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from typing import Union
from .initialization import skip_model_initialization
from .disk_map import DiskMap
from ..device import parse_device_type
class AutoTorchModule(torch.nn.Module):
def __init__(
self,
offload_dtype: torch.dtype = None,
offload_device: Union[str, torch.device] = None,
onload_dtype: torch.dtype = None,
onload_device: Union[str, torch.device] = None,
preparing_dtype: torch.dtype = None,
preparing_device: Union[str, torch.device] = None,
computation_dtype: torch.dtype = None,
computation_device: Union[str, torch.device] = None,
vram_limit: float = None,
):
super().__init__()
self.set_dtype_and_device(
offload_dtype,
offload_device,
onload_dtype,
onload_device,
preparing_dtype,
preparing_device,
computation_dtype,
computation_device,
vram_limit,
)
self.state = 0
self.name = ""
self.computation_device_type = parse_device_type(self.computation_device)
def set_dtype_and_device(
self,
offload_dtype: torch.dtype = None,
offload_device: Union[str, torch.device] = None,
onload_dtype: torch.dtype = None,
onload_device: Union[str, torch.device] = None,
preparing_dtype: torch.dtype = None,
preparing_device: Union[str, torch.device] = None,
computation_dtype: torch.dtype = None,
computation_device: Union[str, torch.device] = None,
vram_limit: float = None,
):
self.offload_dtype = offload_dtype or computation_dtype
self.offload_device = offload_device or computation_dtype
self.onload_dtype = onload_dtype or computation_dtype
self.onload_device = onload_device or computation_dtype
self.preparing_dtype = preparing_dtype or computation_dtype
self.preparing_device = preparing_device or computation_dtype
self.computation_dtype = computation_dtype
self.computation_device = computation_device
self.vram_limit = vram_limit
def cast_to(self, weight, dtype, device):
r = torch.empty_like(weight, dtype=dtype, device=device)
r.copy_(weight)
return r
def check_free_vram(self):
device = self.computation_device if self.computation_device != "npu" else "npu:0"
gpu_mem_state = getattr(torch, self.computation_device_type).mem_get_info(device)
used_memory = (gpu_mem_state[1] - gpu_mem_state[0]) / (1024**3)
return used_memory < self.vram_limit
def offload(self):
if self.state != 0:
self.to(dtype=self.offload_dtype, device=self.offload_device)
self.state = 0
def onload(self):
if self.state != 1:
self.to(dtype=self.onload_dtype, device=self.onload_device)
self.state = 1
def param_name(self, name):
if self.name == "":
return name
else:
return self.name + "." + name
class AutoWrappedModule(AutoTorchModule):
def __init__(
self,
module: torch.nn.Module,
offload_dtype: torch.dtype = None,
offload_device: Union[str, torch.device] = None,
onload_dtype: torch.dtype = None,
onload_device: Union[str, torch.device] = None,
preparing_dtype: torch.dtype = None,
preparing_device: Union[str, torch.device] = None,
computation_dtype: torch.dtype = None,
computation_device: Union[str, torch.device] = None,
vram_limit: float = None,
name: str = "",
disk_map: DiskMap = None,
**kwargs
):
super().__init__(
offload_dtype,
offload_device,
onload_dtype,
onload_device,
preparing_dtype,
preparing_device,
computation_dtype,
computation_device,
vram_limit,
)
self.module = module
if offload_dtype == "disk":
self.name = name
self.disk_map = disk_map
self.required_params = [name for name, _ in self.module.named_parameters()]
self.disk_offload = True
else:
self.disk_offload = False
def load_from_disk(self, torch_dtype, device, copy_module=False):
if copy_module:
module = copy.deepcopy(self.module)
else:
module = self.module
state_dict = {}
for name in self.required_params:
param = self.disk_map[self.param_name(name)]
param = param.to(dtype=torch_dtype, device=device)
state_dict[name] = param
module.load_state_dict(state_dict, assign=True)
module.to(dtype=torch_dtype, device=device)
return module
def offload_to_disk(self, model: torch.nn.Module):
for buf in model.buffers():
