[Doc] Rename offline inference examples (#11927)

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

docs/source/contributing/profiling/profiling_index.md

@@ -26,7 +26,7 @@ Set the env variable VLLM_RPC_TIMEOUT to a big number before you start the serve
 
 ### Offline Inference
 
-Refer to <gh-file:examples/offline_inference/offline_inference_with_profiler.py> for an example.
+Refer to <gh-file:examples/offline_inference/simple_profiling.py> for an example.
 
 ### OpenAI Server
 

docs/source/features/structured_outputs.md

@@ -257,4 +257,4 @@ outputs = llm.generate(
 print(outputs[0].outputs[0].text)
 ```
 
-Full example: <gh-file:examples/offline_inference/offline_inference_structured_outputs.py>
+Full example: <gh-file:examples/offline_inference/structured_outputs.py>

docs/source/getting_started/installation/cpu-x86.md

@@ -95,7 +95,7 @@ $ VLLM_TARGET_DEVICE=cpu python setup.py install
 $ sudo apt-get install libtcmalloc-minimal4 # install TCMalloc library
 $ find / -name *libtcmalloc* # find the dynamic link library path
 $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4:$LD_PRELOAD # prepend the library to LD_PRELOAD
-$ python examples/offline_inference/offline_inference.py # run vLLM
+$ python examples/offline_inference/basic.py # run vLLM
 ```
 
 - When using the online serving, it is recommended to reserve 1-2 CPU cores for the serving framework to avoid CPU oversubscription. For example, on a platform with 32 physical CPU cores, reserving CPU 30 and 31 for the framework and using CPU 0-29 for OpenMP:
@@ -132,7 +132,7 @@ CPU NODE SOCKET CORE L1d:L1i:L2:L3 ONLINE    MAXMHZ   MINMHZ      MHZ
 
 # On this platform, it is recommend to only bind openMP threads on logical CPU cores 0-7 or 8-15
 $ export VLLM_CPU_OMP_THREADS_BIND=0-7
-$ python examples/offline_inference/offline_inference.py
+$ python examples/offline_inference/basic.py
 ```
 
 - If using vLLM CPU backend on a multi-socket machine with NUMA, be aware to set CPU cores using `VLLM_CPU_OMP_THREADS_BIND` to avoid cross NUMA node memory access.

docs/source/getting_started/quickstart.md

@@ -40,7 +40,7 @@ For non-CUDA platforms, please refer [here](#installation-index) for specific in
 
 ## Offline Batched Inference
 
-With vLLM installed, you can start generating texts for list of input prompts (i.e. offline batch inferencing). See the example script: <gh-file:examples/offline_inference/offline_inference.py>
+With vLLM installed, you can start generating texts for list of input prompts (i.e. offline batch inferencing). See the example script: <gh-file:examples/offline_inference/basic.py>
 
 The first line of this example imports the classes {class}`~vllm.LLM` and {class}`~vllm.SamplingParams`:
 

docs/source/models/generative_models.md

@@ -46,7 +46,7 @@ for output in outputs:
     print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
 ```
 
-A code example can be found here: <gh-file:examples/offline_inference/offline_inference.py>
+A code example can be found here: <gh-file:examples/offline_inference/basic.py>
 
 ### `LLM.beam_search`
 
@@ -103,7 +103,7 @@ for output in outputs:
     print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
 ```
 
-A code example can be found here: <gh-file:examples/offline_inference/offline_inference_chat.py>
+A code example can be found here: <gh-file:examples/offline_inference/chat.py>
 
 If the model doesn't have a chat template or you want to specify another one,
 you can explicitly pass a chat template:

docs/source/models/pooling_models.md

@@ -88,7 +88,7 @@ embeds = output.outputs.embedding
 print(f"Embeddings: {embeds!r} (size={len(embeds)})")
 ```
 
-A code example can be found here: <gh-file:examples/offline_inference/offline_inference_embedding.py>
+A code example can be found here: <gh-file:examples/offline_inference/embedding.py>
 
 ### `LLM.classify`
 
@@ -103,7 +103,7 @@ probs = output.outputs.probs
 print(f"Class Probabilities: {probs!r} (size={len(probs)})")
 ```
 
-A code example can be found here: <gh-file:examples/offline_inference/offline_inference_classification.py>
+A code example can be found here: <gh-file:examples/offline_inference/classification.py>
 
 ### `LLM.score`
 
@@ -125,7 +125,7 @@ score = output.outputs.score
 print(f"Score: {score}")
 ```
 
-A code example can be found here: <gh-file:examples/offline_inference/offline_inference_scoring.py>
+A code example can be found here: <gh-file:examples/offline_inference/scoring.py>
 
 ## Online Serving
 

docs/source/serving/multimodal_inputs.md

@@ -60,7 +60,7 @@ for o in outputs:
     print(generated_text)
 ```
 
-Full example: <gh-file:examples/offline_inference/offline_inference_vision_language.py>
+Full example: <gh-file:examples/offline_inference/vision_language.py>
 
 To substitute multiple images inside the same text prompt, you can pass in a list of images instead:
 
@@ -91,7 +91,7 @@ for o in outputs:
     print(generated_text)
 ```
 
-Full example: <gh-file:examples/offline_inference/offline_inference_vision_language_multi_image.py>
+Full example: <gh-file:examples/offline_inference/vision_language_multi_image.py>
 
 Multi-image input can be extended to perform video captioning. We show this with [Qwen2-VL](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) as it supports videos:
 
@@ -125,13 +125,13 @@ for o in outputs:
 You can pass a list of NumPy arrays directly to the `'video'` field of the multi-modal dictionary
 instead of using multi-image input.
 
-Full example: <gh-file:examples/offline_inference/offline_inference_vision_language.py>
+Full example: <gh-file:examples/offline_inference/vision_language.py>
 
 ### Audio
 
 You can pass a tuple `(array, sampling_rate)` to the `'audio'` field of the multi-modal dictionary.
 
-Full example: <gh-file:examples/offline_inference/offline_inference_audio_language.py>
+Full example: <gh-file:examples/offline_inference/audio_language.py>
 
 ### Embedding