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GeezerVison is showing github.com — Fuser/python/python_direct/schedule.cpp at main · NVIDIA/Fuser Expand file tree/ Copy pathschedule.cppMore file actions 440 lines (395 loc) · 14.7 KB / Copy pathschedule.cppFile metadata and controls440 lines (395 loc) · 14.7 KB 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 // clang-format off /* * SPDX-FileCopyrightText: Copyright (c) 2025-present NVIDIA CORPORATION & AFFILIATES. * All rights reserved. * SPDX-License-Identifier: BSD-3-Clause */ // clang-format on #include <bindings.h> #include <direct_utils.h> #include <options.h> #include <scheduler/registry.h> #include <scheduler/runtime_info.h> #include <scheduler/scheduler_types.h> #include <scheduler/tools/inlining.h> #include <scheduler/utils.h> #include <transform_replay.h> namespace nvfuser::python { namespace { void bindTensorviewScheduleOps(py::module_& schedule) { schedule.def( "bounded_transform_backward", [](TensorView* from, int64_t pos, std::vector<TensorView*> to, bool propagate_parallel_type) { using TransformPropagator = scheduler_utils::BoundedDirectionalTransformPropagator; TransformPropagator::Options options; if (propagate_parallel_type) { options.propagateParallelType(); } TransformPropagator::backward(from, pos, to, options); }, R"( Propagate scheduler transformations from a reference TensorView to other TensorViews. Parameters ---------- from : TensorView The reference TensorView whose transformations will be propagated. pos : int The position up to which dimensions should be selected. -1 means all dimensions. to : List[TensorView] List of TensorViews to propagate transformations to. propagate_parallel_type : bool Whether to propagate parallel type. Returns ------- None )", py::arg("from"), py::arg("pos"), py::arg("to"), py::arg("propagate_parallel_type") = false); schedule.def( "transform_like", [](TensorView* reference_tv, const std::vector<TensorView*>& selected_tensors) { TransformPropagator propagator(reference_tv); if (selected_tensors.empty()) { // Propagate scheduler transformations on reference TensorView to the // rest of the fusion. MaxLogicalDomainInfoSpanningTree(reference_tv).traverse(&propagator); } else { // Propagate scheduler transformations on reference TensorView to the // subset of the fusion. std::unordered_set<TensorView*> selected_tv_set( selected_tensors.begin(), selected_tensors.end()); SetSelector selector( {selected_tv_set.begin(), selected_tv_set.end()}); MaxLogicalDomainInfoSpanningTree(reference_tv, &selector) .traverse(&propagator); } }, R"( Propagate scheduler transformations from a reference TensorView to other TensorViews. Parameters ---------- reference_tv : TensorView The reference TensorView whose transformations will be propagated. selected_tensors : List[TensorView], optional List of TensorViews to propagate transformations to. If empty, propagates to all TensorViews. Returns ------- None )", py::arg("reference_tv"), py::arg("selected_tensors") = std::vector<TensorView*>()); schedule.def( "parallelize_like", [](TensorView* reference_tv, int64_t pos, const std::vector<TensorView*>& selected_tensors, const std::unordered_set<ParallelType>& selected_parallel_types, bool propagate_padding) { scheduler_utils::parallelizeAllLike( reference_tv, pos, selected_tensors, selected_parallel_types, propagate_padding); }, R"( Propagate the parallelization from the selected dimensions of the reference tensor to their corresponding dimensions in all selected tensors in the DAG. Parameters ---------- reference_tv : TensorView The reference TensorView whose parallelization will be propagated. pos : int, optional The position up to which dimensions should be selected. -1 means all dimensions. selected_tensors : List[TensorView], optional List of TensorViews to propagate parallelization to. If empty, propagates to all TensorViews. selected_parallel_types : Set[ParallelType], optional Set of parallel types to propagate. If empty, propagates all parallel types. propagate_padding : bool, optional Whether to propagate padding (default: True). Returns ------- None )", py::arg("reference_tv"), py::arg("pos") = -1, py::arg("selected_tensors") = std::vector<TensorView*>(), py::arg("selected_parallel_types") = std::unordered_set<ParallelType>(), py::arg("propagate_padding") = true); schedule.def( "inline_most", [](const std::vector<TensorView*>& selected_tensors) { if (selected_tensors.empty()) { inlineMost(); } else { inlineMost(selected_tensors); } }, R"( Inline operations to the right most allowed position for the selected tensors. Parameters ---------- selected_tensors : List[TensorView], optional List of TensorViews to inline. If empty, inlines all operations. Returns ------- None )", py::arg("selected_tensors") = std::vector<TensorView*>()); schedule.def( "inline_at", [](TensorView* reference_tv, int64_t pos, bool best_effort, const std::vector<TensorView*>& selected_tensors) { if (selected_tensors.empty()) { // Inline to the position corresponding to the reference position in // the reference tensor for all tensors in the current fusion. inlineAllAt(reference_tv, pos, best_effort); } else { // Inline to the position corresponding to the reference position in // the reference tensor for selected tensors in the current fusion. std::unordered_set<TensorView*> selected_tv_set( selected_tensors.begin(), selected_tensors.end()); inlineSelectedAt(selected_tv_set, reference_tv, pos, best_effort); } }, R"( Inline operations at a specific position for the selected tensors. If selected_tensors is empty, inlines all operations. Parameters ---------- reference_tv : TensorView The reference TensorView whose position will be used for inlining. pos : int, optional The position to inline at. -1 means the last position. best_effort : bool, optional Whether to try to inline even if the exact position is not possible (default: False). selected_tensors : List[TensorView], optional List of TensorViews to inline. If empty, inlines all operations. Returns ------- None )", py::arg("reference_tv"), py::arg("pos") = -1, py::arg("best_effort") = false, py::arg("selected_tensors") = std::vector<TensorView*>()); schedule.def( "can_schedule", [](Fusion* fusion, SchedulerType scheduler_type, const py::iterable& inputs) { // Enable collection of messages from canScheduleRejectReason DebugDumpOptionsGuard debug_dump_options_guard; DebugDumpOptionsGuard::getCurOptions().set( DebugDumpOption::FusionSegmenterLog); // Send debug messages to stringstream std::stringstream ss; DebugStreamGuard dsg(ss); // Create runtime info from inputs auto args = from_pyiterable(inputs); SchedulerRuntimeInfo runtime_info(fusion, args); bool can_schedule = Schedule::canSchedule(scheduler_type, fusion, runtime_info); return std::make_tuple(can_schedule, ss.str()); }, py::arg("fusion"), py::arg("scheduler_type"), py::arg("inputs"), R"( Check if a scheduler can schedule the given fusion with the provided inputs. Parameters ---------- fusion : Fusion The fusion to check. scheduler_type : SchedulerType The type of scheduler to check. inputs : iterable The input tensors/values for the fusion. Returns ------- tuple of (bool, str) A tuple containing: - bool: True if the scheduler can schedule the fusion, False otherwise. - str: Debug message explaining why the scheduler was accepted or rejected. )"); schedule.def( "find_compatible_schedulers", [](Fusion* fusion, const py::iterable& inputs) { // Create runtime info from inputs auto args = from_pyiterable(inputs); SchedulerRuntimeInfo runtime_info(fusion, args); std::vector<SchedulerType> compatible_schedulers; // Check all scheduler types except None for (const auto& scheduler_type : all_heuristics_in_priority_order) { if (scheduler_type != SchedulerType::None && Schedule::canSchedule(scheduler_type, fusion, runtime_info)) { compatible_schedulers.push_back(scheduler_type); } } return compatible_schedulers; }, py::arg("fusion"), py::arg("inputs"), R"( Find all schedulers compatible with the given fusion and inputs. Parameters ---------- fusion : Fusion The fusion to check. inputs : iterable The input tensors/values for the fusion. Returns ------- list