def cache_write_under_scope(b: ty.handle, c: ty.handle) -> None:
A = tir.alloc_buffer((128, 128))
B = tir.match_buffer(b, (128, 128))
C = tir.match_buffer(c, (128, 128))
A_global = tir.alloc_buffer((128, 128))
with tir.block([8, 8], "scope") as [i, j]:
A_local = tir.alloc_buffer((128, 128), scope="local")
B_global = tir.alloc_buffer((128, 128))
for x, y in tir.grid(16, 16):
with tir.block([128, 128], "A_local") as [vi, vj]:
tir.bind(vi, i * 16 + x)
tir.bind(vj, j * 16 + y)
A_local[vi, vj] = 1.0
for x, y in tir.grid(16, 16):
with tir.block([128, 128], "A") as [vi, vj]:
tir.bind(vi, i * 16 + x)
tir.bind(vj, j * 16 + y)
A_global[vi, vj] = A_local[vi, vj]
for x, y in tir.grid(16, 16):
with tir.block([128, 128], "B_global") as [vi, vj]:
tir.bind(vi, i * 16 + x)
tir.bind(vj, j * 16 + y)
B_global[vi, vj] = A_global[vi, vj] + 1.0
for x, y in tir.grid(16, 16):
with tir.block([128, 128], "B_global") as [vi, vj]:
tir.bind(vi, i * 16 + x)
tir.bind(vj, j * 16 + y)
B[vi, vj] = B_global[vi, vj]
with tir.block([128, 128], "A_global") as [vi, vj]:
A[vi, vj] = A_global[vi, vj]
with tir.block([128, 128], "C") as [vi, vj]:
C[vi, vj] = A[vi, vj] * 2.0
def transformed_func() -> None:
A = tir.alloc_buffer([128, 128])
with tir.block([128, 128], "") as [i, j]:
A[i, j] = tir.float32(0)
with tir.block([32, 32, tir.reduce_axis(0, 32)], "") as [i, j, k]:
B = tir.alloc_buffer([128, 128])
if k == 0:
for ii, jj in tir.grid(4, 4):
B[i * 4 + ii, j * 4 + jj] = A[i * 4 + ii, j * 4 + jj]
for ii, jj in tir.grid(4, 4):
with tir.block([], ""):
tir.reads([B[((i * 4) + ii), ((j * 4) + jj)]])
tir.writes([B[((i * 4) + ii), ((j * 4) + jj)]])
C = tir.alloc_buffer([128, 128])
for kk in tir.serial(0, 4):
B[((i * 4) + ii),
((j * 4) + jj)] = (B[((i * 4) + ii),
((j * 4) + jj)] + C[((i * 4) + ii),
((k * 4) + kk)])
for kk in tir.serial(0, 4):
with tir.block([], ""):
tir.reads([
B[((i * 4) + ii), ((j * 4) + jj)],
C[((i * 4) + ii), ((k * 4) + kk)],
])
tir.writes([B[((i * 4) + ii), ((j * 4) + jj)]])
D = tir.alloc_buffer([128, 128])
B[((i * 4) + ii),
((j * 4) +
jj)] = B[((i * 4) + ii),
((j * 4) + jj)] + (D[((j * 4) + jj), (
(k * 4) + kk)] * C[((i * 4) + ii),
((k * 4) + kk)])
def compacted_complex_func(a: ty.handle, c: ty.handle, n: ty.int32) -> None:
A = tir.match_buffer(a, (8, 8), "float32")
C = tir.match_buffer(c, (8, 8), "float32")
for i in range(0, 8):
with tir.block([]):
tir.reads(A[0, 8])
tir.writes(C[0, 8])
B = tir.alloc_buffer((1, 8), "float32")
for j in range(0, 4):
with tir.block([]) as []:
D = tir.alloc_buffer((6, 1), "float32")
tir.reads(A[i, j])
tir.writes(B[0, j])
for k in range(4, 8):
D[k - 2, 0] = 1.0
for k in range(2, 4):
tir.store(B.data, j, A[i, j] + D[k - 2, 0])
for j in range(3, 5):
