mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-04-20 14:22:08 +00:00
04e1af94d7
* feat: first draft load multiple lora * feat: load weights within layer and refactor lora pass * fix: refactor and reduce lora math * feat: baseline impl single request multi lora support * feat: prefer lorax implementation and port loading logic * fix: prefer adapter_data and refactors * feat: perfer loraxs custom punica kernels and add mlp loras * fix: adjust batch for bgmv * fix: adjust adapter_segments logic when in batch * fix: refactor and move changes to v3 proto * fix: pass model_id for all flash causal lms * fix: pass model_id for all causal and seq2seq lms * fix: add model_id to model test * feat: add lora support to mistral and refactors * feat: prefer model id in request * fix: include rust code for adapter id * feat: bump launcher and add new lora docs * feat: support base model generation and refactors * fix: rename doc to retry ci build * feat: support if vlm models * fix: add adapter_data param and avoid missing layers * fix: add adapter_data param to phi and neox * fix: update all models forwards to include adapter_data * fix: add model_id to IdeficsCausalLM * Update lora.md Fixed a typo * Update lora.md Fixing spam image * fix: add lora kernel to dockerfile, support running without kernels and refactors * fix: avoid dockerfile conflict * fix: refactors and adjust flash llama lora logic * fix: skip llama test due to CI issue (temp) * fix: skip llama test CI (temp) 2 * fix: revert skips and prefer updated ci token for tests * fix: refactors and helpful comments * fix: add noop in TensorParallelAdapterRowLinear too * fix: refactor and move shard_lora_weights logic * fix: exit early if no adapter_data --------- Co-authored-by: Derek <datavistics@gmail.com>
92 lines
3.0 KiB
Python
92 lines
3.0 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
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from typing import Optional
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from text_generation_server.models import FlashCausalLM
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from text_generation_server.models.custom_modeling.flash_rw_modeling import (
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RWConfig,
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FlashRWForCausalLM,
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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 FlashRWSharded(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.float16 if dtype is None else dtype
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elif SYSTEM == "ipex":
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if hasattr(torch, "xpu") and torch.xpu.is_available():
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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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device = torch.device("cpu")
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dtype = torch.bfloat16 if dtype is None else dtype
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else:
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raise NotImplementedError("FlashRW 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 = RWConfig.from_pretrained(
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model_id, revision=revision, trust_remote_code=trust_remote_code
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)
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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(
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filenames,
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device,
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dtype,
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process_group=self.process_group,
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aliases={
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"lm_head.weight": ["transformer.word_embeddings.weight"],
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"transformer.word_embeddings.weight": ["lm_head.weight"],
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},
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)
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config.quantize = quantize
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config.speculator = speculator
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if config.quantize in ["gptq", "marlin"]:
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weights._set_gptq_params(model_id, revision)
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model = FlashRWForCausalLM(config, weights)
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torch.distributed.barrier(group=self.process_group)
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super(FlashRWSharded, self).__init__(
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model_id=model_id,
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model=model.to(device),
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tokenizer=tokenizer,
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num_layers=len(model.transformer.h),
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num_kv_heads=model.transformer.cache_size,
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head_size=model.transformer.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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