Optimize KV cache distribution for asymmetric pipeline parallelism (#25164)

Signed-off-by: gholmes829 <g.holmes429@gmail.com>

tests/v1/core/test_kv_cache_utils.py

@@ -681,10 +681,10 @@ def test_get_kv_cache_configs_multiple_workers():
             num_blocks=10,
             kv_cache_tensors=[
                 KVCacheTensor(
-                    size=ref_kv_cache_spec.page_size_bytes * 20, shared_by=["layer1"]
+                    size=ref_kv_cache_spec.page_size_bytes * 10, shared_by=["layer1"]
                 ),
                 KVCacheTensor(
-                    size=ref_kv_cache_spec.page_size_bytes * 20, shared_by=["layer2"]
+                    size=ref_kv_cache_spec.page_size_bytes * 10, shared_by=["layer2"]
                 ),
             ],
             kv_cache_groups=[
@@ -718,7 +718,7 @@ def test_get_kv_cache_configs_multiple_workers():
             num_blocks=10,
             kv_cache_tensors=[
                 KVCacheTensor(
-                    size=ref_kv_cache_spec.page_size_bytes * 20, shared_by=["layer1"]
+                    size=ref_kv_cache_spec.page_size_bytes * 10, shared_by=["layer1"]
                 ),
             ],
             kv_cache_groups=[
@@ -802,7 +802,7 @@ def test_get_kv_cache_configs_multiple_workers():
             num_blocks=10,
             kv_cache_tensors=[
                 KVCacheTensor(
-                    size=ref_kv_cache_spec.page_size_bytes * 20, shared_by=["layer3"]
+                    size=ref_kv_cache_spec.page_size_bytes * 10, shared_by=["layer3"]
                 ),
             ],
             kv_cache_groups=[
@@ -813,7 +813,7 @@ def test_get_kv_cache_configs_multiple_workers():
             num_blocks=10,
             kv_cache_tensors=[
                 KVCacheTensor(
-                    size=ref_kv_cache_spec.page_size_bytes * 20, shared_by=["layer3"]
+                    size=ref_kv_cache_spec.page_size_bytes * 10, shared_by=["layer3"]
                 ),
             ],
             kv_cache_groups=[

vllm/config/cache.py

@@ -124,7 +124,7 @@ class CacheConfig:
     gpu_memory_utilization. However, users may want to manually specify
     the kv cache memory size. kv_cache_memory_bytes allows more fine-grain
     control of how much memory gets used when compared with using
-    gpu_memory_memory_utilization. Note that kv_cache_memory_bytes
+    gpu_memory_utilization. Note that kv_cache_memory_bytes
     (when not-None) ignores gpu_memory_utilization"""
 
     def compute_hash(self) -> str:

vllm/entrypoints/llm.py

@@ -143,7 +143,7 @@ class LLM:
             size based on gpu_memory_utilization. However, users may want to
             manually specify the kv cache memory size. kv_cache_memory_bytes
             allows more fine-grain control of how much memory gets used when
-            compared with using gpu_memory_memory_utilization. Note that
+            compared with using gpu_memory_utilization. Note that
             kv_cache_memory_bytes (when not-None) ignores
             gpu_memory_utilization
         swap_space: The size (GiB) of CPU memory per GPU to use as swap space.

vllm/v1/core/kv_cache_utils.py

@@ -1113,35 +1113,12 @@ def get_kv_cache_config_from_groups(
                 KVCacheTensor(size=page_size * num_blocks, shared_by=shared_by)
             )
 
-    kv_cache_config = KVCacheConfig(
+    return KVCacheConfig(
         num_blocks=num_blocks,
         kv_cache_tensors=kv_cache_tensors,
         kv_cache_groups=kv_cache_groups,
     )
 
-    min_block_size = min([group.kv_cache_spec.block_size for group in kv_cache_groups])
-
-    # Print the KV cache size and maximum concurrency.
-    num_tokens = num_blocks // len(kv_cache_groups) * min_block_size
-    if vllm_config.parallel_config.decode_context_parallel_size > 1:
-        num_tokens *= vllm_config.parallel_config.decode_context_parallel_size
-        logger.info(
-            "Multiplying the GPU KV cache size by the dcp_world_size %d.",
-            vllm_config.parallel_config.decode_context_parallel_size,
-        )
-    num_tokens_str = f"{num_tokens:,}"
-    logger.info("GPU KV cache size: %s tokens", num_tokens_str)
-    max_model_len_str = f"{vllm_config.model_config.max_model_len:,}"
-    max_concurrency = get_max_concurrency_for_kv_cache_config(
-        vllm_config, kv_cache_config
-    )
-    logger.info(
-        "Maximum concurrency for %s tokens per request: %.2fx",
-        max_model_len_str,
-        max_concurrency,
-    )
-    return kv_cache_config
-
 
 def unify_hybrid_kv_cache_specs(kv_cache_spec: dict[str, KVCacheSpec]):
     """
@@ -1265,6 +1242,45 @@ def generate_scheduler_kv_cache_config(
     return cfg
 
