162 lines
5.7 KiB
Python
162 lines
5.7 KiB
Python
import torch
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import torch.distributed
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from accelerate import init_empty_weights
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from opentelemetry import trace
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from safetensors import safe_open
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from transformers import AutoTokenizer, AutoConfig
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from typing import Optional, List
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from text_generation_server.models import FlashCausalLM
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from text_generation_server.models.custom_modeling.flash_neox_modeling import (
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FlashGPTNeoXForCausalLM,
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TensorParallelEmbedding,
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TensorParallelRowLinear,
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TensorParallelColumnLinear,
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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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)
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tracer = trace.get_tracer(__name__)
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class FlashNeoX(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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):
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super(FlashNeoX, self).__init__(
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FlashGPTNeoXForCausalLM, model_id, revision, quantize
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)
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class FlashNeoXSharded(FlashNeoX):
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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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):
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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
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else:
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raise NotImplementedError("FlashNeoX is only available on GPU")
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tokenizer = AutoTokenizer.from_pretrained(
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model_id, revision=revision, padding_side="left", truncation_side="left"
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)
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config = AutoConfig.from_pretrained(
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model_id,
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revision=revision,
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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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with init_empty_weights():
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model = FlashGPTNeoXForCausalLM(config, self.process_group)
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torch.distributed.barrier(group=self.process_group)
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self.load_weights(
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model,
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filenames,
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quantize=quantize,
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device=device,
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dtype=dtype,
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rank=rank,
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world_size=world_size,
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)
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self.model = model.eval().to(device)
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torch.distributed.barrier(group=self.process_group)
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super(FlashCausalLM, self).__init__(
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tokenizer=tokenizer,
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requires_padding=False,
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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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@staticmethod
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def load_weights(
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model,
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filenames: List[str],
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quantize: bool,
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device: torch.device,
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dtype: torch.dtype,
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rank: int,
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world_size: int,
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):
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parameters = dict(model.named_parameters())
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for file in filenames:
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with safe_open(
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file, framework="pt", device=str(device) if quantize is None else "cpu"
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) as f:
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for name in f.keys():
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module_name, param_name = name.rsplit(".", 1)
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module = model.get_submodule(module_name)
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current_parameter_tensor = parameters.get(name, None)
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slice_ = f.get_slice(name)
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if isinstance(module, TensorParallelColumnLinear):
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size = slice_.get_shape()[0]
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block_size = size // world_size
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start = rank * block_size
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stop = (rank + 1) * block_size
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tensor = slice_[start:stop]
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elif isinstance(module, TensorParallelRowLinear):
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if param_name == "weight":
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size = slice_.get_shape()[1]
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block_size = size // world_size
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start = rank * block_size
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stop = (rank + 1) * block_size
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tensor = slice_[:, start:stop]
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else:
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tensor = slice_[:]
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# XXX: Hack for Rowlinear to add the bias only once.
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if rank != 0:
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tensor = torch.zeros_like(tensor)
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elif isinstance(module, TensorParallelEmbedding):
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size = slice_.get_shape()[0]
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block_size = size // world_size
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start = rank * block_size
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stop = (rank + 1) * block_size
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tensor = slice_[start:stop]
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elif name == "embed_out.weight" and model.gpt_neox.tp_embeddings:
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size = slice_.get_shape()[0]
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block_size = size // world_size
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start = rank * block_size
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stop = (rank + 1) * block_size
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tensor = slice_[start:stop]
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else:
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try:
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tensor = slice_[:]
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except:
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tensor = f.get_tensor(name)
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if (
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current_parameter_tensor is not None
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and current_parameter_tensor.shape != tensor.shape
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):
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raise ValueError(
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f"Name {name} -- Current {current_parameter_tensor.shape} and got {tensor.shape}"
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)
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tensor = tensor.contiguous().to(dtype)
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if current_parameter_tensor is not None:
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module._parameters[param_name] = tensor
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else:
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module._buffers[param_name] = tensor
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model.post_load_weights(quantize)
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