add a common util for hooks registered on parameter. (#292)

colossalai/engine/paramhooks/__init__.py

+from ._param_hookmgr import BaseParamHookMgr
+__all__ = ["BaseParamHookMgr"]
\ No newline at end of file

colossalai/engine/paramhooks/_param_hookmgr.py

+from typing import Callable, List
+import torch
+import functools
+
+class BaseParamHookMgr(object):
+    def __init__(self, param_list: List[torch.nn.Parameter]) -> None:
+        r"""
+        register backward hook on every parameters of module
+        """
+        self._param_list = param_list
+        self._hook_list = []
+
+    def register_backward_hooks(self, hook_call : Callable) -> None:
+        r"""
+        The hook_call will be called every time a gradient with respect to the a param in self.param_list 
+        is computed. 
+        The hook should have the following signature:
+        ```
+        hook(param, grad) -> Tensor or None
+        ```
+        """
+        if not torch.is_grad_enabled():
+            return  # don't register grad hooks if grad isn't enabled
+        for p in self._param_list:
+            if p.requires_grad and not hasattr(p, '_base_param_hook'):
+                handle = p.register_hook(functools.partial(hook_call, p))
+                p._base_param_hook = handle
+
+    def remove_hooks(self):
+        for p in self._param_list:
+            if p.requires_grad and hasattr(p, '_base_param_hook'):
+                p._base_param_hook.remove()

tests/test_engine/test_engine/test_param_hook.py

+import pytest
+from colossalai.engine.paramhooks import BaseParamHookMgr
+from torch import nn
+import torch
+import torch.nn.functional as F
+import copy
+
+class SubNet(nn.Module):
+    def __init__(self, out_features) -> None:
+        super().__init__()
+        self.bias = nn.Parameter(torch.zeros(out_features))
+
+    def forward(self, x, weight):
+        return F.linear(x, weight, self.bias)
+
+
+class Net(nn.Module):
+    def __init__(self, checkpoint=False) -> None:
+        super().__init__()
+        self.fc1 = nn.Linear(5, 5)
+        self.sub_fc = SubNet(5)
+        self.fc2 = nn.Linear(5, 1)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.sub_fc(x, self.fc1.weight)
+        x = self.fc1(x)
+        x = self.fc2(x)
+        return x
+
+def net_data():
+    return (torch.randn(2, 5, dtype=torch.float, device='cuda'),)
+
+def allclose(tensor_a: torch.Tensor, tensor_b: torch.Tensor, loose=False) -> bool:
+    if loose:
+        return torch.allclose(tensor_a, tensor_b, atol=1e-3, rtol=1e-3)
+    return torch.allclose(tensor_a, tensor_b)
+
+
+def test_base_param_hook():
+    torch.manual_seed(0)
+    model = Net(checkpoint=True).cuda()
+    model.train()
+    inputs = net_data()
+
+    def run_model(model, inputs, use_param_hook = False):
+        if use_param_hook:
+            class HooKWrapper:
+                def __init__(self) -> None:
+                    self.hook_triggered_times = 0
+
+                def wrapper_func(self):
+                    def hook(param, grad) -> torch.Tensor or None:
+                        self.hook_triggered_times += 1
+                        return grad
+                    return hook
+
+            hookwrapper = HooKWrapper()
+            param_list = [p for p in model.parameters()]
+            hook_mgr = BaseParamHookMgr(param_list)
+            hook_mgr.register_backward_hooks(hookwrapper.wrapper_func())
+        
+        model.zero_grad(set_to_none=True)
+
+        with torch.cuda.amp.autocast():
+            y = model(*inputs)
+            loss = y.sum()
+        loss.backward()
+
+        if use_param_hook:
+            hook_mgr.remove_hooks()
+            return hookwrapper.hook_triggered_times
+    
+    model_copy = copy.deepcopy(model)
+
+    run_model(model, inputs, False)
+    ret2 = run_model(model_copy, inputs, True)
+    
+    # Make sure param hook has only be fired once in case of parameter sharing
+    assert ret2 == len(list(model.parameters()))
+
+    for p, p_copy in zip(model.parameters(), model_copy.parameters()):
+        assert allclose(p.grad, p_copy.grad), f"{p.grad} vs {p_copy.grad}"
+
+if __name__ == '__main__':
+    test_base_param_hook()