[Deprecation] Remove prompt_token_ids arg fallback in LLM.generate and LLM.embed (#18800)

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

.buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh

@@ -2,7 +2,7 @@
 # We can use this script to compute baseline accuracy on GSM for transformers.
 #
 # Make sure you have lm-eval-harness installed:
-#   pip install lm-eval==0.4.4
+#   pip install git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d#egg=lm-eval[api]
 
 usage() {
     echo``

.buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh

@@ -3,7 +3,7 @@
 # We use this for fp8, which HF does not support.
 #
 # Make sure you have lm-eval-harness installed:
-#   pip install lm-eval==0.4.4
+#   pip install git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d#egg=lm-eval[api]
 
 usage() {
     echo``

docker/Dockerfile.rocm

@@ -71,7 +71,7 @@ COPY --from=build_vllm ${COMMON_WORKDIR}/vllm /vllm-workspace
 RUN cd /vllm-workspace \
     && rm -rf vllm \
     && python3 -m pip install -e tests/vllm_test_utils \
-    && python3 -m pip install lm-eval[api]==0.4.4 \
+    && python3 -m pip install git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d#egg=lm-eval[api] \
     && python3 -m pip install pytest-shard
 
 # -----------------------

docs/features/quantization/fp8.md

@@ -79,7 +79,7 @@ Since simple RTN does not require data for weight quantization and the activatio
 Install `vllm` and `lm-evaluation-harness` for evaluation:
 
 ```bash
-pip install vllm lm-eval==0.4.4
+pip install vllm git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d#egg=lm-eval[api]
 ```
 
 Load and run the model in `vllm`:

docs/features/quantization/int4.md

@@ -18,7 +18,7 @@ pip install llmcompressor
 Additionally, install `vllm` and `lm-evaluation-harness` for evaluation:
 
 ```bash
-pip install vllm lm-eval==0.4.4
+pip install vllm git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d#egg=lm-eval[api]
 ```
 
 ## Quantization Process

docs/features/quantization/int8.md

@@ -19,7 +19,7 @@ pip install llmcompressor
 Additionally, install `vllm` and `lm-evaluation-harness` for evaluation:
 
 ```bash
-pip install vllm lm-eval==0.4.4
+pip install vllm git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d#egg=lm-eval[api]
 ```
 
 ## Quantization Process

docs/features/quantization/quark.md

@@ -20,7 +20,7 @@ for more installation details.
 Additionally, install `vllm` and `lm-evaluation-harness` for evaluation:
 
 ```bash
-pip install vllm lm-eval==0.4.4
+pip install vllm git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d#egg=lm-eval[api]
 ```
 
 ## Quantization Process

examples/offline_inference/spec_decode.py

@@ -5,6 +5,7 @@ from transformers import AutoTokenizer
 
 from vllm import LLM, SamplingParams
 from vllm.benchmarks.datasets import add_dataset_parser, get_samples
+from vllm.inputs import TokensPrompt
 from vllm.v1.metrics.reader import Counter, Vector
 
 try:
@@ -137,7 +138,8 @@ def main():
     sampling_params = SamplingParams(temperature=args.temp, max_tokens=args.output_len)
     if not args.custom_mm_prompts:
         outputs = llm.generate(
-            prompt_token_ids=prompt_ids, sampling_params=sampling_params
+            TokensPrompt(prompt_token_ids=prompt_ids),
+            sampling_params=sampling_params,
         )
     else:
         outputs = llm.chat(prompts, sampling_params=sampling_params)

examples/offline_inference/structured_outputs.py

@@ -85,7 +85,7 @@ def format_output(title: str, output: str):
 
 
 def generate_output(prompt: str, sampling_params: SamplingParams, llm: LLM):
-    outputs = llm.generate(prompts=prompt, sampling_params=sampling_params)
+    outputs = llm.generate(prompt, sampling_params=sampling_params)
     return outputs[0].outputs[0].text
 
 

requirements/nightly_torch_test.txt

@@ -27,7 +27,7 @@ mistral_common[image,audio] >= 1.8.2 # required for voxtral test
 num2words # required for smolvlm test
 opencv-python-headless >= 4.11.0 # required for video test
 datamodel_code_generator # required for minicpm3 test
-lm-eval[api]==0.4.8 # required for model evaluation test
+lm-eval[api] @ git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d # required for model evaluation test
 mteb>=1.38.11, <2 # required for mteb test
 transformers==4.52.4
 tokenizers==0.21.1

requirements/test.in

@@ -32,7 +32,8 @@ num2words # required for smolvlm test
 open_clip_torch==2.32.0 # Required for nemotron_vl test
 opencv-python-headless >= 4.11.0 # required for video test
 datamodel_code_generator # required for minicpm3 test
-lm-eval[api]==0.4.8 # required for model evaluation test
+# TODO: Use lm-eval[api]==0.4.10 once released
+lm-eval[api] @ git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d # required for model evaluation test
 mteb[bm25s]>=1.38.11, <2 # required for mteb test
 transformers==4.55.2
 tokenizers==0.21.1

requirements/test.txt

@@ -408,7 +408,7 @@ lightning-utilities==0.14.3
     #   torchmetrics
 llvmlite==0.44.0
     # via numba
-lm-eval==0.4.8
+lm-eval @ git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d
     # via -r requirements/test.in
 lxml==5.3.0
     # via

tests/entrypoints/llm/test_chat.py

@@ -18,10 +18,9 @@ def text_llm():
               enforce_eager=True,
               seed=0)
 
