BackendConfig

class torch.ao.quantization.backend_config.BackendConfig(name='') [source]

Config that defines the set of patterns that can be quantized on a given backend, and how reference quantized models can be produced from these patterns.

A pattern in this context refers to a module, a functional, an operator, or a directed acyclic graph of the above. Each pattern supported on the target backend can be individually configured through BackendPatternConfig in terms of:

1. The supported input/output activation, weight, and bias data types
2. How observers and quant/dequant ops are inserted in order to construct the reference pattern, and
3. (Optionally) Fusion, QAT, and reference module mappings.

The format of the patterns is described in: https://github.com/pytorch/pytorch/blob/master/torch/ao/quantization/backend_config/README.md

Example usage:

import torch
from torch.ao.quantization.backend_config import (
    BackendConfig,
    BackendPatternConfig,
    DTypeConfig,
    ObservationType,
)

weighted_int8_dtype_config = DTypeConfig(
    input_dtype=torch.quint8,
    output_dtype=torch.quint8,
    weight_dtype=torch.qint8,
    bias_dtype=torch.float)

def fuse_conv2d_relu(is_qat, conv, relu):
    return torch.ao.nn.intrinsic.ConvReLU2d(conv, relu)

# For quantizing Linear
linear_config = BackendPatternConfig(torch.nn.Linear)             .set_observation_type(ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT)             .add_dtype_config(weighted_int8_dtype_config)             .set_root_module(torch.nn.Linear)             .set_qat_module(torch.ao.nn.qat.Linear)             .set_reference_quantized_module(torch.ao.nn.quantized.reference.Linear)

# For fusing Conv2d + ReLU into ConvReLU2d
conv_relu_config = BackendPatternConfig((torch.nn.Conv2d, torch.nn.ReLU))             .set_observation_type(ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT)             .add_dtype_config(weighted_int8_dtype_config)             .set_fused_module(torch.ao.nn.intrinsic.ConvReLU2d)             .set_fuser_method(fuse_conv2d_relu)

# For quantizing ConvReLU2d
fused_conv_relu_config = BackendPatternConfig(torch.ao.nn.intrinsic.ConvReLU2d)             .set_observation_type(ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT)             .add_dtype_config(weighted_int8_dtype_config)             .set_root_module(torch.nn.Conv2d)             .set_qat_module(torch.ao.nn.intrinsic.qat.ConvReLU2d)             .set_reference_quantized_module(torch.ao.nn.quantized.reference.Conv2d)

backend_config = BackendConfig("my_backend")             .set_backend_pattern_config(linear_config)             .set_backend_pattern_config(conv_relu_config)             .set_backend_pattern_config(fused_conv_relu_config)

property configs: List[BackendPatternConfig]

Return a copy of the list of configs set in this BackendConfig.

classmethod from_dict(backend_config_dict) [source]

Create a BackendConfig from a dictionary with the following items:

“name”: the name of the target backend

“configs”: a list of dictionaries that each represents a BackendPatternConfig

Return type

BackendConfig

set_backend_pattern_config(config) [source]

Set the config for an pattern that can be run on the target backend. This overrides any existing config for the given pattern.

Return type

BackendConfig

set_backend_pattern_configs(configs) [source]

Set the configs for patterns that can be run on the target backend. This overrides any existing config for a given pattern if it was previously registered already.

Return type

BackendConfig

set_name(name) [source]

Set the name of the target backend.

Return type

BackendConfig

to_dict() [source]

Convert this BackendConfig to a dictionary with the items described in from_dict().

Return type

Dict[str, Any]