[torch.compile] Hide KV cache behind torch.compile boundary (vllm-project#11677)

Signed-off-by: Chen Zhang <[email protected]>

Author: heheda12345

tests/kernels/test_encoder_decoder_attn.py

@@ -142,12 +142,18 @@ class that Attention will automatically select when it is constructed.
             torch.tensor([], dtype=torch.float32, device=CUDA_DEVICE))
 
     # Construct KV cache
-    kv_cache = make_kv_cache(test_pt.num_blocks,
-                             test_pt.num_heads,
-                             test_pt.head_size,
-                             test_pt.block_size,
-                             device=CUDA_DEVICE,
-                             backend=test_pt.backend_name)
+    if test_pt.attn_type in (AttentionType.DECODER,
+                             AttentionType.ENCODER_DECODER):
+        kv_cache = make_kv_cache(test_pt.num_blocks,
+                                 test_pt.num_heads,
+                                 test_pt.head_size,
+                                 test_pt.block_size,
+                                 device=CUDA_DEVICE,
+                                 backend=test_pt.backend_name)
+    else:
+        kv_cache = torch.tensor([])
+
+    attn.kv_cache = [kv_cache]
     return TestResources(scale, attn, kv_cache)
 
 

tests/test_utils.py

@@ -7,9 +7,11 @@
 import torch
 from vllm_test_utils import monitor
 
+from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
 from vllm.utils import (FlexibleArgumentParser, PlaceholderModule,
-                        StoreBoolean, deprecate_kwargs, get_open_port,
-                        memory_profiling, merge_async_iterators, supports_kw)
+                        StoreBoolean, bind_kv_cache, deprecate_kwargs,
+                        get_open_port, memory_profiling, merge_async_iterators,
+                        supports_kw)
 
 from .utils import error_on_warning, fork_new_process_for_each_test
 
@@ -325,6 +327,85 @@ def measure_current_non_torch():
     lib.cudaFree(handle2)
 
 
+def test_bind_kv_cache():
+    from vllm.attention import Attention
+
+    ctx = {
+        'layers.0.self_attn': Attention(32, 128, 0.1),
+        'layers.1.self_attn': Attention(32, 128, 0.1),
+        'layers.2.self_attn': Attention(32, 128, 0.1),
+        'layers.3.self_attn': Attention(32, 128, 0.1),
+    }
+    kv_cache = [
+        torch.zeros((1, )),
+        torch.zeros((1, )),
+        torch.zeros((1, )),
+        torch.zeros((1, )),
+    ]
+    bind_kv_cache(ctx, [kv_cache])
+    assert ctx['layers.0.self_attn'].kv_cache[0] is kv_cache[0]
+    assert ctx['layers.1.self_attn'].kv_cache[0] is kv_cache[1]
+    assert ctx['layers.2.self_attn'].kv_cache[0] is kv_cache[2]
+    assert ctx['layers.3.self_attn'].kv_cache[0] is kv_cache[3]
+
+def test_bind_kv_cache_non_attention():
+    from vllm.attention import Attention
+
+    # example from Jamba PP=2
+    ctx = {
+        'model.layers.20.attn': Attention(32, 128, 0.1),
+        'model.layers.28.attn': Attention(32, 128, 0.1),
+    }
+    kv_cache = [
+        torch.zeros((1, )),
+        torch.zeros((1, )),
+    ]
+    bind_kv_cache(ctx, [kv_cache])
+    assert ctx['model.layers.20.attn'].kv_cache[0] is kv_cache[0]
+    assert ctx['model.layers.28.attn'].kv_cache[0] is kv_cache[1]
+
+
+def test_bind_kv_cache_encoder_decoder():
+    from vllm.attention import Attention, AttentionType
+
+    # example from bart
+    ctx = {
+        'encoder.layers.0.self_attn.attn':
+            Attention(32, 128, 0.1, attn_type=AttentionType.ENCODER),
+        'decoder.layers.0.encoder_attn.attn':
+            Attention(32, 128, 0.1, attn_type=AttentionType.ENCODER_DECODER),
+        'decoder.layers.0.self_attn.attn':
+            Attention(32, 128, 0.1, attn_type=AttentionType.DECODER),
+    }
+
+    kv_cache = [
+        torch.zeros((1, )),
+    ]
+    encoder_kv_cache = ctx['encoder.layers.0.self_attn.attn'].kv_cache
+
+    bind_kv_cache(ctx, [kv_cache])
+    assert ctx['encoder.layers.0.self_attn.attn'].kv_cache is encoder_kv_cache
+    assert ctx['decoder.layers.0.encoder_attn.attn'].kv_cache[0] is kv_cache[0]
+    assert ctx['decoder.layers.0.self_attn.attn'].kv_cache[0] is kv_cache[0]
+
+
+def test_bind_kv_cache_pp():
+    cfg = VllmConfig(parallel_config=ParallelConfig(pipeline_parallel_size=2))
+    with set_current_vllm_config(cfg):
+        from vllm.attention import Attention
+
+        ctx = {
+            'layers.0.self_attn': Attention(32, 128, 0.1),
+        }
+        kv_cache = [
+            [torch.zeros((1, ))],
+            [torch.zeros((1, ))]
+        ]
+        bind_kv_cache(ctx, kv_cache)
+        assert ctx['layers.0.self_attn'].kv_cache[0] is kv_cache[0][0]
+        assert ctx['layers.0.self_attn'].kv_cache[1] is kv_cache[1][0]
+
+
 def test_placeholder_module_error_handling():
     placeholder = PlaceholderModule("placeholder_1234")
 

