# Copyright 2019 The JAX Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ cusparse wrappers for performing sparse matrix computations in JAX """ import math from functools import partial import importlib import jaxlib.mlir.ir as ir import numpy as np from jaxlib import xla_client from .hlo_helpers import custom_call, mk_result_types_and_shapes for cuda_module_name in [".cuda", "jax_cuda12_plugin"]: try: _cusparse = importlib.import_module( f"{cuda_module_name}._sparse", package="jaxlib" ) except ImportError: _cusparse = None else: break if _cusparse: for _name, _value in _cusparse.registrations().items(): xla_client.register_custom_call_target(_name, _value, platform="CUDA") try: from .rocm import _sparse as _hipsparse # pytype: disable=import-error except ImportError: _hipsparse = None else: for _name, _value in _hipsparse.registrations().items(): xla_client.register_custom_call_target(_name, _value, platform="ROCM") cuda_is_supported = bool(_cusparse and _cusparse.sparse_supported) rocm_is_supported = bool(_hipsparse and _hipsparse.sparse_supported) def _validate_csr_hlo(data, indices, indptr, shape): data_type = ir.RankedTensorType(data.type) indices_type = ir.RankedTensorType(indices.type) indptr_type = ir.RankedTensorType(indptr.type) nnz, = data_type.shape assert indices_type.shape == [nnz] assert indptr_type.element_type == indices_type.element_type assert indptr_type.shape == [shape[0] + 1] return data_type.element_type, indices_type.element_type, nnz def _validate_coo_hlo(data, row, col): data_type = ir.RankedTensorType(data.type) row_type = ir.RankedTensorType(row.type) col_type = ir.RankedTensorType(col.type) nnz, = data_type.shape assert row_type.shape == [nnz] assert col_type.element_type == row_type.element_type assert col_type.shape == [nnz] return data_type.element_type, row_type.element_type, nnz def _csr_todense_hlo(platform, gpu_sparse, data, indices, indptr, *, shape, data_dtype, index_dtype): """CSR to dense matrix.""" data_type, index_type, nnz = _validate_csr_hlo(data, indices, indptr, shape) rows, cols = shape buffer_size, opaque = gpu_sparse.build_csr_todense_descriptor( data_dtype, index_dtype, rows, cols, nnz) out = custom_call( f"{platform}sparse_csr_todense", result_types=[ ir.RankedTensorType.get(shape, data_type), ir.RankedTensorType.get([buffer_size], ir.IntegerType.get_signless(8)), ], operands=[data, indices, indptr], backend_config=opaque, operand_layouts=[[0]] * 3, result_layouts=[[1, 0], [0]]).results return out[0] cuda_csr_todense = partial(_csr_todense_hlo, "cu", _cusparse) rocm_csr_todense = partial(_csr_todense_hlo, "hip", _hipsparse) def _csr_fromdense_hlo(platform, gpu_sparse, mat, *, nnz, index_dtype, data_dtype, index_type): """CSR from dense matrix.""" mat_type = ir.RankedTensorType(mat.type) rows, cols = mat_type.shape buffer_size, opaque = gpu_sparse.build_csr_fromdense_descriptor( data_dtype, index_dtype, rows, cols, nnz) out = custom_call( f"{platform}sparse_csr_fromdense", result_types=[ ir.RankedTensorType.get([nnz], mat_type.element_type), ir.RankedTensorType.get([nnz], index_type), ir.RankedTensorType.get([rows + 1], index_type), ir.RankedTensorType.get([buffer_size], ir.IntegerType.get_signless(8)), ], operands=[mat], backend_config=opaque, operand_layouts=[[1, 0]], result_layouts=[[0]] * 4).results return out[:3] cuda_csr_fromdense = partial(_csr_fromdense_hlo, "cu", _cusparse) rocm_csr_fromdense = partial(_csr_fromdense_hlo, "hip", _hipsparse) def _csr_matvec_hlo(platform, gpu_sparse, data, indices, indptr, x, *, shape, transpose=False, compute_dtype=None, compute_type=None, data_dtype, index_dtype, x_dtype): """CSR matrix/vector multiply.""" data_type, index_type, nnz = _validate_csr_hlo(data, indices, indptr, shape) rows, cols = shape if compute_dtype is None: compute_dtype = data_dtype compute_type = data_type buffer_size, opaque = gpu_sparse.build_csr_matvec_descriptor( data_dtype, x_dtype, compute_dtype, index_dtype, rows, cols, nnz, transpose) out_size = cols if transpose else rows out = custom_call( f"{platform}sparse_csr_matvec", result_types=[ ir.RankedTensorType.get([out_size], compute_type), ir.RankedTensorType.get([buffer_size], ir.IntegerType.get_signless(8)), ], operands=[data, indices, indptr, x], backend_config=opaque, operand_layouts=[[0]] * 4, result_layouts=[[0]] * 2).results return out[0] cuda_csr_matvec = partial(_csr_matvec_hlo, "cu", _cusparse) rocm_csr_matvec = partial(_csr_matvec_hlo, "hip", _hipsparse) def _csr_matmat_hlo(platform, gpu_sparse, data, indices, indptr, B, *, shape, transpose=False, compute_dtype=None, compute_type=None, index_dtype, data_dtype, B_dtype): """CSR from dense matrix.""" data_type, index_type, nnz = _validate_csr_hlo(data, indices, indptr, shape) rows, cols = shape B_shape = ir.RankedTensorType(B.type).shape _, Ccols = B_shape if compute_dtype is None: compute_dtype = data_dtype compute_type = data_type buffer_size, opaque = gpu_sparse.build_csr_matmat_descriptor( data_dtype, B_dtype, compute_dtype, index_dtype, rows, cols, Ccols, nnz, transpose) out_size = cols if transpose else rows out = custom_call( f"{platform}sparse_csr_matmat", result_types=[ ir.RankedTensorType.get([out_size, Ccols], compute_type), ir.RankedTensorType.get([buffer_size], ir.IntegerType.get_signless(8)), ], operands=[data, indices, indptr, B], backend_config=opaque, operand_layouts=[[0], [0], [0], [1, 0]], result_layouts=[[1, 0], [0]]).results return out[0] cuda_csr_matmat = partial(_csr_matmat_hlo, "cu", _cusparse) rocm_csr_matmat = partial(_csr_matmat_hlo, "hip", _hipsparse) def _coo_todense_hlo(platform, gpu_sparse, data, row, col, *, shape, data_dtype, index_dtype): """COO to dense matrix.""" data_type, _, nnz = _validate_coo_hlo(data, row, col) rows, cols = shape buffer_size, opaque = gpu_sparse.build_coo_todense_descriptor( data_dtype, index_dtype, rows, cols, nnz) out = custom_call( f"{platform}sparse_coo_todense", result_types=[ ir.RankedTensorType.get(shape, data_type), ir.RankedTensorType.get([buffer_size], ir.IntegerType.get_signless(8)), ], operands=[data, row, col], backend_config=opaque, operand_layouts=[[0]] * 3, result_layouts=[[1, 0], [0]]).results return out[0] cuda_coo_todense = partial(_coo_todense_hlo, "cu", _cusparse) rocm_coo_todense = partial(_coo_todense_hlo, "hip", _hipsparse) def _coo_fromdense_hlo(platform, gpu_sparse, mat, *, nnz, data_dtype, index_dtype, index_type): """COO from dense matrix.""" mat_type = ir.RankedTensorType(mat.type) rows, cols = mat_type.shape buffer_size, opaque = gpu_sparse.build_coo_fromdense_descriptor( data_dtype, index_dtype, rows, cols, nnz) out = custom_call( f"{platform}sparse_coo_fromdense", result_types=[ ir.RankedTensorType.get([nnz], mat_type.element_type), ir.RankedTensorType.get([nnz], index_type), ir.RankedTensorType.get([nnz], index_type), ir.RankedTensorType.get([buffer_size], ir.IntegerType.get_signless(8)), ], operands=[mat], backend_config=opaque, operand_layouts=[[1, 0]], result_layouts=[[0]] * 4).results return out[:3] cuda_coo_fromdense = partial(_coo_fromdense_hlo, "cu", _cusparse) rocm_coo_fromdense = partial(_coo_fromdense_hlo, "hip", _hipsparse) def _coo_matvec_hlo(platform, gpu_sparse, data, row, col, x, *, shape, transpose=False, compute_dtype=None, compute_type=None, index_dtype, data_dtype, x_dtype): """COO matrix/vector multiply.""" data_type, _, nnz = _validate_coo_hlo(data, row, col) rows, cols = shape if compute_dtype is None: compute_dtype = data_dtype