tools/train/distributed_shampoo.py

"""File copied from https://github.com/google-research/google-research/edit/master/scalable_shampoo/optax/distributed_shampoo.py"""

# coding=utf-8
# Copyright 2021 The Google Research Authors.
#
# 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
#
#     http://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.

# An implementation of distributed Shampoo optimizer from:
#
#  Scalable Second Order Optimization for Deep Learning
#  Rohan Anil, Vineet Gupta, Tomer Koren, Kevin Regan, Yoram Singer
#  Preprint Paper: https://arxiv.org/abs/2002.09018
#
# This implementation moves computation of inverse pth root back to the
# accelerator (if higher precision is available).
#
# Authors: Rohan Anil (rohananil at google dot com)
#    &     Vineet Gupta (vineet at google dot com)
#

"""Distributed Shampoo Implementation."""

import enum
import functools
import itertools
from typing import Any, List, NamedTuple

import chex
import jax
import jax.experimental.pjit as pjit
import jax.numpy as jnp
import numpy as np
import optax
from flax import struct
from jax import lax


# pylint:disable=no-value-for-parameter
@struct.dataclass
class QuantizedValue:
    """State associated with quantized value."""

    quantized: chex.Array
    diagonal: chex.Array  # Diagonal (if extract_diagonal is set)
    bucket_size: chex.Array
    quantized_dtype: jnp.dtype = struct.field(
        pytree_node=False
    )  # Dtype for the quantized value.
    extract_diagonal: bool = struct.field(pytree_node=False)  # In case its centered.
    shape: Any = struct.field(pytree_node=False)  # Shape of the tensor.

    @classmethod
    def from_float_value(cls, fvalue, quantized_dtype, extract_diagonal=False):
        if isinstance(fvalue, list) and not fvalue:
            return QuantizedValue([], [], [], quantized_dtype, extract_diagonal, [])
        quantized, diagonal_fvalue, bucket_size = QuantizedValue.quantize(
            fvalue, quantized_dtype, extract_diagonal
        )
        return QuantizedValue(
            quantized,
            diagonal_fvalue,
            bucket_size,
            quantized_dtype,
            extract_diagonal,
            list(quantized.shape),
        )

    # Quantization is from Lingvo JAX optimizers.
    # We extend it for int16 quantization of PSD matrices.
    @classmethod
    def quantize(cls, fvalue, quantized_dtype, extract_diagonal=False):
        """Returns quantized value and the bucket."""
        if quantized_dtype == jnp.float32:
            return fvalue, [], []
        elif quantized_dtype == jnp.bfloat16:
            return fvalue.astype(jnp.bfloat16), [], []

        float_dtype = fvalue.dtype
        if quantized_dtype == jnp.int8:
            # value -128 is not used.
            num_buckets = jnp.array(127.0, dtype=float_dtype)
        elif quantized_dtype == jnp.int16:
            # value -32768 is not used.
            num_buckets = jnp.array(32767.0, dtype=float_dtype)
        else:
            raise ValueError(f"Quantized dtype {quantized_dtype} not supported.")
        # max value is mapped to num_buckets

        if extract_diagonal and fvalue.ndim != 2:
            raise ValueError(
                f"Input array {fvalue} must be 2D to work with extract_diagonal."
            )

        diagonal_fvalue = []
        if extract_diagonal:
            diagonal_fvalue = jnp.diag(fvalue)
            # Remove the diagonal entries.
            fvalue = fvalue - jnp.diag(diagonal_fvalue)

        # TODO(rohananil): Extend this by making use of information about the blocks
        # SM3 style which will be useful for diagonal statistics
        # We first decide the scale.
        if fvalue.ndim < 1:
            raise ValueError(
                f"Input array {fvalue} must have a strictly positive number of "
                "dimensions."
            )

        max_abs = jnp.max(jnp.abs(fvalue), axis=0)
        bucket_size = max_abs / num_buckets
        bs_expanded = bucket_size[jnp.newaxis, Ellipsis]
        # To avoid divide by 0.0
        bs_nonzero = jnp.where(
            bs_expanded > 0.0, bs_expanded, jnp.ones_like(bs_expanded)
        )
        ratio = fvalue / bs_nonzero
        # We use rounding to remove bias.
        quantized = jnp.round(ratio)
        return quantized.astype(quantized_dtype), diagonal_fvalue, bucket_size

    def to_float(self):
        """Returns the float value."""
        if isinstance(self.quantized, list) and not self.quantized:
            return self.quantized

        if self.quantized_dtype == jnp.float32:
            return self.quantized

        if self.quantized_dtype == jnp.bfloat16:
            return self.quantized.astype(jnp.float32)

        float_dtype = self.bucket_size.dtype
        bucket_size = self.bucket_size[jnp.newaxis, Ellipsis]
        val = self.quantized.astype(float_dtype) * bucket_size
        if self.extract_diagonal:
            val += jnp.diag(self.diagonal)
        return val


# Per parameter optimizer state used in data-parallel training.
class ParameterStats(NamedTuple):
    """State associated to each parameter of the model being trained."""

    diagonal_statistics: QuantizedValue  # Accumulator for diagonal preconditioner
    statistics: List[Any]  # Statistics (QuantizedValue, chex.Array)
    preconditioners: List[Any]  # Preconditioners (QuantizedValue, chex.Array)
    diagonal_momentum: QuantizedValue  # Momentum for the diagonal preconditioner
    momentum: QuantizedValue  # Momentum for the shampoo preconditioner


# For training extremely large model; We keep a global state with a concatenated
# statistics and preconditioner states for all vars. This is so that we can
# annotate the leading axis to be sharded to save memory at the cost of
# communication.
@struct.dataclass
class GlobalShardedParameterStats:
    statistics: chex.Array  # Statistics
    preconditioners: chex.Array  # Preconditioners


# These are per-parameter local states; All statistics here mirror the parameter
# Thus the sharding is copied over from the param specification.
@struct.dataclass
class LocalShardedParameterStats:
    """State associated to each parameter of the model being trained."""

    diagonal_statistics: QuantizedValue  # Accumulator for diagonal preconditioner
    diagonal_momentum: QuantizedValue  # Momentum for the diagonal preconditioner
    momentum: QuantizedValue  # Momentum for the shampoo preconditioner
    index_start: np.int32 = struct.field(
        pytree_node=False
    )  # Index into global statistics array
    sizes: Any = struct.field(pytree_node=False)  # Sizes of the statistics.


class ShardedShampooStats(NamedTuple):
    """Shampoo state in sharded mode."""

    global_stats: Any
    local_stats: Any


class ShampooState(NamedTuple):
    count: chex.Array
    stats: Any


class GraftingType(enum.IntEnum):
    SGD = 1
    ADAGRAD = 2
    RMSPROP = 3
    RMSPROP_NORMALIZED = 4


def power_iteration(
    matrix, num_iters=100, error_tolerance=1e-6, precision=lax.Precision.HIGHEST
):
    r"""Power iteration algorithm.

    The power iteration algorithm takes a symmetric PSD matrix `A`, and produces
    a scalar `\lambda` , which is the greatest (in absolute value) eigenvalue
    of `A`, and a vector v, which is the corresponding eigenvector of `A`.

