[zero] adapt zero for unsharded paramters (Optimizer part) (#601)

tests/components_to_test/no_leaf_module.py

@@ -42,4 +42,5 @@ def get_training_components():
     testloader = DummyDataLoader()
 
     criterion = torch.nn.CrossEntropyLoss()
-    return model_builder, trainloader, testloader, torch.optim.Adam, criterion
+    from colossalai.nn.optimizer import HybridAdam
+    return model_builder, trainloader, testloader, HybridAdam, criterion

tests/test_moe/test_moe_zero_init.py

@@ -76,8 +76,11 @@ def run_moe_zero_init(init_device_type, shard_strategy_class):
             else:
                 assert param.is_replicated
 
-            assert param.colo_attr.sharded_data_tensor.payload.device.type == init_device.type, \
-                f'{param.colo_attr.sharded_data_tensor.payload.device.type} vs. {init_device.type}'
+            if param.colo_attr.param_is_sharded:
+                assert param.colo_attr.sharded_data_tensor.payload.device.type == init_device.type, \
+                    f'{param.colo_attr.sharded_data_tensor.payload.device.type} vs. {init_device.type}'
+            else:
+                assert param.colo_attr.sharded_data_tensor.payload.device.type == 'cuda'
 
 
 def _run_dist(rank, world_size, port):

tests/test_moe/test_moe_zero_model.py

@@ -67,7 +67,7 @@ def run_dist(rank, world_size, port):
 
 
 @pytest.mark.dist
-@pytest.mark.parametrize("world_size", [1, 2])
+@pytest.mark.parametrize("world_size", [2])
 @rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
 def test_moe_zero_model(world_size):
     run_func = partial(run_dist, world_size=world_size, port=free_port())

tests/test_moe/test_moe_zero_optim.py

@@ -0,0 +1,134 @@
+from functools import partial
+
+import colossalai
+from colossalai.utils.cuda import get_current_device
+import pytest
+import torch
+import torch.multiprocessing as mp
+from colossalai.amp import convert_to_apex_amp
+from colossalai.nn.optimizer import CPUAdam
+from colossalai.testing import parameterize, rerun_on_exception
+from colossalai.utils import free_port
+from colossalai.zero.init_ctx import ZeroInitContext
+from colossalai.zero.shard_utils import (BucketTensorShardStrategy, TensorShardStrategy)
+from colossalai.zero.sharded_model import ShardedModelV2
+from colossalai.zero.sharded_model.utils import col_model_deepcopy
+from colossalai.zero.sharded_optim import ShardedOptimizerV2
+from colossalai.zero.sharded_optim._utils import has_inf_or_nan
+from colossalai.utils import get_current_device
+from tests.components_to_test.registry import non_distributed_component_funcs
+from colossalai.engine.gradient_handler import MoeGradientHandler
+from colossalai.context import MOE_CONTEXT
+from colossalai.testing import assert_equal_in_group
+
+from tests.test_zero_data_parallel.common import CONFIG, check_sharded_model_params
+from tests.test_moe.test_moe_zero_init import MoeModel
+
+
+def _run_step(model, optimizer, data, label, criterion, grad_handler):
+    model.train()
+    optimizer.zero_grad()
+
+    if criterion:
+        y = model(data)
+        loss = criterion(y, label)
+    else:
+        loss = model(data, label)
+
+    loss = loss.float()
+    if isinstance(model, ShardedModelV2):
+        optimizer.backward(loss)
+    else:
+        loss.backward()
+
+    if grad_handler is not None:
+        grad_handler.handle_gradient()
+
+    optimizer.step()
+
+
+@parameterize("cpu_offload", [True, False])
+@parameterize("use_cpuadam", [True, False])
+@parameterize("shard_strategy_class", [TensorShardStrategy, BucketTensorShardStrategy])
+def _run_test_sharded_optim_v2(cpu_offload, shard_strategy_class, use_cpuadam, gpu_margin_mem_ratio=0.0):
+    MOE_CONTEXT.reset_loss()
+    shard_strategy = shard_strategy_class()
+    if use_cpuadam and cpu_offload is False:
+        return
+
+    get_components_func = non_distributed_component_funcs.get_callable('no_leaf_module')
+    _, train_dataloader, _, optimizer_class, criterion = get_components_func()
+
+    with ZeroInitContext(
+            target_device=torch.device('cpu') if cpu_offload else torch.device(f'cuda:{get_current_device()}'),
+            shard_strategy=shard_strategy,
+            shard_param=True,
+            rm_torch_payload_on_the_fly=False):
+        zero_model = MoeModel()
+
+    zero_model = ShardedModelV2(
+        zero_model,
+        shard_strategy,
+        offload_config=dict(device='cpu') if cpu_offload else None,
+        use_memory_tracer=gpu_margin_mem_ratio > 0.0,
+        reuse_fp16_shard=use_cpuadam,
+    )
+
+    # check whether parameters are identical in ddp
+    for name, p in zero_model.named_parameters():
+        if not p.colo_attr.param_is_sharded and p.is_replicated:
+            assert_equal_in_group(p.data.to(get_current_device()))
+
+    model = MoeModel().half()
+    col_model_deepcopy(zero_model, model)
+    model = model.cuda().float()
+
+    if use_cpuadam:
+        optimizer_class = CPUAdam
+    optim = optimizer_class(model.parameters(), lr=1e-3)
+    sharded_optim = optimizer_class(zero_model.parameters(), lr=1e-3)
+    sharded_optim = ShardedOptimizerV2(zero_model,
+                                       sharded_optim,
+                                       cpu_offload=cpu_offload,
+                                       initial_scale=2**5,
+                                       gpu_margin_mem_ratio=gpu_margin_mem_ratio,
+                                       keep_unsharded=True)
+
+    amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False)
+    apex_model, apex_optimizer = convert_to_apex_amp(model, optim, amp_config)
+    apex_grad_handler = MoeGradientHandler(model)
+
+    # Since MOE is not compatible with apex_amp now, we need to convert gate weight to fp32
+    for (n, p), zp in zip(apex_model.named_parameters(), zero_model.parameters()):
+        if 'gate' in n:
+            p.data = p.float()
+            p.data.copy_(zp.data)
+
+    for i, (data, label) in enumerate(train_dataloader):
+        if i > 5:
+            break
+        data, label = data.cuda(), label.cuda()
+        _run_step(apex_model, apex_optimizer, data, label, criterion, apex_grad_handler)
+        _run_step(zero_model, sharded_optim, data, label, criterion, None)
+        check_sharded_model_params(model, zero_model, loose=True, reuse_fp16_shard=use_cpuadam)
+        for param in model.parameters():
+            assert not has_inf_or_nan(param)
+
+
+def _run_dist(rank, world_size, port):
+    colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    MOE_CONTEXT.setup(seed=42)
+    _run_test_sharded_optim_v2()
+
+
+# use_cpuadam = True can be used with cpu_offload = False
+@pytest.mark.dist
+@pytest.mark.parametrize("world_size", [2])
+@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
+def test_moe_zero_optim(world_size):
+    run_func = partial(_run_dist, world_size=world_size, port=free_port())
+    mp.spawn(run_func, nprocs=world_size)
+
+
+if __name__ == '__main__':
+    test_moe_zero_optim(world_size=2)

tests/test_zero_data_parallel/common.py

@@ -124,16 +124,18 @@ def check_params_padding(model, zero_model, loose=False):
 
 def check_sharded_model_params(model, zero_model, loose=False, reuse_fp16_shard=False):
     rank = dist.get_rank()
-    for p, zero_p in zip(model.parameters(), zero_model.parameters()):
-        if reuse_fp16_shard:
-            zero_p = zero_p.data.to(p.device).float()
-        else:
-            zero_p = zero_p.colo_attr.sharded_data_tensor.payload.to(p.device).float()
-        chunks = torch.flatten(p).chunk(dist.get_world_size())
-        if rank >= len(chunks):
-            continue
-        p = chunks[rank].float()
-        if zero_p.size(0) > p.size(0):
-            zero_p = zero_p[:p.size(0)]
+    for (name, p), (zero_name, zero_p) in zip(model.named_parameters(), zero_model.named_parameters()):
+        if zero_p.colo_attr.param_is_sharded:
+            if reuse_fp16_shard:
+                zero_p = zero_p.data.to(p.device).float()
+            else:
+                zero_p = zero_p.colo_attr.sharded_data_tensor.payload.to(p.device).float()
+            chunks = torch.flatten(p).chunk(dist.get_world_size())
+            if rank >= len(chunks):
+                continue
+            p = chunks[rank].float()
+            if zero_p.size(0) > p.size(0):
+                zero_p = zero_p[:p.size(0)]
+
         assert p.dtype == zero_p.dtype
         assert allclose(p, zero_p, loose=loose), f'{p} vs {zero_p}'