add kunlun layernorm

include/core/graph_handler.h

@@ -53,6 +53,7 @@ class GraphHandlerObj {
     Tensor max(Tensor a, Tensor b, Tensor c);
 
     Tensor relu(Tensor x, Tensor y);
+    Tensor leakyRelu(Tensor x, Tensor y, float alpha);
     Tensor silu(Tensor x, Tensor y);
     Tensor gelu(Tensor x, Tensor y);
     Tensor sigmoid(Tensor x, Tensor y);

include/cuda/cuda_unary.h

@@ -15,6 +15,8 @@ template <typename T> void gelu_kernel(T *input, T *output, size_t num);
 template <typename T> void erf_kernel(T *input, T *output, size_t num);
 template <typename T> void hard_sigmoid_kernel(T *input, T *output, size_t num);
 template <typename T> void hard_swish_kernel(T *input, T *output, size_t num);
+template <typename T>
+void leaky_relu_kernel(T *input, T *output, size_t num, float alpha);
 
 template <typename INPUT, typename OUTPUT>
 void cast_kernel(INPUT *input, OUTPUT *output, size_t num);

include/operators/unary.h

@@ -228,6 +228,23 @@ class PReluObj : public OperatorObj {
     vector<int> getOpAttrVector() const override;
 };
 
+class LeakyReluObj : public OperatorObj {
+  public:
+    LeakyReluObj(GraphObj *graph, Tensor input, Tensor output, float alpha);
+    OP_CLONE(LeakyReluObj);
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
+
+    std::string toString() const override;
+    float getAlpha() const { return alphaValue; }
+    int numInputs() const override { return 1; }
+    int numOutputs() const override { return 1; }
+
+  private:
+    float alphaValue;
+    vector<int> getWorkloadVector() const override;
+    vector<int> getOpAttrVector() const override;
+};
+
 class LogObj : public OperatorObj {
   public:
     enum LogType {

pyinfinitensor/src/pyinfinitensor/onnx.py

@@ -85,7 +85,8 @@ class OnnxStub:
         while len(sorted_nodes) < len(model.graph.node):
             updated = False
             for i, node in enumerate(model.graph.node):
-                if all(t in known_edge for t in node.input):
+                # TODO：目前只考虑了resize算子输入为空的情况
+                if all(t in known_edge or t == "" for t in node.input):
                     node.name = str(len(sorted_nodes)) + "_" + node.name
                     sorted_nodes.append(i)
                     known_edge.update(node.output)
@@ -112,7 +113,6 @@ class OnnxStub:
                 )
                 tensors[input.name].set_input()
 