    References:
      [Wikipedia, 2021](https://en.wikipedia.org/wiki/Power_iteration)

    Args:
      matrix: the symmetric PSD matrix.
      num_iters: Number of iterations.
      error_tolerance: Iterative exit condition.
      precision: precision XLA related flag, the available options are:
        a) lax.Precision.DEFAULT (better step time, but not precise)
        b) lax.Precision.HIGH (increased precision, slower)
        c) lax.Precision.HIGHEST (best possible precision, slowest)

    Returns:
      eigen vector, eigen value
    """
    matrix_size = matrix.shape[-1]

    def _iter_condition(state):
        i, unused_v, unused_s, unused_s_v, run_step = state
        return jnp.logical_and(i < num_iters, run_step)

    def _iter_body(state):
        """One step of power iteration."""
        i, new_v, s, s_v, unused_run_step = state
        new_v = new_v / jnp.linalg.norm(new_v)

        s_v = jnp.einsum("ij,j->i", matrix, new_v, precision=precision)
        s_new = jnp.einsum("i,i->", new_v, s_v, precision=precision)
        return (
            i + 1,
            s_v,
            s_new,
            s_v,
            jnp.greater(jnp.abs(s_new - s), error_tolerance),
        )

    # Figure out how to use step as seed for random.
    v_0 = (
        np.random.RandomState(1729).uniform(-1.0, 1.0, matrix_size).astype(matrix.dtype)
    )

    init_state = tuple([0, v_0, jnp.zeros([], dtype=matrix.dtype), v_0, True])
    _, v_out, s_out, _, _ = lax.while_loop(_iter_condition, _iter_body, init_state)
    v_out = v_out / jnp.linalg.norm(v_out)
    return v_out, s_out


def matrix_inverse_pth_root(
    matrix,
    p,
    num_iters=100,
    ridge_epsilon=1e-6,
    error_tolerance=1e-6,
    precision=lax.Precision.HIGHEST,
):
    """Computes `matrix^(-1/p)`, where `p` is a positive integer.

    This function uses the Coupled newton iterations algorithm for
    the computation of a matrix's inverse pth root.


    References:
      [Functions of Matrices, Theory and Computation,
       Nicholas J Higham, Pg 184, Eq 7.18](
       https://epubs.siam.org/doi/book/10.1137/1.9780898717778)

    Args:
      matrix: the symmetric PSD matrix whose power it to be computed
      p: exponent, for p a positive integer.
      num_iters: Maximum number of iterations.
      ridge_epsilon: Ridge epsilon added to make the matrix positive definite.
      error_tolerance: Error indicator, useful for early termination.
      precision: precision XLA related flag, the available options are:
        a) lax.Precision.DEFAULT (better step time, but not precise)
        b) lax.Precision.HIGH (increased precision, slower)
        c) lax.Precision.HIGHEST (best possible precision, slowest)

    Returns:
      matrix^(-1/p)
    """

    # We use float32 for the matrix inverse pth root.
    # Switch to f64 if you have hardware that supports it.
    matrix_size = matrix.shape[0]
    alpha = jnp.asarray(-1.0 / p, jnp.float32)
    identity = jnp.eye(matrix_size, dtype=jnp.float32)
    _, max_ev = power_iteration(
        matrix=matrix, num_iters=100, error_tolerance=1e-6, precision=precision
    )
    ridge_epsilon = ridge_epsilon * jnp.maximum(max_ev, 1e-16)

    def _unrolled_mat_pow_1(mat_m):
        """Computes mat_m^1."""
        return mat_m

    def _unrolled_mat_pow_2(mat_m):
        """Computes mat_m^2."""
        return jnp.matmul(mat_m, mat_m, precision=precision)

    def _unrolled_mat_pow_4(mat_m):
        """Computes mat_m^4."""
        mat_pow_2 = _unrolled_mat_pow_2(mat_m)
        return jnp.matmul(mat_pow_2, mat_pow_2, precision=precision)

    def _unrolled_mat_pow_8(mat_m):
        """Computes mat_m^4."""
        mat_pow_4 = _unrolled_mat_pow_4(mat_m)
        return jnp.matmul(mat_pow_4, mat_pow_4, precision=precision)

    def mat_power(mat_m, p):
        """Computes mat_m^p, for p == 1, 2, 4 or 8.

        Args:
          mat_m: a square matrix
          p: a positive integer

        Returns:
          mat_m^p
        """
        # We unrolled the loop for performance reasons.
        exponent = jnp.round(jnp.log2(p))
        return lax.switch(
            jnp.asarray(exponent, jnp.int32),
            [
                _unrolled_mat_pow_1,
                _unrolled_mat_pow_2,
                _unrolled_mat_pow_4,
                _unrolled_mat_pow_8,
            ],
            (mat_m),
        )

    def _iter_condition(state):
        (i, unused_mat_m, unused_mat_h, unused_old_mat_h, error, run_step) = state
        error_above_threshold = jnp.logical_and(error > error_tolerance, run_step)
        return jnp.logical_and(i < num_iters, error_above_threshold)

    def _iter_body(state):
        (i, mat_m, mat_h, unused_old_mat_h, error, unused_run_step) = state
        mat_m_i = (1 - alpha) * identity + alpha * mat_m
        new_mat_m = jnp.matmul(mat_power(mat_m_i, p), mat_m, precision=precision)
        new_mat_h = jnp.matmul(mat_h, mat_m_i, precision=precision)
        new_error = jnp.max(jnp.abs(new_mat_m - identity))
        # sometimes error increases after an iteration before decreasing and
        # converging. 1.2 factor is used to bound the maximal allowed increase.
        return (i + 1, new_mat_m, new_mat_h, mat_h, new_error, new_error < error * 1.2)

    if matrix_size == 1:
        resultant_mat_h = (matrix + ridge_epsilon) ** alpha
        error = 0
    else:
        damped_matrix = matrix + ridge_epsilon * identity

        z = (1 + p) / (2 * jnp.linalg.norm(damped_matrix))
        new_mat_m_0 = damped_matrix * z
        new_error = jnp.max(jnp.abs(new_mat_m_0 - identity))
        new_mat_h_0 = identity * jnp.power(z, 1.0 / p)
        init_state = tuple([0, new_mat_m_0, new_mat_h_0, new_mat_h_0, new_error, True])
        _, mat_m, mat_h, old_mat_h, error, convergence = lax.while_loop(
            _iter_condition, _iter_body, init_state
        )
        error = jnp.max(jnp.abs(mat_m - identity))
        is_converged = jnp.asarray(convergence, old_mat_h.dtype)
        resultant_mat_h = is_converged * mat_h + (1 - is_converged) * old_mat_h
        resultant_mat_h = jnp.asarray(resultant_mat_h, matrix.dtype)
    return resultant_mat_h, error


def merge_small_dims(shape_to_merge, max_dim):
    """Merge small dimensions.

    If there are some small dimensions, we collapse them:
    e.g. [1, 2, 512, 1, 2048, 1, 3, 4] --> [1024, 2048, 12] if max_dim = 1024
         [1, 2, 768, 1, 2048] --> [2, 768, 2048]

    Args:
      shape_to_merge: Shape to merge small dimensions.
      max_dim: Maximal dimension of output shape used in merging.

    Returns:
      Merged shape.
    """
    resulting_shape = []
    product = 1
    for d in shape_to_merge:
        if product * d <= max_dim:
            product *= d
        else:
            if product > 1:
                resulting_shape.append(product)
            product = d
    if product > 1:
        resulting_shape.append(product)
    return resulting_shape


def pad_matrix(mat, max_size):
    """Pad a matrix to a max_size.

    Args:
      mat: a matrix to pad.
      max_size: matrix size requested.

    Returns:
      Given M returns [[M, 0], [0, I]]
    """
    size = mat.shape[0]
    assert size <= max_size
    if size == max_size:
        return mat
    pad_size = max_size - size
    zs1 = jnp.zeros([size, pad_size], dtype=mat.dtype)
    zs2 = jnp.zeros([pad_size, size], dtype=mat.dtype)
    eye = jnp.eye(pad_size, dtype=mat.dtype)
    mat = jnp.concatenate([mat, zs1], 1)
    mat = jnp.concatenate([mat, jnp.concatenate([zs2, eye], 1)], 0)
    return mat


def pad_vector(vec, max_size):
    """Pad a vector to a max_size.

    Args:
      vec: a vector to pad.
      max_size: matrix size requested.

