36 lines
1.7 KiB
Markdown
36 lines
1.7 KiB
Markdown
# Models
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Common modelzoo such as huggingface/transformers stuggles when using Pytorch native model parallelism. Following the design principle of vLLM, we keep a simple, parallelizable, highly-optimized with packed inputs in verl.
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## Adding a New Huggingface Model
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### Step 1: Copy the model file from HF to verl
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- Add a new file under verl/models/hf
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- Copy ONLY the model file from huggingface/transformers/models to verl/models/hf
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### Step 2: Modify the model file to use packed inputs
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- Remove all the code related to inference (kv cache)
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- Modify the inputs to include only
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- input_ids (total_nnz,)
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- cu_seqlens (total_nnz + 1,)
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- max_seqlen_in_batch: int
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- Note that this requires using flash attention with causal mask.
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### Step 2.5: Add tests
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- Add a test to compare this version and the huggingface version
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- Following the infrastructure and add tests to tests/models/hf
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### Step 3: Add a function to apply tensor parallelism
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- Please follow
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- https://pytorch.org/docs/stable/distributed.tensor.parallel.html
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- https://pytorch.org/tutorials/intermediate/TP_tutorial.html
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- General comments
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- Tensor Parallelism in native Pytorch is NOT auto-parallelism. The way it works is to specify how model parameters and input/output reshards using configs. These configs are then registered as hooks to perform input/output resharding before/after model forward.
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### Step 4: Add a function to apply data parallelism
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- Please use FSDP2 APIs
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- See demo here https://github.com/pytorch/torchtitan/blob/main/torchtitan/parallelisms/parallelize_llama.py#L413
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### Step 5: Add a function to apply pipeline parallelism
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- Comes in Pytorch 2.4
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- Currently only in alpha in nightly version
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- Check torchtitan for more details
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