-    with llm.deprecate_legacy_api():
-        yield weakref.proxy(llm)
+    yield weakref.proxy(llm)
 
-        del llm
+    del llm
 
     cleanup_dist_env_and_memory()
 
@@ -88,10 +87,9 @@ def vision_llm():
         seed=0,
     )
 
-    with llm.deprecate_legacy_api():
-        yield weakref.proxy(llm)
+    yield weakref.proxy(llm)
 
-        del llm
+    del llm
 
     cleanup_dist_env_and_memory()
 
@@ -158,10 +156,9 @@ def thinking_llm():
         seed=0,
     )
 
-    with llm.deprecate_legacy_api():
-        yield weakref.proxy(llm)
+    yield weakref.proxy(llm)
 
-        del llm
+    del llm
 
     cleanup_dist_env_and_memory()
 

tests/entrypoints/llm/test_classify.py

@@ -35,10 +35,9 @@ def llm():
               enforce_eager=True,
               seed=0)
 
-    with llm.deprecate_legacy_api():
-        yield weakref.proxy(llm)
+    yield weakref.proxy(llm)
 
-        del llm
+    del llm
 
     cleanup_dist_env_and_memory()
 

tests/entrypoints/llm/test_embedding.py

@@ -26,10 +26,9 @@ def llm():
               enforce_eager=True,
               seed=0)
 
-    with llm.deprecate_legacy_api():
-        yield weakref.proxy(llm)
+    yield weakref.proxy(llm)
 
-        del llm
+    del llm
 
     cleanup_dist_env_and_memory()
 

tests/entrypoints/llm/test_encode.py

@@ -5,11 +5,9 @@ import weakref
 
 import pytest
 
-from vllm import LLM, PoolingParams, PoolingRequestOutput
+from vllm import LLM, PoolingParams
 from vllm.distributed import cleanup_dist_env_and_memory
 
-from ...models.utils import check_embeddings_close
-
 MODEL_NAME = "intfloat/multilingual-e5-small"
 
 PROMPTS = [
@@ -48,57 +46,13 @@ def llm():
               enforce_eager=True,
               seed=0)
 
-    with llm.deprecate_legacy_api():
-        yield weakref.proxy(llm)
+    yield weakref.proxy(llm)
 
-        del llm
+    del llm
 
     cleanup_dist_env_and_memory()
 
 
-def assert_outputs_match(o1: list[PoolingRequestOutput],
-                         o2: list[PoolingRequestOutput]):
-    check_embeddings_close(
-        embeddings_0_lst=[o.outputs.data for o in o1],
-        embeddings_1_lst=[o.outputs.data for o in o2],
-        name_0="hf",
-        name_1="vllm",
-        tol=1e-2,
-    )
-
-
-@pytest.mark.skip_global_cleanup
-@pytest.mark.parametrize('prompt_token_ids', TOKEN_IDS)
-def test_v1_v2_api_consistency_single_prompt_tokens(llm: LLM,
-                                                    prompt_token_ids):
-    pooling_params = PoolingParams()
-
-    with pytest.warns(DeprecationWarning, match="'prompt_token_ids'"):
-        v1_output = llm.encode(prompt_token_ids=prompt_token_ids,
-                               pooling_params=pooling_params)
-
-    v2_output = llm.encode({"prompt_token_ids": prompt_token_ids},
-                           pooling_params=pooling_params)
-    assert_outputs_match(v1_output, v2_output)
-
-
-@pytest.mark.skip_global_cleanup
-def test_v1_v2_api_consistency_multi_prompt_tokens(llm: LLM):
-    pooling_params = PoolingParams()
-
-    with pytest.warns(DeprecationWarning, match="'prompt_token_ids'"):
-        v1_output = llm.encode(prompt_token_ids=TOKEN_IDS,
-                               pooling_params=pooling_params)
-
-    v2_output = llm.encode(
-        [{
-            "prompt_token_ids": p
-        } for p in TOKEN_IDS],
-        pooling_params=pooling_params,
-    )
-    assert_outputs_match(v1_output, v2_output)
-
-
 @pytest.mark.skip_global_cleanup
 def test_multiple_pooling_params(llm: LLM):
     pooling_params = [

tests/entrypoints/llm/test_generate.py

@@ -5,7 +5,7 @@ import weakref
 
 import pytest
 
-from vllm import LLM, RequestOutput, SamplingParams
+from vllm import LLM, SamplingParams
 from vllm.distributed import cleanup_dist_env_and_memory
 
 MODEL_NAME = "distilbert/distilgpt2"
@@ -41,50 +41,13 @@ def llm():
               gpu_memory_utilization=0.10,
               enforce_eager=True)
 
-    with llm.deprecate_legacy_api():
-        yield weakref.proxy(llm)
+    yield weakref.proxy(llm)
 
-        del llm
+    del llm
 
     cleanup_dist_env_and_memory()
 
