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fix(server): fix decode token (#334) 

Fixes #333

---------

Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
This commit is contained in:
OlivierDehaene 2023-05-16 23:23:27 +02:00 committed by GitHub
parent dbdc587ddd
commit 5a58226130
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GPG Key ID: 4AEE18F83AFDEB23
25 changed files with 396 additions and 350 deletions
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3
.github/workflows/build.yaml vendored
View File

@ -213,12 +213,13 @@ jobs:
sudo mount /dev/nvme1n1 ${{ env.DOCKER_VOLUME }}
- name: Install
run: |
pip install pytest-xdist
make install-integration-tests
- name: Run tests
run: |
export DOCKER_IMAGE=registry.internal.huggingface.tech/api-inference/community/text-generation-inference:sha-${{ env.GITHUB_SHA_SHORT }}
export HUGGING_FACE_HUB_TOKEN=${{ secrets.HUGGING_FACE_HUB_TOKEN }}
pytest -s -vv integration-tests
pytest -s -vv -n 2 --dist loadfile integration-tests
stop-runner:
name: Stop self-hosted EC2 runner

3
.github/workflows/tests.yaml vendored
View File

@ -66,7 +66,8 @@ jobs:
- name: Run server tests
run: |
pip install pytest
make python-server-tests
export HUGGING_FACE_HUB_TOKEN=${{ secrets.HUGGING_FACE_HUB_TOKEN }}
pytest -s -vv server/tests
- name: Run Rust fmt
run: |
cargo fmt --check

2
Makefile
View File

@ -31,7 +31,7 @@ update-integration-tests: install-integration-tests
pytest -s -vv --snapshot-update integration-tests
python-server-tests:
HF_HUB_ENABLE_HF_TRANSFER=1 pytest server/tests
HF_HUB_ENABLE_HF_TRANSFER=1 pytest -s -vv -m "not private" server/tests
python-client-tests:
pytest clients/python/tests

16
integration-tests/conftest.py
View File

@ -1,3 +1,4 @@
import sys
import subprocess
import contextlib
import pytest
@ -7,6 +8,7 @@ import docker
import json
import math
import time
import random
from docker.errors import NotFound
from typing import Optional, List, Dict
@ -205,10 +207,12 @@ def launcher(event_loop):
def local_launcher(
model_id: str, num_shard: Optional[int] = None, quantize: Optional[str] = None
):
port = 9999
master_port = 19999
port = random.randint(8000, 10_000)
master_port = random.randint(10_000, 20_000)
shard_uds_path = f"/tmp/{model_id.replace('/', '--')}-server"
shard_uds_path = (
f"/tmp/tgi-tests-{model_id.split('/')[-1]}-{num_shard}-{quantize}-server"
)
args = [
"text-generation-launcher",
@ -236,7 +240,7 @@ def launcher(event_loop):
process.wait(60)
launcher_output = process.stdout.read().decode("utf-8")
print(launcher_output)
print(launcher_output, file=sys.stderr)
process.stdout.close()
process.stderr.close()
@ -245,7 +249,7 @@ def launcher(event_loop):
def docker_launcher(
model_id: str, num_shard: Optional[int] = None, quantize: Optional[str] = None
):
port = 9999
port = random.randint(8000, 10_000)
args = ["--model-id", model_id, "--env"]
@ -298,7 +302,7 @@ def launcher(event_loop):
pass
container_output = container.logs().decode("utf-8")
print(container_output)
print(container_output, file=sys.stderr)
container.remove()

176
integration-tests/models/__snapshots__/test_flash_llama/test_flash_llama_load.json
View File

@ -1,92 +1,4 @@
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -8.6875,
"text": "Test"
},
{
"id": 2009,
"logprob": -11.5546875,
"text": "request"
}
],
"seed": null,
"tokens": [
{
"id": 363,
"logprob": -1.5322266,
"special": false,
"text": " for"
},
{
"id": 847,
"logprob": -2.5585938,
"special": false,
"text": " /"
},
{
"id": 2754,
"logprob": -2.265625,
"special": false,
"text": "api"
},
{
"id": 29914,
"logprob": -0.034088135,
"special": false,
"text": "/"
},
{
"id": 29894,
"logprob": -0.96240234,
"special": false,
"text": "v"
},
{
"id": 29896,
"logprob": -0.36816406,
"special": false,
"text": "1"
},
{
"id": 29914,
"logprob": -0.013191223,
"special": false,
"text": "/"
},
{
"id": 16418,
"logprob": -3.15625,
"special": false,
"text": "projects"
},
{
"id": 29914,
"logprob": -0.43774414,
"special": false,
"text": "/"
},
{
"id": 29896,
"logprob": -1.9443359,
"special": false,
"text": "1"
}
]
},
"generated_text": "for /api/v1/projects/1"
},
{
"details": {
"best_of_sequences": null,
@ -263,6 +175,94 @@
},
"generated_text": "for /api/v1/projects/1"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "length",
"generated_tokens": 10,
"prefill": [
{
"id": 1,
"logprob": null,
"text": "<s>"
},
{
"id": 4321,
"logprob": -8.6875,
"text": "Test"
},
{
"id": 2009,
"logprob": -11.5546875,
"text": "request"
}
],
"seed": null,
"tokens": [
{
"id": 363,
"logprob": -1.5322266,
"special": false,
"text": " for"
},
{
"id": 847,
"logprob": -2.5585938,
"special": false,
"text": " /"
},
{
"id": 2754,
"logprob": -2.265625,
"special": false,
"text": "api"
},
{
"id": 29914,
"logprob": -0.034088135,
"special": false,
"text": "/"
},
{
"id": 29894,
"logprob": -0.96240234,
"special": false,
"text": "v"
},
{
"id": 29896,
"logprob": -0.36816406,
"special": false,
"text": "1"
},
{
"id": 29914,
"logprob": -0.013191223,
"special": false,
"text": "/"
},
{
"id": 16418,
"logprob": -3.15625,
"special": false,
"text": "projects"
},
{
"id": 29914,
"logprob": -0.43774414,
"special": false,
"text": "/"
},
{
"id": 29896,
"logprob": -1.9443359,
"special": false,
"text": "1"
}
]
},
"generated_text": "for /api/v1/projects/1"
},
{
"details": {
"best_of_sequences": null,

108
integration-tests/models/__snapshots__/test_mt0_base/test_mt0_base_load.json
View File

@ -1,58 +1,4 @@
[
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 6,
"prefill": [
{
"id": 0,
"logprob": null,
"text": "<pad>"
}
],
"seed": null,
"tokens": [
{
"id": 259,
"logprob": -1.3789062,
"special": false,
"text": ""
},
{
"id": 39261,
"logprob": -0.36279297,
"special": false,
"text": "Because"
},
{
"id": 609,
"logprob": -1.0966797,
"special": false,
"text": " it"
},
{
"id": 339,
"logprob": -0.8276367,
"special": false,
"text": " is"
},
{
"id": 16017,
"logprob": -1.6845703,
"special": false,
"text": " blue"
},
{
"id": 1,
"logprob": -0.72753906,
"special": true,
"text": "</s>"
}
]
},
"generated_text": "Because it is blue"
},
{
"details": {
"best_of_sequences": null,
@ -161,6 +107,60 @@
},
"generated_text": "Because it is blue"
},
{
"details": {
"best_of_sequences": null,
"finish_reason": "eos_token",
"generated_tokens": 6,
"prefill": [
{
"id": 0,
"logprob": null,
"text": "<pad>"
}
],
"seed": null,
"tokens": [
{
"id": 259,
"logprob": -1.3789062,
"special": false,
"text": " "
},
{
"id": 39261,
"logprob": -0.36279297,
"special": false,
"text": "Because"
},
{
"id": 609,
"logprob": -1.0966797,
"special": false,
"text": " it"
},
{
"id": 339,
"logprob": -0.8276367,
"special": false,
"text": " is"
},
{
"id": 16017,
"logprob": -1.6845703,
"special": false,
"text": " blue"
},
{
"id": 1,
"logprob": -0.72753906,
"special": true,
"text": "</s>"
}
]
},
"generated_text": "Because it is blue"
},
{
"details": {
"best_of_sequences": null,

2
router/src/main.rs
View File

@ -146,7 +146,7 @@ fn main() -> Result<(), std::io::Error> {
sha: None,
pipeline_tag: None,
},
false => get_model_info(&tokenizer_name, &revision, authorization_token).await.unwrap_or({
false => get_model_info(&tokenizer_name, &revision, authorization_token).await.unwrap_or_else(|| {
tracing::warn!("Could not retrieve model info from the Hugging Face hub.");
HubModelInfo { model_id: tokenizer_name.to_string(), sha: None, pipeline_tag: None }
}),

2
server/Makefile
View File

@ -2,7 +2,7 @@ include Makefile-transformers
include Makefile-flash-att
unit-tests:
python -m pytest tests
pytest -s -vv -m "not private" tests
gen-server:
# Compile protos

78
server/tests/models/test_model.py Normal file
View File

@ -0,0 +1,78 @@
import pytest
import torch
from transformers import AutoTokenizer
from text_generation_server.models import Model
def get_test_model():
class TestModel(Model):
def batch_type(self):
raise NotImplementedError
def generate_token(self, batch):
raise NotImplementedError
tokenizer = AutoTokenizer.from_pretrained("huggingface/llama-7b")
model = TestModel(
torch.nn.Linear(1, 1), tokenizer, False, torch.float32, torch.device("cpu")
)
return model
@pytest.mark.private
def test_decode_streaming_english_spaces():
model = get_test_model()
truth = "Hello here, this is a simple test"
all_input_ids = [15043, 1244, 29892, 445, 338, 263, 2560, 1243]
assert (
all_input_ids == model.tokenizer(truth, add_special_tokens=False)["input_ids"]
)
decoded_text = ""
offset = 0
token_offset = 0
for i in range(len(all_input_ids)):
text, offset, token_offset = model.decode_token(
all_input_ids[: i + 1], offset, token_offset
)
decoded_text += text
assert decoded_text == truth
@pytest.mark.private
def test_decode_streaming_chinese_utf8():
model = get_test_model()
truth = "我很感谢你的热情"
all_input_ids = [
30672,
232,
193,
139,
233,
135,
162,
235,
179,
165,
30919,
30210,
234,
134,
176,
30993,
]
decoded_text = ""
offset = 0
token_offset = 0
for i in range(len(all_input_ids)):
text, offset, token_offset = model.decode_token(
all_input_ids[: i + 1], offset, token_offset
)
decoded_text += text
assert decoded_text == truth

5
server/text_generation_server/models/bloom.py
View File

@ -56,7 +56,7 @@ class BLOOM(CausalLM):
quantize: Optional[str] = None,
):
super(BLOOM, self).__init__(
model_id=model_id, revision=revision, quantize=quantize, decode_buffer=1
model_id=model_id, revision=revision, quantize=quantize
)
@property
@ -104,14 +104,13 @@ class BLOOMSharded(BLOOM):
rank=rank,
world_size=world_size,
)
self.model = model.eval()
torch.distributed.barrier(group=self.process_group)
super(CausalLM, self).__init__(
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,
device=device,
decode_buffer=1,
rank=rank,
world_size=world_size,
)

71
server/text_generation_server/models/causal_lm.py
View File

@ -35,8 +35,8 @@ class CausalLMBatch(Batch):
# Lengths of all generations present in the batch
input_lengths: List[int]
offsets: List[Optional[int]]
token_offsets: List[Optional[int]]
prefix_offsets: List[int]
read_offsets: List[int]
# Generation helpers
next_token_choosers: List[NextTokenChooser]
@ -70,8 +70,8 @@ class CausalLMBatch(Batch):
inputs = []
next_token_choosers = []
stopping_criterias = []
offsets = []
token_offsets = []
prefix_offsets = []
read_offsets = []
requests_idx_mapping = {}
# Parse batch
@ -81,8 +81,6 @@ class CausalLMBatch(Batch):
for i, r in enumerate(pb.requests):
requests_idx_mapping[r.id] = i
inputs.append(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
@ -102,6 +100,10 @@ class CausalLMBatch(Batch):
truncation=True,
max_length=max_truncation,
).to(device)
for _ in pb.requests:
input_len = tokenized_inputs["input_ids"].shape[1]
prefix_offsets.append(0)
read_offsets.append(input_len)
input_lengths = tokenized_inputs["attention_mask"].sum(1)
max_input_length = input_lengths.max()
@ -130,8 +132,8 @@ class CausalLMBatch(Batch):
past_key_values=None,
all_input_ids=list(all_input_ids),
input_lengths=input_lengths.tolist(),
offsets=offsets,
token_offsets=token_offsets,
prefix_offsets=prefix_offsets,
read_offsets=read_offsets,
next_token_choosers=next_token_choosers,
stopping_criterias=stopping_criterias,
max_input_length=max_input_length.item(),
@ -151,8 +153,8 @@ class CausalLMBatch(Batch):
# New values after filtering
requests_idx_mapping = {}
input_lengths = []
offsets = []
token_offsets = []
prefix_offsets = []
read_offsets = []
all_input_ids = []
max_input_length = 0
@ -167,8 +169,8 @@ class CausalLMBatch(Batch):
requests_idx_mapping[r.id] = i
keep_indices.append(idx)
offsets.append(self.offsets[idx])
token_offsets.append(self.token_offsets[idx])
prefix_offsets.append(self.prefix_offsets[idx])
read_offsets.append(self.read_offsets[idx])
all_input_ids.append(self.all_input_ids[idx])
request_input_length = self.input_lengths[idx]
@ -225,8 +227,8 @@ class CausalLMBatch(Batch):
self.position_ids = position_ids
self.all_input_ids = all_input_ids
self.input_lengths = input_lengths
self.offsets = offsets
self.token_offsets = token_offsets
self.prefix_offsets = prefix_offsets
self.read_offsets = read_offsets
self.next_token_choosers = next_token_choosers
self.stopping_criterias = stopping_criterias
self.max_input_length = max_input_length
@ -251,8 +253,8 @@ class CausalLMBatch(Batch):
requests = []
requests_idx_mapping = {}
input_lengths = []
offsets = []
token_offsets = []
prefix_offsets = []
read_offsets = []
all_input_ids = []
next_token_choosers = []
stopping_criterias = []
@ -270,8 +272,8 @@ class CausalLMBatch(Batch):
for i, batch in enumerate(batches):
requests.extend(batch.requests)
input_lengths.extend(batch.input_lengths)
offsets.extend(batch.offsets)
token_offsets.extend(batch.token_offsets)
prefix_offsets.extend(batch.prefix_offsets)
read_offsets.extend(batch.read_offsets)
all_input_ids.extend(batch.all_input_ids)
next_token_choosers.extend(batch.next_token_choosers)
stopping_criterias.extend(batch.stopping_criterias)
@ -428,8 +430,8 @@ class CausalLMBatch(Batch):
past_key_values=past_key_values,
all_input_ids=all_input_ids,
input_lengths=input_lengths,
offsets=offsets,
token_offsets=token_offsets,
prefix_offsets=prefix_offsets,
read_offsets=read_offsets,
next_token_choosers=next_token_choosers,
stopping_criterias=stopping_criterias,
max_input_length=max_input_length,
@ -448,7 +450,6 @@ class CausalLM(Model):
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
decode_buffer: int = 3,
):
if torch.cuda.is_available():
device = torch.device("cuda")
@ -463,25 +464,25 @@ class CausalLM(Model):
tokenizer = AutoTokenizer.from_pretrained(
model_id, revision=revision, padding_side="left", truncation_side="left"
)
self.model = AutoModelForCausalLM.from_pretrained(
model = AutoModelForCausalLM.from_pretrained(
model_id,
revision=revision,
torch_dtype=dtype,
device_map="auto" if torch.cuda.is_available() else None,
load_in_8bit=quantize == "bitsandbytes",
).eval()
)
tokenizer.pad_token_id = (
self.model.config.pad_token_id
if self.model.config.pad_token_id is not None
else self.model.config.eos_token_id
model.config.pad_token_id
if model.config.pad_token_id is not None
else model.config.eos_token_id
)
super(CausalLM, self).__init__(
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,
device=device,
decode_buffer=decode_buffer,
)
@property
@ -528,8 +529,8 @@ class CausalLM(Model):
iterator = zip(
batch.requests,
batch.input_lengths,
batch.offsets,
batch.token_offsets,
batch.prefix_offsets,
batch.read_offsets,
logits,
batch.next_token_choosers,
batch.stopping_criterias,
@ -540,8 +541,8 @@ class CausalLM(Model):
for i, (
request,
input_length,
offset,
token_offset,
prefix_offset,
read_offset,
logits,
next_token_chooser,
stopping_criteria,
@ -559,8 +560,8 @@ class CausalLM(Model):
# Generated token
next_token_logprob = logprobs[-1, next_token_id]
next_token_id_squeezed = next_token_id.squeeze()
next_token_text, offset, token_offset = self.decode_token(
all_input_ids[:, 0], offset, token_offset
next_token_text, prefix_offset, read_offset = self.decode_token(
all_input_ids[:, 0], prefix_offset, read_offset
)
# Evaluate stopping criteria
@ -628,8 +629,8 @@ class CausalLM(Model):
batch.input_ids[i, 0] = next_token_id
batch.all_input_ids[i] = all_input_ids
batch.input_lengths[i] = new_input_length
batch.offsets[i] = offset
batch.token_offsets[i] = token_offset
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

69
server/text_generation_server/models/flash_causal_lm.py
View File

@ -52,8 +52,8 @@ class FlashCausalLMBatch(Batch):
# Lengths of all generations present in the batch
input_lengths: List[int]
offsets: List[Optional[int]]
token_offsets: List[Optional[int]]
prefix_offsets: List[Optional[int]]
read_offsets: List[Optional[int]]
# Generation helpers
next_token_choosers: List[NextTokenChooser]
@ -82,8 +82,8 @@ class FlashCausalLMBatch(Batch):
max_seqlen = 0
input_lengths = []
offsets = []
token_offsets = []
prefix_offsets = []
read_offsets = []
all_input_ids = []
requests_idx_mapping = {}
@ -108,8 +108,8 @@ class FlashCausalLMBatch(Batch):
max_seqlen = max(max_seqlen, input_length)
input_lengths.append(input_length)
offsets.append(None)
token_offsets.append(None)
prefix_offsets.append(0)
read_offsets.append(input_length)
all_input_ids.append(tokenized_input)
@ -151,8 +151,8 @@ class FlashCausalLMBatch(Batch):
max_seqlen=max_seqlen,
past_key_values=None,
input_lengths=input_lengths,
offsets=offsets,
token_offsets=token_offsets,
prefix_offsets=prefix_offsets,
read_offsets=read_offsets,
all_input_ids=all_input_ids,
all_input_ids_tensor=[],
next_token_choosers=next_token_choosers,
@ -190,8 +190,8 @@ class FlashCausalLMBatch(Batch):
all_input_ids_tensor = []
input_lengths = []
offsets = []
token_offsets = []
prefix_offsets = []
read_offsets = []
next_token_choosers = []
stopping_criterias = []
@ -222,8 +222,8 @@ class FlashCausalLMBatch(Batch):
all_input_ids_tensor.append(self.all_input_ids_tensor[idx])
input_lengths.append(request_input_length)
offsets.append(self.offsets[idx])
token_offsets.append(self.token_offsets[idx])
prefix_offsets.append(self.prefix_offsets[idx])
read_offsets.append(self.read_offsets[idx])
next_token_choosers.append(self.next_token_choosers[idx])
@ -269,8 +269,8 @@ class FlashCausalLMBatch(Batch):
max_seqlen=max_seqlen,
past_key_values=past_key_values,
input_lengths=input_lengths,
offsets=offsets,
token_offsets=token_offsets,
prefix_offsets=prefix_offsets,
read_offsets=read_offsets,
all_input_ids=all_input_ids,
all_input_ids_tensor=all_input_ids_tensor,
next_token_choosers=next_token_choosers,
@ -302,8 +302,8 @@ class FlashCausalLMBatch(Batch):
all_input_ids_tensor = []
input_lengths = []
offsets = []
token_offsets = []
prefix_offsets = []
read_offsets = []
next_token_choosers = []
stopping_criterias = []
@ -347,8 +347,8 @@ class FlashCausalLMBatch(Batch):
all_input_ids_tensor.extend(batch.all_input_ids_tensor)
input_lengths.extend(batch.input_lengths)
offsets.extend(batch.offsets)
token_offsets.extend(batch.token_offsets)
prefix_offsets.extend(batch.prefix_offsets)
read_offsets.extend(batch.read_offsets)
next_token_choosers.extend(batch.next_token_choosers)
stopping_criterias.extend(batch.stopping_criterias)
@ -374,8 +374,8 @@ class FlashCausalLMBatch(Batch):
max_seqlen=max_seqlen,
past_key_values=past_key_values,
input_lengths=input_lengths,
offsets=offsets,
token_offsets=token_offsets,
prefix_offsets=prefix_offsets,
read_offsets=read_offsets,
all_input_ids=all_input_ids,
all_input_ids_tensor=all_input_ids_tensor,
next_token_choosers=next_token_choosers,
@ -394,7 +394,6 @@ class FlashCausalLM(Model):
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
decode_buffer: int = 3,
):
if torch.cuda.is_available():
device = torch.device("cuda")
@ -405,23 +404,19 @@ class FlashCausalLM(Model):
tokenizer = AutoTokenizer.from_pretrained(
model_id, revision=revision, padding_side="left", truncation_side="left"
)
self.model = (
model_cls.from_pretrained(
model = model_cls.from_pretrained(
model_id,
revision=revision,
torch_dtype=dtype,
load_in_8bit=quantize == "bitsandbytes",
)
.eval()
.to(device)
)
).to(device)
super(FlashCausalLM, self).__init__(
model=model,
tokenizer=tokenizer,
requires_padding=False,
dtype=dtype,
device=device,
decode_buffer=decode_buffer,
)
@property
@ -645,8 +640,8 @@ class FlashCausalLM(Model):
iterator = zip(
batch.requests,
batch.input_lengths,
batch.offsets,
batch.token_offsets,
batch.prefix_offsets,
batch.read_offsets,
batch.next_token_choosers,
batch.stopping_criterias,
batch.all_input_ids,
@ -659,8 +654,8 @@ class FlashCausalLM(Model):
for i, (
request,
input_length,
offset,
token_offset,
prefix_offset,
read_offset,
next_token_chooser,
stopping_criteria,
all_input_ids,
@ -675,10 +670,10 @@ class FlashCausalLM(Model):
all_input_ids.append(next_token_id)
# Generated token
next_token_text, offset, token_offset = self.decode_token(
next_token_text, prefix_offset, read_offset = self.decode_token(
all_input_ids,
offset,
token_offset,
prefix_offset,
read_offset,
)
# Evaluate stopping criteria
@ -744,8 +739,8 @@ class FlashCausalLM(Model):
# Update values
batch.input_lengths[i] = new_input_length
batch.offsets[i] = offset
batch.token_offsets[i] = token_offset
batch.prefix_offsets[i] = prefix_offset
batch.read_offsets[i] = read_offset
batch.all_input_ids[i] = all_input_ids
batch.max_seqlen = batch.max_seqlen + 1
cumulative_length += input_length

4
server/text_generation_server/models/flash_llama.py
View File

@ -64,9 +64,9 @@ class FlashLlama(FlashCausalLM):
model = FlashLlamaForCausalLM(config)
self.load_weights(model, filenames, quantize, device, dtype)
self.model = model.eval().to(device)
super(FlashCausalLM, self).__init__(
model=model.to(device),
tokenizer=tokenizer,
requires_padding=False,
dtype=dtype,
@ -189,9 +189,9 @@ class FlashLlamaSharded(FlashLlama):
rank=rank,
world_size=world_size,
)
self.model = model.eval().to(device)
torch.distributed.barrier(group=self.process_group)
super(FlashCausalLM, self).__init__(
model=model.to(device),
tokenizer=tokenizer,
requires_padding=False,
dtype=dtype,

2
server/text_generation_server/models/flash_neox.py
View File

@ -73,9 +73,9 @@ class FlashNeoXSharded(FlashNeoX):
rank=rank,
world_size=world_size,
)
self.model = model.eval().to(device)
torch.distributed.barrier(group=self.process_group)
super(FlashCausalLM, self).__init__(
model=model.to(device),
tokenizer=tokenizer,
requires_padding=False,
dtype=dtype,

6
server/text_generation_server/models/flash_santacoder.py
View File

@ -67,14 +67,13 @@ class FlashSantacoder(FlashCausalLM):
dtype,
config.architectures[0].startswith("GPT2"),
)
self.model = model.eval().to(device)
super(FlashCausalLM, self).__init__(
model=model.to(device),
tokenizer=tokenizer,
requires_padding=False,
dtype=dtype,
device=device,
decode_buffer=1,
)
@staticmethod
@ -213,16 +212,15 @@ class FlashSantacoderSharded(FlashSantacoder):
world_size=world_size,
transpose=config.architectures[0].startswith("GPT2"),
)
self.model = model.eval().to(device)
torch.distributed.barrier(group=self.process_group)
super(FlashCausalLM, self).__init__(
model=model.to(device),
tokenizer=tokenizer,
requires_padding=False,
dtype=dtype,
device=device,
rank=rank,
world_size=world_size,
decode_buffer=1,
)
@staticmethod

16
server/text_generation_server/models/galactica.py
View File

@ -94,8 +94,8 @@ class GalacticaCausalLMBatch(CausalLMBatch):
inputs = []
next_token_choosers = []
stopping_criterias = []
offsets = []
token_offsets = []
prefix_offsets = []
read_offsets = []
requests_idx_mapping = {}
# Parse batch
@ -106,8 +106,6 @@ class GalacticaCausalLMBatch(CausalLMBatch):
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
@ -127,6 +125,10 @@ class GalacticaCausalLMBatch(CausalLMBatch):
truncation=True,
max_length=max_truncation,
).to(device)
for _ in pb.requests:
input_len = tokenized_inputs["input_ids"].shape[1]
prefix_offsets.append(0)
read_offsets.append(input_len)
input_lengths = tokenized_inputs["attention_mask"].sum(1)
max_input_length = input_lengths.max()
@ -155,8 +157,8 @@ class GalacticaCausalLMBatch(CausalLMBatch):
past_key_values=None,
all_input_ids=list(all_input_ids),
input_lengths=input_lengths.tolist(),
offsets=offsets,
token_offsets=token_offsets,
prefix_offsets=prefix_offsets,
read_offsets=read_offsets,
next_token_choosers=next_token_choosers,
stopping_criterias=stopping_criterias,
max_input_length=max_input_length.item(),
@ -231,9 +233,9 @@ class GalacticaSharded(Galactica):
rank=rank,
world_size=world_size,
)
self.model = model.eval()
torch.distributed.barrier(group=self.process_group)
super(CausalLM, self).__init__(
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,

2
server/text_generation_server/models/gpt_neox.py
View File

@ -70,9 +70,9 @@ class GPTNeoxSharded(CausalLM):
rank=rank,
world_size=world_size,
)
self.model = model.eval()
torch.distributed.barrier(group=self.process_group)
super(CausalLM, self).__init__(
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,

68
server/text_generation_server/models/model.py
View File

@ -13,23 +13,20 @@ B = TypeVar("B", bound=Batch)
class Model(ABC):
def __init__(
self,
model: torch.nn.Module,
tokenizer: PreTrainedTokenizerBase,
requires_padding: bool,
dtype: torch.dtype,
device: torch.device,
decode_buffer: int = 3,
rank: int = 0,
world_size: int = 1,
):
if decode_buffer < 1:
raise ValueError("decode_buffer must be >= 1")
self.model = model.eval()
self.tokenizer = tokenizer
self.all_special_ids = set(tokenizer.all_special_ids)
self.requires_padding = requires_padding
self.dtype = dtype
self.device = device
self.decode_buffer = decode_buffer
self.rank = rank
self.world_size = world_size
self.check_initialized()
@ -54,52 +51,29 @@ class Model(ABC):
def decode_token(
self,
all_input_ids: List[int],
offset: Optional[int] = None,
token_offset: Optional[int] = None,
) -> Tuple[str, Optional[int], Optional[int]]:
prefix_offset: int = 0,
read_offset: int = 0,
) -> Tuple[str, int, int]:
"""Hack to hopefully support generate_stream for the maximum number of tokenizers"""
if all_input_ids[-1] in self.all_special_ids:
return (
self.tokenizer.decode(all_input_ids[-1], skip_special_tokens=False),
None,
None,
# The prefix text is necessary only to defeat cleanup algorithms in the decode
# which decide to add a space or not depending on the surrounding ids.
prefix_text = self.tokenizer.decode(
all_input_ids[prefix_offset:read_offset], skip_special_tokens=False
)
new_text = self.tokenizer.decode(
all_input_ids[prefix_offset:], skip_special_tokens=False
)
if token_offset is None:
token_offset = len(all_input_ids) - self.decode_buffer
# left token buffer
if self.decode_buffer > 1:
# Decode token_offset token minus last one and token_offset tokens
raw_texts = self.tokenizer.batch_decode(
[all_input_ids[token_offset:-1], all_input_ids[token_offset:]],
skip_special_tokens=False,
)
# default offset is only the last token
offset = len(raw_texts[0])
sequence_text = raw_texts[1]
if len(new_text) > len(prefix_text) and not new_text.endswith("<EFBFBD>"):
# utf-8 char at the end means it's a potential unfinished byte sequence
# from byte fallback tokenization.
# If it's in the middle, it's probably a real invalid id generated
# by the model
new_text = new_text[len(prefix_text) :]
return new_text, read_offset, len(all_input_ids)
else:
# Only decode the last token without using a token buffer
sequence_text = self.tokenizer.decode(
all_input_ids[-1], skip_special_tokens=False
)
# no offset in this case
offset = 0
else:
assert offset is not None
sequence_text = self.tokenizer.decode(
all_input_ids[token_offset:],
skip_special_tokens=False,
)
# get text
token_text = sequence_text[offset:]
# if text is utf-8
if token_text and token_text[-1] != "<EFBFBD>":
return token_text, None, None
else:
return "", offset, token_offset
return "", prefix_offset, read_offset
def check_initialized(self):
uninitialized_parameters = []

2
server/text_generation_server/models/opt.py
View File

@ -86,9 +86,9 @@ class OPTSharded(OPT):
rank=rank,
world_size=world_size,
)
self.model = model.eval()
torch.distributed.barrier(group=self.process_group)
super(CausalLM, self).__init__(
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,

10
server/text_generation_server/models/santacoder.py
View File

@ -46,24 +46,20 @@ class SantaCoder(CausalLM):
}
)
self.model = (
AutoModelForCausalLM.from_pretrained(
model = AutoModelForCausalLM.from_pretrained(
model_id,
revision=revision,
torch_dtype=dtype,
load_in_8bit=quantize == "bitsandbytes",
trust_remote_code=True, # required
)
.to(device)
.eval()
)
).to(device)
super(CausalLM, self).__init__(
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,
device=device,
decode_buffer=1,
)
def decode(self, generated_ids: List[int]) -> str:

66
server/text_generation_server/models/seq2seq_lm.py
View File

@ -42,8 +42,8 @@ class Seq2SeqLMBatch(Batch):
# Lengths of all generations present in the batch
input_lengths: List[int]
decoder_input_lengths: List[int]
offsets: List[Optional[int]]
token_offsets: List[Optional[int]]
prefix_offsets: List[int]
read_offsets: List[int]
# Generation helpers
next_token_choosers: List[NextTokenChooser]
@ -79,8 +79,8 @@ class Seq2SeqLMBatch(Batch):
stopping_criterias = []
decoder_input_lengths = []
offsets = []
token_offsets = []
prefix_offsets = []
read_offsets = []
requests_idx_mapping = {}
# Parse batch
@ -91,8 +91,6 @@ class Seq2SeqLMBatch(Batch):
inputs.append(r.inputs)
requests_idx_mapping[r.id] = i
decoder_input_lengths.append(1)
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
@ -123,6 +121,9 @@ class Seq2SeqLMBatch(Batch):
.repeat(len(pb.requests))
.view(-1, 1)
)
for _ in pb.requests:
prefix_offsets.append(0)
read_offsets.append(1)
all_decoder_input_ids = decoder_input_ids.view(-1).split(1)
max_tokens = len(inputs) * max_input_length + max_decode_tokens
@ -140,8 +141,8 @@ class Seq2SeqLMBatch(Batch):
past_key_values=None,
input_lengths=input_lengths.tolist(),
decoder_input_lengths=decoder_input_lengths,
offsets=offsets,
token_offsets=token_offsets,
prefix_offsets=prefix_offsets,
read_offsets=read_offsets,
next_token_choosers=next_token_choosers,
stopping_criterias=stopping_criterias,
max_input_length=max_input_length.item(),
@ -165,8 +166,8 @@ class Seq2SeqLMBatch(Batch):
requests_idx_mapping = {}
input_lengths = []
decoder_input_lengths = []
offsets = []
token_offsets = []
prefix_offsets = []
read_offsets = []
all_decoder_input_ids = []
@ -184,8 +185,8 @@ class Seq2SeqLMBatch(Batch):
requests_idx_mapping[r.id] = i
keep_indices.append(idx)
offsets.append(self.offsets[idx])
token_offsets.append(self.token_offsets[idx])
prefix_offsets.append(self.prefix_offsets[idx])
read_offsets.append(self.read_offsets[idx])
all_decoder_input_ids.append(self.all_decoder_input_ids[idx])
@ -248,8 +249,8 @@ class Seq2SeqLMBatch(Batch):
self.all_decoder_input_ids = all_decoder_input_ids
self.input_lengths = input_lengths
self.decoder_input_lengths = decoder_input_lengths
self.offsets = offsets
self.token_offsets = token_offsets
self.prefix_offsets = prefix_offsets
self.read_offsets = read_offsets
self.next_token_choosers = next_token_choosers
self.stopping_criterias = stopping_criterias
self.max_input_length = max_input_length
@ -283,8 +284,8 @@ class Seq2SeqLMBatch(Batch):
all_decoder_input_ids = []
input_lengths = []
decoder_input_lengths = []
offsets = []
token_offsets = []
prefix_offsets = []
read_offsets = []
next_token_choosers = []
stopping_criterias = []
max_tokens = 0
@ -306,8 +307,8 @@ class Seq2SeqLMBatch(Batch):
all_decoder_input_ids.extend(batch.all_decoder_input_ids)
input_lengths.extend(batch.input_lengths)
decoder_input_lengths.extend(batch.decoder_input_lengths)
offsets.extend(batch.offsets)
token_offsets.extend(batch.token_offsets)
prefix_offsets.extend(batch.prefix_offsets)
read_offsets.extend(batch.read_offsets)
next_token_choosers.extend(batch.next_token_choosers)
stopping_criterias.extend(batch.stopping_criterias)
@ -482,8 +483,8 @@ class Seq2SeqLMBatch(Batch):
past_key_values=past_key_values,
input_lengths=input_lengths,
decoder_input_lengths=decoder_input_lengths,
offsets=offsets,
token_offsets=token_offsets,
prefix_offsets=prefix_offsets,
read_offsets=read_offsets,
next_token_choosers=next_token_choosers,
stopping_criterias=stopping_criterias,
max_input_length=max_input_length,
@ -502,7 +503,6 @@ class Seq2SeqLM(Model):
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
decode_buffer: int = 3,
):
if torch.cuda.is_available():
device = torch.device("cuda")
@ -514,24 +514,24 @@ class Seq2SeqLM(Model):
device = torch.device("cpu")
dtype = torch.float32
self.model = AutoModelForSeq2SeqLM.from_pretrained(
model = AutoModelForSeq2SeqLM.from_pretrained(
model_id,
revision=revision,
torch_dtype=dtype,
device_map="auto" if torch.cuda.is_available() else None,
load_in_8bit=quantize == "bitsandbytes",
).eval()
)
tokenizer = AutoTokenizer.from_pretrained(
model_id, revision=revision, padding_side="left", truncation_side="left"
)
tokenizer.bos_token_id = self.model.config.decoder_start_token_id
tokenizer.bos_token_id = model.config.decoder_start_token_id
super(Seq2SeqLM, self).__init__(
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,
device=device,
decode_buffer=decode_buffer,
)
@property
@ -608,8 +608,8 @@ class Seq2SeqLM(Model):
iterator = zip(
batch.requests,
batch.input_lengths,
batch.offsets,
batch.token_offsets,
batch.prefix_offsets,
batch.read_offsets,
batch.decoder_input_lengths,
logits,
batch.next_token_choosers,
@ -621,8 +621,8 @@ class Seq2SeqLM(Model):
for i, (
request,
input_length,
offset,
token_offset,
prefix_offset,
read_offset,
decoder_input_length,
logits,
next_token_chooser,
@ -643,8 +643,8 @@ class Seq2SeqLM(Model):
# Generated token
next_token_logprob = logprobs[-1, next_token_id]
next_token_id_squeezed = next_token_id.squeeze()
next_token_text, offset, token_offset = self.decode_token(
all_decoder_input_ids, offset, token_offset
next_token_text, prefix_offset, read_offset = self.decode_token(
all_decoder_input_ids, prefix_offset, read_offset
)
# Evaluate stopping criteria
@ -702,8 +702,8 @@ class Seq2SeqLM(Model):
batch.all_decoder_input_ids[i] = all_decoder_input_ids
batch.input_lengths[i] = input_length
batch.decoder_input_lengths[i] = new_decoder_input_length
batch.offsets[i] = offset
batch.token_offsets[i] = token_offset
batch.prefix_offsets[i] = prefix_offset
batch.read_offsets[i] = read_offset
batch.max_input_length = max(batch.max_input_length, input_length)
batch.max_decoder_input_length = max(
batch.max_decoder_input_length, new_decoder_input_length

5
server/text_generation_server/models/t5.py
View File

@ -16,9 +16,6 @@ from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
)
from text_generation_server.utils.layers import (
FastLinear,
)
from transformers.models.t5.parallel_layers import (
TensorParallelRowLinear,
TensorParallelColumnLinear,
@ -73,9 +70,9 @@ class T5Sharded(Seq2SeqLM):
rank=rank,
world_size=world_size,
)
self.model = model.eval()
torch.distributed.barrier(group=self.process_group)
super(Seq2SeqLM, self).__init__(
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,