core/ndstrides: implement np_transpose() (no axes argument)

The IRRT implementation knows how to handle axes. But the argument is
not in NAC3 yet.

nac3core/irrt/irrt.cpp

@@ -12,3 +12,4 @@
 #include "irrt/ndarray/array.hpp"
 #include "irrt/ndarray/reshape.hpp"
 #include "irrt/ndarray/broadcast.hpp"
+#include "irrt/ndarray/transpose.hpp"

nac3core/irrt/irrt/ndarray/transpose.hpp

@@ -0,0 +1,145 @@
+#pragma once
+
+#include "irrt/debug.hpp"
+#include "irrt/exception.hpp"
+#include "irrt/int_types.hpp"
+#include "irrt/ndarray/def.hpp"
+#include "irrt/slice.hpp"
+
+/*
+ * Notes on `np.transpose(<array>, <axes>)`
+ *
+ * TODO: `axes`, if specified, can actually contain negative indices,
+ * but it is not documented in numpy.
+ *
+ * Supporting it for now.
+ */
+
+namespace {
+namespace ndarray {
+namespace transpose {
+/**
+ * @brief Do assertions on `<axes>` in `np.transpose(<array>, <axes>)`.
+ *
+ * Note that `np.transpose`'s `<axe>` argument is optional. If the argument
+ * is specified but the user, use this function to do assertions on it.
+ *
+ * @param ndims The number of dimensions of `<array>`
+ * @param num_axes Number of elements in `<axes>` as specified by the user.
+ *   This should be equal to `ndims`. If not, a "ValueError: axes don't match array" is thrown.
+ * @param axes The user specified `<axes>`.
+ */
+template<typename SizeT>
+void assert_transpose_axes(SizeT ndims, SizeT num_axes, const SizeT* axes) {
+    if (ndims != num_axes) {
+        raise_exception(SizeT, EXN_VALUE_ERROR, "axes don't match array", NO_PARAM, NO_PARAM, NO_PARAM);
+    }
+
+    // TODO: Optimize this
+    bool* axe_specified = (bool*)__builtin_alloca(sizeof(bool) * ndims);
+    for (SizeT i = 0; i < ndims; i++)
+        axe_specified[i] = false;
+
+    for (SizeT i = 0; i < ndims; i++) {
+        SizeT axis = slice::resolve_index_in_length(ndims, axes[i]);
+        if (axis == -1) {
+            // TODO: numpy actually throws a `numpy.exceptions.AxisError`
+            raise_exception(SizeT, EXN_VALUE_ERROR, "axis {0} is out of bounds for array of dimension {1}", axis, ndims,
+                            NO_PARAM);
+        }
+
+        if (axe_specified[axis]) {
+            raise_exception(SizeT, EXN_VALUE_ERROR, "repeated axis in transpose", NO_PARAM, NO_PARAM, NO_PARAM);
+        }
+
+        axe_specified[axis] = true;
+    }
+}
+
+/**
+ * @brief Create a transpose view of `src_ndarray` and perform proper assertions.
+ *
+ * This function is very similar to doing `dst_ndarray = np.transpose(src_ndarray, <axes>)`.
+ * If `<axes>` is supposed to be `None`, caller can pass in a `nullptr` to `<axes>`.
+ *
+ * The transpose view created is returned by modifying `dst_ndarray`.
+ *
+ * The caller is responsible for setting up `dst_ndarray` before calling this function.
+ * Here is what this function expects from `dst_ndarray` when called:
+ *   - `dst_ndarray->data` does not have to be initialized.
+ *   - `dst_ndarray->itemsize` does not have to be initialized.
+ *   - `dst_ndarray->ndims` must be initialized, must be equal to `src_ndarray->ndims`.
+ *   - `dst_ndarray->shape` must be allocated, through it can contain uninitialized values.
