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https://github.com/hpcaitech/ColossalAI.git
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8daee26989
* add infer_struct and infer_config * update codes * change InferConfig * Add hf_model_config to the engine * rm _get_hf_model_config * update codes * made adjustments according to the feedback from the reviewer. * update codes * add ci test for config and struct * Add the logic of the inference engine * update engine and test * Recover cache_manager.py * add logger * fix conflict * update codes * update codes * update model and tokenizer * fix add the logic about shardformer * change kvcache_manager docstring * add policy * fix ci bug in test_kvcache_manager.py * remove codes related o tokenizer and move model_policy * fix code style * add ordered_set to requirements-infer.txt * Delete extra empty lines * add ordered_set to requirements-test.txt
41 lines
1.2 KiB
Python
Executable File
41 lines
1.2 KiB
Python
Executable File
import copy
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from colossalai.shardformer import ShardConfig, ShardFormer
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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(
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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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)
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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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