misc: correct the int data type for token ids and indices (#969)
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@@ -780,7 +780,7 @@ def top_k_top_p_sampling_from_probs_torch(
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sampled_index = torch.multinomial(probs_sort, num_samples=1)
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sampled_index = torch.multinomial(probs_sort, num_samples=1)
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except RuntimeError:
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except RuntimeError:
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batch_next_token_ids = torch.zeros(
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batch_next_token_ids = torch.zeros(
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(probs_sort.shape[0],), dtype=torch.int64, device=probs.device
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(probs_sort.shape[0],), dtype=torch.int32, device=probs.device
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)
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)
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success = torch.zeros(probs.shape[0], dtype=torch.bool, device=probs.device)
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success = torch.zeros(probs.shape[0], dtype=torch.bool, device=probs.device)
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return batch_next_token_ids, success
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return batch_next_token_ids, success
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@@ -74,7 +74,7 @@ class RadixCache(BasePrefixCache):
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if value:
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if value:
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value = torch.concat(value)
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value = torch.concat(value)
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else:
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else:
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value = torch.tensor([], dtype=torch.int64)
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value = torch.tensor([], dtype=torch.int32)
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return value, last_node[0]
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return value, last_node[0]
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def insert(self, key, value=None):
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def insert(self, key, value=None):
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@@ -102,7 +102,7 @@ class RadixCache(BasePrefixCache):
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if del_in_memory_pool:
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if del_in_memory_pool:
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self.token_to_kv_pool.free(indices)
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self.token_to_kv_pool.free(indices)
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else:
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else:
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return torch.tensor([], dtype=torch.int64), self.root_node
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return torch.tensor([], dtype=torch.int32), self.root_node
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# Radix Cache takes one ref in memory pool
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# Radix Cache takes one ref in memory pool
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self.token_to_kv_pool.free(indices[last_uncached_pos:new_prefix_len])
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self.token_to_kv_pool.free(indices[last_uncached_pos:new_prefix_len])
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