Add send and recv operators based on NCCL (#182)

* baseline sendrecv, bug

* success sendrecv

* get rank from comm

* set output shape

* successful:set output shape equal to input shape

* shape as attribute

* success:shape as attribute

* success send recv, output 0

* add onnx test

* split send and recv

* success split send and recv

* test-onnx bug

* success test-onnx

* modified onnx.py

* solve review

include/core/graph_handler.h

@@ -94,6 +94,9 @@ class GraphHandlerObj {
     Tensor allReduceAvg(Tensor input, Tensor output);
     TensorVec allGather(Tensor input, std::optional<TensorVec> outputs, int n);
     Tensor broadcast(Tensor input, Tensor output, int root);
+    Tensor send(Tensor input, int source, int destination, Tensor output);
+    Tensor recv(Tensor output, int source, int destination, Shape dims,
+                int outputType, Tensor input);
     Tensor depthToSpace(Tensor input, Tensor output, int blocksize,
                         std::string mode);
 

include/core/op_type.h

@@ -232,6 +232,8 @@ struct OpType {
         AllReduceAvg,
         AllGather,
         Broadcast,
+        Send,
+        Recv,
     } type;
 
     constexpr OpType(decltype(type) t) : type(t) {}

include/operators/recv.h

@@ -0,0 +1,46 @@
+#pragma once
+#include "core/operator.h"
+
+namespace infini {
+/**
+ *
+ * https://docs.nvidia.com/deeplearning/nccl/archives/nccl_2193/user-guide/docs/index.html
+ */
+class RecvObj : public OperatorObj {
+
+  public:
+    /**
+     * @brief Construct a new SendRecv object
+     *
+     * @param graph The computation graph that this operator belongs to.
+     * @param input default nullptr, because recv does not have input.
+     * @param output recv output
+     * @param source the send rank
+     * @param destination the recv rank
+     * @param dims The shape of the output tensor.
+     */
+    RecvObj(GraphObj *graph, Tensor output, int source, int destination,
+            Shape dims, int outputType, Tensor input = nullptr);
+    OP_CLONE(RecvObj);
+
+    int numInputs() const override { return inputs.size(); }
+    int numOutputs() const override { return 1; }
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
+    std::string toString() const override;
+    DataType getDType() const;
+    int getSourceRank() const { return source; }
+    int getDestinationRank() const { return destination; }
+    inline Shape getShape() const { return dims; }
+
+  private:
+    vector<int> getWorkloadVector() const override;
+    vector<int> getOpAttrVector() const override;
+    vector<DataType> inferDataType(const TensorVec &inputs) const override;
+
+  protected:
+    int source;
+    int destination;
+    Shape dims;
+    int outputType;
+};
+} // namespace infini

include/operators/send.h

@@ -0,0 +1,42 @@
+#pragma once
+#include "core/operator.h"
+
+namespace infini {
+/**
+ *
+ * https://docs.nvidia.com/deeplearning/nccl/archives/nccl_2193/user-guide/docs/index.html
+ */
+class SendObj : public OperatorObj {
+
+  public:
+    /**
+     * @brief Construct a new SendRecv object
+     *
+     * @param graph The computation graph that this operator belongs to.
+     * @param input send input
+     * @param output recv output
+     * @param source the send rank
+     * @param destination the recv rank
+     */
+    SendObj(GraphObj *graph, Tensor input, int source, int destination,
+            Tensor output = nullptr);
+    OP_CLONE(SendObj);
+
+    int numInputs() const override { return 1; }
+    int numOutputs() const override { return outputs.size(); }
+    std::string toString() const override;
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
+
+    int getSourceRank() const { return source; }
+    int getDestinationRank() const { return destination; }
+
+  private:
+    vector<int> getWorkloadVector() const override;
+    vector<int> getOpAttrVector() const override;
+    vector<DataType> inferDataType(const TensorVec &inputs) const override;
+
+  protected:
+    int source;
+    int destination;
+};
+} // namespace infini

pyinfinitensor/src/pyinfinitensor/onnx.py

@@ -716,14 +716,17 @@ class OnnxStub:
                                 ),
                                 None,
                             )
-                        keepdims = next(
+                        keepdims = (
+                            next(
                                 (
                                     attr.i
                                     for attr in node.attribute
                                     if attr.name == "keepdims"
                                 ),
                                 1,
-                        ) != 0
+                            )
+                            != 0
+                        )
 
