[core] codegen/ndarray: Implement np_reshape

Based on 926e7e93: core/ndstrides: implement np_reshape()

nac3core/irrt/irrt.cpp

@@ -8,4 +8,5 @@
 #include "irrt/ndarray/def.hpp"
 #include "irrt/ndarray/iter.hpp"
 #include "irrt/ndarray/indexing.hpp"
-#include "irrt/ndarray/array.hpp"
+#include "irrt/ndarray/array.hpp"
+#include "irrt/ndarray/reshape.hpp"

nac3core/irrt/irrt/ndarray/reshape.hpp

@@ -0,0 +1,99 @@
+#pragma once
+
+#include "irrt/exception.hpp"
+#include "irrt/int_types.hpp"
+#include "irrt/ndarray/def.hpp"
+
+namespace {
+namespace ndarray {
+namespace reshape {
+/**
+ * @brief Perform assertions on and resolve unknown dimensions in `new_shape` in `np.reshape(<ndarray>, new_shape)`
+ *
+ * If `new_shape` indeed contains unknown dimensions (specified with `-1`, just like numpy), `new_shape` will be
+ * modified to contain the resolved dimension.
+ *
+ * To perform assertions on and resolve unknown dimensions in `new_shape`, we don't need the actual
+ * `<ndarray>` object itself, but only the `.size` of the `<ndarray>`.
+ *
+ * @param size The `.size` of `<ndarray>`
+ * @param new_ndims Number of elements in `new_shape`
+ * @param new_shape Target shape to reshape to
+ */
+template<typename SizeT>
+void resolve_and_check_new_shape(SizeT size, SizeT new_ndims, SizeT* new_shape) {
+    // Is there a -1 in `new_shape`?
+    bool neg1_exists = false;
+    // Location of -1, only initialized if `neg1_exists` is true
+    SizeT neg1_axis_i;
+    // The computed ndarray size of `new_shape`
+    SizeT new_size = 1;
+
+    for (SizeT axis_i = 0; axis_i < new_ndims; axis_i++) {
+        SizeT dim = new_shape[axis_i];
+        if (dim < 0) {
+            if (dim == -1) {
+                if (neg1_exists) {
+                    // Multiple `-1` found. Throw an error.
+                    raise_exception(SizeT, EXN_VALUE_ERROR, "can only specify one unknown dimension", NO_PARAM,
+                                    NO_PARAM, NO_PARAM);
+                } else {
+                    neg1_exists = true;
+                    neg1_axis_i = axis_i;
+                }
+            } else {
+                // TODO: What? In `np.reshape` any negative dimensions is
+                // treated like its `-1`.
+                //
+                // Try running `np.zeros((3, 4)).reshape((-999, 2))`
+                //
+                // It is not documented by numpy.
+                // Throw an error for now...
+
+                raise_exception(SizeT, EXN_VALUE_ERROR, "Found non -1 negative dimension {0} on axis {1}", dim, axis_i,
+                                NO_PARAM);
+            }
+        } else {
+            new_size *= dim;
+        }
+    }
+
+    bool can_reshape;
+    if (neg1_exists) {
+        // Let `x` be the unknown dimension
+        // Solve `x * <new_size> = <size>`
+        if (new_size == 0 && size == 0) {
+            // `x` has infinitely many solutions
+            can_reshape = false;
+        } else if (new_size == 0 && size != 0) {
+            // `x` has no solutions
+            can_reshape = false;
+        } else if (size % new_size != 0) {
+            // `x` has no integer solutions
+            can_reshape = false;
+        } else {
+            can_reshape = true;
+            new_shape[neg1_axis_i] = size / new_size;  // Resolve dimension
+        }
+    } else {
+        can_reshape = (new_size == size);
+    }
+
+    if (!can_reshape) {
+        raise_exception(SizeT, EXN_VALUE_ERROR, "cannot reshape array of size {0} into given shape", size, NO_PARAM,
+                        NO_PARAM);
+    }
+}
+}  // namespace reshape
+}  // namespace ndarray
+}  // namespace
+
+extern "C" {
+void __nac3_ndarray_reshape_resolve_and_check_new_shape(int32_t size, int32_t new_ndims, int32_t* new_shape) {
+    ndarray::reshape::resolve_and_check_new_shape(size, new_ndims, new_shape);
+}
+
+void __nac3_ndarray_reshape_resolve_and_check_new_shape64(int64_t size, int64_t new_ndims, int64_t* new_shape) {
+    ndarray::reshape::resolve_and_check_new_shape(size, new_ndims, new_shape);
+}
+}

nac3core/src/codegen/irrt/ndarray/mod.rs

@@ -20,11 +20,13 @@ pub use array::*;
 pub use basic::*;
 pub use indexing::*;
 pub use iter::*;
+pub use reshape::*;
 
 mod array;
 mod basic;
 mod indexing;
 mod iter;
+mod reshape;
 
