id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
151,570 | import os
import imageio
import numpy as np
import torch
import torchvision
from PIL import Image
from typing import Union
from tqdm import tqdm
from einops import rearrange
import torch.distributed as dist
def ddim_loop(pipeline, ddim_scheduler, latent, num_inv_steps, prompt):
context = init_prompt(prompt, pipelin... | null |
151,571 | import os
import imageio
import numpy as np
import torch
import torchvision
from PIL import Image
from typing import Union
from tqdm import tqdm
from einops import rearrange
import torch.distributed as dist
def video2images(path, step=4, length=16, start=0):
reader = imageio.get_reader(path)
frames = []
fo... | null |
151,572 | import os
import imageio
import numpy as np
import torch
import torchvision
from PIL import Image
from typing import Union
from tqdm import tqdm
from einops import rearrange
import torch.distributed as dist
def images2video(video, path, fps=8):
imageio.mimsave(path, video, fps=fps)
return | null |
151,573 | import os
import imageio
import numpy as np
import torch
import torchvision
from PIL import Image
from typing import Union
from tqdm import tqdm
from einops import rearrange
import torch.distributed as dist
tensor_interpolation = None
def get_tensor_interpolation_method():
return tensor_interpolation | null |
151,574 | import os
import imageio
import numpy as np
import torch
import torchvision
from PIL import Image
from typing import Union
from tqdm import tqdm
from einops import rearrange
import torch.distributed as dist
tensor_interpolation = None
def linear(v1, v2, t):
def slerp(
v0: torch.Tensor, v1: torch.Tensor, t: float, D... | null |
151,575 | import os
import socket
import warnings
import torch
from torch import distributed as dist
def get_rank():
if not dist.is_available():
return 0
if not dist.is_nccl_available():
return 0
if not dist.is_initialized():
return 0
return dist.get_rank()
def is_master():
return get_... | null |
151,577 | from typing import Any, Dict, List, Set, Tuple
import sphinx.ext.autodoc
import sphinx.ext.autosummary.generate as ag
ag.generate_autosummary_content = generate_autosummary_content
def generate_autosummary_content(
name: str,
obj: Any,
parent: Any,
template: ag.AutosummaryRenderer,
template_name: s... | null |
151,578 | import fiddle as fdl
import seqio
from t5.data import mixtures
from t5.data import tasks
from t5x import config_utils
from t5x import eval as t5x_eval
from t5x import partitioning
from t5x import utils
from t5x.fiddle_configs.configs import finetune
from t5x.fiddle_configs.configs import pretrain
from t5x.fiddle_config... | null |
151,579 | import fiddle as fdl
import seqio
from t5.data import mixtures
from t5.data import tasks
from t5x import config_utils
from t5x import eval as t5x_eval
from t5x import partitioning
from t5x import utils
from t5x.fiddle_configs.configs import finetune
from t5x.fiddle_configs.configs import pretrain
from t5x.fiddle_config... | null |
151,580 | import fiddle as fdl
import seqio
from t5.data import mixtures
from t5.data import tasks
from t5x import config_utils
from t5x import eval as t5x_eval
from t5x import partitioning
from t5x import utils
from t5x.fiddle_configs.configs import finetune
from t5x.fiddle_configs.configs import pretrain
from t5x.fiddle_config... | null |
151,581 | import os
from typing import Sequence
from absl import logging
from t5x import export_lib
The provided code snippet includes necessary dependencies for implementing the `_main` function. Write a Python function `def _main(argv: Sequence[str])` to solve the following problem:
True main function.
Here is the function:
... | True main function. |
151,582 | import abc
import collections
import dataclasses
import functools
import typing
from typing import Any, Callable, Optional, Sequence, Set, Tuple, Union
from absl import logging
import cached_property
from flax import traverse_util
from flax.linen import partitioning as flax_partitioning
import jax
from jax import numpy... | Identity function for copying parameters to the devices, sharded. |
151,583 | import functools
import gc
import math
import os
import time
from typing import Callable, Dict, Mapping, Optional, Sequence, Tuple, Type
from absl import logging
from clu import metric_writers
import jax
from jax import random
from jax.experimental import multihost_utils
import jax.numpy as jnp
import numpy as np
impor... | True main function. |
151,584 | import abc
import asyncio
from concurrent.futures import thread
import re
from typing import Any, Callable, Mapping, MutableMapping, Optional, Sequence, Tuple, Union
from flax import traverse_util
import jax
from jax import numpy as jnp
import numpy as np
import tensorflow as tf
import tensorstore as ts
class LazyArray... | null |
151,585 | import abc
import asyncio
from concurrent.futures import thread
import re
from typing import Any, Callable, Mapping, MutableMapping, Optional, Sequence, Tuple, Union
from flax import traverse_util
import jax
from jax import numpy as jnp
import numpy as np
import tensorflow as tf
import tensorstore as ts
SLOT_MAP = {'_s... | null |
151,586 | import abc
import asyncio
from concurrent.futures import thread
import re
