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Merge branch 'master' into compilade/mamba2

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Francis Couture-Harpin
2025-06-18 23:59:21 -04:00
parent 0b6f6becb4 8d94713654
commit a42f239418
91 changed files with 3604 additions and 1088 deletions
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110
convert_hf_to_gguf.py
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@ -519,7 +519,7 @@ class TextModel(ModelBase):
def set_gguf_parameters(self):
self.gguf_writer.add_block_count(self.block_count)
if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx", "n_positions"], optional=True)) is not None:
if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx", "n_positions", "max_length"], optional=True)) is not None:
self.gguf_writer.add_context_length(n_ctx)
logger.info(f"gguf: context length = {n_ctx}")
@ -556,11 +556,8 @@ class TextModel(ModelBase):
logger.info(f"gguf: experts used count = {n_experts_used}")
if (head_dim := self.hparams.get("head_dim")) is not None:
# Workaround for incorrect AutoConfig value for DeepSeekV3 (is set correctly in DeepSeekV2Model class)
# https://github.com/huggingface/transformers/blob/19224c3642705c5b6988c9f5f4251f83323d05ae/src/transformers/models/deepseek_v3/configuration_deepseek_v3.py#L210
if self.hparams.get("model_type") != "deepseek_v3":
self.gguf_writer.add_key_length(head_dim)
self.gguf_writer.add_value_length(head_dim)
self.gguf_writer.add_key_length(head_dim)
self.gguf_writer.add_value_length(head_dim)
self.gguf_writer.add_file_type(self.ftype)
logger.info(f"gguf: file type = {self.ftype}")
@ -1901,9 +1898,7 @@ class LlamaModel(TextModel):
hparams = self.hparams
self.gguf_writer.add_vocab_size(hparams["vocab_size"])
if "head_dim" in hparams:
rope_dim = hparams["head_dim"]
else:
if (rope_dim := hparams.get("head_dim")) is None:
rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
self.gguf_writer.add_rope_dimension_count(rope_dim)
@ -1985,7 +1980,8 @@ class LlamaModel(TextModel):
if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
if rope_scaling.get("rope_type", '').lower() == "llama3":
base = self.hparams.get("rope_theta", 10000.0)
dim = self.hparams.get("head_dim", self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
if (dim := self.hparams.get("head_dim")) is None:
dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
factor = rope_scaling.get("factor", 8.0)
@ -2020,6 +2016,20 @@ class LlamaModel(TextModel):
raise ValueError(f"Unprocessed experts: {experts}")
@ModelBase.register("ArceeForCausalLM")
class ArceeModel(LlamaModel):
model_arch = gguf.MODEL_ARCH.ARCEE
def set_gguf_parameters(self):
super().set_gguf_parameters()
self._try_set_pooling_type()
rope_scaling = self.hparams.get("rope_scaling") or {}
if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling:
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
@ModelBase.register(
"LlavaForConditionalGeneration", # pixtral
"Mistral3ForConditionalGeneration", # mistral small 3.1
@ -2307,9 +2317,7 @@ class DeciModel(TextModel):
hparams = self.hparams
self.gguf_writer.add_vocab_size(hparams["vocab_size"])
if "head_dim" in hparams:
rope_dim = hparams["head_dim"]
else:
if (rope_dim := hparams.get("head_dim")) is None:
rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
self.gguf_writer.add_rope_dimension_count(rope_dim)
@ -2349,7 +2357,8 @@ class DeciModel(TextModel):
if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
if rope_scaling.get("rope_type", '').lower() == "llama3":
base = self.hparams.get("rope_theta", 10000.0)
dim = self.hparams.get("head_dim", self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
if (dim := self.hparams.get("head_dim")) is None:
dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
factor = rope_scaling.get("factor", 8.0)
@ -3667,9 +3676,7 @@ class InternLM3Model(TextModel):
hparams = self.hparams
self.gguf_writer.add_vocab_size(hparams["vocab_size"])
if "head_dim" in hparams:
rope_dim = hparams["head_dim"]
else:
if (rope_dim := hparams.get("head_dim")) is None:
rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
self.gguf_writer.add_rope_dimension_count(rope_dim)
