Rename package from algorithmic-efficiency to algoperf.

Author: fsschneider

.github/workflows/linting.yml

@@ -17,7 +17,7 @@ jobs:
         pip install pylint==2.16.1
     - name: Run pylint
       run: |
-        pylint algorithmic_efficiency
+        pylint algoperf
         pylint reference_algorithms
         pylint prize_qualification_baselines
         pylint submission_runner.py

.gitignore

@@ -12,8 +12,8 @@ makefile
 *.swp
 */data/
 *events.out.tfevents*
-algorithmic_efficiency/workloads/librispeech_conformer/data_dir
-algorithmic_efficiency/workloads/librispeech_conformer/work_dir
+algoperf/workloads/librispeech_conformer/data_dir
+algoperf/workloads/librispeech_conformer/work_dir
 *.flac
 *.npy
 *.csv
@@ -25,4 +25,4 @@ scoring/plots/
 !scoring/test_data/experiment_dir/study_0/mnist_jax/trial_0/eval_measurements.csv
 !scoring/test_data/experiment_dir/study_0/mnist_jax/trial_1/eval_measurements.csv
 
-algorithmic_efficiency/_version.py
+algoperf/_version.py

CHANGELOG.md

@@ -16,8 +16,8 @@
 from tensorflow.io import gfile  # pytype: disable=import-error
 import torch
 
-from algorithmic_efficiency import spec
-from algorithmic_efficiency.pytorch_utils import pytorch_setup
+from algoperf import spec
+from algoperf.pytorch_utils import pytorch_setup
 
 _, _, DEVICE, _ = pytorch_setup()
 CheckpointReturn = Tuple[spec.OptimizerState,

CONTRIBUTING.md

@@ -11,7 +11,7 @@
 from torch.utils.data import DistributedSampler
 from torch.utils.data import Sampler
 
-from algorithmic_efficiency import spec
+from algoperf import spec
 
 
 def shard_and_maybe_pad_np(

DOCUMENTATION.md

@@ -1,7 +1,7 @@
 import jax.dlpack
 import torch
 
-from algorithmic_efficiency import spec
+from algoperf import spec
 
 
 def jax_to_pytorch(x: spec.Tensor, take_ownership: bool = False) -> spec.Tensor:

GETTING_STARTED.md

@@ -18,8 +18,8 @@
 import psutil
 import torch.distributed as dist
 
-from algorithmic_efficiency import spec
-from algorithmic_efficiency.pytorch_utils import pytorch_setup
+from algoperf import spec
+from algoperf.pytorch_utils import pytorch_setup
 
 USE_PYTORCH_DDP, RANK, DEVICE, _ = pytorch_setup()
 

algorithmic_efficiency/__init__.py renamed to algoperf/__init__.py

@@ -6,7 +6,7 @@
 import jax
 from torch import nn
 
-from algorithmic_efficiency import spec
+from algoperf import spec
 
 
 def pytorch_param_shapes(model: nn.Module) -> Dict[str, spec.ShapeTuple]:

algorithmic_efficiency/checkpoint_utils.py renamed to algoperf/checkpoint_utils.py

@@ -7,11 +7,11 @@
 import torch
 import torch.distributed as dist
 
-from algorithmic_efficiency import spec
-from algorithmic_efficiency.profiler import Profiler
-from algorithmic_efficiency.workloads.librispeech_conformer.librispeech_pytorch.models import \
+from algoperf import spec
+from algoperf.profiler import Profiler
+from algoperf.workloads.librispeech_conformer.librispeech_pytorch.models import \
     BatchNorm as ConformerBatchNorm
-from algorithmic_efficiency.workloads.librispeech_deepspeech.librispeech_pytorch.models import \
+from algoperf.workloads.librispeech_deepspeech.librispeech_pytorch.models import \
     BatchNorm as DeepspeechBatchNorm
 
 

algorithmic_efficiency/data_utils.py renamed to algoperf/data_utils.py

@@ -13,8 +13,8 @@
 import tensorflow as tf
 import tensorflow_datasets as tfds
 
-from algorithmic_efficiency import spec
-from algorithmic_efficiency.data_utils import shard_and_maybe_pad_np
+from algoperf import spec
+from algoperf.data_utils import shard_and_maybe_pad_np
 
 
 def preprocess_for_train(image: spec.Tensor,

algorithmic_efficiency/halton.py renamed to algoperf/halton.py

@@ -10,8 +10,8 @@
 from flax import linen as nn
 import jax.numpy as jnp
 
-from algorithmic_efficiency import spec
-from algorithmic_efficiency.workloads.imagenet_resnet.imagenet_jax.models import \
+from algoperf import spec
+from algoperf.workloads.imagenet_resnet.imagenet_jax.models import \
     ResNetBlock
 
