mirror of
https://github.com/kvcache-ai/ktransformers.git
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84 lines
4.1 KiB
Python
84 lines
4.1 KiB
Python
import torch
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from transformers import AutoTokenizer, AutoConfig, GenerationConfig
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from ktransformers.server.backend.interfaces.transformers import TransformersInterface,ConfigArgs, TransformersThreadContext,default_args,TextStreamer
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from ktransformers.server.config.log import logger
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from ktransformers.optimize.optimize import optimize_and_load_gguf
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from ktransformers.models.custom_cache import StaticCache
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from ktransformers.util.cuda_graph_runner import CUDAGraphRunner
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from ktransformers.local_chat import custom_models, default_optimize_rules
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from ktransformers.util.utils import get_device
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class KTransformersThreadContext(TransformersThreadContext):
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pass
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class KTransformersInterface(TransformersInterface):
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def __init__(self,args:ConfigArgs= default_args):
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self.args = args
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torch.set_default_dtype(torch.bfloat16)
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torch.set_grad_enabled(False)
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self.tokenizer = AutoTokenizer.from_pretrained(args.model_dir,device = args.device)
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config=AutoConfig.from_pretrained(args.model_dir, trust_remote_code=True)
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if config.architectures[0] == "Qwen2MoeForCausalLM":
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config._attn_implementation="flash_attention_2"
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with torch.device("meta"):
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self.model=custom_models[config.architectures[0]](config)
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if default_args.optimize_config_path is not None:
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optimize_rule_path = default_optimize_rules[config.architectures[0]]
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else:
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optimize_rule_path = args.optimize_config_path
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# print(optimize_config)
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gguf_path = args.gguf_path
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if gguf_path is None:
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gguf_path = input(
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"please input the path of your gguf file(gguf file in the dir containing input gguf file must all belong to current model):"
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)
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optimize_and_load_gguf(self.model, optimize_rule_path, gguf_path, config)
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device_map = self.model.gguf_loader.tensor_device_map
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logger.info(f'{args.model_name} loaded from {args.model_dir} to {device_map}')
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self.cache = StaticCache(config=self.model.config, max_batch_size=args.batch_size, max_cache_len=args.cache_lens, device=device_map, dtype=self.model.dtype)
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logger.info(f'StaticCache (length={args.cache_lens}) created at {device_map}, batch size:{args.batch_size}')
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self.model.generation_config = GenerationConfig.from_pretrained(args.model_dir)
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if self.model.generation_config.pad_token_id is None:
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self.model.generation_config.pad_token_id = self.model.generation_config.eos_token_id
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self.streamer = TextStreamer(self.tokenizer)
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def decode_one_tokens(self):
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if not hasattr(self, "cuda_graph_runner"):
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device_map = self.model.gguf_loader.tensor_device_map
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torch_device = get_device('blk.0.self_attn', device_map)
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torch_device = "cuda:0" if torch_device == "cuda" else torch_device
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self.cuda_graph_runner = CUDAGraphRunner()
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self.cuda_graph_runner.capture(self.model, self.current_ids, self.active_cache_position.unsqueeze(0), self.active_cache_position, self.cache, main_device=torch_device, return_dict=False, use_cache=True)
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if hasattr(self, "cuda_graph_runner"):
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logits = self.cuda_graph_runner(self.current_ids, self.active_cache_position.unsqueeze(0), self.active_cache_position)
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self.cache.change_seq_length(1)
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torch.cuda.synchronize()
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logits = logits[0,-1,:]
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return self.logits_to_token(logits)
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if self.use_static_cache:
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mask = torch.ones((1,self.seq_length)).to(torch_device)
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logits = self.model(
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self.current_ids,
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cache_position=self.active_cache_position,
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past_key_values=self.cache,
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attention_mask=mask,
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return_dict=False,
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use_cache=True
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)[0]
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else:
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logits = self.model(
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self.current_ids,
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return_dict=False
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)[0]
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logits = logits[0,-1,:]
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return self.logits_to_token(logits)
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