mirror of
https://github.com/ROCm/jax.git
synced 2025-04-16 20:06:05 +00:00
2693afa263
Previously we had no way to tell XLA that inputs and outputs of GPU custom calls must alias. This now works in XLA:GPU so we can just ask XLA to enforce the aliasing we need. This seems to be causing some test failures downstream, so reverting this for the moment until I can debug them. PiperOrigin-RevId: 479670565
503 lines
16 KiB
Python
503 lines
16 KiB
Python
# 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.
|
|
|
|
|
|
import functools
|
|
from functools import partial
|
|
import operator
|
|
|
|
import jaxlib.mlir.ir as ir
|
|
import jaxlib.mlir.dialects.mhlo as mhlo
|
|
|
|
import numpy as np
|
|
|
|
from jaxlib import xla_client
|
|
|
|
from .mhlo_helpers import custom_call
|
|
|
|
try:
|
|
from .cuda import _cublas
|
|
for _name, _value in _cublas.registrations().items():
|
|
xla_client.register_custom_call_target(_name, _value, platform="CUDA")
|
|
except ImportError:
|
|
_cublas = None
|
|
|
|
try:
|
|
from .cuda import _cusolver
|
|
for _name, _value in _cusolver.registrations().items():
|
|
xla_client.register_custom_call_target(_name, _value, platform="CUDA")
|
|
except ImportError:
|
|
_cusolver = None
|
|
|
|
|
|
try:
|
|
from .rocm import _hipblas
|
|
for _name, _value in _hipblas.registrations().items():
|
|
xla_client.register_custom_call_target(_name, _value, platform="ROCM")
|
|
except ImportError:
|
|
_hipblas = None
|
|
|
|
try:
|
|
from .rocm import _hipsolver
|
|
for _name, _value in _hipsolver.registrations().items():
|
|
xla_client.register_custom_call_target(_name, _value, platform="ROCM")
|
|
except ImportError:
|
|
_hipsolver = None
|
|
|
|
|
|
def _real_type(dtype):
|
|
"""Returns the real equivalent of 'dtype'."""
|
|
return np.finfo(dtype).dtype
|
|
|
|
_prod = lambda xs: functools.reduce(operator.mul, xs, 1)
|
|
|
|
|
|
def _trsm_mhlo(platform, gpu_blas, dtype, a, b, left_side=False, lower=False,
|
|
trans_a=False, conj_a=False, diag=False):
|
|
"""Batched triangular solve.
|
|
|
|
XLA implements unbatched triangular solve directly, so we need only implement
|
|
the batched case."""
|
|
b_type = ir.RankedTensorType(b.type)
|
|
dims = b_type.shape
|
|
assert len(dims) >= 2
|
|
m, n = dims[-2:]
|
|
batch_dims = tuple(dims[:-2])
|
|
num_bd = len(batch_dims)
|
|
batch = _prod(batch_dims)
|
|
k = m if left_side else n
|
|
|
|
a_type = ir.RankedTensorType(a.type)
|
|
if (batch_dims + (k, k) != tuple(a_type.shape) or
|
|
a_type.element_type != b_type.element_type):
|
|
raise ValueError("Argument mismatch for trsm, got {} and {}".format(
|
|
a_type, b_type))
|
|
|
|
if conj_a and not trans_a:
|
|
raise NotImplementedError("Conjugation without transposition not supported")
|
|
|
|
lwork, opaque = gpu_blas.build_trsm_batched_descriptor(
|
|
np.dtype(dtype), batch, m, n, left_side, lower, trans_a, conj_a, diag)
|
|
layout = (num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))
|
|
work_type = ir.RankedTensorType.get([lwork], ir.IntegerType.get_signless(8))
|
|
work_layout = [0]
|
|
out = custom_call(
|
|
f"{platform}blas_trsm_batched",
|
|
[b_type, work_type, work_type],
|
|
[a, b],
|
|
backend_config=opaque,
|
|
operand_layouts=[layout] * 2,
|
|
result_layouts=[layout, work_layout, work_layout])
|
|
return out[0]
|
|
|
|
cuda_trsm = partial(_trsm_mhlo, "cu", _cublas)
|
|
rocm_trsm = partial(_trsm_mhlo, "hip", _hipblas)
|
|
|
|
|
|
def _potrf_mhlo(platform, gpu_solver, dtype, a, lower):
|
|
"""Cholesky decomposition."""
