# Copyright 2018 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """JAX user-facing transformations and utilities. The transformations here mostly wrap internal transformations, providing convenience flags to control behavior and handling Python containers of arguments and outputs. The Python containers handled are pytrees (see tree_util.py), which include nested tuples/lists/dicts, where the leaves are arrays. """ import collections import functools from functools import partial import inspect import itertools as it from typing import (Any, Callable, Generator, Iterable, NamedTuple, Mapping, Optional, Sequence, Tuple, TypeVar, Union, overload, Dict, Hashable, List) from typing_extensions import Literal from warnings import warn import numpy as np from contextlib import contextmanager, ExitStack import jax from jax import core from jax import linear_util as lu from jax import stages from jax.core import eval_jaxpr from jax.tree_util import (tree_map, tree_flatten, tree_unflatten, tree_structure, tree_transpose, tree_leaves, treedef_is_leaf, treedef_children, Partial, PyTreeDef, all_leaves, treedef_tuple) from jax._src import device_array from jax._src import dispatch from jax._src import dtypes from jax._src import source_info_util from jax._src import traceback_util from jax._src.api_util import ( flatten_fun, apply_flat_fun, flatten_fun_nokwargs, flatten_fun_nokwargs2, argnums_partial, argnums_partial_except, flatten_axes, donation_vector, rebase_donate_argnums, _ensure_index, _ensure_index_tuple, shaped_abstractify, _ensure_str_tuple, argnames_partial_except, validate_argnames, validate_argnums) from jax._src.lax import lax as lax_internal from jax._src.lib import jax_jit from jax._src.lib import xla_bridge as xb from jax._src.lib import xla_client as xc from jax._src.lib import pmap_lib from jax._src.traceback_util import api_boundary from jax._src.tree_util import broadcast_prefix from jax._src.util import (unzip2, curry, safe_map, safe_zip, prod, split_list, extend_name_stack, new_name_stack, wrap_name, cache, wraps, HashableFunction, weakref_lru_cache) # Unused imports to be exported from jax._src.lib.xla_bridge import (device_count, local_device_count, devices, local_devices, process_index, process_count, host_id, host_ids, host_count, default_backend) from jax.ad_checkpoint import checkpoint_policies, checkpoint as new_checkpoint from jax._src.ad_checkpoint import _remat_static_argnums from jax.core import ShapedArray, raise_to_shaped from jax.custom_batching import custom_vmap from jax.custom_derivatives import (closure_convert, custom_gradient, custom_jvp, custom_vjp, linear_call) from jax.custom_transpose import custom_transpose from jax.interpreters import partial_eval as pe from jax.interpreters import mlir from jax.interpreters import xla from jax.interpreters import pxla from jax.interpreters import ad from jax.interpreters import batching from jax._src.config import ( flags, config, bool_env, disable_jit as _disable_jit, debug_nans as config_debug_nans, debug_infs as config_debug_infs, _thread_local_state as config_thread_local_state, explicit_device_put_scope as config_explicit_device_put_scope, explicit_device_get_scope as config_explicit_device_get_scope) traceback_util.register_exclusion(__file__) _dtype = partial(dtypes.dtype, canonicalize=True) AxisName = Any # These TypeVars are used below to express the fact that function types # (i.e. call signatures) are invariant under the vmap transformation. F = TypeVar("F", bound=Callable) T = TypeVar("T") U = TypeVar("U") map, unsafe_map = safe_map, map zip, unsafe_zip = safe_zip, zip FLAGS = flags.FLAGS flags.DEFINE_bool( "experimental_cpp_jit", bool_env("JAX_CPP_JIT", True), "A flag enabling the C++ jax.jit fast path." "Set this to `False` only if it crashes otherwise and report " "the error to the jax-team.") flags.DEFINE_bool( "experimental_cpp_pmap", bool_env("JAX_CPP_PMAP", True), "A flag enabling the C++ jax.pmap fast path. Until the default " "is switched to True, the feature is not supported and possibly broken " "(e.g. it may use unreleased code from jaxlib.") def _nan_check_posthook(fun, args, kwargs, output): """Hook function called by the C++ jit/pmap to perform NaN checking.""" leaves = tree_leaves(output) buffers = [] for da_or_sda in leaves: if hasattr(da_or_sda, "device_buffer"): buffers.append(da_or_sda.device_buffer) elif hasattr(da_or_sda, "device_buffers"): buffers.extend(da_or_sda.device_buffers) try: dispatch.check_special(xla.xla_call_p, buffers) except FloatingPointError: # compiled_fun can only raise in this case assert config.jax_debug_nans or config.jax_debug_infs print("Invalid nan value encountered in the output of a C++-jit/pmap " "function. Calling the de-optimized version.") fun._cache_miss(*args, **kwargs)[0] # probably won't return def _update_debug_special_global(_): if config._read("jax_debug_nans") or config._read("jax_debug_infs"): jax_jit.global_state().post_hook = _nan_check_posthook else: jax_jit.global_state().post_hook = None def _update_debug_special_thread_local(_): if (getattr(config_thread_local_state, "jax_debug_nans", False) or getattr(config_thread_local_state, "jax_debug_infs", False)): jax_jit.thread_local_state().post_hook = _nan_check_posthook else: jax_jit.thread_local_state().post_hook = None config_debug_nans._add_hooks(_update_debug_special_global, _update_debug_special_thread_local) config_debug_infs._add_hooks(_update_debug_special_global, _update_debug_special_thread_local) float0 = dtypes.float0 def _check_callable(fun): # In Python 3.10+, the only thing stopping us from supporting staticmethods # is that we can't take weak references to them, which the C++ JIT requires. if isinstance(fun, staticmethod): raise TypeError(f"staticmethod arguments are not supported, got {fun}") if not callable(fun): raise TypeError(f"Expected a callable value, got {fun}") if _isgeneratorfunction(fun): raise TypeError(f"Expected a function, got a generator function: {fun}") def _isgeneratorfunction(fun): # TODO 3.9+: remove # re-implemented here because of https://bugs.python.org/issue33261 while inspect.ismethod(fun): fun = fun.__func__ while isinstance(fun, functools.partial): fun = fun.func return inspect.isfunction(fun) and bool(fun.__code__.co_flags & inspect.CO_GENERATOR) _POSITIONAL_OR_KEYWORD = inspect.Parameter.POSITIONAL_OR_KEYWORD def _infer_argnums_and_argnames( sig: inspect.Signature, argnums: Union[int, Iterable[int], None], argnames: Union[str, Iterable[str], None], ) -> Tuple[Tuple[int, ...], Tuple[str, ...]]: """Infer missing argnums and argnames for a function with inspect.""" if argnums is None and argnames is None: return (), () if argnums is not None and argnames is not None: argnums = _ensure_index_tuple(argnums) argnames = _ensure_str_tuple(argnames) return argnums, argnames parameters = sig.parameters if argnums is None: assert argnames is not None argnames = _ensure_str_tuple(argnames) argnums = tuple( i for i, (k, param) in enumerate(parameters.items()) if param.kind == _POSITIONAL_OR_KEYWORD and k in argnames ) else: argnums = _ensure_index_tuple(argnums) argnames = tuple( k for i, (k, param) in enumerate(parameters.items()) if param.kind == _POSITIONAL_OR_KEYWORD and i in argnums ) return argnums, argnames def jit( fun: Callable, *, static_argnums: Union[int, Iterable[int], None] = None, static_argnames: Union[str, Iterable[str], None] = None, device: Optional[xc.Device] = None, backend: Optional[str] = None, donate_argnums: Union[int, Iterable[int]] = (), inline: bool = False, keep_unused: bool = False, abstracted_axes: Optional[Any] = None, ) -> stages.Wrapped: """Sets up ``fun`` for just-in-time compilation with XLA. Args: fun: Function to be jitted. ``fun`` should be a pure function, as side-effects may only be executed once. The arguments and return value of ``fun`` should be arrays, scalars, or (nested) standard Python containers (tuple/list/dict) thereof. Positional arguments indicated by ``static_argnums`` can be anything at all, provided they are hashable and have an equality operation defined. Static arguments are included as part of a compilation cache key, which is why hash and equality operators must be defined. JAX keeps a weak reference to ``fun`` for use as a compilation cache key, so the object ``fun`` must be weakly-referenceable. Most :class:`Callable` objects will already satisfy this requirement. static_argnums: An optional int or collection of ints that specify which positional arguments to treat as static (compile-time constant). Operations that only depend on static arguments will be constant-folded in Python (during tracing), and so the corresponding argument values can be any Python object. Static arguments should be hashable, meaning both ``__hash__`` and ``__eq__`` are implemented, and immutable. Calling the jitted function with different values for these constants will trigger recompilation. Arguments that are not arrays or containers thereof must be marked as static. If neither ``static_argnums`` nor ``static_argnames`` is provided, no arguments are treated as static. If ``static_argnums`` is not provided but ``static_argnames`` is, or vice versa, JAX uses :code:`inspect.signature(fun)` to find any positional arguments that correspond to ``static_argnames`` (or vice versa). If both ``static_argnums`` and ``static_argnames`` are provided, ``inspect.signature`` is not used, and only actual parameters listed in either ``static_argnums`` or ``static_argnames`` will be treated as static. static_argnames: An optional string or collection of strings specifying which named arguments to treat as static (compile-time constant). See the comment on ``static_argnums`` for details. If not provided but ``static_argnums`` is set, the default is based on calling ``inspect.signature(fun)`` to find corresponding named arguments. device: This is an experimental feature and the API is likely to change. Optional, the Device the jitted function will run on. (Available devices can be retrieved via :py:func:`jax.devices`.) The default is inherited from XLA's DeviceAssignment logic and is usually to use ``jax.devices()[0]``. backend: This is an experimental feature and the API is likely to change. Optional, a string representing the XLA backend: ``'cpu'``, ``'gpu'``, or ``'tpu'``. donate_argnums: Specify which positional argument buffers are "donated" to the computation. It is safe to donate argument buffers if you no longer need them once the computation has finished. In some cases XLA can make use of donated buffers to reduce the amount of memory needed to perform a computation, for example recycling one of your input buffers to store a result. You should not reuse buffers that you donate to a computation, JAX will raise an error if you try to. By default, no argument buffers are donated. Note that donate_argnums only work for positional arguments, and keyword arguments will not be donated. For more details on buffer donation see the [FAQ](https://jax.readthedocs.io/en/latest/faq.html#buffer-donation). inline: Specify whether this function should be inlined into enclosing jaxprs (rather than being represented as an application of the xla_call primitive with its own subjaxpr). Default False. keep_unused: If `False` (the default), arguments that JAX determines to be unused by `fun` *may* be dropped from resulting compiled XLA executables. Such arguments will not be transferred to the device nor provided to the underlying executable. If `True`, unused arguments will not be pruned. Returns: A wrapped version of ``fun``, set up for just-in-time compilation. Examples: In the following example, ``selu`` can be compiled into a single fused kernel by XLA: >>> import jax >>> >>> @jax.jit ... def selu(x, alpha=1.67, lmbda=1.05): ... return lmbda * jax.numpy.where(x > 0, x, alpha * jax.numpy.exp(x) - alpha) >>> >>> key = jax.random.PRNGKey(0) >>> x = jax.random.normal(key, (10,)) >>> print(selu(x)) # doctest: +SKIP [-0.54485 0.27744 -0.29255 -0.91421 -0.62452 -0.24748 -0.85743 -0.78232 0.76827 0.59566 ] To pass arguments such as ``static_argnames`` when decorating a function, a common pattern is to use :func:`functools.partial`: >>> from functools import partial >>> >>> @partial(jax.jit, static_argnames=['n']) ... def g(x, n): ... for i in range(n): ... x = x ** 2 ... return x >>> >>> g(jnp.arange(4), 3) DeviceArray([ 0, 1, 256, 6561], dtype=int32) """ if abstracted_axes and not config.jax_dynamic_shapes: raise ValueError("abstracted_axes must be used with --jax_dynamic_shapes") if FLAGS.experimental_cpp_jit and not config.jax_dynamic_shapes: return _jit(True, fun, static_argnums, static_argnames, device, backend, donate_argnums, inline, keep_unused) return _jit(False, fun, static_argnums, static_argnames, device, backend, donate_argnums, inline, keep_unused, abstracted_axes) def _jit( use_cpp_jit: bool, fun: Callable, static_argnums: Union[int, Iterable[int], None] = None, static_argnames: Union[str, Iterable[str], None] = None, device: Optional[xc.Device] = None, backend: Optional[str] = None, donate_argnums: Union[int, Iterable[int]] = (), inline: bool = False, keep_unused: bool = False, abstracted_axes: Optional[Any] = None, ) -> stages.Wrapped: # Implemements common logic between CPP and Python backends _check_callable(fun) # Coerce input donate_argnums = _ensure_index_tuple(donate_argnums) try: sig = inspect.signature(fun) except ValueError: # Some built-in functions don't support signature. # See: https://github.com/python/cpython/issues/73485 # In this case no validation is done static_argnums = () if static_argnums is None else _ensure_index_tuple(static_argnums) static_argnames = () if static_argnames is None else _ensure_str_tuple(static_argnames) else: # Infer argnums and argnames according to docstring static_argnums, static_argnames = _infer_argnums_and_argnames( sig, static_argnums, static_argnames) # Validation validate_argnums(sig, static_argnums, "static_argnums") validate_argnums(sig, donate_argnums, "donate_argnums") validate_argnames(sig, static_argnames, "static_argnames") # Compensate for static argnums absorbing args donate_argnums = rebase_donate_argnums(donate_argnums, static_argnums) if use_cpp_jit: return _cpp_jit( fun, static_argnums=static_argnums, static_argnames=static_argnames, device=device, backend=backend, donate_argnums=donate_argnums, inline=inline, keep_unused=keep_unused) return _python_jit( fun, static_argnums=static_argnums, static_argnames=static_argnames, device=device, backend=backend, donate_argnums=donate_argnums, inline=inline, keep_unused=keep_unused, abstracted_axes=abstracted_axes) def _prepare_jit(fun, static_argnums, static_argnames, donate_argnums, args, kwargs): # Validate donate_argnums if max(donate_argnums, default=-1) >= len(args): raise ValueError( f"jitted function has donate_argnums={donate_argnums} but " f"was called with only {len(args)} positional arguments.") f = lu.wrap_init(fun) f, args = argnums_partial_except(f, static_argnums, args, allow_invalid=True) f, kwargs = argnames_partial_except(f, static_argnames, kwargs) args_flat, in_tree = tree_flatten((args, kwargs)) if donate_argnums: donated_invars = donation_vector(donate_argnums, args, kwargs) else: donated_invars = (False,) * len(args_flat) return f, in_tree, args_flat, donated_invars PytreeOfAbstractedAxesSpec = Any def _python_jit( fun: Callable, *, static_argnums: Tuple[int, ...], static_argnames: Tuple[str, ...], device: Optional[xc.Device], backend: Optional[str], donate_argnums: Tuple[int, ...], inline: bool, keep_unused: bool, abstracted_axes: Optional[PytreeOfAbstractedAxesSpec], ) -> stages.Wrapped: @wraps(fun) @api_boundary def f_jitted(*args, **kwargs): if config.jax_disable_jit: return fun(*args, **kwargs) closed_fun, in_tree, args_flat, donated_invars = _prepare_jit( fun, static_argnums, static_argnames, donate_argnums, args, kwargs) flat_fun, out_tree = flatten_fun(closed_fun, in_tree) for arg in args_flat: _check_arg(arg) if jax.config.jax_dynamic_shapes: axes_specs = (None if abstracted_axes is None else _flat_axes_specs(abstracted_axes, *args, **kwargs)) in_type = pe.infer_lambda_input_type(axes_specs, args_flat) flat_fun = lu.annotate(flat_fun, in_type) out_flat = xla.xla_call( flat_fun, *args_flat, device=device, backend=backend, name=flat_fun.