 /// Generates a call to `__nac3_ndarray_calc_size`. Returns a
 /// [`usize`][CodeGenerator::get_size_type] representing the calculated total size.

nac3core/src/codegen/irrt/ndarray/reshape.rs

@@ -0,0 +1,36 @@
+use inkwell::values::IntValue;
+
+use crate::codegen::{
+    expr::infer_and_call_function,
+    irrt::get_usize_dependent_function_name,
+    values::{ArrayLikeValue, ArraySliceValue},
+    CodeGenContext, CodeGenerator,
+};
+
+pub fn call_nac3_ndarray_reshape_resolve_and_check_new_shape<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &G,
+    ctx: &CodeGenContext<'ctx, '_>,
+    size: IntValue<'ctx>,
+    new_ndims: IntValue<'ctx>,
+    new_shape: ArraySliceValue<'ctx>,
+) {
+    let llvm_usize = generator.get_size_type(ctx.ctx);
+
+    assert_eq!(size.get_type(), llvm_usize);
+    assert_eq!(new_ndims.get_type(), llvm_usize);
+    assert_eq!(new_shape.element_type(ctx, generator), llvm_usize.into());
+
+    let name = get_usize_dependent_function_name(
+        generator,
+        ctx,
+        "__nac3_ndarray_reshape_resolve_and_check_new_shape",
+    );
+    infer_and_call_function(
+        ctx,
+        &name,
+        None,
+        &[size.into(), new_ndims.into(), new_shape.base_ptr(ctx, generator).into()],
+        None,
+        None,
+    );
+}

nac3core/src/codegen/numpy.rs

@@ -21,9 +21,9 @@ use super::{
     types::ndarray::{factory::ndarray_zero_value, NDArrayType},
     values::{
         ndarray::{shape::parse_numpy_int_sequence, NDArrayValue},
-        ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ListValue, ProxyValue,
+        ArrayLikeIndexer, ArrayLikeValue, ArraySliceValue, ProxyValue, TypedArrayLikeAccessor,
-        TypedArrayLikeAccessor, TypedArrayLikeAdapter, TypedArrayLikeMutator,
+        TypedArrayLikeAdapter, TypedArrayLikeMutator, UntypedArrayLikeAccessor,
-        UntypedArrayLikeAccessor, UntypedArrayLikeMutator,
+        UntypedArrayLikeMutator,
     },
     CodeGenContext, CodeGenerator,
 };
@@ -134,46 +134,6 @@ where
     Ok(ndarray)
 }
 
-/// Creates an `NDArray` instance from a constant shape.
-///
-/// * `elem_ty` - The element type of the `NDArray`.
-/// * `shape` - The shape of the `NDArray`, represented am array of [`IntValue`]s.
-pub fn create_ndarray_const_shape<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    elem_ty: Type,
-    shape: &[IntValue<'ctx>],
-) -> Result<NDArrayValue<'ctx>, String> {
-    let llvm_usize = generator.get_size_type(ctx.ctx);
-
-    for &shape_dim in shape {
-        let shape_dim = ctx.builder.build_int_z_extend(shape_dim, llvm_usize, "").unwrap();
-        let shape_dim_gez = ctx
-            .builder
-            .build_int_compare(IntPredicate::SGE, shape_dim, llvm_usize.const_zero(), "")
-            .unwrap();
-
-        ctx.make_assert(
-            generator,
-            shape_dim_gez,
-            "0:ValueError",
-            "negative dimensions not supported",
-            [None, None, None],
-            ctx.current_loc,
-        );
-
-        // TODO: Disallow shape > u32_MAX
-    }
-
-    let llvm_dtype = ctx.get_llvm_type(generator, elem_ty);
-
-    let ndarray = NDArrayType::new(generator, ctx.ctx, llvm_dtype, Some(shape.len() as u64))
-        .construct_dyn_shape(generator, ctx, shape, None);
-    unsafe { ndarray.create_data(generator, ctx) };
-
-    Ok(ndarray)
-}
-
 /// Generates LLVM IR for populating the entire `NDArray` using a lambda with its flattened index as
 /// its input.
 fn ndarray_fill_flattened<'ctx, 'a, G, ValueFn>(
@@ -1455,294 +1415,6 @@ pub fn ndarray_transpose<'ctx, G: CodeGenerator + ?Sized>(
     }
 }
 