    Returns:
      Given V returns [V, 0]
    """
    size = vec.shape[0]
    assert size <= max_size
    if size == max_size:
        return vec
    pad_size = max_size - size
    zs1 = jnp.zeros([pad_size], dtype=vec.dtype)
    return jnp.concatenate([vec, zs1], 0)


def efficient_cond(predicate, compute_fn, init_state, *args, **kwargs):
    """Avoids wasteful buffer allocation with XLA."""

    def _iter_body(unused_state):
        results = compute_fn(*args, **kwargs)
        return tuple([False] + list(results))

    def _iter_condition(state):
        return state[0]

    results = jax.lax.while_loop(
        _iter_condition, _iter_body, tuple([predicate] + init_state)
    )
    return tuple(results[1:])


class BlockPartitioner:
    """Partitions a tensor into smaller tensors."""

    def __init__(self, param, block_size):
        self._shape = param.shape
        self._splits = []
        split_sizes = []
        # We split params into smaller blocks. Here we store the metadata to make
        # that split.
        for i, d in enumerate(param.shape):
            if 0 < block_size < d:
                # d-1, otherwise split appends a 0-size array.
                nsplit = (d - 1) // block_size
                indices = (np.arange(nsplit, dtype=np.int32) + 1) * block_size
                sizes = np.ones(nsplit + 1, dtype=np.int32) * block_size
                sizes[-1] = d - indices[-1]
                self._splits.append((i, indices))
                split_sizes.append(sizes)
            else:
                split_sizes.append(np.array([d], dtype=np.int32))
        self._num_splits = len(split_sizes)
        self._preconditioner_shapes = []
        for t in itertools.product(*split_sizes):
            self._preconditioner_shapes.extend([[d, d] for d in t])

    def shapes_for_preconditioners(self):
        return self._preconditioner_shapes

    def num_splits(self):
        return self._num_splits

    def partition(self, tensor):
        """Partition tensor into blocks."""

        assert tensor.shape == self._shape
        tensors = [tensor]
        for (i, indices) in self._splits:
            tensors_local = []
            for t in tensors:
                tensors_local.extend(jnp.split(t, indices_or_sections=indices, axis=i))
            tensors = tensors_local
        return tensors

    def merge_partitions(self, partitions):
        """Merge partitions back to original shape."""

        for (i, indices) in reversed(self._splits):
            n = len(indices) + 1
            partial_merged_tensors = []
            ind = 0
            while ind < len(partitions):
                partial_merged_tensors.append(
                    jnp.concatenate(partitions[ind : ind + n], axis=i)
                )
                ind += n
            partitions = partial_merged_tensors
        assert len(partitions) == 1
        return partitions[0]


class Preconditioner:
    """Compute statistics/shape from gradients for preconditioning."""

    def __init__(self, param, block_size, best_effort_shape_interpretation):
        self._original_shape = param.shape
        self._transformed_shape = param.shape
        if best_effort_shape_interpretation:
            self._transformed_shape = merge_small_dims(self._original_shape, block_size)
        reshaped_param = jnp.reshape(param, self._transformed_shape)
        self._partitioner = BlockPartitioner(reshaped_param, block_size)

    def statistics_from_grad(self, grad):
        """Compute statistics from gradients.

        Args:
          grad: Gradient to compute statistics from.

        Returns:
          A list of gradient statistics for each partition.
        """
        reshaped_grad = jnp.reshape(grad, self._transformed_shape)
        partitioned_grads = self._partitioner.partition(reshaped_grad)
        stats = []
        for g in partitioned_grads:
            g_stats = []
            rank = len(g.shape)
            for i in range(rank):
                axes = list(range(i)) + list(range(i + 1, rank))
                stat = jnp.tensordot(g, g, axes=(axes, axes))
                g_stats.append(stat)
            stats.extend(g_stats)
        return stats

    def shapes_for_preconditioners(self):
        """Returns shape from statistics."""
        return self._partitioner.shapes_for_preconditioners()

    def exponent_for_preconditioner(self):
        """Returns exponent to use for inverse-pth root M^{-1/p}."""
        return 2 * len(self._transformed_shape)

    def preconditioned_grad(self, grad, preconditioners):
        """Precondition the gradient.

        Args:
          grad: A gradient tensor to precondition.
          preconditioners: A list of preconditioners to apply.

        Returns:
          A preconditioned gradient.
        """

        reshaped_grad = jnp.reshape(grad, self._transformed_shape)
        partitioned_grads = self._partitioner.partition(reshaped_grad)
        preconditioned_partitioned_grads = []
        num_splits = self._partitioner.num_splits()
        for i, g in enumerate(partitioned_grads):
            preconditioners_for_grad = preconditioners[
                i * num_splits : (i + 1) * num_splits
            ]
            rank = len(g.shape)
            precond_g = g
            for j in range(rank):
                precond_g = jnp.tensordot(
                    precond_g, preconditioners_for_grad[j], axes=[[0], [0]]
                )
            preconditioned_partitioned_grads.append(precond_g)
        merged_grad = self._partitioner.merge_partitions(
            preconditioned_partitioned_grads
        )
        return jnp.reshape(merged_grad, self._original_shape)


def _convert_to_parameter_stats(global_stats, local_stat):
    """Creates parameter stats from sharded stats."""
    index_start = int(local_stat.index_start)
    index_end = int(len(local_stat.sizes)) + index_start
    statistics = global_stats.statistics[index_start:index_end, :, :]
    preconditioners = global_stats.preconditioners[index_start:index_end, :, :]
    new_statistics = []
    new_preconditioners = []
    for i, size in enumerate(local_stat.sizes):
        new_statistics.append(statistics[i][:size, :size])
        new_preconditioners.append(preconditioners[i][:size, :size])
    return ParameterStats(
        local_stat.diagonal_statistics,
        new_statistics,
        new_preconditioners,
        local_stat.diagonal_momentum,
        local_stat.momentum,
    )


def _convert_from_parameter_stats(parameter_stats, local_stats):
    """Creates sharded stats from paramter stats."""
    return LocalShardedParameterStats(
        parameter_stats.diagonal_statistics,
        parameter_stats.diagonal_momentum,
        parameter_stats.momentum,
        local_stats.index_start,
        local_stats.sizes,
    )


def batch(x, num_devices):
    """Batch `x` so that so that leading axis is num_devices."""
    n = len(x)
    b = int(n / num_devices)
    return jnp.stack([jnp.stack(x[idx : idx + b]) for idx in range(0, n, b)])


def unbatch(batched_values):
    """Unbatch values across leading axis and return a list of elements."""
    b1, b2 = batched_values.shape[0], batched_values.shape[1]
    results = []
    for v_array in jnp.split(batched_values, indices_or_sections=b1, axis=0):
        v_array = jnp.squeeze(v_array)
        # b2 = batches (number of preconditioner computation) per core.
        if b2 > 1:
            for v in jnp.split(v_array, indices_or_sections=b2, axis=0):
                results.append(jnp.squeeze(v))
        else:
            results.append(v_array)
    return results


def distributed_shampoo(
    learning_rate,
    block_size,
    beta1=0.9,
    beta2=0.999,
    diagonal_epsilon=1e-10,
    matrix_epsilon=1e-6,
    weight_decay=0.0,
    start_preconditioning_step=5,
    preconditioning_compute_steps=1,
    statistics_compute_steps=1,
    best_effort_shape_interpretation=True,
    graft_type=GraftingType.SGD,
    nesterov=True,
    exponent_override=0,
    # Pass pmap 'batch axis name' in pmap mode.
    batch_axis_name=None,
    ### Only set following 3 params in pjit/spmd mode.
    ### WARNING: Experimental
    mesh_axis_names=None,
    num_devices_for_pjit=None,
    shard_optimizer_states=False,
    ###
    ### Experimental memory reduction mode
    best_effort_memory_usage_reduction=False,
    ###
    inverse_failure_threshold=0.1,
    moving_average_for_momentum=False,
    skip_preconditioning_dim_size_gt=4096,
    clip_by_scaled_gradient_norm=None,
    precision=lax.Precision.HIGHEST,
):
    """Distributed Shampoo optimizer.

    Distributed Shampoo is a second-order preconditioned method (concretely, a
    variant of full-matrix Adagrad), that provides significant convergence and
    wall-clock time improvements compared to conventional first-order methods,
    and that has been shown to scale to large state-of-the-art deep learning
    models.

