[V1] Support cross-layer KV sharing (#18212)

Signed-off-by: Yong Hoon Shin <yhshin@meta.com>

tests/v1/worker/test_gpu_model_runner.py

@@ -7,8 +7,11 @@ import pytest
 
 from vllm.attention import Attention
 from vllm.config import (CacheConfig, ModelConfig, ParallelConfig,
-                         SchedulerConfig, VllmConfig)
+                         SchedulerConfig, VllmConfig, set_current_vllm_config)
 from vllm.sampling_params import SamplingParams
+from vllm.utils import GiB_bytes
+from vllm.v1.core.kv_cache_utils import (estimate_max_model_len,
+                                         get_kv_cache_config)
 from vllm.v1.core.sched.output import (CachedRequestData, NewRequestData,
                                        SchedulerOutput)
 from vllm.v1.kv_cache_interface import (FullAttentionSpec, KVCacheConfig,
@@ -19,6 +22,7 @@ from vllm.v1.worker.gpu_model_runner import GPUModelRunner
 
 BLOCK_SIZE = 16
 NUM_BLOCKS = 10
+DEVICE = "cuda"
 
 
 def initialize_kv_cache(runner: GPUModelRunner):
@@ -55,8 +59,7 @@ def initialize_kv_cache(runner: GPUModelRunner):
     runner.initialize_attn_backend(kv_cache_config)
 
 
-@pytest.fixture
-def model_runner():
+def get_vllm_config():
     scheduler_config = SchedulerConfig(
         max_num_seqs=10,
         max_num_batched_tokens=512,
@@ -84,13 +87,18 @@ def model_runner():
         scheduler_config=scheduler_config,
         parallel_config=parallel_config,
     )
-    num_heads = model_config.get_num_kv_heads(parallel_config)
+    return vllm_config
+
+
+@pytest.fixture
+def model_runner():
+    vllm_config = get_vllm_config()
+    model_config = vllm_config.model_config
+    num_heads = model_config.get_num_kv_heads(vllm_config.parallel_config)
     head_size = model_config.get_head_size()
     vllm_config.compilation_config.static_forward_context[
         "layer.0"] = Attention(num_heads, head_size, 0.1)
-
-    device = "cuda"
-    runner = GPUModelRunner(vllm_config, device)
+    runner = GPUModelRunner(vllm_config, DEVICE)
     initialize_kv_cache(runner)
     return runner
 
