# If the function we returned from ``pmap`` were a class instance, # this might naturally be a method, with ``fun`` as a ``self`` and. requests.py. fun: Function whose Jacobian is to be computed. However, it means that', ' executables loaded from the cache may have stale metadata, which', 'jax_hlo_source_file_canonicalization_regex', 'Used to canonicalize the source_path metadata of HLO instructions ', 'by removing the given regex. # simulation data x = np.random.normal (0,1,100) slope = 1.3 p = np.exp (x*slope)/ (1+np.exp (x*slope)) y = np.random.binomial (1,p) # model with pm.Model () as m: sl = pm.Normal ("slope",1,2) logit_p = sl*x pm.Bernoulli ("Y",logit_p=logit_p) # missing observed data with m: tr = sampling_jax.sample_numpyro_nuts () emmmm If any of the platforms in this list are not successfully ', 'initialized, an exception will be raised and the program will be aborted. We read every piece of feedback, and take your input very seriously. """Computes a (forward-mode) Jacobian-vector product of ``fun``. the function and executes each replica on its own XLA device in parallel. avoids the overhead of computing the forward pass. The jax.custom_transforms API was deprecated last year in version 0.1.63 (see the CHANGELOG) and was finally removed in the 0.2.12 release (see #6277). I was not able to reproduce this on an Ubuntu system with jax 0.3.10 and jaxlib 0.3.10, running Python 3.10. This function is always asynchronous, i.e. fun: Function to be differentiated. Calling the jitted function. primals: The primal values at which the Jacobian of ``fun`` should be. I am sorry for that. Hellomy python version=3.6, I have installed jax-0.2.22 and jaxlib-0.1.69. Complete example of how to reproduce the bug: Install jax on Ubuntu 20.04 / Anaconda / Python 3.9.2: Try to follow the example: https://www.tensorflow.org/probability/examples/TensorFlow_Probability_on_JAX. You might notice those if you use a benign side-effecting. """Adds a user specified name to a function when staging out JAX computations. '. Without this ', 'flag, using the standard jax.random pseudo-random number generation ', 'may result in extraneous communication and/or redundant distributed ', 'computation. See the, comment on ``donate_argnums`` for details. Copyright 2023, The JAX Authors. These errors are async, might get lost and are not ', 'very readable. 'Select the transfer guard level for host-to-device transfers. Any value passed via commandline flag or. ', 'If true, exceptions raised when reading or writing to the ', 'persistent compilation cache will be allowed through, halting ', 'program execution if not manually caught. It controls the. import contextlib import functools import itertools import logging import os import sys import threading from typing import Any, Callable, Hashable, NamedTuple, Iterator, Optional from jax._src import lib from jax._src.lib import jax_jit from jax._src.lib import transfer_guard_lib from jax . Prefer using the parameter instead of the flag, ', # Note: bump the default serialization version at least one month after, # we update XlaCallModule to support the new version, so that serialized. If you have a complete runnable reproduction of the problem we'd be delighted to help out! partially initialized module 'jax' has no attribute 'version' (most likely due to a circular import) - AI Search Based Chat | AI for Search Engines import requests def make_request (): # AttributeError: partially initialized module 'requests' # has no attribute 'get' (most likely due to a circular . is not required: only the ``shape`` and ``dtype`` attributes are accessed. # (i.e. with ``__hash__`` and ``__eq__`` defined. operation in a jitted function, like a print: >>> print(f(jax.numpy.array([1, 2, 3]))) # doctest:+ELLIPSIS, Value of y is Tracedwith. This module provides a small set of utility functions for working with tree-like data structures, such as nested tuples, lists, and dicts. We can also use a, The value of the thread-local state or flag can be accessed via, ``config.jax_enable_foo``. For example, assuming 8 XLA devices are available, :py:func:`pmap` can be used, >>> out = pmap(lambda x: x ** 2)(jnp.arange(8)) # doctest: +SKIP, When the leading dimension is smaller than the number of available devices JAX, >>> x = jnp.arange(3 * 2 * 2. backend: This is an experimental feature and the API is likely to change. shape: a sequence of integers representing an array shape, named_shape: (optional) a dictionary representing a named shape, sharding: (optional) a :class:`jax.Sharding` object, "ShapeDtypeStruct: dtype must be specified. representing the array to be replicated to form the output. Valid input into this, function must have the same shape/dtypes/structure as the result of, ``fun(*primals)``. It should return an. You", " can read more here: https://jax.readthedocs.io/en/latest/aot.html", "out_parts has been deprecated. Closing, assuming that this is not a JAX issue anymore. """, """Indicates that the current context is an explicit device_get() call. out JAX programs to TensorFlow using :func:`experimental.jax2tf.convert`. The text was updated successfully, but these errors were encountered: I'm not sure what might have caused this; I tried running this with the versions you have installed and it all seems to work: Can you include a minimal reproducible example of the code that led to the error? ', 'The size in bytes of the buffer used to hold outfeeds from each ', 'device. If platform is None, it is the default backend. AttributeError: module 'jax' has no attribute 'scipy' Issue #10222 or else use the pytree leaf type if example is None. Other levels of abstraction exist internally. ", "Instead, each argument passed by keyword is mapped over its ", "leading axis. ', 'This is a temporary flag that will be used during the process ', 'of deprecating the ``jax_enable_x64`` flag. This is similar to pjit's, The ``out_shardings`` argument is optional. ], [ 0. , 12.317766]]], dtype=float32)}. Optional, a sequence of Devices to map over. one that computes the per-example gradient. On CPUs and GPUs it uses ', 'a lax.associative_scan, while for TPUs it uses the HLO ReduceWindow. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. default: string, a default value for the option. AttributeError: module 'jax' has no attribute 'tree_multimap' jax.tree_util.tree_structure stackoverflow ( AttributeError: 'jaxlib.xla_extension''PmapFunction' )CoLab AlphaFold2 Conceptually, the two, length-4 arrays can be thought of as a sharded length-8 array (in this example, equivalent to jnp.arange(8)) that is mapped over, with the length-8 mapped, axis given name 'i'. *primals: a positional argument tuple of arrays, scalars, or (nested). # all the other arguments stored as attributes. JAX keeps a weak reference to ``fun`` for use as a compilation cache key, so the object ``fun`` must be weakly-referenceable. ', 'The first platform in the list will be the default platform. If the pmapped function is called with fewer, positional arguments than indicated by ``static_argnums`` then an error is. and any other case where :func:`jit` is used within an API's implementation. ], [ 0. , 12.317766]]], dtype=float32). Must be given identically for each process, in multi-process settings (and will therefore include devices across, processes). https://arxiv.org/pdf/2101.11046.pdf) that relies on TensorFlow Probability. Should be either a tuple or a list of tangents, with the same. fun: The function whose output shape should be evaluated. Default False. If ``has_aux`` is True. I am reimplementing some Google/DeepMind research code that uses jax and tensorflow probability (e.g. Sign in then the Hessian leaf has shape ``(out_1, out_2, , in_1_1, in_1_2, , in_2_1, in_2_2, )``. The ``jaxpr`` returned is a trace of ``fun`` abstracted to. If false, the cache will still get hits even', ' if functions or files are moved, etc. which represents an array with a fixed shape and type but an arbitrary value. ', 'For example, config.jax_platforms=cpu,tpu means that CPU and TPU backends ', 'will be initialized, and the CPU backend will be used unless otherwise ', 'specified. **kwargs: a keyword argument dict of arrays, scalars, or (nested) standard, Python containers (pytrees) of those types. out: a nested PyTree containing :class:`jax.ShapeDtypeStruct` objects as leaves. # , AttributeError: module 'jax' has no attribute 'scipy'. * jnp.sin(x) + jnp.cos(x / 2. For example, if you use pip to install packages you can run: Right, I tried that, this is the result I get. (I'm also planning on releasing some public packages that depend on jax and tfp and would rather not require users use the nightly builds if possible.). ``in_axes`` must be a container tree prefix of the positional. For those arguments not, indicated by ``static_argnums``, any object with ``shape`` and ``dtype``, attributes is acceptable (excepting namedtuples, which are treated as Python, >>> def f(x): return jax.numpy.sin(jax.numpy.cos(x)), >>> print(c.as_hlo_text()) # doctest: +SKIP. Thank you for including the function that is causing the issue, but a complete example would also include how the function is called. At this point, my buest guess is that you are passing a keyword argument named mxsteps when you call the function, and you could fix the error by passing mxstep instead. Attaching my nvidia-smi and nvcc -- version results below. '. Thanks! It looks like you have a local file in your directory named jax.py, so when you run import jax it is loading this file instead of the jax package. ', 'The flag exists only temporarily, for backward compatibility.'. ', 'Disable JIT compilation and just call original Python. For more details on data placement see the. Alternatively, the assignment to ``c`` above could be written: >>> scalar = types.SimpleNamespace(shape=(), dtype=np.dtype(np.float32)). example: If specified, cast the components to the matching dtype/weak_type. ", "jacfwd with holomorphic=True requires outputs with complex dtype, ". ], [0. , 0. Related questions No questions were found. * 4. Importing a library from (or near) a script with the same name raises "AttributeError: module has no attribute" or an ImportError or NameError (3 answers) Closed 3 months ago. If `None`, tupling will be enabled when, there are more than 100 arguments, since some platforms have limits on. why hash and equality operators must be defined. Hellomy python version=3.6, I have installed jax-0.2.22 and jaxlib-0.1.69. fun: Function to be jitted. 