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Previously return List[List[Device]] which is not how the function is used. Updated to use the alternative overload.
158 lines
4.5 KiB
Python
158 lines
4.5 KiB
Python
# Copyright 2021 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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"""Experimental IREE backend for JAX.
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This backend is quite incomplete, but exists to allow experimenting with
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using IREE to compile and run JAX computations instead of XLA.
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"""
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# pytype: skip-file
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from typing import Any, List, Sequence
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import iree.compiler
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from iree import runtime as iree_runtime
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from jax._src.lib import xla_client
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import numpy as np
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class IreeDevice:
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def __init__(self, client):
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self.id = 0
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self.host_id = 0
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self.process_index = 0
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self.platform = "iree"
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self.device_kind = "IREE device"
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self.client = client
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def __str__(self) -> str:
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return "IreeDevice"
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def transfer_to_infeed(self, literal: Any):
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raise NotImplementedError("transfer_to_infeed")
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def transfer_from_outfeed(self, shape: xla_client.Shape):
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raise NotImplementedError("transfer_to_outfeed")
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def live_buffers(self) -> List['IreeBuffer']:
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raise NotImplementedError("live_buffers")
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class IreeBuffer(xla_client.DeviceArrayBase):
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def __init__(self, client, device, npy_value):
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self.client = client
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self._device = device
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self._npy_value = np.asarray(npy_value)
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def to_py(self) -> np.ndarray:
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return self._npy_value
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def to_iree(self):
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return self._npy_value
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def platform(self):
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return self.client.platform
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def device(self):
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return self._device
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class IreeExecutable:
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def __init__(self, client, devices, module_object, function_name):
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self.client = client
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self.traceback = None
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self.fingerprint = None
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self._devices = devices
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self.module_object = module_object
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self.function_name = function_name
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def local_devices(self) -> List[IreeDevice]:
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return self._devices
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def execute(self, arguments: Sequence[IreeBuffer]) -> List[IreeBuffer]:
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inputs = [arg.to_iree() for arg in arguments]
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outputs = self.module_object[self.function_name](*inputs)
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# TODO(phawkins): Have a way to just have it always return the list,
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# regardless of arity.
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if not isinstance(outputs, list):
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outputs = [outputs]
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return [
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IreeBuffer(self.client, self._devices[0], output) for output in outputs
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]
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class IreeClient:
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def __init__(self,
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*,
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compile_target_backends: Sequence[str] = ("cpu",),
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runtime_driver: str = "dylib"):
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self.platform = "iree"
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self.platform_version = "0.0.1"
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self.runtime_type = "iree"
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self.iree_config = iree_runtime.system_api.Config(runtime_driver)
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self._devices = [IreeDevice(self)]
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def process_index(self) -> int:
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return 0
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def device_count(self) -> int:
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return len(self._devices)
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def devices(self) -> List[IreeDevice]:
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return self._devices
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def local_devices(self) -> List[IreeDevice]:
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return self._devices
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def local_device_count(self) -> int:
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return len(self._devices)
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def get_default_device_assignment(
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self,
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num_replicas: int) -> List[IreeDevice]:
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if num_replicas != 1:
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raise NotImplementedError("Only single-device computations implemented")
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return [self._devices[0]]
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def compile(self, computation: str,
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compile_options: xla_client.CompileOptions) -> IreeExecutable:
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iree_binary = iree.compiler.compile_str(
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computation, target_backends=["dylib"], input_type="mhlo")
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# Load it into the runtime.
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vm_module = iree_runtime.binding.VmModule.from_flatbuffer(iree_binary)
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module_object = iree_runtime.load_vm_module(vm_module, self.iree_config)
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return IreeExecutable(self, self._devices, module_object, "main")
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def buffer_from_pyval(
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self,
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argument: Any,
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device: IreeDevice,
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force_copy: bool = True,
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host_buffer_semantics: xla_client.HostBufferSemantics = xla_client
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.HostBufferSemantics.ZERO_COPY
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) -> IreeBuffer:
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# TODO(phawkins): IREE's python API will accept a numpy array directly but
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# may want to explicitly construct a lower level BufferView to avoid copies.
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return IreeBuffer(self, device, np.array(argument, copy=True))
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def iree_client_factory():
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return IreeClient()
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