tests/v1/engine/test_engine_core.py

@@ -4,6 +4,7 @@
 import pytest
 from transformers import AutoTokenizer
 
+from tests.utils import fork_new_process_for_each_test
 from vllm import SamplingParams
 from vllm.engine.arg_utils import EngineArgs
 from vllm.platforms import current_platform
@@ -36,6 +37,7 @@ def make_request() -> EngineCoreRequest:
     )
 
 
+@fork_new_process_for_each_test
 def test_engine_core(monkeypatch):
 
     with monkeypatch.context() as m:
@@ -138,6 +140,7 @@ def test_engine_core(monkeypatch):
         assert len(engine_core.scheduler.running) == 0
 
 
+@fork_new_process_for_each_test
 def test_engine_core_advanced_sampling(monkeypatch):
     """
     A basic end-to-end test to verify that the engine functions correctly 

tests/v1/engine/test_engine_core_client.py

@@ -6,6 +6,7 @@
 import pytest
 from transformers import AutoTokenizer
 
+from tests.utils import fork_new_process_for_each_test
 from vllm import SamplingParams
 from vllm.engine.arg_utils import EngineArgs
 from vllm.platforms import current_platform
@@ -75,6 +76,7 @@ async def loop_until_done_async(client: EngineCoreClient, outputs: Dict):
             break
 
 
+@fork_new_process_for_each_test
 @pytest.mark.parametrize("multiprocessing_mode", [True, False])
 def test_engine_core_client(monkeypatch, multiprocessing_mode: bool):
 
@@ -143,6 +145,7 @@ def test_engine_core_client(monkeypatch, multiprocessing_mode: bool):
         client.abort_requests([request.request_id])
 
 
+@fork_new_process_for_each_test
 @pytest.mark.asyncio
 async def test_engine_core_client_asyncio(monkeypatch):
 

vllm/attention/layer.py

@@ -121,6 +121,13 @@ def __init__(
         compilation_config.static_forward_context[prefix] = self
         self.layer_name = prefix
         self.attn_type = attn_type
+        # use a placeholder kv cache tensor during init, which will be replaced
+        # by bind_kv_cache
+        # this variable will not be accessed if use_direct_call is True
+        self.kv_cache = [
+            torch.tensor([]) for _ in range(get_current_vllm_config(
+            ).parallel_config.pipeline_parallel_size)
+        ]
 
     def forward(
         self,
@@ -148,11 +155,11 @@ def forward(
             if value is not None:
                 value = value.view(-1, self.num_kv_heads, self.head_size)
             torch.ops.vllm.unified_attention_with_output(
-                query, key, value, output, kv_cache, self.layer_name)
+                query, key, value, output, self.layer_name)
             return output.view(-1, hidden_size)
         else:
             return torch.ops.vllm.unified_attention(query, key, value,
-                                                    kv_cache, self.layer_name)
+                                                    self.layer_name)
 
