fix(front): 将stub改为可以接收GraphProto作为输入，消除分布式脚本保存额外的onnx文件， 采用int64作为index输入类型

examples/distributed/cuda_launch.py

@@ -80,16 +80,18 @@ def start_worker(
     name: str, world_size: int, rank: int, local_rank: int, model: onnx.ModelProto, data_type: str
 ):
     dist_name = name + "_dist"
-    model = parallel_model(model, world_size, rank)
-    extern_path = f"./{dist_name}_rank{rank}.pb"
-    if os.path.exists(extern_path):
-        os.remove(extern_path)
-    onnx.save_model(
-        model,
-        f"./{dist_name}_rank{rank}.onnx",
-        save_as_external_data=True,
-        location=extern_path,
-    )
+    #model = parallel_model(model, world_size, rank)
+    graph = parallel_model(model, world_size, rank)
+
+    # extern_path = f"./{dist_name}_rank{rank}.pb"
+    # if os.path.exists(extern_path):
+    #     os.remove(extern_path)
+    # onnx.save_model(
+    #     model,
+    #     f"./{dist_name}_rank{rank}.onnx",
+    #     save_as_external_data=True,
+    #     location=extern_path,
+    # )
     #infer_shapes_path(f"./{dist_name}_rank{rank}.onnx")
     runtime = backend.CudaRuntime(local_rank)
     # print("init comm")
@@ -98,7 +100,8 @@ def start_worker(
         world_size,
         rank,
     )
-    run_and_compare(name, model, runtime, data_type)
+    #run_and_compare(name, model, runtime, data_type)
+    run_and_compare(name, graph, runtime, data_type)
 
 
 def start_single(name, model, data_type):

examples/distributed/parallel_opt.py

@@ -243,6 +243,7 @@ def parallel_model(model: ModelProto, tp_world_size: int = 1, tp_rank: int = 0):
         tt = output.type.tensor_type
         if tt.HasField("shape"):
             tt.ClearField("shape")
-    model = helper.make_model(graph)
+    #model = helper.make_model(graph)
     #model = onnx.shape_inference.infer_shapes(model)
-    return model
+    #return model
+    return graph

examples/distributed/run_pytorch.py

@@ -57,7 +57,7 @@ def get_model(modelname):
     return model, voc_size
 
 def run_pytorch(torch_model, voc_size, batchsize, len):
-    data = np.random.randint(0, voc_size, (batchsize, len), dtype=np.int32)
+    data = np.random.randint(0, voc_size, (batchsize, len), dtype=np.int64)
     np.save("test_inputs", data)
     inputs = torch.from_numpy(data).to("cuda")
     torch_model = torch_model.to("cuda")
@@ -177,7 +177,7 @@ def main():
     if export_path is not None:
         filename = "{}_{}_{}.onnx".format(modelname, batchsize, seqlen)
         path = os.path.join(export_path, filename)
-        param = torch.zeros((batchsize, seqlen), dtype=torch.int)
+        param = torch.zeros((batchsize, seqlen), dtype=torch.int64)
         export_onnx(model, param, path, True)
 
     if data_type == "fp16":

pyinfinitensor/src/pyinfinitensor/onnx.py

@@ -6,6 +6,7 @@ from onnx import (
     AttributeProto,
     TensorShapeProto,
     ValueInfoProto,
+    GraphProto,
 )
 from onnx.helper import (
     make_node,
@@ -40,22 +41,27 @@ class OnnxStub:
 