-
         for node_idx in sorted_nodes:
             node = model.graph.node[node_idx]
             if node.op_type == "Conv":
@@ -209,8 +209,8 @@ class OnnxStub:
                 )
             elif node.op_type == "MatMul":
                 tensors[node.output[0]] = self.handler.matmul(
-                    tensors[node.input[0]], # input
-                    tensors[node.input[1]], # weight
+                    tensors[node.input[0]],  # input
+                    tensors[node.input[1]],  # weight
                     tensors.get(node.output[0]),
                     False,
                     False,
@@ -447,6 +447,15 @@ class OnnxStub:
                     tensors[node.input[0]],
                     tensors.get(node.output[0]),
                 )
+            elif node.op_type == "LeakyRelu":
+                tensors[node.output[0]] = self.handler.leakyRelu(
+                    tensors[node.input[0]],
+                    tensors.get(node.output[0]),
+                    next(
+                        (attr.f for attr in node.attribute if attr.name == "alpha"),
+                        0.01,
+                    ),
+                )
             elif node.op_type == "Silu":
                 tensors[node.output[0]] = self.handler.silu(
                     tensors[node.input[0]],
@@ -530,12 +539,16 @@ class OnnxStub:
                 tensors[node.output[0]] = self.handler.clip(
                     tensors[node.input[0]],
                     tensors.get(node.output[0]),
-                    next(_parse_data(data[node.input[1]]).__iter__(), None)
-                    if len(node.input) > 1
-                    else None,
-                    next(_parse_data(data[node.input[2]]).__iter__(), None)
-                    if len(node.input) > 2
-                    else None,
+                    (
+                        next(_parse_data(data[node.input[1]]).__iter__(), None)
+                        if len(node.input) > 1
+                        else None
+                    ),
+                    (
+                        next(_parse_data(data[node.input[2]]).__iter__(), None)
+                        if len(node.input) > 2
+                        else None
+                    ),
                 )
             elif node.op_type == "Transpose":
                 perm = next(
@@ -601,15 +614,15 @@ class OnnxStub:
                         "nearest_mode",
                     ]
                 )
-                if len(node.input) > 1:
+                if len(node.input) > 1 and node.input[1] in data:
                     roiVal = _parse_data(data[node.input[1]])
                 else:
                     roiVal = []
-                if len(node.input) > 2:
+                if len(node.input) > 2 and node.input[2] in data:
                     scalesVal = _parse_data(data[node.input[2]])
                 else:
                     scalesVal = []
-                if len(node.input) > 3:
+                if len(node.input) > 3 and node.input[3] in data:
                     sizesVal = _parse_data(data[node.input[3]])
                 else:
                     sizesVal = []
@@ -617,9 +630,21 @@ class OnnxStub:
                     tensors[node.input[0]],
                     output,
                     axes,
-                    tensors[node.input[3]] if len(node.input) > 3 else None,
-                    tensors[node.input[2]] if len(node.input) > 2 else None,
-                    tensors[node.input[1]] if len(node.input) > 1 else None,
+                    (
+                        tensors[node.input[3]]
+                        if len(node.input) > 3 and node.input[3] != ""
+                        else None
+                    ),
+                    (
+                        tensors[node.input[2]]
+                        if len(node.input) > 2 and node.input[2] != ""
+                        else None
+                    ),
+                    (
+                        tensors[node.input[1]]
+                        if len(node.input) > 1 and node.input[1] != ""
+                        else None
+                    ),
                     sizesVal,
                     scalesVal,
                     roiVal,
@@ -629,18 +654,10 @@ class OnnxStub:
                     coordinate_transformation_mode,
                 )
             elif node.op_type == "Squeeze":
-                axes = (
-                    _parse_data(data[node.input[1]])
-                    if len(node.input) > 1
-                    else None
-                )
+                axes = _parse_data(data[node.input[1]]) if len(node.input) > 1 else None
                 if axes is None:
                     axes = next(
-                        (
-                            attr.ints
-                            for attr in node.attribute
-                            if attr.name == "axes"
-                        ),
+                        (attr.ints for attr in node.attribute if attr.name == "axes"),
                         [],
                     )
                 tensors[node.output[0]] = self.handler.squeeze(
@@ -649,18 +666,10 @@ class OnnxStub:
                     axes,
                 )
             elif node.op_type == "Unsqueeze":
-                axes = (
-                    _parse_data(data[node.input[1]])
-                    if len(node.input) > 1
-                    else None
-                )
+                axes = _parse_data(data[node.input[1]]) if len(node.input) > 1 else None
                 if axes is None:
                     axes = next(
-                        (
-                            attr.ints
-                            for attr in node.attribute
-                            if attr.name == "axes"
-                        )
+                        (attr.ints for attr in node.attribute if attr.name == "axes")
                     )
                 tensors[node.output[0]] = self.handler.unsqueeze(
                     tensors[node.input[0]],
@@ -684,24 +693,18 @@ class OnnxStub:
                     tensors.get(node.output[0]),
                 )
             elif node.op_type == "RoPE":
-                tensors[node.output[0]]= self.handler.RoPE(
+                tensors[node.output[0]] = self.handler.RoPE(
                     tensors[node.input[0]],
                     tensors[node.input[1]],
                     tensors.get(node.output[0]),
                 )
             elif node.op_type == "Split":
                 split = (
-                    _parse_data(data[node.input[1]])
-                    if (len(node.input) > 1)
-                    else None
+                    _parse_data(data[node.input[1]]) if (len(node.input) > 1) else None
                 )
                 if split is None:
                     split = next(
-                        (
-                            attr.ints
-                            for attr in node.attribute
-                            if attr.name == "split"
-                        ),
+                        (attr.ints for attr in node.attribute if attr.name == "split"),
                         None,
                     )
                 for name, tensor in zip(
@@ -710,11 +713,7 @@ class OnnxStub:
                         tensors[node.input[0]],
                         None,
                         next(
-                            (
-                                attr.i
-                                for attr in node.attribute
-                                if attr.name == "axis"
-                            ),
+                            (attr.i for attr in node.attribute if attr.name == "axis"),
                             0,
                         ),
                         split if split is not None else len(node.output),
@@ -767,12 +766,16 @@ class OnnxStub:
                     tensors.get(node.output[0]),
                     clamp(_parse_data(data[node.input[1]])),
                     clamp(_parse_data(data[node.input[2]])),
-                    clamp(_parse_data(data[node.input[3]]))
-                    if len(node.input) > 3
-                    else None,
-                    clamp(_parse_data(data[node.input[4]]))
-                    if len(node.input) > 4
-                    else None,
+                    (
+                        clamp(_parse_data(data[node.input[3]]))
+                        if len(node.input) > 3
+                        else None
+                    ),
+                    (
+                        clamp(_parse_data(data[node.input[4]]))
+                        if len(node.input) > 4
+                        else None
+                    ),
                 )
             elif node.op_type == "Pad":
                 tensors[node.output[0]] = self.handler.pad(
@@ -788,12 +791,16 @@ class OnnxStub:
                         tensors[node.input[0]],
                         tensors.get(node.output[0]),
                         tensors.get(node.output[1]) if len(node.output) > 1 else None,
-                        _parse_data(data[node.input[1]])[0]
-                        if len(node.input) > 1
-                        else 0.5,
-                        _parse_data(data[node.input[2]])[0]
-                        if len(node.input) > 2
-                        else False,
+                        (
+                            _parse_data(data[node.input[1]])[0]
+                            if len(node.input) > 1
+                            else 0.5
+                        ),
+                        (
+                            _parse_data(data[node.input[2]])[0]
+                            if len(node.input) > 2
+                            else False
+                        ),
                     ),
                 ):
                     tensors[name] = tensor
@@ -942,18 +949,25 @@ class OnnxStub:
                     tensors.get(node.output[0]),
                 )
             elif node.op_type == "Where":
-                ## If Y is single -inf, treat Where as Add 
+                ## If Y is single -inf, treat Where as Add
                 ## TODO: deal with cases where Y is single inf or 0
                 if node.input[0] in data and node.input[2] in data:
                     where_condition = to_array(data[node.input[0]])
-                    where_alt =  to_array(data[node.input[2]])
+                    where_alt = to_array(data[node.input[2]])
                     if where_alt.size == 1:
                         if np.isneginf(where_alt) or np.all(where_alt < -3e38):
                             node.input[0] = node.input[0] + "_alt"
                             if node.input[0] not in data:
-                                where_value = np.where(where_condition, 0, -np.inf).astype(where_alt.dtype)
-                                data[node.input[0]] = from_array(where_value, node.input[0])
-                                tensors[node.input[0]] = self.handler.tensor(list(where_value.shape), data[node.input[0]].data_type)
+                                where_value = np.where(
+                                    where_condition, 0, -np.inf
+                                ).astype(where_alt.dtype)
+                                data[node.input[0]] = from_array(
+                                    where_value, node.input[0]
+                                )
+                                tensors[node.input[0]] = self.handler.tensor(
+                                    list(where_value.shape),
+                                    data[node.input[0]].data_type,
+                                )
                                 tensors[node.input[0]].set_weight()
                             tensors[node.output[0]] = self.handler.add(
                                 tensors[node.input[1]],
@@ -980,8 +994,7 @@ class OnnxStub:
                     node, {"alpha": 0.0001, "beta": 0.75, "bias": 1.0, "size": 1}
                 )
                 (alpha, beta, bias, size) = (
-                    attributes[name]
-                    for name in ["alpha", "beta", "bias", "size"]
+                    attributes[name] for name in ["alpha", "beta", "bias", "size"]
                 )
                 tensors[node.output[0]] = self.handler.lrn(
                     tensors[node.input[0]],

