动态计算图验证:

Author: miaobyte

.gitignore

@@ -2,4 +2,8 @@
 **/build/
 .idea
 **/.idea
-**/__pycache__/
+**/__pycache__/
+**/dist/
+**/egg.info/
+front/py/deepx/deepx.egg-info/*
+*.pdf

front/py/deepx/autograd/__init__.py

@@ -1,8 +1,10 @@
 from .graph import Graph
+from .graph_viz import to_dot
 from .node import Node
 from .nodetype import NodeType
 from ._datanode import DataNode
 from ._opnode import OpNode
+
 __all__ = [
     'Graph',
     'Node',

front/py/deepx/autograd/_datanode.py

@@ -6,6 +6,7 @@ def __init__(self, name=None, type=None, data=None):
         super().__init__(name=name, ntype=NodeType.DATA)
         self._data = data
         self._type=type
+    @property
     def data(self):
         return self._data
 

front/py/deepx/autograd/graph.py

@@ -82,5 +82,10 @@ def add_control_flow(self,name,inputs=[]):
             node.add_input(input)
         self.nodes.append(node)
         return node
+def graph_method(f):
+    setattr(Graph, f.__name__, f)
+    return f
+
+
 # 初始化默认图
 Graph._default_graph = Graph()

front/py/deepx/autograd/graph_viz.py

@@ -1,18 +1,11 @@
 import graphviz
-from .graph import Graph
-from ._datanode import TensorNode
+from .graph import Graph,graph_method
+from ._datanode import DataNode
 from ._opnode import OpNode
-from ._controlflownode import ConstArgNode
 
-def graph_method(f):
-    """装饰器:将函数注册为Graph类的方法"""
-    # 延迟到模块加载完成后再绑定方法
-    from .graph import Graph
-    setattr(Graph, f.__name__, f)
-    return f
 
 @graph_method
-def to_dot(self):
+def to_dot(self)->graphviz.Digraph:
         """生成DOT格式的计算图可视化""" 
         dot = graphviz.Digraph(comment='Computational Graph')
         dot.attr(rankdir='TB')  # 从上到下的布局
@@ -27,49 +20,51 @@ def to_dot(self):
                 'labeljust': 'l'
             }
 
-            if isinstance(node, TensorNode):
-                # 张量节点：显示形状和梯度信息
-                label = f"{node.name}\n{node.tensor().shape if node.tensor() else ''}"
-                attrs.update({
-                    'shape': 'box',
-                    'color': 'skyblue',
-                    'style': 'filled',
-                    'fillcolor': 'aliceblue'
-                })
+            if isinstance(node, DataNode):
+                label=f'{node.name}\n'
+                match(node._type):
+                    case "var":
+                        label += f"{node.data}"
+                        attrs.update({
+                            'shape': 'box',
+                            'color': 'orange',
+                            'style': 'filled',
+                            'fillcolor': 'moccasin'
+                        })
+                    case "vector":
+                        label += f"{node.data}"
+                        attrs.update({
+                            'shape': 'box',
+                            'color': 'darkseagreen',
+                            'style': 'filled',
+                            'fillcolor': 'honeydew'
+                        })
+                    case "tensor":
+                        label += f"{node.data.shape if node.data else ''}"
+                        attrs.update({
+                            'shape': 'box',
+                            'color': 'skyblue',
+                            'style': 'filled',
+                            'fillcolor': 'aliceblue'
+                        })
+               
 
             elif isinstance(node, OpNode):
                 # 操作节点：突出显示操作类型
-                label = node.shortchar()
+                label = node.name
                 attrs.update({
                     'shape': 'box',
                     'style': 'filled',
                     'fillcolor': 'lightgray',
                     'color': 'darkslategray',
                     'fontname': 'Courier Bold'
                 })
-                
-            elif isinstance(node, ConstArgNode):
-                # 常量参数节点：显示参数值
-                if node.arg_type == 'int':
-                    value = str(node.get_int())
-                elif node.arg_type == 'float':
-                    value = f"{node.get_float():.2f}f"
-                else:  # string
-                    value = node.get_string()
-                label = value
-                attrs.update({
-                    'shape': 'diamond',
-                    'style': 'filled',
-                    'fillcolor': 'lightyellow',
-                    'color': 'goldenrod'
-                })
-                
             # 添加节点
             dot.node(str(id(node)), label, **attrs)
 
