[feature] new zero implementation (#1623)

tests/test_gemini/update/test_chunk_mgrv2.py

@@ -3,7 +3,7 @@ import colossalai
 import pytest
 import torch.multiprocessing as mp
 from functools import partial
-from colossalai.gemini.update import ChunkManagerV2
+from colossalai.gemini.chunk import ChunkManager
 from colossalai.testing import rerun_if_address_is_in_use, parameterize
 from colossalai.utils import free_port
 from colossalai.tensor import ProcessGroup, ColoTensor, ColoTensorSpec
@@ -19,23 +19,17 @@ CPU_MEM = {True: {True: 0, False: 0}, False: {True: 512, False: 0}}
 def exam_chunk_memory(keep_gathered, pin_memory):
     pg = ProcessGroup()
 
-    debug_print([0], "keep_gathered: {}, pin_memory: {}".format(
-        keep_gathered, pin_memory))
+    debug_print([0], "keep_gathered: {}, pin_memory: {}".format(keep_gathered, pin_memory))
 
     params = [ColoTensor(torch.rand(8, 8), spec=ColoTensorSpec(pg)) for _ in range(3)]
-    config = {
-        2: dict(
-            chunk_size=128,
-            keep_gathered=keep_gathered
-        )
-    }
+    config = {2: dict(chunk_size=128, keep_gathered=keep_gathered)}
 
-    chunk_manager = ChunkManagerV2(config, pin_memory=pin_memory)
+    chunk_manager = ChunkManager(config)
     assert chunk_manager.total_mem['cpu'] == 0
     assert chunk_manager.total_mem['cuda'] == 0
 
     for p in params:
-        chunk_manager.append_tensor(p, 'param', 2)
+        chunk_manager.append_tensor(p, 'param', 2, pin_memory=pin_memory)
     chunk_manager.close_all_groups()
     assert chunk_manager.total_mem['cpu'] == CPU_MEM[keep_gathered][pin_memory]
     assert chunk_manager.total_mem['cuda'] == CUDA_MEM_0[keep_gathered]

tests/test_gemini/update/test_chunkv2.py

@@ -9,7 +9,7 @@ from colossalai.utils import free_port, get_current_device
 from colossalai.tensor import ProcessGroup as ColoProcessGroup
 from colossalai.tensor import ColoParameter
 from colossalai.gemini import TensorState
-from colossalai.gemini.update import ChunkV2
+from colossalai.gemini.chunk import Chunk
 
 
 def dist_sum(x):
@@ -38,14 +38,12 @@ def check_euqal(param, param_cp):
 def exam_chunk_basic(init_device, keep_gathered, pin_memory):
     world_size = torch.distributed.get_world_size()
     pg = ColoProcessGroup()
-    my_chunk = ChunkV2(
-        chunk_size=1024,
-        process_group=pg,
-        dtype=torch.float32,
-        init_device=init_device,
-        keep_gathered=keep_gathered,
-        pin_memory=pin_memory
-    )
+    my_chunk = Chunk(chunk_size=1024,
+                     process_group=pg,
+                     dtype=torch.float32,
+                     init_device=init_device,
+                     keep_gathered=keep_gathered,
+                     pin_memory=pin_memory)
 
     param_list = []
     param_cp_list = []

