[Tensor] Add some attributes to ColoTensor (#877)

* [Tensor] add some function to ColoTensor

* torch.allclose

* rm torch.add

colossalai/tensor/_ops/element_wise.py

@@ -3,6 +3,19 @@ from colossalai.tensor.op_wrapper import colo_op_impl
 from colossalai.tensor import ColoTensor
 
 
+@colo_op_impl(torch.allclose)
+def colo_mean(types, args=(), kwargs=None, pg=None):
+    a = args[0]
+    b = args[1]
+
+    if isinstance(a, ColoTensor):
+        a = a.torch_tensor()
+    elif isinstance(b, ColoTensor):
+        b = b.torch_tensor()
+
+    return torch.allclose(a, b, **kwargs)
+
+
 @colo_op_impl(torch.mean)
 def colo_mean(types, args=(), kwargs=None, pg=None):
     input_t = args[0]

colossalai/tensor/colo_tensor.py

@@ -8,6 +8,7 @@ from colossalai.core import global_context as gpc
 from colossalai.nn.layer.utils import divide
 from colossalai.tensor import TensorSpec, ComputePattern, ParallelAction
 
+
 class ColoTensor(object):
     """ Data Structure for Tensor in Colossal-AI
     1. It contains a torch.Tensor as an attribute.
@@ -37,6 +38,9 @@ class ColoTensor(object):
         self._torch_tensor = torch_tensor
         self._shard_spec = shard_spec
 
+    def __getitem__(self, key):
+        return ColoTensor.init_from_torch_tensor(self.torch_tensor()[key])
+
     @property
     def shard_spec(self) -> TensorSpec:
         return self._shard_spec
@@ -148,7 +152,10 @@ class ColoTensor(object):
                 kwargs = {}
 
             kwargs = {k: v.torch_tensor() if isinstance(v, ColoTensor) else v for k, v in kwargs.items()}
-            return func(*args, **kwargs)
+            return ColoTensor.init_from_torch_tensor(func(*args, **kwargs))
 
-    def backward(self, gradient: Optional[torch.Tensor] = None , retain_graph: bool = False):
+    def backward(self, gradient: Optional[torch.Tensor] = None, retain_graph: bool = False):
         self._torch_tensor.backward(gradient=gradient, retain_graph=retain_graph)
+
+    def __add__(self, o) -> "ColoTensor":
+        return ColoTensor.init_from_torch_tensor(self.torch_tensor() + o.torch_tensor())

tests/test_tensor/test_op.py

@@ -87,19 +87,11 @@ def test_no_wrap_op():
     assert torch.sum(input=t) == torch.sum(input=t_ref)
 
 
-def test_lazy_init_tensor():
-    lazy_t = ColoTensor(2, 3, dtype=torch.float32, requires_grad=True)
-    assert lazy_t._torch_tensor.numel() == 0
-    assert lazy_t.numel() == 6 == lazy_t.torch_tensor().numel()
-
-
 def check_all():
     test_linear()
     test_element_wise()
     test_no_wrap_op()
-    test_lazy_init_tensor()
 
 
 if __name__ == '__main__':
-    # test_lazy_init_ptensor()
+    check_all()
-    test_layernorm()

tests/test_tensor/test_tensor.py

@@ -0,0 +1,15 @@
+import torch
+from colossalai.tensor import ColoTensor
+from numpy import allclose
+
+
+def test_tensor_indexing():
+    torch_t = torch.randn(2, 3)
+    colo_t = ColoTensor.init_from_torch_tensor(torch_t)
+    assert allclose(torch_t[:, 1], colo_t[:, 1].torch_tensor())
+
+
+def test_lazy_init_tensor():
+    lazy_t = ColoTensor(2, 3, dtype=torch.float32, requires_grad=True)
+    assert lazy_t._torch_tensor.numel() == 0
+    assert lazy_t.numel() == 6 == lazy_t.torch_tensor().numel()