conversion/grovemoe.py

from __future__ import annotations

from typing import Iterable, TYPE_CHECKING

import torch

if TYPE_CHECKING:
    from torch import Tensor

from .base import ModelBase, TextModel, gguf, logger


@ModelBase.register("GroveMoeForCausalLM", "modeling_grove_moe.GroveMoeForCausalLM")
class GroveMoeModel(TextModel):
    model_arch = gguf.MODEL_ARCH.GROVEMOE

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
            logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}")
        # FIXME?: Hardcoded https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L299
        self.gguf_writer.add_expert_chunk_feed_forward_length(self.hparams.get("head_dim") or 128)
        # FIXME?: Hardcoded https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L298
        self.gguf_writer.add_experts_per_group(2)
        # FIXME?: Hardcoded https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L376
        self.gguf_writer.add_expert_group_scale(0.05)

    _experts: list[dict[str, Tensor]] | None = None
    _chunk_experts: list[dict[str, Tensor]] | None = None

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if name.endswith(".expert_bias"):
            # FIXME?: Unused https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L303
            return

        # process the experts separately
        if name.find("chunk_experts") != -1:
            n_experts = self.find_hparam(["num_local_experts", "num_experts"]) // 2 # see add_experts_per_group
            assert bid is not None

            if self._chunk_experts is None:
                self._chunk_experts = [{} for _ in range(self.block_count)]

            self._chunk_experts[bid][name] = data_torch

            if len(self._chunk_experts[bid]) >= n_experts * 3:
                # merge the experts into a single 3d tensor
                for w_name in ["down_proj", "gate_proj", "up_proj"]:
                    datas: list[Tensor] = []

                    for xid in range(n_experts):
                        ename = f"model.layers.{bid}.mlp.chunk_experts.{xid}.{w_name}.weight"
                        datas.append(self._chunk_experts[bid][ename])
                        del self._chunk_experts[bid][ename]

                    data_torch = torch.stack(datas, dim=0)

                    merged_name = f"model.layers.{bid}.mlp.chunk_experts.{w_name}.weight"

                    yield from super().modify_tensors(data_torch, merged_name, bid)
                return
            else:
                return
        elif name.find("experts") != -1:
            n_experts = self.find_hparam(["num_local_experts", "num_experts"])
            assert bid is not None

            if self._experts is None:
                self._experts = [{} for _ in range(self.block_count)]

            self._experts[bid][name] = data_torch

            if len(self._experts[bid]) >= n_experts * 3:
                # merge the experts into a single 3d tensor
                for w_name in ["down_proj", "gate_proj", "up_proj"]:
                    datas: list[Tensor] = []

                    for xid in range(n_experts):
                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
                        datas.append(self._experts[bid][ename])
                        del self._experts[bid][ename]

                    data_torch = torch.stack(datas, dim=0)

                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"

                    yield from super().modify_tensors(data_torch, merged_name, bid)
                return
            else:
                return

        yield from super().modify_tensors(data_torch, name, bid)

    def prepare_tensors(self):
        super().prepare_tensors()

        if self._chunk_experts is not None:
            # flatten `list[dict[str, Tensor]]` into `list[str]`
            chunk_experts = [k for d in self._chunk_experts for k in d.keys()]
            if len(chunk_experts) > 0:
                raise ValueError(f"Unprocessed adjugate experts: {chunk_experts}")

        if self._experts is not None:
            # flatten `list[dict[str, Tensor]]` into `list[str]`
            experts = [k for d in self._experts for k in d.keys()]
            if len(experts) > 0:
                raise ValueError(f"Unprocessed experts: {experts}")