core/ndstrides: implement general ndarray reshaping

nac3core/irrt/irrt/ndarray/reshape.hpp

@@ -0,0 +1,117 @@
+#pragma once
+
+#include <irrt/error_context.hpp>
+#include <irrt/int_defs.hpp>
+#include <irrt/ndarray/def.hpp>
+
+namespace {
+namespace ndarray {
+namespace reshape {
+namespace util {
+
+/**
+ * @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(ErrorContext* errctx, 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.
+                    errctx->set_exception(
+                        errctx->exceptions->value_error,
+                        "can only specify one unknown dimension");
+                    return;
+                } 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...
+
+                errctx->set_exception(
+                    errctx->exceptions->value_error,
+                    "Found negative dimension {0} on axis {1}", dim, axis_i);
+                return;
+            }
+        } 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) {
+        errctx->set_exception(
+            errctx->exceptions->value_error,
+            "cannot reshape array of size {0} into given shape", size);
+        return;
+    }
+}
+}  // namespace util
+}  // namespace reshape
+}  // namespace ndarray
+}  // namespace
+
+extern "C" {
+
+void __nac3_ndarray_resolve_and_check_new_shape(ErrorContext* errctx,
+                                                int32_t size, int32_t new_ndims,
+                                                int32_t* new_shape) {
+    ndarray::reshape::util::resolve_and_check_new_shape(errctx, size, new_ndims,
+                                                        new_shape);
+}
+
+void __nac3_ndarray_resolve_and_check_new_shape64(ErrorContext* errctx,
+                                                  int64_t size,
+                                                  int64_t new_ndims,
+                                                  int64_t* new_shape) {
+    ndarray::reshape::util::resolve_and_check_new_shape(errctx, size, new_ndims,
+                                                        new_shape);
+}
+}

nac3core/irrt/irrt_everything.hpp

@@ -7,5 +7,6 @@
 #include <irrt/ndarray/basic.hpp>
 #include <irrt/ndarray/def.hpp>
 #include <irrt/ndarray/indexing.hpp>
+#include <irrt/ndarray/reshape.hpp>
 #include <irrt/slice.hpp>
 #include <irrt/utils.hpp>

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

@@ -1,3 +1,4 @@
 pub mod allocation;
 pub mod basic;
 pub mod indexing;
+pub mod reshape;

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

@@ -0,0 +1,31 @@
+use crate::codegen::{
+    irrt::{
+        error_context::{check_error_context, setup_error_context},
+        util::{function::CallFunction, get_sizet_dependent_function_name},
+    },
+    model::*,
+    CodeGenContext, CodeGenerator,
+};
+
+pub fn call_nac3_ndarray_resolve_and_check_new_shape<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    size: Int<'ctx, SizeT>,
+    new_ndims: Int<'ctx, SizeT>,
+    new_shape: Ptr<'ctx, IntModel<SizeT>>,
+) {
+    let tyctx = generator.type_context(ctx.ctx);
+
+    let perrctx = setup_error_context(tyctx, ctx);
+    CallFunction::begin(
+        tyctx,
+        ctx,
+        &get_sizet_dependent_function_name(tyctx, "__nac3_ndarray_resolve_and_check_new_shape"),
+    )
+    .arg("errctx", perrctx)
+    .arg("size", size)
+    .arg("new_ndims", new_ndims)
+    .arg("new_shape", new_shape)
+    .returning_void();
+    check_error_context(generator, ctx, perrctx);
+}

nac3core/src/codegen/numpy_new/mod.rs

@@ -1,2 +1,3 @@
 pub mod control;
 pub mod factory;
+pub mod view;

