from __future__ import annotations
import math
import re
from typing import Callable, Iterable, TYPE_CHECKING
import torch
if TYPE_CHECKING:
from torch import Tensor
from .base import MmprojModel, ModelBase, TextModel, _MISTRAL_COMMON_DATASET_MEAN, _MISTRAL_COMMON_DATASET_STD, gguf
from .qwen import Qwen3Model
@ModelBase.register("StepVLForConditionalGeneration", "Step3p7ForConditionalGeneration")
@ModelBase.example("stepfun-ai/Step3-VL-10B", "stepfun-ai/Step-3.7-Flash")
class Step3VLVisionModel(MmprojModel):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
assert self.hparams_vision is not None
if not self.hparams_vision.get("intermediate_size"):
hidden_size = self.hparams_vision.get("hidden_size") or self.hparams_vision.get("width") or 0
assert hidden_size > 0
mlp_ratio = float(self.hparams_vision.get("mlp_ratio", 8960 / 1536))
self.hparams_vision["intermediate_size"] = int(round(hidden_size * mlp_ratio))
self.preprocessor_config.setdefault("image_mean", list(_MISTRAL_COMMON_DATASET_MEAN))
self.preprocessor_config.setdefault("image_std", list(_MISTRAL_COMMON_DATASET_STD))
def set_gguf_parameters(self):
super().set_gguf_parameters()
assert self.hparams_vision is not None
projector_stride = int(self.global_config.get("understand_projector_stride", -1))
hidden_size = int(self.hparams_vision.get("hidden_size", self.hparams_vision.get("width", -1)))
num_layers = int(self.hparams_vision.get("num_hidden_layers", self.hparams_vision.get("layers", -1)))
assert (projector_stride, int(self.hparams_vision.get("image_size", -1)), hidden_size, num_layers) == (2, 728, 1536, 47), (
"current Step3-VL conversion path is only validated for Step3-VL-10B"
)
self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.STEP3VL)
self.gguf_writer.add_vision_attention_layernorm_eps(float(self.hparams_vision.get("layer_norm_eps", 1e-5)))
self.gguf_writer.add_vision_projector_scale_factor(projector_stride ** 2)
# 3024 max resize comes from step3-vl-10b processing_step3.py.
self.gguf_writer.add_vision_preproc_image_size(3024)
def tensor_force_quant(self, name, new_name, bid, n_dims):
if ".position_embd." in new_name:
return gguf.GGMLQuantizationType.F32
if ("mm.0." in new_name or "mm.1." in new_name) and new_name.endswith(".weight"):
return gguf.GGMLQuantizationType.F16 if self.ftype == gguf.LlamaFileType.MOSTLY_F16 else 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:
name, gen = item
if name.startswith(("model.", "lm_head.")):
return None
return super().filter_tensors(item)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
if name.startswith("vision_model.vit_downsampler"):
match = re.match(r"vision_model\.vit_downsampler(\d+)\.(weight|bias)", name)
if match is None:
raise ValueError(f"Unexpected Step3-VL projector tensor {name!r}")
proj_id = int(match.group(1)) - 1
suffix = f".{match.group(2)}"
yield (self.format_tensor_name(gguf.MODEL_TENSOR.V_MMPROJ, proj_id, suffix=suffix), data_torch)
return
if name == "vit_large_projector.weight":
yield (self.format_tensor_name(gguf.MODEL_TENSOR.V_MMPROJ_FC), data_torch)
return
if name.startswith("vision_model."):
if name == "vision_model.positional_embedding":
name += ".weight"
elif name.endswith(".gamma") and ".ls_" in name:
name = name.removesuffix(".gamma") + ".weight"
name = name.replace("attn.in_proj_weight", "attn.in_proj.weight")
name = name.replace("attn.in_proj_bias", "attn.in_proj.bias")
yield from super().modify_tensors(data_torch, name, bid)
@ModelBase.register("StepVLForConditionalGeneration")
@ModelBase.example("stepfun-ai/Step3-VL-10B")
class Step3VLTextModel(Qwen3Model):
model_arch = gguf.MODEL_ARCH.QWEN3
@ModelBase.register("Step3p5ForCausalLM", "Step3p7ForConditionalGeneration")
@ModelBase.example("stepfun-ai/Step-3.7-Flash")
class Step35Model(TextModel):
