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core/ndstrides: move functions to numpy_new/util.rs

This commit is contained in:
lyken 2024-07-29 13:34:21 +08:00
parent 2747869a45
commit d5880b119a
8 changed files with 191 additions and 143 deletions
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7
nac3core/src/codegen/expr.rs
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@ -18,6 +18,7 @@ use crate::{
call_memcpy_generic,
},
need_sret, numpy,
numpy_new::util::alloca_ndarray,
stmt::{
gen_for_callback_incrementing, gen_if_callback, gen_if_else_expr_callback, gen_raise,
gen_var,
@ -42,10 +43,8 @@ use nac3parser::ast::{
self, Boolop, Cmpop, Comprehension, Constant, Expr, ExprKind, Located, Location, Operator,
StrRef, Unaryop,
};
use ndarray::{
allocation::alloca_ndarray,
indexing::{call_nac3_ndarray_index, RustNDIndex},
};
use ndarray::indexing::{call_nac3_ndarray_index, RustNDIndex};
use super::{
model::*,

83
nac3core/src/codegen/irrt/ndarray/allocation.rs
View File

@ -1,83 +0,0 @@
use crate::codegen::model::*;
use crate::codegen::util::array_writer::ArrayWriter;
use crate::codegen::{structure::ndarray::NpArray, CodeGenContext, CodeGenerator};
use super::basic::{
call_nac3_ndarray_nbytes, call_nac3_ndarray_set_strides_by_shape,
call_nac3_ndarray_util_assert_shape_no_negative,
};
/**
Allocate an ndarray on the stack given its `ndims`.
`shape` and `strides` will be automatically allocated on the stack.
The returned ndarray's content will be:
- `data`: `nullptr`
- `itemsize`: **uninitialized** value
- `ndims`: initialized value, set to the input `ndims`
- `shape`: initialized pointer to an allocated stack with **uninitialized** values
- `strides`: initialized pointer to an allocated stack with **uninitialized** values
*/
pub fn alloca_ndarray<'ctx, G>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndims: Int<'ctx, SizeT>,
name: &str,
) -> Result<Ptr<'ctx, StructModel<NpArray>>, String>
where
G: CodeGenerator + ?Sized,
{
let tyctx = generator.type_context(ctx.ctx);
let sizet_model = IntModel(SizeT);
let ndarray_model = StructModel(NpArray);
let ndarray_data_model = PtrModel(IntModel(Byte));
// Setup ndarray
let ndarray_ptr = ndarray_model.alloca(tyctx, ctx, name);
let shape = sizet_model.array_alloca(tyctx, ctx, ndims.value, "shape");
let strides = sizet_model.array_alloca(tyctx, ctx, ndims.value, "strides");
ndarray_ptr.gep(ctx, |f| f.data).store(ctx, ndarray_data_model.nullptr(tyctx, ctx.ctx));
ndarray_ptr.gep(ctx, |f| f.ndims).store(ctx, ndims);
ndarray_ptr.gep(ctx, |f| f.shape).store(ctx, shape);
ndarray_ptr.gep(ctx, |f| f.strides).store(ctx, strides);
Ok(ndarray_ptr)
}
/// Initialize an ndarray's `shape` and asserts on.
/// `shape`'s values and prohibit illegal inputs like negative dimensions.
pub fn init_ndarray_shape<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
pndarray: Ptr<'ctx, StructModel<NpArray>>,
shape_writer: &ArrayWriter<'ctx, G, SizeT, IntModel<SizeT>>,
) -> Result<(), String> {
let tyctx = generator.type_context(ctx.ctx);
let shape = pndarray.gep(ctx, |f| f.shape).load(tyctx, ctx, "shape");
(shape_writer.write)(generator, ctx, shape)?;
call_nac3_ndarray_util_assert_shape_no_negative(generator, ctx, shape_writer.len, shape);
Ok(())
}
/// Initialize an ndarray's `data` by allocating a buffer on the stack.
/// The allocated data buffer is considered to be *owned* by the ndarray.
///
/// `strides` of the ndarray will also be updated with `set_strides_by_shape`.
///
/// `shape` and `itemsize` of the ndarray ***must*** be initialized first.
pub fn init_ndarray_data_by_alloca<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
pndarray: Ptr<'ctx, StructModel<NpArray>>,
) {
let tyctx = generator.type_context(ctx.ctx);
let ndarray_data_model = IntModel(Byte);
let nbytes = call_nac3_ndarray_nbytes(generator, ctx, pndarray);
let data = ndarray_data_model.array_alloca(tyctx, ctx, nbytes.value, "data");
pndarray.gep(ctx, |f| f.data).store(ctx, data);
call_nac3_ndarray_set_strides_by_shape(generator, ctx, pndarray);
}