                         tensors[node.output[0]] = self.handler.reduceSum(
                             tensors[node.input[0]],
@@ -775,6 +778,58 @@ class OnnxStub:
                             0,
                         ),
                     )
+                elif node.op_type == "Send":
+                    source = next(
+                        (attr.i for attr in node.attribute if attr.name == "source"),
+                        0,
+                    )
+                    destination = next(
+                        (
+                            attr.i
+                            for attr in node.attribute
+                            if attr.name == "destination"
+                        ),
+                        0,
+                    )
+
+                    self.handler.send(
+                        tensors[node.input[0]],
+                        source,
+                        destination,
+                        None,
+                    )
+                elif node.op_type == "Recv":
+                    source = next(
+                        (attr.i for attr in node.attribute if attr.name == "source"),
+                        0,
+                    )
+                    destination = next(
+                        (
+                            attr.i
+                            for attr in node.attribute
+                            if attr.name == "destination"
+                        ),
+                        0,
+                    )
+
+                    for attr in node.attribute:
+                        if attr.name == "shape":
+                            shapeBasic = attr.ints
+                    shape = []
+                    for item in shapeBasic:
+                        shape.append(item)
+
+                    for attr in node.attribute:
+                        if attr.name == "dataType":
+                            outputType = attr.i
+                    tensors[node.output[0]] = self.handler.recv(
+                        tensors.get(node.output[0]),
+                        source,
+                        destination,
+                        shape,
+                        outputType,
+                        None,
+                    )
                 elif node.op_type == "Expand":
                     shape = _parse_data(data[node.input[1]])
                     tensors[node.output[0]] = self.handler.expand(
@@ -1091,10 +1146,7 @@ class OnnxStub:
             elif ty == backend.OpTypeId.Gather:
                 axis = backend.gather_axis_of(op)
                 ctx.push_node(make_node(ty.name, inputs, outputs, name, axis=axis))
-            elif ty in [
+            elif ty in [backend.OpTypeId.ReduceMean, backend.OpTypeId.ReduceSum]:
-                backend.OpTypeId.ReduceMean,
-                backend.OpTypeId.ReduceSum
-            ]:
                 axes, keepdims = backend.reduce_attrs_of(op)
                 inputs.append(
                     ctx.push_data_input(

pyinfinitensor/tests/test_onnx.py

@@ -508,6 +508,29 @@ class TestStringMethods(unittest.TestCase):
         where = make_node("Where", ["x", "y", "con"], ["output"], name="where")
         make_and_import_model(make_graph([where], "where", [x, y, con], [output]))
 
+    def test_send(self):
+        sendInput = make_tensor_value_info("input", TensorProto.FLOAT, [1, 3, 5, 7])
+        send = make_node("Send", ["input"], [], name="send", source=0, destination=1)
+        graph = make_graph([send], "send", [sendInput], [])
+        model = make_model(graph)
+        from_onnx(model, backend.cpu_runtime())
+
+    def test_recv(self):
+        recvOutput = make_tensor_value_info("output", TensorProto.FLOAT, [1, 3, 5, 7])
+        recv = make_node(
+            "Recv",
+            [],
+            ["output"],
+            name="recv",
+            source=0,
+            destination=1,
+            shape=[1, 3, 5, 7],
+            dataType=1,
+        )
+        graph = make_graph([recv], "recv", [], [recvOutput])
+        model = make_model(graph)
+        from_onnx(model, backend.cpu_runtime())
+
 
