from __future__ import annotations
import json
from typing import Iterable, TYPE_CHECKING
import torch
if TYPE_CHECKING:
from torch import Tensor
from .base import ModelBase, TextModel, gguf
@ModelBase.register("PlamoForCausalLM")
@ModelBase.example("pfnet/plamo-13b")
class PlamoModel(TextModel):
model_arch = gguf.MODEL_ARCH.PLAMO
def set_vocab(self):
self._set_vocab_sentencepiece()
def set_gguf_parameters(self):
hparams = self.hparams
self.gguf_writer.add_context_length(4096) # not in config.json
self.gguf_writer.add_embedding_length(hparams["hidden_size"])
self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_head_count(hparams["num_attention_heads"])
self.gguf_writer.add_head_count_kv(5) # hparams["num_key_value_heads"]) is wrong
self.gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
self.gguf_writer.add_file_type(self.ftype)
def shuffle_attn_q_weight(self, data_torch):
assert data_torch.size() == (5120, 5120)
data_torch = data_torch.reshape(8, 5, 128, 5120)
data_torch = torch.permute(data_torch, (1, 0, 2, 3))
data_torch = torch.reshape(data_torch, (5120, 5120))
return data_torch
def shuffle_attn_output_weight(self, data_torch):
assert data_torch.size() == (5120, 5120)
data_torch = data_torch.reshape(5120, 8, 5, 128)
data_torch = torch.permute(data_torch, (0, 2, 1, 3))
data_torch = torch.reshape(data_torch, (5120, 5120))
return data_torch
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
new_name = self.map_tensor_name(name)
# shuffle for broadcasting of gqa in ggml_mul_mat
if new_name.endswith("attn_q.weight"):
data_torch = self.shuffle_attn_q_weight(data_torch)
elif new_name.endswith("attn_output.weight"):
data_torch = self.shuffle_attn_output_weight(data_torch)
yield from super().modify_tensors(data_torch, name, bid)
@ModelBase.register("Plamo2ForCausalLM", "PLaMo2ForCausalLM")
@ModelBase.example("pfnet/plamo-2-1b")
class Plamo2Model(TextModel):
model_arch = gguf.MODEL_ARCH.PLAMO2
def set_vocab(self):
self._set_vocab_plamo()
def set_gguf_parameters(self):
hparams = self.hparams
self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
# Which layers are Mamba layers
# PLaMo 2 uses mamba_step to indicate the pattern (e.g., 2 means every other layer)
# This logic matches modeling_plamo.py's is_mamba function
mamba_step = hparams.get("mamba_step", 2)
mamba_enabled = hparams.get("mamba_enabled", True)
num_key_value_heads = []
num_attention_heads = []
if mamba_enabled:
for i in range(self.block_count):
if self.block_count <= (mamba_step // 2):
# use attention in last layer
is_mamba = (i != self.block_count - 1)
else:
is_mamba = (i % mamba_step) != (mamba_step // 2)
if is_mamba:
num_key_value_heads.append(0)
num_attention_heads.append(0)
else:
num_key_value_heads.append(hparams.get("num_key_value_heads", 4))
num_attention_heads.append(hparams.get("num_attention_heads", 32))
if num_key_value_heads and num_attention_heads:
self.gguf_writer.add_head_count_kv(num_key_value_heads)
self.gguf_writer.add_head_count(num_attention_heads)
self.gguf_writer.add_context_length(hparams.get("max_position_embeddings", 2048))
self.gguf_writer.add_embedding_length(hparams.get("hidden_size", 4096))
self.gguf_writer.add_key_length(hparams.get("hidden_size_per_head", 128))
self.gguf_writer.add_value_length(hparams.get("hidden_size_per_head", 128))
self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_layer_norm_rms_eps(hparams.get("rms_norm_eps", 1e-06))
self.gguf_writer.add_rope_freq_base(self.rope_parameters.get("rope_theta", 10000))