 
-def assert_outputs_equal(o1: list[RequestOutput], o2: list[RequestOutput]):
-    assert [o.outputs for o in o1] == [o.outputs for o in o2]
-
-
-@pytest.mark.skip_global_cleanup
-@pytest.mark.parametrize('prompt_token_ids', TOKEN_IDS)
-def test_v1_v2_api_consistency_single_prompt_tokens(llm: LLM,
-                                                    prompt_token_ids):
-    sampling_params = SamplingParams(temperature=0.0, top_p=1.0)
-
-    with pytest.warns(DeprecationWarning, match="'prompt_token_ids'"):
-        v1_output = llm.generate(prompt_token_ids=prompt_token_ids,
-                                 sampling_params=sampling_params)
-
-    v2_output = llm.generate({"prompt_token_ids": prompt_token_ids},
-                             sampling_params=sampling_params)
-    assert_outputs_equal(v1_output, v2_output)
-
-
-@pytest.mark.skip_global_cleanup
-def test_v1_v2_api_consistency_multi_prompt_tokens(llm: LLM):
-    sampling_params = SamplingParams(temperature=0.0, top_p=1.0)
-
-    with pytest.warns(DeprecationWarning, match="'prompt_token_ids'"):
-        v1_output = llm.generate(prompt_token_ids=TOKEN_IDS,
-                                 sampling_params=sampling_params)
-
-    v2_output = llm.generate(
-        [{
-            "prompt_token_ids": p
-        } for p in TOKEN_IDS],
-        sampling_params=sampling_params,
-    )
-    assert_outputs_equal(v1_output, v2_output)
-
-
 @pytest.mark.skip_global_cleanup
 def test_multiple_sampling_params(llm: LLM):
     sampling_params = [

tests/entrypoints/llm/test_generate_multiple_loras.py

@@ -48,10 +48,9 @@ def llm(request, monkeypatch_module):
               max_num_seqs=128,
               enforce_eager=True)
 
-    with llm.deprecate_legacy_api():
-        yield weakref.proxy(llm)
+    yield weakref.proxy(llm)
 
-        del llm
+    del llm
 
     cleanup_dist_env_and_memory()
 

tests/entrypoints/llm/test_reward.py

@@ -36,10 +36,9 @@ def llm():
               trust_remote_code=True,
               seed=0)
 
-    with llm.deprecate_legacy_api():
-        yield weakref.proxy(llm)
+    yield weakref.proxy(llm)
 
-        del llm
+    del llm
 
     cleanup_dist_env_and_memory()
 

tests/entrypoints/llm/test_score.py

@@ -33,10 +33,9 @@ def llm():
               enforce_eager=True,
               seed=0)
 
-    with llm.deprecate_legacy_api():
-        yield weakref.proxy(llm)
+    yield weakref.proxy(llm)
 
-        del llm
+    del llm
 
     cleanup_dist_env_and_memory()
 

tests/quantization/test_fp8.py

@@ -38,8 +38,7 @@ def test_model_load_and_run(vllm_runner, model_id: str, force_marlin: bool,
     with vllm_runner(model_id) as llm:
         # note: this does not test accuracy, just that we can run through
         # see lm-eval tests for accuracy
-        outputs = llm.generate_greedy(prompts=["Hello my name is"],
-                                      max_tokens=10)
+        outputs = llm.generate_greedy(["Hello my name is"], max_tokens=10)
         print(outputs[0][1])
 
 
@@ -90,8 +89,7 @@ def test_kv_cache_model_load_and_run(vllm_runner, model_id: str,
 
         # note: this does not test accuracy, just that we can run through
         # see lm-eval tests for accuracy
-        outputs = llm.generate_greedy(prompts=["Hello my name is"],
-                                      max_tokens=10)
+        outputs = llm.generate_greedy(["Hello my name is"], max_tokens=10)
         print(outputs[0][1])
 
 

tests/quantization/test_lm_head.py

@@ -46,5 +46,5 @@ def test_lm_head(
         vllm_model.apply_model(check_model)
 
         print(
-            vllm_model.generate_greedy(prompts=["Hello my name is"],
+            vllm_model.generate_greedy(["Hello my name is"],
                                        max_tokens=10)[0][1])

tests/v1/entrypoints/llm/test_struct_output_generate.py

@@ -127,13 +127,15 @@ def test_structured_output(
         temperature=1.0,
         max_tokens=4096,
         guided_decoding=GuidedDecodingParams(json=sample_json_schema))
-    outputs = llm.generate(prompts=[
-        (f"Give an example JSON for an employee profile that fits this "
-         f"schema. Make the response as short as possible. Schema: "
-         f"{sample_json_schema}")
-    ] * 2,
-                           sampling_params=sampling_params,
-                           use_tqdm=True)
+
+    prompt = ("Give an example JSON for an employee profile that fits this "
+              "schema. Make the response as short as possible. Schema: "
+              f"{sample_json_schema}")
+    outputs = llm.generate(
+        [prompt] * 2,
+        sampling_params=sampling_params,
+        use_tqdm=True,
+    )
 
