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c7ed1bd3a8
For the CUDA and ROCM plugins, we only support exact matches between the plugin and jaxlib version, and bad things can happen if we try and load mismatched versions. This change issues a warning and skips importing a plugin when there is a version mismatch. There are a handful of other places where plugins are imported throughout the JAX codebase (e.g. in lax_numpy, mosaic_gpu, and in the plugins themselves). In a follow up it would be good to add version checking there too, but let's start with just these ones. PiperOrigin-RevId: 731808733
91 lines
3.2 KiB
Python
91 lines
3.2 KiB
Python
# Copyright 2019 The JAX Authors.
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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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from __future__ import annotations
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from functools import partial
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import itertools
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import jaxlib.mlir.ir as ir
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from jaxlib import xla_client
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from .hlo_helpers import custom_call
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from .plugin_support import import_from_plugin
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_cuda_prng = import_from_plugin("cuda", "_prng")
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_hip_prng = import_from_plugin("rocm", "_prng")
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if _cuda_prng:
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for _name, _value in _cuda_prng.registrations().items():
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# TODO(danfm): remove after JAX 0.5.1 release
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api_version = 1 if "_ffi" in _name else 0
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xla_client.register_custom_call_target(_name, _value, platform="CUDA",
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api_version=api_version)
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if _hip_prng:
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for _name, _value in _hip_prng.registrations().items():
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# TODO(danfm): remove after JAX 0.5.1 release
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api_version = 1 if "_ffi" in _name else 0
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xla_client.register_custom_call_target(_name, _value, platform="ROCM",
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api_version=api_version)
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def _threefry2x32_lowering(prng, platform: str, keys, data,
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length: int | ir.Value | None = None,
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output_shape: ir.Value | None = None,
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forward_compatibility_mode: bool = False):
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"""ThreeFry2x32 kernel for GPU.
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In presence of dynamic shapes, `length` is an `ir.Value` and `output_shape`
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is a 1D tensor describing the shape of the two outputs.
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"""
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del forward_compatibility_mode
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assert len(keys) == 2, keys
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assert len(data) == 2, data
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assert (ir.RankedTensorType(keys[0].type).element_type ==
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ir.IntegerType.get_unsigned(32)), keys[0].type
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typ = keys[0].type
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dims = ir.RankedTensorType(typ).shape
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for x in itertools.chain(keys, data):
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assert x.type == typ, (x.type, typ)
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ndims = len(dims)
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layout = tuple(range(ndims - 1, -1, -1))
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operand_layouts = [layout] * 4
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operands = [keys[0], keys[1], data[0], data[1]]
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opaque = {} # Use if not forward_compatibility_mode to trigger the FFI (v4).
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if isinstance(length, int):
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result_shapes = None
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else:
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assert output_shape is not None
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# We also need to pass separately the shapes of the outputs.
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result_shapes = [output_shape, output_shape]
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custom_call_target = f"{platform}_threefry2x32_ffi"
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return custom_call(
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custom_call_target,
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api_version=4,
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result_types=[typ, typ],
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operands=operands,
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backend_config=opaque,
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operand_layouts=operand_layouts,
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result_layouts=[layout] * 2,
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result_shapes=result_shapes).results
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cuda_threefry2x32 = partial(_threefry2x32_lowering, _cuda_prng, "cu")
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rocm_threefry2x32 = partial(_threefry2x32_lowering, _hip_prng, "hip")
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