__name__, donated_invars=donated_invars, inline=inline, keep_unused=keep_unused) return tree_unflatten(out_tree(), out_flat) f_jitted.lower = _jit_lower(fun, static_argnums, static_argnames, device, backend, donate_argnums, inline, keep_unused, abstracted_axes) def clear_cache(): dispatch.xla_callable.evict_function(fun) f_jitted.clear_cache = clear_cache return f_jitted def _flat_axes_specs(abstracted_axes, *args, **kwargs ) -> List[pe.AbstractedAxesSpec]: if kwargs: raise NotImplementedError def ax_leaf(l): return (isinstance(l, dict) and all_leaves(l.values()) or isinstance(l, tuple) and all_leaves(l, lambda x: x is None)) return broadcast_prefix(abstracted_axes, args, ax_leaf) class _BackendAndDeviceInfo(NamedTuple): default_device: xc.Device committed_to_device: bool class _FastpathData(NamedTuple): xla_executable: xla.XlaExecutable out_pytree_def: Any sticky_device: xc.Device avals: Iterable[Any] lazy_exprs: Iterable[Any] kept_var_bitvec: Iterable[bool] _cpp_jit_cache = jax_jit.CompiledFunctionCache() def _cpp_jit_clear_cache(self): self._clear_cache() dispatch.xla_callable.evict_function(self._fun) def _cpp_jit( fun: Callable, *, static_argnums: Tuple[int, ...], static_argnames: Tuple[str, ...], device: Optional[xc.Device], backend: Optional[str], donate_argnums: Tuple[int, ...], inline: bool, keep_unused: bool, ) -> stages.Wrapped: # An implementation of `jit` that tries to do as much as possible in C++. # The goal of this function is to speed up the time it takes to process the # arguments, find the correct C++ executable, start the transfer of arguments # and schedule the computation. # As long as it does not support all features of the Python implementation # the C++ code will fallback to `_python_jit` when it faces some unsupported # feature. if device is not None and backend is not None: raise ValueError("can't specify both a device and a backend for jit, " f"got device={device} and backend={backend}.") @api_boundary def cache_miss(*args, **kwargs): ### This first part is basically the same code as in _python_jit. # An alternative would be for cache_miss to accept from C++ the arguments # (dyn_args, donated_invars, args_flat, in_tree), since otherwise we have # work/code that is redundant between C++ and Python. We can try that later. closed_fun, in_tree, args_flat, donated_invars = _prepare_jit( fun, static_argnums, static_argnames, donate_argnums, args, kwargs) for arg in args_flat: _check_arg(arg) flat_fun, out_tree = flatten_fun(closed_fun, in_tree) if jax.config.jax_dynamic_shapes: in_type = pe.infer_lambda_input_type(None, args_flat) flat_fun = lu.annotate(flat_fun, in_type) out_flat = xla.xla_call( flat_fun, *args_flat, device=device, backend=backend, name=flat_fun.__name__, donated_invars=donated_invars, inline=inline, keep_unused=keep_unused) out_pytree_def = out_tree() out = tree_unflatten(out_pytree_def, out_flat) ### Decide whether we can support the C++ fast path # High level note: The Python tracing mechanism is complex; in particular # to know whether `jax.jit(f)(x)` will execute or trace, it's not enough to # inspect the argument x, we actually do need to execute it and look at the # outputs that could be tracers (if f is capturing `Tracer` by closure). execute: Optional[functools.partial] = ( dispatch.xla_callable.most_recent_entry()) # TODO(sharadmv): Enable fast path for effectful jaxprs # TODO(sharadmv): Clean up usage of `execute.args` use_fastpath = ( # This is if we have already executed this code-path (most-recent entry # has been reset to None). Thus, we do not support the fast-path. execute is not None and execute.func is dispatch._execute_compiled and # not trivial, not pmap # No effects in computation not execute.args[5] and not execute.args[6] and # Not supported: ShardedDeviceArray all(device_array.type_is_device_array(x) for x in out_flat) and # Not supported: dynamic shapes not jax.config.jax_dynamic_shapes and type(execute.args[4]) is dispatch.SimpleResultHandler ) ### If we can use the fastpath, we return required info to the caller. if use_fastpath: (_, xla_executable, _, _, result_handlers, _, _, kept_var_idx) = execute.args sticky_device = None avals = [] lazy_exprs = [None] * len(result_handlers) for result_handler in result_handlers: aval, sticky_device = result_handler.args avals.append(aval) assert len(avals) == len(out_flat) kept_var_bitvec = [i in kept_var_idx for i in range(len(args_flat))] fastpath_data = _FastpathData(xla_executable, out_pytree_def, sticky_device, avals, lazy_exprs, kept_var_bitvec) else: fastpath_data = None return out, fastpath_data def get_device_info(): """Backends do not exist before __main__ is being executed.""" committed_to_device = device is not None or backend is not None if device is not None: default_device = device else: backend_ = xb.get_backend(backend) default_device = backend_.get_default_device_assignment(1)[0] return _BackendAndDeviceInfo(default_device, committed_to_device) jitted_f_kwargs = {} jitted_f_kwargs["has_explicit_device"] = ( device is not None or backend is not None) cpp_jitted_f = jax_jit.jit( fun, cache_miss, get_device_info, static_argnums=static_argnums, static_argnames=static_argnames, donate_argnums=donate_argnums, cache=_cpp_jit_cache, **jitted_f_kwargs) # type: ignore f_jitted = wraps(fun)(cpp_jitted_f) f_jitted.lower = _jit_lower(fun, static_argnums, static_argnames, device, backend, donate_argnums, inline, keep_unused, None) f_jitted._fun = fun type(f_jitted).clear_cache = _cpp_jit_clear_cache return f_jitted def _jit_lower(fun, static_argnums, static_argnames, device, backend, donate_argnums, inline, keep_unused: bool, abstracted_axes: Optional[PytreeOfAbstractedAxesSpec]): """Make a ``lower`` method for jitted functions.""" # If the function we returned from ``jit`` were a class instance, # this might naturally be a method, with ``fun`` as a ``self`` and # all the other arguments stored as attributes. def arg_spec(x): # like xla.arg_spec but duck-types on x.shape and x.dtype aval = None if jax.config.jax_dynamic_shapes else shaped_abstractify(x) device = getattr(x, '_device', None) return aval, device @api_boundary def lower(*args, **kwargs) -> stages.Lowered: """Lower this function for the given arguments. A lowered function is staged out of Python and translated to a compiler's input language, possibly in a backend-dependent manner. It is ready for compilation but not yet compiled. Returns: A ``Lowered`` instance representing the lowering. """ closed_fun, in_tree, args_flat, donated_invars = _prepare_jit( fun, static_argnums, static_argnames, donate_argnums, args, kwargs) flat_fun, out_tree = flatten_fun(closed_fun, in_tree) arg_specs_and_devices = map(arg_spec, args_flat) if jax.config.jax_dynamic_shapes: axes_specs = (None if abstracted_axes is None else _flat_axes_specs(abstracted_axes, *args, **kwargs)) in_type = pe.infer_lambda_input_type(axes_specs, args_flat) flat_fun = lu.annotate(flat_fun, in_type) in_avals = [aval for aval, explicit in in_type if explicit] else: if abstracted_axes: raise ValueError("abstracted_axes must be used with --jax_dynamic_shapes") in_avals, _ = unzip2(arg_specs_and_devices) computation = dispatch.lower_xla_callable( flat_fun, device, backend, flat_fun.__name__, donated_invars, True, keep_unused, *arg_specs_and_devices) return stages.Lowered.from_flat_info( computation, in_tree, in_avals, donate_argnums, out_tree()) return lower @contextmanager def disable_jit(): """Context manager that disables :py:func:`jit` behavior under its dynamic context. For debugging it is useful to have a mechanism that disables :py:func:`jit` everywhere in a dynamic context. Note that this not only disables explicit uses of `jit` by the user, but will also remove any implicit JIT compilation used by the JAX library: this includes implicit JIT computation of `body` and `cond` functions passed to higher-level primitives like :func:`scan` and :func:`while_loop`, JIT used in implementations of :mod:`jax.numpy` functions, and any other case where `jit` is used within an API's implementation. Values that have a data dependence on the arguments to a jitted function are traced and abstracted. For example, an abstract value may be a :py:class:`ShapedArray` instance, representing the set of all possible arrays with a given shape and dtype, but not representing one concrete array with specific values. You might notice those if you use a benign side-effecting operation in a jitted function, like a print: >>> import jax >>> >>> @jax.jit ... def f(x): ... y = x * 2 ... print("Value of y is", y) ... return y + 3 ... >>> print(f(jax.numpy.array([1, 2, 3]))) Value of y is Tracedwith [5 7 9] Here ``y`` has been abstracted by :py:func:`jit` to a :py:class:`ShapedArray`, which represents an array with a fixed shape and type but an arbitrary value. The value of ``y`` is also traced. If we want to see a concrete value while debugging, and avoid the tracer too, we can use the :py:func:`disable_jit` context manager: >>> import jax >>> >>> with jax.disable_jit(): ... print(f(jax.numpy.array([1, 2, 3]))) ... Value of y is [2 4 6] [5 7 9] """ with _disable_jit(True): yield def xla_computation(fun: Callable, static_argnums: Union[int, Iterable[int]] = (), axis_env: Optional[Sequence[Tuple[AxisName, int]]] = None, in_parts=None, out_parts=None, backend: Optional[str] = None, tuple_args: bool = False, instantiate_const_outputs: Optional[bool] = None, return_shape: bool = False, donate_argnums: Union[int, Iterable[int]] = ()) -> Callable: """Creates a function that produces its XLA computation given example args. Args: fun: Function from which to form XLA computations. static_argnums: See the :py:func:`jax.jit` docstring. axis_env: Optional, a sequence of pairs where the first element is an axis name and the second element is a positive integer representing the size of the mapped axis with that name. This parameter is useful when lowering functions that involve parallel communication collectives, and it specifies the axis name/size environment that would be set up by applications of :py:func:`jax.pmap`. See the examples below. in_parts: Optional, how each argument to ``fun`` should be partitioned or replicated. This is used to specify partitioned XLA computations, see ``sharded_jit`` for more info. out_parts: Optional, how each output of ``fun`` should be partitioned or replicated. This is used to specify partitioned XLA computations, see ``sharded_jit`` for more info. backend: This is an experimental feature and the API is likely to change. Optional, a string representing the XLA backend: ``'cpu'``, ``'gpu'``, or ``'tpu'``. tuple_args: Optional bool, defaults to ``False``. If ``True``, the resulting XLA computation will have a single tuple argument that is unpacked into the specified function arguments. If `None`, tupling will be enabled when there are more than 100 arguments, since some platforms have limits on argument arity. instantiate_const_outputs: Deprecated argument, does nothing. return_shape: Optional boolean, defaults to ``False``. If ``True``, the wrapped function returns a pair where the first element is the XLA computation and the second element is a pytree with the same structure as the output of ``fun`` and where the leaves are objects with ``shape``, ``dtype``, and ``named_shape`` attributes representing the corresponding types of the output leaves. donate_argnums: Specify which arguments are "donated" to the computation. It is safe to donate arguments if you no longer need them once the computation has finished. In some cases XLA can make use of donated buffers to reduce the amount of memory needed to perform a computation, for example recycling one of your input buffers to store a result. You should not reuse buffers that you donate to a computation, JAX will raise an error if you try to. Returns: A wrapped version of ``fun`` that when applied to example arguments returns a built XLA Computation (see xla_client.py), from which representations of the unoptimized XLA HLO computation can be extracted using methods like ``as_hlo_text``, ``as_serialized_hlo_module_proto``, and ``as_hlo_dot_graph``. If the argument ``return_shape`` is ``True``, then the wrapped function returns a pair where the first element is the XLA Computation and the second element is a pytree representing the structure, shapes, dtypes, and named shapes of the output of ``fun``. Concrete example arguments are not always necessary. For those arguments not indicated by ``static_argnums``, any object with ``shape`` and ``dtype`` attributes is acceptable (excepting namedtuples, which are treated as Python containers). For example: >>> import jax >>> >>> def f(x): return jax.numpy.sin(jax.numpy.cos(x)) >>> c = jax.xla_computation(f)(3.) >>> print(c.as_hlo_text()) # doctest: +SKIP HloModule xla_computation_f.6 ENTRY xla_computation_f.6 { constant.2 = pred[] constant(false) parameter.1 = f32[] parameter(0) cosine.3 = f32[] cosine(parameter.1) sine.4 = f32[] sine(cosine.3) ROOT tuple.5 = (f32[]) tuple(sine.4) } Alternatively, the assignment to ``c`` above could be written: >>> import types >>> scalar = types.SimpleNamespace(shape=(), dtype=np.dtype(np.float32)) >>> c = jax.xla_computation(f)(scalar) 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) >>> print(c.as_hlo_text()) # doctest: +SKIP HloModule jaxpr_computation.9 primitive_computation.3 { parameter.4 = s32[] parameter(0) parameter.5 = s32[] parameter(1) ROOT add.6 = s32[] add(parameter.4, parameter.5) } ENTRY jaxpr_computation.9 { tuple.1 = () tuple() parameter.2 = s32[] parameter(0) 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. Here's an example that generates more interesting ``replica_groups``: >>> from jax import lax >>> def g(x): ... rowsum = lax.psum(x, 'i') ... colsum = lax.psum(x, 'j') ... allsum = lax.psum(x, ('i', 'j')) ... return rowsum, colsum, allsum ... >>> axis_env = [('i', 4), ('j', 2)] >>> c = xla_computation(g, axis_env=axis_env)(5.) >>> print(c.as_hlo_text()) # doctest: +SKIP HloModule jaxpr_computation__1.19 [removed uninteresting text here] ENTRY jaxpr_computation__1.19 { tuple.1 = () tuple() parameter.2 = f32[] parameter(0) all-reduce.7 = f32[] all-reduce(parameter.2), replica_groups={{0,2,4,6},{1,3,5,7}}, to_apply=primitive_computation__1.3 all-reduce.12 = f32[] all-reduce(parameter.2), replica_groups={{0,1},{2,3},{4,5},{6,7}}, to_apply=primitive_computation__1.8 all-reduce.17 = f32[] all-reduce(parameter.2), replica_groups={{0,1,2,3,4,5,6,7}}, to_apply=primitive_computation__1.13 