mirror of
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.
100 lines
3.3 KiB
Python
100 lines
3.3 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 AutoTokenizer
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from transformers.models.gpt2 import GPT2TokenizerFast
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from text_generation_server.models import FlashCausalLM
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from text_generation_server.models.custom_modeling.flash_dbrx_modeling import (
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FlashDbrxForCausalLM,
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DbrxConfig,
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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 FlashDbrx(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("FlashDBRX is only available on GPU")
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try:
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tokenizer = GPT2TokenizerFast.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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use_fast=True,
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from_slow=False,
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)
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except:
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try:
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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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use_fast=True,
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from_slow=False,
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)
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except:
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# FIXME: change back to model id once the tokenizer.json is merged
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tokenizer = GPT2TokenizerFast.from_pretrained(
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"Xenova/dbrx-instruct-tokenizer",
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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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use_fast=True,
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from_slow=False,
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)
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config = DbrxConfig.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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model = FlashDbrxForCausalLM(config, weights)
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torch.distributed.barrier(group=self.process_group)
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super(FlashDbrx, 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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