     assert outputs is not None
 
@@ -191,20 +193,24 @@ def test_structured_output(
         with pytest.raises(ValueError,
                            match="The provided JSON schema contains features "
                            "not supported by xgrammar."):
+
+            prompt = (f"Give an example JSON for an employee profile that "
+                      f"fits this schema: {unsupported_json_schema}. "
+                      f"Make the response as short as possible.")
             llm.generate(
-                prompts=[(f"Give an example JSON for an employee profile that "
-                          f"fits this schema: {unsupported_json_schema}. "
-                          f"Make the response as short as possible.")] * 2,
+                [prompt] * 2,
                 sampling_params=sampling_params,
-                use_tqdm=True)
+                use_tqdm=True,
+            )
     else:
-        outputs = llm.generate(prompts=(
-            "Give an example JSON object for a grade "
-            "that fits this schema: "
-            f"{unsupported_json_schema}. Make the response as short as "
-            "possible."),
-                               sampling_params=sampling_params,
-                               use_tqdm=True)
+        prompt = (f"Give an example JSON object for a grade that "
+                  f"fits this schema: {unsupported_json_schema}. "
+                  f"Make the response as short as possible.")
+        outputs = llm.generate(
+            prompt,
+            sampling_params=sampling_params,
+            use_tqdm=True,
+        )
         assert outputs is not None
         for output in outputs:
             assert output is not None
@@ -227,10 +233,9 @@ def test_structured_output(
             max_tokens=1000,
             guided_decoding=GuidedDecodingParams(grammar=sample_sql_ebnf))
         outputs = llm.generate(
-            prompts=(
-                "Generate a sql statement that selects col_1 from "
-                "table_1 where it is equal to 1. Make the response as short as "
-                "possible."),
+            ("Generate a sql statement that selects col_1 from "
+             "table_1 where it is equal to 1. Make the response as short as "
+             "possible."),
             sampling_params=sampling_params,
             use_tqdm=True,
         )
@@ -261,10 +266,9 @@ def test_structured_output(
             max_tokens=1000,
             guided_decoding=GuidedDecodingParams(grammar=sample_sql_lark))
         outputs = llm.generate(
-            prompts=(
-                "Generate a sql statement that selects col_1 from "
-                "table_1 where it is equal to 1. Make the response as short as "
-                "possible."),
+            ("Generate a sql statement that selects col_1 from "
+             "table_1 where it is equal to 1. Make the response as short as "
+             "possible."),
             sampling_params=sampling_params,
             use_tqdm=True,
         )
@@ -301,7 +305,6 @@ def test_structured_output(
             guided_decoding=GuidedDecodingParams(grammar="not a grammar"))
         with pytest.raises(ValueError, match="Failed to convert the grammar "):
             llm.generate(
-                prompts=
                 ("Generate a sql statement that selects col_1 from "
                  "table_1 where it is equal to 1. Make the response as short "
                  "as possible."),
@@ -316,11 +319,11 @@ def test_structured_output(
         temperature=0.8,
         top_p=0.95,
         guided_decoding=GuidedDecodingParams(regex=sample_regex))
+
+    prompt = (f"Give an example IPv4 address with this regex: {sample_regex}. "
+              f"Make the response as short as possible.")
     outputs = llm.generate(
-        prompts=[
-            (f"Give an example IPv4 address with this regex: {sample_regex}. "
-             f"Make the response as short as possible.")
-        ] * 2,
+        [prompt] * 2,
         sampling_params=sampling_params,
         use_tqdm=True,
     )
@@ -343,11 +346,13 @@ def test_structured_output(
         temperature=0.8,
         top_p=0.95,
         guided_decoding=GuidedDecodingParams(choice=sample_guided_choice))
+
     outputs = llm.generate(
-        prompts=("The best language for type-safe systems programming is "
-                 "(Make the response as short as possible.) "),
+        ("The best language for type-safe systems programming is "
+         "(Make the response as short as possible.) "),
         sampling_params=sampling_params,
-        use_tqdm=True)
+        use_tqdm=True,
+    )
     assert outputs is not None
     for output in outputs:
         assert output is not None
@@ -367,12 +372,14 @@ def test_structured_output(
         temperature=1.0,
         max_tokens=1000,
         guided_decoding=GuidedDecodingParams(json=json_schema))
-    outputs = llm.generate(prompts=(
-        "Generate a JSON with the brand, model and car_type of the most "
-        "iconic car from the 90's. Make the response as short as "
-        "possible."),
-                           sampling_params=sampling_params,
-                           use_tqdm=True)
+
+    outputs = llm.generate(
+        ("Generate a JSON with the brand, model and car_type of the most "
+         "iconic car from the 90's. Make the response as short as "
+         "possible."),
+        sampling_params=sampling_params,
+        use_tqdm=True,
+    )
 
     assert outputs is not None
 
@@ -411,10 +418,11 @@ def test_structured_output(
         guided_decoding=GuidedDecodingParams(json=json_schema))
 
     outputs = llm.generate(
-        prompts=("Generate a description of a frog using 50 characters. "
-                 "Make the response as short as possible."),
+        ("Generate a description of a frog using 50 characters. "
+         "Make the response as short as possible."),
         sampling_params=sampling_params,
-        use_tqdm=True)
+        use_tqdm=True,
+    )
 
     assert outputs is not None
 
@@ -498,7 +506,7 @@ Make the response as short as possible.
 """
 