     def __init__(
         self,
-        model: ModelProto,
+        model: ModelProto | GraphProto,
         runtime,
         use_naive_allocator: bool = False,
         matmul_compute_type: str = "default",
     ):
-        # We use some user-defined operators for distributed inference
-        try:
-            # onnx simplifier performs inplace simplify
-            model_simp, check = simplify(copy.deepcopy(model))
-            if check:
-                model = model_simp
-        except ValidationError:
-            pass
-        except RuntimeError:
-            pass
-
+        if isinstance(model, ModelProto):
+            # We use some user-defined operators for distributed inference
+            try:
+                # onnx simplifier performs inplace simplify
+                model_simp, check = simplify(copy.deepcopy(model))
+                if check:
+                    model = model_simp
+            except ValidationError:
+                pass
+            except RuntimeError:
+                pass
+            graph = model.graph
+        elif isinstance(model, GraphProto):
+            graph = model
+        else:
+            raise RuntimeError("Expect onnx ModelProto or GraphProto as input.")
         self.inputs: Dict[str, backend.Tensor] = {}
         self.outputs: Dict[str, backend.Tensor] = {}
         self.tensors: Dict[str, backend.Tensor] = {}
@@ -67,10 +73,12 @@ class OnnxStub:
         # except:
         #     warnings.warn("infer_shapes failed.")
         self.handler = backend.GraphHandler(runtime)
+        self.init_graph(graph, matmul_compute_type)
 
+    def init_graph(self, graph: GraphProto, matmul_compute_type: str):
         # 处理重名和匿名算子
         names = {}
-        for node in model.graph.node:
+        for node in graph.node:
             if node.name == "":
                 node.name = "missing_name(" + node.op_type + ")"
             if node.name in names:
@@ -80,11 +88,11 @@ class OnnxStub:
                 names[node.name] = 0
         # 拓扑排序
         sorted_nodes = []
-        known_edge = set(t.name for t in model.graph.input)
-        known_edge.update(t.name for t in model.graph.initializer)
-        while len(sorted_nodes) < len(model.graph.node):
+        known_edge = set(t.name for t in graph.input)
+        known_edge.update(t.name for t in graph.initializer)
+        while len(sorted_nodes) < len(graph.node):
             updated = False
-            for i, node in enumerate(model.graph.node):
+            for i, node in enumerate(graph.node):
                 if all(t in known_edge for t in node.input):
                     node.name = str(len(sorted_nodes)) + "_" + node.name
                     sorted_nodes.append(i)
@@ -98,13 +106,13 @@ class OnnxStub:
         tensors: Dict[str, backend.Tensor] = dict()
         data: Dict[str, TensorProto] = dict()
 
-        for initializer in model.graph.initializer:
+        for initializer in graph.initializer:
             dims = [d for d in initializer.dims]
             tensors[initializer.name] = self.handler.tensor(dims, initializer.data_type)
             data[initializer.name] = initializer
             tensors[initializer.name].set_weight()
 
-        for input in model.graph.input:
+        for input in graph.input:
             dims = _take_shape_dim(input.type.tensor_type.shape)
             if input.name not in tensors.keys():
                 tensors[input.name] = self.handler.tensor(
@@ -114,7 +122,7 @@ class OnnxStub:
 
 
         for node_idx in sorted_nodes:
-            node = model.graph.node[node_idx]
+            node = graph.node[node_idx]
             if node.op_type == "Conv":
                 attributes = _parse_attribute(
                     node,
@@ -994,7 +1002,7 @@ class OnnxStub:
             else:
                 raise Exception('Unsupported operator "{}"'.format(node.op_type))
 
-        for output in model.graph.output:
+        for output in graph.output:
             tensors[output.name].set_output()
         ################################
         # Allocate memory space for data
@@ -1007,7 +1015,7 @@ class OnnxStub:
         for name, obj in tensors.items():
             tensor = data.get(name)
             if tensor == None:
-                if any(input.name == name for input in model.graph.input):
+                if any(input.name == name for input in graph.input):
                     self.inputs[name] = obj
             else:
                 self.initializer[obj.fuid()] = tensor
@@ -1033,7 +1041,7 @@ class OnnxStub:
         for name, obj in tensors.items():
             self.tensors[name] = obj
 
-        for output in model.graph.output:
+        for output in graph.output:
             self.outputs[output.name] = tensors[output.name]
 
     def to_onnx(self, name: str) -> ModelProto: