[V0 deprecation] Remove more V0 references (#29088)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

docs/contributing/model/basic.md

@@ -133,8 +133,6 @@ We consider 3 different scenarios:
 For case (1), we recommend looking at the implementation of [`MambaForCausalLM`](../../../vllm/model_executor/models/mamba.py) (for Mamba-1) or [`Mamba2ForCausalLM`](../../../vllm/model_executor/models/mamba2.py) (for Mamba-2) as a reference.
 The model should inherit protocol `IsAttentionFree` and also implement class methods `get_mamba_state_dtype_from_config` and `get_mamba_state_shape_from_config` to calculate the state shapes and data types from the config.
 For the mamba layers themselves, please use the [`MambaMixer`](../../../vllm/model_executor/layers/mamba/mamba_mixer.py) (for Mamba-1) or [`MambaMixer2`](../../../vllm/model_executor/layers/mamba/mamba_mixer2.py) (for Mamba-2) classes.
-Please *do not* use the `MambaCacheManager` (deprecated in V1) or replicate any of the V0-specific code paths in the existing model implementations.
-V0-only classes and code will be removed in the very near future.
 The model should also be added to the `MODELS_CONFIG_MAP` dictionary in [vllm/model_executor/models/config.py](../../../vllm/model_executor/models/config.py) to ensure that the runtime defaults are optimized.
 
 For case (2), we recommend using as a reference the implementation of [`JambaForCausalLM`](../../../vllm/model_executor/models/jamba.py) (for an example of a model that uses Mamba-1 and attention together) or [`BambaForCausalLM`](../../../vllm/model_executor/models/bamba.py) (for an example of a model that uses Mamba-2 and attention together).

docs/design/prefix_caching.md

@@ -94,9 +94,6 @@ To improve privacy in shared environments, vLLM supports isolating prefix cache
 
 With this setup, cache sharing is limited to users or requests that explicitly agree on a common salt, enabling cache reuse within a trust group while isolating others.
 
-!!! note
-    Cache isolation is not supported in engine V0.
-
 ## Data Structure
 
 The prefix caching in vLLM v1 is implemented in the KV cache manager. The basic building block is the “Block” data class (simplified):

docs/usage/reproducibility.md

 # Reproducibility
 
-vLLM does not guarantee the reproducibility of the results by default, for the sake of performance. You need to do the following to achieve
-reproducible results:
-
-- For V1: Turn off multiprocessing to make the scheduling deterministic by setting `VLLM_ENABLE_V1_MULTIPROCESSING=0`.
-- For V0: Set the global seed (see below).
+vLLM does not guarantee the reproducibility of the results by default, for the sake of performance. To achieve
+reproducible results, you need to turn off multiprocessing to make the scheduling deterministic by setting `VLLM_ENABLE_V1_MULTIPROCESSING=0`.
 
 Example: [examples/offline_inference/reproducibility.py](../../examples/offline_inference/reproducibility.py)
 
@@ -30,8 +27,6 @@ However, in some cases, setting the seed will also [change the random state in u
 
 ### Default Behavior
 
-In V0, the `seed` parameter defaults to `None`. When the `seed` parameter is `None`, the random states for `random`, `np.random`, and `torch.manual_seed` are not set. This means that each run of vLLM will produce different results if `temperature > 0`, as expected.
-
 In V1, the `seed` parameter defaults to `0` which sets the random state for each worker, so the results will remain consistent for each vLLM run even if `temperature > 0`.
 
 !!! note

docs/usage/v1_guide.md

@@ -2,7 +2,7 @@
 
 !!! announcement
 
-    We have started the process of deprecating V0. Please read [RFC #18571](https://github.com/vllm-project/vllm/issues/18571) for more details.
+    We have fully deprecated V0. Please read [RFC #18571](https://github.com/vllm-project/vllm/issues/18571) for more details.
 
