feat(mge): make F.eye numpy compatible

GitOrigin-RevId: fee32537b4

imperative/python/megengine/functional/tensor.py

@@ -57,7 +57,7 @@ __all__ = [
 ]
 
 
-def eye(shape, *, dtype="float32", device: Optional[CompNode] = None) -> Tensor:
+def eye(N, M=None, *, dtype="float32", device: Optional[CompNode] = None) -> Tensor:
     """Returns a 2D tensor with ones on the diagonal and zeros elsewhere.
 
     :param shape: expected shape of output tensor.
@@ -72,8 +72,7 @@ def eye(shape, *, dtype="float32", device: Optional[CompNode] = None) -> Tensor:
         import numpy as np
         import megengine.functional as F
 
-        data_shape = (4, 6)
-        out = F.eye(data_shape, dtype=np.float32)
+        out = F.eye(4, 6, dtype=np.float32)
         print(out.numpy())
 
     Outputs:
@@ -86,8 +85,17 @@ def eye(shape, *, dtype="float32", device: Optional[CompNode] = None) -> Tensor:
          [0. 0. 0. 1. 0. 0.]]
 
     """
+    if M is not None:
+        if isinstance(N, Tensor) or isinstance(M, Tensor):
+            shape = astensor1d((N, M))
+        else:
+            shape = Tensor([N, M], dtype="int32", device=device)
+    elif isinstance(N, Tensor):
+        shape = N
+    else:
+        shape = Tensor(N, dtype="int32", device=device)
     op = builtin.Eye(k=0, dtype=dtype, comp_node=device)
-    (result,) = apply(op, Tensor(shape, dtype="int32", device=device))
+    (result,) = apply(op, shape)
     return result
 
 

imperative/python/test/unit/functional/test_tensor.py

@@ -22,12 +22,20 @@ from megengine.distributed.helper import get_device_count_by_fork
 
 def test_eye():
     dtype = np.float32
-    cases = [{"input": [10, 20]}, {"input": [20, 30]}]
+    cases = [{"input": [10, 20]}, {"input": [30]}]
     for case in cases:
         np.testing.assert_allclose(
             F.eye(case["input"], dtype=dtype).numpy(),
             np.eye(*case["input"]).astype(dtype),
         )
+        np.testing.assert_allclose(
+            F.eye(*case["input"], dtype=dtype).numpy(),
+            np.eye(*case["input"]).astype(dtype),
+        )
+        np.testing.assert_allclose(
+            F.eye(tensor(case["input"]), dtype=dtype).numpy(),
+            np.eye(*case["input"]).astype(dtype),
+        )
 
 
 def test_concat():