Skip to content

outlines_core

Backend class for Outlines Core.

OutlinesCoreBackend

Bases: BaseBackend

Backend for Outlines Core.

Source code in outlines/backends/outlines_core.py 
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
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
class OutlinesCoreBackend(BaseBackend):
    """Backend for Outlines Core."""

    def __init__(self, model: SteerableModel):
        """
        Parameters
        ----------
        model
            The Outlines model of the user.

        """
        if isinstance(model, Transformers):
            vocabulary = model.hf_tokenizer.get_vocab()
            eos_token_id = model.hf_tokenizer.eos_token_id
            eos_token = model.hf_tokenizer.eos_token
        elif isinstance(model, LlamaCpp):
            vocabulary = model.tokenizer.vocabulary
            eos_token_id = model.tokenizer.eos_token_id
            eos_token = model.tokenizer.eos_token
        elif isinstance(model, MLXLM):
            vocabulary = model.mlx_tokenizer._tokenizer.get_vocab()
            eos_token_id = model.mlx_tokenizer._tokenizer.eos_token_id
            eos_token = model.mlx_tokenizer._tokenizer.eos_token
        else:
            raise ValueError(f"Unsupported model type: {type(model)}")

        self.eos_token_id = eos_token_id
        self.vocabulary = self.create_outlines_core_vocabulary(
            vocabulary, eos_token_id, eos_token
        )
        self.tensor_library_name = model.tensor_library_name

    def get_json_schema_logits_processor(
        self, json_schema: str
    ):
        """Create a logits processor from a JSON schema.

        Parameters
        ----------
        json_schema: str
            The JSON schema to create a logits processor from.

        Returns
        -------
        LogitsProcessor
            The logits processor to use to constrain the generation.

        """
        regex = outlines_core.json_schema.build_regex_from_schema(json_schema)
        return self.get_regex_logits_processor(regex)

    def get_regex_logits_processor(self, regex: str):
        """Create a logits processor from a regex.

        Parameters
        ----------
        regex: str
            The regex to create a logits processor from.

        Returns
        -------
        LogitsProcessor
            The logits processor to use to constrain the generation.

        """
        index = Index(regex, self.vocabulary)
        return OutlinesCoreLogitsProcessor(index, self.tensor_library_name)

    def get_cfg_logits_processor(self, grammar):
        raise NotImplementedError(
            "Outlines Core does not support context-free grammar."
        )

    @staticmethod
    def create_outlines_core_vocabulary(
        vocab: Dict[str, int], eos_token_id: int, eos_token: str
    ) -> Vocabulary:
        """Create an Outlines Core Vocabulary instance.

        Parameters
        ----------
        vocab: Dict[str, int]
            The vocabulary to create an Outlines Core vocabulary from.
        eos_token_id: int
            The EOS token ID.
        eos_token: str
            The EOS token.

        Returns
        -------
        Vocabulary
            The Outlines Core Vocabulary instance.

        """
        formatted_vocab = {}
        for token, token_id in vocab.items():
            formatted_vocab[token] = [token_id]
        formatted_vocab.pop(eos_token)
        return Vocabulary(eos_token_id, formatted_vocab)

__init__(model)

Parameters:

Name Type Description Default
model SteerableModel

The Outlines model of the user.

 required
Source code in outlines/backends/outlines_core.py 
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
def __init__(self, model: SteerableModel):
    """
    Parameters
    ----------
    model
        The Outlines model of the user.

    """
    if isinstance(model, Transformers):
        vocabulary = model.hf_tokenizer.get_vocab()
        eos_token_id = model.hf_tokenizer.eos_token_id
        eos_token = model.hf_tokenizer.eos_token
    elif isinstance(model, LlamaCpp):
        vocabulary = model.tokenizer.vocabulary
        eos_token_id = model.tokenizer.eos_token_id
        eos_token = model.tokenizer.eos_token
    elif isinstance(model, MLXLM):
        vocabulary = model.mlx_tokenizer._tokenizer.get_vocab()
        eos_token_id = model.mlx_tokenizer._tokenizer.eos_token_id
        eos_token = model.mlx_tokenizer._tokenizer.eos_token
    else:
        raise ValueError(f"Unsupported model type: {type(model)}")

    self.eos_token_id = eos_token_id
    self.vocabulary = self.create_outlines_core_vocabulary(
        vocabulary, eos_token_id, eos_token
    )
    self.tensor_library_name = model.tensor_library_name

create_outlines_core_vocabulary(vocab, eos_token_id, eos_token) staticmethod

Create an Outlines Core Vocabulary instance.