with tir.block([]) as []:
tir.reads(B[0, j])
tir.writes(C[i, j])
C[i, j] = B[0, j]
for j in range(6, 8):
with tir.block([]) as []:
tir.reads(B[0, j])
tir.writes(C[i, j])
C[i, j] = B[0, j]
def multiple_reduction_blocks_rfactor(a: ty.handle, f: ty.handle) -> None:
A = tir.match_buffer(a, [16, 16, 16])
C = tir.alloc_buffer([16, 16])
D = tir.alloc_buffer([16, 16])
E = tir.alloc_buffer([16, 16])
F = tir.match_buffer(f, [16, 16])
C_rf = tir.alloc_buffer([16, 16, 4])
for i, j1, k1o, k1i in tir.grid(16, 16, 4, 4):
with tir.block([4, 16, 16, tir.reduce_axis(0, 4)],
"C_rf") as [vk1o, ci, cj, vk1i]:
tir.bind(vk1o, k1o)
tir.bind(ci, i)
tir.bind(cj, j1)
tir.bind(vk1i, k1i)
with tir.init():
C_rf[ci, cj, vk1o] = 0.0
C_rf[ci, cj, vk1o] = C_rf[ci, cj, vk1o] + A[ci, cj,
((vk1o * 4) + vk1i)]
for i_1 in tir.serial(0, 16):
for j1_1 in tir.serial(0, 16):
for k1o_1 in tir.serial(0, 4):
with tir.block([tir.reduce_axis(0, 4), 16, 16],
"C") as [vk1o_1, ci_1, cj_1]:
tir.bind(vk1o_1, k1o_1)
tir.bind(ci_1, i_1)
tir.bind(cj_1, j1_1)
with tir.init():
C[ci_1, cj_1] = 0.0
C[ci_1, cj_1] = C[ci_1, cj_1] + C_rf[ci_1, cj_1, vk1o_1]
for k2o, k2i in tir.grid(4, 4):
with tir.block([16, 16, tir.reduce_axis(0, 16)],
"D") as [di, dj, dk]:
tir.bind(di, i_1)
tir.bind(dj, j1_1)
tir.bind(dk, (k2o * 4) + k2i)
with tir.init():
D[di, dj] = 0.0
D[di, dj] = (D[di, dj] + A[di, dj, dk]) + C[di, dj]
for j2 in tir.serial(0, 16):
for k3o, k3i in tir.grid(4, 4):
with tir.block([16, 16, tir.reduce_axis(0, 16)],
"E") as [ei, ej, ek]:
tir.bind(ei, i_1)
tir.bind(ej, j2)
tir.bind(ek, (k3o * 4) + k3i)
with tir.init():
E[ei, ej] = 0.0
E[ei, ej] = (E[ei, ej] + A[ei, ej, ek]) + D[ei, ej]
for k4o, k4i in tir.grid(4, 4):
with tir.block([16, 16, tir.reduce_axis(0, 16)],
"F") as [fi, fj, fk]:
tir.bind(fi, i_1)
tir.bind(fj, j2)
tir.bind(fk, (k4o * 4) + k4i)
with tir.init():
F[fi, fj] = 0.0
F[fi, fj] = (F[fi, fj] + A[fi, fj, fk]) + E[fi, fj]
def func_multi_producer() -> None:
A = tir.alloc_buffer((128))
B = tir.alloc_buffer((128))
with tir.block([128], "A0") as [vi]:
A[vi] = 1.0
with tir.block([128], "A1") as [vi]:
A[vi] = 2.0
with tir.block([128], "B") as [vi]:
B[vi] = A[vi]
def fail_multi_reader_writer(a: ty.handle, d: ty.handle) -> None:
A = tir.match_buffer(a, (128, 128))
B = tir.alloc_buffer((128, 128))
C = tir.alloc_buffer((128, 128))
D = tir.match_buffer(d, (128, 128))
with tir.block([128, 128], "B") as [vi, vj]:
B[vi, vj] = A[vi, vj] * 2.0
C[vi, vj] = A[vi, vj] + 2.0
with tir.block([128, 128], "C") as [vi, vj]:
D[vi, vj] = B[vi, vj] + C[vi, vj]
def opaque_access_func() -> None:
A = tir.alloc_buffer([1024])
B = tir.alloc_buffer([1024])
for i in tir.serial(0, 8):
with tir.block([8]) as [v]:
tir.bind(v, i)
tir.reads([A[v * 128 : v * 128 + 128]])
tir.writes([B[v * 128 : v * 128 + 128]])
tir.evaluate(
tir.call_extern("test", B.data, v * 128, 128, A.data, v * 128, 128, dtype="float32")
)
def cuda_matmul_1(a: ty.handle, b: ty.handle, c: ty.handle) -> None: # pylint: disable=undefined-loop-variable
A = tir.match_buffer(a, [2048, 2048], "float32")
B = tir.match_buffer(b, [2048, 2048], "float32")
C = tir.match_buffer(c, [2048, 2048], "float32")
A_shared = tir.alloc_buffer([2048, 2048], "float32", scope="shared")
B_shared = tir.alloc_buffer([2048, 2048], "float32", scope="shared")
A_shared_local = tir.alloc_buffer([2048, 2048], "float32", scope="local")
B_shared_local = tir.alloc_buffer([2048, 2048], "float32", scope="local")
C_local = tir.alloc_buffer([2048, 2048], "float32", scope="local")
with tir.block([2048, 2048], "A_shared") as [v0, v1]:
A_shared[v0, v1] = A[v0, v1]
with tir.block([2048, 2048], "B_shared") as [v0, v1]:
B_shared[v0, v1] = B[v0, v1]
with tir.block([2048, 2048], "A_shared_local") as [v0, v1]:
A_shared_local[v0, v1] = A_shared[v0, v1]
with tir.block([2048, 2048], "B_shared_local") as [v0, v1]:
B_shared_local[v0, v1] = B_shared[v0, v1]
for by in tir.thread_binding(0, 32, thread="blockIdx.y"):
for bx in tir.thread_binding(0, 32, thread="blockIdx.x"):
for vy in tir.thread_binding(0, 2, thread="vthread.y"):
for vx in tir.thread_binding(0, 2, thread="vthread.x"):
for ty in tir.thread_binding(0, 8, thread="threadIdx.y"):
for tx in tir.thread_binding(0,
8,
thread="threadIdx.x"):
for k_0 in tir.serial(0, 256):
for k_1 in tir.unroll(0, 8):
for _, i, j in tir.grid(1, 4, 4):
with tir.block([
2048, 2048,
tir.reduce_axis(0, 2048)
], "C") as [vi, vj, vk]:
tir.bind(
vi,
by * 64 + vy * 32 + ty * 4 + i)
tir.bind(
vj,
bx * 64 + vx * 32 + tx * 4 + j)
tir.bind(vk, k_0 * 8 + k_1)
with tir.init():
C_local[vi, vj] = 0.0
C_local[vi, vj] = C_local[
vi, vj] + A_shared_local[
vk, vi] * B_shared_local[
vk, vj]
for i, j in tir.grid(4, 4):
with tir.block([2048, 2048],
"C_local") as [vi, vj]:
tir.bind(vi,
by * 64 + vy * 32 + ty * 4 + i)
tir.bind(vj,
bx * 64 + vx * 32 + tx * 4 + j)
C[vi, vj] = C_local[vi, vj]
def compacted_multi_alloc_func(a: ty.handle, d: ty.handle) -> None:
A = tir.match_buffer(a, (32), "float32")
D = tir.match_buffer(d, (32), "float32")
for i in range(0, 32):
with tir.block([]) as []:
tir.reads(A[i])
tir.writes(D[i])
B = tir.alloc_buffer((32, ))
C = tir.alloc_buffer((32, ))
B[i] = A[i] + 1.0
C[i] = A[i] + B[i]
D[i] = C[i] * 2.0
def continuous_cache_write(a: ty.handle, c: ty.handle) -> None:
A = tir.match_buffer(a, (128, 128))
B = tir.alloc_buffer((128, 128))
C = tir.match_buffer(c, (128, 128))
B_shared = tir.alloc_buffer((128, 128), scope="shared")
