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minicpm working without uhd

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This commit is contained in:
Xuan Son Nguyen 2025-01-23 12:14:06 +01:00
parent c0d93dd509
commit 8586d23c8a
9 changed files with 77 additions and 2 deletions
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25
convert_hf_to_gguf.py
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@ -2339,6 +2339,7 @@ class MiniCPMVModel(Qwen2Model):
model_arch = gguf.MODEL_ARCH.QWEN2
proj_type: gguf.constants.CLIPProjectorType | None
resampler_n_embd = 0
tok_embd_tensor: Tensor | None = None
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
@ -2361,6 +2362,8 @@ class MiniCPMVModel(Qwen2Model):
for tname, tensor in self.get_tensors():
if tname == "resampler.ln_post.bias":
self.resampler_n_embd = tensor.shape[0]
if tname.endswith("embed_tokens.weight"):
self.tok_embd_tensor = tensor
if self.resampler_n_embd < 2:
raise ValueError("Failed to detect resampler embedding size")
else:
@ -2372,6 +2375,16 @@ class MiniCPMVModel(Qwen2Model):
self.hparams["vision_feature_layer"] = 0
self.v_tensor_map = gguf.get_tensor_name_map(self.vision_arch, self.vparams["num_hidden_layers"])
def get_embd_of_tokens(self, map_token_to_tensor_name: Iterable[tuple[str, str]]) -> Iterable[tuple[str, Tensor]]:
if self.tok_embd_tensor is None:
raise ValueError("Token embedding tensor not found")
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
for token, tensor_name in map_token_to_tensor_name:
tok_id = tokenizer.get_vocab()[token]
row = self.tok_embd_tensor[tok_id]
yield tensor_name, row
def set_gguf_parameters(self):
super().set_gguf_parameters()
# For vision model
@ -2388,6 +2401,14 @@ class MiniCPMVModel(Qwen2Model):
self.format_tensor_name(gguf.MODEL_TENSOR.V_RESMPL_POS_EMBD_K, is_vision=True),
torch.from_numpy(self._get_2d_sincos_pos_embed(self.resampler_n_embd, (70, 70)))
)
added_tokens = [
("<image>", gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_TOK_EMBD_IMAGE ] + ".weight"),
("</image>", gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_TOK_EMBD_END_IMAGE] + ".weight"),
("<slice>", gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_TOK_EMBD_SLICE ] + ".weight"),
("</slice>", gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_TOK_EMBD_END_SLICE] + ".weight"),
]
for tensor_name, tensor in self.get_embd_of_tokens(added_tokens):
yield tensor_name, tensor
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused
@ -2404,6 +2425,7 @@ class MiniCPMVModel(Qwen2Model):
name_k = name.replace("in_proj_", "in_proj_k.") # in_proj_k.(weight|bias)
name_v = name.replace("in_proj_", "in_proj_v.") # in_proj_v.(weight|bias)
return [
# TODO: permute these
(self.map_tensor_name(name_q), split_tensor[0]),
(self.map_tensor_name(name_k), split_tensor[1]),
(self.map_tensor_name(name_v), split_tensor[2]),
@ -2413,6 +2435,9 @@ class MiniCPMVModel(Qwen2Model):
if name == "resampler.proj" or name == "resampler.query":
name += ".weight"
if name.startswith("resampler.proj"):
data_torch = data_torch.transpose(-1, -2).contiguous()
if "post_layernorm" in name:
return [] # skip post_layernorm