ROOT tuple.18 = (f32[], f32[], f32[]) tuple(all-reduce.7, all-reduce.12, all-reduce.17) } """ del instantiate_const_outputs # Unused _check_callable(fun) static_argnums = _ensure_index_tuple(static_argnums) donate_argnums = _ensure_index_tuple(donate_argnums) donate_argnums = rebase_donate_argnums(donate_argnums, static_argnums) fun_name = getattr(fun, "__name__", "unknown") backend = backend if backend is not None else xb.get_backend().platform def make_axis_env(nreps): if axis_env is None: return xla.AxisEnv(nreps, (), ()) else: nreps = nreps * prod(size for name, size in axis_env) names, sizes = unzip2(axis_env) return xla.AxisEnv(nreps, names, sizes) @wraps(fun) @api_boundary def computation_maker(*args, **kwargs): if max(static_argnums + donate_argnums, default=-1) >= len(args): raise ValueError(f"jitted function has static_argnums={static_argnums}," f" donate_argnums={donate_argnums} but " f"was called with only {len(args)} positional arguments.") f = lu.wrap_init(fun) f, dyn_args = argnums_partial_except(f, static_argnums, args, allow_invalid=False) args_flat, in_tree = tree_flatten((dyn_args, kwargs)) if donate_argnums: donated_invars = donation_vector(donate_argnums, dyn_args, kwargs) else: donated_invars = (False,) * len(args_flat) if in_parts is None: in_parts_flat = None else: in_parts_flat = tuple(flatten_axes( "xla_computation in_parts", in_tree.children()[0], in_parts)) jaxtree_fun, out_tree = flatten_fun(f, in_tree) avals = map(shaped_abstractify, args_flat) with ExitStack() as stack: for axis_name, size in axis_env or []: stack.enter_context(core.extend_axis_env(axis_name, size, None)) jaxpr, out_avals, consts = pe.trace_to_jaxpr_dynamic(jaxtree_fun, avals) jaxpr = dispatch.apply_outfeed_rewriter(jaxpr) axis_env_ = make_axis_env(dispatch.jaxpr_replicas(jaxpr)) if out_parts is None: out_parts_flat = None else: out_parts_flat = tuple(flatten_axes( "xla_computation out_parts", out_tree(), out_parts)) unordered_effects = [eff for eff in jaxpr.effects if eff not in core.ordered_effects] ordered_effects = [eff for eff in jaxpr.effects if eff in core.ordered_effects] lowering_result = mlir.lower_jaxpr_to_module( f"xla_computation_{fun_name}", core.ClosedJaxpr(jaxpr, consts), unordered_effects=unordered_effects, ordered_effects=ordered_effects, platform=backend, axis_context=mlir.ReplicaAxisContext(axis_env_), name_stack=new_name_stack(wrap_name(fun_name, "xla_computation")), donated_args=donated_invars, arg_shardings=(None if in_parts_flat is None else map(xla.sharding_to_proto, in_parts_flat)), result_shardings=(None if out_parts_flat is None else map(xla.sharding_to_proto, out_parts_flat))) should_tuple = tuple_args if tuple_args is not None else (len(avals) > 100) # tuple args not yet supported on IPU should_tuple = False built = xc._xla.mlir.mlir_module_to_xla_computation( mlir.module_to_string(lowering_result.module), use_tuple_args=should_tuple, return_tuple=True) out_shapes_flat = [ ShapeDtypeStruct(a.shape, a.dtype, a.named_shape) for a in out_avals] out_shape = tree_unflatten(out_tree(), out_shapes_flat) for out_aval in out_avals: if not isinstance(out_aval, xla.ShapedArray): raise RuntimeError("As we want to propagate the weak_type, we need " "to get a ShapedArray, otherwise this " "information is lost") if return_shape: return built, out_shape else: return built return computation_maker def grad(fun: Callable, argnums: Union[int, Sequence[int]] = 0, has_aux: bool = False, holomorphic: bool = False, allow_int: bool = False, reduce_axes: Sequence[AxisName] = ()) -> Callable: """Creates a function that evaluates the gradient of ``fun``. Args: fun: Function to be differentiated. Its arguments at positions specified by ``argnums`` should be arrays, scalars, or standard Python containers. Argument arrays in the positions specified by ``argnums`` must be of inexact (i.e., floating-point or complex) type. It should return a scalar (which includes arrays with shape ``()`` but not arrays with shape ``(1,)`` etc.) argnums: Optional, integer or sequence of integers. Specifies which positional argument(s) to differentiate with respect to (default 0). has_aux: Optional, bool. Indicates whether ``fun`` returns a pair where the first element is considered the output of the mathematical function to be differentiated and the second element is auxiliary data. Default False. holomorphic: Optional, bool. Indicates whether ``fun`` is promised to be holomorphic. If True, inputs and outputs must be complex. Default False. allow_int: Optional, bool. Whether to allow differentiating with respect to integer valued inputs. The gradient of an integer input will have a trivial vector-space dtype (float0). Default False. reduce_axes: Optional, tuple of axis names. If an axis is listed here, and ``fun`` implicitly broadcasts a value over that axis, the backward pass will perform a ``psum`` of the corresponding gradient. Otherwise, the gradient will be per-example over named axes. For example, if ``'batch'`` is a named batch axis, ``grad(f, reduce_axes=('batch',))`` will create a function that computes the total gradient while ``grad(f)`` will create one that computes the per-example gradient. Returns: A function with the same arguments as ``fun``, that evaluates the gradient of ``fun``. If ``argnums`` is an integer then the gradient has the same shape and type as the positional argument indicated by that integer. If argnums is a tuple of integers, the gradient is a tuple of values with the same shapes and types as the corresponding arguments. If ``has_aux`` is True then a pair of (gradient, auxiliary_data) is returned. For example: >>> import jax >>> >>> grad_tanh = jax.grad(jax.numpy.tanh) >>> print(grad_tanh(0.2)) 0.961043 """ value_and_grad_f = value_and_grad(fun, argnums, has_aux=has_aux, holomorphic=holomorphic, allow_int=allow_int, reduce_axes=reduce_axes) docstr = ("Gradient of {fun} with respect to positional argument(s) " "{argnums}. Takes the same arguments as {fun} but returns the " "gradient, which has the same shape as the arguments at " "positions {argnums}.") @wraps(fun, docstr=docstr, argnums=argnums) @api_boundary def grad_f(*args, **kwargs): _, g = value_and_grad_f(*args, **kwargs) return g @wraps(fun, docstr=docstr, argnums=argnums) @api_boundary def grad_f_aux(*args, **kwargs): (_, aux), g = value_and_grad_f(*args, **kwargs) return g, aux return grad_f_aux if has_aux else grad_f def value_and_grad(fun: Callable, argnums: Union[int, Sequence[int]] = 0, has_aux: bool = False, holomorphic: bool = False, allow_int: bool = False, reduce_axes: Sequence[AxisName] = () ) -> Callable[..., Tuple[Any, Any]]: """Create a function that evaluates both ``fun`` and the gradient of ``fun``. Args: fun: Function to be differentiated. Its arguments at positions specified by ``argnums`` should be arrays, scalars, or standard Python containers. It should return a scalar (which includes arrays with shape ``()`` but not arrays with shape ``(1,)`` etc.) argnums: Optional, integer or sequence of integers. Specifies which positional argument(s) to differentiate with respect to (default 0). has_aux: Optional, bool. Indicates whether ``fun`` returns a pair where the first element is considered the output of the mathematical function to be differentiated and the second element is auxiliary data. Default False. holomorphic: Optional, bool. Indicates whether ``fun`` is promised to be holomorphic. If True, inputs and outputs must be complex. Default False. allow_int: Optional, bool. Whether to allow differentiating with respect to integer valued inputs. The gradient of an integer input will have a trivial vector-space dtype (float0). Default False. reduce_axes: Optional, tuple of axis names. If an axis is listed here, and ``fun`` implicitly broadcasts a value over that axis, the backward pass will perform a ``psum`` of the corresponding gradient. Otherwise, the gradient will be per-example over named axes. For example, if ``'batch'`` is a named batch axis, ``value_and_grad(f, reduce_axes=('batch',))`` will create a function that computes the total gradient while ``value_and_grad(f)`` will create one that computes the per-example gradient. Returns: A function with the same arguments as ``fun`` that evaluates both ``fun`` and the gradient of ``fun`` and returns them as a pair (a two-element tuple). If ``argnums`` is an integer then the gradient has the same shape and type as the positional argument indicated by that integer. If argnums is a sequence of integers, the gradient is a tuple of values with the same shapes and types as the corresponding arguments. If ``has_aux`` is True then a tuple of ((value, auxiliary_data), gradient) is returned. """ docstr = ("Value and gradient of {fun} with respect to positional " "argument(s) {argnums}. Takes the same arguments as {fun} but " "returns a two-element tuple where the first element is the value " "of {fun} and the second element is the gradient, which has the " "same shape as the arguments at positions {argnums}.") _check_callable(fun) argnums = core.concrete_or_error(_ensure_index, argnums) reduce_axes = _ensure_str_tuple(reduce_axes) @wraps(fun, docstr=docstr, argnums=argnums) @api_boundary def value_and_grad_f(*args, **kwargs): max_argnum = argnums if isinstance(argnums, int) else max(argnums) if max_argnum >= len(args): raise TypeError(f"differentiating with respect to argnums={argnums} requires at least " f"{max_argnum + 1} positional arguments to be passed by the caller, " f"but got only {len(args)} positional arguments.") f = lu.wrap_init(fun, kwargs) f_partial, dyn_args = argnums_partial(f, argnums, args, require_static_args_hashable=False) for leaf in tree_leaves(dyn_args): _check_input_dtype_grad(holomorphic, allow_int, leaf) if not has_aux: ans, vjp_py = _vjp(f_partial, *dyn_args, reduce_axes=reduce_axes) else: ans, vjp_py, aux = _vjp( f_partial, *dyn_args, has_aux=True, reduce_axes=reduce_axes) _check_scalar(ans) tree_map(partial(_check_output_dtype_grad, holomorphic), ans) g = vjp_py(lax_internal._one(ans)) g = g[0] if isinstance(argnums, int) else g if not has_aux: return ans, g else: return (ans, aux), g return value_and_grad_f def _check_scalar(x): msg = "Gradient only defined for scalar-output functions. Output {}.".format try: aval = core.get_aval(x) except TypeError as e: raise TypeError(msg(f"was {x}")) from e else: if isinstance(aval, ShapedArray): if aval.shape != (): raise TypeError(msg(f"had shape: {aval.shape}")) else: raise TypeError(msg(f"had abstract value {aval}")) def _check_input_dtype_revderiv(name, holomorphic, allow_int, x): _check_arg(x) aval = core.get_aval(x) if holomorphic: if not dtypes.issubdtype(aval.dtype, np.complexfloating): raise TypeError(f"{name} with holomorphic=True requires inputs with complex dtype, " f"but got {aval.dtype.name}.") if (dtypes.issubdtype(aval.dtype, np.integer) or dtypes.issubdtype(aval.dtype, np.bool_)): if not allow_int: raise TypeError(f"{name} requires real- or complex-valued inputs (input dtype " f"that is a sub-dtype of np.inexact), but got {aval.dtype.name}. " "If you want to use Boolean- or integer-valued inputs, use vjp " "or set allow_int to True.") elif not dtypes.issubdtype(aval.dtype, np.inexact): raise TypeError(f"{name} requires numerical-valued inputs (input dtype that is a " f"sub-dtype of np.bool_ or np.number), but got {aval.dtype.name}.") _check_input_dtype_grad = partial(_check_input_dtype_revderiv, "grad") def _check_output_dtype_revderiv(name, holomorphic, x): aval = core.get_aval(x) if holomorphic: if not dtypes.issubdtype(aval.dtype, np.complexfloating): raise TypeError(f"{name} with holomorphic=True requires outputs with complex dtype, " f"but got {aval.dtype.name}.") elif dtypes.issubdtype(aval.dtype, np.complexfloating): raise TypeError(f"{name} requires real-valued outputs (output dtype that is " f"a sub-dtype of np.floating), but got {aval.dtype.name}. " "For holomorphic differentiation, pass holomorphic=True. " "For differentiation of non-holomorphic functions involving complex " "outputs, use jax.vjp directly.") elif not dtypes.issubdtype(aval.dtype, np.floating): raise TypeError(f"{name} requires real-valued outputs (output dtype that is " f"a sub-dtype of np.floating), but got {aval.dtype.name}. " "For differentiation of functions with integer outputs, use " "jax.vjp directly.") _check_output_dtype_grad = partial(_check_output_dtype_revderiv, "grad") def jacfwd(fun: Callable, argnums: Union[int, Sequence[int]] = 0, has_aux: bool = False, holomorphic: bool = False) -> Callable: """Jacobian of ``fun`` evaluated column-by-column using forward-mode AD. Args: fun: Function whose Jacobian is to be computed. argnums: Optional, integer or sequence of integers. Specifies which positional argument(s) to differentiate with respect to (default ``0``). has_aux: Optional, bool. Indicates whether ``fun`` returns a pair where the first element is considered the output of the mathematical function to be differentiated and the second element is auxiliary data. Default False. holomorphic: Optional, bool. Indicates whether ``fun`` is promised to be holomorphic. Default False. Returns: A function with the same arguments as ``fun``, that evaluates the Jacobian of ``fun`` using forward-mode automatic differentiation. If ``has_aux`` is True then a pair of (jacobian, auxiliary_data) is returned. >>> import jax >>> import jax.numpy as jnp >>> >>> def f(x): ... return jnp.asarray( ... [x[0], 5*x[2], 4*x[1]**2 - 2*x[2], x[2] * jnp.sin(x[0])]) ... >>> print(jax.jacfwd(f)(jnp.array([1., 2., 3.]))) [[ 1. 0. 0. ] [ 0. 0. 5. ] [ 0. 16. -2. ] [ 1.6209 0. 0.84147]] """ _check_callable(fun) argnums = _ensure_index(argnums) def jacfun(*args, **kwargs): f = lu.wrap_init(fun, kwargs) f_partial, dyn_args = argnums_partial(f, argnums, args, require_static_args_hashable=False) tree_map(partial(_check_input_dtype_jacfwd, holomorphic), dyn_args) if not has_aux: pushfwd = partial(_jvp, f_partial, dyn_args) y, jac = vmap(pushfwd, out_axes=(None, -1))(_std_basis(dyn_args)) else: pushfwd = partial(_jvp, f_partial, dyn_args, has_aux=True) y, jac, aux = vmap(pushfwd, out_axes=(None, -1, None))(_std_basis(dyn_args)) tree_map(partial(_check_output_dtype_jacfwd, holomorphic), y) example_args = dyn_args[0] if isinstance(argnums, int) else dyn_args jac_tree = tree_map(partial(_jacfwd_unravel, example_args), y, jac) if not has_aux: return jac_tree else: return jac_tree, aux return jacfun def _check_input_dtype_jacfwd(holomorphic: bool, x: Any) -> None: _check_arg(x) aval = core.get_aval(x) if holomorphic: if not dtypes.issubdtype(aval.dtype, np.complexfloating): raise TypeError("jacfwd with holomorphic=True requires inputs with complex " f"dtype, but got {aval.dtype.name}.") elif not dtypes.issubdtype(aval.dtype, np.floating): raise TypeError("jacfwd requires real-valued inputs (input dtype that is " f"a sub-dtype of np.floating), but got {aval.dtype.name}. " "For holomorphic differentiation, pass holomorphic=True. " "For differentiation of non-holomorphic functions involving " "complex inputs or integer inputs, use jax.jvp directly.") def _check_output_dtype_jacfwd(holomorphic, x): aval = core.get_aval(x) if holomorphic: if not dtypes.issubdtype(aval.dtype, np.complexfloating): raise TypeError("jacfwd with holomorphic=True requires outputs with complex dtype, " f"but got {aval.dtype.name}.") def jacrev(fun: Callable, argnums: Union[int, Sequence[int]] = 0, has_aux: bool = False, holomorphic: bool = False, allow_int: bool = False) -> Callable: """Jacobian of ``fun`` evaluated row-by-row using reverse-mode AD. Args: fun: Function whose Jacobian is to be computed. argnums: Optional, integer or sequence of integers. Specifies which positional argument(s) to differentiate with respect to (default ``0``). has_aux: Optional, bool. Indicates whether ``fun`` returns a pair where the first element is considered the output of the mathematical function to be differentiated and the second element is auxiliary data. Default False. holomorphic: Optional, bool. Indicates whether ``fun`` is promised to be holomorphic. Default False. allow_int: Optional, bool. Whether to allow differentiating with respect to integer valued inputs. The gradient of an integer input will have a trivial vector-space dtype (float0). Default False. Returns: A function with the same arguments as ``fun``, that evaluates the Jacobian of ``fun`` using reverse-mode automatic differentiation. If ``has_aux`` is True then a pair of (jacobian, auxiliary_data) is returned. >>> import jax >>> import jax.numpy as jnp >>> >>> def f(x): ... return jnp.asarray( ... [x[0], 5*x[2], 4*x[1]**2 - 2*x[2], x[2] * jnp.sin(x[0])]) ... >>> print(jax.jacrev(f)(jnp.array([1., 2., 3.]))) [[ 1. 