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提交 e6d07bcb authored 作者: Brandon T. Willard's avatar Brandon T. Willard 提交者： Brandon T. Willard
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Delete disabled gpu-related config options

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aesara/configdefaults.py
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......@@ -15,7 +15,6 @@ import aesara.configparser
from aesara.configparser import (
BoolParam,
ConfigParam,
ContextsParam,
DeviceParam,
EnumStr,
FloatParam,
......@@ -332,29 +331,11 @@ def add_basic_configvars():
config.add(
"device",
(
"Default device for computations. If cuda* or opencl*, change the"
"default to try to move computation to the GPU. Do not use upper case"
"letters, only lower case even if NVIDIA uses capital letters. "
"'gpu' means let the driver select the gpu (needed for gpu in exclusive mode). "
"'gpuX' mean use the gpu number X."
),
("Default device for computations. only cpu is supported for now"),
DeviceParam("cpu", mutable=False),
in_c_key=False,
)
config.add(
"init_gpu_device",
(
"Initialize the gpu device to use, works only if device=cpu. "
"Unlike 'device', setting this option will NOT move computations, "
"nor shared variables, to the specified GPU. "
"It can be used to run GPU-specific tests on a particular GPU."
),
DeviceParam("", mutable=False),
in_c_key=False,
)
config.add(
"force_device",
"Raise an error if we can't use the specified device",
......@@ -378,79 +359,6 @@ def add_basic_configvars():
in_c_key=False,
)
config.add(
"contexts",
"""
Context map for multi-gpu operation. Format is a
semicolon-separated list of names and device names in the
'name->dev_name' format. An example that would map name 'test' to
device 'cuda0' and name 'test2' to device 'opencl0:0' follows:
"test->cuda0;test2->opencl0:0".
Invalid context names are 'cpu', 'cuda*' and 'opencl*'
""",
ContextsParam(),
in_c_key=False,
)
config.add(
"print_active_device",
"Print active device at when the GPU device is initialized.",
BoolParam(True, mutable=False),
in_c_key=False,
)
# config.add(
# "gpuarray__preallocate",
# """If negative it disables the allocation cache. If
# between 0 and 1 it enables the allocation cache and
# preallocates that fraction of the total GPU memory. If 1
# or greater it will preallocate that amount of memory (in
# megabytes).""",
# FloatParam(0, mutable=False),
# in_c_key=False,
# )
# config.add(
# "gpuarray__sched",
# """The sched parameter passed for context creation to pygpu.
# With CUDA, using "multi" is equivalent to using the parameter
# cudaDeviceScheduleBlockingSync. This is useful to lower the
# CPU overhead when waiting for GPU. One user found that it
# speeds up his other processes that was doing data augmentation.
# """,
# EnumStr("default", ["multi", "single"]),
# )
# config.add(
# "gpuarray__single_stream",
# """
# If your computations are mostly lots of small elements,
# using single-stream will avoid the synchronization
# overhead and usually be faster. For larger elements it
# does not make a difference yet. In the future when true
# multi-stream is enabled in libgpuarray, this may change.
# If you want to make sure to have optimal performance,
# check both options.
# """,
# BoolParam(True),
# in_c_key=False,
# )
# config.add(
# "cuda__root",
# "Location of the cuda installation",
# StrParam(get_cuda_root),
# in_c_key=False,
# )
# config.add(
# "cuda__include_path",
# "Location of the cuda includes",
# StrParam(default_cuda_include),
# in_c_key=False,
# )
# This flag determines whether or not to raise error/warning message if
# there is a CPU Op in the computational graph.
config.add(
......@@ -478,105 +386,6 @@ def add_basic_configvars():
)
# def add_dnn_configvars():
# config.add(
# "dnn__conv__algo_fwd",
# "Default implementation to use for cuDNN forward convolution.",
# EnumStr("small", SUPPORTED_DNN_CONV_ALGO_FWD),
# in_c_key=False,
# )
# config.add(
# "dnn__conv__algo_bwd_data",
# "Default implementation to use for cuDNN backward convolution to "
# "get the gradients of the convolution with regard to the inputs.",
# EnumStr("none", SUPPORTED_DNN_CONV_ALGO_BWD_DATA),
# in_c_key=False,
# )
# config.add(
# "dnn__conv__algo_bwd_filter",
# "Default implementation to use for cuDNN backward convolution to "
# "get the gradients of the convolution with regard to the "
# "filters.",
# EnumStr("none", SUPPORTED_DNN_CONV_ALGO_BWD_FILTER),
# in_c_key=False,
# )
# config.add(
# "dnn__conv__precision",
# "Default data precision to use for the computation in cuDNN "
# "convolutions (defaults to the same dtype as the inputs of the "
# "convolutions, or float32 if inputs are float16).",
# EnumStr("as_input_f32", SUPPORTED_DNN_CONV_PRECISION),
# in_c_key=False,
# )
# config.add(
# "dnn__base_path",
# "Install location of cuDNN.",
# StrParam(default_dnn_base_path),
# in_c_key=False,
# )
# config.add(
# "dnn__include_path",
# "Location of the cudnn header",
# StrParam(default_dnn_inc_path),
# in_c_key=False,
# )
# config.add(
# "dnn__library_path",
# "Location of the cudnn link library.",
# StrParam(default_dnn_lib_path),
# in_c_key=False,
# )
# config.add(
# "dnn__bin_path",
# "Location of the cuDNN load library "
# "(on non-windows platforms, "
# "this is the same as dnn__library_path)",
# StrParam(default_dnn_bin_path),
# in_c_key=False,
# )
# config.add(
# "dnn__enabled",
# "'auto', use cuDNN if available, but silently fall back"
# " to not using it if not present."
# " If True and cuDNN can not be used, raise an error."
# " If False, disable cudnn even if present."
# " If no_check, assume present and the version between header and library match (so less compilation at context init)",
# EnumStr("auto", ["True", "False", "no_check"]),
# in_c_key=False,
# )
# def add_magma_configvars():
# config.add(
# "magma__include_path",
# "Location of the magma header",
# StrParam(""),
# in_c_key=False,
# )
# config.add(
# "magma__library_path",
# "Location of the magma library",
# StrParam(""),
# in_c_key=False,
# )
# config.add(
# "magma__enabled",
# " If True, use magma for matrix computation." " If False, disable magma",
# BoolParam(False),
# in_c_key=False,
# )
def _is_gt_0(x):
return x > 0
......@@ -893,21 +702,6 @@ def add_traceback_configvars():
def add_experimental_configvars():
# config.add(
# "experimental__unpickle_gpu_on_cpu",
# "Allow unpickling of pickled GpuArrays as numpy.ndarrays."
# "This is useful, if you want to open a GpuArray without "
# "having cuda installed."
# "If you have cuda installed, this will force unpickling to"
# "be done on the cpu to numpy.ndarray."
# "Please be aware that this may get you access to the data,"
# "however, trying to unpicke gpu functions will not succeed."
# "This flag is experimental and may be removed any time, when"
# "gpu<>cpu transparency is solved.",
# BoolParam(default=False),
# in_c_key=False,
# )
config.add(
"experimental__local_alloc_elemwise",
"DEPRECATED: If True, enable the experimental"
......@@ -1608,17 +1402,6 @@ def add_caching_dir_configvars():
in_c_key=False,
)
# config.add(
# "gpuarray__cache_path",
# "Directory to cache pre-compiled kernels for the gpuarray backend.",
# ConfigParam(
# _get_default_gpuarray__cache_path,
# apply=_filter_base_compiledir,
# mutable=False,
# ),
# in_c_key=False,
# )
# Those are the options provided by Aesara to choose algorithms at runtime.
SUPPORTED_DNN_CONV_ALGO_RUNTIME = (
......@@ -1676,8 +1459,6 @@ config = aesara.configparser._config
# The functions below register config variables into the config instance above.
add_basic_configvars()
# add_dnn_configvars()
# add_magma_configvars()
add_compile_configvars()
add_tensor_configvars()
add_traceback_configvars()
......
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