# If there are some parameters are registed in buffers (not in state dict),
# We cannot offload the model.
for children in model.children():
self.offload_to_disk(children)
break
else:
model.to("meta")
def offload(self):
# offload / onload / preparing -> offload
if self.state != 0:
if self.disk_offload:
self.offload_to_disk(self.module)
else:
self.to(dtype=self.offload_dtype, device=self.offload_device)
self.state = 0
def onload(self):
# offload / onload / preparing -> onload
if self.state < 1:
if self.disk_offload and self.onload_device != "disk" and self.offload_device == "disk":
self.load_from_disk(self.onload_dtype, self.onload_device)
elif self.onload_device != "disk":
self.to(dtype=self.onload_dtype, device=self.onload_device)
self.state = 1
def preparing(self):
# onload / preparing -> preparing
if self.state != 2:
if self.disk_offload and self.preparing_device != "disk" and self.onload_device == "disk":
self.load_from_disk(self.preparing_dtype, self.preparing_device)
elif self.preparing_device != "disk":
self.to(dtype=self.preparing_dtype, device=self.preparing_device)
self.state = 2
def cast_to(self, module, dtype, device):
return copy.deepcopy(module).to(dtype=dtype, device=device)
def computation(self):
# onload / preparing -> computation (temporary)
if self.state == 2:
torch_dtype, device = self.preparing_dtype, self.preparing_device
else:
torch_dtype, device = self.onload_dtype, self.onload_device
if torch_dtype == self.computation_dtype and device == self.computation_device:
module = self.module
elif self.disk_offload and device == "disk":
module = self.load_from_disk(self.computation_dtype, self.computation_device, copy_module=True)
else:
module = self.cast_to(self.module, dtype=self.computation_dtype, device=self.computation_device)
return module
def forward(self, *args, **kwargs):
if self.state == 1 and (self.vram_limit is None or self.check_free_vram()):
self.preparing()
module = self.computation()
return module(*args, **kwargs)
def __getattr__(self, name):
if name in self.__dict__ or name == "module":
return super().__getattr__(name)
else:
return getattr(self.module, name)
class AutoWrappedNonRecurseModule(AutoWrappedModule):
def __init__(
self,
module: torch.nn.Module,
offload_dtype: torch.dtype = None,
offload_device: Union[str, torch.device] = None,
onload_dtype: torch.dtype = None,
onload_device: Union[str, torch.device] = None,
preparing_dtype: torch.dtype = None,
preparing_device: Union[str, torch.device] = None,
computation_dtype: torch.dtype = None,
computation_device: Union[str, torch.device] = None,
vram_limit: float = None,
name: str = "",
disk_map: DiskMap = None,
**kwargs
):
super().__init__(
module,
offload_dtype,
offload_device,
onload_dtype,
onload_device,
preparing_dtype,
preparing_device,
computation_dtype,
computation_device,
vram_limit,
name,
disk_map,
**kwargs
)
if self.disk_offload:
self.required_params = [name for name, _ in self.module.named_parameters(recurse=False)]
def load_from_disk(self, torch_dtype, device, copy_module=False):
if copy_module:
module = copy.deepcopy(self.module)
else:
module = self.module
state_dict = {}
for name in self.required_params:
param = self.disk_map[self.param_name(name)]
param = param.to(dtype=torch_dtype, device=device)
state_dict[name] = param
module.load_state_dict(state_dict, assign=True, strict=False)
return module
def offload_to_disk(self, model: torch.nn.Module):
for name in self.required_params:
getattr(self, name).to("meta")
def cast_to(self, module, dtype, device):