tests/test_gemini/update/test_fwd_bwd.py

@@ -0,0 +1,109 @@
+import pytest
+import colossalai
+import torch
+import torch.multiprocessing as mp
+from colossalai.testing import rerun_if_address_is_in_use
+from colossalai.utils.cuda import get_current_device
+from colossalai.utils import free_port
+from colossalai.utils.model.colo_init_context import ColoInitContext
+
+from functools import partial
+from tests.test_tensor.common_utils import tensor_equal, set_seed, tensor_shard_equal
+from tests.components_to_test.registry import non_distributed_component_funcs
+from torch.nn.parallel import DistributedDataParallel as DDP
+from colossalai.nn.parallel import ZeroDDP
+from colossalai.nn.optimizer import HybridAdam
+from colossalai.zero import ZeroOptimizer
+from colossalai.testing import parameterize
+from colossalai.amp import convert_to_apex_amp
+from colossalai.gemini.gemini_mgr import GeminiManager
+from colossalai.tensor import ColoTensorSpec, ShardSpec, ComputePattern, ComputeSpec, ProcessGroup, ColoTensor
+from tests.test_tensor.common_utils import debug_print
+
+from time import time
+from colossalai.gemini.chunk import search_chunk_configuration, ChunkManager
+
+
+def check_grad(model: ZeroDDP, torch_model: torch.nn.Module):
+    chunk_manager = model.chunk_manager
+    param_list = [p for p in model.parameters()]
+    chunk_list = chunk_manager.get_chunks(param_list)
+    for chunk in chunk_list:
+        chunk_manager.access_chunk(chunk)
+
+    for (p0, p1) in zip(model.parameters(), torch_model.parameters()):
+        assert torch.allclose(p0, p1.grad, atol=1e-3, rtol=1e-5), "{}".format(torch.max(torch.abs(p0 - p1.grad)).item())
+
+
+def run_fwd_bwd(model, criterion, optimizer, input_ids, attn_mask):
+    optimizer.zero_grad()
+    logits = model(input_ids, attn_mask)
+    logits = logits.float()
+    loss = criterion(logits, input_ids)
+    optimizer.backward(loss)
+    return logits
+
+
+@parameterize('placement_policy', ['cuda', 'cpu', 'auto'])
+def exam_gpt_fwd_bwd(placement_policy):
+    set_seed(42)
+    get_components_func = non_distributed_component_funcs.get_callable('gpt2')
+    model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
+
+    with ColoInitContext(device=get_current_device()):
+        model = model_builder()
+
+    torch_model = model_builder().cuda()
+    for torch_p, p in zip(torch_model.parameters(), model.parameters()):
+        torch_p.data.copy_(p.data)
+
+    world_size = torch.distributed.get_world_size()
+    config_dict = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100)
+    config_dict[world_size]['chunk_size'] = 5000
+    config_dict[world_size]['keep_gathered'] = False
+    chunk_manager = ChunkManager(config_dict)
+    gemini_manager = GeminiManager(placement_policy, chunk_manager)
+    model = ZeroDDP(model, gemini_manager, pin_memory=True)
+
+    pg = ProcessGroup()
+    amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False, loss_scale=1)
+    torch_optim = torch.optim.Adam(torch_model.parameters(), lr=1e-3)
+    torch_model, torch_optim = convert_to_apex_amp(torch_model, torch_optim, amp_config)
+    torch_model = DDP(torch_model, device_ids=[pg.rank()], process_group=pg.dp_process_group())
+
+    model.eval()
+    torch_model.eval()
+
+    set_seed(pg.dp_local_rank())
+    for i, (input_ids, attn_mask) in enumerate(train_dataloader):
+        if i > 0:
+            break
+
+        logits = model(input_ids, attn_mask)
+        logits = logits.float()
+        loss = criterion(logits, input_ids)
+        model.backward(loss)
+
+        torch_logits = run_fwd_bwd(torch_model, criterion, torch_optim, input_ids, attn_mask)
+        assert torch.allclose(logits, torch_logits, rtol=0), "{} {} {}".format(
+            torch.max(torch.abs(logits - torch_logits)).item(), logits, torch_logits)
+
+        check_grad(model, torch_model)
+
+
+def run_dist(rank, world_size, port):
+    config = {}
+    colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    exam_gpt_fwd_bwd()
+
+
+@pytest.mark.dist
+@pytest.mark.parametrize('world_size', [1, 4])
+@rerun_if_address_is_in_use()
+def test_gpt(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_gpt(1)

