id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
22,724 | import contextlib
import dataclasses
import importlib.util
import inspect
import typing as tp
from abc import ABCMeta
from copy import copy
from functools import partial
from types import MappingProxyType
import jax
import numpy as np
from flax.experimental.nnx.nnx import module as modulelib
from flax.experimental.nnx.... | null |
22,725 | import contextlib
import dataclasses
import importlib.util
import inspect
import typing as tp
from abc import ABCMeta
from copy import copy
from functools import partial
from types import MappingProxyType
import jax
import numpy as np
from flax.experimental.nnx.nnx import module as modulelib
from flax.experimental.nnx.... | null |
22,726 | from __future__ import annotations
import dataclasses
import typing as tp
import typing_extensions as tpe
def _identity(x):
return x | null |
22,727 | import dataclasses
import functools
import typing as tp
from abc import ABCMeta
from functools import partial
from typing import Any
import jax
import jax.tree_util as jtu
from flax.experimental.nnx.nnx import reprlib, tracers
from flax.experimental import nnx
jtu.register_pytree_node(
Empty,
lambda empty: ((), Non... | null |
22,728 | import dataclasses
import functools
import typing as tp
from abc import ABCMeta
from functools import partial
from typing import Any
import jax
import jax.tree_util as jtu
from flax.experimental.nnx.nnx import reprlib, tracers
from flax.experimental import nnx
A = tp.TypeVar('A')
class Variable(tp.Generic[A], reprlib.R... | null |
22,729 | import dataclasses
import functools
import typing as tp
from abc import ABCMeta
from functools import partial
from typing import Any
import jax
import jax.tree_util as jtu
from flax.experimental.nnx.nnx import reprlib, tracers
from flax.experimental import nnx
VariableTypeCache: dict[str, tp.Type['Variable[tp.Any]']] =... | null |
22,730 | from __future__ import annotations
import dataclasses
import functools
import typing as tp
import jax
from flax.experimental.nnx.nnx import errors, filterlib, tracers
class ForkedKeys(tp.Mapping[str, jax.Array]):
def __init__(
self,
broadcast_rngs: dict[str, jax.Array],
split_rngs: dict[str, jax.Array],
... | null |
22,731 | from __future__ import annotations
import dataclasses
import functools
import typing as tp
import jax
from flax.experimental.nnx.nnx import errors, filterlib, tracers
class ForkedKeys(tp.Mapping[str, jax.Array]):
def __init__(
self,
broadcast_rngs: dict[str, jax.Array],
split_rngs: dict[str, jax.Array],
... | null |
22,732 | import jax
import jax.core
from jax.core import MainTrace
from flax.experimental.nnx.nnx import reprlib
def get_top_trace(pytree: tp.Union[tp.Any, Tracer]) -> MainTrace:
"""Returns the main top trace of a sequence of tracers."""
if isinstance(pytree, Tracer):
return pytree._trace.main
return jax.core.find_top... | Returns the innermost Jax tracer. |
22,733 | import contextlib
import dataclasses
import threading
import typing as tp
from abc import ABC, abstractmethod
class Object:
type: tp.Union[str, type]
start: str = '('
end: str = ')'
value_sep: str = '='
elem_indent: str = ' '
empty_repr: str = ''
class Attr:
key: str
value: tp.Union[str, tp.Any]
star... | null |
22,734 | from __future__ import annotations
import inspect
import typing as tp
import jax
import jax.numpy as jnp
import numpy as np
import optax
from flax.experimental.nnx.nnx import pytreelib
from flax.experimental.nnx.nnx.module import GraphDef, Module
from flax.experimental.nnx.nnx.proxy_caller import ApplyCaller
from flax.... | Return True if func has keyword-only arguments with the given name. |
22,735 | from __future__ import annotations
import dataclasses
import enum
import typing as tp
import jax
from flax.experimental.nnx.nnx import filterlib, reprlib, tracers
from flax.experimental.nnx.nnx.proxy_caller import (
ApplyCaller,
CallableProxy,
DelayedAccessor,
)
from flax.experimental.nnx.nnx.state import State
f... | null |
22,736 | from __future__ import annotations
import dataclasses
import enum
import typing as tp