@@ -385,3 +393,225 @@ def test_load_model_weights_inplace(dist_init, model_runner, model_runner_2):
     model_runner_2.load_model()  # Load real weights inplace
     assert str(model_runner.get_model().state_dict()) == str(
         model_runner_2.get_model().state_dict())
+
+
+def test_init_kv_cache_with_kv_sharing_invalid_target_layer_order():
+    layer_0 = "model.layers.0.self_attn.attn"
+    layer_1 = "model.layers.1.self_attn.attn"
+    error_msg = f"{layer_1} must come before the current layer"
+    with pytest.raises(ValueError, match=error_msg):
+        fwd_context = {
+            # initialization below will fail because target layer is invalid;
+            # the target layer needs to come before layer 1
+            layer_0:
+            Attention(
+                num_heads=8,
+                head_size=64,
+                scale=1.0,
+                prefix=layer_0,
+                kv_sharing_target_layer_name=layer_1,
+            ),
+            layer_1:
+            Attention(
+                num_heads=8,
+                head_size=64,
+                scale=1.0,
+                prefix=layer_1,
+            )
+        }
+        # suppress var not used error
+        assert fwd_context is not None
+
+
+def test_init_kv_cache_with_kv_sharing_target_layer_not_exist():
+    layer_0 = "model.layers.0.self_attn.attn"
+    layer_1 = "model.layers.1.self_attn.attn"
+    invalid_layer = "model.layers.0.cross_attn.attn"
+    error_msg = f"{invalid_layer} is not a valid Attention layer in the model"
+    with pytest.raises(ValueError, match=error_msg):
+        fwd_context = {
+            layer_0:
+            Attention(
+                num_heads=8,
+                head_size=64,
+                scale=1.0,
+                prefix=layer_0,
+            ),
+            layer_1:
+            Attention(
+                num_heads=8,
+                head_size=64,
+                scale=1.0,
+                prefix=layer_1,
+                # invalid layer: cross_attn.atn doesn't exist!
+                kv_sharing_target_layer_name=invalid_layer,
+            )
+        }
+        # suppress var not used error
+        assert fwd_context is not None
+
+
+def test_init_kv_cache_with_kv_sharing_target_same_as_current():
+    layer_0 = "model.layers.0.self_attn.attn"
+    layer_1 = "model.layers.1.self_attn.attn"
+    error_msg = f"{layer_1} cannot be the same as the current layer"
+    with pytest.raises(ValueError, match=error_msg):
+        fwd_context = {
+            # initialization below will fail because target layer is invalid;
+            # the target layer needs to come before layer 1
+            layer_0:
+            Attention(
+                num_heads=8,
+                head_size=64,
+                scale=1.0,
+                prefix=layer_0,
+            ),
+            layer_1:
+            Attention(
+                num_heads=8,
+                head_size=64,
+                scale=1.0,
+                prefix=layer_1,
+                kv_sharing_target_layer_name=layer_1,
+            )
+        }
+        # suppress var not used error
+        assert fwd_context is not None
+
+
+def test_init_kv_cache_without_kv_sharing():
+    layer_0 = "model.layers.0.self_attn.attn"
+    layer_1 = "model.layers.1.self_attn.attn"
+    vllm_config = get_vllm_config()
+    with set_current_vllm_config(vllm_config):
+        fwd_context = {
+            layer_0:
+            Attention(
+                num_heads=8,
+                head_size=64,
+                scale=1.0,
+                prefix=layer_0,
+            ),
+            layer_1:
+            Attention(
+                num_heads=8,
+                head_size=64,
+                scale=1.0,
+                prefix=layer_1,
+            )
+        }
+        # suppress var not used error
+        assert fwd_context is not None
+    # Set high context length to test max context length estimation
+    vllm_config.model_config.max_model_len = 3_000_000
+    vllm_ctx = vllm_config.compilation_config.static_forward_context
+    runner = GPUModelRunner(vllm_config, DEVICE)
+    kv_cache_spec = runner.get_kv_cache_spec()
+    assert len(kv_cache_spec) == 2
+    assert len(runner.shared_kv_cache_layers) == 0
+
+    available_memory = 20 * GiB_bytes
+    # page size for layer 0's kv_cache_spec is 32KB
+    num_expected_blocks = 327680  # 20GB / 32KB / 2 (num layers)