0. 0. ] [ 0. 0. 5. ] [ 0. 16. -2. ] [ 1.6209 0. 0.84147]] """ _check_callable(fun) def jacfun(*args, **kwargs): f = lu.wrap_init(fun, kwargs) f_partial, dyn_args = argnums_partial(f, argnums, args, require_static_args_hashable=False) tree_map(partial(_check_input_dtype_jacrev, holomorphic, allow_int), dyn_args) if not has_aux: y, pullback = _vjp(f_partial, *dyn_args) else: y, pullback, aux = _vjp(f_partial, *dyn_args, has_aux=True) tree_map(partial(_check_output_dtype_jacrev, holomorphic), y) jac = vmap(pullback)(_std_basis(y)) jac = jac[0] if isinstance(argnums, int) else jac example_args = dyn_args[0] if isinstance(argnums, int) else dyn_args jac_tree = tree_map(partial(_jacrev_unravel, y), example_args, jac) jac_tree = tree_transpose(tree_structure(example_args), tree_structure(y), jac_tree) if not has_aux: return jac_tree else: return jac_tree, aux return jacfun jacobian = jacrev _check_input_dtype_jacrev = partial(_check_input_dtype_revderiv, "jacrev") _check_output_dtype_jacrev = partial(_check_output_dtype_revderiv, "jacrev") def hessian(fun: Callable, argnums: Union[int, Sequence[int]] = 0, has_aux: bool = False, holomorphic: bool = False) -> Callable: """Hessian of ``fun`` as a dense array. Args: fun: Function whose Hessian is to be computed. Its arguments at positions specified by ``argnums`` should be arrays, scalars, or standard Python containers thereof. It should return arrays, scalars, or standard Python containers thereof. argnums: Optional, integer or sequence of integers. Specifies which positional argument(s) to differentiate with respect to (default ``0``). has_aux: Optional, bool. Indicates whether ``fun`` returns a pair where the first element is considered the output of the mathematical function to be differentiated and the second element is auxiliary data. Default False. holomorphic: Optional, bool. Indicates whether ``fun`` is promised to be holomorphic. Default False. Returns: A function with the same arguments as ``fun``, that evaluates the Hessian of ``fun``. >>> import jax >>> >>> g = lambda x: x[0]**3 - 2*x[0]*x[1] - x[1]**6 >>> print(jax.hessian(g)(jax.numpy.array([1., 2.]))) [[ 6. -2.] [ -2. -480.]] :py:func:`hessian` is a generalization of the usual definition of the Hessian that supports nested Python containers (i.e. pytrees) as inputs and outputs. The tree structure of ``jax.hessian(fun)(x)`` is given by forming a tree product of the structure of ``fun(x)`` with a tree product of two copies of the structure of ``x``. A tree product of two tree structures is formed by replacing each leaf of the first tree with a copy of the second. For example: >>> import jax.numpy as jnp >>> f = lambda dct: {"c": jnp.power(dct["a"], dct["b"])} >>> print(jax.hessian(f)({"a": jnp.arange(2.) + 1., "b": jnp.arange(2.) + 2.})) {'c': {'a': {'a': DeviceArray([[[ 2., 0.], [ 0., 0.]], [[ 0., 0.], [ 0., 12.]]], dtype=float32), 'b': DeviceArray([[[ 1. , 0. ], [ 0. , 0. ]], [[ 0. , 0. ], [ 0. , 12.317766]]], dtype=float32)}, 'b': {'a': DeviceArray([[[ 1. , 0. ], [ 0. , 0. ]], [[ 0. , 0. ], [ 0. , 12.317766]]], dtype=float32), 'b': DeviceArray([[[0. , 0. ], [0. , 0. ]], [[0. , 0. ], [0. , 3.843624]]], dtype=float32)}}} Thus each leaf in the tree structure of ``jax.hessian(fun)(x)`` corresponds to a leaf of ``fun(x)`` and a pair of leaves of ``x``. For each leaf in ``jax.hessian(fun)(x)``, if the corresponding array leaf of ``fun(x)`` has shape ``(out_1, out_2, ...)`` and the corresponding array leaves of ``x`` have shape ``(in_1_1, in_1_2, ...)`` and ``(in_2_1, in_2_2, ...)`` respectively, then the Hessian leaf has shape ``(out_1, out_2, ..., in_1_1, in_1_2, ..., in_2_1, in_2_2, ...)``. In other words, the Python tree structure represents the block structure of the Hessian, with blocks determined by the input and output pytrees. In particular, an array is produced (with no pytrees involved) when the function input ``x`` and output ``fun(x)`` are each a single array, as in the ``g`` example above. If ``fun(x)`` has shape ``(out1, out2, ...)`` and ``x`` has shape ``(in1, in2, ...)`` then ``jax.hessian(fun)(x)`` has shape ``(out1, out2, ..., in1, in2, ..., in1, in2, ...)``. To flatten pytrees into 1D vectors, consider using :py:func:`jax.flatten_util.flatten_pytree`. """ return jacfwd(jacrev(fun, argnums, has_aux=has_aux, holomorphic=holomorphic), argnums, has_aux=has_aux, holomorphic=holomorphic) def _std_basis(pytree): leaves, _ = tree_flatten(pytree) ndim = sum(map(np.size, leaves)) dtype = dtypes.result_type(*leaves) flat_basis = jax.numpy.eye(ndim, dtype=dtype) return _unravel_array_into_pytree(pytree, 1, None, flat_basis) def _jacfwd_unravel(input_pytree, output_pytree_leaf, arr): return _unravel_array_into_pytree( input_pytree, -1, output_pytree_leaf, arr) def _jacrev_unravel(output_pytree, input_pytree_leaf, arr): return _unravel_array_into_pytree( output_pytree, 0, input_pytree_leaf, arr) def _possible_downcast(x, example): if (dtypes.issubdtype(x.dtype, np.complexfloating) and not dtypes.issubdtype(_dtype(example), np.complexfloating)): x = x.real dtype = None if example is None else _dtype(example) weak_type = None if example is None else dtypes.is_weakly_typed(example) return lax_internal._convert_element_type(x, dtype, weak_type) def _unravel_array_into_pytree(pytree, axis, example, arr): """Unravel an array into a PyTree with a given structure. Args: pytree: The pytree that provides the structure. axis: The parameter axis is either -1, 0, or 1. It controls the resulting shapes. example: If specified, cast the components to the matching dtype/weak_type, or else use the pytree leaf type if example is None. arr: The array to be unraveled. """ leaves, treedef = tree_flatten(pytree) axis = axis % arr.ndim shapes = [arr.shape[:axis] + np.shape(l) + arr.shape[axis+1:] for l in leaves] parts = _split(arr, np.cumsum(map(np.size, leaves[:-1])), axis) reshaped_parts = [ _possible_downcast(np.reshape(x, shape), leaf if example is None else example) for x, shape, leaf in zip(parts, shapes, leaves)] return tree_unflatten(treedef, reshaped_parts) def _split(x, indices, axis): if isinstance(x, np.ndarray): return np.split(x, indices, axis) else: return x.split(indices, axis) def vmap(fun: F, in_axes: Union[int, Sequence[Any]] = 0, out_axes: Any = 0, axis_name: Optional[Hashable] = None, axis_size: Optional[int] = None, spmd_axis_name: Optional[Hashable] = None) -> F: """Vectorizing map. Creates a function which maps ``fun`` over argument axes. Args: fun: Function to be mapped over additional axes. in_axes: An integer, None, or (nested) standard Python container (tuple/list/dict) thereof specifying which input array axes to map over. If each positional argument to ``fun`` is an array, then ``in_axes`` can be an integer, a None, or a tuple of integers and Nones with length equal to the number of positional arguments to ``fun``. An integer or ``None`` indicates which array axis to map over for all arguments (with ``None`` indicating not to map any axis), and a tuple indicates which axis to map for each corresponding positional argument. Axis integers must be in the range ``[-ndim, ndim)`` for each array, where ``ndim`` is the number of dimensions (axes) of the corresponding input array. If the positional arguments to ``fun`` are container (pytree) types, the corresponding element of ``in_axes`` can itself be a matching container, so that distinct array axes can be mapped for different container elements. ``in_axes`` must be a container tree prefix of the positional argument tuple passed to ``fun``. See this link for more detail: https://jax.readthedocs.io/en/latest/pytrees.html#applying-optional-parameters-to-pytrees Either ``axis_size`` must be provided explicitly, or at least one positional argument must have ``in_axes`` not None. The sizes of the mapped input axes for all mapped positional arguments must all be equal. Arguments passed as keywords are always mapped over their leading axis (i.e. axis index 0). See below for examples. out_axes: An integer, None, or (nested) standard Python container (tuple/list/dict) thereof indicating where the mapped axis should appear in the output. All outputs with a mapped axis must have a non-None ``out_axes`` specification. Axis integers must be in the range ``[-ndim, ndim)`` for each output array, where ``ndim`` is the number of dimensions (axes) of the array returned by the :func:`vmap`-ed function, which is one more than the number of dimensions (axes) of the corresponding array returned by ``fun``. axis_name: Optional, a hashable Python object used to identify the mapped axis so that parallel collectives can be applied. axis_size: Optional, an integer indicating the size of the axis to be mapped. If not provided, the mapped axis size is inferred from arguments.. Returns: Batched/vectorized version of ``fun`` with arguments that correspond to those of ``fun``, but with extra array axes at positions indicated by ``in_axes``, and a return value that corresponds to that of ``fun``, but with extra array axes at positions indicated by ``out_axes``. For example, we can implement a matrix-matrix product using a vector dot product: >>> import jax.numpy as jnp >>> >>> vv = lambda x, y: jnp.vdot(x, y) # ([a], [a]) -> [] >>> mv = vmap(vv, (0, None), 0) # ([b,a], [a]) -> [b] (b is the mapped axis) >>> mm = vmap(mv, (None, 1), 1) # ([b,a], [a,c]) -> [b,c] (c is the mapped axis) Here we use ``[a,b]`` to indicate an array with shape (a,b). Here are some variants: >>> mv1 = vmap(vv, (0, 0), 0) # ([b,a], [b,a]) -> [b] (b is the mapped axis) >>> mv2 = vmap(vv, (0, 1), 0) # ([b,a], [a,b]) -> [b] (b is the mapped axis) >>> mm2 = vmap(mv2, (1, 1), 0) # ([b,c,a], [a,c,b]) -> [c,b] (c is the mapped axis) Here's an example of using container types in ``in_axes`` to specify which axes of the container elements to map over: >>> A, B, C, D = 2, 3, 4, 5 >>> x = jnp.ones((A, B)) >>> y = jnp.ones((B, C)) >>> z = jnp.ones((C, D)) >>> def foo(tree_arg): ... x, (y, z) = tree_arg ... return jnp.dot(x, jnp.dot(y, z)) >>> tree = (x, (y, z)) >>> print(foo(tree)) [[12. 12. 12. 12. 12.] [12. 12. 12. 12. 12.]] >>> from jax import vmap >>> K = 6 # batch size >>> x = jnp.ones((K, A, B)) # batch axis in different locations >>> y = jnp.ones((B, K, C)) >>> z = jnp.ones((C, D, K)) >>> tree = (x, (y, z)) >>> vfoo = vmap(foo, in_axes=((0, (1, 2)),)) >>> print(vfoo(tree).shape) (6, 2, 5) Here's another example using container types in ``in_axes``, this time a dictionary, to specify the elements of the container to map over: >>> dct = {'a': 0., 'b': jnp.arange(5.)} >>> x = 1. >>> def foo(dct, x): ... return dct['a'] + dct['b'] + x >>> out = vmap(foo, in_axes=({'a': None, 'b': 0}, None))(dct, x) >>> print(out) [1. 2. 3. 4. 5.] The results of a vectorized function can be mapped or unmapped. For example, the function below returns a pair with the first element mapped and the second unmapped. Only for unmapped results we can specify ``out_axes`` to be ``None`` (to keep it unmapped). >>> print(vmap(lambda x, y: (x + y, y * 2.), in_axes=(0, None), out_axes=(0, None))(jnp.arange(2.), 4.)) (DeviceArray([4., 5.], dtype=float32), 8.0) If the ``out_axes`` is specified for an unmapped result, the result is broadcast across the mapped axis: >>> print(vmap(lambda x, y: (x + y, y * 2.), in_axes=(0, None), out_axes=0)(jnp.arange(2.), 4.)) (DeviceArray([4., 5.], dtype=float32), DeviceArray([8., 8.], dtype=float32, weak_type=True)) If the ``out_axes`` is specified for a mapped result, the result is transposed accordingly. Finally, here's an example using ``axis_name`` together with collectives: >>> xs = jnp.arange(3. * 4.).reshape(3, 4) >>> print(vmap(lambda x: lax.psum(x, 'i'), axis_name='i')(xs)) [[12. 15. 18. 21.] [12. 15. 18. 21.] [12. 15. 18. 21.]] See the :py:func:`jax.pmap` docstring for more examples involving collectives. """ _check_callable(fun) docstr = ("Vectorized version of {fun}. Takes similar arguments as {fun} " "but with additional array axes over which {fun} is mapped.") if fun.__doc__: docstr += "\n\nOriginal documentation:\n\n" docstr += fun.