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convert : add support for DeepSeek V3 model

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This commit is contained in:
Stanisław Szymczyk 2025-01-02 10:14:39 +01:00
parent a45433ba20
commit 0061955a06
5 changed files with 45 additions and 0 deletions
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23
convert_hf_to_gguf.py
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@ -687,6 +687,9 @@ class Model:
if chkhsh == "d4c8f286ea6b520b3d495c4455483cfa2302c0cfcd4be05d781b6a8a0a7cdaf1":
# ref: https://huggingface.co/Infinigence/Megrez-3B-Instruct
res = "megrez"
if chkhsh == "877081d19cf6996e2c4ff0e1236341e9b7bde288f5311a56a937f0afbbb3aeb5":
# ref: https://huggingface.co/deepseek-ai/DeepSeek-V3
res = "deepseek-v3"
if res is None:
logger.warning("\n")
@ -3831,6 +3834,7 @@ class DeepseekModel(Model):
@Model.register("DeepseekV2ForCausalLM")
@Model.register("DeepseekV3ForCausalLM")
class DeepseekV2Model(Model):
model_arch = gguf.MODEL_ARCH.DEEPSEEK2
@ -3852,6 +3856,15 @@ class DeepseekV2Model(Model):
self.gguf_writer.add_expert_count(hparams["n_routed_experts"])
self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"])
self.gguf_writer.add_expert_weights_scale(hparams["routed_scaling_factor"])
self.gguf_writer.add_expert_weights_norm(hparams["norm_topk_prob"])
if hparams["scoring_func"] == "sigmoid":
self.gguf_writer.add_expert_weights_func(gguf.ExpertWeightsFuncType.SIGMOID)
elif hparams["scoring_func"] == "softmax":
self.gguf_writer.add_expert_weights_func(gguf.ExpertWeightsFuncType.SOFTMAX)
else:
raise ValueError(f"Unsupported scoring_func value: {hparams['scoring_func']}")
self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
@ -3864,6 +3877,16 @@ class DeepseekV2Model(Model):
_experts: list[dict[str, Tensor]] | None = None
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# rename e_score_correction_bias tensors
if name.endswith("e_score_correction_bias"):
name = name.replace("e_score_correction_bias", "e_score_correction.bias")
# skip Multi-Token Prediction (MTP) layers
block_count = self.hparams["num_hidden_layers"]
match = re.match(r"model.layers.(\d+)", name)
if match and int(match.group(1)) >= block_count:
return []
# process the experts separately
if name.find("mlp.experts") != -1:
n_experts = self.hparams["n_routed_experts"]

1
convert_hf_to_gguf_update.py
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@ -107,6 +107,7 @@ models = [
{"name": "roberta-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sentence-transformers/stsb-roberta-base"},
{"name": "gigachat", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct"},
{"name": "megrez", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Infinigence/Megrez-3B-Instruct"},
{"name": "deepseek-v3", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V3"},
]

10
gguf-py/gguf/constants.py
View file

@ -102,6 +102,8 @@ class Keys:
EXPERT_USED_COUNT = "{arch}.expert_used_count"
EXPERT_SHARED_COUNT = "{arch}.expert_shared_count"
EXPERT_WEIGHTS_SCALE = "{arch}.expert_weights_scale"
EXPERT_WEIGHTS_NORM = "{arch}.expert_weights_norm"
EXPERT_WEIGHTS_FUNC = "{arch}.expert_weights_func"
POOLING_TYPE = "{arch}.pooling_type"
LOGIT_SCALE = "{arch}.logit_scale"
DECODER_START_TOKEN_ID = "{arch}.decoder_start_token_id"
@ -312,6 +314,7 @@ class MODEL_TENSOR(IntEnum):
FFN_GATE_SHEXP = auto()
FFN_DOWN_SHEXP = auto()
FFN_UP_SHEXP = auto()
FFN_EXPERT_WEIGHTS_B = auto()
ATTN_Q_NORM = auto()
ATTN_K_NORM = auto()
LAYER_OUT_NORM = auto()
@ -496,6 +499,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
MODEL_TENSOR.FFN_GATE_EXP: "blk.{bid}.ffn_gate_exps",
MODEL_TENSOR.FFN_DOWN_EXP: "blk.{bid}.ffn_down_exps",
MODEL_TENSOR.FFN_UP_EXP: "blk.{bid}.ffn_up_exps",
MODEL_TENSOR.FFN_EXPERT_WEIGHTS_B: "blk.{bid}.expert_weights_b",
MODEL_TENSOR.LAYER_OUT_NORM: "blk.{bid}.layer_output_norm",
MODEL_TENSOR.SSM_IN: "blk.{bid}.ssm_in",
MODEL_TENSOR.SSM_CONV1D: "blk.{bid}.ssm_conv1d",
@ -1276,6 +1280,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_GATE_SHEXP,
MODEL_TENSOR.FFN_DOWN_SHEXP,
MODEL_TENSOR.FFN_UP_SHEXP,
MODEL_TENSOR.FFN_EXPERT_WEIGHTS_B,
],
MODEL_ARCH.CHATGLM : [
MODEL_TENSOR.TOKEN_EMBD,
@ -1576,6 +1581,11 @@ class GGMLQuantizationType(IntEnum):
TQ2_0 = 35
class ExpertWeightsFuncType(IntEnum):
SOFTMAX = 1
SIGMOID = 2
# TODO: add GGMLFileType from ggml_ftype in ggml.h

7
gguf-py/gguf/gguf_writer.py
View file

@ -26,6 +26,7 @@ from .constants import (
RopeScalingType,
PoolingType,
TokenType,
ExpertWeightsFuncType,
)
from .quants import quant_shape_from_byte_shape
@ -715,6 +716,12 @@ class GGUFWriter:
def add_expert_weights_scale(self, value: float) -> None:
self.add_float32(Keys.LLM.EXPERT_WEIGHTS_SCALE.format(arch=self.arch), value)
def add_expert_weights_norm(self, value: bool) -> None:
self.add_bool(Keys.LLM.EXPERT_WEIGHTS_NORM.format(arch=self.arch), value)
def add_expert_weights_func(self, value: ExpertWeightsFuncType) -> None:
self.add_uint32(Keys.LLM.EXPERT_WEIGHTS_FUNC.format(arch=self.arch), value.value)
def add_swin_norm(self, value: bool) -> None:
self.add_bool(Keys.LLM.SWIN_NORM.format(arch=self.arch), value)

4
gguf-py/gguf/tensor_mapping.py
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@ -276,6 +276,10 @@ class TensorNameMap:
"model.layers.{bid}.mlp.shared_expert_gate", # qwen2moe
),
MODEL_TENSOR.FFN_EXPERT_WEIGHTS_B: (
"model.layers.{bid}.mlp.gate.e_score_correction", # deepseek-v3
),
# Feed-forward up
MODEL_TENSOR.FFN_UP: (
"gpt_neox.layers.{bid}.mlp.dense_h_to_4h", # gptneox