-/// LLVM-typed implementation for generating the implementation for `ndarray.reshape`.
-///
-/// * `x1` - `NDArray` to reshape.
-/// * `shape` - The `shape` parameter used to construct the new `NDArray`.
-///   Just like numpy, the `shape` argument can be:
-///   1. A list of `int32`;   e.g., `np.reshape(arr, [600, -1, 3])`
-///   2. A tuple of `int32`;  e.g., `np.reshape(arr, (-1, 800, 3))`
-///   3. A scalar `int32`;     e.g., `np.reshape(arr, 3)`
-///
-/// Note that unlike other generating functions, one of the dimensions in the shape can be negative.
-pub fn ndarray_reshape<'ctx, G: CodeGenerator + ?Sized>(
-    generator: &mut G,
-    ctx: &mut CodeGenContext<'ctx, '_>,
-    x1: (Type, BasicValueEnum<'ctx>),
-    shape: (Type, BasicValueEnum<'ctx>),
-) -> Result<BasicValueEnum<'ctx>, String> {
-    const FN_NAME: &str = "ndarray_reshape";
-    let (x1_ty, x1) = x1;
-    let (_, shape) = shape;
-
-    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 llvm_ndarray_ty = NDArrayType::from_unifier_type(generator, ctx, x1_ty);
-        let n1 = llvm_ndarray_ty.map_value(n1, None);
-        let n_sz = n1.size(generator, ctx);
-
-        let acc = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
-        let num_neg = generator.gen_var_alloc(ctx, llvm_usize.into(), None)?;
-        ctx.builder.build_store(acc, llvm_usize.const_int(1, false)).unwrap();
-        ctx.builder.build_store(num_neg, llvm_usize.const_zero()).unwrap();
-
-        let out = match shape {
-            BasicValueEnum::PointerValue(shape_list_ptr)
-                if ListValue::is_representable(shape_list_ptr, llvm_usize).is_ok() =>
-            {
-                // 1. A list of ints; e.g., `np.reshape(arr, [int64(600), int64(800, -1])`
-
-                let shape_list = ListValue::from_pointer_value(shape_list_ptr, llvm_usize, None);
-                // Check for -1 in dimensions
-                gen_for_callback_incrementing(
-                    generator,
-                    ctx,
-                    None,
-                    llvm_usize.const_zero(),
-                    (shape_list.load_size(ctx, None), false),
-                    |generator, ctx, _, idx| {
-                        let ele =
-                            shape_list.data().get(ctx, generator, &idx, None).into_int_value();
-                        let ele = ctx.builder.build_int_s_extend(ele, llvm_usize, "").unwrap();
-
-                        gen_if_else_expr_callback(
-                            generator,
-                            ctx,
-                            |_, ctx| {
-                                Ok(ctx
-                                    .builder
-                                    .build_int_compare(
-                                        IntPredicate::SLT,
-                                        ele,
-                                        llvm_usize.const_zero(),
-                                        "",
-                                    )
-                                    .unwrap())
-                            },
-                            |_, ctx| -> Result<Option<IntValue>, String> {
-                                let num_neg_value =
-                                    ctx.builder.build_load(num_neg, "").unwrap().into_int_value();
-                                let num_neg_value = ctx
-                                    .builder
-                                    .build_int_add(
-                                        num_neg_value,
-                                        llvm_usize.const_int(1, false),
-                                        "",
-                                    )
-                                    .unwrap();
-                                ctx.builder.build_store(num_neg, num_neg_value).unwrap();
-                                Ok(None)
-                            },
-                            |_, ctx| {
-                                let acc_value =
-                                    ctx.builder.build_load(acc, "").unwrap().into_int_value();
-                                let acc_value =
-                                    ctx.builder.build_int_mul(acc_value, ele, "").unwrap();
-                                ctx.builder.build_store(acc, acc_value).unwrap();
-                                Ok(None)
-                            },
-                        )?;
-                        Ok(())
-                    },
-                    llvm_usize.const_int(1, false),
-                )?;
-                let acc_val = ctx.builder.build_load(acc, "").unwrap().into_int_value();
-                let rem = ctx.builder.build_int_unsigned_div(n_sz, acc_val, "").unwrap();
-                // Generate the output shape by filling -1 with `rem`
-                create_ndarray_dyn_shape(
-                    generator,
-                    ctx,
-                    elem_ty,
-                    &shape_list,
-                    |_, ctx, _| Ok(shape_list.load_size(ctx, None)),
-                    |generator, ctx, shape_list, idx| {
-                        let dim =
-                            shape_list.data().get(ctx, generator, &idx, None).into_int_value();
-                        let dim = ctx.builder.build_int_s_extend(dim, llvm_usize, "").unwrap();
-
-                        Ok(gen_if_else_expr_callback(
-                            generator,
-                            ctx,
-                            |_, ctx| {
-                                Ok(ctx
-                                    .builder
-                                    .build_int_compare(
-                                        IntPredicate::SLT,
-                                        dim,
-                                        llvm_usize.const_zero(),
-                                        "",
-                                    )
-                                    .unwrap())
-                            },
-                            |_, _| Ok(Some(rem)),
-                            |_, _| Ok(Some(dim)),
-                        )?
-                        .unwrap()
-                        .into_int_value())
-                    },