model_arch = gguf.MODEL_ARCH.STEP35
supports_mtp_export = True
# The --mtp / --no-mtp toggles are ModelBase.mtp_only / no_mtp (set in
# convert_hf_to_gguf.py main()). Unlike Qwen3.5, which stores MTP under a
# `mtp.*` namespace, Step3.5 appends MTP layers at
# `model.layers.{num_hidden_layers + i}`, so we filter them by layer index.
# The trunk layer count is captured before indexing so the classmethod
# filter_tensors can tell the appended MTP block(s) apart from the trunk.
_n_main_layers: int | None = None
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# NextN/MTP layers are appended past num_hidden_layers; extend the
# tensor map to cover them so the MTP block's tensors get correctly
# indexed names. When --no-mtp drops the MTP blocks, fall back to the
# base num_hidden_layers so we don't reserve unused slots.
n_nextn = int(self.hparams.get("num_nextn_predict_layers", 0))
if n_nextn > 0 and not self.no_mtp:
self.block_count += n_nextn
self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
def index_tensors(self, remote_hf_model_id: str | None = None):
# filter_tensors is a classmethod and can't reach self.hparams; stash
# the trunk layer count here (before indexing runs) so it can detect
# the appended MTP layers by index.
hparams = {**self.hparams, **self.hparams.get("text_config", {})}
key = next((k for k in ["n_layers", "num_hidden_layers", "n_layer", "num_layers"] if k in hparams), None)
type(self)._n_main_layers = hparams.get(key)
return super().index_tensors(remote_hf_model_id=remote_hf_model_id)
def set_gguf_parameters(self):
rope_theta = self.hparams.get("rope_theta")
if isinstance(rope_theta, list):
self.hparams["rope_theta"] = float(rope_theta[0])
self.hparams["local_rope_theta"] = float(rope_theta[1])
self.rope_parameters["rope_theta"] = self.hparams["rope_theta"]
self.rope_parameters["sliding_attention"] = {"rope_theta": self.hparams["local_rope_theta"]}
super().set_gguf_parameters()
layer_types = self.hparams.get("layer_types") or []
partial_rotary_factors = self.hparams.get("partial_rotary_factors") or []
attn_other = self.hparams.get("attention_other_setting") or {}
n_head_base = self.hparams["num_attention_heads"]
n_kv_base = self.hparams["num_attention_groups"]
n_head_swa = attn_other.get("num_attention_heads", n_head_base)
n_kv_swa = attn_other.get("num_attention_groups", n_kv_base)
n_nextn = int(self.hparams.get("num_nextn_predict_layers", 0))
# The Step3p5 HF checkpoint stores layer_types/partial_rotary_factors
# entries for the MTP blocks past num_hidden_layers; preserve them so
# the MTP layer's attention shape, SWA flag, and partial RoPE dim are
# set correctly. Pad with full-attention defaults if the checkpoint
# truncated them.
def _pad(arr, n, default):
arr = list(arr)
if len(arr) < n:
arr = arr + [default] * (n - len(arr))
return arr[:n]
layer_types = _pad(layer_types, self.block_count, "full_attention")
partial_rotary_factors = _pad(
partial_rotary_factors,
self.block_count,
0.5, # full_attention default for Step3p5
)
assert [1.0 if lt == "sliding_attention" else 0.5 for lt in layer_types] == partial_rotary_factors
head_arr = [n_head_swa if lt == "sliding_attention" else n_head_base for lt in layer_types]
kv_arr = [n_kv_swa if lt == "sliding_attention" else n_kv_base for lt in layer_types]
swa_pat = [lt == "sliding_attention" for lt in layer_types]
self.gguf_writer.add_head_count(head_arr)
self.gguf_writer.add_head_count_kv(kv_arr)
self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
self.gguf_writer.add_sliding_window_pattern(swa_pat)
self.gguf_writer.add_value_length(self.hparams["head_dim"])
# MoE params
self.gguf_writer.add_expert_count(self.hparams["moe_num_experts"])
self.gguf_writer.add_expert_used_count(self.hparams["moe_top_k"])
self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
self.gguf_writer.add_expert_shared_feed_forward_length(self.hparams["share_expert_dim"])
if (moe_router_scaling_factor := self.hparams.get("moe_router_scaling_factor")) is not None:
self.gguf_writer.add_expert_weights_scale(moe_router_scaling_factor)
if (norm_expert_weight := self.hparams.get("norm_expert_weight")) is not None:
self.gguf_writer.add_expert_weights_norm(norm_expert_weight)
# leading dense blocks
leading_dense = 0
moe_layers_enum = self.hparams.get("moe_layers_enum")
if isinstance(moe_layers_enum, str) and moe_layers_enum.strip():
moe_layers = sorted(int(i) for i in moe_layers_enum.strip().split(","))
if moe_layers:
leading_dense = max(0, moe_layers[0])
self.gguf_writer.add_leading_dense_block_count(leading_dense)
self.gguf_writer.add_moe_every_n_layers(int(self.hparams.get("moe_every_n_layer", 1)))
self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("rms_norm_eps", 1e-5))