src/core/graph_handler.cc

@@ -222,6 +222,15 @@ Tensor GraphHandlerObj::pRelu(Tensor x, Tensor slope, Tensor y) {
     }
 }
 
+Tensor GraphHandlerObj::leakyRelu(Tensor x, Tensor y, float alpha) {
+    if (y) {
+        g->addOpWithOutputs<LeakyReluObj>(std::move(x), y, alpha);
+        return y;
+    } else {
+        return g->addOp<LeakyReluObj>(std::move(x), y, alpha)->getOutput();
+    }
+}
+
 Tensor GraphHandlerObj::clip(Tensor x, Tensor y, std::optional<float> min,
                              std::optional<float> max) {
     if (y) {

src/ffi/ffi_infinitensor.cc

@@ -562,6 +562,7 @@ void init_graph_builder(py::module &m) {
         .def("expand", &Handler::expand, policy::move)
         .def("erf", &Handler::erf, policy::move)
         .def("where", &Handler::where, policy::move)
+        .def("leakyRelu", &Handler::leakyRelu, policy::move)
         .def("lrn", &Handler::lrn, policy::move)
         .def("topo_sort", &Handler::topo_sort, policy::automatic)
         .def("optimize", &Handler::optimize, policy::automatic)

src/kernels/bang/activation.cc

@@ -241,8 +241,50 @@ class HardSigmoidCnnl : public UnaryCnnl {
     float getScale() const override { return 0.5f; }
 };
 
+class LeakyReluCnnl : public BangKernelWithoutConfig {
+    void compute(const Operator &_op,
+                 const RuntimeObj *_context) const override {
+        auto op = as<LeakyReluObj>(_op);
+        auto context = dynamic_cast<const BangRuntimeObj *>(_context);
+
+        void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
+        void *const cData = (op->getOutput()->getRawDataPtr<void *>());
+
+        cnnlTensorDescriptor_t aDesc, cDesc;
+        auto aDim = op->getInputs(0)->getDims();
+        auto cDim = op->getOutput()->getDims();
+        auto coef = op->getAlpha();
+
+        checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
+        checkCnnlError(cnnlSetTensorDescriptor(
+            aDesc, CNNL_LAYOUT_NCHW, cnnlDataTypeConvert(op->getDType()),
+            aDim.size(), aDim.data()));
+        checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
+        checkCnnlError(cnnlSetTensorDescriptor(
+            cDesc, CNNL_LAYOUT_NCHW, cnnlDataTypeConvert(op->getDType()),
+            cDim.size(), cDim.data()));
+        cnnlActivationDescriptor_t opDesc;
+        checkCnnlError(cnnlCreateActivationDescriptor(&opDesc));
+        checkCnnlError(cnnlSetActivationDescriptor_v5(
+            opDesc, CNNL_ACTIVATION_LEAKYRELU, CNNL_ACTIVATION_HIGH_PRECISION,
+            CNNL_NOT_PROPAGATE_NAN, coef, 0.0, 0.0, 0.0, true));
+
+        float alpha = 1.f, beta = 0.f;
+        cnnlStatus_t stat =
+            cnnlActivationForward(context->cnnlHandle(), opDesc, &alpha, aDesc,
+                                  aData, &beta, cDesc, cData);
+        if (stat != CNNL_STATUS_SUCCESS)
+            return;
+        checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
+        checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
+        checkCnnlError(cnnlDestroyActivationDescriptor(opDesc));
+    }
+};
+
 REGISTER_KERNEL(Device::BANG, OpType::Relu, ReluCnnl, "Relu_cnnl_BANG");
 REGISTER_KERNEL(Device::BANG, OpType::PRelu, PReluCnnl, "PRelu_cnnl_BANG");
+REGISTER_KERNEL(Device::BANG, OpType::LeakyRelu, LeakyReluCnnl,
+                "LeakyRelu_cnnl_BANG");
 REGISTER_KERNEL(Device::BANG, OpType::Sigmoid, SigmoidCnnl,
                 "Sigmoid_cnnl_BANG");
 REGISTER_KERNEL(Device::BANG, OpType::Round, RoundCnnl, "Round_cnnl_BANG");

src/kernels/bang/batchnorm.cc

@@ -16,10 +16,14 @@ class BatchNormCnnl : public BangKernelWithoutConfig {
         void *const bias = (op->getInputs(4)->getRawDataPtr<void *>());
         void *const output = (op->getOutput()->getRawDataPtr<void *>());
 
