from __future__ import annotations
from typing import Any, Callable, Iterable, TYPE_CHECKING
import torch
if TYPE_CHECKING:
from torch import Tensor
from .base import MmprojModel, ModelBase, TextModel, gguf, logger
from .granite import GraniteHybridModel
@ModelBase.register(
"NemotronH_Nano_VL_V2",
"RADIOModel",
)
@ModelBase.example("nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-BF16")
class NemotronNanoV2VLModel(MmprojModel):
# ViT-Huge architecture parameters for RADIO v2.5-h
_vit_hidden_size = 1280
_vit_intermediate_size = 5120
_vit_num_layers = 32
_vit_num_heads = 16
def get_vision_config(self) -> dict[str, Any] | None:
# RADIO config doesn't have standard ViT parameters, so they need to be constructed manually
vision_config = self.global_config.get("vision_config")
if vision_config is None:
return None
# Add ViT-H parameters
vision_config = {
**vision_config,
"hidden_size": self._vit_hidden_size,
"intermediate_size": self._vit_intermediate_size,
"num_hidden_layers": self._vit_num_layers,
"num_attention_heads": self._vit_num_heads,
"image_size": self.global_config.get("force_image_size", 512),
}
return vision_config
def get_audio_config(self) -> dict[str, Any] | None:
return self.global_config.get("sound_config")
def set_gguf_parameters(self):
if "image_mean" not in self.preprocessor_config:
self.preprocessor_config["image_mean"] = [0.485, 0.456, 0.406]
if "image_std" not in self.preprocessor_config:
self.preprocessor_config["image_std"] = [0.229, 0.224, 0.225]
if self.hparams_audio is not None:
self.has_vision_encoder = True
self.has_audio_encoder = True
self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["num_mel_bins"])
self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)
self.gguf_writer.add_audio_subsampling_factor(self.hparams_audio["subsampling_factor"])
self.gguf_writer.add_audio_conv_kernel_size(self.hparams_audio["conv_kernel_size"])
self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.PARAKEET)
self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.NEMOTRON_V2_VL)
else:
self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.NEMOTRON_V2_VL)
super().set_gguf_parameters()
hparams = self.global_config
self.gguf_writer.add_vision_attention_layernorm_eps(1e-6)
self.gguf_writer.add_vision_use_gelu(True)
downsample_ratio = hparams.get("downsample_ratio", 0.5)
self.gguf_writer.add_vision_projector_scale_factor(int(1.0 / downsample_ratio))
def tensor_force_quant(self, name, new_name, bid, n_dims):
if "sound_encoder" in name or new_name.startswith("mm.a."):
if "bias" in new_name or "norm" in new_name:
return gguf.GGMLQuantizationType.F32
if "conv" in new_name and "weight" in new_name:
return gguf.GGMLQuantizationType.F32
return super().tensor_force_quant(name, new_name, bid, n_dims)
@classmethod
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
if (titem := super().filter_tensors(item)) is None:
return None
name, gen = titem
if "input_conditioner" in name:
return None
# mtmd does not support video yet so skip tensors related to video.
if "radio_model.model.patch_generator.video_embedder" in name:
return None
if not name.startswith(("vision_model.radio_model.model.", "mlp1.", "sound_encoder.", "sound_projection.")):
return None
if "patch_generator.pos_embed" in name:
if not name.endswith(".weight"):
name += ".weight"
