Comments (13)
artek0chumak
commented on June 15, 2024
1
Hello! Thank you for reporting!
We will quickly resolve this issue.
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artek0chumak
commented on June 15, 2024
1
We resolved this issue in recent master update. Just pull new updates.
Thank tou for noticing the issue and waiting fixes.
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artek0chumak
commented on June 15, 2024
1
Thank you for the information. It seems the only change required is this: #574.
We will soon merge it with the main.
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mprishchepo
commented on June 15, 2024
1
Hi!
How is the work on the fixes going, is everything good?
We are really looking for the merge
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artek0chumak
commented on June 15, 2024
1
Sorry for taking so long; the fix is merged into the master.
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mprishchepo
commented on June 15, 2024
Hello!
I observe the same problem. I have tried to diagnose the issue a bit by myselve.
As I understood (if you haven't found it already) the problem is in calculating block size (its parameters). The layer_idx mentioned above is used in load_pretrained_block, but it is not used when calculating block_size and when calculating rps in throughput.
Very much waiting for a solution.
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Qessia
commented on June 15, 2024
Thank you for your quick response!
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mprishchepo
commented on June 15, 2024
Hi!
Original error of this issue doesn't appear anymore, but I've got another error when I try launching private swarm with Mixtral (with GPU, CPU is ok). Also it doesn't appear when I do the same with StableBeluga2
System:
- Python3.10
- Torch2.2.2
- Cuda 12.3
- Ubuntu 22.04
Reproduce
python3 -m petals.cli.run_server SanjiWatsuki/TinyMixtral-32x248M --new_swarmError
File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
exec(code, run_globals)
File "/home/qessia/.local/lib/python3.10/site-packages/petals/cli/run_server.py", line 235, in <module>
main()
File "/home/qessia/.local/lib/python3.10/site-packages/petals/cli/run_server.py", line 219, in main
server = Server(
File "/home/qessia/.local/lib/python3.10/site-packages/petals/server/server.py", line 237, in __init__
throughput_info = get_server_throughput(
File "/home/qessia/.local/lib/python3.10/site-packages/petals/server/throughput.py", line 83, in get_server_throughput
cache[cache_key] = measure_throughput_info(
File "/home/qessia/.local/lib/python3.10/site-packages/petals/server/throughput.py", line 123, in measure_throughput_info
"inference_rps": measure_compute_rps(
File "/home/qessia/.local/lib/python3.10/site-packages/petals/server/throughput.py", line 218, in measure_compute_rps
cache = step(cache)
File "/home/qessia/.local/lib/python3.10/site-packages/petals/server/throughput.py", line 215, in step
outputs = block.forward(dummy_input, use_cache=inference, layer_past=cache_ if inference else None)
File "/home/qessia/.local/lib/python3.10/site-packages/tensor_parallel/tensor_parallel.py", line 99, in forward
return [self.module_shards[0](*args, **kwargs)][self.output_device_index]
File "/home/qessia/.local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/qessia/.local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "/home/qessia/.local/lib/python3.10/site-packages/petals/models/mixtral/block.py", line 74, in forward
outputs = super().forward(
File "/home/qessia/.local/lib/python3.10/site-packages/transformers/models/mixtral/modeling_mixtral.py", line 934, in forward
hidden_states, self_attn_weights, present_key_value = self.self_attn(
File "/home/qessia/.local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/qessia/.local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "/home/qessia/.local/lib/python3.10/site-packages/transformers/models/mixtral/modeling_mixtral.py", line 356, in forward
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
File "/home/qessia/.local/lib/python3.10/site-packages/transformers/cache_utils.py", line 131, in update
self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0! (when checking argument for argument tensors in method wrapper_CUDA_cat)
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artek0chumak
commented on June 15, 2024
Hello!
This is a strange error. Can you also provide a transformers' version?
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mprishchepo
commented on June 15, 2024
Can you also provide a transformers' version?
4.38.2
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jmikedupont2
commented on June 15, 2024
I had that same error on master as well and had a ticket open for it, #575
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jmikedupont2
commented on June 15, 2024
I was able to get the branch mentioned running and my docker work rebased. 
Have now tinymixtral running locally in gpu.
https://github.com/meta-introspector/petals
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Qessia
commented on June 15, 2024
Thank you for fixes!! It works
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