AIMET PyTorch Layer Output Generation API

This API captures and saves intermediate layer-outputs of a model. The model can be original (FP32) or quantsim. The layer-outputs are named according to the exported PyTorch/ONNX/TorchScript model by the quantsim export API. This allows layer-output comparison amongst FP32 model, quantization simulated model and actually quantized model on target-device to debug accuracy miss-match issues.

Top-level API

class aimet_torch.layer_output_utils.LayerOutputUtil(model, dir_path, naming_scheme=<NamingScheme.PYTORCH: 1>, dummy_input=None, onnx_export_args=None)[source]

Implementation to capture and save outputs of intermediate layers of a model (fp32/quantsim).

Constructor for LayerOutputUtil.

Parameters

  • model (Module) – Model whose layer-outputs are needed.

  • dir_path (str) – Directory wherein layer-outputs will be saved.

  • naming_scheme (NamingScheme) – Naming scheme to be followed to name layer-outputs. There are multiple schemes as per the exported model (pytorch, onnx or torchscript). Refer the NamingScheme enum definition.

  • dummy_input (Union[Tensor, Tuple, List[~T], None]) – Dummy input to model. Required if naming_scheme is ‘NamingScheme.ONNX’ or ‘NamingScheme.TORCHSCRIPT’.

  • onnx_export_args (Union[OnnxExportApiArgs, Dict[~KT, ~VT], None]) – Should be same as that passed to quantsim export API to have consistency between layer-output names present in exported onnx model and generated layer-outputs. Required if naming_scheme is ‘NamingScheme.ONNX’.


The following API can be used to Generate Layer Outputs

LayerOutputUtil.generate_layer_outputs(input_batch)[source]

This method captures output of every layer of a model & saves the inputs and corresponding layer-outputs to disk.

Parameters

input_batch (Union[Tensor, List[Tensor], Tuple[Tensor]]) – Batch of inputs for which we want to obtain layer-outputs.

Returns

None


Enum Definition

Naming Scheme Enum

class aimet_torch.layer_output_utils.NamingScheme[source]

Enumeration of layer-output naming schemes.

ONNX = 2

Names outputs according to exported onnx model. Layer output names are generally numeric.

PYTORCH = 1

Names outputs according to exported pytorch model. Layer names are used.

TORCHSCRIPT = 3

Names outputs according to exported torchscript model. Layer output names are generally numeric.


Code Example

Imports

import torch

from aimet_torch.quantsim import QuantizationSimModel, load_encodings_to_sim

from aimet_torch.layer_output_utils import LayerOutputUtil, NamingScheme
from aimet_torch.onnx_utils import OnnxExportApiArgs

Obtain Original or QuantSim model from AIMET Export Artifacts

# Load the model on CPU device. Ensure model definition is present in the PYTHONPATH to successfully load the model.
# If exported on CPU, load this way.
model = torch.load('path/to/aimet_export_artifacts/model.pth')
# Or
# If exported on GPU, load this way.
# model = torch.load('path/to/aimet_export_artifacts/model.pth', map_location=torch.device('cpu'))

dummy_input = torch.rand(1, 3, 224, 224)

# Use same arguments as that were used for the exported QuantSim model. For sake of simplicity only mandatory arguments are passed below.
quantsim = QuantizationSimModel(model=model, dummy_input=dummy_input)

# Load exported encodings into quantsim object
load_encodings_to_sim(quantsim, 'path/to/aimet_export_artifacts/model_torch.encodings')

# Check whether constructed original and quantsim model are running properly before using Layer Output Generation API.
_ = model(dummy_input)
_ = quantsim.model(dummy_input)

Obtain inputs for which we want to generate intermediate layer-outputs

# Use same input pre-processing pipeline as was used for computing the quantization encodings.
input_batches = get_pre_processed_inputs()

Generate layer-outputs

# Use original model to get fp32 layer-outputs
fp32_layer_output_util = LayerOutputUtil(model=model, dir_path='./fp32_layer_outputs', naming_scheme=NamingScheme.ONNX,
                                         dummy_input=dummy_input, onnx_export_args=OnnxExportApiArgs())
# Use quantsim model to get quantsim layer-outputs
quantsim_layer_output_util = LayerOutputUtil(model=quantsim.model, dir_path='./quantsim_layer_outputs', naming_scheme=NamingScheme.ONNX,
                                             dummy_input=dummy_input, onnx_export_args=OnnxExportApiArgs())
for input_batch in input_batches:
    fp32_layer_output_util.generate_layer_outputs(input_batch)
    quantsim_layer_output_util.generate_layer_outputs(input_batch)