# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import ast import dataclasses import os import pprint import time from collections.abc import Sequence from contextlib import contextmanager from typing import Any, Callable, Optional import torch import torch.fx as fx from torch._dispatch.python import enable_python_dispatcher import vllm.envs as envs from vllm.config import CompilationConfig, CUDAGraphMode, VllmConfig from vllm.logger import init_logger from vllm.platforms import current_platform from vllm.utils import is_torch_equal_or_newer, resolve_obj_by_qualname from .compiler_interface import (CompilerInterface, EagerAdaptor, InductorAdaptor, InductorStandaloneAdaptor) from .counter import compilation_counter from .inductor_pass import InductorPass from .pass_manager import PostGradPassManager logger = init_logger(__name__) def make_compiler(compilation_config: CompilationConfig) -> CompilerInterface: if compilation_config.use_inductor: if envs.VLLM_USE_STANDALONE_COMPILE and is_torch_equal_or_newer( "2.8.0.dev"): logger.debug("Using InductorStandaloneAdaptor") return InductorStandaloneAdaptor() else: logger.debug("Using InductorAdaptor") return InductorAdaptor() else: logger.debug("Using EagerAdaptor") return EagerAdaptor() class CompilerManager: """ A manager to manage the compilation process, including caching the compiled graph, loading the compiled graph, and compiling the graph. The cache is a dict mapping `(runtime_shape, graph_index, backend_name)` to `any_data` returned from the compiler. When serializing the cache, we save it to a Python file for readability. We don't use json here because json doesn't support int as key. """ def __init__(self, compilation_config: CompilationConfig): self.cache: dict[tuple[Optional[int], int, str], Any] = dict() self.is_cache_updated = False self.compilation_config = compilation_config self.compiler = make_compiler(compilation_config) def compute_hash(self, vllm_config: VllmConfig) -> str: return self.compiler.compute_hash(vllm_config) def initialize_cache(self, cache_dir: str, disable_cache: bool = False, prefix: str = ""): """ Initialize the cache directory for the compiler. The organization of the cache directory is as follows: cache_dir=/path/to/hash_str/rank_i_j/prefix/ inside cache_dir, there will be: - vllm_compile_cache.py - computation_graph.py - transformed_code.py for multiple prefixes, they can share the same base cache dir of /path/to/hash_str/rank_i_j/ , to store some common compilation artifacts. """ self.disable_cache = disable_cache self.cache_dir = cache_dir self.cache_file_path = os.path.join(cache_dir, "vllm_compile_cache.py") if not disable_cache and os.path.exists(self.cache_file_path): # load the cache from the file with open(self.cache_file_path) as f: # we use ast.literal_eval to parse the data # because it is a safe way to parse Python literals. # do not use eval(), it is unsafe. self.cache = ast.literal_eval(f.read()) self.compiler.initialize_cache(cache_dir=cache_dir, disable_cache=disable_cache, prefix=prefix) def save_to_file(self): if self.disable_cache or not self.is_cache_updated: return printer = pprint.PrettyPrinter(indent=4) data = printer.pformat(self.cache) with open(self.cache_file_path, "w") as f: f.write(data) def load(self, graph: fx.GraphModule, example_inputs: list[Any], graph_index: int, runtime_shape: Optional[int] = None) -> Optional[Callable]: if (runtime_shape, graph_index, self.compiler.name) not in self.cache: return None handle = self.cache[(runtime_shape, graph_index, self.compiler.name)] compiled_graph = self.compiler.load(handle, graph, example_inputs, graph_index, runtime_shape) if runtime_shape is None: logger.debug( "Directly load the %s-th graph for dynamic shape from %s via " "handle %s", graph_index, self.compiler.name, handle) else: logger.debug( "Directly load the %s-th graph for shape %s from %s via " "handle %s", graph_index, str(runtime_shape), self.compiler.name, handle) return compiled_graph def compile(self, graph: fx.GraphModule, example_inputs, additional_inductor_config, compilation_config: CompilationConfig, graph_index: int = 0, num_graphs: int = 1, runtime_shape: Optional[int] = None) -> Any: if graph_index == 0: # before compiling the first graph, record the start time global compilation_start_time compilation_start_time = time.time() compilation_counter.num_backend_compilations += 1 compiled_graph = None # try to load from the cache compiled_graph = self.load(graph, example_inputs, graph_index, runtime_shape) if compiled_graph is not None: if graph_index == num_graphs - 1: # after loading the last graph for this shape, record the time. # there can be multiple graphs due to piecewise compilation. now = time.time() elapsed = now - compilation_start_time if runtime_shape is None: logger.info( "Directly load the compiled graph(s) for dynamic shape " "from the cache, took %.3f s", elapsed) else: logger.info( "Directly load the compiled graph(s) for shape %s " "from the cache, took %.3f s", str(runtime_shape), elapsed) return compiled_graph # no compiler cached the graph, or the cache is disabled, # we need to compile it if isinstance(self.compiler, InductorAdaptor): # Let compile_fx generate a key for us maybe_key = None else: maybe_key = \ f"artifact_shape_{runtime_shape}_subgraph_{graph_index}" compiled_graph, handle = self.compiler.compile( graph, example_inputs, additional_inductor_config, runtime_shape, maybe_key) assert compiled_graph is not None, "Failed to compile the graph" # store the artifact in the cache if not envs.VLLM_DISABLE_COMPILE_CACHE and handle is not None: self.cache[(runtime_shape, graph_index, self.compiler.name)] = handle compilation_counter.num_cache_entries_updated += 1 self.is_cache_updated = True if graph_index == 0: # adds some info logging for the first graph if runtime_shape is None: logger.info( "Cache the graph for dynamic shape for later use") else: logger.info("Cache the graph of shape %s for later use", str(runtime_shape)) if runtime_shape is None: logger.debug( "Store the %s-th graph for dynamic shape from %s via " "handle %s", graph_index, self.compiler.name, handle) else: logger.debug( "Store the %s-th graph for shape %s from %s via handle %s", graph_index, str(runtime_shape), self.compiler.name, handle) # after compiling the last graph, record the end time if graph_index == num_graphs - 1: now = time.time() elapsed = now - compilation_start_time compilation_config.compilation_time += elapsed if runtime_shape is None: logger.info("Compiling a graph for dynamic shape takes %.2f s", elapsed) else: logger.info("Compiling a graph for shape %s takes %.2f s", runtime_shape, elapsed) return compiled_graph @dataclasses.dataclass class SplitItem: submod_name: str graph_id: int is_splitting_graph: bool graph: fx.GraphModule def split_graph(graph: fx.GraphModule, ops: list[str]) -> tuple[fx.GraphModule, list[SplitItem]]: # split graph by ops subgraph_id = 0 node_to_subgraph_id = {} split_op_graphs = [] for node in graph.graph.nodes: if node.op in ("output", "placeholder"): continue if node.op == 'call_function' and str(node.target) in ops: subgraph_id += 1 node_to_subgraph_id[node] = subgraph_id split_op_graphs.append(subgraph_id) subgraph_id += 1 else: node_to_subgraph_id[node] = subgraph_id # `keep_original_order` is important! # otherwise pytorch might reorder the nodes and # the semantics of the graph will change when we # have mutations in the graph split_gm = torch.fx.passes.split_module.split_module( graph, None, lambda node: node_to_subgraph_id[node], keep_original_order=True) outputs = [] names = [name for (name, module) in split_gm.named_modules()] for name in names: if "." in name or name == "": # recursive child module or the root module continue module = getattr(split_gm, name) graph_id = int(name.replace("submod_", "")) outputs.append( SplitItem(name, graph_id, (graph_id in split_op_graphs), module)) # sort by intetger graph_id, rather than string name outputs.sort(key=lambda x: x.graph_id) return split_gm, outputs compilation_start_time = 0.0 class PiecewiseCompileInterpreter(torch.fx.Interpreter): """Code adapted from `torch.fx.passes.shape_prop.ShapeProp`. It runs the given graph with fake inputs, and compile some submodules specified by `compile_submod_names` with the given compilation configs. NOTE: the order in `compile_submod_names` matters, because it will be used to determine the order of the compiled piecewise graphs. The first graph will handle logging, and the last graph has some special cudagraph output handling. """ def __init__(self, module: torch.fx.GraphModule, compile_submod_names: list[str], vllm_config: VllmConfig, graph_pool, vllm_backend: "VllmBackend"): super().__init__(module) from torch._guards import detect_fake_mode self.fake_mode = detect_fake_mode() self.compile_submod_names = compile_submod_names self.compilation_config = vllm_config.compilation_config self.graph_pool = graph_pool self.vllm_config = vllm_config self.vllm_backend = vllm_backend # When True, it annoyingly dumps the torch.fx.Graph on errors. self.extra_traceback = False def run(self, *args): fake_args = [ self.fake_mode.from_tensor(t) if isinstance(t, torch.Tensor) else t for t in args ] with self.fake_mode, enable_python_dispatcher(): return super().run(*fake_args) def call_module(self, target: torch.fx.node.Target, args: tuple[torch.fx.node.Argument, ...], kwargs: dict[str, Any]) -> Any: assert isinstance(target, str) output = super().call_module(target, args, kwargs) if target in self.compile_submod_names: index = self.compile_submod_names.index(target) submod = self.fetch_attr(target) sym_shape_indices = [ i for i, x in enumerate(args) if isinstance(x, torch.SymInt) ] global compilation_start_time compiled_graph_for_dynamic_shape = self.vllm_backend.\ compiler_manager.compile( submod, args, self.compilation_config.inductor_compile_config, self.compilation_config, graph_index=index, num_graphs=len(self.compile_submod_names), runtime_shape=None) # Lazy import here to avoid circular import from .cuda_graph import CUDAGraphOptions from .cuda_piecewise_backend import PiecewiseBackend piecewise_backend = PiecewiseBackend( submod, self.vllm_config, index, len(self.compile_submod_names), sym_shape_indices, compiled_graph_for_dynamic_shape, self.vllm_backend) if self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE: # resolve the static graph wrapper class (e.g. CUDAGraphWrapper # class) as platform dependent. static_graph_wrapper_class = resolve_obj_by_qualname( current_platform.get_static_graph_wrapper_cls()) # Always assign PIECEWISE runtime mode to the # CUDAGraphWrapper for piecewise_backend, to distinguish # it from the FULL cudagraph runtime mode, no matter it # is wrapped on a full or piecewise fx graph. self.module.__dict__[target] = static_graph_wrapper_class( runnable=piecewise_backend, vllm_config=self.vllm_config, runtime_mode=CUDAGraphMode.PIECEWISE, graph_pool=self.graph_pool, cudagraph_options=CUDAGraphOptions( debug_log_enable=piecewise_backend.is_first_graph, gc_disable=not piecewise_backend.is_first_graph, weak_ref_output=piecewise_backend.is_last_graph)) else: self.module.__dict__[target] = piecewise_backend compilation_counter.num_piecewise_capturable_graphs_seen += 1 return output # the tag for the part of model being compiled, # e.g. backbone/eagle_head model_tag: str = "backbone" @contextmanager def set_model_tag(tag: str): """Context manager to set the model tag.""" global model_tag assert tag != model_tag, \ f"Model tag {tag} is the same as the current tag {model_tag}." old_tag = model_tag model_tag = tag try: yield finally: model_tag = old_tag class VllmBackend: """The compilation backend for `torch.compile` with vLLM. It is used for compilation level of `CompilationLevel.PIECEWISE`, where we customize the compilation. The major work of this backend is to split the graph into piecewise graphs, and pass them to the piecewise backend. This backend also adds the PostGradPassManager to Inductor config, which handles the post-grad passes. """ vllm_config: VllmConfig compilation_config: CompilationConfig graph_pool: Any _called: bool = False # the graph we compiled graph: fx.GraphModule # the stiching graph module for all the piecewise graphs split_gm: fx.GraphModule piecewise_graphs: list[SplitItem] returned_callable: Callable # Inductor passes to run on the graph pre-defunctionalization post_grad_passes: Sequence[Callable] sym_tensor_indices: list[int] input_buffers: list[torch.Tensor] compiler_manager: CompilerManager def __init__( self, vllm_config: VllmConfig, prefix: str = "", ): # if the model is initialized with a non-empty prefix, # then usually it's enough to use that prefix, # e.g. launguage_model, vision_model, etc. # when multiple parts are initialized as independent # models, we need to use the model_tag to distinguish # them, e.g. backbone (default), eagle_head, etc. self.prefix = prefix or model_tag global_graph_pool = current_platform.get_global_graph_pool() # TODO: in the future, if we want to use multiple # streams, it might not be safe to share a global pool. # only investigate this when we use multiple streams self.graph_pool = global_graph_pool # Passes to run on the graph post-grad. self.post_grad_pass_manager = PostGradPassManager() self.sym_tensor_indices = [] self.input_buffers = [] self.vllm_config = vllm_config self.compilation_config = vllm_config.compilation_config self.compiler_manager: CompilerManager = CompilerManager( self.compilation_config) # `torch.compile` is JIT compiled, so we don't need to # do anything here def configure_post_pass(self): config = self.compilation_config self.post_grad_pass_manager.configure(self.vllm_config) # Post-grad custom passes are run using the post_grad_custom_post_pass # hook. If a pass for that hook exists, add it to the pass manager. inductor_config = config.inductor_compile_config PASS_KEY = "post_grad_custom_post_pass" if PASS_KEY in inductor_config: # Config should automatically wrap all inductor passes if isinstance(inductor_config[PASS_KEY], PostGradPassManager): assert (inductor_config[PASS_KEY].uuid() == self.post_grad_pass_manager.uuid()) else: assert isinstance(inductor_config[PASS_KEY], InductorPass) self.post_grad_pass_manager.add(inductor_config[PASS_KEY]) inductor_config[PASS_KEY] = self.post_grad_pass_manager def __call__(self, graph: fx.GraphModule, example_inputs) -> Callable: vllm_config = self.vllm_config if not self.compilation_config.cache_dir: # no provided cache dir, generate one based on the known factors # that affects the compilation. if none of the factors change, # the cache dir will be the same so that we can reuse the compiled # graph. factors = [] # 0. factors come from the env, for example, The values of # VLLM_PP_LAYER_PARTITION will affects the computation graph. env_hash = envs.compute_hash() factors.append(env_hash) # 1. factors come from the vllm_config (it mainly summarizes how the # model is created) config_hash = vllm_config.compute_hash() factors.append(config_hash) # 2. factors come from the code files that are traced by Dynamo ( # it mainly summarizes how the model is used in forward pass) forward_code_files = list( sorted(self.compilation_config.traced_files)) self.compilation_config.traced_files.clear() logger.debug( "Traced files (to be considered for compilation cache):\n%s", "\n".join(forward_code_files)) hash_content = [] for filepath in forward_code_files: hash_content.append(filepath) if filepath == "": # This means the function was dynamically generated, with # e.g. exec(). We can't actually check these. continue with open(filepath) as f: hash_content.append(f.read()) import hashlib code_hash = hashlib.md5("\n".join(hash_content).encode(), usedforsecurity=False).hexdigest() factors.append(code_hash) # 3. compiler hash compiler_hash = self.compiler_manager.compute_hash(vllm_config) factors.append(compiler_hash) # combine all factors to generate the cache dir hash_key = hashlib.md5(str(factors).encode(), usedforsecurity=False).hexdigest()[:10] cache_dir = os.path.join( envs.VLLM_CACHE_ROOT, "torch_compile_cache", hash_key, ) self.compilation_config.cache_dir = cache_dir cache_dir = self.compilation_config.cache_dir os.makedirs(cache_dir, exist_ok=True) self.compilation_config.cache_dir = cache_dir rank = vllm_config.parallel_config.rank dp_rank = vllm_config.parallel_config.data_parallel_rank local_cache_dir = os.path.join(cache_dir, f"rank_{rank}_{dp_rank}", self.prefix) os.makedirs(local_cache_dir, exist_ok=True) self.compilation_config.local_cache_dir = local_cache_dir disable_cache = envs.VLLM_DISABLE_COMPILE_CACHE if disable_cache: logger.info("vLLM's torch.compile cache is disabled.") else: logger.info("Using cache directory: %s for vLLM's torch.compile", local_cache_dir) self.compiler_manager.initialize_cache(local_cache_dir, disable_cache, self.prefix) # when dynamo calls the backend, it means the bytecode # transform and analysis are done compilation_counter.num_graphs_seen += 1 from .monitor import torch_compile_start_time dynamo_time = time.time() - torch_compile_start_time logger.info("Dynamo bytecode transform time: %.2f s", dynamo_time) self.compilation_config.compilation_time += dynamo_time # we control the compilation process, each instance can only be # called once assert not self._called, "VllmBackend can only be called once" self.graph = graph self.configure_post_pass() self.split_gm, self.piecewise_graphs = split_graph( graph, self.compilation_config.splitting_ops) from torch._dynamo.utils import lazy_format_graph_code # depyf will hook lazy_format_graph_code and dump the graph # for debugging, no need to print the graph here lazy_format_graph_code("before split", self.graph) lazy_format_graph_code("after split", self.split_gm) compilation_counter.num_piecewise_graphs_seen += len( self.piecewise_graphs) submod_names_to_compile = [ item.submod_name for item in self.piecewise_graphs if not item.is_splitting_graph ] # propagate the split graph to the piecewise backend, # compile submodules with symbolic shapes PiecewiseCompileInterpreter(self.split_gm, submod_names_to_compile, self.vllm_config, self.graph_pool, self).run(*example_inputs) graph_path = os.path.join(local_cache_dir, "computation_graph.py") if not os.path.exists(graph_path): # code adapted from https://github.com/thuml/depyf/blob/dab831108a752d1facc00acdd6d4243891845c37/depyf/explain/patched_lazy_format_graph_code.py#L30 # noqa # use `print_readable` because it can include submodules src = "from __future__ import annotations\nimport torch\n" + \ self.split_gm.print_readable(print_output=False) src = src.replace("", "GraphModule") with open(graph_path, "w") as f: f.write(src) logger.debug("Computation graph saved to %s", graph_path) self._called = True if self.compilation_config.cudagraph_mode == CUDAGraphMode.NONE or \ not self.compilation_config.cudagraph_copy_inputs: return self.split_gm # if we need to copy input buffers for cudagraph from torch._guards import detect_fake_mode fake_mode = detect_fake_mode() fake_args = [ fake_mode.from_tensor(t) if isinstance(t, torch.Tensor) else t for t in example_inputs ] # index of tensors that have symbolic shapes (batch size) # for weights and static buffers, they will have concrete shapes. # symbolic shape only happens for input tensors. from torch.fx.experimental.symbolic_shapes import is_symbolic self.sym_tensor_indices = [ i for i, x in enumerate(fake_args) if isinstance(x, torch._subclasses.fake_tensor.FakeTensor) and \ any(is_symbolic(d) for d in x.size()) ] # compiler managed cudagraph input buffers # we assume the first run with symbolic shapes # has the maximum size among all the tensors self.input_buffers = [ example_inputs[x].clone() for x in self.sym_tensor_indices ] # this is the callable we return to Dynamo to run def copy_and_call(*args): list_args = list(args) for i, index in enumerate(self.sym_tensor_indices): runtime_tensor = list_args[index] runtime_shape = runtime_tensor.shape[0] static_tensor = self.input_buffers[i][:runtime_shape] # copy the tensor to the static buffer static_tensor.copy_(runtime_tensor) # replace the tensor in the list_args to the static buffer list_args[index] = static_tensor return self.split_gm(*list_args) return copy_and_call