py:unsqueeze修正&约束broadcast_shape

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

@@ -1,6 +1,6 @@
 from typing import Union
 from deepx import Tensor
-from .leaffunc_changeshape import reshape,indexselect, concat
+from .leaffunc_changeshape import reshape,indexselect, concat,broadcastTo
 from .leaffunc_init import newtensor,arange
 def squeeze(t:Tensor,dim:int)->Tensor:
     assert isinstance(dim,int)
@@ -13,7 +13,7 @@ def squeeze(t:Tensor,dim:int)->Tensor:
 def unsqueeze(t:Tensor,dim:int)->Tensor:
     assert isinstance(dim,int)
     assert isinstance(t,Tensor)
-    dim=dim%t.ndim
+    dim = dim % (t.ndim + 1)
     newshape=list(t.shape)
     newshape.insert(dim,1)
     return reshape(t,tuple(newshape))
@@ -29,4 +29,7 @@ def sliceselect(t:Tensor,sliceobj:slice,dim:int=-1,out:Union[Tensor,str]='')->Te
     index=arange(start,stop,dtype='int32')
     return  indexselect(t,index,dim=dim,out=out)
 
-cat= concat
+cat= concat
+# 参考 PyTorch 文档，broadcastTo和expand是作用一样
+#  https://docs.pytorch.org/docs/stable/generated/torch.broadcast_to.html
+expand = broadcastTo

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

@@ -61,25 +61,36 @@ def concat(tensors:Union[list[Tensor],tuple[Tensor,...]],dim:int,out:Union[Tenso
     rtf_concat(tensors,dim,outtensor,defaultauthor['concat'])
     return outtensor
 
-def broadcastTo(t:Tensor,new_shape:tuple[int,...],out:Union[Tensor,str]='',requires_grad:bool=False,author='miaobyte')->Tensor:
-    assert isinstance(new_shape,tuple)
-    for i in new_shape:
-        assert isinstance(i,int) and i>0
-
+def broadcastTo(t:Tensor,newshape:tuple[int,...],out:Union[Tensor,str]='')->Tensor:
+    assert isinstance(newshape,tuple)
+    assert len(newshape)==t.ndim
+    new_shape=[]
+    for idx,i in enumerate(newshape):
+        assert isinstance(i,int)
+        if i==-1:
+            new_shape.append(t.shape[idx])
+        elif i>0:
+            new_shape.append(i)
+        else:
+            raise ValueError(f"新形状参数不合法，维度 {idx} 的值{i}")
+    new_shape=tuple(new_shape)
     if t.shape==new_shape:
         return t
+
     bshape=Shape.broadcast_shape(t.shape,new_shape)
-    if bshape!=tuple(new_shape):
-        raise ValueError(f"广播失败：{t.shape} 无法广播为 {new_shape} ")
+    if bshape!=new_shape:
+        raise ValueError(f"广播失败：{t.shape} 无法广播为 {new_shape}，请先reshape")
     outtensor=out
     if isinstance(out,str) or out is None:
         outshape=new_shape
         outtensor=newtensor(outshape,dtype=t.dtype,name=out)
     from .rtf_changeshape import rtf_broadcastTo
     rtf_broadcastTo(t,new_shape,outtensor,defaultauthor['broadcastTo'])
     return outtensor
+
 broadcast_to = broadcastTo
 
+
 def indexselect(input:Tensor,indices:Tensor,dim:int,out:Union[Tensor,str]='')->Tensor:
     assert dim>=0 and dim<input.ndim
     outtensor=out

front/py/deepx/tensor/changeshape.py

@@ -42,6 +42,8 @@ def transpose_(self):
     transpose_func(self,self)
     return self
 