import jax
from flax.experimental.nnx.nnx import filterlib, reprlib, tracers
from flax.experimental.nnx.nnx.proxy_caller import (
ApplyCaller,
CallableProxy,
DelayedAccessor,
)
from flax.experimental.nnx.nnx.state import State
f... | null |
22,737 | from __future__ import annotations
import dataclasses
import enum
import typing as tp
import jax
from flax.experimental.nnx.nnx import filterlib, reprlib, tracers
from flax.experimental.nnx.nnx.proxy_caller import (
ApplyCaller,
CallableProxy,
DelayedAccessor,
)
from flax.experimental.nnx.nnx.state import State
f... | null |
22,738 | from __future__ import annotations
import dataclasses
import enum
import typing as tp
import jax
from flax.experimental.nnx.nnx import filterlib, reprlib, tracers
from flax.experimental.nnx.nnx.proxy_caller import (
ApplyCaller,
CallableProxy,
DelayedAccessor,
)
from flax.experimental.nnx.nnx.state import State
f... | null |
22,739 | from __future__ import annotations
import dataclasses
import enum
import typing as tp
import jax
from flax.experimental.nnx.nnx import filterlib, reprlib, tracers
from flax.experimental.nnx.nnx.proxy_caller import (
ApplyCaller,
CallableProxy,
DelayedAccessor,
)
from flax.experimental.nnx.nnx.state import State
f... | Unflattens a graphdef into a node with the given state. Args: graphdef: A GraphDef instance. state: A State instance. ref_cache: A mapping from indexes to existing nodes that can be reused. When an reference is reused, ``GraphNodeImpl.clear`` is called to leave the object in an empty state and then filled by the unflat... |
22,740 | from __future__ import annotations
import dataclasses
import enum
import typing as tp
import jax
from flax.experimental.nnx.nnx import filterlib, reprlib, tracers
from flax.experimental.nnx.nnx.proxy_caller import (
ApplyCaller,
CallableProxy,
DelayedAccessor,
)
from flax.experimental.nnx.nnx.state import State
f... | null |
22,741 | from __future__ import annotations
import dataclasses
import enum
import typing as tp
import jax
from flax.experimental.nnx.nnx import filterlib, reprlib, tracers
from flax.experimental.nnx.nnx.proxy_caller import (
ApplyCaller,
CallableProxy,
DelayedAccessor,
)
from flax.experimental.nnx.nnx.state import State
f... | null |
22,742 | from __future__ import annotations
import dataclasses
import enum
import typing as tp
import jax
from flax.experimental.nnx.nnx import filterlib, reprlib, tracers
from flax.experimental.nnx.nnx.proxy_caller import (
ApplyCaller,
CallableProxy,
DelayedAccessor,
)
from flax.experimental.nnx.nnx.state import State
f... | null |
22,743 | from __future__ import annotations
import dataclasses
import enum
import typing as tp
import jax
from flax.experimental.nnx.nnx import filterlib, reprlib, tracers
from flax.experimental.nnx.nnx.proxy_caller import (
ApplyCaller,
CallableProxy,
DelayedAccessor,
)
from flax.experimental.nnx.nnx.state import State
f... | null |
22,744 | from __future__ import annotations
import dataclasses
import enum
import typing as tp
import jax
from flax.experimental.nnx.nnx import filterlib, reprlib, tracers
from flax.experimental.nnx.nnx.proxy_caller import (
ApplyCaller,
CallableProxy,
DelayedAccessor,
)
from flax.experimental.nnx.nnx.state import State
f... | null |
22,745 | from __future__ import annotations
import dataclasses
import enum
import typing as tp
import jax
from flax.experimental.nnx.nnx import filterlib, reprlib, tracers
from flax.experimental.nnx.nnx.proxy_caller import (
ApplyCaller,
CallableProxy,
DelayedAccessor,
)
from flax.experimental.nnx.nnx.state import State
f... | null |
22,746 | from __future__ import annotations
import dataclasses
import enum
import typing as tp
import jax
from flax.experimental.nnx.nnx import filterlib, reprlib, tracers
from flax.experimental.nnx.nnx.proxy_caller import (
ApplyCaller,
CallableProxy,
DelayedAccessor,
)
from flax.experimental.nnx.nnx.state import State
f... | null |
22,747 | from __future__ import annotations
import dataclasses
import enum
import typing as tp
import jax
from flax.experimental.nnx.nnx import filterlib, reprlib, tracers
