diff --git a/tests/test_amp/test_torch_fp16.py b/tests/test_amp/test_torch_fp16.py
new file mode 100644
index 000000000..1372b08fa
--- /dev/null
+++ b/tests/test_amp/test_torch_fp16.py
@@ -0,0 +1,90 @@
+import torch
+import colossalai
+import torch.multiprocessing as mp
+from tests.components_to_test.registry import non_distributed_component_funcs
+from colossalai.testing import assert_close_loose, rerun_if_address_is_in_use
+from colossalai.utils import free_port
+from colossalai.amp import convert_to_torch_amp, convert_to_apex_amp
+
+import copy
+import pytest
+from functools import partial
+
+
+def run_torch_amp():
+    """
+    In this test, we compare the torch amp and apex amp implemented in colossalai
+    """
+
+    torch.backends.cudnn.benchmark = False
+    torch.backends.cudnn.deterministic = True
+
+    # create layer
+    test_models = ['resnet18', 'simple_net']
+    for test_name in test_models:
+        get_component_func = non_distributed_component_funcs.get_callable(test_name)
+        model_builder, train_dataloader, _, optim_class, _ = get_component_func()
+
+        # create model
+        torch_amp_model = model_builder(checkpoint=True).cuda()
+        apex_amp_model = copy.deepcopy(torch_amp_model)
+
+        # create optimizer
+        torch_amp_optimizer = optim_class(torch_amp_model.parameters(), lr=1e-3)
+        apex_amp_optimizer = optim_class(apex_amp_model.parameters(), lr=1e-3)
+
+        # inject torch and apex amp
+        torch_amp_config = dict(init_scale=1280, enabled=True)
+        torch_amp_model, torch_amp_optimizer, _ = convert_to_torch_amp(torch_amp_model,
+                                                                       torch_amp_optimizer,
+                                                                       amp_config=torch_amp_config)
+        apex_amp_config = dict(opt_level='O1', loss_scale=1280)
+        apex_amp_model, apex_amp_optimizer = convert_to_apex_amp(apex_amp_model, apex_amp_optimizer, apex_amp_config)
+
+        # create data
+        data_iter = iter(train_dataloader)
+        data, label = next(data_iter)
+        data = data.cuda()
+
+        # forward pass
+        torch_amp_output = torch_amp_model(data)
+        apex_amp_output = apex_amp_model(data)
+        assert_close_loose(torch_amp_output, apex_amp_output)
+
+        for torch_amp_param, apex_amp_param in zip(torch_amp_model.parameters(), apex_amp_model.parameters()):
+            assert_close_loose(torch_amp_param, apex_amp_param)
+
+        # backward
+        torch_amp_optimizer.backward(torch_amp_output.mean())
+        apex_amp_optimizer.backward(apex_amp_output.mean())
+
+        # check grad
+        # In apex amp, grad is not scaled before backward, but torch amp does
+        for torch_amp_param, apex_amp_param in zip(torch_amp_model.parameters(), apex_amp_model.parameters()):
+            assert_close_loose(torch_amp_param.grad, apex_amp_param.grad * apex_amp_config['loss_scale'])
+
+        # step
+        torch_amp_optimizer.step()
+        apex_amp_optimizer.step()
+
+        # check updated param and grad
+        for torch_amp_param, apex_amp_param in zip(torch_amp_model.parameters(), apex_amp_model.parameters()):
+            assert_close_loose(torch_amp_param.grad, apex_amp_param.grad)
+            assert_close_loose(torch_amp_param, apex_amp_param)
+
+
+def run_dist(rank, world_size, port):
+    colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost')
+    run_torch_amp()
+
+
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+def test_torch_amp():
+    world_size = 1
+    run_func = partial(run_dist, world_size=world_size, port=free_port())
+    mp.spawn(run_func, nprocs=world_size)
+
+
+if __name__ == '__main__':
+    test_torch_amp()