-                )
-            }
-            BasicValueEnum::StructValue(shape_tuple) => {
-                // 2. A tuple of `int32`;  e.g., `np.reshape(arr, (-1, 800, 3))`
-
-                let ndims = shape_tuple.get_type().count_fields();
-                // Check for -1 in dims
-                for dim_i in 0..ndims {
-                    let dim = ctx
-                        .builder
-                        .build_extract_value(shape_tuple, dim_i, "")
-                        .unwrap()
-                        .into_int_value();
-                    let dim = ctx.builder.build_int_s_extend(dim, llvm_usize, "").unwrap();
-
-                    gen_if_else_expr_callback(
-                        generator,
-                        ctx,
-                        |_, ctx| {
-                            Ok(ctx
-                                .builder
-                                .build_int_compare(
-                                    IntPredicate::SLT,
-                                    dim,
-                                    llvm_usize.const_zero(),
-                                    "",
-                                )
-                                .unwrap())
-                        },
-                        |_, ctx| -> Result<Option<IntValue>, String> {
-                            let num_negs =
-                                ctx.builder.build_load(num_neg, "").unwrap().into_int_value();
-                            let num_negs = ctx
-                                .builder
-                                .build_int_add(num_negs, llvm_usize.const_int(1, false), "")
-                                .unwrap();
-                            ctx.builder.build_store(num_neg, num_negs).unwrap();
-                            Ok(None)
-                        },
-                        |_, ctx| {
-                            let acc_val = ctx.builder.build_load(acc, "").unwrap().into_int_value();
-                            let acc_val = ctx.builder.build_int_mul(acc_val, dim, "").unwrap();
-                            ctx.builder.build_store(acc, acc_val).unwrap();
-                            Ok(None)
-                        },
-                    )?;
-                }
-
-                let acc_val = ctx.builder.build_load(acc, "").unwrap().into_int_value();
-                let rem = ctx.builder.build_int_unsigned_div(n_sz, acc_val, "").unwrap();
-                let mut shape = Vec::with_capacity(ndims as usize);
-
-                // Reconstruct shape filling negatives with rem
-                for dim_i in 0..ndims {
-                    let dim = ctx
-                        .builder
-                        .build_extract_value(shape_tuple, dim_i, "")
-                        .unwrap()
-                        .into_int_value();
-                    let dim = ctx.builder.build_int_s_extend(dim, llvm_usize, "").unwrap();
-
-                    let dim = gen_if_else_expr_callback(
-                        generator,
-                        ctx,
-                        |_, ctx| {
-                            Ok(ctx
-                                .builder
-                                .build_int_compare(
-                                    IntPredicate::SLT,
-                                    dim,
-                                    llvm_usize.const_zero(),
-                                    "",
-                                )
-                                .unwrap())
-                        },
-                        |_, _| Ok(Some(rem)),
-                        |_, _| Ok(Some(dim)),
-                    )?
-                    .unwrap()
-                    .into_int_value();
-                    shape.push(dim);
-                }
-                create_ndarray_const_shape(generator, ctx, elem_ty, shape.as_slice())
-            }
-            BasicValueEnum::IntValue(shape_int) => {
-                // 3. A scalar `int32`;     e.g., `np.reshape(arr, 3)`
-                let shape_int = gen_if_else_expr_callback(
-                    generator,
-                    ctx,
-                    |_, ctx| {
-                        Ok(ctx
-                            .builder
-                            .build_int_compare(
-                                IntPredicate::SLT,
-                                shape_int,
-                                llvm_usize.const_zero(),
-                                "",
-                            )
-                            .unwrap())
-                    },
-                    |_, _| Ok(Some(n_sz)),
-                    |_, ctx| {
-                        Ok(Some(ctx.builder.build_int_s_extend(shape_int, llvm_usize, "").unwrap()))
-                    },
-                )?
-                .unwrap()
-                .into_int_value();
-                create_ndarray_const_shape(generator, ctx, elem_ty, &[shape_int])
-            }
-            _ => codegen_unreachable!(ctx),
-        }
-        .unwrap();
-
-        // Only allow one dimension to be negative
-        let num_negs = ctx.builder.build_load(num_neg, "").unwrap().into_int_value();
-        ctx.make_assert(
-            generator,
-            ctx.builder
-                .build_int_compare(IntPredicate::ULT, num_negs, llvm_usize.const_int(2, false), "")
-                .unwrap(),
-            "0:ValueError",
-            "can only specify one unknown dimension",
-            [None, None, None],
-            ctx.current_loc,
-        );
-
-        // The new shape must be compatible with the old shape
-        let out_sz = out.size(generator, ctx);
-        ctx.make_assert(
-            generator,
-            ctx.builder.build_int_compare(IntPredicate::EQ, out_sz, n_sz, "").unwrap(),
-            "0:ValueError",
-            "cannot reshape array of size {0} into provided shape of size {1}",
-            [Some(n_sz), Some(out_sz), None],
-            ctx.current_loc,
-        );
-
-        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) };
-                unsafe { out.data().set_unchecked(ctx, generator, &idx, 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/values/ndarray/view.rs