__doc__ axis_name = core.no_axis_name if axis_name is None else axis_name if isinstance(in_axes, list): # To be a tree prefix of the positional args tuple, in_axes can never be a # list: if in_axes is not a leaf, it must be a tuple of trees. However, # in cases like these users expect tuples and lists to be treated # essentially interchangeably, so we canonicalize lists to tuples here # rather than raising an error. https://github.com/google/jax/issues/2367 in_axes = tuple(in_axes) if not all(type(l) is int or type(l) in batching.spec_types for l in tree_leaves(in_axes)): raise TypeError("vmap in_axes must be an int, None, or (nested) container " f"with those types as leaves, but got {in_axes}.") if not all(type(l) is int or type(l) in batching.spec_types for l in tree_leaves(out_axes)): raise TypeError("vmap out_axes must be an int, None, or (nested) container " f"with those types as leaves, but got {out_axes}.") @wraps(fun, docstr=docstr) @api_boundary def vmap_f(*args, **kwargs): args_flat, in_tree = tree_flatten((args, kwargs), is_leaf=batching.is_vmappable) f = lu.wrap_init(fun) flat_fun, out_tree = batching.flatten_fun_for_vmap(f, in_tree) in_axes_flat = flatten_axes("vmap in_axes", in_tree, (in_axes, 0), kws=True) axis_size_ = (axis_size if axis_size is not None else _mapped_axis_size(in_tree, args_flat, in_axes_flat, "vmap", kws=True)) out_flat = batching.batch( flat_fun, axis_name, axis_size_, in_axes_flat, lambda: flatten_axes("vmap out_axes", out_tree(), out_axes), spmd_axis_name=spmd_axis_name ).call_wrapped(*args_flat) return tree_unflatten(out_tree(), out_flat) return vmap_f def _mapped_axis_size(tree, vals, dims, name, *, kws=False): if not vals: args, kwargs = tree_unflatten(tree, vals) raise ValueError( f"{name} wrapped function must be passed at least one argument " f"containing an array, got empty *args={args} and **kwargs={kwargs}" ) def _get_axis_size(name: str, shape: Tuple[core.AxisSize, ...], axis: int ) -> core.AxisSize: try: return shape[axis] except (IndexError, TypeError) as e: min_rank = axis + 1 if axis >= 0 else -axis raise ValueError( f"{name} was requested to map its argument along axis {axis}, " f"which implies that its rank should be at least {min_rank}, " f"but is only {len(shape)} (its shape is {shape})") from e axis_sizes = core.dedup_referents( _get_axis_size(name, np.shape(x), d) for x, d in zip(vals, dims) if d is not None) if len(axis_sizes) == 1: return axis_sizes[0] if not axis_sizes: msg = f"{name} must have at least one non-None value in in_axes" raise ValueError(msg) msg = f"{name} got inconsistent sizes for array axes to be mapped:\n" + "{}" # we switch the error message based on whether args is a tuple of arrays, # in which case we can produce an error message based on argument indices, # or if it has nested containers. if kws: position_only_tree, leaf = treedef_children(tree) if not treedef_is_leaf(leaf): sizes = [x.shape[d] if d is not None else None for x, d in zip(vals, dims)] sizes = tree_unflatten(tree, sizes) raise ValueError(msg.format(f"the tree of axis sizes is:\n{sizes}")) from None # if keyword arguments are included in the tree, we adapt the error # message only to be about the positional arguments tree = position_only_tree # TODO(mattjj,phawkins): add a way to inspect pytree kind more directly if tree == tree_flatten((0,) * tree.num_leaves)[1]: lines1 = [f"arg {i} has shape {np.shape(x)} and axis {d} is to be mapped" for i, (x, d) in enumerate(zip(vals, dims))] sizes = collections.defaultdict(list) for i, (x, d) in enumerate(zip(vals, dims)): if d is not None: sizes[x.shape[d]].append(i) lines2 = ["{} {} {} {} to be mapped of size {}".format( "args" if len(idxs) > 1 else "arg", ", ".join(map(str, idxs)), "have" if len(idxs) > 1 else "has", "axes" if len(idxs) > 1 else "an axis", size) for size, idxs in sizes.items()] raise ValueError(msg.format("\n".join(lines1 + ["so"] + lines2))) from None else: sizes = [x.shape[d] if d is not None else None for x, d in zip(vals, dims)] sizes = tree_unflatten(tree, sizes) raise ValueError(msg.format(f"the tree of axis sizes is:\n{sizes}")) from None def pmap( fun: Callable, axis_name: Optional[AxisName] = None, *, in_axes=0, out_axes=0, static_broadcasted_argnums: Union[int, Iterable[int]] = (), devices: Optional[Sequence[xc.Device]] = None, # noqa: F811 backend: Optional[str] = None, axis_size: Optional[int] = None, donate_argnums: Union[int, Iterable[int]] = (), global_arg_shapes: Optional[Tuple[Tuple[int, ...], ...]] = None, ) -> Any: """Parallel map with support for collective operations. The purpose of :py:func:`pmap` is to express single-program multiple-data (SPMD) programs. Applying :py:func:`pmap` to a function will compile the function with XLA (similarly to :py:func:`jit`), then execute it in parallel on XLA devices, such as multiple GPUs or multiple TPU cores. Semantically it is comparable to :py:func:`vmap` because both transformations map a function over array axes, but where :py:func:`vmap` vectorizes functions by pushing the mapped axis down into primitive operations, :py:func:`pmap` instead replicates the function and executes each replica on its own XLA device in parallel. The mapped axis size must be less than or equal to the number of local XLA devices available, as returned by :py:func:`jax.local_device_count()` (unless ``devices`` is specified, see below). For nested :py:func:`pmap` calls, the product of the mapped axis sizes must be less than or equal to the number of XLA devices. .. note:: :py:func:`pmap` compiles ``fun``, so while it can be combined with :py:func:`jit`, it's usually unnecessary. **Multi-process platforms:** On multi-process platforms such as TPU pods, :py:func:`pmap` is designed to be used in SPMD Python programs, where every process is running the same Python code such that all processes run the same pmapped function in the same order. Each process should still call the pmapped function with mapped axis size equal to the number of *local* devices (unless ``devices`` is specified, see below), and an array of the same leading axis size will be returned as usual. However, any collective operations in ``fun`` will be computed over *all* participating devices, including those on other processes, via device-to-device communication. Conceptually, this can be thought of as running a pmap over a single array sharded across processes, where each process "sees" only its local shard of the input and output. The SPMD model requires that the same multi-process pmaps must be run in the same order on all devices, but they can be interspersed with arbitrary operations running in a single process. Args: fun: Function to be mapped over argument axes. Its arguments and return value should be arrays, scalars, or (nested) standard Python containers (tuple/list/dict) thereof. Positional arguments indicated by ``static_broadcasted_argnums`` can be anything at all, provided they are hashable and have an equality operation defined. axis_name: Optional, a hashable Python object used to identify the mapped axis so that parallel collectives can be applied. in_axes: A non-negative integer, None, or nested Python container thereof that specifies which axes of positional arguments to map over. Arguments passed as keywords are always mapped over their leading axis (i.e. axis index 0). See :py:func:`vmap` for details. out_axes: A non-negative integer, None, or nested Python container thereof indicating where the mapped axis should appear in the output. All outputs with a mapped axis must have a non-None ``out_axes`` specification (see :py:func:`vmap`). static_broadcasted_argnums: An int or collection of ints specifying which positional arguments to treat as static (compile-time constant). Operations that only depend on static arguments will be constant-folded. Calling the pmapped function with different values for these constants will trigger recompilation. If the pmapped function is called with fewer positional arguments than indicated by ``static_argnums`` then an error is raised. Each of the static arguments will be broadcasted to all devices. Arguments that are not arrays or containers thereof must be marked as static. Defaults to (). Static arguments must be hashable, meaning both ``__hash__`` and ``__eq__`` are implemented, and should be immutable. devices: This is an experimental feature and the API is likely to change. Optional, a sequence of Devices to map over. (Available devices can be retrieved via jax.devices()). Must be given identically for each process in multi-process settings (and will therefore include devices across processes). If specified, the size of the mapped axis must be equal to the number of devices in the sequence local to the given process. Nested :py:func:`pmap` s with ``devices`` specified in either the inner or outer :py:func:`pmap` are not yet supported. backend: This is an experimental feature and the API is likely to change. Optional, a string representing the XLA backend. 'cpu', 'gpu', or 'tpu'. axis_size: Optional; the size of the mapped axis. donate_argnums: Specify which positional argument buffers are "donated" to the computation. It is safe to donate argument buffers if you no longer need them once the computation has finished. In some cases XLA can make use of donated buffers to reduce the amount of memory needed to perform a computation, for example recycling one of your input buffers to store a result. You should not reuse buffers that you donate to a computation, JAX will raise an error if you try to. Note that donate_argnums only work for positional arguments, and keyword arguments will not be donated. For more details on buffer donation see the [FAQ](https://jax.readthedocs.io/en/latest/faq.html#buffer-donation). global_arg_shapes: Optional, must be set when using pmap(sharded_jit) and the partitioned values span multiple processes. The global cross-process per-replica shape of each argument, i.e. does not include the leading pmapped dimension. Can be None for replicated arguments. This API is likely to change in the future. Returns: A parallelized version of ``fun`` with arguments that correspond to those of ``fun`` but with extra array axes at positions indicated by ``in_axes`` and with output that has an additional leading array axis (with the same size). For example, assuming 8 XLA devices are available, :py:func:`pmap` can be used as a map along a leading array axis: >>> import jax.numpy as jnp >>> >>> out = pmap(lambda x: x ** 2)(jnp.arange(8)) # doctest: +SKIP >>> print(out) # doctest: +SKIP [0, 1, 4, 9, 16, 25, 36, 49] When the leading dimension is smaller than the number of available devices JAX will simply run on a subset of devices: >>> x = jnp.arange(3 * 2 * 2.).reshape((3, 2, 2)) >>> y = jnp.arange(3 * 2 * 2.).reshape((3, 2, 2)) ** 2 >>> out = pmap(jnp.dot)(x, y) # doctest: +SKIP >>> print(out) # doctest: +SKIP [[[ 4. 9.] [ 12. 29.]] [[ 244. 345.] [ 348. 493.]] [[ 1412. 1737.] [ 1740. 2141.]]] If your leading dimension is larger than the number of available devices you will get an error: >>> 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 mapped, across the replicas: >>> x, y = jnp.arange(2.), 4. >>> out = pmap(lambda x, y: (x + y, y * 2.), in_axes=(0, None))(x, y) # doctest: +SKIP >>> print(out) # doctest: +SKIP ([4., 5.], [8., 8.]) Note that :py:func:`pmap` always returns values mapped over their leading axis, equivalent to using ``out_axes=0`` in :py:func:`vmap`. In addition to expressing pure maps, :py:func:`pmap` can also be used to express parallel single-program multiple-data (SPMD) programs that communicate via collective operations. For example: >>> f = lambda x: x / jax.lax.psum(x, axis_name='i') >>> out = pmap(f, axis_name='i')(jnp.arange(4.)) # doctest: +SKIP >>> print(out) # doctest: +SKIP [ 0. 0.16666667 0.33333334 0.5 ] >>> print(out.sum()) # doctest: +SKIP 1.0 In this example, ``axis_name`` is a string, but it can be any Python object with ``__hash__`` and ``__eq__`` defined. The argument ``axis_name`` to :py:func:`pmap` names the mapped axis so that collective operations, like :func:`jax.lax.psum`, can refer to it. Axis names are important particularly in the case of nested :py:func:`pmap` functions, where collective operations can operate over distinct axes: >>> from functools import partial >>> import jax >>> >>> @partial(pmap, axis_name='rows') ... @partial(pmap, axis_name='cols') ... def normalize(x): ... row_normed = x / jax.lax.psum(x, 'rows') ... col_normed = x / jax.lax.psum(x, 'cols') ... doubly_normed = x / jax.lax.psum(x, ('rows', 'cols')) ... return row_normed, col_normed, doubly_normed >>> >>> x = jnp.arange(8.).reshape((4, 2)) >>> row_normed, col_normed, doubly_normed = normalize(x) # doctest: +SKIP >>> print(row_normed.sum(0)) # doctest: +SKIP [ 1. 1.] >>> print(col_normed.sum(1)) # doctest: +SKIP [ 1. 1. 1. 1.] >>> print(doubly_normed.sum((0, 1))) # doctest: +SKIP 1.0 On multi-process platforms, collective operations operate over all devices, including those on other processes. For example, assuming the following code 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 >>> print(out) # doctest: +SKIP [28 29 30 31] # on process 0 [32 33 34 35] # on process 1 Each process passes in a different length-4 array, corresponding to its 4 local devices, and the psum operates over all 8 values. 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'. The pmap call on each process then returns the corresponding length-4 output shard. The ``devices`` argument can be used to specify exactly which devices are used to run the parallel computation. For example, again assuming a single process with 8 devices, the following code defines two parallel computations, one which runs on the first six devices and one on the remaining two: >>> from functools import partial >>> @partial(pmap, axis_name='i', devices=jax.devices()[:6]) ... def f1(x): ... return x / jax.lax.psum(x, axis_name='i') >>> >>> @partial(pmap, axis_name='i', devices=jax.devices()[-2:]) ... def f2(x): ... return jax.lax.psum(x ** 2, axis_name='i') >>> >>> print(f1(jnp.arange(6.))) # doctest: +SKIP [0. 0.06666667 0.13333333 0.2 0.26666667 0.33333333] >>> print(f2(jnp.array([2., 3.]))) # doctest: +SKIP [ 13. 13.] """ if FLAGS.experimental_cpp_pmap: func = _cpp_pmap else: func = _python_pmap return func( fun, axis_name, in_axes=in_axes, out_axes=out_axes, static_broadcasted_argnums=static_broadcasted_argnums, devices=devices, backend=backend, axis_size=axis_size, donate_argnums=donate_argnums, global_arg_shapes=global_arg_shapes) class PmapCallInfo(NamedTuple): flat_fun: lu.WrappedFun in_tree: PyTreeDef out_tree: PyTreeDef flat_args: Sequence[Any] donated_invars: Sequence[bool] in_axes_flat: Sequence[Optional[int]] local_axis_size: int global_arg_shapes_flat: Sequence[Optional[Tuple[int, ...]]] out_axes_thunk: HashableFunction devices: Optional[Sequence[xc.Device]] def _check_in_pmap_sharding_with_arrays(args, in_axes_flat, in_devices): from jax.experimental.sharding import PmapSharding from jax.experimental.array import Array if not args: return first_device_assignment = None for a, i in safe_zip(args, in_axes_flat): if not isinstance(a, Array): continue if not isinstance(a.sharding, PmapSharding): raise NotImplementedError('pmap only works with PmapSharding.') if first_device_assignment is None: first_device_assignment = a.sharding._device_assignment arr_sharding = a.sharding.sharded_dim arr_device_assignment = a.sharding._device_assignment if arr_sharding != i: raise ValueError('Array and pmap sharding does not match. Got pmap ' f'sharding: {i}, Array sharding: {arr_sharding} for ' f'arg: {a}') if (in_devices is not None and arr_device_assignment is not None and arr_device_assignment != in_devices): raise ValueError('Devices passed to pmap and Array should be equal. ' f'Got pmap devices: {in_devices}, Array devices: ' f'{arr_device_assignment} for arg: {a}') if (in_devices is None and arr_device_assignment != first_device_assignment): raise ValueError('Devices of all `Array` inputs should be the same. ' f'Got array device: {arr_device_assignment}, ' f'another array device: {first_device_assignment}') def _prepare_pmap(fun, in_axes, out_axes, static_broadcasted_tuple, donate_tuple, global_arg_shapes, in_devices, args, kwargs): f = lu.wrap_init(fun) if static_broadcasted_tuple: if max(static_broadcasted_tuple) >= len(args): raise ValueError( f"pmapped function has static_broadcasted_argnums={static_broadcasted_tuple}" f" but was called with only {len(args)} positional " f"argument{'s' if len(args) > 1 else ''}. " "All static broadcasted arguments must be passed positionally.") dyn_argnums = [i for i in range(len(args)) if i not in static_broadcasted_tuple] f, dyn_args = argnums_partial(f, dyn_argnums, args) if isinstance(in_axes, tuple): dyn_in_axes = tuple(in_axes[i] for i in dyn_argnums) else: dyn_in_axes = in_axes dyn_global_arg_shapes = global_arg_shapes if isinstance(global_arg_shapes, tuple): dyn_global_arg_shapes = tuple(global_arg_shapes[i] for i in dyn_argnums) else: dyn_global_arg_shapes = global_arg_shapes else: dyn_args, dyn_in_axes = args, in_axes dyn_global_arg_shapes = global_arg_shapes args, in_tree = tree_flatten((dyn_args, kwargs)) if donate_tuple: donated_invars = donation_vector(donate_tuple, dyn_args, kwargs) else: donated_invars = (False,) * len(args) in_axes_flat = tuple(flatten_axes("pmap in_axes", in_tree, (dyn_in_axes, 0))) global_arg_shapes_flat = tuple(flatten_axes( "pmap