Skip to content

[LoadStoreToLLVM] Refactor the 2D block load lowering. #4615

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Draft
wants to merge 2 commits into
base: main
Choose a base branch
from
Draft

[LoadStoreToLLVM] Refactor the 2D block load lowering. #4615

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
c684d08
[LoadStoreOpToLLVM] Refactor block load lowering of tt.load with tens…
chengjunlu Jul 21, 2025
f2b464f
Transposed 2d load.
chengjunlu Aug 11, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
27 changes: 20 additions & 7 deletions python/test/unit/intel/test_block_io.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -119,9 +119,9 @@ def warps_per_cta(layout):
@pytest.mark.parametrize("dtype_str", ["float32", "float16", "int8"])
@pytest.mark.parametrize("layout", layouts)
@pytest.mark.parametrize("load_block_ptr, store_block_ptr", [(True, True), (False, False), (True, False),
(False, True)])
@pytest.mark.parametrize("transpose", [True, False]) (False, True)])
@pytest.mark.skipif(not is_xpu(), reason="Block store tests are specific to the XPU backend")
def test_block_io(M, N, dtype_str, layout, load_block_ptr, store_block_ptr, device, tmp_path: pathlib.Path):
def test_block_io(M, N, dtype_str, layout, load_block_ptr, store_block_ptr, transpose, device, tmp_path: pathlib.Path):

warps = warps_per_cta(layout)
num_warps = int(np.prod(warps))
Expand All @@ -132,16 +132,18 @@ def test_block_io(M, N, dtype_str, layout, load_block_ptr, store_block_ptr, devi

support_block_io = torch.xpu.get_device_capability()['has_subgroup_2d_block_io']

block_io = "\"column_major\"" if transpose else "\"row_major\""

if load_block_ptr:
load_ops = f"""
%src_ptr = tt.make_tensor_ptr %src, [%M_i64, %N_i64], [%N_i64, %c1_i64], [%c0_i32, %c0_i32] {{order = array<i32: 1, 0>}} : <tensor<{M}x{N}x{ty}, #layout>>
%store_val = tt.load %src_ptr {{ttig.block_io = "row_major", boundaryCheck = array<i32: 0, 1>, padding = 1 : i32}} : !tt.ptr<tensor<{M}x{N}x{ty}, #layout>>
%src_ptr = tt.make_tensor_ptr %src, [%M_i64, %N_i64], {"[%c1_i64, %M_i64]" if transpose else "[%N_i64, %c1_i64]"}, [%c0_i32, %c0_i32] {{order = array<i32: 1, 0>}} : <tensor<{M}x{N}x{ty}, #layout>>
%store_val = tt.load %src_ptr {{ttig.block_io = {block_io}, boundaryCheck = array<i32: 0, 1>, padding = 1 : i32}} : !tt.ptr<tensor<{M}x{N}x{ty}, #layout>>
"""
else:
load_ops = f"""
%src_base = tt.splat %src : !tt.ptr<{ty}> -> tensor<{M}x{N}x!tt.ptr<{ty}>, #layout>
%src_ptr = tt.addptr %src_base, %row_major_off : tensor<{M}x{N}x!tt.ptr<{ty}>, #layout>, tensor<{M}x{N}xi32, #layout>
%store_val = tt.load %src_ptr {{ttig.block_io = "row_major"}} : tensor<{M}x{N}x!tt.ptr<{ty}>, #layout>
%src_ptr = tt.addptr %src_base, {"%col_major_off" if transpose else "%row_major_off" } : tensor<{M}x{N}x!tt.ptr<{ty}>, #layout>, tensor<{M}x{N}xi32, #layout>
%store_val = tt.load %src_ptr {{ttig.block_io = {block_io}}} : tensor<{M}x{N}x!tt.ptr<{ty}>, #layout>
"""
if store_block_ptr:
store_ops = f"""
Expand All @@ -164,7 +166,6 @@ def test_block_io(M, N, dtype_str, layout, load_block_ptr, store_block_ptr, devi
%N_i64 = arith.constant {N} : i64
%c1_i64 = arith.constant 1 : i64
%c0_i32 = arith.constant 0 : i32

%stride_N = arith.constant dense<{N}> : tensor<{M}x1xi32, #layout>
%1 = tt.make_range {{end = {M} : i32, start = 0 : i32}} : tensor<{M}xi32, #ttg.slice<{{dim = 1, parent = #layout}}>>
%2 = tt.expand_dims %1 {{axis = 1 : i32}} : tensor<{M}xi32, #ttg.slice<{{dim = 1, parent = #layout}}>> -> tensor<{M}x1xi32, #layout>
Expand All @@ -175,6 +176,14 @@ def test_block_io(M, N, dtype_str, layout, load_block_ptr, store_block_ptr, devi
%7 = tt.broadcast %5 : tensor<1x{N}xi32, #layout> -> tensor<{M}x{N}xi32, #layout>
%row_major_off = arith.addi %6, %7 : tensor<{M}x{N}xi32, #layout>


%stride_M = arith.constant dense<{M}> : tensor<1x{N}xi32, #layout>
%col_stride = arith.muli %5, %stride_M : tensor<1x{N}xi32, #layout>
%8 = tt.broadcast %2 : tensor<{M}x1xi32, #layout> -> tensor<{M}x{N}xi32, #layout>
%9 = tt.broadcast %col_stride : tensor<1x{N}xi32, #layout> -> tensor<{M}x{N}xi32, #layout>
%col_major_off = arith.addi %8, %9 : tensor<{M}x{N}xi32, #layout>
{load_ops}

{load_ops}
{store_ops}

Expand All @@ -195,10 +204,14 @@ def test_block_io(M, N, dtype_str, layout, load_block_ptr, store_block_ptr, devi
temp_file.write_text(ir)
kernel = triton.compile(str(temp_file))

a = a.permute(1, 0).contiguous().permute(1, 0) if transpose else a

kernel[(1, 1, 1)](a, x)
assert torch.equal(a, x)

if support_block_io:
if not load_block_ptr:
assert 'spirv_Subgroup2DBlockLoad' in kernel.asm['llir'] or 'GenISA.LSC2DBlockRead' in kernel.asm['llir']
assert 'spirv_Subgroup2DBlockStoreINTEL' in kernel.asm['llir'] or 'GenISA.LSC2DBlockWrite' in kernel.asm['llir']
if not block_ptr:
assert 'spirv_Subgroup2DBlockLoad' in kernel.asm['llir'] or 'GenISA.LSC2DBlockRead' in kernel.asm['llir']
Loading
Loading