    References:
      Scalable Second Order Optimization for Deep Learning,
      Rohan Anil, Vineet Gupta, Tomer Koren, Kevin Regan, Yoram Singer

      Preprint: https://arxiv.org/abs/2002.09018

    Args:
      learning_rate: the step size used to update the parameters.
      block_size: Block size for large layers (if > 0). Preconditioning compute
        operation is cubic in the dimension of the tensor. Block size allows us to
        chunk the layers into sub-layers of maximal dimension dictated by this
        value. Use 128 as default (increase if you have compute budget).
      beta1: momentum parameter.
      beta2: second moment averaging parameter.
      diagonal_epsilon: epsilon for diagonal adagrad (only if layerwise grafting
        to AdaGrad is enabled).
      matrix_epsilon: epsilon to add to statistics before computing inverse pth
        root. If you are running in f32 precision for inverse pth root
        (recommended today) this can go upto 1e-6. If you have latest hardware
        with native f64 precision, set this upto 1e-12.
      weight_decay: Weight decay for regularization.
      start_preconditioning_step: When to start Shampoo update before which
        diagonal update is used. This is because we dont have enough information
        to do stable inverse.
      preconditioning_compute_steps: How often to compute preconditioner.
        Performance tuning params for controlling memory and compute requirements.
        Ideally set this and statistics_compute_steps params to 1.
      statistics_compute_steps: How often to compute statistics.
      best_effort_shape_interpretation: If there are some small dimensions,
        collapse them e.g. [1, 2, 512, 1, 2048, 1, 3, 4] --> [1024, 2048, 12] if
        block = 1024, [1, 2, 768, 1, 2048] --> [2, 768, 2048]
      graft_type: Grafting is a technique to fix the layerwise scale of Shampoo
        optimizer. This allows us to plugin the Shampoo optimizer into settings
        where SGD/AdaGrad is already well tuned. Available options are:
          GraftingType.SGD and GraftingType.ADAGRAD.
      nesterov: Nesterov momentum.
      exponent_override: Override the exponent used in matrix inverse.
      batch_axis_name: labeled axis over pmap for data-parallel training the
        optimizer used for.
      mesh_axis_names: Axis names for the mesh (used in pjit).
      num_devices_for_pjit: Number of devices to parallelize over when using pjit.
      shard_optimizer_states: Shard optimizer states to save memory in model
        parallel training.
      best_effort_memory_usage_reduction: Best effort memory usage reduction.
        diagonal_statistics -> jnp.bfloat16
        momentum buffers (2x) -> jnp.int8
        statistics, preconditioners -> jnp.int16 + diagonals
      inverse_failure_threshold: numerics are hard and inverses fail sometimes; we
        determine that using this threshold.
      moving_average_for_momentum: Whether to use moving average for momentum
        instead of exponential moving average.
      skip_preconditioning_dim_size_gt: Skip if preconditioning dim size is
          greater than this value.
      clip_by_scaled_gradient_norm: Clip by scaled gradient norm (only useful
        when using RMSProp Grafting).
      precision: precision XLA related flag, the available options are: a)
        lax.Precision.DEFAULT (better step time, but not precise) b)
        lax.Precision.HIGH (increased precision, slower) c) lax.Precision.HIGHEST
        (best possible precision, slowest)

    Returns:
      a GradientTransformation.
    """

    def quantized_dtype_for_momentum_buffers():
        return jnp.int8 if best_effort_memory_usage_reduction else jnp.float32

    # TODO(rohananil): Explore int8-16 quantization with non-linear bucket sizes.
    def quantized_dtype_for_diagonal_statistics_buffers():
        return jnp.bfloat16 if best_effort_memory_usage_reduction else jnp.float32

    # Preconditioner and statistics are both stores as int16 in this mode.
    # We take out the diagonal to make quantization easier.
    def quantized_dtype_for_second_moment_statistics_buffers():
        return (
            jnp.int16
            if best_effort_memory_usage_reduction and batch_axis_name
            else jnp.float32
        )

    # Preconditioner and statistics are both stores as int16 in this mode.
    # We take out the diagonal to make quantization easier.
    def quantized_dtype_for_second_moment_preconditioner_buffers():
        return (
            jnp.int16
            if best_effort_memory_usage_reduction and batch_axis_name
            else jnp.float32
        )

    def _to_float(maybe_quantized):
        if isinstance(maybe_quantized, QuantizedValue):
            return maybe_quantized.to_float()
        else:
            return maybe_quantized

    def _maybe_quantize_statistics(statistics_list):
        return _maybe_quantize_matrices_with_dtype(
            statistics_list, quantized_dtype_for_second_moment_statistics_buffers()
        )

    def _maybe_quantize_preconditioners(statistics_list):
        return _maybe_quantize_matrices_with_dtype(
            statistics_list, quantized_dtype_for_second_moment_preconditioner_buffers()
        )

    def _maybe_quantize_matrices_with_dtype(statistics_list, quantized_dtype):
        if quantized_dtype != jnp.float32:
            return [
                QuantizedValue.from_float_value(
                    s, quantized_dtype, extract_diagonal=True
                )
                for s in statistics_list
            ]
        else:
            return statistics_list

    def _maybe_dequantize_preconditioners(preconditioner_list):
        return _maybe_dequantize_matrices_with_dtype(
            preconditioner_list,
            quantized_dtype_for_second_moment_preconditioner_buffers(),
        )

    def _maybe_dequantize_matrices_with_dtype(statistics_list, quantized_dtype):
        if quantized_dtype != jnp.float32:
            return [s.to_float() for s in statistics_list]
        else:
            return statistics_list

    def _quantize_diagonal_statistics(diagonal_statistics):
        return QuantizedValue.from_float_value(
            diagonal_statistics, quantized_dtype_for_diagonal_statistics_buffers()
        )

    def _quantize_momentum(momentum_statistics):
        return QuantizedValue.from_float_value(
            momentum_statistics, quantized_dtype_for_momentum_buffers()
        )

    def sharded_init_fn(params):
        params_flat, treedef = jax.tree_flatten(params)
        # Find max size to pad to.
        max_size = 0
        for param in params_flat:
            preconditioner = Preconditioner(
                param, block_size, best_effort_shape_interpretation
            )
            if not _skip_preconditioning(param):
                shapes = preconditioner.shapes_for_preconditioners()
                sizes = [s[0] for s in shapes]
                max_size = max(max(sizes), max_size)

        padded_statistics = []
        padded_preconditioners = []
        local_stats_flat = []
        for param in params_flat:
            preconditioner = Preconditioner(
                param, block_size, best_effort_shape_interpretation
            )
            shapes = preconditioner.shapes_for_preconditioners()
            sizes = []

            statistics = []
            preconditioners = []
            index_start = len(padded_statistics)
            if not _skip_preconditioning(param):
                sizes = [s[0] for s in shapes]
                shapes = preconditioner.shapes_for_preconditioners()
                statistics = [matrix_epsilon * jnp.eye(max_size) for s in shapes]
                preconditioners = [jnp.eye(max_size) for s in shapes]
                padded_statistics.extend(statistics)
                padded_preconditioners.extend(preconditioners)

            diagonal_statistics = []
            if graft_type != GraftingType.SGD:
                diagonal_statistics = jnp.zeros_like(param)
            local_stats_flat.append(
                LocalShardedParameterStats(
                    _quantize_diagonal_statistics(diagonal_statistics),
                    _quantize_momentum(jnp.zeros_like(param)),
                    _quantize_momentum(jnp.zeros_like(param)),
                    index_start,
                    sizes,
                )
            )

        local_stats = jax.tree_unflatten(treedef, local_stats_flat)
        # Pad the statistics and preconditioner matrices to be a multiple of
        # num devices.
        # TODO(rohananil): Relax to only the size of the mesh axis where the dim
        # is split on.
        to_pad = -len(padded_statistics) % num_devices_for_pjit
        padded_statistics.extend(
            [jnp.eye(max_size, dtype=padded_statistics[0].dtype) for _ in range(to_pad)]
        )
        padded_preconditioners.extend(
            [jnp.eye(max_size, dtype=padded_statistics[0].dtype) for _ in range(to_pad)]
        )
        global_stats = GlobalShardedParameterStats(
            jnp.stack(padded_statistics), jnp.stack(padded_preconditioners)
        )
        return ShampooState(
            count=jnp.zeros([], jnp.int32),
            stats=ShardedShampooStats(global_stats, local_stats),
        )

    def sharded_update_fn(grads, state, params):
        """Transform the input gradient and update all statistics in sharded mode.

