Fix format issue reported by yapf

Signed-off-by: Chendi Xue <[email protected]>

examples/offline_inference_spec_decode.py

@@ -15,7 +15,8 @@ def time_generation(llm: LLM, prompts: List[str],
     start = time.time()
     outputs = llm.generate(prompts, sampling_params)
     end = time.time()
-    latency_per_token = (end - start) / sum([len(o.outputs[0].token_ids) for o in outputs])
+    latency_per_token = (end - start) / sum(
+        [len(o.outputs[0].token_ids) for o in outputs])
     # Print the outputs.
     ret = []
     for output in outputs:
@@ -36,7 +37,8 @@ def time_generation(llm: LLM, prompts: List[str],
     print("==============Without speculation==================")
     llm = LLM(model="facebook/opt-6.7b")
 
-    ret_non_spec,latency_per_token_non_spec = time_generation(llm, prompts, sampling_params)
+    ret_non_spec, latency_per_token_non_spec = time_generation(
+        llm, prompts, sampling_params)
 
     del llm
     gc.collect()
@@ -46,19 +48,20 @@ def time_generation(llm: LLM, prompts: List[str],
     llm = LLM(
         model="facebook/opt-6.7b",
         speculative_model="facebook/opt-125m",
-        num_speculative_tokens = 5,
+        num_speculative_tokens=5,
         # These are currently required for MLPSpeculator decoding
         use_v2_block_manager=True,
     )
 
-    ret_spec,latency_per_token_spec = time_generation(llm, prompts, sampling_params)
+    ret_spec, latency_per_token_spec = time_generation(llm, prompts,
+                                                       sampling_params)
 
     del llm
     gc.collect()
     print("================= Summary =====================")
     print("input is ", prompts, "\n")
-    print("Non Spec Decode - latency_per_token is ", latency_per_token_non_spec)
+    print("Non Spec Decode - latency_per_token is ",
+          latency_per_token_non_spec)
     print("Generated Text is :", ret_non_spec, "\n")
     print("Spec Decode - latency_per_token is ", latency_per_token_spec)
     print("Generated Text is :", ret_spec)
-

tests/samplers/test_rejection_sampler.py

@@ -23,16 +23,17 @@ def mock_causal_accepted_tensor(
     """
     batch_size = last_accepted_indices.shape[0]
 
-    accepted = (torch.arange(k).expand(batch_size, k) <=
-                last_accepted_indices.unsqueeze(-1).broadcast_to(
+    accepted = (torch.arange(k).expand(batch_size, k)
+                <= last_accepted_indices.unsqueeze(-1).broadcast_to(
                     batch_size, k))
 
     # Sprinkle accepted values after the contiguous initial accepted values.
     # This replicates the behavior of rejection sampling, which may "accept"
     # a token that cannot be accepted because of causality.
-    sprinkle_candidates = (
-        torch.arange(k).expand(batch_size, k) >
-        last_accepted_indices.unsqueeze(-1).broadcast_to(batch_size, k) + 1)
+    sprinkle_candidates = (torch.arange(k).expand(
+        batch_size,
+        k) > last_accepted_indices.unsqueeze(-1).broadcast_to(batch_size, k) +
+                           1)
     sprinkle = torch.rand(batch_size, k) > 0.5
     accepted[sprinkle_candidates] = sprinkle[sprinkle_candidates]
     return accepted
@@ -382,8 +383,8 @@ def test_rejection_sampling_approximates_target_distribution(
         distance_wrt_reference)
 
     expected_improvement_multiplier = 20
-    assert (relative_change_in_distance_wrt_target >
-            relative_change_in_distance_wrt_reference *
+    assert (relative_change_in_distance_wrt_target
+            > relative_change_in_distance_wrt_reference *
             expected_improvement_multiplier)
 
 

vllm/attention/backends/hpu_attn.py

@@ -25,7 +25,7 @@ class HPUAttentionBackend(AttentionBackend):
     @staticmethod
     def get_name() -> str:
         return "hpu-attn"
-    
+
     @staticmethod
     def get_impl_cls() -> Type["HPUAttentionImpl"]:
         return HPUAttentionImpl

vllm/executor/hpu_executor.py

@@ -144,13 +144,13 @@ def execute_model(
             with gc_ctx as gc_local_metric, \
                 cpu_fallback_ctx as cpu_fallback_local_metric:
                 output = self.driver_worker.execute_model(execute_model_req)
-            if (log_graph_compilation and gc_local_metric.stats()[0][1] > 0
-                ) or log_graph_compilation_all:
+            if (log_graph_compilation and gc_local_metric.stats()[0][1]
+                    > 0) or log_graph_compilation_all:
                 msg = ("VLLM_HPU_STEP_GRAPH_COMPILATION: "
                        f"{gc_local_metric.stats()}, {input_stats}")
                 logger.warning(msg)
-            if (log_cpu_fallbacks and cpu_fallback_local_metric.stats()[0][1] >
-                    0) or log_cpu_fallbacks_all:
+            if (log_cpu_fallbacks and cpu_fallback_local_metric.stats()[0][1]
+                    > 0) or log_cpu_fallbacks_all:
                 msg = ("VLLM_HPU_STEP_CPU_FALLBACK: "
                        f"{cpu_fallback_local_metric.stats()}, {input_stats}")
                 logger.warning(msg)

vllm/model_executor/layers/spec_decode_base_sampler.py

@@ -30,7 +30,9 @@ def __init__(self, strict_mode: bool = False):
         self.num_emitted_tokens: Optional[torch.Tensor] = None
         self.num_draft_tokens: int = 0
 
-    def init_tensors(self, device: Union[int, str], device_type: str = 'cuda') -> None:
+    def init_tensors(self,
+                     device: Union[int, str],
+                     device_type: str = 'cuda') -> None:
         assert self.num_accepted_tokens is None
         if isinstance(device, int):
             device = f"{device_type}:{device}"

vllm/spec_decode/batch_expansion.py

@@ -231,12 +231,17 @@ def _contract_batch_all_spec(
         # of shape [batch_size * k + 1] back to [batch_size, k + 1].
         contracted_bs, k = proposals.proposal_token_ids.shape
 
-        (target_sampler_output.sampled_token_ids,
-         target_sampler_output.sampled_token_probs,
-         target_sampler_output.logprobs,
-         target_sampler_output.hidden_states,
-         _, _, _, _,) = self._split_scoring_output(
-             target_sampler_output, num_scoring_tokens)
+        (
+            target_sampler_output.sampled_token_ids,
+            target_sampler_output.sampled_token_probs,
+            target_sampler_output.logprobs,
+            target_sampler_output.hidden_states,
+            _,
+            _,
+            _,
+            _,
+        ) = self._split_scoring_output(target_sampler_output,
+                                       num_scoring_tokens)
 
         # Reshape tensors to original batch size
         target_token_ids = target_sampler_output.sampled_token_ids.reshape(

vllm/spec_decode/draft_model_runner.py

@@ -24,9 +24,8 @@
         from vllm.attention.backends.rocm_flash_attn import (
             ROCmFlashAttentionMetadata as FlashAttentionMetadata)
 except:
-    logger.warning(
-        "Draft model speculative decoding currently only supports"
-        "CUDA and ROCm flash attention backend.")
+    logger.warning("Draft model speculative decoding currently only supports"
+                   "CUDA and ROCm flash attention backend.")
 
 # A flag to enable debug prints for the updated input tensors
 # before each step.

vllm/spec_decode/spec_decode_worker.py

@@ -461,8 +461,8 @@ def _should_disable_all_speculation(
             self, execute_model_req: ExecuteModelRequest) -> bool:
         # When the batch size is too large, disable speculative decoding
         # to stop trading off throughput for latency.
-        return (execute_model_req.running_queue_size >=
-                self.disable_by_batch_size)
+        return (execute_model_req.running_queue_size
+                >= self.disable_by_batch_size)
 
     def _maybe_disable_speculative_tokens(
             self, disable_all_speculation: bool,

vllm/spec_decode/util.py

@@ -40,13 +40,15 @@ def get_sampled_token_logprobs(
     """
     num_steps, batch_size, vocab_size = logprob_tensor.shape
 
-    selected_logprobs = logprob_tensor[torch.arange(num_steps).unsqueeze(1),
-                                       torch.arange(batch_size),
-                                       sampled_token_ids, ]
+    selected_logprobs = logprob_tensor[
+        torch.arange(num_steps).unsqueeze(1),
+        torch.arange(batch_size),
+        sampled_token_ids,
+    ]
     expanded_selected_logprobs = selected_logprobs.unsqueeze(-1).expand(
         -1, -1, vocab_size)
-    sampled_token_ids_ranks = (logprob_tensor >
-                               expanded_selected_logprobs).sum(-1).add_(1)
+    sampled_token_ids_ranks = (logprob_tensor
+                               > expanded_selected_logprobs).sum(-1).add_(1)
 
     return sampled_token_ids_ranks, selected_logprobs
 

vllm/worker/selector.py

@@ -1,5 +1,6 @@
 from vllm.config import DeviceConfig
 
+
 def init_worker(*args, **kwargs):
     device_config: DeviceConfig = kwargs.get("device_config")
     if device_config.device_type == 'neuron':
@@ -22,4 +23,4 @@ def init_worker(*args, **kwargs):
         return XPUWorker(*args, **kwargs)
     else:
         from vllm.worker.worker import Worker
-        return Worker(*args, **kwargs)
+        return Worker(*args, **kwargs)