 class TestDynamicTensor(unittest.TestCase):
     def test_dynamic_tensor(self):
@@ -517,6 +540,7 @@ class TestDynamicTensor(unittest.TestCase):
         for root, dirs, files in os.walk(current_path):
             if filename in files:
                 model_file = os.path.join(root, filename)
+
         model = OnnxStub(onnx.load(model_file), backend.cpu_runtime())
         output_key = list(model.outputs.keys())[0]
         old_output_shape = model.getShape(output_key)

src/core/graph.cc

@@ -11,20 +11,33 @@ GraphObj::GraphObj(Runtime runtime, OpVec ops_in)
     map<UidBaseType, Tensor> tensorPool;
     // Clone tensors
     for (const auto &op : ops_in) {
-        for (const auto &t : op->getInputs())
+        for (const auto &t : op->getInputs()) {
+            if (t) {
                 if (tensorPool.find(t->getFuid()) == tensorPool.end())
                     tensorPool[t->getFuid()] = cloneTensor(t);
-        for (const auto &t : op->getOutputs())
+            }
+        }
+        for (const auto &t : op->getOutputs()) {
+            if (t) {
                 if (tensorPool.find(t->getFuid()) == tensorPool.end())
                     tensorPool[t->getFuid()] = cloneTensor(t);
             }
+        }
+    }
     // Clone operators and add connections
     for (const auto &op : ops_in) {
         TensorVec inputs, outputs;
-        for (const auto &t : op->getInputs())
+        for (const auto &t : op->getInputs()) {
+            if (t) {
                 inputs.emplace_back(tensorPool.at(t->getFuid()));
-        for (const auto &t : op->getOutputs())
+            }
+        }
+
+        for (const auto &t : op->getOutputs()) {
+            if (t) {
                 outputs.emplace_back(tensorPool.at(t->getFuid()));
+            }
+        }
         addOperatorAndConnect(op->clone(inputs, outputs));
     }
 }
@@ -33,13 +46,16 @@ void GraphObj::addOperatorAndConnect(const Operator &op) {
     sorted = false;
     ops.push_back(op);
     for (auto &input : op->getInputs()) {
+        if (input) {
             input->addTarget(op);
             if (auto pred = input->getSource()) {
                 pred->addSuccessors(op);
                 op->addPredecessors(pred);
             }
         }
+    }
     for (auto &output : op->getOutputs()) {
+        if (output) {
             output->setSource(op);
             for (auto &succ : output->getTargets()) {
                 succ->addPredecessors(op);
@@ -47,6 +63,7 @@ void GraphObj::addOperatorAndConnect(const Operator &op) {
             }
         }
     }
+}
 
 string GraphObj::toString() const {
     std::ostringstream oss;
@@ -88,8 +105,9 @@ bool GraphObj::topo_sort() {
             const auto is_head = std::all_of(
                 this_inputs.begin(), this_inputs.end(), [&](const auto &input) {
                     auto src = input->getSource();
-                    return src // If the source node is in the waiting list,
+                    return src // If the source node is in the waiting
-                               // means that this node is not the head node.
+                               // list, means that this node is not the
+                               // head node.
                                ? waiting.find(src) == waiting.end()
                                // This tensor has no source node,
                                // it must be a input tensor.
@@ -110,7 +128,6 @@ bool GraphObj::topo_sort() {
             return false;
         }
     }
-
     // Done.
     this->ops = std::move(sorted);
     return this->sorted = true;
@@ -155,6 +172,7 @@ void GraphObj::shape_infer() {
 