# num_batches is only used for training not inference.
if "conv.norm" in name and "num_batches" in name:
return None
return name, gen
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# RADIO's pos_embed doesn't have .weight suffix, but clip.cpp expects it
if "patch_generator.pos_embed" in name:
# Downsample position embeddings for fixed 512x512 image size
import torch.nn.functional as F
n_embd = self.hparams["hidden_size"]
image_size = self.global_config.get("force_image_size", 512)
patch_size = self.hparams["patch_size"]
target_patches_per_side = image_size // patch_size # 32
max_patches_per_side = int((data_torch.shape[1]) ** 0.5) # 128
if target_patches_per_side != max_patches_per_side:
# Reshape to grid, interpolate, flatten back
data_torch = data_torch.reshape(1, max_patches_per_side, max_patches_per_side, n_embd)
data_torch = data_torch.permute(0, 3, 1, 2).float() # [1, n_embd, 128, 128]
data_torch = F.interpolate(data_torch, size=(target_patches_per_side, target_patches_per_side),
mode='bilinear', align_corners=True)
data_torch = data_torch.permute(0, 2, 3, 1) # [1, 32, 32, n_embd]
data_torch = data_torch.reshape(1, target_patches_per_side * target_patches_per_side, n_embd)
# Reshape linear patch embedding to conv2d format for ggml_conv_2d
# From [n_embd, patch_size*patch_size*3] to [n_embd, 3, patch_size, patch_size]
if "patch_generator.embedder" in name:
patch_size = self.hparams["patch_size"]
n_embd = self.hparams["hidden_size"]
data_torch = data_torch.reshape(n_embd, 3, patch_size, patch_size)
if "depthwise_conv.weight" in name:
data_torch = data_torch.unsqueeze(-1)
data_torch = data_torch.permute(3, 1, 0, 2).contiguous()
if "pointwise_conv" in name and name.endswith(".weight"):
if len(data_torch.shape) == 3 and data_torch.shape[2] == 1:
data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[1])
if "subsampling.layers" in name and name.endswith(".bias"):
if len(data_torch.shape) == 1:
data_torch = data_torch.reshape(1, -1, 1, 1)
if "pointwise_conv" in name and name.endswith(".bias"):
if len(data_torch.shape) == 1:
data_torch = data_torch.reshape(1, -1, 1, 1)
for mapped_name, tensor in super().modify_tensors(data_torch, name, bid):
if name.startswith("sound_projection.") and mapped_name.startswith("mm.model.mlp."):
mapped_name = mapped_name.replace("mm.model.mlp.", "mm.a.mlp.")
yield mapped_name, tensor
@ModelBase.register("NemotronForCausalLM")
@ModelBase.example("nvidia/Minitron-4B-Base")
class NemotronModel(TextModel):
model_arch = gguf.MODEL_ARCH.NEMOTRON
def set_vocab(self):
self._set_vocab_sentencepiece()
self.gguf_writer.add_pad_token_id(0)
self.gguf_writer.add_unk_token_id(1)
def set_gguf_parameters(self):
super().set_gguf_parameters()
hparams = self.hparams
self.gguf_writer.add_vocab_size(hparams["vocab_size"])
f_norm_eps = self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon", "norm_eps"])
self.gguf_writer.add_layer_norm_eps(f_norm_eps)
# * Partial RoPE
rot_pct = self.rope_parameters["partial_rotary_factor"]
n_embd = self.find_hparam(["hidden_size", "n_embd"])
n_head = self.find_hparam(["num_attention_heads", "n_head"])
self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)
# * RopeScaling for Nemotron
factor = self.hparams.get("factor") or self.rope_parameters.get("factor")
if factor is None:
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
else:
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
self.gguf_writer.add_rope_scaling_factor(factor)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# * Adding +1 to LayerNorm's weights here to implement layernorm1p w/o changing anything on the GGML engine side
# model.layers.{l}.input_layernorm.weight
# model.layers.{l}.post_attention_layernorm.weight
# model.norm.weight
if name.endswith("norm.weight"):
data_torch = data_torch + 1
yield from super().modify_tensors(data_torch, name, bid)
@ModelBase.register("NemotronHForCausalLM")
@ModelBase.example("nvidia/Nemotron-H-8B-Base-8K")
class NemotronHModel(GraniteHybridModel):
"""Hybrid mamba2/attention model from NVIDIA"""
model_arch = gguf.MODEL_ARCH.NEMOTRON_H
is_moe: bool = False
supports_mtp_export = True
def __init__(self, *args, **kwargs):