# Parameter casting is implemented in the model architecture.
return module
def __getattr__(self, name):
if name in self.__dict__ or name == "module":
return super().__getattr__(name)
else:
return getattr(self.module, name)
class AutoWrappedLinear(torch.nn.Linear, AutoTorchModule):
def __init__(
self,
module: torch.nn.Linear,
offload_dtype: torch.dtype = None,
offload_device: Union[str, torch.device] = None,
onload_dtype: torch.dtype = None,
onload_device: Union[str, torch.device] = None,
preparing_dtype: torch.dtype = None,
preparing_device: Union[str, torch.device] = None,
computation_dtype: torch.dtype = None,
computation_device: Union[str, torch.device] = None,
vram_limit: float = None,
name: str = "",
disk_map: DiskMap = None,
**kwargs
):
with skip_model_initialization():
super().__init__(
in_features=module.in_features,
out_features=module.out_features,
bias=module.bias is not None,
)
self.set_dtype_and_device(
offload_dtype,
offload_device,
onload_dtype,
onload_device,
preparing_dtype,
preparing_device,
computation_dtype,
computation_device,
vram_limit,
)
self.weight = module.weight
self.bias = module.bias
self.state = 0
self.name = name
self.lora_A_weights = []
self.lora_B_weights = []
self.lora_merger = None
self.enable_fp8 = computation_dtype in [torch.float8_e4m3fn, torch.float8_e4m3fnuz]
self.computation_device_type = parse_device_type(self.computation_device)
if offload_dtype == "disk":
self.disk_map = disk_map
self.disk_offload = True
else:
self.disk_offload = False
def fp8_linear(
self,
input: torch.Tensor,
weight: torch.Tensor,
bias: torch.Tensor = None,
) -> torch.Tensor:
device = input.device
origin_dtype = input.dtype
origin_shape = input.shape
input = input.reshape(-1, origin_shape[-1])
x_max = torch.max(torch.abs(input), dim=-1, keepdim=True).values
fp8_max = 448.0
# For float8_e4m3fnuz, the maximum representable value is half of that of e4m3fn.
# To avoid overflow and ensure numerical compatibility during FP8 computation,
# we scale down the input by 2.0 in advance.
# This scaling will be compensated later during the final result scaling.
if self.computation_dtype == torch.float8_e4m3fnuz:
fp8_max = fp8_max / 2.0
scale_a = torch.clamp(x_max / fp8_max, min=1.0).float().to(device=device)
scale_b = torch.ones((weight.shape[0], 1)).to(device=device)
input = input / (scale_a + 1e-8)
input = input.to(self.computation_dtype)
weight = weight.to(self.computation_dtype)
bias = bias.to(torch.bfloat16)
result = torch._scaled_mm(
input,
weight.T,
scale_a=scale_a,
scale_b=scale_b.T,
bias=bias,
out_dtype=origin_dtype,
)
new_shape = origin_shape[:-1] + result.shape[-1:]
result = result.reshape(new_shape)
return result
def load_from_disk(self, torch_dtype, device, assign=True):
weight = self.disk_map[self.name + ".weight"].to(dtype=torch_dtype, device=device)
bias = None if self.bias is None else self.disk_map[self.name + ".bias"].to(dtype=torch_dtype, device=device)
if assign:
state_dict = {"weight": weight}
if bias is not None: state_dict["bias"] = bias
self.load_state_dict(state_dict, assign=True)
return weight, bias
def offload(self):
# offload / onload / preparing -> offload
if self.state != 0:
if self.disk_offload:
self.to("meta")
else:
self.to(dtype=self.offload_dtype, device=self.offload_device)
self.state = 0
def onload(self):
# offload / onload / preparing -> onload
if self.state < 1:
if self.disk_offload and self.onload_device != "disk" and self.offload_device == "disk":
self.load_from_disk(self.onload_dtype, self.onload_device)
elif self.onload_device != "disk":
self.to(dtype=self.onload_dtype, device=self.onload_device)
self.state = 1
def preparing(self):
# onload / preparing -> preparing
if self.state != 2:
if self.disk_offload and self.preparing_device != "disk" and self.onload_device == "disk":
self.load_from_disk(self.preparing_dtype, self.preparing_device)
elif self.preparing_device != "disk":
self.to(dtype=self.preparing_dtype, device=self.preparing_device)