global_arg_shapes", in_tree, (dyn_global_arg_shapes, None), kws=True)) local_axis_size = _mapped_axis_size( in_tree, args, in_axes_flat, "pmap", kws=True) flat_fun, out_tree = flatten_fun(f, in_tree) if config.jax_array: _check_in_pmap_sharding_with_arrays(args, in_axes_flat, in_devices) if any(out_axis is None for out_axis in tree_flatten(out_axes)): raise NotImplementedError("None out_axes in pmap are not supported yet") # NOTE: We don't put out_tree() in the closure, because it's (1) non-hashable, # (2) depends deterministically on flat_fun (at least that's the assumption # that we make). if out_axes == 0: # TODO(apaszke,mattjj): flatten_axes assumes that the output pytree is # functorial (i.e. it can hold leaves of any type), but some user code # breaks this assumption. This is a stop-gap solution to keep the old # out_axes == 0 path working as we look for a better solution. out_axes_thunk = HashableFunction( lambda: (0,) * out_tree().num_leaves, closure=out_axes) else: # out_axes_thunk closes over the out_axes, they are flattened here to make # them hashable. out_axes_leaves, out_axes_treedef = tree_flatten(out_axes) out_axes_thunk = HashableFunction( lambda: tuple(flatten_axes("pmap out_axes", out_tree(), tree_unflatten(out_axes_treedef, list(out_axes_leaves)))), closure=(tuple(out_axes_leaves), out_axes_treedef)) return PmapCallInfo(flat_fun=flat_fun, in_tree=in_tree, out_tree=out_tree, flat_args=args, donated_invars=donated_invars, in_axes_flat=in_axes_flat, local_axis_size=local_axis_size, global_arg_shapes_flat=global_arg_shapes_flat, out_axes_thunk=out_axes_thunk, devices=None if in_devices is None else tuple(in_devices)) def _get_f_mapped( *, fun: Callable, axis_name: Optional[AxisName], in_axes=0, out_axes=0, static_broadcasted_tuple: Tuple[int], devices: Optional[Sequence[xc.Device]], # noqa: F811 backend: Optional[str], axis_size: Optional[int], donate_tuple: Tuple[int], global_arg_shapes: Optional[Tuple[Tuple[int, ...], ...]], ): def pmap_f(*args, **kwargs): p = _prepare_pmap( fun, in_axes, out_axes, static_broadcasted_tuple, donate_tuple, global_arg_shapes, devices, args, kwargs) for arg in p.flat_args: _check_arg(arg) out = pxla.xla_pmap( p.flat_fun, *p.flat_args, backend=backend, axis_name=axis_name, axis_size=p.local_axis_size, global_axis_size=axis_size, devices=p.devices, in_axes=p.in_axes_flat, out_axes_thunk=p.out_axes_thunk, name=p.flat_fun.__name__, donated_invars=p.donated_invars, global_arg_shapes=p.global_arg_shapes_flat) return p.out_tree, out return pmap_f def _shared_code_pmap(fun, axis_name, static_broadcasted_argnums, donate_argnums, in_axes, out_axes): # axis_size is an optional integer representing the global axis size. The # aggregate size (across all processes) size of the mapped axis must match the # given value. _check_callable(fun) axis_name = core._TempAxisName(fun) if axis_name is None else axis_name static_broadcasted_tuple = _ensure_index_tuple(static_broadcasted_argnums) donate_tuple = rebase_donate_argnums( _ensure_index_tuple(donate_argnums), static_broadcasted_tuple) if not all(type(l) is int for l in tree_leaves(in_axes)): raise TypeError("pmap in_axes must be an int, None, or (nested) container " f"with those types as leaves, but got {in_axes}.") if not all(type(l) is int for l in tree_leaves(out_axes)): raise TypeError("pmap out_axes must be an int, None, or (nested) container " f"with those types as leaves, but got {out_axes}.") return axis_name, static_broadcasted_tuple, donate_tuple def _python_pmap( fun: Callable, axis_name: Optional[AxisName] = None, *, in_axes=0, out_axes=0, static_broadcasted_argnums: Union[int, Iterable[int]] = (), devices: Optional[Sequence[xc.Device]] = None, # noqa: F811 backend: Optional[str] = None, axis_size: Optional[int] = None, donate_argnums: Union[int, Iterable[int]] = (), global_arg_shapes: Optional[Tuple[Tuple[int, ...], ...]] = None, ) -> stages.Wrapped: """The Python only implementation.""" axis_name, static_broadcasted_tuple, donate_tuple = _shared_code_pmap( fun, axis_name, static_broadcasted_argnums, donate_argnums, in_axes, out_axes) @wraps(fun) @api_boundary def pmap_f(*args, **kwargs): f_pmapped_ = _get_f_mapped( fun=fun, axis_name=axis_name, in_axes=in_axes, out_axes=out_axes, static_broadcasted_tuple=static_broadcasted_tuple, devices=devices, backend=backend, axis_size=axis_size, global_arg_shapes=global_arg_shapes, donate_tuple=donate_tuple) out_tree, out_flat = f_pmapped_(*args, **kwargs) return tree_unflatten(out_tree(), out_flat) pmap_f.lower = _pmap_lower( fun, axis_name, in_axes, out_axes, static_broadcasted_tuple, devices, backend, axis_size, global_arg_shapes, donate_tuple) return pmap_f class _PmapFastpathData(NamedTuple): version: int # For forward and backward compatibility xla_executable: xla.XlaExecutable in_handler: Any out_handler: Any out_pytree_def: Any # Data needed to handle the inputs. input_sharding_specs: Sequence[pxla.ShardingSpec] input_devices: Sequence[xc.Device] input_indices: Sequence[pxla.Index] # Data needed to build the ShardedDeviceArray from C++. out_sharding_specs: Sequence[pxla.ShardingSpec] out_indices: Sequence[pxla.Index] out_avals: Sequence[Any] def _cpp_pmap( fun: Callable, axis_name: Optional[AxisName] = None, *, in_axes=0, out_axes=0, static_broadcasted_argnums: Union[int, Iterable[int]] = (), devices: Optional[Sequence[xc.Device]] = None, # noqa: F811 backend: Optional[str] = None, axis_size: Optional[int] = None, donate_argnums: Union[int, Iterable[int]] = (), global_arg_shapes: Optional[Tuple[Tuple[int, ...], ...]] = None, ) -> Any: axis_name, static_broadcasted_tuple, donate_tuple = _shared_code_pmap( fun, axis_name, static_broadcasted_argnums, donate_argnums, in_axes, out_axes) del static_broadcasted_argnums, donate_argnums @api_boundary def cache_miss(*args, **kwargs): f_pmapped_ = _get_f_mapped( fun=fun, axis_name=axis_name, in_axes=in_axes, out_axes=out_axes, static_broadcasted_tuple=static_broadcasted_tuple, devices=devices, backend=backend, axis_size=axis_size, global_arg_shapes=global_arg_shapes, donate_tuple=donate_tuple) out_tree, out_flat = f_pmapped_(*args, **kwargs) out_pytree_def = out_tree() out = tree_unflatten(out_pytree_def, out_flat) ### Decide whether we can support the C++ fast path execute: Optional[functools.partial] = None execute = pxla.parallel_callable.most_recent_entry() use_fastpath = ( execute is not None and # We don't support JAX extension backends. isinstance(execute[0], pxla.ExecuteReplicated) and # TODO(sharadmv): Enable effects in replicated computation not execute[0].has_unordered_effects and # No tracers in the outputs. Checking for ShardedDeviceArray should be # sufficient, but we use the more general `DeviceArray`. all(isinstance(x, device_array.DeviceArray) for x in out_flat)) ### If we can use the fastpath, we return required info to the caller. if use_fastpath: execute_replicated = execute[0] out_handler = execute_replicated.out_handler in_handler = execute_replicated.in_handler out_indices = [tuple(s.devices_indices_map(a.shape).values()) for s, a in safe_zip(out_handler.out_shardings, out_handler.out_avals)] fastpath_data = _PmapFastpathData( version=1, xla_executable=execute_replicated.xla_executable, in_handler=in_handler, out_handler=out_handler, out_pytree_def=out_pytree_def, input_sharding_specs=[i.sharding_spec for i in in_handler.in_shardings], input_devices=in_handler.local_devices, input_indices=in_handler.input_indices, out_sharding_specs=[s.sharding_spec for s in out_handler.out_shardings], out_indices=out_indices, out_avals=out_handler.out_avals, ) else: fastpath_data = None return out, fastpath_data cpp_mapped_f = pmap_lib.pmap( fun, cache_miss, static_broadcasted_tuple, partial(pxla._shard_arg, mode=pxla.InputsHandlerMode.pmap)) pmap_f = wraps(fun)(cpp_mapped_f) pmap_f.lower = _pmap_lower( fun, axis_name, in_axes, out_axes, static_broadcasted_tuple, devices, backend, axis_size, global_arg_shapes, donate_tuple) return pmap_f def _pmap_lower(fun, axis_name, in_axes, out_axes, static_broadcasted_tuple, devices, backend, axis_size, global_arg_shapes, donate_tuple): # noqa: F811 """Make a ``lower`` method for pmapped functions.""" # If the function we returned from ``pmap`` were a class instance, # this might naturally be a method, with ``fun`` as a ``self`` and # all the other arguments stored as attributes. @api_boundary def lower(*args, **kwargs) -> stages.Lowered: """Lower a parallel-mapped form of this function for the given arguments. 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. It is ready for compilation but is not yet compiled. It represents a function intended for SPMD execution on multiple devices. Returns: A ``Lowered`` instance representing the post-map lowering. """ p = _prepare_pmap( fun, in_axes, out_axes, static_broadcasted_tuple, donate_tuple, global_arg_shapes, devices, args, kwargs) abstract_args = list(map(shaped_abstractify, p.flat_args)) computation = pxla.lower_parallel_callable( p.flat_fun, backend, axis_name, axis_size=p.local_axis_size, global_axis_size=axis_size, devices=p.devices, name=p.flat_fun.__name__, in_axes=p.in_axes_flat, out_axes_thunk=p.out_axes_thunk, donated_invars=p.donated_invars, global_arg_shapes=p.global_arg_shapes_flat, avals=abstract_args) return stages.Lowered.from_flat_info( computation, p.in_tree, abstract_args, donate_tuple, p.out_tree()) return lower def jvp( fun: Callable, primals, tangents, has_aux: bool = False ) -> Tuple[Any, ...]: """Computes a (forward-mode) Jacobian-vector product of ``fun``. Args: fun: Function to be differentiated. Its arguments should be arrays, scalars, or standard Python containers of arrays or scalars. It should return an array, scalar, or standard Python container of arrays or scalars. primals: The primal values at which the Jacobian of ``fun`` should be evaluated. Should be either a tuple or a list of arguments, and its length should be equal to the number of positional parameters of ``fun``. tangents: The tangent vector for which the Jacobian-vector product should be evaluated. Should be either a tuple or a list of tangents, with the same tree structure and array shapes as ``primals``. has_aux: Optional, bool. Indicates whether ``fun`` returns a pair where the first element is considered the output of the mathematical function to be differentiated and the second element is auxiliary data. Default False. Returns: If ``has_aux`` is ``False``, returns a ``(primals_out, tangents_out)`` pair, where ``primals_out`` is ``fun(*primals)``, and ``tangents_out`` is the Jacobian-vector product of ``function`` evaluated at ``primals`` with ``tangents``. The ``tangents_out`` value has the same Python tree structure and shapes as ``primals_out``. If ``has_aux`` is ``True``, returns a ``(primals_out, tangents_out, aux)`` tuple where ``aux`` is the auxiliary data returned by ``fun``. For example: >>> import jax >>> >>> primals, tangents = jax.jvp(jax.numpy.sin, (0.1,), (0.2,)) >>> print(primals) 0.09983342 >>> print(tangents) 0.19900084 """ _check_callable(fun) return _jvp(lu.wrap_init(fun), primals, tangents, has_aux=has_aux) def _jvp(fun: lu.WrappedFun, primals, tangents, has_aux=False): """Variant of jvp() that takes an lu.WrappedFun.""" if (not isinstance(primals, (tuple, list)) or not isinstance(tangents, (tuple, list))): raise TypeError("primal and tangent arguments to jax.jvp must be tuples or lists; " f"found {type(primals).__name__} and {type(tangents).__name__}.") ps_flat, tree_def = tree_flatten(primals) ts_flat, tree_def_2 = tree_flatten(tangents) if tree_def != tree_def_2: raise TypeError("primal and tangent arguments to jax.jvp must have the same tree " f"structure; primals have tree structure {tree_def} whereas tangents have " f"tree structure {tree_def_2}.") for p, t in safe_zip(ps_flat, ts_flat): if core.primal_dtype_to_tangent_dtype(_dtype(p)) != _dtype(t): raise TypeError("primal and tangent arguments to jax.jvp do not match; " "dtypes must be equal, or in case of int/bool primal dtype " "the tangent dtype must be float0." f"Got primal dtype {_dtype(p)} and so expected tangent dtype " f"{core.primal_dtype_to_tangent_dtype(_dtype(p))}, but got " f"tangent dtype {_dtype(t)} instead.") if np.shape(p) != np.shape(t): raise ValueError("jvp called with different primal and tangent shapes;" f"Got primal shape {np.shape(p)} and tangent shape as {np.shape(t)}") if not has_aux: flat_fun, out_tree = flatten_fun_nokwargs(fun, tree_def) out_primals, out_tangents = ad.jvp(flat_fun).call_wrapped(ps_flat, ts_flat) out_tree = out_tree() return (tree_unflatten(out_tree, out_primals), tree_unflatten(out_tree, out_tangents)) else: flat_fun, out_aux_trees = flatten_fun_nokwargs2(fun, tree_def) jvp_fun, aux = ad.jvp(flat_fun, has_aux=True) out_primals, out_tangents = jvp_fun.call_wrapped(ps_flat, ts_flat) out_tree, aux_tree = out_aux_trees() return (tree_unflatten(out_tree, out_primals), tree_unflatten(out_tree, out_tangents), tree_unflatten(aux_tree, aux())) def linearize(fun: Callable, *primals) -> Tuple[Any, Callable]: """Produces a linear approximation to ``fun`` using :py:func:`jvp` and partial eval. Args: fun: Function to be differentiated. Its arguments should be arrays, scalars, or standard Python containers of arrays or scalars. It should return an array, scalar, or standard python container of arrays or scalars. primals: The primal values at which the Jacobian of ``fun`` should be evaluated. Should be a tuple of arrays, scalar, or standard Python container thereof. The length of the tuple is equal to the number of positional parameters of ``fun``. 