@@ -1,9 +1,16 @@
 use std::iter::{once, repeat_n};
 
+use inkwell::values::IntValue;
 use itertools::Itertools;
 
 use crate::codegen::{
-    values::ndarray::{NDArrayValue, RustNDIndex},
+    irrt,
+    stmt::gen_if_callback,
+    types::ndarray::NDArrayType,
+    values::{
+        ndarray::{NDArrayValue, RustNDIndex},
+        ArrayLikeValue, ProxyValue, TypedArrayLikeAccessor,
+    },
     CodeGenContext, CodeGenerator,
 };
 
@@ -33,4 +40,69 @@ impl<'ctx> NDArrayValue<'ctx> {
             *self
         }
     }
+
+    /// Create a reshaped view on this ndarray like `np.reshape()`.
+    ///
+    /// If there is a `-1` in `new_shape`, it will be resolved; `new_shape` would **NOT** be modified as a result.
+    ///
+    /// If reshape without copying is impossible, this function will allocate a new ndarray and copy contents.
+    ///
+    /// * `new_ndims` - The number of dimensions of `new_shape` as a [`Type`].
+    /// * `new_shape` - The target shape to do `np.reshape()`.
+    #[must_use]
+    pub fn reshape_or_copy<G: CodeGenerator + ?Sized>(
+        &self,
+        generator: &mut G,
+        ctx: &mut CodeGenContext<'ctx, '_>,
+        new_ndims: u64,
+        new_shape: &impl TypedArrayLikeAccessor<'ctx, G, IntValue<'ctx>>,
+    ) -> Self {
+        assert_eq!(new_shape.element_type(ctx, generator), self.llvm_usize.into());
+
+        // TODO: The current criterion for whether to do a full copy or not is by checking
+        //       `is_c_contiguous`, but this is not optimal - there are cases when the ndarray is
+        //       not contiguous but could be reshaped without copying data. Look into how numpy does
+        //       it.
+
+        let dst_ndarray = NDArrayType::new(generator, ctx.ctx, self.dtype, Some(new_ndims))
+            .construct_uninitialized(generator, ctx, None);
+        dst_ndarray.copy_shape_from_array(generator, ctx, new_shape.base_ptr(ctx, generator));
+
+        // Resolve negative indices
+        let size = self.size(generator, ctx);
+        let dst_ndims = self.llvm_usize.const_int(dst_ndarray.get_type().ndims().unwrap(), false);
+        let dst_shape = dst_ndarray.shape();
+        irrt::ndarray::call_nac3_ndarray_reshape_resolve_and_check_new_shape(
+            generator,
+            ctx,
+            size,
+            dst_ndims,
+            dst_shape.as_slice_value(ctx, generator),
+        );
+
+        gen_if_callback(
+            generator,
+            ctx,
+            |generator, ctx| Ok(self.is_c_contiguous(generator, ctx)),
+            |generator, ctx| {
+                // Reshape is possible without copying
+                dst_ndarray.set_strides_contiguous(generator, ctx);
+                dst_ndarray.store_data(ctx, self.data().base_ptr(ctx, generator));
+
+                Ok(())
+            },
+            |generator, ctx| {
+                // Reshape is impossible without copying
+                unsafe {
+                    dst_ndarray.create_data(generator, ctx);
+                }
+                dst_ndarray.copy_data_from(generator, ctx, *self);
+
+                Ok(())
+            },
+        )
+        .unwrap();
+
+        dst_ndarray
+    }
 }

nac3core/src/toplevel/builtins.rs

@@ -5,8 +5,10 @@ use inkwell::{values::BasicValue, IntPredicate};
 use strum::IntoEnumIterator;
 
 use super::{
-    helper::{debug_assert_prim_is_allowed, make_exception_fields, PrimDef, PrimDefDetails},
+    helper::{
-    numpy::make_ndarray_ty,
+        debug_assert_prim_is_allowed, extract_ndims, make_exception_fields, PrimDef, PrimDefDetails,
+    },
+    numpy::{make_ndarray_ty, unpack_ndarray_var_tys},
     *,
 };
 use crate::{
@@ -15,7 +17,7 @@ use crate::{
         numpy::*,
         stmt::exn_constructor,
         types::ndarray::NDArrayType,
-        values::{ProxyValue, RangeValue},
+        values::{ndarray::shape::parse_numpy_int_sequence, ProxyValue, RangeValue},
     },
     symbol_resolver::SymbolValue,
     typecheck::typedef::{into_var_map, iter_type_vars, TypeVar, VarMap},
@@ -193,7 +195,6 @@ struct BuiltinBuilder<'a> {
 
