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Adding ctranslate2 quantization and inference: moving the contribution (#1) 

* rebaseing the commit on preemo fork.

* reformatting and changes.

* update dockerfile

* update changes for dockerfile

* adapt path

* rebaseing the commit on preemo fork.

* reformatting and changes.

* update dockerfile

* update changes for dockerfile

* adapt path

---------

Co-authored-by: michaelfeil <me@michaelfeil.eu>
This commit is contained in:
Michael Feil 2023-10-02 20:12:49 +02:00 committed by GitHub
parent 012c917b6f
commit ff703cb867
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GPG Key ID: 4AEE18F83AFDEB23
9 changed files with 483 additions and 5 deletions
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2
Dockerfile
View File

@ -188,7 +188,7 @@ COPY server/Makefile server/Makefile
RUN cd server && \
make gen-server && \
pip install -r requirements.txt && \
pip install ".[bnb, accelerate, quantize]" --no-cache-dir
pip install ".[bnb, accelerate, quantize, ct2]" --no-cache-dir
# Install benchmarker
COPY --from=builder /usr/src/target/release/text-generation-benchmark /usr/local/bin/text-generation-benchmark

6
launcher/src/main.rs
View File

@ -25,6 +25,7 @@ enum Quantization {
BitsandbytesNF4,
BitsandbytesFP4,
Gptq,
Ct2,
}
impl std::fmt::Display for Quantization {
@ -43,6 +44,9 @@ impl std::fmt::Display for Quantization {
Quantization::Gptq => {
write!(f, "gptq")
}
Quantization::Ct2 => {
write!(f, "ct2")
}
}
}
}
@ -104,7 +108,7 @@ struct Args {
num_shard: Option<usize>,
/// Whether you want the model to be quantized. This will use `bitsandbytes` for
/// quantization on the fly, or `gptq`. 4bit quantization is available through
/// quantization on the fly, `bnb` or `gptq`, or `ctranslate2`. 4bit quantization is available through
/// `bitsandbytes` by providing the `bitsandbytes-fp4` or `bitsandbytes-nf4` options.
#[clap(long, env, value_enum)]
quantize: Option<Quantization>,

2
server/Makefile
View File

@ -21,7 +21,7 @@ install-torch:
install: gen-server install-torch
pip install pip --upgrade
pip install -r requirements.txt
pip install -e ".[bnb, accelerate]"
pip install -e ".[bnb, accelerate, quantize, ct2]"
run-dev:
SAFETENSORS_FAST_GPU=1 python -m torch.distributed.run --nproc_per_node=2 text_generation_server/cli.py serve bigscience/bloom-560m --sharded

2
server/pyproject.toml
View File

@ -16,6 +16,7 @@ grpcio-reflection = "^1.51.1"
grpc-interceptor = "^0.15.0"
typer = "^0.6.1"
accelerate = { version = "^0.19.0", optional = true }
ctranslate2 = { version = "^3.20.0", optional = true }
bitsandbytes = { version = "^0.40.0", optional = true }
safetensors = "0.3.1"
loguru = "^0.6.0"
@ -35,6 +36,7 @@ datasets = { version = "^2.14.0", optional = true }
accelerate = ["accelerate"]
bnb = ["bitsandbytes"]
quantize = ["texttable", "datasets", "accelerate"]
ct2 = ["ctranslate2"]
[tool.poetry.group.dev.dependencies]
grpcio-tools = "^1.51.1"

99
server/tests/models/test_ct2.py Normal file
View File

@ -0,0 +1,99 @@
import pytest
import torch
from text_generation_server.pb import generate_pb2
from text_generation_server.models.causal_lm import CausalLMBatch
from text_generation_server.models.ct2_causal_lm import CT2CausalLM
@pytest.fixture(scope="session")
def default_santacoder():
return CT2CausalLM("bigcode/gpt_bigcode-santacoder", dtype=torch.float16)
@pytest.fixture
def default_pb_request(default_pb_parameters, default_pb_stop_parameters):
return generate_pb2.Request(
id=0,
inputs="def",
prefill_logprobs=True,
truncate=100,
parameters=default_pb_parameters,
stopping_parameters=default_pb_stop_parameters,
)
@pytest.fixture
def default_pb_batch(default_pb_request):
return generate_pb2.Batch(id=0, requests=[default_pb_request], size=1)
@pytest.fixture
def default_fim_pb_request(default_pb_parameters, default_pb_stop_parameters):
return generate_pb2.Request(
id=0,
inputs="<fim-prefix>def<fim-suffix>world<fim-middle>",
prefill_logprobs=True,
truncate=100,
parameters=default_pb_parameters,
stopping_parameters=default_pb_stop_parameters,
)
@pytest.fixture
def default_fim_pb_batch(default_fim_pb_request):
return generate_pb2.Batch(id=0, requests=[default_fim_pb_request], size=1)
def test_ct2santa_generate_token_completion(default_santacoder, default_pb_batch):
batch = CausalLMBatch.from_pb(
default_pb_batch,
default_santacoder.tokenizer,
default_santacoder.dtype,
default_santacoder.device,
)
next_batch = batch
for _ in range(batch.stopping_criterias[0].max_new_tokens - 1):
generations, next_batch = default_santacoder.generate_token(next_batch)
assert len(generations) == len(next_batch)
generations, next_batch = default_santacoder.generate_token(next_batch)
assert next_batch is None
assert len(generations) == 1
assert generations[0].generated_text.text in (" test_get_all_users_with_", ' test_get_all_users(client):')
assert generations[0].request_id == batch.requests[0].id
assert (
generations[0].generated_text.generated_tokens
== batch.stopping_criterias[0].max_new_tokens
)
def test_fim_ct2santacoder_generate_token_completion(
default_santacoder, default_fim_pb_batch
):
batch = CausalLMBatch.from_pb(
default_fim_pb_batch,
default_santacoder.tokenizer,
default_santacoder.dtype,
default_santacoder.device,
)
next_batch = batch
for _ in range(batch.stopping_criterias[0].max_new_tokens - 1):
generations, next_batch = default_santacoder.generate_token(next_batch)
assert len(generations) == len(next_batch)
generations, next_batch = default_santacoder.generate_token(next_batch)
assert next_batch is None
assert len(generations) == 1
assert (
generations[0].generated_text.text
== """ineProperty(exports, "__esModule", { value"""
)
assert generations[0].request_id == batch.requests[0].id
assert (
generations[0].generated_text.generated_tokens
== batch.stopping_criterias[0].max_new_tokens
)

5
server/text_generation_server/cli.py
View File

@ -16,6 +16,7 @@ class Quantization(str, Enum):
bitsandbytes_nf4 = "bitsandbytes-nf4"
bitsandbytes_fp4 = "bitsandbytes-fp4"
gptq = "gptq"
ct2 = "ct2"
class Dtype(str, Enum):
@ -73,7 +74,7 @@ def serve(
# Downgrade enum into str for easier management later on
quantize = None if quantize is None else quantize.value
dtype = None if dtype is None else dtype.value
if dtype is not None and quantize is not None:
if dtype is not None and quantize is not None and quantize != Quantization.ct2:
raise RuntimeError(
"Only 1 can be set between `dtype` and `quantize`, as they both decide how goes the final model."
)
@ -90,6 +91,7 @@ def download_weights(
auto_convert: bool = True,
logger_level: str = "INFO",
json_output: bool = False,
trust_remote_code: bool = False
):
# Remove default handler
logger.remove()
@ -169,6 +171,7 @@ def download_weights(
config = AutoConfig.from_pretrained(
model_id,
revision=revision,
trust_remote_code=trust_remote_code
)
architecture = config.architectures[0]

11
server/text_generation_server/models/__init__.py
View File

@ -18,6 +18,7 @@ from text_generation_server.models.galactica import GalacticaSharded
from text_generation_server.models.santacoder import SantaCoder
from text_generation_server.models.t5 import T5Sharded
from text_generation_server.models.gpt_neox import GPTNeoxSharded
from text_generation_server.models.ct2_causal_lm import CT2CausalLM
# The flag below controls whether to allow TF32 on matmul. This flag defaults to False
# in PyTorch 1.12 and later.
@ -75,6 +76,7 @@ def get_model(
dtype: Optional[str],
trust_remote_code: bool,
) -> Model:
dtype_ct2 = dtype
if dtype is None:
dtype = torch.float16
elif dtype == "float16":
@ -84,6 +86,15 @@ def get_model(
else:
raise RuntimeError(f"Unknown dtype {dtype}")
if quantize is not None and "ct2" in quantize:
return CT2CausalLM(
model_id,
revision,
quantize=quantize,
dtype=dtype_ct2,
trust_remote_code=trust_remote_code,
)
if "facebook/galactica" in model_id:
return GalacticaSharded(
model_id,

359
server/text_generation_server/models/ct2_causal_lm.py Normal file
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@ -0,0 +1,359 @@
# coding=utf-8
# Copyright 2023 Michael Feil.
#
# This code is loosely based on Huggingface text-generation-inference v0.9.3's causal_lm.py implementation.
# While it remains licensed under Apache License, Version 2.0,
# text-generation-inference itself on 7/28/2023 has changed its license.
# This code remains unaffected by this change.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import numpy as np
import os
import multiprocessing
from pathlib import Path
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE
from opentelemetry import trace
from transformers import (
AutoTokenizer,
AutoConfig,
)
from typing import Optional, Tuple, List, Type, Dict
from text_generation_server.models import Model
from text_generation_server.models.types import (
PrefillTokens,
Generation,
GeneratedText,
)
from text_generation_server.utils import Sampling
from text_generation_server.models.causal_lm import CausalLMBatch
try:
import ctranslate2
except ImportError:
ctranslate2 = None
tracer = trace.get_tracer(__name__)
class CT2CausalLM(Model):
def __init__(
self,
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
if ctranslate2 is None:
raise ValueError(
"for quantization with ct2, the installation requires the pip package ctranslate2. "
"install via `text-generation-server[ct2]` or `pip install ctranslate2` is required.",
)
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
# Start CT2
ct2_generator_kwargs = {
"inter_threads": int(os.environ.get("TGI_CT2_INTER_THREADS", 1))
}
if torch.cuda.is_available():
self.ct2_device = "cuda"
ct2_generator_kwargs["intra_threads"] = int(
os.environ.get("TGI_CT2_INTRA_THREADS", 1)
)
else:
self.ct2_device = "cpu"
ct2_generator_kwargs["intra_threads"] = int(
os.environ.get(
"TGI_CT2_INTRA_THREADS", multiprocessing.cpu_count() // 2
)
)
if dtype == torch.float16 and self.ct2_device == "cuda":
ct2_compute_type = "float16"
elif dtype == torch.bfloat16 and self.ct2_device == "cuda":
ct2_compute_type = "bfloat16"
elif self.ct2_device == "cpu" and dtype in [torch.float16, torch.bfloat16]:
# float16 is not available on CPU
# and int16 has no stable implementation
ct2_compute_type = "float32"
else:
# default, int8 quantization.
if "cuda" in self.ct2_device:
# int8 for int8 layers, float16 for non-quantized layers
ct2_compute_type = "int8_float16"
else:
# int8 for int8 layers, float32 for non-quantized layers
ct2_compute_type = "int8"
# Start CT2 - conversion
out_dir = (
Path(HUGGINGFACE_HUB_CACHE)
/ "ct2models" / f"{model_id.replace('/','--')}--{ct2_compute_type}"
)
if not os.path.exists(out_dir / "model.bin"):
try:
converter = ctranslate2.converters.TransformersConverter(
model_id,
activation_scales=None,
load_as_float16=ct2_compute_type != "bfloat16",
revision=revision,
low_cpu_mem_usage=True,
trust_remote_code=trust_remote_code,
)
converter.convert(
output_dir=out_dir,
vmap=None,
quantization=ct2_compute_type,
force=True,
)
except Exception as ex:
raise ValueError(
f"conversion with ctranslate2 for {model_id} failed : Error {ex}"
)
if not os.path.exists(out_dir / "model.bin"):
raise ValueError(
f"no ctranslate2 model for {model_id} found after conversion in {out_dir}"
)
# Start CT2
self.ct2_model = ctranslate2.Generator(
str(out_dir),
device=self.ct2_device,
compute_type=ct2_compute_type,
**ct2_generator_kwargs,
)
class DummyModel(torch.nn.Module):
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.config = AutoConfig.from_pretrained(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
model = DummyModel()
if tokenizer.pad_token_id is None:
if model.config.pad_token_id is not None:
tokenizer.pad_token_id = model.config.pad_token_id
elif model.config.eos_token_id is not None:
tokenizer.pad_token_id = model.config.eos_token_id
elif tokenizer.eos_token_id is not None:
tokenizer.pad_token_id = tokenizer.eos_token_id
else:
tokenizer.add_special_tokens({"pad_token": "[PAD]"})
super().__init__(
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=torch.int8 if "int8" in ct2_compute_type else torch.float16,
device=torch.device(self.ct2_device),
)
@property
def batch_type(self) -> Type[CausalLMBatch]:
return CausalLMBatch
def decode(self, generated_ids: List[int]) -> str:
return self.tokenizer.decode(
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
def forward_ct2(
self,
all_input_ids,
input_lengths,
) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
# CT2 forward requires a list of list of input tokens ids and lengths
ids_input = (
torch.nested.to_padded_tensor(
torch.nested.nested_tensor(all_input_ids), 1234567
)
.flatten(1)
.to(torch.int32)
)
# lengths of the padded ids_input, i.e. how often not pad=1234567 is used.
lengths = np.array(input_lengths, dtype=np.int32)
if self.ct2_device == "cuda":
lengths = torch.from_numpy(lengths).to(self.ct2_device)
elif self.ct2_device == "cpu":
ids_input = ids_input.numpy()
ids_input = ctranslate2.StorageView.from_array(ids_input)
lengths = ctranslate2.StorageView.from_array(lengths)
# now, forward through the network
logits = self.ct2_model.forward_batch(ids_input, lengths)
# continue with logits as torch tensor
if self.ct2_device == "cpu":
# logits is a float32 torch cpu tensor
logits = torch.from_numpy(np.asarray(logits))
else:
# logits is a float16 torch cuda tensor
logits = torch.as_tensor(logits, device=self.ct2_device)
return logits, None
@tracer.start_as_current_span("generate_token")
def generate_token(
self, batch: CausalLMBatch
) -> Tuple[List[Generation], Optional[CausalLMBatch]]:
logits, past = self.forward_ct2(batch.all_input_ids, batch.input_lengths)
# Results
generations: List[Generation] = []
stopped = True
# Zipped iterator
iterator = zip(
batch.requests,
batch.input_lengths,
batch.prefix_offsets,
batch.read_offsets,
logits,
batch.next_token_choosers,
batch.stopping_criterias,
batch.all_input_ids,
)
# For each member of the batch
for i, (
request,
input_length,
prefix_offset,
read_offset,
logits,
next_token_chooser,
stopping_criteria,
all_input_ids,
) in enumerate(iterator):
# Select next token
next_token_id, logprobs = next_token_chooser(
all_input_ids.view(1, -1), logits[-1:, :]
)
# Append next token to all tokens
all_input_ids = torch.cat([all_input_ids, next_token_id])
new_input_length = input_length + 1
# Generated token
next_token_logprob = logprobs[-1, next_token_id]
next_token_id_squeezed = next_token_id.squeeze()
next_token_text, prefix_offset, read_offset = self.decode_token(
all_input_ids[:, 0], prefix_offset, read_offset
)
# Evaluate stopping criteria
stop, reason = stopping_criteria(
next_token_id_squeezed,
next_token_text,
)
if not stop:
stopped = False
# Shard generations
# All generations will be appended in the rust sharded client
if i % self.world_size == self.rank:
if stop:
# Decode generated tokens
output_text = self.decode(
all_input_ids[-stopping_criteria.current_tokens :, 0]
)
# Get seed
if isinstance(next_token_chooser.choice, Sampling):
seed = next_token_chooser.choice.seed
else:
seed = None
generated_text = GeneratedText(
output_text, stopping_criteria.current_tokens, reason, seed
)
else:
generated_text = None
# Prefill
if stopping_criteria.current_tokens == 1 and request.prefill_logprobs:
# Remove generated token to only have prefill and add nan for first prompt token
prefill_logprobs = [float("nan")] + torch.log_softmax(
logits, -1
).gather(1, all_input_ids[1:]).squeeze(1)[
-new_input_length:-1
].tolist()
prefill_token_ids = all_input_ids[-new_input_length:-1]
prefill_texts = self.tokenizer.batch_decode(
prefill_token_ids,
clean_up_tokenization_spaces=False,
skip_special_tokens=False,
)
prefill_tokens = PrefillTokens(
prefill_token_ids, prefill_logprobs, prefill_texts
)
else:
prefill_tokens = None
generation = Generation(
request.id,
prefill_tokens,
next_token_id_squeezed,
next_token_logprob,
next_token_text,
next_token_id_squeezed.item() in self.all_special_ids,
generated_text,
)
generations.append(generation)
# Update values
batch.input_ids[i, 0] = next_token_id
batch.all_input_ids[i] = all_input_ids
batch.input_lengths[i] = new_input_length
batch.prefix_offsets[i] = prefix_offset
batch.read_offsets[i] = read_offset
batch.max_input_length = max(batch.max_input_length, new_input_length)
# We finished all generations in the batch; there is no next batch
if stopped:
return generations, None
# Slice unused values from prefill
batch.input_ids = batch.input_ids[:, :1]
# Update attention_mask as we added a new token to input_ids
batch.attention_mask[:, -batch.padding_right_offset] = 1
# Decrease right offset
batch.padding_right_offset -= 1
# Update position_ids
batch.position_ids = batch.position_ids[:, -1:] + 1
# Update past key values
batch.past_key_values = past
return generations, batch

2
server/text_generation_server/utils/logits_process.py
View File

@ -42,7 +42,7 @@ class StaticWarper:
self.static_next_logprob = None
def __call__(self, scores):
if torch.cuda.is_available():
if scores.device.type == "cuda":
if self.cuda_graph is None:
self.static_scores = scores
self.cuda_graph = torch.cuda.CUDAGraph()