0.06666667 0.13333333 0.2 0.26666667 0.33333333], >>> print(f2(jnp.array([2., 3.]))) equivalent to using ``out_axes=0`` in :py:func:`vmap`. If we want to see a concrete value while, debugging, and avoid the tracer too, we can use the :py:func:`disable_jit`, print(f(jax.numpy.array([1, 2, 3]))). Have a question about this project? Sign in objects will already satisfy this requirement. ), in_axes=(0, None), out_axes=(0, None))(jnp.arange(2. runs on two processes with 4 XLA devices each: >>> f = lambda x: x + jax.lax.psum(x, axis_name='i'), >>> data = jnp.arange(4) if jax.process_index() == 0 else jnp.arange(4, 8), >>> out = pmap(f, axis_name='i')(data) # doctest: +SKIP, Each process passes in a different length-4 array, corresponding to its 4, local devices, and the psum operates over all 8 values. It is currently an error for. ", 'Enable logging useful for debugging multi-process distributed ', 'computations. We plan on having a mainline release soon that will be compatible with the newest JAX version. If argnums is, a sequence of integers, the gradient is a tuple of values with the same, shapes and types as the corresponding arguments. Here's an example that involves a parallel collective and axis name: >>> def f(x): return x - jax.lax.psum(x, 'i'), >>> c = jax.xla_computation(f, axis_env=[('i', 4)])(2), ROOT add.6 = s32[] add(parameter.4, parameter.5), all-reduce.7 = s32[] all-reduce(parameter.2), replica_groups={{0,1,2,3}}, to_apply=primitive_computation.3, ROOT subtract.8 = s32[] subtract(parameter.2, all-reduce.7), Notice the ``replica_groups`` that were generated. to your account. Optional, a string representing the XLA backend: ``'cpu'``, ``'gpu'``, or, inline: Specify whether this function should be inlined into enclosing, jaxprs (rather than being represented as an application of the xla_call. It represents a function intended for SPMD execution on. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. NumPy and SciPy documentation are copyright the respective authors.. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. raised. With tensorflow probability: AttributeError: module 'jax' has no Each of the static arguments will be broadcasted to all devices. extra_description: string, optional: extra information to add to the. GitHub deepmind dm-haiku Notifications Fork Star 2.6k Code Pull requests Insights New issue ', 'Set to None to use the system default device. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. I want to use pandas to process a csv file. Well occasionally send you account related emails. file A imports file B and vice versa. Most :class:`Callable`. Already on GitHub? ).reshape((3, 2, 2)) ** 2, >>> out = pmap(jnp.dot)(x, y) # doctest: +SKIP, If your leading dimension is larger than the number of available devices you, >>> pmap(lambda x: x ** 2)(jnp.arange(9)) # doctest: +SKIP, ValueError: requires 9 replicas, but only 8 XLA devices are available, As with :py:func:`vmap`, using ``None`` in ``in_axes`` indicates that an, argument doesn't have an extra axis and should be broadcasted, rather than, >>> out = pmap(lambda x, y: (x + y, y * 2. By clicking Sign up for GitHub, you agree to our terms of service and A parallel-mapped and lowered function is staged out of Python and, translated to a compiler's input language, possibly in a, backend-dependent manner. jax.tree_util module JAX documentation - Read the Docs JAX is available to install via the Python Package Index . : removes hidden frames from tracebacks, and adds ". " Its arguments should be arrays, scalars, or standard Python containers of arrays or scalars. If ``has_aux`` is ``False``, returns a pair where the first element is the value of, ``f(*primals)`` and the second element is a function that evaluates the, (forward-mode) Jacobian-vector product of ``fun`` evaluated at ``primals`` without, re-doing the linearization work. JAX will infer the shardings, from the input :py:class:`jax.Array`'s and defaults to replicating the input. 'Some platforms, like TPU, offer configurable precision levels for ', 'matrix multiplication and convolution computations, trading off ', 'accuracy for speed. differentiated and the second element is auxiliary data. pip install --upgrade jax jaxlib after installing this worked for my Sequential machine learning model. # TODO(b/262050896): Set to true after bug is fixed, # TODO(mattjj): remove this flag when we ensure we only succeed at trace-staging, # if the intended backend can handle lowering the result, 'Enables experimental features for staging out computations with '. the same device to participate twice in the same `pmap`. "gradient, which has the same shape as the arguments at ". i meet the same problem as you didi guess you are working on the lnn problem which is writen by cranmerif you solve this problem nowi hope you can tell mei will appreciate it.