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hf_text-generation-inference/server/text_generation_server/models/galactica.py

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import re
import torch
import torch.distributed
from typing import List, Optional, Type, Tuple
from accelerate import init_empty_weights
from safetensors import safe_open
from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
AutoConfig,
PreTrainedTokenizerBase,
)
from transformers.models.opt.parallel_layers import (
TensorParallelColumnLinear,
TensorParallelEmbedding,
TensorParallelRowLinear,
)
from text_generation_server.models import CausalLM
from text_generation_server.models.causal_lm import CausalLMBatch
from text_generation_server.pb import generate_pb2
from text_generation_server.models.opt import OPT
from text_generation_server.utils import (
NextTokenChooser,
StoppingCriteria,
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
# CREDIT: Papers with code => https://github.com/paperswithcode/galai/blob/main/galai/utils.py
# we split individual characters inside special tokens like [START_DNA]
CUSTOM_SEQ_RE = re.compile(r"(\[START_(DNA|SMILES|I_SMILES|AMINO)])(.*?)(\[END_\2])")
# token added to implement a custom sequence tokenization. This token is added at
# corpus cleaning step and removed in pretokenization. The digits are added to increase the chance
# that they do not occur in the corpus. The digits are escaped so that the token does not appear
# literally in the source code in case we ever include it in the training data.
SPLIT_MARKER = f"SPL{1}T-TH{1}S-Pl3A5E"
def _insert_split_marker(m: re.Match):
"""
Applies split marker based on a regex match of special tokens such as
[START_DNA].
Parameters
----------
n : str
Input text to split
Returns
----------
str - the text with the split token added
"""
start_token, _, sequence, end_token = m.groups()
sequence = re.sub(r"(.)", rf"{SPLIT_MARKER}\1", sequence, flags=re.DOTALL)
return f"{start_token}{sequence}{SPLIT_MARKER}{end_token}"
def escape_custom_split_sequence(text):
"""
Applies custom splitting to the text for GALILEO's tokenization
Parameters
----------
text : str
Input text to split
Returns
----------
str - the text with the split token added
"""
return CUSTOM_SEQ_RE.sub(_insert_split_marker, text)
# END CREDIT
class GalacticaCausalLMBatch(CausalLMBatch):
@classmethod
def from_pb(
cls,
pb: generate_pb2.Batch,
tokenizer: PreTrainedTokenizerBase,
device: torch.device,
) -> "GalacticaCausalLMBatch":
inputs = []
next_token_choosers = []
stopping_criterias = []
offsets = []
token_offsets = []
requests_idx_mapping = {}
# Parse batch
max_truncation = 0
padding_right_offset = 0
max_decode_tokens = 0
for i, r in enumerate(pb.requests):
requests_idx_mapping[r.id] = i
# Add escape_custom_split_sequence to the CausalLMBatch logic
inputs.append(escape_custom_split_sequence(r.inputs))
offsets.append(None)
token_offsets.append(None)
next_token_choosers.append(NextTokenChooser.from_pb(r.parameters, device))
stopping_criteria = StoppingCriteria.from_pb(
r.stopping_parameters, tokenizer
)
stopping_criterias.append(stopping_criteria)
max_truncation = max(max_truncation, r.truncate)
max_decode_tokens += stopping_criteria.max_new_tokens
padding_right_offset = max(
padding_right_offset, stopping_criteria.max_new_tokens
)
tokenized_inputs = tokenizer(
inputs,
return_tensors="pt",
padding=True,
return_token_type_ids=False,
truncation=True,
max_length=max_truncation,
).to(device)
input_lengths = tokenized_inputs["attention_mask"].sum(1)
max_input_length = input_lengths.max()
input_ids = tokenized_inputs["input_ids"]
# Allocate maximum attention_mask
attention_mask = input_ids.new_zeros(
(pb.size, max_input_length + padding_right_offset)
)
# Copy tokenizer attention_mask into fully allocated attention_mask
attention_mask[:, :max_input_length] = tokenized_inputs["attention_mask"]
position_ids = tokenized_inputs["attention_mask"].long().cumsum(-1) - 1
position_ids.masked_fill_(tokenized_inputs["attention_mask"] == 0, 1)
all_input_ids = tokenized_inputs["input_ids"].T.split(1, dim=1)
max_tokens = len(inputs) * max_input_length + max_decode_tokens
return cls(
batch_id=pb.id,
requests=pb.requests,
requests_idx_mapping=requests_idx_mapping,
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=None,
all_input_ids=list(all_input_ids),
input_lengths=input_lengths.tolist(),
offsets=offsets,
token_offsets=token_offsets,
next_token_choosers=next_token_choosers,
stopping_criterias=stopping_criterias,
max_input_length=max_input_length.item(),
padding_right_offset=padding_right_offset,
max_tokens=max_tokens,
)
class Galactica(OPT):
@property
def batch_type(self) -> Type[CausalLMBatch]:
return GalacticaCausalLMBatch
def decode(self, generated_ids: List[int]) -> str:
# Do not skip special tokens as they are used for custom parsing rules of the generated text
return self.tokenizer.decode(
generated_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False
)
def forward(
self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
"""Overwrite forward to ignore position_ids"""
# Model Forward
outputs = self.model.forward(
input_ids=input_ids,
attention_mask=attention_mask,
past_key_values=past_key_values,
use_cache=True,
)
return outputs.logits, outputs.past_key_values
class GalacticaSharded(Galactica):
def __init__(
self, model_id: str, revision: Optional[str] = None, quantize: bool = False
):
self.process_group, self.rank, self.world_size = initialize_torch_distributed()
self.master = self.rank == 0
if torch.cuda.is_available():
device = torch.device(f"cuda:{self.rank}")
dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32
else:
device = torch.device("cpu")
dtype = torch.float32
tokenizer = AutoTokenizer.from_pretrained(
model_id, revision=revision, padding_side="left", truncation_side="left"
)
config = AutoConfig.from_pretrained(
model_id, revision=revision, tp_parallel=True
)
tokenizer.pad_token_id = config.pad_token_id
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)
torch.distributed.barrier(group=self.process_group)
self.load_weights(
model,
filenames,
quantize=quantize,
device=device,
dtype=dtype,
rank=self.rank,
world_size=self.world_size,
)
self.model = model.eval()
torch.distributed.barrier(group=self.process_group)
super(CausalLM, self).__init__(
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,
device=device,
)
@staticmethod
def load_weights(
model,
filenames: List[str],
quantize: bool,
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 not quantize else "cpu"
) as f:
for name in f.keys():
if name == "lm_head.weight":
continue
module_name, param_name = name.rsplit(".", 1)
module = model.get_submodule(module_name)
current_tensor = parameters[name]
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]
else:
tensor = slice_[:]
if current_tensor.shape != tensor.shape:
raise ValueError(
f"Name {name} -- Current {current_tensor.shape} and got {tensor.shape}"
)
tensor = tensor.contiguous().to(dtype)
if quantize:
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)
else:
tensor = tensor.to(device)
module._parameters[param_name] = tensor
if name == "model.decoder.embed_tokens.weight":
model.lm_head._parameters["weight"] = tensor
uninitialized_parameters = []
for n, p in model.named_parameters():
if p.data.device == torch.device("meta"):
uninitialized_parameters.append(n)
if uninitialized_parameters:
raise RuntimeError(
f"found uninitialized parameters in model: {uninitialized_parameters}"
)
def forward(
self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
):
outputs = self.model.forward(
input_ids=input_ids,
attention_mask=attention_mask,
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
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