|
| 1 | +import ast |
1 | 2 | import copy
|
2 | 3 | import dataclasses
|
| 4 | +import os |
| 5 | +import pprint |
3 | 6 | import time
|
| 7 | +from collections import defaultdict |
4 | 8 | from contextlib import ExitStack
|
5 | 9 | from typing import Any, Callable, Dict, List, Optional, Sequence, Set, Tuple
|
6 | 10 | from unittest.mock import patch
|
|
21 | 25 | logger = init_logger(__name__)
|
22 | 26 |
|
23 | 27 |
|
| 28 | +class InductorHashCache: |
| 29 | + """ |
| 30 | + Disk format: a Python list of tuples, each tuple is |
| 31 | + (runtime_shape, graph_index, hash_str) |
| 32 | + We use list of tuple for readability. |
| 33 | +
|
| 34 | + In-memory format: a defaultdict of dict, where the key is |
| 35 | + runtime_shape, and the value is a dict of graph_index to hash_str. |
| 36 | +
|
| 37 | + The data is essentially `Dict[Optional[int], Dict[int, str]]`, |
| 38 | + we don't use json here because json doesn't support int as key. |
| 39 | +
|
| 40 | + TODO: better off-the-shelf solution to serialize the data? |
| 41 | + """ |
| 42 | + |
| 43 | + def __init__(self, cache_dir: str, disabled: bool = False): |
| 44 | + self.cache: defaultdict = defaultdict(dict) |
| 45 | + self.disabled = disabled |
| 46 | + self.cache_dir = cache_dir |
| 47 | + self.cache_file_path = os.path.join(cache_dir, |
| 48 | + "inductor_hash_cache.py") |
| 49 | + if disabled: |
| 50 | + return |
| 51 | + # set flags so that Inductor and Triton store their cache |
| 52 | + # in the cache_dir, then users only need to copy the cache_dir |
| 53 | + # to another machine to reuse the cache. |
| 54 | + inductor_cache = os.path.join(cache_dir, "inductor_cache") |
| 55 | + os.makedirs(inductor_cache, exist_ok=True) |
| 56 | + os.environ["TORCHINDUCTOR_CACHE_DIR"] = inductor_cache |
| 57 | + triton_cache = os.path.join(cache_dir, "triton_cache") |
| 58 | + os.makedirs(triton_cache, exist_ok=True) |
| 59 | + os.environ["TRITON_CACHE_DIR"] = triton_cache |
| 60 | + if os.path.exists(self.cache_file_path): |
| 61 | + with open(self.cache_file_path) as f: |
| 62 | + self.deserialize(f.read()) |
| 63 | + |
| 64 | + def deserialize(self, data: str): |
| 65 | + # we use ast.literal_eval to parse the data |
| 66 | + # because it is a safe way to parse Python literals. |
| 67 | + # do not use eval(), it is unsafe. |
| 68 | + list_data = ast.literal_eval(data) |
| 69 | + for runtime_shape, graph_index, hash_str in list_data: |
| 70 | + self.cache[runtime_shape][graph_index] = hash_str |
| 71 | + |
| 72 | + def serialize(self) -> str: |
| 73 | + data = [] |
| 74 | + for runtime_shape, graph_index_to_hash_str in self.cache.items(): |
| 75 | + for graph_index, hash_str in graph_index_to_hash_str.items(): |
| 76 | + data.append((runtime_shape, graph_index, hash_str)) |
| 77 | + printer = pprint.PrettyPrinter(indent=4) |
| 78 | + return printer.pformat(data) |
| 79 | + |
| 80 | + def save_to_file(self): |
| 81 | + if self.disabled: |
| 82 | + return |
| 83 | + with open(self.cache_file_path, "w") as f: |
| 84 | + f.write(self.serialize()) |
| 85 | + |
| 86 | + def __contains__(self, key: Tuple[Optional[int], int]) -> bool: |
| 87 | + if self.disabled: |
| 88 | + return False |
| 89 | + runtime_shape, graph_index = key |
| 90 | + return runtime_shape in self.cache and graph_index in self.cache[ |
| 91 | + runtime_shape] |
| 92 | + |
| 93 | + def __getitem__(self, key: Tuple[Optional[int], int]) -> str: |
| 94 | + if self.disabled: |
| 95 | + raise KeyError("cannot read from disabled cache") |
| 96 | + runtime_shape, graph_index = key |
| 97 | + return self.cache[runtime_shape][graph_index] |
| 98 | + |
