Full codegen erf, erfc, erfinv, and exp

[wonjoolee95]

Full codegen erf, erfc, erfinv, and exp

---

Generated LazyIr.h:

class Erf : public XlaNode {
 public:
  static torch::lazy::OpKind ClassOpKind() {
    return torch::lazy::OpKind(at::aten::erf);
  }

  Erf(const torch::lazy::Value& self, std::vector<torch::lazy::Shape>&& shapes)
      : XlaNode(torch::lazy::OpKind(at::aten::erf),
              {self}, std::move(shapes),
              [&]() { return ErfOutputShape(self); },
              /* num_outputs */ 1,
              torch::lazy::MHash())
  {
    
  }

  std::string ToString() const override {
    std::stringstream ss;
    ss << XlaNode::ToString();
    
    return ss.str();
  }

  

  bool CanBeReused(const torch::lazy::Value& self) const {
    return false;
    }

  torch_xla::XlaOpVector Lower(LoweringContext* loctx) const override;

  
  

};

class Erfc : public XlaNode {
 public:
  static torch::lazy::OpKind ClassOpKind() {
    return torch::lazy::OpKind(at::aten::erfc);
  }

  Erfc(const torch::lazy::Value& self, std::vector<torch::lazy::Shape>&& shapes)
      : XlaNode(torch::lazy::OpKind(at::aten::erfc),
              {self}, std::move(shapes),
              [&]() { return ErfcOutputShape(self); },
              /* num_outputs */ 1,
              torch::lazy::MHash())
  {
    
  }

  std::string ToString() const override {
    std::stringstream ss;
    ss << XlaNode::ToString();
    
    return ss.str();
  }

  

  bool CanBeReused(const torch::lazy::Value& self) const {
    return false;
    }

  torch_xla::XlaOpVector Lower(LoweringContext* loctx) const override;

  
  

};

class Erfinv : public XlaNode {
 public:
  static torch::lazy::OpKind ClassOpKind() {
    return torch::lazy::OpKind(at::aten::erfinv);
  }

  Erfinv(const torch::lazy::Value& self, std::vector<torch::lazy::Shape>&& shapes)
      : XlaNode(torch::lazy::OpKind(at::aten::erfinv),
              {self}, std::move(shapes),
              [&]() { return ErfinvOutputShape(self); },
              /* num_outputs */ 1,
              torch::lazy::MHash())
  {
    
  }

  std::string ToString() const override {
    std::stringstream ss;
    ss << XlaNode::ToString();
    
    return ss.str();
  }

  

  bool CanBeReused(const torch::lazy::Value& self) const {
    return false;
    }

  torch_xla::XlaOpVector Lower(LoweringContext* loctx) const override;

  
  

};

class Exp : public XlaNode {
 public:
  static torch::lazy::OpKind ClassOpKind() {
    return torch::lazy::OpKind(at::aten::exp);
  }

  Exp(const torch::lazy::Value& self, std::vector<torch::lazy::Shape>&& shapes)
      : XlaNode(torch::lazy::OpKind(at::aten::exp),
              {self}, std::move(shapes),
              [&]() { return ExpOutputShape(self); },
              /* num_outputs */ 1,
              torch::lazy::MHash())
  {
    
  }

  std::string ToString() const override {
    std::stringstream ss;
    ss << XlaNode::ToString();
    
    return ss.str();
  }

  

  bool CanBeReused(const torch::lazy::Value& self) const {
    return false;
    }

  torch_xla::XlaOpVector Lower(LoweringContext* loctx) const override;

  
  

};

---

Generated XLANativeFunctions.cpp:

    at::Tensor XLANativeFunctions::erf(const at::Tensor & self) {
        
        XLA_FN_COUNTER("xla::");
        auto common_device = torch_xla::bridge::GetXlaDevice(self);
        TORCH_INTERNAL_ASSERT(common_device);
        
        torch_xla::XLATensorPtr lazy_self = torch_xla::bridge::GetXlaTensorOrCreateForWrappedNumber(self, *common_device);
        torch::lazy::NodePtr node = torch::lazy::ReuseNode<Erf>(lazy_self->GetIrValue());
        if (!node) {
            auto out_meta = at::meta::erf(self);
            std::vector<torch::lazy::Shape> shapes{
        torch::lazy::Shape(out_meta.scalar_type(), out_meta.sizes().vec())};
            TORCH_INTERNAL_ASSERT(shapes.size() == 1);
            if(torch::lazy::symbolicShapeEnabled()){
                std::vector<torch::jit::IValue> inputs = { self };
                const char* schema_str = "aten::erf(Tensor self) -> Tensor";
                applySymbolicShapesOnLT(schema_str, inputs, shapes);
            }
        
