code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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def jit_trivial_dispatch(state):
"""Benchmarks only the duration for jitted_f to return the future."""
f = jax.jit(swap)
a, b = f(1, 2)
x = f(a, b)
while state:
x = f(a, b)
x[0].block_until_ready() | Benchmarks only the duration for jitted_f to return the future. | jit_trivial_dispatch | python | jax-ml/jax | benchmarks/api_benchmark.py | https://github.com/jax-ml/jax/blob/master/benchmarks/api_benchmark.py | Apache-2.0 |
def add_global_arguments(parser: argparse.ArgumentParser):
"""Adds all the global arguments that applies to all the CLI subcommands."""
parser.add_argument(
"--python_version",
type=str,
default=f"{sys.version_info.major}.{sys.version_info.minor}",
help=
"""
Hermetic Python v... | Adds all the global arguments that applies to all the CLI subcommands. | add_global_arguments | python | jax-ml/jax | build/build.py | https://github.com/jax-ml/jax/blob/master/build/build.py | Apache-2.0 |
def add_artifact_subcommand_arguments(parser: argparse.ArgumentParser):
"""Adds all the arguments that applies to the artifact subcommands."""
parser.add_argument(
"--wheels",
type=str,
default="jaxlib",
help=
"""
A comma separated list of JAX wheels to build. E.g: --wheels="... | Adds all the arguments that applies to the artifact subcommands. | add_artifact_subcommand_arguments | python | jax-ml/jax | build/build.py | https://github.com/jax-ml/jax/blob/master/build/build.py | Apache-2.0 |
def parse_test_log(log_file):
"""Parses the test module log file to extract test modules and functions."""
test_files = set()
with open(log_file, "r") as f:
for line in f:
report = json.loads(line)
if "nodeid" in report:
module = report["nodeid"].split("::")[0... | Parses the test module log file to extract test modules and functions. | parse_test_log | python | jax-ml/jax | build/rocm/run_single_gpu.py | https://github.com/jax-ml/jax/blob/master/build/rocm/run_single_gpu.py | Apache-2.0 |
def install_amdgpu_installer_internal(rocm_version):
"""
Download and install the "amdgpu-installer" package from internal builds
on the current system.
"""
md = os_release_meta()
url, fn = _build_installer_url(rocm_version, md)
try:
# download installer
LOG.info("Downloadin... |
Download and install the "amdgpu-installer" package from internal builds
on the current system.
| install_amdgpu_installer_internal | python | jax-ml/jax | build/rocm/tools/get_rocm.py | https://github.com/jax-ml/jax/blob/master/build/rocm/tools/get_rocm.py | Apache-2.0 |
def get_libc_version():
"""
Detect and return glibc version that the current Python is linked against.
This mimics the detection behavior of the 'wheel' and 'auditwheel' projects,
but without any PyPy or libmusl support.
"""
try:
version_str = os.confstr("CS_GNU_LIBC_VERSION")
... |
Detect and return glibc version that the current Python is linked against.
This mimics the detection behavior of the 'wheel' and 'auditwheel' projects,
but without any PyPy or libmusl support.
| get_libc_version | python | jax-ml/jax | build/rocm/tools/libc.py | https://github.com/jax-ml/jax/blob/master/build/rocm/tools/libc.py | Apache-2.0 |
async def _process_log_stream(stream, result: CommandResult):
"""Logs the output of a subprocess stream."""
while True:
line_bytes = await stream.readline()
if not line_bytes:
break
line = line_bytes.decode().rstrip()
result.logs += line
logger.info("%s", line) | Logs the output of a subprocess stream. | _process_log_stream | python | jax-ml/jax | build/tools/command.py | https://github.com/jax-ml/jax/blob/master/build/tools/command.py | Apache-2.0 |
async def run(self, cmd: str, dry_run: bool = False, detailed_timestamped_log: bool = False) -> CommandResult:
"""
Executes a subprocess command.
Args:
cmd: The command to execute.
dry_run: If True, prints the command instead of executing it.
Returns:
A CommandResult instance.
... |
Executes a subprocess command.
Args:
cmd: The command to execute.
dry_run: If True, prints the command instead of executing it.
Returns:
A CommandResult instance.
| run | python | jax-ml/jax | build/tools/command.py | https://github.com/jax-ml/jax/blob/master/build/tools/command.py | Apache-2.0 |
def download_and_verify_bazel():
"""Downloads a bazel binary from GitHub, verifying its SHA256 hash."""
package = bazel_packages.get((platform.system(), platform.machine()))
if package is None:
return None
if not os.access(package.file, os.X_OK):
uri = (package.base_uri or BAZEL_BASE_URI) + package.fil... | Downloads a bazel binary from GitHub, verifying its SHA256 hash. | download_and_verify_bazel | python | jax-ml/jax | build/tools/utils.py | https://github.com/jax-ml/jax/blob/master/build/tools/utils.py | Apache-2.0 |
def get_bazel_paths(bazel_path_flag):
"""Yields a sequence of guesses about bazel path.
Some of sequence elements can be None. The resulting iterator is lazy and
potentially has a side effects.
"""
yield bazel_path_flag
yield shutil.which("bazel")
yield download_and_verify_bazel() | Yields a sequence of guesses about bazel path.
Some of sequence elements can be None. The resulting iterator is lazy and
potentially has a side effects.
| get_bazel_paths | python | jax-ml/jax | build/tools/utils.py | https://github.com/jax-ml/jax/blob/master/build/tools/utils.py | Apache-2.0 |
def get_bazel_path(bazel_path_flag):
"""Returns the path to a Bazel binary, downloading Bazel if not found.
Also, checks Bazel's version is at least newer than 7.4.1
A manual version check is needed only for really old bazel versions.
Newer bazel releases perform their own version check against .bazelversion
... | Returns the path to a Bazel binary, downloading Bazel if not found.
Also, checks Bazel's version is at least newer than 7.4.1
A manual version check is needed only for really old bazel versions.
Newer bazel releases perform their own version check against .bazelversion
(see for details
https://blog.bazel.bu... | get_bazel_path | python | jax-ml/jax | build/tools/utils.py | https://github.com/jax-ml/jax/blob/master/build/tools/utils.py | Apache-2.0 |
def get_jax_configure_bazel_options(bazel_command: list[str], use_new_wheel_build_rule: bool):
"""Returns the bazel options to be written to .jax_configure.bazelrc."""
# Get the index of the "run" parameter. Build options will come after "run" so
# we find the index of "run" and filter everything after it. If we ... | Returns the bazel options to be written to .jax_configure.bazelrc. | get_jax_configure_bazel_options | python | jax-ml/jax | build/tools/utils.py | https://github.com/jax-ml/jax/blob/master/build/tools/utils.py | Apache-2.0 |
def msys_to_windows_path(msys_path):
"""Converts an MSYS path to a Windows path using cygpath.
Args:
msys_path: The MSYS path to convert.
Returns:
The corresponding Windows path.
"""
try:
# Use cygpath with the -w flag to convert to Windows format
process = subprocess.run(['cygpath', '-w', m... | Converts an MSYS path to a Windows path using cygpath.
Args:
msys_path: The MSYS path to convert.
