mirror of https://github.com/llvm/torch-mlir
Add MLIRContext.dense_elements_attr to create an attribute from a python buffer/array.
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@ -4,3 +4,5 @@
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build
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build-mlir
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install-mlir
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__pycache__
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@ -2,6 +2,7 @@
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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 numpy as np
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from npcomp.dialect import Basicpy
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from _npcomp.mlir import ir
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@ -35,6 +36,16 @@ class DialectHelper(Basicpy.DialectHelper):
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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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dense<[1, 2, 3, 4]> : tensor<4xsi64>
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>>> c.dense_elements_attr(np.asarray([[1, 2], [3, 4]]))
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dense<[[1, 2], [3, 4]]> : tensor<2x2xsi64>
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>>> c.dense_elements_attr(np.asarray([[1., 2.], [3., 4.]]))
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dense<[[1.000000e+00, 2.000000e+00], [3.000000e+00, 4.000000e+00]]> : tensor<2x2xf64>
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>>> c.dense_elements_attr(np.asarray([[1., 2.], [3., 4.]], dtype=np.float32))
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dense<[[1.000000e+00, 2.000000e+00], [3.000000e+00, 4.000000e+00]]> : tensor<2x2xf32>
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Types:
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>>> t = DialectHelper(ir.MLIRContext())
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>>> t.numpy_any_dtype
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@ -76,6 +76,58 @@ using PyBlockList = PyIpListWrapper<Region::BlockListType, PyBlockRef>;
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template class PyIpListWrapper<Block::OpListType, PyOperationRef>;
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using PyOperationList = PyIpListWrapper<Block::OpListType, PyOperationRef>;
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//===----------------------------------------------------------------------===//
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// Conversions
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//===----------------------------------------------------------------------===//
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Type mapBufferFormatToType(MLIRContext *context, const std::string &format,
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py::ssize_t itemSize) {
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// Floating point formats.
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if (format == "f")
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return FloatType::getF32(context);
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if (format == "d")
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return FloatType::getF64(context);
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if (format == "D")
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return ComplexType::get(FloatType::getF64(context));
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// Signed integer formats.
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if (format == "b" || format == "h" || format == "i" || format == "l" ||
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format == "L") {
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unsigned width = itemSize * 8;
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return IntegerType::get(width, IntegerType::SignednessSemantics::Signed,
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context);
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}
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// Unsigned integer format.
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if (format == "B" || format == "H" || format == "I" || format == "k" ||
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format == "K") {
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unsigned width = itemSize * 8;
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return IntegerType::get(width, IntegerType::SignednessSemantics::Unsigned,
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context);
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}
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return Type();
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}
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/// Creates a DenseElementsAttr from a python buffer which must have been
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/// requested to be C-Contiguous.
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Attribute createDenseElementsAttrFromBuffer(MLIRContext *context,
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py::buffer_info &array) {
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Type elementType =
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mapBufferFormatToType(context, array.format, array.itemsize);
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if (!elementType) {
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throw py::raiseValueError(
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"Unsupported buffer/array type for conversion to DenseElementsAttr");
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}
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SmallVector<int64_t, 4> shape(array.shape.begin(),
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array.shape.begin() + array.ndim);
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RankedTensorType type = RankedTensorType::get(shape, elementType);
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const char *rawBufferPtr = reinterpret_cast<const char *>(array.ptr);
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ArrayRef<char> rawBuffer(rawBufferPtr, array.size * array.itemsize);
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return DenseElementsAttr::getFromRawBuffer(type, rawBuffer, false);
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}
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//===----------------------------------------------------------------------===//
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// Diagnostics
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//===----------------------------------------------------------------------===//
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@ -359,7 +411,8 @@ void PyContext::bind(py::module m) {
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[](PyContext &self, const std::string &s) -> PyAttribute {
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return FlatSymbolRefAttr::get(s, &self.context);
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})
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.def("dictionary_attr", [](PyContext &self, py::dict d) -> PyAttribute {
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.def("dictionary_attr",
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[](PyContext &self, py::dict d) -> PyAttribute {
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SmallVector<NamedAttribute, 4> attrs;
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for (auto &it : d) {
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auto key = it.first.cast<std::string>();
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@ -368,7 +421,21 @@ void PyContext::bind(py::module m) {
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attrs.emplace_back(keyIdent, value.attr);
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}
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return DictionaryAttr::get(attrs, &self.context);
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});
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})
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.def("dense_elements_attr",
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[](PyContext &self, py::buffer array) -> PyAttribute {
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// Request a contiguous view.
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int flags = PyBUF_C_CONTIGUOUS | PyBUF_FORMAT;
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Py_buffer *view = new Py_buffer();
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if (PyObject_GetBuffer(array.ptr(), view, flags) != 0) {
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delete view;
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throw py::error_already_set();
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}
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py::buffer_info array_info(view);
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return createDenseElementsAttrFromBuffer(&self.context,
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array_info);
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},
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py::arg("array"));
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}
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PyModuleOp PyContext::parseAsm(const std::string &asm_text) {
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