nac3core/src/codegen/numpy_new/view.rs

@@ -0,0 +1,135 @@
+use inkwell::values::PointerValue;
+use nac3parser::ast::StrRef;
+
+use crate::{
+    codegen::{
+        irrt::ndarray::{
+            allocation::{alloca_ndarray, init_ndarray_shape},
+            basic::{
+                call_nac3_ndarray_is_c_contiguous, call_nac3_ndarray_nbytes,
+                call_nac3_ndarray_set_strides_by_shape, call_nac3_ndarray_size,
+            },
+            reshape::call_nac3_ndarray_resolve_and_check_new_shape,
+        },
+        model::*,
+        structure::ndarray::NpArray,
+        util::{array_writer::ArrayWriter, shape::make_shape_writer},
+        CodeGenContext, CodeGenerator,
+    },
+    symbol_resolver::ValueEnum,
+    toplevel::DefinitionId,
+    typecheck::typedef::{FunSignature, Type},
+};
+
+fn gen_reshape_ndarray_or_copy<'ctx, G: CodeGenerator + ?Sized>(
+    generator: &mut G,
+    ctx: &mut CodeGenContext<'ctx, '_>,
+    src_ndarray: Ptr<'ctx, StructModel<NpArray>>,
+    new_shape: &ArrayWriter<'ctx, G, SizeT, IntModel<SizeT>>,
+) -> Result<Ptr<'ctx, StructModel<NpArray>>, String> {
+    /*
+        Reference pseudo-code:
+        ```c
+        NDArray<SizeT>* src_ndarray;
+
+        NDArray<SizeT>* dst_ndarray = __builtin_alloca(...);
+        dst_ndarray->ndims = ...
+        dst_ndarray->strides = __builtin_alloca(...);
+        dst_ndarray->shape = ... // Directly set by user, may contain -1, or even illegal values.
+        dst_ndarray->itemsize = src_ndarray->itemsize;
+        set_strides_by_shape(dst_ndarray);
+
+        // Do assertions on `dst_ndarray->shape` and resolve -1
+
+        resolve_and_check_new_shape(ndarray_size(src_ndarray), dst_ndarray->shape);
+
+        if (is_c_contiguous(src_ndarray)) {
+            dst_ndarray->data = src_ndarray->data;
+        } else {
+            dst_ndarray->data = __builtin_alloca( ndarray_nbytes(dst_ndarray) );
+            copy_data(src_ndarray, dst_ndarray);
+        }
+
+        return dst_ndarray;
+        ```
+    */
+
+    let tyctx = generator.type_context(ctx.ctx);
+    let byte_model = IntModel(Byte);
+
+    let current_bb = ctx.builder.get_insert_block().unwrap();
+    let then_bb = ctx.ctx.insert_basic_block_after(current_bb, "then");
+    let else_bb = ctx.ctx.insert_basic_block_after(then_bb, "else_bb");
+    let end_bb = ctx.ctx.insert_basic_block_after(else_bb, "end_bb");
+
+    // Inserting into current_bb
+    let dst_ndarray = alloca_ndarray(generator, ctx, new_shape.len, "ndarray").unwrap();
+
+    init_ndarray_shape(generator, ctx, dst_ndarray, new_shape)?;
+    dst_ndarray
+        .gep(ctx, |f| f.itemsize)
+        .store(ctx, src_ndarray.gep(ctx, |f| f.itemsize).load(tyctx, ctx, "itemsize"));
+
+    call_nac3_ndarray_set_strides_by_shape(generator, ctx, dst_ndarray);
+
+    let src_ndarray_size = call_nac3_ndarray_size(generator, ctx, src_ndarray);
+    call_nac3_ndarray_resolve_and_check_new_shape(
+        generator,
+        ctx,
+        src_ndarray_size,
+        dst_ndarray.gep(ctx, |f| f.ndims).load(tyctx, ctx, "ndims"),
+        dst_ndarray.gep(ctx, |f| f.shape).load(tyctx, ctx, "shape"),
+    );
+
+    let is_c_contiguous = call_nac3_ndarray_is_c_contiguous(generator, ctx, src_ndarray);
+    ctx.builder.build_conditional_branch(is_c_contiguous.value, then_bb, else_bb).unwrap();
+
+    // Inserting into then_bb: reshape is possible without copying
+    ctx.builder.position_at_end(then_bb);
+    dst_ndarray
+        .gep(ctx, |f| f.data)
+        .store(ctx, src_ndarray.gep(ctx, |f| f.data).load(tyctx, ctx, "data"));
+    ctx.builder.build_unconditional_branch(end_bb).unwrap();
+
+    // Inserting into else_bb: reshape is impossible without copying
+    ctx.builder.position_at_end(else_bb);
+    let dst_ndarray_nbytes = call_nac3_ndarray_nbytes(generator, ctx, dst_ndarray);
+    let data = byte_model.array_alloca(tyctx, ctx, dst_ndarray_nbytes.value, "new_data");
+    dst_ndarray.gep(ctx, |f| f.data).store(ctx, data);
+    ctx.builder.build_unconditional_branch(end_bb).unwrap();
+
+    // Reposition for continuation
+    ctx.builder.position_at_end(end_bb);
+
+    Ok(dst_ndarray)
+}
+
+/// Generates LLVM IR for `np.reshape`.
+pub fn gen_ndarray_reshape<'ctx>(
+    context: &mut CodeGenContext<'ctx, '_>,
+    obj: &Option<(Type, ValueEnum<'ctx>)>,
+    fun: (&FunSignature, DefinitionId),
+    args: &[(Option<StrRef>, ValueEnum<'ctx>)],
+    generator: &mut dyn CodeGenerator,
+) -> Result<PointerValue<'ctx>, String> {
+    assert!(obj.is_none());
+    assert_eq!(args.len(), 2);
+
+    // Parse argument #1 ndarray
+    let ndarray_ty = fun.0.args[0].ty;
+    let ndarray_arg = args[0].1.clone().to_basic_value_enum(context, generator, ndarray_ty)?;
+
+    // Parse argument #2 shape
+    let shape_ty = fun.0.args[1].ty;
+    let shape_arg = args[1].1.clone().to_basic_value_enum(context, generator, shape_ty)?;
+
+    let tyctx = generator.type_context(context.ctx);
+    let pndarray_model = PtrModel(StructModel(NpArray));
+
+    let src_ndarray = pndarray_model.check_value(tyctx, context.ctx, ndarray_arg).unwrap();
+    let new_shape = make_shape_writer(generator, context, shape_arg, shape_ty);
+
+    let reshaped_ndarray =
+        gen_reshape_ndarray_or_copy(generator, context, src_ndarray, &new_shape)?;
+    Ok(reshaped_ndarray.value)
+}