Returns:
The corresponding Windows path.
| msys_to_windows_path | python | jax-ml/jax | ci/utilities/convert_msys_paths_to_win_paths.py | https://github.com/jax-ml/jax/blob/master/ci/utilities/convert_msys_paths_to_win_paths.py | Apache-2.0 |
def should_convert(var: str,
convert: list[str] | None):
"""Check the variable name against convert list"""
if var in convert:
return True
else:
return False | Check the variable name against convert list | should_convert | python | jax-ml/jax | ci/utilities/convert_msys_paths_to_win_paths.py | https://github.com/jax-ml/jax/blob/master/ci/utilities/convert_msys_paths_to_win_paths.py | Apache-2.0 |
def jax_issue_role(name, rawtext, text, lineno, inliner, options=None,
content=()):
"""Generate links to jax issues or PRs in sphinx.
Usage::
:jax-issue:`1234`
This will output a hyperlink of the form
`#1234 <http://github.com/jax-ml/jax/issues/1234>`_. These links work even
for PR... | Generate links to jax issues or PRs in sphinx.
Usage::
:jax-issue:`1234`
This will output a hyperlink of the form
`#1234 <http://github.com/jax-ml/jax/issues/1234>`_. These links work even
for PR numbers.
| jax_issue_role | python | jax-ml/jax | docs/sphinxext/jax_extensions.py | https://github.com/jax-ml/jax/blob/master/docs/sphinxext/jax_extensions.py | Apache-2.0 |
def create_field_item(label, content):
"""Create a field list item with a label and content side by side.
Args:
label: The label text for the field name
content: The content to add (a node or text)
Returns:
A field list item with the label and content side by side.
"""
# Create a field list item... | Create a field list item with a label and content side by side.
Args:
label: The label text for the field name
content: The content to add (a node or text)
Returns:
A field list item with the label and content side by side.
| create_field_item | python | jax-ml/jax | docs/sphinxext/jax_list_config_options.py | https://github.com/jax-ml/jax/blob/master/docs/sphinxext/jax_list_config_options.py | Apache-2.0 |
def clipped_grad(params, l2_norm_clip, single_example_batch):
"""Evaluate gradient for a single-example batch and clip its grad norm."""
grads = grad(loss)(params, single_example_batch)
nonempty_grads, tree_def = tree_flatten(grads)
total_grad_norm = jnp.linalg.norm(
jnp.array([jnp.linalg.norm(neg.ravel()... | Evaluate gradient for a single-example batch and clip its grad norm. | clipped_grad | python | jax-ml/jax | examples/differentially_private_sgd.py | https://github.com/jax-ml/jax/blob/master/examples/differentially_private_sgd.py | Apache-2.0 |
def private_grad(params, batch, rng, l2_norm_clip, noise_multiplier,
batch_size):
"""Return differentially private gradients for params, evaluated on batch."""
clipped_grads = vmap(clipped_grad, (None, None, 0))(params, l2_norm_clip, batch)
clipped_grads_flat, grads_treedef = tree_flatten(clipped... | Return differentially private gradients for params, evaluated on batch. | private_grad | python | jax-ml/jax | examples/differentially_private_sgd.py | https://github.com/jax-ml/jax/blob/master/examples/differentially_private_sgd.py | Apache-2.0 |
def cov_map(cov_func, xs, xs2=None):
"""Compute a covariance matrix from a covariance function and data points.
Args:
cov_func: callable function, maps pairs of data points to scalars.
xs: array of data points, stacked along the leading dimension.
Returns:
A 2d array `a` such that `a[i, j... | Compute a covariance matrix from a covariance function and data points.
Args:
cov_func: callable function, maps pairs of data points to scalars.
xs: array of data points, stacked along the leading dimension.
Returns:
A 2d array `a` such that `a[i, j] = cov_func(xs[i], xs[j])`.
| cov_map | python | jax-ml/jax | examples/gaussian_process_regression.py | https://github.com/jax-ml/jax/blob/master/examples/gaussian_process_regression.py | Apache-2.0 |
def elbo(rng, params, images):
"""Monte Carlo estimate of the negative evidence lower bound."""
enc_params, dec_params = params
mu_z, sigmasq_z = encode(enc_params, images)
logits_x = decode(dec_params, gaussian_sample(rng, mu_z, sigmasq_z))
return bernoulli_logpdf(logits_x, images) - gaussian_kl(mu_z, sigmas... | Monte Carlo estimate of the negative evidence lower bound. | elbo | python | jax-ml/jax | examples/mnist_vae.py | https://github.com/jax-ml/jax/blob/master/examples/mnist_vae.py | Apache-2.0 |
def image_sample(rng, params, nrow, ncol):
"""Sample images from the generative model."""
_, dec_params = params
code_rng, img_rng = random.split(rng)
logits = decode(dec_params, random.normal(code_rng, (nrow * ncol, 10)))
sampled_images = random.bernoulli(img_rng, jnp.logaddexp(0., logits))
return image_gr... | Sample images from the generative model. | image_sample | python | jax-ml/jax | examples/mnist_vae.py | https://github.com/jax-ml/jax/blob/master/examples/mnist_vae.py | Apache-2.0 |
def image_grid(nrow, ncol, imagevecs, imshape):
"""Reshape a stack of image vectors into an image grid for plotting."""
images = iter(imagevecs.reshape((-1,) + imshape))
return jnp.vstack([jnp.hstack([next(images).T for _ in range(ncol)][::-1])
for _ in range(nrow)]).T | Reshape a stack of image vectors into an image grid for plotting. | image_grid | python | jax-ml/jax | examples/mnist_vae.py | https://github.com/jax-ml/jax/blob/master/examples/mnist_vae.py | Apache-2.0 |
def onnx_maxpool(x, kernel_shape, pads=None, strides=None):
"""Numpy-backed implementation of ONNX MaxPool op."""
prefix = (1,) * (x.ndim - len(kernel_shape))
dims = prefix + tuple(kernel_shape)
pads = tuple(pads) if pads else [0] * len(kernel_shape)
strides = (prefix + tuple(strides)) if strides else [1] * l... | Numpy-backed implementation of ONNX MaxPool op. | onnx_maxpool | python | jax-ml/jax | examples/onnx2xla.py | https://github.com/jax-ml/jax/blob/master/examples/onnx2xla.py | Apache-2.0 |
def onnx_conv(x, w, b=0, group=1, kernel_shape=None, pads=None, strides=None,
dilations=None, auto_pad=None):
"""Numpy-backed implementation of ONNX Conv op."""
assert group == 1
kernel_shape = kernel_shape or w.shape
strides = strides or [1] * (w.ndim - 2)
if auto_pad:
auto_pad = 'SAME' if ... | Numpy-backed implementation of ONNX Conv op. | onnx_conv | python | jax-ml/jax | examples/onnx2xla.py | https://github.com/jax-ml/jax/blob/master/examples/onnx2xla.py | Apache-2.0 |
def onnx_add(a, b, axis=None, broadcast=True):
"""Numpy-backed implementation of ONNX Add op."""
if broadcast:
axis = (a.dim - b.ndim) if axis is None else axis % a.ndim
assert a.shape[axis:][:b.ndim] == b.shape
b_shape = np.ones(a.ndim, dtype='int64')
b_shape[axis:axis + b.ndim] = b.shape
b = j... | Numpy-backed implementation of ONNX Add op. | onnx_add | python | jax-ml/jax | examples/onnx2xla.py | https://github.com/jax-ml/jax/blob/master/examples/onnx2xla.py | Apache-2.0 |
def _get_version_for_build() -> str:
"""Determine the version at build time.
