[test] merge zero optim tests (#428)

tests/test_zero_data_parallel/test_sharded_optim_v2.py

@@ -12,11 +12,12 @@ from colossalai.zero.sharded_model import ShardedModelV2
 from colossalai.zero.sharded_optim import ShardedOptimizerV2
 from tests.components_to_test.registry import non_distributed_component_funcs
 from torch.nn.parallel import DistributedDataParallel as DDP
+from colossalai.nn.optimizer import CPUAdam
 
 from common import CONFIG, check_sharded_params_padding
 
 
-def run_step(model, optimizer, data, label, criterion, enable_autocast=False):
+def _run_step(model, optimizer, data, label, criterion, enable_autocast=False):
     model.train()
     optimizer.zero_grad()
     with torch.cuda.amp.autocast(enabled=enable_autocast):
@@ -34,13 +35,17 @@ def run_step(model, optimizer, data, label, criterion, enable_autocast=False):
     optimizer.step()
 
 
-def run_dist(rank, world_size, port, cpu_offload, shard_strategy):
+def _run_dist(rank, world_size, port, cpu_offload, shard_strategy, use_cpuadam):
     colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
     test_models = ['repeated_computed_layers', 'resnet18', 'bert']
     shard_strategy = shard_strategy()
+
+    if use_cpuadam and cpu_offload is False:
+        return
+
     for model_name in test_models:
         get_components_func = non_distributed_component_funcs.get_callable(model_name)
-        model, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
+        model, train_dataloader, _, optimizer_class, criterion = get_components_func()
         model = model(checkpoint=True).cuda()
         zero_model = ShardedModelV2(copy.deepcopy(model),
                                     shard_strategy,
@@ -48,33 +53,59 @@ def run_dist(rank, world_size, port, cpu_offload, shard_strategy):
         if dist.get_world_size() > 1:
             model = DDP(model)
         lr = 1e-3
-        optim = optimizer_class(model.parameters(), lr=lr)
-        sharded_optim = ShardedOptimizerV2(zero_model,
-                                           optimizer_class,
-                                           cpu_offload=cpu_offload,
-                                           initial_scale=2**5,
-                                           lr=lr)
+        if use_cpuadam:
+            optim = torch.optim.Adam(model.parameters(), lr=lr)
+            sharded_optim = ShardedOptimizerV2(zero_model, CPUAdam, cpu_offload=cpu_offload, initial_scale=2**5, lr=lr)
+        else:
+            optim = optimizer_class(model.parameters(), lr=lr)
+            sharded_optim = ShardedOptimizerV2(zero_model,
+                                               optimizer_class,
+                                               cpu_offload=cpu_offload,
+                                               initial_scale=2**5,
+                                               lr=lr)
         for i, (data, label) in enumerate(train_dataloader):
-            if i > 2:
+            #FIXME() if i > 5, the unittest will fail
+            if i > 3:
                 break
             data, label = data.cuda(), label.cuda()
-            run_step(model, optim, data, label, criterion, False)
-            run_step(zero_model, sharded_optim, data, label, criterion, False)
+            _run_step(model, optim, data, label, criterion, False)
+            _run_step(zero_model, sharded_optim, data, label, criterion, False)
             check_sharded_params_padding(model, zero_model, loose=True)
 
 
+# use_cpuadam = True can be used with cpu_offload = False
+@pytest.mark.dist
+@pytest.mark.parametrize("world_size", [1, 2])
+@pytest.mark.parametrize("cpu_offload", [False])
+@pytest.mark.parametrize("use_cpuadam", [False])
+@pytest.mark.parametrize("shard_strategy", [TensorShardStrategy, BucketTensorShardStrategy])
+def test_sharded_optim_v2(world_size, cpu_offload, shard_strategy, use_cpuadam):
+    run_func = partial(_run_dist,
+                       world_size=world_size,
+                       port=free_port(),
+                       cpu_offload=cpu_offload,
+                       shard_strategy=shard_strategy,
+                       use_cpuadam=use_cpuadam)
+    mp.spawn(run_func, nprocs=world_size)
+
+
 @pytest.mark.dist
 @pytest.mark.parametrize("world_size", [1, 2])
-@pytest.mark.parametrize("cpu_offload", [True, False])
+@pytest.mark.parametrize("cpu_offload", [True])
 @pytest.mark.parametrize("shard_strategy", [TensorShardStrategy, BucketTensorShardStrategy])
-def test_sharded_optim_v2(world_size, cpu_offload, shard_strategy):
-    run_func = partial(run_dist,
+@pytest.mark.parametrize("use_cpuadam", [True, False])
+def test_sharded_optim_v2_cpu_adam(world_size, cpu_offload, shard_strategy, use_cpuadam):
+    run_func = partial(_run_dist,
                        world_size=world_size,
                        port=free_port(),
                        cpu_offload=cpu_offload,
-                       shard_strategy=shard_strategy)
+                       shard_strategy=shard_strategy,
+                       use_cpuadam=use_cpuadam)
     mp.spawn(run_func, nprocs=world_size)
 
 
 if __name__ == '__main__':
-    test_sharded_optim_v2(world_size=2, cpu_offload=True, shard_strategy=TensorShardStrategy)
+    test_sharded_optim_v2_cpu_adam(world_size=2,
\ No newline at end of file
+                                   cpu_offload=False,
+                                   shard_strategy=TensorShardStrategy,
+                                   use_cpuadam=True)

tests/test_zero_data_parallel/test_sharded_optim_v2_with_cpu_adam.py

-#!/usr/bin/env python
-# -*- encoding: utf-8 -*-
-
-import copy
-from functools import partial
-
-import colossalai
-import pytest
-import torch
-import torch.distributed as dist
-import torch.multiprocessing as mp
-from colossalai.nn.optimizer import CPUAdam
-from colossalai.utils import free_port
-from colossalai.zero.shard_utils import (BucketTensorShardStrategy, TensorShardStrategy)
-from colossalai.zero.sharded_model import ShardedModelV2
-from colossalai.zero.sharded_optim import ShardedOptimizerV2
-from tests.components_to_test.registry import non_distributed_component_funcs
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.optim import Adam
-
-from common import CONFIG, check_sharded_params_padding
-
-
-def run_step(model, optimizer, data, label, criterion, enable_autocast=False):
-    model.train()
-    optimizer.zero_grad()
-    with torch.cuda.amp.autocast(enabled=enable_autocast):
-        y = model(data)
-        loss = criterion(y, label)
-    loss = loss.float()
-    if isinstance(model, ShardedModelV2):
-        optimizer.backward(loss)
-    else:
-        loss.backward()
-    optimizer.step()
-
-
-def run_step_no_criterion(model, optimizer, data, label, enable_autocast=False):
-    model.train()
-    optimizer.zero_grad()
-    with torch.cuda.amp.autocast(enabled=enable_autocast):
-        loss = model(data, label)
-    if isinstance(model, ShardedModelV2):
-        optimizer.backward(loss)
-    else:
-        loss.backward()
-    optimizer.step()
-
-
-def run_dist(rank, world_size, port, shard_strategy):
-    colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
-    test_models = ['repeated_computed_layers', 'resnet18', 'bert']
-    shard_strategy = shard_strategy()
-    for model_name in test_models:
-        get_components_func = non_distributed_component_funcs.get_callable(model_name)
-        model, train_dataloader, test_dataloader, optimizer, criterion = get_components_func()
-        model = model(checkpoint=True).cuda()
-        zero_model = ShardedModelV2(copy.deepcopy(model), shard_strategy, offload_config={'device': 'cpu'})
-        if dist.get_world_size() > 1:
-            model = DDP(model)
-        optim = Adam(model.parameters(), lr=1e-3)
-        sharded_optim = ShardedOptimizerV2(zero_model, CPUAdam, initial_scale=2**5, cpu_offload=True, lr=1e-3)
-        for i, (data, label) in enumerate(train_dataloader):
-            if i > 2:
-                break
-            data, label = data.cuda(), label.cuda()
-            if criterion is None:
-                run_step_no_criterion(model, optim, data, label, False)
-                run_step_no_criterion(zero_model, sharded_optim, data, label, False)
-            else:
-                run_step(model, optim, data, label, criterion, False)
-                run_step(zero_model, sharded_optim, data, label, criterion, False)
-            check_sharded_params_padding(model, zero_model, loose=True)
-
-
-@pytest.mark.dist
-@pytest.mark.parametrize("world_size", [1, 2])
-@pytest.mark.parametrize("shard_strategy", [TensorShardStrategy, BucketTensorShardStrategy])
-def test_sharded_optim_v2(world_size, shard_strategy):
-    run_func = partial(run_dist, world_size=world_size, port=free_port(), shard_strategy=shard_strategy)
-    mp.spawn(run_func, nprocs=world_size)
-
-
-if __name__ == '__main__':
-    test_sharded_optim_v2(world_size=2, shard_strategy=TensorShardStrategy)