[BugFix] Fix quantization for all other methods (#11547)

Signed-off-by: youkaichao <youkaichao@gmail.com>

README.md

@@ -60,7 +60,7 @@ vLLM is flexible and easy to use with:
 
 vLLM seamlessly supports most popular open-source models on HuggingFace, including:
 - Transformer-like LLMs (e.g., Llama)
-- Mixture-of-Expert LLMs (e.g., Mixtral)
+- Mixture-of-Expert LLMs (e.g., Mixtral, Deepseek-V2 and V3)
 - Embedding Models (e.g. E5-Mistral)
 - Multi-modal LLMs (e.g., LLaVA)
 

docs/source/models/supported_models.md

@@ -137,6 +137,11 @@ See [this page](#generative-models) for more information on how to use generativ
     - :code:`deepseek-ai/DeepSeek-V2`, :code:`deepseek-ai/DeepSeek-V2-Chat` etc.
     -
     - ✅︎
+  * - :code:`DeepseekV3ForCausalLM`
+    - DeepSeek-V3
+    - :code:`deepseek-ai/DeepSeek-V3-Base`, :code:`deepseek-ai/DeepSeek-V3` etc.
+    -
+    - ✅︎
   * - :code:`ExaoneForCausalLM`
     - EXAONE-3
     - :code:`LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct`, etc.

docs/source/serving/openai_compatible_server.md

@@ -112,7 +112,13 @@ completion = client.chat.completions.create(
 
 ## Extra HTTP Headers
 
-Only `X-Request-Id` HTTP request header is supported for now.
+Only `X-Request-Id` HTTP request header is supported for now. It can be enabled
+with `--enable-request-id-headers`. 
+
+> Note that enablement of the headers can impact performance significantly at high QPS
+> rates. We recommend implementing HTTP headers at the router level (e.g. via Istio),
+> rather than within the vLLM layer for this reason.
+> See https://github.com/vllm-project/vllm/pull/11529 for more details.
 
 ```python
 completion = client.chat.completions.create(

tests/models/registry.py

@@ -61,6 +61,8 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
     "DeepseekForCausalLM": _HfExamplesInfo("deepseek-ai/deepseek-llm-7b-chat"),
     "DeepseekV2ForCausalLM": _HfExamplesInfo("deepseek-ai/DeepSeek-V2-Lite-Chat",  # noqa: E501
                                          trust_remote_code=True),
+    "DeepseekV3ForCausalLM": _HfExamplesInfo("deepseek-ai/DeepSeek-V3",  # noqa: E501
+                                         trust_remote_code=True),
     "ExaoneForCausalLM": _HfExamplesInfo("LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct"),  # noqa: E501
     "FalconForCausalLM": _HfExamplesInfo("tiiuae/falcon-7b"),
     "GemmaForCausalLM": _HfExamplesInfo("google/gemma-2b"),

tests/v1/sample/test_sampler.py

@@ -68,7 +68,7 @@ def _create_default_sampling_metadata(
         no_top_p=True,
         no_top_k=True,
         generators={},
-        max_num_logprobs=VOCAB_SIZE,
+        max_num_logprobs=0,
         prompt_token_ids=_create_prompt_tokens_tensor(prompt_token_ids,
                                                       vocab_size, device),
         output_token_ids=output_token_ids,
@@ -169,20 +169,14 @@ def test_sampler_min_tokens_penalty(device: str, batch_size: int):
     sampling_metadata.min_tokens = min_tokens
     sampling_metadata.stop_token_ids = stop_token_ids
     sampler = Sampler()
-    sampler_output = sampler(fake_logits, sampling_metadata)
+    logits = sampler.apply_penalties(fake_logits, sampling_metadata)
+    logits = logits.cpu()
     for batch_idx in range(batch_size):
-        for vocab in range(VOCAB_SIZE):
-            # Verify that the logprobs for stop token ids is set
-            # to -inf.
-            logprob_index = torch.where(
-                sampler_output.logprob_token_ids[batch_idx] ==
-                vocab)[0].item()
-            if vocab in stop_token_ids[batch_idx]:
-                assert sampler_output.logprobs[batch_idx][
-                    logprob_index] == -float("inf")
+        for token_id in range(VOCAB_SIZE):
+            if token_id in stop_token_ids[batch_idx]:
+                assert logits[batch_idx][token_id] == -float("inf")
             else:
-                assert sampler_output.logprobs[batch_idx][
-                    logprob_index] != -float("inf")
+                assert logits[batch_idx][token_id] != -float("inf")
 
 
 @pytest.mark.parametrize("device", CUDA_DEVICES)
@@ -205,18 +199,14 @@ def test_sampler_presence_penalty(device: str, batch_size: int,
         batch_size, presence_penalty, torch.device(device))
     sampling_metadata.no_penalties = False
     sampler = Sampler()
-    sampler_output = sampler(fake_logits, sampling_metadata)
+    logits = sampler.apply_penalties(fake_logits, sampling_metadata)
+    logits = logits.cpu()
     for batch_idx in range(batch_size):
-        # The logprobs in the SamplerOutput are arranged in descending order.
-        # Since all tokens initially have the same logprobs, the non-penalized
-        # tokens will appear at the beginning, while the penalized tokens
-        #  will appear at the end of the list.
-        penalized_token_id = sampler_output.logprob_token_ids[batch_idx][
-            VOCAB_SIZE - 1]
-        penalized_log_prod = sampler_output.logprobs[batch_idx][VOCAB_SIZE - 1]
-        non_penalized_token_id = sampler_output.logprob_token_ids[batch_idx][0]
-        non_penalized_log_prod = sampler_output.logprobs[batch_idx][0]
-        assert non_penalized_log_prod > penalized_log_prod
+        # Since all tokens initially have the same logits, the non-penalized
+        # token ID will be the one with the highest logit value, while the
+        # penalized token ID will be the one with the lowest logit value.
