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GeezerVison is showing github.com — Fuser/python/python_direct/runtime.cpp at main · NVIDIA/Fuser Expand file tree/ Copy pathruntime.cppMore file actions 631 lines (567 loc) · 18.4 KB / Copy pathruntime.cppFile metadata and controls631 lines (567 loc) · 18.4 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 // 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 <python_common/distributed_tensor.h> #include <python_utils.h> #include <fusion.h> #include <options.h> #include <runtime/executor_kernel_arg.h> #include <runtime/fusion_executor_cache.h> #include <runtime/fusion_kernel_runtime.h> namespace nvfuser::python { // direct_bindings/runtime.cpp contains Fusion and FusionExecutorCache. // TODO IrContainer and Kernel would be implemented here. namespace { void bindFusion(py::module& nvfuser) { py::class_<FusionGuard>(nvfuser, "FusionGuard") .def( py::init<Fusion*>(), py::arg("fusion"), R"( Create a new FusionGuard to manage the active fusion context. A FusionGuard is a RAII-style guard that sets the active fusion context for the current scope. When the guard is created, it sets the provided fusion as the active fusion. When the guard is destroyed, it restores the previous fusion context. Parameters ---------- fusion : Fusion The fusion to set as the active fusion context. )"); // NOTE: manage, get_managed, get_managed_safe, stop_managing, has_managed are // template functions in the Fusion class. Templates do not exist in the // python language. To bind these functions, you must instantiate a full // (explicit) template specialization. py::class_<Fusion, py::smart_holder>(nvfuser, "Fusion") .def(py::init<>(), R"( Create a new Fusion. A Fusion represents a computation graph that can be compiled and executed on CUDA devices. It manages the IR nodes, inputs/outputs, and transformations needed to generate efficient CUDA kernels. Examples -------- >>> fusion = Fusion() >>> # Add inputs >>> t0 = fusion.add_input(...) >>> # Define computations >>> t1 = ops.add(t0, t0) >>> # Register outputs >>> fusion.add_output(t1) )") .def( "__eq__", &Fusion::sameDefinition, R"( Whether the fusion definitions are the same. Returns ------- bool The equality of the fusion definitions. )") .def( "inputs", &Fusion::inputs, py::return_value_policy::reference, R"( Get the inputs of the fusion. Returns ------- list of Val The inputs of the fusion. )") .def( "outputs", &Fusion::outputs, py::return_value_policy::reference, R"( Get the outputs of the fusion. Returns ------- list of Val The outputs of the fusion. )") .def( "vals", [](Fusion& self) { return self.vals(); }, py::return_value_policy::reference, R"( Return all Vals registered in the fusion. Returns ------- list of Val The Vals registered in the fusion. )") .def("add_input", &Fusion::addInput, py::arg("input"), R"( Register a value as an input to the fusion. Parameters ---------- input : Val The value to register as an input. Returns ------- Val The registered input value. Notes ----- - The input must not already be registered as an input. - The input must not have a definition within the fusion. )") .def("add_output", &Fusion::addOutput, py::arg("output"), R"( Register a value as an output of the fusion. Parameters ---------- output : Val The value to register as an output. Returns ------- None Notes ----- - The output must be defined within the fusion or be an input. - The same value can be registered as an output multiple times. )") .def( "add_output", [](Fusion& self, Val* output, Val* alias_input) { self.aliasOutputToInput( output, alias_input, AllocationType::ReuseBuffer); }, py::arg("output"), py::arg("alias_input"), R"( Alias an output to an input. Parameters ---------- output : Val The value to alias as an output. alias_input : Val The value to alias the output to. Returns ------- None Notes ----- - This output is not returned from the fusion. - The same value can be registered as a regular output, so it is returned from the fusion. )") .def( "print_math", [](Fusion& f, bool from_outputs_only) { // Send debug messages to stringstream std::stringstream ss; DebugStreamGuard dsg(ss); f.printMath(from_outputs_only); return ss.str(); }, py::arg("from_outputs_only") = true, R"( Return a string representing the arithmetic expressions in the fusion. Parameters ---------- from_outputs_only : bool, optional If True, only print expressions reachable from outputs. If False, print all expressions. Default is True. Returns ------- str The fusion intermediate representation (IR) as a string. )") .def( "print_kernel", [](Fusion& f, const CompileParams& compile_params) { // Send debug messages to stringstream std::stringstream ss; DebugStreamGuard dsg(ss); f.printKernel(compile_params); return ss.str(); }, py::arg("compile_params") = CompileParams(), R"( Lower the fusion and return the generated CUDA kernel as a string. Parameters ---------- compile_params : CompileParams, optional Parameters to control the