The returned version string depends on which environment variables are set:
- if WHEEL_VERSION_SUFFIX is set: version looks like "0.5.1.dev20230906+ge58560fdc"
Here the WHEEL_VERSION_SUFFIX value is ".dev20230906+ge58560fdc".
Pl... | Determine the version at build time.
The returned version string depends on which environment variables are set:
- if WHEEL_VERSION_SUFFIX is set: version looks like "0.5.1.dev20230906+ge58560fdc"
Here the WHEEL_VERSION_SUFFIX value is ".dev20230906+ge58560fdc".
Please note that the WHEEL_VERSION_SUFFIX va... | _get_version_for_build | python | jax-ml/jax | jax/version.py | https://github.com/jax-ml/jax/blob/master/jax/version.py | Apache-2.0 |
def _is_prerelease() -> bool:
"""Determine if this is a pre-release ("rc" wheels) build."""
rc_version = os.getenv("WHEEL_VERSION_SUFFIX", "")
return True if rc_version.startswith("rc") else False | Determine if this is a pre-release ("rc" wheels) build. | _is_prerelease | python | jax-ml/jax | jax/version.py | https://github.com/jax-ml/jax/blob/master/jax/version.py | Apache-2.0 |
def _write_version(fname: str) -> None:
"""Used by setup.py to write the specified version info into the source tree."""
release_version = _get_version_for_build()
old_version_string = "_release_version: str | None = None"
new_version_string = f"_release_version: str = {release_version!r}"
fhandle = pathlib.P... | Used by setup.py to write the specified version info into the source tree. | _write_version | python | jax-ml/jax | jax/version.py | https://github.com/jax-ml/jax/blob/master/jax/version.py | Apache-2.0 |
def optimizer(opt_maker: Callable[...,
tuple[Callable[[Params], State],
Callable[[Step, Updates, Params], Params],
Callable[[State], Params]]]) -> Callable[..., Optimizer]:
"""Decorator to make an optimizer defined for arrays generalize to containers.
With this decorator, you can write init, upda... | Decorator to make an optimizer defined for arrays generalize to containers.
With this decorator, you can write init, update, and get_params functions that
each operate only on single arrays, and convert them to corresponding
functions that operate on pytrees of parameters. See the optimizers defined in
optimiz... | optimizer | python | jax-ml/jax | jax/example_libraries/optimizers.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/optimizers.py | Apache-2.0 |
def sgd(step_size):
"""Construct optimizer triple for stochastic gradient descent.
Args:
step_size: positive scalar, or a callable representing a step size schedule
that maps the iteration index to a positive scalar.
Returns:
An (init_fun, update_fun, get_params) triple.
"""
step_size = make_s... | Construct optimizer triple for stochastic gradient descent.
Args:
step_size: positive scalar, or a callable representing a step size schedule
that maps the iteration index to a positive scalar.
Returns:
An (init_fun, update_fun, get_params) triple.
| sgd | python | jax-ml/jax | jax/example_libraries/optimizers.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/optimizers.py | Apache-2.0 |
def momentum(step_size: Schedule, mass: float):
"""Construct optimizer triple for SGD with momentum.
Args:
step_size: positive scalar, or a callable representing a step size schedule
that maps the iteration index to a positive scalar.
mass: positive scalar representing the momentum coefficient.
Re... | Construct optimizer triple for SGD with momentum.
Args:
step_size: positive scalar, or a callable representing a step size schedule
that maps the iteration index to a positive scalar.
mass: positive scalar representing the momentum coefficient.
Returns:
An (init_fun, update_fun, get_params) trip... | momentum | python | jax-ml/jax | jax/example_libraries/optimizers.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/optimizers.py | Apache-2.0 |
def nesterov(step_size: Schedule, mass: float):
"""Construct optimizer triple for SGD with Nesterov momentum.
Args:
step_size: positive scalar, or a callable representing a step size schedule
that maps the iteration index to a positive scalar.
mass: positive scalar representing the momentum coefficie... | Construct optimizer triple for SGD with Nesterov momentum.
Args:
step_size: positive scalar, or a callable representing a step size schedule
that maps the iteration index to a positive scalar.
mass: positive scalar representing the momentum coefficient.
Returns:
An (init_fun, update_fun, get_par... | nesterov | python | jax-ml/jax | jax/example_libraries/optimizers.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/optimizers.py | Apache-2.0 |
def adagrad(step_size, momentum=0.9):
"""Construct optimizer triple for Adagrad.
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization:
http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf
Args:
step_size: positive scalar, or a callable representing a step size schedule
t... | Construct optimizer triple for Adagrad.
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization:
http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf
Args:
step_size: positive scalar, or a callable representing a step size schedule
that maps the iteration index to a positive ... | adagrad | python | jax-ml/jax | jax/example_libraries/optimizers.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/optimizers.py | Apache-2.0 |
def rmsprop(step_size, gamma=0.9, eps=1e-8):
"""Construct optimizer triple for RMSProp.
Args:
step_size: positive scalar, or a callable representing a step size schedule
that maps the iteration index to a positive scalar.
gamma: Decay parameter.
eps: Epsilon parameter.
Returns:
An (ini... | Construct optimizer triple for RMSProp.
Args:
step_size: positive scalar, or a callable representing a step size schedule
that maps the iteration index to a positive scalar.
gamma: Decay parameter.
eps: Epsilon parameter.
Returns:
An (init_fun, update_fun, get_params) triple.
| rmsprop | python | jax-ml/jax | jax/example_libraries/optimizers.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/optimizers.py | Apache-2.0 |
def rmsprop_momentum(step_size, gamma=0.9, eps=1e-8, momentum=0.9):
"""Construct optimizer triple for RMSProp with momentum.
This optimizer is separate from the rmsprop optimizer because it needs to
keep track of additional parameters.
Args:
step_size: positive scalar, or a callable representing a step si... | Construct optimizer triple for RMSProp with momentum.
This optimizer is separate from the rmsprop optimizer because it needs to
keep track of additional parameters.
Args:
step_size: positive scalar, or a callable representing a step size schedule
that maps the iteration index to a positive scalar.
... | rmsprop_momentum | python | jax-ml/jax | jax/example_libraries/optimizers.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/optimizers.py | Apache-2.0 |
def adam(step_size, b1=0.9, b2=0.999, eps=1e-8):
"""Construct optimizer triple for Adam.
Args:
step_size: positive scalar, or a callable representing a step size schedule
that maps the iteration index to a positive scalar.
b1: optional, a positive scalar value for beta_1, the exponential decay rate
... | Construct optimizer triple for Adam.
Args:
step_size: positive scalar, or a callable representing a step size schedule
that maps the iteration index to a positive scalar.
b1: optional, a positive scalar value for beta_1, the exponential decay rate
for the first moment estimates (default 0.9).
... | adam | python | jax-ml/jax | jax/example_libraries/optimizers.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/optimizers.py | Apache-2.0 |
def adamax(step_size, b1=0.9, b2=0.999, eps=1e-8):
"""Construct optimizer triple for AdaMax (a variant of Adam based on infinity norm).
Args:
step_size: positive scalar, or a callable representing a step size schedule
that maps the iteration index to a positive scalar.
b1: optional, a positive scalar... | Construct optimizer triple for AdaMax (a variant of Adam based on infinity norm).
Args:
step_size: positive scalar, or a callable representing a step size schedule
that maps the iteration index to a positive scalar.
b1: optional, a positive scalar value for beta_1, the exponential decay rate
for ... | adamax | python | jax-ml/jax | jax/example_libraries/optimizers.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/optimizers.py | Apache-2.0 |
def sm3(step_size, momentum=0.9):
"""Construct optimizer triple for SM3.
Memory-Efficient Adaptive Optimization for Large-Scale Learning.
https://arxiv.org/abs/1901.11150
Args:
step_size: positive scalar, or a callable representing a step size schedule
that maps the iteration index to a positive sca... | Construct optimizer triple for SM3.
Memory-Efficient Adaptive Optimization for Large-Scale Learning.
https://arxiv.org/abs/1901.11150
Args:
step_size: positive scalar, or a callable representing a step size schedule
that maps the iteration index to a positive scalar.
momentum: optional, a positive... | sm3 | python | jax-ml/jax | jax/example_libraries/optimizers.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/optimizers.py | Apache-2.0 |
def l2_norm(tree):
"""Compute the l2 norm of a pytree of arrays. Useful for weight decay."""
leaves, _ = jax.tree.flatten(tree)
return jnp.sqrt(sum(jnp.vdot(x, x) for x in leaves)) | Compute the l2 norm of a pytree of arrays. Useful for weight decay. | l2_norm | python | jax-ml/jax | jax/example_libraries/optimizers.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/optimizers.py | Apache-2.0 |
def unpack_optimizer_state(opt_state):
"""Converts an OptimizerState to a marked pytree.
Converts an OptimizerState to a marked pytree with the leaves of the outer
pytree represented as JoinPoints to avoid losing information. This function is
intended to be useful when serializing optimizer states.
Args:
... | Converts an OptimizerState to a marked pytree.
Converts an OptimizerState to a marked pytree with the leaves of the outer
pytree represented as JoinPoints to avoid losing information. This function is
intended to be useful when serializing optimizer states.
Args:
opt_state: An OptimizerState
Returns:
... | unpack_optimizer_state | python | jax-ml/jax | jax/example_libraries/optimizers.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/optimizers.py | Apache-2.0 |
def pack_optimizer_state(marked_pytree):
"""Converts a marked pytree to an OptimizerState.
The inverse of unpack_optimizer_state. Converts a marked pytree with the
leaves of the outer pytree represented as JoinPoints back into an
OptimizerState. This function is intended to be useful when deserializing
optim... | Converts a marked pytree to an OptimizerState.
The inverse of unpack_optimizer_state. Converts a marked pytree with the
leaves of the outer pytree represented as JoinPoints back into an
OptimizerState. This function is intended to be useful when deserializing
optimizer states.
Args:
marked_pytree: A pyt... | pack_optimizer_state | python | jax-ml/jax | jax/example_libraries/optimizers.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/optimizers.py | Apache-2.0 |
def Dense(out_dim, W_init=glorot_normal(), b_init=normal()):
"""Layer constructor function for a dense (fully-connected) layer."""
def init_fun(rng, input_shape):
output_shape = input_shape[:-1] + (out_dim,)
k1, k2 = random.split(rng)
W, b = W_init(k1, (input_shape[-1], out_dim)), b_init(k2, (out_dim,))... | Layer constructor function for a dense (fully-connected) layer. | Dense | python | jax-ml/jax | jax/example_libraries/stax.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/stax.py | Apache-2.0 |
def GeneralConv(dimension_numbers, out_chan, filter_shape,
strides=None, padding='VALID', W_init=None,
b_init=normal(1e-6)):
"""Layer construction function for a general convolution layer."""
lhs_spec, rhs_spec, out_spec = dimension_numbers
one = (1,) * len(filter_shape)
strides ... | Layer construction function for a general convolution layer. | GeneralConv | python | jax-ml/jax | jax/example_libraries/stax.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/stax.py | Apache-2.0 |
def GeneralConvTranspose(dimension_numbers, out_chan, filter_shape,
strides=None, padding='VALID', W_init=None,
b_init=normal(1e-6)):
"""Layer construction function for a general transposed-convolution layer."""
lhs_spec, rhs_spec, out_spec = dimension_numbers
one... | Layer construction function for a general transposed-convolution layer. | GeneralConvTranspose | python | jax-ml/jax | jax/example_libraries/stax.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/stax.py | Apache-2.0 |
def BatchNorm(axis=(0, 1, 2), epsilon=1e-5, center=True, scale=True,
beta_init=zeros, gamma_init=ones):
"""Layer construction function for a batch normalization layer."""
_beta_init = lambda rng, shape: beta_init(rng, shape) if center else ()
_gamma_init = lambda rng, shape: gamma_init(rng, shape) i... | Layer construction function for a batch normalization layer. | BatchNorm | python | jax-ml/jax | jax/example_libraries/stax.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/stax.py | Apache-2.0 |
def elementwise(fun, **fun_kwargs):
"""Layer that applies a scalar function elementwise on its inputs."""
init_fun = lambda rng, input_shape: (input_shape, ())
apply_fun = lambda params, inputs, **kwargs: fun(inputs, **fun_kwargs)
return init_fun, apply_fun | Layer that applies a scalar function elementwise on its inputs. | elementwise | python | jax-ml/jax | jax/example_libraries/stax.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/stax.py | Apache-2.0 |
def PoolingLayer(window_shape, strides=None, padding='VALID', spec=None):
"""Layer construction function for a pooling layer."""
strides = strides or (1,) * len(window_shape)
rescale = rescaler(window_shape, strides, padding) if rescaler else None
if spec is None:
non_spatial_axes = 0, len(window... | Layer construction function for a pooling layer. | PoolingLayer | python | jax-ml/jax | jax/example_libraries/stax.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/stax.py | Apache-2.0 |
def Flatten():
"""Layer construction function for flattening all but the leading dim."""
def init_fun(rng, input_shape):
output_shape = input_shape[0], functools.reduce(op.mul, input_shape[1:], 1)
return output_shape, ()
def apply_fun(params, inputs, **kwargs):
return jnp.reshape(inputs, (inputs.shape... | Layer construction function for flattening all but the leading dim. | Flatten | python | jax-ml/jax | jax/example_libraries/stax.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/stax.py | Apache-2.0 |
def Identity():
"""Layer construction function for an identity layer."""
init_fun = lambda rng, input_shape: (input_shape, ())
apply_fun = lambda params, inputs, **kwargs: inputs
return init_fun, apply_fun | Layer construction function for an identity layer. | Identity | python | jax-ml/jax | jax/example_libraries/stax.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/stax.py | Apache-2.0 |
def FanOut(num):
"""Layer construction function for a fan-out layer."""
init_fun = lambda rng, input_shape: ([input_shape] * num, ())
apply_fun = lambda params, inputs, **kwargs: [inputs] * num
return init_fun, apply_fun | Layer construction function for a fan-out layer. | FanOut | python | jax-ml/jax | jax/example_libraries/stax.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/stax.py | Apache-2.0 |
def FanInSum():
"""Layer construction function for a fan-in sum layer."""
init_fun = lambda rng, input_shape: (input_shape[0], ())
apply_fun = lambda params, inputs, **kwargs: sum(inputs)
return init_fun, apply_fun | Layer construction function for a fan-in sum layer. | FanInSum | python | jax-ml/jax | jax/example_libraries/stax.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/stax.py | Apache-2.0 |
def FanInConcat(axis=-1):
"""Layer construction function for a fan-in concatenation layer."""
def init_fun(rng, input_shape):
ax = axis % len(input_shape[0])
concat_size = sum(shape[ax] for shape in input_shape)
out_shape = input_shape[0][:ax] + (concat_size,) + input_shape[0][ax+1:]
return out_shap... | Layer construction function for a fan-in concatenation layer. | FanInConcat | python | jax-ml/jax | jax/example_libraries/stax.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/stax.py | Apache-2.0 |
def Dropout(rate, mode='train'):
"""Layer construction function for a dropout layer with given rate."""
def init_fun(rng, input_shape):
return input_shape, ()
def apply_fun(params, inputs, **kwargs):
rng = kwargs.get('rng', None)
if rng is None:
msg = ("Dropout layer requires apply_fun to be cal... | Layer construction function for a dropout layer with given rate. | Dropout | python | jax-ml/jax | jax/example_libraries/stax.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/stax.py | Apache-2.0 |
def serial(*layers):
"""Combinator for composing layers in serial.
Args:
*layers: a sequence of layers, each an (init_fun, apply_fun) pair.
Returns:
A new layer, meaning an (init_fun, apply_fun) pair, representing the serial
composition of the given sequence of layers.
"""
nlayers = len(layers)
... | Combinator for composing layers in serial.
Args:
*layers: a sequence of layers, each an (init_fun, apply_fun) pair.
Returns:
A new layer, meaning an (init_fun, apply_fun) pair, representing the serial
composition of the given sequence of layers.
| serial | python | jax-ml/jax | jax/example_libraries/stax.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/stax.py | Apache-2.0 |
def parallel(*layers):
"""Combinator for composing layers in parallel.
The layer resulting from this combinator is often used with the FanOut and
FanInSum layers.
Args:
*layers: a sequence of layers, each an (init_fun, apply_fun) pair.
Returns:
A new layer, meaning an (init_fun, apply_fun) pair, re... | Combinator for composing layers in parallel.
The layer resulting from this combinator is often used with the FanOut and
FanInSum layers.
Args:
*layers: a sequence of layers, each an (init_fun, apply_fun) pair.
Returns:
A new layer, meaning an (init_fun, apply_fun) pair, representing the
parallel ... | parallel | python | jax-ml/jax | jax/example_libraries/stax.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/stax.py | Apache-2.0 |
def shape_dependent(make_layer):
"""Combinator to delay layer constructor pair until input shapes are known.
Args:
make_layer: a one-argument function that takes an input shape as an argument
(a tuple of positive integers) and returns an (init_fun, apply_fun) pair.
Returns:
A new layer, meaning an... | Combinator to delay layer constructor pair until input shapes are known.
Args:
make_layer: a one-argument function that takes an input shape as an argument
(a tuple of positive integers) and returns an (init_fun, apply_fun) pair.
Returns:
A new layer, meaning an (init_fun, apply_fun) pair, represent... | shape_dependent | python | jax-ml/jax | jax/example_libraries/stax.py | https://github.com/jax-ml/jax/blob/master/jax/example_libraries/stax.py | Apache-2.0 |
def broadcast_one_to_all(in_tree: Any, is_source: bool | None = None) -> Any:
"""Broadcast data from a source host (host 0 by default) to all other hosts.
Args:
in_tree: pytree of arrays - each array *must* have the same shape across the
hosts.
is_source: optional bool denoting whether the caller is ... | Broadcast data from a source host (host 0 by default) to all other hosts.
Args:
in_tree: pytree of arrays - each array *must* have the same shape across the
hosts.
is_source: optional bool denoting whether the caller is the source. Only
'source host' will contribute the data for the broadcast. If... | broadcast_one_to_all | python | jax-ml/jax | jax/experimental/multihost_utils.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/multihost_utils.py | Apache-2.0 |
def sync_global_devices(name: str):
"""Creates a barrier across all hosts/devices."""
h = np.uint32(zlib.crc32(name.encode()))
assert_equal(h, f"sync_global_devices name mismatch ('{name}')") | Creates a barrier across all hosts/devices. | sync_global_devices | python | jax-ml/jax | jax/experimental/multihost_utils.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/multihost_utils.py | Apache-2.0 |
def process_allgather(in_tree: Any, tiled: bool = False) -> Any:
"""Gather data from across processes.
Args:
in_tree: pytree of arrays - each array _must_ have the same shape across the
hosts.
tiled: Whether to stack or concat the output. Defaults to False i.e. stack
into a new positional axis ... | Gather data from across processes.
Args:
in_tree: pytree of arrays - each array _must_ have the same shape across the
hosts.
tiled: Whether to stack or concat the output. Defaults to False i.e. stack
into a new positional axis at index 0.
Returns:
Pytrees of numpy arrays.
* If the in... | process_allgather | python | jax-ml/jax | jax/experimental/multihost_utils.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/multihost_utils.py | Apache-2.0 |
def assert_equal(in_tree, fail_message: str = ''):
"""Verifies that all the hosts have the same tree of values."""
expected = broadcast_one_to_all(in_tree)
if not jax.tree_util.tree_all(
jax.tree_util.tree_map(lambda *x: np.all(np.equal(*x)), in_tree, expected)):
raise AssertionError(
f'{fail_me... | Verifies that all the hosts have the same tree of values. | assert_equal | python | jax-ml/jax | jax/experimental/multihost_utils.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/multihost_utils.py | Apache-2.0 |
def reached_preemption_sync_point(step_id: int) -> bool:
"""Determine whether all hosts have reached a preemption sync step.
When any host receives a preemption notice, the notice is propagated to all
hosts and triggers a synchronization protocol in the background. The
synchronization protocol calculates the m... | Determine whether all hosts have reached a preemption sync step.
When any host receives a preemption notice, the notice is propagated to all
hosts and triggers a synchronization protocol in the background. The
synchronization protocol calculates the maximum step ids from all hosts, and
uses the next step id (i... | reached_preemption_sync_point | python | jax-ml/jax | jax/experimental/multihost_utils.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/multihost_utils.py | Apache-2.0 |
def host_local_array_to_global_array(
local_inputs: Any, global_mesh: jax.sharding.Mesh, pspecs: Any):
r"""Converts a host local value to a globally sharded jax.Array.
This function takes host-local data (which might be different
across hosts), and populates a global array with this data, where each
device... | Converts a host local value to a globally sharded jax.Array.
This function takes host-local data (which might be different
across hosts), and populates a global array with this data, where each
device on each host, get the appropriate slice of the data according to
sharding defined by the global_mesh/pspects.
... | host_local_array_to_global_array | python | jax-ml/jax | jax/experimental/multihost_utils.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/multihost_utils.py | Apache-2.0 |
def global_array_to_host_local_array(
global_inputs: Any, global_mesh: jax.sharding.Mesh, pspecs: Any):
r"""Converts a global `jax.Array` to a host local `jax.Array`.
You can use this function to transition to `jax.Array`. Using `jax.Array` with
pjit has the same semantics of using GDA with pjit i.e. all `ja... | Converts a global `jax.Array` to a host local `jax.Array`.
