[zero] fix memory leak for zero2 (#1955)

colossalai/zero/sharded_optim/low_level_optim.py

@@ -48,7 +48,7 @@ class LowLevelZeroOptimizer(ColossalaiOptimizer):
             verbose=False,
 
     # communication
-            reduce_bucket_size=500000000,
+            reduce_bucket_size=50000000,
             communication_dtype=torch.float16,
             overlap_communication=False,
 
@@ -125,14 +125,14 @@ class LowLevelZeroOptimizer(ColossalaiOptimizer):
         # partition these param groups for data parallel training
         # and add buffers to parameter store for future access
         for group_id, param_group in enumerate(self._optimizer.param_groups):
-            params = param_group['params']
+            group_params = param_group['params']
 
             # add the fp16 params to fp16_param_groups for bookkeeping
-            self._fp16_param_groups[group_id] = params
+            self._fp16_param_groups[group_id] = group_params
 
             # assign parameters to ranks
             # the params in the list are sorted
-            params_per_rank = self._partition_param_list(params)
+            params_per_rank = self._partition_param_list(group_params)
 
             # store the mapping between param to rank
             # each param should belong to only one rank
@@ -143,14 +143,15 @@ class LowLevelZeroOptimizer(ColossalaiOptimizer):
 
             # move to cpu to make room to create the flat tensor
             # move_tensor(params, device='cpu')
-            for param in params:
+            for param in group_params:
                 param.data = param.data.cpu()
 
             # flatten the reordered tensors
             for rank in range(self._world_size):
                 tensor_list = self._param_store.get_fp16_params_by_rank_group(rank, group_id)
-                flat_tensor = flatten(tensor_list)
-                flat_tensor = flat_tensor.cuda()
+                with torch.no_grad():
+                    flat_tensor = flatten(tensor_list)
+                flat_tensor = flat_tensor.data.cuda()
                 self._param_store.add_flat_fp16_param_by_rank_group(rank, group_id, flat_tensor)
 
             # sync parameters
@@ -161,7 +162,7 @@ class LowLevelZeroOptimizer(ColossalaiOptimizer):
 
             # create a copy of fp32 weights of the parameters for which this rank is responsible
             fp16_flat_current_rank = self._param_store.get_flat_fp16_param_by_rank_group(self._local_rank, group_id)
-            fp32_flat_current_rank = fp16_flat_current_rank.clone().float().detach()
+            fp32_flat_current_rank = fp16_flat_current_rank.float()
             device = 'cpu' if self._cpu_offload else get_current_device()
             fp32_flat_current_rank = fp32_flat_current_rank.to(device)
             fp32_flat_current_rank.requires_grad = True
@@ -384,7 +385,7 @@ class LowLevelZeroOptimizer(ColossalaiOptimizer):
     # torch.optim.Optimizer methods
     ################################
 
-    def backward(self, loss, retain_graph=True):
+    def backward(self, loss, retain_graph=False):
         loss = self.loss_scale * loss
         loss.backward(retain_graph=retain_graph)
 

tests/test_zero/low_level_zero/test_grad_clip.py

+import copy
+from functools import partial
+
+import pytest
+import torch
+import torch.multiprocessing as mp
+import torch.nn as nn
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+import colossalai
+from colossalai.utils import free_port
+from colossalai.zero import LowLevelZeroOptimizer
+
+
+def check_equal(a, b, rtol=1e-4, atol=1e-3):
+    """
+    This function checks if two tensors are equal within tolerance
+    """
+    assert torch.allclose(a.float(), b.float(), rtol=rtol, atol=atol), f'a = {a}, b = {b}'
+
+
+def check_completely_equal(a, b):
+    """
+    This function checks if two tensors are completely equal
+    """
+    assert torch.all(a == b), f'a = {a}, b = {b}'
+
+
+class TestModel(nn.Module):
+
+    def __init__(self):
+        super(TestModel, self).__init__()
+        self.linear1 = nn.Linear(128, 256)
+        self.linear2 = nn.Linear(256, 512)
+
+    def forward(self, x):
+        x = self.linear1(x)
+        x = self.linear2(x)
+        return x
+
+
+def exam_zero_1_2_grad_clip():
+    # create model
+    zero1_model = TestModel().cuda().half()
+    zero2_model = copy.deepcopy(zero1_model)
+
+    # create optimizer
+    zero1_optimizer = torch.optim.Adam(zero1_model.parameters(), lr=0.001)
+    zero2_optimizer = torch.optim.Adam(zero2_model.parameters(), lr=0.001)
+    zero1_optimizer = LowLevelZeroOptimizer(zero1_optimizer,
+                                            overlap_communication=True,
+                                            initial_scale=32,
+                                            clip_grad_norm=1.0,
+                                            verbose=True)
+    zero2_optimizer = LowLevelZeroOptimizer(zero2_optimizer,
+                                            overlap_communication=True,
+                                            partition_grad=True,
+                                            initial_scale=32,
+                                            clip_grad_norm=1.0)
+
+    # create
+    input_data = torch.rand(32, 128).cuda().half()
+
+    # forward
+    zero1_output = zero1_model(input_data)
+    zero2_output = zero2_model(input_data)
+    check_completely_equal(zero1_output, zero2_output)
+
+    # backward
+    zero1_optimizer.backward(zero1_output.mean().float())
+    zero2_optimizer.backward(zero2_output.mean().float())
+
+    # check grad
+    # as this param is small, the backward reduction
+    # will not be fired
+    for z1p, z2p in zip(zero1_model.parameters(), zero2_model.parameters()):
+        check_completely_equal(z1p.grad, z2p.grad)
+
+    # step
+    zero1_optimizer.sync_grad()
+    zero2_optimizer.sync_grad()
+
+    # step
+    zero1_optimizer.step()
+    zero2_optimizer.step()
+
+    # check updated param
+    for z1p, z2p in zip(zero1_model.parameters(), zero2_model.parameters()):
+        check_completely_equal(z1p.data, z2p.data)
+
+
+def exam_zero_1_grad_clip():
+    # create models
+    zero_model = TestModel()
+    torch_model = copy.deepcopy(zero_model)
+
+    zero_model = zero_model.cuda().half()
+    torch_model = DDP(torch_model.cuda())
+
+    # create optimizer
+    zero_optimizer = torch.optim.Adam(zero_model.parameters(), lr=0.001)
+
+    # we only test stage 1 here
+    # in `check_sharded_param_consistency.py`, we will test whether
+    # level 1 and 2 will produce exactly the same results
+    zero_optimizer = LowLevelZeroOptimizer(zero_optimizer,
+                                           overlap_communication=True,
+                                           initial_scale=1,
+                                           clip_grad_norm=1.0)
+
+    torch_optimizer = torch.optim.Adam(torch_model.parameters(), lr=0.001)
+
+    # create
+    input_data = torch.rand(32, 128).cuda()
+
+    # zero-dp forward
+    zero_output = zero_model(input_data.half())
+
+    # torch-ddp forward
+    torch_output = torch_model(input_data)
+    check_equal(zero_output, torch_output)
+
+    # zero-dp backward
+    zero_optimizer.backward(zero_output.mean().float())
+
+    # torch-ddp backward
+    torch_output.mean().backward()
+
+    # check grad
+    for p, z1p in zip(torch_model.parameters(), zero_model.parameters()):
+        check_equal(p.grad, z1p.grad)
+
+    # zero-dp step
+    zero_optimizer.sync_grad()
+    zero_optimizer.step()
+
+    # torch ddp step
+    torch.nn.utils.clip_grad_norm_(torch_model.parameters(), 1.0)
+    torch_optimizer.step()
+
+    # check updated param
+    for p, z1p in zip(torch_model.parameters(), zero_model.parameters()):
+        check_equal(p.data, z1p.data, atol=5e-4)
+
+
+def run_dist(rank, world_size, port):
+    colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost')
+
+    exam_zero_1_2_grad_clip()
+    exam_zero_1_grad_clip()
+
+
+@pytest.mark.dist
+def test_grad_clip():
+    world_size = 2
+    run_func = partial(run_dist, world_size=world_size, port=free_port())
+    mp.spawn(run_func, nprocs=world_size)
+
+
+if __name__ == '__main__':
+    test_grad_clip()