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[tutorial] removed duplicated tutorials (#1904)

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examples/tutorial/handson3/README.md → examples/tutorial/auto_parallel/README.md
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......@@ -2,7 +2,7 @@
## Prepare Dataset
We use CIFAR10 dataset in this example. The dataset will be downloaded to `./data` by default.
We use CIFAR10 dataset in this example. The dataset will be downloaded to `./data` by default.
If you wish to use customized directory for the dataset. You can set the environment variable `DATA` via the following command.
```bash
......@@ -14,4 +14,4 @@ export DATA=/path/to/data
```bash
colossalai run --nproc_per_node 4 auto_parallel_demo.py
```
\ No newline at end of file
```
examples/tutorial/handson3/auto_parallel_demo.py → examples/tutorial/auto_parallel/auto_parallel_demo.py
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import os
from pathlib import Path
from colossalai.logging import get_dist_logger
import colossalai
import torch
import os
from titans.utils import barrier_context
from torch.fx import GraphModule
from colossalai.auto_parallel.passes.runtime_apply_pass import runtime_apply_pass
from colossalai.auto_parallel.passes.runtime_preparation_pass import runtime_preparation_pass
from colossalai.core import global_context as gpc
from colossalai.utils import get_dataloader
from torchvision import transforms
from colossalai.nn.lr_scheduler import CosineAnnealingLR
from torchvision.datasets import CIFAR10
from torchvision.models import resnet50
from tqdm import tqdm
from titans.utils import barrier_context
import colossalai
from colossalai.auto_parallel.passes.runtime_apply_pass import runtime_apply_pass
from colossalai.auto_parallel.passes.runtime_preparation_pass import runtime_preparation_pass
from colossalai.auto_parallel.tensor_shard.solver.cost_graph import CostGraph
from colossalai.auto_parallel.tensor_shard.solver.graph_analysis import GraphAnalyser
from colossalai.auto_parallel.tensor_shard.solver.options import SolverOptions
from colossalai.auto_parallel.tensor_shard.solver.solver import Solver
from colossalai.auto_parallel.tensor_shard.solver.strategies_constructor import StrategiesConstructor
from colossalai.core import global_context as gpc
from colossalai.device.device_mesh import DeviceMesh
from colossalai.fx.tracer.tracer import ColoTracer
from colossalai.logging import get_dist_logger
from colossalai.nn.lr_scheduler import CosineAnnealingLR
from colossalai.utils import get_dataloader
DATA_ROOT = Path(os.environ.get('DATA', './data'))
BATCH_SIZE = 1024
......
examples/tutorial/handson1/README.md deleted 100644 → 0
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# Handson 1: Multi-dimensional Parallelism with Colossal-AI
## Install Colossal-AI and other dependencies
```bash
sh install.sh
```
## Prepare Dataset
We use CIFAR10 dataset in this example. The dataset will be downloaded to `../data` by default.
If you wish to use customized directory for the dataset. You can set the environment variable `DATA` via the following command.
```bash
export DATA=/path/to/data
```
## Run on 2*2 device mesh
Current configuration setting on `config.py` is TP=2, PP=2.
