llama.cpp/conversion/hrm_text.py

from __future__ import annotations

import re

from typing import Iterable, TYPE_CHECKING

if TYPE_CHECKING:
    from torch import Tensor

from .base import ModelBase, TextModel, gguf


@ModelBase.register("HrmTextForCausalLM")
@ModelBase.example("danish-foundation-models/DFM-Mimir")
class HrmTextModel(TextModel):
    model_arch = gguf.MODEL_ARCH.HRM_TEXT

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        # training-style configs store the per-stack count in num_hidden_layers,
        # transformers-style configs keep it in num_layers_per_stack
        self.layers_per_stack = self.hparams.get("num_layers_per_stack") or self.hparams["num_hidden_layers"]
        self.h_cycles = self.hparams["H_cycles"]
        self.l_cycles = self.hparams["L_cycles"]

        # block_count is the expanded cache-slot count; the file only holds
        # 2 * layers_per_stack physical blocks
        self.block_count = self.layers_per_stack * self.h_cycles * (self.l_cycles + 1)
        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, 2 * self.layers_per_stack)

    def set_vocab(self):
        self._set_vocab_gpt2()

    def set_gguf_parameters(self):
        super().set_gguf_parameters()

        head_dim = self.hparams.get("head_dim") or self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
        self.gguf_writer.add_rope_dimension_count(head_dim)
        self.gguf_writer.add_embedding_scale(self.hparams["embedding_scale"])
        self.gguf_writer.add_hrm_layers_per_stack(self.layers_per_stack)
        self.gguf_writer.add_hrm_h_cycles(self.h_cycles)
        self.gguf_writer.add_hrm_l_cycles(self.l_cycles)
        self.gguf_writer.add_hrm_prefix_lm(bool(self.hparams.get("prefix_lm", False)))

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        if name == "model.embed_tokens.weight":
            yield self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD), data_torch
            return
        if name == "lm_head.weight":
            yield self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch
            return
        if name == "model.z_L_init":
            yield self.format_tensor_name(gguf.MODEL_TENSOR.HRM_Z_L_INIT, suffix=""), data_torch
            return

        match = re.fullmatch(r"model\.([LH])_module\.layers\.(\d+)\.(.+)", name)
        if match is None:
            raise ValueError(f"can not map tensor: {name}")

        stack, layer_s, tensor_name = match.groups()
        # the L stack occupies blocks [0, layers_per_stack), the H stack follows it
        layer_idx = int(layer_s) + (self.layers_per_stack if stack == "H" else 0)

        if tensor_name == "attn.gqkv_proj.weight":
            gate, q, k, v = data_torch.chunk(4, dim=0)
            yield self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_GATE, layer_idx), gate.contiguous()
            yield self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, layer_idx), q.contiguous()
            yield self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, layer_idx), k.contiguous()
            yield self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, layer_idx), v.contiguous()
        elif tensor_name == "mlp.gate_up_proj.weight":
            gate, up = data_torch.chunk(2, dim=0)
            yield self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, layer_idx), gate.contiguous()
            yield self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, layer_idx), up.contiguous()
        else:
            if tensor_name.startswith("attn."):
                tensor_name = "self_attn." + tensor_name[len("attn."):]
            tensor_name = "model.layers.{bid}." + tensor_name
            yield from super().modify_tensors(data_torch, tensor_name.format(bid=layer_idx), layer_idx)