tests/test_gemini/update/test_optim.py

@@ -0,0 +1,118 @@
+import pytest
+import colossalai
+import torch
+import torch.multiprocessing as mp
+import torch.distributed as dist
+from colossalai.testing import rerun_if_address_is_in_use
+from colossalai.utils.cuda import get_current_device
+from colossalai.utils import free_port
+from colossalai.utils.model.colo_init_context import ColoInitContext
+
+from functools import partial
+from tests.test_tensor.common_utils import tensor_equal, set_seed, tensor_shard_equal
+from tests.components_to_test.registry import non_distributed_component_funcs
+from torch.nn.parallel import DistributedDataParallel as DDP
+from colossalai.nn.parallel import ZeroDDP
+from colossalai.nn.optimizer import HybridAdam
+from colossalai.zero import ZeroOptimizer
+from colossalai.testing import parameterize
+from colossalai.amp import convert_to_apex_amp
+from colossalai.gemini.gemini_mgr import GeminiManager
+from tests.test_tensor.common_utils import debug_print
+
+from time import time
+from colossalai.gemini.chunk import search_chunk_configuration, ChunkManager
+
+
+def check_param(model: ZeroDDP, torch_model: torch.nn.Module):
+    zero_dict = model.state_dict(only_rank_0=False)
+    torch_dict = torch_model.state_dict()
+
+    for key, value in torch_dict.items():
+        # key is 'module.model.PARAMETER', so we truncate it
+        key = key[7:]
+        if key == 'model.lm_head.weight':
+            continue
+        assert key in zero_dict, "{} not in ZeRO dictionary.".format(key)
+        temp_zero_value = zero_dict[key].to(device=value.device, dtype=value.dtype)
+        # debug_print([0], "max range: ", key, torch.max(torch.abs(value - temp_zero_value)))
+        assert torch.allclose(value, temp_zero_value, rtol=1e-3, atol=1e-2), "parameter '{}' has problem.".format(key)
+
+
+def run_fwd_bwd(model, criterion, optimizer, input_ids, attn_mask):
+    optimizer.zero_grad()
+    logits = model(input_ids, attn_mask)
+    logits = logits.float()
+    loss = criterion(logits, input_ids)
+    optimizer.backward(loss)
+    return logits
+
+
+@parameterize('placement_policy', ['cuda', 'cpu', 'auto'])
+def exam_gpt_fwd_bwd(placement_policy):
+    set_seed(42)
+    get_components_func = non_distributed_component_funcs.get_callable('gpt2')
+    model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
+
+    with ColoInitContext(device=get_current_device()):
+        model = model_builder()
+
+    torch_model = model_builder().cuda()
+    for torch_p, p in zip(torch_model.parameters(), model.parameters()):
+        torch_p.data.copy_(p.data)
+
+    world_size = torch.distributed.get_world_size()
+    config_dict = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100)
+    config_dict[world_size]['chunk_size'] = 5000
+    config_dict[world_size]['keep_gathered'] = False
+    if placement_policy != 'cuda':
+        init_device = torch.device('cpu')
+    else:
+        init_device = None
+    chunk_manager = ChunkManager(config_dict, init_device=init_device)
+    gemini_manager = GeminiManager(placement_policy, chunk_manager)
+    model = ZeroDDP(model, gemini_manager, pin_memory=True)
+
+    optimizer = HybridAdam(model.parameters(), lr=1e-3)
+    zero_optim = ZeroOptimizer(optimizer, model, initial_scale=2)
+
+    amp_config = dict(opt_level='O2', keep_batchnorm_fp32=False, loss_scale=1)
+    torch_optim = torch.optim.Adam(torch_model.parameters(), lr=1e-3)
+    torch_model, torch_optim = convert_to_apex_amp(torch_model, torch_optim, amp_config)
+    torch_model = DDP(torch_model, device_ids=[dist.get_rank()])
+
+    model.eval()
+    torch_model.eval()
+
+    set_seed(dist.get_rank() * 3 + 128)
+    for i, (input_ids, attn_mask) in enumerate(train_dataloader):
+        if i > 2:
+            break
+
+        zero_logits = run_fwd_bwd(model, criterion, zero_optim, input_ids, attn_mask)
+        torch_logits = run_fwd_bwd(torch_model, criterion, torch_optim, input_ids, attn_mask)
+        assert torch.allclose(zero_logits, torch_logits, rtol=1e-3, atol=1e-2)
+        # debug_print([0], zero_logits, torch_logits)
+
+        zero_optim.step()
+        torch_optim.step()
+
+        check_param(model, torch_model)
+
+
+def run_dist(rank, world_size, port):
+    config = {}
+    colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    exam_gpt_fwd_bwd()
+
+
+@pytest.mark.dist
+@pytest.mark.parametrize('world_size', [1, 4])
+@rerun_if_address_is_in_use()
+def test_gpt(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_gpt(1)

tests/test_gemini/update/test_search.py

@@ -8,7 +8,7 @@ import torch.distributed as dist
 
 import colossalai
 from colossalai.testing import rerun_if_address_is_in_use
-from colossalai.gemini.update import search_chunk_configuration
+from colossalai.gemini.chunk import search_chunk_configuration
 from colossalai.utils import free_port, get_current_device
 from colossalai.utils.model.colo_init_context import ColoInitContext
 from colossalai.tensor import ShardSpec, ComputePattern, ComputeSpec, ProcessGroup
@@ -35,12 +35,11 @@ def exam_search_chunk_size():
     with ColoInitContext(device=get_current_device()):
         model = model_builder()
     init_1d_row_spec(model, pg_tp)
-    config_dict = search_chunk_configuration(
-        model,
-        search_range_mb=1,
-        search_interval_byte=16,
-        min_chunk_size_mb=0,
-        filter_exlarge_params=True)
+    config_dict = search_chunk_configuration(model,
+                                             search_range_mb=1,
+                                             search_interval_byte=16,
+                                             min_chunk_size_mb=0,
+                                             filter_exlarge_params=True)
 
     for key in config_dict:
         chunk_size = config_dict[key]['chunk_size']

tests/test_gemini/update/test_zeroddp_state_dict.py

@@ -0,0 +1,114 @@
+import pytest
+import colossalai
+import torch
+import torch.multiprocessing as mp
+import torch.distributed as dist
+from colossalai.testing import rerun_if_address_is_in_use
+from colossalai.utils.cuda import get_current_device
+from colossalai.utils import free_port
+from colossalai.utils.model.colo_init_context import ColoInitContext
+
+from functools import partial
+from tests.test_tensor.common_utils import set_seed
+from tests.components_to_test.registry import non_distributed_component_funcs
+from colossalai.nn.parallel import ZeroDDP
+from colossalai.zero import ZeroOptimizer
+from colossalai.testing import parameterize
+from colossalai.gemini.gemini_mgr import GeminiManager
+from tests.test_tensor.common_utils import debug_print
+
+from colossalai.gemini.chunk import search_chunk_configuration, ChunkManager
+
+
+@parameterize('placement_policy', ['cuda', 'cpu', 'auto'])
+@parameterize('keep_gathered', [True, False])
+def exam_state_dict(placement_policy, keep_gathered):
+    set_seed(431)
+    get_components_func = non_distributed_component_funcs.get_callable('gpt2')
+    model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
+
+    with ColoInitContext(device=get_current_device()):
+        model = model_builder()
+
+    torch_model = model_builder()
+    for torch_p, p in zip(torch_model.parameters(), model.parameters()):
+        torch_p.data.copy_(p.data)
+
+    world_size = torch.distributed.get_world_size()
+    config_dict = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100)
+    config_dict[world_size]['chunk_size'] = 5000
+    config_dict[world_size]['keep_gathered'] = keep_gathered
+    chunk_manager = ChunkManager(config_dict)
+    gemini_manager = GeminiManager(placement_policy, chunk_manager)
+    model = ZeroDDP(model, gemini_manager, pin_memory=True)
+    model.train()
+
+    zero_dict = model.state_dict(only_rank_0=False)
+    torch_dict = torch_model.state_dict()
+
+    for key, value in torch_dict.items():
+        if key == 'model.lm_head.weight':
+            continue
+        assert key in zero_dict, "{} not in ZeRO dictionary.".format(key)
+        temp_zero_value = zero_dict[key].to(device=value.device, dtype=value.dtype)
+        assert torch.equal(value, temp_zero_value), "parameter '{}' has problem.".format(key)
+
+
+@parameterize('placement_policy', ['cuda', 'cpu', 'auto'])
+@parameterize('keep_gathered', [True, False])
+def exam_load_state_dict(placement_policy, keep_gathered):
+    set_seed(431)
+    get_components_func = non_distributed_component_funcs.get_callable('gpt2')
+    model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
+
+    with ColoInitContext(device=get_current_device()):
+        model = model_builder()
+
+    set_seed(451)
+    torch_model = model_builder()    # get a different model
+
+    world_size = torch.distributed.get_world_size()
+    config_dict = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100)
+    config_dict[world_size]['chunk_size'] = 5000
+    config_dict[world_size]['keep_gathered'] = keep_gathered
+
+    if placement_policy != 'cuda':
+        init_device = torch.device('cpu')
+    else:
+        init_device = None
+    chunk_manager = ChunkManager(config_dict, init_device=init_device)
+    gemini_manager = GeminiManager(placement_policy, chunk_manager)
+    model = ZeroDDP(model, gemini_manager, pin_memory=True)
+
+    optimizer = torch.optim.Adam(model.parameters())
+    optim = ZeroOptimizer(optimizer, model)    # initialize the link between chunk16 and chunk32
+
+    torch_dict = torch_model.state_dict()
+    model.load_state_dict(torch_dict, strict=False)
+    zero_dict = model.state_dict(only_rank_0=False)
+
+    for key, value in torch_dict.items():
+        if key == 'model.lm_head.weight':
+            continue
+        assert key in zero_dict, "{} not in ZeRO dictionary.".format(key)
+        temp_zero_value = zero_dict[key].to(device=value.device, dtype=value.dtype)
+        assert torch.equal(value, temp_zero_value), "parameter '{}' has problem.".format(key)
+
+
+def run_dist(rank, world_size, port):
+    config = {}
+    colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    exam_state_dict()
+    exam_load_state_dict()
+
+
+@pytest.mark.dist
+@pytest.mark.parametrize('world_size', [1, 4])
+@rerun_if_address_is_in_use()
+def test_zero_ddp(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_zero_ddp(1)

tests/test_gemini/update/test_zerooptim_state_dict.py

@@ -0,0 +1,81 @@
+import pytest
+import colossalai
+import torch
+import torch.multiprocessing as mp
+import torch.distributed as dist
+from colossalai.testing import rerun_if_address_is_in_use
+from colossalai.utils.cuda import get_current_device
+from colossalai.utils import free_port
+from colossalai.utils.model.colo_init_context import ColoInitContext
+
+from functools import partial
+from tests.test_tensor.common_utils import set_seed
+from tests.components_to_test.registry import non_distributed_component_funcs
+from colossalai.nn.parallel import ZeroDDP
+from colossalai.zero import ZeroOptimizer
+from colossalai.testing import parameterize
+from colossalai.gemini.gemini_mgr import GeminiManager
+from tests.test_tensor.common_utils import debug_print
+
+from colossalai.gemini.chunk import search_chunk_configuration, ChunkManager
+
+
+@parameterize('placement_policy', ['cuda', 'cpu', 'auto'])
+@parameterize('keep_gathered', [True, False])
+def exam_zero_optim_state_dict(placement_policy, keep_gathered):
+    set_seed(431)
+    get_components_func = non_distributed_component_funcs.get_callable('gpt2')
+    model_builder, train_dataloader, test_dataloader, optimizer_class, criterion = get_components_func()
+
+    with ColoInitContext(device=get_current_device()):
+        model = model_builder()
+
+    set_seed(451)
+    torch_model = model_builder()    # get a different model
+
+    world_size = torch.distributed.get_world_size()
+    config_dict = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100)
+    config_dict[world_size]['chunk_size'] = 5000
+    config_dict[world_size]['keep_gathered'] = keep_gathered
+
+    if placement_policy != 'cuda':
+        init_device = torch.device('cpu')
+    else:
+        init_device = None
+    chunk_manager = ChunkManager(config_dict, init_device=init_device)
+    gemini_manager = GeminiManager(placement_policy, chunk_manager)
+    model = ZeroDDP(model, gemini_manager, pin_memory=True)
+
+    optimizer = torch.optim.Adam(model.parameters())
+    optim = ZeroOptimizer(optimizer, model)    # initialize the link between chunk16 and chunk32
+
+    set_seed(dist.get_rank() * 3 + 128)
+    for i, (input_ids, attn_mask) in enumerate(train_dataloader):
+        if i > 0:
+            break
+        optim.zero_grad()
+        logits = model(input_ids, attn_mask)
+        logits = logits.float()
+        loss = criterion(logits, input_ids)
+        optim.backward(loss)
+
+    optim_state_dict = optim.state_dict()
+    optim.load_state_dict(optim_state_dict)
+
+
+def run_dist(rank, world_size, port):
+    config = {}
+    colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    exam_zero_optim_state_dict()
+
+
+@pytest.mark.dist
+@pytest.mark.parametrize('world_size', [1, 4])
+@rerun_if_address_is_in_use()
+def test_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_zero_optim(1)