Hacktoberfest 2026: los issues que los mantenedores marcaron para octubre, abiertos y aptos para principiantes. Explorar issues de Hacktoberfest

Support for Recurrent layers for Quantization Aware Training.

Abierto
  • ¿Algo no está bien?
#1,114 1 comentario 0 reacciones 0 asignados Ver en GitHub

Nadie ha tomado este issue todavía.

Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
25/100
Tipo de issue
Nueva funcionalidad
Claridad
Bastante claro
Estado de actividad
Estancado
Stack tecnológico
keras, python, tensorflow
Área
machine-learning

Línea de trabajo

Comience leyendo default_8bit_quantize_registry.py alrededor de la línea 159, donde la compatibilidad con RNN está deshabilitada, y reproduzca el error de quantize_apply con el caso de GRU de TensorFlow 2.15 indicado. Investigue la QuantizeConfig personalizada y el error posterior al guardar en H5. Se considera terminado cuando las capas recurrentes admiten la cuantización de 8 bits predeterminada mediante quantize_apply y el modelo cuantizado puede guardarse correctamente.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

feature request

System information

  • TensorFlow version (you are using): 2.15
  • Are you willing to contribute it (Yes/No): Yes

Motivation
I am trying to train RNN model with quantization aware training for embedded devices.

Describe the feature
I am looking for a way to train with default 8bit weights & activations quantization using quantize_apply API without passing in custom config.

Describe how the feature helps achieve the use case

Describe how existing APIs don't satisfy your use case (optional if obvious)

I tried to use quantize_apply API but I received this error.
RuntimeError: Layer gru:<class 'keras.src.layers.rnn.gru.GRU'> is not supported. You can quantize this layer by passing a `tfmot.quantization.keras.QuantizeConfig` instance to the `quantize_annotate_layer` API.

After using quantize_annotate_layer, I was able to train the model but Model fails to save with following error:

  keras.models.save_model(model, filepath=model_filename, save_format="h5")
Traceback (most recent call last):
  File "/workspaces/project-embedded/syntiant-ndp-model-converter/examples/train_audio_model.py", line 169, in <module>
    keras.models.save_model(model, filepath=model_filename, save_format="h5")
  File "/home/vscode/tf_venv/lib/python3.10/site-packages/keras/src/saving/saving_api.py", line 167, in save_model
    return legacy_sm_saving_lib.save_model(
  File "/home/vscode/tf_venv/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 70, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/home/vscode/tf_venv/lib/python3.10/site-packages/h5py/_hl/group.py", line 183, in create_dataset
    dsid = dataset.make_new_dset(group, shape, dtype, data, name, **kwds)
  File "/home/vscode/tf_venv/lib/python3.10/site-packages/h5py/_hl/dataset.py", line 163, in make_new_dset
    dset_id = h5d.create(parent.id, name, tid, sid, dcpl=dcpl, dapl=dapl)
  File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
  File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
  File "h5py/h5d.pyx", line 137, in h5py.h5d.create
ValueError: Unable to synchronously create dataset (name already exists)

I used following QuantizeConfig

class GruQuantizeConfig(tfmot.quantization.keras.QuantizeConfig):
    # Configure how to quantize weights.
    def get_weights_and_quantizers(self, layer):
        return [
            (
                layer.cell.kernel,
                LastValueQuantizer(
                    num_bits=8, symmetric=True, narrow_range=False, per_axis=False
                ),
            ),
            (
                layer.cell.recurrent_kernel,
                LastValueQuantizer(
                    num_bits=8, symmetric=True, narrow_range=False, per_axis=False
                ),
            ),
        ]

    # Configure how to quantize activations.
    def get_activations_and_quantizers(self, layer):
        return [
            (
                layer.cell.activation,
                MovingAverageQuantizer(
                    num_bits=8, symmetric=False, narrow_range=False, per_axis=False
                ),
            ),
            (
                layer.cell.recurrent_activation,
                MovingAverageQuantizer(
                    num_bits=8, symmetric=False, narrow_range=False, per_axis=False
                ),
            ),
        ]

    def set_quantize_weights(self, layer, quantize_weights):
        # Add this line for each item returned in `get_weights_and_quantizers`
        # , in the same order
        layer.cell.kernel = quantize_weights[0]
        layer.cell.recurrent_kernel = quantize_weights[1]

    def set_quantize_activations(self, layer, quantize_activations):
        # Add this line for each item returned in `get_activations_and_quantizers`
        # , in the same order.
        layer.cell.activation = quantize_activations[0]
        layer.cell.recurrent_activation = quantize_activations[1]

    # Configure how to quantize outputs (may be equivalent to activations).
    def get_output_quantizers(self, layer):
        return []

    def get_config(self):
        return {}

I looked at the source code. It seems that the support for RNN is disabled here for some reason.

I was wondering if this can be enabled back?

Lenguaje dominante
Python
Estrellas
1.6k
Forks
349
Merge medio
3 d 2 h
PR fusionados (30 d)
1

Preparar el entorno

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

Más de tensorflow/model-optimization

Todos los issues de tensorflow/model-optimization

Issues similares

Más issues de Python

Recibe los nuevos issues en tu correo

Un resumen breve de issues de GitHub para principiantes.