Closed hayyaw closed 2 weeks ago
tinyllama model: https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0
Hi @hayyaw, I think because the checkpoint you are using have additional tensors (i.e. only the default 22 layers) it is failing. Did you have a checkpoint in mind for 1 layer? or did you only want to port 1 of the layers?
I only want to port 1 of the layers. I remember it could be exported with 1 layer about 3 months ago. After I update the main branch, it failed. How can I export it successfully with num_layers=1 for tinyllama model checkpoint(22layers)( https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)? Looking forward to your reply. Thanks. @pkgoogle
if you just run the export with num_layers=1, but you are using the original 22-layer tinyllama checkpoint, then export will fail. This is b/c the checkpoint and the model isn't matching. You can always export a single layer tinyllama with random weights though (skipping checkpoint loading part).
Hi @hayyaw, you can make it work like @haozha111 says, but you will get a poor performing model with random weights... if you only port 1 layer it will probably be the same. You can construct your own 1-layer version and train/fine-tune that, then convert that and that will behave better, or you can train post-conversion but most users don't train on device, but if you want to go that route we can try.
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Description of the bug:
To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. Traceback (most recent call last): File "/home/wangzhiqun/ai-edge-torch/ai_edge_torch/generative/examples/tiny_llama/convert_to_tflite.py", line 80, in
app.run(main)
File "/home/wangzhiqun/miniconda3/envs/torch2tflite_3.11/lib/python3.11/site-packages/absl/app.py", line 308, in run
_run_main(main, args)
File "/home/wangzhiqun/miniconda3/envs/torch2tflite_3.11/lib/python3.11/site-packages/absl/app.py", line 254, in _run_main
sys.exit(main(argv))
^^^^^^^^^^
File "/home/wangzhiqun/ai-edge-torch/ai_edge_torch/generative/examples/tiny_llama/convert_to_tflite.py", line 65, in main
pytorch_model = tiny_llama.build_model(
^^^^^^^^^^^^^^^^^^^^^^^
File "/home/wangzhiqun/ai-edge-torch/ai_edge_torch/generative/examples/tiny_llama/tiny_llama.py", line 79, in build_model
return model_builder.build_decoder_only_model(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/wangzhiqun/ai-edge-torch/ai_edge_torch/generative/utilities/model_builder.py", line 137, in build_decoder_only_model
loader.load(
File "/home/wangzhiqun/ai-edge-torch/ai_edge_torch/generative/utilities/loader.py", line 188, in load
raise ValueError(
ValueError: Failed to map all tensor. Remaing tensor are: ['model.layers.1.input_layernorm.weight', 'model.layers.1.mlp.down_proj.weight', 'model.layers.1.mlp.gate_proj.weight', 'model.layers.1.mlp.up_proj.weight', .........
Actual vs expected behavior:
main master commit ddb7bf76d5343787cb4ad2780a5f194bf5b646fd
cd ai_edge_torch/generative/examples/tiny_llama config = cfg.ModelConfig( vocab_size=32000, num_layers=1, max_seq_len=2048, embedding_dim=2048, kv_cache_max_len=kv_cache_max_len, block_configs=block_config, final_norm_config=norm_config, lm_head_share_weight_with_embedding=False, enable_hlfb=True, )
python convert_to_tflite.py
Any other information you'd like to share?
No response