-        auto dims = op->getInputs(0)->getDims();
-        auto outDims = op->getOutput()->getDims();
-        if (dims.size() != 4)
-            IT_TODO_HALT();
+        auto padDims = [](Shape shape) {
+            for (size_t i = shape.size(); i < 4; ++i) {
+                shape.push_back(1);
+            }
+            return shape;
+        };
+        auto dims = padDims(op->getInputs(0)->getDims());
+        auto outDims = padDims(op->getOutput()->getDims());
 
         int dimsTrans[4] = {dims[0], dims[2], dims[3], dims[1]};
         int dimsOutTrans[4] = {outDims[0], outDims[2], outDims[3], outDims[1]};

src/kernels/bang/resize.cc

@@ -0,0 +1,144 @@
+#include "operators/resize.h"
+#include "bang/bang_kernel_without_config.h"
+#include "bang/bang_runtime.h"
+#include <iostream>
+
+namespace infini {
+class ResizeCnnl : public BangKernelWithoutConfig {
+    void compute(const Operator &_op,
+                 const RuntimeObj *_context) const override {
+        auto op = as<ResizeObj>(_op);
+        IT_ASSERT(op->getDType() == DataType::Float32);
+        auto context = dynamic_cast<const BangRuntimeObj *>(_context);
+
+        void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
+        void *const cData = (op->getOutput()->getRawDataPtr<void *>());
+
+        auto nDims = op->getInputs(0)->getRank();
+        if (nDims != 4) {
+            IT_TODO_HALT();
+        }
+        auto aDim = op->getInputs(0)->getDims();
+        auto cDim = op->getOutput()->getDims();
+        std::vector<int> aTransDim = {aDim[0], aDim[2], aDim[3], aDim[1]};
+        std::vector<int> cTransDim = {cDim[0], cDim[2], cDim[3], cDim[1]};
+
+        cnnlTensorDescriptor_t aDesc, cDesc, aTransDesc, cTransDesc;
+        // input
+        checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
+        checkCnnlError(cnnlSetTensorDescriptor(
+            aDesc, CNNL_LAYOUT_NCHW, cnnlDataTypeConvert(op->getDType()),
+            aDim.size(), aDim.data()));
+        checkCnnlError(cnnlCreateTensorDescriptor(&aTransDesc));
+        checkCnnlError(cnnlSetTensorDescriptor(
+            aTransDesc, CNNL_LAYOUT_NHWC, cnnlDataTypeConvert(op->getDType()),
+            aTransDim.size(), aTransDim.data()));
+        // output
+        checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
+        checkCnnlError(cnnlSetTensorDescriptor(
+            cDesc, CNNL_LAYOUT_NCHW, cnnlDataTypeConvert(op->getDType()),
+            cDim.size(), cDim.data()));
+        checkCnnlError(cnnlCreateTensorDescriptor(&cTransDesc));
+        checkCnnlError(cnnlSetTensorDescriptor(
+            cTransDesc, CNNL_LAYOUT_NHWC, cnnlDataTypeConvert(op->getDType()),
+            cTransDim.size(), cTransDim.data()));
+
+        // transpose
+        BangPtr aTransData = context->getWorkspace(
+            cnnlGetTensorElementNum(aTransDesc) * op->getDType().getSize());
+        BangPtr cTransData = context->getWorkspace(
+            cnnlGetTensorElementNum(cTransDesc) * op->getDType().getSize());
+
+        int permuteIn[4] = {0, 2, 3, 1};
+        cnnlTransposeDescriptor_t inDesc;
+        checkCnnlError(cnnlCreateTransposeDescriptor(&inDesc));
+        checkCnnlError(cnnlSetTransposeDescriptor(inDesc, 4, permuteIn));
+        size_t wsSizeIn;
+        cnnlGetTransposeWorkspaceSize(context->cnnlHandle(), aDesc, inDesc,
+                                      &wsSizeIn);
+        BangPtr wsDataIn = context->getWorkspace(wsSizeIn);
+
+        checkCnnlError(cnnlTranspose_v2(context->cnnlHandle(), inDesc, aDesc,
+                                        aData, aTransDesc, aTransData, wsDataIn,
+                                        wsSizeIn));
+
+        cnnlTensorDescriptor_t boxesDesc, boxesIndexDesc;
+        checkCnnlError(cnnlCreateTensorDescriptor(&boxesDesc));
+        auto nBatch = aDim[0];
+        std::vector<int> boxesDim = {nBatch, 4};
+        checkCnnlError(cnnlSetTensorDescriptor(
+            boxesDesc, CNNL_LAYOUT_ARRAY, cnnlDataTypeConvert(op->getDType()),
+            boxesDim.size(), boxesDim.data()));
+
+        checkCnnlError(cnnlCreateTensorDescriptor(&boxesIndexDesc));
+        std::vector<int> boxesIndexDim = {nBatch};
+        checkCnnlError(cnnlSetTensorDescriptor(
+            boxesIndexDesc, CNNL_LAYOUT_ARRAY, CNNL_DTYPE_INT32,
+            boxesIndexDim.size(), boxesIndexDim.data()));
+        std::vector<int32_t> boxesIndex(nBatch);
+        std::iota(boxesIndex.begin(), boxesIndex.end(), 0);
+        BangPtr boxesIndexData =
+            context->getWorkspace(nBatch * sizeof(int32_t));
+        context->copyBlobFromCPU(boxesIndexData, boxesIndex.data(),
+                                 nBatch * sizeof(int32_t));
+
+        cnnlCropAndResizeMode_t mode;
+        auto coefMode = op->getMode();
+        if (coefMode == ResizeObj::ECoeffMode::nearest) {
+            // CNNL uses round by default and
+            // does not support other nearest modes
+            mode = CNNL_CROP_AND_RESIZE_NEAREST;
+        } else if (coefMode == ResizeObj::ECoeffMode::linear) {
+            mode = CNNL_CROP_AND_RESIZE_BILINEAR;
+        } else {
+            IT_TODO_HALT();
+        }
+
+        std::vector<float> box;
+        auto transMode = op->getCoordinateTransMode();
+        if (transMode ==
+            enum_to_underlying(
+                ResizeObj::ECoordinateTransMode::tfCropAndResize)) {
+            box = {op->getRoi(2), op->getRoi(3), op->getRoi(6), op->getRoi(7)};
+        } else {
+            box = {0, 0, 1.0, 1.0};
+        }
+
+        BangPtr boxesData =
+            context->getWorkspace(nBatch * box.size() * sizeof(float));
+        for (auto i = 0; i < nBatch; i++) {
+            context->copyBlobFromCPU(boxesData + i * box.size() * sizeof(float),
+                                     box.data(), box.size() * sizeof(float));
+        }
+
+        checkCnnlError(cnnlCropAndResize(
+            context->cnnlHandle(), aTransDesc, aTransData, boxesDesc, boxesData,
+            boxesIndexDesc, boxesIndexData, mode, 0.0, cTransDesc, cTransData));
+
+        // transpose
+        int permuteOut[4] = {0, 3, 1, 2};
+        cnnlTransposeDescriptor_t outDesc;
+        checkCnnlError(cnnlCreateTransposeDescriptor(&outDesc));
+        checkCnnlError(cnnlSetTransposeDescriptor(outDesc, 4, permuteOut));
+        size_t wsSizeOut;
+        cnnlGetTransposeWorkspaceSize(context->cnnlHandle(), cTransDesc,
+                                      outDesc, &wsSizeOut);
+        BangPtr wsDataOut = context->getWorkspace(wsSizeOut);
+
+        checkCnnlError(cnnlTranspose_v2(context->cnnlHandle(), outDesc,
+                                        cTransDesc, cTransData, cDesc, cData,
+                                        wsDataOut, wsSizeOut));
+
+        checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
+        checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
+        checkCnnlError(cnnlDestroyTensorDescriptor(aTransDesc));
+        checkCnnlError(cnnlDestroyTensorDescriptor(cTransDesc));
+        checkCnnlError(cnnlDestroyTensorDescriptor(boxesDesc));
+        checkCnnlError(cnnlDestroyTensorDescriptor(boxesIndexDesc));
+        checkCnnlError(cnnlDestroyTransposeDescriptor(inDesc));
+        checkCnnlError(cnnlDestroyTransposeDescriptor(outDesc));
+    }
+};
+
+REGISTER_KERNEL(Device::BANG, OpType::Resize, ResizeCnnl, "Resize_cnnl_BANG");
+}; // namespace infini