 ModuleDef = nn.Module

algorithmic_efficiency/init_utils.py renamed to algoperf/init_utils.py

@@ -11,12 +11,12 @@
 import optax
 import tensorflow_datasets as tfds
 
-from algorithmic_efficiency import param_utils
-from algorithmic_efficiency import spec
-from algorithmic_efficiency.workloads.cifar.cifar_jax import models
-from algorithmic_efficiency.workloads.cifar.cifar_jax.input_pipeline import \
+from algoperf import param_utils
+from algoperf import spec
+from algoperf.workloads.cifar.cifar_jax import models
+from algoperf.workloads.cifar.cifar_jax.input_pipeline import \
     create_input_iter
-from algorithmic_efficiency.workloads.cifar.workload import BaseCifarWorkload
+from algoperf.workloads.cifar.workload import BaseCifarWorkload
 
 
 class CifarWorkload(BaseCifarWorkload):

algorithmic_efficiency/interop_utils.py renamed to algoperf/interop_utils.py

@@ -10,13 +10,13 @@
 import torch
 from torch import nn
 
-from algorithmic_efficiency import spec
-from algorithmic_efficiency.init_utils import pytorch_default_init
-from algorithmic_efficiency.workloads.imagenet_resnet.imagenet_pytorch.models import \
+from algoperf import spec
+from algoperf.init_utils import pytorch_default_init
+from algoperf.workloads.imagenet_resnet.imagenet_pytorch.models import \
     BasicBlock
-from algorithmic_efficiency.workloads.imagenet_resnet.imagenet_pytorch.models import \
+from algoperf.workloads.imagenet_resnet.imagenet_pytorch.models import \
     Bottleneck
-from algorithmic_efficiency.workloads.imagenet_resnet.imagenet_pytorch.models import \
+from algoperf.workloads.imagenet_resnet.imagenet_pytorch.models import \
     conv1x1
 
 

algorithmic_efficiency/logger_utils.py renamed to algoperf/logger_utils.py

@@ -12,13 +12,13 @@
 from torchvision import transforms
 from torchvision.datasets import CIFAR10
 
-from algorithmic_efficiency import data_utils
-from algorithmic_efficiency import param_utils
-from algorithmic_efficiency import pytorch_utils
-from algorithmic_efficiency import spec
-from algorithmic_efficiency.workloads.cifar.cifar_pytorch.models import \
+from algoperf import data_utils
+from algoperf import param_utils
+from algoperf import pytorch_utils
+from algoperf import spec
+from algoperf.workloads.cifar.cifar_pytorch.models import \
     resnet18
-from algorithmic_efficiency.workloads.cifar.workload import BaseCifarWorkload
+from algoperf.workloads.cifar.workload import BaseCifarWorkload
 
 USE_PYTORCH_DDP, RANK, DEVICE, N_GPUS = pytorch_utils.pytorch_setup()
 

algorithmic_efficiency/param_utils.py renamed to algoperf/param_utils.py

@@ -7,9 +7,9 @@
 import jax
 import torch
 
-from algorithmic_efficiency import spec
-from algorithmic_efficiency.pytorch_utils import pytorch_setup
-import algorithmic_efficiency.random_utils as prng
+from algoperf import spec
+from algoperf.pytorch_utils import pytorch_setup
+import algoperf.random_utils as prng
 
 USE_PYTORCH_DDP, _, _, _ = pytorch_setup()
 

algorithmic_efficiency/profiler.py renamed to algoperf/profiler.py

@@ -8,10 +8,10 @@
 import jax.numpy as jnp
 import numpy as np
 
-from algorithmic_efficiency import param_utils
-from algorithmic_efficiency import spec
-from algorithmic_efficiency.workloads.criteo1tb.criteo1tb_jax import models
-from algorithmic_efficiency.workloads.criteo1tb.workload import \
+from algoperf import param_utils
+from algoperf import spec
+from algoperf.workloads.criteo1tb.criteo1tb_jax import models
+from algoperf.workloads.criteo1tb.workload import \
     BaseCriteo1TbDlrmSmallWorkload
 
 

algorithmic_efficiency/pytorch_utils.py renamed to algoperf/pytorch_utils.py

@@ -7,11 +7,11 @@
 import torch.distributed as dist
 from torch.nn.parallel import DistributedDataParallel as DDP
 
-from algorithmic_efficiency import param_utils
-from algorithmic_efficiency import spec
-from algorithmic_efficiency.pytorch_utils import pytorch_setup
-from algorithmic_efficiency.workloads.criteo1tb.criteo1tb_pytorch import models
-from algorithmic_efficiency.workloads.criteo1tb.workload import \
+from algoperf import param_utils
+from algoperf import spec
+from algoperf.pytorch_utils import pytorch_setup
+from algoperf.workloads.criteo1tb.criteo1tb_pytorch import models
+from algoperf.workloads.criteo1tb.workload import \
     BaseCriteo1TbDlrmSmallWorkload
 
 USE_PYTORCH_DDP, RANK, DEVICE, N_GPUS = pytorch_setup()

algorithmic_efficiency/random_utils.py renamed to algoperf/random_utils.py

@@ -12,7 +12,7 @@
 
 import tensorflow as tf
 
-from algorithmic_efficiency import data_utils
+from algoperf import data_utils
 
 _NUM_DAY_23_FILES = 36
 

algorithmic_efficiency/spec.py renamed to algoperf/spec.py

@@ -7,8 +7,8 @@
 from absl import flags
 import torch.distributed as dist
 
-from algorithmic_efficiency import spec
-from algorithmic_efficiency.workloads.criteo1tb import input_pipeline
+from algoperf import spec
+from algoperf.workloads.criteo1tb import input_pipeline
 
 FLAGS = flags.FLAGS
 

algorithmic_efficiency/workloads/__init__.py renamed to algoperf/workloads/__init__.py

@@ -8,12 +8,12 @@
 import jax
 import jax.numpy as jnp
 
-from algorithmic_efficiency import param_utils
-from algorithmic_efficiency import spec
-import algorithmic_efficiency.random_utils as prng
-from algorithmic_efficiency.workloads.fastmri.fastmri_jax.models import UNet
-from algorithmic_efficiency.workloads.fastmri.fastmri_jax.ssim import ssim
-from algorithmic_efficiency.workloads.fastmri.workload import \
+from algoperf import param_utils
+from algoperf import spec
+import algoperf.random_utils as prng
+from algoperf.workloads.fastmri.fastmri_jax.models import UNet
+from algoperf.workloads.fastmri.fastmri_jax.ssim import ssim
+from algoperf.workloads.fastmri.workload import \
     BaseFastMRIWorkload
 
 

algorithmic_efficiency/workloads/cifar/__init__.py renamed to algoperf/workloads/cifar/__init__.py

@@ -12,7 +12,7 @@
 from torch import Tensor
 from torch.nn import functional as F
 
-from algorithmic_efficiency import init_utils
+from algoperf import init_utils
 
 
 class UNet(nn.Module):

algorithmic_efficiency/workloads/cifar/cifar_jax/__init__.py renamed to algoperf/workloads/cifar/cifar_jax/__init__.py

@@ -6,7 +6,7 @@
 import torch.nn.functional as F
 from torchvision.transforms.functional import pad as pad_fn
 
-from algorithmic_efficiency.pytorch_utils import pytorch_setup
+from algoperf.pytorch_utils import pytorch_setup
 
 DEVICE = pytorch_setup()[2]
 

algorithmic_efficiency/workloads/cifar/cifar_jax/input_pipeline.py renamed to algoperf/workloads/cifar/cifar_jax/input_pipeline.py

@@ -9,14 +9,14 @@
 import torch.nn.functional as F
 from torch.nn.parallel import DistributedDataParallel as DDP
 
-from algorithmic_efficiency import param_utils
-from algorithmic_efficiency import pytorch_utils
-from algorithmic_efficiency import spec
-import algorithmic_efficiency.random_utils as prng
-from algorithmic_efficiency.workloads.fastmri.fastmri_pytorch.models import \
+from algoperf import param_utils
+from algoperf import pytorch_utils
+from algoperf import spec
+import algoperf.random_utils as prng
+from algoperf.workloads.fastmri.fastmri_pytorch.models import \
     UNet
-from algorithmic_efficiency.workloads.fastmri.fastmri_pytorch.ssim import ssim
-from algorithmic_efficiency.workloads.fastmri.workload import \
+from algoperf.workloads.fastmri.fastmri_pytorch.ssim import ssim
+from algoperf.workloads.fastmri.workload import \
     BaseFastMRIWorkload
 
 USE_PYTORCH_DDP, RANK, DEVICE, N_GPUS = pytorch_utils.pytorch_setup()