Add the shark_downloader for the torch_models. #184
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Also, update the minilm_jit example. The results are: Copying gs://shark_tank/microsoft_MiniLM-L12-H384-uncased/function_name.npy... Copying gs://shark_tank/microsoft_MiniLM-L12-H384-uncased/golden_out.npz... Copying gs://shark_tank/microsoft_MiniLM-L12-H384-uncased/inputs.npz... Copying gs://shark_tank/microsoft_MiniLM-L12-H384-uncased/microsoft_MiniLM-L12-H384-uncased.mlir... - [4 files][254.7 MiB/254.7 MiB] Operation completed over 4 objects/254.7 MiB. Target triple found:x86_64-linux-gnu The obtained result via shark is: [[ 0.00985309 -0.00083516]] The golden result is: (array([ 0.00985307, -0.00083515], dtype=float32),) Running shark_module with random_inputs is: [[0.00674547 0.00371962]]
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Also, update the minilm_jit example.
The results are:
Copying gs://shark_tank/microsoft_MiniLM-L12-H384-uncased/function_name.npy...
Copying gs://shark_tank/microsoft_MiniLM-L12-H384-uncased/golden_out.npz...
Copying gs://shark_tank/microsoft_MiniLM-L12-H384-uncased/inputs.npz...
Copying gs://shark_tank/microsoft_MiniLM-L12-H384-uncased/microsoft_MiniLM-L12-H384-uncased.mlir...
Operation completed over 4 objects/254.7 MiB.
Target triple found:x86_64-linux-gnu
The obtained result via shark is: [[ 0.00985309 -0.00083516]]
The golden result is: (array([ 0.00985307, -0.00083515], dtype=float32),)
Running shark_module with random_inputs is: [[0.00674547 0.00371962]]