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GeezerVison is showing github.com — Fuser/python/python_direct/ir.cpp at main · NVIDIA/Fuser Expand file tree/ Copy pathir.cppMore file actions 816 lines (748 loc) · 21.7 KB / Copy pathir.cppFile metadata and controls816 lines (748 loc) · 21.7 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 // 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 <ranges> #include <bindings.h> #include <python_utils.h> // size and shape operations are a part of TensorView bindings but not a // part of TensorView IR node. #include <ops/arith.h> #include <fusion.h> #include <ir/base_nodes.h> #include <ir/interface_nodes.h> #include <ir/internal_base_nodes.h> namespace nvfuser::python { // For all nodes, use multiple inheritance to disable destructor with // std::unique_ptr<Statement, py::nodelete>. This class will // disable memory management because it is handled automatically by IrContainer. namespace { void bindBaseNodes(py::module& nvfuser) { // Statement py::class_<Statement, std::unique_ptr<Statement, py::nodelete>>( nvfuser, "Statement") .def( "__str__", [](Statement* self) { return self->toString(); }, R"(Get string representation of Statement.)"); // Val py::class_<Val, Statement, std::unique_ptr<Val, py::nodelete>>(nvfuser, "Val") .def( "is_symbolic", &Val::isSymbolic, R"( Check if this value is symbolic (not a concrete value). Returns ------- bool True if the value is symbolic, False otherwise. )") .def( "is_tensor", [](Val* self) { return self->isA<TensorView>(); }, R"( Check if this value is a TensorView. Returns ------- bool True if the value is a TensorView, False otherwise. )") .def( "definition", &Val::definition, R"( Get the definition of this expression. Returns ------- Expr The definition of this expression. )") .def( "uses", &Val::uses, R"( Get the uses of this expression. Returns ------- Expr The uses of this expression. )"); // Expr py::class_<Expr, Statement, std::unique_ptr<Expr, py::nodelete>>( nvfuser, "Expr") .def( "input", &Expr::input, py::arg("index"), py::return_value_policy::reference, R"( Get the input of this expression. Parameters ---------- index : int The index of the input. Returns ------- Expr The input of this expression. )") .def( "output", &Expr::output, py::arg("index"), py::return_value_policy::reference, R"( Get the output of this expression. Parameters ---------- index : int The index of the output. Returns ------- Expr The output of this expression. )"); } void bindInternalBaseNodes(py::module& nvfuser) { // IterDomain py::class_<IterDomain, Val, std::unique_ptr<IterDomain, py::nodelete>>( nvfuser, "IterDomain") .def( "__str__", [](IterDomain* self) { return self->toString(/*indent_size=*/0); }, "Convert the IterDomain to a string representation.") .def( "is_reduction", &IterDomain::isReduction, R"( Check if this domain is a reduction domain. Returns ------- bool True if the domain is a reduction domain, False otherwise. )") .def( "extent", &IterDomain::extent, R"( Get the extent of this domain. Returns ------- Val The extent of this domain. )") .def( "parallelize", &IterDomain::parallelize, py::arg("parallel_type"), R"( Set the parallel type of this domain. Parameters ---------- parallel_type : ParallelType The type of parallelization to apply (e.g., BIDx, TIDx, etc.). Notes ----- This is a key function used in scheduling to specify how the domain should be parallelized across CUDA threads and blocks. )"); // TensorDomain py::class_<TensorDomain, Val, std::unique_ptr<TensorDomain, py::nodelete>>( nvfuser, "TensorDomain") .def( "__str__", [](TensorDomain* self) { return self->toString(/*indent_size=*/0); }, "Convert the TensorDomain to a string representation."); } void bindInterfaceNodes(py::module& nvfuser) { py::class_<TensorView, Val, std::unique_ptr<TensorView, py::nodelete>>( nvfuser, "TensorView") .def( "__str__", [](TensorView* self) { return self->toString(/*indent_size=*/0); }, "Convert the TensorView to a string representation.") .def_property_readonly( "ndim", [](TensorView* self) { return std::ranges::distance( self->getLogicalDomain() | TensorDomain::kNoReductions); }, R"( Get the number of dimensions in this tensor. Returns ------- int The number of dimensions. )") .def( "size", [](TensorView* self, int64_t dim) { return size(self, dim); }, py::arg("dim"), py::return_value_policy::reference, R"( Get the size of this tensor. Parameters ---------- dim : int The dimension in the tensor. Returns ------- int The size of the dimension. )") .def( "shape", [](TensorView* self) { return