[v1] fix compilation cache (#11598)

Signed-off-by: youkaichao <[email protected]>

Author: youkaichao

tests/compile/piecewise/test_toy_llama.py

@@ -7,7 +7,7 @@
 initialized randomly with a fixed seed.
 """
 from dataclasses import dataclass
-from typing import Optional, Tuple
+from typing import Any, List, Optional, Tuple
 
 import torch
 from torch import nn
@@ -54,6 +54,16 @@ class LlamaConfig:
     tractable_init: bool = False
     random_seed: int = 0
 
+    def compute_hash(self) -> str:
+        factors: List[Any] = []
+        for k, v in self.__dict__.items():
+            if k == "random_seed":
+                continue
+            factors.append((k, v))
+        factors.sort()
+        import hashlib
+        return hashlib.md5(str(factors).encode()).hexdigest()
+
     def __post_init__(self):
         assert self.mlp_size >= self.hidden_size
 
@@ -263,7 +273,8 @@ def run_model(llama_config,
         compilation_config = CompilationConfig(
             level=CompilationLevel.NO_COMPILATION, )
 
-    vllm_config = VllmConfig(compilation_config=compilation_config)
+    vllm_config = VllmConfig(compilation_config=compilation_config,
+                             additional_config=llama_config)
     with set_current_vllm_config(vllm_config):
         model = LlamaModel(config=llama_config,
                            vllm_config=vllm_config,

vllm/compilation/backends.py

@@ -619,21 +619,28 @@ def __init__(self, graph: fx.GraphModule, vllm_config: VllmConfig,
         # the entries for different shapes that we need to either
         # compile or capture cudagraph
         self.concrete_size_entries: Dict[int, ConcreteSizeEntry] = {}
-        self.to_be_compiled_sizes: Set[int] = self.compile_sizes.union(
-            self.capture_sizes)
+
+        # to_be_compiled_sizes tracks the remaining sizes to compile,
+        # and updates during the compilation process, so we need to copy it
+        self.to_be_compiled_sizes: Set[int] = self.compile_sizes.copy()
         for shape in self.compile_sizes.union(self.capture_sizes):
             self.concrete_size_entries[shape] = ConcreteSizeEntry(
                 runtime_shape=shape,
                 need_to_compile=shape in self.compile_sizes,
                 use_cudagraph=shape in self.capture_sizes,
             )
 
+    def check_for_ending_compilation(self):
+        if self.is_last_graph and not self.to_be_compiled_sizes:
+            # no specific sizes to compile
+            # save the hash of the inductor graph for the next run
+            self.compilation_config.inductor_hash_cache.save_to_file()
+            end_monitoring_torch_compile(self.vllm_config)
+
     def __call__(self, *args) -> Any:
         if not self.first_run_finished:
             self.first_run_finished = True
-            # no specific sizes to compile
-            if self.is_last_graph and not self.to_be_compiled_sizes:
-                end_monitoring_torch_compile(self.vllm_config)
+            self.check_for_ending_compilation()
             return self.compiled_graph_for_general_shape(*args)
 
         runtime_shape = args[self.sym_shape_indices[0]]
@@ -662,10 +669,7 @@ def __call__(self, *args) -> Any:
 
             # finished compilations for all required shapes
             if self.is_last_graph and not self.to_be_compiled_sizes:
-
-                # save the hash of the inductor graph for the next run
-                self.compilation_config.inductor_hash_cache.save_to_file()
-                end_monitoring_torch_compile(self.vllm_config)
+                self.check_for_ending_compilation()
 
         if not entry.use_cudagraph:
             return entry.runnable(*args)

vllm/config.py

@@ -9,8 +9,8 @@
 from dataclasses import dataclass, field, replace
 from pathlib import Path
 from typing import (TYPE_CHECKING, Any, Callable, ClassVar, Counter, Dict,
-                    Final, List, Literal, Mapping, Optional, Set, Tuple, Type,
-                    Union)
+                    Final, List, Literal, Mapping, Optional, Protocol, Set,
+                    Tuple, Type, Union)
 
 import torch
 from pydantic import BaseModel, Field, PrivateAttr
@@ -75,6 +75,12 @@
                                              PretrainedConfig]]
 
 
+class SupportsHash(Protocol):
+
+    def compute_hash(self) -> str:
+        ...
+
+
 class ModelConfig:
     """Configuration for the model.
 
@@ -2969,6 +2975,10 @@ class VllmConfig:
                                                   init=True)  # type: ignore
     kv_transfer_config: KVTransferConfig = field(default=None,
                                                  init=True)  # type: ignore
+    # some opaque config, only used to provide additional information
+    # for the hash computation, mainly used for testing and debugging.
+    additional_config: SupportsHash = field(default=None,
+                                            init=True)  # type: ignore
     instance_id: str = ""
 
     def compute_hash(self) -> str:
@@ -3000,33 +3010,62 @@ def compute_hash(self) -> str:
         vllm_factors.append(__version__)
         if self.model_config:
             vllm_factors.append(self.model_config.compute_hash())
+        else:
+            vllm_factors.append("None")
         if self.cache_config:
             vllm_factors.append(self.cache_config.compute_hash())
+        else:
+            vllm_factors.append("None")
         if self.parallel_config:
             vllm_factors.append(self.parallel_config.compute_hash())
+        else:
+            vllm_factors.append("None")
         if self.scheduler_config:
             vllm_factors.append(self.scheduler_config.compute_hash())
+        else:
+            vllm_factors.append("None")
         if self.device_config:
             vllm_factors.append(self.device_config.compute_hash())
+        else:
+            vllm_factors.append("None")
         if self.load_config:
             vllm_factors.append(self.load_config.compute_hash())
+        else:
+            vllm_factors.append("None")
         if self.lora_config:
             vllm_factors.append(self.lora_config.compute_hash())
+        else:
+            vllm_factors.append("None")
         if self.speculative_config:
             vllm_factors.append(self.speculative_config.compute_hash())
+        else:
+            vllm_factors.append("None")
         if self.decoding_config:
             vllm_factors.append(self.decoding_config.compute_hash())
+        else:
+            vllm_factors.append("None")
         if self.observability_config:
             vllm_factors.append(self.observability_config.compute_hash())
+        else:
+            vllm_factors.append("None")
         if self.prompt_adapter_config:
             vllm_factors.append(self.prompt_adapter_config.compute_hash())
+        else:
+            vllm_factors.append("None")
         if self.quant_config:
             pass  # should be captured by model_config.quantization
         if self.compilation_config:
             vllm_factors.append(self.compilation_config.compute_hash())
+        else:
+            vllm_factors.append("None")
         if self.kv_transfer_config:
             vllm_factors.append(self.kv_transfer_config.compute_hash())
-
+        else:
+            vllm_factors.append("None")
+        if self.additional_config:
+            vllm_factors.append(self.additional_config.compute_hash())
+        else:
+            vllm_factors.append("None")
         factors.append(vllm_factors)
 
         hash_str = hashlib.md5(str(factors).encode()).hexdigest()[:10]

vllm/v1/worker/gpu_worker.py

@@ -48,6 +48,7 @@ def __init__(
         self.prompt_adapter_config = vllm_config.prompt_adapter_config
         self.observability_config = vllm_config.observability_config
 
+        self.parallel_config.rank = rank
         self.local_rank = local_rank
         self.rank = rank
         self.distributed_init_method = distributed_init_method