You can use this function to transition to `jax.Array`. Using `jax.Array` with
pjit has the same semantics of using GDA with pjit i.e. all `jax.Array`
inputs to pjit should be globally shaped and the output from pjit will also
be globally shaped jax.Ar... | global_array_to_host_local_array | python | jax-ml/jax | jax/experimental/multihost_utils.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/multihost_utils.py | Apache-2.0 |
def odeint(func, y0, t, *args, rtol=1.4e-8, atol=1.4e-8, mxstep=jnp.inf, hmax=jnp.inf):
"""Adaptive stepsize (Dormand-Prince) Runge-Kutta odeint implementation.
Args:
func: function to evaluate the time derivative of the solution `y` at time
`t` as `func(y, t, *args)`, producing the same shape/structure ... | Adaptive stepsize (Dormand-Prince) Runge-Kutta odeint implementation.
Args:
func: function to evaluate the time derivative of the solution `y` at time
`t` as `func(y, t, *args)`, producing the same shape/structure as `y0`.
y0: array or pytree of arrays representing the initial value for the state.
... | odeint | python | jax-ml/jax | jax/experimental/ode.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/ode.py | Apache-2.0 |
def _W_ih_l(layer_i: int, input_size: int, hidden_size: int,
bidirectional: bool) -> Shape:
"""Shape of W_ii|W_if|W_ig|W_io.
Note that layer_i is an index of pseudo-layers.
"""
if layer_i == 0 or (layer_i == 1 and bidirectional):
return (4 * hidden_size, input_size)
else:
num_directions =... | Shape of W_ii|W_if|W_ig|W_io.
Note that layer_i is an index of pseudo-layers.
| _W_ih_l | python | jax-ml/jax | jax/experimental/rnn.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/rnn.py | Apache-2.0 |
def _get_params_shapes_in_lstm(input_size: int, hidden_size: int,
num_layers: int,
bidirectional: bool) -> list[Shape]:
"""Get flat param shapes in LSTM. See module docstring for layout."""
layer_shapes = []
num_directions = 2 if bidirectional else 1
... | Get flat param shapes in LSTM. See module docstring for layout. | _get_params_shapes_in_lstm | python | jax-ml/jax | jax/experimental/rnn.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/rnn.py | Apache-2.0 |
def init_lstm_weight(rng: PRNGKeyArray, input_size: int, hidden_size: int,
num_layers: int, bidirectional: bool):
"""Random initialize LSTM weights from U(-k, k), k=sqrt(1/hidden_size)."""
param_count = get_num_params_in_lstm(input_size, hidden_size, num_layers,
... | Random initialize LSTM weights from U(-k, k), k=sqrt(1/hidden_size). | init_lstm_weight | python | jax-ml/jax | jax/experimental/rnn.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/rnn.py | Apache-2.0 |
def swap_lstm_gates(weights, input_size, hidden_size, num_layers, bidirectional):
"""Swaps the weights for the input and output gates for an LSTM model."""
weights = jnp.asarray(weights) # Ensure weights are JAX arrays
flat_shapes = _get_params_shapes_in_lstm(input_size, hidden_size, num_layers, bidirectional)
... | Swaps the weights for the input and output gates for an LSTM model. | swap_lstm_gates | python | jax-ml/jax | jax/experimental/rnn.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/rnn.py | Apache-2.0 |
def unpack_lstm_weights(
weights: Array, input_size: int, hidden_size: int, num_layers: int,
bidirectional: bool
) -> tuple[dict[int, Array], dict[int, Array], dict[int, Array], dict[int,
Array]]:
"""Unpack cudnn LSTM weights into individua... | Unpack cudnn LSTM weights into individual weights.
CUDNN LSTM weight layout: (num_layers, num_directions, W_ih, W_hh, b_ih, b_hh)
Returns W_ih, W_hh, b_ih, b_hh. e.g. W_ih[2][1] is the concat weights of
4 weights (W_ii, W_if, W_ig, W_io), each of shape (hidden_size, input_size)
at 2nd layer for the reverse dir... | unpack_lstm_weights | python | jax-ml/jax | jax/experimental/rnn.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/rnn.py | Apache-2.0 |
def lstm(x: Array, h_0: Array, c_0: Array, weights: Array, seq_lengths: Array,
input_size: int, hidden_size: int, num_layers: int, dropout: float,
bidirectional: bool, precision: lax.PrecisionLike = None) -> tuple[Array, Array, Array]:
"""LSTM via CuDNN or HIPDNN (not-yet-supported).
Assume batch... | LSTM via CuDNN or HIPDNN (not-yet-supported).
Assume batch-first inputs.
Arguments:
x: (batch_size, max_seq_length, input_size)
h_0: (num_directions * num_layers, batch_size, hidden_size)
c_0: (num_directions * num_layers, batch_size, hidden_size)
weights: (num_params,) where num_params = get_num_... | lstm | python | jax-ml/jax | jax/experimental/rnn.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/rnn.py | Apache-2.0 |
def lstm_ref(x: Array, h_0: Array, c_0: Array, W_ih: dict[int, Array],
W_hh: dict[int, Array], b_ih: dict[int, Array],
b_hh: dict[int, Array], seq_lengths: Array, input_size: int,
hidden_size: int, num_layers: int, dropout: float,
bidirectional: bool) -> tuple[Array, ... | Reference implementation of LSTM.
See https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html#lstm
https://docs.nvidia.com/deeplearning/cudnn/api/index.html#cudnnRNNMode_t
| lstm_ref | python | jax-ml/jax | jax/experimental/rnn.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/rnn.py | Apache-2.0 |
def serialize(compiled: jax.stages.Compiled):
"""Serializes a compiled binary.
Because pytrees are not serializable, they are returned so that
the user can handle them properly.
"""
unloaded_executable = getattr(compiled._executable,
'_unloaded_executable', None)
if unloaded... | Serializes a compiled binary.
Because pytrees are not serializable, they are returned so that
the user can handle them properly.
| serialize | python | jax-ml/jax | jax/experimental/serialize_executable.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/serialize_executable.py | Apache-2.0 |
def deserialize_and_load(serialized,
in_tree,
out_tree,
backend: str | xc.Client | None = None,
execution_devices: Sequence[xc.Device] | None = None):
"""Constructs a jax.stages.Compiled from a serialized executable.""... | Constructs a jax.stages.Compiled from a serialized executable. | deserialize_and_load | python | jax-ml/jax | jax/experimental/serialize_executable.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/serialize_executable.py | Apache-2.0 |
def shard_map(
f: Callable, mesh: Mesh | AbstractMesh, in_specs: Specs, out_specs: Specs,
check_rep: bool = True, auto: frozenset[AxisName] = frozenset()):
"""Map a function over shards of data.
Note:
``shard_map`` is an experimental API, and still subject to change. For an
introduction to sharded ... | Map a function over shards of data.
Note:
``shard_map`` is an experimental API, and still subject to change. For an
introduction to sharded data, refer to :ref:`sharded-computation`. For a more
in-depth look at using ``shard_map``, refer to `SPMD multi-device parallelism with shard_map`_.
Args:
f:... | shard_map | python | jax-ml/jax | jax/experimental/shard_map.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/shard_map.py | Apache-2.0 |
def pull(self, uuid: int, xs: Any) -> Any:
"""Fetches a pytree of arrays from a remote device.
Args:
uuid: identifier for the request
xs: A pytree of ShapeDtypeStruct.
Returns:
A pytree of arrays.
"""
xs_flat, tree = jax.tree.flatten(xs)
if not xs_flat:
return xs
... | Fetches a pytree of arrays from a remote device.
Args:
uuid: identifier for the request
xs: A pytree of ShapeDtypeStruct.
Returns:
A pytree of arrays.
| pull | python | jax-ml/jax | jax/experimental/transfer.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/transfer.py | Apache-2.0 |
def _get_unique_sync_key() -> str | None:
"""Generate a thread-local key for ensuring all host finish (de)serializing"""
if jax.process_count() == 1:
return None
# broadcast a thread-local unique barrier name
sync_key_unique = multihost_utils.broadcast_one_to_all(
np.frombuffer(uuid4().bytes, dtype=np... | Generate a thread-local key for ensuring all host finish (de)serializing | _get_unique_sync_key | python | jax-ml/jax | jax/experimental/array_serialization/pytree_serialization.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/array_serialization/pytree_serialization.py | Apache-2.0 |
def _is_str_same_on_all_hosts(path: str | PathLike[str]) -> bool:
"""All-gather the location of the checkpoint and check if it's the same."""
if jax.process_count() <= 1:
return False
path_b = str(path).encode("utf-8")
if len(path_b) > _MAX_PATH_LENGTH:
raise ValueError(f"Path exceeds maximum length of ... | All-gather the location of the checkpoint and check if it's the same. | _is_str_same_on_all_hosts | python | jax-ml/jax | jax/experimental/array_serialization/pytree_serialization.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/array_serialization/pytree_serialization.py | Apache-2.0 |
def _is_remote_path(path: str | PathLike[str]):
"""Check whether a path is remote by examining the prefix."""
# we need to truncate e.g., gs:// to gs:/ because pathlib.Path collapses //
return any(str(path).startswith(prefix[:-1])
for prefix in _REMOTE_URL_PREFIXES) | Check whether a path is remote by examining the prefix. | _is_remote_path | python | jax-ml/jax | jax/experimental/array_serialization/pytree_serialization.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/array_serialization/pytree_serialization.py | Apache-2.0 |
def _set_up_destination(root: str | PathLike[str], overwrite: bool,
pytree_repr: dict[str, Any], distinct_locations: bool,
sync_key: str | None) -> dict[str, Any]:
"""Inspect the destination, set it up for writing, potentially read existing data."""
root = _norm_path(... | Inspect the destination, set it up for writing, potentially read existing data. | _set_up_destination | python | jax-ml/jax | jax/experimental/array_serialization/pytree_serialization.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/array_serialization/pytree_serialization.py | Apache-2.0 |
def _prepare_directory(root: str | PathLike[str], overwrite: bool,
pytreedef_repr: dict[str, Any], distinct_locations: bool,
sync_key: str | None):
"""Prepare the directory: check destination, potentially read existing data
and overwrite.
Raises:
RuntimeError: If... | Prepare the directory: check destination, potentially read existing data
and overwrite.
Raises:
RuntimeError: If the destination directory cannot be created.
| _prepare_directory | python | jax-ml/jax | jax/experimental/array_serialization/pytree_serialization.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/array_serialization/pytree_serialization.py | Apache-2.0 |
def _finalize_array_store(kvstore_path, distinct_locations: bool):
"""When multiple processes are writing, they must write to a per-process
location followed by combining them via no-copy links to the final location.
"""
# only in multiprocess case and only process 0
if distinct_locations or jax.process_count... | When multiple processes are writing, they must write to a per-process
location followed by combining them via no-copy links to the final location.
| _finalize_array_store | python | jax-ml/jax | jax/experimental/array_serialization/pytree_serialization.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/array_serialization/pytree_serialization.py | Apache-2.0 |
def _write_pytreedef(directory: Any, pytree_repr: dict[str, Any],
distinct_locations: bool):
"""Write the pytreedef to the destination directory and aux data to the archive."""
if not (jax.process_index() == 0 or distinct_locations):
return
root = _norm_path(directory)
(root / _PYTREEDE... | Write the pytreedef to the destination directory and aux data to the archive. | _write_pytreedef | python | jax-ml/jax | jax/experimental/array_serialization/pytree_serialization.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/array_serialization/pytree_serialization.py | Apache-2.0 |
def _tree_broadcast(a, b, is_leaf=lambda x: x is None):
"""Broadcast the prefix tree `a` to the full tree `b`
Uses `flatten_axes` for better error messages on mismatched arity but allowing
for custom is_leaf in the `a` and `b` trees.
"""
a_leaves, a_struct = jax.tree.flatten(a, is_leaf=is_leaf)
a_idx2leaf_... | Broadcast the prefix tree `a` to the full tree `b`
Uses `flatten_axes` for better error messages on mismatched arity but allowing
for custom is_leaf in the `a` and `b` trees.
| _tree_broadcast | python | jax-ml/jax | jax/experimental/array_serialization/pytree_serialization.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/array_serialization/pytree_serialization.py | Apache-2.0 |
def save(data: PyTreeT, directory: str | PathLike[str], *,
overwrite: bool = True, ts_specs: PyTreeT | None = None) -> None:
"""Saves the given data structure to the provided directory path.
This function provides functionality to serialize and save a data structure
comprising JAX arrays, along with its... | Saves the given data structure to the provided directory path.
This function provides functionality to serialize and save a data structure
comprising JAX arrays, along with its structure to a given directory. It
leverages `PyTree` for flattening and reconstructing the data structure.
This is a simple experime... | save | python | jax-ml/jax | jax/experimental/array_serialization/pytree_serialization.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/array_serialization/pytree_serialization.py | Apache-2.0 |
def load_pytreedef(directory: str | PathLike[str]) -> PyTreeT:
"""Loads a pytree from the given directory.
This is a simple experimental array serialization API, for anything more
complex and for all checkpointing prefer: https://github.com/google/orbax
Args:
directory: Directory path to load from.
Retu... | Loads a pytree from the given directory.
This is a simple experimental array serialization API, for anything more
complex and for all checkpointing prefer: https://github.com/google/orbax
Args:
directory: Directory path to load from.
Returns:
The loaded pytree with arrays represented as jax.ShapeDtype... | load_pytreedef | python | jax-ml/jax | jax/experimental/array_serialization/pytree_serialization.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/array_serialization/pytree_serialization.py | Apache-2.0 |
def load(directory: str | PathLike[str], shardings: PyTreeT, *,
mask: PyTreeT | None = None, ts_specs: PyTreeT | None = None
) -> PyTreeT:
"""Loads and reconstructs a data structure from a directory.
This is a simple experimental array serialization API, for anything more
complex and for all ch... | Loads and reconstructs a data structure from a directory.
This is a simple experimental array serialization API, for anything more
complex and for all checkpointing prefer: https://github.com/google/orbax
Args:
directory: Directory path where the data is stored.
shardings: Sharding strategy for array ob... | load | python | jax-ml/jax | jax/experimental/array_serialization/pytree_serialization.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/array_serialization/pytree_serialization.py | Apache-2.0 |
def nonblocking_save(data: PyTreeT, directory: str | PathLike[str], *,
overwrite: bool = True, ts_specs: PyTreeT | None = None
) -> utils.PyTreeFuture:
"""Nonblocking alias of save, return an awaitable future with a pytree stub.
This is a simple experimental array serializ... | Nonblocking alias of save, return an awaitable future with a pytree stub.
