Support TP-compatible Torch AMP and Update trainer API (#27)

* Add gradient accumulation, fix lr scheduler

* fix FP16 optimizer and adapted torch amp with tensor parallel (#18)

* fixed bugs in compatibility between torch amp and tensor parallel and performed some minor fixes

* fixed trainer

* Revert "fixed trainer"

This reverts commit 2e0b0b7699.

* improved consistency between trainer, engine and schedule (#23)

Co-authored-by: 1SAA <c2h214748@gmail.com>

Co-authored-by: 1SAA <c2h214748@gmail.com>
Co-authored-by: ver217 <lhx0217@gmail.com>

tests/test_engine/configs/non_pipeline_resnet.py

@@ -38,5 +38,3 @@ optimizer = dict(type='Adam', lr=0.001)
 
 loss = dict(type='CrossEntropyLoss')
 
-# set_device_func = lambda global_rank, world_size: global_rank % 4
-seed = 1024

tests/test_engine/configs/non_pipeline_resnet_apex_amp.py

@@ -40,6 +40,3 @@ optimizer = dict(type='Adam', lr=0.001)
 
 loss = dict(type='CrossEntropyLoss')
 fp16 = dict(mode=AMP_TYPE.APEX)
-
-# set_device_func = lambda global_rank, world_size: global_rank % 4
-seed = 1024

tests/test_engine/configs/non_pipeline_resnet_torch_amp.py

@@ -40,6 +40,3 @@ optimizer = dict(type='Adam', lr=0.001)
 
 loss = dict(type='CrossEntropyLoss')
 fp16 = dict(mode=AMP_TYPE.TORCH)
-
-# set_device_func = lambda global_rank, world_size: global_rank % 4
-seed = 1024

tests/test_engine/configs/pipeline_vanilla_resnet.py

@@ -38,11 +38,9 @@ parallel = dict(
     tensor=dict(size=1, mode=None)
 )
 
-schedule = dict(
-    num_microbatches=4
+engine = dict(
+    schedule=dict(
+        num_microbatches=4
+    )
 )
-num_pipeling_batches = 2
-seed = 1024
-lr_scheduler = dict(type='LinearWarmupLR', warmup_steps=5)
-
 num_epochs = 10

tests/test_engine/test_non_pipeline_engine/test_engine_apex_amp.py

@@ -8,7 +8,6 @@ import torch
 
 from colossalai import initialize
 from colossalai.core import global_context as gpc
-from colossalai.engine import Engine
 from colossalai.logging import get_global_dist_logger
 from colossalai.utils import report_memory_usage
 
@@ -24,20 +23,13 @@ NO_PIPE_CONFIG_PATH = osp.join(DIR_PATH, '../configs/non_pipeline_resnet_apex_am
 
 
 def run_no_pipeline(config):
-    model, train_dataloader, test_dataloader, criterion, optimizer, schedule, lr_scheduler = initialize(config)
+    engine, train_dataloader, test_dataloader = initialize(config)
     logger = get_global_dist_logger()
     rank = torch.distributed.get_rank()
 
-    engine = Engine(model=model,
-                    train_dataloader=train_dataloader,
-                    criterion=criterion,
-                    optimizer=optimizer,
-                    schedule=schedule)
     engine.train()
-    logger.info('lr = %g' % engine.get_lr())
-    output, label, loss = engine.step()
+    output, label, loss = engine.step(iter(train_dataloader))
     logger.info('Rank {} returns: {}'.format(rank, loss.item()))
-    logger.info('lr = %g' % engine.get_lr())
 
     gpc.destroy()
     logger.info('Test engine finished')

tests/test_engine/test_non_pipeline_engine/test_engine_no_amp.py

@@ -8,7 +8,6 @@ import torch
 
 from colossalai import initialize
 from colossalai.core import global_context as gpc
-from colossalai.engine import Engine
 from colossalai.logging import get_global_dist_logger
 from colossalai.utils import report_memory_usage
 
@@ -26,21 +25,14 @@ NO_PIPE_CONFIG_PATH = osp.join(DIR_PATH, '../configs/non_pipeline_resnet.py')
 def test_no_pipeline(config):
     print('Test no pipeline engine start')
 
-    model, train_dataloader, test_dataloader, criterion, optimizer, schedule, lr_scheduler = initialize(config)
+    engine, train_dataloader, test_dataloader = initialize(config)
     logger = get_global_dist_logger()
 
     rank = torch.distributed.get_rank()
-    engine = Engine(model=model,
-                    train_dataloader=train_dataloader,
-                    criterion=criterion,
-                    optimizer=optimizer,
-                    schedule=schedule)
 
     engine.train()
-    logger.info('lr = %g' % engine.get_lr())
-    output, label, loss = engine.step()
+    output, label, loss = engine.step(iter(train_dataloader))
     logger.info('Rank {} returns: {}'.format(rank, loss.item()))
-    logger.info('lr = %g' % engine.get_lr())
 
     gpc.destroy()
     logger.info('Test engine finished')

tests/test_engine/test_non_pipeline_engine/test_engine_torch_amp.py

@@ -8,7 +8,6 @@ import torch
 
 from colossalai import initialize
 from colossalai.core import global_context as gpc
-from colossalai.engine import Engine
 from colossalai.logging import get_global_dist_logger
 from colossalai.utils import report_memory_usage
 
@@ -26,21 +25,13 @@ NO_PIPE_CONFIG_PATH = osp.join(DIR_PATH, '../configs/non_pipeline_resnet_torch_a
 def test_no_pipeline(config):
     print('Test no pipeline engine start')
 
-    model, train_dataloader, test_dataloader, criterion, optimizer, schedule, lr_scheduler = initialize(config)
+    engine, train_dataloader, test_dataloader = initialize(config)
     logger = get_global_dist_logger()
-
     rank = torch.distributed.get_rank()
-    engine = Engine(model=model,
-                    train_dataloader=train_dataloader,
-                    criterion=criterion,
-                    optimizer=optimizer,
-                    schedule=schedule)
 
     engine.train()
-    logger.info('lr = %g' % engine.get_lr())
-    output, label, loss = engine.step()
+    output, label, loss = engine.step(iter(train_dataloader))
     logger.info('Rank {} returns: {}'.format(rank, loss.item()))
-    logger.info('lr = %g' % engine.get_lr())
 
     gpc.destroy()
     logger.info('Test engine finished')

tests/test_engine/test_pipeline/test_schedule.py

@@ -5,6 +5,7 @@ import os.path as osp
 
 import pytest
 
+from colossalai.context import ParallelMode
 from colossalai.core import global_context as gpc
 from colossalai.initialize import initialize
 from colossalai.logging import get_global_dist_logger
@@ -22,13 +23,25 @@ CONFIG_PATH = osp.join(DIR_PATH, '../configs/pipeline_vanilla_resnet.py')
 @pytest.mark.skip("This test should be invoked using the test.sh provided")
 @pytest.mark.dist
 def test_schedule():
-    model, train_dataloader, test_dataloader, criterion, optimizer, schedule, lr_scheduler = initialize(CONFIG_PATH)
+    engine, train_dataloader, test_dataloader = initialize(CONFIG_PATH)
     logger = get_global_dist_logger()
 
-    schedule.zero_grad()
-    output, label, losses = schedule.forward_backward_step(forward_only=False)
-    schedule.step()
-    logger.info('losses: {}'.format([loss.item() for loss in losses]))
+    model = engine.model
+    optimizer = engine.optimizer
+    criterion = engine.criterion
+    schedule = engine._schedule
+
+    output, label, loss = schedule.forward_backward_step(
+        data_iter=iter(train_dataloader),
+        model=model,
+        optimizer=optimizer,
+        criterion=criterion,
+        forward_only=False
+    )
+    schedule.optimizer_step(model, optimizer)
+
+    if gpc.is_last_rank(ParallelMode.PIPELINE):
+        logger.info('losses: {}'.format(loss))
 
     gpc.destroy()
     logger.info('training finished')

tests/test_engine/test_pipeline_engine/test_engine.py

@@ -9,7 +9,6 @@ import torch
 from colossalai import initialize
 from colossalai.context import ParallelMode
 from colossalai.core import global_context as gpc
-from colossalai.engine import Engine
 from colossalai.logging import get_global_dist_logger
 
 NUM_BATCH = 128
@@ -23,22 +22,14 @@ PIPE_CONFIG_PATH = osp.join(DIR_PATH, '../configs/pipeline_vanilla_resnet.py')
 
 
 def run_pipeline(config):
-    model, train_dataloader, test_dataloader, criterion, optimizer, schedule, lr_scheduler = initialize(config)
+    engine, train_dataloader, test_dataloader = initialize(config)
     logger = get_global_dist_logger()
     rank = torch.distributed.get_rank()
-    engine = Engine(model=model,
-                    train_dataloader=train_dataloader,
-                    criterion=criterion,
-                    optimizer=optimizer,
-                    lr_scheduler=lr_scheduler,
-                    schedule=schedule)
 
     engine.train()
-    logger.info('lr = %g' % engine.get_lr())
-    outputs, labels, loss = engine.step()
+    outputs, labels, loss = engine.step(iter(train_dataloader))
     if gpc.is_last_rank(ParallelMode.PIPELINE):
         logger.info('losses: {}'.format(rank, loss.item()))
-    logger.info('lr = %g' % engine.get_lr())
 
     gpc.destroy()
     logger.info('Test engine pipeline finished')