nac3core/src/toplevel/builtins.rs

@@ -496,6 +496,8 @@ impl<'a> BuiltinBuilder<'a> {
             | PrimDef::FunNpEye
             | PrimDef::FunNpIdentity => self.build_ndarray_other_factory_function(prim),
 
+            PrimDef::FunNpReshape => self.build_ndarray_view_functions(prim),
+
             PrimDef::FunStr => self.build_str_function(),
 
             PrimDef::FunFloor | PrimDef::FunFloor64 | PrimDef::FunCeil | PrimDef::FunCeil64 => {
@@ -1333,6 +1335,39 @@ impl<'a> BuiltinBuilder<'a> {
         }
     }
 
+    // Build functions related to NDArray views
+    fn build_ndarray_view_functions(&mut self, prim: PrimDef) -> TopLevelDef {
+        debug_assert_prim_is_allowed(prim, &[PrimDef::FunNpReshape]);
+
+        match prim {
+            PrimDef::FunNpReshape => {
+                let new_ndim_ty = self.unifier.get_fresh_var(Some("NewNDim".into()), None);
+                let returned_ndarray_ty = make_ndarray_ty(
+                    self.unifier,
+                    self.primitives,
+                    Some(self.ndarray_dtype_tvar.ty),
+                    Some(new_ndim_ty.ty),
+                );
+
+                create_fn_by_codegen(
+                    self.unifier,
+                    &into_var_map([self.ndarray_dtype_tvar, self.ndarray_ndims_tvar, new_ndim_ty]),
+                    prim.name(),
+                    returned_ndarray_ty,
+                    &[
+                        (self.primitives.ndarray, "array"),
+                        (self.ndarray_factory_fn_shape_arg_tvar.ty, "shape"),
+                    ],
+                    Box::new(|ctx, obj, fun, args, generator| {
+                        numpy_new::view::gen_ndarray_reshape(ctx, &obj, fun, &args, generator)
+                            .map(|val| Some(val.as_basic_value_enum()))
+                    }),
+                )
+            }
+            _ => unreachable!(),
+        }
+    }
+
     /// Build the `str()` function.
     fn build_str_function(&mut self) -> TopLevelDef {
         let prim = PrimDef::FunStr;

nac3core/src/toplevel/helper.rs

@@ -52,6 +52,9 @@ pub enum PrimDef {
     FunNpEye,
     FunNpIdentity,
 
+    // NumPy view functions
+    FunNpReshape,
+
     // Miscellaneous NumPy & SciPy functions
     FunNpRound,
     FunNpFloor,
@@ -223,6 +226,9 @@ impl PrimDef {
             PrimDef::FunNpEye => fun("np_eye", None),
             PrimDef::FunNpIdentity => fun("np_identity", None),
 