         # Change this once other backends support structural_tag
-        outputs = llm.generate(prompts=prompt,
+        outputs = llm.generate(prompt,
                                sampling_params=sampling_params,
                                use_tqdm=True)
         assert outputs is not None
@@ -639,15 +647,13 @@ def test_structured_output_auto_mode(
         f"{unsupported_json_schema}. Make the response as short as possible.")
     # This would fail with the default of "xgrammar", but in "auto"
     # we will handle fallback automatically.
-    outputs = llm.generate(prompts=prompts,
+    outputs = llm.generate(prompts,
                            sampling_params=sampling_params,
                            use_tqdm=True)
     # Make sure `auto` backend handling doesn't mess up sampling_params
     # and that we can reuse it without error.
     outputs.extend(
-        llm.generate(prompts=prompts,
-                     sampling_params=sampling_params,
-                     use_tqdm=True))
+        llm.generate(prompts, sampling_params=sampling_params, use_tqdm=True))
 
     assert outputs is not None
     for output in outputs:
@@ -705,7 +711,7 @@ def test_guidance_no_additional_properties(monkeypatch: pytest.MonkeyPatch):
                                          max_tokens=256,
                                          guided_decoding=guided_params)
 
-        outputs = llm.generate(prompts=prompt, sampling_params=sampling_params)
+        outputs = llm.generate(prompt, sampling_params=sampling_params)
         assert outputs is not None
         generated_text = outputs[0].outputs[0].text
         assert generated_text is not None

vllm/entrypoints/llm.py

@@ -3,15 +3,13 @@
 
 import itertools
 from collections.abc import Sequence
-from contextlib import contextmanager
-from typing import (TYPE_CHECKING, Any, Callable, ClassVar, Optional, Union,
-                    cast, overload)
+from typing import TYPE_CHECKING, Any, Callable, Optional, Union, cast
 
 import cloudpickle
 import torch.nn as nn
 from pydantic import ValidationError
 from tqdm.auto import tqdm
-from typing_extensions import TypeVar, deprecated
+from typing_extensions import TypeVar
 
 import vllm.envs as envs
 from vllm.beam_search import (BeamSearchInstance, BeamSearchOutput,
@@ -40,7 +38,6 @@ from vllm.entrypoints.score_utils import (ScoreContentPartParam,
 from vllm.entrypoints.utils import (_validate_truncation_size,
                                     log_non_default_args)
 from vllm.inputs import PromptType, SingletonPrompt, TextPrompt, TokensPrompt
-from vllm.inputs.parse import parse_and_batch_prompt
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.model_executor.layers.quantization import QuantizationMethods
@@ -54,7 +51,7 @@ from vllm.tasks import PoolingTask
 from vllm.transformers_utils.tokenizer import (AnyTokenizer, MistralTokenizer,
                                                get_cached_tokenizer)
 from vllm.usage.usage_lib import UsageContext
-from vllm.utils import Counter, Device, deprecate_kwargs, is_list_of
+from vllm.utils import Counter, Device, is_list_of
 from vllm.v1.sample.logits_processor import LogitsProcessor
 
 if TYPE_CHECKING:
@@ -157,18 +154,6 @@ class LLM:
         serving, use the [AsyncLLMEngine][vllm.AsyncLLMEngine] class instead.
     """
 
-    DEPRECATE_LEGACY: ClassVar[bool] = True
-    """A flag to toggle whether to deprecate the legacy generate/encode API."""
-
-    @classmethod
-    @contextmanager
-    def deprecate_legacy_api(cls):
-        cls.DEPRECATE_LEGACY = True
-
-        yield
-
-        cls.DEPRECATE_LEGACY = False
-
     def __init__(
         self,
         model: str,
@@ -325,99 +310,14 @@ class LLM:
             return SamplingParams.from_optional(**self.default_sampling_params)
         return SamplingParams()
 
