[Gemini] add unitests to check gemini correctness (#2015)

tests/components_to_test/__init__.py

 from . import bert, gpt, inline_op_model, nested_model, no_leaf_module, repeated_computed_layer, resnet, simple_net
+from .utils import run_fwd_bwd

tests/components_to_test/gpt.py

 import torch
 import torch.nn as nn
-from .registry import non_distributed_component_funcs
 from transformers import GPT2Config, GPT2LMHeadModel
-from .utils.dummy_data_generator import DummyDataGenerator
+
 from colossalai.utils.cuda import get_current_device
 
+from .registry import non_distributed_component_funcs
+from .utils.dummy_data_generator import DummyDataGenerator
+
 
 class DummyDataLoader(DummyDataGenerator):
     vocab_size = 128
@@ -15,8 +17,7 @@ class DummyDataLoader(DummyDataGenerator):
         input_ids = torch.randint(0,
                                   DummyDataLoader.vocab_size, (DummyDataLoader.batch_size, DummyDataLoader.seq_len),
                                   device=get_current_device())
-        attention_mask = torch.ones_like(input_ids)
-        return input_ids, attention_mask
+        return input_ids, input_ids
 
 
 class GPTLMModel(nn.Module):
@@ -43,8 +44,9 @@ class GPTLMModel(nn.Module):
         if checkpoint:
             self.model.gradient_checkpointing_enable()
 
-    def forward(self, input_ids, attention_mask):
+    def forward(self, input_ids):
         # Only return lm_logits
+        attention_mask = torch.ones_like(input_ids)
         return self.model(input_ids=input_ids, attention_mask=attention_mask, use_cache=not self.checkpoint)[0]
 
 

tests/components_to_test/inline_op_model.py

@@ -38,7 +38,7 @@ class DummyDataLoader(DummyDataGenerator):
         return data, label
 
 
-@non_distributed_component_funcs.register(name='inline_op_module')
+@non_distributed_component_funcs.register(name='inline_op_model')
 def get_training_components():
 
     def model_builder(checkpoint=True):

tests/components_to_test/utils/__init__.py

-from .dummy_data_generator import DummyDataGenerator
+from .dummy_data_generator import DummyDataGenerator
+from .executor import run_fwd_bwd

tests/components_to_test/utils/executor.py

+import torch
+
+
+def run_fwd_bwd(model, data, label, criterion, enable_autocast=False, use_init_ctx=False):
+    with torch.cuda.amp.autocast(enabled=enable_autocast):
+        if criterion:
+            y = model(data)
+            loss = criterion(y, label)
+        else:
+            loss = model(data, label)
+        loss = loss.float()
+    if use_init_ctx:
+        model.backward(loss)
+    else:
+        loss.backward()

tests/test_gemini/test_gemini_train.py

+from functools import partial
+
+import pytest
+import torch
+import torch.multiprocessing as mp
+
+import colossalai
+from colossalai.logging import disable_existing_loggers, get_dist_logger
+from colossalai.nn.parallel import ZeroDDP
+from colossalai.testing import rerun_if_address_is_in_use
+from colossalai.utils import free_port, get_current_device
+from colossalai.utils.model.colo_init_context import ColoInitContext
+from tests.components_to_test import run_fwd_bwd
+from tests.components_to_test.registry import non_distributed_component_funcs
+
+
+def run_gemini_fwd_bwd(rank, world_size, port, model_name: str, iter_num=2):
+    PLACEMENT_POLICY = 'cuda'
+    disable_existing_loggers()
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+
+    get_components_func = non_distributed_component_funcs.get_callable(model_name)
+    model_builder, train_dataloader, _, _, criterion = get_components_func()
+
+    # build torch model
+    model_torch = model_builder(checkpoint=False).cuda()
+
+    for i, (data, label) in enumerate(train_dataloader):
+        if i >= iter_num:
+            break
+        run_fwd_bwd(model_torch, data.cuda(), label.cuda(), criterion, False, use_init_ctx=False)
+
+    # build CAI model
+    with ColoInitContext(device=get_current_device()):
+        model = model_builder(checkpoint=False)
+
+    from colossalai.gemini import ChunkManager, GeminiManager, search_chunk_configuration
+    config_dict, _ = search_chunk_configuration(model, search_range_mb=1, search_interval_byte=100)
+    chunk_manager = ChunkManager(config_dict, init_device=GeminiManager.get_default_device(PLACEMENT_POLICY))
+    gemini_manager = GeminiManager(PLACEMENT_POLICY, chunk_manager)
+    model = ZeroDDP(model, gemini_manager)
+
+    model.train()
+
+    for i, (data, label) in enumerate(train_dataloader):
+        if i >= iter_num:
+            break
+        run_fwd_bwd(model, data.cuda(), label.cuda(), criterion, False, use_init_ctx=True)
+
+    for p1, p2 in zip(model.parameters(), model_torch.parameters()):
+        torch.allclose(p1.to(torch.float), p2.to(torch.float))
+    print(f'pass test {model_name}')
+
+
+@pytest.mark.parametrize("model_name", ['bert'])
+@rerun_if_address_is_in_use()
+def test_gemini_train(model_name, iter_num=2):
+    run_func = partial(run_gemini_fwd_bwd, world_size=1, port=free_port(), model_name=model_name, iter_num=iter_num)
+    mp.spawn(run_func, nprocs=1)
+
+
+if __name__ == '__main__':
+    # for model_name in ["bert", "resnet18", "inline_op_model"]:
+    # bert, gpt, inline_op_model, nested_model, no_leaf_module,
+    # repeated_computed_layer, resnet, simple_net
+    for model_name in ["nested_model", "no_leaf_module"]:
+        test_gemini_train(model_name=model_name, iter_num=4)

tests/test_gemini/test_mem_tracer.py

@@ -8,20 +8,10 @@ import colossalai
 from colossalai.gemini.memory_tracer import MemtracerWrapper
 from colossalai.testing import rerun_if_address_is_in_use
 from colossalai.utils import free_port
+from tests.components_to_test import run_fwd_bwd
 from tests.components_to_test.registry import non_distributed_component_funcs
 
 
-def run_fwd_bwd(model, data, label, criterion, enable_autocast=False):
-    with torch.cuda.amp.autocast(enabled=enable_autocast):
-        if criterion:
-            y = model(data)
-            loss = criterion(y, label)
-        else:
-            loss = model(data, label)
-        loss = loss.float()
-    model.backward(loss)
-
-
 def run_tracer(rank, world_size, port, use_grad_check=True):
     colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
     test_models = ['repeated_computed_layers', 'resnet18', 'no_leaf_module', 'bert']
@@ -43,7 +33,7 @@ def run_tracer(rank, world_size, port, use_grad_check=True):
             data = data.cuda()
             label = label.cuda()
 
-            run_fwd_bwd(model, data, label, criterion, False)
+            run_fwd_bwd(model, data, label, criterion, False, use_init_ctx=False)
 
         model._ophook_list[0].print_non_model_data()
 
@@ -58,4 +48,4 @@ def test_tracer(world_size, use_grad_check):
 
 
 if __name__ == '__main__':
-    test_tracer(1)
+    test_tracer(1, True)

tests/test_gemini/test_param_op.py

@@ -50,7 +50,7 @@ def run_model(model, inputs, label, criterion, use_param_hook=False):
 
 
 def test_base_param_hook():
-    test_models = ['repeated_computed_layers', 'resnet18', 'no_leaf_module', 'inline_op_module']
+    test_models = ['repeated_computed_layers', 'resnet18', 'no_leaf_module', 'inline_op_model']
     # test_models = ['bert']
 
     for model_name in test_models:

tests/test_gemini/update/test_fwd_bwd.py

@@ -30,9 +30,9 @@ def check_grad(model: ZeroDDP, torch_model: torch.nn.Module):
         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):
+def run_fwd_bwd(model, criterion, optimizer, input_ids):
     optimizer.zero_grad()
-    logits = model(input_ids, attn_mask)
+    logits = model(input_ids)
     logits = logits.float()
     loss = criterion(logits, input_ids)
     optimizer.backward(loss)
@@ -71,16 +71,16 @@ def exam_gpt_fwd_bwd(placement_policy, keep_gather):
     torch_model.eval()
 
     set_seed(pg.dp_local_rank())
-    for i, (input_ids, attn_mask) in enumerate(train_dataloader):
+    for i, (input_ids, label) in enumerate(train_dataloader):
         if i > 0:
             break
 
-        logits = model(input_ids, attn_mask)
+        logits = model(input_ids)
         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)
+        torch_logits = run_fwd_bwd(torch_model, criterion, torch_optim, input_ids)
         assert torch.allclose(logits, torch_logits, rtol=0), "{} {} {}".format(
             torch.max(torch.abs(logits - torch_logits)).item(), logits, torch_logits)
 

tests/test_gemini/update/test_optim.py

@@ -37,9 +37,9 @@ def check_param(model: ZeroDDP, torch_model: torch.nn.Module):
         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):
+def run_fwd_bwd(model, criterion, optimizer, input_ids):
     optimizer.zero_grad()
-    logits = model(input_ids, attn_mask)
+    logits = model(input_ids)
     logits = logits.float()
     loss = criterion(logits, input_ids)
     optimizer.backward(loss)
@@ -83,12 +83,12 @@ def exam_gpt_fwd_bwd(placement_policy):
     torch_model.eval()
 
     set_seed(dist.get_rank() * 3 + 128)
-    for i, (input_ids, attn_mask) in enumerate(train_dataloader):
+    for i, (input_ids, label) 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)
+        zero_logits = run_fwd_bwd(model, criterion, zero_optim, input_ids)
+        torch_logits = run_fwd_bwd(torch_model, criterion, torch_optim, input_ids)
         assert torch.allclose(zero_logits, torch_logits, rtol=1e-3, atol=1e-2)
         # debug_print([0], zero_logits, torch_logits)
 
@@ -127,12 +127,12 @@ def exam_tiny_example(placement_policy):
     torch_model.eval()
 
     set_seed(dist.get_rank() * 3 + 128)
-    for i, (input_ids, attn_mask) in enumerate(train_dataloader):
+    for i, (input_ids, label) 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)
+        zero_logits = run_fwd_bwd(model, criterion, zero_optim, input_ids)
+        torch_logits = run_fwd_bwd(torch_model, criterion, torch_optim, input_ids)
         assert torch.allclose(zero_logits, torch_logits, rtol=1e-3, atol=1e-2)
         # debug_print([0], zero_logits, torch_logits)
 

tests/test_gemini/update/test_zerooptim_state_dict.py

@@ -50,11 +50,11 @@ def exam_zero_optim_state_dict(placement_policy, keep_gathered):
 
     set_seed(dist.get_rank() * 3 + 128)
     model.train()
-    for i, (input_ids, attn_mask) in enumerate(train_dataloader):
+    for i, (input_ids, label) in enumerate(train_dataloader):
         if i > 0:
             break
         optim.zero_grad()
-        logits = model(input_ids, attn_mask)
+        logits = model(input_ids)
         logits = logits.float()
         loss = criterion(logits, input_ids)
         optim.backward(loss)

tests/test_tensor/model/test_gpt2.py

-import pytest
-
 from functools import partial
-from tests.test_tensor.common_utils import tensor_equal, tensor_shard_equal, set_seed
 
+import pytest
 import torch
-from torch.nn.parallel import DistributedDataParallel as DDP
 import torch.multiprocessing as mp
+from torch.nn.parallel import DistributedDataParallel as DDP
 
 import colossalai
+from colossalai.nn.parallel.data_parallel import ColoDDP
+from colossalai.tensor import ColoTensor, ColoTensorSpec, ComputePattern, ComputeSpec, ProcessGroup, ShardSpec
 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.cuda import get_current_device
 from colossalai.utils.model.colo_init_context import ColoInitContext
-from colossalai.tensor import ShardSpec, ComputePattern, ComputeSpec, ProcessGroup, ColoTensor, ColoTensorSpec
-from colossalai.nn.parallel.data_parallel import ColoDDP
 from tests.components_to_test.registry import non_distributed_component_funcs
-from tests.test_tensor.common_utils import split_param_col_tp1d, split_param_row_tp1d, debug_print
+from tests.test_tensor.common_utils import (
+    debug_print,
+    set_seed,
+    split_param_col_tp1d,
+    split_param_row_tp1d,
+    tensor_equal,
+    tensor_shard_equal,
+)
 
 
 def init_1d_row_spec(model, pg: ProcessGroup):
@@ -107,10 +112,10 @@ def run_gpt(init_spec_func, use_ddp):
     torch_model.eval()
     set_seed(pg.dp_local_rank())
     torch.distributed.barrier()
-    for i, (input_ids, attn_mask) in enumerate(train_dataloader):
+    for i, (input_ids, label) in enumerate(train_dataloader):
         colo_input = ColoTensor.from_torch_tensor(input_ids, ColoTensorSpec(pg))
-        logits = model(colo_input, attn_mask)
-        torch_logits = torch_model(input_ids, attn_mask)
+        logits = model(colo_input)
+        torch_logits = torch_model(input_ids)
         assert tensor_equal(torch_logits, logits), f"{torch_logits - logits}"
         loss = criterion(logits, input_ids)
         torch_loss = criterion(torch_logits, input_ids)

tests/test_tensor/test_tp_with_zero.py

@@ -36,9 +36,9 @@ def check_param(model: ZeroDDP, torch_model: torch.nn.Module, pg: ProcessGroup):
             "parameter '{}' has problem.".format(key)
 
 
-def run_fwd_bwd(model, criterion, optimizer, input_ids, attn_mask):
+def run_fwd_bwd(model, criterion, optimizer, input_ids):
     optimizer.zero_grad()
-    logits = model(input_ids, attn_mask)
+    logits = model(input_ids)
     logits = logits.float()
     loss = criterion(logits, input_ids)
     optimizer.backward(loss)
@@ -117,12 +117,12 @@ def run_gpt(placement_policy, tp_init_spec_func=None):
     torch_model.eval()
 
     set_seed(pg.dp_local_rank())
-    for i, (input_ids, attn_mask) in enumerate(train_dataloader):
+    for i, (input_ids, label) in enumerate(train_dataloader):
         if i > 2:
             break
         input_ids_colo = ColoTensor.from_torch_tensor(input_ids, ColoTensorSpec(pg))
-        zero_logits = run_fwd_bwd(model, criterion, zero_optim, input_ids_colo, attn_mask)
-        torch_logits = run_fwd_bwd(torch_model, criterion, torch_optim, input_ids, attn_mask)
+        zero_logits = run_fwd_bwd(model, criterion, zero_optim, input_ids_colo)
+        torch_logits = run_fwd_bwd(torch_model, criterion, torch_optim, input_ids)
         assert torch.allclose(zero_logits, torch_logits, rtol=1e-3, atol=1e-2)
 
         zero_optim.step()