ -647,8 +646,7 @@ def generate_coarse(
relevant_logits = logits[0, 0, logit_start_idx:logit_end_idx] relevant_logits = logits[0, 0, logit_start_idx:logit_end_idx]
if top_p is not None: if top_p is not None:
# faster to convert to numpy # faster to convert to numpy
logits_device = relevant_logits.device original_device = relevant_logits.device
logits_dtype = relevant_logits.type()
relevant_logits = relevant_logits.detach().cpu().type(torch.float32).numpy() relevant_logits = relevant_logits.detach().cpu().type(torch.float32).numpy()
sorted_indices = np.argsort(relevant_logits)[::-1] sorted_indices = np.argsort(relevant_logits)[::-1]
sorted_logits = relevant_logits[sorted_indices] sorted_logits = relevant_logits[sorted_indices]
@@ -658,7 +656,7 @@ def generate_coarse(
sorted_indices_to_remove[0] = False sorted_indices_to_remove[0] = False
relevant_logits[sorted_indices[sorted_indices_to_remove]] = -np.inf relevant_logits[sorted_indices[sorted_indices_to_remove]] = -np.inf
relevant_logits = torch.from_numpy(relevant_logits) relevant_logits = torch.from_numpy(relevant_logits)
relevant_logits = relevant_logits.to(logits_device) relevant_logits = relevant_logits.to(original_device)
if top_k is not None: if top_k is not None:
v, _ = torch.topk(relevant_logits, min(top_k, relevant_logits.size(-1))) v, _ = torch.topk(relevant_logits, min(top_k, relevant_logits.size(-1)))
relevant_logits[relevant_logits < v[-1]] = -float("Inf") relevant_logits[relevant_logits < v[-1]] = -float("Inf")