| 99 | + def __setitem__(self, key: Tuple[Optional[int], int], value: str): |
| 100 | + # setitem for disabled cache is fine, because we |
| 101 | + # don't actually write to the disk |
| 102 | + runtime_shape, graph_index = key |
| 103 | + self.cache[runtime_shape][graph_index] = value |
| 104 | + |
| 105 | + |
| 106 | +class AlwaysHitShapeEnv: |
| 107 | + """ |
| 108 | + Why do we need this class: |
| 109 | +
|
| 110 | + For normal `torch.compile` usage, every compilation will have |
| 111 | + one Dynamo bytecode compilation and one Inductor compilation. |
| 112 | + The Inductor compilation happens under the context of the |
| 113 | + Dynamo bytecode compilation, and that context is used to |
| 114 | + determine the dynamic shape information, etc. |
| 115 | +
|
| 116 | + For our use case, we only run Dynamo bytecode compilation once, |
| 117 | + and run Inductor compilation multiple times with different shapes |
| 118 | + plus a general shape. The compilation for specific shapes happens |
| 119 | + outside of the context of the Dynamo bytecode compilation. At that |
| 120 | + time, we don't have shape environment to provide to Inductor, and |
| 121 | + it will fail the Inductor code cache lookup. |
| 122 | +
|
| 123 | + By providing a dummy shape environment that always hits, we can |
| 124 | + make the Inductor code cache lookup always hit, and we can |
| 125 | + compile the graph for different shapes as needed. |
| 126 | +
|
| 127 | + The following dummy methods are obtained by trial-and-error |
| 128 | + until it works. |
| 129 | + """ |
| 130 | + |
| 131 | + def __init__(self) -> None: |
| 132 | + self.guards: List[Any] = [] |
| 133 | + |
| 134 | + def evaluate_guards_expression(self, *args, **kwargs): |
| 135 | + return True |
| 136 | + |
| 137 | + def get_pruned_guards(self, *args, **kwargs): |
| 138 | + return [] |
| 139 | + |
| 140 | + def produce_guards_expression(self, *args, **kwargs): |
| 141 | + return "" |
| 142 | + |
| 143 | + |
24 | 144 | def wrap_inductor(graph,
|
25 | 145 | example_inputs,
|
26 | 146 | additional_inductor_config,
|
@@ -55,9 +175,93 @@ def wrap_inductor(graph,
|
55 | 175 | # inductor can inplace modify the graph, so we need to copy it
|
56 | 176 | # see https://github.com/pytorch/pytorch/issues/138980
|
57 | 177 | graph = copy.deepcopy(graph)
|
58 |
| - compiled_graph = compile_fx(graph, |
59 |
| - example_inputs, |
60 |
| - config_patches=current_config) |
| 178 | + |
| 179 | + cache_data = compilation_config.inductor_hash_cache |
| 180 | + if (runtime_shape, graph_index) in cache_data: |
| 181 | + # we compiled this graph before |
| 182 | + # so we can directly lookup the compiled graph via hash |
| 183 | + hash_str = cache_data[(runtime_shape, graph_index)] |
| 184 | + if graph_index == 0: |
| 185 | + # adds some info logging for the first graph |
| 186 | + logger.info( |
| 187 | + "Directly lookup the graph for shape %s from the cache", |
| 188 | + str(runtime_shape)) # noqa |
| 189 | + logger.debug( |
| 190 | + "directly lookup the %s-th graph for shape %s via hash %s", |
| 191 | + graph_index, str(runtime_shape), hash_str) |
| 192 | + from torch._inductor.codecache import FxGraphCache |
| 193 | + with patch("torch._inductor.codecache.FxGraphCache._get_shape_env", |
| 194 | + lambda *args, **kwargs: AlwaysHitShapeEnv()): |
| 195 | + inductor_compiled_graph = FxGraphCache._lookup_graph( |
| 196 | + hash_str, example_inputs, True, False) |
| 197 | + assert inductor_compiled_graph is not None, ( |
| 198 | + "Inductor cache lookup failed. Please remove" |
| 199 | + f"the cache file {compilation_config.inductor_hash_cache.cache_file_path} and try again." # noqa |
| 200 | + ) |
