mirror of https://github.com/hpcaitech/ColossalAI
54 lines
1.9 KiB
Python
54 lines
1.9 KiB
Python
import copy
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import torch
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import torch.distributed as dist
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from torch import Tensor
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from torch import distributed as dist
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from torch.distributed import ProcessGroup
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from torch.nn import Module
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from torch.optim import Adam, Optimizer
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from colossalai.booster import Booster
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from colossalai.booster.plugin import HybridParallelPlugin
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from colossalai.booster.plugin.hybrid_parallel_plugin import HybridParallelModule
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from colossalai.shardformer import ShardConfig, ShardFormer
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from colossalai.shardformer._utils import getattr_
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from colossalai.shardformer.policies.auto_policy import Policy
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from colossalai.tensor.d_tensor.api import is_customized_distributed_tensor, is_distributed_tensor
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def build_model(
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model_fn,
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enable_fused_normalization=False,
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enable_tensor_parallelism=False,
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enable_flash_attention=False,
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enable_jit_fused=False,
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):
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# create new model
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org_model = model_fn()
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# shard model
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shard_config = ShardConfig(enable_fused_normalization=enable_fused_normalization,
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enable_tensor_parallelism=enable_tensor_parallelism,
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enable_flash_attention=enable_flash_attention,
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enable_jit_fused=enable_jit_fused,
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inference_only=True)
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model_copy = copy.deepcopy(org_model)
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shard_former = ShardFormer(shard_config=shard_config)
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sharded_model, shared_params = shard_former.optimize(model_copy)
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return org_model.cuda(), sharded_model.cuda()
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def run_infer(original_model, sharded_model, data_gen_fn, output_transform_fn):
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# prepare input
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data = data_gen_fn()
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data = {k: v.cuda() for k, v in data.items()}
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# run forward
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org_output = original_model(**data)
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org_output = output_transform_fn(org_output)
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shard_output = sharded_model(**data)
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shard_output = output_transform_fn(shard_output)
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return org_output, shard_output
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