This is a simple experimental array serialization API, for anything more
complex and for all checkpointing prefer: https://github.com/google/orbax
Examples:
>>> fut = nonblocking_save(data, directory)
>>> print(fut.pytree) # a py... | nonblocking_save | python | jax-ml/jax | jax/experimental/array_serialization/pytree_serialization.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/array_serialization/pytree_serialization.py | Apache-2.0 |
def nonblocking_load(directory: str | PathLike[str], shardings: PyTreeT, *,
mask: PyTreeT | None = None,
ts_specs: PyTreeT | None = None) -> utils.PyTreeFuture:
"""Nonblocking alias of load, return an awaitable future with a pytree stub.
This is a simple experimental array... | Nonblocking alias of load, return an awaitable future with a pytree stub.
This is a simple experimental array serialization API, for anything more
complex and for all checkpointing prefer: https://github.com/google/orbax
Examples:
>>> fut = nonblocking_load(directory)
>>> print(fut.pytree) # a pytree o... | nonblocking_load | python | jax-ml/jax | jax/experimental/array_serialization/pytree_serialization.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/array_serialization/pytree_serialization.py | Apache-2.0 |
def is_remote_storage(tspec: dict[str, Any] | str) -> bool:
"""Detect if user is using cloud storages.
This can detect common defines and unable to detect some corner cases such as
using gcsfuse.
"""
if isinstance(tspec, str):
# KvStoreUrl
if re.match(rf'^({"|".join(_REMOTE_URL_PREFIXES)})', tspec):
... | Detect if user is using cloud storages.
This can detect common defines and unable to detect some corner cases such as
using gcsfuse.
| is_remote_storage | python | jax-ml/jax | jax/experimental/array_serialization/serialization.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/array_serialization/serialization.py | Apache-2.0 |
def check_for_errors(self):
"""Checks if any errors have been raised in the child thread.
This is a non-blocking call that can be called in the main thread.
""" | Checks if any errors have been raised in the child thread.
This is a non-blocking call that can be called in the main thread.
| check_for_errors | python | jax-ml/jax | jax/experimental/array_serialization/serialization.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/array_serialization/serialization.py | Apache-2.0 |
def merge_nested_ts_specs(dict1: dict[Any, Any], dict2: dict[Any, Any] | None):
"""Merge two ts specs, dict2 takes precedence."""
if dict2 is None: # nothing to do
return dict1
# TODO(rdyro): this is an opinionated merge, we should get user feedback
# merge kvstore explicitly
kvstore = dict1.get("kvstore... | Merge two ts specs, dict2 takes precedence. | merge_nested_ts_specs | python | jax-ml/jax | jax/experimental/array_serialization/tensorstore_impl.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/array_serialization/tensorstore_impl.py | Apache-2.0 |
def verify_tensorstore_spec(spec: dict[str, Any], arr: jax.Array | None,
path: str | os.PathLike[str], ocdbt: bool,
check_metadata: bool = True) -> None:
"""Verify the minimum requirements for a tensorstore spec."""
if ocdbt:
if spec.get("kvstore", {}).get... | Verify the minimum requirements for a tensorstore spec. | verify_tensorstore_spec | python | jax-ml/jax | jax/experimental/array_serialization/tensorstore_impl.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/array_serialization/tensorstore_impl.py | Apache-2.0 |
async def combine_kvstores(combined_kvstore: dict[str, Any],
kvstores: list[dict[str, Any]],
context: ts.Context | dict[str, Any] = _TS_CONTEXT
) -> None:
"""Merge a list of kvstores into a single kvstore. NOT multi-process safe."""
co... | Merge a list of kvstores into a single kvstore. NOT multi-process safe. | combine_kvstores | python | jax-ml/jax | jax/experimental/array_serialization/tensorstore_impl.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/array_serialization/tensorstore_impl.py | Apache-2.0 |
def _run_serialization(arrays, tensorstore_specs):
"""Legacy serialization of a list of arrays."""
async def _run_serializer():
future_writer = jax.tree_util.tree_map(async_serialize, arrays, tensorstore_specs)
return await asyncio.gather(*future_writer)
asyncio.run(_run_serializer()) | Legacy serialization of a list of arrays. | _run_serialization | python | jax-ml/jax | jax/experimental/array_serialization/tensorstore_impl.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/array_serialization/tensorstore_impl.py | Apache-2.0 |
async def async_deserialize(
user_in_sharding: jax.sharding.Sharding | Format,
tensorstore_spec: ts.Spec | dict[str, Any],
global_shape: Sequence[int] | None = None,
dtype=None,
byte_limiter: _LimitInFlightBytes | None = None,
context=_TS_CONTEXT,
chunk_layout=_TS_CHUNK_LAYOUT,
assume_me... | Main performant deserialization routine for arrays using tensorstore. | async_deserialize | python | jax-ml/jax | jax/experimental/array_serialization/tensorstore_impl.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/array_serialization/tensorstore_impl.py | Apache-2.0 |
def _run_deserialization(shardings: Sequence[jax.sharding.Sharding | Format],
tensorstore_specs: Sequence[dict[str, Any]],
global_shapes: Sequence[array.Shape] | None = None,
dtypes: Sequence[typing.DTypeLike] | None = None,
... | Legacy deserialization of a list of arrays. Optionally pass global_shapes
and dtypes for type-checking.
| _run_deserialization | python | jax-ml/jax | jax/experimental/array_serialization/tensorstore_impl.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/array_serialization/tensorstore_impl.py | Apache-2.0 |
def colocated_cpu_devices(
devices: Sequence[jax.Device],
) -> Sequence[jax.Device]:
"""Finds CPU devices colocated with the given devices."""
if not isinstance(devices, tuple):
devices = tuple(devices)
try:
return _colocated_cpu_devices_cached(devices)
except (ValueError, AttributeError):
retur... | Finds CPU devices colocated with the given devices. | colocated_cpu_devices | python | jax-ml/jax | jax/experimental/colocated_python/api.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/colocated_python/api.py | Apache-2.0 |
def colocated_python(fun: Callable[..., Any]) -> Callable[..., Any]:
"""Executes the given Python function on the same devices as the arguments."""
return make_callable(
fun, api_util.fun_sourceinfo(fun), api_util.fun_signature(fun)
) | Executes the given Python function on the same devices as the arguments. | colocated_python | python | jax-ml/jax | jax/experimental/colocated_python/api.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/colocated_python/api.py | Apache-2.0 |
def _get_spec(x: Any) -> api.ShapeDtypeStruct:
"""Extracts a spec for a value, which must be a JAX Array."""
# TODO(hyeontaek): Allow Python values and automatically apply `shard_arg`
# with a suitable sharding and layout.
if not isinstance(x, jax.Array):
raise ValueError(
"colocated_python only sup... | Extracts a spec for a value, which must be a JAX Array. | _get_spec | python | jax-ml/jax | jax/experimental/colocated_python/func.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/colocated_python/func.py | Apache-2.0 |
def _infer_devices_from_args(args: Sequence[Any]) -> xc.DeviceList | None:
"""Returns a representative device list from function call arguments."""
device_list_set: set[xc.DeviceList] = set()
for x in args:
sharding = getattr(x, "sharding", None)
if sharding is not None:
device_list_set.add(x.shardi... | Returns a representative device list from function call arguments. | _infer_devices_from_args | python | jax-ml/jax | jax/experimental/colocated_python/func.py | https://github.com/jax-ml/jax/blob/master/jax/experimental/colocated_python/func.py | Apache-2.0 |
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