self.state = 2
def computation(self):
# onload / preparing -> computation (temporary)
if self.state == 2:
torch_dtype, device = self.preparing_dtype, self.preparing_device
else:
torch_dtype, device = self.onload_dtype, self.onload_device
if torch_dtype == self.computation_dtype and device == self.computation_device:
weight, bias = self.weight, self.bias
elif self.disk_offload and device == "disk":
weight, bias = self.load_from_disk(self.computation_dtype, self.computation_device, assign=False)
else:
weight = self.cast_to(self.weight, self.computation_dtype, self.computation_device)
bias = None if self.bias is None else self.cast_to(self.bias, self.computation_dtype, self.computation_device)
return weight, bias
def linear_forward(self, x, weight, bias):
if self.enable_fp8:
out = self.fp8_linear(x, weight, bias)
else:
out = torch.nn.functional.linear(x, weight, bias)
return out
def lora_forward(self, x, out):
if self.lora_merger is None:
for lora_A, lora_B in zip(self.lora_A_weights, self.lora_B_weights):
out = out + x @ lora_A.T @ lora_B.T
else:
lora_output = []
for lora_A, lora_B in zip(self.lora_A_weights, self.lora_B_weights):
lora_output.append(x @ lora_A.T @ lora_B.T)
lora_output = torch.stack(lora_output)
out = self.lora_merger(out, lora_output)
return out
def forward(self, x, *args, **kwargs):
if self.state == 1 and (self.vram_limit is None or self.check_free_vram()):
self.preparing()
weight, bias = self.computation()
out = self.linear_forward(x, weight, bias)
if len(self.lora_A_weights) > 0:
out = self.lora_forward(x, out)
return out
def enable_vram_management_recursively(model: torch.nn.Module, module_map: dict, vram_config: dict, vram_limit=None, name_prefix="", disk_map=None, **kwargs):
if isinstance(model, AutoWrappedNonRecurseModule):
model = model.module
for name, module in model.named_children():
layer_name = name if name_prefix == "" else name_prefix + "." + name
for source_module, target_module in module_map.items():
if isinstance(module, source_module):
module_ = target_module(module, **vram_config, vram_limit=vram_limit, name=layer_name, disk_map=disk_map, **kwargs)
if isinstance(module_, AutoWrappedNonRecurseModule):
enable_vram_management_recursively(module_, module_map, vram_config, vram_limit=vram_limit, name_prefix=layer_name, disk_map=disk_map, **kwargs)
setattr(model, name, module_)
break
else:
enable_vram_management_recursively(module, module_map, vram_config, vram_limit=vram_limit, name_prefix=layer_name, disk_map=disk_map, **kwargs)
def fill_vram_config(model, vram_config):
vram_config_ = vram_config.copy()
vram_config_["onload_dtype"] = vram_config["computation_dtype"]
vram_config_["onload_device"] = vram_config["computation_device"]
vram_config_["preparing_dtype"] = vram_config["computation_dtype"]
vram_config_["preparing_device"] = vram_config["computation_device"]
for k in vram_config:
if vram_config[k] != vram_config_[k]:
print(f"No fine-grained VRAM configuration is provided for {model.__class__.__name__}. [`onload`, `preparing`, `computation`] will be the same state. `vram_config` is set to {vram_config_}")
break
return vram_config_
def enable_vram_management(model: torch.nn.Module, module_map: dict, vram_config: dict, vram_limit=None, disk_map=None, **kwargs):
for source_module, target_module in module_map.items():
# If no fine-grained VRAM configuration is provided, the entire model will be managed uniformly.
if isinstance(model, source_module):
vram_config = fill_vram_config(model, vram_config)
model = target_module(model, **vram_config, vram_limit=vram_limit, disk_map=disk_map, **kwargs)
break
else:
enable_vram_management_recursively(model, module_map, vram_config, vram_limit=vram_limit, disk_map=disk_map, **kwargs)
# `vram_management_enabled` is a flag that allows the pipeline to determine whether VRAM management is enabled.
model.vram_management_enabled = True
return model
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