+ *   - `dst_ndarray->strides` must be allocated, through it can contain uninitialized values.
+ * When this function call ends:
+ *   - `dst_ndarray->data` is set to `src_ndarray->data` (`dst_ndarray` is just a view to `src_ndarray`)
+ *   - `dst_ndarray->itemsize` is set to `src_ndarray->itemsize`
+ *   - `dst_ndarray->ndims` is unchanged
+ *   - `dst_ndarray->shape` is updated according to how `np.transpose` works
+ *   - `dst_ndarray->strides` is updated according to how `np.transpose` works
+ *
+ * @param src_ndarray The NDArray to build a transpose view on
+ * @param dst_ndarray The resulting NDArray after transpose. Further details in the comments above,
+ * @param num_axes Number of elements in axes. Unused if `axes` is nullptr.
+ * @param axes Axes permutation. Set it to `nullptr` if `<axes>` is `None`.
+ */
+template<typename SizeT>
+void transpose(const NDArray<SizeT>* src_ndarray, NDArray<SizeT>* dst_ndarray, SizeT num_axes, const SizeT* axes) {
+    debug_assert_eq(SizeT, src_ndarray->ndims, dst_ndarray->ndims);
+    const auto ndims = src_ndarray->ndims;
+
+    if (axes != nullptr)
+        assert_transpose_axes(ndims, num_axes, axes);
+
+    dst_ndarray->data = src_ndarray->data;
+    dst_ndarray->itemsize = src_ndarray->itemsize;
+
+    // Check out https://ajcr.net/stride-guide-part-2/ to see how `np.transpose` works behind the scenes.
+    if (axes == nullptr) {
+        // `np.transpose(<array>, axes=None)`
+
+        /*
+         * Minor note: `np.transpose(<array>, axes=None)` is equivalent to
+         * `np.transpose(<array>, axes=[N-1, N-2, ..., 0])` - basically it
+         * is reversing the order of strides and shape.
+         *
+         * This is a fast implementation to handle this special (but very common) case.
+         */
+
+        for (SizeT axis = 0; axis < ndims; axis++) {
+            dst_ndarray->shape[axis] = src_ndarray->shape[ndims - axis - 1];
+            dst_ndarray->strides[axis] = src_ndarray->strides[ndims - axis - 1];
+        }
+    } else {
+        // `np.transpose(<array>, <axes>)`
+
+        // Permute strides and shape according to `axes`, while resolving negative indices in `axes`
+        for (SizeT axis = 0; axis < ndims; axis++) {
+            // `i` cannot be OUT_OF_BOUNDS because of assertions
+            SizeT i = slice::resolve_index_in_length(ndims, axes[axis]);
+
+            dst_ndarray->shape[axis] = src_ndarray->shape[i];
+            dst_ndarray->strides[axis] = src_ndarray->strides[i];
+        }
+    }
+}
+}  // namespace transpose
+}  // namespace ndarray
+}  // namespace
+
+extern "C" {
+using namespace ndarray::transpose;
+void __nac3_ndarray_transpose(const NDArray<int32_t>* src_ndarray,
+                              NDArray<int32_t>* dst_ndarray,
+                              int32_t num_axes,
+                              const int32_t* axes) {
+    transpose(src_ndarray, dst_ndarray, num_axes, axes);
+}
+
+void __nac3_ndarray_transpose64(const NDArray<int64_t>* src_ndarray,
+                                NDArray<int64_t>* dst_ndarray,
+                                int64_t num_axes,
+                                const int64_t* axes) {
+    transpose(src_ndarray, dst_ndarray, num_axes, axes);
+}
+}