B_local = tir.alloc_buffer((128, 128), scope="local")
with tir.block([128, 128], "B") as [vi, vj]:
B_local[vi, vj] = A[vi, vj] * 2.0
with tir.block([128, 128], "B") as [vi, vj]:
B_shared[vi, vj] = B_local[vi, vj]
with tir.block([128, 128], "B") as [vi, vj]:
B[vi, vj] = B_shared[vi, vj]
with tir.block([128, 128], "C") as [vi, vj]:
C[vi, vj] = B[vi, vj] + 1.0
def cache_write_elementwise(a: ty.handle, c: ty.handle) -> None:
A = tir.match_buffer(a, (128, 128))
C = tir.match_buffer(c, (128, 128))
B = tir.alloc_buffer((128, 128))
B_global = tir.alloc_buffer((128, 128), scope="local")
C_local = tir.alloc_buffer((128, 128))
with tir.block([128, 128], "B_global") as [vi, vj]:
B_global[vi, vj] = A[vi, vj] * 2.0
with tir.block([128, 128], "B") as [vi, vj]:
B[vi, vj] = B_global[vi, vj]
with tir.block([128, 128], "C_local") as [vi, vj]:
C_local[vi, vj] = B[vi, vj] + 1.0
with tir.block([128, 128], "C") as [vi, vj]:
C[vi, vj] = C_local[vi, vj]
def fail_all_producers_under_loop(a: ty.handle, d: ty.handle) -> None:
A = tir.match_buffer(a, (128, 128), "float32")
B = tir.alloc_buffer((128, 128), "float32")
C = tir.alloc_buffer((128, 128), "float32")
D = tir.match_buffer(d, (128, 128), "float32")
for i, j in tir.grid(128, 128):
with tir.block([128, 128], "B") as [vi, vj]:
B[vi, vj] = A[vi, vj] * 2.0
for i, j in tir.grid(128, 128):
with tir.block([128, 128], "C") as [vi, vj]:
C[vi, vj] = A[vi, vj] + 1.0
for i, j in tir.grid(128, 128):
with tir.block([128, 128], "D") as [vi, vj]:
D[vi, vj] = B[vi, vj] + C[vi, vj]
def match_buffer_func() -> None:
C = tir.alloc_buffer((128, 128))
with tir.block([128]) as [vi]:
C0 = tir.match_buffer(C[vi, 0:128], (128))
with tir.block([128]) as [jj]:
C1 = tir.match_buffer(C0[jj], ())
C1[()] = 0
def opaque_block_func() -> None:
with tir.block([], "root"):
A = tir.alloc_buffer((16, 16), "float32")
B = tir.alloc_buffer((16, 16), "float32")
tir.reads([])
tir.writes([])
# Need add read/write region manually to avoid triggering block access region detector
for i in range(0, 16):
with tir.block([]):
tir.reads(A[i, 0:16])
tir.writes([B[i, 0:16]])
for j in range(0, 16):
with tir.block([]):
tir.reads(A[i, j])
tir.writes(B[i, j])
B[i, j] = A[i, j] + 1.0
def func_with_bufferslice_indices(data: ty.handle, index: ty.handle) -> None:
data_buf = tir.match_buffer(data, (16, 16), "float32")
index_buf = tir.match_buffer(index, (1, ), "int32")
out_buf = tir.alloc_buffer((16, 16), "float32")
with tir.block([16, 16]) as [vi, vj]:
out_buf[vi, vj] = data_buf[vi, index_buf[0]]
def element_wise_invalid_annotation(a: ty.handle, c: ty.handle) -> None:
C = tir.match_buffer(c, [128, 128],
elem_offset=0,
align=128,
offset_factor=1)
A = tir.match_buffer(a, [128, 128],
elem_offset=0,
align=128,