from flax.experimental.nnx.nnx.proxy_caller import (
ApplyCaller,
CallableProxy,
DelayedAccessor,
)
from flax.experimental.nnx.nnx.state import State
f... | null |
22,748 | from __future__ import annotations
import dataclasses
import enum
import typing as tp
import jax
from flax.experimental.nnx.nnx import filterlib, reprlib, tracers
from flax.experimental.nnx.nnx.proxy_caller import (
ApplyCaller,
CallableProxy,
DelayedAccessor,
)
from flax.experimental.nnx.nnx.state import State
f... | null |
22,749 | from __future__ import annotations
import dataclasses
import enum
import typing as tp
import jax
from flax.experimental.nnx.nnx import filterlib, reprlib, tracers
from flax.experimental.nnx.nnx.proxy_caller import (
ApplyCaller,
CallableProxy,
DelayedAccessor,
)
from flax.experimental.nnx.nnx.state import State
f... | null |
22,750 | from __future__ import annotations
import dataclasses
import enum
import typing as tp
import jax
from flax.experimental.nnx.nnx import filterlib, reprlib, tracers
from flax.experimental.nnx.nnx.proxy_caller import (
ApplyCaller,
CallableProxy,
DelayedAccessor,
)
from flax.experimental.nnx.nnx.state import State
f... | null |
22,751 | from __future__ import annotations
import dataclasses
import enum
import typing as tp
import jax
from flax.experimental.nnx.nnx import filterlib, reprlib, tracers
from flax.experimental.nnx.nnx.proxy_caller import (
ApplyCaller,
CallableProxy,
DelayedAccessor,
)
from flax.experimental.nnx.nnx.state import State
f... | null |
22,752 | from __future__ import annotations
import dataclasses
import enum
import typing as tp
import jax
from flax.experimental.nnx.nnx import filterlib, reprlib, tracers
from flax.experimental.nnx.nnx.proxy_caller import (
ApplyCaller,
CallableProxy,
DelayedAccessor,
)
from flax.experimental.nnx.nnx.state import State
f... | null |
22,753 | from __future__ import annotations
import dataclasses
import enum
import typing as tp
import jax
from flax.experimental.nnx.nnx import filterlib, reprlib, tracers
from flax.experimental.nnx.nnx.proxy_caller import (
ApplyCaller,
CallableProxy,
DelayedAccessor,
)
from flax.experimental.nnx.nnx.state import State
f... | null |
22,754 | from __future__ import annotations
import dataclasses
import enum
import typing as tp
import jax
from flax.experimental.nnx.nnx import filterlib, reprlib, tracers
from flax.experimental.nnx.nnx.proxy_caller import (
ApplyCaller,
CallableProxy,
DelayedAccessor,
)
from flax.experimental.nnx.nnx.state import State
f... | null |
22,755 | from __future__ import annotations
import dataclasses
import enum
import typing as tp
import jax
from flax.experimental.nnx.nnx import filterlib, reprlib, tracers
from flax.experimental.nnx.nnx.proxy_caller import (
ApplyCaller,
CallableProxy,
DelayedAccessor,
)
from flax.experimental.nnx.nnx.state import State
f... | null |
22,756 | from __future__ import annotations
import dataclasses
import enum
import typing as tp
import jax
from flax.experimental.nnx.nnx import filterlib, reprlib, tracers
from flax.experimental.nnx.nnx.proxy_caller import (
ApplyCaller,
CallableProxy,
DelayedAccessor,
)
from flax.experimental.nnx.nnx.state import State
f... | null |
22,757 | from __future__ import annotations
import dataclasses
import enum
import typing as tp
import jax
from flax.experimental.nnx.nnx import filterlib, reprlib, tracers
from flax.experimental.nnx.nnx.proxy_caller import (
ApplyCaller,
CallableProxy,
DelayedAccessor,
)
from flax.experimental.nnx.nnx.state import State
f... | null |
22,758 | from __future__ import annotations
import dataclasses
import enum
import typing as tp
import jax
from flax.experimental.nnx.nnx import filterlib, reprlib, tracers
from flax.experimental.nnx.nnx.proxy_caller import (
ApplyCaller,
CallableProxy,
DelayedAccessor,
)
from flax.experimental.nnx.nnx.state import State
f... | null |
22,759 | from __future__ import annotations
import dataclasses
import enum
import typing as tp
import jax
from flax.experimental.nnx.nnx import filterlib, reprlib, tracers
from flax.experimental.nnx.nnx.proxy_caller import (