Parameters:

Name Type Description Default
vocab Dict[str, int]

The vocabulary to create an Outlines Core vocabulary from.

 required
eos_token_id int

The EOS token ID.

 required
eos_token str

The EOS token.

 required

Returns:

Type Description
Vocabulary

The Outlines Core Vocabulary instance.
Source code in outlines/backends/outlines_core.py 
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
@staticmethod
def create_outlines_core_vocabulary(
    vocab: Dict[str, int], eos_token_id: int, eos_token: str
) -> Vocabulary:
    """Create an Outlines Core Vocabulary instance.

    Parameters
    ----------
    vocab: Dict[str, int]
        The vocabulary to create an Outlines Core vocabulary from.
    eos_token_id: int
        The EOS token ID.
    eos_token: str
        The EOS token.

    Returns
    -------
    Vocabulary
        The Outlines Core Vocabulary instance.

    """
    formatted_vocab = {}
    for token, token_id in vocab.items():
        formatted_vocab[token] = [token_id]
    formatted_vocab.pop(eos_token)
    return Vocabulary(eos_token_id, formatted_vocab)

get_json_schema_logits_processor(json_schema)

Create a logits processor from a JSON schema.

Parameters:

Name Type Description Default
json_schema str

The JSON schema to create a logits processor from.

 required

Returns:

Type Description
LogitsProcessor

The logits processor to use to constrain the generation.
Source code in outlines/backends/outlines_core.py 
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
def get_json_schema_logits_processor(
    self, json_schema: str
):
    """Create a logits processor from a JSON schema.

    Parameters
    ----------
    json_schema: str
        The JSON schema to create a logits processor from.

    Returns
    -------
    LogitsProcessor
        The logits processor to use to constrain the generation.

    """
    regex = outlines_core.json_schema.build_regex_from_schema(json_schema)
    return self.get_regex_logits_processor(regex)

get_regex_logits_processor(regex)

Create a logits processor from a regex.

Parameters:

Name Type Description Default
regex str

The regex to create a logits processor from.

 required

Returns:

Type Description
LogitsProcessor

The logits processor to use to constrain the generation.
Source code in outlines/backends/outlines_core.py 
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
def get_regex_logits_processor(self, regex: str):
    """Create a logits processor from a regex.

    Parameters
    ----------
    regex: str
        The regex to create a logits processor from.

    Returns
    -------
    LogitsProcessor
        The logits processor to use to constrain the generation.

    """
    index = Index(regex, self.vocabulary)
    return OutlinesCoreLogitsProcessor(index, self.tensor_library_name)

OutlinesCoreLogitsProcessor

Bases: OutlinesLogitsProcessor

Logits processor for Outlines Core.

Source code in outlines/backends/outlines_core.py 
 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
class OutlinesCoreLogitsProcessor(OutlinesLogitsProcessor):
    """Logits processor for Outlines Core."""

    def __init__(
        self, index: Index, tensor_library_name: str
    ):
        """
        Parameters
        ----------
        index: Index
            The Outlines Core `Index` instance to use to create the Outlines
            Core `Guide` instances that will be used to bias the logits
        tensor_library_name: str
            The tensor library name to use for the logits processor.

        """
        self.index = index
        self.tensor_library_name = tensor_library_name
        self.is_first_token = True
        super().__init__(tensor_library_name)

    def reset(self) -> None:
        """Reset the logits processor."""
        self.is_first_token = True

    def _setup(self, batch_size: int, vocab_size: int) -> None:
        """Set the guides, bitmasks and some functions used in the
        `process_logits` method.

        This method is called when the first token is generated instead of
        at initialization because we need to know the batch size.

        """
        if self.tensor_library_name == "torch":
            from outlines_core.kernels.torch import allocate_token_bitmask

            self.allocate_token_bitmask = allocate_token_bitmask
            self.bias_logits = self._bias_logits_torch

        elif self.tensor_library_name == "numpy":
            from outlines_core.kernels.numpy import allocate_token_bitmask

            self.allocate_token_bitmask = allocate_token_bitmask
            self.bias_logits = self._bias_logits_numpy

        elif self.tensor_library_name == "mlx":
            from outlines_core.kernels.mlx import (
                allocate_token_bitmask
            )

            self.allocate_token_bitmask = allocate_token_bitmask
            self.bias_logits = self._bias_logits_mlx

        else:
            raise ValueError(
                f"Unsupported tensor library: {self.tensor_library_name}"
            )

        self._guides = [Guide(self.index) for _ in range(batch_size)]
        self._bitmasks = [
            self.allocate_token_bitmask(vocab_size)
            for _ in range(batch_size)
        ]

    def _bias_logits_mlx( # pragma: no cover
        self, batch_size: int, logits: TensorType
    ) -> TensorType:
        """Bias the logits for MLX tensors."""
        from outlines_core.kernels.mlx import (
            apply_token_bitmask,
            fill_next_token_bitmask
        )