compilation process. Default is default-constructed CompileParams. Returns ------- str The CUDA kernel as a string. )"); } void bindFusionExecutorCache(py::module& nvfuser) { py::class_<FusionExecutorCache>(nvfuser, "FusionExecutorCache") .def( py::init([](std::unique_ptr<Fusion> fusion, int64_t fusion_id, bool auto_schedule) { // Move fusion to FusionExecutorCache. return std::make_unique<FusionExecutorCache>( std::move(fusion), fusion_id, auto_schedule); }), py::arg("fusion"), py::arg("fusion_id") = 0, py::arg("auto_schedule") = true, R"( Create a new FusionExecutorCache. The cache automatically handles compilation and execution of the fusion for different input configurations. Parameters ---------- fusion : Fusion The fusion to be executed. The FusionExecutorCache takes ownership of this pointer. fusion_id : int, optional A unique identifier for this fusion. Default is 0. auto_schedule : bool, optional Whether to automatically schedule the fusion. If False, the fusion must be manually scheduled. Default is True. Examples -------- >>> fusion = Fusion() >>> # ... define fusion operations ... >>> executor_cache = FusionExecutorCache(fusion) >>> outputs = executor_cache.execute([input1, input2]) )") .def( "execute", [](FusionExecutorCache& self, const py::iterable& iter, std::optional<int64_t> device, std::vector<std::string> _enable_options, std::vector<std::string> _disable_options) { KernelArgumentHolder args = from_pyiterable(iter, device); EnableOptionsGuard enable_opt_guard; for (const auto& _enable_option : _enable_options) { std::optional<EnableOption> opt = stringToEnableOption(_enable_option); NVF_CHECK( opt.has_value(), "Unrecognized enable_option: ", _enable_option); EnableOptionsGuard::getCurOptions().set(opt.value()); } DisableOptionsGuard disable_opt_guard; for (const auto& _disable_option : _disable_options) { std::optional<DisableOption> opt = stringToDisableOption(_disable_option); NVF_CHECK( opt.has_value(), "Unrecognized disable_option: ", _disable_option); DisableOptionsGuard::getCurOptions().set(opt.value()); } KernelArgumentHolder outputs = self.runFusionWithInputs( args, std::nullopt, args.getDeviceIndex()); return to_tensor_vector(outputs); }, py::arg("inputs"), py::kw_only(), py::arg("device") = py::none(), py::arg("_enable_options") = py::list(), py::arg("_disable_options") = py::list(), R"( Execute the fusion with the given inputs. Parameters ---------- inputs : iterable An iterable of input tensors or values. All tensor inputs must be on the same device. Cpu scalar tensor can interoperate with gpu tensors. device : int, optional The device index to execute the fusion on. It must be a non-negative integer less than 256. If None, uses the device of the input tensors. Default is None. _enable_options : list of str, optional A list of enable options. Default is None. _disable_options : list of str, optional A list of disable options. Default is None. Returns ------- list of torch.Tensor The output tensors produced by the fusion. )") .def( "is_compiled", [](FusionExecutorCache& self, const py::iterable& iter, std::optional<int64_t> device) { return self.isCompiled(from_pyiterable(iter, device)); }, py::arg("inputs"), py::arg("device") = 0, R"( Check if a compiled kernel exists for the given input configuration. Parameters ---------- inputs : iterable An iterable of input tensors or values to check. device : int, optional The target device index. Default is 0. Returns ------- bool True if a compiled kernel exists for the input configuration. )") .def( "fusion", static_cast<Fusion* (FusionExecutorCache::*)()>( &FusionExecutorCache::fusion), py::return_value_policy::reference, R"( Get the underlying fusion object. Returns ------- Fusion The fusion object being executed by this cache. )") .def( "get_cuda_kernel", [](FusionExecutorCache& self, const py::iterable& iter, bool intrinsic_code, std::optional<int64_t> device) { return self.getCodeFor( from_pyiterable(iter, device), intrinsic_code); }, py::arg("inputs"), py::arg("intrinsic_code") = false, py::arg("device") = 0, R"( Get the CUDA kernel code for the given input configuration. Parameters ---------- inputs : iterable An iterable of input tensors or values. device : int, optional The target device index. Default is 0. Returns ------- str The generated CUDA kernel code as a string. )") .def( "get_scheduled_ir", [](FusionExecutorCache& self, const py::iterable& iter, bool tensor_transforms, std::optional<int64_t> device) { return self.getScheduledIrFor( from_pyiterable(iter, device), tensor_transforms); }, py::arg("inputs"), py::arg("tensor_transforms") = false, py::arg("device") = 0, R"( Get the scheduled IR for the given input configuration. Parameters ---------- inputs : iterable An iterable of input tensors or values. tensor_transforms : bool, optional Whether to include tensor transformations in the output. Default is False. device : int, optional The target device index. Default is 0. Returns ------- str The scheduled intermediate representation (IR) as a string. )") .def( "get_most_recent_scheduled_ir", &FusionExecutorCache::getMostRecentScheduledIr, py::arg("tensor_transforms") = false, R"( Get the scheduled IR from the most recent execution. Parameters ---------- tensor_transforms : bool, optional Whether to include tensor transformations in the output. Default is False. Returns ------- str The scheduled intermediate representation (IR) as a string. Notes ----- - Returns None if execution has occurred yet. )") .def( "get_output_shardings", [](FusionExecutorCache& self) { Fusion* fusion = self.getMostRecentKernelRuntime() ->fusionSegments() ->completeFusion(); std::vector<Sharding> output_shardings = getOutputShardings(fusion); NVF_ERROR( output_shardings.empty() || std::ssize(output_shardings) == (int64_t)fusion->outputs().size(), "Found ", std::ssize(output_shardings), " output shardings but expected ", fusion->outputs().size(), " or 0."); return output_shardings; }, R"( Get the output shardings of the fusion. Returns ------- list of Sharding The output shardings of the fusion. )"); } void bindKernelExecutor(py::module& nvfuser) { py::class_<KernelExecutor>(nvfuser, "KernelExecutor") .def( py::init<int64_t, int64_t, int64_t, int64_t>(), R"( Create a new KernelExecutor. A KernelExecutor is responsible for compiling and executing CUDA kernels. It manages the compilation process, kernel caching, and runtime execution. Parameters ---------- fusion_id : int, optional A unique identifier for the fusion. Used for caching compiled kernels. Default is 0. concrete_id : int, optional A unique identifier for the concrete implementation of the fusion. Used for caching compiled kernels. Default is 0. runtime_id : int, optional A unique identifier for the runtime instance. Used for caching compiled kernels. Default is 0. group_id : int, optional A unique identifier for the group of operations. Used for segmented fusions. Default is 0. Examples -------- >>> executor = KernelExecutor() >>> executor.compile(fusion) >>> outputs = executor.run(inputs) )", py::arg("fusion_id") = 0, py::arg("concrete_id") = 0, py::arg("runtime_id") = 0, py::arg("group_id") = 0) .def( "compile", [](KernelExecutor& self, Fusion* fusion, const py::iterable& args, std::optional<LaunchParams> launch_constraints, const CompileParams& compile_params, SchedulerType scheduler_type) { // launch_constraints is optional to avoid creating default // LaunchParams when importing shared library. self.compile( fusion, from_pyiterable(args), launch_constraints.value_or(LaunchParams()), compile_params, scheduler_type); }, R"( Compile a fusion into a CUDA kernel. Parameters ---------- fusion : Fusion The fusion to compile. args : KernelArgumentHolder, optional The kernel arguments. If empty, will be populated during run. launch_constraints : LaunchParams, optional Constraints for kernel launch parameters. compile_params : CompileParams, optional Parameters for kernel compilation. scheduler_type : SchedulerType, optional The type of scheduler to use (default: None). Returns ------- None )", py::arg("fusion"), py::arg("args") = py::list(), py::arg("launch_constraints") = py::none(), py::arg("compile_params") = CompileParams(), py::arg("scheduler_type") = SchedulerType::None) .def( "run", [](KernelExecutor& self, const py::iterable& args, std::optional<LaunchParams> launch_constraints, const CompileParams& compile_params) { // launch_constraints is optional to avoid creating default // LaunchParams when importing shared library. KernelArgumentHolder outputs = self.run( from_pyiterable(args), {}, launch_constraints.value_or(LaunchParams()), compile_params); return to_tensor_vector(outputs); }, R"( Run the compiled kernel with the given arguments. Parameters ---------- args : KernelArgumentHolder The input arguments for the kernel. launch_constraints : LaunchParams, optional Constraints for kernel launch parameters. compile_params : CompileParams, optional Parameters for kernel compilation. Returns ------- KernelArgumentHolder The output arguments containing the results. )", py::arg("args"), py::arg("launch_constraints") = py::none(), py::arg("compile_params") = CompileParams()) .def( "is_compiled", &KernelExecutor::isCompiled, R"( Check if the kernel has been compiled. Returns ------- bool True if the kernel has been compiled, False otherwise. )") .def( "is_programmatic_dependent_launch_enabled", [](KernelExecutor& self) -> bool { if (!self.isCompiled()) { return false; } return self.compiledKernel() ->kernel() ->summary() .enable_programmatic_dependent_launch; }, R"( Check if programmatic dependent launch is enabled for this kernel. Returns ------- bool True if programmatic dependent launch is enabled, False otherwise. )"); } } // namespace void bindRuntime(py::module& nvfuser) { bindFusion(nvfuser); bindFusionExecutorCache(nvfuser); bindKernelExecutor(nvfuser); } } // namespace nvfuser::python |
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