-    @overload
     def generate(
         self,
         prompts: Union[PromptType, Sequence[PromptType]],
-        /,
         sampling_params: Optional[Union[SamplingParams,
                                         Sequence[SamplingParams]]] = None,
         *,
         use_tqdm: Union[bool, Callable[..., tqdm]] = True,
         lora_request: Optional[Union[list[LoRARequest], LoRARequest]] = None,
-    ) -> list[RequestOutput]:
-        ...
-
-    @overload  # LEGACY: single (prompt + optional token ids)
-    @deprecated("'prompt_token_ids' will become part of 'prompts'")
-    def generate(
-        self,
-        prompts: str,
-        sampling_params: Optional[Union[SamplingParams,
-                                        list[SamplingParams]]] = None,
-        prompt_token_ids: Optional[list[int]] = None,
-        use_tqdm: Union[bool, Callable[..., tqdm]] = True,
-        lora_request: Optional[Union[list[LoRARequest], LoRARequest]] = None,
-    ) -> list[RequestOutput]:
-        ...
-
-    @overload  # LEGACY: multi (prompt + optional token ids)
-    @deprecated("'prompt_token_ids' will become part of 'prompts'")
-    def generate(
-        self,
-        prompts: list[str],
-        sampling_params: Optional[Union[SamplingParams,
-                                        list[SamplingParams]]] = None,
-        prompt_token_ids: Optional[list[list[int]]] = None,
-        use_tqdm: Union[bool, Callable[..., tqdm]] = True,
-        lora_request: Optional[Union[list[LoRARequest], LoRARequest]] = None,
-    ) -> list[RequestOutput]:
-        ...
-
-    @overload  # LEGACY: single (token ids + optional prompt)
-    @deprecated("'prompt_token_ids' will become part of 'prompts'")
-    def generate(
-        self,
-        prompts: Optional[str] = None,
-        sampling_params: Optional[Union[SamplingParams,
-                                        list[SamplingParams]]] = None,
-        *,
-        prompt_token_ids: list[int],
-        use_tqdm: Union[bool, Callable[..., tqdm]] = True,
-        lora_request: Optional[Union[list[LoRARequest], LoRARequest]] = None,
-    ) -> list[RequestOutput]:
-        ...
-
-    @overload  # LEGACY: multi (token ids + optional prompt)
-    @deprecated("'prompt_token_ids' will become part of 'prompts'")
-    def generate(
-        self,
-        prompts: Optional[list[str]] = None,
-        sampling_params: Optional[Union[SamplingParams,
-                                        list[SamplingParams]]] = None,
-        *,
-        prompt_token_ids: list[list[int]],
-        use_tqdm: Union[bool, Callable[..., tqdm]] = True,
-        lora_request: Optional[Union[list[LoRARequest], LoRARequest]] = None,
-    ) -> list[RequestOutput]:
-        ...
-
-    @overload  # LEGACY: single or multi token ids [pos-only]
-    @deprecated("'prompt_token_ids' will become part of 'prompts'")
-    def generate(
-        self,
-        prompts: None,
-        sampling_params: None,
-        prompt_token_ids: Union[list[int], list[list[int]]],
-        use_tqdm: Union[bool, Callable[..., tqdm]] = True,
-        lora_request: Optional[Union[list[LoRARequest], LoRARequest]] = None,
-    ) -> list[RequestOutput]:
-        ...
-
-    @deprecate_kwargs(
-        "prompt_token_ids",
-        is_deprecated=lambda: LLM.DEPRECATE_LEGACY,
-        additional_message="Please use the 'prompts' parameter instead.",
-    )
-    def generate(
-        self,
-        prompts: Union[Union[PromptType, Sequence[PromptType]],
-                       Optional[Union[str, list[str]]]] = None,
-        sampling_params: Optional[Union[SamplingParams,
-                                        Sequence[SamplingParams]]] = None,
-        prompt_token_ids: Optional[Union[list[int], list[list[int]]]] = None,
-        use_tqdm: Union[bool, Callable[..., tqdm]] = True,
-        lora_request: Optional[Union[list[LoRARequest], LoRARequest]] = None,
         priority: Optional[list[int]] = None,
     ) -> list[RequestOutput]:
         """Generates the completions for the input prompts.
@@ -460,15 +360,6 @@ class LLM:
                 "Try passing `--runner generate` to use the model as a "
                 "generative model.")
 
-        if prompt_token_ids is not None:
-            parsed_prompts = self._convert_v1_inputs(
-                prompts=cast(Optional[Union[str, list[str]]], prompts),
-                prompt_token_ids=prompt_token_ids,
-            )
-        else:
-            parsed_prompts = cast(Union[PromptType, Sequence[PromptType]],
-                                  prompts)
-
         if sampling_params is None:
             # Use default sampling params.
             sampling_params = self.get_default_sampling_params()
@@ -483,10 +374,10 @@ class LLM:
 
         # Add any modality specific loras to the corresponding prompts
         lora_request = self._get_modality_specific_lora_reqs(
-            parsed_prompts, lora_request)
+            prompts, lora_request)
 
         self._validate_and_add_requests(
-            prompts=parsed_prompts,
+            prompts=prompts,
             params=sampling_params,
             use_tqdm=use_tqdm,
             lora_request=lora_request,
@@ -498,7 +389,7 @@ class LLM:
         return self.engine_class.validate_outputs(outputs, RequestOutput)
 
     def _get_modality_specific_lora_reqs(
-            self, parsed_prompts: Union[PromptType, Sequence[PromptType]],
+            self, prompts: Union[PromptType, Sequence[PromptType]],
             lora_request: Optional[Union[list[LoRARequest], LoRARequest]]):
         # Grab the lora config off the vllm config on the engine,
         # since this is the same for both v0 & v1.
@@ -511,35 +402,33 @@ class LLM:
                 or (lora_config and lora_config.default_mm_loras is None)):
             return lora_request
 
-        if not isinstance(parsed_prompts, Sequence):
-            parsed_prompts = [parsed_prompts]
+        if not isinstance(prompts, Sequence):
+            prompts = [prompts]
 
-        optional_loras = ([lora_request] * len(parsed_prompts)
+        optional_loras = ([lora_request] * len(prompts)
                           if not isinstance(lora_request, Sequence) else
                           lora_request)
 
         return [
             self._resolve_single_prompt_mm_lora(
-                parsed_prompt,
+                prompt,
                 opt_lora_req,
                 lora_config.default_mm_loras,
-            ) for parsed_prompt, opt_lora_req in zip(parsed_prompts,
-                                                     optional_loras)
+            ) for prompt, opt_lora_req in zip(prompts, optional_loras)
         ]
 
