Support multiple attention groups for KV sharing

tests/v1/test_kv_sharing.py

@@ -0,0 +1,189 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from unittest.mock import Mock
+
+import torch
+
+from vllm.v1.attention.backends.flash_attn import (
+    FlashAttentionBackend, FlashAttentionMetadataBuilder)
+from vllm.v1.attention.backends.flex_attention import (
+    FlexAttentionBackend, FlexAttentionMetadataBuilder)
+from vllm.v1.kv_cache_interface import FullAttentionSpec, KVCacheGroupSpec
+from vllm.v1.worker.utils import (AttentionGroup,
+                                  initialize_kv_cache_for_kv_sharing)
+
+
+def new_kv_cache_spec():
+    return FullAttentionSpec(16, 1, 1, torch.float32, False)
+
+
+def test_initialize_kv_cache_for_kv_sharing_different_attn_groups():
+    """
+    Test initializing KV cache sharing with different attention groups.
+    Layers in the same KV cache group might be placed in different attn groups
+    if they have different attention backends.
+    """
+    shared_kv_cache_layers = {
+        "model.layers.2": "model.layers.0",
+        "model.layers.3": "model.layers.1",
+    }
+
+    # Layers 0 and 1 both belong in KV cache group 0
+    # However, if they have have different attention backends, they will be
+    # placed in different attention groups for KV cache group 0
+    kv_cache_groups = [
+        KVCacheGroupSpec(["model.layers.0", "model.layers.1"],
+                         new_kv_cache_spec()),
+    ]
+
+    attn_groups = [
+        # KV cache group 0 has two attention groups
+        [
+            AttentionGroup(
+                backend=FlashAttentionBackend,
+                metadata_builder=Mock(spec=FlashAttentionMetadataBuilder),
+                layer_names=["model.layers.0"],
+            ),
+            AttentionGroup(
+                backend=FlexAttentionBackend,
+                metadata_builder=Mock(spec=FlexAttentionMetadataBuilder),
+                layer_names=["model.layers.1"],
+            ),
+        ],
+    ]
+
+    # Only layers 0 and 1 will have KV caches allocated
+    kv_caches = {
+        "model.layers.0": torch.zeros(1, 2, 3),
+        "model.layers.1": torch.ones(1, 2, 3),
+    }
+
+    initialize_kv_cache_for_kv_sharing(
+        shared_kv_cache_layers=shared_kv_cache_layers,
+        kv_cache_groups=kv_cache_groups,
+        kv_caches=kv_caches,
+        attn_groups=attn_groups,
+    )
+
+    # Check that the KV caches were shared correctly
+    assert kv_caches["model.layers.2"].data_ptr(
+    ) == kv_caches["model.layers.0"].data_ptr()
+    assert kv_caches["model.layers.3"].data_ptr(
+    ) == kv_caches["model.layers.1"].data_ptr()
+
+    # Check that the layers were added to the correct KV cache group
+    assert len(kv_cache_groups) == 1
+    assert kv_cache_groups[0].layer_names == [
+        "model.layers.0", "model.layers.1", "model.layers.2", "model.layers.3"
+    ]
+
+    # Check that the layers were added to the attention groups
+    assert len(attn_groups) == 1 and len(attn_groups[0]) == 2
+    assert attn_groups[0][0].layer_names == [
+        "model.layers.0", "model.layers.2"
+    ]
+    assert attn_groups[0][1].layer_names == [
+        "model.layers.1", "model.layers.3"
+    ]
+
+
+def test_initialize_kv_cache_for_kv_sharing_same_attn_groups():
+    """
+    Test case assuming that all layers in the same KV cache group have the same
+    attention backends. This is true for most models.
+    """
+    shared_kv_cache_layers = {
+        "model.layers.2": "model.layers.0",
+        "model.layers.3": "model.layers.1",
+    }
+
+    kv_cache_groups = [
+        KVCacheGroupSpec(["model.layers.0", "model.layers.1"],
+                         new_kv_cache_spec()),
+    ]
+
+    attn_groups = [
+        # KV cache group 0 has a single attention group
+        # as all layers have the same flash attention backend
+        [
+            AttentionGroup(
+                backend=FlashAttentionBackend,
+                metadata_builder=Mock(spec=FlashAttentionMetadataBuilder),
+                layer_names=["model.layers.0", "model.layers.1"],
+            ),
+        ],
+    ]
+
+    kv_caches = {
+        "model.layers.0": torch.zeros(1, 2, 3),
+        "model.layers.1": torch.ones(1, 2, 3),
+    }
+
+    initialize_kv_cache_for_kv_sharing(
+        shared_kv_cache_layers=shared_kv_cache_layers,
+        kv_cache_groups=kv_cache_groups,
+        kv_caches=kv_caches,
+        attn_groups=attn_groups,
+    )
+
+    # Check that the KV caches were shared correctly
+    assert kv_caches["model.layers.2"].data_ptr(
+    ) == kv_caches["model.layers.0"].data_ptr()
+    assert kv_caches["model.layers.3"].data_ptr(
+    ) == kv_caches["model.layers.1"].data_ptr()
+
+    # Check that the layers were added to the correct KV cache group
+    assert len(kv_cache_groups) == 1
+    assert kv_cache_groups[0].layer_names == [
+        "model.layers.0", "model.layers.1", "model.layers.2", "model.layers.3"
+    ]
+
+    # Check that the layers were added to the attention groups
+    assert len(attn_groups) == 1 and len(attn_groups[0]) == 1
+    assert attn_groups[0][0].layer_names == [
+        "model.layers.0", "model.layers.1", "model.layers.2", "model.layers.3"
+    ]
+
+
+def test_initialize_kv_cache_for_kv_sharing_no_attn_groups():
+    """
+    Test KV sharing set up when no attention groups are provided.
+    This is the case for the TPU model runner, which doesn't have 
+    support for attention groups yet.
+    """
+    shared_kv_cache_layers = {
+        "model.layers.2": "model.layers.0",
+        "model.layers.3": "model.layers.1",
+    }
+
+    kv_cache_groups = [
+        KVCacheGroupSpec(["model.layers.0"], new_kv_cache_spec()),
+        KVCacheGroupSpec(["model.layers.1"], new_kv_cache_spec()),
+    ]
+
+    kv_caches = {
+        "model.layers.0": torch.zeros(1, 2, 3),
+        "model.layers.1": torch.ones(1, 2, 3),
+    }
+
+    initialize_kv_cache_for_kv_sharing(
+        shared_kv_cache_layers=shared_kv_cache_layers,
+        kv_cache_groups=kv_cache_groups,
+        kv_caches=kv_caches,
+    )
+
+    # Check that the KV caches were shared correctly
+    assert kv_caches["model.layers.2"].data_ptr(
+    ) == kv_caches["model.layers.0"].data_ptr()
+    assert kv_caches["model.layers.3"].data_ptr(
+    ) == kv_caches["model.layers.1"].data_ptr()
+
+    # Check that the layers were added to the correct KV cache group
+    assert len(kv_cache_groups) == 2
+    assert kv_cache_groups[0].layer_names == [
+        "model.layers.0", "model.layers.2"
+    ]
+    assert kv_cache_groups[1].layer_names == [
+        "model.layers.1", "model.layers.3"
+    ]

