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Update benchmarks.sh
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torchao/_models/llama/benchmarks.sh

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@@ -21,6 +21,7 @@ export MODEL_REPO=meta-llama/Meta-Llama-3.1-8B
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python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --write_result benchmark_results.txt
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python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization int8wo --write_result benchmark_results.txt
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python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization int4wo-64 --write_result benchmark_results.txt
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# Runs on H100, float8 is not supported on CUDA arch < 8.9
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python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization float8wo --write_result benchmark_results.txt
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python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization float8dq-tensor --write_result benchmark_results.txt
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python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --quantization float8dq-wo --write_result benchmark_results.txt

torchao/quantization/README.md

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@@ -139,9 +139,6 @@ change_linear_weights_to_int8_dqtensors(model)
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from torchao.quantization import quantize_, float8_dynamic_activation_float8_weight
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from torchao.quantization.observer import PerTensor
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quantize_(model, float8_dynamic_activation_float8_weight(granularity=PerTensor()))
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from torchao.quantization.observer import PerTensor
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quantize_(model, float8_dynamic_activation_float8_weight(granularity=PerTensor()))
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```
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#### A16W6 Floating Point WeightOnly Quantization

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