[torch.compile] store inductor compiled Python file

vllm/compilation/backends.py

@@ -25,23 +25,30 @@
 logger = init_logger(__name__)
 
 
+@dataclasses.dataclass
+class InductorArtifact:
+    hash_str: str = ""
+    file_path: str = ""
+
+
 class InductorHashCache:
     """
     Disk format: a Python list of tuples, each tuple is
-    (runtime_shape, graph_index, hash_str)
+    (runtime_shape, graph_index, hash_str, file_path)
     We use list of tuple for readability.
 
     In-memory format: a defaultdict of dict, where the key is
     runtime_shape, and the value is a dict of graph_index to hash_str.
 
-    The data is essentially `Dict[Optional[int], Dict[int, str]]`,
+    The data is essentially `Dict[Optional[int], Dict[int, InductorArtifact]]`,
     we don't use json here because json doesn't support int as key.
 
     TODO: better off-the-shelf solution to serialize the data?
     """
 
     def __init__(self, cache_dir: str, disabled: bool = False):
-        self.cache: defaultdict = defaultdict(dict)
+        self.cache: Dict[Optional[int],
+                         Dict[int, InductorArtifact]] = defaultdict(dict)
         self.disabled = disabled
         self.cache_dir = cache_dir
         self.cache_file_path = os.path.join(cache_dir,
@@ -66,14 +73,25 @@ def deserialize(self, data: str):
         # because it is a safe way to parse Python literals.
         # do not use eval(), it is unsafe.
         list_data = ast.literal_eval(data)
-        for runtime_shape, graph_index, hash_str in list_data:
-            self.cache[runtime_shape][graph_index] = hash_str
+        for item in list_data:
+            runtime_shape = item[0]
+            graph_index = item[1]
+            hash_str = item[2]
+            # for compatibility of old version,
+            # where we don't have file_path.
+            # NOTE: after running the new code, the file_path
+            # will be updated.
+            file_path = "" if len(item) == 3 else item[3]
+            self.cache[runtime_shape][graph_index] = InductorArtifact(
+                hash_str=hash_str, file_path=file_path)
 
     def serialize(self) -> str:
         data = []
-        for runtime_shape, graph_index_to_hash_str in self.cache.items():
-            for graph_index, hash_str in graph_index_to_hash_str.items():
-                data.append((runtime_shape, graph_index, hash_str))
+        for runtime_shape, value in self.cache.items():
+            for graph_index, inductor_artifact in value.items():
+                data.append(
+                    (runtime_shape, graph_index, inductor_artifact.hash_str,
+                     inductor_artifact.file_path))
         printer = pprint.PrettyPrinter(indent=4)
         return printer.pformat(data)
 
@@ -90,13 +108,14 @@ def __contains__(self, key: Tuple[Optional[int], int]) -> bool:
         return runtime_shape in self.cache and graph_index in self.cache[
             runtime_shape]
 
-    def __getitem__(self, key: Tuple[Optional[int], int]) -> str:
+    def __getitem__(self, key: Tuple[Optional[int], int]) -> InductorArtifact:
         if self.disabled:
             raise KeyError("cannot read from disabled cache")
         runtime_shape, graph_index = key
         return self.cache[runtime_shape][graph_index]
 
-    def __setitem__(self, key: Tuple[Optional[int], int], value: str):
+    def __setitem__(self, key: Tuple[Optional[int], int],
+                    value: InductorArtifact):
         # setitem for disabled cache is fine, because we
         # don't actually write to the disk
         runtime_shape, graph_index = key
@@ -181,7 +200,8 @@ def wrap_inductor(graph: fx.GraphModule,
     if (runtime_shape, graph_index) in cache_data:
         # we compiled this graph before
         # so we can directly lookup the compiled graph via hash
-        hash_str = cache_data[(runtime_shape, graph_index)]
+        inductor_artifact = cache_data[(runtime_shape, graph_index)]
+        hash_str = inductor_artifact.hash_str
         if graph_index == 0:
             # adds some info logging for the first graph
             logger.info(
@@ -199,6 +219,7 @@ def wrap_inductor(graph: fx.GraphModule,
                 "Inductor cache lookup failed. Please remove"
                 f"the cache file {cache_data.cache_file_path} and try again."  # noqa
             )
+            inductor_artifact.file_path = inductor_compiled_graph.current_callable.__code__.co_filename  # noqa
 
