mirror of
https://github.com/ROCm/jax.git
synced 2025-04-17 04:16:07 +00:00
65450d165e
The backend support for the new custom call was added on June 28th. Also add backwards compatibility test for the new custom call. PiperOrigin-RevId: 658011228
114 lines
3.6 KiB
Python
114 lines
3.6 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.
|
|
|
|
from __future__ import annotations
|
|
|
|
import functools
|
|
from functools import partial
|
|
import importlib
|
|
import itertools
|
|
import operator
|
|
|
|
import jaxlib.mlir.ir as ir
|
|
|
|
from jaxlib import xla_client
|
|
|
|
from .hlo_helpers import custom_call
|
|
|
|
|
|
for cuda_module_name in [".cuda", "jax_cuda12_plugin"]:
|
|
try:
|
|
_cuda_prng = importlib.import_module(
|
|
f"{cuda_module_name}._prng", package="jaxlib"
|
|
)
|
|
except ImportError:
|
|
_cuda_prng = None
|
|
else:
|
|
break
|
|
|
|
if _cuda_prng:
|
|
for _name, _value in _cuda_prng.registrations().items():
|
|
# TODO(b/338022728): remove after 6 months, always api_version=1
|
|
api_version = 1 if "_ffi" in _name else 0
|
|
xla_client.register_custom_call_target(_name, _value, platform="CUDA",
|
|
api_version=api_version)
|
|
|
|
for rocm_module_name in [".rocm", "jax_rocm60_plugin"]:
|
|
try:
|
|
_hip_prng = importlib.import_module(
|
|
f"{rocm_module_name}._prng", package="jaxlib"
|
|
)
|
|
except ImportError:
|
|
_hip_prng = None
|
|
else:
|
|
break
|
|
|
|
if _hip_prng:
|
|
for _name, _value in _hip_prng.registrations().items():
|
|
# TODO(b/338022728): remove after 6 months, always api_version=1
|
|
api_version = 1 if "_ffi" in _name else 0
|
|
xla_client.register_custom_call_target(_name, _value, platform="ROCM",
|
|
api_version=api_version)
|
|
|
|
_prod = lambda xs: functools.reduce(operator.mul, xs, 1)
|
|
|
|
|
|
def _threefry2x32_lowering(prng, platform: str, keys, data,
|
|
length: int | ir.Value | None = None,
|
|
output_shape: ir.Value | None = None,
|
|
forward_compatibility_mode: bool = False):
|
|
"""ThreeFry2x32 kernel for GPU.
|
|
|
|
In presence of dynamic shapes, `length` is an `ir.Value` and `output_shape`
|
|
is a 1D tensor describing the shape of the two outputs.
|
|
"""
|
|
del forward_compatibility_mode
|
|
assert len(keys) == 2, keys
|
|
assert len(data) == 2, data
|
|
assert (ir.RankedTensorType(keys[0].type).element_type ==
|
|
ir.IntegerType.get_unsigned(32)), keys[0].type
|
|
|
|
typ = keys[0].type
|
|
dims = ir.RankedTensorType(typ).shape
|
|
|
|
for x in itertools.chain(keys, data):
|
|
assert x.type == typ, (x.type, typ)
|
|
ndims = len(dims)
|
|
layout = tuple(range(ndims - 1, -1, -1))
|
|
operand_layouts = [layout] * 4
|
|
operands = [keys[0], keys[1], data[0], data[1]]
|
|
|
|
opaque = {} # Use if not forward_compatibility_mode to trigger the FFI (v4).
|
|
if isinstance(length, int):
|
|
result_shapes = None
|
|
else:
|
|
assert output_shape is not None
|
|
# We also need to pass separately the shapes of the outputs.
|
|
result_shapes = [output_shape, output_shape]
|
|
|
|
custom_call_target = f"{platform}_threefry2x32_ffi"
|
|
return custom_call(
|
|
custom_call_target,
|
|
api_version=4,
|
|
result_types=[typ, typ],
|
|
operands=operands,
|
|
backend_config=opaque,
|
|
operand_layouts=operand_layouts,
|
|
result_layouts=[layout] * 2,
|
|
result_shapes=result_shapes).results
|
|
|
|
|
|
cuda_threefry2x32 = partial(_threefry2x32_lowering, _cuda_prng, "cu")
|
|
rocm_threefry2x32 = partial(_threefry2x32_lowering, _hip_prng, "hip")
|