GeezerVisonThe World Wide Web, restored to 800 pixels of glory. |
| Home|About|Gaming cabinet|Archives|How it works|Starter sites |
GeezerVison is showing github.com — Fuser/python/python_direct/python_translate.cpp at main · NVIDIA/Fuser Expand file tree/ Copy pathpython_translate.cppMore file actions 1713 lines (1540 loc) · 59.3 KB / Copy pathpython_translate.cppFile metadata and controls1713 lines (1540 loc) · 59.3 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 // 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 <python_utils.h> #include <translation_names.h> #include <fusion.h> #include <ir/all_nodes.h> #include <ir/container.h> #include <ir/utils.h> #include <ops/all_ops.h> #include <type.h> #include "base.h" #include <algorithm> #include <iterator> #include <ranges> #include <type_traits> #include <utility> namespace nvfuser::python { namespace { // Check if a type is an optional via type_traits template <typename T> struct is_optional : std::false_type {}; template <typename T> struct is_optional<std::optional<T>> : std::true_type {}; template <typename T> inline constexpr bool is_optional_v = is_optional<T>::value; // A struct to hold default values for keyword arguments. template <typename T> struct KeywordArgument { using type = T; // The data type of the argument std::string name; std::optional<T> default_value; }; // Check if the NvFuser Val can be represented as a Python scalar. bool isPythonScalar(const Val* v) { // short_circuit: Symbolic values are not Python scalars. if (v->isSymbolic()) { return false; } // Check if the dtype is compatible with a Python scalar. // e.g., Python scalar cannot distiguish between ComplexDouble and // ComplexFloat. In this case, define_scalar must be used to specify custom // dtype. PrimDataType value_dtype(std::get<PrimDataType>(v->dtype().type)); switch (value_dtype) { case PrimDataType::Bool: case PrimDataType::Int: case PrimDataType::Index: case PrimDataType::ComplexDouble: case PrimDataType::Double: return true; default: return false; } } class PythonPrinter { public: PythonPrinter(std::ostream& os) : os_(&os) {} // Generate a python string for a string value. std::string toString(const std::string& s) { return s; } // Generate a python string for a boolean value. std::string toString(bool b) { return b ? "True" : "False"; } // Generate a python string for an int64_t value. std::string toString(int64_t i) { return std::to_string(i); } // Generate a python string for an size_t value. std::string toString(size_t i) { return std::to_string(i); } // Generate a python string for a complex double value. std::string toString(std::complex<double> c) { std::stringstream ss; ss << std::showpoint << std::real(c) << "+" << std::showpoint << std::imag(c) << "j"; return ss.str(); } // Generate a python string for a double value. std::string toString(double d) { if (std::isinf(d)) { if (std::signbit(d)) { return "float(\"-inf\")"; } else { return "float(\"inf\")"; } } else if (std::isnan(d)) { return "float(\"nan\")"; } else { std::stringstream ss; ss << std::showpoint << d; return ss.str(); } } // Generate a python string for a Datatype. std::string toString(DataType dtype) { return dtypeToPyString(std::get<PrimDataType>(dtype.type)); } // Generate a python string for a PolymorphicValue with simple types. std::string toString(const PolymorphicValue& pv) { if (pv.is<bool>()) { return toString(pv.as<bool>()); } else if (pv.is<int64_t>()) { return toString(pv.as<int64_t>()); } else if (pv.is<std::complex<double>>()) { return toString(pv.as<std::complex<double>>()); } else if (pv.is<double>()) { return toString(pv.as<double>()); } else if (pv.is<std::monostate>()) { return "None"; } else { NVF_THROW("Unsupported PolymorphicValue type"); } } // Generate a unique name for a Val. Map val to name to track Val's lifetime. std::string toString(const nvfuser::Val* v, bool is_lvalue = false) { std::stringstream ss; if (v == nullptr) { return "None"; } else if (v->isA<TensorView>()) { ss << "tv" << v->name(); } else if (!is_lvalue && isPythonScalar(v)) { ss << toString(v->value()); } else { ss << "c" << v->name(); } return ss.str(); } // Generate a python string for an optional value. template <typename T> std::string toString(std::optional<T> optional, bool skip_none = true) { if (optional.has_value()) { return toString(optional.value()); } else if (!skip_none) { return "None"; } else { return ""; } } // Generate a python string for a keyword argument. template <typename T> std::string toString( const std::string& name, T value, const std::string& separator) { std::string result = toString(value); if (result.empty()) { return ""; } return separator + name + "=" + result; } // Generate a python list of values. template <typename T> std::string toString(const std::vector<T>& vec, bool is_list = true) { std::stringstream ss; if (is_list) { ss << "["; } for (auto&& [i, val] : enumerate(vec)) { if constexpr (is_optional_v<T>) { ss << toString(val, /*skip_none=*/false); } else { ss << toString(val); } if (i < std::ssize(vec) - 1) { ss << ", "; } } if (is_list) { ss << "]"; } return ss.str(); } // Generate a python list of values. std::string generateOutputs(const std::vector<const nvfuser::Val*>& vec) { std::stringstream ss; for (auto&& [i, val] : enumerate(vec)) { if (val == nullptr) { ss << "_"; } else { NVF_ERROR( !isPythonScalar(val), "A constant scalar Val* cannot be an output lvalue. Got\t", val->toString()); ss << toString(val, /*is_lvalue=*/true); } if (i < std::ssize(vec) - 1) { ss << ", "; } } return ss.str(); } // Generate a python list of values. template <typename... Ts> std::string generateList(std::tuple<Ts...> const& args) { if (sizeof...(Ts) == 0) { return ""; } std::stringstream ss; std::apply( [&](Ts const&... tuple_args) { size_t i = 0; (((ss << (i > 0 ? ", " : "") << toString(tuple_args)), ++i), ...); }, args); return ss.str(); } // Generate a python list of values with string keyword arguments. template <typename... Ts> std::string generateNamedList( const std::vector<std::string>& argument_names, std::tuple<Ts...> const& args) { NVF_ERROR( argument_names.size() == sizeof...(Ts), "Input argument names and args must have the same size."); // Use std::apply to unpack tuple of arguments into a lambda. The lambda // contains a C++17 fold expression on a comma operator that writes each // argument to stringstream and increments argument position. std::stringstream ss; std::apply( [this, &ss, &argument_names](Ts const&... tuple_args) { size_t i = 0; (((ss << toString( argument_names[i], tuple_args, (i > 0 ? ", " : ""))), ++i), ...); }, args); return ss.str(); } // Generate a python list of values with string keyword arguments. // * A tuple of default argument values is provided to the function. // * If the default argument is optional and equal to the provided argument, // then skip printing the keyword-argument pair. template <typename... Ds, typename... Ts> std::string generateNamedList( std::tuple<Ds...> const& default_args, std::tuple<Ts...> const& args) { NVF_ERROR( sizeof...(Ds) == sizeof...(Ts), "The default and given arguments must have the same size."); // This immediately-invoked generic lambda uses a C++17 fold expression // to emulate a loop over the tuple elements. // // 1. `std::make_index_sequence` generates a compile-time sequence of // indices (0, 1, 2...). // 2. The lambda accepts this sequence, deducing the indices into the // template pack `Is...`. // 3. A fold expression over the comma operator expands the code for each // index. // 4. A ternary operator `(condition ? ... : ...)` performs the conditional // logic. // 5. If condition is true, another comma operator // `(write_to_stream, increment_counters)` chains the side effects of // writing to the stringstream and advancing the counters. // 6. If condition is false, increment `printed_arg_pos` counter by zero. std::stringstream ss; [&]<std::size_t... Is>(std::index_sequence<Is...>) { size_t printed_arg_pos = 0; (((!std::get<Is>(default_args).default_value.has_value() || std::get<Is>(default_args).default_value.value() != std::get<Is>(args)) ? ((ss << toString( std::get<Is>(default_args).name, std::get<Is>(args), (printed_arg_pos > 0 ? ", " : ""))), ++printed_arg_pos) : (printed_arg_pos += 0)), ...); }(std::make_index_sequence<sizeof...(Ts)>{}); // Generate indices 0..N-1 return ss.str(); } // Generate a python operation with a list of inputs and outputs. void generateOperation( const std::string& op_name, const std::vector<const nvfuser::Val*>& inputs, const std::vector<const nvfuser::Val*>& outputs) { (*os_) << kTab; if (!outputs.empty()) { (*os_) << generateOutputs(outputs) << " = "; } (*os_) << op_name << "(" << toString(inputs, /*is_list=*/false) << ")\n"; } // Generate a python operation with a list of inputs and outputs. // A string keyword argument is added for each input. The default_kwargs // argument allows skipping arguments if it isn't strictly necessary. template < typename... arg_types, typename... default_kwarg_types, typename... kwargs_types> void generateKwargsOperation( const std::string& op_name, const std::tuple<arg_types...>& args, const std::tuple<default_kwarg_types...>& default_kwargs, const std::tuple<kwargs_types...>& kwargs, const std::vector<const nvfuser::Val*>& outputs) { std::string kwargs_str = generateNamedList(default_kwargs, kwargs); constexpr bool any_arguments = sizeof...(arg_types) == 0; std::string connect = (any_arguments || kwargs_str.empty()) ? "" : ", "; (*os_) << kTab << generateOutputs(outputs) << " = " << op_name << "(" << generateList(args) << connect << kwargs_str << ")\n"; } // Generate a python operation with a list of inputs and outputs. // A string is added for each keyword argument. // // NOTES // ------ // - args and kwargs are a tuple, so it accepts a fixed set of arguments of // any type at compile-time. // - outputs is a vector of nvfuser values that is converted into a string. template <typename... arg_types, typename... kwargs_types> void generateKwargsOperation( const std::string& op_name, const std::tuple<arg_types...>& args, const std::vector<std::string>& kwargs_names, const std::tuple<kwargs_types...>& kwargs, const std::vector<const nvfuser::Val*>& outputs) { std::string connect = (sizeof...(arg_types) == 0) ? "" : ", "; (*os_) << kTab << generateOutputs(outputs) << " = " << op_name << "(" << generateList(args) << connect << generateNamedList(kwargs_names, kwargs) << ")\n"; } // Generate a python operation with a list of inputs and a single output. // A string is added for each keyword argument. // // NOTES // ------ // - args and kwargs are a tuple, so it accepts a fixed set of arguments of // any type at compile-time. // - output_name is a string. template <typename... arg_types, typename... kwargs_types> void generateKwargsOperation( const std::string& op_name, const std::tuple<arg_types...>& args, const std::vector<std::string>& kwargs_names, const std::tuple<kwargs_types...>& kwargs, const std::string& output_name) { std::string connect = (sizeof...(arg_types) == 0) ? "" : ", "; (*os_) << kTab << output_name << " = " << op_name << "(" << generateList(args) << connect << generateNamedList(kwargs_names, kwargs) << ")\n"; } // Generate a python operation with a list of inputs and outputs. // A string is added for each keyword argument. // // NOTES // ------ // - args and outputs are vectors of nvfuser values that are converted into // strings. // - kwargs is a tuple, so it accepts a fixed set of arguments of // any type at compile-time. template <typename... kwargs_types> void generateKwargsOperation( const std::string& op_name, const std::vector<Val*>& args, const std::vector<std::string>& kwargs_names, const std::tuple<kwargs_types...>& kwargs, const std::vector<const nvfuser::Val*>& outputs) { std::string connect = args.empty() ? "" : ", "; (*os_) << kTab << generateOutputs(outputs) << " = " << op_name << "(" << toString(args) << connect << generateNamedList(kwargs_names, kwargs) << ")\n"; } // Generate a python definition for a FusionDefinition. void generateFusionDefinition() { (*os_) << "def nvfuser_fusion(fd : FusionDefinition) -> None :\n"; } private: //! The stream to print the python function to. std::ostream* os_; //! Indentation for python code. static constexpr const char* kTab = " "; }; // PythonTranslator converts CPP Fusion to an equivalent python definition. // // How to add support for an expression not yet overriden by PythonTranslator? // 1. Create handle function for expression. // a. void handle(const SomeOp* op) final // 2. Check if IR node pointer is not nullptr. // 3. Add output values for Expr node to visited_vals_. // 4. Create scalar input arguments. This step is for view and expand // operations. // a. TensorView input arguments are handled via DAG traversal. // 5. Use PythonPrinter to create string for operation. // a. output = operation(inputs...) // 6. Use `PythonPrinter::generateOperation` if the operation only uses // positional arguments. This is mainly used for unary and binary // operations. // 7. Use `PythonPrinter::generateKwargsOperation` if the operation uses // keyword arguments. // a. If none of the keyword arguments have default arguments, create a // static vector of strings. // b. If some of the keyword arguments have default arguments, create a // vector of KeywordArgument. The KeywordArgument struct hold default // values for keyword arguments. Use `std::nullopt` for keyword // arguments without default values. // // How to debug PythonTranslator? // 1. Recompile with debug symbols // `export NVFUSER_BUILD_BUILD_TYPE=RelwithDebInfo` // 2. Run `gdb python` // 3. Catch exception in gdb `(gdb) catch throw`. // 4. Run failing test. // `r -m pytest test_python_frontend.py -k [your_failing_test]` // 5. At gdb Catchpoint, get backtrace for call stack using `(gdb) bt`. // 6. Find and fix failure in PythonTranslate. // // TODO: Python operations without a corresponding Fusion IR node require // pattern matching. // 1. Map a series of scalar values to `define_vector` // 2. Map Squeeze, Reduction, and Broadcast to a single reduction operation // with keepdim argument. // 3. Map Broadcast and Expand to `broadcast_in_dim` // 4. var_mean // 5. var class PythonTranslator : public OptInConstDispatch { public: // Returns a map from the values in the CPP fusion to its corresponding // FusionDefinition State index. static void print(std::ostream& os, Fusion* fusion) { PythonTranslator translator(os, fusion); translator.translate(); } private: PythonTranslator(std::ostream& os, Fusion* fusion) : printer_(os), fusion_(fusion) {} // The output TensorView shape can be dynamic for operations like ReshapeOp. // Check that all dynamic scalar dependencies are handled first before // handling the expression. bool checkDynamicShapeDependency(const Expr* op) { // short-circuit: Only check operations with dynamic shapes encoded in the // output TensorView. if (!op->isOneOf<ReshapeOp, ExpandOp, FullOp>()) { return true; } NVF_ERROR_EQ(op->outputs().size(), 1); const std::vector<IterDomain*>& logical_out_domain = op->output(0)->as<TensorView>()->domain()->logical(); std::vector<Val*> logical_domain_extents; std::ranges::copy( logical_out_domain | std::views::transform([](IterDomain* id) { return id->getMaybeExpandedExtent(); }), std::back_inserter(logical_domain_extents)); return std::ranges::all_of(logical_domain_extents, [&](Val* v) { return v->definition() == nullptr || visited_vals_.count(v) > 0; }); } // Gather the expressions necessary to create a scalar value. std::vector<Expr*> gatherScalarExpressions(Val* v) { NVF_ERROR(v != nullptr); NVF_ERROR(v->isScalar()); // short-circuit: v does not have a definition. if (v->definition() == nullptr) { return {}; } std::vector<Expr*> expression_chain; std::unordered_set<Expr*> visited; std::vector<Expr*> to_visit = {v->definition()}; while (!to_visit.empty()) { Expr* e = to_visit.back(); to_visit.pop_back(); expression_chain.push_back(e); visited.insert(e); for (Val* input : e->inputs()) { // short-circuit: input does not have a definition. if (input->definition() == nullptr) { continue; } // short-circuit: input definition is already visited. if (visited.count(input->definition()) > 0) { continue; } to_visit.push_back(input->definition()); } } return expression_chain; } // Gather the scalar expressions necessary to create the logical domain for a // TensorView. std::vector<Expr*> gatherScalarExpressions(TensorView* tv) { NVF_ERROR(tv != nullptr); std::vector<Expr*> logical_domain_expressions; const std::vector<IterDomain*>& logical_out_domain = tv->domain()->logical(); for (IterDomain* id : logical_out_domain) { std::vector<Expr*> extent_definitions = gatherScalarExpressions(id->getMaybeExpandedExtent()); logical_domain_expressions.insert( logical_domain_expressions.end(), extent_definitions.begin(), extent_definitions.end()); } return logical_domain_expressions; } // Check that all of the expression's inputs are defined in FusionDefinition. bool checkExpressionDependencies(Expr* e) { // short-circuit: Found an operation without all its dynamic shape // dependencies. if (!checkDynamicShapeDependency(e)) { return false; } return std::all_of( e->inputs().begin(), e->inputs().end(), [&](const Val* v) { return isPythonScalar(v) || visited_vals_.count(v) > 0; }); } void translate() { printer_.generateFusionDefinition(); // Add Fusion inputs to FusionDefinition for (nvfuser::Val* v : fusion_->inputs()) { dispatch(v); } // Gather all expressions in CPP Fusion. const std::vector<nvfuser::Expr*> fusion_exprs = fusion_->exprs(); std::deque<nvfuser::Expr*> to_visit( fusion_exprs.begin(), fusion_exprs.end()); // Scalar expressions are not handled by Fusion::exprs, so gather them // manually. for (Expr* e : to_visit) { if (e->isOneOf<ReshapeOp, ExpandOp, FullOp>()) { NVF_ERROR_EQ(e->outputs().size(), 1); std::vector<Expr*> extent_definitions = gatherScalarExpressions(e->output(0)->as<TensorView>()); to_visit.insert( to_visit.end(), extent_definitions.begin(), extent_definitions.end()); } } // Topological search of Fusion expressions size_t skip_count = 0; std::unordered_set<nvfuser::Expr*> visited; while (!to_visit.empty()) { Expr* e = to_visit.front(); to_visit.pop_front(); NVF_ERROR( skip_count <= to_visit.size(), "Cycle detected: None of the expressions can be processed!"); // short-circuit: skip if already visited if (visited.count(e) > 0) { continue; } // TODO: short-circuit: skip Split and Merge expressions created by // Reshape // TODO: short-circuit: skip Resize expressions created by Slice // TODO: direct bindings does not support scheduled expressions. // Handle scalars and constants not generated by separate expression. std::vector<Val*> scalars; std::ranges::copy_if( e->inputs(), std::back_inserter(scalars), [](Val* v) { return v->isScalar(); }); std::ranges::for_each(scalars, [this](const Val* v) { dispatch(v); }); // short-circuit: add to back of stack if not all of the expression's // dependencies are satisfied. if (!checkExpressionDependencies(e)) { ++skip_count; to_visit.push_back(e); continue; } // Create string representation given inputs, outputs, and attributes. visited.insert(e); dispatch(e); skip_count = 0; } // Add tensor outputs and handle aliased outputs std::unordered_set<nvfuser::Val*> visited_alias_output; for (nvfuser::Val* v : fusion_->outputs()) { NVF_ERROR(v->isA<TensorView>()); const AliasInfo& alias_info = fusion_->getOutputAlias(v); switch (alias_info.type) { case AllocationType::New: { handleOutput(v->as<TensorView>()); break; } case AllocationType::ReuseBuffer: { // Only apply aliasing once if (visited_alias_output.count(v) == 0) { visited_alias_output.insert(v); handleOutput(v->as<TensorView>(), alias_info); } // If not hide_output, then the aliased output is returned as a // fusion output. if (alias_info.visibility == OutputVisibility::kVisible) { handleOutput(v->as<TensorView>()); } break; } default: NVF_THROW("Unsupported AllocationType"); } } } // ================================================================================= // Filter Functions // Gather all TensorViews and FusionDefinition indices std::vector<const nvfuser::Val*> tensors() { std::vector<const nvfuser::Val*> tensors; std::ranges::copy_if( visited_vals_, std::back_inserter(tensors), [](const nvfuser::Val* v) { return v->isA<TensorView>(); }); return tensors; } // ================================================================================= // Create scalar for given nvfuser value. The nvfuser value must not already // exist and have a definition. It can be a fusion input, a constant, or a // tensor's extent. void handle(const Val* v) final { NVF_ERROR(v != nullptr); // short-circuit: scalar definition has a definition if (v->definition() != nullptr) { return; } // short-circuit: value already exists in FusionDefinition if (visited_vals_.count(v) > 0) { return; } // short-circuit: print python scalar directly if (isPythonScalar(v)) { return; } visited_vals_.insert(v); // Since scalars can come from TensorView dimension sizes, search through // all TensorViews for an iterDomain whose extent matches the desired // value and then use size op. for (const nvfuser::Val* tv_val : tensors()) { const auto* tv = tv_val->as<TensorView>(); // Get extents for each IterDomain std::vector<IterDomain*> filtered_logical_domain = TensorDomain::noReductions(tv->domain()->logical()); std::vector<Val*> extents; extents.reserve(filtered_logical_domain.size()); std::ranges::copy( filtered_logical_domain | std::views::transform([](IterDomain* id) { return id->getMaybeExpandedExtent(); }), std::back_inserter(extents)); // Check if value matches iterdomain extent auto iter = std::ranges::find(extents, v); if (iter == extents.end()) { continue; } int64_t dim = std::distance(extents.begin(), iter); static const std::vector<std::string> argument_names = {"dim"}; printer_.generateKwargsOperation( "fd.ops.size", std::make_tuple(tv), argument_names, std::make_tuple(dim), {v}); return; } static const std::vector<std::string> argument_names = {"dtype"}; printer_.generateKwargsOperation( "fd.define_scalar", std::make_tuple(v->value()), argument_names, std::make_tuple(v->dtype()), {v}); } // Add Tensor value to Fusion Definition void handle(const TensorView* tv) final { NVF_ERROR(tv != nullptr); // short-circuit: value already exists in FusionDefinition if (visited_vals_.count(tv) > 0) { return; } visited_vals_.insert(tv); std::vector<int64_t> shape; std::transform( tv->domain()->logical().begin(), tv->domain()->logical().end(), std::back_inserter(shape), [](IterDomain* id) { return (id->getMaybeExpandedExtent()->isConstScalar()) ? id->getMaybeExpandedExtent()->evaluate().as<int64_t>() : -1; }); const std::vector<int64_t>& stride_order = tv->domain()->strideOrder(); static const std::vector<std::string> argument_names = { "shape", "contiguity", "dtype", "is_cpu", "stride_order"}; printer_.generateKwargsOperation( "fd.define_tensor", std::make_tuple(), argument_names, std::make_tuple( shape, tv->domain()->contiguity(), tv->dtype(), tv->isCpuScalar(), (stride_order.empty()) ? std::nullopt : std::make_optional(stride_order)), {tv}); } // ================================================================================= // Utility functions // Create a vector for the logical domain of TensorView. // Used with ReshapeOp, ExpandOp, and FullOp handlers std::vector<Val*> getShape(TensorView* tv) { const std::vector<IterDomain*>& logical_out_domain = tv->domain()->logical(); std::vector<Val*> logical_domain_extents; // Use expanded extent if available for IterDomain. std::ranges::copy( logical_out_domain | std::views::transform([](IterDomain* id) { return id->getMaybeExpandedExtent(); }), std::back_inserter(logical_domain_extents)); return logical_domain_extents; } // Find integer index corresponding with reduction iterDomains std::vector<int64_t> getReductionAxes(TensorView* tv) { std::vector<int64_t> axes; const std::vector<IterDomain*>& logical_domain = tv->domain()->logical(); for (int64_t dim : c10::irange((int64_t)logical_domain.size())) { if (logical_domain.at(dim)->isReduction()) { axes.push_back(dim); } } return axes; } // ================================================================================= // Handle add_output variants // Add Tensor output to FusionDefinition void handleOutput(const TensorView* tv) { NVF_ERROR(tv != nullptr); printer_.generateOperation("fd.add_output", {tv}, {}); } // Alias output Tensor with input tensor void handleOutput(const TensorView* tv, const AliasInfo& alias_info) { NVF_ERROR(tv != nullptr); printer_.generateOperation( "fd.add_output", {tv, alias_info.aliased_io}, {}); } // ================================================================================= // Map CPP Expression classes to corresponding RecordFunctors in // python_frontend void handle(const UnaryOp* uop) final { NVF_ERROR(uop != nullptr); // short-circuit: Handle cast operation separately if (uop->getUnaryOpType() == UnaryOpType::Cast) { return handleCastOp(uop); } // Map remaining UnaryOp to python_frontend visited_vals_.insert(uop->out()); printer_.generateOperation( "fd.ops." + nvfuser::python::toString(uop), {uop->in()}, {uop->out()}); } void handleCastOp(const UnaryOp* uop) { NVF_ERROR(uop->getUnaryOpType() == UnaryOpType::Cast); visited_vals_.insert(uop->out()); static const std::vector<std::string> argument_names = {"dtype"}; printer_.generateKwargsOperation( "fd.ops.cast", std::make_tuple(uop->in()), argument_names, std::make_tuple(uop->out()->dtype()), {uop->out()}); } void handle(const BinaryOp* bop) final { NVF_ERROR(bop != nullptr); if (visited_vals_.count(bop->out()) > 0) { return; } visited_vals_.insert(bop->out()); printer_.generateOperation( "fd.ops." + nvfuser::python::toString(bop), {bop->lhs(), bop->rhs()}, {bop->out()}); } void handle(const TernaryOp* top) final { NVF_ERROR(top != nullptr); visited_vals_.insert(top->out()); printer_.generateOperation( "fd.ops." + nvfuser::python::toString(top), {top->in1(), top->in2(), top->in3()}, {top->out()}); } void handle(const ReductionOp* rop) final { NVF_ERROR(rop != nullptr); NVF_ERROR(rop->out()->isA<TensorView>()); visited_vals_.insert(rop->out()); // The min and max reduction operations expect the dtype argument to by // PrimDataType::Null DataType dtype = (rop->getReductionOpType() == BinaryOpType::Min || rop->getReductionOpType() == BinaryOpType::FMin || rop->getReductionOpType() == BinaryOpType::Max || rop->getReductionOpType() == BinaryOpType::FMax) ? DataType::Null : rop->out()->dtype(); std::vector<int64_t> dims = getReductionAxes(rop->out()->as<TensorView>()); // TODO: keepdim is always False in ReductionOp because a separate // BroadcastOp node exists if keepdim is True. Detect this pattern to // minify the python definition. static const auto default_args = std::make_tuple( KeywordArgument<decltype(dims)>{ .name = "dims", .default_value = std::nullopt}, KeywordArgument<bool>{.name = "keepdim", .default_value = false}, KeywordArgument<DataType>{ .name = "dtype", .default_value = DataType::Null}); printer_.generateKwargsOperation( "fd.ops." + nvfuser::python::toString(rop), std::make_tuple(rop->in()), default_args, std::make_tuple(dims, false, dtype), {rop->out()}); } void handle(const ScanOp* sop) final { NVF_ERROR(sop != nullptr); visited_vals_.insert(sop->out()); static const auto default_args = std::make_tuple( KeywordArgument<int64_t>{.name = "dim", .default_value = std::nullopt}); printer_.generateKwargsOperation( "fd.ops." + toString(sop), std::make_tuple(sop->in()), default_args, std::make_tuple(sop->dim()), {sop->out()}); } void handle(const WelfordOp* wop) final { NVF_ERROR(wop != nullptr); NVF_ERROR(wop->initAvg()->evaluate().as<double>() == 0.0); NVF_ERROR(wop->initVar()->evaluate().as<double>() == 0.0); NVF_ERROR(wop->initN()->evaluate().as<int64_t>() == 0); visited_vals_.insert(wop->outAvg()); visited_vals_.insert(wop->outVar()); visited_vals_.insert(wop->outN()); static const std::vector<std::string> argument_names = {"dims"}; printer_.generateKwargsOperation( "fd.ops.welford", std::make_tuple(wop->in()), argument_names, std::make_tuple(getReductionAxes(wop->outAvg()->as<TensorView>())), {wop->outAvg(), wop->outVar(), wop->outN()}); } void handle(const BroadcastOp* bcast_op) final { NVF_ERROR(bcast_op != nullptr); visited_vals_.insert(bcast_op->out()); static const std::vector<std::string> broadcast_argument_names = { "is_broadcast_dim"}; printer_.generateKwargsOperation( "fd.ops.broadcast", std::make_tuple(bcast_op->in()), broadcast_argument_names, std::make_tuple(bcast_op->getBroadcastDimFlags()), {bcast_op->out()}); } void handle(const MatmulOp* matmul_op) final { NVF_ERROR(matmul_op != nullptr); visited_vals_.insert(matmul_op->out()); printer_.generateOperation( "fd.ops.matmul", {matmul_op->inA(), matmul_op->inB()}, {matmul_op->out()}); } void handle(const LinearOp* lop) final { NVF_ERROR(lop != nullptr); visited_vals_.insert(lop->out()); static const auto default_args = std::make_tuple( KeywordArgument<TensorView*>{.name = "bias", .default_value = nullptr}); printer_.generateKwargsOperation( "fd.ops.linear", std::make_tuple(lop->inA(), lop->inB()), default_args, std::make_tuple(lop->bias()), {lop->out()}); } void handle(const GroupedMmaOp* gmm_op) final { NVF_ERROR(gmm_op != nullptr); TensorView* out_tv = gmm_op->out(); visited_vals_.insert(gmm_op->out()); int64_t out_block_scale_size = 0; PrimDataType out_block_scale_dtype = DataType::BFloat16; bool out_gamma = false; TensorView* out_block_scale_tv = gmm_op->outScale(); if (out_block_scale_tv != nullptr) { visited_vals_.insert(gmm_op->outScale()); const std::vector<IterDomain*>& logical = out_block_scale_tv->getLogicalDomain(); Val* block_size_extent = logical.at(logical.size() - 1)->extent(); NVF_CHECK( block_size_extent->isConstInt(), "Block size extent needs to be a constant integer"); out_block_scale_size = block_size_extent->evaluate().as<int64_t>(); out_block_scale_dtype = std::get<PrimDataType>(out_block_scale_tv->dtype().type); } TensorView* out_gamma_tv = gmm_op->outGamma(); if (out_gamma_tv != nullptr) { visited_vals_.insert(gmm_op->outGamma()); out_gamma = true; } |
| You are visitor 01402520 — GeezerVison does not store the pages you look up. Best viewed with Netscape Navigator 4.7 or Internet Explorer 6 at 800×600. No cookies required. |