# We have to determine the correct model architecture (MoE vs non-MoE) before
# calling the parent __init__. This is because the parent constructor
# uses self.model_arch to build the tensor name map, and all MoE-specific
# mappings would be missed if it were called with the default non-MoE arch.
hparams = kwargs.pop("hparams", None)
if hparams is None:
hparams = ModelBase.load_hparams(args[0], self.is_mistral_format)
has_moe_params = (
"num_experts_per_tok" in hparams
or (isinstance(hparams.get("llm_config"), dict) and "num_experts_per_tok" in hparams["llm_config"])
)
if has_moe_params:
self.model_arch = gguf.MODEL_ARCH.NEMOTRON_H_MOE
self.is_moe = True
layers_block_type = hparams.get("layers_block_type")
if layers_block_type is not None:
hparams["num_hidden_layers"] = len(layers_block_type)
super().__init__(*args, hparams=hparams, **kwargs)
# Save the top-level head_dim for later
self.head_dim = self.hparams.get("head_dim", self.hparams.get("attention_head_dim"))
assert self.head_dim is not None, "Could not find the attention head dim in config"
# Don't use expand to calculate d_inner
self.d_inner = self.find_hparam(["num_heads"]) * self.d_model
# Update the ssm / attn / mlp layers
# M: Mamba2, *: Attention, -: MLP
# MoE:
# M: Mamba2, *: Attention, E: Expert
pattern = self.hparams.get("hybrid_override_pattern") or self.hparams.get("layers_block_type")
if pattern is None:
self._ssm_layers = []
self._mlp_layers = []
elif isinstance(pattern, str):
self._ssm_layers = [i for i, val in enumerate(pattern) if val == "M"]
self._mlp_layers = [i for i, val in enumerate(pattern) if val == ("E" if self.is_moe else "-")]
else:
self._ssm_layers = [i for i, val in enumerate(pattern) if val == "mamba"]
self._mlp_layers = [i for i, val in enumerate(pattern) if val == "moe"]
# `--no-mtp` drops it entirely; `--mtp` exports only the MTP head
self._mtp_bid: int | None = None
if self.is_moe and not self.no_mtp:
n_nextn = self.hparams.get("num_nextn_predict_layers", 0) or 0
if n_nextn > 0:
assert n_nextn == 1, (
"NemotronH MTP conversion currently supports num_nextn_predict_layers == 1"
)
self._mtp_bid = self.block_count
self.block_count += 1
# The folded MTP block carries both an attention sub-layer and a
# MoE sub-layer, so register it as both so the per-layer metadata arrays cover it
self._attn_layers.append(self._mtp_bid)
self._mlp_layers.append(self._mtp_bid)
self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
if self.mtp_only and self._mtp_bid is None:
raise ValueError("--mtp was requested, but this model does not contain a supported MTP head")
def get_attn_layers(self):
pattern = self.hparams.get("hybrid_override_pattern") or self.hparams.get("layers_block_type")
if pattern is None:
return []
assert len(pattern) == self.block_count, f"Mismatch between pattern ({len(pattern)}) and block_count ({self.block_count})!"
if isinstance(pattern, str):
return [i for i, val in enumerate(pattern) if val == "*"]
return [i for i, val in enumerate(pattern) if val == "attention"]
@classmethod
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
name, gen = item
if name.startswith("mtp."):
# --no-mtp: drop the MTP head entirely
if cls.no_mtp:
return None
elif cls.mtp_only:
# --mtp: export the MTP head plus the tensors it shares with the target model
# Include lm_head scale sidecars so NVFP4 packing sees them.
keep = name in (
"backbone.embeddings.weight",
"backbone.norm_f.weight",
"lm_head.weight",
"lm_head.weight_scale",
"lm_head.weight_scale_2",
"lm_head.weight_scale_inv",
"lm_head.input_scale",
"lm_head.input_global_scale",
"lm_head.weight_global_scale",
"lm_head.weight_packed",
)
if not keep:
return None
return super().filter_tensors((name, gen))
def prepare_metadata(self, vocab_only: bool):
from_dir = self.fname_out.is_dir()
super().prepare_metadata(vocab_only=vocab_only)
if not self.mtp_only or not from_dir:
return
output_type: str = self.ftype.name.partition("_")[2]
fname_default: str = gguf.naming_convention(
self.metadata.name, self.metadata.basename, self.metadata.finetune,
self.metadata.version, size_label=None, output_type=output_type, model_type=None)
self.fname_out = self.fname_out.parent / f"mtp-{fname_default}.gguf"
def set_gguf_parameters(self):
super().set_gguf_parameters()
head_dim = self.head_dim
if head_dim is None:
raise ValueError("Could not find the attention head dim in config")
self.gguf_writer.add_key_length(head_dim)
self.gguf_writer.add_value_length(head_dim)
# Set feed_forward_length
# NOTE: This will trigger an override warning. This is preferable to
# duplicating all the parent logic
if not self.is_moe:
n_ff = self.find_hparam(["intermediate_size", "n_inner", "hidden_dim"])
self.gguf_writer.add_feed_forward_length([
n_ff if i in self._mlp_layers else 0 for i in range(self.block_count)
])
else:
moe_intermediate_size = self.hparams["moe_intermediate_size"]
self.gguf_writer.add_feed_forward_length([
moe_intermediate_size if i in self._mlp_layers else 0 for i in range(self.block_count)
])
self.gguf_writer.add_expert_used_count(self.hparams["num_experts_per_tok"])
self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
self.gguf_writer.add_expert_shared_feed_forward_length(self.hparams["moe_shared_expert_intermediate_size"])
self.gguf_writer.add_expert_count(self.hparams["n_routed_experts"])
self.gguf_writer.add_expert_shared_count(self.hparams["n_shared_experts"])
self.gguf_writer.add_expert_weights_norm(self.hparams["norm_topk_prob"])
self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])
self.gguf_writer.add_expert_group_count(self.hparams["n_group"])
# number of experts used per token (top-k)
if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None:
self.gguf_writer.add_expert_used_count(n_experts_used)
if (latent_size := self.hparams.get("moe_latent_size")) is not None:
self.gguf_writer.add_moe_latent_size(latent_size)
# MTP head: number of trailing NextN blocks
if self._mtp_bid is not None:
self.gguf_writer.add_nextn_predict_layers(self.hparams["num_nextn_predict_layers"])
def set_vocab(self):
# The NemotronH config uses pattern characters (e.g. '-') that may not
# be supported by the installed transformers version. AutoTokenizer
# internally calls AutoConfig which triggers this parsing failure.
# Using trust_remote_code=True to load the model's own config class.
tokens: list[str] = []
toktypes: list[int] = []
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
# Pad vocab size (from Mamba2Model/GraniteHybridModel)
self.hparams["pad_vocab_size_multiple"] = 8 # Setting this here since GraniteHybridModel.set_vocab() isn't being invoked now.
# From Mamba2Model.set_vocab():
vocab_size = self.hparams["vocab_size"]
pad_vocab = self.hparams.get("pad_vocab_size_multiple", 16)
# ref: https://stackoverflow.com/a/17511341/22827863
vocab_size = -(vocab_size // -pad_vocab) * pad_vocab
self.hparams["vocab_size"] = vocab_size
assert max(tokenizer.vocab.values()) < vocab_size # ty: ignore[unresolved-attribute]
tokpre = self.get_vocab_base_pre(tokenizer)
reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()} # ty: ignore[unresolved-attribute]
added_vocab = tokenizer.get_added_vocab() # ty: ignore[unresolved-attribute]
added_tokens_decoder = tokenizer.added_tokens_decoder # ty: ignore[unresolved-attribute]
for i in range(vocab_size):
if i not in reverse_vocab:
tokens.append(f"[PAD{i}]")
toktypes.append(gguf.TokenType.UNUSED)
else:
token: str = reverse_vocab[i]
if token in added_vocab:
if not added_tokens_decoder[i].normalized:
previous_token = token
token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False)) # ty: ignore[unresolved-attribute, invalid-assignment]
if previous_token != token:
logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer")
if added_tokens_decoder[i].special or self.does_token_look_special(token):
toktypes.append(gguf.TokenType.CONTROL)
else:
token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ") # pre-normalize user-defined spaces
toktypes.append(gguf.TokenType.USER_DEFINED)
else:
toktypes.append(gguf.TokenType.NORMAL)
tokens.append(token)
# From TextModel.set_vocab_gpt2():
self.gguf_writer.add_tokenizer_model("gpt2")
self.gguf_writer.add_tokenizer_pre(tokpre)
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
special_vocab.add_to_gguf(self.gguf_writer)
# The tokenizer _does_ add a BOS token (via post_processor type
# TemplateProcessing) but does not set add_bos_token to true in the
# config, so we need to explicitly override it here.
if not self.is_moe:
self.gguf_writer.add_add_bos_token(True)
_MTP_SPECIAL_RENAMES = {
"mtp.layers.0.enorm.weight": "model.layers.{bid}.enorm.weight",
"mtp.layers.0.hnorm.weight": "model.layers.{bid}.hnorm.weight",
"mtp.layers.0.eh_proj.weight": "model.layers.{bid}.eh_proj.weight",
"mtp.layers.1.norm.weight": "model.layers.{bid}.post_attention_layernorm.weight",
"mtp.layers.1.final_layernorm.weight": "model.layers.{bid}.shared_head.norm.weight",
}
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# mtp.layers.0: NextN input fusion + attention
# mtp.layers.1: MoE + final head norm
if self._mtp_bid is not None and name.startswith(("mtp.layers.0.", "mtp.layers.1.")):
suffix = name.split(".", 3)[3]
bid = self._mtp_bid
renamed = self._MTP_SPECIAL_RENAMES.get(name)
name = renamed.format(bid=bid) if renamed else f"backbone.layers.{bid}.{suffix}"
if self.is_moe and bid is not None:
if name.endswith("mixer.gate.e_score_correction.bias"):
yield from ModelBase.modify_tensors(self, data_torch, name, bid)
return
if name.endswith("mixer.dt_bias"):
new_name = name.replace("dt_bias", "dt.bias")
yield from ModelBase.modify_tensors(self, data_torch, new_name, bid)
return
if name.endswith("mixer.conv1d.weight"):
squeezed_data = data_torch.squeeze()
yield from ModelBase.modify_tensors(self, squeezed_data, name, bid)
return
if name.endswith("mixer.A_log"):
transformed_data = -torch.exp(data_torch)
reshaped_data = transformed_data.squeeze().reshape(-1, 1)
yield from ModelBase.modify_tensors(self, reshaped_data, name, bid)
return
if name.endswith("mixer.D"):
reshaped_data = data_torch.squeeze().reshape(-1, 1)
yield from ModelBase.modify_tensors(self, reshaped_data, name, bid)
return
if name.endswith("mixer.norm.weight"):
reshaped_data = data_torch.reshape(self.n_group, -1)
yield from ModelBase.modify_tensors(self, reshaped_data, name, bid)
return
if name.find("mixer.experts") != -1:
n_experts = self.hparams["n_routed_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 * 2:
# merge the experts into a single tensor
for w_name in ["down_proj", "up_proj"]:
datas: list[Tensor] = []
for xid in range(n_experts):
ename = f"backbone.layers.{bid}.mixer.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 ModelBase.modify_tensors(self, 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._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}")