        Args:
          grads: the gradient tensors for the parameters.
          state: a named tuple containing the state of the optimizer
          params: the parameters that should be updated.

        Returns:
          A tuple containing the new parameters and the new optimizer state.
        """
        params_flat, treedef = jax.tree_flatten(params)
        grads_flat = treedef.flatten_up_to(grads)

        global_stats = state.stats.global_stats
        local_stats_flat = treedef.flatten_up_to(state.stats.local_stats)
        stats_flat = [
            _convert_to_parameter_stats(global_stats, local_stat)
            for local_stat in local_stats_flat
        ]
        new_stats_flat = jax.tree_multimap(
            lambda g, s, p: _compute_stats(g, s, p, state.count),
            grads_flat,
            stats_flat,
            params_flat,
        )

        exponents = []
        for stat, param in zip(new_stats_flat, params_flat):
            num_statistics = len(stat.statistics)
            if num_statistics > 0:
                preconditioner = Preconditioner(
                    param, block_size, best_effort_shape_interpretation
                )
                exponent = (
                    preconditioner.exponent_for_preconditioner()
                    if exponent_override == 0
                    else exponent_override
                )
                exponents.extend([exponent] * num_statistics)

        outputs = jax.tree_multimap(
            lambda g, s, p: _transform_grad(g, s, p, state.count),
            grads_flat,
            new_stats_flat,
            params_flat,
        )
        updates_flat, new_stats_flat = list(zip(*outputs)) if outputs else ((), ())

        updates = jax.tree_unflatten(treedef, updates_flat)
        # Create new local_stats
        new_local_stats_flat = [
            _convert_from_parameter_stats(new_stat, local_stat)
            for new_stat, local_stat in zip(new_stats_flat, local_stats_flat)
        ]
        new_local_stats = jax.tree_unflatten(treedef, new_local_stats_flat)

        max_size = global_stats.statistics.shape[1]
        new_padded_statistics = []
        for stat in new_stats_flat:
            new_padded_statistics.extend(
                [pad_matrix(stat, max_size) for stat in stat.statistics]
            )

        # Create global stats
        # TODO(rohananil): Preconditioner is not updated every step, so cost of
        # stack/pad can be obviated away.
        # Pad the statistics and preconditioner matrices to be a multiple of
        # num devices.
        # TODO(rohananil): Relax to only the size of the mesh axis where the dim
        # is split on.
        to_pad = -len(new_padded_statistics) % num_devices_for_pjit
        new_padded_statistics.extend(
            [
                jnp.eye(max_size, dtype=new_padded_statistics[0].dtype)
                for _ in range(to_pad)
            ]
        )
        exponents.extend([1 for _ in range(to_pad)])
        new_stacked_padded_statistics = jnp.stack(new_padded_statistics)
        new_stacked_exponents = jnp.stack(exponents)

        def _matrix_inverse_pth_root_vmap(xs, ps):
            mi_pth_root = functools.partial(
                matrix_inverse_pth_root,
                ridge_epsilon=matrix_epsilon,
                precision=precision,
            )
            preconditioners, errors = jax.vmap(mi_pth_root)(xs, ps)
            return preconditioners, errors

        def _internal_inverse_pth_root_all():
            preconditioners, errors = _matrix_inverse_pth_root_vmap(
                new_stacked_padded_statistics, new_stacked_exponents
            )
            return preconditioners, errors

        if preconditioning_compute_steps == 1:
            new_preconditioners, errors = _internal_inverse_pth_root_all()
        else:
            # Passing statistics instead of preconditioners as they are similarly
            # shaped tensors. Note statistics will be ignored as we are passing in
            # a large init value for error.
            preconditioners_init = new_stacked_padded_statistics
            errors_init = np.stack([inverse_failure_threshold] * len(exponents))
            init_state = [preconditioners_init, errors_init]
            perform_step = state.count % preconditioning_compute_steps == 0
            new_preconditioners, errors = efficient_cond(
                perform_step, _internal_inverse_pth_root_all, init_state
            )

        errors = errors.reshape((-1, 1, 1))
        predicate = jnp.logical_or(
            jnp.isnan(errors), errors >= inverse_failure_threshold
        ).astype(new_preconditioners.dtype)
        # TODO(rohananil): Check for numerical instabilities.
        new_conditional_preconditioners = (
            predicate * global_stats.preconditioners
            + (1.0 - predicate) * new_preconditioners
        )
        new_global_stats = GlobalShardedParameterStats(
            new_stacked_padded_statistics, new_conditional_preconditioners
        )
        new_shampoo_state = ShampooState(
            count=state.count + 1,
            stats=ShardedShampooStats(new_global_stats, new_local_stats),
        )
        return updates, new_shampoo_state

    def init_fn(params):
        """Initialise the optimiser's state."""

        def _init(param):
            preconditioner = Preconditioner(
                param, block_size, best_effort_shape_interpretation
            )
            statistics = []
            preconditioners = []
            if not _skip_preconditioning(param):
                shapes = preconditioner.shapes_for_preconditioners()
                statistics = [matrix_epsilon * jnp.eye(s[0]) for s in shapes]
                preconditioners = [jnp.eye(s[0]) for s in shapes]

            diagonal_statistics = []
            if graft_type != GraftingType.SGD:
                diagonal_statistics = jnp.zeros_like(param)
            return ParameterStats(
                _quantize_diagonal_statistics(diagonal_statistics),
                _maybe_quantize_statistics(statistics),
                _maybe_quantize_preconditioners(preconditioners),
                _quantize_momentum(jnp.zeros_like(param)),
                _quantize_momentum(jnp.zeros_like(param)),
            )

        return ShampooState(
            count=jnp.zeros([], jnp.int32), stats=jax.tree_map(_init, params)
        )

    def _skip_preconditioning(param):
        return len(param.shape) < 1 or any(
            [s > skip_preconditioning_dim_size_gt for s in param.shape]
        )

    def _compute_stats(grad, state, param, step):
        """Compute per-parameter statistics."""
        preconditioner = Preconditioner(
            param, block_size, best_effort_shape_interpretation
        )
        new_statistics = [[]] * len(state.statistics)
        w1 = beta2
        w2 = beta2 if beta2 == 1.0 else (1.0 - beta2)
        if not _skip_preconditioning(param):

            def compute_updated_statistics():
                new_stats = preconditioner.statistics_from_grad(grad)
                new_stats_accumulators = []
                for stat, stat_accumulator in zip(new_stats, state.statistics):
                    new_stats_accumulators.append(
                        w1 * _to_float(stat_accumulator) + w2 * stat
                    )
                return _maybe_quantize_statistics(new_stats_accumulators)

            if statistics_compute_steps > 1:
                perform_step = step % statistics_compute_steps == 0
                init_state = state.statistics
                new_statistics = list(
                    efficient_cond(perform_step, compute_updated_statistics, init_state)
                )
            else:
                new_statistics = compute_updated_statistics()
        return ParameterStats(
            state.diagonal_statistics,
            new_statistics,
            state.preconditioners,
            state.diagonal_momentum,
            state.momentum,
        )

    def _matrix_inverse_pth_root_vmap(xs, ps):
        mi_pth_root = functools.partial(
            matrix_inverse_pth_root, ridge_epsilon=matrix_epsilon, precision=precision
        )
        return jax.vmap(mi_pth_root)(xs, ps)

    def _quantized_matrix_inverse_pth_root_vmap(qxs, qds, qbs, ps):
        def _quantized_to_float(qx, qd, qb):
            qv = QuantizedValue(qx, qd, qb, qx.dtype, True, list(qx.shape))
            return qv.to_float()

        def matrix_inverse_pth_root_wrapper(qx, qd, qb, p):
            v = _quantized_to_float(qx, qd, qb)
            preconditioner, error = matrix_inverse_pth_root(
                v, p, ridge_epsilon=matrix_epsilon, precision=precision
            )
            qp = QuantizedValue.from_float_value(preconditioner, qx.dtype, True)
            return qp.quantized, qp.diagonal, qp.bucket_size, error