nac3core/src/codegen/irrt/mod.rs

@@ -1203,3 +1203,20 @@ pub fn call_nac3_ndarray_broadcast_shapes<'ctx, G: CodeGenerator + ?Sized>(
         .arg(dst_shape)
         .returning_void();
 }
+
+pub fn call_nac3_ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    src_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
+    dst_ndarray: Instance<'ctx, Ptr<Struct<NDArray>>>,
+    num_axes: Instance<'ctx, Int<SizeT>>,
+    axes: Instance<'ctx, Ptr<Int<SizeT>>>,
+) {
+    let name = get_sizet_dependent_function_name(generator, ctx, "__nac3_ndarray_transpose");
+    FnCall::builder(generator, ctx, &name)
+        .arg(src_ndarray)
+        .arg(dst_ndarray)
+        .arg(num_axes)
+        .arg(axes)
+        .returning_void();
+}

nac3core/src/codegen/numpy.rs

@@ -1990,113 +1990,6 @@ pub fn gen_ndarray_fill<'ctx>(
     Ok(())
 }
 
-/// Generates LLVM IR for `ndarray.transpose`.
-pub fn ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    x1: (Type, BasicValueEnum<'ctx>),
-) -> Result<BasicValueEnum<'ctx>, String> {
-    const FN_NAME: &str = "ndarray_transpose";
-    let (x1_ty, x1) = x1;
-    let llvm_usize = generator.get_size_type(ctx.ctx);
-
-    if let BasicValueEnum::PointerValue(n1) = x1 {
-        let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, x1_ty);
-        let n1 = NDArrayValue::from_ptr_val(n1, llvm_usize, None);
-        let n_sz = call_ndarray_calc_size(generator, ctx, &n1.dim_sizes(), (None, None));
-
-        // Dimensions are reversed in the transposed array
-        let out = create_ndarray_dyn_shape(
-            generator,
-            ctx,
-            elem_ty,
-            &n1,
-            |_, ctx, n| Ok(n.load_ndims(ctx)),
-            |generator, ctx, n, idx| {
-                let new_idx = ctx.builder.build_int_sub(n.load_ndims(ctx), idx, "").unwrap();
-                let new_idx = ctx
-                    .builder
-                    .build_int_sub(new_idx, new_idx.get_type().const_int(1, false), "")
-                    .unwrap();
-                unsafe { Ok(n.dim_sizes().get_typed_unchecked(ctx, generator, &new_idx, None)) }
-            },
-        )
-        .unwrap();
-
-        gen_for_callback_incrementing(
-            generator,
-            ctx,
-            None,
-            llvm_usize.const_zero(),
-            (n_sz, false),
-            |generator, ctx, _, idx| {
-                let elem = unsafe { n1.data().get_unchecked(ctx, generator, &idx, None) };
-
-                let new_idx = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
-                let rem_idx = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
-                ctx.builder.build_store(new_idx, llvm_usize.const_zero()).unwrap();
-                ctx.builder.build_store(rem_idx, idx).unwrap();
-
-                // Incrementally calculate the new index in the transposed array
-                // For each index, we first decompose it into the n-dims and use those to reconstruct the new index
-                // The formula used for indexing is:
-                // idx = dim_n * ( ... (dim2 * (dim0 * dim1) + dim1) + dim2 ... ) + dim_n
-                gen_for_callback_incrementing(
-                    generator,
-                    ctx,
-                    None,
-                    llvm_usize.const_zero(),
-                    (n1.load_ndims(ctx), false),
-                    |generator, ctx, _, ndim| {
-                        let ndim_rev =
-                            ctx.builder.build_int_sub(n1.load_ndims(ctx), ndim, "").unwrap();
-                        let ndim_rev = ctx
-                            .builder
-                            .build_int_sub(ndim_rev, llvm_usize.const_int(1, false), "")
-                            .unwrap();
-                        let dim = unsafe {
-                            n1.dim_sizes().get_typed_unchecked(ctx, generator, &ndim_rev, None)
-                        };
-
-                        let rem_idx_val =
-                            ctx.builder.build_load(rem_idx, "").unwrap().into_int_value();
-                        let new_idx_val =
-                            ctx.builder.build_load(new_idx, "").unwrap().into_int_value();
-
-                        let add_component =
-                            ctx.builder.build_int_unsigned_rem(rem_idx_val, dim, "").unwrap();
-                        let rem_idx_val =
-                            ctx.builder.build_int_unsigned_div(rem_idx_val, dim, "").unwrap();
-
-                        let new_idx_val = ctx.builder.build_int_mul(new_idx_val, dim, "").unwrap();
-                        let new_idx_val =
-                            ctx.builder.build_int_add(new_idx_val, add_component, "").unwrap();
-
-                        ctx.builder.build_store(rem_idx, rem_idx_val).unwrap();
-                        ctx.builder.build_store(new_idx, new_idx_val).unwrap();
-
-                        Ok(())
-                    },
-                    llvm_usize.const_int(1, false),
-                )?;
-
-                let new_idx_val = ctx.builder.build_load(new_idx, "").unwrap().into_int_value();
-                unsafe { out.data().set_unchecked(ctx, generator, &new_idx_val, elem) };
-                Ok(())
-            },
-            llvm_usize.const_int(1, false),
-        )?;
-
-        Ok(out.as_base_value().into())
-    } else {
-        codegen_unreachable!(
-            ctx,
-            "{FN_NAME}() not supported for '{}'",
-            format!("'{}'", ctx.unifier.stringify(x1_ty))
-        )
-    }
-}
-
 /// Generates LLVM IR for `ndarray.dot`.
 /// Calculate inner product of two vectors or literals
 /// For matrix multiplication use `np_matmul`

