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🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
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TF Lite model created from TFWhisperForConditionalGeneration.from_pretrained craches #32125

Closed dsame closed 2 months ago

dsame commented 4 months ago

System Info

The main branch still does not include the fix: https://github.com/huggingface/transformers/issues/19691#issuecomment-1791869884

namely

    #new_scores = tf.ones_like(scores, dtype=scores.dtype) * -float("inf")
    new_scores = tf.ones_like(scores, dtype=scores.dtype) * -float(1)

which causes TFLite Whisper model to crash when used as:

    interpreter = tf.lite.Interpreter(tflite_model_path)
    tflite_generate = interpreter.get_signature_runner()
    output = tflite_generate(input_features=input_features)

Who can help?

@ArthurZucker

Information

Tasks

Reproduction

class GenerateModel(tf.Module):
    def __init__(self, model, forced_decoder_ids):
        super(GenerateModel, self).__init__()
        self.model = model
        self.forced_decoder_ids = forced_decoder_ids

    @tf.function(
        # shouldn't need static batch size, but throws exception without it (needs to be fixed)
        input_signature=[
            tf.TensorSpec((1, 80, 3000), tf.float32, name="input_features"),
        ],
    )
    def serving(self, input_features):
        outputs = self.model.generate(
            input_features,
            forced_decoder_ids=self.forced_decoder_ids,
            #max_new_tokens=223,  # change as needed
            return_dict_in_generate=True,
        )
        return {"sequences": outputs["sequences"]}

model = TFWhisperForConditionalGeneration.from_pretrained(model_name)
generate_model = GenerateModel(model=model, forced_decoder_ids=forced_decoder_ids)
tf.saved_model.save(generate_model, saved_model_dir, signatures={"serving_default": generate_model.serving})

# Convert the model
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
converter.target_spec.supported_ops = [
        tf.lite.OpsSet.TFLITE_BUILTINS,  # enable TensorFlow Lite ops.
        tf.lite.OpsSet.SELECT_TF_OPS  # enable TensorFlow ops.
]

converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()

# Save the model
with open(tflite_model_path, 'wb') as f:
        f.write(tflite_model)

....
    interpreter = tf.lite.Interpreter(tflite_model_path)
    tflite_generate = interpreter.get_signature_runner()
    output = tflite_generate(input_features=input_features)

causes crache on tflite_generate.

Applying the patch https://github.com/huggingface/transformers/issues/19691#issuecomment-1791869884 solves the problem

Expected behavior

The code below should work without patch

class GenerateModel(tf.Module):
    def __init__(self, model, forced_decoder_ids):
        super(GenerateModel, self).__init__()
        self.model = model
        self.forced_decoder_ids = forced_decoder_ids

    @tf.function(
        # shouldn't need static batch size, but throws exception without it (needs to be fixed)
        input_signature=[
            tf.TensorSpec((1, 80, 3000), tf.float32, name="input_features"),
        ],
    )
    def serving(self, input_features):
        outputs = self.model.generate(
            input_features,
            forced_decoder_ids=self.forced_decoder_ids,
            #max_new_tokens=223,  # change as needed
            return_dict_in_generate=True,
        )
        return {"sequences": outputs["sequences"]}

model = TFWhisperForConditionalGeneration.from_pretrained(model_name)
generate_model = GenerateModel(model=model, forced_decoder_ids=forced_decoder_ids)
tf.saved_model.save(generate_model, saved_model_dir, signatures={"serving_default": generate_model.serving})

# Convert the model
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
converter.target_spec.supported_ops = [
        tf.lite.OpsSet.TFLITE_BUILTINS,  # enable TensorFlow Lite ops.
        tf.lite.OpsSet.SELECT_TF_OPS  # enable TensorFlow ops.
]

converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()

# Save the model
with open(tflite_model_path, 'wb') as f:
        f.write(tflite_model)

....
    interpreter = tf.lite.Interpreter(tflite_model_path)
    tflite_generate = interpreter.get_signature_runner()
    output = tflite_generate(input_features=input_features)
dsame commented 4 months ago

The colab notebook: https://colab.research.google.com/drive/1CIDh8wqZOS7ifkUQkEIxM5osEXAeYMof

amyeroberts commented 4 months ago

cc @Rocketknight1

dsame commented 4 months ago

The exact error:

2024-07-28 09:56:51.045333: I tensorflow/compiler/mlir/lite/flatbuffer_export.cc:3064] Estimated count of arithmetic ops: 76.370 G  ops, equivalently 38.185 G  MACs
Traceback (most recent call last):
  File "/home/dsa/whishper2tflite/whisper2tflite.py", line 96, in <module>
    test()
  File "/home/dsa/whishper2tflite/test.py", line 49, in test
    output = tflite_generate(input_features=input_features)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/dsa/whishper2tflite/.venv/lib/python3.12/site-packages/tensorflow/lite/python/interpreter.py", line 249, in __call__
    self._interpreter_wrapper.Invoke(self._subgraph_index)
RuntimeError: tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.tensorflow/lite/kernels/reduce.cc:445 std::apply(optimized_ops::Mean<T, U>, args) was not true.gather index out of boundsNode number 34 (GATHER) failed to invoke.Node number 618 (WHILE) failed to invoke.

python3 --version Python 3.12.3

pip freeze

absl-py==2.1.0
astunparse==1.6.3
certifi==2024.7.4
cffi==1.16.0
charset-normalizer==3.3.2
coloredlogs==15.0.1
filelock==3.15.4
flatbuffers==24.3.25
fsspec==2024.6.1
gast==0.6.0
google-pasta==0.2.0
grpcio==1.65.1
h5py==3.11.0
huggingface-hub==0.24.2
humanfriendly==10.0
idna==3.7
Jinja2==3.1.4
keras==3.4.1
libclang==18.1.1
Markdown==3.6
markdown-it-py==3.0.0
MarkupSafe==2.1.5
mdurl==0.1.2
ml-dtypes==0.3.2
mpmath==1.3.0
namex==0.0.8
networkx==3.3
numpy==1.26.4
onnx==1.16.1
onnxruntime==1.18.1
opt-einsum==3.3.0
optree==0.12.1
packaging==24.1
protobuf==4.25.4
pycparser==2.22
Pygments==2.18.0
PySoundFile==0.9.0.post1
PyYAML==6.0.1
regex==2024.7.24
requests==2.32.3
rich==13.7.1
safetensors==0.4.3
setuptools==71.1.0
six==1.16.0
sympy==1.13.1
tensorboard==2.16.2
tensorboard-data-server==0.7.2
tensorflow==2.16.1
tensorflow-io==0.37.1
tensorflow-io-gcs-filesystem==0.37.1
termcolor==2.4.0
tf_keras==2.16.0
tokenizers==0.19.1
torch==2.4.0
torchaudio==2.4.0
tqdm==4.66.4
transformers==4.43.3
typing_extensions==4.12.2
urllib3==2.2.2
Werkzeug==3.0.3
wheel==0.43.0
wrapt==1.16.0
Rocketknight1 commented 4 months ago

@dsame can you test the PR at #32301 and let me know if it fixes the issue for you?

You can install from the PR branch with pip install --upgrade git+https://github.com/huggingface/transformers.git@fix_whisper_tflite_export

github-actions[bot] commented 3 months ago

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