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import safetensors
import torch
import torch.nn as nn
import re
from contextlib import contextmanager
from typing import Callable, List
from .text import build_text_model
from .config import TextConfig
# Our custom linear has an module named linear, so we add linear to the name
def add_linear_to_key(k: str) -> str:
k = k.replace("model.", "")
if k.startswith("text.") and ".linear." not in k:
k = re.sub(
r"(attn\.(?:qkv|proj)|mlp\.fc[12])\.(weight|bias)$",
r"\1.linear.\2",
k,
)
return k
@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)
def get_keys() -> List[str]:
return st.keys()
get_tensor.keys = get_keys
yield get_tensor
def _load_weights(
get_tensor: Callable[[str], torch.Tensor],
model: nn.Module,
is_quantized: bool = False,
) -> None:
"""Internal function to load weights using a tensor getter function."""
model = model.to(dtype=torch.float16)
vision = model.vision
region = model.region
weight_map = {
"vision_encoder.encoder.model.visual.patch_embed.linear.weight": vision[
"patch_emb"
].weight,
"vision_encoder.encoder.model.visual.patch_embed.linear.bias": vision[
"patch_emb"
].bias,
"vision_encoder.encoder.model.visual.pos_embed": vision.pos_emb,
"vision_encoder.encoder.model.visual.norm.weight": vision["post_ln"].weight,
"vision_encoder.encoder.model.visual.norm.bias": vision["post_ln"].bias,
"vision_encoder.projection.mlp.fc1.weight": vision["proj_mlp"]["fc1"].weight,
"vision_encoder.projection.mlp.fc1.bias": vision["proj_mlp"]["fc1"].bias,
"vision_encoder.projection.mlp.fc2.weight": vision["proj_mlp"]["fc2"].weight,
"vision_encoder.projection.mlp.fc2.bias": vision["proj_mlp"]["fc2"].bias,
"text_model.transformer.embd.wte.weight": model.text.wte,
"text_model.lm_head.ln.weight": model.text["post_ln"].weight,
"text_model.lm_head.ln.bias": model.text["post_ln"].bias,
"text_model.lm_head.linear.weight": model.text["lm_head"].weight,
"text_model.lm_head.linear.bias": model.text["lm_head"].bias,
"region_model.coordinate_encoder.weight": region["coord_encoder"].weight,
"region_model.coordinate_encoder.bias": region["coord_encoder"].bias,
"region_model.coordinate_decoder.fc1.weight": region["coord_decoder"][
"fc1"
].weight,
"region_model.coordinate_decoder.fc1.bias": region["coord_decoder"]["fc1"].bias,
"region_model.coordinate_decoder.fc2.weight": region["coord_decoder"][
"fc2"
].weight,
"region_model.coordinate_decoder.fc2.bias": region["coord_decoder"]["fc2"].bias,
"region_model.size_encoder.weight": region["size_encoder"].weight,
"region_model.size_encoder.bias": region["size_encoder"].bias,
"region_model.size_decoder.fc1.weight": region["size_decoder"]["fc1"].weight,
"region_model.size_decoder.fc1.bias": region["size_decoder"]["fc1"].bias,
"region_model.size_decoder.fc2.weight": region["size_decoder"]["fc2"].weight,
"region_model.size_decoder.fc2.bias": region["size_decoder"]["fc2"].bias,
}
for i in range(len(model.vision["blocks"])):
prefix = f"vision_encoder.encoder.model.visual.blocks.{i}"
blk = model.vision["blocks"][i]
weight_map.update(
{
f"{prefix}.norm1.weight": blk["ln1"].weight,
f"{prefix}.norm1.bias": blk["ln1"].bias,
f"{prefix}.norm2.weight": blk["ln2"].weight,
f"{prefix}.norm2.bias": blk["ln2"].bias,
f"{prefix}.attn.qkv.weight": blk["attn"]["qkv"].weight,
f"{prefix}.attn.qkv.bias": blk["attn"]["qkv"].bias,
f"{prefix}.attn.proj.weight": blk["attn"]["proj"].weight,
f"{prefix}.attn.proj.bias": blk["attn"]["proj"].bias,
f"{prefix}.mlp.fc1.weight": blk["mlp"]["fc1"].weight,
f"{prefix}.mlp.fc1.bias": blk["mlp"]["fc1"].bias,
f"{prefix}.mlp.fc2.weight": blk["mlp"]["fc2"].weight,
f"{prefix}.mlp.fc2.bias": blk["mlp"]["fc2"].bias,
}
)
if not is_quantized:
for i in range(len(model.text["blocks"])):
prefix = f"text_model.transformer.h.{i}"
blk = model.text["blocks"][i]
weight_map.update(
{
f"{prefix}.ln.weight": blk["ln"].weight,
f"{prefix}.ln.bias": blk["ln"].bias,
f"{prefix}.mixer.Wqkv.weight": blk["attn"]["qkv"].weight,
f"{prefix}.mixer.Wqkv.bias": blk["attn"]["qkv"].bias,
f"{prefix}.mixer.out_proj.weight": blk["attn"]["proj"].weight,
f"{prefix}.mixer.out_proj.bias": blk["attn"]["proj"].bias,
f"{prefix}.mlp.fc1.weight": blk["mlp"]["fc1"].weight,
f"{prefix}.mlp.fc1.bias": blk["mlp"]["fc1"].bias,
f"{prefix}.mlp.fc2.weight": blk["mlp"]["fc2"].weight,
f"{prefix}.mlp.fc2.bias": blk["mlp"]["fc2"].bias,
}
)
else: # add special quantized path. this is specific to how bitblas expects weights to be loaded (.qweight)
for i in range(len(model.text["blocks"])):
prefix = f"text_model.transformer.h.{i}"
blk = model.text["blocks"][i]
weight_map.update(
{
f"{prefix}.ln.qweight": blk["ln"].weight,
f"{prefix}.ln.bias": blk["ln"].bias,
f"{prefix}.mixer.Wqkv.qweight": blk["attn"]["qkv"].weight,
f"{prefix}.mixer.Wqkv.bias": blk["attn"]["qkv"].bias,
f"{prefix}.mixer.out_proj.qweight": blk["attn"]["proj"].weight,
f"{prefix}.mixer.out_proj.bias": blk["attn"]["proj"].bias,
f"{prefix}.mlp.fc1.qweight": blk["mlp"]["fc1"].weight,
f"{prefix}.mlp.fc1.bias": blk["mlp"]["fc1"].bias,
f"{prefix}.mlp.fc2.qweight": blk["mlp"]["fc2"].weight,
f"{prefix}.mlp.fc2.bias": blk["mlp"]["fc2"].bias,
}
)
for key, tensor in weight_map.items():
tensor.data.copy_(get_tensor(key))
region.coord_features.data.copy_(
get_tensor("region_model.coordinate_features.weight").T
)
region.size_features.data.copy_(get_tensor("region_model.size_features.weight").T)
def load_weights_from_safetensors(weights_file: str, model: nn.Module) -> None:
"""Load weights from a safetensors file into a MoondreamModel instance."""
with safetensors_open(weights_file) as get_tensor:
all_keys = get_tensor.keys()
is_quantized = any(
".qweight" in key or "_quantized" in key or "quant." in key
for key in all_keys
)
if "text_model.transformer.h.0.ln.weight" in all_keys:
layernorm_dtype = get_tensor("text_model.transformer.h.0.ln.weight").dtype
else:
layernorm_dtype = torch.float16
linear_dtype = torch.int8 if is_quantized else torch.float16
model.text = build_text_model(
TextConfig, linear_dtype=linear_dtype, layernorm_dtype=layernorm_dtype
)
if model.setup_caches_flag:
model._setup_caches()
if (
"vision.blocks.0.attn.proj.bias" in all_keys
or "model.vision.blocks.0.attn.proj.bias" in all_keys
):
with safetensors_open(weights_file) as get_tensor:
tensors = {add_linear_to_key(k): get_tensor(k) for k in all_keys}
model.load_state_dict(tensors, strict=False)
else:
# Wrap the get_tensor function to handle key normalization
name_map = {k.replace("._orig_mod", ""): k for k in all_keys}
_load_weights(
lambda x: get_tensor(name_map[x]).to(dtype=torch.float16),
model,
is_quantized,
)
def load_weights_from_pt(weights_file: str, model: nn.Module) -> None:
"""Load weights from a PyTorch file into a MoondreamModel instance."""
tensors = torch.load(weights_file, map_location="cpu", weights_only=True)
all_keys = tensors.keys()
is_quantized = any(
".qweight" in key or "_quantized" in key or "quant." in key for key in all_keys
)
if "text.blocks.0.ln.weight" in all_keys:
layernorm_dtype = tensors["text.blocks.0.ln.weight"].dtype
else:
layernorm_dtype = torch.float16
linear_dtype = torch.int8 if is_quantized else torch.float16
model.text = build_text_model(
TextConfig, linear_dtype=linear_dtype, layernorm_dtype=layernorm_dtype
)
if model.setup_caches_flag:
model._setup_caches()
if (
"vision.blocks.0.attn.proj.bias" in all_keys
or "model.vision.blocks.0.attn.proj.bias" in all_keys
):
tensors = {add_linear_to_key(k): v for k, v in tensors.items()}
model.load_state_dict(tensors, strict=False)
else:
tensors = {
k.replace("._orig_mod", ""): v.to(dtype=torch.float16)
for k, v in tensors.items()
}
_load_weights(lambda x: tensors[x], model, is_quantized)
def load_weights_into_model(weights_file: str, model: nn.Module) -> None:
"""
Load weights from either a safetensors or PyTorch file directly into a MoondreamModel instance.
Args:
weights_file: Path to weights file (either .safetensors or .pt)
model: MoondreamModel instance to load weights into
"""
if weights_file.endswith(".safetensors"):
load_weights_from_safetensors(weights_file, model)
else:
load_weights_from_pt(weights_file, model)
# Make all parameters contiguous
for param in model.parameters():
param.data = param.data.contiguous()
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