ApplyCaller,
CallableProxy,
DelayedAccessor,
)
from flax.experimental.nnx.nnx.state import State
f... | null |
22,760 | from __future__ import annotations
import dataclasses
import enum
import typing as tp
import jax
from flax.experimental.nnx.nnx import filterlib, reprlib, tracers
from flax.experimental.nnx.nnx.proxy_caller import (
ApplyCaller,
CallableProxy,
DelayedAccessor,
)
from flax.experimental.nnx.nnx.state import State
f... | null |
22,761 | from __future__ import annotations
import typing as tp
import jax
import jax.tree_util as jtu
from flax import traverse_util
from flax.experimental.nnx.nnx import filterlib, reprlib
from flax.experimental.nnx.nnx.variables import Variable
from flax.typing import Path
class State(tp.MutableMapping[Key, tp.Any], reprlib.... | null |
22,762 | from __future__ import annotations
import typing as tp
import jax
import jax.tree_util as jtu
from flax import traverse_util
from flax.experimental.nnx.nnx import filterlib, reprlib
from flax.experimental.nnx.nnx.variables import Variable
from flax.typing import Path
class State(tp.MutableMapping[Key, tp.Any], reprlib.... | null |
22,763 | from __future__ import annotations
import typing as tp
import jax
import jax.tree_util as jtu
from flax import traverse_util
from flax.experimental.nnx.nnx import filterlib, reprlib
from flax.experimental.nnx.nnx.variables import Variable
from flax.typing import Path
class State(tp.MutableMapping[Key, tp.Any], reprlib.... | null |
22,764 | from __future__ import annotations
import typing as tp
import jax
import jax.tree_util as jtu
from flax import traverse_util
from flax.experimental.nnx.nnx import filterlib, reprlib
from flax.experimental.nnx.nnx.variables import Variable
from flax.typing import Path
FlatState = dict[Path, Variable[Variable]]
class Sta... | null |
22,765 | import dataclasses
import typing as tp
from typing import Any
from flax import linen
from flax.experimental.nnx.nnx import variables as variableslib
from flax.experimental.nnx.nnx.module import GraphDef, Module
from flax.experimental.nnx.nnx.rnglib import Rngs
from flax.experimental.nnx.nnx.state import State
M = tp.Ty... | null |
22,766 | from __future__ import annotations
import dataclasses
import typing as tp
from abc import ABCMeta
from copy import deepcopy
from functools import partial
import jax
import jax.tree_util as jtu
import numpy as np
import typing_extensions as tpe
from flax.experimental.nnx.nnx import (
errors,
filterlib,
graph_utils... | null |
22,767 | from __future__ import annotations
import dataclasses
import typing as tp
from abc import ABCMeta
from copy import deepcopy
from functools import partial
import jax
import jax.tree_util as jtu
import numpy as np
import typing_extensions as tpe
from flax.experimental.nnx.nnx import (
errors,
filterlib,
graph_utils... | null |
22,768 | from __future__ import annotations
import dataclasses
import typing as tp
from abc import ABCMeta
from copy import deepcopy
from functools import partial
import jax
import jax.tree_util as jtu
import numpy as np
import typing_extensions as tpe
from flax.experimental.nnx.nnx import (
errors,
filterlib,
graph_utils... | null |
22,769 | from __future__ import annotations
import dataclasses
import typing as tp
from abc import ABCMeta
from copy import deepcopy
from functools import partial
import jax
import jax.tree_util as jtu
import numpy as np
import typing_extensions as tpe
from flax.experimental.nnx.nnx import (
errors,
filterlib,
graph_utils... | null |
22,770 | from __future__ import annotations
import dataclasses
import typing as tp
from abc import ABCMeta
from copy import deepcopy
from functools import partial
import jax
import jax.tree_util as jtu
import numpy as np
import typing_extensions as tpe
from flax.experimental.nnx.nnx import (
errors,
filterlib,
graph_utils... | null |
22,771 | from __future__ import annotations
import dataclasses
import typing as tp
from abc import ABCMeta
from copy import deepcopy
from functools import partial
import jax
import jax.tree_util as jtu
import numpy as np
import typing_extensions as tpe