-    def _resolve_single_prompt_mm_lora(self, parsed_prompt: PromptType,
+    def _resolve_single_prompt_mm_lora(self, prompt: PromptType,
                                        lora_request: Optional[LoRARequest],
                                        default_mm_loras: Optional[dict[str,
                                                                        str]]):
-        if (not default_mm_loras or not isinstance(parsed_prompt, dict)
-                or "multi_modal_data" not in parsed_prompt):
+        if (not default_mm_loras or not isinstance(prompt, dict)
+                or "multi_modal_data" not in prompt):
             return lora_request
 
-        parsed_prompt = cast(Union[TextPrompt, TokensPrompt], parsed_prompt)
+        prompt = cast(Union[TextPrompt, TokensPrompt], prompt)
 
-        intersection = set(
-            parsed_prompt["multi_modal_data"].keys()).intersection(
-                default_mm_loras.keys())
+        intersection = set(prompt["multi_modal_data"].keys()) \
+            .intersection(default_mm_loras.keys())
         if not intersection:
             return lora_request
         if len(intersection) > 1:
@@ -933,11 +822,9 @@ class LLM:
             lora_request=lora_request,
         )
 
-    @overload
     def encode(
         self,
         prompts: Union[PromptType, Sequence[PromptType]],
-        /,
         pooling_params: Optional[Union[PoolingParams,
                                        Sequence[PoolingParams]]] = None,
         *,
@@ -946,107 +833,6 @@ class LLM:
         lora_request: Optional[Union[list[LoRARequest], LoRARequest]] = None,
         pooling_task: PoolingTask = "encode",
         tokenization_kwargs: Optional[dict[str, Any]] = None,
-    ) -> list[PoolingRequestOutput]:
-        ...
-
-    @overload  # LEGACY: single (prompt + optional token ids)
-    @deprecated("'prompt_token_ids' will become part of 'prompts'")
-    def encode(
-        self,
-        prompts: str,
-        pooling_params: Optional[Union[PoolingParams,
-                                       Sequence[PoolingParams]]] = None,
-        prompt_token_ids: Optional[list[int]] = None,
-        truncate_prompt_tokens: Optional[int] = None,
-        use_tqdm: Union[bool, Callable[..., tqdm]] = True,
-        lora_request: Optional[Union[list[LoRARequest], LoRARequest]] = None,
-        pooling_task: PoolingTask = "encode",
-        tokenization_kwargs: Optional[dict[str, Any]] = None,
-    ) -> list[PoolingRequestOutput]:
-        ...
-
-    @overload  # LEGACY: multi (prompt + optional token ids)
-    @deprecated("'prompt_token_ids' will become part of 'prompts'")
-    def encode(
-        self,
-        prompts: list[str],
-        pooling_params: Optional[Union[PoolingParams,
-                                       Sequence[PoolingParams]]] = None,
-        prompt_token_ids: Optional[list[list[int]]] = None,
-        truncate_prompt_tokens: Optional[int] = None,
-        use_tqdm: Union[bool, Callable[..., tqdm]] = True,
-        lora_request: Optional[Union[list[LoRARequest], LoRARequest]] = None,
-        pooling_task: PoolingTask = "encode",
-        tokenization_kwargs: Optional[dict[str, Any]] = None,
-    ) -> list[PoolingRequestOutput]:
-        ...
-
-    @overload  # LEGACY: single (token ids + optional prompt)
-    @deprecated("'prompt_token_ids' will become part of 'prompts'")
-    def encode(
-        self,
-        prompts: Optional[str] = None,
-        pooling_params: Optional[Union[PoolingParams,
-                                       Sequence[PoolingParams]]] = None,
-        *,
-        prompt_token_ids: list[int],
-        truncate_prompt_tokens: Optional[int] = None,
-        use_tqdm: Union[bool, Callable[..., tqdm]] = True,
-        lora_request: Optional[Union[list[LoRARequest], LoRARequest]] = None,
-        pooling_task: PoolingTask = "encode",
-        tokenization_kwargs: Optional[dict[str, Any]] = None,
-    ) -> list[PoolingRequestOutput]:
-        ...
-
-    @overload  # LEGACY: multi (token ids + optional prompt)
-    @deprecated("'prompt_token_ids' will become part of 'prompts'")
-    def encode(
-        self,
-        prompts: Optional[list[str]] = None,
-        pooling_params: Optional[Union[PoolingParams,
-                                       Sequence[PoolingParams]]] = None,
-        *,
-        prompt_token_ids: list[list[int]],
-        truncate_prompt_tokens: Optional[int] = None,
-        use_tqdm: Union[bool, Callable[..., tqdm]] = True,
-        lora_request: Optional[Union[list[LoRARequest], LoRARequest]] = None,
-        pooling_task: PoolingTask = "encode",
-        tokenization_kwargs: Optional[dict[str, Any]] = None,
-    ) -> list[PoolingRequestOutput]:
-        ...
-
-    @overload  # LEGACY: single or multi token ids [pos-only]
-    @deprecated("'prompt_token_ids' will become part of 'prompts'")
-    def encode(
-        self,
-        prompts: None,
-        pooling_params: None,
-        prompt_token_ids: Union[list[int], list[list[int]]],
-        truncate_prompt_tokens: Optional[int] = None,
-        use_tqdm: Union[bool, Callable[..., tqdm]] = True,
-        lora_request: Optional[Union[list[LoRARequest], LoRARequest]] = None,
-        pooling_task: PoolingTask = "encode",
-        tokenization_kwargs: Optional[dict[str, Any]] = None,
-    ) -> list[PoolingRequestOutput]:
-        ...
-
-    @deprecate_kwargs(
-        "prompt_token_ids",
-        is_deprecated=lambda: LLM.DEPRECATE_LEGACY,
-        additional_message="Please use the 'prompts' parameter instead.",
-    )
-    def encode(
-        self,
-        prompts: Union[Union[PromptType, Sequence[PromptType]],
-                       Optional[Union[str, list[str]]]] = None,
-        pooling_params: Optional[Union[PoolingParams,
-                                       Sequence[PoolingParams]]] = None,
-        prompt_token_ids: Optional[Union[list[int], list[list[int]]]] = None,
-        truncate_prompt_tokens: Optional[int] = None,
-        use_tqdm: Union[bool, Callable[..., tqdm]] = True,
-        lora_request: Optional[Union[list[LoRARequest], LoRARequest]] = None,
-        pooling_task: Optional[PoolingTask] = None,
-        tokenization_kwargs: Optional[dict[str, Any]] = None,
     ) -> list[PoolingRequestOutput]:
         """Apply pooling to the hidden states corresponding to the input
         prompts.
@@ -1108,15 +894,6 @@ class LLM:
             raise ValueError(
                 f"pooling_task must be one of {self.supported_tasks}.")
 