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. In terms of values computed, :py:func:`linearize` behaves much like a curried :py:func:`jvp`, where these two code blocks compute the same values:: y, out_tangent = jax.jvp(f, (x,), (in_tangent,)) y, f_jvp = jax.linearize(f, x) out_tangent = f_jvp(in_tangent) However, the difference is that :py:func:`linearize` uses partial evaluation so that the function ``f`` is not re-linearized on calls to ``f_jvp``. In general that means the memory usage scales with the size of the computation, much like in reverse-mode. (Indeed, :py:func:`linearize` has a similar signature to :py:func:`vjp`!) This function is mainly useful if you want to apply ``f_jvp`` multiple times, i.e. to evaluate a pushforward for many different input tangent vectors at the same linearization point. Moreover if all the input tangent vectors are known at once, it can be more efficient to vectorize using :py:func:`vmap`, as in:: pushfwd = partial(jvp, f, (x,)) y, out_tangents = vmap(pushfwd, out_axes=(None, 0))((in_tangents,)) By using :py:func:`vmap` and :py:func:`jvp` together like this we avoid the stored-linearization memory cost that scales with the depth of the computation, which is incurred by both :py:func:`linearize` and :py:func:`vjp`. Here's a more complete example of using :py:func:`linearize`: >>> import jax >>> import jax.numpy as jnp >>> >>> def f(x): return 3. * jnp.sin(x) + jnp.cos(x / 2.) ... >>> jax.jvp(f, (2.,), (3.,)) (DeviceArray(3.26819, dtype=float32, weak_type=True), DeviceArray(-5.00753, dtype=float32, weak_type=True)) >>> y, f_jvp = jax.linearize(f, 2.) >>> print(y) 3.2681944 >>> print(f_jvp(3.)) -5.007528 >>> print(f_jvp(4.)) -6.676704 """ _check_callable(fun) f = lu.wrap_init(fun) primals_flat, in_tree = tree_flatten((primals, {})) jaxtree_fun, out_tree = flatten_fun(f, in_tree) out_primals, out_pvals, jaxpr, consts = ad.linearize(jaxtree_fun, *primals_flat) out_tree = out_tree() out_primal_py = tree_unflatten(out_tree, out_primals) primal_avals = list(map(core.get_aval, primals_flat)) # Ensure that lifted_jvp is a PyTree lifted_jvp = Partial(partial(_lift_linearized, jaxpr, primal_avals, (in_tree, out_tree), out_pvals), consts) return out_primal_py, lifted_jvp def _lift_linearized(jaxpr, primal_avals, io_tree, out_pvals, consts, *py_args): def fun(*tangents): tangent_avals = list(map(core.get_aval, tangents)) for primal_aval, tangent_aval in zip(primal_avals, tangent_avals): if not core.typecompat(primal_aval.at_least_vspace(), tangent_aval): raise ValueError("linearized function called on tangent values inconsistent with " "the original primal values: " f"got {tangent_aval} for primal aval {primal_aval}") tangents_out = eval_jaxpr(jaxpr, consts, *tangents) tangents_out_ = iter(tangents_out) full_out = [pval.get_known() if pval.is_known() else next(tangents_out_) for pval in out_pvals] assert next(tangents_out_, None) is None return full_out return apply_flat_fun(fun, io_tree, *py_args) def _vjp_pullback_wrapper(cotangent_dtypes, cotangent_shapes, io_tree, fun, py_args): in_tree_expected, out_tree = io_tree args, in_tree = tree_flatten(py_args) if in_tree != in_tree_expected: raise TypeError(f"Tree structure of cotangent input {in_tree}, does not match structure of " f"primal output {in_tree_expected}.") for arg, ct_dtype, ct_shape in safe_zip(args, cotangent_dtypes, cotangent_shapes): expected_tangent_dtype = core.primal_dtype_to_tangent_dtype(_dtype(arg)) if expected_tangent_dtype != ct_dtype: raise TypeError( f"Type of cotangent input to vjp pullback function ({ct_dtype}) is not " f"the expected tangent type ({expected_tangent_dtype}) of corresponding primal output " f"with dtype {_dtype(arg)}.") if np.shape(arg) != ct_shape: raise ValueError( f"Shape of cotangent input to vjp pullback function {np.shape(arg)} " "must be the same as the shape of corresponding primal input " f"{ct_shape}.") ans = fun(*args) return tree_unflatten(out_tree, ans) @overload def vjp(fun: Callable[..., T], *primals: Any, has_aux: Literal[False] = False, reduce_axes: Sequence[AxisName] = ()) -> Tuple[T, Callable]: ... @overload def vjp(fun: Callable[..., Tuple[T, U]], *primals: Any, has_aux: Literal[True], reduce_axes: Sequence[AxisName] = ()) -> Tuple[T, Callable, U]: ... def vjp( # type: ignore fun: Callable, *primals, has_aux: bool = False, reduce_axes=() ) -> Union[Tuple[Any, Callable], Tuple[Any, Callable, Any]]: """Compute a (reverse-mode) vector-Jacobian product of ``fun``. :py:func:`grad` is implemented as a special case of :py:func:`vjp`. Args: fun: Function to be differentiated. Its arguments should be arrays, scalars, or standard Python containers of arrays or scalars. It should return an array, scalar, or standard Python container of arrays or scalars. primals: A sequence of primal values at which the Jacobian of ``fun`` should be evaluated. The length of ``primals`` should be equal to the number of positional parameters to ``fun``. Each primal value should be a tuple of arrays, scalar, or standard Python containers thereof. has_aux: Optional, bool. Indicates whether ``fun`` returns a pair where the first element is considered the output of the mathematical function to be differentiated and the second element is auxiliary data. Default False. reduce_axes: Optional, tuple of axis names. If an axis is listed here, and ``fun`` implicitly broadcasts a value over that axis, the backward pass will perform a ``psum`` of the corresponding gradient. Otherwise, the VJP will be per-example over named axes. For example, if ``'batch'`` is a named batch axis, ``vjp(f, *args, reduce_axes=('batch',))`` will create a VJP function that sums over the batch while ``vjp(f, *args)`` will create a per-example VJP. Returns: If ``has_aux`` is ``False``, returns a ``(primals_out, vjpfun)`` pair, where ``primals_out`` is ``fun(*primals)``. ``vjpfun`` is a function from a cotangent vector with the same shape as ``primals_out`` to a tuple of cotangent vectors with the same shape as ``primals``, representing the vector-Jacobian product of ``fun`` evaluated at ``primals``. If ``has_aux`` is ``True``, returns a ``(primals_out, vjpfun, aux)`` tuple where ``aux`` is the auxiliary data returned by ``fun``. >>> import jax >>> >>> def f(x, y): ... return jax.numpy.sin(x), jax.numpy.cos(y) ... >>> primals, f_vjp = jax.vjp(f, 0.5, 1.0) >>> xbar, ybar = f_vjp((-0.7, 0.3)) >>> print(xbar) -0.61430776 >>> print(ybar) -0.2524413 """ _check_callable(fun) reduce_axes = _ensure_str_tuple(reduce_axes) return _vjp( lu.wrap_init(fun), *primals, has_aux=has_aux, reduce_axes=reduce_axes) def _vjp(fun: lu.WrappedFun, *primals, has_aux=False, reduce_axes=()): """Variant of vjp() that takes an lu.WrappedFun.""" primals_flat, in_tree = tree_flatten(primals) for arg in primals_flat: _check_arg(arg) if not has_aux: flat_fun, out_tree = flatten_fun_nokwargs(fun, in_tree) out_primal, out_vjp = ad.vjp( flat_fun, primals_flat, reduce_axes=reduce_axes) out_tree = out_tree() else: flat_fun, out_aux_trees = flatten_fun_nokwargs2(fun, in_tree) out_primal, out_vjp, aux = ad.vjp( flat_fun, primals_flat, has_aux=True, reduce_axes=reduce_axes) out_tree, aux_tree = out_aux_trees() out_primal_py = tree_unflatten(out_tree, out_primal) ct_dtypes = [core.primal_dtype_to_tangent_dtype(_dtype(x)) for x in out_primal] ct_shapes = [np.shape(x) for x in out_primal] # Ensure that vjp_py is a PyTree so that we can pass it from the forward to the # backward pass in a custom VJP. vjp_py = Partial(partial(_vjp_pullback_wrapper, ct_dtypes, ct_shapes, (out_tree, in_tree)), out_vjp) if not has_aux: return out_primal_py, vjp_py else: return out_primal_py, vjp_py, tree_unflatten(aux_tree, aux) def linear_transpose(fun: Callable, *primals, reduce_axes=()) -> Callable: """Transpose a function that is promised to be linear. For linear functions, this transformation is equivalent to ``vjp``, but avoids the overhead of computing the forward pass. The outputs of the transposed function will always have the exact same dtypes as ``primals``, even if some values are truncated (e.g., from complex to float, or from float64 to float32). To avoid truncation, use dtypes in ``primals`` that match the full range of desired outputs from the transposed function. Integer dtypes are not supported. Args: fun: the linear function to be transposed. *primals: a positional argument tuple of arrays, scalars, or (nested) standard Python containers (tuples, lists, dicts, namedtuples, i.e., pytrees) of those types used for evaluating the shape/dtype of ``fun(*primals)``. These arguments may be real scalars/ndarrays, but that is not required: only the ``shape`` and ``dtype`` attributes are accessed. See below for an example. (Note that the duck-typed objects cannot be namedtuples because those are treated as standard Python containers.) reduce_axes: Optional, tuple of axis names. If an axis is listed here, and ``fun`` implicitly broadcasts a value over that axis, the backward pass will perform a ``psum`` of the corresponding cotangent. Otherwise, the transposed function will be per-example over named axes. For example, if ``'batch'`` is a named batch axis, ``linear_transpose(f, *args, reduce_axes=('batch',))`` will create a transpose function that sums over the batch while ``linear_transpose(f, args)`` will create a per-example transpose. Returns: A callable that calculates the transpose of ``fun``. Valid input into this function must have the same shape/dtypes/structure as the result of ``fun(*primals)``. Output will be a tuple, with the same shape/dtypes/structure as ``primals``. >>> import jax >>> import types >>> >>> f = lambda x, y: 0.5 * x - 0.5 * y >>> scalar = types.SimpleNamespace(shape=(), dtype=np.dtype(np.float32)) >>> f_transpose = jax.linear_transpose(f, scalar, scalar) >>> f_transpose(1.0) (DeviceArray(0.5, dtype=float32), DeviceArray(-0.5, dtype=float32)) """ reduce_axes = _ensure_str_tuple(reduce_axes) primals_flat, in_tree = tree_flatten(primals) flat_fun, out_tree = flatten_fun_nokwargs(lu.wrap_init(fun), in_tree) in_avals = map(shaped_abstractify, primals_flat) in_dtypes = map(dtypes.dtype, in_avals) in_pvals = map(pe.PartialVal.unknown, in_avals) jaxpr, out_pvals, const = pe.trace_to_jaxpr_nounits(flat_fun, in_pvals, instantiate=True) out_avals, _ = unzip2(out_pvals) out_dtypes = map(dtypes.dtype, out_avals) if not (all(dtypes.issubdtype(d, np.inexact) for d in in_dtypes + out_dtypes) or all(dtypes.issubdtype(d, np.integer) for d in in_dtypes + out_dtypes)): raise TypeError("linear_transpose only supports [float or complex] -> " "[float or complex], and integer -> integer functions, " f"but got {in_dtypes} -> {out_dtypes}.") @api_boundary def transposed_fun(const, out_cotangent): out_cts, out_tree2 = tree_flatten(out_cotangent) if out_tree() != out_tree2: raise TypeError("cotangent tree does not match function output, " f"expected {out_tree()} but got {out_tree2}") if not all(map(core.typecheck, out_avals, out_cts)): raise TypeError("cotangent type does not match function output, " f"expected {out_avals} but got {out_cts}") dummies = [ad.UndefinedPrimal(a) for a in in_avals] in_cts = ad.backward_pass(jaxpr, reduce_axes, True, const, dummies, out_cts) in_cts = map(ad.instantiate_zeros, in_cts) return tree_unflatten(in_tree, in_cts) # Ensure that transposed_fun is a PyTree return Partial(transposed_fun, const) def make_jaxpr(fun: Callable, static_argnums: Union[int, Iterable[int]] = (), axis_env: Optional[Sequence[Tuple[AxisName, int]]] = None, return_shape: bool = False, abstracted_axes: Optional[Any] = None, ) -> Callable[..., core.ClosedJaxpr]: """Creates a function that produces its jaxpr given example args. Args: fun: The function whose ``jaxpr`` is to be computed. Its positional arguments and return value should be arrays, scalars, or standard Python containers (tuple/list/dict) thereof. static_argnums: See the :py:func:`jax.jit` docstring. axis_env: Optional, a sequence of pairs where the first element is an axis name and the second element is a positive integer representing the size of the mapped axis with that name. This parameter is useful when lowering functions that involve parallel communication collectives, and it specifies the axis name/size environment that would be set up by applications of :py:func:`jax.pmap`. return_shape: Optional boolean, defaults to ``False``. If ``True``, the wrapped function returns a pair where the first element is the XLA computation and the second element is a pytree with the same structure as the output of ``fun`` and where the leaves are objects with ``shape``, ``dtype``, and ``named_shape`` attributes representing the corresponding types of the output leaves. Returns: A wrapped version of ``fun`` that when applied to example arguments returns a ``ClosedJaxpr`` representation of ``fun`` on those arguments. If the argument ``return_shape`` is ``True``, then the returned function instead returns a pair where the first element is the ``ClosedJaxpr`` representation of ``fun`` and the second element is a pytree representing the structure, shape, dtypes, and named shapes of the output of ``fun``. A ``jaxpr`` is JAX's intermediate representation for program traces. The ``jaxpr`` language is based on the simply-typed first-order lambda calculus with let-bindings. :py:func:`make_jaxpr` adapts a function to return its ``jaxpr``, which we can inspect to understand what JAX is doing internally. The ``jaxpr`` returned is a trace of ``fun`` abstracted to :py:class:`ShapedArray` level. Other levels of abstraction exist internally. We do not describe the semantics of the ``jaxpr`` language in detail here, but instead give a few examples. >>> import jax >>> >>> def f(x): return jax.numpy.sin(jax.numpy.cos(x)) >>> print(f(3.0)) -0.83602 >>> jax.make_jaxpr(f)(3.0) { lambda ; a:f32[]. let b:f32[] = cos a; c:f32[] = sin b in (c,) } >>> jax.make_jaxpr(jax.grad(f))(3.0) { lambda ; a:f32[]. let b:f32[] = cos a c:f32[] = sin a _:f32[] = sin b d:f32[] = cos b e:f32[] = mul 1.0 d f:f32[] = neg e g:f32[] = mul f c in (g,) } """ _check_callable(fun) static_argnums = _ensure_index_tuple(static_argnums) def abstractify(args, kwargs): flat_args, in_tree = tree_flatten((args, kwargs)) if abstracted_axes is None: return map(shaped_abstractify, flat_args), in_tree, [True] * len(flat_args) else: axes_specs = _flat_axes_specs(abstracted_axes, *args, **kwargs) in_type = pe.infer_lambda_input_type(axes_specs, flat_args) in_avals, keep_inputs = unzip2(in_type) return in_avals, in_tree, keep_inputs @wraps(fun) @api_boundary def make_jaxpr_f(*args, **kwargs): f = lu.wrap_init(fun) if static_argnums: dyn_argnums = [i for i in range(len(args)) if i not in static_argnums] f, args = argnums_partial(f, dyn_argnums, args) in_avals, in_tree, keep_inputs = abstractify(args, kwargs) in_type = tuple(zip(in_avals, keep_inputs)) f, out_tree = flatten_fun(f, in_tree) f = lu.annotate(f, in_type) with ExitStack() as stack: for axis_name, size in axis_env or []: stack.enter_context(core.extend_axis_env(axis_name, size, None)) jaxpr, out_type, consts = pe.trace_to_jaxpr_dynamic2(f) closed_jaxpr = core.ClosedJaxpr(jaxpr, consts) if return_shape: out_avals, _ = unzip2(out_type) out_shapes_flat = [ ShapeDtypeStruct(a.shape, a.dtype, a.named_shape) for a in out_avals] return closed_jaxpr, tree_unflatten(out_tree(), out_shapes_flat) return closed_jaxpr make_jaxpr_f.__name__ = f"make_jaxpr({make_jaxpr.