[chore] move fair_dev into fairscale (#1078)

Co-authored-by: Min Xu <min.xu.public@gmail.com>

benchmarks/experimental/experimental_async_approaches.py

@@ -21,8 +21,8 @@ from torch.utils.data import DataLoader
 import torchtext
 from torchtext.data.utils import get_tokenizer
 
-from fair_dev.testing.testing import dist_init, get_worker_map
 from fairscale.experimental.nn.ampnet_pipe import pipe
+from fairscale.fair_dev.testing.testing import dist_init, get_worker_map
 from fairscale.nn.model_parallel import initialize_model_parallel
 from fairscale.nn.model_parallel.initialize import get_pipeline_parallel_group
 from fairscale.nn.pipe import LazyModule

benchmarks/pipe.py

@@ -16,7 +16,7 @@ from torch.nn.parallel import DistributedDataParallel as DDP
 import utils
 
 from benchmarks.golden_configs.lm_wikitext2 import Pipe as lm_wikitext2
-from fair_dev.testing.testing import dist_init
+from fairscale.fair_dev.testing.testing import dist_init
 from fairscale.nn import Pipe
 from fairscale.nn.model_parallel import initialize_model_parallel
 

fairscale/README.md

+NOTE:
+
+The experimental and fair_dev submodules are not part of the fairscale public
+API. There can be breaking changes in them at anytime.

fairscale/__init__.py

@@ -5,6 +5,10 @@
 
 ################################################################################
 # Import most common subpackages
+#
+# NOTE: we don't maintain any public APIs in both experimental and fair_dev
+#       sub-modules. Code in them are experimental or for developer only. They
+#       can be changed, removed, anytime.
 ################################################################################
 
 from typing import List

tests/experimental/nn/ampnet_pipe_process/test_ampnet_pipe.py

@@ -22,8 +22,8 @@ from torch import nn
 from torch.optim.optimizer import Optimizer
 from torch.utils.data import DataLoader, Dataset
 
-from fair_dev.testing.testing import get_worker_map, torch_spawn
 from fairscale.experimental.nn.ampnet_pipe.pipe import AMPnetPipe
+from fairscale.fair_dev.testing.testing import get_worker_map, torch_spawn
 
 
 class MySGD(Optimizer):

tests/experimental/nn/data_parallel/test_gossip.py

@@ -15,8 +15,8 @@ from torch import nn
 import torch.distributed
 import torch.nn.functional as F
 
-from fair_dev.testing.testing import skip_if_single_gpu, spawn_for_all_world_sizes
 import fairscale.experimental.nn.data_parallel.gossip as gossip
+from fairscale.fair_dev.testing.testing import skip_if_single_gpu, spawn_for_all_world_sizes
 
 # Enfore CUBLAS reproducibility, see https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility
 os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"

tests/experimental/nn/test_mevo.py

@@ -12,9 +12,9 @@ import os
 import pytest
 import torch
 
-from fair_dev.testing.testing import skip_if_no_cuda
 from fairscale.experimental.nn import MEVO
 from fairscale.experimental.nn.mevo import BaselineSoftmaxNllLoss, get_data
+from fairscale.fair_dev.testing.testing import skip_if_no_cuda
 
 
 @pytest.fixture(scope="session", params=[torch.float16, torch.float32])

tests/experimental/nn/test_multiprocess_pipe.py

@@ -20,8 +20,8 @@ import torch.distributed.rpc as rpc
 import torch.multiprocessing as mp
 import torch.nn as nn
 
-from fair_dev.testing.testing import skip_due_to_flakyness, skip_if_single_gpu
 from fairscale.experimental.nn.distributed_pipeline import DistributedLoss, DistributedPipeline, PipelineModulesGraph
+from fairscale.fair_dev.testing.testing import skip_due_to_flakyness, skip_if_single_gpu
 from fairscale.internal import torch_version
 
 pytestmark = pytest.mark.skipif(

tests/experimental/nn/test_offload.py

@@ -14,8 +14,8 @@ import numpy as np
 import pytest
 import torch
 
-from fair_dev.testing.testing import skip_if_no_cuda
 from fairscale.experimental.nn.offload import OffloadModel
+from fairscale.fair_dev.testing.testing import skip_if_no_cuda
 from fairscale.internal import torch_version
 
 if torch_version() >= (1, 8, 0):

tests/experimental/tooling/test_layer_memory_tracker.py

@@ -10,12 +10,12 @@ import torch.multiprocessing as mp
 import torch.nn as nn
 from torch.nn.parallel import DistributedDataParallel
 
-from fair_dev.testing.testing import GPT2, dist_init, skip_if_no_cuda, skip_if_single_gpu, temp_files_ctx
 from fairscale.experimental.tooling.layer_memory_tracker import (
     LayerwiseMemoryTracker,
     ProcessGroupTracker,
     find_best_reset_points,
 )
+from fairscale.fair_dev.testing.testing import GPT2, dist_init, skip_if_no_cuda, skip_if_single_gpu, temp_files_ctx
 from fairscale.nn import FullyShardedDataParallel
 
 

tests/experimental/wgit/test_sha1_store.py

@@ -11,8 +11,8 @@ import pytest
 import torch
 from torch import nn
 
-from fair_dev.testing.testing import objects_are_equal
 from fairscale.experimental.wgit.sha1_store import SHA1_Store
+from fairscale.fair_dev.testing.testing import objects_are_equal
 
 # Get the absolute path of the parent at the beginning before any os.chdir(),
 # so that we can proper clean it up at any CWD.

tests/experimental/wgit/test_signal_sparsity.py

@@ -6,8 +6,8 @@
 import pytest
 import torch
 
-from fair_dev.testing.testing import objects_are_equal
 from fairscale.experimental.wgit.signal_sparsity import SignalSparsity, random_sparse_mask
+from fairscale.fair_dev.testing.testing import objects_are_equal
 
 # Our own tolerance
 ATOL = 1e-6

tests/experimental/wgit/test_signal_sparsity_profiling.py

@@ -8,8 +8,8 @@ import time
 import pytest
 import torch
 
-from fair_dev.testing.testing import objects_are_equal, skip_if_no_cuda
 from fairscale.experimental.wgit.signal_sparsity_profiling import EnergyConcentrationProfile as ECP
+from fairscale.fair_dev.testing.testing import objects_are_equal, skip_if_no_cuda
 
 # Our own tolerance
 ATOL = 1e-6

tests/nn/checkpoint/test_checkpoint_activations.py

@@ -10,7 +10,7 @@ import torch
 import torch.nn as nn
 from torch.utils.checkpoint import checkpoint as torch_checkpoint_wrapper
 
-from fair_dev.testing.testing import skip_if_no_cuda
+from fairscale.fair_dev.testing.testing import skip_if_no_cuda
 from fairscale.internal import torch_version
 from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper, disable_checkpointing
 from fairscale.nn.misc import FlattenParamsWrapper

tests/nn/checkpoint/test_checkpoint_activations_norm.py

@@ -14,7 +14,7 @@ import torch
 from torch.nn import BatchNorm2d, LayerNorm, Linear, Sequential
 from torch.optim import SGD
 
-from fair_dev.testing.testing import objects_are_equal
+from fairscale.fair_dev.testing.testing import objects_are_equal
 from fairscale.internal import torch_version
 from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper
 

tests/nn/data_parallel/test_fsdp.py

@@ -18,7 +18,7 @@ import torch
 from torch import nn
 import torch.distributed
 
-from fair_dev.testing.testing import (
+from fairscale.fair_dev.testing.testing import (
     DeviceAndTypeCheckModule,
     DummyProcessGroup,
     dist_init,