250 lines
9.5 KiB
Python
250 lines
9.5 KiB
Python
import torch
|
|
import torch.distributed
|
|
|
|
from typing import List, Optional
|
|
|
|
from accelerate import init_empty_weights
|
|
from safetensors import safe_open
|
|
from transformers import (
|
|
AutoTokenizer,
|
|
AutoModelForCausalLM,
|
|
AutoConfig,
|
|
)
|
|
from transformers.models.gpt_neox.parallel_layers import (
|
|
TensorParallelColumnLinear,
|
|
TensorParallelEmbedding,
|
|
TensorParallelRowLinear,
|
|
)
|
|
|
|
from text_generation_server.models import CausalLM
|
|
from text_generation_server.utils import (
|
|
initialize_torch_distributed,
|
|
weight_files,
|
|
)
|
|
|
|
HAS_BITS_AND_BYTES = True
|
|
try:
|
|
import bitsandbytes as bnb
|
|
from bitsandbytes.nn import Int8Params
|
|
except Exception as e:
|
|
HAS_BITS_AND_BYTES = False
|
|
|
|
|
|
class GPTNeoxSharded(CausalLM):
|
|
def __init__(
|
|
self,
|
|
model_id: str,
|
|
revision: Optional[str] = None,
|
|
quantize: Optional[str] = None,
|
|
trust_remote_code: bool = False,
|
|
):
|
|
self.process_group, rank, world_size = initialize_torch_distributed()
|
|
if torch.cuda.is_available():
|
|
device = torch.device(f"cuda:{rank}")
|
|
dtype = torch.float16
|
|
else:
|
|
device = torch.device("cpu")
|
|
dtype = torch.float32
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
model_id,
|
|
revision=revision,
|
|
padding_side="left",
|
|
truncation_side="left",
|
|
trust_remote_code=trust_remote_code,
|
|
)
|
|
tokenizer.pad_token = tokenizer.eos_token
|
|
|
|
config = AutoConfig.from_pretrained(
|
|
model_id,
|
|
revision=revision,
|
|
tp_parallel=True,
|
|
trust_remote_code=trust_remote_code,
|
|
)
|
|
|
|
torch.distributed.barrier(group=self.process_group)
|
|
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
|
|
|
|
with init_empty_weights():
|
|
model = AutoModelForCausalLM.from_config(
|
|
config, trust_remote_code=trust_remote_code
|
|
)
|
|
|
|
torch.distributed.barrier(group=self.process_group)
|
|
self.load_weights(
|
|
model,
|
|
filenames,
|
|
quantize=quantize,
|
|
device=device,
|
|
dtype=dtype,
|
|
rank=rank,
|
|
world_size=world_size,
|
|
)
|
|
torch.distributed.barrier(group=self.process_group)
|
|
super(CausalLM, self).__init__(
|
|
model=model,
|
|
tokenizer=tokenizer,
|
|
requires_padding=True,
|
|
dtype=dtype,
|
|
device=device,
|
|
rank=rank,
|
|
world_size=world_size,
|
|
)
|
|
|
|
@staticmethod
|
|
def load_weights(
|
|
model,
|
|
filenames: List[str],
|
|
quantize: Optional[str],
|
|
device: torch.device,
|
|
dtype: torch.dtype,
|
|
rank: int,
|
|
world_size: int,
|
|
):
|
|
parameters = dict(model.named_parameters())
|
|
for file in filenames:
|
|
with safe_open(
|
|
file, framework="pt", device=str(device) if quantize is None else "cpu"
|
|
) as f:
|
|
for name in f.keys():
|
|
module_name, param_name = name.rsplit(".", 1)
|
|
module = model.get_submodule(module_name)
|
|
|
|
current_parameter_tensor = parameters.get(name, None)
|
|
|
|
slice_ = f.get_slice(name)
|
|
|
|
if isinstance(module, TensorParallelColumnLinear):
|
|
size = slice_.get_shape()[0]
|
|
block_size = size // world_size
|
|
start = rank * block_size
|
|
stop = (rank + 1) * block_size
|
|
tensor = slice_[start:stop]
|
|
elif isinstance(module, TensorParallelRowLinear):
|
|
if param_name == "weight":
|
|
size = slice_.get_shape()[1]
|
|
block_size = size // world_size
|
|
start = rank * block_size
|
|
stop = (rank + 1) * block_size
|
|
tensor = slice_[:, start:stop]
|
|
else:
|
|
tensor = slice_[:]
|
|
# XXX: Hack for Rowlinear to add the bias only once.
|
|
if rank != 0:
|
|
tensor = torch.zeros_like(tensor)
|
|
elif isinstance(module, TensorParallelEmbedding):
|
|
size = slice_.get_shape()[0]
|
|
block_size = size // world_size
|
|
start = rank * block_size
|
|
stop = (rank + 1) * block_size
|
|
tensor = slice_[start:stop]
|
|
elif name == "embed_out.weight" and model.gpt_neox.tp_embeddings:
|
|
size = slice_.get_shape()[0]
|
|
block_size = size // world_size
|
|
start = rank * block_size
|
|
stop = (rank + 1) * block_size
|
|
tensor = slice_[start:stop]
|
|
else:
|
|
try:
|
|
tensor = slice_[:]
|
|
except:
|
|
tensor = f.get_tensor(name)
|
|
|
|
if (
|
|
current_parameter_tensor is not None
|
|
and current_parameter_tensor.shape != tensor.shape
|
|
):
|
|
raise ValueError(
|
|
f"Name {name} -- Current {current_parameter_tensor.shape} and got {tensor.shape}"
|
|
)
|
|
|
|
tensor = tensor.contiguous().to(dtype)
|
|
|
|
if quantize == "bitsandbytes":
|
|
if not HAS_BITS_AND_BYTES:
|
|
raise ImportError(
|
|
"bitsandbytes is not available on your machine either because it is not installed "
|
|
"or you don't have a GPU.\n"
|
|
"You can install it with `pip install bitsandbytes`."
|
|
)
|
|
|
|
if (
|
|
type(module)
|
|
in [TensorParallelRowLinear, TensorParallelColumnLinear]
|
|
and param_name == "weight"
|
|
):
|
|
tensor = Int8Params(
|
|
tensor,
|
|
has_fp16_weights=False,
|
|
requires_grad=False,
|
|
).to(device)
|
|
state = bnb.MatmulLtState()
|
|
state.threshold = 6.0
|
|
state.has_fp16_weights = False
|
|
state.memory_efficient_backward = False
|
|
state.use_pool = True
|
|
state.CB = tensor.CB
|
|
state.SCB = tensor.SCB
|
|
tensor.CB = None
|
|
tensor.SCB = None
|
|
|
|
def replace_linear(state):
|
|
def linear(input, weight, bias):
|
|
out = bnb.matmul(
|
|
input,
|
|
weight,
|
|
state=state,
|
|
threshold=state.threshold,
|
|
bias=bias,
|
|
)
|
|
|
|
if state.CB is not None:
|
|
# we converted 8-bit row major to turing/ampere format
|
|
# in the first inference pass
|
|
# we no longer need the row-major weight
|
|
del state.CB
|
|
weight.data = state.CxB
|
|
|
|
return out
|
|
|
|
return linear
|
|
|
|
module.linear = replace_linear(state)
|
|
elif quantize == "gptq":
|
|
raise NotImplementedError("`gptq` is not implemented for now")
|
|
elif quantize is None:
|
|
tensor = tensor.to(device)
|
|
else:
|
|
raise ValueError(f"Unexpected quantize `{quantize}`")
|
|
|
|
if current_parameter_tensor is not None:
|
|
module._parameters[param_name] = tensor
|
|
else:
|
|
module._buffers[param_name] = tensor
|
|
|
|
def forward(
|
|
self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
|
|
):
|
|
if self.model.gpt_neox.tp_embeddings:
|
|
outputs = self.model.forward(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
use_cache=True,
|
|
)
|
|
|
|
# Logits are sharded, so we need to gather them
|
|
logits = [torch.empty_like(outputs.logits) for _ in range(self.world_size)]
|
|
torch.distributed.all_gather(
|
|
logits, outputs.logits, group=self.process_group
|
|
)
|
|
logits = torch.cat(logits, dim=2)
|
|
|
|
return logits, outputs.past_key_values
|
|
# While the model itself is sharded, the embeddings might not as they might not be dividable by num-shard
|
|
else:
|
|
return super(GPTNeoxSharded, self).forward(
|
|
input_ids, attention_mask, position_ids, past_key_values
|
|
)
|