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f004bcb7b8
Some folks want to be able to run JAX-generated HLO computations from C++, and those computations may refer to JAX's custom kernels. This change splits the custom kernels into separate modules that may be used independently of Python.
The general pattern is that each extension now has two parts:
* xyz_kernels.{cc, h} — the C++ parts
* xyz.cc — Python bindings around the C++ parts, including code to build any descriptor objects.
There's also a new (minimally supported) module named "gpu_kernels.cc" which registers JAX's GPU kernels with the XLA C++ custom kernel registry.
PiperOrigin-RevId: 394460343
430 lines
15 KiB
Python
430 lines
15 KiB
Python
# Copyright 2019 Google LLC
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#
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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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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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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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import functools
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import operator
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import numpy as np
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from jaxlib import xla_client
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try:
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from . import _cublas
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for _name, _value in _cublas.registrations().items():
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xla_client.register_custom_call_target(_name, _value, platform="CUDA")
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except ImportError:
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pass
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try:
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from . import _cusolver
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for _name, _value in _cusolver.registrations().items():
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xla_client.register_custom_call_target(_name, _value, platform="CUDA")
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except ImportError:
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pass
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_ops = xla_client.ops
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_Shape = xla_client.Shape
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# TODO(phawkins): remove after we no longer need to support old jax releases.
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def _unpack_builder(c):
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# If `c` is a ComputationBuilder object, extracts the underlying XlaBuilder.
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return getattr(c, "_builder", c)
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def _real_type(dtype):
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"""Returns the real equivalent of 'dtype'."""
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if dtype == np.float32:
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return np.float32
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elif dtype == np.float64:
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return np.float64
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elif dtype == np.complex64:
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return np.float32
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elif dtype == np.complex128:
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return np.float64
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else:
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raise NotImplementedError("Unsupported dtype {}".format(dtype))
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_prod = lambda xs: functools.reduce(operator.mul, xs, 1)
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def trsm(c, a, b, left_side=False, lower=False, trans_a=False, conj_a=False,
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diag=False):
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"""Batched triangular solve.
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XLA implements unbatched triangular solve directly, so we need only implement
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the batched case."""
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c = _unpack_builder(c)
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b_shape = c.get_shape(b)
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dtype = b_shape.element_type()
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dims = b_shape.dimensions()
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assert len(dims) >= 2
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m, n = dims[-2:]
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batch_dims = tuple(dims[:-2])
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num_bd = len(batch_dims)
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batch = _prod(batch_dims)
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k = m if left_side else n
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a_shape = c.get_shape(a)
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if (batch_dims + (k, k) != a_shape.dimensions() or
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a_shape.element_type() != dtype):
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raise ValueError("Argument mismatch for trsm, got {} and {}".format(
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a_shape, b_shape))
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if conj_a and not trans_a:
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raise NotImplementedError("Conjugation without transposition not supported")
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lwork, opaque = _cublas.build_trsm_batched_descriptor(
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np.dtype(dtype), batch, m, n, left_side, lower, trans_a, conj_a, diag)
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layout = (num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))
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out = _ops.CustomCallWithLayout(
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c, b"cublas_trsm_batched",
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operands=(a, b),
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shape_with_layout=_Shape.tuple_shape((
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_Shape.array_shape(dtype, b_shape.dimensions(), layout),
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_Shape.array_shape(np.dtype(np.int8), (lwork,), (0,)),
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_Shape.array_shape(np.dtype(np.int8), (lwork,), (0,)))),
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operand_shapes_with_layout=(
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_Shape.array_shape(dtype, a_shape.dimensions(), layout),
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_Shape.array_shape(dtype, b_shape.dimensions(), layout),
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),
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opaque=opaque,
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api_version=xla_client.ops.CustomCallApiVersion
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.API_VERSION_STATUS_RETURNING)
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return _ops.GetTupleElement(out, 0)
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def potrf(c, a, lower):
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"""Cholesky decomposition."""