src/kernels/cuda/batch_norm.cc

@@ -18,9 +18,16 @@ class BatchNormCudnn : public CudaKernelWithoutConfig {
         void *const scaleData = (op->getInputs(3)->getRawDataPtr<void *>());
         void *const biasData = (op->getInputs(4)->getRawDataPtr<void *>());
 
-        auto dims = op->getInputs(0)->getDims();
         // Only 4D and 5D tensors are supported by
         // cudnnBatchNormalizationForwardInference
+        if (auto dims = op->getInputs(0)->getDims(); dims.size() < 4) {
+            auto dims_t = dims;
+            for (size_t i = dims_t.size(); i < 4; ++i) {
+                dims_t.push_back(1);
+            }
+            op->getInputs(0)->setShape(dims_t);
+        }
+        auto dims = op->getInputs(0)->getDims();
         IT_ASSERT(dims.size() == 4);
 
         int dimArray[4], strideArray[4], dimPArray[4], stridePArray[4];

src/kernels/cuda/unary.cc

@@ -173,6 +173,25 @@ class TanhCudnn : public ActivationCudnn {
     }
 };
 
+class LeakyReluCuda : public CudaKernelWithoutConfig {
+    void compute(const Operator &_op,
+                 const RuntimeObj *_context) const override {
+
+        auto op = as<LeakyReluObj>(_op);
+        auto alpha = op->getAlpha();
+        size_t num = op->getOutput()->size();
+        void *const inputData = (op->getInputs(0)->getRawDataPtr<void *>());
+        void *const outputData = (op->getOutput()->getRawDataPtr<void *>());
+
+        if (op->getDType() == DataType::Float32) {
+            leaky_relu_kernel<float>((float *)inputData, (float *)outputData,
+                                     num, alpha);
+        } else {
+            IT_TODO_HALT();
+        }
+    }
+};
+
 REGISTER_KERNEL(Device::CUDA, OpType::Relu, ReluCudnn, "Relu_CUDA");
 REGISTER_KERNEL(Device::CUDA, OpType::Sigmoid, SigmoidCudnn, "Sigmoid_CUDA");
 REGISTER_KERNEL(Device::CUDA, OpType::HardSigmoid, UnaryCuda,
@@ -185,11 +204,14 @@ REGISTER_KERNEL(Device::CUDA, OpType::Gelu, UnaryCuda, "Gelu_CUDA");
 REGISTER_KERNEL(Device::CUDA, OpType::Silu, UnaryCuda, "Silu_CUDA");
 REGISTER_KERNEL(Device::CUDA, OpType::Neg, UnaryCuda, "Neg_CUDA");
 REGISTER_KERNEL(Device::CUDA, OpType::Erf, UnaryCuda, "Erf_CUDA");
+REGISTER_KERNEL(Device::CUDA, OpType::LeakyRelu, LeakyReluCuda,
+                "LeakyRelu_CUDA");
 
 REGISTER_KERNEL(Device::CUDA, OpType::Cast, CastCuda, "Cast_CUDA");
 
-// REGISTER_KERNEL(Device::CUDA, OpType::Softmax, UnaryCuda, "Softmax_CUDA");
-// REGISTER_KERNEL(Device::CUDA, OpType::Relu, UnaryCuda,
+// REGISTER_KERNEL(Device::CUDA, OpType::Softmax, UnaryCuda,
+// "Softmax_CUDA"); REGISTER_KERNEL(Device::CUDA, OpType::Relu,
+// UnaryCuda,
 //                 "Relu_CUDA");
 // REGISTER_KERNEL(Device::CUDA, OpType::Sigmoid, UnaryCuda,
 //                 "Sigmoid_CUDA");

src/kernels/cuda/unary.cu

@@ -110,7 +110,8 @@ __global__ void _silu_kernel(T *input, T *output, size_t n) {
     int stride = blockDim.x * gridDim.x;
     for (int i = index; i < n; i += stride) {
         float x = input[i];
-        output[i] = x / (1.0 + expf(-x));;
+        output[i] = x / (1.0 + expf(-x));
+        ;
     }
 }
 
@@ -143,33 +144,40 @@ __global__ void _cast_kernel(INPUT *input, OUTPUT *output, size_t n) {
     }
 }
 
+template <typename T>
+__global__ void _leaky_relu_kernel(T *input, T *output, size_t n, float alpha) {
+    size_t index = threadIdx.x + blockIdx.x * blockDim.x;
+    size_t stride = blockDim.x * gridDim.x;
+    for (size_t i = index; i < n; i += stride) {
+        output[i] = input[i] > 0 ? input[i] : alpha * input[i];
+    }
+}
+
 namespace infini {
 template <typename T> void softmax_kernel(T *input, T *output, size_t num) {
 
     int blocksize = block_work_size();
     int gridsize = (num + block_work_size() - 1) / block_work_size();
     _softmax_kernel1<T>
-        <<<1, 1, 0, CUDAStream::getCurrentStream()>>>
-        (input, output, num);
+        <<<1, 1, 0, CUDAStream::getCurrentStream()>>>(input, output, num);
     _softmax_kernel2<T>
-        <<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>
-        (input, output, num);
+        <<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>(
+            input, output, num);
 }
 template <typename T> void relu_kernel(T *input, T *output, size_t num) {
 
     int blocksize = block_work_size();
     int gridsize = (num + block_work_size() - 1) / block_work_size();
-    _relu_kernel<T>
-        <<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>
-        (input, output, num);
+    _relu_kernel<T><<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>(
+        input, output, num);
 }
 template <typename T> void sigmoid_kernel(T *input, T *output, size_t num) {
 
     int blocksize = block_work_size();
     int gridsize = (num + block_work_size() - 1) / block_work_size();
     _sigmoid_kernel<T>
-        <<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>
-        (input, output, num);
+        <<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>(
+            input, output, num);
 }
 template <typename T>
 void hard_sigmoid_kernel(T *input, T *output, size_t num) {
@@ -177,75 +185,78 @@ void hard_sigmoid_kernel(T *input, T *output, size_t num) {
     int blocksize = block_work_size();
     int gridsize = (num + block_work_size() - 1) / block_work_size();
     _hard_sigmoid_kernel<T>
-        <<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>
-        (input, output, num);
+        <<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>(
+            input, output, num);
 }
 template <typename T> void hard_swish_kernel(T *input, T *output, size_t num) {
 
     int blocksize = block_work_size();
     int gridsize = (num + block_work_size() - 1) / block_work_size();
     _hard_swish_kernel<T>
-        <<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>
-        (input, output, num);
+        <<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>(
+            input, output, num);
 }
 template <typename T> void tanh_kernel(T *input, T *output, size_t num) {
 
     int blocksize = block_work_size();
     int gridsize = (num + block_work_size() - 1) / block_work_size();
-    _tanh_kernel<T>
-        <<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>
-        (input, output, num);
+    _tanh_kernel<T><<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>(
+        input, output, num);
 }
 template <typename T> void abs_kernel(T *input, T *output, size_t num) {
 
     int blocksize = block_work_size();
     int gridsize = (num + block_work_size() - 1) / block_work_size();
-    _abs_kernel<T>
-        <<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>
-        (input, output, num);
+    _abs_kernel<T><<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>(
+        input, output, num);
 }
 template <typename T> void sqrt_kernel(T *input, T *output, size_t num) {
 
     int blocksize = block_work_size();
     int gridsize = (num + block_work_size() - 1) / block_work_size();
-    _sqrt_kernel
-        <<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>
-        ((T *)input, (T *)output, num);
+    _sqrt_kernel<<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>(
+        (T *)input, (T *)output, num);
 }
 
 template <typename T> void gelu_kernel(T *input, T *output, size_t num) {
 
     int blocksize = block_work_size();
     int gridsize = (num + block_work_size() - 1) / block_work_size();
-    _gelu_kernel<T>
-        <<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>
-        (input, output, num);
+    _gelu_kernel<T><<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>(
+        input, output, num);
 }
 
 template <typename T> void silu_kernel(T *input, T *output, size_t num) {
 
     int blocksize = block_work_size();
     int gridsize = (num + block_work_size() - 1) / block_work_size();
-    _silu_kernel<T>
-        <<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>
-        (input, output, num);
+    _silu_kernel<T><<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>(
+        input, output, num);
 }
 
 template <typename T> void erf_kernel(T *input, T *output, size_t num) {
 
     int blocksize = block_work_size();
     int gridsize = (num + block_work_size() - 1) / block_work_size();
-    _erf_kernel<T>
-        <<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>
-        (input, output, num);
+    _erf_kernel<T><<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>(
+        input, output, num);
 }
 template <typename T> void neg_kernel(T *input, T *output, size_t num) {
 
     int blocksize = block_work_size();
     int gridsize = (num + block_work_size() - 1) / block_work_size();
-    _neg_kernel<T>
-        <<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>
-        (input, output, num);
+    _neg_kernel<T><<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>(
+        input, output, num);
+}
+
+template <typename T>
+void leaky_relu_kernel(T *input, T *output, size_t num, float alpha) {
+
+    int blocksize = block_work_size();
+    int gridsize = (num + block_work_size() - 1) / block_work_size();
+    _leaky_relu_kernel<T>
+        <<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>(
+            input, output, num, alpha);
 }
 