 void GraphObj::dataMalloc(bool useNaiveAllocator, size_t memPoolSize) {
     // topological sorting first
+
     IT_ASSERT(topo_sort() == true);
     if (useNaiveAllocator) {
         // can not set memory pool when use naive allocator
@@ -222,13 +240,16 @@ void GraphObj::dataMalloc(bool useNaiveAllocator, size_t memPoolSize) {
         // memory should be allocated for the op's output first
         auto outputs = op->getOutputs();
         for (auto &tensor : outputs) {
+            if (tensor) {
                 if (tensor->isOthers()) {
                     tensorToOffset[tensor.get()] =
                         allocator.alloc(tensor->getBytes());
                 }
             }
+        }
         auto inputs = op->getInputs();
         for (auto &tensor : inputs) {
+            if (tensor) {
                 if (tensor->isOthers()) {
                     auto tensorIter = tensorToRefCount.find(tensor.get());
                     IT_ASSERT(tensorIter != tensorToRefCount.end());
@@ -244,6 +265,7 @@ void GraphObj::dataMalloc(bool useNaiveAllocator, size_t memPoolSize) {
                 }
             }
         }
+    }
 
     // perform actual memory allocation for non-weight tensors
     for (auto &tensor : tensors) {

src/core/graph_handler.cc

@@ -13,8 +13,10 @@
 #include "operators/matmul.h"
 #include "operators/pad.h"
 #include "operators/pooling.h"
+#include "operators/recv.h"
 #include "operators/reduce.h"
 #include "operators/reshape.h"
+#include "operators/send.h"
 #include "operators/slice.h"
 #include "operators/softmax.h"
 #include "operators/split.h"
@@ -434,6 +436,39 @@ Tensor GraphHandlerObj::broadcast(Tensor input, Tensor output, int root) {
     }
 }
 
+Tensor GraphHandlerObj::send(Tensor input, int source, int destination,
+                             Tensor output) {
+    if (output) {
+
+        g->addOpWithOutputs<SendObj>(std::move(input), source, destination,
+                                     output);
+
+        return output;
+    } else {
+        return g->addOp<SendObj>(std::move(input), source, destination, output)
+            ->getOutput();
+    }
+}
+
+Tensor GraphHandlerObj::recv(Tensor output, int source, int destination,
+                             Shape dims, int outputType, Tensor input) {
+
+    if (output) {
+
+        g->addOpWithOutputs<RecvObj>(output, source, destination,
+                                     std::move(dims), outputType,
+                                     std::move(input));
+
+        return output;
+    } else {
+
+        return g
+            ->addOp<RecvObj>(output, source, destination, std::move(dims),
+                             outputType, std::move(input))
+            ->getOutput();
+    }
+}
+
 Tensor GraphHandlerObj::cast(Tensor input, Tensor output, int to) {
     if (output) {
         g->addOpWithOutputs<CastObj>(std::move(input), output,

src/core/operator.cc

@@ -6,9 +6,11 @@ namespace infini {
 
 OperatorObj::OperatorObj(OpType opType, TensorVec inputs, TensorVec outputs)
     : type(opType), inputs(inputs), outputs(outputs) {
+    if (opType != OpType::Recv) {
         for (const auto &t : inputs)
             IT_ASSERT(t);
     }
+}
 
 void OperatorObj::removePredecessors(const Operator &op) {
     for (auto it = predecessors.begin(); it != predecessors.end();) {

src/ffi/ffi_infinitensor.cc

@@ -511,6 +511,8 @@ void init_graph_builder(py::module &m) {
         .def("allReduceAvg", &Handler::allReduceAvg, policy::move)
         .def("allGather", &Handler::allGather, policy::move)
         .def("broadcast", &Handler::broadcast, policy::move)
+        .def("send", &Handler::send, policy::move)
+        .def("recv", &Handler::recv, policy::move)
         .def("cast", &Handler::cast, policy::move)
         .def("expand", &Handler::expand, policy::move)
         .def("erf", &Handler::erf, policy::move)

