251 lines
9.5 KiB
Python
251 lines
9.5 KiB
Python
import torch
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import torch.distributed
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from typing import List, Optional, Tuple
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from accelerate import init_empty_weights
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from safetensors import safe_open
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from transformers import (
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AutoTokenizer,
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AutoModelForCausalLM,
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AutoConfig,
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)
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from transformers.models.gpt_neox.parallel_layers import (
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TensorParallelColumnLinear,
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TensorParallelEmbedding,
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TensorParallelRowLinear,
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)
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from text_generation.models import CausalLM
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from text_generation.utils import (
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initialize_torch_distributed,
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weight_files,
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download_weights,
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)
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HAS_BITS_AND_BYTES = True
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try:
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import bitsandbytes as bnb
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from bitsandbytes.nn import Int8Params
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except Exception as e:
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HAS_BITS_AND_BYTES = False
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class GPTNeox(CausalLM):
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def forward(
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self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
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) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
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"""Overwrite forward to ignore position_ids"""
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# Model Forward
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outputs = self.model.forward(
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input_ids=input_ids,
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attention_mask=attention_mask,
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past_key_values=past_key_values,
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use_cache=True,
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)
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return outputs.logits, outputs.past_key_values
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class GPTNeoxSharded(GPTNeox):
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def __init__(
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self, model_id: str, revision: Optional[str] = None, quantize: bool = False
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):
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self.process_group, self.rank, self.world_size = initialize_torch_distributed()
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self.master = self.rank == 0
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if torch.cuda.is_available():
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device = torch.device(f"cuda:{self.rank}")
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dtype = torch.bfloat16
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else:
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device = torch.device("cpu")
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dtype = torch.float32
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tokenizer = AutoTokenizer.from_pretrained(
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model_id, revision=revision, padding_side="left"
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)
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tokenizer.pad_token = tokenizer.eos_token
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config = AutoConfig.from_pretrained(
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model_id, revision=revision, tp_parallel=True
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)
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# Only master download weights
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if self.master:
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download_weights(model_id, revision=revision, extension=".safetensors")
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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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if not filenames:
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raise ValueError("No safetensors weights found")
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with init_empty_weights():
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model = AutoModelForCausalLM.from_config(config)
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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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rank=self.rank,
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world_size=self.world_size,
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)
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self.model = model.eval().to(dtype)
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torch.distributed.barrier(group=self.process_group)
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super(CausalLM, self).__init__(
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tokenizer=tokenizer,
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device=device,
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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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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 not quantize 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()
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if quantize:
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if not HAS_BITS_AND_BYTES:
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raise ImportError(
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"bitsandbytes is not available on your machine either because it is not installed "
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"or you don't have a GPU.\n"
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"You can install it with `pip install bitsandbytes`."
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)
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if (
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type(module)
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in [TensorParallelRowLinear, TensorParallelColumnLinear]
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and param_name == "weight"
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):
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tensor = Int8Params(
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tensor,
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has_fp16_weights=False,
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requires_grad=False,
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).to(device)
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state = bnb.MatmulLtState()
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state.threshold = 6.0
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state.has_fp16_weights = False
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state.memory_efficient_backward = False
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state.use_pool = True
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state.CB = tensor.CB
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state.SCB = tensor.SCB
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tensor.CB = None
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tensor.SCB = None
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def replace_linear(state):
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def linear(input, weight, bias):
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out = bnb.matmul(
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input,
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weight,
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state=state,
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threshold=state.threshold,
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bias=bias,
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)
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if state.CB is not None:
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# we converted 8-bit row major to turing/ampere format
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# in the first inference pass
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# we no longer need the row-major weight
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del state.CB
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weight.data = state.CxB
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return out
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return linear
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module.linear = replace_linear(state)
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else:
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tensor = tensor.to(device)
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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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def forward(
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self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
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):
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if self.model.gpt_neox.tp_embeddings:
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outputs = self.model.forward(
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input_ids=input_ids,
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attention_mask=attention_mask,
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past_key_values=past_key_values,
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use_cache=True,
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)
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# Logits are sharded, so we need to gather them
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logits = [torch.empty_like(outputs.logits) for _ in range(self.world_size)]
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torch.distributed.all_gather(
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logits, outputs.logits, group=self.process_group
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)
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logits = torch.cat(logits, dim=2)
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return logits, outputs.past_key_values
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# While the model itself is sharded, the embeddings might not as they might not be dividable by num-shard
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else:
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return super(GPTNeoxSharded, self).forward(
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input_ids, attention_mask, position_ids, past_key_values
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)
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