lint: formatting changes from linter

Signed-off-by: Travis Johnson <[email protected]>

tests/samplers/test_sampler.py

@@ -396,15 +396,20 @@ def run_test_case(*,
 
             if should_penalize:
                 for token_id in tokens_to_check:
-                    assert logits[logits_idx, token_id] == -float('inf'), \
-                    f"Expected token {token_id} for sequence {logits_idx} to be penalized"
+                    assert logits[logits_idx, token_id] == -float(
+                        'inf'
+                    ), f"Expected token {token_id} for logits row {logits_idx}"
+                    " to be penalized"
                 # no other tokens should be set to -inf
-                assert torch.count_nonzero(logits[logits_idx, :] == -float('inf')) == len(tokens_to_check), \
-                    f"Expected only {len(tokens_to_check)} to be penalized"
+                assert torch.count_nonzero(
+                    logits[logits_idx, :] == -float('inf')) == len(
+                        tokens_to_check
+                    ), f"Expected only {len(tokens_to_check)} to be penalized"
             else:
                 # no tokens should be set to -inf
-                assert torch.count_nonzero(logits[logits_idx, :] == -float('inf')) == 0, \
-                    "No tokens should have been penalized"
+                assert torch.count_nonzero(
+                    logits[logits_idx, :] ==
+                    -float('inf')) == 0, "No tokens should have been penalized"
 
         del model_runner
 

vllm/engine/llm_engine.py

@@ -269,7 +269,8 @@ def add_request(
         # Defensive copy of SamplingParams, which are used by the sampler,
         # this doesn't deep-copy LogitsProcessor objects
         sampling_params = sampling_params.clone()
-        # inject the eos token id into the sampling_params to support min_tokens processing
+        # inject the eos token id into the sampling_params to support min_tokens
+        # processing
         sampling_params.eos_token_id = self.get_tokenizer_for_seq(
             seq).eos_token_id
 

vllm/model_executor/layers/sampler.py

@@ -168,7 +168,7 @@ def _apply_min_tokens_penalty(
                     seqs_to_penalize.append(i)
 
             if seqs_to_penalize:
-                # convert from the index for this seq_group to the index into logits
+                # convert to the index into logits
                 seqs_to_penalize = [start_idx + i for i in seqs_to_penalize]
                 # use set() to remove any duplicates
                 token_ids_to_penalize = set(sampling_params.stop_token_ids +

vllm/sampling_params.py

@@ -198,13 +198,12 @@ def _verify_args(self) -> None:
             raise ValueError(
                 f"max_tokens must be at least 1, got {self.max_tokens}.")
         if self.min_tokens < 0:
-            raise ValueError(
-                f"min_tokens must be greater than or equal to 0, got {self.min_tokens}."
-            )
+            raise ValueError(f"min_tokens must be greater than or equal to 0, "
+                             f"got {self.min_tokens}.")
         if self.max_tokens is not None and self.min_tokens > self.max_tokens:
             raise ValueError(
-                f"min_tokens must be less than or equal to max_tokens={self.max_tokens}, got {self.min_tokens}."
-            )
+                f"min_tokens must be less than or equal to "
+                f"max_tokens={self.max_tokens}, got {self.min_tokens}.")
         if self.logprobs is not None and self.logprobs < 0:
             raise ValueError(
                 f"logprobs must be non-negative, got {self.logprobs}.")