-        if prompt_token_ids is not None:
-            parsed_prompts = self._convert_v1_inputs(
-                prompts=cast(Optional[Union[str, list[str]]], prompts),
-                prompt_token_ids=prompt_token_ids,
-            )
-        else:
-            parsed_prompts = cast(Union[PromptType, Sequence[PromptType]],
-                                  prompts)
-
         if pooling_params is None:
             # Use default pooling params.
             pooling_params = PoolingParams()
@@ -1134,7 +911,7 @@ class LLM:
                                       tokenization_kwargs)
 
         self._validate_and_add_requests(
-            prompts=parsed_prompts,
+            prompts=prompts,
             params=pooling_params,
             use_tqdm=use_tqdm,
             lora_request=lora_request,
@@ -1148,7 +925,6 @@ class LLM:
     def embed(
         self,
         prompts: Union[PromptType, Sequence[PromptType]],
-        /,
         *,
         truncate_prompt_tokens: Optional[int] = None,
         use_tqdm: Union[bool, Callable[..., tqdm]] = True,
@@ -1198,7 +974,6 @@ class LLM:
     def classify(
         self,
         prompts: Union[PromptType, Sequence[PromptType]],
-        /,
         *,
         use_tqdm: Union[bool, Callable[..., tqdm]] = True,
         pooling_params: Optional[Union[PoolingParams,
@@ -1348,7 +1123,7 @@ class LLM:
         _validate_truncation_size(model_config.max_model_len,
                                   truncate_prompt_tokens, tokenization_kwargs)
 
-        parsed_prompts = []
+        prompts = list[PromptType]()
 
         input_pairs = [(t1, t2) for t1, t2 in zip(data_1, data_2)]
 
@@ -1372,10 +1147,10 @@ class LLM:
             else:
                 pooling_params_list.append(pooling_params)
 
-            parsed_prompts.append(engine_prompt)
+            prompts.append(engine_prompt)
 
         self._validate_and_add_requests(
-            prompts=parsed_prompts,
+            prompts=prompts,
             params=pooling_params_list,
             use_tqdm=use_tqdm,
             lora_request=lora_request,
@@ -1585,48 +1360,6 @@ class LLM:
         assert isinstance(self.llm_engine, V1LLMEngine)
         return self.llm_engine.get_metrics()
 
-    # LEGACY
-    def _convert_v1_inputs(
-        self,
-        prompts: Optional[Union[str, list[str]]],
-        prompt_token_ids: Optional[Union[list[int], list[list[int]]]],
-    ):
-        # skip_tokenizer_init is now checked in engine
-
-        if prompts is None and prompt_token_ids is None:
-            raise ValueError(
-                "Either prompts or prompt_token_ids must be provided.")
-        if prompts is not None and prompt_token_ids is not None \
-                and len(prompts) != len(prompt_token_ids):
-            raise ValueError(
-                "The lengths of prompts and prompt_token_ids must be the same."
-            )
-
-        if prompts is not None:
-            prompts = [p["content"] for p in parse_and_batch_prompt(prompts)]
-        if prompt_token_ids is not None:
-            prompt_token_ids = [
-                p["content"] for p in parse_and_batch_prompt(prompt_token_ids)
-            ]
-        if prompts is not None:
-            num_requests = len(prompts)
-        elif prompt_token_ids is not None:
-            num_requests = len(prompt_token_ids)
-        parsed_prompts: list[PromptType] = []
-        for i in range(num_requests):
-            item: PromptType
-
-            if prompts is not None:
-                item = TextPrompt(prompt=prompts[i])
-            elif prompt_token_ids is not None:
-                item = TokensPrompt(prompt_token_ids=prompt_token_ids[i])
-            else:
-                raise AssertionError
-
-            parsed_prompts.append(item)
-
-        return parsed_prompts
-
     def _validate_and_add_requests(
         self,
         prompts: Union[PromptType, Sequence[PromptType]],