[Core] Enable decode of context length equal to max model length (#26168)

Signed-off-by: Yannick Schnider <yannick.schnider1@ibm.com>

tests/entrypoints/llm/test_generate.py

@@ -82,10 +82,11 @@ def test_max_model_len():
     for output in outputs:
         num_total_tokens = len(output.prompt_token_ids) + len(
             output.outputs[0].token_ids)
-        # Total tokens must not exceed max_model_len.
+        # 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)
         # 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
+        assert num_total_tokens <= max_model_len + 1
 
 
 def test_log_stats():

tests/v1/e2e/test_context_length.py

@@ -4,15 +4,22 @@
 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 a prefill that fills the
-model's maximum context length and then request a single new token.
+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 textual prompt that tokenizes to exactly ``max_model_len`` tokens.
-- Run vLLM generation requesting a single new token (max_tokens=1).
-- Run HF generation on the same prompt requesting a single token too.
+- 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
@@ -27,11 +34,16 @@ from vllm.inputs import TokensPrompt
 
 @create_new_process_for_each_test()
 @pytest.mark.parametrize("model", ["JackFram/llama-160m"])
-@pytest.mark.parametrize("max_model_len", [2048])
-@pytest.mark.parametrize("max_tokens", [1])
-def test_prefill_max_context_length(
+@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,
-    max_model_len: int,
+    prompt_len: int,
     max_tokens: int,
 ) -> None:
     """Compare vLLM and HuggingFace when the prompt already fills the
@@ -42,8 +54,8 @@ def test_prefill_max_context_length(
     single token when given the same inputs.
     """
 
-    # Construct a prompt of size max_model_len
-    prompt_ids = [[43] * max_model_len]
+    # Construct a prompt of size prompt_len
+    prompt_ids = [[43] * prompt_len]
 
     # Generate max_tokens new tokens deterministically.
     sampling_params = [
@@ -54,6 +66,7 @@ def test_prefill_max_context_length(
     llm = LLM(
         model=model,
         tokenizer=model,
+        max_model_len=2048,
         max_num_seqs=1,
         tensor_parallel_size=1,
     )
@@ -81,6 +94,9 @@ def test_prefill_max_context_length(
         # HF returns the prompt + generated tokens. Slice off the prompt.
         hf_output_ids = hf_generated.cpu().tolist()[0][len(prompt_ids[0]):]
 
+    # 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(

vllm/v1/core/sched/scheduler.py

@@ -224,7 +224,7 @@ class Scheduler(SchedulerInterface):
             # This is necessary when using spec decoding.
             num_new_tokens = min(
                 num_new_tokens,
-                self.max_model_len - 1 - request.num_computed_tokens)
+                self.max_model_len - request.num_computed_tokens)
 
             # Schedule encoder inputs.
             encoder_inputs_to_schedule = None

vllm/v1/core/sched/utils.py

@@ -43,7 +43,7 @@ def remove_all(lst: list, items_to_remove: set) -> list:
 def check_stop(request: Request,
                max_model_len: int,
                pooler_output: Optional[torch.Tensor] = None) -> bool:
-    if (request.num_tokens >= max_model_len
+    if (request.num_tokens > max_model_len
             or request.num_output_tokens >= request.max_tokens):
         request.status = RequestStatus.FINISHED_LENGTH_CAPPED
         return True