mirror of https://github.com/llvm/torch-mlir
Refactor old tracing tests and remove deprecated ops.
* Old doctests to run under lit. * Old custom filecheck tests -> pytest directory (under lit). * Rename some old ufunc ops in the tracer.pull/1/head
Stella Laurenzo
parent
7ca292ade5
commit
046751254f
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@ -82,8 +82,6 @@ LLVM_COMMIT="$(cat ./built_tools/llvm.version)"
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cd build
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ninja
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ninja check-npcomp
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# Note: currently, some python tests run separately
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./python/run_tests.py
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# Setup PYTHONPATH for interactive use
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export PYTHONPATH="$(realpath build/python):$(realpath build/python_native):$(realpath build/iree/bindings/python)"
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@ -98,9 +96,6 @@ if files are added/removed).
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Configuring the `PYTHONPATH` as above should be sufficient to run any
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interactive tooling (`python3`, Jupyter/Colab, etc).
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The `run_tests.py` script is special in that it sets up the PYTHONPATH
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correctly when run.
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Note that running the `cmake_configure.sh` script will also output a `.env`
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file in the workspace folder with the correct PYTHONPATH set. This allows
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tools like VSCode to work by default for debugging.
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@ -102,67 +102,6 @@ def Numpy_BuiltinUfuncCallOp : Numpy_Op<"builtin_ufunc_call"> {
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}];
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}
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def Numpy_BuiltinUfuncOp : Numpy_Op<"builtin_ufunc", [Symbol]> {
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let summary = "References a built-in universal function";
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let description = [{
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This module-level op binds by name to a fully-qualified numpy built-in
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ufunc (i.e. "numpy.add") and carries metadata associated with it.
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Deprecated: Remove once using new builtin_ufunc_call.
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}];
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}
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def Numpy_GenericUfuncOp : Numpy_Op<"generic_ufunc", [
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IsolatedFromAbove,
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Symbol]> {
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let summary = "Defines a ufunc in terms of overloaded element-wise functions";
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let description = [{
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Deprecated: Remove once using new builtin_ufunc_call.
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}];
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let arguments = (ins
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TypeArrayAttr:$overload_types);
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let regions = (region
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VariadicRegion<AnyRegion>:$overloads);
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}
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def Numpy_UfuncReturnOp : Numpy_Op<"ufunc_return", [
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Terminator,
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HasParent<"Numpy::GenericUfuncOp">]> {
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let summary = "Return a value from a generic_ufunc";
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let description = [{
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Must terminate the basic block of a generic_ufunc overload.
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Deprecated: Remove once using new builtin_ufunc_call.
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}];
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let arguments = (ins
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Variadic<AnyType>:$operands
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);
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let assemblyFormat = "attr-dict ($operands^ `:` type($operands))?";
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}
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def Numpy_UfuncCallOp : Numpy_Op<"ufunc_call", []> {
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let summary = "Default operation on a func";
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let description = [{
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Invokes a ufunc with the given arguments. This variant models the __call__
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behavior of a python ufunc except that it does not model the `out`
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parameter, which indicates an in-place update.
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Deprecated: Remove once using new builtin_ufunc_call.
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}];
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let arguments = (ins
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FlatSymbolRefAttr:$ufunc_ref,
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Variadic<Numpy_AnyArray>:$operands
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);
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let results = (outs
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Numpy_AnyArray:$result
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);
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let assemblyFormat = [{
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$ufunc_ref `(` operands `)` attr-dict `:` functional-type(operands, results)
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}];
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}
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//----------------------------------------------------------------------------//
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// Built-in array functions
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//
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@ -16,135 +16,6 @@
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namespace mlir {
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namespace NPCOMP {
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namespace Numpy {
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//===----------------------------------------------------------------------===//
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// BuildinUfuncOp
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//===----------------------------------------------------------------------===//
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static ParseResult parseBuiltinUfuncOp(OpAsmParser &parser,
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OperationState *result) {
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// Parse the name as a symbol.
