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.
75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
import torch
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import torch.distributed
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from opentelemetry import trace
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from typing import Optional
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from transformers import AutoConfig, AutoTokenizer
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from text_generation_server.models import FlashCausalLM
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from text_generation_server.models.custom_modeling.flash_gemma_modeling import (
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FlashGemmaForCausalLM,
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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 FlashGemma(FlashCausalLM):
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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.bfloat16 if dtype is None else dtype
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else:
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raise NotImplementedError("FlashGemma 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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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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# TODO hardcoded
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prefix = ""
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model = FlashGemmaForCausalLM(prefix, config, weights, causal=True)
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torch.distributed.barrier(group=self.process_group)
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super(FlashGemma, 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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)
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