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feature: quantization added #21

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feature: quantization added #21

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7 changes: 6 additions & 1 deletion interleaved_generation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,12 +62,17 @@ def split_token_sequence(
def main(args: argparse.Namespace):
"""Main function to generate and process model output."""
# Load Chameleon model
model = ChameleonInferenceModel(
unquantized_model = ChameleonInferenceModel(
MODEL_7B_PATH.as_posix(),
TOKENIZER_TEXT_PATH.as_posix(),
TOKENIZER_IMAGE_CFG_PATH.as_posix(),
TOKENIZER_IMAGE_PATH.as_posix(),
)
model = torch.quantization.quantize_dynamic(
unquantized_model, # The model to be quantized
{torch.nn.Linear, torch.nn.LSTM}, # Layers to be dynamically quantized
dtype=torch.qint8 # Data type for quantization
)
# Print model configuration
print(f"Model path: {MODEL_7B_PATH}")
print(f"Text tokenizer path: {TOKENIZER_TEXT_PATH}")
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8 changes: 6 additions & 2 deletions text2image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,13 +20,17 @@ def main(args: argparse.Namespace):
print(f"Batch size: {args.batch_size}")

# Load Chameleon model
model = ChameleonInferenceModel(
unquantized_model = ChameleonInferenceModel(
MODEL_7B_PATH.as_posix(),
TOKENIZER_TEXT_PATH.as_posix(),
TOKENIZER_IMAGE_CFG_PATH.as_posix(),
TOKENIZER_IMAGE_PATH.as_posix(),
)

model = torch.quantization.quantize_dynamic(
unquantized_model, # The model to be quantized
{torch.nn.Linear, torch.nn.LSTM}, # Layers to be dynamically quantized
dtype=torch.qint8 # Data type for quantization
)
# Generate options
options = Options()
options.txt = False
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