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https://github.com/huggingface/text-generation-inference.git
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093a27c528
Add support for GPTQ Marlin kernels GPTQ Marlin extends the Marlin kernels to support common GPTQ configurations: - bits: 4 or 8 - groupsize: -1, 32, 64, or 128 - desc_act: true/false Using the GPTQ Marlin kernels requires repacking the parameters in the Marlin quantizer format. The kernels were contributed by Neural Magic to VLLM. We vendor them here for convenience.
85 lines
2.6 KiB
Python
85 lines
2.6 KiB
Python
import math
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import torch
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import torch.distributed
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from opentelemetry import trace
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from transformers import AutoTokenizer, AutoConfig
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from typing import Optional
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from text_generation_server.models.flash_mistral import (
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BaseFlashMistral,
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set_sliding_window,
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)
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from text_generation_server.models.custom_modeling.flash_qwen2_modeling import (
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Qwen2ForCausalLM,
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)
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from text_generation_server.utils import (
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initialize_torch_distributed,
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weight_files,
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Weights,
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)
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tracer = trace.get_tracer(__name__)
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class FlashQwen2(BaseFlashMistral):
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def __init__(
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self,
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model_id: str,
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revision: Optional[str] = None,
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quantize: Optional[str] = None,
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speculator: Optional[str] = None,
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dtype: Optional[torch.dtype] = None,
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trust_remote_code: bool = False,
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):
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self.process_group, rank, world_size = initialize_torch_distributed()
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if torch.cuda.is_available():
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device = torch.device(f"cuda:{rank}")
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dtype = torch.float16 if dtype is None else dtype
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else:
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raise NotImplementedError("FlashQwen2 is only available on GPU")
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tokenizer = AutoTokenizer.from_pretrained(
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model_id,
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revision=revision,
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padding_side="left",
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truncation_side="left",
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trust_remote_code=trust_remote_code,
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)
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config = AutoConfig.from_pretrained(
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model_id, revision=revision, trust_remote_code=trust_remote_code
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)
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config.quantize = quantize
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config.speculator = speculator
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# Set context windows
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if config.sliding_window is not None:
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set_sliding_window(config.sliding_window)
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torch.distributed.barrier(group=self.process_group)
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filenames = weight_files(model_id, revision=revision, extension=".safetensors")
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weights = Weights(filenames, device, dtype, process_group=self.process_group)
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if config.quantize in ["gptq", "awq", "marlin"]:
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weights._set_gptq_params(model_id, revision)
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model = Qwen2ForCausalLM(config, weights)
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self.cuda_graphs = {}
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torch.distributed.barrier(group=self.process_group)
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super(BaseFlashMistral, self).__init__(
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model=model,
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tokenizer=tokenizer,
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num_layers=len(model.model.layers),
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num_kv_heads=model.model.num_key_value_heads,
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head_size=model.model.head_size,
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dtype=dtype,
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device=device,
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rank=rank,
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world_size=world_size,
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sliding_window=config.sliding_window,
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)
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