of SchedulerType A list of scheduler types that can schedule the fusion. )"); schedule.def( "compute_heuristics", [](Fusion* fusion, SchedulerType scheduler_type, const py::iterable& inputs) { auto args = from_pyiterable(inputs); SchedulerRuntimeInfo runtime_info(fusion, args); NVF_ERROR( Schedule::canSchedule(scheduler_type, fusion, runtime_info), "Could not schedule fusion with the SchedulerType: ", scheduler_type); auto scheduler_instance = SchedulerEntry::makeSchedulerInstance(scheduler_type); return scheduler_instance->computeHeuristics(fusion, runtime_info); }, py::arg("fusion"), py::arg("scheduler_type"), py::arg("inputs"), R"( Compute the heuristics for the specified scheduler type. Parameters ---------- fusion : Fusion The fusion to compute heuristics for. scheduler_type : SchedulerType The type of scheduler to compute heuristics for. inputs : iterable The input tensors/values for the fusion. Returns ------- HeuristicParams The heuristics for the given fusion. Notes ----- This function will raise an error if the scheduler cannot schedule the fusion. )"); schedule.def( "schedule", [](Fusion* fusion, SchedulerType scheduler_type, const py::iterable& inputs) { auto args = from_pyiterable(inputs); return SchedulerEntry::scheduleWith( fusion, scheduler_type, args, /*validate_scheduler=*/true); }, py::arg("fusion"), py::arg("scheduler_type"), py::arg("inputs"), R"( Schedule the fusion with the specified scheduler type. Parameters ---------- fusion : Fusion The fusion to schedule. scheduler_type : SchedulerType The type of scheduler to use. inputs : iterable The input tensors/values for the fusion. Returns ------- HeuristicParams The heuristics for the scheduled fusion. Notes ----- This function will raise an error if the scheduler cannot schedule the fusion. )"); schedule.def( "schedule", [](Fusion* fusion, SchedulerType scheduler_type, const HeuristicParams* heuristic_params) { auto scheduler_instance = SchedulerEntry::makeSchedulerInstance(scheduler_type); scheduler_instance->schedule(fusion, heuristic_params); }, py::arg("fusion"), py::arg("scheduler_type"), py::arg("heuristic_params"), R"( Schedule the fusion with the specified scheduler type. Parameters ---------- fusion : Fusion The fusion to schedule. scheduler_type : SchedulerType The type of scheduler to use. heuristic_params : HeuristicParams The heuristics for the scheduled fusion. Returns ------- None )"); } void bindCircularBuffering(py::module_& schedule) { schedule.def( "warp_specialize", [](TensorView* tv, int64_t number_of_stages, int64_t prefetch_distance, ParallelType parallel_type, std::optional<std::pair<int64_t, int64_t>> num_registers) { CircularBufferType circular_buffer_type = (num_registers.has_value()) ? WarpSpecialized(parallel_type, num_registers.value()) : WarpSpecialized(parallel_type); tv->circularBuffer( number_of_stages, prefetch_distance, circular_buffer_type); }, R"( Apply warp specialization circular buffering to the given TensorView. Parameters ---------- tv : TensorView The TensorView to apply warp specialization circular buffering to. number_of_stages : int The number of stages in the circular buffer. prefetch_distance : int The prefetch distance for the circular buffer. parallel_type : ParallelType The parallel type to apply warp specialization to. num_registers : tuple of int, optional A tuple specifying the number of registers for (async_warps, compute_warps). Both values must be between 24 and 256 (inclusive), multiples of 8, and the first value must be <= the second value. If not provided, register sharing is disabled. )", py::arg("tv"), py::arg("number_of_stages"), py::arg("prefetch_distance"), py::arg("parallel_type"), py::arg("num_registers") = py::none()); } } // namespace void bindScheduleOperators(py::module& nvfuser) { py::module_ nvf_schedule = nvfuser.def_submodule( "schedule", "This submodule contains all schedule operators for NvFuser."); bindTensorviewScheduleOps(nvf_schedule); bindCircularBuffering(nvf_schedule); } } // namespace nvfuser::python |
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