from typing import Any, Callable, Mapping, MutableMapping, Optional, Sequence, Tuple, Union
from flax import traverse_util
import jax
from jax import numpy as jnp
import numpy as np
import tensorflow as tf
import tensorstore as ts
SLOT_MAP = {'_s... | null |
151,587 | import abc
import asyncio
from concurrent.futures import thread
import re
from typing import Any, Callable, Mapping, MutableMapping, Optional, Sequence, Tuple, Union
from flax import traverse_util
import jax
from jax import numpy as jnp
import numpy as np
import tensorflow as tf
import tensorstore as ts
SLOT_MAP = {'_s... | Process relpos bias assuming that they are not shared across layers. |
151,588 | import abc
import asyncio
from concurrent.futures import thread
import re
from typing import Any, Callable, Mapping, MutableMapping, Optional, Sequence, Tuple, Union
from flax import traverse_util
import jax
from jax import numpy as jnp
import numpy as np
import tensorflow as tf
import tensorstore as ts
SLOT_MAP = {'_s... | Process attention layers. |
151,589 | import abc
import asyncio
from concurrent.futures import thread
import re
from typing import Any, Callable, Mapping, MutableMapping, Optional, Sequence, Tuple, Union
from flax import traverse_util
import jax
from jax import numpy as jnp
import numpy as np
import tensorflow as tf
import tensorstore as ts
SLOT_MAP = {'_s... | Process MLP blocks. |
151,590 | import abc
import asyncio
from concurrent.futures import thread
import re
from typing import Any, Callable, Mapping, MutableMapping, Optional, Sequence, Tuple, Union
from flax import traverse_util
import jax
from jax import numpy as jnp
import numpy as np
import tensorflow as tf
import tensorstore as ts
SLOT_MAP = {'_s... | Process layer norms assuming that they are pre-layernorms. |
151,591 | import abc
import asyncio
from concurrent.futures import thread
import re
from typing import Any, Callable, Mapping, MutableMapping, Optional, Sequence, Tuple, Union
from flax import traverse_util
import jax
from jax import numpy as jnp
import numpy as np
import tensorflow as tf
import tensorstore as ts
SLOT_MAP = {'_s... | Process final layer norms. |
151,592 | import abc
import asyncio
from concurrent.futures import thread
import re
from typing import Any, Callable, Mapping, MutableMapping, Optional, Sequence, Tuple, Union
from flax import traverse_util
import jax
from jax import numpy as jnp
import numpy as np
import tensorflow as tf
import tensorstore as ts
SLOT_MAP = {'_s... | null |
151,593 | import abc
import asyncio
from concurrent.futures import thread
import re
from typing import Any, Callable, Mapping, MutableMapping, Optional, Sequence, Tuple, Union
from flax import traverse_util
import jax
from jax import numpy as jnp
import numpy as np
import tensorflow as tf
import tensorstore as ts
class LazyArray... | Load T5 checkpoint and update Adafactor optimizer and T5 model from it. We require that the final translated checkpoint structure exactly matches that of the Flax Adafactor + Transformer data, up to shape agreement of the leaves. Args: state_dict: Flax Adafactor Optimizer for T5 transformer encoder-decoder. path: a pat... |
151,594 | import enum
import os
from typing import Any, BinaryIO, Optional
from absl import logging
from etils import epath
import msgpack
from tensorflow.io import gfile
def pinned_checkpoint_filepath(ckpt_dir: str) -> str:
"""Full path of the pinned checkpoint file."""
return os.path.join(ckpt_dir, _PINNED_CHECKPOINT_FILEN... | Pin a checkpoint so it does not get deleted by the normal pruning process. Creates a PINNED file in the checkpoint directory to indicate the checkpoint should be excluded from the deletion of old checkpoints. Args: ckpt_dir: The checkpoint step dir that is to be always kept. txt: Text to be written into the checkpoints... |
151,595 | import enum
import os
from typing import Any, BinaryIO, Optional
from absl import logging
from etils import epath
import msgpack
from tensorflow.io import gfile
def pinned_checkpoint_filepath(ckpt_dir: str) -> str:
"""Full path of the pinned checkpoint file."""
return os.path.join(ckpt_dir, _PINNED_CHECKPOINT_FILEN... | Removes the pinned status of the checkpoint so it is open for deletion. |
151,596 | import enum
import os
from typing import Any, BinaryIO, Optional
from absl import logging
from etils import epath
import msgpack
from tensorflow.io import gfile
def is_pinned_checkpoint(ckpt_dir: str) -> bool:
"""Returns whether the checkpoint is pinned, and should NOT be removed."""
pinned_ckpt_file = pinned_check... | Removes the checkpoint dir if it is not pinned. |
151,597 | import enum
import os
from typing import Any, BinaryIO, Optional
from absl import logging
from etils import epath
import msgpack
from tensorflow.io import gfile
def is_pinned_checkpoint(ckpt_dir: str) -> bool:
"""Returns whether the checkpoint is pinned, and should NOT be removed."""
pinned_ckpt_file = pinned_check... | Removes dataset checkpoints if the checkpoint is not pinned. |
151,598 | import enum
import os
from typing import Any, BinaryIO, Optional
from absl import logging
from etils import epath
import msgpack
from tensorflow.io import gfile
def _read_msgpack_keys(file_like: BinaryIO) -> PyTree:
"""Returns a tree containing all keys but no values from a msgpack file."""