+            // NumPy view functions
+            PrimDef::FunNpReshape => fun("np_reshape", None),
+
             // Miscellaneous NumPy & SciPy functions
             PrimDef::FunNpRound => fun("np_round", None),
             PrimDef::FunNpFloor => fun("np_floor", None),

nac3core/src/toplevel/snapshots/nac3core__toplevel__test__test_analyze__generic_class.snap

@@ -5,7 +5,7 @@ expression: res_vec
 [
     "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(245)]\n}\n",
+    "Function {\nname: \"Generic_A.fun\",\nsig: \"fn[[a:int32], V]\",\nvar_id: [TypeVarId(248)]\n}\n",
     "Class {\nname: \"B\",\nancestors: [\"B\"],\nfields: [\"aa\"],\nmethods: [(\"__init__\", \"fn[[], none]\"), (\"foo\", \"fn[[b:T], none]\")],\ntype_vars: []\n}\n",
     "Function {\nname: \"B.__init__\",\nsig: \"fn[[], none]\",\nvar_id: []\n}\n",
     "Function {\nname: \"B.foo\",\nsig: \"fn[[b:T], none]\",\nvar_id: []\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.fun\",\nsig: \"fn[[a:T], V]\",\nvar_id: [TypeVarId(252)]\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(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: \"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",
     "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",
     "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",
 ]

nac3core/src/typecheck/type_inferencer/mod.rs

@@ -1390,6 +1390,55 @@ impl<'a> Inferencer<'a> {
             }));
         }
 
+        // Handle `np.reshape(<array>, <shape>)`
+        if ["np_reshape".into()].contains(id) && args.len() == 2 {
+            // Extract arguments
+            let array_expr = args.remove(0);
+            let shape_expr = args.remove(0);
+
+            // Fold `<array>`
+            let array = self.fold_expr(array_expr)?;
+            let array_ty = array.custom.unwrap();
+            let (array_dtype, _) = unpack_ndarray_var_tys(self.unifier, array_ty);
+
+            // Fold `<shape>`
+            let (target_ndims, target_shape) =
+                self.fold_numpy_function_call_shape_argument(*id, 0, shape_expr)?;
+            let target_shape_ty = target_shape.custom.unwrap();
+            // ... and deduce the return type of the call
+            let target_ndims_ty =
+                self.unifier.get_fresh_literal(vec![SymbolValue::U64(target_ndims)], None);
+            let ret = make_ndarray_ty(
+                self.unifier,
+                self.primitives,
+                Some(array_dtype),
+                Some(target_ndims_ty),
+            );
+
+            let custom = self.unifier.add_ty(TypeEnum::TFunc(FunSignature {
+                args: vec![
+                    FuncArg { name: "array".into(), ty: array_ty, default_value: None },
+                    FuncArg { name: "shape".into(), ty: target_shape_ty, default_value: None },
+                ],
+                ret,
+                vars: VarMap::new(),
+            }));
+
+            return Ok(Some(Located {
+                location,
+                custom: Some(ret),
+                node: ExprKind::Call {
+                    func: Box::new(Located {
+                        custom: Some(custom),
+                        location: func.location,
+                        node: ExprKind::Name { id: *id, ctx: *ctx },
+                    }),
+                    args: vec![array, target_shape],
+                    keywords: vec![],
+                },
+            }));
+        }
+
         // 2-argument ndarray n-dimensional creation functions
         if id == &"np_full".into() && args.len() == 2 {
             let ExprKind::List { elts, .. } = &args[0].node else {

nac3standalone/demo/interpret_demo.py

@@ -178,6 +178,9 @@ def patch(module):
     module.np_identity = np.identity
     module.np_array = np.array
 
+    # NumPy view functions
+    module.np_reshape = np.reshape
+
     # NumPy Math functions
     module.np_isnan = np.isnan
     module.np_isinf = np.isinf