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Fixes multihost Wan 2.1 weight loading #212

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Merged
entrpn merged 1 commit into from
Jul 28, 2025
Merged

Fixes multihost Wan 2.1 weight loading #212

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34 changes: 21 additions & 13 deletions src/maxdiffusion/models/wan/wan_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -57,8 +57,10 @@ def rename_for_custom_trasformer(key):
return renamed_pt_key


def load_fusionx_transformer(pretrained_model_name_or_path: str, eval_shapes: dict, device: str, hf_download: bool = True):
device = jax.devices(device)[0]
def load_fusionx_transformer(
pretrained_model_name_or_path: str, eval_shapes: dict, device: str, hf_download: bool = True, num_layers: int = 40
):
device = jax.local_devices(backend=device)[0]
with jax.default_device(device):
if hf_download:
ckpt_shard_path = hf_hub_download(pretrained_model_name_or_path, filename="Wan14BT2VFusioniX_fp16_.safetensors")
Expand Down Expand Up @@ -97,7 +99,7 @@ def load_fusionx_transformer(pretrained_model_name_or_path: str, eval_shapes: di
if flax_key in flax_state_dict:
new_tensor = flax_state_dict[flax_key]
else:
new_tensor = jnp.zeros((40,) + flax_tensor.shape)
new_tensor = jnp.zeros((num_layers,) + flax_tensor.shape)
flax_tensor = new_tensor.at[block_index].set(flax_tensor)
flax_state_dict[flax_key] = jax.device_put(jnp.asarray(flax_tensor), device=cpu)
validate_flax_state_dict(eval_shapes, flax_state_dict)
Expand All @@ -107,8 +109,10 @@ def load_fusionx_transformer(pretrained_model_name_or_path: str, eval_shapes: di
return flax_state_dict


def load_causvid_transformer(pretrained_model_name_or_path: str, eval_shapes: dict, device: str, hf_download: bool = True):
device = jax.devices(device)[0]
def load_causvid_transformer(
pretrained_model_name_or_path: str, eval_shapes: dict, device: str, hf_download: bool = True, num_layers: int = 40
):
device = jax.local_devices(backend=device)[0]
with jax.default_device(device):
if hf_download:
ckpt_shard_path = hf_hub_download(pretrained_model_name_or_path, filename="causal_model.pt")
Expand Down Expand Up @@ -145,7 +149,7 @@ def load_causvid_transformer(pretrained_model_name_or_path: str, eval_shapes: di
if flax_key in flax_state_dict:
new_tensor = flax_state_dict[flax_key]
else:
new_tensor = jnp.zeros((40,) + flax_tensor.shape)
new_tensor = jnp.zeros((num_layers,) + flax_tensor.shape)
flax_tensor = new_tensor.at[block_index].set(flax_tensor)
flax_state_dict[flax_key] = jax.device_put(jnp.asarray(flax_tensor), device=cpu)
validate_flax_state_dict(eval_shapes, flax_state_dict)
Expand All @@ -155,18 +159,22 @@ def load_causvid_transformer(pretrained_model_name_or_path: str, eval_shapes: di
return flax_state_dict


def load_wan_transformer(pretrained_model_name_or_path: str, eval_shapes: dict, device: str, hf_download: bool = True):
def load_wan_transformer(
pretrained_model_name_or_path: str, eval_shapes: dict, device: str, hf_download: bool = True, num_layers: int = 40
):

if pretrained_model_name_or_path == CAUSVID_TRANSFORMER_MODEL_NAME_OR_PATH:
return load_causvid_transformer(pretrained_model_name_or_path, eval_shapes, device, hf_download)
return load_causvid_transformer(pretrained_model_name_or_path, eval_shapes, device, hf_download, num_layers)
elif pretrained_model_name_or_path == WAN_21_FUSION_X_MODEL_NAME_OR_PATH:
return load_fusionx_transformer(pretrained_model_name_or_path, eval_shapes, device, hf_download)
return load_fusionx_transformer(pretrained_model_name_or_path, eval_shapes, device, hf_download, num_layers)
else:
return load_base_wan_transformer(pretrained_model_name_or_path, eval_shapes, device, hf_download)
return load_base_wan_transformer(pretrained_model_name_or_path, eval_shapes, device, hf_download, num_layers)


def load_base_wan_transformer(pretrained_model_name_or_path: str, eval_shapes: dict, device: str, hf_download: bool = True):
device = jax.devices(device)[0]
def load_base_wan_transformer(
pretrained_model_name_or_path: str, eval_shapes: dict, device: str, hf_download: bool = True, num_layers: int = 40
):
device = jax.local_devices(backend=device)[0]
subfolder = "transformer"
filename = "diffusion_pytorch_model.safetensors.index.json"
local_files = False
Expand Down Expand Up @@ -237,7 +245,7 @@ def load_base_wan_transformer(pretrained_model_name_or_path: str, eval_shapes: d
if flax_key in flax_state_dict:
new_tensor = flax_state_dict[flax_key]
else:
new_tensor = jnp.zeros((40,) + flax_tensor.shape)
new_tensor = jnp.zeros((num_layers,) + flax_tensor.shape)
flax_tensor = new_tensor.at[block_index].set(flax_tensor)
flax_state_dict[flax_key] = jax.device_put(jnp.asarray(flax_tensor), device=cpu)
validate_flax_state_dict(eval_shapes, flax_state_dict)
Expand Down
4 changes: 3 additions & 1 deletion src/maxdiffusion/pipelines/wan/wan_pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -95,7 +95,9 @@ def create_model(rngs: nnx.Rngs, wan_config: dict):
# 4. Load pretrained weights and move them to device using the state shardings from (3) above.
# This helps with loading sharded weights directly into the accelerators without fist copying them
# all to one device and then distributing them, thus using low HBM memory.
params = load_wan_transformer(config.wan_transformer_pretrained_model_name_or_path, params, "cpu")
params = load_wan_transformer(
config.wan_transformer_pretrained_model_name_or_path, params, "cpu", num_layers=wan_config["num_layers"]
)
params = jax.tree_util.tree_map(lambda x: x.astype(config.weights_dtype), params)
for path, val in flax.traverse_util.flatten_dict(params).items():
sharding = logical_state_sharding[path].value
Expand Down
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