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cc7200bf12
* move conversion code to a dedicated conversion directory and split the files akin to the src/models architecture --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
54 lines
1.9 KiB
Python
54 lines
1.9 KiB
Python
from __future__ import annotations
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from typing import TYPE_CHECKING
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import torch
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if TYPE_CHECKING:
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from torch import Tensor
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from .base import ModelBase, TextModel, gguf
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@ModelBase.register("MiniMaxM2ForCausalLM")
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class MiniMaxM2Model(TextModel):
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model_arch = gguf.MODEL_ARCH.MINIMAXM2
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_experts_cache: dict[int, dict[str, Tensor]] = {}
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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self.gguf_writer.add_expert_feed_forward_length(self.find_hparam(["intermediate_size"]))
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self.gguf_writer.add_rope_dimension_count(self.find_hparam(["rotary_dim"]))
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
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# merge expert weights
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if 'experts' in name:
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n_experts = self.find_hparam(["num_local_experts", "num_experts"])
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assert bid is not None
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expert_cache = self._experts_cache.setdefault(bid, {})
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expert_cache[name] = data_torch
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expert_weights = ["w1", "w2", "w3"]
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# not enough expert weights to merge
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if len(expert_cache) < n_experts * len(expert_weights):
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return
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for w_name in expert_weights:
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datas: list[Tensor] = []
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for xid in range(n_experts):
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ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{w_name}.weight"
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datas.append(expert_cache[ename])
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del expert_cache[ename]
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data_torch = torch.stack(datas, dim=0)
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merged_name = f"model.layers.{bid}.block_sparse_moe.experts.{w_name}.weight"
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new_name = self.map_tensor_name(merged_name)
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yield from super().modify_tensors(data_torch, new_name, bid)
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del self._experts_cache[bid]
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return
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yield from super().modify_tensors(data_torch, name, bid)
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