diff --git a/latest/_sources/autoapi/neural_compressor/torch/algorithms/weight_only/autoround/index.rst.txt b/latest/_sources/autoapi/neural_compressor/torch/algorithms/weight_only/autoround/index.rst.txt
index f3581bc72aa..a559adeae63 100644
--- a/latest/_sources/autoapi/neural_compressor/torch/algorithms/weight_only/autoround/index.rst.txt
+++ b/latest/_sources/autoapi/neural_compressor/torch/algorithms/weight_only/autoround/index.rst.txt
@@ -9,6 +9,14 @@
Module Contents
---------------
+Classes
+~~~~~~~
+
+.. autoapisummary::
+
+ neural_compressor.torch.algorithms.weight_only.autoround.AutoRoundQuantizer
+
+
Functions
~~~~~~~~~
@@ -16,10 +24,25 @@ Functions
.. autoapisummary::
neural_compressor.torch.algorithms.weight_only.autoround.get_autoround_default_run_fn
- neural_compressor.torch.algorithms.weight_only.autoround.autoround_quantize
+.. py:class:: AutoRoundQuantizer(weight_config: dict = {}, enable_full_range: bool = False, batch_size: int = 8, amp: bool = True, device=None, lr_scheduler=None, use_quant_input: bool = True, enable_minmax_tuning: bool = True, lr: float = None, minmax_lr: float = None, low_gpu_mem_usage: bool = True, iters: int = 200, seqlen: int = 2048, n_samples: int = 512, sampler: str = 'rand', seed: int = 42, n_blocks: int = 1, gradient_accumulate_steps: int = 1, not_use_best_mse: bool = False, dynamic_max_gap: int = -1, scale_dtype='fp32')
+
+
+
+
+ The base quantizer for all algorithm quantizers.
+
+ The `Quantizer` unifies the interfaces across various quantization algorithms, including GPTQ, RTN, etc.
+ Given a float model, `Quantizer` apply the quantization algorithm to the model according to the `quant_config`.
+
+ To implement a new quantization algorithm,, inherit from `Quantizer` and implement the following methods:
+ - `prepare`: prepare a given model for convert.
+ - `convert`: convert a prepared model to a quantized model.
+ Note: `quantize` and `execute` are optional for new quantization algorithms.
+
+
.. py:function:: get_autoround_default_run_fn(model, tokenizer, dataset_name='NeelNanda/pile-10k', n_samples=512, seqlen=2048, seed=42, bs=8, dataset_split: str = 'train', dataloader=None)
Perform calibration for quantization.
@@ -34,51 +57,3 @@ Functions
:type n_samples: int
-.. py:function:: autoround_quantize(model, weight_config: dict = {}, enable_full_range: bool = False, batch_size: int = 8, amp: bool = True, device=None, lr_scheduler=None, use_quant_input: bool = True, enable_minmax_tuning: bool = True, lr: float = None, minmax_lr: float = None, low_gpu_mem_usage: bool = True, iters: int = 200, seqlen: int = 2048, n_samples: int = 512, sampler: str = 'rand', seed: int = 42, n_blocks: int = 1, gradient_accumulate_steps: int = 1, not_use_best_mse: bool = False, dynamic_max_gap: int = -1, scale_dtype='fp16', run_fn=None, run_args=None)
-
- The entry point of the autoround weight-only quantization.
- Args:
- model: The PyTorch model to be quantized.
- weight_config (dict): Configuration for weight quantization (default is an empty dictionary).
- weight_config={
- 'layer1':##layer_name
- {
- 'data_type': 'int',
- 'bits': 4,
- 'group_size': 32,
- 'sym': False,
- }
- ...
- }
- keys:
- data_type (str): The data type to be used (default is "int").
- bits (int): Number of bits for quantization (default is 4).
- group_size (int): Size of the quantization group (default is 128).
- sym (bool): Whether to use symmetric quantization. (default is None).
- enable_full_range (bool): Whether to enable full range quantization (default is False).
- batch_size (int): Batch size for training (default is 8).
- amp (bool): Whether to use automatic mixed precision (default is True). Automatically detect and set.
- device: The device to be used for tuning (default is None). Automatically detect and set.
- lr_scheduler: The learning rate scheduler to be used.
- use_quant_input (bool): Whether to use quantized input data (default is True).
- enable_minmax_tuning (bool): Whether to enable min-max tuning (default is True).
- lr (float): The learning rate (default is 0.005).
- minmax_lr (float): The learning rate for min-max tuning (default is None).
- low_gpu_mem_usage (bool): Whether to use low GPU memory (default is True).
- iters (int): Number of iterations (default is 200).
- seqlen (int): Length of the sequence.
- n_samples (int): Number of samples (default is 512).
- sampler (str): The sampling method (default is "rand").
- seed (int): The random seed (default is 42).
- n_blocks (int): Number of blocks (default is 1).
- gradient_accumulate_steps (int): Number of gradient accumulation steps (default is 1).
- not_use_best_mse (bool): Whether to use mean squared error (default is False).
- dynamic_max_gap (int): The dynamic maximum gap (default is -1).
- scale_dtype (str): The data type of quantization scale to be used (default is "float32"), different kernels
- have different choices.
- run_fn: a calibration function for calibrating the model. Defaults to None.
- run_args: positional arguments for `run_fn`. Defaults to None.
-
- :returns: The quantized model.
-
-
diff --git a/latest/autoapi/block_mask/index.html b/latest/autoapi/block_mask/index.html
index 982cf46745a..985f9a6d7d6 100644
--- a/latest/autoapi/block_mask/index.html
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@@ -107,7 +107,7 @@
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index 096c8e9e0d5..03baeb88d9f 100644
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@@ -146,7 +146,7 @@ Functions using a
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@@ -217,7 +217,7 @@ Package Contents using a
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index cfab8dc804d..636025764e0 100644
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index 2eebea46a19..f99165bc0ad 100644
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@@ -106,7 +106,7 @@
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diff --git a/latest/autoapi/neural_compressor/adaptor/keras_utils/depthwise_conv2d/index.html b/latest/autoapi/neural_compressor/adaptor/keras_utils/depthwise_conv2d/index.html
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@@ -106,7 +106,7 @@
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index a354e437a44..bf997106350 100644
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@@ -106,7 +106,7 @@
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index be10c71afa6..8a601f432c3 100644
--- a/latest/autoapi/neural_compressor/adaptor/keras_utils/pool2d/index.html
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@@ -106,7 +106,7 @@
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provided by Read the Docs.
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@@ -106,7 +106,7 @@
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