Revert #24446 and #26168 (#26332)

Signed-off-by: Thomas Parnell <tpa@zurich.ibm.com>

tests/entrypoints/llm/test_generate.py

@@ -85,11 +85,10 @@ def test_max_model_len():
         num_total_tokens = len(output.prompt_token_ids) + len(
             output.outputs[0].token_ids
         )
-        # Total tokens must not exceed max_model_len + 1 (the last token can be
-        # generated with the context length equal to the max model length)
+        # Total tokens must not exceed max_model_len.
         # It can be less if generation finishes due to other reasons (e.g., EOS)
         # before reaching the absolute model length limit.
-        assert num_total_tokens <= max_model_len + 1
+        assert num_total_tokens <= max_model_len
 
 
 def test_log_stats():

tests/v1/e2e/test_context_length.py

-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""
-end-to-end tests for context length corner cases of vLLM v1 model runner
-versus HuggingFace's transformers.
-
-This test verifies the following behavior: allow prefill and decodes on the
-model's maximum context length ``max_model_len`` and get one more token.
-
-Test strategy
-- Build a prompt consisting of exactly ``prompt_len`` tokens.
-- Run vLLM generation requesting ``max_tokens`` new tokens.
-- Run HF generation on the same prompt requesting the same number of tokens.
-- Assert both return the same number of generated tokens and the same ids.
-
-Test cases
-- Prefill a prompt of ``max_model_len`` (2048) and request a single token which
-will be sampled after the prefill (context length ``max_model_len``).
-- Prefill a prompt of ``max_model_len`` - 1 (2047) and request two tokens where
-the 1st will be sampled after the prefill and the 2nd after the first decode
-(context length ``max_model_len``).
-
-"""
-
-import pytest
-
-from tests.conftest import HfRunner, VllmRunner
-from tests.models.utils import check_outputs_equal
-from tests.utils import create_new_process_for_each_test
-
-
-@create_new_process_for_each_test()
-@pytest.mark.parametrize("model", ["JackFram/llama-160m"])
-@pytest.mark.parametrize(
-    "prompt_len, max_tokens",
-    [
-        (2048, 1),  # prompt_len = max_model_len
-        (2047, 2),  # prompt_len = max_model_len - 1
-    ],
-)
-def test_max_context_length(
-    model: str,
-    vllm_runner: type[VllmRunner],
-    hf_runner: type[HfRunner],
-    prompt_len: int,
-    max_tokens: int,
-) -> None:
-    """Compare vLLM and HuggingFace when the prompt already fills the
-    model's maximum context length and we request a single new token.
-
-    The test ensures vLLM does not raise the "Sampled token IDs exceed the
-    max model length" assertion and that both vLLM and HF produce the same
-    single token when given the same inputs.
-    """
-
-    # Construct a prompt of size prompt_len
-    prompt_ids = [[43] * prompt_len]
-
-    # --- vLLM generation ---
-    with vllm_runner(
-        model_name=model,
-        tokenizer_name=model,
-        max_model_len=2048,
-        max_num_seqs=1,
-        tensor_parallel_size=1,
-    ) as vllm_model:
-        # Generate max_tokens new tokens deterministically.
-        vllm_outputs = vllm_model.generate_greedy(prompt_ids, max_tokens)
-
-    # --- HuggingFace generation ---
-    with hf_runner(
-        model_name=model,
-    ) as hf_model:
-        hf_outputs = hf_model.generate_greedy(prompt_ids, max_tokens)
-
-    # vLLM and HF runners return prompt + generated tokens. Slice off the prompt.
-    vllm_output_ids = vllm_outputs[0][0][prompt_len:]
-    hf_output_ids = hf_outputs[0][0][prompt_len:]
-
-    # check that exactly max_tokens tokens were generated with vLLM and HF
-    assert len(vllm_output_ids) == len(hf_output_ids) == max_tokens
-
-    # check that vLLM outputs (token ids) match HF outputs
-    # Note: for simplicity don't pass detokenized string
-    check_outputs_equal(
-        outputs_0_lst=[(hf_output_ids, "")],
-        outputs_1_lst=[(vllm_output_ids, "")],
-        name_0="hf",
-        name_1="vllm",
-    )

vllm/v1/core/sched/scheduler.py

@@ -223,7 +223,7 @@ class Scheduler(SchedulerInterface):
             # Make sure the input position does not exceed the max model len.
             # This is necessary when using spec decoding.
             num_new_tokens = min(
-                num_new_tokens, self.max_model_len - request.num_computed_tokens
+                num_new_tokens, self.max_model_len - 1 - request.num_computed_tokens
             )
 
             # Schedule encoder inputs.

vllm/v1/core/sched/utils.py

@@ -44,7 +44,7 @@ def check_stop(
     request: Request, max_model_len: int, pooler_output: Optional[torch.Tensor] = None
 ) -> bool:
     if (
-        request.num_tokens > max_model_len
+        request.num_tokens >= max_model_len
         or request.num_output_tokens >= request.max_tokens
     ):
         request.status = RequestStatus.FINISHED_LENGTH_CAPPED

vllm/v1/worker/gpu_model_runner.py

@@ -2317,30 +2317,14 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
 
             start_idx = self.input_batch.num_tokens_no_spec[req_idx]
             end_idx = start_idx + len(sampled_ids)
-            assert end_idx <= self.max_model_len + 1, (
-                "Sampled token IDs exceed the max model length + 1. "
-                f"Total number of tokens: {end_idx} > max_model_len + 1: "
-                f"{self.max_model_len + 1}"
+            assert end_idx <= self.max_model_len, (
+                "Sampled token IDs exceed the max model length. "
+                f"Total number of tokens: {end_idx} > max_model_len: "
+                f"{self.max_model_len}"
             )
 
-            n_tokens_cache = len(sampled_ids)
-
-            # Sampled token IDs exceed the max model length by 1. This is
-            # legitimate as we can still sample 1 last token when the context
-            # length equals the max model length. Note that we do not need to
-            # cache this token ID as the sequence finishes after this step.
-            # Additionally, the buffers token_ids_cpu and is_token_ids are of
-            # size max model length only.
-            if end_idx == self.max_model_len + 1:
-                n_tokens_cache -= 1
-
-            self.input_batch.token_ids_cpu[
-                req_idx, start_idx : (start_idx + n_tokens_cache)
-            ] = sampled_ids[:n_tokens_cache]
-            self.input_batch.is_token_ids[
-                req_idx, start_idx : (start_idx + n_tokens_cache)
-            ] = True
-
+            self.input_batch.token_ids_cpu[req_idx, start_idx:end_idx] = sampled_ids
+            self.input_batch.is_token_ids[req_idx, start_idx:end_idx] = True
             self.input_batch.num_tokens_no_spec[req_idx] = end_idx
             self.input_batch.num_tokens[req_idx] = end_idx