+        non_penalized_token_id = logits[batch_idx].argmax().item()
+        penalized_token_id = logits[batch_idx].argmin().item()
         if presence_penalty > 0:
             # If `presence_penalty` is set to a value greater than 0, it
             # indicates a preference for new tokens over those already
@@ -256,11 +246,11 @@ def test_sampler_frequency_penalty(device: str, batch_size: int,
     sampling_metadata.output_token_ids = output_token_ids
     sampling_metadata.no_penalties = False
     sampler = Sampler()
-    sampler_output = sampler(fake_logits, sampling_metadata)
+    logits = sampler.apply_penalties(fake_logits, sampling_metadata)
+    logits = logits.cpu()
     for batch_idx in range(batch_size):
-        logprobs_token_ids = sampler_output.logprob_token_ids[batch_idx]
-        non_penalized_token_id = logprobs_token_ids[0]
-        penalized_token_id = logprobs_token_ids[VOCAB_SIZE - 1]
+        non_penalized_token_id = logits[batch_idx].argmax().item()
+        penalized_token_id = logits[batch_idx].argmin().item()
         distinct_sorted_token_ids_in_output = \
             sorted_token_ids_in_output[batch_idx]
         most_frequent_token_id = distinct_sorted_token_ids_in_output[
@@ -305,11 +295,11 @@ def test_sampler_repetition_penalty(device: str, batch_size: int,
         batch_size, repetition_penalty, torch.device(device))
     sampling_metadata.no_penalties = False
     sampler = Sampler()
-    sampler_output = sampler(fake_logits, sampling_metadata)
+    logits = sampler.apply_penalties(fake_logits, sampling_metadata)
+    logits = logits.cpu()
     for batch_idx in range(batch_size):
-        logprobs_token_ids = sampler_output.logprob_token_ids[batch_idx]
-        non_penalized_token_id = logprobs_token_ids[0]
-        penalized_token_id = logprobs_token_ids[VOCAB_SIZE - 1]
+        non_penalized_token_id = logits[batch_idx].argmax().item()
+        penalized_token_id = logits[batch_idx].argmin().item()
         prompt_tokens = sampling_metadata.prompt_token_ids[
             batch_idx][:].tolist()
         output_tokens = sampling_metadata.output_token_ids[batch_idx]

vllm/compilation/backends.py

@@ -208,8 +208,8 @@ def wrap_inductor(graph: fx.GraphModule,
         from torch._inductor.compile_fx import graph_returns_tuple
         returns_tuple = graph_returns_tuple(graph)
 
-        # this is the graph we return to Dynamo to run
-        def compiled_graph(*args) -> Optional[fx.CompiledFxGraph]:
+        # this is the callable we return to Dynamo to run
+        def compiled_graph(*args):
             # convert args to list
             list_args = list(args)
             graph_output = inductor_compiled_graph(list_args)
@@ -537,7 +537,8 @@ class VllmBackend:
             example_inputs[x].clone() for x in self.sym_tensor_indices
         ]
 
-        def copy_and_call(*args) -> fx.GraphModule:
+        # this is the callable we return to Dynamo to run
+        def copy_and_call(*args):
             list_args = list(args)
             for i, index in enumerate(self.sym_tensor_indices):
                 runtime_tensor = list_args[index]

vllm/envs.py

@@ -30,7 +30,7 @@ if TYPE_CHECKING:
     VLLM_LOGGING_CONFIG_PATH: Optional[str] = None
     VLLM_TRACE_FUNCTION: int = 0
     VLLM_ATTENTION_BACKEND: Optional[str] = None
-    VLLM_USE_FLASHINFER_SAMPLER: bool = False
+    VLLM_USE_FLASHINFER_SAMPLER: Optional[bool] = None
     VLLM_USE_FLASHINFER_REJECTION_SAMPLER: bool = False
     VLLM_FLASHINFER_FORCE_TENSOR_CORES: bool = False
     VLLM_PP_LAYER_PARTITION: Optional[str] = None
@@ -277,7 +277,8 @@ environment_variables: Dict[str, Callable[[], Any]] = {
 
     # If set, vllm will use flashinfer sampler
     "VLLM_USE_FLASHINFER_SAMPLER":
-    lambda: bool(int(os.getenv("VLLM_USE_FLASHINFER_SAMPLER", "0"))),
+    lambda: bool(int(os.environ["VLLM_USE_FLASHINFER_SAMPLER"]))
+    if "VLLM_USE_FLASHINFER_SAMPLER" in os.environ else None,
 
     # If set, vllm will force flashinfer to use tensor cores;
     # otherwise will use heuristic based on model architecture.

vllm/model_executor/layers/fused_moe/layer.py

@@ -41,9 +41,20 @@ class FusedMoEMethodBase(QuantizeMethodBase):
         raise NotImplementedError
 
     @abstractmethod
-    def apply(self, layer: torch.nn.Module, x: torch.Tensor,
-              router_logits: torch.Tensor, top_k: int, renormalize: bool,
-              use_grouped_topk: bool) -> torch.Tensor:
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool,
+        use_grouped_topk: bool = False,
+        topk_group: Optional[int] = None,
+        num_expert_group: Optional[int] = None,
+        custom_routing_function: Optional[Callable] = None,
+        scoring_func: str = "softmax",
+        e_score_correction_bias: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
         raise NotImplementedError
 
 
@@ -79,7 +90,7 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
         router_logits: torch.Tensor,
         top_k: int,
         renormalize: bool,
-        use_grouped_topk: bool,
+        use_grouped_topk: bool = False,
         topk_group: Optional[int] = None,
         num_expert_group: Optional[int] = None,
         custom_routing_function: Optional[Callable] = None,

vllm/model_executor/layers/quantization/awq_marlin.py

@@ -440,11 +440,13 @@ class AWQMoEMethod(FusedMoEMethodBase):
         x: torch.Tensor,
         router_logits: torch.Tensor,
         top_k: int,
-        renormalize: bool = True,
+        renormalize: bool,
         use_grouped_topk: bool = False,
-        num_expert_group: Optional[int] = None,
         topk_group: Optional[int] = None,
+        num_expert_group: Optional[int] = None,
         custom_routing_function: Optional[Callable] = None,
+        scoring_func: str = "softmax",
+        e_score_correction_bias: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         topk_weights, topk_ids = FusedMoE.select_experts(
             hidden_states=x,
@@ -454,7 +456,9 @@ class AWQMoEMethod(FusedMoEMethodBase):
             renormalize=renormalize,
             topk_group=topk_group,
             num_expert_group=num_expert_group,
-            custom_routing_function=custom_routing_function)
+            custom_routing_function=custom_routing_function,
+            scoring_func=scoring_func,
+            e_score_correction_bias=e_score_correction_bias)
 
         return torch.ops.vllm.fused_marlin_moe(
             x,

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py

@@ -203,13 +203,14 @@ class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod):
         x: torch.Tensor,
         router_logits: torch.Tensor,
         top_k: int,
