source
stringclasses 470
values | url
stringlengths 49
167
| file_type
stringclasses 1
value | chunk
stringlengths 1
512
| chunk_id
stringlengths 5
9
|
|---|---|---|---|---|
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#gptqconfig | .md | use_cuda_fp16 (`bool`, *optional*, defaults to `False`):
Whether or not to use optimized cuda kernel for fp16 model. Need to have model in fp16.
model_seqlen (`int`, *optional*):
The maximum sequence length that the model can take.
block_name_to_quantize (`str`, *optional*):
The transformers block name to quantize. If None, we will infer the block name using common patterns (e.g. model.layers)
module_name_preceding_first_block (`List[str]`, *optional*): | 468_7_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#gptqconfig | .md | module_name_preceding_first_block (`List[str]`, *optional*):
The layers that are preceding the first Transformer block.
batch_size (`int`, *optional*, defaults to 1):
The batch size used when processing the dataset
pad_token_id (`int`, *optional*):
The pad token id. Needed to prepare the dataset when `batch_size` > 1.
use_exllama (`bool`, *optional*):
Whether to use exllama backend. Defaults to `True` if unset. Only works with `bits` = 4.
max_input_length (`int`, *optional*): | 468_7_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#gptqconfig | .md | Whether to use exllama backend. Defaults to `True` if unset. Only works with `bits` = 4.
max_input_length (`int`, *optional*):
The maximum input length. This is needed to initialize a buffer that depends on the maximum expected input
length. It is specific to the exllama backend with act-order.
exllama_config (`Dict[str, Any]`, *optional*):
The exllama config. You can specify the version of the exllama kernel through the `version` key. Defaults
to `{"version": 1}` if unset. | 468_7_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#gptqconfig | .md | to `{"version": 1}` if unset.
cache_block_outputs (`bool`, *optional*, defaults to `True`):
Whether to cache block outputs to reuse as inputs for the succeeding block.
modules_in_block_to_quantize (`List[List[str]]`, *optional*):
List of list of module names to quantize in the specified block. This argument is useful to exclude certain linear modules from being quantized. | 468_7_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#gptqconfig | .md | The block to quantize can be specified by setting `block_name_to_quantize`. We will quantize each list sequentially. If not set, we will quantize all linear layers.
Example: `modules_in_block_to_quantize =[["self_attn.k_proj", "self_attn.v_proj", "self_attn.q_proj"], ["self_attn.o_proj"]]`.
In this example, we will first quantize the q,k,v layers simultaneously since they are independent.
Then, we will quantize `self_attn.o_proj` layer with the q,k,v layers quantized. This way, we will get | 468_7_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#gptqconfig | .md | Then, we will quantize `self_attn.o_proj` layer with the q,k,v layers quantized. This way, we will get
better results since it reflects the real input `self_attn.o_proj` will get when the model is quantized. | 468_7_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#bitsandbytesconfig | .md | This is a wrapper class about all possible attributes and features that you can play with a model that has been
loaded using `bitsandbytes`.
This replaces `load_in_8bit` or `load_in_4bit`therefore both options are mutually exclusive.
Currently only supports `LLM.int8()`, `FP4`, and `NF4` quantization. If more methods are added to `bitsandbytes`,
then more arguments will be added to this class.
Args:
load_in_8bit (`bool`, *optional*, defaults to `False`): | 468_8_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#bitsandbytesconfig | .md | then more arguments will be added to this class.
Args:
load_in_8bit (`bool`, *optional*, defaults to `False`):
This flag is used to enable 8-bit quantization with LLM.int8().
load_in_4bit (`bool`, *optional*, defaults to `False`):
This flag is used to enable 4-bit quantization by replacing the Linear layers with FP4/NF4 layers from
`bitsandbytes`.
llm_int8_threshold (`float`, *optional*, defaults to 6.0): | 468_8_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#bitsandbytesconfig | .md | `bitsandbytes`.
llm_int8_threshold (`float`, *optional*, defaults to 6.0):
This corresponds to the outlier threshold for outlier detection as described in `LLM.int8() : 8-bit Matrix
Multiplication for Transformers at Scale` paper: https://arxiv.org/abs/2208.07339 Any hidden states value
that is above this threshold will be considered an outlier and the operation on those values will be done
in fp16. Values are usually normally distributed, that is, most values are in the range [-3.5, 3.5], but | 468_8_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#bitsandbytesconfig | .md | in fp16. Values are usually normally distributed, that is, most values are in the range [-3.5, 3.5], but
there are some exceptional systematic outliers that are very differently distributed for large models.