offset_factor=1)
# body
with tir.block([], "root"):
tir.reads([])
tir.writes([])
B = tir.alloc_buffer([128, 128],
elem_offset=0,
align=128,
offset_factor=1)
for i0 in tir.serial(0, 128):
for ax1 in tir.serial(0, 128):
with tir.block([128, 128], "B") as [vi, vj]:
tir.block_attr({"buffer_dim_align": [0]})
tir.bind(vi, i0)
tir.bind(vj, ax1)
tir.reads([A[vi, vj]])
tir.writes([B[vi, vj]])
B[vi, vj] = (A[vi, vj] * tir.float32(2))
for i1 in tir.serial(0, 128):
with tir.block([128, 128], "C") as [vi_1, vj_1]:
tir.bind(vi_1, i0)
tir.bind(vj_1, i1)
tir.reads([B[vi_1, vj_1]])
tir.writes([C[vi_1, vj_1]])
C[vi_1, vj_1] = (B[vi_1, vj_1] + tir.float32(1))
def expected_recursive_bufferslice_indices(data: ty.handle,
index: ty.handle) -> None:
index_buf = tir.match_buffer(index, [1],
dtype="int32",
elem_offset=0,
align=128,
offset_factor=1)
data_buf = tir.match_buffer(data, [16, 16],
elem_offset=0,
align=128,
offset_factor=1)
with tir.block([], "root"):
tir.reads([])
tir.writes([])
out_buf = tir.alloc_buffer([16, 16],
elem_offset=0,
align=128,
offset_factor=1)
for i0, i1 in tir.grid(16, 16):
with tir.block([16, 16], "") as [vi, vj]:
tir.bind(vi, i0)
tir.bind(vj, i1)
tir.reads([data_buf[0:16, 0:16], index_buf[0]])
tir.writes([out_buf[vi, vj]])
out_buf[vi, vj] = data_buf[index_buf[index_buf[0]],
index_buf[0]]
def buffer_load_store_func(a: ty.handle, b: ty.handle) -> None:
A = tir.match_buffer(a, (128, 128), "float32")
B = tir.match_buffer(b, (128, 128), "float32")
C = tir.alloc_buffer((128, 128), "float32")
D = tir.alloc_buffer((128, 128), "float32")
with tir.block([128, 128]) as [i, j]:
A[i, j] = tir.float32(0)
with tir.block([32, 32, tir.reduce_axis(0, 32)]) as [i, j, k]:
with tir.init():
for ii, jj in tir.grid(4, 4):
B[i * 4 + ii, j * 4 + jj] = A[i * 4 + ii, j * 4 + jj]
for ii, jj in tir.grid(4, 4):
for kk in range(0, 4):
B[i * 4 + ii, j * 4 + jj] += C[i * 4 + ii, k * 4 + kk]
for kk in range(0, 4):
B[i * 4 + ii, j * 4 + jj] += D[j * 4 + jj, k * 4 + kk] * C[i * 4 + ii, k * 4 + kk]
def transformed_opaque_access(a: ty.handle, b: ty.handle) -> None:
A = tir.match_buffer(a, [1024])
B = tir.match_buffer(b, [1024])
for i in tir.serial(0, 8):
with tir.block([8]) as [vi]:
tir.reads(A[vi * 128:vi * 128 + 128])
tir.writes(B[vi * 128:vi * 128 + 128])
A_cache = tir.alloc_buffer([1024])
with tir.block([8]) as [v]:
tir.bind(v, vi)
tir.reads([A[v * 128:v * 128 + 128]])
tir.writes([A_cache[v * 128:v * 128 + 128]])
tir.evaluate(
tir.call_extern("test",
A_cache.data,
v * 128,
128,
A.data,
v * 128,
128,
dtype="float32"))
for j in tir.serial(0, 128):
with tir.block([1024]) as [v]:
tir.bind(v, ((vi * 128) + j))
tir.reads([A_cache[v]])
tir.writes([B[v]])
B[v] = A_cache[v]