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c = _unpack_builder(c)
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a_shape = c.get_shape(a)
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dtype = a_shape.element_type()
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dims = a_shape.dimensions()
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m, n = dims[-2:]
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assert m == n
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batch_dims = tuple(dims[:-2])
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num_bd = len(batch_dims)
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batch = _prod(batch_dims)
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lwork, opaque = _cusolver.build_potrf_descriptor(
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np.dtype(dtype), lower, batch, n)
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kernel = b"cusolver_potrf"
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out = _ops.CustomCallWithLayout(
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c, kernel,
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operands=(a,),
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shape_with_layout=_Shape.tuple_shape((
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_Shape.array_shape(
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dtype, batch_dims + (n, n),
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(num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))),
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_Shape.array_shape(
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np.dtype(np.int32), batch_dims, tuple(range(num_bd - 1, -1, -1))),
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_Shape.array_shape(np.dtype(np.int8), (lwork,), (0,)),
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)),
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operand_shapes_with_layout=(_Shape.array_shape(
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dtype, batch_dims + (n, n),
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(num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))),),
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opaque=opaque,
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api_version=xla_client.ops.CustomCallApiVersion
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.API_VERSION_STATUS_RETURNING)
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return _ops.GetTupleElement(out, 0), _ops.GetTupleElement(out, 1)
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def getrf(c, a):
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"""LU decomposition."""
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c = _unpack_builder(c)
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a_shape = c.get_shape(a)
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dtype = a_shape.element_type()
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dims = a_shape.dimensions()
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assert len(dims) >= 2
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m, n = dims[-2:]
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batch_dims = tuple(dims[:-2])
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num_bd = len(batch_dims)
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batch = _prod(batch_dims)
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if batch > 1 and m == n and m // batch <= 128:
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lwork, opaque = _cublas.build_getrf_batched_descriptor(
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np.dtype(dtype), batch, m)
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workspace = _Shape.array_shape(np.dtype(np.int8), (lwork,), (0,))
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kernel = b"cublas_getrf_batched"
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else:
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lwork, opaque = _cusolver.build_getrf_descriptor(
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np.dtype(dtype), batch, m, n)
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workspace = _Shape.array_shape(dtype, (lwork,), (0,))
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kernel = b"cusolver_getrf"
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out = _ops.CustomCallWithLayout(
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c, kernel,
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operands=(a,),
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shape_with_layout=_Shape.tuple_shape((
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_Shape.array_shape(
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dtype, batch_dims + (m, n),
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(num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))),
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_Shape.array_shape(
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np.dtype(np.int32), batch_dims + (min(m, n),),
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tuple(range(num_bd, -1, -1))),
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_Shape.array_shape(
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np.dtype(np.int32), batch_dims, tuple(range(num_bd - 1, -1, -1))),
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workspace,
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)),
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operand_shapes_with_layout=(_Shape.array_shape(
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dtype, batch_dims + (m, n),
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(num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))),),
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opaque=opaque,
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api_version=xla_client.ops.CustomCallApiVersion
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.API_VERSION_STATUS_RETURNING)
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return (_ops.GetTupleElement(out, 0), _ops.GetTupleElement(out, 1),
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_ops.GetTupleElement(out, 2))
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def geqrf(c, a):
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"""QR decomposition."""
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c = _unpack_builder(c)
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a_shape = c.get_shape(a)
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dtype = a_shape.element_type()
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dims = a_shape.dimensions()
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assert len(dims) >= 2
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m, n = dims[-2:]
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batch_dims = tuple(dims[:-2])
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num_bd = len(batch_dims)
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batch = _prod(batch_dims)
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lwork, opaque = _cusolver.build_geqrf_descriptor(
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np.dtype(dtype), batch, m, n)
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workspace = _Shape.array_shape(dtype, (lwork,), (0,))
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kernel = b"cusolver_geqrf"
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out = _ops.CustomCallWithLayout(
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c, kernel,
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operands=(a,),
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shape_with_layout=_Shape.tuple_shape((
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_Shape.array_shape(
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dtype, batch_dims + (m, n),
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(num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))),
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_Shape.array_shape(
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dtype, batch_dims + (min(m, n),),
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tuple(range(num_bd, -1, -1))),
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_Shape.array_shape(
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np.dtype(np.int32), batch_dims, tuple(range(num_bd - 1, -1, -1))),
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workspace,
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)),
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operand_shapes_with_layout=(_Shape.array_shape(
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dtype, batch_dims + (m, n),
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(num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))),),
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opaque=opaque,
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api_version=xla_client.ops.CustomCallApiVersion
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.API_VERSION_STATUS_RETURNING)
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return (_ops.GetTupleElement(out, 0), _ops.GetTupleElement(out, 1),
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_ops.GetTupleElement(out, 2))
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def orgqr(c, a, tau):
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"""Product of elementary Householder reflections."""