|
|
a_type = ir.RankedTensorType(a.type)
|
|
dims = a_type.shape
|
|
m, n = dims[-2:]
|
|
assert m == n
|
|
batch_dims = tuple(dims[:-2])
|
|
num_bd = len(batch_dims)
|
|
batch = _prod(batch_dims)
|
|
|
|
lwork, opaque = gpu_solver.build_potrf_descriptor(
|
|
np.dtype(dtype), lower, batch, n)
|
|
|
|
layout = (num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))
|
|
info_layout = tuple(range(num_bd - 1, -1, -1))
|
|
i32_type = ir.IntegerType.get_signless(32)
|
|
info_type = ir.RankedTensorType.get(batch_dims, i32_type)
|
|
work_layout = [0]
|
|
out = custom_call(
|
|
f"{platform}solver_potrf",
|
|
[
|
|
a.type,
|
|
info_type,
|
|
ir.RankedTensorType.get([lwork], ir.IntegerType.get_signless(8)),
|
|
],
|
|
[a],
|
|
backend_config=opaque,
|
|
operand_layouts=[layout],
|
|
result_layouts=[layout, info_layout, work_layout])
|
|
return out[:2]
|
|
|
|
cuda_potrf = partial(_potrf_mhlo, "cu", _cusolver)
|
|
rocm_potrf = partial(_potrf_mhlo, "hip", _hipsolver)
|
|
|
|
|
|
def _getrf_mhlo(platform, gpu_blas, gpu_solver, dtype, a):
|
|
"""LU decomposition."""
|
|
a_type = ir.RankedTensorType(a.type)
|
|
dims = a_type.shape
|
|
assert len(dims) >= 2
|
|
m, n = dims[-2:]
|
|
batch_dims = tuple(dims[:-2])
|
|
num_bd = len(batch_dims)
|
|
batch = _prod(batch_dims)
|
|
|
|
if batch > 1 and m == n and m // batch <= 128:
|
|
lwork, opaque = gpu_blas.build_getrf_batched_descriptor(
|
|
np.dtype(dtype), batch, m)
|
|
workspace = ir.RankedTensorType.get([lwork], ir.IntegerType.get_signless(8))
|
|
kernel = f"{platform}blas_getrf_batched"
|
|
else:
|
|
lwork, opaque = gpu_solver.build_getrf_descriptor(
|
|
np.dtype(dtype), batch, m, n)
|
|
workspace = ir.RankedTensorType.get([lwork], a_type.element_type)
|
|
kernel = f"{platform}solver_getrf"
|
|
|
|
layout = (num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))
|
|
i32_type = ir.IntegerType.get_signless(32)
|
|
out = custom_call(
|
|
kernel,
|
|
[
|
|
a.type,
|
|
ir.RankedTensorType.get(batch_dims + (min(m, n),), i32_type),
|
|
ir.RankedTensorType.get(batch_dims, i32_type),
|
|
workspace,
|
|
],
|
|
[a],
|
|
backend_config=opaque,
|
|
operand_layouts=[layout],
|
|
result_layouts=[
|
|
layout,
|
|
tuple(range(num_bd, -1, -1)),
|
|
tuple(range(num_bd - 1, -1, -1)),
|
|
[0],
|
|
])
|
|
return out[:3]
|
|
|
|
cuda_getrf = partial(_getrf_mhlo, "cu", _cublas, _cusolver)
|
|
rocm_getrf = partial(_getrf_mhlo, "hip", _hipblas, _hipsolver)
|
|
|
|
|
|
def _geqrf_mhlo(platform, gpu_solver, dtype, a):
|
|
"""QR decomposition."""
|
|
a_type = ir.RankedTensorType(a.type)
|
|
dims = a_type.shape
|
|
assert len(dims) >= 2
|
|
m, n = dims[-2:]
|
|
batch_dims = tuple(dims[:-2])
|
|
num_bd = len(batch_dims)
|
|
batch = _prod(batch_dims)
|
|
|
|
lwork, opaque = gpu_solver.build_geqrf_descriptor(
|
|
np.dtype(dtype), batch, m, n)
|
|
|
|
layout = (num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))
|
|
i32_type = ir.IntegerType.get_signless(32)
|
|
out = custom_call(
|
|
f"{platform}solver_geqrf",
|
|
[
|
|
a.type,
|
|
ir.RankedTensorType.get(batch_dims + (min(m, n),), a_type.element_type),
|
|
ir.RankedTensorType.get(batch_dims, i32_type),
|
|
ir.RankedTensorType.get([lwork], a_type.element_type),
|
|
],
|
|
[a],
|
|
backend_config=opaque,
|
|
operand_layouts=[layout],
|
|
result_layouts=[
|
|
layout,
|
|
tuple(range(num_bd, -1, -1)),
|
|
tuple(range(num_bd - 1, -1, -1)),
|
|
[0],
|
|
])
|
|
return out[:3]
|
|
|
|
cuda_geqrf = partial(_geqrf_mhlo, "cu", _cusolver)
|
|
rocm_geqrf = partial(_geqrf_mhlo, "hip", _hipsolver)
|
|
|
|
def _geqrf_batched_mhlo(platform, gpu_blas, dtype, a):
|
|
"""Batched QR decomposition."""
|
|
a_type = ir.RankedTensorType(a.type)
|
|
dims = a_type.shape
|
|
assert len(dims) >= 2
|
|
m, n = dims[-2:]
|
|
batch_dims = tuple(dims[:-2])
|
|
num_bd = len(batch_dims)
|
|
batch = _prod(batch_dims)
|
|
|
|
lwork, opaque = gpu_blas.build_geqrf_batched_descriptor(
|
|
np.dtype(dtype), batch, m, n)
|
|
|
|
layout = (num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))
|
|
out = custom_call(
|
|
f"{platform}blas_geqrf_batched",
|
|
[
|
|
a.type,
|
|
ir.RankedTensorType.get(batch_dims + (min(m, n),), a_type.element_type),
|
|
ir.RankedTensorType.get([lwork], ir.IntegerType.get_signless(8)),
|
|
ir.RankedTensorType.get([lwork], ir.IntegerType.get_signless(8)),
|
|
],
|
|
[a],
|
|
backend_config=opaque,
|
|
operand_layouts=[layout],
|
|
result_layouts=[
|
|
layout,
|
|
tuple(range(num_bd, -1, -1)),
|
|
[0],
|
|
[0],
|
|
])
|
|
return out[:2]
|
|
|
|
cuda_geqrf_batched = partial(_geqrf_batched_mhlo, "cu", _cublas)
|
|
rocm_geqrf_batched = partial(_geqrf_batched_mhlo, "hip", _hipblas)
|
|
|
|
|
|
def _csrlsvqr_mhlo(platform, gpu_solver, dtype, data,
|
|
indices, indptr, b, tol, reorder):
|
|
"""Sparse solver via QR decomposition. CUDA only."""
|
|
b_type = ir.RankedTensorType(b.type)
|
|
data_type = ir.RankedTensorType(data.type)
|
|
|
|
n = b_type.shape[0]
|
|
nnz = data_type.shape[0]
|
|
opaque = gpu_solver.build_csrlsvqr_descriptor(
|
|
np.dtype(dtype), n, nnz, reorder, tol
|
|
)
|
|
|
|
out = custom_call(
|
|
f"{platform}solver_csrlsvqr", # call_target_name
|
|
[b.type], # out_types
|
|
[data, indptr, indices, b], # operands
|
|
backend_config=opaque, # backend_config
|
|
operand_layouts=[(0,), (0,), (0,), (0,)], # operand_layouts
|
|
result_layouts=[(0,)] # result_layouts
|
|
)
|
|
return [out]
|
|
|
|
cuda_csrlsvqr = partial(_csrlsvqr_mhlo, "cu", _cusolver)
|
|
|
|
|
|
def _orgqr_mhlo(platform, gpu_solver, dtype, a, tau):
|
|
"""Product of elementary Householder reflections."""
|
|
a_type = ir.RankedTensorType(a.type)
|
|
dims = a_type.shape
|
|
assert len(dims) >= 2
|
|
m, n = dims[-2:]
|
|
batch_dims = tuple(dims[:-2])
|
|
num_bd = len(batch_dims)
|
|
batch = _prod(batch_dims)
|
|
|
|
tau_dims = ir.RankedTensorType(tau.type).shape
|
|
assert tau_dims[:-1] == dims[:-2]
|
|
k = tau_dims[-1]
|
|
|
|
lwork, opaque = gpu_solver.build_orgqr_descriptor(
|
|
np.dtype(dtype), batch, m, n, k)
|
|
|
|
layout = (num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))
|
|
i32_type = ir.IntegerType.get_signless(32)
|
|
out = custom_call(
|
|
f"{platform}solver_orgqr",
|
|
[
|
|
a.type,
|
|
ir.RankedTensorType.get(batch_dims, i32_type),
|
|
ir.RankedTensorType.get([lwork], a_type.element_type),
|
|
],
|
|
[a, tau],
|
|
backend_config=opaque,
|
|
operand_layouts=[
|
|
layout,
|
|
tuple(range(num_bd, -1, -1)),
|
|
],
|
|
result_layouts=[
|
|
layout,
|
|
tuple(range(num_bd - 1, -1, -1)),
|
|
[0],
|
|
])
|
|
return out[:2]
|
|
|
|
cuda_orgqr = partial(_orgqr_mhlo, "cu", _cusolver)
|
|
rocm_orgqr = partial(_orgqr_mhlo, "hip", _hipsolver)
|
|
|
|
|
|
def _syevd_mhlo(platform, gpu_solver, have_jacobi_solver, dtype, a,
|
|
lower=False):
|
|
"""Symmetric (Hermitian) eigendecomposition."""