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StringAttr nameAttr;
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if (parser.parseSymbolName(nameAttr, SymbolTable::getSymbolAttrName(),
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result->attributes)) {
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return failure();
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}
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if (failed(parser.parseOptionalAttrDict(result->attributes))) {
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return failure();
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}
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return success();
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}
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static void printBuiltinUfuncOp(OpAsmPrinter &p, BuiltinUfuncOp op) {
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p << op.getOperationName() << " ";
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p.printSymbolName(op.getName());
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p.printOptionalAttrDict(op.getAttrs(), {SymbolTable::getSymbolAttrName()});
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}
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//===----------------------------------------------------------------------===//
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// GenericUfuncOp
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//===----------------------------------------------------------------------===//
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static ParseResult parseGenericUfuncOp(OpAsmParser &parser,
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OperationState *result) {
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Builder b(result->getContext());
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// Parse the name as a symbol.
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StringAttr nameAttr;
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if (parser.parseSymbolName(nameAttr, SymbolTable::getSymbolAttrName(),
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result->attributes))
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return failure();
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// Parse the body of overloads.
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if (parser.parseLParen())
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return failure();
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SmallVector<Attribute, 4> overloadTypes;
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for (bool first = true;; first = false) {
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if (first) {
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if (parser.parseOptionalKeyword("overload"))
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break;
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}
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if (!first) {
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if (parser.parseOptionalComma())
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break;
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if (parser.parseKeyword("overload"))
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return failure();
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}
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SmallVector<OpAsmParser::OperandType, 2> argNames;
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SmallVector<Type, 2> argTypes;
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SmallVector<Type, 1> resultTypes;
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SmallVector<NamedAttrList, 1> unusedAttrs;
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bool isVariadic = false;
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if (::mlir::impl::parseFunctionSignature(parser, false, argNames, argTypes,
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unusedAttrs, isVariadic,
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resultTypes, unusedAttrs))
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return failure();
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overloadTypes.push_back(TypeAttr::get(
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FunctionType::get(argTypes, resultTypes, result->getContext())));
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auto *region = result->addRegion();
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if (parser.parseRegion(*region, argNames, argTypes))
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return failure();
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}
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if (parser.parseRParen())
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return failure();
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// Parse 'attributes {...}'
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if (parser.parseOptionalAttrDictWithKeyword(result->attributes))
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return failure();
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result->addAttribute(b.getIdentifier("overload_types"),
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b.getArrayAttr(overloadTypes));
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return success();
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}
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static void printGenericUfuncOp(OpAsmPrinter &p, GenericUfuncOp op) {
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p << op.getOperationName() << " @" << op.getName() << "(";
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bool first = true;
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for (auto it : llvm::enumerate(op.getRegions())) {
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auto *region = it.value();
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if (first)
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first = false;
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else
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p << ", ";
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if (region->empty()) {
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p << "<<OVERLOAD_ERROR>>";
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continue;
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}
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Block &entryBlock = region->front();
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p << "overload(";
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if (it.index() >= op.overload_types().size()) {
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p << "<<MISSING OVERLOAD TYPE>>";
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continue;
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}
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TypeAttr tattr = op.overload_types()[it.index()].cast<TypeAttr>();
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FunctionType overloadType = tattr.getValue().dyn_cast<FunctionType>();
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if (!overloadType) {
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p << "<<ILLEGAL OVERLOAD TYPE>>";
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continue;
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}
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if (overloadType.getNumInputs() != entryBlock.getNumArguments()) {
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p << "<<OVERLOAD ARG MISMATCH>>";
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continue;
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}
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for (unsigned i = 0, e = entryBlock.getNumArguments(); i < e; ++i) {
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auto arg = entryBlock.getArgument(i);
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if (i > 0)
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p << ", ";
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p.printOperand(arg);
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p << ": ";
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p.printType(overloadType.getInputs()[i]);
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}
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p << ")";
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p.printArrowTypeList(overloadType.getResults());
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p.printRegion(*region, false, true);
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}
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p << ")";
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}
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#define GET_OP_CLASSES
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#include "npcomp/Dialect/Numpy/IR/NumpyOps.cpp.inc"
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} // namespace Numpy
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@ -1,12 +1,16 @@
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# RUN: %PYTHON %s | FileCheck %s --dump-input=fail
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# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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# See https://llvm.org/LICENSE.txt for license information.