# Optional per-layer SwiGLU clamps. MTP layers default to no clamping (0.0).
if (limits := self.hparams.get("swiglu_limits")) is not None:
limits_f = _pad(
[0.0 if v is None else float(v) for v in limits],
self.block_count,
0.0,
)
self.gguf_writer.add_swiglu_clamp_exp(limits_f)
if (limits_shared := self.hparams.get("swiglu_limits_shared")) is not None:
limits_shared_f = _pad(
[0.0 if v is None else float(v) for v in limits_shared],
self.block_count,
0.0,
)
self.gguf_writer.add_swiglu_clamp_shexp(limits_shared_f)
if n_nextn > 0 and not self.no_mtp:
self.gguf_writer.add_nextn_predict_layers(n_nextn)
@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
# Map router bias (expert selection bias) to a GGUF bias tensor
if name.endswith(".moe.router_bias"):
name += ".bias"
# Step3.5 appends the MTP block(s) past num_hidden_layers.
assert cls._n_main_layers is not None
is_mtp = (m := re.match(r"model\.layers\.(\d+)\.", name)) is not None and int(m.group(1)) >= cls._n_main_layers
# --no-mtp: drop the appended MTP block(s) entirely.
if is_mtp and cls.no_mtp:
return None
# --mtp: keep ONLY MTP-block tensors plus the shared embeddings/norm/
# lm_head (so the resulting GGUF carries just the draft head).
if cls.mtp_only and not is_mtp and name not in (
"model.embed_tokens.weight", "model.norm.weight", "lm_head.weight",
):
return None
# The checkpoint nests the per-MTP-layer shared head under
# `model.layers.{N+i}.transformer.shared_head.{norm,output}.weight`;
# strip the `transformer.` infix and rename `output` → `head` so the
# existing NEXTN_SHARED_HEAD_{NORM,HEAD} tensor mapping picks them up.
# Mirrors vllm's `_rewrite_spec_layer_name` (step3p5_mtp.py).
if is_mtp:
name = name.replace(".transformer.", ".")
name = name.replace("shared_head.output", "shared_head.head")
return name, gen
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
if name.endswith("norm.weight"):
data_torch += 1.0
if name.endswith((".self_attn.g_proj.weight", ".moe.gate.weight", ".moe.up_proj.weight", ".moe.gate_proj.weight", ".moe.down_proj.weight")):
data_torch = data_torch.squeeze().contiguous()
yield from super().modify_tensors(data_torch, name, bid)
def prepare_metadata(self, vocab_only: bool):
from_dir = self.fname_out.is_dir()
super().prepare_metadata(vocab_only=vocab_only)
# Mirror Qwen3.5's behavior: when emitting a draft-only file into a
# directory, prefix with "mtp-" so it doesn't collide with the trunk.
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 generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
# Step35 can optionally use Llama-3 style RoPE scaling (HF: rope_scaling.rope_type == "llama3").
# llama.cpp represents this via a single extra tensor: "rope_freqs.weight" (aka MODEL_TENSOR.ROPE_FREQS).
rope_params = self.rope_parameters.get("full_attention", self.rope_parameters)
rope_type = rope_params.get("rope_type") or ""
if rope_type.lower() != "llama3":
return
# Step35 configs can carry per-layer rope_theta as a list; for llama3 rope factors we use the base value.
rope_theta = self.hparams.get("rope_theta", 10000.0)
if isinstance(rope_theta, list):
rope_theta = rope_theta[0]
base = float(rope_theta)
if (storage_dim := self.hparams.get("head_dim")) is None:
storage_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
storage_dim = int(storage_dim)
# Llama 3 factors apply only to the rotary dims used by full_attention layers
# (partial_rotary_factor * head_dim). Remaining slots are padded with 1.0 so
# sliding_attention layers remain unaffected. set_gguf_parameters already
# guarantees at least one full_attention layer.
layer_types = (self.hparams.get("layer_types") or [])[: self.block_count]
partial_rotary_factors = (self.hparams.get("partial_rotary_factors") or [])[: self.block_count]
full_attention_factor = next(
float(f) for lt, f in zip(layer_types, partial_rotary_factors) if lt == "full_attention"
)
rotary_dim = int(storage_dim * full_attention_factor)
freqs = 1.0 / (base ** (torch.arange(0, rotary_dim, 2, dtype=torch.float32) / rotary_dim))
factor = float(rope_params.get("factor", 8.0))
low_freq_factor = float(rope_params.get("low_freq_factor", 1.0))
high_freq_factor = float(rope_params.get("high_freq_factor", 4.0))
old_context_len = int(rope_params.get("original_max_position_embeddings", 8192))
low_freq_wavelen = old_context_len / low_freq_factor
high_freq_wavelen = old_context_len / high_freq_factor
rope_factors: list[float] = []
for freq in freqs:
wavelen = 2 * math.pi / float(freq)
if wavelen < high_freq_wavelen:
rope_factors.append(1.0)
elif wavelen > low_freq_wavelen:
rope_factors.append(factor)
else:
smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
rope_factors.append(1.0 / ((1.0 - smooth) / factor + smooth))
# Pad to head_dim/2 with 1.0 so non-scaled layers remain neutral.
if len(rope_factors) < storage_dim // 2:
rope_factors.extend([1.0] * (storage_dim // 2 - len(rope_factors)))
yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))