73 lines
2.5 KiB
Python
73 lines
2.5 KiB
Python
# Copyright 2020 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 jax import numpy as jnp
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from jax._src import array
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from jax._src.typing import Array
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from jax._src import xla_bridge
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from jax._src.lib import xla_client
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SUPPORTED_DTYPES = frozenset({
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jnp.int8, jnp.int16, jnp.int32, jnp.int64, jnp.uint8, jnp.uint16,
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jnp.uint32, jnp.uint64, jnp.float16, jnp.bfloat16, jnp.float32,
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jnp.float64, jnp.complex64, jnp.complex128})
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def to_dlpack(x: Array, take_ownership: bool = False):
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"""Returns a DLPack tensor that encapsulates a ``DeviceArray`` `x`.
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Takes ownership of the contents of ``x``; leaves `x` in an invalid/deleted
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state.
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Args:
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x: a ``DeviceArray``, on either CPU or GPU.
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take_ownership: If ``True``, JAX hands ownership of the buffer to DLPack,
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and the consumer is free to mutate the buffer; the JAX buffer acts as if
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it were deleted. If ``False``, JAX retains ownership of the buffer; it is
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undefined behavior if the DLPack consumer writes to a buffer that JAX
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owns.
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"""
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if not isinstance(x, array.ArrayImpl):
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raise TypeError("Argument to to_dlpack must be a jax.Array, "
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f"got {type(x)}")
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assert len(x.devices()) == 1
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return xla_client._xla.buffer_to_dlpack_managed_tensor(
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x.addressable_data(0), take_ownership=take_ownership) # type: ignore
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def from_dlpack(dlpack):
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"""Returns a ``DeviceArray`` representation of a DLPack tensor.
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The returned ``DeviceArray`` shares memory with ``dlpack``.
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Args:
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dlpack: a DLPack tensor, on either CPU or GPU.
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"""
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cpu_backend = xla_bridge.get_backend("cpu")
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try:
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gpu_backend = xla_bridge.get_backend("cuda")
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except RuntimeError:
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gpu_backend = None
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# Try ROCm if CUDA backend not found
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if gpu_backend is None:
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try:
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gpu_backend = xla_bridge.get_backend("rocm")
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except RuntimeError:
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gpu_backend = None
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return jnp.asarray(xla_client._xla.dlpack_managed_tensor_to_buffer(
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dlpack, cpu_backend, gpu_backend))
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