These outliers are often in the interval [-60, -6] or [6, 60]. Int8 quantization works well for values of
magnitude ~5, but beyond that, there is a significant performance penalty. A good default threshold is 6,
but a lower threshold might be needed for more unstable models (small models, fine-tuning). | 468_8_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#bitsandbytesconfig | .md | but a lower threshold might be needed for more unstable models (small models, fine-tuning).
llm_int8_skip_modules (`List[str]`, *optional*):
An explicit list of the modules that we do not want to convert in 8-bit. This is useful for models such as
Jukebox that has several heads in different places and not necessarily at the last position. For example
for `CausalLM` models, the last `lm_head` is kept in its original `dtype`.
llm_int8_enable_fp32_cpu_offload (`bool`, *optional*, defaults to `False`): | 468_8_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#bitsandbytesconfig | .md | llm_int8_enable_fp32_cpu_offload (`bool`, *optional*, defaults to `False`):
This flag is used for advanced use cases and users that are aware of this feature. If you want to split
your model in different parts and run some parts in int8 on GPU and some parts in fp32 on CPU, you can use
this flag. This is useful for offloading large models such as `google/flan-t5-xxl`. Note that the int8
operations will not be run on CPU.
llm_int8_has_fp16_weight (`bool`, *optional*, defaults to `False`): | 468_8_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#bitsandbytesconfig | .md | operations will not be run on CPU.
llm_int8_has_fp16_weight (`bool`, *optional*, defaults to `False`):
This flag runs LLM.int8() with 16-bit main weights. This is useful for fine-tuning as the weights do not
have to be converted back and forth for the backward pass.
bnb_4bit_compute_dtype (`torch.dtype` or str, *optional*, defaults to `torch.float32`):
This sets the computational type which might be different than the input type. For example, inputs might be | 468_8_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#bitsandbytesconfig | .md | This sets the computational type which might be different than the input type. For example, inputs might be
fp32, but computation can be set to bf16 for speedups.
bnb_4bit_quant_type (`str`, *optional*, defaults to `"fp4"`):
This sets the quantization data type in the bnb.nn.Linear4Bit layers. Options are FP4 and NF4 data types
which are specified by `fp4` or `nf4`.
bnb_4bit_use_double_quant (`bool`, *optional*, defaults to `False`): | 468_8_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#bitsandbytesconfig | .md | which are specified by `fp4` or `nf4`.
bnb_4bit_use_double_quant (`bool`, *optional*, defaults to `False`):
This flag is used for nested quantization where the quantization constants from the first quantization are
quantized again.
bnb_4bit_quant_storage (`torch.dtype` or str, *optional*, defaults to `torch.uint8`):
This sets the storage type to pack the quanitzed 4-bit prarams.
kwargs (`Dict[str, Any]`, *optional*):
Additional parameters from which to initialize the configuration object. | 468_8_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#hfquantizer | .md | quantizers.base.HfQuantizer
Abstract class of the HuggingFace quantizer. Supports for now quantizing HF transformers models for inference and/or quantization.
This class is used only for transformers.PreTrainedModel.from_pretrained and cannot be easily used outside the scope of that method
yet.
Attributes
quantization_config (`transformers.utils.quantization_config.QuantizationConfigMixin`):
The quantization config that defines the quantization parameters of your model that you want to quantize. | 468_9_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#hfquantizer | .md | The quantization config that defines the quantization parameters of your model that you want to quantize.
modules_to_not_convert (`List[str]`, *optional*):
The list of module names to not convert when quantizing the model.
required_packages (`List[str]`, *optional*):
The list of required pip packages to install prior to using the quantizer
requires_calibration (`bool`):
Whether the quantization method requires to calibrate the model before using it.
requires_parameters_quantization (`bool`): | 468_9_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#hfquantizer | .md | Whether the quantization method requires to calibrate the model before using it.
requires_parameters_quantization (`bool`):
Whether the quantization method requires to create a new Parameter. For example, for bitsandbytes, it is
required to create a new xxxParameter in order to properly quantize the model. | 468_9_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#higgsconfig | .md | HiggsConfig is a configuration class for quantization using the HIGGS method.
Args:
bits (int, *optional*, defaults to 4):
Number of bits to use for quantization. Can be 2, 3 or 4. Default is 4.
p (int, *optional*, defaults to 2):
Quantization grid dimension. 1 and 2 are supported. 2 is always better in practice. Default is 2.
modules_to_not_convert (`list`, *optional*, default to ["lm_head"]):
List of linear layers that should not be quantized.
hadamard_size (int, *optional*, defaults to 512): | 468_10_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#higgsconfig | .md | List of linear layers that should not be quantized.
hadamard_size (int, *optional*, defaults to 512):
Hadamard size for the HIGGS method. Default is 512. Input dimension of matrices is padded to this value. Decreasing this below 512 will reduce the quality of the quantization.
group_size (int, *optional*, defaults to 256):
Group size for the HIGGS method. Can be 64, 128 or 256. Decreasing it barely affects the performance. Default is 256. Must be a divisor of hadamard_size. | 468_10_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#hqqconfig | .md | This is wrapper around hqq's BaseQuantizeConfig.
Args:
nbits (`int`, *optional*, defaults to 4):
Number of bits. Supported values are (8, 4, 3, 2, 1).
group_size (`int`, *optional*, defaults to 64):
Group-size value. Supported values are any value that is divisble by weight.shape[axis]).
view_as_float (`bool`, *optional*, defaults to `False`):
View the quantized weight as float (used in distributed training) if set to `True`.
axis (`Optional[int]`, *optional*): | 468_11_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#hqqconfig | .md | View the quantized weight as float (used in distributed training) if set to `True`.
axis (`Optional[int]`, *optional*):
Axis along which grouping is performed. Supported values are 0 or 1.
dynamic_config (dict, *optional*):
Parameters for dynamic configuration. The key is the name tag of the layer and the value is a quantization config.
If set, each layer specified by its id will use its dedicated quantization configuration.
skip_modules (`List[str]`, *optional*, defaults to `['lm_head']`): | 468_11_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#hqqconfig | .md | skip_modules (`List[str]`, *optional*, defaults to `['lm_head']`):
List of `nn.Linear` layers to skip.
kwargs (`Dict[str, Any]`, *optional*):
Additional parameters from which to initialize the configuration object. | 468_11_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#fbgemmfp8config | .md | This is a wrapper class about all possible attributes and features that you can play with a model that has been
loaded using fbgemm fp8 quantization.
Args:
activation_scale_ub (`float`, *optional*, defaults to 1200.0):
The activation scale upper bound. This is used when quantizing the input activation.
modules_to_not_convert (`list`, *optional*, default to `None`):
The list of modules to not quantize, useful for quantizing models that explicitly require to have | 468_12_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#fbgemmfp8config | .md | The list of modules to not quantize, useful for quantizing models that explicitly require to have
some modules left in their original precision. | 468_12_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#compressedtensorsconfig | .md | This is a wrapper class that handles compressed-tensors quantization config options.
It is a wrapper around `compressed_tensors.QuantizationConfig`
Args:
config_groups (`typing.Dict[str, typing.Union[ForwardRef('QuantizationScheme'), typing.List[str]]]`, *optional*):
dictionary mapping group name to a quantization scheme definition
format (`str`, *optional*, defaults to `"dense"`):
format the model is represented as. Set `run_compressed` True to execute model as the
compressed format if not `dense` | 468_13_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#compressedtensorsconfig | .md | format the model is represented as. Set `run_compressed` True to execute model as the
compressed format if not `dense`
quantization_status (`QuantizationStatus`, *optional*, defaults to `"initialized"`):
status of model in the quantization lifecycle, ie 'initialized', 'calibration', 'frozen'
kv_cache_scheme (`typing.Union[QuantizationArgs, NoneType]`, *optional*):
specifies quantization of the kv cache. If None, kv cache is not quantized. | 468_13_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#compressedtensorsconfig | .md | specifies quantization of the kv cache. If None, kv cache is not quantized.
global_compression_ratio (`typing.Union[float, NoneType]`, *optional*):
0-1 float percentage of model compression
ignore (`typing.Union[typing.List[str], NoneType]`, *optional*):
layer names or types to not quantize, supports regex prefixed by 're:'
sparsity_config (`typing.Dict[str, typing.Any]`, *optional*):
configuration for sparsity compression
quant_method (`str`, *optional*, defaults to `"compressed-tensors"`): | 468_13_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#compressedtensorsconfig | .md | configuration for sparsity compression
quant_method (`str`, *optional*, defaults to `"compressed-tensors"`):
do not override, should be compressed-tensors
run_compressed (`bool`, *optional*, defaults to `True`): alter submodules (usually linear) in order to
emulate compressed model execution if True, otherwise use default submodule | 468_13_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#torchaoconfig | .md | This is a config class for torchao quantization/sparsity techniques.
