Replace single-line defensive getattrs with direct access (#25435)
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@@ -75,7 +75,7 @@ class SchedulerOutputProcessorMixin:
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"host": req.cached_tokens_host,
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"host": req.cached_tokens_host,
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}
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}
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# Only include storage fields if L3 storage is enabled
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# Only include storage fields if L3 storage is enabled
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if getattr(self, "enable_hicache_storage", False):
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if self.enable_hicache_storage:
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details["storage"] = req.cached_tokens_storage
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details["storage"] = req.cached_tokens_storage
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details["storage_backend"] = self._get_storage_backend_type()
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details["storage_backend"] = self._get_storage_backend_type()
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return details
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return details
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@@ -717,7 +717,7 @@ class SchedulerPPMixin:
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):
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):
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return None
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return None
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max_chunk_size = getattr(self, "max_prefill_tokens", None)
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max_chunk_size = self.max_prefill_tokens
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predicted_size = self.length_predictor.predict_next_chunk_size(
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predicted_size = self.length_predictor.predict_next_chunk_size(
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history_len=history_len,
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history_len=history_len,
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base_chunk_size=self.chunked_prefill_size,
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base_chunk_size=self.chunked_prefill_size,
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@@ -261,7 +261,7 @@ class TokenizerManagerScoreMixin:
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has_phs = False
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has_phs = False
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prompt_tokens = 0
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prompt_tokens = 0
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is_generation = getattr(self, "is_generation", True)
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is_generation = self.is_generation
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if is_generation:
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if is_generation:
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for result in results:
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for result in results:
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# For single-item scoring, logprobs are in output_token_ids_logprobs
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# For single-item scoring, logprobs are in output_token_ids_logprobs
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@@ -563,7 +563,7 @@ class TokenizerManagerScoreMixin:
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return_pooled_hidden_states=True and the model supports it;
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return_pooled_hidden_states=True and the model supports it;
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None otherwise.
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None otherwise.
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"""
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"""
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is_generation = getattr(self, "is_generation", True)
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is_generation = self.is_generation
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if is_generation and label_token_ids is None:
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if is_generation and label_token_ids is None:
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raise ValueError(
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raise ValueError(
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@@ -676,7 +676,7 @@ class TokenizerManagerScoreMixin:
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"It requires a model with a task-specific head "
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"It requires a model with a task-specific head "
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"(e.g. SequenceClassification or RewardModel)."
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"(e.g. SequenceClassification or RewardModel)."
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)
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)
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model_config = getattr(self, "model_config", None)
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model_config = self.model_config
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if model_config is not None:
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if model_config is not None:
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archs = getattr(model_config.hf_config, "architectures", []) or []
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archs = getattr(model_config.hf_config, "architectures", []) or []
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if is_cross_encoding_pooler_model(archs):
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if is_cross_encoding_pooler_model(archs):
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