     def extra_repr(self) -> str:
         s = f"head_size={self.impl.head_size}"  # type: ignore
@@ -230,12 +237,12 @@ def unified_attention(
     query: torch.Tensor,
     key: torch.Tensor,
     value: torch.Tensor,
-    kv_cache: torch.Tensor,
     layer_name: str,
 ) -> torch.Tensor:
     forward_context: ForwardContext = get_forward_context()
-    attn_metadata = forward_context.dynamic_forward_context
-    self = forward_context.static_forward_context[layer_name]
+    attn_metadata = forward_context.attn_metadata
+    self = forward_context.attn_layers[layer_name]
+    kv_cache = self.kv_cache[forward_context.virtual_engine]
     return self.impl.forward(query, key, value, kv_cache, attn_metadata,
                              self._k_scale, self._v_scale)
 
@@ -244,7 +251,6 @@ def unified_attention_fake(
     query: torch.Tensor,
     key: torch.Tensor,
     value: torch.Tensor,
-    kv_cache: torch.Tensor,
     layer_name: str,
 ) -> torch.Tensor:
     return torch.empty_like(query).contiguous()
@@ -253,7 +259,7 @@ def unified_attention_fake(
 direct_register_custom_op(
     op_name="unified_attention",
     op_func=unified_attention,
-    mutates_args=["kv_cache"],
+    mutates_args=[],
     fake_impl=unified_attention_fake,
     dispatch_key=current_platform.dispatch_key,
 )
@@ -264,12 +270,12 @@ def unified_attention_with_output(
     key: torch.Tensor,
     value: torch.Tensor,
     output: torch.Tensor,
-    kv_cache: torch.Tensor,
     layer_name: str,
 ) -> None:
     forward_context: ForwardContext = get_forward_context()
-    attn_metadata = forward_context.dynamic_forward_context
-    self = forward_context.static_forward_context[layer_name]
+    attn_metadata = forward_context.attn_metadata
+    self = forward_context.attn_layers[layer_name]
+    kv_cache = self.kv_cache[forward_context.virtual_engine]
     self.impl.forward(query,
                       key,
                       value,
@@ -285,7 +291,6 @@ def unified_attention_with_output_fake(
     key: torch.Tensor,
     value: torch.Tensor,
     output: torch.Tensor,
-    kv_cache: torch.Tensor,
     layer_name: str,
 ) -> None:
     return
@@ -294,7 +299,7 @@ def unified_attention_with_output_fake(
 direct_register_custom_op(
     op_name="unified_attention_with_output",
     op_func=unified_attention_with_output,
-    mutates_args=["kv_cache", "output"],
+    mutates_args=["output"],
     fake_impl=unified_attention_with_output_fake,
     dispatch_key=current_platform.dispatch_key,
 )

vllm/config.py

@@ -2780,7 +2780,6 @@ def model_post_init(self, __context: Any) -> None:
     compilation_time: float = PrivateAttr
 
     # Per-model forward context
-    # Mainly used to store attention cls
     # Map from layer name to the attention cls
     static_forward_context: Dict[str, Any] = PrivateAttr
 

vllm/forward_context.py

@@ -2,14 +2,17 @@
 from collections import defaultdict
 from contextlib import contextmanager
 from dataclasses import dataclass
-from typing import Any, Dict, Optional
+from typing import TYPE_CHECKING, Any, Dict, Optional
 
 import torch
 
 import vllm.envs as envs
 from vllm.config import VllmConfig
 from vllm.logger import init_logger
 
+if TYPE_CHECKING:
+    from vllm.attention.backends.abstract import AttentionMetadata
+
 logger = init_logger(__name__)
 
 track_batchsize: bool = envs.VLLM_LOG_BATCHSIZE_INTERVAL >= 0
@@ -21,9 +24,12 @@
 
 @dataclass
 class ForwardContext:
-    static_forward_context: Dict[str, Any]
+    # copy from vllm_config.compilation_config.static_forward_context
+    attn_layers: Dict[str, Any]
     # TODO: extend to support per-layer dynamic forward context
-    dynamic_forward_context: Any
+    attn_metadata: "AttentionMetadata"  # set dynamically for each forward pass
+    # TODO: remove after making all virtual_engines share the same kv cache
+    virtual_engine: int  # set dynamically for each forward pass
 