@ -4062,6 +4069,34 @@ class NomicBertModel(BertModel):
raise ValueError(f"unknown tokenizer: {toktyp}")
@ModelBase.register("NeoBERT", "NeoBERTLMHead", "NeoBERTForSequenceClassification")
class NeoBert(BertModel):
model_arch = gguf.MODEL_ARCH.NEO_BERT
def set_gguf_parameters(self):
super().set_gguf_parameters()
# NeoBERT uses 2/3 of the intermediate size as feed forward length
self.gguf_writer.add_feed_forward_length(int(2 * self.hparams["intermediate_size"] / 3))
self.gguf_writer.add_rope_freq_base(10000.0) # default value for NeoBERT
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
f_rms_eps = self.hparams.get("norm_eps", 1e-6) # default value for NeoBERT
self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps)
logger.info(f"gguf: rms norm epsilon = {f_rms_eps}")
self.gguf_writer.add_pooling_type(gguf.PoolingType.CLS) # https://huggingface.co/chandar-lab/NeoBERT#how-to-use
def modify_tensors(self, data_torch, name, bid):
if name.startswith("decoder."):
return []
if name.startswith("model."):
name = name[6:]
return super().modify_tensors(data_torch, name, bid)
@ModelBase.register("XLMRobertaModel", "XLMRobertaForSequenceClassification")
class XLMRobertaModel(BertModel):
model_arch = gguf.MODEL_ARCH.BERT
@ -5158,9 +5193,7 @@ class DeepseekModel(TextModel):
def set_gguf_parameters(self):
super().set_gguf_parameters()
hparams = self.hparams
if "head_dim" in hparams:
rope_dim = hparams["head_dim"]
else:
if (rope_dim := hparams.get("head_dim")) is None:
rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
self.gguf_writer.add_rope_dimension_count(rope_dim)
@ -5364,6 +5397,34 @@ class DeepseekV2Model(TextModel):
raise ValueError(f"Unprocessed experts: {experts}")
@ModelBase.register("Dots1ForCausalLM")
class Dots1Model(Qwen2MoeModel):
model_arch = gguf.MODEL_ARCH.DOTS1
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.hparams["num_experts"] = self.hparams["n_routed_experts"]
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_leading_dense_block_count(self.hparams["first_k_dense_replace"])
self.gguf_writer.add_expert_shared_count(self.hparams["n_shared_experts"])
self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])
self.gguf_writer.add_expert_weights_norm(self.hparams["norm_topk_prob"])
if self.hparams["scoring_func"] == "noaux_tc":
self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
else:
raise ValueError(f"Unsupported scoring_func value: {self.hparams['scoring_func']}")
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
if name.endswith("e_score_correction_bias"):
name = name.replace("e_score_correction_bias", "e_score_correction.bias")
if "shared_experts" in name:
return [(self.map_tensor_name(name), data_torch)]
return super().modify_tensors(data_torch, name, bid)
@ModelBase.register("PLMForCausalLM")
class PLMModel(TextModel):
model_arch = gguf.MODEL_ARCH.PLM
@ -6022,7 +6083,8 @@ class ExaoneModel(TextModel):
if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
if rope_scaling.get("rope_type", '').lower() == "llama3":
base = self.hparams.get("rope_theta", 10000.0)
dim = self.hparams.get("head_dim", self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
if (dim := self.hparams.get("head_dim")) is None:
dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
factor = rope_scaling.get("factor", 8.0)
@ -6134,7 +6196,8 @@ class BailingMoeModel(TextModel):
def set_gguf_parameters(self):
super().set_gguf_parameters()
hparams = self.hparams
rope_dim = hparams.get("head_dim") or hparams["hidden_size"] // hparams["num_attention_heads"]
if (rope_dim := hparams.get("head_dim")) is None:
rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
self.gguf_writer.add_rope_dimension_count(rope_dim)
rope_scaling = self.hparams.get("rope_scaling") or {}
@ -6166,7 +6229,8 @@ class BailingMoeModel(TextModel):
n_head = self.hparams["num_attention_heads"]
n_kv_head = self.hparams.get("num_key_value_heads")
n_embd = self.hparams["hidden_size"]
head_dim = self.hparams.get("head_dim") or n_embd // n_head
if (head_dim := self.hparams.get("head_dim")) is None:
head_dim = n_embd // n_head
output_name = self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT)