[core] gemma2 full context length support

tests/basic_correctness/test_basic_correctness.py

@@ -14,11 +14,12 @@
 from vllm.platforms import current_platform
 from vllm.worker.model_runner import ModelInputForGPUWithSamplingMetadata
 
+from ..conftest import VllmRunner
 from ..models.utils import check_outputs_equal
 from ..utils import multi_gpu_test
 
 MODELS = [
-    "facebook/opt-125m",
+    "google/gemma-2-2b-it",
     "meta-llama/Llama-3.2-1B",
 ]
 
@@ -42,8 +43,6 @@ def test_vllm_gc_ed():
 @pytest.mark.parametrize("enforce_eager", [False, True])
 def test_models(
     hf_runner,
-    vllm_runner,
-    example_prompts,
     model: str,
     backend: str,
     dtype: str,
@@ -56,13 +55,21 @@ def test_models(
 
     os.environ["VLLM_ATTENTION_BACKEND"] = backend
 
+    # 5042 tokens for gemma2
+    # gemma2 has alternating sliding window size of 4096
+    # we need a prompt with more than 4096 tokens to test the sliding window
+    prompt = "The following numbers of the sequence " + ", ".join(
+        str(i) for i in range(1024)) + " are:"
+    example_prompts = [prompt]
+
     with hf_runner(model, dtype=dtype) as hf_model:
         hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
 
-    with vllm_runner(model,
-                     dtype=dtype,
-                     enforce_eager=enforce_eager,
-                     gpu_memory_utilization=0.7) as vllm_model:
+    with VllmRunner(model,
+                    max_model_len=8192,
+                    dtype=dtype,
+                    enforce_eager=enforce_eager,
+                    gpu_memory_utilization=0.7) as vllm_model:
         vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
 
     check_outputs_equal(

vllm/attention/layer.py

@@ -40,18 +40,26 @@ def __init__(
         quant_config: Optional[QuantizationConfig] = None,
         blocksparse_params: Optional[Dict[str, Any]] = None,
         logits_soft_cap: Optional[float] = None,
+        per_layer_sliding_window: Optional[int] = None,
         prefix: str = "",
     ) -> None:
         super().__init__()
+        if per_layer_sliding_window is not None:
+            # per-layer sliding window
+            sliding_window = per_layer_sliding_window
+        elif cache_config is not None:
+            # model-level sliding window
+            sliding_window = cache_config.sliding_window
+        else:
+            sliding_window = None
+
         if cache_config is not None:
             kv_cache_dtype = cache_config.cache_dtype
             block_size = cache_config.block_size
-            sliding_window = cache_config.sliding_window
             is_attention_free = cache_config.is_attention_free
         else:
             kv_cache_dtype = "auto"
             block_size = 16
-            sliding_window = None
             is_attention_free = False
         if num_kv_heads is None:
             num_kv_heads = num_heads

vllm/config.py

@@ -232,16 +232,15 @@ def __init__(
             isinstance(sliding_window, list) or
             (self.hf_text_config.model_type in ["gemma2"]))
 
-        if (not self.disable_sliding_window and has_interleaved_attention):
-            sliding_window_len_min = get_min_sliding_window(
-                self.hf_text_config.sliding_window)
-
-            print_warning_once(
-                f"{self.hf_text_config.model_type} has interleaved attention, "
-                "which is currently not supported by vLLM. Disabling sliding "
-                "window and capping the max length to the sliding window size "
-                f"({sliding_window_len_min}).")
-            self.disable_sliding_window = True
+        if has_interleaved_attention:
+            # for a model with interleaved attention,
+            # the scheduler and the model treat it as full attention
+            # (i.e., not dropping any tokens outside the window).
+            # only the attention layer itself is aware of the sliding window,
+            # and use the window size to compute the attention.
+            self.hf_text_config.interleaved_sliding_window = sliding_window
+            delattr(self.hf_text_config, "sliding_window")
+            sliding_window = None
 
         self.max_model_len = _get_and_verify_max_len(
             hf_config=self.hf_text_config,

vllm/model_executor/models/gemma2.py

@@ -143,19 +143,20 @@ def __init__(self,
             is_neox_style=True,
         )
 
-        # FIXME(woosuk): While Gemma 2 uses sliding window attention for every
-        # odd layer, vLLM currently ignores it and uses global attention for
-        # all layers.
-        use_sliding_window = (layer_idx % 2 == 1
-                              and config.sliding_window is not None)
-        del use_sliding_window  # Unused.
+        # reference:
+        # https://github.com/huggingface/transformers/blob/54be2d7ae87e873482b984cc956e165ca4dc0ba3/src/transformers/models/gemma2/modeling_gemma2.py#L312 # noqa
+        use_sliding_window = (layer_idx % 2 == 0 and
+                              config.interleaved_sliding_window is not None)
+        sliding_window = config.interleaved_sliding_window if \
+            use_sliding_window else None
         self.attn = Attention(self.num_heads,
                               self.head_dim,
                               self.scaling,
                               num_kv_heads=self.num_kv_heads,
                               cache_config=cache_config,
                               quant_config=quant_config,
                               logits_soft_cap=attn_logits_soft_cap,
+                              per_layer_sliding_window=sliding_window,
                               prefix=f"{prefix}.attn")
 
     def forward(