         # 添加边连接
         for node in self.nodes:
-            for input_name, input_node in node.inputs().items():
+            for input_node in node.inputs:
                 dot.edge(
                     str(id(input_node)), 
                     str(id(node)),

front/py/deepx/autograd/node.py

@@ -27,6 +27,11 @@ def graph(self):
     @property
     def name(self):
         return self._name
- 
+    
+    @property
+    def inputs(self):
+        return self._inputs
+
+    
     def add_input(self, input_node):
         self._inputs.append(input_node)

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

@@ -2,15 +2,15 @@
 from .new import newtensor
 from .print import printtensor
 from .matmul import matmul
-from .init import constant,full,zeros,ones,uniform_,arange,rand,randn,eye
+from .init import constant,full,zeros,ones,uniform,arange,rand,randn,eye
 from .reduce import max,min,sum,prod,mean
 from .transpose import transpose
 from .activite import relu
 
 __all__ = [
     "newtensor",
     "printtensor",
-    "constant","full","zeros","ones","uniform_","arange","rand","randn","eye",
+    "constant","full","zeros","ones","uniform","arange","rand","randn","eye",
     "add","sub","mul","div","clamp",
     "matmul",
     "max","min","sum","prod","mean",

front/py/deepx/nn/functional/activite.py

@@ -3,11 +3,12 @@
 from deepx.scheduler import send
 
 def relu(t: Tensor,inplace:bool=False)->Tensor:
-    out=t
-    if not inplace:
+    from .reduce import max as max_func
+    if inplace:
+        max_func(t,0,t)
+    else:
         out=Tensor(shape=t.shape, dtype=t.dtype, device=t.device)
-    ir=DeepxIR("max_scalar",t.dtype,[t._node.name,0], [out._node.name])
-    send(ir)
+        max_func(t,0,None,out)
     return out
 
  # 数学公式：σ(x) = 1 / (1 + exp(-x))

front/py/deepx/nn/functional/elementwise.py

@@ -21,14 +21,14 @@ def _A_b_elementwiseop_C(
         b: Optional[Union[ float, int]] = None, 
         op:str=None,
         out:Tensor=None):
+    varnode=a.graph.add_var("",b)
     opnode = a.graph.add_op(op)
     opnode.add_input(a.node)
-    varnode=a.graph.add_var("",b)
     opnode.add_input(varnode)
     out.node.add_input(opnode)
     if a.graph.eager:
         varir=DeepxIR("argset", a.dtype, [b], [varnode.name])
-        send(str(varir))
+        send(varir)
         ir=DeepxIR(op, a.dtype, [a.node.name,varnode.name], [out.node.name])
         send(ir)
 #add

front/py/deepx/nn/functional/init.py

@@ -1,18 +1,21 @@
-from typing import Optional
+from typing import Optional,Union
 
 from deepx import Tensor
 from deepx.autograd.graph import OpNode
 from deepx.nn.deepxir import DeepxIR
 from deepx.scheduler import send
 
-def constant(t:Tensor, fill_value):
+OpNode.register("constant")
+
+def constant(t:Tensor, value:Optional[Union[
+    float,int
+]]=None) -> Tensor:
     opnode = t.graph.add_op("constant")
-    opnode.add_input(t.node)
-    argnode=t.graph.add_var('',fill_value)
+    argnode=t.graph.add_var('',value)   
     opnode.add_input(argnode)
     t.node.add_input(opnode)
     if t.graph.eager:
-        ir=DeepxIR("constant", t.dtype, [fill_value], [t.node.name])
+        ir=DeepxIR("constant", t.dtype, [value], [t.node.name])
         send(ir)
     return t
 
@@ -30,13 +33,15 @@ def ones(*size, dtype=None, device=None):
     return full(*size, fill_value=1, dtype=dtype, device=device)
 
 OpNode.register("uniform")
-def uniform_(t:Tensor,low=0, high=1)->Tensor:
+def uniform(t:Tensor,low=0, high=1)->Tensor:
     if low >= high:
         raise ValueError(f"low({low})必须小于high({high})")
-    opnode = t.graph.add_op("uniform")
-    opnode.add_input(t.node)
-    arglow=t.graph.add_var('',low)
-    arghigh=t.graph.add_var('',high)
+    if t is None:
+        raise ValueError("t不能为None")
+    g=t.graph
+    arglow=g.add_var('',low)
+    arghigh=g.add_var('',high)
+    opnode = g.add_op("uniform")
     opnode.add_input(arglow)
     opnode.add_input(arghigh)
     t.node.add_input(opnode)