     ndarray_float: Type,
     ndarray_float_2d: Type,
-    ndarray_num_ty: Type,
 
     float_or_ndarray_ty: TypeVar,
     float_or_ndarray_var_map: VarMap,
@@ -307,7 +308,6 @@ impl<'a> BuiltinBuilder<'a> {
 
             ndarray_float,
             ndarray_float_2d,
-            ndarray_num_ty,
 
             float_or_ndarray_ty,
             float_or_ndarray_var_map,
@@ -1330,30 +1330,25 @@ impl<'a> BuiltinBuilder<'a> {
     fn build_ndarray_view_function(&mut self, prim: PrimDef) -> TopLevelDef {
         debug_assert_prim_is_allowed(prim, &[PrimDef::FunNpTranspose, PrimDef::FunNpReshape]);
 
+        let in_ndarray_ty = self.unifier.get_fresh_var_with_range(
+            &[self.primitives.ndarray],
+            Some("T".into()),
+            None,
+        );
+
         match prim {
-            PrimDef::FunNpTranspose => {
+            PrimDef::FunNpTranspose => create_fn_by_codegen(
-                let ndarray_ty = self.unifier.get_fresh_var_with_range(
+                self.unifier,
-                    &[self.ndarray_num_ty],
+                &into_var_map([in_ndarray_ty]),
-                    Some("T".into()),
+                prim.name(),
-                    None,
+                in_ndarray_ty.ty,
-                );
+                &[(in_ndarray_ty.ty, "x")],
-                create_fn_by_codegen(
+                Box::new(move |ctx, _, fun, args, generator| {
-                    self.unifier,
+                    let arg_ty = fun.0.args[0].ty;
-                    &into_var_map([ndarray_ty]),
+                    let arg_val = args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
-                    prim.name(),
+                    Ok(Some(ndarray_transpose(generator, ctx, (arg_ty, arg_val))?))
-                    ndarray_ty.ty,
+                }),
-                    &[(ndarray_ty.ty, "x")],
+            ),
-                    // TODO(Derppening): Fix this refactor - This currently causes an unresolved TVar
-                    // self.ndarray_num_ty,
-                    // &[(self.ndarray_num_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)?;
-                        Ok(Some(ndarray_transpose(generator, ctx, (arg_ty, arg_val))?))
-                    }),
-                )
-            }
 
             // NOTE: on `ndarray_factory_fn_shape_arg_tvar` and
             // the `param_ty` for `create_fn_by_codegen`.
@@ -1361,20 +1356,41 @@ impl<'a> BuiltinBuilder<'a> {
             // Similar to `build_ndarray_from_shape_factory_function` we delegate the responsibility of typechecking
             // to [`typecheck::type_inferencer::Inferencer::fold_numpy_function_call_shape_argument`],
             // and use a dummy [`TypeVar`] `ndarray_factory_fn_shape_arg_tvar` as a placeholder for `param_ty`.
-            PrimDef::FunNpReshape => create_fn_by_codegen(
+            PrimDef::FunNpReshape => {
-                self.unifier,
+                let ret_ty = self.unifier.get_dummy_var().ty; // Handled by special holding
-                &VarMap::new(),
+
-                prim.name(),
+                create_fn_by_codegen(
-                self.ndarray_num_ty,
+                    self.unifier,
-                &[(self.ndarray_num_ty, "x"), (self.ndarray_factory_fn_shape_arg_tvar.ty, "shape")],
+                    &VarMap::new(),
-                Box::new(move |ctx, _, fun, args, generator| {
+                    prim.name(),
-                    let x1_ty = fun.0.args[0].ty;
+                    ret_ty,
-                    let x1_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x1_ty)?;
+                    &[
-                    let x2_ty = fun.0.args[1].ty;
+                        (in_ndarray_ty.ty, "x"),
-                    let x2_val = args[1].1.clone().to_basic_value_enum(ctx, generator, x2_ty)?;
+                        (self.ndarray_factory_fn_shape_arg_tvar.ty, "shape"), // Handled by special folding
-                    Ok(Some(ndarray_reshape(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
+                    ],
-                }),
+                    Box::new(move |ctx, _, fun, args, generator| {
-            ),
+                        let ndarray_ty = fun.0.args[0].ty;
+                        let ndarray_val =
+                            args[0].1.clone().to_basic_value_enum(ctx, generator, ndarray_ty)?;
+
+                        let shape_ty = fun.0.args[1].ty;
+                        let shape_val =
+                            args[1].1.clone().to_basic_value_enum(ctx, generator, shape_ty)?;
+
+                        let ndarray = NDArrayType::from_unifier_type(generator, ctx, ndarray_ty)
+                            .map_value(ndarray_val.into_pointer_value(), None);
+
+                        let shape = parse_numpy_int_sequence(generator, ctx, (shape_ty, shape_val));
+
+                        // The ndims after reshaping is gotten from the return type of the call.
+                        let (_, ndims) = unpack_ndarray_var_tys(&mut ctx.unifier, fun.0.ret);
+                        let ndims = extract_ndims(&ctx.unifier, ndims);
+
+                        let new_ndarray = ndarray.reshape_or_copy(generator, ctx, ndims, &shape);
+                        Ok(Some(new_ndarray.as_base_value().as_basic_value_enum()))
+                    }),
+                )
+            }
 
