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5617a02fa4
XLA supports batched triangular solve on GPU and has since February 2022, which is older than the minimum jaxlib version. We can therefore delete our implementation and just use XLA's implementation. PiperOrigin-RevId: 482031830
84 lines
2.8 KiB
C++
84 lines
2.8 KiB
C++
/* Copyright 2021 The JAX Authors.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include <algorithm>
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#include <stdexcept>
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#include <utility>
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#include <vector>
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#include "absl/container/flat_hash_map.h"
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#include "absl/strings/str_format.h"
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#include "include/pybind11/numpy.h"
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#include "include/pybind11/pybind11.h"
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#include "include/pybind11/stl.h"
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#include "jaxlib/rocm/hipblas_kernels.h"
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#include "jaxlib/kernel_pybind11_helpers.h"
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#include "rocm/include/hip/hip_runtime_api.h"
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#include "rocm/include/hipblas.h"
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namespace jax {
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namespace {
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namespace py = pybind11;
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// Converts a NumPy dtype to a Type.
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HipblasType DtypeToHipblasType(const py::dtype& np_type) {
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static auto* types =
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new absl::flat_hash_map<std::pair<char, int>, HipblasType>({
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{{'f', 4}, HipblasType::F32},
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{{'f', 8}, HipblasType::F64},
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{{'c', 8}, HipblasType::C64},
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{{'c', 16}, HipblasType::C128},
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});
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auto it = types->find({np_type.kind(), np_type.itemsize()});
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if (it == types->end()) {
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throw std::invalid_argument(
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absl::StrFormat("Unsupported dtype %s", py::repr(np_type)));
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}
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return it->second;
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}
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// Returns the descriptor for a GetrfBatched operation.
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std::pair<size_t, py::bytes> BuildGetrfBatchedDescriptor(const py::dtype& dtype,
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int b, int n) {
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HipblasType type = DtypeToHipblasType(dtype);
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size_t size = b * sizeof(void*);
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return {size, PackDescriptor(GetrfBatchedDescriptor{type, b, n})};
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}
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// Returns the descriptor for a GetrfBatched operation.
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std::pair<size_t, py::bytes> BuildGeqrfBatchedDescriptor(const py::dtype& dtype,
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int b, int m, int n) {
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HipblasType type = DtypeToHipblasType(dtype);
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size_t size = b * sizeof(void*);
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return {size, PackDescriptor(GeqrfBatchedDescriptor{type, b, m, n})};
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}
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py::dict Registrations() {
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py::dict dict;
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dict["hipblas_getrf_batched"] = EncapsulateFunction(GetrfBatched);
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dict["hipblas_geqrf_batched"] = EncapsulateFunction(GeqrfBatched);
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return dict;
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}
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PYBIND11_MODULE(_hipblas, m) {
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m.def("registrations", &Registrations);
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m.def("build_getrf_batched_descriptor", &BuildGetrfBatchedDescriptor);
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m.def("build_geqrf_batched_descriptor", &BuildGeqrfBatchedDescriptor);
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}
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} // namespace
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} // namespace jax
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