Correct capitalisation: VLLM -> vLLM (#14562)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

Author: hmellor

benchmarks/kernels/benchmark_rmsnorm.py

@@ -139,7 +139,7 @@ def calculate_diff(batch_size, seq_len, hidden_size, use_residual=True):
 
     print(f"Naive output={output_naive}")
     print(f"FlashInfer output={output_flashinfer}")
-    print(f"VLLM output={output_vllm}")
+    print(f"vLLM output={output_vllm}")
 
     if torch.allclose(output_naive, output_flashinfer, atol=1e-2,
                       rtol=1e-2) and torch.allclose(

docs/source/contributing/vulnerability_management.md

@@ -37,7 +37,7 @@ you may contact the following individuals:
 
 ## Slack Discussion
 
-You may use the `#security` channel in the [VLLM Slack](https://slack.vllm.ai)
+You may use the `#security` channel in the [vLLM Slack](https://slack.vllm.ai)
 to discuss security-related topics. However, please do not disclose any
 vulnerabilities in this channel. If you need to report a vulnerability, please
 use the GitHub security advisory system or contact a VMT member privately.

docs/source/design/v1/metrics.md

@@ -509,7 +509,7 @@ cache to complete other requests), we swap kv cache blocks out to CPU
 memory. This is also known as "KV cache offloading" and is configured
 with `--swap-space` and `--preemption-mode`.
 
-In v0, [VLLM has long supported beam
+In v0, [vLLM has long supported beam
 search](gh-issue:6226). The
 SequenceGroup encapsulated the idea of N Sequences which
 all shared the same prompt kv blocks. This enabled KV cache block

examples/offline_inference/disaggregated_prefill_lmcache.py

@@ -5,7 +5,7 @@
 We will launch 2 vllm instances (GPU 0 for prefill and GPU 1 for decode),
 and launch an additional LMCache server.
 KV cache is transferred in the following manner: 
-VLLM prefill node -> LMCache server -> VLLM decode node.
+vLLM prefill node -> LMCache server -> vLLM decode node.
 
 Note that `pip install lmcache` is needed to run this example.
 Learn more about LMCache in https://github.com/LMCache/LMCache.

tests/tpu/test_quantization_accuracy.py

@@ -25,7 +25,7 @@ def get_model_args(self) -> str:
     GSM8KAccuracyTestConfig(
         model_name="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a8",
         excepted_value=0.76),  # no bias
-    # NOTE(rob): We cannot re-initialize VLLM in the same process for TPU,
+    # NOTE(rob): We cannot re-initialize vLLM in the same process for TPU,
     # so only one of these tests can run in a single call to pytest. As
     # a follow up, move this into the LM-EVAL section of the CI.
     # GSM8KAccuracyTestConfig(

vllm/attention/selector.py

@@ -51,7 +51,7 @@ def get_env_variable_attn_backend() -> Optional[_Backend]:
 # (default behavior if this variable is None)
 #
 # THIS SELECTION TAKES PRECEDENCE OVER THE
-# VLLM ATTENTION BACKEND ENVIRONMENT VARIABLE
+# VLLM_ATTENTION_BACKEND ENVIRONMENT VARIABLE
 forced_attn_backend: Optional[_Backend] = None
 
 

vllm/compilation/backends.py

@@ -278,7 +278,7 @@ def call_module(self, target: torch.fx.node.Target,
 
 
 class VllmBackend:
-    """The compilation backend for `torch.compile` with VLLM.
+    """The compilation backend for `torch.compile` with vLLM.
     It is used for compilation level of `CompilationLevel.PIECEWISE`,
     where we customize the compilation.
 

vllm/compilation/compiler_interface.py

@@ -31,7 +31,7 @@ def initialize_cache(self, cache_dir: str, disable_cache: bool = False):
 
     def compute_hash(self, vllm_config: VllmConfig) -> str:
         """
-        Gather all the relevant information from the VLLM config,
+        Gather all the relevant information from the vLLM config,
         to compute a hash so that we can cache the compiled model.
 
         See :meth:`VllmConfig.compute_hash` to check what information

vllm/config.py

@@ -3572,11 +3572,11 @@ def __str__(self):
 @contextmanager
 def set_current_vllm_config(vllm_config: VllmConfig, check_compile=False):
     """
-    Temporarily set the current VLLM config.
+    Temporarily set the current vLLM config.
     Used during model initialization.
-    We save the current VLLM config in a global variable,
+    We save the current vLLM config in a global variable,
     so that all modules can access it, e.g. custom ops
-    can access the VLLM config to determine how to dispatch.
+    can access the vLLM config to determine how to dispatch.
     """
     global _current_vllm_config
     old_vllm_config = _current_vllm_config
@@ -3611,7 +3611,7 @@ def get_current_vllm_config() -> VllmConfig:
         # in ci, usually when we test custom ops/modules directly,
         # we don't set the vllm config. In that case, we set a default
         # config.
-        logger.warning("Current VLLM config is not set.")
+        logger.warning("Current vLLM config is not set.")
         from vllm.config import VllmConfig
         return VllmConfig()
     return _current_vllm_config

vllm/entrypoints/openai/protocol.py

@@ -237,7 +237,7 @@ class ChatCompletionRequest(OpenAIBaseModel):
     tool_choice: Optional[Union[Literal["none"], Literal["auto"],
                                 ChatCompletionNamedToolChoiceParam]] = "none"
 
-    # NOTE this will be ignored by VLLM -- the model determines the behavior
+    # NOTE this will be ignored by vLLM -- the model determines the behavior
     parallel_tool_calls: Optional[bool] = False
     user: Optional[str] = None