1
nac3core/src/codegen/irrt/ndarray/mod.rs
View File

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

49
nac3core/src/codegen/numpy_new/control.rs
View File

@ -1,49 +0,0 @@
use crate::codegen::{
irrt::ndarray::basic::{call_nac3_ndarray_get_nth_pelement, call_nac3_ndarray_size},
model::*,
stmt::BreakContinueHooks,
structure::ndarray::NpArray,
util::control::gen_model_for,
CodeGenContext, CodeGenerator,
};
/// Iterate through all elements in an ndarray.
///
/// `body` is given the index of an element and an opaque pointer (as an `uint8_t*`, you might want to cast it) to the element.
///
/// Short-circuiting is possible with the given [`BreakContinueHooks`].
pub fn gen_foreach_ndarray_elements<'ctx, G, F>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
pndarray: Ptr<'ctx, StructModel<NpArray>>,
body: F,
) -> Result<(), String>
where
G: CodeGenerator + ?Sized,
F: Fn(
&mut G,
&mut CodeGenContext<'ctx, '_>,
BreakContinueHooks<'ctx>,
Int<'ctx, SizeT>,
Ptr<'ctx, IntModel<Byte>>,
) -> Result<(), String>,
{
// TODO: Make this more efficient - use a special NDArray iterator?
let tyctx = generator.type_context(ctx.ctx);
let sizet_model = IntModel(SizeT);
let size = call_nac3_ndarray_size(generator, ctx, pndarray);
gen_model_for(
generator,
ctx,
sizet_model.const_0(tyctx, ctx.ctx),
size,
sizet_model.const_1(tyctx, ctx.ctx),
|generator, ctx, hooks, index| {
let pelement = call_nac3_ndarray_get_nth_pelement(generator, ctx, pndarray, index);
body(generator, ctx, hooks, index, pelement)
},
)
}

6
nac3core/src/codegen/numpy_new/factory.rs
View File

@ -7,10 +7,8 @@ use nac3parser::ast::StrRef;
use crate::{
codegen::{
irrt::ndarray::allocation::{
alloca_ndarray, init_ndarray_data_by_alloca, init_ndarray_shape,
},
model::*,
numpy_new::util::{alloca_ndarray, init_ndarray_data_by_alloca, init_ndarray_shape},
structure::ndarray::NpArray,
util::shape::make_shape_writer,
CodeGenContext, CodeGenerator,
@ -20,7 +18,7 @@ use crate::{
typecheck::typedef::{FunSignature, Type},
};
use super::control::gen_foreach_ndarray_elements;
use super::util::gen_foreach_ndarray_elements;
/// Helper function to create an ndarray with uninitialized values
///

