191 lines
7.7 KiB
Python
191 lines
7.7 KiB
Python
from transformers import AutoModelForCausalLM
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import torch
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from utils import replace_module
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from modules import ConvertingLinear, Linear
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from random import randint
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def find_all_linear_module_names(model) -> list[str]:
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module_names = set()
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for name, module in model.named_modules():
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if isinstance(module, torch.nn.Linear) or isinstance(module, ConvertingLinear):
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module_names.add(name)
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if 'lm_head' in module_names: # needed for 16-bit
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module_names.remove('lm_head')
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return list(module_names)
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def find_all_outher_module_names(model) -> list[str]:
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module_names = set()
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for name, module in model.named_modules():
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if not (isinstance(module, torch.nn.Linear) or isinstance(module, ConvertingLinear)):
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module_names.add(name)
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return list(module_names)
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class LinearGroup:
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def __init__(self, model, group_names: list):
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self.modules = list()
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model_modules = dict(model.named_modules())
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for name in group_names:
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self.modules.append(model_modules[name])
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assert isinstance(self.modules[0], ConvertingLinear)
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assert isinstance(self.modules[-1], ConvertingLinear)
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def inplaceTo(self, dtype: torch.dtype = None, device: torch.device = None, output_device: torch.device = None) -> None:
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for module in self.modules:
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module.inplaceTo(dtype, device)
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self.modules[-1].setOutputDevice(output_device)
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def setFrozen(self, frozen: bool) -> None:
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for module in self.modules:
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module.setFrozen(frozen)
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def isFrozen(self) -> bool:
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return self.modules[0].isFrozen()
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def parameters(self) -> list[torch.nn.Parameter]:
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params = list()
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for module in self.modules:
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params.extend(module.parameters())
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return params
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def paramCount(self) -> int:
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return sum(p.numel() for p in self.parameters())
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class DyntrainModel:
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def __init__(self, model_name_or_path: str, cache_dir: str,
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target_active_params: int, gradient_checkpointing: bool, trust_remote_code: bool = False):
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self.model = AutoModelForCausalLM.from_pretrained(
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model_name_or_path,
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cache_dir=cache_dir,
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torch_dtype=torch.float32,
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trust_remote_code=trust_remote_code,
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device_map=None,
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)
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self.linear_groups = list()
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self.target_active_params = target_active_params
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self._prepare()
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self.reshuffleActive()
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def _get_nonlinear_names(layer: torch.nn.Module):
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names = list()
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modules = dict(layer.named_modules())
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for key in modules.keys():
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if not isinstance(modules[key], torch.nn.Linear):
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names.append(key)
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return names
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def _get_linear_group_names(layer: torch.nn.Module) -> list[list[str]]:
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linear_groups = list()
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list_counter = 0
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in_sequence = False
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modules = dict(layer.named_modules())
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for key in modules.keys():
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if isinstance(modules[key], torch.nn.Linear) and key != "lm_head":
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if not in_sequence:
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linear_groups.append(list())
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in_sequence = True
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linear_groups[list_counter].append(key)
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elif in_sequence:
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in_sequence = False
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list_counter = list_counter + 1
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return linear_groups
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def _prepare(self) -> None:
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modules = dict(self.model.named_modules())
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linear_groups = DyntrainModel._get_linear_group_names(self.model)
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for group in linear_groups:
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replace_module(self.model, group[0], ConvertingLinear.fromLinear(modules[group[0]].to(torch.float16), output_dtype=torch.float16))
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replace_module(self.model, group[-1], ConvertingLinear.fromLinear(modules[group[-1]].to(torch.float16), output_dtype=torch.float32))
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if len(group) > 2:
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for index in range(1, len(group) - 1):
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replace_module(self.model, group[index], Linear.fromLinear(modules[group[index]].to(torch.float16)))
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self.linear_groups.append(LinearGroup(self.model, group))
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def dynamicParameters(self) -> list:
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parameters = list()
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for group in self.linear_groups:
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parameters.extend(group.parameters())
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return parameters
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def staticParameters(self) -> list:
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modules = dict(self.model.named_modules())
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dynamic_param_ids = set([id(p) for p in self.dynamicParameters()])
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parameters = list()
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for key in modules.keys():
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for param in modules[key].parameters():
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if id(param) not in dynamic_param_ids:
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parameters.append(param)
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return parameters
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def dynamicParameterCount(self) -> int:
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return sum(p.numel() for p in self.dynamicParameters())
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def staticParameterCount(self) -> int:
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return sum(p.numel() for p in self.staticParameters())
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def activeParameterCount(self) -> int:
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total_params = self.dynamicParameters() + self.staticParameters()
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return sum(p.numel() for p in total_params if total_params)
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def reshuffleActive(self) -> None:
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for group in self.linear_groups:
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group.setFrozen(True)
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indecies = list(range(0, len(self.linear_groups)))
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params = self.staticParameterCount()
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while params < self.target_active_params and len(indecies) > 0:
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i = randint(0, len(indecies) - 1)
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self.linear_groups[indecies[i]].setFrozen(False)
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params += self.linear_groups[indecies[i]].paramCount()
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indecies.pop(i)
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for group in self.linear_groups:
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if group.isFrozen():
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group.inplaceTo(dtype=torch.float16)
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else:
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group.inplaceTo(dtype=torch.float32)
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print(group.modules[0].weight.dtype)
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def toDevices(self, primary_device: torch.device, secondary_devices: list[torch.device]) -> None:
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modules = dict(self.model.named_modules())
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total_memory = sum(torch.cuda.get_device_properties(d).total_memory for d in secondary_devices)
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total_memory += torch.cuda.get_device_properties(primary_device).total_memory * 0.8
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static_param_count = self.staticParameterCount()
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total_parameter_count = static_param_count + self.dynamicParameterCount()
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params_per_byte = total_parameter_count / float(total_memory)
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print(f"{1/params_per_byte} bytes available per parameter")
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breakpoint()
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for key in DyntrainModel._get_nonlinear_names(self.model):
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replace_module(self.model, key, modules[key].to(primary_device))
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breakpoint()
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group_index = 0
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params_for_primary = torch.cuda.get_device_properties(primary_device).total_memory * params_per_byte * 0.8 - static_param_count
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primary_params = static_param_count
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while params_for_primary > primary_params and group_index < len(self.linear_groups):
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self.linear_groups[group_index].inplaceTo(device=primary_device)
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primary_params += self.linear_groups[group_index].paramCount()
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group_index += 1
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for device in secondary_devices[:-1]:
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params_for_device = torch.cuda.get_device_properties(primary_device).total_memory * params_per_byte
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params = 0
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while params_for_device > params and group_index < len(self.linear_groups):
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self.linear_groups[group_index].inplaceTo(device=device, output_device=primary_device)
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params += self.linear_groups[group_index].paramCount()
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group_index += 1
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while group_index < len(self.linear_groups):
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self.linear_groups[group_index].inplaceTo(device=secondary_devices[-1], output_device=primary_device)
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