def func_multi_consumer() -> None:
A = tir.alloc_buffer((128))
B = tir.alloc_buffer((128))
C = tir.alloc_buffer((128))
for i in tir.grid(8):
for j in tir.grid(16):
with tir.block([128], "A") as [vi]:
tir.bind(vi, i * 16 + j)
A[vi] = 1.0
for j in tir.grid(16):
with tir.block([128], "B") as [vi]:
tir.bind(vi, i * 16 + j)
B[vi] = A[vi] + 1.0
for i in tir.grid(128):
with tir.block([128], "C") as [vi]:
C[vi] = A[vi]
def original_func() -> None:
A = tir.alloc_buffer((128, 128), "float32")
with tir.block([128, 128]) as [i, j]:
A[i, j] = tir.float32(0)
with tir.block([32, 32, tir.reduce_axis(0, 32)]) as [i, j, k]:
B = tir.alloc_buffer((128, 128), "float32")
C = tir.alloc_buffer((128, 128), "float32")
D = tir.alloc_buffer((128, 128), "float32")
if k == 0:
for ii, jj in tir.grid(4, 4):
B[i * 4 + ii, j * 4 + jj] = A[i * 4 + ii, j * 4 + jj]
for ii, jj in tir.grid(4, 4):
for kk in range(0, 4):
B[i * 4 + ii, j * 4 + jj] += C[i * 4 + ii, k * 4 + kk]
for kk in range(0, 4):
B[i * 4 + ii, j * 4 +
jj] += D[j * 4 + jj, k * 4 + kk] * C[i * 4 + ii, k * 4 + kk]
def func() -> None:
A = tir.alloc_buffer((128, 128), "float32")
B = tir.alloc_buffer((128, 128), "float32")
C = tir.alloc_buffer((128, 128), "float32")
D = tir.alloc_buffer((128, 128), "float32")
with tir.block([]):
# Need add read/write region manually to avoid triggering block access region detector
tir.reads([B[0, 0], C[0:16, 0:16], A[4:12, 4:12]])
tir.writes([A[0:12, 0:12]])
for i, j in tir.grid(8, 8):
A[i, j] = B[0, 0] + C[0, 0]
with tir.block([2, 2]) as [vi, vj]:
tir.reads([A[vi * 4 + 4 : vi * 4 + 8, vj * 4 + 4 : vj * 4 + 8], C[12:16, 12:16]])
tir.writes([A[vi * 4 + 4 : vi * 4 + 8, vj * 4 + 4 : vj * 4 + 8]])
for i, j in tir.grid(4, 4):
A[vi * 4 + 4 + i, vj * 4 + 4 + j] += C[i + 12, j + 12]
tir.evaluate(D.data)
def multiple_reduction_blocks(a: ty.handle, f: ty.handle) -> None:
A = tir.match_buffer(a, (16, 16, 16))
C = tir.alloc_buffer((16, 16))
D = tir.alloc_buffer((16, 16))
E = tir.alloc_buffer((16, 16))
F = tir.match_buffer(f, (16, 16))
for i in tir.serial(0, 16):
for j1 in tir.serial(0, 16):
for k1o, k1i in tir.grid(4, 4):
with tir.block([16, 16, tir.reduce_axis(0, 16)],
"C") as [ci, cj, ck]:
tir.bind(ci, i)
tir.bind(cj, j1)
tir.bind(ck, k1o * 4 + k1i)
with tir.init():
C[ci, cj] = 0.0
C[ci, cj] = C[ci, cj] + A[ci, cj, ck]
for k2o, k2i in tir.grid(4, 4):
with tir.block([16, 16, tir.reduce_axis(0, 16)],
"D") as [di, dj, dk]:
tir.bind(di, i)
tir.bind(dj, j1)
tir.bind(dk, k2o * 4 + k2i)
with tir.init():
D[di, dj] = 0.0
D[di, dj] = D[di, dj] + A[di, dj, dk] + C[di, dj]
for j2 in tir.serial(0, 16):
for k3o, k3i in tir.grid(4, 4):
with tir.block([16, 16, tir.reduce_axis(0, 16)],
"E") as [ei, ej, ek]:
tir.bind(ei, i)
tir.bind(ej, j2)