unpacker = msgpack.Unpac... | Returns the checkpoint type by reading the `.checkpoint` metadata file. Args: checkpoint_path: The path of the `.checkpoint` file. expected: The expected checkpoint type. If the checkpoint type is not as expected, this function will log a warning but will not raise an error. Returns: The checkpoint type. |
151,599 | import collections
import collections.abc
from concurrent.futures import thread
import contextlib
import dataclasses
import functools
import importlib
import inspect
import os
import re
import time
import typing
from typing import Any, Callable, Iterable, Mapping, Optional, Sequence, Tuple, Type, Union
import warnings
... | Create jax.Array from input arrays. Example: Consider a case where the global input array has length 128. The global mesh specifies that the data dimension be sharded into 8 shards. This means we want shards of length 16. The data_layout, defined by the partitioner object, specifies that the data should be divided into... |
151,600 | import collections
import collections.abc
from concurrent.futures import thread
import contextlib
import dataclasses
import functools
import importlib
import inspect
import os
import re
import time
import typing
from typing import Any, Callable, Iterable, Mapping, Optional, Sequence, Tuple, Type, Union
import warnings
... | Random uniform method that uses non-deterministic accelerator hardware. |
151,601 | import collections
import collections.abc
from concurrent.futures import thread
import contextlib
import dataclasses
import functools
import importlib
import inspect
import os
import re
import time
import typing
from typing import Any, Callable, Iterable, Mapping, Optional, Sequence, Tuple, Type, Union
import warnings
... | null |
151,602 | import collections
import collections.abc
from concurrent.futures import thread
import contextlib
import dataclasses
import functools
import importlib
import inspect
import os
import re
import time
import typing
from typing import Any, Callable, Iterable, Mapping, Optional, Sequence, Tuple, Type, Union
import warnings
... | Round up vocabulary size for improved TPU performance. |
151,603 | import collections
import collections.abc
from concurrent.futures import thread
import contextlib
import dataclasses
import functools
import importlib
import inspect
import os
import re
import time
import typing
from typing import Any, Callable, Iterable, Mapping, Optional, Sequence, Tuple, Type, Union
import warnings
... | Flattens a nested dictionary to have string keys and '/' separators. |
151,604 | import collections
import collections.abc
from concurrent.futures import thread
import contextlib
import dataclasses
import functools
import importlib
import inspect
import os
import re
import time
import typing
from typing import Any, Callable, Iterable, Mapping, Optional, Sequence, Tuple, Type, Union
import warnings
... | Applies parameter axis names overrides to axes variables. Args: model_variables: the original model variables containing the 'params_axes' collection. params_axes_names_override: a priority-ordered mapping from regex patterns (fully matching parameter names) to tuples containing string logical axis names to replace mod... |
151,605 | import functools
from typing import Callable, Mapping, Optional, Tuple, Union
import flax
import jax
from jax import lax
from jax import random
import jax.numpy as jnp
from t5x import binary_search
from t5x import decoding
from t5x.decoding import _is_tracer
from t5x.decoding import DecodingState
from t5x.decoding impo... | Temperature sampling for language model generation. The temperature sampling is performed `num_decodes` times in a vectorized manner by expanding the batch dimension. This is similar to how beam search expands the batch dimension to process each batch element with multiple beams. This function dynamically updates the `... |
151,606 | from typing import Any, Callable, Optional, Sequence, Tuple, Union
from absl import logging
import cached_property
from flax import core as flax_core
import jax
from jax.experimental.pjit import pjit
from jax.sharding import Mesh
import numpy as np
from t5x import adafactor
from t5x import optimizers
from t5x import pa... | Trivial MoE mesh for CPU Testing. |
151,607 | from typing import Any, Callable, Optional, Sequence, Tuple, Union
from absl import logging
import cached_property
from flax import core as flax_core
import jax
from jax.experimental.pjit import pjit
from jax.sharding import Mesh
import numpy as np
from t5x import adafactor
from t5x import optimizers
from t5x import pa... | Simple MoE mesh for GPUs. |
151,608 | from typing import Any, Callable, Optional, Sequence, Tuple, Union
from absl import logging
import cached_property
from flax import core as flax_core
import jax
from jax.experimental.pjit import pjit
from jax.sharding import Mesh
import numpy as np
from t5x import adafactor
from t5x import optimizers
from t5x import pa... | Construct default xmap/pjit mesh for MoE. Unlike the vanilla T5X mesh, this mesh has three resource axes: - 'expert': 1D submesh with length that divides into `num_expert_partitions`, - 'model': specified by the provided `model_parallel_submesh` shape, and - 'data', which covers the rest of the mesh. Relative to the va... |
151,609 | from typing import Any, Callable, Optional, Sequence, Tuple, Union
from absl import logging
import cached_property
from flax import core as flax_core
import jax
from jax.experimental.pjit import pjit
from jax.sharding import Mesh
import numpy as np
from t5x import adafactor
from t5x import optimizers
from t5x import pa... | Returns partitioning rules for MoE models. MoE params and state are partitioned along the 'expert' axis. Data is partitioned along both of the 'data' AND 'expert' axes. The partitioning rules vary based on whether the expert and data axes need to be decoupled; see also MoePjitPartitioner for details of when expert and ... |
151,610 | from typing import Any, Callable, Optional, Sequence, Tuple, Union