# Mamba parameters
self.gguf_writer.add_ssm_state_size(hparams.get("mamba_d_state", 64))
self.gguf_writer.add_ssm_conv_kernel(hparams.get("mamba_d_conv", 4))
self.gguf_writer.add_ssm_time_step_rank(hparams.get("mamba_num_heads", 64))
intermediate_size = hparams.get("mamba_num_heads", 64) * hparams.get("hidden_size_per_head", 128)
self.gguf_writer.add_ssm_inner_size(intermediate_size)
self.gguf_writer.add_ssm_group_count(0)
# MLP feed forward parameters (for attention layers)
self.gguf_writer.add_feed_forward_length(hparams.get("intermediate_size", 13312))
self.gguf_writer.add_file_type(self.ftype)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
if name.endswith(".A_log"):
data_torch = -torch.exp(data_torch)
elif name.endswith(".dt_bias"):
name = name.rpartition(".dt_bias")[0] + ".dt_proj.bias"
elif name.endswith(".dt_norm_weight"):
name = name.rpartition(".dt_norm_weight")[0] + ".dt_norm.weight"
elif name.endswith(".B_norm_weight"):
name = name.rpartition(".B_norm_weight")[0] + ".B_norm.weight"
elif name.endswith(".C_norm_weight"):
name = name.rpartition(".C_norm_weight")[0] + ".C_norm.weight"
elif name.endswith(".k_weight"):
name = name.rpartition(".k_weight")[0] + ".k.weight"
elif name.endswith(".q_weight"):
name = name.rpartition(".q_weight")[0] + ".q.weight"
elif name.endswith(".conv1d.weight"):
data_torch = torch.squeeze(data_torch) # remove (, 1, )
assert data_torch.ndim == 2
elif name.endswith(".pre_mixer_norm.weight"):
data_torch += 1.0
elif name.endswith(".post_mixer_norm.weight"):
data_torch += 1.0 / 5
elif name.endswith(".pre_mlp_norm.weight"):
data_torch += 1.0
elif name.endswith(".post_mlp_norm.weight"):
data_torch += 1.0 / (5**1.5)
elif name.endswith(".norm.weight"):
data_torch += 1.0
yield from super().modify_tensors(data_torch, name, bid)
@ModelBase.register("Plamo3ForCausalLM", "PLaMo3ForCausalLM")
# [TAG_HF_EXAMPLE_GATED] pfnet/plamo-3-nict-2b-base is gated
@ModelBase.example("midorin-Linux/plamo-3-12b-self-merged-base")
class Plamo3Model(TextModel):
model_arch = gguf.MODEL_ARCH.PLAMO3
def set_vocab(self):
self._set_vocab_plamo()
tokenizer_config_path = self.dir_model / "tokenizer_config.json"
tokenizer_config = {}
if tokenizer_config_path.is_file():
with open(tokenizer_config_path, encoding="utf-8") as f:
tokenizer_config = json.load(f)
chat_template = tokenizer_config.get("chat_template")
chat_template_jinja = self.dir_model / "chat_template.jinja"
if chat_template_jinja.is_file():
with open(chat_template_jinja, encoding="utf-8") as f:
chat_template = f.read()
if chat_template:
self.gguf_writer.add_chat_template(chat_template)
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
if (sliding_window := self.find_hparam(["window_size", "sliding_window"], optional=True)) is not None:
self.gguf_writer.add_sliding_window(sliding_window)
self.gguf_writer.add_sliding_window_pattern(self.hparams["sliding_window_pattern"])
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
if name.endswith(".pre_mixer_norm.weight"):
data_torch = data_torch + 1.0
elif name.endswith(".post_mixer_norm.weight"):
data_torch = data_torch + 1.0 / 5
elif name.endswith(".pre_mlp_norm.weight"):
data_torch = data_torch + 1.0
elif name.endswith(".post_mlp_norm.weight"):
data_torch = data_torch + 1.0 / (5**1.5)
elif name.endswith((".mixer.q_norm.weight", ".mixer.k_norm.weight")):
data_torch = data_torch + 1.0
elif name.endswith(".norm.weight"):
data_torch = data_torch + 1.0
yield from super().modify_tensors(data_torch, name, bid)