[fix][torch.compile] Fix cold-start compilation time increase by adding kv...

[fix][torch.compile] Fix cold-start compilation time increase by adding kv cache update to splitting ops (#33441)
Signed-off-by: Luka Govedič <lgovedic@redhat.com>
Co-authored-by: Richard Zou <zou3519@gmail.com>
(cherry picked from commit 15f40b20)

tests/compile/test_cold_start.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from torch._dynamo.utils import counters
+
+from vllm import LLM
+from vllm.config import CompilationConfig, CompilationMode, CUDAGraphMode
+
+
+def test_moe_compilation_cold_start(monkeypatch, use_fresh_inductor_cache):
+    # Run in same process so we can access PyTorch's internal counters
+    monkeypatch.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", "0")
+
+    # I'm not sure if this is going to affect the numbers
+    monkeypatch.setenv("VLLM_USE_AOT_COMPILE", "0")
+
+    # Force cold compilation
+    monkeypatch.setenv("VLLM_DISABLE_COMPILE_CACHE", "1")
+
+    compilation_config = CompilationConfig(
+        mode=CompilationMode.VLLM_COMPILE,
+        cudagraph_mode=CUDAGraphMode.NONE,  # make the model loading faster
+    )
+
+    counters.clear()
+
+    _ = LLM(
+        model="microsoft/Phi-tiny-MoE-instruct",
+        max_model_len=256,
+        load_format="dummy",  # make the model loading faster
+        compilation_config=compilation_config,
+        num_gpu_blocks_override=8,  # make the model loading faster
+    )
+
+    # vLLM-compile cold start is special. By default, we do
+    # one full dynamo capture of the entire forward pass.
+    # The forward pass consists of 32 transformer layers.
+    # Then, we split on the attention operation. This results in
+    # 33 subgraphs (not including the attention operation).
+    # The 33 subgraphs then get standalone_compile'd.
+    #
+    # There are actually only 3 unique subgraphs for this model
+    # (all of its transformer layers are the same modulo weights);
+    # this is true for most vLLM models.
+    # So we test that during cold start, the aot_autograd cache
+    # misses for 3 subgraphs and hits for the rest.
+    assert counters["aot_autograd"]["autograd_cache_miss"] == 3
+    assert counters["aot_autograd"]["autograd_cache_hit"] == 30

tests/compile/test_graph_partition.py

@@ -8,6 +8,10 @@ import torch
 from torch.fx.experimental.proxy_tensor import make_fx
 
 from vllm.compilation.backends import split_graph
+from vllm.compilation.fx_utils import find_op_nodes
+
+# This import automatically registers `torch.ops.silly.attention`
+from . import silly_attention  # noqa: F401
 
 
 def test_getitem_moved_to_producer_subgraph():
@@ -122,3 +126,61 @@ def test_no_tuple_inputs_with_multiple_consumers():
     output_split = split_gm(new_x)
 
     assert torch.allclose(output_original, output_split), "Output mismatch after split"
+
+
+def test_consecutive_ops_in_split():
+    """
+    Test that consecutive splitting operations are grouped into the same subgraph
+    """
+
+    def model_fn(x: torch.Tensor) -> torch.Tensor:
+        """
+        Define a simple model where consecutive operations create opportunities
+        for splitting subgraphs.
+        """
+        # Apply silly attention followed by consecutive operations
+        intermediate = torch.relu(x)
+        attn_inout = torch.sqrt(intermediate)
+        torch.ops.silly.attention(intermediate, intermediate, attn_inout, attn_inout)
+        final_result = torch.sigmoid(attn_inout)
+        return final_result
+
+    torch.set_default_device("cuda")
+
+    # Create the traced FX graph for the model
+    x = torch.randn(8, 4)
+
+    gm = make_fx(model_fn)(x)
+
+    # Assert presence of the expected operations in the setup
+    assert (
+        len(list(find_op_nodes(torch.ops.aten.relu, gm.graph))) == 1
+        and len(list(find_op_nodes(torch.ops.aten.sqrt, gm.graph))) == 1
+    ), "Test setup failed: Expected sqrt and relu operations in the graph."
+
+    # Configure split operations to test
+    splitting_ops = ["silly::attention", "aten::sqrt"]
+    split_gm, split_items = split_graph(gm, splitting_ops)
+
+    # Validate the number of partitions
+    assert len(split_items) == 3, (
+        "Consecutive splitting operations were not grouped correctly."
+    )
+
+    # Validate that correctness is preserved
+    new_x = torch.randn(8, 4)
+    output_original = gm(new_x)
+    output_split = split_gm(new_x)
+    assert torch.allclose(output_original, output_split), (
+        "Output mismatch after splitting."
+    )
+
+    # Check the splitting item has 2 nodes exactly (relu and attn)
+    splitting_items = list(s for s in split_items if s.is_splitting_graph)
+    assert len(splitting_items) == 1, "Expecting a single splitting graph"
+    print(splitting_items[0].graph.graph)
+    splitting_gm = splitting_items[0].graph
+    assert len(splitting_gm.graph.nodes) == 4, "Expecting 4 nodes in splitting graph"
+    assert [node.op for node in splitting_gm.graph.nodes] == ["placeholder"] + 2 * [
+        "call_function"
+    ] + ["output"]

vllm/compilation/backends.py

@@ -361,6 +361,13 @@ def split_graph(
             subgraph_id += 1
             node_to_subgraph_id[node] = subgraph_id
             split_op_graphs.append(subgraph_id)
+
+            # keep consecutive splitting ops together
+            # (we know node.next exists because node isn't the last (output) node)
+            if should_split(node.next, splitting_ops):
+                # this will get incremented by the next node
+                subgraph_id -= 1
+            else:
                 subgraph_id += 1
         else:
             node_to_subgraph_id[node] = subgraph_id

vllm/config/compilation.py

@@ -925,6 +925,15 @@ class CompilationConfig:
                 # for details. Make a copy to avoid mutating the class-level
                 # list via reference.
                 self.splitting_ops = list(self._attention_ops)
+
+                # unified_kv_cache_update has a string param that prevents Inductor
+                # from reusing piecewise graphs. Remove it from the compiled graph.
+                # This has the side-effect of excluding cache from cudagraphs but
+                # that doesn't seem to affect performance.
+                # https://github.com/vllm-project/vllm/issues/33267
+                if not self.use_inductor_graph_partition:
+                    self.splitting_ops.append("vllm::unified_kv_cache_update")
+
             elif len(self.splitting_ops) == 0:
                 if (
                     self.cudagraph_mode == CUDAGraphMode.PIECEWISE