jax.lax module#

jax.lax is a library of primitives operations that underpins libraries such as jax.numpy. Transformation rules, such as JVP and batching rules, are typically defined as transformations on jax.lax primitives.

Many of the primitives are thin wrappers around equivalent XLA operations, described by the XLA operation semantics documentation. In a few cases JAX diverges from XLA, usually to ensure that the set of operations is closed under the operation of JVP and transpose rules.

Where possible, prefer to use libraries such as jax.numpy instead of using jax.lax directly. The jax.numpy API follows NumPy, and is therefore more stable and less likely to change than the jax.lax API.

Operators#

 Elementwise absolute value: $$|x|$$. Elementwise arc cosine: $$\mathrm{acos}(x)$$. Elementwise inverse hyperbolic cosine: $$\mathrm{acosh}(x)$$. add(x, y) Elementwise addition: $$x + y$$. after_all(*operands) Merges one or more XLA token values. approx_max_k(operand, k[, ...]) Returns max k values and their indices of the operand in an approximate manner. approx_min_k(operand, k[, ...]) Returns min k values and their indices of the operand in an approximate manner. argmax(operand, axis, index_dtype) Computes the index of the maximum element along axis. argmin(operand, axis, index_dtype) Computes the index of the minimum element along axis. Elementwise arc sine: $$\mathrm{asin}(x)$$. Elementwise inverse hyperbolic sine: $$\mathrm{asinh}(x)$$. Elementwise arc tangent: $$\mathrm{atan}(x)$$. atan2(x, y) Elementwise arc tangent of two variables: $$\mathrm{atan}({x \over y})$$. Elementwise inverse hyperbolic tangent: $$\mathrm{atanh}(x)$$. batch_matmul(lhs, rhs[, precision]) Batch matrix multiplication. Exponentially scaled modified Bessel function of order 0: $$\mathrm{i0e}(x) = e^{-|x|} \mathrm{i0}(x)$$ Exponentially scaled modified Bessel function of order 1: $$\mathrm{i1e}(x) = e^{-|x|} \mathrm{i1}(x)$$ betainc(a, b, x) Elementwise regularized incomplete beta integral. bitcast_convert_type(operand, new_dtype) Elementwise bitcast. bitwise_and(x, y) Elementwise AND: $$x \wedge y$$. Elementwise NOT: $$\neg x$$. bitwise_or(x, y) Elementwise OR: $$x \vee y$$. bitwise_xor(x, y) Elementwise exclusive OR: $$x \oplus y$$. Elementwise popcount, count the number of set bits in each element. broadcast(operand, sizes) Broadcasts an array, adding new leading dimensions broadcast_in_dim(operand, shape, ...) Wraps XLA's BroadcastInDim operator. Returns the shape that results from NumPy broadcasting of shapes. broadcast_to_rank(x, rank) Adds leading dimensions of 1 to give x rank rank. broadcasted_iota(dtype, shape, dimension) Convenience wrapper around iota. Elementwise cube root: $$\sqrt[3]{x}$$. Elementwise ceiling: $$\left\lceil x \right\rceil$$. clamp(min, x, max) Elementwise clamp. Elementwise count-leading-zeros. collapse(operand, start_dimension[, ...]) Collapses dimensions of an array into a single dimension. complex(x, y) Elementwise make complex number: $$x + jy$$. concatenate(operands, dimension) Concatenates a sequence of arrays along dimension. Elementwise complex conjugate function: $$\overline{x}$$. conv(lhs, rhs, window_strides, padding[, ...]) Convenience wrapper around conv_general_dilated. convert_element_type(operand, new_dtype) Elementwise cast. conv_dimension_numbers(lhs_shape, rhs_shape, ...) Converts convolution dimension_numbers to a ConvDimensionNumbers. conv_general_dilated(lhs, rhs, ...[, ...]) General n-dimensional convolution operator, with optional dilation. conv_general_dilated_local(lhs, rhs, ...[, ...]) General n-dimensional unshared convolution operator with optional dilation. conv_general_dilated_patches(lhs, ...[, ...]) Extract patches subject to the receptive field of conv_general_dilated. conv_transpose(lhs, rhs, strides, padding[, ...]) Convenience wrapper for calculating the N-d convolution "transpose". conv_with_general_padding(lhs, rhs, ...[, ...]) Convenience wrapper around conv_general_dilated. Elementwise cosine: $$\mathrm{cos}(x)$$. Elementwise hyperbolic cosine: $$\mathrm{cosh}(x)$$. cumlogsumexp(operand[, axis, reverse]) Computes a cumulative logsumexp along axis. cummax(operand[, axis, reverse]) Computes a cumulative maximum along axis. cummin(operand[, axis, reverse]) Computes a cumulative minimum along axis. cumprod(operand[, axis, reverse]) Computes a cumulative product along axis. cumsum(operand[, axis, reverse]) Computes a cumulative sum along axis. Elementwise digamma: $$\psi(x)$$. div(x, y) Elementwise division: $$x \over y$$. dot(lhs, rhs[, precision, ...]) Vector/vector, matrix/vector, and matrix/matrix multiplication. dot_general(lhs, rhs, dimension_numbers[, ...]) General dot product/contraction operator. dynamic_index_in_dim(operand, index[, axis, ...]) Convenience wrapper around dynamic_slice to perform int indexing. dynamic_slice(operand, start_indices, ...) Wraps XLA's DynamicSlice operator. dynamic_slice_in_dim(operand, start_index, ...) Convenience