src/kernels/cuda/recv.cc

@@ -0,0 +1,47 @@
+#ifdef INFINI_USE_NCCL
+#include "operators/recv.h"
+#include "cuda/cuda_kernel_wihtout_config.h"
+#include "cuda/cuda_runtime.h"
+#include "cuda/nccl_communicator.h"
+
+namespace infini {
+class RecvNCCL : public CudaKernelWithoutConfig {
+  public:
+    void compute(const Operator &_op,
+                 const RuntimeObj *_context) const override {
+        auto op = as<RecvObj>(_op);
+        auto context = dynamic_cast<const CudaRuntimeObj *>(_context);
+
+        void *output = op->getOutput(0)->getRawDataPtr<void *>();
+        IT_ASSERT(op->getDType() == DataType::Float32);
+        const auto shape = op->getShape();
+        int nDims = shape.size();
+        int outputCount = 1;
+        for (int i = 0; i < nDims; i++) {
+            outputCount *= shape[i];
+        }
+
+        ncclComm_t comm =
+            dynamic_cast<NcclCommunicatorObj &>(context->getCommunicator())
+                .getNcclComm();
+        // TODO: Using default stream 0 for now.
+        int rank;
+
+        checkNcclError(ncclCommUserRank(comm, &rank));
+
+        int source = op->getSourceRank();
+        int destination = op->getDestinationRank();
+
+        if (rank == destination) {
+
+            checkNcclError(
+                ncclRecv(output, outputCount, ncclFloat, source, comm, 0));
+        }
+    }
+};
+
+REGISTER_KERNEL(Device::CUDA, OpType::Recv, DataType::Float32, RecvNCCL,
+                "Recv_NCCL_CUDA_Float32");
+} // namespace infini
+
+#endif

src/kernels/cuda/send.cc

@@ -0,0 +1,43 @@
+#ifdef INFINI_USE_NCCL
+#include "operators/send.h"
+#include "cuda/cuda_kernel_wihtout_config.h"
+#include "cuda/cuda_runtime.h"
+#include "cuda/nccl_communicator.h"
+
+namespace infini {
+class SendNCCL : public CudaKernelWithoutConfig {
+  public:
+    void compute(const Operator &_op,
+                 const RuntimeObj *_context) const override {
+        auto op = as<SendObj>(_op);
+        auto context = dynamic_cast<const CudaRuntimeObj *>(_context);
+        void *input = op->getInputs(0)->getRawDataPtr<void *>();
+
+        IT_ASSERT(op->getDType() == DataType::Float32);
+        size_t inputCount =
+            op->getInputs(0)->getBytes() / op->getDType().getSize();
+
+        ncclComm_t comm =
+            dynamic_cast<NcclCommunicatorObj &>(context->getCommunicator())
+                .getNcclComm();
+        // TODO: Using default stream 0 for now.
+        int rank;
+
+        checkNcclError(ncclCommUserRank(comm, &rank));
+
+        int source = op->getSourceRank();
+        int destination = op->getDestinationRank();
+
+        if (rank == source) {
+
+            checkNcclError(
+                ncclSend(input, inputCount, ncclFloat, destination, comm, 0));
+        }
+    }
+};
+
+REGISTER_KERNEL(Device::CUDA, OpType::Send, DataType::Float32, SendNCCL,
+                "Send_NCCL_CUDA_Float32");
+} // namespace infini
+
+#endif

