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import math
from contextlib import contextmanager
from dataclasses import dataclass
from typing import List, Callable
import safetensors
import torch
from .layers import AttentionWeights, LayerNormWeights, LinearWeights, MLPWeights
@dataclass
class VisionBlock:
ln1: LayerNormWeights
attn: AttentionWeights
ln2: LayerNormWeights
mlp: MLPWeights
@dataclass
class VisionModel:
patch_size: int
patch_emb: LinearWeights
pos_emb: torch.Tensor
blocks: List[VisionBlock]
post_ln: LayerNormWeights
proj_mlp: MLPWeights
@dataclass
class TextBlock:
ln: LayerNormWeights
attn: AttentionWeights
mlp: MLPWeights
@dataclass
class TextModel:
wte: torch.Tensor
blocks: List[TextBlock]
post_ln: LayerNormWeights
lm_head: LinearWeights
@dataclass
class MoondreamModel:
vision: VisionModel
text: TextModel
@contextmanager
def safetensors_open(safetensors_file: str):
"""
Simplify interfacing with safetensors files. Eliminates the need to ignore
type errors when using the `safe_open` function.
"""
with safetensors.safe_open(
safetensors_file, framework="pt"
) as st: # pyright: ignore
def get_tensor(name: str) -> torch.Tensor:
return st.get_tensor(name)
yield get_tensor
def load_model(
get_tensor: Callable[[str], torch.Tensor],
vision_blocks: int = 27,
text_blocks: int = 24,
vision_n_heads: int = 16,
text_n_heads: int = 32,
) -> MoondreamModel:
## Vision encoder
prefix = "vision_encoder.encoder.model.visual.patch_embed.linear"
patch_emb = LinearWeights(
weight=get_tensor(f"{prefix}.weight"), bias=get_tensor(f"{prefix}.bias")
)
patch_size = int(math.sqrt(patch_emb.weight.shape[1] // 3))
pos_emb = get_tensor("vision_encoder.encoder.model.visual.pos_embed")
post_ln = LayerNormWeights(
weight=get_tensor("vision_encoder.encoder.model.visual.norm.weight"),
bias=get_tensor("vision_encoder.encoder.model.visual.norm.bias"),
)
blocks = []
for i in range(vision_blocks):
prefix = f"vision_encoder.encoder.model.visual.blocks.{i}"
blocks.append(
VisionBlock(
ln1=LayerNormWeights(
weight=get_tensor(f"{prefix}.norm1.weight"),
bias=get_tensor(f"{prefix}.norm1.bias"),
),
attn=AttentionWeights(
qkv=LinearWeights(
weight=get_tensor(f"{prefix}.attn.qkv.weight"),
bias=get_tensor(f"{prefix}.attn.qkv.bias"),
),
proj=LinearWeights(
weight=get_tensor(f"{prefix}.attn.proj.weight"),
bias=get_tensor(f"{prefix}.attn.proj.bias"),
),
n_heads=vision_n_heads,
),
ln2=LayerNormWeights(
weight=get_tensor(f"{prefix}.norm2.weight"),
bias=get_tensor(f"{prefix}.norm2.bias"),
),
mlp=MLPWeights(
fc1=LinearWeights(
weight=get_tensor(f"{prefix}.mlp.fc1.weight"),
bias=get_tensor(f"{prefix}.mlp.fc1.bias"),
),
fc2=LinearWeights(
weight=get_tensor(f"{prefix}.mlp.fc2.weight"),
bias=get_tensor(f"{prefix}.mlp.fc2.bias"),
),
),
)
)
proj_mlp = MLPWeights(
fc1=LinearWeights(
weight=get_tensor("vision_encoder.projection.mlp.fc1.weight"),
bias=get_tensor("vision_encoder.projection.mlp.fc1.bias"),
),
fc2=LinearWeights(
weight=get_tensor("vision_encoder.projection.mlp.fc2.weight"),
bias=get_tensor("vision_encoder.projection.mlp.fc2.bias"),
),
act="gelu_approx",
)
vision = VisionModel(
patch_size=patch_size,
patch_emb=patch_emb,
pos_emb=pos_emb,
blocks=blocks,
post_ln=post_ln,
proj_mlp=proj_mlp,
)
## Text decoder model
wte = get_tensor("text_model.transformer.embd.wte.weight")
post_ln = LayerNormWeights(
weight=get_tensor("text_model.lm_head.ln.weight"),
bias=get_tensor("text_model.lm_head.ln.bias"),
)
lm_head = LinearWeights(
weight=get_tensor("text_model.lm_head.linear.weight"),
bias=get_tensor("text_model.lm_head.linear.bias"),
)
blocks = []
for i in range(text_blocks):
prefix = f"text_model.transformer.h.{i}"
blocks.append(
TextBlock(
ln=LayerNormWeights(
weight=get_tensor(f"{prefix}.ln.weight"),
bias=get_tensor(f"{prefix}.ln.bias"),
),
attn=AttentionWeights(
qkv=LinearWeights(
weight=get_tensor(f"{prefix}.mixer.Wqkv.weight"),
bias=get_tensor(f"{prefix}.mixer.Wqkv.bias"),
),
proj=LinearWeights(
weight=get_tensor(f"{prefix}.mixer.out_proj.weight"),
bias=get_tensor(f"{prefix}.mixer.out_proj.bias"),
),
n_heads=text_n_heads,
),
mlp=MLPWeights(
fc1=LinearWeights(
weight=get_tensor(f"{prefix}.mlp.fc1.weight"),
bias=get_tensor(f"{prefix}.mlp.fc1.bias"),
),
fc2=LinearWeights(
weight=get_tensor(f"{prefix}.mlp.fc2.weight"),
bias=get_tensor(f"{prefix}.mlp.fc2.bias"),
),
act="gelu_approx",
),
)
)
text = TextModel(wte=wte, blocks=blocks, post_ln=post_ln, lm_head=lm_head)
return MoondreamModel(vision=vision, text=text)
def load_from_safetensors(
safetensors_file: str,
vision_blocks: int = 27,
text_blocks: int = 24,
**kwargs,
) -> MoondreamModel:
with safetensors_open(safetensors_file) as get_tensor:
return load_model(get_tensor, vision_blocks, text_blocks, **kwargs)
def load_from_pt(
pt_file: str,
vision_blocks: int = 27,
text_blocks: int = 24,
**kwargs,
) -> MoondreamModel:
device = str(torch.empty(0).device)
tensors = torch.load(pt_file, map_location=device, weights_only=True)
tensors = {
k.replace("._orig_mod", ""): v.to(dtype=torch.float16)
for k, v in tensors.items()
}
return load_model(lambda x: tensors[x], vision_blocks, text_blocks, **kwargs)
if __name__ == "__main__":
weights = load_from_safetensors("model.safetensors")
print(weights)
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