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# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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"""Test for the MLIR IR Python bindings"""
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"""Test for the MLIR IR Python bindings.
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TODO: These tests were just for bootstrapping and are not authoritative at this
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point.
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"""
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from _npcomp.mlir import ir
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from npcomp.utils import test_utils
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test_utils.start_filecheck_test()
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c = ir.MLIRContext()
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# CHECK-LABEL: module @parseSuccess
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@ -37,5 +41,3 @@ try:
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except ValueError as e:
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# CHECK: [ERROR]: expected operation name in quotes
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print(e)
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test_utils.end_filecheck_test(__file__)
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@ -1,3 +1,5 @@
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# RUN: %PYTHON %s | FileCheck %s --dump-input=fail
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# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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# See https://llvm.org/LICENSE.txt for license information.
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# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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@ -5,9 +7,7 @@
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from _npcomp.mlir import ir
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from _npcomp.mlir import passes
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from npcomp.utils import test_utils
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test_utils.start_filecheck_test()
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c = ir.MLIRContext()
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pm = passes.PassManager(c)
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@ -38,5 +38,3 @@ print("PASSES:", str(pm))
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pm.run(m)
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print(m.to_asm())
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# CHECK-NOT: func @notUsed
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test_utils.end_filecheck_test(__file__)
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@ -1,13 +1,15 @@
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# RUN: %PYTHON %s | FileCheck %s --dump-input=fail
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# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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# See https://llvm.org/LICENSE.txt for license information.
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# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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import os
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os.environ["NUMPY_EXPERIMENTAL_ARRAY_FUNCTION"] = "1"
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from npcomp.types import *
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from npcomp.exporter import *
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from npcomp.tracing.mlir_trace import *
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from npcomp.utils import test_utils
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test_utils.start_filecheck_test()
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def simple_mul(a: np.ndarray, b: np.ndarray) -> np.ndarray:
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@ -29,12 +31,10 @@ exp.simple_mul.sig.result += DType(np.float32)
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mb = ModuleBuilder()
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mb.trace(exp.simple_mul)
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# CHECK: func @simple_mul(%arg0: tensor<?x4xf32>, %arg1: tensor<1xf32>) -> tensor<?x4xf32> {
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# CHECK: %0 = numpy.ufunc_call @numpy.multiply(%arg0, %arg1) : (tensor<?x4xf32>, tensor<1xf32>) -> tensor<*x!numpy.any_dtype>
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# CHECK: %1 = numpy.ufunc_call @numpy.add(%0, %arg0) : (tensor<*x!numpy.any_dtype>, tensor<?x4xf32>) -> tensor<*x!numpy.any_dtype>
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# CHECK: %2 = numpy.ufunc_call @numpy.add(%1, %arg1) : (tensor<*x!numpy.any_dtype>, tensor<1xf32>) -> tensor<*x!numpy.any_dtype>
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# CHECK: %3 = numpy.narrow %2 : (tensor<*x!numpy.any_dtype>) -> tensor<?x4xf32>
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# CHECK: %0 = numpy.builtin_ufunc_call<"numpy.multiply"> (%arg0, %arg1) : (tensor<?x4xf32>, tensor<1xf32>) -> tensor<*x!basicpy.UnknownType>
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# CHECK: %1 = numpy.builtin_ufunc_call<"numpy.add"> (%0, %arg0) : (tensor<*x!basicpy.UnknownType>, tensor<?x4xf32>) -> tensor<*x!basicpy.UnknownType>
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# CHECK: %2 = numpy.builtin_ufunc_call<"numpy.add"> (%1, %arg1) : (tensor<*x!basicpy.UnknownType>, tensor<1xf32>) -> tensor<*x!basicpy.UnknownType>
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# CHECK: %3 = numpy.narrow %2 : (tensor<*x!basicpy.UnknownType>) -> tensor<?x4xf32>
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# CHECK: return %3 : tensor<?x4xf32>
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# CHECK: }
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print(mb.module.to_asm())
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test_utils.end_filecheck_test(__file__)
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@ -1,15 +1,37 @@
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# RUN: %PYTHON %s
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import os
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os.environ["NUMPY_EXPERIMENTAL_ARRAY_FUNCTION"] = "1"
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import traceback
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def run_doctest(mod):
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print("TESTING:", mod)
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print("\n\nTESTING:", mod)
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print("--------")
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import doctest
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import sys
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import importlib
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try:
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m = importlib.import_module(mod)
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except:
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print("ERROR IMPORTING MODULE:", mod)
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sys.exit(1)
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fc, _ = doctest.testmod(m)
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if fc:
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sys.exit(1)
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run_doctest("npcomp.compiler.py_value_utils")
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TEST_MODULES = (
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"npcomp.compiler.py_value_utils",
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"npcomp.dialect.Basicpy",
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"npcomp.dialect.Numpy",
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"npcomp.tracing.context",
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"npcomp.tracing.emitters",
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"npcomp.tracing.mlir_trace",
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"npcomp.types",
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"npcomp.exporter",
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)
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for mname in TEST_MODULES:
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run_doctest(mname)
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@ -17,24 +17,6 @@ class DialectHelper(Basicpy.DialectHelper):
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>>> c = ir.MLIRContext()
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>>> h = DialectHelper(c, ir.OpBuilder(c))
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>>> m = c.new_module()
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>>> tensor_type = h.tensor_type(h.f32_type)
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>>> h.builder.insert_block_start(m.first_block)
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>>> f = h.func_op("foobar", h.function_type(
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... [tensor_type, tensor_type], [tensor_type]),
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... create_entry_block=True)
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>>> uf = h.numpy_ufunc_call_op("numpy.add", tensor_type,
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... *f.first_block.args)
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>>> _ = h.return_op(uf.results)
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>>> print(m.to_asm())
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<BLANKLINE>
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<BLANKLINE>
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module {
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func @foobar(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> {
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%0 = numpy.ufunc_call @numpy.add(%arg0, %arg1) : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
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return %0 : tensor<*xf32>
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}
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}
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DenseElementsAttrs:
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>>> c.dense_elements_attr(np.asarray([1, 2, 3, 4]))
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@ -61,7 +43,7 @@ class DialectHelper(Basicpy.DialectHelper):
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tensor<*xf32>
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>>> t.function_type([t.i32_type], [t.f32_type])
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(i32) -> f32
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>>> t.unknown_tensor_type
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>>> t.numpy_unknown_tensor_type
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tensor<*x!basicpy.UnknownType>
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"""
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@ -84,13 +66,6 @@ class DialectHelper(Basicpy.DialectHelper):
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attrs = c.dictionary_attr({"qualified_name": c.string_attr(qualified_name)})
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return self.op("numpy.builtin_ufunc_call", [result_type], args, attrs)
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def numpy_ufunc_call_op(self, callee_symbol, result_type, *args):
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"""Creates a numpy.ufunc_call op."""
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c = self.context
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attrs = c.dictionary_attr(
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{"ufunc_ref": c.flat_symbol_ref_attr(callee_symbol)})
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return self.op("numpy.ufunc_call", [result_type], args, attrs)
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def numpy_narrow_op(self, result_type, operand):
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"""Creates a numpy.narrow op."""
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return self.op("numpy.narrow", [result_type], [operand])
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@ -100,34 +75,6 @@ class DialectHelper(Basicpy.DialectHelper):
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[array] + list(slice_elements))
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def load_builtin_module(context=None):
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"""Loads a module populated with numpy built-ins.
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This is not a long-term solution but overcomes some bootstrapping
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issues.
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>>> m = load_builtin_module()
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>>> op = m.region(0).blocks.front.operations.front
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>>> op.is_registered
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True
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>>> op.name
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'numpy.builtin_ufunc'
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Args:
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context: The MLIRContext to use (None to create a new one).
