Couple fixes.

gen/gen.ml

@@ -32,6 +32,7 @@ let excluded_functions =
     ; "_cummax_helper"
     ; "retain_grad"
     ; "_validate_sparse_coo_tensor_args"
+    ; "_sparse_semi_structured_addmm"
     ; "_backward"
     ; "size"
     ; "stride"

src/wrappers/tensor_fallible_generated.rs

@@ -6489,25 +6489,6 @@ impl Tensor {
         Ok((Tensor { c_tensor: c_tensors[0] }, Tensor { c_tensor: c_tensors[1] }))
     }
 
-    pub fn f_internal_sparse_semi_structured_addmm(
-        &self,
-        mat1: &Tensor,
-        mat1_meta: &Tensor,
-        mat2: &Tensor,
-        out_dtype: impl Into<Option<Kind>>,
-    ) -> Result<Tensor, TchError> {
-        let mut c_tensors = [std::ptr::null_mut(); 1];
-        unsafe_torch_err!(atg__sparse_semi_structured_addmm(
-            c_tensors.as_mut_ptr(),
-            self.c_tensor,
-            mat1.c_tensor,
-            mat1_meta.c_tensor,
-            mat2.c_tensor,
-            out_dtype.into().map_or(-1, |s| s.c_int())
-        ));
-        Ok(Tensor { c_tensor: c_tensors[0] })
-    }
-
     pub fn f_internal_sparse_semi_structured_apply(
         &self,
         thread_masks: &Tensor,

src/wrappers/tensor_generated.rs

@@ -3893,16 +3893,6 @@ impl Tensor {
         self.f_internal_sparse_mm_reduce_impl(other, reduce).unwrap()
     }
 
-    pub fn internal_sparse_semi_structured_addmm(
-        &self,
-        mat1: &Tensor,
-        mat1_meta: &Tensor,
-        mat2: &Tensor,
-        out_dtype: impl Into<Option<Kind>>,
-    ) -> Tensor {
-        self.f_internal_sparse_semi_structured_addmm(mat1, mat1_meta, mat2, out_dtype).unwrap()
-    }
-
     pub fn internal_sparse_semi_structured_apply(&self, thread_masks: &Tensor) -> (Tensor, Tensor) {
         self.f_internal_sparse_semi_structured_apply(thread_masks).unwrap()
     }

src/wrappers/utils.rs

@@ -120,9 +120,6 @@ pub fn has_lazy() -> bool {
 pub fn has_mps() -> bool {
     unsafe_torch!(torch_sys::at_context_has_mps())
 }
-pub fn has_ort() -> bool {
-    unsafe_torch!(torch_sys::at_context_has_ort())
-}
 pub fn version_cudnn() -> i64 {
     unsafe_torch!(torch_sys::at_context_version_cudnn())
 }

torch-sys/libtch/torch_api.cpp

@@ -1109,13 +1109,6 @@ bool at_context_has_mps() {
   return 0;
 }
 
-bool at_context_has_ort() {
-  PROTECT (
-  return at::globalContext().hasORT();
-  )
-  return 0;
-}
-
 module atm_load(char *filename) {
   PROTECT(
     return new torch::jit::script::Module(torch::jit::load(filename));

torch-sys/libtch/torch_api.h

@@ -171,7 +171,6 @@ bool at_context_has_ipu();
 bool at_context_has_xla();
 bool at_context_has_lazy();
 bool at_context_has_mps();
-bool at_context_has_ort();
 
 
 /// Returns the number of CUDA devices available.

torch-sys/libtch/torch_api_generated.cpp

@@ -2645,13 +2645,6 @@ void atg__sparse_mm_reduce_impl(tensor *out__, tensor self, tensor other, char*
   )
 }
 
-void atg__sparse_semi_structured_addmm(tensor *out__, tensor input, tensor mat1, tensor mat1_meta, tensor mat2, int out_dtype) {
-  PROTECT(
-    auto outputs__ = torch::_sparse_semi_structured_addmm(*input, *mat1, *mat1_meta, *mat2, out_dtype < 0 ? c10::nullopt : c10::optional<at::ScalarType>(at::ScalarType(out_dtype)));
-    out__[0] = new torch::Tensor(outputs__);
-  )
-}
-
 void atg__sparse_semi_structured_apply(tensor *out__, tensor input, tensor thread_masks) {
   PROTECT(
     auto outputs__ = torch::_sparse_semi_structured_apply(*input, *thread_masks);

torch-sys/libtch/torch_api_generated.h

@@ -359,7 +359,6 @@ void atg__sparse_mask_projection_out(tensor *, tensor out, tensor self, tensor m
 void atg__sparse_mm(tensor *, tensor sparse, tensor dense);
 void atg__sparse_mm_reduce(tensor *, tensor sparse, tensor dense, char* reduce_ptr, int reduce_len);
 void atg__sparse_mm_reduce_impl(tensor *, tensor self, tensor other, char* reduce_ptr, int reduce_len);
-void atg__sparse_semi_structured_addmm(tensor *, tensor input, tensor mat1, tensor mat1_meta, tensor mat2, int out_dtype);
 void atg__sparse_semi_structured_apply(tensor *, tensor input, tensor thread_masks);
 void atg__sparse_semi_structured_apply_dense(tensor *, tensor input, tensor thread_masks);
 void atg__sparse_semi_structured_linear(tensor *, tensor input, tensor weight, tensor meta, tensor bias, char* activation_ptr, int activation_len, int out_dtype);

torch-sys/src/c_generated.rs

@@ -2663,14 +2663,6 @@ extern "C" {
         reduce_ptr: *const u8,
         reduce_len: c_int,
     );
-    pub fn atg__sparse_semi_structured_addmm(
-        out__: *mut *mut C_tensor,
-        input_: *mut C_tensor,
-        mat1_: *mut C_tensor,
-        mat1_meta_: *mut C_tensor,
-        mat2_: *mut C_tensor,
-        out_dtype_: c_int,
-    );
     pub fn atg__sparse_semi_structured_apply(
         out__: *mut *mut C_tensor,
         input_: *mut C_tensor,

torch-sys/src/lib.rs

@@ -156,7 +156,6 @@ extern "C" {
     pub fn at_context_has_xla() -> bool;
     pub fn at_context_has_lazy() -> bool;
     pub fn at_context_has_mps() -> bool;
-    pub fn at_context_has_ort() -> bool;
     pub fn at_context_version_cudnn() -> i64;
     pub fn at_context_version_cudart() -> i64;
 }