            node = torch::lazy::MakeNode<Erf>(lazy_self->GetIrValue(), std::move(shapes));
            CacheNode(node);
        }
        
        auto result = torch_xla::bridge::AtenFromXlaTensor(
                torch_xla::XLATensor::Create(std::move(node), *common_device));
        return result;
    };

    
    at::Tensor XLANativeFunctions::erfc(const at::Tensor & self) {
        
        XLA_FN_COUNTER("xla::");
        auto common_device = torch_xla::bridge::GetXlaDevice(self);
        TORCH_INTERNAL_ASSERT(common_device);
        
        torch_xla::XLATensorPtr lazy_self = torch_xla::bridge::GetXlaTensorOrCreateForWrappedNumber(self, *common_device);
        torch::lazy::NodePtr node = torch::lazy::ReuseNode<Erfc>(lazy_self->GetIrValue());
        if (!node) {
            auto out_meta = at::meta::erfc(self);
            std::vector<torch::lazy::Shape> shapes{
        torch::lazy::Shape(out_meta.scalar_type(), out_meta.sizes().vec())};
            TORCH_INTERNAL_ASSERT(shapes.size() == 1);
            if(torch::lazy::symbolicShapeEnabled()){
                std::vector<torch::jit::IValue> inputs = { self };
                const char* schema_str = "aten::erfc(Tensor self) -> Tensor";
                applySymbolicShapesOnLT(schema_str, inputs, shapes);
            }
        
            node = torch::lazy::MakeNode<Erfc>(lazy_self->GetIrValue(), std::move(shapes));
            CacheNode(node);
        }
        
        auto result = torch_xla::bridge::AtenFromXlaTensor(
                torch_xla::XLATensor::Create(std::move(node), *common_device));
        return result;
    };

    
    at::Tensor XLANativeFunctions::erfinv(const at::Tensor & self) {
        
        XLA_FN_COUNTER("xla::");
        auto common_device = torch_xla::bridge::GetXlaDevice(self);
        TORCH_INTERNAL_ASSERT(common_device);
        
        torch_xla::XLATensorPtr lazy_self = torch_xla::bridge::GetXlaTensorOrCreateForWrappedNumber(self, *common_device);
        torch::lazy::NodePtr node = torch::lazy::ReuseNode<Erfinv>(lazy_self->GetIrValue());
        if (!node) {
            auto out_meta = at::meta::erfinv(self);
            std::vector<torch::lazy::Shape> shapes{
        torch::lazy::Shape(out_meta.scalar_type(), out_meta.sizes().vec())};
            TORCH_INTERNAL_ASSERT(shapes.size() == 1);
            if(torch::lazy::symbolicShapeEnabled()){
                std::vector<torch::jit::IValue> inputs = { self };
                const char* schema_str = "aten::erfinv(Tensor self) -> Tensor";
                applySymbolicShapesOnLT(schema_str, inputs, shapes);
            }
        
            node = torch::lazy::MakeNode<Erfinv>(lazy_self->GetIrValue(), std::move(shapes));
            CacheNode(node);
        }
        
        auto result = torch_xla::bridge::AtenFromXlaTensor(
                torch_xla::XLATensor::Create(std::move(node), *common_device));
        return result;
    };

    
    at::Tensor XLANativeFunctions::exp(const at::Tensor & self) {
        
        XLA_FN_COUNTER("xla::");
        auto common_device = torch_xla::bridge::GetXlaDevice(self);
        TORCH_INTERNAL_ASSERT(common_device);
        
        torch_xla::XLATensorPtr lazy_self = torch_xla::bridge::GetXlaTensorOrCreateForWrappedNumber(self, *common_device);
        torch::lazy::NodePtr node = torch::lazy::ReuseNode<Exp>(lazy_self->GetIrValue());
        if (!node) {
            auto out_meta = at::meta::exp(self);
            std::vector<torch::lazy::Shape> shapes{
        torch::lazy::Shape(out_meta.scalar_type(), out_meta.sizes().vec())};
            TORCH_INTERNAL_ASSERT(shapes.size() == 1);
            if(torch::lazy::symbolicShapeEnabled()){
                std::vector<torch::jit::IValue> inputs = { self };
                const char* schema_str = "aten::exp(Tensor self) -> Tensor";
                applySymbolicShapesOnLT(schema_str, inputs, shapes);
            }
        
            node = torch::lazy::MakeNode<Exp>(lazy_self->GetIrValue(), std::move(shapes));
            CacheNode(node);
        }
        
        auto result = torch_xla::bridge::AtenFromXlaTensor(
                torch_xla::XLATensor::Create(std::move(node), *common_device));
        return result;
    };

[JackCaoG]

We should just have a macro for all of these functions that just return the input shape.. we can do that in a separate pr.

[JackCaoG]

Thanks!