Adaptor¶

Introduction
Adaptor Support Matrix
Working Flow
Get Start with Adaptor API

4.1 Query API
Example of adding a new backend support

5.1 Capability

5.2 Implement ONNXRTAdaptor Class

Introduction¶

Intel® Neural Compressor builds the low-precision inference solution on popular deep learning frameworks such as TensorFlow, PyTorch, MXNet, and ONNX Runtime. The adaptor layer is the bridge between the tuning strategy and vanilla framework quantization APIs.

Adaptor Support Matrix¶

Framework	Adaptor
TensorFlow	✔
PyTorch	✔
ONNX	✔
MXNet	✔

Working Flow¶

Adaptor only provide framework API for tuning strategy. So we can find complete working flow in tuning strategy working flow.

Get Start with Adaptor API¶

Neural Compressor supports a new adaptor extension by implementing a subclass Adaptor class in the neural_compressor.adaptor package and registering this adaptor by the adaptor_registry decorator.

For example, a user can implement an Abc adaptor like below:

@adaptor_registry
class AbcAdaptor(Adaptor):
    def __init__(self, framework_specific_info):
        ...

    def quantize(self, tune_cfg, model, dataloader, q_func=None):
        ...

    def evaluate(self, model, dataloader, postprocess=None,
                 metric=None, measurer=None, iteration=-1, tensorboard=False):
        ...

    def query_fw_capability(self, model):
        ...

    def query_fused_patterns(self, model):
        ...

quantize function is used to perform quantization for post-training quantization and quantization-aware training. Quantization processing includes calibration and conversion processing for post-training quantization, while for quantization-aware training, it includes training and conversion processing.
evaluate function is used to run an evaluation on a validation dataset. It is a built-in function, if user wants to use specific evaluation function, he can pass the evaluation function to quantizer.
query_fw_capability function is used to run a query framework quantization capability and intersects with the user yaml configuration.
query_fused_patterns function is used to run a query framework graph fusion capability and decide the fusion tuning space.

Query API¶

Background¶

Besides the adaptor API, we also introduced the Query API which describes the behavior of a specific framework. With this API, Neural Compressor can easily query the following information on the current runtime framework.

The runtime version information.
The Quantizable ops type.
The supported sequence of each quantizable op.
The instance of each sequence.

In the past, the above information was generally defined and hidden in every corner of the code which made effective maintenance difficult. With the Query API, we only need to create one unified yaml file and call the corresponding API to get the information. For example, the tensorflow.yaml keeps the current Tensorflow framework ability. We recommend that the end user not make modifications if requirements are not clear.

Below is a fragment of the Tensorflow configuration file.

precisions field defines the supported precision for Neural Compressor.
- valid_mixed_precision enumerates all supported precision combinations for specific scenario. For example, if one hardware doesn’t support bf16， it should be int8 + fp32.
ops field defines the valid OP type list for each precision.
capabilities field focuses on the quantization ability of specific ops such as granularity, scheme, and algorithm. The activation assumes the same data type for both input and output activation by default based on op semantics defined by frameworks.
patterns field defines the supported fusion sequence of each op.

Query API Introduction¶

The abstract class QueryBackendCapability is defined in query.py. Each framework should inherit it and implement the member function if needed. Refer to Tensorflow implementation TensorflowQuery.

Example of Adding a New Backend Support¶

Look at onnxruntime as an example. ONNX Runtime is a backend proposed by Microsoft, and is based on the MLAS kernel by default. Onnxruntime already has quantization tools, so the question becomes how to integrate onnxruntime quantization tools into Neural Compressor.

Capability¶

The user should explore quantization capability first. According to onnx_quantizer, the quantization tools support the following attributes:

whether per_channel
whether reduce_range
QLinear mode, QDQ mode or Integer mode (which is only seen in onnxruntime)
whether static (static quantization or dynamic quantization)
weight_qtype (choices are float32, int8 and uint8)
input_qtype (choices are float32, int8 and uint8)
quantization_params (None if dynamic quantization)
nodes_to_quantize, nodes_to_exclude
op_types_to_quantize

We define three configuration files to describe the capability of ONNXRT. Please refer to onnxrt_qlinear.yaml, onnxrt_integer.yaml and onnxrt_qdq.yaml.

Implement ONNXRTAdaptor Class¶

The base class ONNXRTAdaptor inherits from the Adaptor class. Please refer to onnxrt.py.

 @adaptor_registry
 class ONNXRT_QLinearOpsAdaptor(ONNXRTAdaptor):
   @dump_elapsed_time("Pass quantize model")
   def quantize(self, tune_cfg, model, data_loader, q_func=None):
     ......

   @dump_elapsed_time("Pass recover model")
   def recover(self, model, q_config):
     ......

   def inspect_tensor(self, model, dataloader, op_list=[],
                    iteration_list=[],
                    inspect_type='activation',
                    save_to_disk=False,
                    save_path=None,
                    quantization_cfg=None):
     ......

   def set_tensor(self, model, tensor_dict):
     ......

   def query_fw_capability(self, model):
     ......

   def evaluate(self, input_graph, dataloader, postprocess=None,
              metrics=None, measurer=None, iteration=-1,
              tensorboard=False, fp32_baseline=False):
     ......

   def diagnosis_helper(self, fp32_model, int8_model, tune_cfg=None, save_path=None):
     ......

   def save(self, model, path):
     ......