nac3core/src/codegen/object/ndarray/view.rs

@@ -1,7 +1,8 @@
 use super::{indexing::RustNDIndex, NDArrayObject};
 use crate::codegen::{
-    irrt::call_nac3_ndarray_reshape_resolve_and_check_new_shape, model::*, CodeGenContext,
+    irrt::{call_nac3_ndarray_reshape_resolve_and_check_new_shape, call_nac3_ndarray_transpose},
-    CodeGenerator,
+    model::*,
+    CodeGenContext, CodeGenerator,
 };
 
 impl<'ctx> NDArrayObject<'ctx> {
@@ -85,4 +86,33 @@ impl<'ctx> NDArrayObject<'ctx> {
 
         dst_ndarray
     }
+
+    /// Create a transposed view on this ndarray like `np.transpose(<ndarray>, <axes> = None)`.
+    /// * `axes` - If specified, should be an array of the permutation (negative indices are **allowed**).
+    #[must_use]
+    pub fn transpose<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        axes: Option<Instance<'ctx, Ptr<Int<SizeT>>>>,
+    ) -> Self {
+        // Define models
+        let transposed_ndarray = NDArrayObject::alloca(generator, ctx, self.dtype, self.ndims);
+
+        let num_axes = self.ndims_llvm(generator, ctx.ctx);
+
+        // `axes = nullptr` if `axes` is unspecified.
+        let axes = axes.unwrap_or_else(|| Ptr(Int(SizeT)).nullptr(generator, ctx.ctx));
+
+        call_nac3_ndarray_transpose(
+            generator,
+            ctx,
+            self.instance,
+            transposed_ndarray.instance,
+            num_axes,
+            axes,
+        );
+
+        transposed_ndarray
+    }
 }

nac3core/src/toplevel/builtins.rs

@@ -1483,7 +1483,8 @@ impl<'a> BuiltinBuilder<'a> {
         );
 
         match prim {
-            PrimDef::FunNpTranspose => create_fn_by_codegen(
+            PrimDef::FunNpTranspose => {
+                create_fn_by_codegen(
                     self.unifier,
                     &into_var_map([in_ndarray_ty]),
                     prim.name(),
@@ -1491,10 +1492,17 @@ impl<'a> BuiltinBuilder<'a> {
                     &[(in_ndarray_ty.ty, "x")],
                     Box::new(move |ctx, _, fun, args, generator| {
                         let arg_ty = fun.0.args[0].ty;
-                    let arg_val = args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
+                        let arg_val =
-                    Ok(Some(ndarray_transpose(generator, ctx, (arg_ty, arg_val))?))
+                            args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
+
+                        let arg = AnyObject { ty: arg_ty, value: arg_val };
+                        let ndarray = NDArrayObject::from_object(generator, ctx, arg);
+
+                        let ndarray = ndarray.transpose(generator, ctx, None); // TODO: Add axes argument
+                        Ok(Some(ndarray.instance.value.as_basic_value_enum()))
                     }),
-            ),
+                )
+            }
 
             // NOTE: on `ndarray_factory_fn_shape_arg_tvar` and
             // the `param_ty` for `create_fn_by_codegen`.