|
|
a_type = ir.RankedTensorType(a.type)
|
|
dims = a_type.shape
|
|
assert len(dims) >= 2
|
|
m, n = dims[-2:]
|
|
assert m == n
|
|
batch_dims = tuple(dims[:-2])
|
|
num_bd = len(batch_dims)
|
|
batch = _prod(batch_dims)
|
|
layout = (num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))
|
|
|
|
if have_jacobi_solver and n <= 32:
|
|
kernel = f"{platform}solver_syevj"
|
|
lwork, opaque = gpu_solver.build_syevj_descriptor(
|
|
np.dtype(dtype), lower, batch, n)
|
|
else:
|
|
kernel = f"{platform}solver_syevd"
|
|
lwork, opaque = gpu_solver.build_syevd_descriptor(
|
|
np.dtype(dtype), lower, batch, n)
|
|
|
|
if ir.ComplexType.isinstance(a_type.element_type):
|
|
eigvals_type = ir.ComplexType(a_type.element_type).element_type
|
|
else:
|
|
eigvals_type = a_type.element_type
|
|
|
|
i32_type = ir.IntegerType.get_signless(32)
|
|
out = custom_call(
|
|
kernel,
|
|
[
|
|
a.type,
|
|
ir.RankedTensorType.get(batch_dims + (n,), eigvals_type),
|
|
ir.RankedTensorType.get(batch_dims, i32_type),
|
|
ir.RankedTensorType.get([lwork], a_type.element_type),
|
|
],
|
|
[a],
|
|
backend_config=opaque,
|
|
operand_layouts=[layout],
|
|
result_layouts=[
|
|
layout,
|
|
tuple(range(num_bd, -1, -1)),
|
|
tuple(range(num_bd - 1, -1, -1)),
|
|
[0],
|
|
])
|
|
return out[:3]
|
|
|
|
cuda_syevd = partial(_syevd_mhlo, "cu", _cusolver, True)
|
|
rocm_syevd = partial(_syevd_mhlo, "hip", _hipsolver, True)
|
|
|
|
|
|
def _gesvd_mhlo(platform, gpu_solver, have_jacobi_solver, dtype, a,
|
|
full_matrices=True, compute_uv=True):
|
|
"""Singular value decomposition."""
|
|
a_type = ir.RankedTensorType(a.type)
|
|
dims = a_type.shape
|
|
assert len(dims) >= 2
|
|
m, n = dims[-2:]
|
|
batch_dims = tuple(dims[:-2])
|
|
num_bd = len(batch_dims)
|
|
b = _prod(batch_dims)
|
|
if ir.ComplexType.isinstance(a_type.element_type):
|
|
singular_vals_type = ir.ComplexType(a_type.element_type).element_type
|
|
else:
|
|
singular_vals_type = a_type.element_type
|
|
|
|
scalar_layout = tuple(range(num_bd - 1, -1, -1))
|
|
vector_layout = (num_bd,) + tuple(range(num_bd - 1, -1, -1))
|
|
i32_type = ir.IntegerType.get_signless(32)
|
|
|
|
if have_jacobi_solver and m < 32 and n < 32:
|
|
# The batched kernel doesn't support "econ" mode.
|
|
econ = not full_matrices and b == 1
|
|
lwork, opaque = gpu_solver.build_gesvdj_descriptor(
|
|
np.dtype(dtype), b, m, n, compute_uv, 1 if econ else 0)
|
|
k = min(m, n)
|
|
matrix_layout = (num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))
|
|
_, s, u, v, info, _ = custom_call(
|
|
f"{platform}solver_gesvdj",
|
|
[
|
|
a.type,
|
|
ir.RankedTensorType.get(batch_dims + (min(m, n),), singular_vals_type),
|
|
ir.RankedTensorType.get(batch_dims + (m, k if econ else m),
|
|
a_type.element_type),
|
|
ir.RankedTensorType.get(batch_dims + (n, k if econ else n),
|
|
a_type.element_type),
|
|
ir.RankedTensorType.get(batch_dims, i32_type),
|
|
ir.RankedTensorType.get([lwork], a_type.element_type),
|
|
],
|
|
[a],
|
|
backend_config=opaque,
|
|
operand_layouts=[matrix_layout],
|
|
result_layouts=[
|
|
matrix_layout,
|
|
vector_layout,
|
|
matrix_layout,
|
|
matrix_layout,
|
|
scalar_layout,
|
|
[0],
|
|
])
|
|
vt = mhlo.TransposeOp(
|
|
v,
|
|
ir.DenseIntElementsAttr.get(np.array(tuple(range(num_bd)) + (num_bd + 1, num_bd)))).result
|
|
if np.issubdtype(dtype, np.complexfloating):
|
|
vt = mhlo.ComplexOp(mhlo.RealOp(vt), mhlo.NegOp(mhlo.ImagOp(vt))).result
|
|
if not full_matrices and not econ:
|
|
u = mhlo.SliceOp(
|
|
u,
|
|
ir.DenseIntElementsAttr.get(np.zeros([len(dims)], np.int64)),
|
|
ir.DenseIntElementsAttr.get(np.array(batch_dims + (m, min(m, n)))),
|
|
ir.DenseIntElementsAttr.get(np.ones([len(dims)], np.int64))).result
|
|
vt = mhlo.SliceOp(
|
|
vt,
|
|
ir.DenseIntElementsAttr.get(np.zeros([len(dims)], np.int64)),
|
|
ir.DenseIntElementsAttr.get(np.array(batch_dims + (min(m, n), n))),
|
|
ir.DenseIntElementsAttr.get(np.ones([len(dims)], np.int64))).result
|
|
elif m < n:
|
|
lwork, opaque = gpu_solver.build_gesvd_descriptor(
|
|
np.dtype(dtype), b, n, m, compute_uv, full_matrices)
|
|
k = n if full_matrices else m
|
|
matrix_layout = (num_bd + 1, num_bd) + tuple(range(num_bd - 1, -1, -1))
|
|
_, s, vt, u, info, _ = custom_call(
|
|
f"{platform}solver_gesvd",
|
|
[
|
|
a.type,
|
|
ir.RankedTensorType.get(batch_dims + (min(m, n),), singular_vals_type),
|
|
ir.RankedTensorType.get(batch_dims + (k, n), a_type.element_type),
|
|
ir.RankedTensorType.get(batch_dims + (m, m), a_type.element_type),
|
|
ir.RankedTensorType.get(batch_dims, i32_type),
|
|
ir.RankedTensorType.get([lwork], a_type.element_type),
|
|
],
|
|
[a],
|
|
backend_config=opaque,
|
|
operand_layouts=[matrix_layout],
|
|
result_layouts=[
|
|
matrix_layout,
|
|
vector_layout,
|
|
matrix_layout,
|
|
matrix_layout,
|
|
scalar_layout,
|
|
[0],
|
|
])
|
|
else:
|
|
lwork, opaque = gpu_solver.build_gesvd_descriptor(
|
|
np.dtype(dtype), b, m, n, compute_uv, full_matrices)
|
|
k = m if full_matrices else n
|
|
matrix_layout = (num_bd, num_bd + 1) + tuple(range(num_bd - 1, -1, -1))
|
|
_, s, u, vt, info, _ = custom_call(
|
|
f"{platform}solver_gesvd",
|
|
[
|
|
a.type,
|
|
ir.RankedTensorType.get(batch_dims + (min(m, n),), singular_vals_type),
|
|
ir.RankedTensorType.get(batch_dims + (m, k), a_type.element_type),
|
|
ir.RankedTensorType.get(batch_dims + (n, n), a_type.element_type),
|
|
ir.RankedTensorType.get(batch_dims, i32_type),
|
|
ir.RankedTensorType.get([lwork], a_type.element_type),
|
|
],
|
|
[a],
|
|
backend_config=opaque,
|
|
operand_layouts=[matrix_layout],
|
|
result_layouts=[
|
|
matrix_layout,
|
|
vector_layout,
|
|
matrix_layout,
|
|
matrix_layout,
|
|
scalar_layout,
|
|
[0],
|
|
])
|
|
return s, u, vt, info
|
|
|
|
cuda_gesvd = partial(_gesvd_mhlo, "cu", _cusolver, True)
|
|
rocm_gesvd = partial(_gesvd_mhlo, "hip", _hipsolver, False)
|