Comments (1)
tensor_parallel
has a few distinct features compared to deepspeed:
- It works with any PyTorch model out of the box. From what I've seen, deepspeed Automatic Tensor Parallelism only works for a selection of Hugging Face models.
- It works in an interactive environment in a Jupyter Notebook. Deepspeed doesn't.
- It supports training. I haven't been able to find whether deepspeed Automatic Tensor Parallelism does.
from tensor_parallel.
Related Issues (20)
- How to use trained models? HOT 3
- Support LLaMA Models, including HuggingFace-adapted variants HOT 7
- Slow inference performance for large Llama models compared to naive MP HOT 22
- How to load lora weights? HOT 13
- set distributed=True, return AttributeError: 'NoneType' object HOT 2
- cuda memory not evenly distributed between devices HOT 6
- Huggingface Accelerate HOT 1
- Great work! and can this work with deepspeedzero?
- Torch version requirement HOT 4
- Error in README.Md, hence not able to load model with limited memory. HOT 5
- Not work with 4bit quant HOT 6
- Support for PEFT LoRA and 4-bit quantization HOT 6
- Request to fix the content about parallelformers in README. HOT 1
- Can I parallelize just one large layer? HOT 1
- Does tensor_parallel support multi-node tensor parallel training? HOT 3
- Does tensor_parallel support data parallel and tensor parallel hybrid training?
- Does tensor_parallel support the model inference concurrently or in multi-threads? HOT 2
- Question on custom models HOT 23
- TypeError when multi-thread inference using tensor_parallel HOT 1
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