vllm/v1/worker/utils.py

@@ -225,26 +225,34 @@ def initialize_kv_cache_for_kv_sharing(
             Note that layers in shared_kv_cache_layers.keys() are not
             originally included as it only contains layers which have its own
             KV cache allocation.
+        attn_groups: Optional list of attention groups. Layers in the same KV
+            cache group may be placed in different attention groups if they
+            have different attention backends.  Currently only provided by 
+            GPU model runner.
     """
-    # Record index of KV cache group for each layer that allocates a KV cache.
-    layer_to_kv_cache_group_idx: dict[str, int] = {}
-    for i, kv_cache_group in enumerate(kv_cache_groups):
-        for layer_name in kv_cache_group.layer_names:
-            layer_to_kv_cache_group_idx[layer_name] = i
+    # mapping from layer name to tuple of (kv_cache_group_idx, attn_group_idx)
+    layer_to_attn_group_idx: dict[str, tuple[int, int]] = {}
+    if attn_groups:
+        for kv_cache_group_idx, kv_attn_groups in enumerate(attn_groups):
+            for attn_group_idx, attn_group in enumerate(kv_attn_groups):
+                for layer_name in attn_group.layer_names:
+                    layer_to_attn_group_idx[layer_name] = (kv_cache_group_idx,
+                                                           attn_group_idx)
+    else:
+        for kv_cache_group_idx, kv_cache_group in enumerate(kv_cache_groups):
+            for layer_name in kv_cache_group.layer_names:
+                # attn group idx default to 0 if not provided
+                layer_to_attn_group_idx[layer_name] = (kv_cache_group_idx, 0)
 
     for layer_name, target_layer_name in shared_kv_cache_layers.items():
         kv_caches[layer_name] = kv_caches[target_layer_name]
-        group_idx = layer_to_kv_cache_group_idx[target_layer_name]
-        kv_cache_groups[group_idx].layer_names.append(layer_name)
-
-        if attn_groups is not None:
-            assert len(attn_groups[group_idx]) == 1, (
-                "Only one attention group per KV cache group is supported "
-                "for KV-cache sharing for now.")
-            # TODO(lucas): I think in the future the layers that re-use a
-            # KV cache will be in a different attention group so we can
-            # remove this code from here.
-            attn_groups[group_idx][0].layer_names.append(layer_name)
+        kv_cache_group_idx = layer_to_attn_group_idx[target_layer_name][0]
+        kv_cache_groups[kv_cache_group_idx].layer_names.append(layer_name)
+
+        if attn_groups:
+            attn_group_idx = layer_to_attn_group_idx[target_layer_name][1]
+            attn_groups[kv_cache_group_idx][attn_group_idx].layer_names.append(
+                layer_name)
 
 
 def bind_kv_cache(