wrapper around lax.dynamic_slice() applied to one dimension. dynamic_update_index_in_dim(operand, update, ...) Convenience wrapper around dynamic_update_slice() to update a slice of size 1 in a single axis. dynamic_update_slice(operand, update, ...) Wraps XLA's DynamicUpdateSlice operator. dynamic_update_slice_in_dim(operand, update, ...) Convenience wrapper around dynamic_update_slice() to update a slice in a single axis. eq(x, y) Elementwise equals: $$x = y$$. Elementwise error function: $$\mathrm{erf}(x)$$. Elementwise complementary error function: $$\mathrm{erfc}(x) = 1 - \mathrm{erf}(x)$$. Elementwise inverse error function: $$\mathrm{erf}^{-1}(x)$$. Elementwise exponential: $$e^x$$. expand_dims(array, dimensions) Insert any number of size 1 dimensions into an array. Elementwise $$e^{x} - 1$$. fft(x, fft_type, fft_lengths) Elementwise floor: $$\left\lfloor x \right\rfloor$$. full(shape, fill_value[, dtype, sharding]) Returns an array of shape filled with fill_value. full_like(x, fill_value[, dtype, shape, ...]) Create a full array like np.full based on the example array x. gather(operand, start_indices, ...[, ...]) Gather operator. ge(x, y) Elementwise greater-than-or-equals: $$x \geq y$$. gt(x, y) Elementwise greater-than: $$x > y$$. igamma(a, x) Elementwise regularized incomplete gamma function. igammac(a, x) Elementwise complementary regularized incomplete gamma function. Elementwise extract imaginary part: $$\mathrm{Im}(x)$$. index_in_dim(operand, index[, axis, keepdims]) Convenience wrapper around lax.slice() to perform int indexing. index_take(src, idxs, axes) integer_pow(x, y) Elementwise power: $$x^y$$, where $$y$$ is a fixed integer. iota(dtype, size) Wraps XLA's Iota operator. Elementwise $$\mathrm{isfinite}$$. le(x, y) Elementwise less-than-or-equals: $$x \leq y$$. Elementwise log gamma: $$\mathrm{log}(\Gamma(x))$$. Elementwise natural logarithm: $$\mathrm{log}(x)$$. Elementwise $$\mathrm{log}(1 + x)$$. Elementwise logistic (sigmoid) function: $$\frac{1}{1 + e^{-x}}$$. lt(x, y) Elementwise less-than: $$x < y$$. max(x, y) Elementwise maximum: $$\mathrm{max}(x, y)$$ min(x, y) Elementwise minimum: $$\mathrm{min}(x, y)$$ mul(x, y) Elementwise multiplication: $$x \times y$$. ne(x, y) Elementwise not-equals: $$x \neq y$$. Elementwise negation: $$-x$$. nextafter(x1, x2) Returns the next representable value after x1 in the direction of x2. pad(operand, padding_value, padding_config) Applies low, high, and/or interior padding to an array. polygamma(m, x) Elementwise polygamma: $$\psi^{(m)}(x)$$. Elementwise popcount, count the number of set bits in each element. pow(x, y) Elementwise power: $$x^y$$. Elementwise derivative of samples from Gamma(a, 1). Elementwise extract real part: $$\mathrm{Re}(x)$$. Elementwise reciprocal: $$1 \over x$$. reduce(operands, init_values, computation, ...) Wraps XLA's Reduce operator. reduce_precision(operand, exponent_bits, ...) Wraps XLA's ReducePrecision operator. reduce_window(operand, init_value, ...[, ...]) rem(x, y) Elementwise remainder: $$x \bmod y$$. reshape(operand, new_sizes[, dimensions]) Wraps XLA's Reshape operator. rev(operand, dimensions) Wraps XLA's Rev operator. rng_bit_generator(key, shape[, dtype, algorithm]) Stateless PRNG bit generator. rng_uniform(a, b, shape) Stateful PRNG generator. round(x[, rounding_method]) Elementwise round. Elementwise reciprocal square root: $$1 \over \sqrt{x}$$. scatter(operand, scatter_indices, updates, ...) Scatter-update operator. scatter_add(operand, scatter_indices, ...[, ...]) Scatter-add operator. scatter_apply(operand, scatter_indices, ...) Scatter-apply operator. scatter_max(operand, scatter_indices, ...[, ...]) Scatter-max operator. scatter_min(operand, scatter_indices, ...[, ...]) Scatter-min operator. scatter_mul(operand, scatter_indices, ...[, ...]) Scatter-multiply operator. shift_left(x, y) Elementwise left shift: $$x \ll y$$. Elementwise arithmetic right shift: $$x \gg y$$. Elementwise logical right shift: $$x \gg y$$. Elementwise sign. Elementwise sine: $$\mathrm{sin}(x)$$. Elementwise hyperbolic sine: $$\mathrm{sinh}(x)$$. slice(operand, start_indices, limit_indices) Wraps XLA's Slice operator. slice_in_dim(operand, start_index, limit_index) Convenience wrapper around lax.slice() applying to only one dimension. Wraps XLA's Sort operator. sort_key_val(keys, values[, dimension, ...]) Sorts keys along dimension and applies the same permutation to values. Elementwise square root: $$\sqrt{x}$$. Elementwise square: $$x^2$$. squeeze(array, dimensions) Squeeze any number of size 1 dimensions from an array. sub(x, y) Elementwise subtraction: $$x - y$$. Elementwise tangent: $$\mathrm{tan}(x)$$. Elementwise hyperbolic tangent: $$\mathrm{tanh}(x)$$. top_k(operand, k) Returns top k values and their indices along the last axis of operand. transpose(operand, permutation) Wraps XLA's Transpose operator. zeta(x, q) Elementwise Hurwitz zeta function: $$\zeta(x, q)$$