compute_type = data_type buffer_size, opaque = gpu_sparse.build_coo_matvec_descriptor( data_dtype, x_dtype, compute_dtype, index_dtype, rows, cols, nnz, transpose) out_size = cols if transpose else rows out = custom_call( f"{platform}sparse_coo_matvec", result_types=[ ir.RankedTensorType.get([out_size], compute_type), ir.RankedTensorType.get([buffer_size], ir.IntegerType.get_signless(8)), ], operands=[data, row, col, x], backend_config=opaque, operand_layouts=[[0]] * 4, result_layouts=[[0]] * 2).results return out[0] cuda_coo_matvec = partial(_coo_matvec_hlo, "cu", _cusparse) rocm_coo_matvec = partial(_coo_matvec_hlo, "hip", _hipsparse) def _coo_matmat_hlo(platform, gpu_sparse, data, row, col, B, *, shape, transpose=False, compute_dtype=None, compute_type=None, x_dtype, data_dtype, index_dtype): """COO from dense matrix.""" data_type, _, nnz = _validate_coo_hlo(data, row, col) is_batched_matmat = False batch_count = 1 if len(shape) == 2: rows, cols = shape elif len(shape) == 3: is_batched_matmat = True batch_count, rows, cols = shape # Redefine nnz as nnz per batch. nnz = nnz // batch_count B_shape = ir.RankedTensorType(B.type).shape _, Ccols = B_shape if compute_dtype is None: compute_dtype = data_dtype compute_type = data_type # TODO(tianjianlu): use batch stride to trigger different mode of batch # computation. Currently batch_stride = 0 is not allowed because of the issue # in cusparse https://github.com/NVIDIA/CUDALibrarySamples/issues/81#issuecomment-1205562643 # Set batch stride to be the matrix size for now. lhs_batch_stride = nnz B_rows = rows if transpose else cols rhs_batch_stride = B_rows * Ccols buffer_size, opaque = gpu_sparse.build_coo_matmat_descriptor( data_dtype, x_dtype, compute_dtype, index_dtype, rows, cols, Ccols, nnz, transpose, batch_count, lhs_batch_stride, rhs_batch_stride) out_size = cols if transpose else rows if is_batched_matmat: out_shape = [batch_count, out_size, Ccols] out_layout = [2, 1, 0] else: out_shape = [out_size, Ccols] out_layout = [1, 0] out = custom_call( f"{platform}sparse_coo_matmat", result_types=[ ir.RankedTensorType.get(out_shape, compute_type), ir.RankedTensorType.get([buffer_size], ir.IntegerType.get_signless(8)), ], operands=[data, row, col, B], backend_config=opaque, operand_layouts=[[0], [0], [0], [1, 0]], result_layouts=[out_layout, [0]]).results return out[0] cuda_coo_matmat = partial(_coo_matmat_hlo, "cu", _cusparse) rocm_coo_matmat = partial(_coo_matmat_hlo, "hip", _hipsparse) def _gtsv2_hlo( platform, gpu_sparse, dl, d, du, B, *, m, n, ldb, t, b_shape_vals=None): """Calls `cusparsegtsv2(dl, d, du, B, m, n, ldb)`.""" assert len(b_shape_vals) >= 2 batch_dim_vals = b_shape_vals[:-2] batch_size = math.prod(batch_dim_vals) num_bd = len(b_shape_vals) - 2 f32 = (t == np.float32) if f32: buffer_size = gpu_sparse.gtsv2_f32_buffer_size(m, n, ldb) else: buffer_size = gpu_sparse.gtsv2_f64_buffer_size(m, n, ldb) b_layout = (num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1)) d_layout = (num_bd,) + tuple(range(num_bd - 1, -1, -1)) b_type = ir.RankedTensorType(B.type) shape_type_pairs = [ (batch_dim_vals + (ldb, n), b_type.element_type), ((buffer_size,), ir.IntegerType.get_signless(8)) ] result_types, result_shapes = mk_result_types_and_shapes(shape_type_pairs) out = custom_call( f"{platform}sparse_gtsv2_" + ("f32" if f32 else "f64"), result_types=result_types, operands=[dl, d, du, B], backend_config=gpu_sparse.build_gtsv2_descriptor(batch_size, m, n, ldb), operand_layouts=[d_layout] * 3 + [b_layout], result_layouts=[b_layout, [0]], operand_output_aliases={3: 0}, result_shapes=result_shapes).results return out[0] cuda_gtsv2 = partial(_gtsv2_hlo, "cu", _cusparse) rocm_gtsv2 = partial(_gtsv2_hlo, "hip", _hipsparse)