-        renormalize: bool = True,
+        renormalize: bool,
         use_grouped_topk: bool = False,
-        num_expert_group: Optional[int] = None,
         topk_group: Optional[int] = None,
+        num_expert_group: Optional[int] = None,
         custom_routing_function: Optional[Callable] = None,
+        scoring_func: str = "softmax",
+        e_score_correction_bias: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
-
         from vllm.model_executor.layers.fused_moe import fused_experts
 
         topk_weights, topk_ids = FusedMoE.select_experts(
@@ -220,7 +221,9 @@ class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod):
             renormalize=renormalize,
             topk_group=topk_group,
             num_expert_group=num_expert_group,
-            custom_routing_function=custom_routing_function)
+            custom_routing_function=custom_routing_function,
+            scoring_func=scoring_func,
+            e_score_correction_bias=e_score_correction_bias)
 
         return fused_experts(x,
                              layer.w13_weight,
@@ -476,12 +479,15 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
         x: torch.Tensor,
         router_logits: torch.Tensor,
         top_k: int,
-        renormalize: bool = True,
+        renormalize: bool,
         use_grouped_topk: bool = False,
-        num_expert_group: Optional[int] = None,
         topk_group: Optional[int] = None,
+        num_expert_group: Optional[int] = None,
         custom_routing_function: Optional[Callable] = None,
+        scoring_func: str = "softmax",
+        e_score_correction_bias: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
+
         topk_weights, topk_ids = FusedMoE.select_experts(
             hidden_states=x,
             router_logits=router_logits,
@@ -490,7 +496,9 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
             renormalize=renormalize,
             topk_group=topk_group,
             num_expert_group=num_expert_group,
-            custom_routing_function=custom_routing_function)
+            custom_routing_function=custom_routing_function,
+            scoring_func=scoring_func,
+            e_score_correction_bias=e_score_correction_bias)
 
         return torch.ops.vllm.fused_marlin_moe(
             x,

vllm/model_executor/layers/quantization/experts_int8.py

@@ -99,11 +99,13 @@ class ExpertsInt8MoEMethod(FusedMoEMethodBase):
         x: torch.Tensor,
         router_logits: torch.Tensor,
         top_k: int,
-        renormalize: bool = True,
+        renormalize: bool,
         use_grouped_topk: bool = False,
-        num_expert_group: Optional[int] = None,
         topk_group: Optional[int] = None,
+        num_expert_group: Optional[int] = None,
         custom_routing_function: Optional[Callable] = None,
+        scoring_func: str = "softmax",
+        e_score_correction_bias: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         from vllm.model_executor.layers.fused_moe import fused_experts
 
@@ -115,7 +117,9 @@ class ExpertsInt8MoEMethod(FusedMoEMethodBase):
             renormalize=renormalize,
             topk_group=topk_group,
             num_expert_group=num_expert_group,
-            custom_routing_function=custom_routing_function)
+            custom_routing_function=custom_routing_function,
+            scoring_func=scoring_func,
+            e_score_correction_bias=e_score_correction_bias)
 
         return fused_experts(x,
                              layer.w13_weight,

vllm/model_executor/layers/quantization/fp8.py

@@ -601,14 +601,13 @@ class Fp8MoEMethod(FusedMoEMethodBase):
         router_logits: torch.Tensor,
         top_k: int,
         renormalize: bool,
-        use_grouped_topk: bool,
+        use_grouped_topk: bool = False,
         topk_group: Optional[int] = None,
         num_expert_group: Optional[int] = None,
         custom_routing_function: Optional[Callable] = None,
         scoring_func: str = "softmax",
         e_score_correction_bias: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
-
         from vllm.model_executor.layers.fused_moe import fused_experts
 
         topk_weights, topk_ids = FusedMoE.select_experts(

vllm/model_executor/layers/quantization/gptq_marlin.py

@@ -532,11 +532,13 @@ class GPTQMarlinMoEMethod(FusedMoEMethodBase):
         x: torch.Tensor,
         router_logits: torch.Tensor,
         top_k: int,
-        renormalize: bool = True,
+        renormalize: bool,
         use_grouped_topk: bool = False,
-        num_expert_group: Optional[int] = None,
         topk_group: Optional[int] = None,
+        num_expert_group: Optional[int] = None,
         custom_routing_function: Optional[Callable] = None,
+        scoring_func: str = "softmax",
+        e_score_correction_bias: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         # The input must currently be float16
         orig_dtype = x.dtype
@@ -550,7 +552,9 @@ class GPTQMarlinMoEMethod(FusedMoEMethodBase):
             renormalize=renormalize,
             topk_group=topk_group,
             num_expert_group=num_expert_group,
-            custom_routing_function=None)
+            custom_routing_function=custom_routing_function,
+            scoring_func=scoring_func,
+            e_score_correction_bias=e_score_correction_bias)
 
         return torch.ops.vllm.fused_marlin_moe(
             x,

vllm/v1/engine/async_llm.py

@@ -9,14 +9,13 @@ from vllm.inputs import INPUT_REGISTRY, InputRegistry, PromptType
 from vllm.inputs.preprocess import InputPreprocessor
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
-from vllm.outputs import PoolingRequestOutput, RequestOutput
+from vllm.outputs import RequestOutput
 from vllm.pooling_params import PoolingParams
 from vllm.prompt_adapter.request import PromptAdapterRequest
 from vllm.sampling_params import SamplingParams
 from vllm.transformers_utils.tokenizer import AnyTokenizer
 from vllm.transformers_utils.tokenizer_group import init_tokenizer_from_configs
 from vllm.usage.usage_lib import UsageContext
-from vllm.v1.engine.async_stream import AsyncStream
 from vllm.v1.engine.core_client import EngineCoreClient
 from vllm.v1.engine.detokenizer import Detokenizer
 from vllm.v1.engine.processor import Processor
@@ -54,10 +53,8 @@ class AsyncLLM(EngineClient):
             lora_config=vllm_config.lora_config)
         self.tokenizer.ping()
 
-        # Request streams (map of request_id -> AsyncStream).
-        self.request_streams: Dict[str, AsyncStream] = {}
-        # List of cancelled request ids to be aborted.
-        self.client_aborted_requests: List[str] = []
+        # Request streams (map of request_id -> queue).
