mirror of
https://github.com/huggingface/text-generation-inference.git
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f1f28404e7
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.
122 lines
3.9 KiB
Python
122 lines
3.9 KiB
Python
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, Tuple
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from text_generation_server.models import FlashCausalLM
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from text_generation_server.models.flash_causal_lm import set_sliding_window
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from text_generation_server.models.custom_modeling.flash_mistral_modeling import (
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FlashMistralForCausalLM,
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MistralConfig,
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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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from text_generation_server.utils.import_utils import SYSTEM
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tracer = trace.get_tracer(__name__)
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class BaseFlashMistral(FlashCausalLM):
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def __init__(
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self,
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model_cls,
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model_id: str,
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config_cls=AutoConfig,
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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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tokenizer_class=AutoTokenizer,
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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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elif SYSTEM == "xpu":
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device = torch.device(f"xpu:{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("FlashMistral is only available on GPU")
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tokenizer = tokenizer_class.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 = config_cls.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 getattr(config, "sliding_window", None) is not None:
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set_sliding_window(config.sliding_window)
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else:
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config.sliding_window = None
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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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prefix = ""
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model = model_cls(prefix, config, weights)
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self.cuda_graphs = {}
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torch.distributed.barrier(group=self.process_group)
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num_layers, num_kv_heads, head_size = self.get_layer_config(model)
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super().__init__(
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model=model,
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tokenizer=tokenizer,
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num_layers=num_layers,
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num_kv_heads=num_kv_heads,
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head_size=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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def get_layer_config(self, model) -> Tuple[int, int, int]:
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return (
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len(model.model.layers),
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model.model.num_key_value_heads,
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model.model.head_size,
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)
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class FlashMistral(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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super(FlashMistral, self).__init__(
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config_cls=MistralConfig,
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model_cls=FlashMistralForCausalLM,
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model_id=model_id,
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revision=revision,
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quantize=quantize,
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speculator=speculator,
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dtype=dtype,
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trust_remote_code=trust_remote_code,
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)
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