int4

Performs INT4 WoQ on an ONNX model, and returns the ONNX ModelProto.

Classes

AWQClipHelper

AWQ calibration helper class.

AWQLiteHelper

AWQ Lite calibration helper class.

Functions

dq_tensor

Dequantizes w with scale factors s.

find_scales

Find scale factors for w via s = max(w.block(block_size)) / 7.

get_act_scale

Get scale tensors for inputs.

get_act_to_weight_map_and_act_to_wa_pack_map

Method to return subgraph related maps based on activation-name as key.

get_scale

Get AWQ lite scales as described by 's' in the paper.

get_weight_scale

Get scale tensors for weights.

get_x_w_mean_for_subgraph

This method returns x-mean and w-mean.

quant_tensor

Quantize a tensor using alpha etc.

quantize

Applies INT4 WoQ (Weight-Only-Quantization) to an ONNX file.

quantize_rtn

Quantizes onnx_model using the RTN (Round-to-Nearest) algorithm.

rtn

Quantizes w with scale factors s via Round-to-Nearest.

run_awq_scale_search_per_node

Method that iterates over each quantizable node for scale search.

run_awq_scale_search_per_subgraph

Method that iterates over each quantizable subgraph/siblings for scale search.
class AWQClipHelper

Bases: object

AWQ calibration helper class.

__init__(w, block_size, **kwargs)

Initializes AWQClipHelper with a module weight.

Parameters:

block_size (int) –

alpha_step = 0.05
min_alpha = 0.5
update_best_params()

Updates the loss dictionary.

class AWQLiteHelper

Bases: object

AWQ Lite calibration helper class.

__init__(x, w, block_size, **kwargs)

Initializes AWQLiteHelper with a module weight.

Parameters:

block_size (int) –

alpha_step = 0.1
update_best_params()

Updates best-alpha and best-scale.

dq_tensor(w, s, block_size, zp=None)

Dequantizes w with scale factors s.

Parameters:

  • w (ndarray) –

  • s (ndarray) –

  • block_size (int) –

  • zp (ndarray) –

Return type:

ndarray

find_scales(w, block_size, alpha=1.0, use_zero_point=False)

Find scale factors for w via s = max(w.block(block_size)) / 7.

Parameters:

  • w (ndarray) –

  • block_size (int) –

  • alpha (float) –

  • use_zero_point (bool) –

get_act_scale(x)

Get scale tensors for inputs.

get_act_to_weight_map_and_act_to_wa_pack_map(wa_pack)

Method to return subgraph related maps based on activation-name as key.

This method returns 2 maps: (a) map of act-name to input-node’s weights dimensions (b) map of act-name to wa_pack indices with same act-name

Parameters:

wa_pack (List[Tuple[Tensor, Tensor, bool, int]]) –

get_scale(x_max, w_max, alpha, reduce_across_tp=False)

Get AWQ lite scales as described by ‘s’ in the paper.

get_weight_scale(weight, block_size=None)

Get scale tensors for weights.

get_x_w_mean_for_subgraph(wa_pack, wa_pack_idx_list, augmented_onnx_path, x, block_size)

This method returns x-mean and w-mean.

Parameters:

wa_pack (List[Tuple[Tensor, Tensor, bool, int]]) –

quant_tensor(w, block_size, alpha=1.0, use_zero_point=False)

Quantize a tensor using alpha etc. and return the quantized tensor.

Parameters:

  • w (ndarray) –

  • block_size (int) –

  • alpha (float) –

  • use_zero_point (bool) –

quantize(onnx_path, calibration_method='awq_clip', calibration_data_reader=None, calibration_eps=['cuda:0', 'cpu'], use_external_data_format=True, use_zero_point=False, block_size=None, nodes_to_exclude=['/lm_head'], **kwargs)

Applies INT4 WoQ (Weight-Only-Quantization) to an ONNX file.

Currently only GEMM quantization is supported.

use_zero_point:

Use zero-point based quantization, if True.

block_size:

Block size parameter for int4 quantization.

kwargs:

Additional keyword arguments for int4 quantization, including: - awqlite_alpha_step (float): Alpha step for lite, range [0, 1]. - awqclip_alpha_step (float): Min alpha step for clip, range [awqclip_alpha_step, 1]. - awqclip_alpha_min (float): Alpha step to find best alpha for clip. - awqclip_bsz_col (int): Batch size for processing the column dimension in clip.

Parameters:

  • onnx_path (str | ModelProto) –

  • calibration_method (str) –

  • calibration_data_reader (CalibrationDataReader) –

  • calibration_eps (List[str]) –

  • use_external_data_format (bool) –

  • use_zero_point (bool) –

  • block_size (int | None) –

  • nodes_to_exclude (List[str] | None) –

  • kwargs (Any) –

Return type:

ModelProto

quantize_rtn(onnx_model, gemm_io_type, block_size, dq_only=False)

Quantizes onnx_model using the RTN (Round-to-Nearest) algorithm.

This algorithm computes scale factors by computing s = max(abs(block)) / 8, for each block. The quantized weights are computed via Q(w) = round_to_even(w / s), where round_to_even denotes rounding ties to the nearest even integer (i.e. 1.5, 2.5 both round to 2).

Always selects the first dimension (0) to block over. This is because we must batch over the Cin dimension, and in ONNX, weights are always plugged into the RHS (i.e. y = x @ W).

Parameters:

  • onnx_model (ModelProto) –

  • gemm_io_type (<google.protobuf.internal.enum_type_wrapper.EnumTypeWrapper object at 0x7f4d2fb110d0>) –

  • block_size (int) –

  • dq_only (bool) –

Return type:

ModelProto

rtn(w, s, block_size, zp=None)

Quantizes w with scale factors s via Round-to-Nearest.

Ties are broken by rounding to the nearest even number.

Parameters:

  • w (ndarray) –

  • s (ndarray) –

  • block_size (int) –

  • zp (ndarray) –

Return type:

ndarray

run_awq_scale_search_per_node(wa_pack, augmented_onnx_path, block_size, use_zero_point, session, awq_lite, inputs, tqdm_msg_append_str, enable_weight_clipping, enable_fast_path_using_high_sysram, output_data, clip_alphas, **kwargs)

Method that iterates over each quantizable node for scale search.

Parameters:

  • wa_pack (List[Tuple[Tensor, Tensor, bool, int]]) –

  • kwargs (Any) –

run_awq_scale_search_per_subgraph(wa_pack, augmented_onnx_path, block_size, use_zero_point, session, awq_lite, inputs, tqdm_msg_append_str, **kwargs)

Method that iterates over each quantizable subgraph/siblings for scale search.

Parameters:

  • wa_pack (List[Tuple[Tensor, Tensor, bool, int]]) –

  • kwargs (Any) –