 
 _forward_context: Optional[ForwardContext] = None
@@ -38,34 +44,35 @@ def get_forward_context() -> ForwardContext:
 
 
 @contextmanager
-def set_forward_context(context: Any, vllm_config: VllmConfig):
+def set_forward_context(attn_metadata: Any,
+                        vllm_config: VllmConfig,
+                        virtual_engine: int = 0):
     """A context manager that stores the current forward context,
     can be attention metadata, etc.
     Here we can inject common logic for every model forward pass.
     """
     global forward_start_time
-    need_to_track_batchsize = track_batchsize and context is not None
+    need_to_track_batchsize = track_batchsize and attn_metadata is not None
     if need_to_track_batchsize:
         forward_start_time = time.perf_counter()
     global _forward_context
     prev_context = _forward_context
     _forward_context = ForwardContext(
-        static_forward_context=vllm_config.compilation_config.
-        static_forward_context,
-        dynamic_forward_context=context)
+        attn_layers=vllm_config.compilation_config.static_forward_context,
+        virtual_engine=virtual_engine,
+        attn_metadata=attn_metadata)
     try:
         yield
     finally:
-        global batchsize_counter
         global last_logging_time, batchsize_logging_interval
         if need_to_track_batchsize:
-            if hasattr(context, "num_prefill_tokens"):
+            if hasattr(attn_metadata, "num_prefill_tokens"):
                 # for v0 attention backends
-                batchsize = context.num_prefill_tokens + \
-                    context.num_decode_tokens
+                batchsize = attn_metadata.num_prefill_tokens + \
+                    attn_metadata.num_decode_tokens
             else:
                 # for v1 attention backends
-                batchsize = context.num_input_tokens
+                batchsize = attn_metadata.num_input_tokens
             # we use synchronous scheduling right now,
             # adding a sync point here should not affect
             # scheduling of the next batch

vllm/utils.py

@@ -2138,3 +2138,38 @@ def get_mp_context():
     _check_multiproc_method()
     mp_method = envs.VLLM_WORKER_MULTIPROC_METHOD
     return multiprocessing.get_context(mp_method)
+
+
+def bind_kv_cache(
+        ctx: Dict[str, Any],
+        kv_cache: List[List[torch.Tensor]],  # [virtual_engine][layer_index]
+) -> None:
+    # Bind the kv_cache tensor to Attention modules, similar to
+    # ctx[layer_name].kv_cache[ve]=kv_cache[ve][extract_layer_index(layer_name)]
+    # Special things handled here:
+    # 1. Some models have non-attention layers, e.g., Jamba
+    # 2. Pipeline parallelism, each rank only has a subset of layers
+    # 3. Encoder attention has no kv cache
+    # 4. Encoder-decoder models, encoder-decoder attention and decoder-only
+    #    attention of the same layer (e.g., bart's decoder.layers.1.self_attn
+    #    and decoder.layers.1.encoder_attn) is mapped to the same kv cache
+    #    tensor
+    from vllm.attention import AttentionType
+    from vllm.model_executor.models.utils import extract_layer_index
+    layer_need_kv_cache = [
+        layer_name for layer_name in ctx
+        if ctx[layer_name].attn_type in (AttentionType.DECODER,
+                                         AttentionType.ENCODER_DECODER)
+    ]
+    layer_index_sorted = sorted(
+        set(
+            extract_layer_index(layer_name)
+            for layer_name in layer_need_kv_cache))
+    for layer_name in layer_need_kv_cache:
+        kv_cache_idx = layer_index_sorted.index(
+            extract_layer_index(layer_name))
+        forward_ctx = ctx[layer_name]
+        assert len(forward_ctx.kv_cache) == len(kv_cache)
+        for ve, ve_kv_cache in enumerate(kv_cache):
+            assert forward_ctx.kv_cache[ve].numel() == 0
+            forward_ctx.kv_cache[ve] = ve_kv_cache[kv_cache_idx]