src/operators/recv.cc

@@ -0,0 +1,49 @@
+#include "operators/recv.h"
+
+namespace infini {
+RecvObj::RecvObj(GraphObj *graph, Tensor output, int source, int destination,
+                 Shape dims, int outputType, [[maybe_unused]] Tensor input)
+    : OperatorObj(OpType::Recv, input ? TensorVec{input} : TensorVec{},
+                  TensorVec{output}),
+      source(source), destination(destination), dims(std::move(dims)),
+      outputType(outputType) {
+
+    IT_ASSERT(checkValid(graph));
+}
+optional<vector<Shape>> RecvObj::inferShape(const TensorVec &inputs) {
+    return {{dims}};
+}
+vector<DataType> RecvObj::inferDataType(const TensorVec &inputs) const {
+    return {{DataType(outputType)}};
+}
+DataType RecvObj::getDType() const { return getOutput(0)->getDType(); }
+std::string RecvObj::toString() const {
+    std::ostringstream os;
+    os << "Recv"
+       << "[" << getGuid() << "]";
+    os << "(";
+    os << vecToString(dims) << ",";
+    os << "output=" << outputs[0]->getGuid() << ",";
+    os << "dims=" << vecToString(dims) << ")";
+    return os.str();
+}
+
+vector<int> RecvObj::getWorkloadVector() const {
+    vector<int> ret = dims;
+    ret.insert(ret.end(), dims.begin(), dims.end());
+    ret.emplace(ret.begin(), type.underlying());
+
+    ret.emplace_back(source);
+    ret.emplace_back(destination);
+
+    return ret;
+}
+
+vector<int> RecvObj::getOpAttrVector() const {
+    vector<int> ret = dims;
+    ret.emplace(ret.begin(), type.underlying());
+    ret.emplace_back(source);
+    ret.emplace_back(destination);
+    return ret;
+}
+} // namespace infini

src/operators/send.cc

@@ -0,0 +1,46 @@
+#include "operators/send.h"
+
+namespace infini {
+SendObj::SendObj(GraphObj *graph, Tensor input, int source, int destination,
+                 [[maybe_unused]] Tensor output)
+    : OperatorObj(OpType::Send, TensorVec{input},
+                  TensorVec{output ? output : nullptr}),
+      source(source), destination(destination) {
+
+    IT_ASSERT(checkValid(graph));
+}
+optional<vector<Shape>> SendObj::inferShape(const TensorVec &inputs) {
+    return {{inputs[0]->getDims()}};
+}
+vector<DataType> SendObj::inferDataType(const TensorVec &inputs) const {
+    return {{inputs[0]->getDType()}};
+}
+
+std::string SendObj::toString() const {
+    std::ostringstream os;
+    os << "Send"
+       << "[" << getGuid() << "]";
+    os << "(";
+    os << vecToString(inputs[0]->getDims()) << ",";
+    os << "input=" << inputs[0]->getGuid() << ")";
+    return os.str();
+}
+
+vector<int> SendObj::getWorkloadVector() const {
+    vector<int> ret = inputs[0]->getDims();
+
+    ret.emplace(ret.begin(), type.underlying());
+    ret.emplace_back(source);
+    ret.emplace_back(destination);
+
+    return ret;
+}
+
+vector<int> SendObj::getOpAttrVector() const {
+    vector<int> ret = inputs[0]->getDims();
+    ret.emplace(ret.begin(), type.underlying());
+    ret.emplace_back(source);
+    ret.emplace_back(destination);
+    return ret;
+}
+} // namespace infini