         # Inductor calling convention (function signature):
         # f(list) -> tuple
@@ -224,19 +245,20 @@ def compiled_graph(*args):
         # the assumption is that we don't have nested Inductor compilation.
         # compiled_fx_graph_hash will only be called once, and we can hook
         # it to get the hash of the compiled graph directly.
-        from torch._inductor.codecache import compiled_fx_graph_hash
+
+        inductor_artifact = InductorArtifact()
+        from torch._inductor.codecache import (FxGraphCache,
+                                               compiled_fx_graph_hash)
+        original_load = FxGraphCache.load
+
+        def hijack_load(*args, **kwargs):
+            inductor_compiled_graph = original_load(*args, **kwargs)
+            inductor_artifact.file_path = inductor_compiled_graph.current_callable.__code__.co_filename  # noqa
+            return inductor_compiled_graph
 
         def hijack_compiled_fx_graph_hash(*args, **kwargs):
             out = compiled_fx_graph_hash(*args, **kwargs)
-            # store the hash in the cache
-            nonlocal cache_data
-            cache_data[(runtime_shape, graph_index)] = out[0]
-            if graph_index == 0:
-                # adds some info logging for the first graph
-                logger.info("Cache the graph of shape %s for later use",
-                            str(runtime_shape))
-            logger.debug("store the %s-th graph for shape %s via hash %s",
-                         graph_index, str(runtime_shape), out[0])
+            inductor_artifact.hash_str = out[0]
             return out
 
         def _check_can_cache(*args, **kwargs):
@@ -255,6 +277,11 @@ def _get_shape_env() -> AlwaysHitShapeEnv:
             if not cache_data.disabled:
                 # compilation cache is enabled, patch several functions
 
+                # hijack to get the compiled graph itself
+                stack.enter_context(
+                    patch("torch._inductor.codecache.FxGraphCache.load",
+                          hijack_load))
+
                 # for hijacking the hash of the compiled graph
                 stack.enter_context(
                     patch("torch._inductor.codecache.compiled_fx_graph_hash",
@@ -275,7 +302,16 @@ def _get_shape_env() -> AlwaysHitShapeEnv:
             compiled_graph = compile_fx(graph,
                                         example_inputs,
                                         config_patches=current_config)
-
+        # store the inductor_artifact in the cache
+        cache_data[(runtime_shape, graph_index)] = inductor_artifact
+        if graph_index == 0:
+            # adds some info logging for the first graph
+            logger.info("Cache the graph of shape %s for later use",
+                        str(runtime_shape))
+        logger.debug(
+            "store the %s-th graph for shape %s via hash %s from file %s",
+            graph_index, str(runtime_shape), inductor_artifact.hash_str,
+            inductor_artifact.file_path)
     # after compiling the last graph, record the end time
     if graph_index == num_graphs - 1:
         now = time.time()

vllm/config.py

@@ -2862,17 +2862,8 @@ def model_post_init(self, __context: Any) -> None:
                     "vllm.unified_attention_with_output",
                 ]
             else:
-                # v0 can use full graph compilation without splitting,
-                # splitting is optional.
-                # right now we still need it. kv cache shape
-                # will be included in the graph if we don't split
-                # the graph.
-                # TODO: hide kv cache in static forward context
-                # so that inductor does not see it.
-                self.splitting_ops = [
-                    "vllm.unified_attention",
-                    "vllm.unified_attention_with_output",
-                ]
+                # v0 uses full graph compilation
+                self.splitting_ops = []
 
         for k, v in self.inductor_passes.items():
             if not isinstance(v, str):