Args:
quant_type (`str`):
The type of quantization we want to use, currently supporting: `int4_weight_only`, `int8_weight_only` and `int8_dynamic_activation_int8_weight`.
modules_to_not_convert (`list`, *optional*, default to `None`):
The list of modules to not quantize, useful for quantizing models that explicitly require to have
some modules left in their original precision.
kwargs (`Dict[str, Any]`, *optional*): | 468_14_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#torchaoconfig | .md | some modules left in their original precision.
kwargs (`Dict[str, Any]`, *optional*):
The keyword arguments for the chosen type of quantization, for example, int4_weight_only quantization supports two keyword arguments
`group_size` and `inner_k_tiles` currently. More API examples and documentation of arguments can be found in
https://github.com/pytorch/ao/tree/main/torchao/quantization#other-available-quantization-techniques
Example:
```python | 468_14_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#torchaoconfig | .md | https://github.com/pytorch/ao/tree/main/torchao/quantization#other-available-quantization-techniques
Example:
```python
quantization_config = TorchAoConfig("int4_weight_only", group_size=32)
# int4_weight_only quant is only working with *torch.bfloat16* dtype right now
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda", torch_dtype=torch.bfloat16, quantization_config=quantization_config)
``` | 468_14_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/quantization.md | https://huggingface.co/docs/transformers/en/main_classes/quantization/#bitnetconfig | .md | BitNetConfig(modules_to_not_convert: Optional[List] = None, **kwargs) | 468_15_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/onnx.md | https://huggingface.co/docs/transformers/en/main_classes/onnx/ | .md | <!--Copyright 2020 The HuggingFace Team. All rights reserved.
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 | 469_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/onnx.md | https://huggingface.co/docs/transformers/en/main_classes/onnx/ | .md | 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.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
--> | 469_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/onnx.md | https://huggingface.co/docs/transformers/en/main_classes/onnx/#exporting--transformers-models-to-onnx | .md | 🤗 Transformers provides a `transformers.onnx` package that enables you to
convert model checkpoints to an ONNX graph by leveraging configuration objects.
See the [guide](../serialization) on exporting 🤗 Transformers models for more
details. | 469_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/onnx.md | https://huggingface.co/docs/transformers/en/main_classes/onnx/#onnx-configurations | .md | We provide three abstract classes that you should inherit from, depending on the
type of model architecture you wish to export:
* Encoder-based models inherit from [`~onnx.config.OnnxConfig`]
* Decoder-based models inherit from [`~onnx.config.OnnxConfigWithPast`]
* Encoder-decoder models inherit from [`~onnx.config.OnnxSeq2SeqConfigWithPast`] | 469_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/onnx.md | https://huggingface.co/docs/transformers/en/main_classes/onnx/#onnxconfig | .md | onnx.config.OnnxConfig
Base class for ONNX exportable model describing metadata on how to export the model through the ONNX format. | 469_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/onnx.md | https://huggingface.co/docs/transformers/en/main_classes/onnx/#onnxconfigwithpast | .md | onnx.config.OnnxConfig
Base class for ONNX exportable model describing metadata on how to export the model through the ONNX format.
WithPast | 469_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/onnx.md | https://huggingface.co/docs/transformers/en/main_classes/onnx/#onnxseq2seqconfigwithpast | .md | onnx.config.OnnxSeq2SeqConfigWithPast | 469_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/onnx.md | https://huggingface.co/docs/transformers/en/main_classes/onnx/#onnx-features | .md | Each ONNX configuration is associated with a set of _features_ that enable you
to export models for different types of topologies or tasks. | 469_6_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/main_classes/onnx.md | https://huggingface.co/docs/transformers/en/main_classes/onnx/#featuresmanager | .md | onnx.features.FeaturesManager | 469_7_0 |