__name__})" return make_jaxpr_f def device_put(x, device: Optional[xc.Device] = None): """Transfers ``x`` to ``device``. Args: x: An array, scalar, or (nested) standard Python container thereof. device: The (optional) :py:class:`Device` to which ``x`` should be transferred. If given, then the result is committed to the device. If the ``device`` parameter is ``None``, then this operation behaves like the identity function if the operand is on any device already, otherwise it transfers the data to the default device, uncommitted. For more details on data placement see the :ref:`FAQ on data placement `. This function is always asynchronous, i.e. returns immediately. Returns: A copy of ``x`` that resides on ``device``. """ with config_explicit_device_put_scope(): return tree_map(lambda y: dispatch.device_put_p.bind(y, device=device), x) def device_put_sharded(shards: Sequence[Any], devices: Sequence[xc.Device]): # noqa: F811 """Transfer array shards to specified devices and form ShardedDeviceArray(s). Args: shards: A sequence of arrays, scalars, or (nested) standard Python containers thereof representing the shards to be stacked together to form the output. The length of ``shards`` must equal the length of ``devices``. devices: A sequence of :py:class:`Device` instances representing the devices to which corresponding shards in ``shards`` will be transferred. This function is always asynchronous, i.e. returns immediately. Returns: A ShardedDeviceArray or (nested) Python container thereof representing the elements of ``shards`` stacked together, with each shard backed by physical device memory specified by the corresponding entry in ``devices``. Examples: Passing a list of arrays for ``shards`` results in a sharded array containing a stacked version of the inputs: >>> import jax >>> devices = jax.local_devices() >>> x = [jax.numpy.ones(5) for device in devices] >>> y = jax.device_put_sharded(x, devices) >>> np.allclose(y, jax.numpy.stack(x)) True Passing a list of nested container objects with arrays at the leaves for ``shards`` corresponds to stacking the shards at each leaf. This requires all entries in the list to have the same tree structure: >>> x = [(i, jax.numpy.arange(i, i + 4)) for i in range(len(devices))] >>> y = jax.device_put_sharded(x, devices) >>> type(y) >>> y0 = jax.device_put_sharded([a for a, b in x], devices) >>> y1 = jax.device_put_sharded([b for a, b in x], devices) >>> np.allclose(y[0], y0) True >>> np.allclose(y[1], y1) True See Also: - device_put - device_put_replicated """ # TODO(jakevdp): provide a default for devices that considers both local # devices and pods if not isinstance(shards, Sequence): raise ValueError("device_put_sharded `shards` input must be a sequence; " f"got {type(shards)}") if len(shards) != len(devices): raise ValueError(f"len(shards) = {len(shards)} must equal " f"len(devices) = {len(devices)}.") def _device_put_sharded(*xs): avals = [core.raise_to_shaped(core.get_aval(x)) for x in xs] if not all(a1 == a2 for a1, a2 in zip(avals[:-1], avals[1:])): a1, a2 = next((a1, a2) for a1, a2 in zip(avals[:-1], avals[1:]) if a1 != a2) raise ValueError("the shards passed to device_put_sharded must have " f"consistent shape and dtype, but got {a1} and {a2}.") stacked_aval = avals[0].update(shape=(len(devices),) + avals[0].shape) buffers = [buf for x, d in zip(xs, devices) for buf in dispatch.device_put(x, d)] if config.jax_array: from jax.experimental import array, sharding sharding_spec = pxla._create_pmap_sharding_spec(stacked_aval) return array.Array( stacked_aval.shape, sharding.PmapSharding(np.array(devices), sharding_spec), buffers, committed=True) else: return pxla.make_sharded_device_array(stacked_aval, None, buffers) with config_explicit_device_put_scope(): return tree_map(_device_put_sharded, *shards) def device_put_replicated(x: Any, devices: Sequence[xc.Device]): # noqa: F811 """Transfer array(s) to each specified device and form ShardedDeviceArray(s). Args: x: an array, scalar, or (nested) standard Python container thereof representing the array to be replicated to form the output. devices: A sequence of :py:class:`Device` instances representing the devices to which ``x`` will be transferred. This function is always asynchronous, i.e. returns immediately. Returns: A ShardedDeviceArray or (nested) Python container thereof representing the value of ``x`` broadcasted along a new leading axis of size ``len(devices)``, with each slice along that new leading axis backed by memory on the device specified by the corresponding entry in ``devices``. Examples: Passing an array: >>> import jax >>> devices = jax.local_devices() >>> x = jax.numpy.array([1., 2., 3.]) >>> y = jax.device_put_replicated(x, devices) >>> np.allclose(y, jax.numpy.stack([x for _ in devices])) True See Also: - device_put - device_put_sharded """ if not isinstance(devices, Sequence) or not devices: raise ValueError("`devices` argument to `device_put_replicated must be " "a non-empty sequence.") def _device_put_replicated(x): aval = core.unmapped_aval(len(devices), core.no_axis_name, 0, core.raise_to_shaped(core.get_aval(x))) assert (isinstance(aval, ShapedArray) and len(xla.aval_to_xla_shapes(aval)) == 1) buf, = dispatch.device_put(x, devices[0]) rest_bufs = [buf.copy_to_device(d) for d in devices[1:]] if config.jax_array: from jax.experimental import array, sharding sharding_spec = pxla._create_pmap_sharding_spec(aval) return array.Array( aval.shape, sharding.PmapSharding(np.array(devices), sharding_spec), [buf, *rest_bufs], committed=True) else: return pxla.make_sharded_device_array(aval, None, [buf, *rest_bufs]) with config_explicit_device_put_scope(): return tree_map(_device_put_replicated, x) # TODO(mattjj): consider revising def _device_get(x): if isinstance(x, core.Tracer): return x try: toarray = x.__array__ except AttributeError: return x else: return toarray() def device_get(x: Any): """Transfer ``x`` to host. If ``x`` is a pytree, then the individual buffers are copied in parallel. Args: x: An array, scalar, DeviceArray or (nested) standard Python container thereof representing the array to be transferred to host. Returns: An array or (nested) Python container thereof representing the value of ``x``. Examples: Passing a DeviceArray: >>> import jax >>> x = jax.numpy.array([1., 2., 3.]) >>> jax.device_get(x) array([1., 2., 3.], dtype=float32) Passing a scalar (has no effect): >>> jax.device_get(1) 1 See Also: - device_put - device_put_sharded - device_put_replicated """ with config_explicit_device_get_scope(): for y in tree_leaves(x): try: y.copy_to_host_async() except AttributeError: pass return tree_map(_device_get, x) def _check_arg(arg): if not (isinstance(arg, core.Tracer) or _valid_jaxtype(arg)): raise TypeError(f"Argument '{arg}' of type {type(arg)} is not a valid JAX type.") # TODO(mattjj,necula): this duplicates code in core.valid_jaxtype, but one # internal user relies on it for duck-typing. must fix downstream user! def _valid_jaxtype(arg): try: xla.abstractify(arg) # faster than core.get_aval except TypeError: return core.valid_jaxtype(arg) else: return True class ShapeDtypeStruct: __slots__ = ["shape", "dtype", "named_shape"] def __init__(self, shape, dtype, named_shape=None): self.shape = shape self.dtype = np.dtype(dtype) self.named_shape = {} if named_shape is None else dict(named_shape) size = property(lambda self: prod(self.shape)) ndim = property(lambda self: len(self.shape)) def __len__(self): try: return self.shape[0] except IndexError as e: raise TypeError("len() of unsized object") from e # same as numpy error def __repr__(self): ns = f", named_shape={self.named_shape}" if self.named_shape else "" return f"{type(self).__name__}(shape={self.shape}, dtype={self.dtype.name}{ns})" __str__ = __repr__ def __eq__(self, other): if not isinstance(other, ShapeDtypeStruct): return False else: return (other.shape, other.dtype, other.named_shape) == ( self.shape, self.dtype, self.named_shape) def __hash__(self): # TODO(frostig): avoid the conversion from dict by addressing # https://github.com/google/jax/issues/8182 named = frozenset(self.named_shape.items()) return hash((self.shape, self.dtype, named)) def eval_shape(fun: Callable, *args, **kwargs): """Compute the shape/dtype of ``fun`` without any FLOPs. This utility function is useful for performing shape inference. Its input/output behavior is defined by:: def eval_shape(fun, *args, **kwargs): out = fun(*args, **kwargs) return jax.tree_util.tree_map(shape_dtype_struct, out) def shape_dtype_struct(x): return ShapeDtypeStruct(x.shape, x.dtype) class ShapeDtypeStruct: __slots__ = ["shape", "dtype"] def __init__(self, shape, dtype): self.shape = shape self.dtype = dtype In particular, the output is a pytree of objects that have ``shape`` and ``dtype`` attributes, but nothing else about them is guaranteed by the API. But instead of applying ``fun`` directly, which might be expensive, it uses JAX's abstract interpretation machinery to evaluate the shapes without doing any FLOPs. Using :py:func:`eval_shape` can also catch shape errors, and will raise same shape errors as evaluating ``fun(*args, **kwargs)``. Args: fun: The function whose output shape should be evaluated. *args: a positional argument tuple of arrays, scalars, or (nested) standard Python containers (tuples, lists, dicts, namedtuples, i.e. pytrees) of those types. Since only the ``shape`` and ``dtype`` attributes are accessed, only values that duck-type arrays are required, rather than real ndarrays. The duck-typed objects cannot be namedtuples because those are treated as standard Python containers. See the example below. **kwargs: a keyword argument dict of arrays, scalars, or (nested) standard Python containers (pytrees) of those types. As in ``args``, array values need only be duck-typed to have ``shape`` and ``dtype`` attributes. For example: >>> import jax >>> import jax.numpy as jnp >>> >>> f = lambda A, x: jnp.tanh(jnp.dot(A, x)) >>> class MyArgArray(object): ... def __init__(self, shape, dtype): ... self.shape = shape ... self.dtype = jnp.dtype(dtype) ... >>> A = MyArgArray((2000, 3000), jnp.float32) >>> x = MyArgArray((3000, 1000), jnp.float32) >>> out = jax.eval_shape(f, A, x) # no FLOPs performed >>> print(out.shape) (2000, 1000) >>> print(out.dtype) float32 """ args_flat, in_tree = tree_flatten((args, kwargs)) wrapped_fun, out_tree = flatten_fun(lu.wrap_init(fun), in_tree) debug_info = pe.debug_info(fun, in_tree, True, "eval_shape") out = pe.abstract_eval_fun(wrapped_fun.call_wrapped, *map(shaped_abstractify, args_flat), debug_info=debug_info) out = [ShapeDtypeStruct(x.shape, x.dtype, x.named_shape) for x in out] return tree_unflatten(out_tree(), out) @functools.wraps(new_checkpoint) # config.jax_new_checkpoint is True by default def checkpoint(fun: Callable, *, concrete: bool = False, prevent_cse: bool = True, static_argnums: Union[int, Tuple[int, ...]] = (), policy: Optional[Callable[..., bool]] = None, ) -> Callable: if concrete: msg = ("The 'concrete' option to jax.checkpoint / jax.remat is deprecated; " "in its place, you can use its `static_argnums` option, and if " "necessary the `jax.ensure_compile_time_eval()` context manager.\n" "\n" "For example, if using `concrete=True` for an `is_training` flag:\n" "\n" " from functools import partial\n" "\n" " @partial(jax.checkpoint, concrete=True)\n" " def foo(x, is_training):\n" " if is_training:\n" " return f(x)\n" " else:\n" " return g(x)\n" "\n" "replace it with a use of `static_argnums`:\n" "\n" " @partial(jax.checkpoint, static_argnums=(1,))\n" " def foo(x, is_training):\n" " ...\n" "\n" "If jax.numpy operations need to be performed on static arguments, " "we can use the `jax.ensure_compile_time_eval()` context manager. " "For example, we can replace this use of `concrete=True`\n:" "\n" " @partial(jax.checkpoint, concrete=True)\n" " def foo(x, y):\n" " if y > 0:\n" " return f(x)\n" " else:\n" " return g(x)\n" "\n" "with this combination of `static_argnums` and " "`jax.ensure_compile_time_eval()`:\n" "\n" " @partial(jax.checkpoint, static_argnums=(1,))\n" " def foo(x, y):\n" " with jax.ensure_compile_time_eval():\n" " y_pos = y > 0\n" " if y_pos:\n" " return f(x)\n" " else:\n" " return g(x)\n" "\n") if config.jax_new_checkpoint: raise NotImplementedError(msg) else: warn(msg, DeprecationWarning) if config.jax_new_checkpoint: return new_checkpoint(fun, prevent_cse=prevent_cse, policy=policy, static_argnums=static_argnums) @wraps(fun) @api_boundary def remat_f(*args, **kwargs): f, args = _remat_static_argnums(fun, static_argnums, args) args_flat, in_tree = tree_flatten((args, kwargs)) flat_fun, out_tree = flatten_fun(lu.wrap_init(f), in_tree) out_flat = pe.remat_call(flat_fun, *args_flat, name=flat_fun.__name__, concrete=concrete, prevent_cse=prevent_cse, differentiated=False, policy=policy) return tree_unflatten(out_tree(), out_flat) return remat_f remat = checkpoint # type: ignore def named_call( fun: Callable[..., Any], *, name: Optional[str] = None, ) -> Callable[..., Any]: """Adds a user specified name to a function when staging out JAX computations. When staging out computations for just-in-time compilation to XLA (or other backends such as TensorFlow) JAX runs your Python program but by default does not preserve any of the function names or other metadata associated with it. This can make debugging the staged out (and/or compiled) representation of your program complicated because there is limited context information for each operation being executed. `named_call` tells JAX to stage the given function out as a subcomputation with a specific name. When the staged out program is compiled with XLA these named subcomputations are preserved and show up in debugging utilities like the TensorFlow Profiler in TensorBoard. Names are also preserved when staging out JAX programs to TensorFlow using :func:`experimental.jax2tf.convert`. Args: fun: Function to be wrapped. This can be any Callable. name: Optional. The prefix to use to name all sub computations created within the name scope. Use the fun.__name__ if not specified. Returns: A version of `fun` that is wrapped in a name_scope. """ if name is None: name = fun.__name__ _, in_tree = tree_flatten(()) if config.jax_experimental_name_stack: return source_info_util.extend_name_stack(name)(fun) @functools.wraps(fun) def named_call_f(*args, **kwargs): lu_f = lu.wrap_init(lambda: fun(*args, **kwargs)) flat_f, out_tree = flatten_fun_nokwargs(lu_f, in_tree) out_flat = core.named_call_p.bind(flat_f, name=name) return tree_unflatten(out_tree(), out_flat) return named_call_f @contextmanager def named_scope( name: str, ) -> Generator[None, None, None]: """A context manager that adds a user specified name to the JAX name stack. When staging out computations for just-in-time compilation to XLA (or other backends such as TensorFlow) JAX does not, by default, preserve the names (or other source metadata) of Python functions it encounters. This can make debugging the staged out (and/or compiled) representation of your program complicated because there is limited context information for each operation being executed. ``named_scope`` tells JAX to stage the given function with additional annotations on the underlying operations. JAX internally keeps track of these annotations in a name stack. When the staged out program is compiled with XLA these annotations are preserved and show up in debugging utilities like the TensorFlow Profiler in TensorBoard. Names are also preserved when staging out JAX programs to TensorFlow using :func:`experimental.jax2tf.convert`. Args: name: The prefix to use to name all operations created within the name scope. Yields: Yields ``None``, but enters a context in which `name` will be appended to the active name stack. Examples: ``named_scope`` can be used as a context manager inside compiled functions: >>> import jax >>> >>> @jax.jit ... def layer(w, x): ... with jax.named_scope("dot_product"): ... logits = w.dot(x) ... with jax.named_scope("activation"): ... return jax.nn.relu(logits) It can also be used as a decorator: >>> @jax.jit ... @jax.named_scope("layer") ... def layer(w, x): ... logits = w.dot(x) ... return jax.nn.relu(logits) """ with source_info_util.extend_name_stack(name): yield def effects_barrier(): """Waits until existing functions have completed any side-effects.""" dispatch.runtime_tokens.block_until_ready() def block_until_ready(x): """ Tries to call a ``block_until_ready`` method on pytree leaves. Args: x: a pytree, usually with at least some JAX array instances at its leaves. Returns: A pytree with the same structure and values of the input, where the values of all JAX array leaves are ready. """ def try_to_block(x): try: return x.block_until_ready() except AttributeError: return x return jax.tree_util.tree_map(try_to_block, x) def clear_backends(): """ Clear all backend clients so that new backend clients can be created later. """ if xc._version < 79: raise RuntimeError("clear_backends is not supported in the jaxlib used." "Please update your jaxlib package.") xb._clear_backends() jax.lib.xla_bridge._backends = {} dispatch.xla_callable.cache_clear() # type: ignore dispatch.xla_primitive_callable.cache_clear() _cpp_jit_cache.clear() jax_jit.CompiledFunctionCache.clear_all()