        return jax.vmap(matrix_inverse_pth_root_wrapper)(qxs, qds, qbs, ps)

    def _matrix_inverse_pth_root_pjit(xs, ps):
        mesh_axis_names_tuple = tuple(mesh_axis_names)
        # Partition the concatenated statistics matrix across all cores.
        partitioned_xs, partitioned_ps = pjit.pjit(
            lambda x, y: (x, y),
            in_axis_resources=None,
            out_axis_resources=pjit.PartitionSpec(
                mesh_axis_names_tuple,
            ),
        )(xs, ps)
        # Run matrix inverse pth root on each shard.
        partitioned_preconditioners, partitioned_errors = _matrix_inverse_pth_root_vmap(
            partitioned_xs, partitioned_ps
        )
        # Recombine the outputs at each core.
        preconditioners, errors = pjit.pjit(
            lambda x, y: (x, y),
            in_axis_resources=(
                pjit.PartitionSpec(
                    mesh_axis_names_tuple,
                ),
                pjit.PartitionSpec(
                    mesh_axis_names_tuple,
                ),
            ),
            out_axis_resources=(None, None),
        )(partitioned_preconditioners, partitioned_errors)
        return preconditioners, errors

    def _pmap_compute_preconditioners(
        states,
        step,
        statistics,
        num_statistics_per_state,
        original_shapes,
        exponents,
        max_size,
        prev_preconditioners,
    ):
        """Computes preconditioners for given statistics in states in PMAP mode.

        Args:
          states: A list of optimizer states.
          step: Current step number
          statistics: A list of statistics for all variables (for every dim)
          num_statistics_per_state: Number of statistis per state to reconstruct
            output states.
          original_shapes: A list of shapes of the statistics.
          exponents: Exponent power to use for inverse-pth roots.
          max_size: Maximum dim of the statistics to pad.
          prev_preconditioners: Previously available preconditioner.

        Returns:
          New optimizer states after computing the preconditioner.
        """
        num_devices = lax.psum(1, batch_axis_name)
        num_statistics = len(statistics)
        # Pad statistics and exponents to next multiple of num_devices.
        packed_statistics = [pad_matrix(stat, max_size) for stat in statistics]
        to_pad = -num_statistics % num_devices
        packed_statistics.extend(
            [jnp.eye(max_size, dtype=packed_statistics[0].dtype) for _ in range(to_pad)]
        )
        exponents.extend([1 for _ in range(to_pad)])

        if not packed_statistics:
            return states

        all_statistics = batch(packed_statistics, num_devices)
        all_exponents = batch(exponents, num_devices)

        def _internal_inverse_pth_root_all():
            current_replica = lax.axis_index(batch_axis_name)
            preconditioners, errors = _matrix_inverse_pth_root_vmap(
                all_statistics[current_replica], all_exponents[current_replica]
            )
            preconditioners = jax.lax.all_gather(preconditioners, batch_axis_name)
            errors = jax.lax.all_gather(errors, batch_axis_name)
            preconditioners_flat = unbatch(preconditioners)
            errors_flat = unbatch(errors)
            return preconditioners_flat, errors_flat

        if preconditioning_compute_steps == 1:
            preconditioners_flat, errors_flat = _internal_inverse_pth_root_all()
        else:
            # Passing statistics instead of preconditioners as they are similarly
            # shaped tensors. Note statistics will be ignored as we are passing in
            # a large init value for error.
            preconditioners_init = packed_statistics
            errors_init = [inverse_failure_threshold] * len(packed_statistics)
            init_state = [preconditioners_init, errors_init]
            perform_step = step % preconditioning_compute_steps == 0
            preconditioners_flat, errors_flat = efficient_cond(
                perform_step, _internal_inverse_pth_root_all, init_state
            )

        def _skip(error):
            condition = jnp.logical_or(
                jnp.isnan(error), error >= inverse_failure_threshold
            )
            return condition.astype(error.dtype)

        def _select_preconditioner(error, new_p, old_p):
            return lax.cond(
                _skip(error), lambda _: old_p, lambda _: new_p, operand=None
            )

        new_preconditioners_flat = []
        for p, shape, prev_p, error in zip(
            preconditioners_flat, original_shapes, prev_preconditioners, errors_flat
        ):
            new_preconditioners_flat.append(
                _select_preconditioner(error, p[: shape[0], : shape[1]], prev_p)
            )

        assert len(states) == len(num_statistics_per_state)
        assert len(new_preconditioners_flat) == num_statistics

        # Add back empty preconditioners so we that we can set the optimizer state.
        preconditioners_for_states = []
        idx = 0
        for num_statistics, state in zip(num_statistics_per_state, states):
            if num_statistics == 0:
                preconditioners_for_states.append([])
            else:
                preconditioners_for_state = new_preconditioners_flat[
                    idx : idx + num_statistics
                ]
                assert len(state.statistics) == len(preconditioners_for_state)
                preconditioners_for_states.append(preconditioners_for_state)
                idx += num_statistics
        new_states = []
        for state, new_preconditioners in zip(states, preconditioners_for_states):
            new_states.append(
                ParameterStats(
                    state.diagonal_statistics,
                    state.statistics,
                    new_preconditioners,
                    state.diagonal_momentum,
                    state.momentum,
                )
            )

        return new_states

    def _pmap_quantized_compute_preconditioners(
        states,
        step,
        statistics,
        num_statistics_per_state,
        original_shapes,
        exponents,
        max_size,
        prev_preconditioners,
    ):
        """Computes preconditioners for given statistics in states in PMAP mode.

        For quantization, each statistic is represented by three values:
          quantized matrix, diagonal, and bucket sizes, we run inverse pth-roots
          without ever recreating the original matrix in f32.

        Args:
          states: A list of optimizer states.
          step: Current step number
          statistics: A list of statistics for all variables (for every dim)
          num_statistics_per_state: Number of statistis per state to reconstruct
            output states.
          original_shapes: A list of shapes of the statistics.
          exponents: Exponent power to use for inverse-pth roots.
          max_size: Maximum dim of the statistics to pad.
          prev_preconditioners: Previously available preconditioner.

        Returns:
          New optimizer states after computing the preconditioner.
        """
        num_devices = lax.psum(1, batch_axis_name)
        num_statistics = len(statistics)
        quantized_dtype = quantized_dtype_for_second_moment_statistics_buffers()
        # Complexity here is around: shapes needing be statically shaped,
        # our custom quantization type requires a different type of packing.

        # Parallel tensors:
        # quantized [dxd]
        # diagonals [d] f32
        # bucket_sizes [d] f32
        packed_quantized_statistics = [
            pad_matrix(stat.quantized, max_size) for stat in statistics
        ]
        packed_quantized_diagonals = [
            pad_vector(stat.diagonal, max_size) for stat in statistics
        ]
        packed_quantized_bucket_sizes = [
            pad_vector(stat.bucket_size, max_size) for stat in statistics
        ]

        to_pad = -num_statistics % num_devices
        padded_eye = jnp.eye(max_size, dtype=jnp.float32)
        quantized_eye = QuantizedValue.from_float_value(
            padded_eye, quantized_dtype, True
        )
        packed_quantized_statistics.extend(
            [quantized_eye.quantized for _ in range(to_pad)]
        )
        packed_quantized_diagonals.extend(
            [quantized_eye.diagonal for _ in range(to_pad)]
        )
        packed_quantized_bucket_sizes.extend(
            [quantized_eye.bucket_size for _ in range(to_pad)]
        )
        exponents.extend([1 for _ in range(to_pad)])

        if not packed_quantized_statistics:
            return states

        all_quantized_statistics = batch(packed_quantized_statistics, num_devices)
        all_quantized_diagonals = batch(packed_quantized_diagonals, num_devices)
        all_quantized_bucket_sizes = batch(packed_quantized_bucket_sizes, num_devices)
        all_exponents = batch(exponents, num_devices)

        def _internal_inverse_pth_root_all():
            current_replica = lax.axis_index(batch_axis_name)
            (
                quantized_preconditioners,
                quantized_diagonals,
                quantized_bucket_sizes,
                errors,
            ) = _quantized_matrix_inverse_pth_root_vmap(
                all_quantized_statistics[current_replica],
                all_quantized_diagonals[current_replica],
                all_quantized_bucket_sizes[current_replica],
                all_exponents[current_replica],
            )
            quantized_preconditioners = jax.lax.all_gather(
                quantized_preconditioners, batch_axis_name
            )
            quantized_diagonals = jax.lax.all_gather(
                quantized_diagonals, batch_axis_name
            )
            quantized_bucket_sizes = jax.lax.all_gather(
                quantized_bucket_sizes, batch_axis_name
            )
            errors = jax.lax.all_gather(errors, batch_axis_name)
            quantized_preconditioners_flat = unbatch(quantized_preconditioners)
            quantized_diagonals_flat = unbatch(quantized_diagonals)
            quantized_bucket_sizes_flat = unbatch(quantized_bucket_sizes)
            errors_flat = unbatch(errors)
            return (
                quantized_preconditioners_flat,
                quantized_diagonals_flat,
                quantized_bucket_sizes_flat,
                errors_flat,
            )

        if preconditioning_compute_steps == 1:
            (
                quantized_preconditioners_flat,
                quantized_diagonals_flat,
                quantized_bucket_sizes_flat,
                errors_flat,
            ) = _internal_inverse_pth_root_all()
        else:
            # Passing statistics instead of preconditioners as they are similarly
            # shaped tensors. Note statistics will be ignored as we are passing in
            # a large init value for error.
            quantized_preconditioners_init = packed_quantized_statistics
            quantized_diagonals_init = packed_quantized_diagonals
            quantized_bucket_sizes_init = packed_quantized_bucket_sizes
            errors_init = [inverse_failure_threshold] * len(
                quantized_preconditioners_init
            )
            init_state = [
                quantized_preconditioners_init,
                quantized_diagonals_init,
                quantized_bucket_sizes_init,
                errors_init,
            ]
            perform_step = step % preconditioning_compute_steps == 0
            (
                quantized_preconditioners_flat,
                quantized_diagonals_flat,
                quantized_bucket_sizes_flat,
                errors_flat,
            ) = efficient_cond(perform_step, _internal_inverse_pth_root_all, init_state)

        def _skip(error):
            condition = jnp.logical_or(
                jnp.isnan(error), error >= inverse_failure_threshold
            )
            return condition.astype(error.dtype)

        def _select_preconditioner(error, new_p, old_p):
            return lax.cond(
                _skip(error), lambda _: old_p, lambda _: new_p, operand=None
            )

        new_quantized_preconditioners_flat = []
        new_quantized_diagonals_flat = []
        new_quantized_bucket_sizes_flat = []
        for p, d, b, shape, prev_p, error in zip(
            quantized_preconditioners_flat,
            quantized_diagonals_flat,
            quantized_bucket_sizes_flat,
            original_shapes,
            prev_preconditioners,
            errors_flat,
        ):
            new_quantized_preconditioners_flat.append(
                _select_preconditioner(
                    error, p[: shape[0], : shape[1]], prev_p.quantized
                )
            )
            new_quantized_diagonals_flat.append(
                _select_preconditioner(error, d[: shape[0]], prev_p.diagonal)
            )
            new_quantized_bucket_sizes_flat.append(
                _select_preconditioner(error, b[: shape[0]], prev_p.bucket_size)
            )

        assert len(states) == len(num_statistics_per_state)
        assert len(new_quantized_preconditioners_flat) == num_statistics
        assert len(new_quantized_diagonals_flat) == num_statistics
        assert len(new_quantized_bucket_sizes_flat) == num_statistics

        # Add back empty preconditioners so we that we can set the optimizer state.
        preconditioners_for_states = []
        idx = 0
        for num_statistics, state in zip(num_statistics_per_state, states):
            if num_statistics == 0:
                preconditioners_for_states.append([])
            else:
                quantized_preconditioners_for_state = (
                    new_quantized_preconditioners_flat[idx : idx + num_statistics]
                )
                quantized_diagonals_for_state = new_quantized_diagonals_flat[
                    idx : idx + num_statistics
                ]
                quantized_bucket_sizes_for_state = new_quantized_bucket_sizes_flat[
                    idx : idx + num_statistics
                ]

                assert len(state.statistics) == len(quantized_preconditioners_for_state)
                assert len(state.statistics) == len(quantized_diagonals_for_state)
                assert len(state.statistics) == len(quantized_bucket_sizes_for_state)

                quantized_preconditioners = []
                for qv, qd, qb in zip(
                    quantized_preconditioners_for_state,
                    quantized_diagonals_for_state,
                    quantized_bucket_sizes_for_state,
                ):
                    quantized_preconditioners.append(
                        QuantizedValue(qv, qd, qb, qv.dtype, True, list(qv.shape))
                    )
                preconditioners_for_states.append(quantized_preconditioners)
                idx += num_statistics
        new_states = []
        for state, new_preconditioners in zip(states, preconditioners_for_states):
            new_states.append(
                ParameterStats(
                    state.diagonal_statistics,
                    state.statistics,
                    new_preconditioners,
                    state.diagonal_momentum,
                    state.momentum,
                )
            )

        return new_states

    def _pjit_compute_preconditioners(
        states,
        step,
        statistics,
        num_statistics_per_state,
        original_shapes,
        exponents,
        max_size,
        prev_preconditioners,
    ):
        """Computes preconditioners for given statistics in states in PJIT mode.

        Args:
          states: A list of optimizer states.
          step: Current step number
          statistics: A list of statistics for all variables (for every dim)
          num_statistics_per_state: Number of statistis per state to reconstruct
            output states.
          original_shapes: A list of shapes of the statistics.
          exponents: Exponent power to use for inverse-pth roots.
          max_size: Maximum dim of the statistics to pad.
          prev_preconditioners: Previously available preconditioner.

        Returns:
          New optimizer states after computing the preconditioner.
        """
        num_statistics = len(statistics)
        to_pad = -num_statistics % num_devices_for_pjit
        padded_statistics = [pad_matrix(stat, max_size) for stat in statistics]
        padded_statistics.extend(
            [jnp.eye(max_size, dtype=padded_statistics[0].dtype) for _ in range(to_pad)]
        )
        exponents.extend([1 for _ in range(to_pad)])
        all_statistics = jnp.stack(padded_statistics)
        all_exponents = jnp.stack(exponents)

        def _internal_inverse_pth_root_all():
            preconditioners, errors = _matrix_inverse_pth_root_pjit(
                all_statistics, all_exponents
            )
            b1 = preconditioners.shape[0]

            def split(batched_values):
                return [
                    jnp.squeeze(v)
                    for v in jnp.split(batched_values, indices_or_sections=b1, axis=0)
                ]

            return split(preconditioners), split(errors)

        if preconditioning_compute_steps == 1:
            preconditioners_flat, errors_flat = _internal_inverse_pth_root_all()
        else:
            # Passing statistics instead of preconditioners as they are similarly
            # shaped tensors. Note statistics will be ignored as we are passing in
            # a large init value for error.
            preconditioners_init = padded_statistics
            errors_init = [inverse_failure_threshold] * len(padded_statistics)
            init_state = [preconditioners_init, errors_init]
            perform_step = step % preconditioning_compute_steps == 0
            preconditioners_flat, errors_flat = efficient_cond(
                perform_step, _internal_inverse_pth_root_all, init_state
            )

        def _skip(error):
            condition = jnp.logical_or(
                jnp.isnan(error), error >= inverse_failure_threshold
            )
            return condition.astype(error.dtype)

        def _select_preconditioner(error, new_p, old_p):
            return lax.cond(
                _skip(error), lambda _: old_p, lambda _: new_p, operand=None
            )

        new_preconditioners_flat = []
        for p, shape, prev_p, error in zip(
            preconditioners_flat, original_shapes, prev_preconditioners, errors_flat
        ):
            new_preconditioners_flat.append(
                _select_preconditioner(error, p[: shape[0], : shape[1]], prev_p)
            )

        assert len(states) == len(num_statistics_per_state)
        assert len(new_preconditioners_flat) == num_statistics

        # Add back empty preconditioners so we that we can set the optimizer state.
        preconditioners_for_states = []
        idx = 0
        for num_statistics, state in zip(num_statistics_per_state, states):
            if num_statistics == 0:
                preconditioners_for_states.append([])
            else:
                preconditioners_for_state = new_preconditioners_flat[
                    idx : idx + num_statistics
                ]
                assert len(state.statistics) == len(preconditioners_for_state)
                preconditioners_for_states.append(preconditioners_for_state)
                idx += num_statistics
        new_states = []
        for state, new_preconditioners in zip(states, preconditioners_for_states):
            new_states.append(
                ParameterStats(
                    state.diagonal_statistics,
                    state.statistics,
                    new_preconditioners,
                    state.diagonal_momentum,
                    state.momentum,
                )
            )

        return new_states

    def _compute_preconditioners(states, params, step):
        """Computes preconditioners for given statistics in states.

