Spaces:
Sleeping
Sleeping
File size: 7,763 Bytes
85bd48b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 |
# Copyright 2021 DeepMind Technologies Limited
#
# 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.
"""Specialized mapping functions."""
import functools
from typing import Any, Callable, Optional, Sequence, Union
import haiku as hk
import jax
import jax.numpy as jnp
PYTREE = Any
PYTREE_JAX_ARRAY = Any
partial = functools.partial
PROXY = object()
def _maybe_slice(array, i, slice_size, axis):
if axis is PROXY:
return array
else:
return jax.lax.dynamic_slice_in_dim(
array, i, slice_size=slice_size, axis=axis)
def _maybe_get_size(array, axis):
if axis == PROXY:
return -1
else:
return array.shape[axis]
def _expand_axes(axes, values, name='sharded_apply'):
values_tree_def = jax.tree_flatten(values)[1]
flat_axes = jax.api_util.flatten_axes(name, values_tree_def, axes)
# Replace None's with PROXY
flat_axes = [PROXY if x is None else x for x in flat_axes]
return jax.tree_unflatten(values_tree_def, flat_axes)
def sharded_map(
fun: Callable[..., PYTREE_JAX_ARRAY],
shard_size: Union[int, None] = 1,
in_axes: Union[int, PYTREE] = 0,
out_axes: Union[int, PYTREE] = 0) -> Callable[..., PYTREE_JAX_ARRAY]:
"""Sharded vmap.
Maps `fun` over axes, in a way similar to vmap, but does so in shards of
`shard_size`. This allows a smooth trade-off between memory usage
(as in a plain map) vs higher throughput (as in a vmap).
Args:
fun: Function to apply smap transform to.
shard_size: Integer denoting shard size.
in_axes: Either integer or pytree describing which axis to map over for each
input to `fun`, None denotes broadcasting.
out_axes: integer or pytree denoting to what axis in the output the mapped
over axis maps.
Returns:
function with smap applied.
"""
vmapped_fun = hk.vmap(fun, in_axes, out_axes)
return sharded_apply(vmapped_fun, shard_size, in_axes, out_axes)
def sharded_apply(
fun: Callable[..., PYTREE_JAX_ARRAY], # pylint: disable=g-bare-generic
shard_size: Union[int, None] = 1,
in_axes: Union[int, PYTREE] = 0,
out_axes: Union[int, PYTREE] = 0,
new_out_axes: bool = False) -> Callable[..., PYTREE_JAX_ARRAY]:
"""Sharded apply.
Applies `fun` over shards to axes, in a way similar to vmap,
but does so in shards of `shard_size`. Shards are stacked after.
This allows a smooth trade-off between
memory usage (as in a plain map) vs higher throughput (as in a vmap).
Args:
fun: Function to apply smap transform to.
shard_size: Integer denoting shard size.
in_axes: Either integer or pytree describing which axis to map over for each
input to `fun`, None denotes broadcasting.
out_axes: integer or pytree denoting to what axis in the output the mapped
over axis maps.
new_out_axes: whether to stack outputs on new axes. This assumes that the
output sizes for each shard (including the possible remainder shard) are
the same.
Returns:
function with smap applied.
"""
docstr = ('Mapped version of {fun}. Takes similar arguments to {fun} '
'but with additional array axes over which {fun} is mapped.')
if new_out_axes:
raise NotImplementedError('New output axes not yet implemented.')
# shard size None denotes no sharding
if shard_size is None:
return fun
@jax.util.wraps(fun, docstr=docstr)
def mapped_fn(*args):
# Expand in axes and Determine Loop range
in_axes_ = _expand_axes(in_axes, args)
in_sizes = jax.tree_util.tree_map(_maybe_get_size, args, in_axes_)
flat_sizes = jax.tree_flatten(in_sizes)[0]
in_size = max(flat_sizes)
assert all(i in {in_size, -1} for i in flat_sizes)
num_extra_shards = (in_size - 1) // shard_size
# Fix Up if necessary
last_shard_size = in_size % shard_size
last_shard_size = shard_size if last_shard_size == 0 else last_shard_size
def apply_fun_to_slice(slice_start, slice_size):
input_slice = jax.tree_util.tree_map(
lambda array, axis: _maybe_slice(array, slice_start, slice_size, axis
), args, in_axes_)
return fun(*input_slice)
remainder_shape_dtype = hk.eval_shape(
partial(apply_fun_to_slice, 0, last_shard_size))
out_dtypes = jax.tree_map(lambda x: x.dtype, remainder_shape_dtype)
out_shapes = jax.tree_map(lambda x: x.shape, remainder_shape_dtype)
out_axes_ = _expand_axes(out_axes, remainder_shape_dtype)
if num_extra_shards > 0:
regular_shard_shape_dtype = hk.eval_shape(
partial(apply_fun_to_slice, 0, shard_size))
shard_shapes = jax.tree_map(lambda x: x.shape, regular_shard_shape_dtype)
def make_output_shape(axis, shard_shape, remainder_shape):
return shard_shape[:axis] + (
shard_shape[axis] * num_extra_shards +
remainder_shape[axis],) + shard_shape[axis + 1:]
out_shapes = jax.tree_util.tree_map(make_output_shape, out_axes_, shard_shapes,
out_shapes)
# Calls dynamic Update slice with different argument order
# This is here since tree_multimap only works with positional arguments
def dynamic_update_slice_in_dim(full_array, update, axis, i):
return jax.lax.dynamic_update_slice_in_dim(full_array, update, i, axis)
def compute_shard(outputs, slice_start, slice_size):
slice_out = apply_fun_to_slice(slice_start, slice_size)
update_slice = partial(
dynamic_update_slice_in_dim, i=slice_start)
return jax.tree_util.tree_map(update_slice, outputs, slice_out, out_axes_)
def scan_iteration(outputs, i):
new_outputs = compute_shard(outputs, i, shard_size)
return new_outputs, ()
slice_starts = jnp.arange(0, in_size - shard_size + 1, shard_size)
def allocate_buffer(dtype, shape):
return jnp.zeros(shape, dtype=dtype)
outputs = jax.tree_util.tree_map(allocate_buffer, out_dtypes, out_shapes)
if slice_starts.shape[0] > 0:
outputs, _ = hk.scan(scan_iteration, outputs, slice_starts)
if last_shard_size != shard_size:
remainder_start = in_size - last_shard_size
outputs = compute_shard(outputs, remainder_start, last_shard_size)
return outputs
return mapped_fn
def inference_subbatch(
module: Callable[..., PYTREE_JAX_ARRAY],
subbatch_size: int,
batched_args: Sequence[PYTREE_JAX_ARRAY],
nonbatched_args: Sequence[PYTREE_JAX_ARRAY],
low_memory: bool = True,
input_subbatch_dim: int = 0,
output_subbatch_dim: Optional[int] = None) -> PYTREE_JAX_ARRAY:
"""Run through subbatches (like batch apply but with split and concat)."""
assert len(batched_args) > 0 # pylint: disable=g-explicit-length-test
if not low_memory:
args = list(batched_args) + list(nonbatched_args)
return module(*args)
if output_subbatch_dim is None:
output_subbatch_dim = input_subbatch_dim
def run_module(*batched_args):
args = list(batched_args) + list(nonbatched_args)
return module(*args)
sharded_module = sharded_apply(run_module,
shard_size=subbatch_size,
in_axes=input_subbatch_dim,
out_axes=output_subbatch_dim)
return sharded_module(*batched_args)
|