```bash
colossalai run --nproc_per_node 4 train.py --config config.py
```
\ No newline at end of file
examples/tutorial/handson1/config.py deleted 100644 → 0
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from colossalai.amp import AMP_TYPE
# hyperparameters
# BATCH_SIZE is as per GPU
# global batch size = BATCH_SIZE x data parallel size
BATCH_SIZE = 256
LEARNING_RATE = 3e-3
WEIGHT_DECAY = 0.3
NUM_EPOCHS = 10
WARMUP_EPOCHS = 3
# model config
IMG_SIZE = 224
PATCH_SIZE = 16
HIDDEN_SIZE = 512
DEPTH = 4
NUM_HEADS = 4
MLP_RATIO = 2
NUM_CLASSES = 1000
CHECKPOINT = False
SEQ_LENGTH = (IMG_SIZE // PATCH_SIZE)**2 + 1 # add 1 for cls token
# parallel setting
TENSOR_PARALLEL_SIZE = 2
TENSOR_PARALLEL_MODE = '1d'
parallel = dict(
pipeline=2,
tensor=dict(mode=TENSOR_PARALLEL_MODE, size=TENSOR_PARALLEL_SIZE),
)
fp16 = dict(mode=AMP_TYPE.NAIVE)
clip_grad_norm = 1.0
# pipeline config
NUM_MICRO_BATCHES = parallel['pipeline']
examples/tutorial/handson1/install.sh deleted 100644 → 0
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pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113
pip install colossalai==0.1.10+torch1.12cu11.3 -f https://release.colossalai.org
pip install titans
colossalai check -i
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examples/tutorial/handson1/train.py deleted 100644 → 0
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import os
import colossalai
import torch
from tqdm import tqdm
from colossalai.context import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.logging import get_dist_logger
from colossalai.nn import CrossEntropyLoss
from colossalai.nn.lr_scheduler import CosineAnnealingWarmupLR
from colossalai.utils import is_using_pp, get_dataloader
from colossalai.pipeline.pipelinable import PipelinableContext
from titans.model.vit.vit import _create_vit_model
from titans.dataloader.cifar10 import build_cifar
def main():
# initialize distributed setting
parser = colossalai.get_default_parser()
args = parser.parse_args()
# launch from torch
colossalai.launch_from_torch(config=args.config)
# get logger
logger = get_dist_logger()
logger.info("initialized distributed environment", ranks=[0])
if hasattr(gpc.config, 'LOG_PATH'):
if gpc.get_global_rank() == 0:
log_path = gpc.config.LOG_PATH
if not os.path.exists(log_path):
os.mkdir(log_path)
logger.log_to_file(log_path)
use_pipeline = is_using_pp()
# create model
model_kwargs = dict(img_size=gpc.config.IMG_SIZE,
patch_size=gpc.config.PATCH_SIZE,
hidden_size=gpc.config.HIDDEN_SIZE,
depth=gpc.config.DEPTH,
num_heads=gpc.config.NUM_HEADS,
mlp_ratio=gpc.config.MLP_RATIO,
num_classes=10,
init_method='jax',
checkpoint=gpc.config.CHECKPOINT)
if use_pipeline:
pipelinable = PipelinableContext()
with pipelinable:
model = _create_vit_model(**model_kwargs)
pipelinable.to_layer_list()
pipelinable.policy = "uniform"
model = pipelinable.partition(
1, gpc.pipeline_parallel_size, gpc.get_local_rank(ParallelMode.PIPELINE))
else:
model = _create_vit_model(**model_kwargs)
# count number of parameters
total_numel = 0
for p in model.parameters():
total_numel += p.numel()
if not gpc.is_initialized(ParallelMode.PIPELINE):
pipeline_stage = 0
else:
pipeline_stage = gpc.get_local_rank(ParallelMode.PIPELINE)
logger.info(
f"number of parameters: {total_numel} on pipeline stage {pipeline_stage}")
# create dataloaders
root = os.environ.get('DATA', '../data/cifar10')
train_dataloader, test_dataloader = build_cifar(
gpc.config.BATCH_SIZE, root, pad_if_needed=True)
# create loss function
criterion = CrossEntropyLoss(label_smoothing=0.1)
# create optimizer
optimizer = torch.optim.AdamW(model.parameters(
), lr=gpc.config.LEARNING_RATE, weight_decay=gpc.config.WEIGHT_DECAY)
# create lr scheduler
lr_scheduler = CosineAnnealingWarmupLR(optimizer=optimizer,
total_steps=gpc.config.NUM_EPOCHS,
warmup_steps=gpc.config.WARMUP_EPOCHS)
# initialize
engine, train_dataloader, test_dataloader, _ = colossalai.initialize(model=model,
optimizer=optimizer,
criterion=criterion,
train_dataloader=train_dataloader,
test_dataloader=test_dataloader)
logger.info("Engine is built", ranks=[0])
data_iter = iter(train_dataloader)
for epoch in range(gpc.config.NUM_EPOCHS):
# training
engine.train()
if gpc.get_global_rank() == 0:
description = 'Epoch {} / {}'.format(epoch, gpc.config.NUM_EPOCHS)
progress = tqdm(range(len(train_dataloader)), desc=description)
else:
progress = range(len(train_dataloader))
for _ in progress:
engine.zero_grad()
engine.execute_schedule(data_iter, return_output_label=False)
engine.step()
lr_scheduler.step()
if __name__ == '__main__':
main()
examples/tutorial/handson2/README.md deleted 100644 → 0
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# Handson 2: Sequence Parallelism with BERT
## Prepare Dataset
We use CIFAR10 dataset in this example. The dataset will be downloaded to `../data` by default.
If you wish to use customized directory for the dataset. You can set the environment variable `DATA` via the following command.
```bash
export DATA=/path/to/data
```
## Run on 2*2 device mesh
Current configuration setting on `config.py` is TP=2, PP=2.
```bash
colossalai run --nproc_per_node 4 train.py --config config.py
```
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examples/tutorial/handson2/config.py deleted 100644 → 0
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from colossalai.amp import AMP_TYPE
# hyperparameters
# BATCH_SIZE is as per GPU
# global batch size = BATCH_SIZE x data parallel size
BATCH_SIZE = 256
LEARNING_RATE = 3e-3
WEIGHT_DECAY = 0.3
NUM_EPOCHS = 10
WARMUP_EPOCHS = 3
# model config
IMG_SIZE = 224
PATCH_SIZE = 16
HIDDEN_SIZE = 512
DEPTH = 4
NUM_HEADS = 4
MLP_RATIO = 2
NUM_CLASSES = 1000
CHECKPOINT = False
SEQ_LENGTH = (IMG_SIZE // PATCH_SIZE)**2 + 1 # add 1 for cls token
# parallel setting
TENSOR_PARALLEL_SIZE = 1
TENSOR_PARALLEL_MODE = '1d'
parallel = dict(
tensor=dict(size=4, mode='sequence')
)
fp16 = dict(mode=AMP_TYPE.NAIVE)
clip_grad_norm = 1.0
# pipeline config
NUM_MICRO_BATCHES = parallel['pipeline']
examples/tutorial/handson2/train.py deleted 100644 → 0
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import os
import colossalai
import torch
from tqdm import tqdm
from colossalai.context import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.logging import get_dist_logger
from colossalai.nn import CrossEntropyLoss
from colossalai.nn.lr_scheduler import CosineAnnealingWarmupLR
from colossalai.utils import is_using_pp, get_dataloader
from colossalai.pipeline.pipelinable import PipelinableContext
from titans.model.vit.vit import _create_vit_model
from titans.dataloader.cifar10 import build_cifar
def main():
# initialize distributed setting
parser = colossalai.get_default_parser()
args = parser.parse_args()
# launch from torch
colossalai.launch_from_torch(config=args.config)
# get logger
logger = get_dist_logger()
logger.info("initialized distributed environment", ranks=[0])
if hasattr(gpc.config, 'LOG_PATH'):
if gpc.get_global_rank() == 0:
log_path = gpc.config.LOG_PATH
if not os.path.exists(log_path):
os.mkdir(log_path)
logger.log_to_file(log_path)
use_pipeline = is_using_pp()
# create model
model_kwargs = dict(img_size=gpc.config.IMG_SIZE,
patch_size=gpc.config.PATCH_SIZE,
hidden_size=gpc.config.HIDDEN_SIZE,
depth=gpc.config.DEPTH,
num_heads=gpc.config.NUM_HEADS,
mlp_ratio=gpc.config.MLP_RATIO,
num_classes=10,
init_method='jax',
checkpoint=gpc.config.CHECKPOINT)
if use_pipeline:
pipelinable = PipelinableContext()
with pipelinable:
model = _create_vit_model(**model_kwargs)
pipelinable.to_layer_list()
pipelinable.policy = "uniform"
model = pipelinable.partition(
1, gpc.pipeline_parallel_size, gpc.get_local_rank(ParallelMode.PIPELINE))
else:
model = _create_vit_model(**model_kwargs)
# count number of parameters
total_numel = 0
for p in model.parameters():
total_numel += p.numel()
if not gpc.is_initialized(ParallelMode.PIPELINE):
pipeline_stage = 0
else:
pipeline_stage = gpc.get_local_rank(ParallelMode.PIPELINE)
logger.info(
f"number of parameters: {total_numel} on pipeline stage {pipeline_stage}")
# create dataloaders
root = os.environ.get('DATA', '../data/cifar10')
train_dataloader, test_dataloader = build_cifar(
gpc.config.BATCH_SIZE, root, pad_if_needed=True)
# create loss function
criterion = CrossEntropyLoss(label_smoothing=0.1)
# create optimizer
optimizer = torch.optim.AdamW(model.parameters(
), lr=gpc.config.LEARNING_RATE, weight_decay=gpc.config.WEIGHT_DECAY)
# create lr scheduler
lr_scheduler = CosineAnnealingWarmupLR(optimizer=optimizer,
total_steps=gpc.config.NUM_EPOCHS,
warmup_steps=gpc.config.WARMUP_EPOCHS)
# initialize
engine, train_dataloader, test_dataloader, _ = colossalai.initialize(model=model,
optimizer=optimizer,
criterion=criterion,
train_dataloader=train_dataloader,
test_dataloader=test_dataloader)
logger.info("Engine is built", ranks=[0])
data_iter = iter(train_dataloader)
for epoch in range(gpc.config.NUM_EPOCHS):
# training
engine.train()
if gpc.get_global_rank() == 0:
description = 'Epoch {} / {}'.format(epoch, gpc.config.NUM_EPOCHS)
progress = tqdm(range(len(train_dataloader)), desc=description)
else:
progress = range(len(train_dataloader))
for _ in progress:
engine.zero_grad()
engine.execute_schedule(data_iter, return_output_label=False)
engine.step()
lr_scheduler.step()
if __name__ == '__main__':
main()
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# Handson 4: Comparison of Large Batch Training Optimization
## Prepare Dataset
We use CIFAR10 dataset in this example. The dataset will be downloaded to `../data` by default.
If you wish to use customized directory for the dataset. You can set the environment variable `DATA` via the following command.
```bash
export DATA=/path/to/data
```
## Run on 2*2 device mesh
```bash
colossalai run --nproc_per_node 4 train.py --config config.py
```
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examples/tutorial/handson4/config.py deleted 100644 → 0
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from colossalai.amp import AMP_TYPE
# hyperparameters
# BATCH_SIZE is as per GPU
# global batch size = BATCH_SIZE x data parallel size
BATCH_SIZE = 512
LEARNING_RATE = 3e-3
WEIGHT_DECAY = 0.3
NUM_EPOCHS = 10
WARMUP_EPOCHS = 3
# model config
IMG_SIZE = 224
PATCH_SIZE = 16
HIDDEN_SIZE = 512
DEPTH = 4
NUM_HEADS = 4
MLP_RATIO = 2
NUM_CLASSES = 1000
CHECKPOINT = False
SEQ_LENGTH = (IMG_SIZE // PATCH_SIZE)**2 + 1 # add 1 for cls token
# parallel setting
TENSOR_PARALLEL_SIZE = 2
TENSOR_PARALLEL_MODE = '1d'
parallel = dict(
pipeline=2,
tensor=dict(mode=TENSOR_PARALLEL_MODE, size=TENSOR_PARALLEL_SIZE),
)
fp16 = dict(mode=AMP_TYPE.NAIVE)
clip_grad_norm = 1.0
# pipeline config
NUM_MICRO_BATCHES = parallel['pipeline']
examples/tutorial/handson4/train.py deleted 100644 → 0
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import os
import colossalai
import torch
from tqdm import tqdm
from colossalai.context import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.logging import get_dist_logger
from colossalai.nn import CrossEntropyLoss
from colossalai.nn.lr_scheduler import CosineAnnealingWarmupLR
from colossalai.nn.optimizer import Lars, Lamb
from colossalai.utils import is_using_pp, get_dataloader
from colossalai.pipeline.pipelinable import PipelinableContext
from titans.model.vit.vit import _create_vit_model
from titans.dataloader.cifar10 import build_cifar
def main():
# initialize distributed setting
parser = colossalai.get_default_parser()
args = parser.parse_args()
# launch from torch
colossalai.launch_from_torch(config=args.config)
# get logger
logger = get_dist_logger()
logger.info("initialized distributed environment", ranks=[0])
if hasattr(gpc.config, 'LOG_PATH'):
if gpc.get_global_rank() == 0:
log_path = gpc.config.LOG_PATH
if not os.path.exists(log_path):
os.mkdir(log_path)
logger.log_to_file(log_path)
use_pipeline = is_using_pp()
# create model
model_kwargs = dict(img_size=gpc.config.IMG_SIZE,
patch_size=gpc.config.PATCH_SIZE,
hidden_size=gpc.config.HIDDEN_SIZE,
depth=gpc.config.DEPTH,
num_heads=gpc.config.NUM_HEADS,
mlp_ratio=gpc.config.MLP_RATIO,
num_classes=10,
init_method='jax',
checkpoint=gpc.config.CHECKPOINT)
if use_pipeline:
pipelinable = PipelinableContext()
with pipelinable:
model = _create_vit_model(**model_kwargs)
pipelinable.to_layer_list()
pipelinable.policy = "uniform"
model = pipelinable.partition(
1, gpc.pipeline_parallel_size, gpc.get_local_rank(ParallelMode.PIPELINE))
else:
model = _create_vit_model(**model_kwargs)
# count number of parameters
total_numel = 0
for p in model.parameters():
total_numel += p.numel()
if not gpc.is_initialized(ParallelMode.PIPELINE):
pipeline_stage = 0
else:
pipeline_stage = gpc.get_local_rank(ParallelMode.PIPELINE)
logger.info(
f"number of parameters: {total_numel} on pipeline stage {pipeline_stage}")
# create dataloaders
root = os.environ.get('DATA', '../data/cifar10')
train_dataloader, test_dataloader = build_cifar(
gpc.config.BATCH_SIZE, root, pad_if_needed=True)
# create loss function
criterion = CrossEntropyLoss(label_smoothing=0.1)
# create optimizer
optimizer = Lars(model.parameters(), lr=gpc.config.LEARNING_RATE,
weight_decay=gpc.config.WEIGHT_DECAY)
# create lr scheduler
lr_scheduler = CosineAnnealingWarmupLR(optimizer=optimizer,
total_steps=gpc.config.NUM_EPOCHS,
warmup_steps=gpc.config.WARMUP_EPOCHS)
# initialize
engine, train_dataloader, test_dataloader, _ = colossalai.initialize(model=model,
optimizer=optimizer,
criterion=criterion,
train_dataloader=train_dataloader,
test_dataloader=test_dataloader)
logger.info("Engine is built", ranks=[0])
data_iter = iter(train_dataloader)
for epoch in range(gpc.config.NUM_EPOCHS):
# training
engine.train()
if gpc.get_global_rank() == 0:
description = 'Epoch {} / {}'.format(epoch, gpc.config.NUM_EPOCHS)
progress = tqdm(range(len(train_dataloader)), desc=description)
else:
progress = range(len(train_dataloader))
for _ in progress:
engine.zero_grad()
engine.execute_schedule(data_iter, return_output_label=False)
engine.step()
lr_scheduler.step()
if __name__ == '__main__':
main()
examples/tutorial/handson5/README.md deleted 100644 → 0
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# Handson 5: Fine-tuning and Serving for OPT from Hugging Face
examples/tutorial/handson5/inference/README.md deleted 100644 → 0
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# Overview
This is an example showing how to run OPT generation. The OPT model is implemented using ColossalAI.
It supports tensor parallelism, batching and caching.
# How to run
Run OPT-125M:
```shell
python opt_fastapi.py opt-125m
```
It will launch a HTTP server on `0.0.0.0:7070` by default and you can customize host and port. You can open `localhost:7070/docs` in your browser to see the openapi docs.
## Configure
### Configure model
```shell
python opt_fastapi.py <model>
```
Available models: opt-125m, opt-6.7b, opt-30b, opt-175b.
### Configure tensor parallelism
```shell
python opt_fastapi.py <model> --tp <TensorParallelismWorldSize>
```
The `<TensorParallelismWorldSize>` can be an integer in `[1, #GPUs]`. Default `1`.
### Configure checkpoint
```shell
python opt_fastapi.py <model> --checkpoint <CheckpointPath>
```
The `<CheckpointPath>` can be a file path or a directory path. If it's a directory path, all files under the directory will be loaded.
### Configure queue
```shell
python opt_fastapi.py <model> --queue_size <QueueSize>
```
The `<QueueSize>` can be an integer in `[0, MAXINT]`. If it's `0`, the request queue size is infinite. If it's a positive integer, when the request queue is full, incoming requests will be dropped (the HTTP status code of response will be 406).
### Configure bathcing
```shell
python opt_fastapi.py <model> --max_batch_size <MaxBatchSize>
```
The `<MaxBatchSize>` can be an integer in `[1, MAXINT]`. The engine will make batch whose size is less or equal to this value.
Note that the batch size is not always equal to `<MaxBatchSize>`, as some consecutive requests may not be batched.
### Configure caching
```shell
python opt_fastapi.py <model> --cache_size <CacheSize> --cache_list_size <CacheListSize>
```
This will cache `<CacheSize>` unique requests. And for each unique request, it cache `<CacheListSize>` different results. A random result will be returned if the cache is hit.
The `<CacheSize>` can be an integer in `[0, MAXINT]`. If it's `0`, cache won't be applied. The `<CacheListSize>` can be an integer in `[1, MAXINT]`.
### Other configurations
```shell
python opt_fastapi.py -h
```
# How to benchmark
```shell
cd benchmark
locust
```
Then open the web interface link which is on your console.
# Pre-process pre-trained weights
## OPT-66B
See [script/processing_ckpt_66b.py](./script/processing_ckpt_66b.py).
## OPT-175B
See [script/process-opt-175b](./script/process-opt-175b/).
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examples/tutorial/handson5/inference/batch.py deleted 100644 → 0
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import torch
from typing import List, Deque, Tuple, Hashable, Any
from energonai import BatchManager, SubmitEntry, TaskEntry
class BatchManagerForGeneration(BatchManager):
def __init__(self, max_batch_size: int = 1, pad_token_id: int = 0) -> None:
super().__init__()
self.max_batch_size = max_batch_size
self.pad_token_id = pad_token_id
def _left_padding(self, batch_inputs):
max_len = max(len(inputs['input_ids']) for inputs in batch_inputs)
outputs = {'input_ids': [], 'attention_mask': []}
for inputs in batch_inputs:
input_ids, attention_mask = inputs['input_ids'], inputs['attention_mask']
padding_len = max_len - len(input_ids)
input_ids = [self.pad_token_id] * padding_len + input_ids
attention_mask = [0] * padding_len + attention_mask
outputs['input_ids'].append(input_ids)
outputs['attention_mask'].append(attention_mask)
for k in outputs:
outputs[k] = torch.tensor(outputs[k])
return outputs, max_len
@staticmethod
def _make_batch_key(entry: SubmitEntry) -> tuple:
data = entry.data
return (data['top_k'], data['top_p'], data['temperature'])
def make_batch(self, q: Deque[SubmitEntry]) -> Tuple[TaskEntry, dict]:
entry = q.popleft()
uids = [entry.uid]
batch = [entry.data]
while len(batch) < self.max_batch_size:
if len(q) == 0:
break
if self._make_batch_key(entry) != self._make_batch_key(q[0]):
break
if q[0].data['max_tokens'] > entry.data['max_tokens']:
break
e = q.popleft()
batch.append(e.data)
uids.append(e.uid)
inputs, max_len = self._left_padding(batch)
trunc_lens = []
for data in batch:
trunc_lens.append(max_len + data['max_tokens'])
inputs['top_k'] = entry.data['top_k']
inputs['top_p'] = entry.data['top_p']
inputs['temperature'] = entry.data['temperature']
inputs['max_tokens'] = max_len + entry.data['max_tokens']
return TaskEntry(tuple(uids), inputs), {'trunc_lens': trunc_lens}
def split_batch(self, task_entry: TaskEntry, trunc_lens: List[int] = []) -> List[Tuple[Hashable, Any]]:
retval = []
for uid, output, trunc_len in zip(task_entry.uids, task_entry.batch, trunc_lens):
retval.append((uid, output[:trunc_len]))
return retval
examples/tutorial/handson5/inference/benchmark/locustfile.py deleted 100644 → 0
View file @ 351f0f64
from locust import HttpUser, task
from json import JSONDecodeError
class GenerationUser(HttpUser):
@task
def generate(self):
prompt = 'Question: What is the longest river on the earth? Answer:'
for i in range(4, 9):
data = {'max_tokens': 2**i, 'prompt': prompt}
with self.client.post('/generation', json=data, catch_response=True) as response:
if response.status_code in (200, 406):
response.success()
else:
response.failure('Response wrong')
examples/tutorial/handson5/inference/cache.py deleted 100644 → 0
View file @ 351f0f64
from collections import OrderedDict
from threading import Lock
from contextlib import contextmanager
from typing import List, Any, Hashable, Dict
class MissCacheError(Exception):
pass
class ListCache:
def __init__(self, cache_size: int, list_size: int, fixed_keys: List[Hashable] = []) -> None:
"""Cache a list of values. The fixed keys won't be removed. For other keys, LRU is applied.
When the value list is not full, a cache miss occurs. Otherwise, a cache hit occurs. Redundant values will be removed.
Args:
cache_size (int): Max size for LRU cache.
list_size (int): Value list size.
fixed_keys (List[Hashable], optional): The keys which won't be removed. Defaults to [].
"""
self.cache_size = cache_size
self.list_size = list_size
self.cache: OrderedDict[Hashable, List[Any]] = OrderedDict()
self.fixed_cache: Dict[Hashable, List[Any]] = {}
for key in fixed_keys:
self.fixed_cache[key] = []
self._lock = Lock()
def get(self, key: Hashable) -> List[Any]:
with self.lock():
if key in self.fixed_cache:
l = self.fixed_cache[key]
if len(l) >= self.list_size:
return l
elif key in self.cache:
self.cache.move_to_end(key)
l = self.cache[key]
if len(l) >= self.list_size:
return l
raise MissCacheError()
def add(self, key: Hashable, value: Any) -> None:
with self.lock():
if key in self.fixed_cache:
l = self.fixed_cache[key]
if len(l) < self.list_size and value not in l:
l.append(value)
elif key in self.cache:
self.cache.move_to_end(key)
l = self.cache[key]
if len(l) < self.list_size and value not in l:
l.append(value)
else:
if len(self.cache) >= self.cache_size:
self.cache.popitem(last=False)
self.cache[key] = [value]
@contextmanager
def lock(self):
try:
self._lock.acquire()
yield
finally:
self._lock.release()
examples/tutorial/handson5/inference/opt_fastapi.py deleted 100644 → 0
View file @ 351f0f64
import argparse
import logging
import random
from typing import Optional
import uvicorn
from energonai import QueueFullError, launch_engine
from energonai.model import opt_6B, opt_30B, opt_125M, opt_175B
from fastapi import FastAPI, HTTPException, Request
from pydantic import BaseModel, Field
from transformers import GPT2Tokenizer
from batch import BatchManagerForGeneration
from cache import ListCache, MissCacheError
class GenerationTaskReq(BaseModel):
max_tokens: int = Field(gt=0, le=256, example=64)
prompt: str = Field(
min_length=1, example='Question: Where were the 2004 Olympics held?\nAnswer: Athens, Greece\n\nQuestion: What is the longest river on the earth?\nAnswer:')
top_k: Optional[int] = Field(default=None, gt=0, example=50)
top_p: Optional[float] = Field(default=None, gt=0.0, lt=1.0, example=0.5)
temperature: Optional[float] = Field(default=None, gt=0.0, lt=1.0, example=0.7)
app = FastAPI()
@app.post('/generation')
async def generate(data: GenerationTaskReq, request: Request):
logger.info(f'{request.client.host}:{request.client.port} - "{request.method} {request.url.path}" - {data}')
key = (data.prompt, data.max_tokens)
try:
if cache is None:
raise MissCacheError()
outputs = cache.get(key)
output = random.choice(outputs)
logger.info('Cache hit')
except MissCacheError:
inputs = tokenizer(data.prompt, truncation=True, max_length=512)
inputs['max_tokens'] = data.max_tokens
inputs['top_k'] = data.top_k
inputs['top_p'] = data.top_p
inputs['temperature'] = data.temperature
try:
uid = id(data)
engine.submit(uid, inputs)
output = await engine.wait(uid)
output = tokenizer.decode(output, skip_special_tokens=True)
if cache is not None:
cache.add(key, output)
except QueueFullError as e:
raise HTTPException(status_code=406, detail=e.args[0])
return {'text': output}
@app.on_event("shutdown")
async def shutdown(*_):
engine.shutdown()
server.should_exit = True
server.force_exit = True
await server.shutdown()
def get_model_fn(model_name: str):
model_map = {
'opt-125m': opt_125M,
'opt-6.7b': opt_6B,
'opt-30b': opt_30B,
'opt-175b': opt_175B
}
return model_map[model_name]
def print_args(args: argparse.Namespace):
print('\n==> Args:')
for k, v in args.__dict__.items():
print(f'{k} = {v}')
FIXED_CACHE_KEYS = [
('Question: What is the name of the largest continent on earth?\nAnswer: Asia\n\nQuestion: What is at the center of the solar system?\nAnswer:', 64),
('A chat between a salesman and a student.\n\nSalesman: Hi boy, are you looking for a new phone?\nStudent: Yes, my phone is not functioning well.\nSalesman: What is your budget? \nStudent: I have received my scholarship so I am fine with any phone.\nSalesman: Great, then perhaps this latest flagship phone is just right for you.', 64),
("English: I am happy today.\nChinese: 我今天很开心。\n\nEnglish: I am going to play basketball.\nChinese: 我一会去打篮球。\n\nEnglish: Let's celebrate our anniversary.\nChinese:", 64)
]
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('model', choices=['opt-125m', 'opt-6.7b', 'opt-30b', 'opt-175b'])
parser.add_argument('--tp', type=int, default=1)
parser.add_argument('--master_host', default='localhost')
parser.add_argument('--master_port', type=int, default=19990)
parser.add_argument('--rpc_port', type=int, default=19980)
parser.add_argument('--max_batch_size', type=int, default=8)
parser.add_argument('--pipe_size', type=int, default=1)
parser.add_argument('--queue_size', type=int, default=0)
parser.add_argument('--http_host', default='0.0.0.0')
parser.add_argument('--http_port', type=int, default=7070)
parser.add_argument('--checkpoint', default=None)
parser.add_argument('--cache_size', type=int, default=0)
parser.add_argument('--cache_list_size', type=int, default=1)
args = parser.parse_args()
print_args(args)
model_kwargs = {}
if args.checkpoint is not None:
model_kwargs['checkpoint'] = args.checkpoint
logger = logging.getLogger(__name__)
tokenizer = GPT2Tokenizer.from_pretrained('facebook/opt-30b')
if args.cache_size > 0:
cache = ListCache(args.cache_size, args.cache_list_size, fixed_keys=FIXED_CACHE_KEYS)
else:
cache = None
engine = launch_engine(args.tp, 1, args.master_host, args.master_port, args.rpc_port, get_model_fn(args.model),
batch_manager=BatchManagerForGeneration(max_batch_size=args.max_batch_size,
pad_token_id=tokenizer.pad_token_id),
pipe_size=args.pipe_size,
queue_size=args.queue_size,
**model_kwargs)
config = uvicorn.Config(app, host=args.http_host, port=args.http_port)
server = uvicorn.Server(config=config)
server.run()
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