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e52be9bba2
Deepseek V2 is a MoE model from Deepseek. Relevant variations
compared to other models:
- Grouped top-K in expert selection.
- mscale in yarn is calculated using the `mscale` and `mscale_all_dim`
configuration options.
- `mscale_all_dim` is also used in scaling attention softmax.
- Permuting of the query/key representations before applying rotary
embeddings.
- Some projections cannot be sharded (`q_a_proj`, `kv_a_proj_with_mqa`).
So, we need weight loads that supports quantized weights. To this
end `{Weights,WeightLoader}.get_weight` was added.
- The query/key head dimensionality differs from that of the value,
so we need to pad during attention.
- Heads with size 192, needs an extension to our paged attention
fork and we need to ensure that the KV cache is allocated with the
correct size.
- Shared experts.
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|---|---|---|
| .. | ||
| attention | ||
| awq | ||
| gptq | ||
| __init__.py | ||
| bnb.py | ||
| conv.py | ||
| eetq.py | ||
| exl2.py | ||
| fp8.py | ||
| layernorm.py | ||
| linear.py | ||
| lora.py | ||
| marlin.py | ||
| medusa.py | ||
| mlp.py | ||
| rotary.py | ||
| speculative.py | ||
| tensor_parallel.py | ||