 V1 is now enabled by default for all supported use cases, and we will gradually enable it for every use case we plan to support. Please share any feedback on [GitHub](https://github.com/vllm-project/vllm) or in the [vLLM Slack](https://inviter.co/vllm-slack).
 

examples/offline_inference/reproducibility.py

@@ -11,13 +11,9 @@ import random
 
 from vllm import LLM, SamplingParams
 
-# V1 only: Turn off multiprocessing to make the scheduling deterministic.
+# Turn off multiprocessing to make the scheduling deterministic.
 os.environ["VLLM_ENABLE_V1_MULTIPROCESSING"] = "0"
 
-# V0 only: Set the global seed. The default seed is None, which is
-# not reproducible.
-SEED = 42
-
 prompts = [
     "Hello, my name is",
     "The president of the United States is",
@@ -28,7 +24,7 @@ sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
 
 
 def main():
-    llm = LLM(model="facebook/opt-125m", seed=SEED)
+    llm = LLM(model="facebook/opt-125m")
     outputs = llm.generate(prompts, sampling_params)
     print("-" * 50)
     for output in outputs:

examples/offline_inference/rlhf_utils.py

@@ -30,8 +30,8 @@ class WorkerExtension:
     """
     The class for vLLM's worker to inherit from.
     By defining an extension class, the code can work no matter what is
-    the underlying worker class. This way, the code can be compatible
-    with both vLLM V0 and V1.
+    the underlying worker class.
+
     NOTE: we define this class in a separate module, and the main module
     should pass the full qualified name as `worker_extension_cls` argument.
     """
@@ -96,8 +96,8 @@ class ColocateWorkerExtension:
     """
     The class for vLLM's worker to inherit from, in the colocate setting.
     By defining an extension class, the code can work no matter what is
-    the underlying worker class. This way, the code can be compatible
-    with both vLLM V0 and V1.
+    the underlying worker class.
+
     NOTE: we define this class in a separate module, and the main module
     should pass the full qualified name as `worker_extension_cls` argument.
     """

examples/offline_inference/save_sharded_state.py

@@ -67,22 +67,9 @@ def main(args):
     Path(args.output).mkdir(exist_ok=True)
     # Dump worker states to output directory
 
-    # Check which engine version is being used
-    is_v1_engine = hasattr(llm.llm_engine, "engine_core")
-
-    if is_v1_engine:
-        # For V1 engine, we need to use engine_core.save_sharded_state
-        print("Using V1 engine save path")
-        llm.llm_engine.engine_core.save_sharded_state(
-            path=args.output, pattern=args.file_pattern, max_size=args.max_file_size
-        )
-    else:
-        # For V0 engine
-        print("Using V0 engine save path")
-        model_executor = llm.llm_engine.model_executor
-        model_executor.save_sharded_state(
-            path=args.output, pattern=args.file_pattern, max_size=args.max_file_size
-        )
+    llm.llm_engine.engine_core.save_sharded_state(
+        path=args.output, pattern=args.file_pattern, max_size=args.max_file_size
+    )
 
     # Copy metadata files to output directory
     for file in os.listdir(model_path):

examples/offline_inference/spec_decode.py

@@ -158,11 +158,7 @@ def main(args):
             print(f"generated text: {output.outputs[0].text}")
             print("-" * 50)
 
-    try:
-        metrics = llm.get_metrics()
-    except AssertionError:
-        print("Metrics are not supported in the V0 engine.")
-        return
+    metrics = llm.get_metrics()
 
     total_num_output_tokens = sum(
         len(output.outputs[0].token_ids) for output in outputs

tests/model_executor/model_loader/test_sharded_state_loader.py

@@ -60,18 +60,9 @@ def llama_3p2_1b_files():
 
 def _run_writer(input_dir, output_dir, weights_patterns, **kwargs):
     llm_sharded_writer = LLM(model=input_dir, **kwargs)
-    # Check which engine version is being used
-    is_v1_engine = hasattr(llm_sharded_writer.llm_engine, "engine_core")
+
     # Dump worker states to output directory
-    if is_v1_engine:
-        # For V1 engine, we need to use engine_core.save_sharded_state
-        print("Using V1 engine save path")
-        llm_sharded_writer.llm_engine.engine_core.save_sharded_state(path=output_dir)
-    else:
-        # For V0 engine
-        print("Using V0 engine save path")
-        model_executor = llm_sharded_writer.llm_engine.model_executor
-        model_executor.save_sharded_state(path=output_dir)
+    llm_sharded_writer.llm_engine.engine_core.save_sharded_state(path=output_dir)
 
     # Copy metadata files to output directory
     for file in os.listdir(input_dir):

tests/tool_use/utils.py

@@ -140,21 +140,22 @@ CONFIGS: dict[str, ServerConfig] = {
         "without calling a tool. DO NOT CALL A TOOL THAT IS IRRELEVANT "
         "to the user's question - just respond to it normally.",
     },
-    # V1 Test: Passing locally but failing in CI. This runs the
-    # V0 Engine because of CPU offloading. Need to debug why.
+    # FIXME: This test currently fails, need to debug why.
     # "granite20b": {
-    #     "model":
-    #     "mbayser/granite-20b-functioncalling-FP8-KV",
+    #     "model": "mbayser/granite-20b-functioncalling-FP8-KV",
     #     "arguments": [
-    #         "--tool-call-parser", "granite-20b-fc", "--chat-template",
-    #         str(VLLM_PATH /
-    #             "examples/tool_chat_template_granite_20b_fc.jinja"),
-    #         "--max_num_seqs", "1", "--enforce-eager", "--cpu-offload-gb", "20"
+    #         "--tool-call-parser",
+    #         "granite-20b-fc",
+    #         "--chat-template",
+    #         str(VLLM_PATH / "examples/tool_chat_template_granite_20b_fc.jinja"),
+    #         "--max_num_seqs",
+    #         "1",
+    #         "--enforce-eager",
+    #         "--cpu-offload-gb",
+    #         "20",
     #     ],
-    #     "supports_parallel":
-    #     False,
-    #     "supports_rocm":
-    #     False,
+    #     "supports_parallel": False,
+    #     "supports_rocm": False,
     # },
     "granite-3.0-8b": {
         "model": "ibm-granite/granite-3.0-8b-instruct",

vllm/entrypoints/llm.py

@@ -339,7 +339,6 @@ class LLM:
 
         log_non_default_args(engine_args)
 
-        # Create the Engine (autoselects V0 vs V1)
         self.llm_engine = LLMEngine.from_engine_args(
             engine_args=engine_args, usage_context=UsageContext.LLM_CLASS
         )

vllm/entrypoints/openai/protocol.py

@@ -377,7 +377,7 @@ class ResponsesRequest(OpenAIBaseModel):
             "environments. The salt should be random, protected from "
             "access by 3rd parties, and long enough to be "
             "unpredictable (e.g., 43 characters base64-encoded, corresponding "
-            "to 256 bit). Not supported by vLLM engine V0."
+            "to 256 bit)."
         ),
     )
 
@@ -763,7 +763,7 @@ class ChatCompletionRequest(OpenAIBaseModel):
             "environments. The salt should be random, protected from "
             "access by 3rd parties, and long enough to be "
             "unpredictable (e.g., 43 characters base64-encoded, corresponding "
-            "to 256 bit). Not supported by vLLM engine V0."
+            "to 256 bit)."
         ),
     )
     kv_transfer_params: dict[str, Any] | None = Field(
@@ -1249,7 +1249,7 @@ class CompletionRequest(OpenAIBaseModel):
             "environments. The salt should be random, protected from "
             "access by 3rd parties, and long enough to be "
             "unpredictable (e.g., 43 characters base64-encoded, corresponding "
-            "to 256 bit). Not supported by vLLM engine V0."
+            "to 256 bit)."
         ),
     )
 

vllm/model_executor/layers/mamba/mamba_mixer2.py

@@ -590,7 +590,6 @@ class MambaMixer2(MambaBase, CustomOp):
             hidden_states, _B, _C = self.split_hidden_states_B_C_fn(hidden_states_B_C)
             return hidden_states
 
-        # NOTE: V0 put prefill before decode, v1 puts decode before prefill
         num_prefills = attn_metadata.num_prefills  # request count
         num_decodes = attn_metadata.num_decode_tokens  # token count (=request)
         num_prefill_tokens = attn_metadata.num_prefill_tokens  # token count

vllm/model_executor/models/interfaces.py

@@ -586,13 +586,11 @@ class IsHybrid(Protocol):
     def get_mamba_state_shape_from_config(
         cls,
         vllm_config: VllmConfig,
-        use_v1: bool = True,
     ) -> tuple[tuple[int, int], tuple[int, int, int]]:
         """Calculate shapes for Mamba's convolutional and state caches.
 
         Args:
             vllm_config: vLLM config
-            use_v1: Get shapes for V1 (or V0)
 
         Returns:
             Tuple containing:

vllm/model_executor/models/plamo2.py

@@ -290,7 +290,6 @@ class Plamo2MambaMixer(MambaBase, CustomOp):
         has_decode = num_decodes > 0
         num_actual_tokens = num_prefill_tokens + num_decodes
 
-        # NOTE: V0 put prefill before decode, v1 puts decode before prefill
         # Separate prefill and decode by splitting varlen input
         # Split along token dimension
         hidden_states_d, hidden_states_p = torch.split(