Comments (4)
jfrery
commented on July 29, 2024
Hi @dopc,
Could you share the error you are getting?
Some parts from the transformer architecture might be missing (e.g. softmax). They will be supported soon.
For now you can check the use case available on LLMs. It's using GPT2: https://github.com/zama-ai/concrete-ml/tree/main/use_case_examples/llm.
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dopc
commented on July 29, 2024
Hey @jfrery, thanks for your quick reply.
Good to see GPT2 model works! I will look at it.
Here is my error trace:
File "/opt/conda/lib/python3.10/site-packages/concrete/ml/torch/compile.py", line 303, in compile_torch_model
return _compile_torch_or_onnx_model(
File "/opt/conda/lib/python3.10/site-packages/concrete/ml/torch/compile.py", line 195, in _compile_torch_or_onnx_model
quantized_module = build_quantized_module(
File "/opt/conda/lib/python3.10/site-packages/concrete/ml/torch/compile.py", line 117, in build_quantized_module
numpy_model = NumpyModule(model, dummy_input_for_tracing)
File "/opt/conda/lib/python3.10/site-packages/concrete/ml/torch/numpy_module.py", line 45, in __init__
self.numpy_forward, self._onnx_model = get_equivalent_numpy_forward_from_torch(
File "/opt/conda/lib/python3.10/site-packages/concrete/ml/onnx/convert.py", line 145, in get_equivalent_numpy_forward_from_torch
torch.onnx.export(
File "/opt/conda/lib/python3.10/site-packages/torch/onnx/utils.py", line 504, in export
_export(
File "/opt/conda/lib/python3.10/site-packages/torch/onnx/utils.py", line 1529, in _export
graph, params_dict, torch_out = _model_to_graph(
File "/opt/conda/lib/python3.10/site-packages/torch/onnx/utils.py", line 1111, in _model_to_graph
graph, params, torch_out, module = _create_jit_graph(model, args)
File "/opt/conda/lib/python3.10/site-packages/torch/onnx/utils.py", line 987, in _create_jit_graph
graph, torch_out = _trace_and_get_graph_from_model(model, args)
File "/opt/conda/lib/python3.10/site-packages/torch/onnx/utils.py", line 891, in _trace_and_get_graph_from_model
trace_graph, torch_out, inputs_states = torch.jit._get_trace_graph(
File "/opt/conda/lib/python3.10/site-packages/torch/jit/_trace.py", line 1184, in _get_trace_graph
outs = ONNXTracedModule(f, strict, _force_outplace, return_inputs, _return_inputs_states)(*args, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/torch/jit/_trace.py", line 127, in forward
graph, out = torch._C._create_graph_by_tracing(
File "/opt/conda/lib/python3.10/site-packages/torch/jit/_trace.py", line 118, in wrapper
outs.append(self.inner(*trace_inputs))
File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1182, in _slow_forward
result = self.forward(*input, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/transformers/models/distilbert/modeling_distilbert.py", line 1225, in forward
outputs = self.distilbert(
File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1182, in _slow_forward
result = self.forward(*input, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/transformers/models/distilbert/modeling_distilbert.py", line 806, in forward
embeddings = self.embeddings(input_ids, inputs_embeds) # (bs, seq_length, dim)
File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1182, in _slow_forward
result = self.forward(*input, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/transformers/models/distilbert/modeling_distilbert.py", line 129, in forward
input_embeds = self.word_embeddings(input_ids) # (bs, max_seq_length, dim)
File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
return forward_call(*input, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1182, in _slow_forward
result = self.forward(*input, **kwargs)
File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/sparse.py", line 160, in forward
return F.embedding(
File "/opt/conda/lib/python3.10/site-packages/torch/nn/functional.py", line 2210, in embedding
return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
RuntimeError: Expected tensor for argument #1 'indices' to have one of the following scalar types: Long, Int; but got torch.FloatTensor instead (while checking arguments for embedding)
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jfrery
commented on July 29, 2024
Hey my bad I missed your answer. Concrete ML doesn't support embedding layer. We will support that very soon.
If you don't mind, we can convert your issue to a Feature request to make sure we have the embedding supported asap.
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dopc
commented on July 29, 2024
Thanks for the answer and for converting the issue to a feature request.
Looking forward to the support.
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