Control flow operators#

 associative_scan(fn, elems[, reverse, axis]) Performs a scan with an associative binary operation, in parallel. cond(pred, true_fun, false_fun, *operands[, ...]) Conditionally apply true_fun or false_fun. fori_loop(lower, upper, body_fun, init_val, *) Loop from lower to upper by reduction to jax.lax.while_loop(). map(f, xs) Map a function over leading array axes. scan(f, init[, xs, length, reverse, unroll, ...]) Scan a function over leading array axes while carrying along state. select(pred, on_true, on_false) Selects between two branches based on a boolean predicate. select_n(which, *cases) Selects array values from multiple cases. switch(index, branches, *operands[, operand]) Apply exactly one of the branches given by index. while_loop(cond_fun, body_fun, init_val) Call body_fun repeatedly in a loop while cond_fun is True.

 Stops gradient computation. custom_linear_solve(matvec, b, solve[, ...]) Perform a matrix-free linear solve with implicitly defined gradients. custom_root(f, initial_guess, solve, ...[, ...]) Differentiably solve for the roots of a function.

Parallel operators#

 all_gather(x, axis_name, *[, ...]) Gather values of x across all replicas. all_to_all(x, axis_name, split_axis, ...[, ...]) Materialize the mapped axis and map a different axis. pdot(x, y, axis_name[, pos_contract, ...]) psum(x, axis_name, *[, axis_index_groups]) Compute an all-reduce sum on x over the pmapped axis axis_name. psum_scatter(x, axis_name, *[, ...]) Like psum(x, axis_name) but each device retains only part of the result. pmax(x, axis_name, *[, axis_index_groups]) Compute an all-reduce max on x over the pmapped axis axis_name. pmin(x, axis_name, *[, axis_index_groups]) Compute an all-reduce min on x over the pmapped axis axis_name. pmean(x, axis_name, *[, axis_index_groups]) Compute an all-reduce mean on x over the pmapped axis axis_name. ppermute(x, axis_name, perm) Perform a collective permutation according to the permutation perm. pshuffle(x, axis_name, perm) Convenience wrapper of jax.lax.ppermute with alternate permutation encoding pswapaxes(x, axis_name, axis, *[, ...]) Swap the pmapped axis axis_name with the unmapped axis axis. axis_index(axis_name) Return the index along the mapped axis axis_name.

Linear algebra operators (jax.lax.linalg)#

 cholesky(x, *[, symmetrize_input]) Cholesky decomposition. eig(x, *[, compute_left_eigenvectors, ...]) Eigendecomposition of a general matrix. eigh(x, *[, lower, symmetrize_input, ...]) Eigendecomposition of a Hermitian matrix. Reduces a square matrix to upper Hessenberg form. lu(x) LU decomposition with partial pivoting. householder_product(a, taus) Product of elementary Householder reflectors. qdwh(x, *[, is_hermitian, max_iterations, ...]) QR-based dynamically weighted Halley iteration for polar decomposition. qr(x, *[, full_matrices]) QR decomposition. schur(x, *[, compute_schur_vectors, ...]) Singular value decomposition. triangular_solve(a, b, *[, left_side, ...]) Triangular solve. tridiagonal(a, *[, lower]) Reduces a symmetric/Hermitian matrix to tridiagonal form. tridiagonal_solve(dl, d, du, b) Computes the solution of a tridiagonal linear system.