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Returns:
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A ModuleOp.
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"""
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if context is None:
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context = ir.MLIRContext()
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return context.parse_asm(_BUILTIN_MODULE_ASM)
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_BUILTIN_MODULE_ASM = r"""
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numpy.builtin_ufunc @numpy.add
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numpy.builtin_ufunc @numpy.multiply
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"""
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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@ -168,8 +168,9 @@ class GenericCallUfuncEmitter(FuncEmitter):
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def emit(self, request: EmissionRequest):
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h = request.dialect_helper
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op_result_type = h.tensor_type(h.numpy_any_dtype)
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call_op = h.numpy_ufunc_call_op(self._ufunc_name, op_result_type,
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*request.input_ssa_values)
|
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call_op = h.numpy_builtin_ufunc_call_op(*request.input_ssa_values,
|
||||
qualified_name=self._ufunc_name,
|
||||
result_type=op_result_type)
|
||||
return call_op.results
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -20,9 +20,7 @@ class ModuleBuilder:
|
|||
|
||||
def __init__(self, mlir_context=None, emitter_registry=None):
|
||||
self.context = mlir_context if mlir_context else ir.MLIRContext()
|
||||
# TODO: Instead of bootstrapping a large module, populate imports
|
||||
# dynamically.
|
||||
self.module = Numpy.load_builtin_module(self.context)
|
||||
self.module = self.context.new_module()
|
||||
self.helper = Numpy.DialectHelper(self.context, ir.OpBuilder(self.context))
|
||||
self.emitters = (emitter_registry
|
||||
if emitter_registry else EmitterRegistry.create_default())
|
||||
|
|
|
|||
|
|
@ -1,61 +0,0 @@
|
|||
#!/usr/bin/env python3
|
||||
|
||||
import os
|
||||
import subprocess
|
||||
import sys
|
||||
|
||||
TEST_MODULES = (
|
||||
"npcomp.mlir_ir_test",
|
||||
"npcomp.mlir_pass_test",
|
||||
"npcomp.dialect.Basicpy",
|
||||
"npcomp.dialect.Numpy",
|
||||
"npcomp.tracing.context",
|
||||
"npcomp.tracing.mlir_trace",
|
||||
"npcomp.types",
|
||||
"npcomp.exporter",
|
||||
"npcomp.tracing.mlir_trace_test",
|
||||
)
|
||||
|
||||
# Compute PYTHONPATH for sub processes.
|
||||
DIRSEP = os.path.pathsep
|
||||
LOCAL_PYTHONPATH_COMPONENTS = [
|
||||
# This directory.
|
||||
os.path.abspath(os.path.dirname(__file__)),
|
||||
# The parallel python_native directory (assuming in the build tree).
|
||||
os.path.abspath(
|
||||
os.path.join(os.path.dirname(__file__), "..", "python_native"))
|
||||
]
|
||||
PYTHONPATH = DIRSEP.join(LOCAL_PYTHONPATH_COMPONENTS)
|
||||
if "PYTHONPATH" in os.environ:
|
||||
PYTHONPATH = PYTHONPATH + DIRSEP + os.environ["PYTHONPATH"]
|
||||
CHILD_ENVIRON = dict(os.environ)
|
||||
CHILD_ENVIRON["PYTHONPATH"] = PYTHONPATH
|
||||
|
||||
# Configure filecheck.
|
||||
FILECHECK_BINARY = os.path.abspath(
|
||||
os.path.join(os.path.dirname(__file__), "..", "..", "..", "bin",
|
||||
"FileCheck"))
|
||||
if os.path.exists(FILECHECK_BINARY):
|
||||
CHILD_ENVIRON["FILECHECK_BINARY"] = FILECHECK_BINARY
|
||||
else:
|
||||
print("WARNING! Built FileCheck not found. Leaving to path resolution")
|
||||
|
||||
passed = []
|
||||
failed = []
|
||||
|
||||
for test_module in TEST_MODULES:
|
||||
print("--------====== RUNNING %s ======--------" % test_module)
|
||||
try:
|
||||
subprocess.check_call([sys.executable, "-Wignore", "-m", test_module],
|
||||
env=CHILD_ENVIRON)
|
||||
print("--------====== DONE %s ======--------\n" % test_module)
|
||||
passed.append(test_module)
|
||||
except subprocess.CalledProcessError:
|
||||
print("!!!!!!!!====== ERROR %s ======!!!!!!!!\n" % test_module)
|
||||
failed.append(test_module)
|
||||
|
||||
print("Done: %d passed, %d failed" % (len(passed), len(failed)))
|
||||
if failed:
|
||||
for test_module in failed:
|
||||
print(" %s: FAILED" % test_module)
|
||||
sys.exit(1)
|
||||
|
|
@ -1,52 +1,8 @@
|
|||
// RUN: npcomp-opt -split-input-file %s | npcomp-opt | FileCheck --dump-input=fail %s
|
||||
|
||||
// CHECK-LABEL: @any_dtype
|
||||
func @any_dtype(%arg0: tensor<*x!numpy.any_dtype>) -> (tensor<*x!numpy.any_dtype>) {
|
||||
return %arg0 : tensor<*x!numpy.any_dtype>
|
||||
}
|
||||
|
||||
// -----
|
||||
// CHECK-LABEL: @builtin_ufunc
|
||||
module @builtin_ufunc {
|
||||
// CHECK: numpy.builtin_ufunc @numpy.add
|
||||
numpy.builtin_ufunc @numpy.add
|
||||
// CHECK: numpy.builtin_ufunc @numpy.custom_sub {some_attr = "foobar"}
|
||||
numpy.builtin_ufunc @numpy.custom_sub { some_attr = "foobar" }
|
||||
}
|
||||
|
||||
// -----
|
||||
// CHECK-LABEL: @example_generic_ufunc
|
||||
module @example_generic_ufunc {
|
||||
// CHECK: numpy.generic_ufunc @numpy.add(
|
||||
numpy.generic_ufunc @numpy.add (
|
||||
// CHECK-SAME: overload(%arg0: i32, %arg1: i32) -> i32 {
|
||||
overload(%arg0: i32, %arg1: i32) -> i32 {
|
||||
// CHECK: addi
|
||||
%0 = addi %arg0, %arg1 : i32
|
||||
numpy.ufunc_return %0 : i32
|
||||
},
|
||||
// CHECK: overload(%arg0: f32, %arg1: f32) -> f32 {
|
||||
overload(%arg0: f32, %arg1: f32) -> f32 {
|
||||
// CHECK: addf
|
||||
%0 = addf %arg0, %arg1 : f32
|
||||
numpy.ufunc_return %0 : f32
|
||||
}
|
||||
)
|
||||
}
|
||||
|
||||
// -----
|
||||
// CHECK-LABEL: @ufunc_apply_ops
|
||||
module @ufunc_apply_ops {
|
||||
numpy.generic_ufunc @numpy.add (
|
||||
overload(%arg0: i32, %arg1: i32) -> i32 {
|
||||
%0 = addi %arg0, %arg1 : i32
|
||||
numpy.ufunc_return %0 : i32
|
||||
}
|
||||
)
|
||||
|
||||
func @example(%arg0: tensor<*xi32>, %arg1: tensor<*xi32>) -> tensor<*xi32> {
|
||||
%0 = numpy.ufunc_call @numpy.add(%arg0, %arg1) : (tensor<*xi32>, tensor<*xi32>)
|
||||
-> tensor<*xi32>
|
||||
return %0 : tensor<*xi32>
|
||||
}
|
||||
func @builtin_ufunc(%arg0 : tensor<3xf64>, %arg1 : tensor<3xf64>) -> tensor<3xf64> {
|
||||
%0 = numpy.builtin_ufunc_call<"numpy.add"> (%arg0, %arg1) : (tensor<3xf64>, tensor<3xf64>) -> tensor<3xf64>
|
||||
return %0 : tensor<3xf64>
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue