py:tensor __getitem__ 已支持

front/py/deepx/nn/functional/__init__.py

@@ -37,7 +37,7 @@
     "mean",
     "rsqrt",
     "softmax",
-    "squeeze","unsqueeze",
+    "squeeze","unsqueeze","sliceselect","cat",
 
     #other
     "calculate_fan_in_and_fan_out",

front/py/deepx/nn/functional/changeshape.py

@@ -1,6 +1,7 @@
+from typing import Union
 from deepx import Tensor
-from .leaffunc_changeshape import reshape
-
+from .leaffunc_changeshape import reshape,indexselect, concat
+from .leaffunc_init import newtensor,arange
 def squeeze(t:Tensor,dim:int)->Tensor:
     assert isinstance(dim,int)
     assert isinstance(t,Tensor)
@@ -15,4 +16,17 @@ def unsqueeze(t:Tensor,dim:int)->Tensor:
     dim=dim%t.ndim
     newshape=list(t.shape)
     newshape.insert(dim,1)
-    return reshape(t,tuple(newshape))
+    return reshape(t,tuple(newshape))
+
+def sliceselect(t:Tensor,sliceobj:slice,dim:int=-1,out:Union[Tensor,str]='')->Tensor:
+    assert isinstance(dim,int)
+    assert isinstance(sliceobj,slice)
+    assert isinstance(t,Tensor)
+    dim=dim%t.ndim
+    start=start = 0 if sliceobj.start is None else sliceobj.start % t.shape[dim]
+    stop= t.shape[dim] if sliceobj.stop is None else sliceobj.stop % t.shape[dim]
+
+    index=arange(start,stop,dtype='int32')
+    return  indexselect(t,index,dim=dim,out=out)
+
+cat= concat

front/py/deepx/nn/functional/leaffunc_changeshape.py

@@ -80,17 +80,16 @@ def broadcastTo(t:Tensor,new_shape:tuple[int,...],out:Union[Tensor,str]='',requi
     return outtensor
 broadcast_to = broadcastTo
 
-def indexselect(input:Tensor,indices:Tensor,gatheraxis:int,out:Union[Tensor,str]='')->Tensor:
-    assert gatheraxis>=0 and gatheraxis<input.ndim
-
+def indexselect(input:Tensor,indices:Tensor,dim:int,out:Union[Tensor,str]='')->Tensor:
+    assert dim>=0 and dim<input.ndim
     outtensor=out
     if isinstance(out,str) or out is None:
-        outshape=Shape.indexselectshape(input.shape,indices.shape,gatheraxis)
+        outshape=Shape.indexselectshape(input.shape,indices.shape,dim)
         outtensor=newtensor(outshape,dtype=input.dtype,name=out)
     assert outtensor.shape==outshape
 
     from .rtf_changeshape import rtf_indexselect
-    rtf_indexselect(input,indices,gatheraxis,outtensor,defaultauthor['indexselect'])
+    rtf_indexselect(input,indices,dim,outtensor,defaultauthor['indexselect'])
     return outtensor
 
 def repeat(input:Tensor,repeats:tuple[int,...],out:Union[Tensor,str]=''):

front/py/deepx/nn/functional/leaffunc_life.py

@@ -1,7 +1,7 @@
 from deepx.tensor import Tensor
 from typing import Union
 
-def newtensor(shape:tuple[int,...],dtype:str='float32',name:str=None):
+def newtensor(shape:tuple[int,...],dtype:str='float32',name:str=None)->Tensor:
     assert isinstance(shape,tuple)
     for i in shape:
         assert isinstance(i,int)

front/py/deepx/tensor/changeshape.py

@@ -62,6 +62,14 @@ def indexselect(self,index:Tensor,gatheraxis:int=0,out:Union[Tensor,str]='')->Te
     result=indexselect_func(self,index,gatheraxis,out)
     return result
 
+@tensor_method
+def sliceselect(self,index:slice,dim:int=0,out:Union[Tensor,str]='')->Tensor:
+    assert isinstance(index,slice)
+    gatheraxis=dim%self.ndim
+    from deepx.nn.functional import sliceselect as sliceselect_func
+    result=sliceselect_func(self,index,gatheraxis,out)
+    return result
+
 @tensor_method
 def squeeze(self,dim:int)->Tensor:
     from deepx.nn.functional import squeeze as squeeze_func

front/py/deepx/tensor/tensor.py

@@ -159,8 +159,48 @@ def __matmul__(self, other:'Tensor'):
     def __rmatmul__(self, other:'Tensor'):
         return other.matmul(self)
 
-    def __getitem__(self, index:'Tensor'):
-        return self.indexselect(index)
+    def __getitem__(self, idx):
+        # 简单操作
+        if  isinstance(idx,Tensor):
+            return self.indexselect(idx)
+        if isinstance(idx, int): 
+            return self.sliceselect(slice(idx,idx+1)).squeeze(dim=0)
+
+        ## 阶段1,
+        if isinstance(idx, slice):
+            indices = [idx]
+        elif isinstance(idx, tuple):
+            indices = list(idx)
+        else:
+            raise TypeError(f"Index must be an integer, slice, tuple, or Tensor, not {type(idx).__name__}")
+        # 阶段2
+        result = self
+        new_axis_positions = []
+        dim_cursor = 0
+
+        for item in  indices:
+            if item is None:
+                # 如果是 None，则表示在该位置添加一个新的维度
+                new_axis_positions.append(dim_cursor)
+                continue
+            if item == Ellipsis:
+                num_ellipsis = self.ndim - len(indices) + 1
+                dim_cursor += num_ellipsis
+                continue
+            # 如果是完整的切片 (e.g., ':')，则无需操作，直接进入下一维度
+            if item == slice(None, None, None):
+                dim_cursor += 1
+                continue
+            result=result.sliceselect(item,dim=dim_cursor)
+            dim_cursor += 1
+
+        # 2. 在指定位置添加新维度（由 None 产生）
+        i=0
+        for pos in sorted(new_axis_positions):
+            result = result.unsqueeze(pos+i)
+            i += 1
+
+        return result
 
     #shape操作
     @property

front/py/deepx/transformer/models/llama/attention.py

@@ -1,15 +1,13 @@
 from typing import Optional,Tuple
 from deepx.nn.modules import Module,Linear
-from deepx import Tensor,matmul,softmax,concat,arange,dropout as dropout_func
+from deepx import Tensor,matmul,softmax,cat,dropout as dropout_func
 
 
 
 def rotate_half(x:Tensor):
-    index_front=arange(0,x.shape[-1]//2,dtype="int32")
-    index_back=arange(x.shape[-1]//2,x.shape[-1],dtype="int32")
-    x1 = x.indexselect(gatheraxis=-1,index=index_front)
-    x2 = x.indexselect(gatheraxis=-1,index=index_back)
-    return concat((-x2, x1,), dim=-1)
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return  cat((-x2, x1,), dim=-1)
 
 def apply_rotary_pos_emb(q:Tensor, k:Tensor, cos:Tensor, sin:Tensor, unsqueeze_dim:int=1):
     cos = cos.unsqueeze(unsqueeze_dim)

front/py/examples/1_tensor/getitem/getitem.py

@@ -0,0 +1,13 @@
+from deepx import newtensor,arange
+t = newtensor((2, 3, 13))
+t.arange_()
+print()
+t2 = t[None, :, None]
+t2.print()
+t3=t[:,None,:]
+t3.print()
+x=t
+x1 = x[..., : x.shape[-1] // 2]
+x2 = x[..., x.shape[-1] // 2 :]
+x1.print()
+x2.print()

front/py/examples/1_tensor/getitem/getitem_torch.py

@@ -0,0 +1,11 @@
+import torch
+
+t = torch.full((2, 3, 13), 1)
+t2 = t[None, :, None]
+print(t2.shape)
+print(t2)
+x=t
+x1 = x[..., : x.shape[-1] // 2]
+x2 = x[..., x.shape[-1] // 2 :]
+print(x1)
+print(x2)

front/py/examples/1_tensor/getitem/tensor.py

@@ -0,0 +1,53 @@
+class Tensor:
+    def __init__(self, data):
+        # 假设 data 是嵌套 list 或 numpy ndarray
+        import numpy as np
+        self.data = np.array(data)
+
+    def __getitem__(self, idx):
+        # 支持 None, int, slice, tuple 等
+        # 重点：遇到 None 时，插入新轴
+        import numpy as np
+
+        if not isinstance(idx, tuple):
+            idx = (idx,)
+
+        # 统计原始索引和 None 的位置，组装成新的索引
+        new_idx = []
+        expand_axes = []
+        for i, ix in enumerate(idx):
+            if ix is None:
+                expand_axes.append(len(new_idx))
+            else:
+                new_idx.append(ix)
+
+        # 先索引
+        result = self.data[tuple(new_idx)]
+        # 再插入新轴
+        for ax in expand_axes:
+            result = np.expand_dims(result, axis=ax)
+
+        # 返回新 Tensor
+        ret = Tensor(result)
+        return ret
+
+    @property
+    def shape(self):
+        return self.data.shape
+
+    def __repr__(self):
+        return f"Tensor(shape={self.shape}, data=\n{self.data})"
+
+
+# 测试代码
+t = Tensor([[1, 2, 3], [4, 5, 6]])
+print("原始shape:", t.shape)  # (2, 3)
+
+t2 = t[None]
+print("t[None].shape:", t2.shape)  # (1, 2, 3)
+
+t3 = t[:, None]
+print("t[:, None].shape:", t3.shape)  # (2, 1, 3)
+
+t4 = t[None, :, None]
+print("t[None, :, None].shape:", t4.shape)  # (1, 2, 1, 3)