2
nac3core/src/codegen/numpy_new/mod.rs
View File

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

184
nac3core/src/codegen/numpy_new/util.rs Normal file
View File

@ -0,0 +1,184 @@
use inkwell::{types::BasicType, values::BasicValueEnum};
use crate::{
codegen::{
irrt::ndarray::basic::{
call_nac3_ndarray_get_nth_pelement, call_nac3_ndarray_nbytes,
call_nac3_ndarray_set_strides_by_shape, call_nac3_ndarray_size,
call_nac3_ndarray_util_assert_shape_no_negative,
},
model::*,
stmt::BreakContinueHooks,
structure::ndarray::NpArray,
util::{array_writer::ArrayWriter, control::gen_model_for},
CodeGenContext, CodeGenerator,
},
toplevel::numpy::unpack_ndarray_var_tys,
typecheck::typedef::{Type, TypeEnum},
};
/**
Allocate an ndarray on the stack given its `ndims`.
`shape` and `strides` will be automatically allocated on the stack.
The returned ndarray's content will be:
- `data`: `nullptr`
- `itemsize`: **uninitialized** value
- `ndims`: initialized value, set to the input `ndims`
- `shape`: initialized pointer to an allocated stack with **uninitialized** values
- `strides`: initialized pointer to an allocated stack with **uninitialized** values
*/
pub fn alloca_ndarray<'ctx, G>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
ndims: Int<'ctx, SizeT>,
name: &str,
) -> Result<Ptr<'ctx, StructModel<NpArray>>, String>
where
G: CodeGenerator + ?Sized,
{
let tyctx = generator.type_context(ctx.ctx);
let sizet_model = IntModel(SizeT);
let ndarray_model = StructModel(NpArray);
let ndarray_data_model = PtrModel(IntModel(Byte));
// Setup ndarray
let ndarray_ptr = ndarray_model.alloca(tyctx, ctx, name);
let shape = sizet_model.array_alloca(tyctx, ctx, ndims.value, "shape");
let strides = sizet_model.array_alloca(tyctx, ctx, ndims.value, "strides");
ndarray_ptr.gep(ctx, |f| f.data).store(ctx, ndarray_data_model.nullptr(tyctx, ctx.ctx));
ndarray_ptr.gep(ctx, |f| f.ndims).store(ctx, ndims);
ndarray_ptr.gep(ctx, |f| f.shape).store(ctx, shape);
ndarray_ptr.gep(ctx, |f| f.strides).store(ctx, strides);
Ok(ndarray_ptr)
}
/// Initialize an ndarray's `shape` and asserts on.
/// `shape`'s values and prohibit illegal inputs like negative dimensions.
pub fn init_ndarray_shape<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
pndarray: Ptr<'ctx, StructModel<NpArray>>,
shape_writer: &ArrayWriter<'ctx, G, SizeT, IntModel<SizeT>>,
) -> Result<(), String> {
let tyctx = generator.type_context(ctx.ctx);
let shape = pndarray.gep(ctx, |f| f.shape).load(tyctx, ctx, "shape");
(shape_writer.write)(generator, ctx, shape)?;
call_nac3_ndarray_util_assert_shape_no_negative(generator, ctx, shape_writer.len, shape);
Ok(())
}
/// Initialize an ndarray's `data` by allocating a buffer on the stack.
/// The allocated data buffer is considered to be *owned* by the ndarray.
///
/// `strides` of the ndarray will also be updated with `set_strides_by_shape`.
///
/// `shape` and `itemsize` of the ndarray ***must*** be initialized first.
pub fn init_ndarray_data_by_alloca<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
pndarray: Ptr<'ctx, StructModel<NpArray>>,
) {
let tyctx = generator.type_context(ctx.ctx);
let ndarray_data_model = IntModel(Byte);
let nbytes = call_nac3_ndarray_nbytes(generator, ctx, pndarray);
let data = ndarray_data_model.array_alloca(tyctx, ctx, nbytes.value, "data");
pndarray.gep(ctx, |f| f.data).store(ctx, data);
call_nac3_ndarray_set_strides_by_shape(generator, ctx, pndarray);
}
