Add vLLM v0.18.1 source tree with KV transfer abort fix

third_party/vllm/ now tracked in git for direct patch management.
Based on vLLM v0.18.1 release with one patch applied:

  vllm/v1/core/sched/scheduler.py:
    Replace fatal assert with graceful skip when KV transfer callback
    arrives for an already-aborted request during PD disaggregated serving.

Future vLLM modifications should be made directly in third_party/vllm/
and committed normally. The patches/ directory is kept as documentation
of what changed from upstream.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

third_party/vllm/tests/compile/test_compile_ranges.py

@@ -0,0 +1,257 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Any
+
+import torch
+from torch import fx as fx
+from torch import nn
+
+# This import automatically registers `torch.ops.silly.attention`
+import tests.compile.silly_attention  # noqa
+from vllm.compilation.counter import compilation_counter
+from vllm.compilation.decorators import support_torch_compile
+from vllm.compilation.passes.inductor_pass import (
+    InductorPass,
+    get_pass_context,
+)
+from vllm.config import (
+    VllmConfig,
+    set_current_vllm_config,
+)
+from vllm.config.compilation import CompilationConfig, CompilationMode
+from vllm.config.scheduler import SchedulerConfig
+from vllm.config.utils import Range
+from vllm.forward_context import set_forward_context
+
+BATCH_SIZE = 64
+MLP_SIZE = 128
+
+
+@support_torch_compile
+class TestModel(nn.Module):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "", **kwargs) -> None:
+        super().__init__()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = x + x
+        attn_output = torch.empty_like(x)
+        torch.ops.silly.attention(x, x, x, attn_output)
+        x = attn_output
+        x = x * 3
+        return x
+
+
+@torch.inference_mode
+def run_model(vllm_config: VllmConfig, model: nn.Module, batch_sizes: list[int]):
+    with set_forward_context({}, vllm_config=vllm_config):
+        model(torch.randn(BATCH_SIZE, MLP_SIZE))
+        for batch_size in batch_sizes:
+            model(torch.randn(batch_size, MLP_SIZE))
+
+
+class PostGradRangeChecker(InductorPass):
+    def __init__(self, ranges: list[Range]):
+        self.ranges = ranges
+        self.num_calls = 0
+
+    def __call__(self, graph: fx.Graph):
+        compile_range = get_pass_context().compile_range
+        assert compile_range in self.ranges, (
+            f"Compile range {compile_range} not in {self.ranges}"
+        )
+        self.num_calls += 1
+
+    def uuid(self) -> str:
+        state: dict[str, Any] = {}
+        return InductorPass.hash_dict(state)
+
+
+def test_compile_ranges(use_fresh_inductor_cache):
+    post_grad_range_checker = PostGradRangeChecker(
+        [
+            Range(start=1, end=8),
+            Range(start=16, end=16),
+            Range(start=9, end=32),
+            Range(start=64, end=64),
+            Range(start=128, end=128),
+            Range(start=33, end=8192),
+        ]
+    )
+    torch.set_default_device("cuda")
+    vllm_config = VllmConfig(
+        scheduler_config=SchedulerConfig(
+            max_num_batched_tokens=8192,
+            max_model_len=8192,
+            is_encoder_decoder=False,
+        ),
+        compilation_config=CompilationConfig(
+            mode=CompilationMode.VLLM_COMPILE,
+            compile_ranges_endpoints=[8, 32],
+            compile_sizes=[16, 64, 128],
+            inductor_compile_config={
+                "post_grad_custom_post_pass": post_grad_range_checker,
+            },
+        ),
+    )
+
+    with set_current_vllm_config(vllm_config):
+        model = TestModel(vllm_config=vllm_config, prefix="").eval()
+        # Number of compilations: 3 compile ranges + 3 compile sizes
+        batch_sizes = [1, 4, 16, 24, 48, 64, 8192]
+
+        with compilation_counter.expect(
+            num_graphs_seen=1,
+            num_piecewise_graphs_seen=1,
+            num_backend_compilations=6,
+        ):
+            run_model(vllm_config, model, batch_sizes)
+        assert post_grad_range_checker.num_calls == 6
+
+
+def test_compile_config_get_compile_ranges():
+    compilation_config = CompilationConfig(
+        compile_ranges_endpoints=[8, 32],
+    )
+    VllmConfig(
+        scheduler_config=SchedulerConfig(
+            max_num_batched_tokens=8192,
+            max_model_len=8192,
+            is_encoder_decoder=False,
+        ),
+        compilation_config=compilation_config,
+    )
+    assert compilation_config.get_compile_ranges() == [
+        Range(start=1, end=8),
+        Range(start=9, end=32),
+        Range(start=33, end=8192),
+    ]
+
+
+class PostGradStaticShapeChecker(InductorPass):
+    """Asserts that compile_sizes entries produce graphs with fully concrete
+    (non-symbolic) shapes, and compile_ranges entries have symbolic shapes."""
+
+    def __init__(self):
+        self.num_static_calls = 0
+        self.num_dynamic_calls = 0
+
+    def __call__(self, graph: fx.Graph):
+        from torch.fx.experimental.symbolic_shapes import is_symbolic
+
+        compile_range = get_pass_context().compile_range
+        is_single = compile_range.is_single_size()
+
+        for node in graph.nodes:
+            val = node.meta.get("val")
+            if val is None:
+                val = node.meta.get("example_value")
+            if isinstance(val, torch.Tensor):
+                has_symbolic = any(is_symbolic(d) for d in val.shape)
+                if is_single:
+                    assert not has_symbolic, (
+                        f"compile_sizes entry {compile_range}: "
+                        f"node '{node.name}' has symbolic shape "
+                        f"{val.shape}"
+                    )
+                else:
+                    # compile_ranges should have at least some
+                    # symbolic shapes (the batch dimension)
+                    if has_symbolic:
+                        self.num_dynamic_calls += 1
+                        return
+
+        if is_single:
+            self.num_static_calls += 1
+
+    def uuid(self) -> str:
+        state: dict[str, Any] = {}
+        return InductorPass.hash_dict(state)
+
+
+def test_compile_sizes_produce_static_shapes(use_fresh_inductor_cache):
+    """Verify that compile_sizes entries are compiled with fully concrete
+    shapes (no SymInts), while compile_ranges entries retain dynamic shapes."""
+    checker = PostGradStaticShapeChecker()
+    torch.set_default_device("cuda")
+    vllm_config = VllmConfig(
+        scheduler_config=SchedulerConfig(
+            max_num_batched_tokens=8192,
+            max_model_len=8192,
+            is_encoder_decoder=False,
+        ),
+        compilation_config=CompilationConfig(
+            mode=CompilationMode.VLLM_COMPILE,
+            compile_ranges_endpoints=[8],
+            compile_sizes=[16],
+            inductor_compile_config={
+                "post_grad_custom_post_pass": checker,
+            },
+        ),
+    )
+
+    with set_current_vllm_config(vllm_config):
+        model = TestModel(vllm_config=vllm_config, prefix="").eval()
+        # 3 compilations: Range(1,8), Range(9,8192), single-size 16
+        with compilation_counter.expect(
+            num_graphs_seen=1,
+            num_piecewise_graphs_seen=1,
+            num_backend_compilations=3,
+        ):
+            run_model(vllm_config, model, [1, 16, 64])
+
+    # compile_sizes=16 should produce static shapes
+    assert checker.num_static_calls == 1, (
+        f"Expected 1 static compilation, got {checker.num_static_calls}"
+    )
+    # compile_ranges should produce dynamic shapes
+    assert checker.num_dynamic_calls == 2, (
+        f"Expected 2 dynamic compilations, got {checker.num_dynamic_calls}"
+    )
+
+
+def test_inductor_cache_compile_ranges(monkeypatch, use_fresh_inductor_cache):
+    # To force multiple compilations, we disable the compile cache
+    monkeypatch.setenv("VLLM_DISABLE_COMPILE_CACHE", "1")
+
+    post_grad_range_checker = PostGradRangeChecker(
+        ranges=[
+            Range(start=1, end=8),
+            Range(start=9, end=8192),
+        ]
+    )
+    scheduler_config = SchedulerConfig(
+        max_num_batched_tokens=8192,
+        max_model_len=8192,
+        is_encoder_decoder=False,
+    )
+    torch.set_default_device("cuda")
+
+    def create_vllm_config():
+        return VllmConfig(
+            scheduler_config=scheduler_config,
+            compilation_config=CompilationConfig(
+                mode=CompilationMode.VLLM_COMPILE,
+                compile_ranges_endpoints=[8],
+                inductor_compile_config={
+                    "post_grad_custom_post_pass": post_grad_range_checker,
+                },
+            ),
+        )
+
+    vllm_config_1 = create_vllm_config()
+    with set_current_vllm_config(vllm_config_1):
+        model1 = TestModel(vllm_config=vllm_config_1, prefix="").eval()
+        batch_sizes = [1, 16]
+        run_model(vllm_config_1, model1, batch_sizes)
+        assert post_grad_range_checker.num_calls == 2
+
+    post_grad_range_checker.num_calls = 0
+    # Create a new vllm config with the new pass context
+    vllm_config_2 = create_vllm_config()
+    with set_current_vllm_config(vllm_config_2):
+        model2 = TestModel(vllm_config=vllm_config_2, prefix="").eval()
+        batch_sizes = [4, 32]
+        run_model(vllm_config_2, model2, batch_sizes)
+        # Check that cache is used, so the number of calls
+        # should be 0
+        assert post_grad_range_checker.num_calls == 0