        biased_logits_array = []
        for i in range(batch_size):
            fill_next_token_bitmask(self._guides[i], self._bitmasks[i])
            biased_logits = apply_token_bitmask(
                self.tensor_adapter.unsqueeze(logits[i]), self._bitmasks[i] # type: ignore
            )
            biased_logits_array.append(biased_logits)

        return self.tensor_adapter.concatenate(biased_logits_array)

    def _bias_logits_torch(
        self, batch_size: int, logits: TensorType
    ) -> TensorType:
        """Bias the logits for Torch tensors."""
        from outlines_core.kernels.torch import (
            apply_token_bitmask_inplace,
            fill_next_token_bitmask
        )

        for i in range(batch_size):
            fill_next_token_bitmask(self._guides[i], self._bitmasks[i])
            apply_token_bitmask_inplace(
                self.tensor_adapter.unsqueeze(logits[i]), # type: ignore
                self._bitmasks[i]
            )

        return logits

    def _bias_logits_numpy(
        self, batch_size: int, logits: TensorType
    ) -> TensorType:
        """Bias the logits for Numpy tensors."""
        from outlines_core.kernels.numpy import (
            apply_token_bitmask_inplace,
            fill_next_token_bitmask
        )

        for i in range(batch_size):
            fill_next_token_bitmask(self._guides[i], self._bitmasks[i])
            apply_token_bitmask_inplace(
                self.tensor_adapter.unsqueeze(logits[i]), # type: ignore
                self._bitmasks[i]
            )

        return logits

    def process_logits(
        self, input_ids: TensorType, logits: TensorType
    ) -> TensorType:
        """Use the guides to bias the logits.

        Parameters
        ----------
        input_ids
            The ids of the tokens of the existing sequences.
        logits
            The logits for the current generation step.

        Returns
        -------
        TensorType
            The biased logits.

        """
        batch_size = self.tensor_adapter.shape(input_ids)[0]
        vocab_size = self.tensor_adapter.shape(logits)[1]

        if self.is_first_token:
            self._setup(batch_size, vocab_size)
            self.is_first_token = False
        else:
            for i in range(batch_size):
                last_token_id = self.tensor_adapter.to_scalar(input_ids[i][-1]) # type: ignore
                if not self._guides[i].is_finished():
                    self._guides[i].advance(
                        token_id=last_token_id,
                        return_tokens=False
                    )

        return self.bias_logits(batch_size, logits)

__init__(index, tensor_library_name)

Parameters:

Name Type Description Default
index Index

The Outlines Core Index instance to use to create the Outlines Core Guide instances that will be used to bias the logits

 required
tensor_library_name str

The tensor library name to use for the logits processor.

 required
Source code in outlines/backends/outlines_core.py 
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
def __init__(
    self, index: Index, tensor_library_name: str
):
    """
    Parameters
    ----------
    index: Index
        The Outlines Core `Index` instance to use to create the Outlines
        Core `Guide` instances that will be used to bias the logits
    tensor_library_name: str
        The tensor library name to use for the logits processor.

    """
    self.index = index
    self.tensor_library_name = tensor_library_name
    self.is_first_token = True
    super().__init__(tensor_library_name)

process_logits(input_ids, logits)

Use the guides to bias the logits.

Parameters:

Name Type Description Default
input_ids TensorType

The ids of the tokens of the existing sequences.

 required
logits TensorType

The logits for the current generation step.

 required

Returns:

Type Description
TensorType

The biased logits.
Source code in outlines/backends/outlines_core.py 
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
def process_logits(
    self, input_ids: TensorType, logits: TensorType
) -> TensorType:
    """Use the guides to bias the logits.

    Parameters
    ----------
    input_ids
        The ids of the tokens of the existing sequences.
    logits
        The logits for the current generation step.

    Returns
    -------
    TensorType
        The biased logits.

    """
    batch_size = self.tensor_adapter.shape(input_ids)[0]
    vocab_size = self.tensor_adapter.shape(logits)[1]

    if self.is_first_token:
        self._setup(batch_size, vocab_size)
        self.is_first_token = False
    else:
        for i in range(batch_size):
            last_token_id = self.tensor_adapter.to_scalar(input_ids[i][-1]) # type: ignore
            if not self._guides[i].is_finished():
                self._guides[i].advance(
                    token_id=last_token_id,
                    return_tokens=False
                )

    return self.bias_logits(batch_size, logits)

reset()

Reset the logits processor.

Source code in outlines/backends/outlines_core.py 
41
42
43
def reset(self) -> None:
    """Reset the logits processor."""
    self.is_first_token = True