mirror of https://github.com/hpcaitech/ColossalAI
98 lines
3.8 KiB
Python
98 lines
3.8 KiB
Python
import torch
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import torch.nn as nn
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from colossalai.cluster import ProcessGroupMesh
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from colossalai.pipeline.schedule.generate import GenerateSchedule
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from colossalai.pipeline.stage_manager import PipelineStageManager
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from colossalai.shardformer import ShardConfig, ShardFormer
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from colossalai.shardformer.policies.base_policy import Policy
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from .microbatch_manager import MicroBatchManager
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class PPInferEngine:
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"""
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PPInferEngine is a class that handles the pipeline parallel inference.
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Args:
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pp_size (int): the number of pipeline stages.
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pp_model (`nn.Module`): the model already in pipeline parallelism style.
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model (`nn.Module`): the model not in pipeline style, and will be modified with `ShardFormer`.
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model_policy (`colossalai.shardformer.policies.base_policy.Policy`): the policy to shardformer model.
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micro_batch_size (int): the micro batch size.
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micro_batch_buffer_size (int): the buffer size for micro batch. Normally, it should be the same as the number of pipeline stages.
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new_length (int): the new length of the input sequence.
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early_stopping (bool): whether to stop early.
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Example:
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```python
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from colossalai.ppinference import PPInferEngine
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from transformers import GPT2LMHeadModel, GPT2Tokenizer
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model = transformers.GPT2LMHeadModel.from_pretrained('gpt2')
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# assume the model is infered with 4 pipeline stages
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inferengine = PPInferEngine(pp_size=4, model=model, model_policy={Your own policy for pipeline sharding})
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input = ["Hello, my dog is cute, and I like"]
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tokenized_input = tokenizer(input, return_tensors='pt')
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output = engine.inference([tokenized_input])
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```
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"""
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def __init__(
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self,
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pp_size: int,
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dtype: str = "fp16",
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pp_model: nn.Module = None,
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model: nn.Module = None,
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model_policy: Policy = None,
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new_length: int = 32,
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micro_batch_size: int = 1,
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micro_batch_buffer_size: int = None,
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verbose: bool = False,
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# TODO: implement early_stopping, and various gerneration options
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early_stopping: bool = False,
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do_sample: bool = False,
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num_beams: int = 1,
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) -> None:
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assert pp_model or (model and model_policy), "Either pp_model or model with model_policy should be provided."
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self.pp_size = pp_size
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self.pg_mesh = ProcessGroupMesh(pp_size)
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self.stage_manager = PipelineStageManager(self.pg_mesh, 0, True)
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self.mb_manager = MicroBatchManager(
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self.stage_manager.stage, new_length, micro_batch_size, micro_batch_buffer_size or pp_size
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)
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self.verbose = verbose
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self.schedule = GenerateSchedule(self.stage_manager, self.mb_manager, verbose)
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assert dtype in ["fp16", "fp32", "bf16"], "dtype should be one of 'fp16', 'fp32', 'bf16'"
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if dtype == "fp16":
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model.half()
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elif dtype == "bf16":
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model.to(torch.bfloat16)
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self.model = pp_model or self._shardformer(model, model_policy)
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def inference(self, input_list):
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out, timestamp = self.schedule.generate_step(self.model, iter(input_list))
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if self.verbose:
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return out, timestamp
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else:
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return out
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def _shardformer(self, model, model_policy):
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shardconfig = ShardConfig(
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tensor_parallel_process_group=None,
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pipeline_stage_manager=self.stage_manager,
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enable_tensor_parallelism=False,
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enable_fused_normalization=False,
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enable_all_optimization=False,
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enable_flash_attention=False,
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enable_jit_fused=False,
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enable_sequence_parallelism=False,
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)
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shardformer = ShardFormer(shard_config=shardconfig)
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shard_model, _ = shardformer.optimize(model, model_policy)
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return shard_model.cuda()
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