+    kv_cache_config = get_kv_cache_config(vllm_config, kv_cache_spec,
+                                          available_memory)
+    assert kv_cache_config.num_blocks == num_expected_blocks
+    assert len(kv_cache_config.tensors) == 2
+    assert kv_cache_config.tensors[layer_0].size == available_memory // 2
+    assert kv_cache_config.tensors[layer_1].size == available_memory // 2
+
+    max_context_len =\
+        estimate_max_model_len(vllm_config, kv_cache_spec, 5 * GiB_bytes)
+    # max context len with KV sharing should be 2x as large as without
+    assert max_context_len == 1310720
+
+    # important: override tensor size to prevent large mem alloc during test
+    # this will only allocate 2 block worth of memory (2 * 32kb)
+    kv_cache_config.num_blocks = 1
+    for layer in kv_cache_config.tensors:
+        kv_cache_config.tensors[layer].size =\
+            kv_cache_spec[layer].page_size_bytes
+
+    runner.initialize_kv_cache(kv_cache_config)
+
+    layer_0_kv = vllm_ctx[layer_0].kv_cache[0]
+    layer_1_kv = vllm_ctx[layer_1].kv_cache[0]
+    # check layer 1 kv cache does NOT share memory with layer 0
+    assert id(layer_1_kv) != id(layer_0_kv)
+
+    # check layer 1 added to kv cache group's layer names
+    assert len(kv_cache_config.kv_cache_groups) == 1
+    assert len(kv_cache_config.kv_cache_groups[0].layer_names) == 2
+    assert kv_cache_config.kv_cache_groups[0].layer_names[0] == layer_0
+    assert kv_cache_config.kv_cache_groups[0].layer_names[1] == layer_1
+
+
+def test_init_kv_cache_with_kv_sharing_valid():
+    layer_0 = "model.layers.0.self_attn.attn"
+    layer_1 = "model.layers.1.self_attn.attn"
+    vllm_config = get_vllm_config()
+    with set_current_vllm_config(vllm_config):
+        fwd_context = {
+            layer_0:
+            Attention(
+                num_heads=8,
+                head_size=64,
+                scale=1.0,
+                prefix=layer_0,
+            ),
+            layer_1:
+            Attention(
+                num_heads=8,
+                head_size=64,
+                scale=1.0,
+                prefix=layer_1,
+                kv_sharing_target_layer_name="model.layers.0.self_attn.attn",
+            )
+        }
+        # suppress var not used error
+        assert fwd_context is not None
+    # Set high context length to test max context length estimation
+    vllm_config.model_config.max_model_len = 3_000_000
+    vllm_ctx = vllm_config.compilation_config.static_forward_context
+    runner = GPUModelRunner(vllm_config, DEVICE)
+    kv_cache_spec = runner.get_kv_cache_spec()
+    assert len(kv_cache_spec) == 1
+    assert layer_0 in kv_cache_spec
+    assert runner.shared_kv_cache_layers[layer_1] == layer_0
+
+    available_memory = 20 * GiB_bytes
+    # page size for layer 0's kv_cache_spec is 32KB
+    # with KV sharing, we can allocate (available_mem//page_size//1) blocks
+    # which is twice as many as without KV sharing
+    num_expected_blocks = 655360  # 20GB / 32KB
+    kv_cache_config = get_kv_cache_config(vllm_config, kv_cache_spec,
+                                          available_memory)
+    assert kv_cache_config.num_blocks == num_expected_blocks
+    assert len(kv_cache_config.tensors) == 1
+    # Each layer now has twice the available memory for KV cache
+    # compared to no KV sharing
+    assert kv_cache_config.tensors[layer_0].size == available_memory
+
+    max_context_len =\
+        estimate_max_model_len(vllm_config, kv_cache_spec, 5 * GiB_bytes)
+    # max context len with KV sharing should be 2x as large as without
+    assert max_context_len == 2 * 1310720
+
+    # important: override tensor size to prevent large mem alloc during test
+    # this will only allocate 1 block worth of memory (32kb)
+    kv_cache_config.num_blocks = 1
+    kv_cache_config.tensors[layer_0].size =\
+        kv_cache_spec[layer_0].page_size_bytes
+
+    runner.initialize_kv_cache(kv_cache_config)
+
+    layer_0_kv = vllm_ctx[layer_0].kv_cache[0]
+    layer_1_kv = vllm_ctx[layer_1].kv_cache[0]
+    # check layer 1 kv cache shares memory with layer 0
+    assert id(layer_1_kv) == id(layer_0_kv)
+
+    # check layer 1 added to kv cache group's layer names
+    assert len(kv_cache_config.kv_cache_groups) == 1
+    assert len(kv_cache_config.kv_cache_groups[0].layer_names) == 2
+    assert kv_cache_config.kv_cache_groups[0].layer_names[0] == layer_0
+    assert kv_cache_config.kv_cache_groups[0].layer_names[1] == layer_1