+        self.rid_to_queue: Dict[str, asyncio.Queue] = {}
 
         # Processor (converts Inputs --> EngineCoreRequests).
         self.processor = Processor(
@@ -153,14 +150,13 @@ class AsyncLLM(EngineClient):
         trace_headers: Optional[Mapping[str, str]] = None,
         prompt_adapter_request: Optional[PromptAdapterRequest] = None,
         priority: int = 0,
-    ) -> AsyncGenerator[Union[RequestOutput, PoolingRequestOutput], None]:
+    ) -> asyncio.Queue[RequestOutput]:
         """Add new request to the AsyncLLM."""
 
-        if self.detokenizer.is_request_active(request_id):
-            raise ValueError(f"Request {request_id} already exists.")
-
-        # 1) Create a new AsyncStream for the request.
-        stream = self._add_request_to_streams(request_id)
+        # 1) Create a new output queue for the request.
+        if request_id in self.rid_to_queue:
+            raise ValueError(f"Request id {request_id} already running.")
+        self.rid_to_queue[request_id] = asyncio.Queue()
 
         # 2) Convert input --> DetokenizerRequest / EngineCoreRequest.
         detokenizer_req, engine_core_req = self.processor.process_inputs(
@@ -173,8 +169,10 @@ class AsyncLLM(EngineClient):
         # 4) Add the EngineCoreRequest to EngineCore (separate process).
         await self.engine_core.add_request_async(engine_core_req)
 
-        # 5) Return the generator.
-        return stream.generator()
+        if self.log_requests:
+            logger.info("Added request %s.", request_id)
+
+        return self.rid_to_queue[request_id]
 
     # TODO: we should support multiple prompts in one call, as you
     # can do with LLM.generate. So that for multi-prompt completion
@@ -194,7 +192,7 @@ class AsyncLLM(EngineClient):
         """
         Main function called by the API server to kick off a request
             * 1) Making an AsyncStream corresponding to the Request.
-            # 2) Processing the Input.
+            * 2) Processing the Input.
             * 3) Adding the Request to the Detokenizer.
             * 4) Adding the Request to the EngineCore (separate process).
 
@@ -206,14 +204,15 @@ class AsyncLLM(EngineClient):
         returning the RequestOutput back to the caller.
         """
 
-        # We start the output_handler on the first call to generate() so that
-        # we can call __init__ before the event loop starts, which enables us
+        try:
+            # We start the output_handler on the first call to generate() so
+            # we can call __init__ before the event loop, which enables us
             # to handle startup failure gracefully in the OpenAI server.
             if self.output_handler is None:
                 self.output_handler = asyncio.create_task(
                     self._run_output_handler())
 
-        async for output in await self.add_request(
+            q = await self.add_request(
                 request_id,
                 prompt,
                 sampling_params,
@@ -221,79 +220,42 @@ class AsyncLLM(EngineClient):
                 trace_headers=trace_headers,
                 prompt_adapter_request=prompt_adapter_request,
                 priority=priority,
-        ):
-            yield output
+            )
 
-    def _finish_stream(self, request_id: str):
-        stream = self.request_streams.pop(request_id, None)
-        if stream is not None:
-            stream.finish()
+            # The output_handler task pushes items into the queue.
+            # This task pulls from the queue and yields to caller.
+            while True:
+                # Note: drain queue without await if possible (avoids
+                # task switching under load which helps performance).
+                out = q.get_nowait() if q.qsize() > 0 else await q.get()
 
-    def _add_request_to_streams(
-        self,
-        request_id: str,
-    ) -> AsyncStream:
+                # Note: both Detokenizer and EngineCore handle their
+                # own request cleanup based on finished.
+                if out.finished:
+                    del self.rid_to_queue[request_id]
+                    yield out
+                    break
 
-        if request_id in self.request_streams:
-            raise ValueError(f"Request id {request_id} already running.")
+                yield out
 
-        # Avoid streams having circular ref to parent AsyncLLM object.
-        aborted_reqs = self.client_aborted_requests
-        stream = AsyncStream(request_id, aborted_reqs.append)
-        self.request_streams[request_id] = stream
-
-        if self.log_requests:
-            logger.info("Added request %s.", request_id)
-
-        return stream
-
-    async def _process_cancellations(self) -> None:
-        """
-        Process requests cancelled from user disconnecting.
-
-        When a client disconnects, AsyncStream._cancel() is called.
-        We passed a callback to AsyncStream(), which appends to 
-        self.client_aborted_requests.
-
-        As a result, if any requests are canceled from the user side
-        the request_id will show up in self.client_aborted_requests.
-        """
-
-        # Avoid streams having circular ref to parent AsyncLLM object.
-        if not self.client_aborted_requests:
-            return
-        reqs_to_abort = self.client_aborted_requests.copy()
-        self.client_aborted_requests.clear()
-
-        # Remove from Detokenizer.
-        self.detokenizer.abort_requests(reqs_to_abort)
-
-        # Remove from RequestStreams.
-        for request_id in reqs_to_abort:
-            if self.log_requests:
-                logger.info("User-cancelled request %s.", request_id)
-            self._finish_stream(request_id)
-
-        # Remove from EngineCore.
-        await self.engine_core.abort_requests_async(reqs_to_abort)
+        # If the request is disconnected by the client, the
+        # generate() task will be canceled. So, we abort the
+        # request if we end up here.
+        except asyncio.CancelledError:
+            await self.abort(request_id)
+            raise
 
     def _process_request_outputs(self, request_outputs: List[RequestOutput]):
-        """Process outputs by putting them into per-request AsyncStreams."""
+        """Process outputs by putting them into per-request queues."""
 
         for request_output in request_outputs:
             request_id = request_output.request_id
-            assert request_id in self.request_streams
 
-            # Each request in the API server pulls from the per-request stream.
-            stream = self.request_streams.get(request_id)
-            if stream is not None:
-                stream.put(request_output)
-
-                # If finished, remove from the tracker.
-                if request_output.finished:
-                    if self.log_requests:
-                        logger.info("Finished request %s.", request_id)
-                    self._finish_stream(request_id)
+            # Note: it is possible a request was aborted and removed from
+            # the state due to client cancellations, so if we encounter a
+            # request id not in the state, we skip.
+            if request_id in self.rid_to_queue:
+                self.rid_to_queue[request_id].put_nowait(request_output)
 
     async def _run_output_handler(self):
         """Background loop: pulls from EngineCore and pushes to AsyncStreams."""
@@ -306,24 +268,27 @@ class AsyncLLM(EngineClient):
                 # 2) Detokenize based on the output.
                 request_outputs, reqs_to_abort = self.detokenizer.step(outputs)
 
-                # 3) Put the RequestOutputs into the per-request AsyncStreams.
+                # 3) Put the RequestOutputs into the per-request queues.
                 self._process_request_outputs(request_outputs)
 
                 # 4) Abort any requests that finished due to stop strings.
                 await self.engine_core.abort_requests_async(reqs_to_abort)
 
-                # 5) Abort any requests due to client cancellations.
-                await self._process_cancellations()
-
         except BaseException as e:
             logger.error(e)
             raise e
 
-    # TODO: can we eliminate these?
-
     async def abort(self, request_id: str) -> None:
-        # Note: Who Calls this? I dont think this is actually used.
-        raise ValueError("Not Supported on V1 yet.")
+        """Abort RequestId in self, detokenizer, and engine core."""
+
+        request_ids = [request_id]
+        await self.engine_core.abort_requests_async(request_ids)
+        self.detokenizer.abort_requests(request_ids)
+
+        # If a request finishes while we await then the request_id
+        # will be removed from the tracked queues before we get here.
+        if request_id in self.rid_to_queue:
+            del self.rid_to_queue[request_id]
 
     def encode(
         self,

vllm/v1/engine/async_stream.py

@@ -1,55 +0,0 @@
-import asyncio
-from typing import Any, AsyncGenerator, Callable, Optional, Type, Union
-
-from vllm.outputs import PoolingRequestOutput, RequestOutput
-
-
-class AsyncStream:
-    """A stream of RequestOutputs or PoolingRequestOutputs for a request
-    that can be iterated over asynchronously via an async generator."""
-
-    STOP_ITERATION = Exception()  # Sentinel
-
-    def __init__(self, request_id: str, cancel: Callable[[str], None]) -> None:
-        self.request_id = request_id
-        self._cancel = cancel
-        self._queue: asyncio.Queue = asyncio.Queue()
-        self._finished = False
-
-    def put(self, item: Union[RequestOutput, PoolingRequestOutput,
-                              Exception]) -> None:
-        if not self._finished:
-            self._queue.put_nowait(item)
-
-    def finish(
-        self,
-        exception: Optional[Union[BaseException, Type[BaseException]]] = None,
-    ) -> None:
-        if not self._finished:
-            self._finished = True
-            self._queue.put_nowait(exception if self._is_raisable(exception)
-                                   else AsyncStream.STOP_ITERATION)
-
-    async def generator(
-        self
-    ) -> AsyncGenerator[Union[RequestOutput, PoolingRequestOutput], None]:
-        finished = False
-        try:
-            while True:
-                result = await self._queue.get()
-                if self._is_raisable(result):
-                    finished = True
-                    if result == AsyncStream.STOP_ITERATION:
-                        return
-                    raise result
-                yield result
-        finally:
-            self._finished = True
-            if not finished:
-                self._cancel(self.request_id)
-
-    @staticmethod
-    def _is_raisable(value: Any):
-        return isinstance(value, BaseException) or \
-                (isinstance(value, type) and \
-                 issubclass(value, BaseException))

vllm/v1/engine/core.py

@@ -32,7 +32,7 @@ logger = init_logger(__name__)
 
 POLLING_TIMEOUT_MS = 5000
 POLLING_TIMEOUT_S = POLLING_TIMEOUT_MS // 1000
-LOGGING_TIME_S = 5000
+LOGGING_TIME_S = POLLING_TIMEOUT_S
 
 
 class EngineCore:

vllm/v1/sample/ops/penalties.py

@@ -0,0 +1,59 @@
+from typing import List, Set, Tuple
+
+import torch
+
+from vllm.model_executor.layers.utils import apply_penalties
+from vllm.utils import is_pin_memory_available, make_tensor_with_pad
+
+
+def apply_min_token_penalties(logits: torch.Tensor,
+                              output_token_ids: List[List[int]],
+                              stop_token_ids: List[Set[int]],
+                              min_tokens: List[int]) -> None:
+    """
+    Applies minimum token penalty by setting the logits of the stop tokens
+    to -inf.
+    """
+    min_tokens_logits_to_penalize: List[Tuple[int, int]] = []
+    for index, min_token in enumerate(min_tokens):
+        if len(output_token_ids[index]) < min_token:
+            for stop_token_id in stop_token_ids[index]:
+                min_tokens_logits_to_penalize.append((index, stop_token_id))
+    if min_tokens_logits_to_penalize:
+        logits[tuple(zip(*min_tokens_logits_to_penalize))] = -float("inf")
+
+
+def apply_all_penalties(
+    logits: torch.Tensor,
+    prompt_token_ids: torch.Tensor,
+    presence_penalties: torch.Tensor,
+    frequency_penalties: torch.Tensor,
+    repetition_penalties: torch.Tensor,
+    output_token_ids: List[List[int]],
+) -> torch.Tensor:
+    """
+    Applies presence, frequency and repetition penalties to the logits.
+    """
+    _, vocab_size = logits.shape
+    output_tokens_t = _convert_to_tensors(output_token_ids, vocab_size,
+                                          logits.device)
+    return apply_penalties(logits, prompt_token_ids, output_tokens_t,
+                           presence_penalties, frequency_penalties,
+                           repetition_penalties)
+
+
+def _convert_to_tensors(output_token_ids: List[List[int]], vocab_size: int,
+                        device: torch.device) -> torch.Tensor:
+    """
+    Convert the different list data structures to tensors.
+    """
+    output_tokens_tensor = make_tensor_with_pad(
+        output_token_ids,
+        # Use the value of vocab_size as a pad since we don't have a
+        # token_id of this value.
+        pad=vocab_size,
+        device="cpu",
+        dtype=torch.int64,
+        pin_memory=is_pin_memory_available(),
+    )
+    return output_tokens_tensor.to(device, non_blocking=True)

vllm/v1/sample/ops/topk_topp_sampler.py

@@ -0,0 +1,201 @@
+from typing import Dict
+
+import torch
+import torch.nn as nn
+
+from vllm import envs
+from vllm.logger import init_logger
+from vllm.platforms import current_platform
+
+logger = init_logger(__name__)
+
+try:
+    import flashinfer.sampling
+    is_flashinfer_available = True
+except ImportError:
+    is_flashinfer_available = False
+
+
+class TopKTopPSampler(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        if current_platform.is_cuda:
+            if is_flashinfer_available:
+                if envs.VLLM_USE_FLASHINFER_SAMPLER is not False:
+                    # NOTE(woosuk): The V0 sampler doesn't use FlashInfer for
+                    # sampling unless VLLM_USE_FLASHINFER_SAMPLER=1 (i.e., by
+                    # default it is unused). For backward compatibility, we set
+                    # `VLLM_USE_FLASHINFER_SAMPLER` as None by default and
+                    # interpret it differently in V0 and V1 samplers: In V0,
+                    # None means False, while in V1, None means True. This is
+                    # why we use the condition
+                    # `envs.VLLM_USE_FLASHINFER_SAMPLER is not False` here.
+                    logger.info("Using FlashInfer for top-p & top-k sampling.")
+                    self.forward = self.forward_cuda
+                else:
+                    logger.warning(
+                        "FlashInfer is available, but it is not enabled. "
+                        "Falling back to the PyTorch-native implementation of "
+                        "top-p & top-k sampling. For the best performance, "
+                        "please set VLLM_USE_FLASHINFER_SAMPLER=1.")
+                    self.forward = self.forward_native
+            else:
+                logger.warning(
+                    "FlashInfer is not available. Falling back to the PyTorch-"
+                    "native implementation of top-p & top-k sampling. For the "
+                    "best performance, please install FalshInfer.")
+                self.forward = self.forward_native
+        else:
+            self.forward = self.forward_native
+
+    def forward_native(
+        self,
+        logits: torch.Tensor,
+        generators: Dict[int, torch.Generator],
+        no_top_k: bool,
+        k: torch.Tensor,
+        no_top_p: bool,
+        p: torch.Tensor,
+    ) -> torch.Tensor:
+        """PyTorch-native implementation of top-k and top-p sampling."""
+        logits = apply_top_k_top_p(logits, no_top_k, k, no_top_p, p)
+        probs = logits.softmax(dim=-1, dtype=torch.float32)
+        return random_sample(probs, generators)
+
+    def forward_cuda(
+        self,
+        logits: torch.Tensor,
+        generators: Dict[int, torch.Generator],
+        no_top_k: bool,
+        k: torch.Tensor,
+        no_top_p: bool,
+        p: torch.Tensor,
+    ) -> torch.Tensor:
+        """More optimized implementation for top-k and top-p sampling."""
+        probs = logits.softmax(dim=-1, dtype=torch.float32)
+        if no_top_k and no_top_p:
+            # We prefer `random_sample` over `flashinfer_sample` when sorting is
+            # not needed. This is because `random_sample` does not require
+            # CPU-GPU synchronization while `flashinfer_sample` does.
+            return random_sample(probs, generators)
+        return flashinfer_sample(probs, no_top_k, k, no_top_p, p, generators)
+
+
+def apply_top_k_top_p(
+    logits: torch.Tensor,
+    no_top_k: bool,
+    k: torch.Tensor,
+    no_top_p: bool,
+    p: torch.Tensor,
+) -> torch.Tensor:
+    """Apply top-k and top-p masks to the logits.
+
+    This function sorts the logits tensor, which can be slow for large batches.
+    """
+    if no_top_k and no_top_p:
+        return logits
+    logits_sort, logits_idx = logits.sort(dim=-1, descending=False)
+
+    if not no_top_k:
+        # Apply top-k.
+        top_k_mask = logits_sort.size(1) - k.to(torch.long)
+        # Get all the top_k values.
+        top_k_mask = logits_sort.gather(1, top_k_mask.unsqueeze(dim=1))
+        top_k_mask = logits_sort < top_k_mask
+        logits_sort.masked_fill_(top_k_mask, -float("inf"))
+
+    if not no_top_p:
+        # Apply top-p.
+        probs_sort = logits_sort.softmax(dim=-1)
+        probs_sum = probs_sort.cumsum(dim=-1)
+        top_p_mask = probs_sum <= 1 - p.unsqueeze(dim=1)
+        # at least one
+        top_p_mask[:, -1] = False
+        logits_sort.masked_fill_(top_p_mask, -float("inf"))
+
+    # Re-sort the probabilities.
+    logits = logits_sort.scatter(dim=-1, index=logits_idx, src=logits_sort)
+    return logits
+
+
+def random_sample(
+    probs: torch.Tensor,
+    generators: Dict[int, torch.Generator],
+) -> torch.Tensor:
+    """Randomly sample from the probabilities.
+
+    We use this function instead of torch.multinomial because torch.multinomial
+    causes CPU-GPU synchronization.
+    """
+    q = torch.empty_like(probs)
+    # NOTE(woosuk): To batch-process the requests without their own seeds,
+    # which is the common case, we first assume that every request does
+    # not have its own seed. Then, we overwrite the values for the requests
+    # that have their own seeds.
+    if len(generators) != probs.shape[0]:
+        q.exponential_()
+    if generators:
+        # TODO(woosuk): This can be slow because we handle each request
+        # one by one. Optimize this.
+        for i, generator in generators.items():
+            q[i].exponential_(generator=generator)
+    return probs.div_(q).argmax(dim=-1).view(-1)
+
+
+def flashinfer_sample(
+    probs: torch.Tensor,
+    no_top_k: bool,
+    k: torch.Tensor,
+    no_top_p: bool,
+    p: torch.Tensor,
+    generators: Dict[int, torch.Generator],
+) -> torch.Tensor:
+    """Sample from the probabilities using FlashInfer.
+
+    Statistically, this function is equivalent to the `random_sample` function.
+    However, this function is faster because it avoids sorting the logits tensor
+    via rejection sampling.
+    
+    NOTE: The outputs of this function do not necessarily match the outputs of
+    the `random_sample` function. It only guarantees that the outputs are
+    statistically equivalent.
+
+    NOTE: This function includes CPU-GPU synchronization, while `random_sample`
+    does not. Call this function at the end of the forward pass to minimize
+    the synchronization overhead.
+    """
+    assert not (no_top_k and no_top_p)
+    max_top_k_round = 32
+    batch_size = probs.shape[0]
+    uniform_samples = torch.empty((max_top_k_round, batch_size),
+                                  device=probs.device)
+    if len(generators) != batch_size:
+        uniform_samples.uniform_()
+    if generators:
+        for i, generator in generators.items():
+            uniform_samples[:, i].uniform_(generator=generator)
+
+    if no_top_k:
+        # Top-p only.
+        next_token_ids, success = flashinfer.sampling.top_p_sampling_from_probs(
+            probs, uniform_samples, p, deterministic=True)
+    elif no_top_p:
+        # Top-k only.
+        next_token_ids, success = flashinfer.sampling.top_k_sampling_from_probs(
+            probs, uniform_samples, k, deterministic=True)
+    else:
+        # Both top-k and top-p.
+        next_token_ids, success = (
+            flashinfer.sampling.top_k_top_p_sampling_from_probs(
+                probs, uniform_samples, k, p, deterministic=True))
+
+    # NOTE: CPU-GPU synchronization happens here.
+    if not success.all():
+        if not no_top_k:
+            probs = flashinfer.sampling.top_k_renorm_prob(probs, k)
+        if not no_top_p:
+            probs = flashinfer.sampling.top_p_renorm_prob(probs, p)
+        next_token_ids = flashinfer.sampling.sampling_from_probs(
+            probs, uniform_samples[0], deterministic=True)
+    return next_token_ids.view(-1)

vllm/v1/sample/sampler.py

@@ -1,53 +1,55 @@
 """A layer that samples the next tokens from the model's outputs."""
-from typing import Dict, List, Set, Tuple
+from typing import Tuple
 
 import torch
 import torch.nn as nn
 
-from vllm.model_executor.layers.utils import apply_penalties
-from vllm.utils import is_pin_memory_available, make_tensor_with_pad
 from vllm.v1.outputs import SamplerOutput
 from vllm.v1.sample.metadata import SamplingMetadata
+from vllm.v1.sample.ops.penalties import (apply_all_penalties,
+                                          apply_min_token_penalties)
+from vllm.v1.sample.ops.topk_topp_sampler import TopKTopPSampler
 
 _SAMPLING_EPS = 1e-5
 
 
 class Sampler(nn.Module):
 
+    def __init__(self):
+        super().__init__()
+        self.topk_topp_sampler = TopKTopPSampler()
+
     def forward(
         self,
         logits: torch.Tensor,
         sampling_metadata: SamplingMetadata,
     ) -> SamplerOutput:
-        _apply_min_token_penalties(logits, sampling_metadata.output_token_ids,
-                                   sampling_metadata.stop_token_ids,
-                                   sampling_metadata.min_tokens)
-        if not sampling_metadata.no_penalties:
-            assert sampling_metadata.prompt_token_ids is not None
-            _apply_penalties(logits, sampling_metadata.prompt_token_ids,
-                             sampling_metadata.presence_penalties,
-                             sampling_metadata.frequency_penalties,
-                             sampling_metadata.repetition_penalties,
-                             sampling_metadata.output_token_ids)
-        logits = self.apply_temperature(logits, sampling_metadata.temperature)
-        logits = self.apply_top_k_top_p(logits, sampling_metadata)
-        probs = self.get_probs(logits)
-        sampled = self.sample(probs, sampling_metadata)
-        # Use int32 to reduce the tensor size.
-        sampled = sampled.to(torch.int32)
-
-        if sampling_metadata.max_num_logprobs > 0:
-            logprobs = self.get_logprobs(logits)
-            # FIXME: Mask the sampled token_id, get topk logprobs,
-            # and concatenate the topk with the sampled token_id.
-            topk_logprobs, topk_indices = torch.topk(
-                logprobs, sampling_metadata.max_num_logprobs, dim=-1)
-            # Use int32 to reduce the tensor size.
-            topk_indices = topk_indices.to(torch.int32)
+        needs_logprobs = sampling_metadata.max_num_logprobs > 0
+        if needs_logprobs:
+            # NOTE(woosuk): Use the original logits (before any penalties or
+            # temperature scaling) for the top-k logprobs.
+            # This is different from the V0 sampler, which uses the logits that
+            # is used for sampling (after penalties and temperature scaling).
+            # NOTE: We compute logprobs first because the below ops may
+            # modify the logits tensor in-place (and we don't want to clone
+            # the logits tensor for memory efficiency).
+            topk_logprobs, topk_indices = self.get_topk_logprobs(
+                logits, sampling_metadata)
         else:
             topk_logprobs = None
             topk_indices = None
 
+        # Use float32 for the logits.
+        logits = logits.to(torch.float32)
+        # Apply penalties (e.g., min_tokens, freq_penalties).
+        logits = self.apply_penalties(logits, sampling_metadata)
+        # Apply temperature.
+        logits = self.apply_temperature(logits, sampling_metadata.temperature)
+        # Sample the next token.
+        sampled = self.sample(logits, sampling_metadata)
+        # Use int32 to reduce the tensor size.
+        sampled = sampled.to(torch.int32)
+
         # NOTE: CPU-GPU synchronization happens here.
         sampler_output = SamplerOutput(
             sampled_token_ids=sampled.tolist(),
@@ -63,71 +65,37 @@ class Sampler(nn.Module):
         logits: torch.Tensor,
         temp: torch.Tensor,
     ) -> torch.Tensor:
-        # Use float32 to apply temperature scaling.
-        logits = logits.to(torch.float32)
         # Avoid division by zero.
         temp = torch.where(temp < _SAMPLING_EPS, 1.0, temp)
         # Use in-place division to avoid creating a new tensor.
         logits.div_(temp.unsqueeze(dim=1))
         return logits
 
-    def apply_top_k_top_p(
-        self,
-        logits: torch.Tensor,
-        sampling_metadata: SamplingMetadata,
-    ) -> torch.Tensor:
-        return _apply_top_k_top_p(
-            logits,
-            sampling_metadata.no_top_k,
-            sampling_metadata.top_k,
-            sampling_metadata.no_top_p,
-            sampling_metadata.top_p,
-        )
-
-    def get_probs(self, logits: torch.Tensor) -> torch.Tensor:
-        return torch.softmax(logits, dim=-1, dtype=torch.float32)
-
-    def get_logprobs(self, logits: torch.Tensor) -> torch.Tensor:
-        return torch.log_softmax(logits, dim=-1, dtype=torch.float32)
-
-    def greedy_sample(self, probs: torch.Tensor) -> torch.Tensor:
-        return probs.argmax(dim=-1).view(-1)
-
-    def random_sample(
-        self,
-        probs: torch.Tensor,
-        generators: Dict[int, torch.Generator],
-    ) -> torch.Tensor:
-        q = torch.empty_like(probs)
-        # NOTE(woosuk): To batch-process the requests without their own seeds,
-        # which is the common case, we first assume that every request does
-        # not have its own seed. Then, we overwrite the values for the requests
-        # that have their own seeds.
