Fix llama conversion with smooth quant #1650
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without this it errors out with:
Traceback (most recent call last):
File "/app/tensorrt_llm/examples/llama/convert_checkpoint.py", line 456, in <module>
main()
File "/app/tensorrt_llm/examples/llama/convert_checkpoint.py", line 448, in main
convert_and_save_hf(args)
File "/app/tensorrt_llm/examples/llama/convert_checkpoint.py", line 353, in convert_and_save_hf
LLaMAForCausalLM.quantize(args.model_dir,
File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/models/llama/model.py", line 405, in quantize
convert.quantize(
File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/models/llama/convert.py", line 1395, in quantize
weights = load_weights_from_hf(
File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/models/llama/convert.py", line 1437, in load_weights_from_hf
weights = convert_hf_llama(
File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/models/llama/convert.py", line 1089, in convert_hf_llama
convert_layer(l)
File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/models/llama/convert.py", line 725, in convert_layer
get_tllm_linear_sq_weight(int8_weights,
File "/usr/local/lib/python3.10/dist-packages/tensorrt_llm/models/llama/convert.py", line 610, in get_tllm_linear_sq_weight
results[prefix + 'per_channel_scale'] = torch.Tensor([
ValueError: only one element tensors can be converted to Python scalars
we can also check the shapes:
cur_per_channel_value.shape -> torch.Size([6144])
col_shape -> [1, 6144]
so it's clear that we meant to convert the tensor without []
with these the model works and provides sensible output
Merged
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hi @kaiyux great, thank you! I think only the first commit was integrated, but the two others were not, but they are also required -- although they fix the error which would happen when running the engine. I'm experimenting with smooth quant llama 3 right now and need all commits to get it working. Do you mind having another look? |
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This PR fixes a few errors which appear when following the README https://github.com/NVIDIA/TensorRT-LLM/blob/main/examples/llama/README.md#smoothquant on current latest commit.
Note: the first commit looks quite obvious (although I'm not sure how this could have worked before), while I'm less sure about the second, I just just going by error messages during engine conversion, there might be a better place for the fix. So feel free to treat this as a bug report instead. I verified that engine built in this way has reasonable outputs and expected performance. The model I was testing this on is Mistral 7B (mistral-7b-v0.1-instruct) but I assume other llama 2 and 3 should also work (didn't get to llama 3 yet).