 void unary_kernel(const Operator &_op) {
@@ -315,7 +326,7 @@ void unary_kernel(const Operator &_op) {
     } else if (op->getOpType() == OpType::Silu) {
         if (_op->getDType() == DataType::Float32) {
             silu_kernel<float>((float *)inputData, (float *)outputData, num);
-        } else if (_op->getDType() == DataType::Float16){
+        } else if (_op->getDType() == DataType::Float16) {
             silu_kernel<half>((half *)inputData, (half *)outputData, num);
         } else {
             IT_TODO_HALT();
@@ -346,8 +357,8 @@ void cast_kernel(INPUT *input, OUTPUT *output, size_t num) {
     int blocksize = block_work_size();
     int gridsize = (num + block_work_size() - 1) / block_work_size();
     _cast_kernel<INPUT, OUTPUT>
-        <<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>
-        (input, output, num);
+        <<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>(
+            input, output, num);
 }
 
 template void cast_kernel<float, half>(float *input, half *output, size_t num);
@@ -359,4 +370,6 @@ template void cast_kernel<float, int8_t>(float *input, int8_t *output,
 template void cast_kernel<int8_t, float>(int8_t *input, float *output,
                                          size_t num);
 
+template void leaky_relu_kernel<float>(float *input, float *output, size_t num,
+                                       float alpha);
 }; // namespace infini

src/kernels/kunlun/batch_norm.cc

@@ -19,13 +19,17 @@ class BatchNormXdnn : public KUNLUNKernelWithoutConfig {
 
         auto dims = op->getInputs(0)->getDims();
 
-        if (dims.size() != 4)
-            IT_TODO_HALT();
+        int n, c, h, w;
+        if (dims.size() != 4) {
+            h = 1;
+            w = 1;
+        }
+
+        w = dims[3];
+        h = dims[2];
+        c = dims[1];
+        n = dims[0];
 
-        int w = dims[3];
-        int h = dims[2];
-        int c = dims[1];
-        int n = dims[0];
         auto ret = xdnn::batch_norm_infer<float>(
             context->KUNLUNHandle(), (float *)input, (float *)output, n, c, h,
             w, op->getEps(), (float *)scale, (float *)bias, (float *)mean,

src/kernels/kunlun/layer_norm.cc

@@ -0,0 +1,45 @@
+#include "operators/layer_norm.h"
+#include "kunlun/kunlun_kernel_without_config.h"
+#include "kunlun/kunlun_runtime.h"
+
+namespace infini {
+class LayerNormXdnn : public KUNLUNKernelWithoutConfig {
+    void compute(const Operator &_op,
+                 const RuntimeObj *_context) const override {
+        auto op = as<LayerNormObj>(_op);
+        auto context = static_cast<const KUNLUNRuntimeObj *>(_context);
+
+        void *const inputData = (op->getInputs(0)->getRawDataPtr<void *>());
+        void *const scaleData = (op->getInputs(1)->getRawDataPtr<void *>());
+        void *const outputData = (op->getOutput()->getRawDataPtr<void *>());
+
+        float eps = op->getEps();
+        // int axis = op->getAxis();
+
+        const auto &opInputShape = op->getInputs(0)->getDims();
+        const auto &opOutputShape = op->getOutput()->getDims();
+        IT_ASSERT(opInputShape.size() == 2);
+
+        int ret;
+        if (op->numInputs() == 3) {
+            // with bias
+            void *const biasData = op->getInputs(2)->getRawDataPtr<void *>();
+            ret = xdnn::layer_norm<float, float>(
+                context->KUNLUNHandle(), (float const *)inputData,
+                (float *)outputData, opInputShape[0], opInputShape[1], eps,
+                (float *)scaleData, (float *)biasData, nullptr, nullptr);
+        } else {
+            // without bias
+            ret = xdnn::layer_norm<float, float>(
+                context->KUNLUNHandle(), (float const *)inputData,
+                (float *)outputData, opInputShape[0], opInputShape[1], eps,
+                (float *)scaleData, nullptr, nullptr, nullptr);
+        }
+        assert(ret == 0);
+    }
+};
+
+REGISTER_KERNEL(Device::KUNLUN, OpType::LayerNormalization, LayerNormXdnn,
+                "LayerNorm_xdnn_KUNLUN");
+
+}; // namespace infini

src/kernels/kunlun/unary.cc

@@ -21,6 +21,26 @@ class ReluXdnn : public KUNLUNKernelWithoutConfig {
     }
 };
 
+class LeakyReluXdnn : public KUNLUNKernelWithoutConfig {
+    void compute(const Operator &_op,
+                 const RuntimeObj *_context) const override {
+        auto op = as<LeakyReluObj>(_op);
+        IT_ASSERT(op->getDType() == DataType::Float32);
+        auto context = dynamic_cast<const KUNLUNRuntimeObj *>(_context);
+
+        void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
+        void *const cData = (op->getOutput()->getRawDataPtr<void *>());
+        auto len = op->getInputs(0)->size();
+        auto alpha = op->getAlpha();
+
+        auto ret = xdnn::leaky_relu<float>(context->KUNLUNHandle(),
+                                           (float *const)aData, (float *)cData,
+                                           len, alpha);
+        assert(ret == 0);
+        return;
+    }
+};
+
 class SigmoidXdnn : public KUNLUNKernelWithoutConfig {
     void compute(const Operator &_op,
                  const RuntimeObj *_context) const override {
@@ -552,6 +572,8 @@ class ATanhXdnn : public KUNLUNKernelWithoutConfig {
 };
 
 REGISTER_KERNEL(Device::KUNLUN, OpType::Relu, ReluXdnn, "Relu_xdnn_KUNLUN");
+REGISTER_KERNEL(Device::KUNLUN, OpType::LeakyRelu, LeakyReluXdnn,
+                "LeakyRelu_xdnn_KUNLUN");
 REGISTER_KERNEL(Device::KUNLUN, OpType::Sigmoid, SigmoidXdnn,
                 "Sigmoid_xdnn_KUNLUN");
 REGISTER_KERNEL(Device::KUNLUN, OpType::Tanh, TanhXdnn, "Tanh_xdnn_KUNLUN");

src/operators/unary.cc

@@ -283,6 +283,38 @@ vector<int> PReluObj::getWorkloadVector() const {
 
 vector<int> PReluObj::getOpAttrVector() const { return {type.underlying()}; }
 
+LeakyReluObj::LeakyReluObj(GraphObj *graph, Tensor input, Tensor output,
+                           float alpha)
+    : OperatorObj(OpType::LeakyRelu, {input}, {output}), alphaValue(alpha) {
+    IT_ASSERT(checkValid(graph));
+}
+
+optional<vector<Shape>> LeakyReluObj::inferShape(const TensorVec &inputs) {
+    const auto A = inputs[0];
+    return {{A->getDims()}};
+}
+
+std::string LeakyReluObj::toString() const {
+    std::ostringstream os;
+    os << type.toString() << "[" << getGuid() << "]";
+    os << "(";
+    os << vecToString(inputs[0]->getDims()) << ",";
+    os << "input=" << inputs[0]->getGuid() << ",";
+    os << "output=" << outputs[0]->getGuid() << ")";
+    return os.str();
+}
+
+vector<int> LeakyReluObj::getWorkloadVector() const {
+    vector<int> ret{type.underlying()};
+    const Shape shape = outputs[0]->getDims();
+    ret.insert(ret.end(), shape.begin(), shape.end());
+    return ret;
+}
+
+vector<int> LeakyReluObj::getOpAttrVector() const {
+    return {type.underlying()};
+}
+
 LogObj::LogObj(GraphObj *graph, Tensor input, Tensor output, LogType type)
     : OperatorObj(OpType::Log, {input}, {output}), logType(type) {
     IT_ASSERT(checkValid(graph));

test/kernels/bang/test_bang_resize.cc

@@ -0,0 +1,65 @@
+#include "bang/bang_runtime.h"
+#include "cmath"
+#include "core/graph.h"
+#include "core/runtime.h"
+#include "operators/resize.h"
+#include "test.h"
+namespace infini {
+TEST(Resize, Bang_downsample_sizes_nearest) {
+    Runtime runtime = NativeCpuRuntimeObj::getInstance();
+    Graph gCpu = make_ref<GraphObj>(runtime);
+
+    auto input = gCpu->addTensor({1, 1, 2, 4}, DataType::Float32);
+    auto scales = gCpu->addTensor({4}, DataType::Float32);
+    gCpu->dataMalloc();
+    input->copyin(vector<float>{1, 2, 3, 4, 5, 6, 7, 8});
+    scales->copyin(vector<float>{1, 1, 0.6, 0.6});
+
+    auto bangRuntime = make_ref<BangRuntimeObj>();
+    Graph gMlu = make_ref<GraphObj>(bangRuntime);
+
+    auto inputMlu = gMlu->cloneTensor(input);
+    auto scalesMlu = gMlu->cloneTensor(scales);
+    auto op = gMlu->addOp<ResizeObj>(inputMlu, nullptr, std::nullopt, nullptr,
+                                     scalesMlu, nullptr);
+    gMlu->dataMalloc();
+    inputMlu->copyin(vector<float>{1, 2, 3, 4, 5, 6, 7, 8});
+    scalesMlu->copyin(vector<float>{1, 1, 0.6, 0.6});
+
+    bangRuntime->run(gMlu);
+
+    //  copy output from CUDA to CPU
+    auto oCpu = gCpu->cloneTensor(op->getOutput(0));
+    EXPECT_TRUE(oCpu->equalData(vector<float>{5, 8}));
+}
+
+TEST(Resize, Bang_upsample_sizes_nearest) {
+    Runtime runtime = NativeCpuRuntimeObj::getInstance();
+    Graph gCpu = make_ref<GraphObj>(runtime);
+
+    auto input = gCpu->addTensor({1, 1, 2, 2}, DataType::Float32);
+    auto scales = gCpu->addTensor({4}, DataType::Float32);
+    gCpu->dataMalloc();
+    input->copyin(vector<float>{1, 2, 3, 4});
+    scales->copyin(vector<float>{1, 1, 2, 3});
+
+    auto bangRuntime = make_ref<BangRuntimeObj>();
+    Graph gMlu = make_ref<GraphObj>(bangRuntime);
+
+    auto inputMlu = gMlu->cloneTensor(input);
+    auto scalesMlu = gMlu->cloneTensor(scales);
+    auto op = gMlu->addOp<ResizeObj>(inputMlu, nullptr, std::nullopt, nullptr,
+                                     scalesMlu, nullptr);
+    gMlu->dataMalloc();
+    inputMlu->copyin(vector<float>{1, 2, 3, 4});
+    scalesMlu->copyin(vector<float>{1, 1, 2, 3});
+
+    bangRuntime->run(gMlu);
+
+    //  copy output from CUDA to CPU
+    auto oCpu = gCpu->cloneTensor(op->getOutput(0));
+    EXPECT_TRUE(
+        oCpu->equalData(vector<float>{1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2,
+                                      3, 3, 3, 4, 4, 4, 3, 3, 3, 4, 4, 4}));
+}
+} // namespace infini