[checkpoint] use gather_tensor in checkpoint and update its unit test

colossalai/tensor/colo_tensor.py

@@ -262,7 +262,7 @@ def view_global(self, *args) -> 'ColoTensor':
         replicated_t = self.redistribute(dist_spec=ReplicaSpec())
         return replicated_t.view(*args)
 
-    def size_global(self, args: Optional[int] = None):
+    def size_global(self, args: Optional[int] = None) -> torch.Size:
         """override the torch buildin size()
         the shape passed in must be in a replicate placement.
         Returns:

colossalai/tensor/process_group.py

@@ -141,9 +141,18 @@ def __eq__(self, obj: 'ProcessGroup') -> bool:
     def rank(self):
         return self._rank
 
+    def ranks_in_group(self):
+        return self._rank_list
+
     def world_size(self):
         return self._world_size
 
+    def tp_rank_list(self):
+        return self._tp_rank_list
+
+    def dp_rank_list(self):
+        return self._dp_rank_list
+
     def tp_local_rank(self):
         return self._rank % self._tp_degree
 

colossalai/utils/checkpoint/module_checkpoint.py

@@ -1,8 +1,8 @@
 import torch
 import torch.distributed as dist
-from colossalai.tensor import ColoTensor, DistSpecManager
+from colossalai.tensor import ColoTensor
 from colossalai.nn.optimizer import ColossalaiOptimizer
-from copy import copy
+from colossalai.utils.checkpoint.utils import gather_tensor, scatter_tensor
 from typing import Optional
 
 
@@ -22,37 +22,52 @@ def save_checkpoint(dire: str,
         optimizer (ColossalaiOptimizer, optional): optimizers. Defaults to None.
         lr_scheduler (torch.optim.lr_scheduler._LRScheduler, optional): lr schedule. Defaults to None.
     """
-
-    mapping = dict()
-    new_dict = dict()
-
+    rank = dist.get_rank()
+    model_state = model.state_dict()
     # save the dist context about the tensors in a new dict, while still maintain the original dict.
-    for k, v in model.state_dict().items():
+    for k, v in model_state.items():
         if isinstance(v, ColoTensor):
-            mapping[k] = (v.dist_spec, v.compute_spec)
-            new_dict[k] = v.to_replicate().detach()
-        else:
-            new_dict[k] = v
-    if dist.get_rank() == 0:
-        for k, v in new_dict.items():
+            gather_tensor(v)  # gather shared tensors to rank0
+            # don't recover tensors in rank0, since the dict is only a copy of model
+
+    if rank == 0:
+        # sanity check
+        for k, v in model_state.items():
             if isinstance(v, ColoTensor):
+                assert v.save_ready
                 assert v.is_replicate()
+                delattr(v, 'save_ready')
+        # model saving
+        save_state = {'epoch': epoch, 'model': model_state}
+        torch.save(save_state, dire + '/epoch_{}_model.pth'.format(epoch))
 
-        model_state = {'epoch': epoch, 'model': new_dict}
-        torch.save(model_state, dire + '/epoch_{}_model.pth'.format(epoch))
-
-    # delete the new dict
-    del new_dict
+    # delete old dicts
+    del model_state
+    # synchronize all the processes
+    dist.barrier()
 
-    optim_state_copy = copy(optimizer.state_dict())
-    for k, v in optim_state_copy['state'].items():
+    mapping = dict()
+    optim_state = optimizer.state_dict()
+    for k, v in optim_state['state'].items():
         for n, t in v.items():
             if isinstance(t, ColoTensor):
-                t.to_replicate_()
-    if dist.get_rank() == 0:
-        model_state = {'epoch': epoch, 'optim': optim_state_copy}
-        torch.save(model_state, dire + '/epoch_{}_optim.pth'.format(epoch))
-    del optim_state_copy
+                mapping[(k, n)] = t.dist_spec
+                gather_tensor(t)
+
+    if rank == 0:
+        save_state = {'epoch': epoch, 'optim': optim_state}
+        torch.save(save_state, dire + '/epoch_{}_optim.pth'.format(epoch))
+        # recover colo tensors in rank0
+        for k, v in optimizer.state_dict()['state'].items():
+            for n, t in v.items():
+                if isinstance(t, ColoTensor):
+                    assert hasattr(t, 'save_ready')
+                    t.set_dist_spec(mapping[(k, n)])
+                    delattr(t, 'save_ready')
+
+    del optim_state
+    del mapping
+    dist.barrier()
 
 
 def load_checkpoint(dire,
@@ -72,39 +87,42 @@ def load_checkpoint(dire,
         optimizer (ColossalaiOptimizer, optional): _description_. Defaults to None.
         lr_scheduler (torch.optim.lr_scheduler._LRScheduler, optional): _description_. Defaults to None.
     """
-
+    rank = dist.get_rank()
     mapping = dict()
-    for k, v in model.state_dict().items():
-        if isinstance(v, ColoTensor):
-            mapping[k] = (v.dist_spec, v.compute_spec)
-            v.to_replicate_()
-
-    model_state = torch.load(dire + '/epoch_{}_model.pth'.format(epoch))
-    model.load_state_dict(model_state['model'])
-
-    # reset tensors to original dist spec.
-    with DistSpecManager.no_grad():
-        for k, v in model.state_dict().items():
-            if isinstance(v, ColoTensor):
-                v.set_tensor_spec(*mapping[k])
-
+    for n, p in model.named_parameters():
+        if isinstance(p, ColoTensor):
+            mapping[n] = p.dist_spec
+            gather_tensor(p)
+
+    if rank == 0:
+        load_state = torch.load(dire + '/epoch_{}_model.pth'.format(epoch))
+        model.load_state_dict(load_state['model'])
+    dist.barrier()
+
+    # scatter loaded parameters
+    for n, p in model.named_parameters():
+        if isinstance(p, ColoTensor):
+            scatter_tensor(p, mapping[n])
+            if rank == 0:
+                assert hasattr(p, 'save_ready')
+                delattr(p, 'save_ready')
     del mapping
-    mapping = dict()
 
+    mapping = dict()
     for k, v in optimizer.state_dict()['state'].items():
         for n, t in v.items():
             if isinstance(t, ColoTensor):
-                mapping[(k, n)] = (t.dist_spec, t.compute_spec)
-                t.to_replicate_()
+                mapping[(k, n)] = t.dist_spec
+                gather_tensor(t)
 
-    colo_checkpoint = torch.load(dire + '/epoch_{}_optim.pth'.format(epoch))
-    optimizer.load_state_dict(colo_checkpoint['optim'])
+    if rank == 0:
+        colo_checkpoint = torch.load(dire + '/epoch_{}_optim.pth'.format(epoch))
+        optimizer.load_state_dict(colo_checkpoint['optim'])
+    dist.barrier()
 
     for k, v in optimizer.state_dict()['state'].items():
         for n, t in v.items():
             if isinstance(t, ColoTensor):
-                # skip key not in mapping.
-                # For Adam, if it dose not execute step() once, there will be not exp_avg and exp_avg_sq in optimizer
-                if (k, n) not in mapping:
-                    continue
-                t.set_tensor_spec(*mapping[(k, n)])
+                scatter_tensor(t, mapping[(k, n)])
+
+    del mapping

colossalai/utils/checkpoint/utils.py

@@ -0,0 +1,50 @@
+import torch
+import torch.distributed as dist
+from colossalai.tensor import ColoTensor, ColoTensorSpec
+from colossalai.tensor.distspec import _DistSpec
+
+
+def gather_tensor(colo_tensor: ColoTensor) -> None:
+    """Make colo_tensor replicated when the rank is 0
+    """
+    if not colo_tensor.is_replicate():
+        pg = colo_tensor.get_process_group()
+        # for the group which contains rank 0
+        if pg.tp_rank_list()[0] == 0:
+            old_dist_spec = colo_tensor.dist_spec
+            colo_tensor.to_replicate_()
+            if dist.get_rank() != 0:
+                colo_tensor.set_dist_spec(old_dist_spec)
+
+        # synchronize all processes for unexpected problems
+        dist.barrier()
+
+    if dist.get_rank() == 0:
+        setattr(colo_tensor, 'save_ready', True)  # set saving signitrue
+
+
+def scatter_tensor(colo_tensor: ColoTensor, dist_spec: _DistSpec) -> None:
+    """Reversal operation of `gather_tensor`.
+    """
+    if dist_spec.placement == 'r':
+        dist.broadcast(colo_tensor.data, 0)
+    else:
+        global_size = colo_tensor.size_global()
+
+        if dist.get_rank() == 0:
+            entire_data = colo_tensor.data
+        else:
+            entire_data = torch.empty(global_size, device=colo_tensor.device)
+        dist.broadcast(entire_data, 0)
+
+        if dist.get_rank() == 0:
+            colo_tensor.set_dist_spec(dist_spec)
+        else:
+            rep_tensor = ColoTensor(entire_data, ColoTensorSpec(
+                pg=colo_tensor.get_process_group(),
+                compute_attr=colo_tensor.compute_spec))
+            rep_tensor.set_dist_spec(dist_spec)
+            with torch.no_grad():
+                colo_tensor.data.copy_(rep_tensor.data)
+        # synchronize all processes for unexpected problems
+        dist.barrier()

tests/test_utils/test_colo_checkpoint.py

@@ -1,6 +1,7 @@
 import os, shutil
 import torch
 import pytest
+from copy import deepcopy
 from functools import partial
 
 import torch.multiprocessing as mp
@@ -15,8 +16,7 @@
 from colossalai.utils.cuda import get_current_device
 from colossalai.utils import free_port
 from colossalai.utils.model.colo_init_context import ColoInitContext
-from colossalai.tensor import ComputePattern, ComputeSpec, ColoTensor, ShardSpec, ProcessGroup, DistSpecManager, ReplicaSpec
-from colossalai.nn.parallel.data_parallel import ColoDDP
+from colossalai.tensor import ComputePattern, ComputeSpec, ColoTensor, ShardSpec, ProcessGroup
 from colossalai.utils.checkpoint import save_checkpoint, load_checkpoint
 from colossalai.nn.optimizer import ColossalaiOptimizer
 
@@ -63,8 +63,8 @@ def init_1d_row_for_linear_weight_spec(model, pg: ProcessGroup):
 
 
 def check_param_equal(model, torch_model):
-    for p, torch_p in zip(model.parameters(), torch_model.parameters()):
-        assert torch.allclose(torch_p, p, rtol=1e-3, atol=1e-1)
+    for (n, p), (tn, tp) in zip(model.named_parameters(), torch_model.named_parameters()):
+        assert torch.all(p.data == tp.data), "{} went wrong.\n {} vs {}\n{}".format(n, p, tp, p.shape)
 
 
 def remove(path):
@@ -84,9 +84,13 @@ def compare_optims(optim1, optim2):
         if k not in state2:
             continue
         p2 = state2[k]
-        if isinstance(p1, ColoTensor):
-            assert isinstance(p2, ColoTensor)
-            assert torch.allclose(p1.to_replicate_(), p2.to_replicate_(), rtol=1e-3, atol=1e-1)
+        for n, t1 in p1.items():
+            if n not in p2:
+                continue
+            t2 = p2[n]
+            if isinstance(t1, ColoTensor):
+                assert isinstance(t2, ColoTensor)
+                assert torch.allclose(t1, t2, rtol=0, atol=0)
 
 
 def _run_checkpoint(model_name, init_spec_func, use_ddp, use_mp_reload, test_scheduler, pg):
@@ -99,7 +103,6 @@ def _run_checkpoint(model_name, init_spec_func, use_ddp, use_mp_reload, test_sch
     # set_seed(1)
     with ColoInitContext(device=get_current_device()):
         model = model_builder(checkpoint=True)
-        model_reload = model_builder(checkpoint=True)
 
     if use_mp_reload:
         if 'bert' == model_name:
@@ -119,25 +122,26 @@ def _run_checkpoint(model_name, init_spec_func, use_ddp, use_mp_reload, test_sch
                 elif 'token_type_embeddings' in name and 'weight' in name:
                     init_1d_col_embedding(p, pg)
                 elif p.process_group.tp_world_size() == 1:
-                    p.redistribute(ReplicaSpec(), pg)
+                    p.set_process_group(pg)
         elif "simple_net" == model_name:
             init_spec_func(model, pg)
 
+    model_reload = deepcopy(model)
     model = model.cuda()
-    model.train()
+    model.eval()
 
     model_reload = model_reload.cuda()
-    model_reload.train()
+    model_reload.eval()
 
     opt_class = torch.optim.Adam
     colo_optimizer = ColossalaiOptimizer(opt_class(model.parameters(), lr=0.1))
     colo_optimizer_reload = ColossalaiOptimizer(opt_class(model_reload.parameters(), lr=0.1))
-    run_reload = False
 
     for i, (data, label) in enumerate(train_dataloader):
 
         # Zero grad
         colo_optimizer.zero_grad()
+        colo_optimizer_reload.zero_grad()
 
         data = data.to(get_current_device())
         label = label.to(get_current_device())
@@ -155,43 +159,33 @@ def _run_checkpoint(model_name, init_spec_func, use_ddp, use_mp_reload, test_sch
         loss.backward()
         loss_reload.backward()
 
-        if run_reload:
-            colo_optimizer_reload.zero_grad()
-            if criterion:
-                output_reload = model_reload(data)
-                loss_reload = criterion(output_reload, label)
-            else:
-                loss_reload = model_reload(data, label)
-            loss_reload.backward()
-            colo_optimizer_reload.step()
+        colo_optimizer.step()
+        colo_optimizer_reload.step()
 
         if i > 2:
             break
 
     if not os.path.isdir('./checkpoint') and rank == 0:
         os.mkdir('./checkpoint')
-    save_checkpoint('./checkpoint', 0, model, colo_optimizer, None)
-    dist.barrier()
-    load_checkpoint('./checkpoint', 0, model_reload, colo_optimizer_reload, None)
     dist.barrier()
 
-    # Since model is sharded, we merge them before param checking.
-    for p in model.parameters():
-        p.to_replicate_()
-
-    for p in model_reload.parameters():
-        p.to_replicate_()
+    save_checkpoint('./checkpoint', 0, model, colo_optimizer, None)
+    load_checkpoint('./checkpoint', 0, model_reload, colo_optimizer_reload, None)
 
     check_param_equal(model, model_reload)
     compare_optims(colo_optimizer, colo_optimizer_reload)
+
     if rank == 0:
         remove('./checkpoint')
+    dist.barrier()
 
 
 def run_dist(rank, world_size, port, use_ddp, use_mp_reload, test_scheduler):
     colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
     pg = ProcessGroup(tp_degree=world_size)
-    for model_name in ['simple_net', 'bert']:
+    # TODO(haichen) add BERT in the test
+    # the data loader of BERT is in DDP mode, causing the input data is not replicated in the TP context
+    for model_name in ['simple_net']:
         _run_checkpoint(model_name,
                         init_1d_row_for_linear_weight_spec,
                         use_ddp,