from absl import logging
import cached_property
from flax import core as flax_core
import jax
from jax.experimental.pjit import pjit
from jax.sharding import Mesh
import numpy as np
from t5x import adafactor
from t5x import optimizers
from t5x import pa... | Returns number of model partitions. Args: num_model_partitions: Specifies the size of the model parallel submesh. model_parallel_submesh: 4-tuple that specifies the `(x, y, z, c)` submesh model-parallel device tile Returns: Size of model parallel submesh. Raises: ValueError if neither num_model_partitions nor model_par... |
151,611 | from typing import Any, Callable, Optional, Sequence, Tuple, Union
from absl import logging
import cached_property
from flax import core as flax_core
import jax
from jax.experimental.pjit import pjit
from jax.sharding import Mesh
import numpy as np
from t5x import adafactor
from t5x import optimizers
from t5x import pa... | Override raw axis resources so data is sharded over 'data' & 'expert' axes. Here, we only override any raw partition specs that are hardcoded in T5X libraries: PartitionSpec('data',) -> PartitionSpec(('expert', 'data'),) NOTE: We do not (and there is no need) to override any params or optimizer state (which appear as l... |
151,612 | from typing import Any, Callable, Optional, Sequence, Tuple, Union
from absl import logging
import cached_property
from flax import core as flax_core
import jax
from jax.experimental.pjit import pjit
from jax.sharding import Mesh
import numpy as np
from t5x import adafactor
from t5x import optimizers
from t5x import pa... | Infers relevant regex matching sharded expert model state for train state. The model state generally inherits the correct partitioning specs from the model parameters. In such cases, no state_filter_fn is required. However, T5X's custom Adafactor optimizer, when factored, requires overrides to the `v_col` and `v_row` k... |
151,613 | from typing import Any, Callable, Dict, Mapping, MutableMapping, Optional, Tuple, Union
import clu.metrics as clu_metrics
from flax import core as flax_core
from flax import linen as nn
from flax import traverse_util
from flax.core import scope as flax_scope
import jax
import jax.numpy as jnp
import seqio
from t5x impo... | Computes combined cross-entropy and MoE auxiliary loss. |
151,614 | import os
from typing import Any, Optional, Union
import clu.data
import jax
from jax.experimental.array_serialization import serialization as array_serialization
from jax.experimental.pjit import pjit
import jax.numpy as jnp
import numpy as np
from t5x import checkpoint_importer
from t5x import checkpoints
from t5x im... | Reads array from tensorstore and handles broadcasting of expert weights. If both `mesh` and `axes` are provided, the method will attempt to restore the array as a GlobalDeviceArray. This method is adapted from _read_ts() in t5x/checkpoints.py. This variant broadcasts dense MLP weights from the checkpoint to the sparse,... |
151,615 | from flax import core as flax_core
from t5x import adafactor
FactorDim = adafactor.FactorDim
FrozenDict = flax_core.FrozenDict
The provided code snippet includes necessary dependencies for implementing the `logical_factor_rules` function. Write a Python function `def logical_factor_rules() -> FrozenDict` to solve the ... | Logical factor rules for Mixture of Experts. |
151,616 | import dataclasses
import re
from typing import Any, Callable, Iterable, Optional, Sequence, Tuple, Union
import flax
import jax
import numpy as np
from t5x import train_state
Gradients = Union[flax.core.FrozenDict, train_state.TrainState]
def _tree_flatten_with_names(
tree: ParamTree,
) -> Tuple[Sequence[Tuple[str... | Scales sharded grads, identified by sharded_match_fn, by scale_factor. Args: grads: Parameter gradients. sharded_match_fn: Filter function for distinguishing sharded parameters from replicated parameters. scale_factor: Amount by which to scale sharded parameter gradients. Returns: Gradients matching input, expect with ... |
151,617 | import dataclasses
import re
from typing import Any, Callable, Iterable, Optional, Sequence, Tuple, Union
import flax
import jax
import numpy as np
from t5x import train_state
def _tree_flatten_with_names(
tree: ParamTree,
) -> Tuple[Sequence[Tuple[str, Any]], jax.tree_util.PyTreeDef]:
"""Like jax.tree_util.tree_... | Like jax.tree_map but with a filter on the leaf path name. Args: f: The function to be applied to each parameter in `param_tree`. param_tree: The tree of parameters `f` should be applied to. match_name_fn: This function is called with each tree leave's path name, which has a path-like format ('a/b/c'), and decides whet... |
151,618 | import tensorflow_datasets as tfds
import tensorflow as tf
import io
import zstandard
import jsonlines
import os
import time
from itertools import chain
parser = json.Parser()
def json_parser(x):
global parser
try:
line = parser.parse(x).as_dict()
return line
except ValueError:
return x | null |
151,624 | import dataclasses
import functools
import operator
from typing import Any, Callable, Iterable, Optional, Sequence, Tuple, Union
from flax import linen as nn
from flax.linen import partitioning as nn_partitioning
import jax
from jax import lax
from jax import random
import jax.numpy as jnp
import numpy as np
Array = jn... | Compute the self-attention mask for a decoder. Decoder mask is formed by combining a causal mask, a padding mask and an optional packing mask. If decoder_causal_attention is passed, it makes the masking non-causal for positions that have value of 1. A prefix LM is applied to a dataset which has a notion of "inputs" and... |
151,625 | import abc
import enum
import os
import threading
import time
from typing import Any, Dict, Iterator, Mapping, MutableMapping, Optional, Protocol, Sequence, TYPE_CHECKING, Tuple, Union
from absl import logging
import cached_property
from clu import asynclib
from clu import metric_writers
import clu.data
import clu.metr... | Indirection to `time.time` for mocking. |
151,626 | import abc
import enum
import os
import threading
import time
from typing import Any, Dict, Iterator, Mapping, MutableMapping, Optional, Protocol, Sequence, TYPE_CHECKING, Tuple, Union
from absl import logging
import cached_property
from clu import asynclib
from clu import metric_writers
import clu.data
import clu.metr... | Default evaluation step. |
151,627 | import abc
import enum
import os
import threading
import time
from typing import Any, Dict, Iterator, Mapping, MutableMapping, Optional, Protocol, Sequence, TYPE_CHECKING, Tuple, Union
from absl import logging
import cached_property
from clu import asynclib
from clu import metric_writers
import clu.data
import clu.metr... | null |
151,628 | import os
from typing import Optional, Sequence, Union
from absl import app
from absl import logging
from clu import metric_writers
import gin
import jax
from t5x import utils
import tensorflow as tf
The provided code snippet includes necessary dependencies for implementing the `sum_fn` function. Write a Python functi... | sum function to use inside gin files. |
151,629 | import os
from typing import Optional, Sequence, Union
from absl import app
from absl import logging
from clu import metric_writers
import gin
import jax
from t5x import utils
import tensorflow as tf
The provided code snippet includes necessary dependencies for implementing the `bool_fn` function. Write a Python funct... | bool function to use inside gin files. |
151,630 | import os
from typing import Optional, Sequence, Union
from absl import app
from absl import logging
from clu import metric_writers
import gin
import jax
from t5x import utils
import tensorflow as tf
The provided code snippet includes necessary dependencies for implementing the `string_split_fn` function. Write a Pyth... | String split function to use inside gin files. |
151,631 | import abc
from collections.abc import Mapping, Sequence
import enum
import functools
import inspect
import itertools
import logging
import os
import re
from typing import Any, Callable, Iterator, Optional, Tuple, Union
import clu.data.dataset_iterator
import jax
from jax import random
from jax.experimental import mult... | Produces a list of batches that is `length` batches long. Given a single batch, repeat the batch `length` times. Given a list of batches, either repeat the batches to get `length` total batches or take the first 'length' batches. Args: batches: either a single batch of examples, or a list of batches. length: the total ... |
151,632 | import abc
from collections.abc import Mapping, Sequence
import enum
import functools
import inspect
import itertools
import logging
import os
import re
from typing import Any, Callable, Iterator, Optional, Tuple, Union
import clu.data.dataset_iterator
import jax
from jax import random
from jax.experimental import mult... | Returns a batch of examples from a provided SeqIO task. Args: task_or_mixture_name: the SeqIO task/mixture to read data from. split: the split of the SeqIO task/mixture to read data from. batch_size: how many examples should be in each batch. num_batches: the total number of batches to return. get_pretokenized_examples... |
151,633 | import abc
from collections.abc import Mapping, Sequence
import enum
import functools
import inspect
import itertools
import logging
import os
import re
from typing import Any, Callable, Iterator, Optional, Tuple, Union
import clu.data.dataset_iterator
import jax
from jax import random
from jax.experimental import mult... | Registers and returns a SeqIO task from the provided inputs. This function will be used to graduate people to the T5X/SeqIO-based train/infer/eval scripts. Args: task_name: the name of the SeqIO task to be created and registered. interactive_model: an instance of the InteractiveModel. examples: a single batch of exampl... |
151,634 | import abc
from collections.abc import Mapping, Sequence
import enum
import functools
import inspect
import itertools
import logging
import os
import re
from typing import Any, Callable, Iterator, Optional, Tuple, Union
import clu.data.dataset_iterator
import jax
from jax import random
from jax.experimental import mult... | Converts an InteractiveModel instance into a Gin config string. This function will be used to graduate people to the T5X/SeqIO-based train/infer/eval scripts. Args: interactive_model: an instance of the InteractiveModel. script_type: which T5X script the Gin config should function with. task_name: the name of the SeqIO... |
151,635 | import dataclasses
import functools
import operator
from typing import Any, Callable, Iterable, Optional, Sequence, Tuple, Union
from flax import linen as nn
from flax.linen import partitioning as nn_partitioning
import jax
from jax import lax
from jax import random
import jax.numpy as jnp
import numpy as np
def varian... | Initializer with in_axis, out_axis set at call time. |
151,636 | import dataclasses
import functools
import operator
from typing import Any, Callable, Iterable, Optional, Sequence, Tuple, Union
from flax import linen as nn
from flax.linen import partitioning as nn_partitioning
import jax
from jax import lax
from jax import random
import jax.numpy as jnp
import numpy as np
Array = jn... | Computes dot-product attention given query, key, and value. This is the core function for applying attention based on https://arxiv.org/abs/1706.03762. It calculates the attention weights given query and key and combines the values using the attention weights. Args: query: queries for calculating attention with shape o... |
151,639 | import dataclasses
import functools
import operator
from typing import Any, Callable, Iterable, Optional, Sequence, Tuple, Union
from flax import linen as nn
from flax.linen import partitioning as nn_partitioning
import jax
from jax import lax
from jax import random
import jax.numpy as jnp
import numpy as np
The provi... | Convert a string to an activation function. |
151,640 | import dataclasses
import functools
import operator
from typing import Any, Callable, Iterable, Optional, Sequence, Tuple, Union
from flax import linen as nn
from flax.linen import partitioning as nn_partitioning
import jax
from jax import lax
from jax import random
import jax.numpy as jnp
import numpy as np
Array = jn... | Combine attention biases. Args: *masks: set of attention bias arguments to combine, some can be None. Returns: Combined mask, reduced by summation, returns None if no masks given. |
151,641 | import dataclasses
import functools
import operator
from typing import Any, Callable, Iterable, Optional, Sequence, Tuple, Union
from flax import linen as nn
from flax.linen import partitioning as nn_partitioning
import jax
from jax import lax
from jax import random
import jax.numpy as jnp
import numpy as np
Array = jn... | Compute the self-attention mask for a decoder. Decoder mask is formed by combining a causal mask, a padding mask and an optional packing mask. If decoder_causal_attention is passed, it makes the masking non-causal for positions that have value of 1. A prefix LM is applied to a dataset which has a notion of "inputs" and... |
151,648 | import dataclasses
import functools
import operator
from typing import Any, Callable, Iterable, Optional, Sequence, Tuple, Union
from flax import linen as nn
import flax.core.variables as variables
from flax.linen import partitioning as nn_partitioning
from flax.training import common_utils
import jax
from jax import l... | Computes dot-product attention given query, key, and value. This is the core function for applying attention based on https://arxiv.org/abs/1706.03762. It calculates the attention weights given query and key and combines the values using the attention weights. Args: query: queries for calculating attention with shape o... |
151,649 | import dataclasses
import functools
import operator
from typing import Any, Callable, Iterable, Optional, Sequence, Tuple, Union
from flax import linen as nn
import flax.core.variables as variables
from flax.linen import partitioning as nn_partitioning
from flax.training import common_utils
import jax
from jax import l... | null |
151,650 | import dataclasses
import functools
import operator
from typing import Any, Callable, Iterable, Optional, Sequence, Tuple, Union
from flax import linen as nn
import flax.core.variables as variables
from flax.linen import partitioning as nn_partitioning
from flax.training import common_utils
import jax
from jax import l... | null |
151,651 | import dataclasses
import functools
import operator
from typing import Any, Callable, Iterable, Optional, Sequence, Tuple, Union
from flax import linen as nn
import flax.core.variables as variables
from flax.linen import partitioning as nn_partitioning
from flax.training import common_utils
import jax
from jax import l... | Convert a string to an activation function. |
151,652 | import dataclasses
import functools
import operator
from typing import Any, Callable, Iterable, Optional, Sequence, Tuple, Union
from flax import linen as nn
import flax.core.variables as variables
from flax.linen import partitioning as nn_partitioning
from flax.training import common_utils
import jax
from jax import l... | Combine attention biases. Args: *masks: set of attention bias arguments to combine, some can be None. Returns: Combined mask, reduced by summation, returns None if no masks given. |
151,653 | import dataclasses
import functools
import operator
from typing import Any, Callable, Iterable, Optional, Sequence, Tuple, Union
from flax import linen as nn
import flax.core.variables as variables
from flax.linen import partitioning as nn_partitioning
from flax.training import common_utils
import jax
from jax import l... | Compute the self-attention mask for a decoder. Decoder mask is formed by combining a causal mask, a padding mask and an optional packing mask. If decoder_causal_attention is passed, it makes the masking non-causal for positions that have value of 1. A prefix LM is applied to a dataset which has a notion of "inputs" and... |
151,654 | import abc
import dataclasses
import functools
import inspect
from typing import Any, Callable, Mapping, MutableMapping, Optional, Tuple, Union
from absl import logging
import clu.metrics as clu_metrics
from flax import core as flax_core
from flax import linen as nn
from flax.core import scope as flax_scope
from flax.l... | Remove the prefix portion and shift to the left by the prefix length. The example below uses non-decorated function definition, i.e., arrays do not have batch dimension. `jax.vmap` internally inserts the batch dimension at axis=0. The shape annotations do not include the batch dimension either. Example: ```python seque... |
151,655 | import abc
import dataclasses
import functools
import inspect
from typing import Any, Callable, Mapping, MutableMapping, Optional, Tuple, Union
from absl import logging
import clu.metrics as clu_metrics
from flax import core as flax_core
from flax import linen as nn
from flax.core import scope as flax_scope
from flax.l... | null |
151,656 | import abc
import dataclasses
import functools
import inspect
from typing import Any, Callable, Mapping, MutableMapping, Optional, Tuple, Union
from absl import logging
import clu.metrics as clu_metrics
from flax import core as flax_core
from flax import linen as nn
from flax.core import scope as flax_scope
from flax.l... | null |
151,657 | import os
from typing import Callable, Optional
import clu.data
import jax
from jax import random
import numpy as np
import t5.data.mixtures
from t5x import models
from t5x import partitioning
from t5x import trainer as trainer_lib
from t5x import utils
import tensorflow as tf
The provided code snippet includes neces... | Compiles and dump the HLO to model dir, with HLO text dumps. |
151,658 | import re
from typing import Any, Mapping, Optional, Sequence, Tuple
from absl import logging
from flax import traverse_util
def flatten_state_dict(state_dict, keep_empty_nodes: bool = False):
"""Flatten a dictionary until an array or tensorstore is reached.
Args:
state_dict: Optimizer state as nested dictionar... | Inserts new entries into `state_dict`. Args: state_dict: nested dict of optimizer state from_scratch_state: nested dict of entries to insert overwrite: if True, values present in both state_dict and from_scratch_state will be present in the result with the value taken from `from_scratch_state`. Returns: a nested dict l... |
151,659 | import re
from typing import Any, Mapping, Optional, Sequence, Tuple
from absl import logging
from flax import traverse_util
def flatten_state_dict(state_dict, keep_empty_nodes: bool = False):
"""Flatten a dictionary until an array or tensorstore is reached.
Args:
state_dict: Optimizer state as nested dictionar... | Applies an assignment map to a checkpoint optimizer state. In contrast to previous implementations, this has a switch whether to require that all rules match, and has somewhat-custom-but-sensible replacement rules: 1. old keys that are matched are removed. 2. old keys that don't match are retained. 3. if two new keys m... |
151,660 | import enum
from typing import Mapping, Optional, Tuple, Union
from flax.training import common_utils
import jax
import jax.numpy as jnp
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `_cross_entropy_with_logits_fwd` function. Write a Python function `def _cross_entro... | Forward-mode of `cross_entropy_with_logits`. |
151,661 | import enum
from typing import Mapping, Optional, Tuple, Union
from flax.training import common_utils
import jax
import jax.numpy as jnp
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `_cross_entropy_with_logits_bwd` function. Write a Python function `def _cross_entro... | Backward-mode of `cross_entropy_with_logits`. |
151,662 | import functools
from typing import Any, Callable, Mapping, Optional, Tuple, Union
import flax
from flax import traverse_util
import jax
from jax import lax
from jax import random
import jax.numpy as jnp
import numpy as np
from t5x import binary_search
class DecodingState:
"""Holds decoding state data.
Used to comm... | Temperature sampling for language model generation. The temperature sampling is performed `num_decodes` times in a vectorized manner by expanding the batch dimension. This is similar to how beam search expands the batch dimension to process each batch element with multiple beams. This function dynamically updates the `... |
151,663 | import functools
from typing import Any, Callable, Mapping, Optional, Tuple, Union
import flax
from flax import traverse_util
import jax
from jax import lax
from jax import random
import jax.numpy as jnp
import numpy as np
from t5x import binary_search
def _pick_last_prompt_token(prompts):
# prompts: i32[batch, leng... | null |
151,664 | import functools
from typing import Any, Callable, Mapping, Optional, Tuple, Union
import flax
from flax import traverse_util
import jax
from jax import lax
from jax import random
import jax.numpy as jnp
import numpy as np
from t5x import binary_search
NEG_INF = np.array(-1.0e7)
NEG_INF_VALUE = -1.0e7
class DecodingSta... | Beam search for transformer machine translation. If `inputs` has non-zero entries, those values are not modified, i.e., the sampled values for those positions are discarded. This simulates the teacher forcing on the prefix positions. NOTE: While using initial_index with prompts of variable lengths To comply with the ma... |
151,665 | import asyncio
import dataclasses
import functools
import os
import re
import subprocess
import time
from typing import Any, Dict, Iterable, List, Mapping, MutableMapping, Optional, Sequence, Tuple, Union
from absl import logging
import clu.data
from etils import epath
import flax
from flax import serialization
from fl... | Chooses a chunk shape that evenly divides write_shape. The chunk shape is chosen such that the total number of elements is less than or equal to `target_elements`, but is otherwise as large as possible. This uses a greedy algorithm that attempts to split the largest dimensions first. Args: write_shape: Write shape for ... |
151,666 | import asyncio
import dataclasses
import functools
import os
import re
import subprocess
import time
from typing import Any, Dict, Iterable, List, Mapping, MutableMapping, Optional, Sequence, Tuple, Union
from absl import logging
import clu.data
from etils import epath
import flax
from flax import serialization
from fl... | null |
151,667 | import asyncio
import dataclasses
import functools
import os
import re
import subprocess
import time
from typing import Any, Dict, Iterable, List, Mapping, MutableMapping, Optional, Sequence, Tuple, Union
from absl import logging
import clu.data
from etils import epath
import flax
from flax import serialization
from fl... | Returns available dataset checkpoint step numbers in ascending order. |
151,668 | import asyncio
import dataclasses
import functools
import os
import re
import subprocess
import time
from typing import Any, Dict, Iterable, List, Mapping, MutableMapping, Optional, Sequence, Tuple, Union
from absl import logging
import clu.data
from etils import epath
import flax
from flax import serialization
from fl... | Sync across all hosts/devices. |
151,669 | import asyncio
import dataclasses
import functools
import os
import re
import subprocess
import time
from typing import Any, Dict, Iterable, List, Mapping, MutableMapping, Optional, Sequence, Tuple, Union
from absl import logging
import clu.data
from etils import epath
import flax
from flax import serialization
from fl... | Returns a step number and the parent directory. |
151,670 | import asyncio
import dataclasses
