def test_tile_with_tensor_intrin_conv2d_nchwc_vnni():
s = Schedule(Conv2dNCHWcVNNIModule)
block = s.get_block("conv2d_NCHWc_int8")
tiled_loop = tile_with_tensor_intrin(s, block, VNNI_DOT_16x4_INTRIN)
tiled_loops = s.get_loops(block)
assert len(tiled_loops) == 12
assert s.get(tiled_loop) == s.get(tiled_loops[-2])
tvm.ir.assert_structural_equal(s.mod, Conv2dNCHWcVNNIModuleTiled)
def test_tile_with_tensor_intrin_dense_vnni():
s = Schedule(DenseVNNIModule)
block = s.get_block("compute")
tiled_loop = tile_with_tensor_intrin(s, block, VNNI_DOT_16x4_INTRIN)
_, _, _, i1_1, _ = s.get_loops(block)
assert s.get(tiled_loop) == s.get(i1_1)
tvm.ir.assert_structural_equal(s.mod, DenseVNNIModuleTiled)
def test_get_tensorize_loop_mapping_dense_vnni():
s = Schedule(DenseVNNIModule)
block = s.get_block("compute")
info = get_tensorize_loop_mapping(s, block, dot_product_16x4_u8i8i32_desc)
assert isinstance(info, TensorizeInfo)
desc_loop_to_sref = dict((v, k) for k, v in info.loop_map.items())
desc_loops = collect_loops(dot_product_16x4_u8i8i32_desc)
_, loop_j, loop_k = s.get_loops(block)
assert desc_loops[0] in desc_loop_to_sref and desc_loops[
1] in desc_loop_to_sref
assert s.get(desc_loop_to_sref[desc_loops[0]]) == s.get(loop_j)
assert s.get(desc_loop_to_sref[desc_loops[1]]) == s.get(loop_k)
def test_get_tensorize_loop_mapping_conv2d_nchwc_vnni():
s = Schedule(Conv2dNCHWcVNNIModule)
block = s.get_block("conv2d_NCHWc_int8")
info = get_tensorize_loop_mapping(s, block, dot_product_16x4_u8i8i32_desc)
desc_loop_to_sref = dict((v, k) for k, v in info.loop_map.items())
desc_loops = collect_loops(dot_product_16x4_u8i8i32_desc)
# i4 corresonds to the inner output channel axis of the NCHWc output tensor
# for i0, i1, i2, i3, i4, i5, i6, i7, i8, i9 in T.grid(1, 16, 56, 56, 16, 1, 1, 4, 4, 4):
_, _, _, _, i4, _, _, _, _, i9 = s.get_loops(block)
assert desc_loops[0] in desc_loop_to_sref and desc_loops[
1] in desc_loop_to_sref
assert s.get(desc_loop_to_sref[desc_loops[0]]) == s.get(i4)
assert s.get(desc_loop_to_sref[desc_loops[1]]) == s.get(i9)
def test_get_tensorize_loop_mapping_matmul_mma():
@T.prim_func
def matmul_16x16x16xf16f16f16_desc(
A: T.Buffer((16, 16), "float16", align=128, offset_factor=1),
B: T.Buffer((16, 16), "float16", align=128, offset_factor=1),
C: T.Buffer((16, 16), "float16", align=128, offset_factor=1),
) -> None:
with T.block("root"):
T.reads(C[0:16, 0:16], A[0:16, 0:16], B[0:16, 0:16])
T.writes(C[0:16, 0:16])
for i, j, k in T.grid(16, 16, 16):
with T.block("update"):
vii, vjj, vkk = T.axis.remap("SSR", [i, j, k])
C[vii, vjj] = C[vii, vjj] + A[vii, vkk] * B[vjj, vkk]
matmul = create_prim_func(te_workload.matmul_relu(
n=512,
m=512,
k=512,
))
s = Schedule(matmul)
block = s.get_block("C")
i0, i1, i2 = s.get_loops(block)
desc_loops = collect_loops(matmul_16x16x16xf16f16f16_desc)
for do_reorder in [False, True]:
# Mapping should be invariant to the loop permutation
if do_reorder:
s.reorder(i2, i0, i1)
info = get_tensorize_loop_mapping(s, block,
matmul_16x16x16xf16f16f16_desc)
assert info is not None
desc_loop_to_sref = dict((v, k) for k, v in info.loop_map.items())
for i in range(3):
assert desc_loops[i] in desc_loop_to_sref
assert s.get(desc_loop_to_sref[desc_loops[0]]) == s.get(i0)
assert s.get(desc_loop_to_sref[desc_loops[1]]) == s.get(i1)
assert s.get(desc_loop_to_sref[desc_loops[2]]) == s.get(i2)
