from labm8 import app
from util.photolib.shutterbug import shutterbug
FLAGS = app.FLAGS
app.DEFINE_string(
'src_dir', None,
'The directory to create chunks from. All files in this directory are '
'packed into chunks.')
app.DEFINE_string(
'chunks_dir', None,
'The root directory of the chunks. Each chunk is a directory containing '
'files and a manifest.')
app.DEFINE_integer(
'size_mb', 4695,
'The smaximum size of each chunk in megabytes. This excludes the MANIFEST '
'and README files which are generated.')
app.DEFINE_string('chunk_prefix', 'chunk_',
'The name to prepend to generated chunks.')
app.DEFINE_boolean(
'random_ordering', True,
'Whether to randomize the ordering of files across and within chunks. If '
'--norandom_ordering is used, the files are arranged in chunks in the order '
'in which they are found in --src_dir. This is not recommended, as it means '
'the loss of a chunk causes a loss in a contiguous block of files.')
app.DEFINE_integer(
'random_ordering_seed', 0,
'The number used to seed the random number generator. Not used if '
'--norandom_ordering is set. Using the same seed produces the same ordering '
'of files.')
app.DEFINE_boolean(
from deeplearning.clgen import sample_observers as sample_observers_lib
from deeplearning.clgen import samplers
from deeplearning.clgen.models import models
from deeplearning.clgen.models import pretrained
from deeplearning.clgen.proto import clgen_pb2
from deeplearning.clgen.proto import model_pb2
from labm8 import app
from labm8 import pbutil
from labm8 import prof
FLAGS = app.FLAGS
app.DEFINE_string('config', '/clgen/config.pbtxt',
'Path to a clgen.Instance proto file.')
app.DEFINE_integer(
'min_samples', 0,
'The minimum number of samples to make. If <= 0, sampling continues '
'indefinitely and never terminates.')
app.DEFINE_boolean('print_samples', True,
'If set, print the generated samples.')
app.DEFINE_boolean('cache_samples', False,
'If set, cache the generated sample protobufs.')
app.DEFINE_string('sample_text_dir', None,
'A directory to write plain text samples to.')
app.DEFINE_string('stop_after', None,
'Stop CLgen early. Valid options are: "corpus", or "train".')
app.DEFINE_string(
'print_cache_path', None,
'Print the directory of a cache and exit. Valid options are: "corpus", '
'"model", or "sampler".')
app.DEFINE_string(
'export_model', None,
"""
import collections
import pathlib
import re
import subprocess
import sys
import typing
from compilers.llvm import llvm
from labm8 import app
from labm8 import bazelutil
from labm8 import system
FLAGS = app.FLAGS
app.DEFINE_integer('clang_timeout_seconds', 60,
'The maximum number of seconds to allow process to run.')
_LLVM_REPO = 'llvm_linux' if system.is_linux() else 'llvm_mac'
# Path to clang binary.
CLANG = bazelutil.DataPath(f'{_LLVM_REPO}/bin/clang')
# Valid optimization levels.
OPTIMIZATION_LEVELS = {"-O0", "-O1", "-O2", "-O3", "-Ofast", "-Os", "-Oz"}
# A structured representation of the output of clang's bisect debugging, e.g.
# $ clang foo.c -mllvm -opt-bisect-limit=-1.
# The output is of the form:
# BISECT: running pass (<number>) <name> on <target_type> (<target>)
#
# See ClangBisectMessageToInvocation() for the conversion.
from compilers.llvm import opt_util <<<<<<< HEAD:deeplearning/ml4pl/graphs/labelled/dataflow/alias_set/alias_set.py from labm8.py import app from labm8.py import decorators ======= from labm8 import app from labm8 import decorators >>>>>>> d5cd0e23d... Reduce verbosity of alias_set.:deeplearning/ml4pl/graphs/labelled/alias_set/alias_set.py FLAGS = app.FLAGS app.DEFINE_integer( "alias_set_min_size", 2, "The minimum number of pointers in an alias set to be used as a labelled " "example.", ) @decorators.timeout(seconds=120) def AnnotateAliasSet( g: nx.MultiDiGraph, root_identifier: str, identifiers_in_set: typing.List[str], x_label: str = "x", y_label: str = "y", false=False, true=True, ) -> int: """
from deeplearning.clgen import samplers
from deeplearning.clgen import telemetry
from deeplearning.clgen.models import backends
from deeplearning.clgen.models import data_generators
from deeplearning.clgen.proto import model_pb2
from labm8 import app
from labm8 import humanize
FLAGS = app.FLAGS
app.DEFINE_boolean(
'clgen_tf_backend_reset_inference_state_between_batches', False,
'If set, reset the network state between sample batches. Else, the model '
'state is unaffected.')
app.DEFINE_integer(
'clgen_tf_backend_tensorboard_summary_step_count', 10,
'The number of steps between writing tensorboard summaries.')
class TensorFlowBackend(backends.BackendBase):
"""A model with an embedding layer, using a keras backend."""
def __init__(self, *args, **kwargs):
"""Instantiate a model.
Args:
args: Arguments to be passed to BackendBase.__init__().
kwargs: Arguments to be passed to BackendBase.__init__().
"""
super(TensorFlowBackend, self).__init__(*args, **kwargs)
# Attributes that will be lazily set.
'Path to a clgen.Instance proto file containing a full '
'CLgen configuration.')
app.DEFINE_string('clgen_working_dir',
str(pathlib.Path('~/.cache/clgen').expanduser()),
'The directory for CLgen working files.')
# Corpus options.
app.DEFINE_string('clgen_corpus_dir',
"/mnt/cc/data/datasets/github/corpuses/opencl",
"Directory where the corpus is stored.")
app.DEFINE_boolean('clgen_multichar_tokenizer', False,
'If true, use multichar OpenCL token.')
# Model options.
app.DEFINE_integer('clgen_layer_size', 512, 'Size of LSTM model layers.')
app.DEFINE_integer('clgen_num_layers', 2, 'Number of layers in LSTM model.')
app.DEFINE_integer('clgen_max_sample_length', 20000,
'The maximum length of CLgen samples. If 0, no limit.')
# Training options.
app.DEFINE_integer("clgen_num_epochs", 50, "The number of training epochs.")
app.DEFINE_integer("clgen_training_sequence_length", 64,
"CLgen training sequence length.")
app.DEFINE_integer("clgen_training_batch_size", 64,
"CLgen training batch size.")
# Sampling options.
app.DEFINE_string("clgen_seed_text", "kernel void ", "CLgen sample seed text.")
app.DEFINE_float("clgen_sample_temperature", 1.0, "CLgen sampling temperature.")
app.DEFINE_integer("clgen_sample_sequence_length", 1024,
import pathlib
import pytest
import re
import tempfile
import typing
from importlib import util as importutil
from labm8 import app
FLAGS = app.FLAGS
app.DEFINE_boolean('test_color', False, 'Colorize pytest output.')
app.DEFINE_boolean('test_skip_slow', True,
'Skip tests that have been marked slow.')
app.DEFINE_integer(
'test_maxfail', 1,
'The maximum number of tests that can fail before execution terminates. '
'If --test_maxfail=0, all tests will execute.')
app.DEFINE_boolean('test_capture_output', True,
'Capture stdout and stderr during test execution.')
app.DEFINE_boolean(
'test_print_durations', True,
'Print the duration of the slowest tests at the end of execution. Use '
'--test_durations to set the number of tests to print the durations of.')
app.DEFINE_integer(
'test_durations', 3,
'The number of slowest tests to print the durations of after execution. '
'If --test_durations=0, the duration of all tests is printed.')
app.DEFINE_string(
'test_coverage_data_dir', None,
'Run tests with statement coverage and write coverage.py data files to '
'this directory. The directory is created. Existing files are untouched.')