import functools
import os
import re
import subprocess
import time
from typing import Any, Dict, Iterable, List, Mapping, MutableMapping, Optional, Sequence, Tuple, Union
from absl import logging
import clu.data
from etils import epath
import flax
from flax import serialization
from fl... | Get ts.Spec from array and name information. |
151,671 | import asyncio
import dataclasses
import functools
import os
import re
import subprocess
import time
from typing import Any, Dict, Iterable, List, Mapping, MutableMapping, Optional, Sequence, Tuple, Union
from absl import logging
import clu.data
from etils import epath
import flax
from flax import serialization
from fl... | Makes a sharded array from non-sharded array if necessary. Args: arr: array to maybe shard. mesh: jax.sharding.Mesh. axes: mesh_axes. restore_dtype: type to restore as. params_on_devices: If true, the array will be placed on device. Otherwise, it will be stored in the host(s) RAM. Returns: Sharded or unsharded array. |
151,672 | import asyncio
import dataclasses
import functools
import os
import re
import subprocess
import time
from typing import Any, Dict, Iterable, List, Mapping, MutableMapping, Optional, Sequence, Tuple, Union
from absl import logging
import clu.data
from etils import epath
import flax
from flax import serialization
from fl... | Iterate through summary event files and return metrics for `steps`. |
151,673 | import asyncio
import dataclasses
import functools
import os
import re
import subprocess
import time
from typing import Any, Dict, Iterable, List, Mapping, MutableMapping, Optional, Sequence, Tuple, Union
from absl import logging
import clu.data
from etils import epath
import flax
from flax import serialization
from fl... | Applies transformations to the state dict and parameter infos PyTrees. |
151,674 | import asyncio
import dataclasses
import functools
import os
import re
import subprocess
import time
from typing import Any, Dict, Iterable, List, Mapping, MutableMapping, Optional, Sequence, Tuple, Union
from absl import logging
import clu.data
from etils import epath
import flax
from flax import serialization
from fl... | null |
151,675 | import asyncio
import dataclasses
import functools
import os
import re
import subprocess
import time
from typing import Any, Dict, Iterable, List, Mapping, MutableMapping, Optional, Sequence, Tuple, Union
from absl import logging
import clu.data
from etils import epath
import flax
from flax import serialization
from fl... | Create SaveArgs for Orbax saving. |
151,676 | import asyncio
import dataclasses
import functools
import os
import re
import subprocess
import time
from typing import Any, Dict, Iterable, List, Mapping, MutableMapping, Optional, Sequence, Tuple, Union
from absl import logging
import clu.data
from etils import epath
import flax
from flax import serialization
from fl... | Create RestoreArgs for Orbax restoration. |
151,677 | import asyncio
import dataclasses
import functools
import os
import re
import subprocess
import time
from typing import Any, Dict, Iterable, List, Mapping, MutableMapping, Optional, Sequence, Tuple, Union
from absl import logging
import clu.data
from etils import epath
import flax
from flax import serialization
from fl... | Construct _OrbaxParamInfo tree for TrainState parameters. |
151,678 | import asyncio
import dataclasses
import functools
import os
import re
import subprocess
import time
from typing import Any, Dict, Iterable, List, Mapping, MutableMapping, Optional, Sequence, Tuple, Union
from absl import logging
import clu.data
from etils import epath
import flax
from flax import serialization
from fl... | Construct transformations and restoration arguments for Orbax classes. |
151,679 | import asyncio
import dataclasses
import functools
import os
import re
import subprocess
import time
from typing import Any, Dict, Iterable, List, Mapping, MutableMapping, Optional, Sequence, Tuple, Union
from absl import logging
import clu.data
from etils import epath
import flax
from flax import serialization
from fl... | Restore from a TensorFlow-based T5 checkpoint. |
151,680 | import enum
import re
import typing
from typing import Any, Mapping, Optional, Sequence, Tuple, Union
from absl import logging
from flax import struct
from flax.core import freeze
from flax.core import FrozenDict
from flax.core import unfreeze
from flax.serialization import from_state_dict
from flax.serialization impor... | null |
151,681 | import enum
import re
import typing
from typing import Any, Mapping, Optional, Sequence, Tuple, Union
from absl import logging
from flax import struct
from flax.core import freeze
from flax.core import FrozenDict
from flax.core import unfreeze
from flax.serialization import from_state_dict
from flax.serialization impor... | null |
151,682 | import enum
import re
import typing
from typing import Any, Mapping, Optional, Sequence, Tuple, Union
from absl import logging
from flax import struct
from flax.core import freeze
from flax.core import FrozenDict
from flax.core import unfreeze
from flax.serialization import from_state_dict
from flax.serialization impor... | null |
151,683 | import dataclasses
import functools
import inspect
import itertools
import json
import os
import os.path
import typing
from typing import Any, Callable, Dict, List, Mapping, Optional, Sequence, Tuple, Type, Union
from absl import logging
from flax.core import frozen_dict
import flax.traverse_util
import jax
from jax.ex... | Builds a function based on the config task to tokenize and batch the input text. |
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