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[shardformer] init shardformer code structure (#3731)
* init shardformer code structure * add implement of sharder (inject and replace) * add implement of replace layer to colossal layer * separate different layer policy, add some notion * implement 1d and 2d slicer, can tell col or row * fix bug when slicing and inject model * fix some bug; add inference test example
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FrankLeeeee
238
colossalai/shardformer/shard/sharder.py
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238
colossalai/shardformer/shard/sharder.py
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import torch
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import torch.nn as nn
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from typing import Any, Dict, Iterable, List, Optional, Tuple, Type, Union, Callable
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from .shardconfig import ShardConfig
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from dataclasses import dataclass
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from ..policies.basepolicy import Policy, Layer
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from ..policies.autopolicy import get_autopolicy
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from .slicer import Slicer
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from ..utils.utils import hasattr_, setattr_, getattr_
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import colossalai.nn as col_nn
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from colossalai.logging import get_dist_logger
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import os
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logger = get_dist_logger()
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class ModelSharder(object):
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"""
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Shard the original huggingface model according to the policy
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Args:
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policy: The policy to shard the model
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model: The model to shard
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dist_setting: The setting of distributed model
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"""
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def __init__(
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self,
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model: nn.Module,
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policy: Policy,
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shard_config: ShardConfig = None, # TODO
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) -> None:
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self.model = model
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self.policy = get_autopolicy(self.model) if policy is None else policy
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self.slicer = Slicer(shard_config)
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self.shard_config = shard_config
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self.model_config = self.model.config
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self.binding_map = {}
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def shard(self) -> None:
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self.inject_model(self.model)
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self.replace_layer(self.model)
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def inject_model(
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self,
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model: nn.Module,
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) -> None:
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"""
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Replace the model to policy defined model
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Mainly modify the forward and backward to fit distributed model
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e.g.
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BertForMaskedLM.forward -> BertForMaskedLM_.forward
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"""
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inject_policy = self.policy.inject_policy()
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org_model_cls = inject_policy[0]
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shard_model_cls = inject_policy[1]
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if model.__class__ == org_model_cls:
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for key in shard_model_cls.__dict__.keys():
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if hasattr(model.__class__, key):
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setattr(
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model.__class__,
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key,
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getattr(shard_model_cls,key),
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)
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else:
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raise NotImplementedError(f"{model.__class__} is not implemented so far")
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def replace_layer(
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self,
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model: nn.Module,
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) -> None:
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"""
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Replace the layer according to the policy, and replace the layer one by one
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Args:
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layer: The layer to shard
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"""
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argument_policies = self.policy.argument_policy(self.model_config, self.shard_config.world_size)
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for argument_policy in argument_policies.items():
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origin_layer_cls = argument_policy[0]
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attr_dict = argument_policy[1].attr_dict
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param_funcs = argument_policy[1].param_funcs
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binding_layers = argument_policy[1].binding_layers
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# if binding_layer is not None:
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# self.binding_map[origin_layer_cls] = binding_layer
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self.reverse_replace_layer(model, origin_layer_cls, attr_dict, param_funcs, binding_layers)
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def reverse_replace_layer(
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self,
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layer: nn.Module,
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origin_cls: nn.Module,
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attr_dict: Dict[str, Any],
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param_funcs: List[Callable],
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binding_layers: List[nn.Module]
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) -> None:
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"""
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Reverse the replace layer operation
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Args:
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layer: The object of layer to shard
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origin_cls: The origin layer class
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attr_dict: The attribute dict to modify
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policy_cls: The policy class
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"""
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for name, child in layer.named_children():
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if child.__class__ == origin_cls:
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# replac_layer = child
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for k, v in attr_dict.items():
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setattr_(child, k, v, ignore=True)
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# print(f"Sharding {name} layer", replac_layer.attention.self.__dict__)
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# setattr_(layer, name, self.shard_one_layer(child, policy_cls))
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self.shard_one_layer(child, param_funcs, binding_layers)
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continue
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self.reverse_replace_layer(child, origin_cls, attr_dict, param_funcs, binding_layers)
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return layer
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def shard_one_layer(
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self,
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org_layer: nn.Module,
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param_funcs: List[Callable],
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binding_layers: List[nn.Module]
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) -> None:
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"""
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Shard one layer according to the policy, the layer should be the same class as the key in policy's argument_policy return dict
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Args:
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org_layer: The origin layer object to shard
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param_funcs: The function list to get shard information in policy class
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"""
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# print(org_layer)
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for func in param_funcs:
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policy_layers = func()
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for policy_layer in policy_layers:
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weight = None
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bias = None
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weight_attr = policy_layer.weight
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bias_attr = policy_layer.bias
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replace_layer_cls = policy_layer.replace_layer
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ignore = policy_layer.ignore
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if policy_layer.__class__.__name__ == "Col_Layer":
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gather_output = policy_layer.gather_output
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print(gather_output)
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if weight_attr is not None:
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if hasattr_(org_layer, weight_attr):
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weight = getattr_(org_layer, weight_attr)
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elif not ignore:
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raise ValueError(f"Layer {org_layer.__class__.__qualname__} has no attribute {weight_attr}")
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if bias_attr is not None:
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if hasattr_(org_layer, bias_attr):
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bias = getattr_(org_layer, bias_attr)
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elif not ignore:
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raise ValueError(f"Layer {org_layer.__class__.__qualname__} has no attribute {bias_attr}")
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# dont have the attribute in policy, and ignore is true
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if weight is None and bias is None and ignore:
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continue
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# set the sliced weight and bias to the new nn_col layer
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assert weight is not None or bias is not None
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layer_attr = (lambda x: x[:x.rfind(".")])(weight_attr or bias_attr)
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# slice weight and bias
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weight, bias = self.slicer.slice_weight_bias(weight, bias, policy_layer.__class__)
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print(os.environ['RANK'], policy_layer.__class__, weight.shape, bias.shape if bias is not None else None)
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# save the binding information
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for binding_layer in binding_layers:
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self.binding_map[binding_layer] = dict(weight=weight, bias=bias)
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# create new object to replace the origin layer
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if replace_layer_cls is not None:
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# print(f"RANK {os.environ['RANK']}: replace {getattr_(org_layer, layer_attr).__class__} to {replace_layer_cls}, shape is {weight.shape}")
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if isinstance(getattr_(org_layer, layer_attr), nn.Linear):
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if replace_layer_cls.__name__ == "Linear1D_Row":
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replace_layer = replace_layer_cls(weight.shape[1], weight.shape[0], bias=False if bias is None else True)
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elif replace_layer_cls.__name__ == "Linear1D_Col":
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replace_layer = replace_layer_cls(weight.shape[0], weight.shape[1], bias=False if bias is None else True, gather_output=gather_output)
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setattr_(org_layer, layer_attr, replace_layer, ignore=ignore)
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self.set_param(replace_layer, weight, bias)
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elif isinstance(getattr_(org_layer, layer_attr), nn.Embedding):
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replace_layer = replace_layer_cls(weight.shape[0], weight.shape[1], getattr_(org_layer, f"{layer_attr}.padding_idx", ignore=True))
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setattr_(org_layer, layer_attr, replace_layer, ignore=ignore)
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self.set_param(replace_layer, weight, bias)
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else:
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raise NotImplementedError(f"Replacing {getattr_(org_layer, layer_attr).__class__} is not implemented so far")
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# do not replace the layer object, just replace the weight and bias
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else:
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self.set_param(org_layer, layer_attr, weight, bias)
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def set_param(
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self,
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layer: Any,
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layer_attr: str = "",
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weight: torch.Tensor = None,
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bias: torch.Tensor = None
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) -> None:
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"""
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Reset the weight and bias of the layer object
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Args:
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layer: The layer object
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layer_attr: The attribute name of the layer
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weight: The weight of the layer
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bias: The bias of the layer
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"""
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assert weight is not None or bias is not None
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if weight is not None:
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setattr_(layer, "weight" if layer_attr == "" else layer_attr+".weight", nn.Parameter(weight))
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self.set_layer_size(layer, layer_attr, weight.shape)
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if bias is not None:
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setattr_(layer, "bias" if layer_attr == "" else layer_attr+".bias", nn.Parameter(bias))
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def set_layer_size(self, layer: nn.Module, layer_attr: str, size: torch.Size) -> None:
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"""
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Set the layer attribute
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Args:
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layer: The layer object
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layer_attr: The attribute name of the layer
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size: Torch.size
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"""
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# Tensor.shape[0] -> out_features, Tensor.shape[1] -> in_features
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attrs = ["out_features", "in_features"]
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for i, attr in enumerate(attrs):
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if hasattr_(layer, f"{layer_attr}.{attr}"):
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setattr_(layer, f"{layer_attr}.{attr}", size[i])
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