test/kernels/cuda/test_cuda_sendrecv.cc

@@ -0,0 +1,90 @@
+#ifdef INFINI_USE_NCCL
+#include "core/graph.h"
+#include "core/runtime.h"
+#include "cuda/cuda_runtime.h"
+#include "cuda/cuda_utility.h"
+#include "operators/recv.h"
+#include "operators/send.h"
+#include "test.h"
+#include <nccl.h>
+#include <thread>
+
+namespace infini {
+
+void sendrecv(const string taskName, int deviceID, vector<float> data,
+              const Shape &dataShape, int WORLD_SIZE, int source,
+              int destination) {
+    // Create Runtimes and initiate communication
+    Runtime cpuRuntime = NativeCpuRuntimeObj::getInstance();
+    Runtime cudaRuntime = make_ref<CudaRuntimeObj>(deviceID);
+    cudaRuntime->initComm(taskName, WORLD_SIZE, deviceID);
+
+    if (deviceID == source) {
+        Graph gSend = make_ref<GraphObj>(cudaRuntime);
+        auto input = gSend->addTensor(Shape{static_cast<int>(data.size())},
+                                      DataType::Float32);
+        auto opSend =
+            gSend->addOp<SendObj>(input, source, destination, nullptr);
+
+        // Copy data from CPU to GPU
+        gSend->dataMalloc();
+        input->copyin(data);
+        cudaRuntime->run(gSend);
+    }
+
+    // ----------------
+
+    if (deviceID == destination) {
+        Graph gRecv = make_ref<GraphObj>(cudaRuntime);
+        int outputType = 1;
+        // auto input =
+        // gRecv->addTensor(Shape{static_cast<int>(data.size())},DataType::Float32);
+        auto opRecv = gRecv->addOp<RecvObj>(nullptr, source, destination,
+                                            dataShape, outputType, nullptr);
+        gRecv->dataMalloc();
+        cudaRuntime->run(gRecv);
+
+        auto result = opRecv->getOutput()->clone(cpuRuntime);
+        EXPECT_TRUE(result->equalData(data));
+    }
+}
+
+TEST(CUDA_SendRecv1, run) {
+    // Only 1 device gets data. Every rank should have the same data after
+    // sendrecv.
+    vector<float> data = {2., 3., 5., 6.};
+
+    int WORLD_SIZE = 4;
+    int source = 0;
+    int destination = 2;
+    std::vector<std::thread> threads;
+    for (int gpu = 0; gpu < WORLD_SIZE; ++gpu) {
+        threads.emplace_back(sendrecv, "test_sendrecv", gpu, data, Shape{2, 2},
+                             WORLD_SIZE, source, destination);
+    }
+
+    for (auto &thread : threads) {
+        thread.join();
+    }
+}
+
+TEST(CUDA_SendRecv2, run) {
+    // Only 1 device gets data. Every rank should have the same data after
+    // sendrecv.
+    vector<float> data = {2., 3., 5., 6.};
+
+    int WORLD_SIZE = 3;
+    int source = 0;
+    int destination = 2;
+    std::vector<std::thread> threads;
+    for (int gpu = 0; gpu < WORLD_SIZE; ++gpu) {
+        threads.emplace_back(sendrecv, "test_sendrecv", gpu, data, Shape{2, 2},
+                             WORLD_SIZE, source, destination);
+    }
+
+    for (auto &thread : threads) {
+        thread.join();
+    }
+}
+} // namespace infini
+#endif

test/operators/test_sendrecv.cc

@@ -0,0 +1,38 @@
+#include "core/graph.h"
+#include "core/runtime.h"
+#include "operators/recv.h"
+#include "operators/send.h"
+#include "test.h"
+
+namespace infini {
+TEST(Send, ShapeTypeInfer) {
+    auto runtime = NativeCpuRuntimeObj::getInstance();
+    int source = 0;
+    int destination = 1;
+    Shape dims = {1, 3, 2, 4};
+    {
+        Graph g = make_ref<GraphObj>(runtime);
+        Tensor input = g->addTensor(dims, DataType::Float32);
+        auto op = g->addOp<SendObj>(input, source, destination, nullptr);
+        EXPECT_EQ(op->getOpType(), OpType::Send);
+        EXPECT_EQ(op->getInputs(0)->getDims(), (dims));
+        EXPECT_EQ(op->getInputs(0)->getDType(), DataType::Float32);
+    }
+}
+TEST(Recv, ShapeTypeInfer) {
+    auto runtime = NativeCpuRuntimeObj::getInstance();
+    int source = 0;
+    int destination = 1;
+    Shape dims = {1, 3, 2, 4};
+    int outputType = 1;
+    {
+        Graph g = make_ref<GraphObj>(runtime);
+        Tensor input = g->addTensor(dims, DataType::Float32);
+        auto op = g->addOp<RecvObj>(nullptr, source, destination, dims,
+                                    outputType, input);
+        EXPECT_EQ(op->getOpType(), OpType::Recv);
+        EXPECT_EQ(op->getOutput()->getDims(), (dims));
+        EXPECT_EQ(op->getOutput()->getDType(), DataType::Float32);
+    }
+}
+} // namespace infini