        Args:
          states: A list of optimizer states.
          params: A list of params.
          step: Current step number

        Returns:
          New optimizer states after computing the preconditioner.
        """
        statistics = []
        num_statistics_per_state = []
        original_shapes = []
        exponents = []
        max_size = 0
        prev_preconditioners = []

        for state, param in zip(states, params):
            num_statistics = len(state.statistics)
            num_statistics_per_state.append(num_statistics)
            original_shapes_for_state = []
            if num_statistics > 0:
                preconditioner = Preconditioner(
                    param, block_size, best_effort_shape_interpretation
                )
                for statistic in state.statistics:
                    exponents.append(
                        preconditioner.exponent_for_preconditioner()
                        if exponent_override == 0
                        else exponent_override
                    )
                    original_shapes_for_state.append(statistic.shape)
                    max_size = max(max_size, statistic.shape[0])

                statistics.extend(state.statistics)
                prev_preconditioners.extend(state.preconditioners)
                original_shapes.extend(original_shapes_for_state)

        if batch_axis_name:
            # Quantization is only enabled if batch_axis_name is not set.
            quantized_dtype = quantized_dtype_for_second_moment_statistics_buffers()

            if quantized_dtype == jnp.float32:
                return _pmap_compute_preconditioners(
                    states,
                    step,
                    statistics,
                    num_statistics_per_state,
                    original_shapes,
                    exponents,
                    max_size,
                    prev_preconditioners,
                )
            else:
                return _pmap_quantized_compute_preconditioners(
                    states,
                    step,
                    statistics,
                    num_statistics_per_state,
                    original_shapes,
                    exponents,
                    max_size,
                    prev_preconditioners,
                )

        else:
            return _pjit_compute_preconditioners(
                states,
                step,
                statistics,
                num_statistics_per_state,
                original_shapes,
                exponents,
                max_size,
                prev_preconditioners,
            )

    def _transform_grad(grad, state, param, step):
        """Transform per-parameter gradients."""
        preconditioner = Preconditioner(
            param, block_size, best_effort_shape_interpretation
        )
        sgd_update = grad
        new_diagonal_statistics = state.diagonal_statistics.to_float()
        if graft_type == GraftingType.ADAGRAD:
            new_diagonal_statistics = state.diagonal_statistics.to_float() + jnp.square(
                grad
            )
            adagrad_update = grad / (
                jnp.sqrt(new_diagonal_statistics) + diagonal_epsilon
            )
            grafting_update = adagrad_update
        elif (
            graft_type == GraftingType.RMSPROP
            or graft_type == GraftingType.RMSPROP_NORMALIZED
        ):

            scaled_grad = grad
            if graft_type == GraftingType.RMSPROP_NORMALIZED:
                scaled_grad = grad / jnp.linalg.norm(grad)

            w1 = beta2
            w2 = beta2 if beta2 == 1.0 else (1.0 - beta2)

            new_diagonal_statistics = (
                w1 * state.diagonal_statistics.to_float() + w2 * jnp.square(scaled_grad)
            )
            rmsprop_update = scaled_grad / (
                jnp.sqrt(new_diagonal_statistics) + diagonal_epsilon
            )

            if clip_by_scaled_gradient_norm:
                scaled_grad_norm = jnp.linalg.norm(rmsprop_update) / (
                    jnp.sqrt(float(rmsprop_update.size))
                )
                clipping_denom = jnp.maximum(
                    1.0, scaled_grad_norm / clip_by_scaled_gradient_norm
                )
                rmsprop_update /= clipping_denom

            grafting_update = rmsprop_update
        else:
            grafting_update = sgd_update

        precond_grad = grad
        if not _skip_preconditioning(param):
            precond_grad = preconditioner.preconditioned_grad(
                precond_grad, _maybe_dequantize_preconditioners(state.preconditioners)
            )
        else:
            precond_grad = grafting_update

        grafting_update_norm = jnp.linalg.norm(grafting_update)
        precond_grad_norm = jnp.linalg.norm(precond_grad)

        multiplier = grafting_update_norm / (precond_grad_norm + 1e-16)
        shampoo_update = precond_grad * multiplier

        shampoo_update_with_wd = shampoo_update
        grafting_update_with_wd = grafting_update
        if weight_decay != 0:
            shampoo_update_with_wd = shampoo_update + weight_decay * param
            grafting_update_with_wd = grafting_update + weight_decay * param

        w = (1.0 - beta1) if moving_average_for_momentum else 1.0
        shampoo_update_with_wd_momentum = (
            state.momentum.to_float() * beta1 + w * shampoo_update_with_wd
        )
        grafting_update_with_wd_momentum = (
            state.diagonal_momentum.to_float() * beta1 + w * grafting_update_with_wd
        )

        run_shampoo = (step >= start_preconditioning_step).astype(
            grafting_update_with_wd_momentum.dtype
        )

        momentum_update = (
            run_shampoo * shampoo_update_with_wd_momentum
            + (1.0 - run_shampoo) * grafting_update_with_wd_momentum
        )

        wd_update = (
            run_shampoo * shampoo_update_with_wd
            + (1.0 - run_shampoo) * grafting_update_with_wd
        )

        if nesterov:
            momentum_update = w * wd_update + beta1 * momentum_update

        lr = learning_rate
        if callable(learning_rate):
            lr = learning_rate(step)
        transformed_update = -1.0 * lr * momentum_update

        param_stats = ParameterStats(
            _quantize_diagonal_statistics(new_diagonal_statistics),
            state.statistics,
            state.preconditioners,
            _quantize_momentum(grafting_update_with_wd_momentum),
            _quantize_momentum(shampoo_update_with_wd_momentum),
        )
        return transformed_update, param_stats

    def update_fn(grads, state, params):
        """Transform the input gradient and update all statistics.

        Args:
          grads: the gradient tensors for the parameters.
          state: a named tuple containing the state of the optimizer
          params: the parameters that should be updated.

        Returns:
          A tuple containing the new parameters and the new optimizer state.
        """
        params_flat, treedef = jax.tree_flatten(params)
        stats_flat = treedef.flatten_up_to(state.stats)
        grads_flat = treedef.flatten_up_to(grads)

        new_stats_flat = jax.tree_multimap(
            lambda g, s, p: _compute_stats(g, s, p, state.count),
            grads_flat,
            stats_flat,
            params_flat,
        )
        new_stats_flat = _compute_preconditioners(
            new_stats_flat, params_flat, state.count
        )

        outputs = jax.tree_multimap(
            lambda g, s, p: _transform_grad(g, s, p, state.count),
            grads_flat,
            new_stats_flat,
            params_flat,
        )
        updates_flat, new_stats_flat = list(zip(*outputs)) if outputs else ((), ())

        updates = jax.tree_unflatten(treedef, updates_flat)
        new_stats = jax.tree_unflatten(treedef, new_stats_flat)

        new_state = ShampooState(count=state.count + 1, stats=new_stats)
        return updates, new_state

    if shard_optimizer_states:
        return optax.GradientTransformation(sharded_init_fn, sharded_update_fn)
    else:
        return optax.GradientTransformation(init_fn, update_fn)