+# broadcast_to==broadcastTo==expand
+# https://docs.pytorch.org/docs/stable/generated/torch.broadcast_to.html
 @tensor_method
 def broadcastTo(self,shape:tuple[int,...],out:Union[Tensor,str]='')->Tensor:
     from deepx.nn.functional import broadcastTo as broadcastTo_func
@@ -54,6 +56,12 @@ def broadcast_to(self,shape:tuple[int,...],out:Union[Tensor,str]='')->Tensor:
     result=broadcast_to_func(self,shape,out)
     return result
 
+@tensor_method
+def expand(self,shape:tuple[int,...],out:Union[Tensor,str]='')->Tensor:
+    from deepx.nn.functional import broadcastTo as expand_func
+    result=expand_func(self,shape,out)
+    return result
+
 @tensor_method
 def indexselect(self,index:Tensor,gatheraxis:int=0,out:Union[Tensor,str]='')->Tensor:
     assert isinstance(index,Tensor)
@@ -88,8 +96,3 @@ def repeat(self,repeats:tuple[int,...])->Tensor:
     result=repeat_func(self,repeats)
     return result
 
-# @tensor_method
-# def expand(self,shape:tuple)->Tensor:
-#     from deepx.nn.functional import expand as expand_func
-#     result=expand_func(self,shape,False)
-#     return result

front/py/deepx/tensor/elementwise.py

@@ -228,9 +228,45 @@ def switch_(self,cases:Union[Tensor,float,int]):
 # 类型转换
 @tensor_method
 def todtype(self,dtype:str):
+    assert isinstance(dtype,str)
+    if str== self.dtype:
+        return self
     from deepx.nn.functional import newtensor
     dest=newtensor(self.shape,dtype=dtype)
     from deepx.nn.functional import todtype as todtype_func
     todtype_func(self,dest)
     return dest
-
+
+@tensor_method
+def double(self)->Tensor:
+    """将张量转换为float64类型"""
+    return self.todtype('float64')
+
+@tensor_method
+def float(self)->Tensor:
+    """将张量转换为float32类型"""
+    return self.todtype('float32')
+
+@tensor_method
+def half(self)->Tensor:
+    """将张量转换为float16类型"""
+    return self.todtype('float16')
+
+@tensor_method
+def long(self)->Tensor:
+    """将张量转换为int64类型"""
+    return self.todtype('int64')
+
+
+@tensor_method
+def int(self)->Tensor:
+    """将张量转换为int32类型"""
+    return self.todtype('int32')
+
+@tensor_method
+def bool(self)->Tensor:
+    """将张量转换为bool类型"""
+    return self.todtype('bool')
+
+
+

front/py/deepx/tensor/shape.py

@@ -142,34 +142,17 @@ def matmul(cls,shape:tuple[int],other:tuple[int])->tuple[int]:
 
     @classmethod
     def broadcast_shape(cls,shape_a: tuple[int,...], shape_b: tuple[int,...]) -> tuple[int,...]:
-        """计算两个形状的广播后形状"""
-        # 获取形状的长度
-        len_a, len_b = len(shape_a), len(shape_b)
-
-        # 创建结果形状
+        assert isinstance(shape_a,tuple) and isinstance(shape_b,tuple)
+        assert len(shape_b)==len(shape_a)
+        """计算两个形状的广播后形状（长度必须一致）"""
         result_shape = []
-
-        # 从右往左对齐并计算每个维度
-        for i in range(1, min(len_a, len_b) + 1):
-            dim_a = shape_a[-i]
-            dim_b = shape_b[-i]
-
+        for dim_a, dim_b in zip(shape_a, shape_b):
             if dim_a == 1 or dim_b == 1:
-                # 广播规则：如果一个维度为1，取另一个维度的值
-                result_shape.insert(0, max(dim_a, dim_b))
+                result_shape.append(max(dim_a, dim_b))
             elif dim_a == dim_b:
-                # 维度相同，保持不变
-                result_shape.insert(0, dim_a)
+                result_shape.append(dim_a)
             else:
-                # 维度不同且都不为1，无法广播
-                raise ValueError(f"无法广播的形状：{shape_a} 和 {shape_b}")
-
-        # 添加较长形状中多出的前导维度
-        if len_a > len_b:
-            result_shape = list(shape_a[:len_a - len_b]) + result_shape
-        elif len_b > len_a:
-            result_shape = list(shape_b[:len_b - len_a]) + result_shape
-
+                raise ValueError(f"无法广播的形状：{shape_a} 和 {shape_b},请先reshape")
         return tuple(result_shape)
 
 

front/py/deepx/tensor/tensor.py

@@ -1,7 +1,5 @@
 from typing import Optional,Union,TypeAlias
-
 from triton.language.semantic import equal
-
 from .shape import Shape
 
 

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

@@ -39,12 +39,12 @@ def __init__(self,config:dict):
 
     def forward(self, x, position_ids):
         # 扩展旋转频率
-        inv_freq_expanded = self.inv_freq.unsqueeze(dim=0).unsqueeze(dim=2).todtype('float32')
-        broadcast_shape=(position_ids.shape[0], self.inv_freq.shape[0], 1)
-        inv_freq_expanded = inv_freq_expanded.reshape(broadcast_shape)
-
+        inv_freq_expanded = self.inv_freq[None, :, None].todtype('float32').expand((position_ids.shape[0], -1, 1))
+
         # 使用torch.unsqueeze和type转换替代索引操作
-        position_ids_expanded = position_ids.unsqueeze(dim=1).todtype(x.dtype)
+        position_ids_expanded = position_ids[:, None, :].float()
+
+
         # 计算频率
         freqs = (inv_freq_expanded @ position_ids_expanded).T
         # 拼接频率

front/py/examples/1_tensor/getitem/getitem_2.py

@@ -0,0 +1,6 @@
+from deepx import newtensor,arange
+t = newtensor((64,))
+t.arange_()
+print()
+t2 = t[None, :, None]
+t2.print()

front/py/examples/2_ir/4_changeshape_broadcast.py

@@ -11,11 +11,11 @@
 
 
 ########====DEEPX====########
-from deepx import Tensor,arange,broadcastTo
+from deepx import  arange
 
 a=arange(start=0,end=4*2*3,name="a").reshape_((4,2,3))
 b=arange(start=0,end=2,name='b').reshape((2,1))
-bb=b.broadcastTo( a.shape,out="b.broadcasted")
+bb=b.unsqueeze(0).broadcastTo(a.shape,out="b.broadcasted")
 bb.print()
 
 c=a[None:,]

front/py/examples/2_ir/4_changeshape_concat.py

@@ -15,9 +15,9 @@
 from deepx import Tensor,zeros, ones, concat
 
 
-t1 = ones([3,4,5],dtype='float32',name='t1')
-t2=ones([3,4,5],dtype='float32',name='t2')
-t3=ones([3,4,5],dtype='float32',name='t3')
+t1 = ones( (3,4,5),dtype='float32',name='t1')
+t2=ones((3,4,5),dtype='float32',name='t2')
+t3=ones((3,4,5),dtype='float32',name='t3')
 
-t=concat([t1,t2,t3],dim=1,out='t')
+t=concat((t1,t2,t3),dim=1,out='t')
 t.print()

front/py/examples/3_functional/changeshape_broadcast.py

@@ -13,7 +13,7 @@
 #######-----------------deepx-----------------#######
 from deepx import Tensor,broadcast_to,arange
 deepx_x = arange(0,6).reshape_((1,2,3))      # shape=(2,3)
-deepx_y = broadcast_to(deepx_x, (3,2,3))    # 需要原维度为1
+deepx_y = deepx_x.broadcast_to((3,2,3))    # 需要原维度为1
 deepx_y.print()
 
 

front/py/examples/3_module/1_linear.py

@@ -14,7 +14,7 @@
 from deepx import ones
 
 net = Linear(64, 4)
-input=ones(1,64,name='input')
+input=ones((1,64),name='input')
 out=net.forward(input)
 out.print()