-        if len(generators) != probs.shape[0]:
-            # This might still be done here unnecessarily if there are greedies
-            q.exponential_()
-        if generators:
-            # TODO(woosuk): This can be slow because we handle each request
-            # one by one. Optimize this.
-            for i, generator in generators.items():
-                q[i].exponential_(generator=generator)
-        return probs.div_(q).argmax(dim=-1).view(-1)
+    def greedy_sample(self, logits: torch.Tensor) -> torch.Tensor:
+        return logits.argmax(dim=-1).view(-1)
 
     def sample(
         self,
-        probs: torch.Tensor,
+        logits: torch.Tensor,
         sampling_metadata: SamplingMetadata,
     ) -> torch.Tensor:
         assert not (sampling_metadata.all_greedy
                     and sampling_metadata.all_random)
         if sampling_metadata.all_greedy:
-            return self.greedy_sample(probs)
-        if sampling_metadata.all_random:
-            return self.random_sample(probs, sampling_metadata.generators)
+            return self.greedy_sample(logits)
 
-        greedy_sampled = self.greedy_sample(probs)
-        random_sampled = self.random_sample(probs,
-                                            sampling_metadata.generators)
+        random_sampled = self.topk_topp_sampler(
+            logits,
+            sampling_metadata.generators,
+            sampling_metadata.no_top_k,
+            sampling_metadata.top_k,
+            sampling_metadata.no_top_p,
+            sampling_metadata.top_p,
+        )
+        if sampling_metadata.all_random:
+            return random_sampled
+
+        greedy_sampled = self.greedy_sample(logits)
         sampled = torch.where(
             sampling_metadata.temperature < _SAMPLING_EPS,
             greedy_sampled,
@@ -135,86 +103,34 @@ class Sampler(nn.Module):
         )
         return sampled
 
-
-# TODO(woosuk): Optimize this with a custom kernel.
-def _apply_top_k_top_p(
+    def get_topk_logprobs(
+        self,
         logits: torch.Tensor,
-    no_top_k: bool,
-    k: torch.Tensor,
-    no_top_p: bool,
-    p: torch.Tensor,
-) -> torch.Tensor:
-    if no_top_k and no_top_p:
+        sampling_metadata: SamplingMetadata,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        logprobs = logits.log_softmax(dim=-1, dtype=torch.float32)
+        # FIXME: Mask the sampled token_id, get topk logprobs,
+        # and concatenate the topk with the sampled token_id.
+        topk_logprobs, topk_indices = torch.topk(
+            logprobs, sampling_metadata.max_num_logprobs, dim=-1)
+        # Use int32 to reduce the tensor size.
+        topk_indices = topk_indices.to(torch.int32)
+        return topk_logprobs, topk_indices
+
+    def apply_penalties(
+        self,
+        logits: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> torch.Tensor:
+        apply_min_token_penalties(logits, sampling_metadata.output_token_ids,
+                                  sampling_metadata.stop_token_ids,
+                                  sampling_metadata.min_tokens)
+        if not sampling_metadata.no_penalties:
+            assert sampling_metadata.prompt_token_ids is not None
+            logits = apply_all_penalties(
+                logits, sampling_metadata.prompt_token_ids,
+                sampling_metadata.presence_penalties,
+                sampling_metadata.frequency_penalties,
+                sampling_metadata.repetition_penalties,
+                sampling_metadata.output_token_ids)
         return logits
-    logits_sort, logits_idx = logits.sort(dim=-1, descending=False)
-
-    if not no_top_k:
-        # Apply top-k.
-        top_k_mask = logits_sort.size(1) - k.to(torch.long)
-        # Get all the top_k values.
-        top_k_mask = logits_sort.gather(1, top_k_mask.unsqueeze(dim=1))
-        top_k_mask = logits_sort < top_k_mask
-        logits_sort.masked_fill_(top_k_mask, -float("inf"))
-
-    if not no_top_p:
-        # Apply top-p.
-        probs_sort = logits_sort.softmax(dim=-1)
-        probs_sum = probs_sort.cumsum(dim=-1)
-        top_p_mask = probs_sum <= 1 - p.unsqueeze(dim=1)
-        # at least one
-        top_p_mask[:, -1] = False
-        logits_sort.masked_fill_(top_p_mask, -float("inf"))
-
-    # Re-sort the probabilities.
-    logits = logits_sort.scatter(dim=-1, index=logits_idx, src=logits_sort)
-    return logits
-
-
-def _apply_min_token_penalties(logits: torch.Tensor,
-                               output_token_ids: List[List[int]],
-                               stop_token_ids: List[Set[int]],
-                               min_tokens: List[int]):
-    """
-    Applies minimum token penalty by setting the logits of the stop tokens
-    to -inf.
-    """
-    min_tokens_logits_to_penalize: List[Tuple[int, int]] = []
-    for index, min_token in enumerate(min_tokens):
-        if (len(output_token_ids[index]) < min_token):
-            for stop_token_id in stop_token_ids[index]:
-                min_tokens_logits_to_penalize.append((index, stop_token_id))
-    if min_tokens_logits_to_penalize:
-        logits[tuple(zip(*min_tokens_logits_to_penalize))] = -float("inf")
-
-
-def _apply_penalties(logits: torch.Tensor, prompt_token_ids: torch.Tensor,
-                     presence_penalties: torch.Tensor,
-                     frequency_penalties: torch.Tensor,
-                     repetition_penalties: torch.Tensor,
-                     output_token_ids: List[List[int]]):
-    """
-    Applies presence, frequency and repetition penalties to the logits.
-    """
-    _, vocab_size = logits.shape
-    output_tokens_t = _convert_to_tensors(output_token_ids, vocab_size,
-                                          logits.device)
-    return apply_penalties(logits, prompt_token_ids, output_tokens_t,
-                           presence_penalties, frequency_penalties,
-                           repetition_penalties)
-
-
-def _convert_to_tensors(output_token_ids: List[List[int]], vocab_size: int,
-                        device: torch.device) -> torch.Tensor:
-    """
-    Convert the different list data structures to tensors.
-    """
-    output_tokens_tensor = make_tensor_with_pad(
-        output_token_ids,
-        # Use the value of vocab_size as a pad since we don't have a
-        # token_id of this value.
-        pad=vocab_size,
-        device="cpu",
-        dtype=torch.int64,
-        pin_memory=is_pin_memory_available(),
-    )
-    return output_tokens_tensor.to(device, non_blocking=True)