[Fix] Move ScheduleBatch out of SamplingInfo (#1556)
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@@ -423,10 +423,14 @@ class ScheduleBatch:
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# Stream
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has_stream: bool = False
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# Has regex
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has_regex: bool = False
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@classmethod
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def init_new(cls, reqs, req_to_token_pool, token_to_kv_pool, tree_cache):
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return_logprob = any(req.return_logprob for req in reqs)
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has_stream = any(req.stream for req in reqs)
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has_regex = any(req.regex_fsm for req in reqs)
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return cls(
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reqs=reqs,
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@@ -435,6 +439,7 @@ class ScheduleBatch:
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tree_cache=tree_cache,
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return_logprob=return_logprob,
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has_stream=has_stream,
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has_regex=has_regex,
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)
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def batch_size(self):
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@@ -750,7 +755,9 @@ class ScheduleBatch:
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]
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else:
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self.top_logprobs_nums = None
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self.has_stream = any(req.stream for req in self.reqs)
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self.has_regex = any(req.regex_fsm for req in self.reqs)
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self.sampling_info.filter_batch(unfinished_indices, new_indices)
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@@ -771,9 +778,11 @@ class ScheduleBatch:
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self.top_logprobs_nums.extend([0] * len(other.reqs))
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elif other.return_logprob:
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self.top_logprobs_nums = [0] * len(self.reqs) + other.top_logprobs_nums
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self.has_stream = any(req.stream for req in self.reqs)
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self.reqs.extend(other.reqs)
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self.return_logprob = self.return_logprob or other.return_logprob
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self.has_stream = self.has_stream or other.has_stream
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self.has_regex = self.has_regex or other.has_regex
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def get_model_worker_batch(self):
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if self.forward_mode.is_decode():
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@@ -787,7 +796,11 @@ class ScheduleBatch:
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image_inputs = [r.image_inputs for r in self.reqs]
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lora_paths = [req.lora_path for req in self.reqs]
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self.sampling_info.regex_fsm_states = [req.regex_fsm_state for req in self.reqs]
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if self.has_regex:
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self.sampling_info.regex_fsms = [req.regex_fsm for req in self.reqs]
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self.sampling_info.regex_fsm_states = [
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req.regex_fsm_state for req in self.reqs
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]
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return ModelWorkerBatch(
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forward_mode=self.forward_mode,
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@@ -84,10 +84,6 @@ class SamplingBatchInfo:
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# Handle logit bias but only allocate when needed
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ret.logit_bias = None
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# This is only for regex_fsm. We notice a regression if we maintain the list of regex_fsm
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# in SamplingBatchInfo, so we keep it here.
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ret.schedule_batch = batch
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return ret
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def __len__(self):
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@@ -113,7 +109,7 @@ class SamplingBatchInfo:
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self.linear_penalties = penalizer.apply(self.linear_penalties)
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def update_regex_vocab_mask(self):
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has_regex = any(req.regex_fsm is not None for req in self.schedule_batch.reqs)
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has_regex = self.regex_fsms and any(regex_fsm for regex_fsm in self.regex_fsms)
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# Reset the vocab mask
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self.vocab_mask = None
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@@ -122,11 +118,11 @@ class SamplingBatchInfo:
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self.vocab_mask = torch.zeros(
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len(self.temperatures), self.vocab_size, dtype=torch.bool, device="cuda"
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)
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for i, req in enumerate(self.schedule_batch.reqs):
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if req.regex_fsm is not None:
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for i, regex_fsm in enumerate(self.regex_fsms):
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if regex_fsm is not None:
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self.vocab_mask[i].fill_(1)
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self.vocab_mask[i][
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req.regex_fsm.get_next_instruction(req.regex_fsm_state).tokens
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regex_fsm.get_next_instruction(self.regex_fsm_states[i]).tokens
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] = 0
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def filter_batch(self, unfinished_indices: List[int], new_indices: torch.Tensor):
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