FM / formatting

/usr/local/lib/python3.6/site-packages/datasets/formatting/

/usr/local/lib/python3.6/site-packages/datasets/formatting

Name	Size	Mode	Actions
dataset_wrappers/	-	0755	rm
__pycache__/	-	0755	rm
formatting.py	23158	0644	edit dl rm
jax_formatter.py	3000	0644	edit dl rm
tf_formatter.py	3479	0644	edit dl rm
torch_formatter.py	2561	0644	edit dl rm
__init__.py	5087	0644	edit dl rm

Edit: /usr/local/lib/python3.6/site-packages/datasets/formatting/jax_formatter.py (3000B)

# Lint as: python3
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from ..utils.py_utils import map_nested
from .formatting import Formatter

if TYPE_CHECKING:
    import jax.numpy as jnp

class JaxFormatter(Formatter[dict, "jnp.ndarray", dict]):
    def __init__(self, features=None, decoded=True, **jnp_array_kwargs):
        self.jnp_array_kwargs = jnp_array_kwargs
        import jax.numpy as jnp  # noqa import jax at initialization

def _tensorize(self, value):
        import jax
        import jax.numpy as jnp

default_dtype = {}
        if np.issubdtype(value.dtype, np.integer):
            # the default int precision depends on the jax config
            # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
            if jax.config.jax_enable_x64:
                default_dtype = {"dtype": jnp.int64}
            else:
                default_dtype = {"dtype": jnp.int32}
        elif np.issubdtype(value.dtype, np.floating):
            default_dtype = {"dtype": jnp.float32}

# calling jnp.array on a np.ndarray does copy the data
        # see https://github.com/google/jax/issues/4486
        return jnp.array(value, **{**default_dtype, **self.jnp_array_kwargs})

def _recursive_tensorize(self, data_struct: dict):
        # support for nested types like struct of list of struct
        if isinstance(data_struct, (list, np.ndarray)):
            data_struct = np.array(data_struct, copy=False)
            if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
                return [self.recursive_tensorize(substruct) for substruct in data_struct]
        return self._tensorize(data_struct)

def recursive_tensorize(self, data_struct: dict):
        return map_nested(self._recursive_tensorize, data_struct, map_list=False)

def format_row(self, pa_table: pa.Table) -> dict:
        row = self.numpy_arrow_extractor().extract_row(pa_table)
        return self.recursive_tensorize(row)

def format_column(self, pa_table: pa.Table) -> "jnp.ndarray":
        col = self.numpy_arrow_extractor().extract_column(pa_table)
        return self.recursive_tensorize(col)

def format_batch(self, pa_table: pa.Table) -> dict:
        batch = self.numpy_arrow_extractor().extract_batch(pa_table)
        return self.recursive_tensorize(batch)

# Copyright 2021 The HuggingFace 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. # Lint as: python3 from typing import TYPE_CHECKING import numpy as np import pyarrow as pa from ..utils.py_utils import map_nested from .formatting import Formatter if TYPE_CHECKING: import jax.numpy as jnp class JaxFormatter(Formatter[dict, "jnp.ndarray", dict]): def __init__(self, features=None, decoded=True, **jnp_array_kwargs): self.jnp_array_kwargs = jnp_array_kwargs import jax.numpy as jnp # noqa import jax at initialization def _tensorize(self, value): import jax import jax.numpy as jnp default_dtype = {} if np.issubdtype(value.dtype, np.integer): # the default int precision depends on the jax config # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision if jax.config.jax_enable_x64: default_dtype = {"dtype": jnp.int64} else: default_dtype = {"dtype": jnp.int32} elif np.issubdtype(value.dtype, np.floating): default_dtype = {"dtype": jnp.float32} # calling jnp.array on a np.ndarray does copy the data # see https://github.com/google/jax/issues/4486 return jnp.array(value, **{**default_dtype, **self.jnp_array_kwargs}) def _recursive_tensorize(self, data_struct: dict): # support for nested types like struct of list of struct if isinstance(data_struct, (list, np.ndarray)): data_struct = np.array(data_struct, copy=False) if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects return [self.recursive_tensorize(substruct) for substruct in data_struct] return self._tensorize(data_struct) def recursive_tensorize(self, data_struct: dict): return map_nested(self._recursive_tensorize, data_struct, map_list=False) def format_row(self, pa_table: pa.Table) -> dict: row = self.numpy_arrow_extractor().extract_row(pa_table) return self.recursive_tensorize(row) def format_column(self, pa_table: pa.Table) -> "jnp.ndarray": col = self.numpy_arrow_extractor().extract_column(pa_table) return self.recursive_tensorize(col) def format_batch(self, pa_table: pa.Table) -> dict: batch = self.numpy_arrow_extractor().extract_batch(pa_table) return self.recursive_tensorize(batch)