 
+def _report_kv_cache_config(
+    vllm_config: VllmConfig, kv_cache_config: KVCacheConfig
+) -> None:
+    """
+    Log resolved KV cache configuration.
+
+    Args:
+        vllm_config: The global VllmConfig
+        kv_cache_config: The resolved KV cache configuration
+    """
+    min_block_size = min(
+        [group.kv_cache_spec.block_size for group in kv_cache_config.kv_cache_groups]
+    )
+
+    # Log the KV cache size and maximum concurrency.
+    num_tokens = (
+        kv_cache_config.num_blocks
+        // len(kv_cache_config.kv_cache_groups)
+        * min_block_size
+    )
+    if vllm_config.parallel_config.decode_context_parallel_size > 1:
+        num_tokens *= vllm_config.parallel_config.decode_context_parallel_size
+        logger.info(
+            "Multiplying the GPU KV cache size by the dcp_world_size %d.",
+            vllm_config.parallel_config.decode_context_parallel_size,
+        )
+    num_tokens_str = f"{num_tokens:,}"
+    logger.info("GPU KV cache size: %s tokens", num_tokens_str)
+    max_model_len_str = f"{vllm_config.model_config.max_model_len:,}"
+    max_concurrency = get_max_concurrency_for_kv_cache_config(
+        vllm_config, kv_cache_config
+    )
+    logger.info(
+        "Maximum concurrency for %s tokens per request: %.2fx",
+        max_model_len_str,
+        max_concurrency,
+    )
+
+
 def get_kv_cache_configs(
     vllm_config: VllmConfig,
     kv_cache_specs: list[dict[str, KVCacheSpec]],
@@ -1284,7 +1300,8 @@ def get_kv_cache_configs(
     3. Generate the KV cache configs for each worker based on the KV cache
        grouping strategy. (This is reasonable because the layer ratio of
        different PP stages are similar.)
-    4. Change the num_blocks of each worker to the smallest among all workers.
+    4. Change the num_blocks of each worker to the smallest among all workers
+       and shrink tensor sizes proportionally to avoid allocating unused memory.
 
     Args:
         vllm_config: The global VllmConfig
@@ -1345,13 +1362,22 @@ def get_kv_cache_configs(
             )
         )
 
-    # Change the num_blocks of each rank to the smallest among all ranks. We
-    # do not need to shrink the tensor size because it is valid to only use the
-    # first `num_blocks` blocks of the tensor.
+    # Change the num_blocks of each rank to the smallest among all ranks.
+    # We also need to shrink the tensor size proportionally to avoid
+    # allocating unused memory.
     min_num_blocks = min(
         kv_cache_config.num_blocks for kv_cache_config in kv_cache_configs
     )
     for kv_cache_config in kv_cache_configs:
+        num_blocks_old = kv_cache_config.num_blocks
         kv_cache_config.num_blocks = min_num_blocks
 
+        # Shrink tensor size proportionally
+        for tensor in kv_cache_config.kv_cache_tensors:
+            assert tensor.size % num_blocks_old == 0
+            tensor.size = tensor.size // num_blocks_old * min_num_blocks
+
+        if len(kv_cache_config.kv_cache_groups) > 0:
+            _report_kv_cache_config(vllm_config, kv_cache_config)
+
     return kv_cache_configs

vllm/v1/worker/gpu_worker.py

@@ -253,10 +253,10 @@ class Worker(WorkerBase):
             self.model_runner.profile_run()
 
             msg = (
-                f"Initial free memory {GiB(self.init_snapshot.free_memory)} "
-                f"GiB, reserved {GiB(kv_cache_memory_bytes):.2f}GiB memory for "
+                f"Initial free memory {GiB(self.init_snapshot.free_memory):.2f} "
+                f"GiB, reserved {GiB(kv_cache_memory_bytes):.2f} GiB memory for "
                 "KV Cache as specified by kv_cache_memory_bytes config and "
-                "skipped memory profiling. This does does not respect the "
+                "skipped memory profiling. This does not respect the "
                 "gpu_memory_utilization config. Only use kv_cache_memory_bytes "
                 "config when you want manual control of KV cache memory "
                 "size. If OOM'ed, check the difference of initial free "