from flax.experimental.nnx.nnx import (
errors,
filterlib,
graph_utils... | null |
22,772 | from __future__ import annotations
import dataclasses
import typing as tp
from abc import ABCMeta
from copy import deepcopy
from functools import partial
import jax
import jax.tree_util as jtu
import numpy as np
import typing_extensions as tpe
from flax.experimental.nnx.nnx import (
errors,
filterlib,
graph_utils... | null |
22,773 | from __future__ import annotations
import dataclasses
import typing as tp
from abc import ABCMeta
from copy import deepcopy
from functools import partial
import jax
import jax.tree_util as jtu
import numpy as np
import typing_extensions as tpe
from flax.experimental.nnx.nnx import (
errors,
filterlib,
graph_utils... | Return the first non-None argument. If all arguments are None, raise a ValueError with the given error message. Args: *args: the arguments to check error_msg: the error message to raise if all arguments are None Returns: The first non-None argument. |
22,774 | import collections
import dataclasses
import os
import input_pipeline
import jax
import jax.numpy as jnp
import models
import numpy as np
import optax
import temperature_sampler
import tensorflow as tf
import utils
from absl import logging
from clu import metric_writers, periodic_actions
from configs import default
fro... | Execute psum on in_tree"s leaves over one device per host. |
22,775 | import collections
import dataclasses
import os
import input_pipeline
import jax
import jax.numpy as jnp
import models
import numpy as np
import optax
import temperature_sampler
import tensorflow as tf
import utils
from absl import logging
from clu import metric_writers, periodic_actions
from configs import default
fro... | Runs a training and evaluation loop. Args: config: Configuration to use. workdir: Working directory for checkpoints and TF summaries. If this contains checkpoint training will be resumed from the latest checkpoint. |
22,776 | import dataclasses
import os
import tempfile
import time
from typing import Any, Dict, Iterable, Tuple
import jax
import tensorflow as tf
import tensorflow_text as tftxt
from absl import logging
from sentencepiece import SentencePieceTrainer
def _train_sentencepiece(
dataset: tf.data.Dataset,
*,
vocab_size: int,
... | Loads the tokenizer at `vocab_path` or trains a one from `dataset`. |
22,777 | from __future__ import annotations
import dataclasses
from typing import Any, Optional
import jax
import jax.numpy as jnp
import numpy as np
from jax import lax
from flax.experimental import nnx
from flax.experimental.nnx.examples.lm1b.configs import default
def shift_right(x: jax.Array, axis: int = 1):
"""Shift the ... | Shift inputs and replace EOS by 0 for packed inputs. |
22,778 | from __future__ import annotations
import dataclasses
from typing import Any, Optional
import jax
import jax.numpy as jnp
import numpy as np
from jax import lax
from flax.experimental import nnx
from flax.experimental.nnx.examples.lm1b.configs import default
The provided code snippet includes necessary dependencies fo... | 1D Sinusoidal Position Embedding Initializer. Args: max_len: maximum possible length for the input. min_scale: float: minimum frequency-scale in sine grating. max_scale: float: maximum frequency-scale in sine grating. Returns: output: init function returning `(1, max_len, d_feature)` |
22,779 | import jax.numpy as jnp
from jax import lax, random
EOS_ID = 2
The provided code snippet includes necessary dependencies for implementing the `temperature_sample` function. Write a Python function `def temperature_sample( prompt_inputs, init_cache, tokens_to_logits, prng_key, temperature=1.0, topk=20, eo... | Temperature sampling for language model generation. Args: prompt_inputs: array: [batch_size, max_decode_len] int32 sequence of tokens. init_cache: flax attention cache. tokens_to_logits: fast autoregressive decoder function taking single token slices and cache and returning next-token logits and updated cache. prng_key... |
22,780 | from __future__ import annotations
import dataclasses
class Config:
# Path to load or store sentencepiece vocab file.
vocab_path: str | None = None
# Vocabulary size if `vocab_path` is not given.
vocab_size: int = 30_000
# Maximum number of characters to use for training.
max_corpus_chars: int = 10**7
# N... | Get the default hyperparameter configuration. |
22,781 | import os
from typing import Dict, List, Optional, Union
import tensorflow as tf
import tensorflow_datasets as tfds
import tokenizer
from clu import deterministic_data
from configs import default
AUTOTUNE = tf.data.experimental.AUTOTUNE
def get_raw_dataset(
dataset_builder: tfds.core.DatasetBuilder, split: str
) -> t... | Load and return dataset of batched examples for use during training. |
22,782 | import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
import optax
from flax.experimental import nnx
X = np.linspace(0, 1, 100)[:, None]
Y = 0.8 * X**2 + 0.1 + np.random.normal(0, 0.1, size=X.shape)
def dataset(batch_size):
while True:
idx = np.random.choice(len(X), size=batch_siz... | null |
22,783 | import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
import optax
from flax.experimental import nnx
class Count(nnx.Variable[nnx.A]):
pass
class MLP(nnx.Module):
def __init__(self, din, dhidden, dout, *, rngs: nnx.Rngs):
self.count = Count(jnp.array(0))
self.linear1 = Linear... | null |
22,784 | import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
import optax
from flax.experimental import nnx
class MLP(nnx.Module):
def __init__(self, din, dhidden, dout, *, rngs: nnx.Rngs):
self.count = Count(jnp.array(0))
self.linear1 = Linear(din, dhidden, rngs=rngs)
self.linear... | null |
22,785 | import jax
from flax.experimental import nnx
def load_pretrained():
return nnx.Linear(784, 128, rngs=nnx.Rngs(0)) | null |
22,786 | import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
from flax.experimental import nnx
X = np.linspace(0, 1, 100)[:, None]
Y = 0.8 * X**2 + 0.1 + np.random.normal(0, 0.1, size=X.shape)
def dataset(batch_size):
while True:
idx = np.random.choice(len(X), size=batch_size)
yield ... | null |
22,787 | import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
from flax.experimental import nnx
class MLP(nnx.Module):
def __init__(self, din, dhidden, dout, *, rngs: nnx.Rngs):
self.count = Count(jnp.array(0))
self.linear1 = Linear(din, dhidden, rngs=rngs)
self.linear2 = Linear(dh... | null |
22,788 | import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
from flax.experimental import nnx
class MLP(nnx.Module):
def __init__(self, din, dhidden, dout, *, rngs: nnx.Rngs):
self.count = Count(jnp.array(0))
self.linear1 = Linear(din, dhidden, rngs=rngs)
self.linear2 = Linear(dh... | null |
22,789 | from tempfile import TemporaryDirectory
import jax
import jax.numpy as jnp
import orbax.checkpoint as orbax
from flax.experimental import nnx
def create_model(seed: int):
return MLP(10, 20, 30, rngs=nnx.Rngs(seed))
def create_and_save(seed: int, path: str):
model = create_model(seed)
state = model.get_state()
... | null |
22,790 | from tempfile import TemporaryDirectory
import jax
import jax.numpy as jnp
import orbax.checkpoint as orbax
from flax.experimental import nnx
class MLP(nnx.Module):
def __init__(self, din: int, dmid: int, dout: int, *, rngs: nnx.Rngs):
def __call__(self, x: jax.Array) -> jax.Array:
def create_model(seed: int)... | null |
22,792 | import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
from flax.experimental import nnx
class Count(nnx.Variable[nnx.A]):
pass
y_pred = model(X)
def train_step(params, counts, batch):
x, y = batch
def loss_fn(params):
y_pred, (updates, _) = modeldef.apply(params, counts)(x)
... | null |
22,793 | import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
from flax.experimental import nnx
y_pred = model(X)
def test_step(params: nnx.State, counts: nnx.State, batch):
x, y = batch
y_pred, _ = modeldef.apply(params, counts)(x)
loss = jnp.mean((y - y_pred) ** 2)
return {'loss': los... | null |
22,794 | import dataclasses
import typing as tp
import jax
import jax.numpy as jnp
import numpy as np
from jax.sharding import PartitionSpec as P
from flax.experimental import nnx
The provided code snippet includes necessary dependencies for implementing the `nd_dense_init` function. Write a Python function `def nd_dense_init(... | Initializer with in_axis, out_axis set at call time. |
22,795 | import dataclasses
import typing as tp
import jax
import jax.numpy as jnp
import numpy as np
from jax.sharding import PartitionSpec as P
from flax.experimental import nnx
def make_attention_mask(
query_input: tp.Any,
key_input: tp.Any,
pairwise_fn: tp.Callable = jnp.multiply,
dtype: tp.Any = jnp.float32,
):
m... | null |
22,796 | import dataclasses
import typing as tp
import jax
import jax.numpy as jnp
import numpy as np
from jax.sharding import PartitionSpec as P
from flax.experimental import nnx
def sine_table(features, length, min_timescale=1.0, max_timescale=10000.0):
fraction = jnp.arange(0, features, 2, dtype=jnp.float32) / features
... | null |
22,797 | import dataclasses
import typing as tp
import jax
import jax.numpy as jnp
import numpy as np
from jax.sharding import PartitionSpec as P
from flax.experimental import nnx
def rotate_half(x):
x1, x2 = jnp.split(x, 2, axis=-1)
x = jnp.concatenate([-x2, x1], axis=-1)
return x
The provided code snippet includes nece... | Helper function to apply Rotary Embeddings. |
22,798 | import dataclasses
import typing as tp
import jax
import jax.numpy as jnp
import numpy as np
from jax.sharding import PartitionSpec as P
from flax.experimental import nnx
def rms_norm(cfg, scale, x):
x = jnp.asarray(x, jnp.float32)
mean2 = jnp.mean(jax.lax.square(x), axis=-1, keepdims=True)
y = jnp.asarray(x * j... | null |
22,799 | import dataclasses
import typing as tp
import jax
import jax.numpy as jnp
import numpy as np
from jax.sharding import PartitionSpec as P
from flax.experimental import nnx
class Config:
def dropout(cfg: Config, x, broadcast_dims=(-2,), *, rngs: nnx.Rngs):
if cfg.dropout_rate == 0.0:
return x
broadcast_shape = l... | null |
22,800 | import typing as tp
from functools import partial
import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
import optax
from datasets import load_dataset
from flax.experimental import nnx
class Loss(nnx.Variable):
pass
class VAE(nnx.Module):
def __init__(
self,
din: int,
hid... | null |
22,801 | import typing as tp
from functools import partial
import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
import optax
from datasets import load_dataset
from flax.experimental import nnx
class VAE(nnx.Module):
def __init__(
self,
din: int,
hidden_size: int,
latent_size: i... | null |
22,802 | import typing as tp
from functools import partial
import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
import optax
from datasets import load_dataset
from flax.experimental import nnx
class VAE(nnx.Module):
def __init__(
self,
din: int,
hidden_size: int,
latent_size: i... | null |
22,803 | import typing as tp
from functools import partial
import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
import optax
from datasets import load_dataset
from flax.experimental import nnx
def diff_round(x) -> jax.Array:
diff_round.defvjp(diff_round_fwd, diff_round_bwd)
def diff_round_fwd(x... | null |
22,804 | import typing as tp
from functools import partial
import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
import optax
from datasets import load_dataset
from flax.experimental import nnx
def diff_round_bwd(_, g):
return (g,) | null |
22,805 | import typing as tp
from functools import partial
import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
import optax
from datasets import load_dataset
from flax.experimental import nnx
def diff_clip(x, low, high) -> jax.Array:
return jnp.clip(x, low, high)
diff_clip.defvjp(diff_clip_fw... | null |
22,806 | import typing as tp
from functools import partial
import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
import optax
from datasets import load_dataset
from flax.experimental import nnx
def diff_clip_bwd(_, _1, _2, dy):
return (dy,) | null |
22,807 | import typing as tp
from functools import partial
import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
import optax
from datasets import load_dataset
from flax.experimental import nnx
def diff_round(x) -> jax.Array:
y = jnp.round(x)
return y
diff_round.defvjp(diff_round_fwd, diff_ro... | null |
22,808 | import typing as tp
from functools import partial
import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
import optax
from datasets import load_dataset
from flax.experimental import nnx
class MLP(nnx.Module):
def __init__(self, din: int, dmid: int, dout: int, *, rngs: nnx.Rngs):
sel... | null |
22,809 | import typing as tp
from functools import partial
import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
import optax
from datasets import load_dataset
from flax.experimental import nnx
class MLP(nnx.Module):
def __init__(self, din: int, dmid: int, dout: int, *, rngs: nnx.Rngs):
sel... | null |
22,810 | import typing as tp
from functools import partial
import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
import optax
from datasets import load_dataset
from flax.experimental import nnx
class MLP(nnx.Module):
def __init__(self, din: int, dmid: int, dout: int, *, rngs: nnx.Rngs):
sel... | null |
22,811 | import typing as tp
from functools import partial
import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import numpy as np
import optax
from datasets import load_dataset
from flax.experimental import nnx
x = jnp.linspace(-1.5, 1.5, 100)
print('X_train:', X_train.shape, X_train.dtype)
print('X_test:', X_tes... | null |
22,812 | import abc
import copy
import dataclasses
import warnings
from typing import Any, Callable
import jax
import flax
from flax.core.scope import VariableDict
from flax.typing import PathParts
from . import struct
empty_node = _EmptyNode()
def flatten_dict(xs, keep_empty_nodes=False, is_leaf=None, sep=None):
"""Flatten a... | A map function that operates over nested dictionary structures while taking the path to each leaf into account. Example:: >>> import jax.numpy as jnp >>> from flax import traverse_util >>> params = {'a': {'x': 10, 'y': 3}, 'b': {'x': 20}} >>> f = lambda path, x: x + 5 if 'x' in path else -x >>> traverse_util.path_aware... |
22,813 | import abc
import copy
import dataclasses
import warnings
from typing import Any, Callable
import jax
import flax
from flax.core.scope import VariableDict
from flax.typing import PathParts
from . import struct
def _is_namedtuple(t):
return issubclass(t, tuple) and hasattr(t, '_fields') | null |
22,814 | import abc
import copy
import dataclasses
import warnings
from typing import Any, Callable
import jax
import flax
from flax.core.scope import VariableDict
from flax.typing import PathParts
from . import struct
def _get_params_dict(inputs):
if isinstance(inputs, (dict, flax.core.FrozenDict)):
return flax.core.unf... | null |
22,815 | import setuptools
with open("README.md", "r", encoding="utf8") as fh:
long_description = fh.read()
def _get_version():
with open('rlcard/__init__.py') as f:
for line in f:
if line.startswith('__version__'):
g = {}
exec(line, g)
return g['__ver... | null |
22,816 | import os
import argparse
import torch
import rlcard
from rlcard.agents import RandomAgent
from rlcard.utils import (
get_device,
set_seed,
tournament,
reorganize,
Logger,
plot_curve,
)
def train(args):
# Check whether gpu is available
device = get_device()
# Seed numpy, t... | null |
22,817 | import os
import argparse
import torch
from pettingzoo.classic import (
leduc_holdem_v4,
texas_holdem_v4,
texas_holdem_no_limit_v6,
gin_rummy_v4,
)
from rlcard.agents.pettingzoo_agents import RandomAgentPettingZoo
from rlcard.utils import (
get_device,
set_seed,
Logger,
plot_curve,
... | null |
22,818 | import os
import argparse
from pettingzoo.classic import (
leduc_holdem_v4,
texas_holdem_v4,
dou_dizhu_v4,
mahjong_v4,
texas_holdem_no_limit_v6,
uno_v4,
gin_rummy_v4,
)
from rlcard.agents.dmc_agent import DMCTrainer
env_name_to_env_func = {
"leduc-holdem": leduc_holdem_v4,
"limit-hol... | null |
22,819 | import argparse
import pprint
import rlcard
from rlcard.agents import RandomAgent
from rlcard.utils import set_seed
def run(args):
# Make environment
env = rlcard.make(
args.env,
config={
'seed': 42,
}
)
# Seed numpy, torch, random
set_seed(42)
# Set agents... | null |
22,820 | from typing import TYPE_CHECKING
import rlcard
from rlcard.agents import RandomAgent
from rlcard.models.gin_rummy_rule_models import GinRummyNoviceRuleAgent
from rlcard.agents.human_agents.gin_rummy_human_agent.gin_rummy_human_agent import HumanAgent
from rlcard.agents.human_agents.gin_rummy_human_agent.gui_gin_rummy.g... | null |
22,821 | import os
import argparse
import torch
import rlcard
from rlcard.agents.dmc_agent import DMCTrainer
def train(args):
# Make the environment
env = rlcard.make(args.env)
# Initialize the DMC trainer
trainer = DMCTrainer(
env,
cuda=args.cuda,
load_model=args.load_model,
x... | null |
22,822 | import os
import argparse
import rlcard
from rlcard.agents import (
DQNAgent,
RandomAgent,
)
from rlcard.utils import (
get_device,
set_seed,
tournament,
)
def load_model(model_path, env=None, position=None, device=None):
if os.path.isfile(model_path): # Torch model
import torch
... | null |
22,823 | import os
import argparse
import rlcard
from rlcard.agents import (
CFRAgent,
RandomAgent,
)
from rlcard.utils import (
set_seed,
tournament,
Logger,
plot_curve,
)
def train(args):
# Make environments, CFR only supports Leduc Holdem
env = rlcard.make(
'leduc-holdem',
con... | null |
22,824 | from rlcard.utils.utils import print_card
def print_card(cards):
''' Nicely print a card or list of cards
Args:
card (string or list): The card(s) to be printed
'''
if cards is None:
cards = [None]
if isinstance(cards, str):
cards = [cards]
lines = [[] for _ in range(9... | Print out the state Args: state (dict): A dictionary of the raw state action_record (list): A list of the historical actions |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.