Argument classes#

class jax.lax.ConvDimensionNumbers(lhs_spec, rhs_spec, out_spec)[source]#

Describes batch, spatial, and feature dimensions of a convolution.

Parameters:
• lhs_spec (Sequence[int]) – a tuple of nonnegative integer dimension numbers containing (batch dimension, feature dimension, spatial dimensions…).

• rhs_spec (Sequence[int]) – a tuple of nonnegative integer dimension numbers containing (out feature dimension, in feature dimension, spatial dimensions…).

• out_spec (Sequence[int]) – a tuple of nonnegative integer dimension numbers containing (batch dimension, feature dimension, spatial dimensions…).

jax.lax.ConvGeneralDilatedDimensionNumbers#
class jax.lax.GatherDimensionNumbers(offset_dims, collapsed_slice_dims, start_index_map)[source]#

Describes the dimension number arguments to an XLA’s Gather operator. See the XLA documentation for more details of what the dimension numbers mean.

Parameters:
• offset_dims (tuple[int, ...]) – the set of dimensions in the gather output that offset into an array sliced from operand. Must be a tuple of integers in ascending order, each representing a dimension number of the output.

• collapsed_slice_dims (tuple[int, ...]) – the set of dimensions i in operand that have slice_sizes[i] == 1 and that should not have a corresponding dimension in the output of the gather. Must be a tuple of integers in ascending order.

• start_index_map (tuple[int, ...]) – for each dimension in start_indices, gives the corresponding dimension in the operand that is to be sliced. Must be a tuple of integers with size equal to start_indices.shape[-1].

Unlike XLA’s GatherDimensionNumbers structure, index_vector_dim is implicit; there is always an index vector dimension and it must always be the last dimension. To gather scalar indices, add a trailing dimension of size 1.

class jax.lax.GatherScatterMode(value)[source]#

Describes how to handle out-of-bounds indices in a gather or scatter.

Possible values are:

CLIP:

Indices will be clamped to the nearest in-range value, i.e., such that the entire window to be gathered is in-range.

FILL_OR_DROP:

If any part of a gathered window is out of bounds, the entire window that is returned, even those elements that were otherwise in-bounds, will be filled with a constant. If any part of a scattered window is out of bounds, the entire window will be discarded.

PROMISE_IN_BOUNDS:

The user promises that indices are in bounds. No additional checking will be performed. In practice, with the current XLA implementation this means that out-of-bounds gathers will be clamped but out-of-bounds scatters will be discarded. Gradients will not be correct if indices are out-of-bounds.

class jax.lax.Precision(value)[source]#

Precision enum for lax functions

The precision argument to JAX functions generally controls the tradeoff between speed and accuracy for array computations on accelerator backends, (i.e. TPU and GPU). Members are:

DEFAULT:

Fastest mode, but least accurate. Performs computations in bfloat16. Aliases: 'default', 'fastest', 'bfloat16'.

HIGH:

Slower but more accurate. Performs float32 computations in 3 bfloat16 passes, or using tensorfloat32 where available. Aliases: 'high', 'bfloat16_3x', 'tensorfloat32'.

HIGHEST:

Slowest but most accurate. Performs computations in float32 or float64 as applicable. Aliases: 'highest', 'float32'.

jax.lax.PrecisionLike#
class jax.lax.RoundingMethod(value)[source]#

An enumeration.

class jax.lax.ScatterDimensionNumbers(update_window_dims, inserted_window_dims, scatter_dims_to_operand_dims)[source]#

Describes the dimension number arguments to an XLA’s Scatter operator. See the XLA documentation for more details of what the dimension numbers mean.

Parameters:
• update_window_dims (Sequence[int]) – the set of dimensions in the updates that are window dimensions. Must be a tuple of integers in ascending order, each representing a dimension number.

• inserted_window_dims (Sequence[int]) – the set of size 1 window dimensions that must be inserted into the shape of updates. Must be a tuple of integers in ascending order, each representing a dimension number of the output. These are the mirror image of collapsed_slice_dims in the case of gather.

• scatter_dims_to_operand_dims (Sequence[int]) – for each dimension in scatter_indices, gives the corresponding dimension in operand. Must be a sequence of integers with size equal to scatter_indices.shape[-1].

Unlike XLA’s ScatterDimensionNumbers structure, index_vector_dim is implicit; there is always an index vector dimension and it must always be the last dimension. To scatter scalar indices, add a trailing dimension of size 1.