shape(self); }, py::return_value_policy::reference, R"( Get the shape of this tensor. Returns ------- list of Val The shape of this tensor. )") .def( "dtype", [](TensorView* self) -> PrimDataType { DataType dt = self->dtype(); NVF_CHECK( std::holds_alternative<PrimDataType>(dt.type), "Expected PrimDataType but got type: ", dt); return std::get<PrimDataType>(dt.type); }, R"( Get the data type of this tensor. Returns ------- DataType The data type of this tensor. )") .def("has_root", &TensorView::hasRoot, R"( Check if this tensor has a root domain. Returns ------- bool True if the tensor has a root domain, False otherwise. )") .def( "domain", &TensorView::domain, R"( Get the TensorDomain of this tensor. Returns ------- TensorDomain The tensor domain object that describes the dimensionality and properties of this tensor. The tensor domain contains information about: - Root domain (The original dimensions if logical domain contains rFactor iterDomains.) - Logical domain (The original dimensions. It may contain rFactor iterDomains.) - Allocation domain (How the memory is allocated for the tensor?) - Loop domain (The for-loop structure for the tensor.) )") .def( "get_logical_domain", &TensorView::getLogicalDomain, R"( Get the logical domain of this tensor. Returns ------- list of IterDomain The logical iteration domain. )") .def( "get_loop_domain", &TensorView::getLoopDomain, R"( Get the loop domain of this tensor. Returns ------- list of IterDomain The loop iteration domains. )") .def( "get_root_domain", &TensorView::getRootDomain, R"( Get the root domain of this tensor. Returns ------- list of IterDomain The root iteration domains. )") .def( "cache_after", &TensorView::cacheAfter, py::arg("op_type") = LoadStoreOpType::Set, py::arg("cache_op") = CacheOp::Unspecified, py::arg("propagate_allocation_domain") = true, py::arg("cached_uses") = py::list(), py::return_value_policy::reference, R"( Cache the TensorView after the specified operation. Parameters ---------- op_type : LoadStoreOpType, optional The type of load/store operation (default: Set). cache_op : CacheOp, optional The type of cache operation (default: Unspecified). Returns ------- TensorView The new cached TensorView. )") .def( "cache_before", &TensorView::cacheBefore, py::arg("op_type") = LoadStoreOpType::Set, py::return_value_policy::reference, R"( Cache the TensorView before the specified operation. Parameters ---------- op_type : LoadStoreOpType, optional The type of load/store operation (default: Set). Returns ------- TensorView The new cached TensorView. )") .def( "set_memory_type", &TensorView::setMemoryType, py::arg("memory_type"), R"( Set the memory type for the TensorView. Parameters ---------- memory_type : MemoryType The memory type to set. Returns ------- None )") .def( "split", static_cast<TensorView* (TensorView::*)(int64_t, int64_t, bool)>( &TensorView::split), py::arg("axis"), py::arg("factor"), py::arg("inner_split") = true, py::return_value_policy::reference, R"( Split an axis into two axes. Parameters ---------- axis : int The axis to split. factor : int The factor to split by. inner_split : bool, optional If True, the factor determines the size of the inner domain. If False, the factor determines the size of the outer domain. Default is True. Returns ------- TensorView A TensorView with the split axes in its loop domain. )") .def( "inner_split", [](TensorView* self, int64_t axis, int64_t factor) { return self->split(axis, factor, true); }, py::arg("axis"), py::arg("factor"), py::return_value_policy::reference, R"( Split an axis into inner-first order (alias of split with inner_split=True). Parameters ---------- axis : int The axis to split. factor : int The factor to split by (inner size). Returns ------- TensorView A TensorView with the split axes in its loop domain. )") .def( "outer_split", [](TensorView* self, int64_t axis, int64_t factor) { return self->split(axis, factor, false); }, py::arg("axis"), py::arg("factor"), py::return_value_policy::reference, R"( Split an axis into outer-first order (alias of split with inner_split=False). Parameters ---------- axis : int The axis to split. factor : int The factor to split by (outer size). Returns ------- TensorView A TensorView with the split axes in its loop domain. )") .def( "merge", static_cast<TensorView* (TensorView::*)(int64_t, int64_t)>( &TensorView::merge), py::arg("axis_o"), py::arg("axis_i"), py::return_value_policy::reference, R"( Merge two axes into one axis. Parameters ---------- axis_o : int The outer axis to