| 201 | + |
| 202 | + # Inductor calling convention (function signature): |
| 203 | + # f(list) -> tuple |
| 204 | + # Dynamo calling convention (function signature): |
| 205 | + # f(*args) -> Any |
| 206 | + |
| 207 | + # need to know if the graph returns a tuple |
| 208 | + from torch._inductor.compile_fx import graph_returns_tuple |
| 209 | + returns_tuple = graph_returns_tuple(graph) |
| 210 | + |
| 211 | + # this is the graph we return to Dynamo to run |
| 212 | + def compiled_graph(*args): |
| 213 | + # convert args to list |
| 214 | + list_args = list(args) |
| 215 | + graph_output = inductor_compiled_graph(list_args) |
| 216 | + # unpack the tuple if needed |
| 217 | + if returns_tuple: |
| 218 | + return graph_output |
| 219 | + else: |
| 220 | + return graph_output[0] |
| 221 | + else: |
| 222 | + # it's the first time we compile this graph |
| 223 | + # the assumption is that we don't have nested Inductor compilation. |
| 224 | + # compiled_fx_graph_hash will only be called once, and we can hook |
| 225 | + # it to get the hash of the compiled graph directly. |
| 226 | + from torch._inductor.codecache import compiled_fx_graph_hash |
| 227 | + |
| 228 | + def hijack_compiled_fx_graph_hash(*args, **kwargs): |
| 229 | + out = compiled_fx_graph_hash(*args, **kwargs) |
| 230 | + # store the hash in the cache |
| 231 | + nonlocal cache_data |
| 232 | + cache_data[(runtime_shape, graph_index)] = out[0] |
| 233 | + if graph_index == 0: |
| 234 | + # adds some info logging for the first graph |
| 235 | + logger.info("Cache the graph of shape %s for later use", |
| 236 | + str(runtime_shape)) |
| 237 | + logger.debug("store the %s-th graph for shape %s via hash %s", |
| 238 | + graph_index, str(runtime_shape), out[0]) |
| 239 | + return out |
| 240 | + |
| 241 | + def _check_can_cache(*args, **kwargs): |
| 242 | + # no error means it can be cached. |
| 243 | + # Inductor refuses to cache the graph outside of Dynamo |
| 244 | + # tracing context, and also disables caching for graphs |
| 245 | + # with high-order ops. |
| 246 | + # For vLLM, in either case, we want to cache the graph. |
| 247 | + # see https://github.com/pytorch/pytorch/blob/9f5ebf3fc609105a74eab4ccc24932d6353ff566/torch/_inductor/codecache.py#L1221 # noqa |
| 248 | + return |
| 249 | + |
| 250 | + def _get_shape_env(): |
| 251 | + return AlwaysHitShapeEnv() |
| 252 | + |
| 253 | + with patch(# for hijacking the hash of the compiled graph |
| 254 | + "torch._inductor.codecache.compiled_fx_graph_hash", |
| 255 | + hijack_compiled_fx_graph_hash), \ |
| 256 | + patch(# for providing a dummy shape environment |
| 257 | + "torch._inductor.codecache.FxGraphCache._get_shape_env", |
| 258 | + _get_shape_env), \ |
| 259 | + patch(# for forcing the graph to be cached |
| 260 | + "torch._inductor.codecache.FxGraphCache._check_can_cache", |
| 261 | + _check_can_cache): |
| 262 | + compiled_graph = compile_fx(graph, |
| 263 | + example_inputs, |
| 264 | + config_patches=current_config) |
61 | 265 |
|
62 | 266 | # after compiling the last graph, record the end time
|
63 | 267 | if graph_index == num_graphs - 1:
|
@@ -457,6 +661,9 @@ def __call__(self, *args) -> Any:
|
457 | 661 |
|
458 | 662 | # finished compilations for all required shapes
|
459 | 663 | if self.is_last_graph and not self.to_be_compiled_sizes:
|
| 664 | + |
| 665 | + # save the hash of the inductor graph for the next run |
| 666 | + self.compilation_config.inductor_hash_cache.save_to_file() |
460 | 667 | end_monitoring_torch_compile(self.vllm_config)
|
461 | 668 |
|
462 | 669 | if not entry.use_cudagraph:
|
|
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