/// Convert `input` to an ndarray - behaves similarly to `np.asarray`.
///
/// Returns the ndarray interpretation of `input` and **the element type** of the ndarray.
///
/// Here are the exact details:
/// - If `input` is an ndarray, the function returns back the **same** ndarray and the `dtype`
/// of the ndarray.
/// - If `input` is not an ndarray, the function creates an ndarray with a single element `input`,
/// and returns the created ndarray and `input_ty`. Note that the created ndarray's `ndims` will
/// be `0` (an *unsized* ndarray).
pub fn as_ndarray<'ctx, G: CodeGenerator + ?Sized>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
input: BasicValueEnum<'ctx>,
input_ty: Type,
) -> Result<(Ptr<'ctx, StructModel<NpArray>>, Type), String> {
let tyctx = generator.type_context(ctx.ctx);
let sizet_model = IntModel(SizeT);
let pbyte_model = PtrModel(IntModel(Byte));
let pndarray_model = PtrModel(StructModel(NpArray));
let input_ty_enum = ctx.unifier.get_ty(input_ty);
match &*input_ty_enum {
TypeEnum::TObj { obj_id, .. }
if *obj_id == ctx.primitives.ndarray.obj_id(&ctx.unifier).unwrap() =>
{
let pndarray = pndarray_model.check_value(tyctx, ctx.ctx, input).unwrap();
let (elem_ty, _) = unpack_ndarray_var_tys(&mut ctx.unifier, input_ty);
Ok((pndarray, elem_ty))
}
_ => {
let ndims = sizet_model.const_0(tyctx, ctx.ctx);
let pndarray = alloca_ndarray(generator, ctx, ndims, "ndarray")?;
// We have to put `input` onto the stack to get a data pointer.
let data = ctx.builder.build_alloca(input.get_type(), "as_ndarray_scalar").unwrap();
ctx.builder.build_store(data, input).unwrap();
let data = pbyte_model.transmute(tyctx, ctx, data, "data");
pndarray.gep(ctx, |f| f.data).store(ctx, data);
let itemsize = input.get_type().size_of().unwrap();
let itemsize = sizet_model.check_value(tyctx, ctx.ctx, itemsize).unwrap();
pndarray.gep(ctx, |f| f.itemsize).store(ctx, itemsize);
Ok((pndarray, input_ty))
}
}
}
/// Iterate through all elements in an ndarray.
///
/// `body` is given the index of an element and an opaque pointer (as an `uint8_t*`, you might want to cast it) to the element.
///
/// Short-circuiting is possible with the given [`BreakContinueHooks`].
pub fn gen_foreach_ndarray_elements<'ctx, G, F>(
generator: &mut G,
ctx: &mut CodeGenContext<'ctx, '_>,
pndarray: Ptr<'ctx, StructModel<NpArray>>,
body: F,
) -> Result<(), String>
where
G: CodeGenerator + ?Sized,
F: Fn(
&mut G,
&mut CodeGenContext<'ctx, '_>,
BreakContinueHooks<'ctx>,
Int<'ctx, SizeT>,
Ptr<'ctx, IntModel<Byte>>,
) -> Result<(), String>,
{
// TODO: Make this more efficient - use a special NDArray iterator?
let tyctx = generator.type_context(ctx.ctx);
let sizet_model = IntModel(SizeT);
let size = call_nac3_ndarray_size(generator, ctx, pndarray);
gen_model_for(
generator,
ctx,
sizet_model.const_0(tyctx, ctx.ctx),
size,
sizet_model.const_1(tyctx, ctx.ctx),
|generator, ctx, hooks, index| {
let pelement = call_nac3_ndarray_get_nth_pelement(generator, ctx, pndarray, index);
body(generator, ctx, hooks, index, pelement)
},
)
}

2
nac3core/src/codegen/numpy_new/view.rs
View File

@ -4,7 +4,6 @@ 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,
@ -12,6 +11,7 @@ use crate::{
reshape::call_nac3_ndarray_resolve_and_check_new_shape,
},
model::*,
numpy_new::util::{alloca_ndarray, init_ndarray_shape},
structure::ndarray::NpArray,
util::{array_writer::ArrayWriter, shape::make_shape_writer},
CodeGenContext, CodeGenerator,