merge. axis_i : int The inner axis to merge. Returns ------- TensorView A TensorView with the merged axes in its loop domain. )") .def( "reorder", [](TensorView* self, std::unordered_map<int64_t, int64_t>& old2new) { return self->reorder(old2new); }, py::arg("old2new") = py::dict(), py::return_value_policy::reference, R"( Reorder the axes of this tensor. Parameters ---------- old2new : dict of int to int The new order of the axes. Returns ------- TensorView A TensorView with the reordered axes in its loop domain. )") .def( "swizzle", [](TensorView* self, int64_t x, int64_t y) { return self->swizzle(SwizzleType::XOR, x, y); }, py::return_value_policy::reference, py::arg("x"), py::arg("y"), R"( Swizzle the axes of this tensor. Parameters ---------- x : int The x axis to swizzle. y : int The y axis to swizzle. Returns ------- TensorView A TensorView with the swizzled axes in its loop domain. )") .def( "swizzle1d", [](TensorView* self, int64_t x, ParallelType parallel_type) { return self->swizzle1d(x, parallel_type); }, py::return_value_policy::reference, py::arg("x"), py::arg("parallel_type"), R"( Swizzle the specified axis with the device index corresponding to the given parallel type. Parameters ---------- x : int The axis to swizzle. parallel_type : ParallelType The device parallel type for the 1D swizzle. Returns ------- TensorView A TensorView with the swizzled axis in its loop domain. )") .def( "rfactor", static_cast<TensorView* (TensorView::*)(const std::vector<int64_t>&)>( &TensorView::rFactor), py::arg("axes"), py::return_value_policy::reference, R"( Perform an rfactor transformation on the specified axes. Parameters ---------- axes : list of int The axes to apply rfactor to. Returns ------- TensorView The newly created rfactor tensor. )") .def( "set_allocation_domain", static_cast<void (TensorView::*)(std::vector<IterDomain*>, bool)>( &TensorView::setAllocationDomain), py::arg("new_allocation_domain"), py::arg("new_contiguity"), R"( Set the allocation domain of this tensor. Parameters ---------- new_allocation_domain : list of IterDomain The new allocation iteration domains. new_contiguity : bool The new contiguity flag. )") .def( "set_device_mesh", &TensorView::setDeviceMesh, py::arg("mesh"), R"( Set the device mesh of this tensor. Parameters ---------- mesh : DeviceMesh The device mesh to set. )") .def( "axis", &TensorView::axis, py::arg("index"), py::return_value_policy::reference, R"( Get the iteration domain at the specified axis. Parameters ---------- index : int The axis index. Returns ------- IterDomain The iteration domain at the specified axis. )"); } // Creates a new TensorView with the specified properties. // // This function creates a tensor with the given shape, contiguity, data type, // and stride order. It handles both CPU and CUDA tensors, with special handling // for CPU scalar tensors. // // Parameters // ---------- // shape : vector<int64_t> // The shape of the tensor // contiguity : vector<optional<bool>> // The contiguity flags for each dimension. None indicates a broadcast // dimension. // dtype : PrimDataType // The data type of the tensor (e.g., Float, Half, Int) // is_cpu : bool, optional // Whether this is a CPU tensor. Default is false. // stride_order : vector<int64_t>, optional // The stride order of dimensions. Default is empty. // // Returns // ------- // TensorView* // A pointer to the newly created TensorView. // // Notes // ----- // - CPU tensors are only supported for scalar tensors (empty shape) // - The stride order is normalized and validated before use // - Expanded dimensions are automatically determined based on shape and // contiguity // - The tensor is created using the TensorViewBuilder pattern TensorView* defineTensor( std::vector<int64_t> shape, std::vector<std::optional<bool>> contiguity, PrimDataType dtype, bool is_cpu = false, std::vector<int64_t> stride_order = {}) { normalizeStrideOrder(stride_order); TensorView* tv = TensorViewBuilder() .contiguity(contiguity) .shape(shape) .dtype(dtype) .expanded(getExpanded(shape, contiguity, stride_order)) .strideOrder(stride_order) .build(); if (shape.empty() && is_cpu) { tv->setCpuScalar(true); } else { NVF_CHECK(!is_cpu, "CPU non-scalar tensor is not supported!"); } return tv; } // return the unpacked shape and dtype for a given packed dtype, where we need // to double the size of the inner most dimension. std::tuple<std::vector<int64_t>, PrimDataType> translatePackedDtype( const std::vector<int64_t>& shape, const PrimDataType dtype, const std::vector<int64_t>& stride_order) { // TODO: switch to isPackedType when the pack width is retrieved through // utility functions as well. NVF_CHECK(dtype == DataType::Float4_e2m1fn_x2); int64_t fastest_dim = std::ssize(shape) - 1; for (const auto& [i, val] : enumerate(stride_order)) { if (val == 0) { fastest_dim = i; break; } } std::vector<int64_t> un_packed_shape = shape; un_packed_shape[fastest_dim] *= 2; return {un_packed_shape, DataType::Float4_e2m1fn}; } void bindDefineTensor(py::module& nvfuser) { nvfuser .def( "define_tensor", [](const std::vector<int64_t>& shape, const std::vector<std::optional<bool>>& contiguity, const PrimDataType dtype = DataType::Float, const bool is_cpu = false, const std::vector<int64_t>& stride_order = {}) -> TensorView* { verifyShape(shape); if (!isPackedType(dtype)) { return defineTensor( shape, contiguity, dtype, is_cpu, stride_order); } else { auto&& [new_shape, new_dtype] = translatePackedDtype(shape, dtype, stride_order); return defineTensor( new_shape, contiguity, new_dtype, is_cpu, stride_order); } }, py::arg("shape"), py::arg("contiguity"), py::arg("dtype") = DataType::Float, py::arg("is_cpu") = false, py::arg("stride_order") = py::list(), py::return_value_policy::reference) .def( "define_tensor", [](const std::vector<int64_t>& shape, // Contiguity for non-broadcast dimensions. const bool contiguity = false, const PrimDataType dtype = DataType::Float, const bool is_cpu = false, const std::vector<int64_t>& stride_order = {}) -> TensorView* { verifyShape(shape); if (!isPackedType(dtype)) { return defineTensor( shape, getContiguityVec(shape, stride_order, contiguity), dtype, is_cpu, stride_order); } else { auto&& [new_shape, new_dtype] = translatePackedDtype(shape, dtype, stride_order); return defineTensor( new_shape, getContiguityVec(new_shape, stride_order, contiguity), new_dtype, is_cpu, stride_order); } }, py::arg("shape"), py::arg("contiguity") = false, py::arg("dtype") = DataType::Float, py::arg("is_cpu") = false, py::arg("stride_order") = py::list(), py::return_value_policy::reference) .def( "define_tensor", [](const std::vector<int64_t>& sizes, const std::vector<int64_t>& strides, const PrimDataType dtype = DataType::Float, const bool static_sizes = false, const bool is_cpu = false) -> TensorView* { NVF_CHECK( sizes.size() == strides.size(), "The number of sizes does not match the number of strides.", sizes.size(), strides.size()); std::vector<std::optional<bool>> contiguity; std::vector<int64_t> stride_order; std::tie(contiguity, stride_order) = computeTensorDescriptor(sizes, strides); if (!isPackedType(dtype)) { return defineTensor( getTensorViewBuilderSizes(sizes, static_sizes), contiguity, dtype, is_cpu, stride_order); } else { auto&& [new_sizes, new_dtype] = translatePackedDtype(sizes, dtype, stride_order); return defineTensor( getTensorViewBuilderSizes(new_sizes, static_sizes), contiguity, new_dtype, is_cpu, stride_order); } }, py::arg("sizes"), py::arg("strides"), py::arg("dtype") = DataType::Float, py::arg("static_sizes") = false, py::arg("is_cpu") = false, py::return_value_policy::reference); } void bindDefineScalar(py::module& nvfuser) { // The symbolic define_scalar must come before the constant version because of // pybind11 rules for overload resolution. Essentially, overload functions are // tried in the order they were registered with pybind11. If the order is // reversed, the PrimDataType is cast to its corresponding Enum integer and // used as a Fusion contant. // // Reference: // https://pybind11.readthedocs.io/en/stable/advanced/functions.html#overload-resolution-order nvfuser.def( "define_scalar", [](PrimDataType dtype = DataType::Double) { return IrBuilder::create<Val>(std::monostate{}, dtype); }, py::arg("dtype") = DataType::Double, py::return_value_policy::reference); nvfuser.def( "define_scalar", [](PolymorphicValue::VariantType value, std::optional<PrimDataType> dtype) { PolymorphicValue cast_value( dtype.has_value() ? castToDtype(std::move(value), dtype.value()) : std::move(value)); PrimDataType value_dtype( dtype.has_value() ? dtype.value() : std::get<PrimDataType>(getDataType(cast_value).type)); return IrBuilder::create<Val>(cast_value, value_dtype); }, py::arg("value"), py::arg("dtype") = std::nullopt, py::return_value_policy::reference); } } // namespace void bindFusionIr(py::module& nvfuser) { bindBaseNodes(nvfuser); bindInternalBaseNodes(nvfuser); bindInterfaceNodes(nvfuser); bindDefineTensor(nvfuser); bindDefineScalar(nvfuser); } } // namespace nvfuser::python |
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