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c = _unpack_builder(c)
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a_shape = c.get_shape(a)
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dtype = a_shape.element_type()
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dims = a_shape.dimensions()
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assert len(dims) >= 2
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m, n = dims[-2:]
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batch_dims = tuple(dims[:-2])
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num_bd = len(batch_dims)
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batch = _prod(batch_dims)
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tau_dims = c.get_shape(tau).dimensions()
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assert tau_dims[:-1] == dims[:-2]
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k = tau_dims[-1]
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lwork, opaque = _cusolver.build_orgqr_descriptor(
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np.dtype(dtype), batch, m, n, k)
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workspace = _Shape.array_shape(dtype, (lwork,), (0,))
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kernel = b"cusolver_orgqr"
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out = _ops.CustomCallWithLayout(
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c, kernel,
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operands=(a, tau),
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shape_with_layout=_Shape.tuple_shape((
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_Shape.array_shape(
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dtype, batch_dims + (m, n),
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(num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))),
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_Shape.array_shape(
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np.dtype(np.int32), batch_dims, tuple(range(num_bd - 1, -1, -1))),
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workspace,
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)),
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operand_shapes_with_layout=(
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_Shape.array_shape(
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dtype, batch_dims + (m, n),
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(num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))),
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_Shape.array_shape(
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dtype, batch_dims + (k,),
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tuple(range(num_bd, -1, -1))),
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),
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opaque=opaque,
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api_version=xla_client.ops.CustomCallApiVersion
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.API_VERSION_STATUS_RETURNING)
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return (_ops.GetTupleElement(out, 0), _ops.GetTupleElement(out, 1))
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def syevd(c, a, lower=False):
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"""Symmetric (Hermitian) eigendecomposition."""
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c = _unpack_builder(c)
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a_shape = c.get_shape(a)
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dtype = a_shape.element_type()
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dims = a_shape.dimensions()
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assert len(dims) >= 2
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m, n = dims[-2:]
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assert m == n
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batch_dims = tuple(dims[:-2])
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num_bd = len(batch_dims)
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batch = _prod(batch_dims)
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layout = (num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))
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if n <= 32:
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kernel = b"cusolver_syevj"
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lwork, opaque = _cusolver.build_syevj_descriptor(
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np.dtype(dtype), lower, batch, n)
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else:
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kernel = b"cusolver_syevd"
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lwork, opaque = _cusolver.build_syevd_descriptor(
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np.dtype(dtype), lower, batch, n)
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eigvals_type = _real_type(dtype)
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out = _ops.CustomCallWithLayout(
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c, kernel,
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operands=(a,),
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shape_with_layout=_Shape.tuple_shape((
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_Shape.array_shape(dtype, dims, layout),
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_Shape.array_shape(
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np.dtype(eigvals_type), batch_dims + (n,),
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tuple(range(num_bd, -1, -1))),
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_Shape.array_shape(
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np.dtype(np.int32), batch_dims,
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tuple(range(num_bd - 1, -1, -1))),
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_Shape.array_shape(dtype, (lwork,), (0,))
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)),
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operand_shapes_with_layout=(
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_Shape.array_shape(dtype, dims, layout),
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),
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opaque=opaque,
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api_version=xla_client.ops.CustomCallApiVersion
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.API_VERSION_STATUS_RETURNING)
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return (_ops.GetTupleElement(out, 0), _ops.GetTupleElement(out, 1),
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_ops.GetTupleElement(out, 2))
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def gesvd(c, a, full_matrices=True, compute_uv=True):
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"""Singular value decomposition."""
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c = _unpack_builder(c)
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a_shape = c.get_shape(a)
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dims = a_shape.dimensions()
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dtype = a_shape.element_type()
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assert len(dims) >= 2
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m, n = dims[-2:]
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batch_dims = tuple(dims[:-2])
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num_bd = len(batch_dims)
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b = _prod(batch_dims)
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singular_vals_dtype = np.dtype(_real_type(dtype))
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if m < 32 and n < 32:
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lwork, opaque = _cusolver.build_gesvdj_descriptor(
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np.dtype(dtype), b, m, n, compute_uv)
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scalar_layout = tuple(range(num_bd - 1, -1, -1))
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vector_layout = (num_bd,) + scalar_layout
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matrix_layout = (num_bd, num_bd + 1) + scalar_layout
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out = _ops.CustomCallWithLayout(
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c, b"cusolver_gesvdj",
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operands=(a,),
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shape_with_layout=_Shape.tuple_shape((
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_Shape.array_shape(dtype, batch_dims + (m, n), matrix_layout),
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_Shape.array_shape(singular_vals_dtype, batch_dims + (min(m, n),),
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vector_layout),
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_Shape.array_shape(dtype, batch_dims + (m, m), matrix_layout),
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_Shape.array_shape(dtype, batch_dims + (n, n), matrix_layout),
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_Shape.array_shape(np.dtype(np.int32), batch_dims, scalar_layout),
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_Shape.array_shape(dtype, (lwork,), (0,)),
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)),
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operand_shapes_with_layout=(
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_Shape.array_shape(dtype, batch_dims + (m, n), matrix_layout),
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),
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opaque=opaque,
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api_version=xla_client.ops.CustomCallApiVersion
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.API_VERSION_STATUS_RETURNING)
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s = _ops.GetTupleElement(out, 1)
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u = _ops.GetTupleElement(out, 2)
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v = _ops.GetTupleElement(out, 3)
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info = _ops.GetTupleElement(out, 4)
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vt = _ops.Transpose(v, tuple(range(num_bd)) + (num_bd + 1, num_bd))
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if np.issubdtype(dtype, np.complexfloating):
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vt = _ops.Conj(vt)
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elif m < n:
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lwork, opaque = _cusolver.build_gesvd_descriptor(
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np.dtype(dtype), b, n, m, compute_uv, full_matrices)
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scalar_layout = tuple(range(num_bd - 1, -1, -1))
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vector_layout = (num_bd,) + scalar_layout
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matrix_layout = (num_bd + 1, num_bd) + scalar_layout
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out = _ops.CustomCallWithLayout(
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c, b"cusolver_gesvd",
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operands=(a,),
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shape_with_layout=_Shape.tuple_shape((
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_Shape.array_shape(dtype, batch_dims + (m, n), matrix_layout),
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_Shape.array_shape(singular_vals_dtype, batch_dims + (min(m, n),),
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vector_layout),
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_Shape.array_shape(dtype, batch_dims + (n, n), matrix_layout),
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_Shape.array_shape(dtype, batch_dims + (m, m), matrix_layout),
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_Shape.array_shape(np.dtype(np.int32), batch_dims, scalar_layout),
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_Shape.array_shape(dtype, (lwork,), (0,)),
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)),
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operand_shapes_with_layout=(
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_Shape.array_shape(dtype, batch_dims + (m, n), matrix_layout),
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),
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opaque=opaque,
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api_version=xla_client.ops.CustomCallApiVersion
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.API_VERSION_STATUS_RETURNING)
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s = _ops.GetTupleElement(out, 1)
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vt = _ops.GetTupleElement(out, 2)
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u = _ops.GetTupleElement(out, 3)
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info = _ops.GetTupleElement(out, 4)
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else:
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lwork, opaque = _cusolver.build_gesvd_descriptor(
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np.dtype(dtype), b, m, n, compute_uv, full_matrices)
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scalar_layout = tuple(range(num_bd - 1, -1, -1))
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vector_layout = (num_bd,) + scalar_layout
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matrix_layout = (num_bd, num_bd + 1) + scalar_layout
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out = _ops.CustomCallWithLayout(
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c, b"cusolver_gesvd",
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operands=(a,),
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shape_with_layout=_Shape.tuple_shape((
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_Shape.array_shape(dtype, batch_dims + (m, n), matrix_layout),
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_Shape.array_shape(singular_vals_dtype, batch_dims + (min(m, n),),
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vector_layout),
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_Shape.array_shape(dtype, batch_dims + (m, m), matrix_layout),
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_Shape.array_shape(dtype, batch_dims + (n, n), matrix_layout),
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_Shape.array_shape(np.dtype(np.int32), batch_dims, scalar_layout),
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_Shape.array_shape(dtype, (lwork,), (0,)),
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)),
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operand_shapes_with_layout=(
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_Shape.array_shape(dtype, batch_dims + (m, n), matrix_layout),
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),
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|
opaque=opaque,
|
|
api_version=xla_client.ops.CustomCallApiVersion
|
|
.API_VERSION_STATUS_RETURNING)
|
|
s = _ops.GetTupleElement(out, 1)
|
|
u = _ops.GetTupleElement(out, 2)
|
|
vt = _ops.GetTupleElement(out, 3)
|
|
info = _ops.GetTupleElement(out, 4)
|
|
if not full_matrices:
|
|
u = _ops.Slice(u, (0,) * len(dims), batch_dims + (m, min(m, n)),
|
|
(1,) * len(dims))
|
|
vt = _ops.Slice(vt, (0,) * len(dims), batch_dims + (min(m, n), n),
|
|
(1,) * len(dims))
|
|
return s, u, vt, info
|