tir.bind(ek, k3o * 4 + k3i)
with tir.init():
E[ei, ej] = 0.0
E[ei, ej] = E[ei, ej] + A[ei, ej, ek] + D[ei, ej]
for k4o, k4i in tir.grid(4, 4):
with tir.block([16, 16, tir.reduce_axis(0, 16)],
"F") as [fi, fj, fk]:
tir.bind(fi, i)
tir.bind(fj, j2)
tir.bind(fk, k4o * 4 + k4i)
with tir.init():
F[fi, fj] = 0.0
F[fi, fj] = F[fi, fj] + A[fi, fj, fk] + E[fi, fj]
def elementwise(a: ty.handle, c: ty.handle) -> None:
A = tir.match_buffer(a, (128, 128))
B = tir.alloc_buffer((128, 128))
C = tir.match_buffer(c, (128, 128))
with tir.block([128, 128], "B") as [vi, vj]:
B[vi, vj] = A[vi, vj] * 2.0
with tir.block([128, 128], "C") as [vi, vj]:
C[vi, vj] = B[vi, vj] + 1.0
def elementwise_multi_reverse_loads(a: ty.handle, c: ty.handle) -> None:
A = tir.match_buffer(a, (128, 128))
B = tir.alloc_buffer((128, 128))
C = tir.match_buffer(c, (128, 128))
with tir.block([128, 128], "B") as [vi, vj]:
B[vi, vj] = A[vi, vj] * 2.0
with tir.block([128, 128], "C") as [vi, vj]:
C[vi, vj] = (B[vi, vj] + 1.0) * (B[vi, vj] * 2.0) + 3.0
def transformed_match_buffer_func() -> None:
for i in range(0, 128):
with tir.block([128]) as [vi]:
tir.bind(vi, i)
C = tir.alloc_buffer((128, 128))
C0 = tir.match_buffer(C[vi, 0:128], (128))
with tir.block([128]) as [jj]:
C1 = tir.match_buffer(C0[jj], ())
C1[()] = 0
def tir_element_wise(a: ty.handle, c: ty.handle) -> None:
A = tir.match_buffer(a, (128, 128))
C = tir.match_buffer(c, (128, 128))
B = tir.alloc_buffer((128, 128))
with tir.block([128, 128]) as [i, j]:
B[i, j] = A[i, j] * 2.0
with tir.block([128, 128]) as [i, j]:
C[i, j] = B[i, j] + 1.0
def buffer_matched(a: ty.handle, c: ty.handle) -> None:
A = tir.match_buffer(a, (128, 128))
B = tir.alloc_buffer((128, 128))
C = tir.match_buffer(c, (128, 128))
with tir.block([128, 128], "B") as [vi, vj]:
B[vi, vj] = A[vi, vj] * 2.0
with tir.block([128, 128], "C") as [vi, vj]:
Bb = tir.match_buffer(B[vi:vi + 1, vj], (1, 1))
C[vi, vj] = Bb[0, 0] + 1.0
def elementwise_predicate(a: ty.handle, c: ty.handle) -> None:
A = tir.match_buffer(a, (128, 128))
B = tir.alloc_buffer((128, 128))
C = tir.match_buffer(c, (128, 128))
with tir.block([128, 128], "B") as [vi, vj]:
B[vi, vj] = A[vi, vj] * 2.0
for i, j in tir.grid(128, 128):
with tir.block([128, 128], "C") as [vi, vj]:
tir.where(B[i, j] < 10.0)
C[vi, vj] = B[vi, vj] + 1.0
def opaque_access_load(a: ty.handle, c: ty.handle) -> None:
A = tir.match_buffer(a, (128, 128))
B = tir.alloc_buffer((128, 128))
C = tir.match_buffer(c, (128, 128))
with tir.block([128, 128], "B") as [vi, vj]:
B[vi, vj] = A[vi, vj] * 2.0
with tir.block([128, 128], "C") as [vi, vj]:
tir.reads(B[0:128, 0:128])
tir.writes(C[0:128, 0:128])
C[vi, vj] = tir.load("float32", B.data, vi * 128 + vj) + 1.0