             _ => unreachable!(),
         }

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__generic_class.snap

@@ -8,5 +8,5 @@ expression: res_vec
     "Function {\nname: \"B.foo\",\nsig: \"fn[[b:T], none]\",\nvar_id: []\n}\n",
     "Class {\nname: \"Generic_A\",\nancestors: [\"Generic_A[V]\", \"B\"],\nfields: [\"aa\", \"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"foo\", \"fn[[b:T], none]\"), (\"fun\", \"fn[[a:int32], V]\")],\ntype_vars: [\"V\"]\n}\n",
     "Function {\nname: \"Generic_A.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
-    "Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [TypeVarId(250)]\n}\n",
+    "Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [TypeVarId(253)]\n}\n",
 ]

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__inheritance_override.snap

@@ -7,7 +7,7 @@ expression: res_vec
     "Function {\nname: \"A.__init__\",\nsig: \"fn[[t:T], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.fun\",\nsig: \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.foo\",\nsig: \"fn[[c:C], none]\",\nvar_id: []\n}\n",
-    "Class {\nname: \"B\",\nancestors: [\"B[typevar234]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: [\"typevar234\"]\n}\n",
+    "Class {\nname: \"B\",\nancestors: [\"B[typevar237]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: [\"typevar237\"]\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"B.fun\",\nsig: \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\",\nvar_id: []\n}\n",
     "Class {\nname: \"C\",\nancestors: [\"C\", \"B[bool]\", \"A[float]\"],\nfields: [\"a\", \"b\", \"c\", \"d\", \"e\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:int32, b:T], list[virtual[B[bool]]]]\"), (\"foo\", \"fn[[c:C], none]\")],\ntype_vars: []\n}\n",

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__list_tuple_generic.snap

@@ -5,8 +5,8 @@ expression: res_vec
 [
     "Function {\nname: \"foo\",\nsig: \"fn[[a:list[int32], b:tuple[T, float]], A[B, bool]]\",\nvar_id: []\n}\n",
     "Class {\nname: \"A\",\nancestors: [\"A[T, V]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[v:V], none]\"), (\"fun\", \"fn[[a:T], V]\")],\ntype_vars: [\"T\", \"V\"]\n}\n",
-    "Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [TypeVarId(247)]\n}\n",
+    "Function {\nname: \"A.__init__\",\nsig: \"fn[[v:V], none]\",\nvar_id: [TypeVarId(250)]\n}\n",
-    "Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(252)]\n}\n",
+    "Function {\nname: \"A.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(255)]\n}\n",
     "Function {\nname: \"gfun\",\nsig: \"fn[[a:A[list[float], int32]], none]\",\nvar_id: []\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\"],\nfields: [],\nmethods: [(\"__init__\", \"fn[[], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__self1.snap

@@ -3,7 +3,7 @@ source: nac3core/src/toplevel/test.rs
 expression: res_vec
 ---
 [
-    "Class {\nname: \"A\",\nancestors: [\"A[typevar233, typevar234]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar233\", \"typevar234\"]\n}\n",
+    "Class {\nname: \"A\",\nancestors: [\"A[typevar236, typevar237]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[a:A[float, bool], b:B], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\")],\ntype_vars: [\"typevar236\", \"typevar237\"]\n}\n",
     "Function {\nname: \"A.__init__\",\nsig: \"fn[[a:A[float, bool], b:B], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.fun\",\nsig: \"fn[[a:A[float, bool]], A[bool, int32]]\",\nvar_id: []\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\", \"A[int64, bool]\"],\nfields: [\"a\", \"b\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[a:A[float, bool]], A[bool, int32]]\"), (\"foo\", \"fn[[b:B], B]\"), (\"bar\", \"fn[[a:A[list[B], int32]], tuple[A[virtual[A[B, int32]], bool], B]]\")],\ntype_vars: []\n}\n",

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__simple_class_compose.snap

@@ -6,12 +6,12 @@ expression: res_vec
     "Class {\nname: \"A\",\nancestors: [\"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"A.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"A.fun\",\nsig: \"fn[[b:B], none]\",\nvar_id: []\n}\n",
-    "Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [TypeVarId(253)]\n}\n",
+    "Function {\nname: \"A.foo\",\nsig: \"fn[[a:T, b:V], none]\",\nvar_id: [TypeVarId(256)]\n}\n",
     "Class {\nname: \"C\",\nancestors: [\"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"C.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"C.fun\",\nsig: \"fn[[b:B], none]\",\nvar_id: []\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\", \"C\", \"A\"],\nfields: [\"a\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"fun\", \"fn[[b:B], none]\"), (\"foo\", \"fn[[a:T, b:V], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"foo\",\nsig: \"fn[[a:A], none]\",\nvar_id: []\n}\n",
-    "Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(261)]\n}\n",
+    "Function {\nname: \"ff\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(264)]\n}\n",
 ]

nac3standalone/demo/src/ndarray.py

@@ -68,6 +68,13 @@ def output_ndarray_float_2(n: ndarray[float, Literal[2]]):
         for c in range(len(n[r])):
             output_float64(n[r][c])
 
+def output_ndarray_float_4(n: ndarray[float, Literal[4]]):
+    for x in range(len(n)):
+        for y in range(len(n[x])):
+            for z in range(len(n[x][y])):
+                for w in range(len(n[x][y][z])):
+                    output_float64(n[x][y][z][w])
+
 def consume_ndarray_1(n: ndarray[float, Literal[1]]):
     pass
 
@@ -197,6 +204,38 @@ def test_ndarray_nd_idx():
     output_float64(x[1, 0])
     output_float64(x[1, 1])
 
+def test_ndarray_reshape():
+    w: ndarray[float, 1] = np_array([1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])
+    x = np_reshape(w, (1, 2, 1, -1))
+    y = np_reshape(x, [2, -1])
+    z = np_reshape(y, 10)
+
+    output_int32(np_shape(w)[0])
+    output_ndarray_float_1(w)
+
+    output_int32(np_shape(x)[0])
+    output_int32(np_shape(x)[1])
+    output_int32(np_shape(x)[2])
+    output_int32(np_shape(x)[3])
+    output_ndarray_float_4(x)
+
+    output_int32(np_shape(y)[0])
+    output_int32(np_shape(y)[1])
+    output_ndarray_float_2(y)
+
+    output_int32(np_shape(z)[0])
+    output_ndarray_float_1(z)
+
+    x1: ndarray[int32, 1] = np_array([1, 2, 3, 4])
+    x2: ndarray[int32, 2] = np_reshape(x1, (2, 2))
+
+    output_int32(np_shape(x1)[0])
+    output_ndarray_int32_1(x1)
+
+    output_int32(np_shape(x2)[0])
+    output_int32(np_shape(x2)[1])
+    output_ndarray_int32_2(x2)
+
 def test_ndarray_add():
     x = np_identity(2)
     y = x + np_ones([2, 2])
@@ -1448,19 +1487,6 @@ def test_ndarray_transpose():
     output_ndarray_float_2(x)
     output_ndarray_float_2(y)
 
-def test_ndarray_reshape():
-    w: ndarray[float, 1] = np_array([1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])
-    x = np_reshape(w, (1, 2, 1, -1))
-    y = np_reshape(x, [2, -1])
-    z = np_reshape(y, 10)
-
-    x1: ndarray[int32, 1] = np_array([1, 2, 3, 4])
-    x2: ndarray[int32, 2] = np_reshape(x1, (2, 2))
-
-    output_ndarray_float_1(w)
-    output_ndarray_float_2(y)
-    output_ndarray_float_1(z)
-
 def test_ndarray_dot():
     x1: ndarray[float, 1] = np_array([5.0, 1.0, 4.0, 2.0])
     y1: ndarray[float, 1] = np_array([5.0, 1.0, 6.0, 6.0])
@@ -1592,6 +1618,8 @@ def run() -> int32:
     test_ndarray_slices()
     test_ndarray_nd_idx()
 
+    test_ndarray_reshape()
+
     test_ndarray_add()
     test_ndarray_add_broadcast()
     test_ndarray_add_broadcast_lhs_scalar()
@@ -1756,7 +1784,6 @@ def run() -> int32:
     test_ndarray_nextafter_broadcast_lhs_scalar()
     test_ndarray_nextafter_broadcast_rhs_scalar()
     test_ndarray_transpose()
-    test_ndarray_reshape()
 
     test_ndarray_dot()
     test_ndarray_cholesky()