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path: add fastvit #11

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1c9be2e
path: add fastvit
YassineYousfi Jun 23, 2026
3e5390a
no pretrain
YassineYousfi Jun 23, 2026
7375930
recomp limit higher
YassineYousfi Jun 24, 2026
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4 changes: 4 additions & 0 deletions torchtitan/experiments/path/config_registry.py
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Original file line number Diff line number Diff line change
Expand Up @@ -89,6 +89,10 @@ def convnext_xxlarge() -> PathTrainer.Config:
return _path("convnext_xxlarge")


def fastvit_t12() -> PathTrainer.Config:
return _path("fastvit_t12")


def _path(flavor: str) -> PathTrainer.Config:
steps = 1024*100
validation_freq = 1024
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81 changes: 0 additions & 81 deletions torchtitan/experiments/path/convnext.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,6 @@

from __future__ import annotations

import re
from functools import partial

import torch
Expand All @@ -20,19 +19,12 @@
to_ntuple,
trunc_normal_,
)
from timm.models import build_model_with_cfg
from timm.models._manipulate import checkpoint_seq, named_apply


__all__ = [
"CONVNEXT_FLAVORS",
"ConvNeXt",
"convnext_base",
"convnext_small",
"convnext_tiny",
"convnext_xxlarge",
"create_convnext",
"pretrained_name",
]


Expand Down Expand Up @@ -352,76 +344,3 @@ def _init_weights(module: nn.Module, name: str | None = None, head_init_scale: f
if name and "head." in name:
module.weight.data.mul_(head_init_scale)
module.bias.data.mul_(head_init_scale)


def checkpoint_filter_fn(state_dict, model):
if "head.norm.weight" in state_dict or "norm_pre.weight" in state_dict:
return state_dict
if "model" in state_dict:
state_dict = state_dict["model"]

out_dict = {}
if "visual.trunk.stem.0.weight" in state_dict:
out_dict = {k.replace("visual.trunk.", ""): v for k, v in state_dict.items() if k.startswith("visual.trunk.")}
if "visual.head.proj.weight" in state_dict:
out_dict["head.fc.weight"] = state_dict["visual.head.proj.weight"]
out_dict["head.fc.bias"] = torch.zeros(state_dict["visual.head.proj.weight"].shape[0])
elif "visual.head.mlp.fc1.weight" in state_dict:
out_dict["head.pre_logits.fc.weight"] = state_dict["visual.head.mlp.fc1.weight"]
out_dict["head.pre_logits.fc.bias"] = state_dict["visual.head.mlp.fc1.bias"]
out_dict["head.fc.weight"] = state_dict["visual.head.mlp.fc2.weight"]
out_dict["head.fc.bias"] = torch.zeros(state_dict["visual.head.mlp.fc2.weight"].shape[0])
return out_dict

for k, v in state_dict.items():
k = k.replace("downsample_layers.0.", "stem.")
k = re.sub(r"stages.([0-9]+).([0-9]+)", r"stages.\1.blocks.\2", k)
k = re.sub(r"downsample_layers.([0-9]+).([0-9]+)", r"stages.\1.downsample.\2", k)
k = k.replace("dwconv", "conv_dw")
k = k.replace("pwconv", "mlp.fc")
k = k.replace("head.", "head.fc.")
if k.startswith("norm."):
k = k.replace("norm", "head.norm")
if v.ndim == 2 and "head" not in k:
v = v.reshape(model.state_dict()[k].shape)
out_dict[k] = v
return out_dict

def _create_convnext(variant: str, pretrained: bool = False, **kwargs):
return build_model_with_cfg(
ConvNeXt,
variant,
pretrained,
pretrained_filter_fn=checkpoint_filter_fn,
feature_cfg=dict(out_indices=(0, 1, 2, 3), flatten_sequential=True),
**kwargs,
)


def pretrained_name(flavor: str) -> str:
return CONVNEXT_FLAVORS[flavor]["pretrained"]


def create_convnext(flavor: str, pretrained: bool = False, **kwargs) -> ConvNeXt:
model_args = {
"depths": CONVNEXT_FLAVORS[flavor]["depths"],
"dims": CONVNEXT_FLAVORS[flavor]["dims"],
"norm_eps": kwargs.pop("norm_eps", 1e-5),
}
return _create_convnext(flavor, pretrained=pretrained, **dict(model_args, **kwargs))


def convnext_tiny(pretrained: bool = False, **kwargs) -> ConvNeXt:
return create_convnext("convnext_tiny", pretrained=pretrained, **kwargs)


def convnext_small(pretrained: bool = False, **kwargs) -> ConvNeXt:
return create_convnext("convnext_small", pretrained=pretrained, **kwargs)


def convnext_base(pretrained: bool = False, **kwargs) -> ConvNeXt:
return create_convnext("convnext_base", pretrained=pretrained, **kwargs)


def convnext_xxlarge(pretrained: bool = False, **kwargs) -> ConvNeXt:
return create_convnext("convnext_xxlarge", pretrained=pretrained, **kwargs)
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