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 get_valid_counts(
data: ty.handle,
valid_count: ty.handle,
out: ty.handle,
out_indices: ty.handle,
score_threshold: ty.float32,
id_index: ty.int32,
score_index: ty.int32,
) -> None:
data_buf = tir.match_buffer(data, (1, 2500, 6), "float32")
valid_count_buf = tir.match_buffer(valid_count, (1, ), "int32")
out_buf = tir.match_buffer(out, (1, 2500, 6), "float32")
out_indices_buf = tir.match_buffer(out_indices, (1, 2500), "int32")
with tir.block([1], "init") as [vi]:
valid_count_buf[vi] = tir.int32(0)
with tir.block([2500], "update") as [vj]:
tir.reads([data_buf[vi, vj, 6]])
tir.writes([
valid_count_buf[vi], out_indices_buf[vi, vj], out_buf[vi, vj,
6]
])
if (data_buf[vi, vj, score_index] > score_threshold) and (
(id_index < 0) or
(data_buf[vi, vj, id_index] >= tir.float32(0))):
for k in tir.serial(0, 6):
out_buf[vi, valid_count_buf[vi], k] = data_buf[vi, vj, k]
out_indices_buf[vi, valid_count_buf[vi]] = vj
valid_count_buf[vi] = valid_count_buf[vi] + 1
if vj >= valid_count_buf[vi]:
for k in tir.serial(0, 6):
out_buf[vi, vj, k] = tir.float32(-1)
out_indices_buf[vi, vj] = tir.int32(-1)
def func_with_opaque_block(a: ty.handle, b: ty.handle, c: ty.handle) -> None:
A = tir.match_buffer(a, [128, 128])
B = tir.match_buffer(b, [128, 128])
C = tir.match_buffer(c, [128, 128])
with tir.block([]) as []:
with tir.block([]) as []:
B[0, 0] = A[0, 0] + tir.float32(1)
with tir.block([128, 128]) as [vi, vj]:
C[vi, vj] = B[vi, vj] + tir.float32(1)
def elementwise_with_root(a: ty.handle, b: ty.handle, c: ty.handle) -> None:
A = tir.match_buffer(a, [128, 128])
B = tir.match_buffer(b, [128, 128])
C = tir.match_buffer(c, [128, 128])
with tir.block([]) as []:
with tir.block([128, 128]) as [vi, vj]:
B[vi, vj] = A[vi, vj] + tir.float32(1)
with tir.block([128, 128]) as [vi, vj]:
C[vi, vj] = B[vi, vj] + tir.float32(1)
def func_with_part_access_region(a: ty.handle, b: ty.handle, c: ty.handle) -> None:
A = tir.match_buffer(a, [128, 128])
B = tir.match_buffer(b, [128, 128])
C = tir.match_buffer(c, [128, 128])
with tir.block([]) as []:
with tir.block([128, 128]) as [vi, vj]:
tir.reads(A[vi, vj])
B[vi, vj] = A[vi, vj] + tir.float32(1)
with tir.block([128, 128]) as [vi, vj]:
tir.writes(C[vi, vj])
C[vi, vj] = B[vi, vj] + tir.float32(1)
def buffer_opaque_access(b: ty.handle, c: ty.handle) -> None:
B = tir.match_buffer(b, [16, 16], "float32")
C = tir.match_buffer(c, [16, 16], "float32")
with tir.block([]):
tir.reads([])
tir.writes(B[0:16, 0:16])
A = tir.allocate([256], "float32", "global")
for i, j in tir.grid(16, 16):
tir.store(A, i * 16 + j, 1)
for i in range(0, 16):
for j in range(0, 16):
tir.evaluate(tir.load("float32", A, i * 16 + j))
for j in range(0, 16):
tir.evaluate(
tir.tvm_fill_fragment(B.data,
16,
16,
16,
0,
tir.float32(0),
dtype="handle"))
for i, j in tir.grid(16, 16):
with tir.block([16, 16]) as [vi, vj]:
tir.bind(vi, i)
tir.bind(vj, j)
C[vi, vj] = B[vi, vj]
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 matmul(a: ty.handle, b: ty.handle, c: ty.handle) -> None:
A = tir.match_buffer(a, [128, 128])
B = tir.match_buffer(b, [128, 128])
C = tir.match_buffer(c, [128, 128])
with tir.block([128, 128, tir.reduce_axis(0, 128)], "update") as [vi, vj, vk]:
with tir.init():
C[vi, vj] = tir.float32(0)
C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
def main(a: ty.handle, b: ty.handle) -> None:
A = tir.match_buffer(a, [64, 64, 64])
B = tir.match_buffer(b, [64])
with tir.block([64,
tir.reduce_axis(0, 64),
tir.reduce_axis(32, 64)]) as [i, j, k]:
if (j == 0) and (k == 32):
B[i] = tir.float32(0)
B[i] += A[i, j, k]
def matmul(a: ty.handle, b: ty.handle, c: ty.handle) -> None:
A = tir.match_buffer(a, [128, 128])
B = tir.match_buffer(b, [128, 128])
C = tir.match_buffer(c, [128, 128])
for i, j in tir.grid(128, 128):
with tir.block([128, 128], "init") as [vi, vj]:
C[vi, vj] = tir.float32(0)
for k in range(0, 128):
with tir.block([128, 128, tir.reduce_axis(0, 128)], "update") as [vi, vj, vk]:
C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk]
def main(a: ty.handle, b: ty.handle) -> None:
A = tir.match_buffer(a, [64, 64, 64])
B = tir.match_buffer(b, [64])
with tir.block([64,
tir.reduce_axis(0, 64),
tir.reduce_axis(32, 64)]) as [i, j, k]:
BB = tir.match_buffer(B[i], ())
AA = tir.match_buffer(A[i, 0:64, 0:64], (64, 64))
if (j == 0) and (k == 32):
BB[()] = tir.float32(0)
BB[()] += AA[j, k]
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 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 matmul_original(a: ty.handle, b: ty.handle, c: ty.handle) -> None:
A = tir.match_buffer(a, [128, 128])
B = tir.match_buffer(b, [128, 128])
C = tir.match_buffer(c, [128, 128])
for i, j in tir.grid(32, 32):
with tir.block([32, 32], "init") as [vi, vj]:
for ii, jj in tir.grid(4, 4):
C[vi * 4 + ii, vj * 4 + jj] = tir.float32(0)
for k in range(0, 32):
with tir.block([128, 128, tir.reduce_axis(0, 128)], "update") as [vi, vj, vk]:
for ii, jj, kk in tir.grid(4, 4, 4):
C[vi * 4 + ii, vj * 4 + jj] = (
C[vi * 4 + ii, vj * 4 + jj]
+ A[vi * 4 + ii, vk * 4 + kk] * B[vj * 4 + jj, vk * 4 + kk]
)
def cuda_matmul_3(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]
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 k0 in tir.serial(0, 256):
for k1 in tir.unroll(0, 8):
for i, j in tir.grid(1, 4):
with tir.block(
[2048, 2048],
"A_shared_local") as [v0, v1]:
tir.bind(v0, k0 * 8 + k1 + i)
tir.bind(
v1,
by * 64 + vy * 32 + ty * 4 + j)
A_shared_local[v0,
v1] = A_shared[v0,
v1]
for i, j in tir.grid(1, 4):
with tir.block(
[2048, 2048],
"B_shared_local") as [v0, v1]:
tir.bind(v0, k0 * 8 + k1 + i)
tir.bind(
v1,
bx * 64 + vx * 32 + tx * 4 + j)
B_shared_local[v0,
v1] = B_shared[v0,
v1]
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, k0 * 8 + k1)
with tir.init():
C_local[vi,
vj] = tir.float32(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 [v0, v1]:
tir.bind(v0,
by * 64 + vy * 32 + ty * 4 + i)
tir.bind(v1,
bx * 64 + vx * 32 + tx * 4 + j)
C[v0, v1] = C_local[v0, v1]
def main(
placeholder: ty.handle,
placeholder_1: ty.handle,
placeholder_2: ty.handle,
ethosu_conv2d: ty.handle,
) -> None:
# function attr dict
tir.func_attr({"global_symbol": "main", "tir.noalias": True})
placeholder_3 = tir.match_buffer(placeholder, [1, 8, 8, 3],
dtype="uint8",
elem_offset=0,
align=128,
offset_factor=1)
placeholder_4 = tir.match_buffer(placeholder_1, [48],
dtype="uint8",
elem_offset=0,
align=128,
offset_factor=1)
placeholder_5 = tir.match_buffer(placeholder_2, [16],
dtype="int32",
elem_offset=0,
align=128,
offset_factor=1)
ethosu_conv2d_1 = tir.match_buffer(ethosu_conv2d, [1, 8, 8, 16],
dtype="uint8",
elem_offset=0,
align=128,
offset_factor=1)
# body
tir.evaluate(
tir.call_extern(
"ethosu_conv2d",
"uint8",
8,
8,
3,
8,
0,
8,
tir.load("uint8", placeholder_3.data, 0),
0,
0,
0,
tir.float32(0.5),
10,
"NHWC",
24,
3,
1,
"uint8",
8,
8,
16,
8,
0,
8,
tir.load("uint8", ethosu_conv2d_1.data, 0),
0,
0,
0,
tir.float32(0.25),
14,
"NHWC",
128,
16,
1,
1,
1,
1,
1,
1,
1,
tir.load("uint8", placeholder_4.data, 0),
0,
12,
tir.load("uint8", placeholder_5.data, 0),
0,
0,
0,
0,
0,
"CLIP",
0,
0,
"NONE",
dtype="uint8",
))
def tensorcore_gemm(a: ty.handle, b: ty.handle, c: ty.handle) -> None:
# match buffer
A = tir.match_buffer(a, [1024, 1024], "float16")
B = tir.match_buffer(b, [1024, 1024], "float16")
C = tir.match_buffer(c, [1024, 1024], "float32")
# body
for blockIdx_x in tir.thread_binding(0, 16, "blockIdx.x"):
for blockIdx_y in tir.thread_binding(0, 8, "blockIdx.y"):
with tir.block([16, 8]) as [bx, by]:
tir.bind(bx, blockIdx_x)
tir.bind(by, blockIdx_y)
shared_A = tir.alloc_buffer([1024, 1024], "float16", scope="shared")
shared_B = tir.alloc_buffer([1024, 1024], "float16", scope="shared")
wmma_A = tir.alloc_buffer([1024, 1024], "float16", scope="wmma.matrix_a")
wmma_B = tir.alloc_buffer([1024, 1024], "float16", scope="wmma.matrix_b")
wmma_C = tir.alloc_buffer([1024, 1024], "float32", scope="wmma.accumulator")
for ty in tir.thread_binding(0, 2, "threadIdx.y"):
for tz in tir.thread_binding(0, 2, "threadIdx.z"):
for i, j in tir.grid(2, 4):
with tir.block([64, 64]) as [vi, vj]:
tir.bind(vi, bx * 4 + ty * 2 + i)
tir.bind(vj, by * 8 + tz * 4 + j)
tir.reads([])
tir.writes(wmma_C[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
C0 = tir.match_buffer(
wmma_C[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16],
(16, 16),
"float32",
strides=[16 * 4, 1],
scope="wmma.accumulator",
offset_factor=1,
)
tir.evaluate(
tir.tvm_fill_fragment(
C0.data,
16,
16,
16,
i * 4 + j,
tir.float32(0),
dtype="handle",
)
)
for ko in range(0, 32):
# copy data from global to shared
for tx in tir.thread_binding(0, 32, "threadIdx.x"):
for i0, j0 in tir.grid(1, 4):
for j1 in tir.vectorized(0, 4):
with tir.block([1024, 1024]) as [vi, vj]:
tir.bind(vi, bx * 64 + ty * 32 + tx + i0)
tir.bind(vj, ko * 32 + tz * 16 + j0 * 4 + j1)
shared_A[vi, vj + 8] = A[vi, vj]
for i0, j0 in tir.grid(2, 4):
for j1 in tir.vectorized(0, 4):
with tir.block([1024, 1024]) as [vi, vj]:
tir.bind(vi, by * 128 + ty * 64 + tx * 2 + i0)
tir.bind(vj, ko * 32 + tz * 16 + j0 * 4 + j1)
shared_B[vi, vj + 8] = B[vi, vj]
for ki in range(0, 2):
for i in range(0, 2):
with tir.block([64, 64]) as [vi, vk]:
tir.bind(vi, bx * 4 + ty * 2 + i)
tir.bind(vk, ko * 2 + ki)
tir.reads(
shared_A[
vi * 16 : vi * 16 + 16,
vk * 16 : vk * 16 + 16 + 8,
]
)
tir.writes(
wmma_A[vi * 16 : vi * 16 + 16, vk * 16 : vk * 16 + 16]
)
s0 = tir.var("int32")
s1 = tir.var("int32")
A0 = tir.match_buffer(
shared_A[
vi * 16 : vi * 16 + 16,
vk * 16 : vk * 16 + 16 + 8,
],
(16, 16 + 8),
"float16",
strides=[s0, s1],
scope="shared",
offset_factor=1,
)
wmma_A0 = tir.match_buffer(
wmma_A[vi * 16 : vi * 16 + 16, vk * 16 : vk * 16 + 16],
(16, 16),
"float16",
strides=[16, 1],
scope="wmma.matrix_a",
offset_factor=1,
)
tir.evaluate(
tir.tvm_load_matrix_sync(
wmma_A0.data,
16,
16,
16,
i,
tir.tvm_access_ptr(
tir.type_annotation(dtype="float16"),
A0.data,
A0.elem_offset + 8,
A0.strides[0],
1,
dtype="handle",
),
A0.strides[0],
"row_major",
dtype="handle",
)
)
for j in range(0, 4):
with tir.block([64, 64]) as [vj, vk]:
tir.bind(vj, by * 8 + tz * 4 + j)
tir.bind(vk, ko * 2 + ki)
tir.reads(
shared_B[
vj * 16 : vj * 16 + 16,
vk * 16 : vk * 16 + 16 + 8,
]
)
tir.writes(
wmma_B[vj * 16 : vj * 16 + 16, vk * 16 : vk * 16 + 16]
)
s0 = tir.var("int32")
s1 = tir.var("int32")
B0 = tir.match_buffer(
shared_B[
vj * 16 : vj * 16 + 16,
vk * 16 : vk * 16 + 16 + 8,
],
(16, 16 + 8),
"float16",
strides=[s0, s1],
scope="shared",
offset_factor=1,
)
wmma_B0 = tir.match_buffer(
wmma_B[vj * 16 : vj * 16 + 16, vk * 16 : vk * 16 + 16],
(16, 16),
"float16",
strides=[16, 1],
scope="wmma.matrix_b",
offset_factor=1,
)
tir.evaluate(
tir.tvm_load_matrix_sync(
wmma_B0.data,
16,
16,
16,
j,
tir.tvm_access_ptr(
tir.type_annotation(dtype="float16"),
B0.data,
B0.elem_offset + 8,
B0.strides[0],
1,
dtype="handle",
),
B0.strides[0],
"col_major",
dtype="handle",
)
)
for i, j in tir.grid(2, 4):
with tir.block([64, 64, tir.reduce_axis(0, 64)]) as [
vi,
vj,
vk,
]:
tir.bind(vi, bx * 4 + ty * 2 + i)
tir.bind(vj, by * 8 + tz * 4 + j)
tir.bind(vk, ko * 2 + ki)
tir.reads(
[
wmma_A[
vi * 16 : vi * 16 + 16, vk * 16 : vk * 16 + 16
],
wmma_B[
vj * 16 : vj * 16 + 16, vk * 16 : vk * 16 + 16
],
wmma_C[
vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16
],
]
)
tir.writes(
wmma_C[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16]
)
wmma_A1 = tir.match_buffer(
wmma_A[vi * 16 : vi * 16 + 16, vk * 16 : vk * 16 + 16],
(16, 16),
"float16",
strides=[16, 1],
scope="wmma.matrix_a",
offset_factor=1,
)
wmma_B1 = tir.match_buffer(
wmma_B[vj * 16 : vj * 16 + 16, vk * 16 : vk * 16 + 16],
(16, 16),
"float16",
strides=[16, 1],
scope="wmma.matrix_b",
offset_factor=1,
)
wmma_C1 = tir.match_buffer(
wmma_C[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16],
(16, 16),
"float32",
strides=[16 * 4, 1],
scope="wmma.accumulator",
offset_factor=1,
)
tir.evaluate(
tir.tvm_mma_sync(
wmma_C1.data,
i * 4 + j,
wmma_A1.data,
i,
wmma_B1.data,
j,
wmma_C1.data,
i * 4 + j,
dtype="handle",
)
)
for i, j in tir.grid(2, 4):
with tir.block([64, 64]) as [vi, vj]:
tir.bind(vi, bx * 4 + ty * 2 + i)
tir.bind(vj, by * 8 + tz * 4 + j)
tir.reads(wmma_C[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
tir.writes(C[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16])
s0 = tir.var("int32")
s1 = tir.var("int32")
wmma_C2 = tir.match_buffer(
wmma_C[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16],
(16, 16),
"float32",
strides=[16 * 4, 1],
scope="wmma.accumulator",
offset_factor=1,
)
C1 = tir.match_buffer(
C[vi * 16 : vi * 16 + 16, vj * 16 : vj * 16 + 16],
(16, 16),
"float32",
strides=[s0, s1],
offset_factor=1,
)
tir.evaluate(
tir.tvm_store_matrix_sync(
wmma_C2.data,
16,
16,
16,
i * 4 + j,
tir.tvm_access_ptr(
tir.type_annotation(dtype="float32"),
C1.data,
C1.elem_offset,
C1.strides[0],
1,
dtype="handle",
),
C1.strides[0],
"row_major",
dtype="handle",
)
)
def main(placeholder: ty.handle, placeholder_1: ty.handle,
placeholder_2: ty.handle, ethosu_write: ty.handle,
placeholder_3: ty.handle, placeholder_4: ty.handle,
placeholder_5: ty.handle, placeholder_6: ty.handle,
placeholder_7: ty.handle, placeholder_8: ty.handle,
placeholder_9: ty.handle, placeholder_10: ty.handle) -> None:
# function attr dict
tir.func_attr({
"from_legacy_te_schedule": True,
"global_symbol": "main",
"tir.noalias": True
})
buffer = tir.match_buffer(placeholder_7, [80],
dtype="uint8",
elem_offset=0,
align=128,
offset_factor=1)
buffer_1 = tir.match_buffer(placeholder_5, [80],
dtype="uint8",
elem_offset=0,
align=128,
offset_factor=1)
buffer_2 = tir.match_buffer(placeholder_3, [80],
dtype="uint8",
elem_offset=0,
align=128,
offset_factor=1)
buffer_3 = tir.match_buffer(placeholder_4, [32],
dtype="uint8",
elem_offset=0,
align=128,
offset_factor=1)
buffer_4 = tir.match_buffer(placeholder_9, [80],
dtype="uint8",
elem_offset=0,
align=128,
offset_factor=1)
buffer_5 = tir.match_buffer(placeholder_6, [32],
dtype="uint8",
elem_offset=0,
align=128,
offset_factor=1)
placeholder_11 = tir.match_buffer(placeholder, [1, 16, 16, 32],
dtype="int8",
elem_offset=0,
align=128,
offset_factor=1)
buffer_6 = tir.match_buffer(placeholder_1, [592],
dtype="uint8",
elem_offset=0,
align=128,
offset_factor=1)
ethosu_write_1 = tir.match_buffer(ethosu_write, [1, 16, 16, 8],
dtype="int8",
elem_offset=0,
align=128,
offset_factor=1)
buffer_7 = tir.match_buffer(placeholder_2, [160],
dtype="uint8",
elem_offset=0,
align=128,
offset_factor=1)
buffer_8 = tir.match_buffer(placeholder_8, [32],
dtype="uint8",
elem_offset=0,
align=128,
offset_factor=1)
buffer_9 = tir.match_buffer(placeholder_10, [32],
dtype="uint8",
elem_offset=0,
align=128,
offset_factor=1)
# body
ethosu_write_2 = tir.allocate([4096], "int8", "global")
placeholder_global = tir.allocate([80], "uint8", "global")
placeholder_d_global = tir.allocate([32], "uint8", "global")
tir.evaluate(
tir.call_extern("ethosu_conv2d",
"int8",
16,
16,
32,
16,
0,
16,
tir.load("int8", placeholder_11.data, 0),
0,
0,
0,
tir.float32(0.5),
10,
"NHWC",
512,
32,
1,
"int8",
16,
16,
16,
16,
0,
16,
tir.load("int8", ethosu_write_2, 0),
0,
0,
0,
tir.float32(0.25),
14,
"NHWC",
256,
16,
1,
1,
1,
1,
1,
1,
1,
tir.load("uint8", buffer_6.data, 0),
592,
12,
tir.load("uint8", buffer_7.data, 0),
160,
0,
0,
0,
0,
"NONE",
0,
0,
"NONE",
dtype="handle"))
tir.evaluate(
tir.call_extern("ethosu_copy",
tir.load("uint8", buffer_2.data, 0),
80,
tir.load("uint8", placeholder_global, 0),
dtype="handle"))
tir.evaluate(
tir.call_extern("ethosu_copy",
tir.load("uint8", buffer_3.data, 0),
32,
tir.load("uint8", placeholder_d_global, 0),
dtype="handle"))
tir.evaluate(
tir.call_extern("ethosu_conv2d",
"int8",
16,
16,
16,
16,
0,
16,
tir.load("int8", ethosu_write_2, 0),
0,
0,
0,
tir.float32(0.5),
10,
"NHWC",
256,
16,
1,
"int8",
16,
16,
2,
16,
0,
16,
tir.load("int8", ethosu_write_1.data, 0),
0,
0,
0,
tir.float32(0.25),
14,
"NHWC",
128,
8,
1,
1,
1,
1,
1,
1,
1,
tir.load("uint8", placeholder_global, 0),
80,
12,
tir.load("uint8", placeholder_d_global, 0),
32,
0,
0,
0,
0,
"NONE",
0,
0,
"NONE",
dtype="handle"))
tir.evaluate(
tir.call_extern("ethosu_copy",
tir.load("uint8", buffer_1.data, 0),
80,
tir.load("uint8", placeholder_global, 0),
dtype="handle"))
tir.evaluate(
tir.call_extern("ethosu_copy",
tir.load("uint8", buffer_5.data, 0),
32,
tir.load("uint8", placeholder_d_global, 0),
dtype="handle"))
tir.evaluate(
tir.call_extern("ethosu_conv2d",
"int8",
16,
16,
16,
16,
0,
16,
tir.load("int8", ethosu_write_2, 0),
0,
0,
0,
tir.float32(0.5),
10,
"NHWC",
256,
16,
1,
"int8",
16,
16,
2,
16,
0,
16,
tir.load("int8", ethosu_write_1.data, 2),
0,
0,
0,
tir.float32(0.25),
14,
"NHWC",
128,
8,
1,
1,
1,
1,
1,
1,
1,
tir.load("uint8", placeholder_global, 0),
80,
12,
tir.load("uint8", placeholder_d_global, 0),
32,
0,
0,
0,
0,
"NONE",
0,
0,
"NONE",
dtype="handle"))
tir.evaluate(
tir.call_extern("ethosu_copy",
tir.load("uint8", buffer.data, 0),
80,
tir.load("uint8", placeholder_global, 0),
dtype="handle"))
tir.evaluate(
tir.call_extern("ethosu_copy",
tir.load("uint8", buffer_8.data, 0),
32,
tir.load("uint8", placeholder_d_global, 0),
dtype="handle"))
tir.evaluate(
tir.call_extern("ethosu_conv2d",
"int8",
16,
16,
16,
16,
0,
16,
tir.load("int8", ethosu_write_2, 0),
0,
0,
0,
tir.float32(0.5),
10,
"NHWC",
256,
16,
1,
"int8",
16,
16,
2,
16,
0,
16,
tir.load("int8", ethosu_write_1.data, 4),
0,
0,
0,
tir.float32(0.25),
14,
"NHWC",
128,
8,
1,
1,
1,
1,
1,
1,
1,
tir.load("uint8", placeholder_global, 0),
80,
12,
tir.load("uint8", placeholder_d_global, 0),
32,
0,
0,
0,
0,
"NONE",
0,
0,
"NONE",
dtype="handle"))
tir.evaluate(
tir.call_extern("ethosu_copy",
tir.load("uint8", buffer_4.data, 0),
80,
tir.load("uint8", placeholder_global, 0),
dtype="handle"))
tir.evaluate(
tir.call_extern("ethosu_copy",
tir.load("uint8", buffer_9.data, 0),
32,
tir.load("uint8", placeholder_d_global, 0),
dtype="handle"))
tir.evaluate(
tir.call_extern("ethosu_conv2d",
"int8",
16,
16,
16,
16,
0,
16,
tir.load("int8", ethosu_write_2, 0),
0,
0,
0,
tir.float32(0.5),
10,
"NHWC",
256,
16,
1,
"int8",
16,
16,
2,
16,
0,
16,
tir.load("int8", ethosu_write_1.data, 6),
0,
0,
0,
tir.float32(0.25),
14,
"NHWC",
128,
8,
1,
1,
1,
1,
1,
1,
1,
tir.load("uint8", placeholder_global, 0),
80,
12,
tir.load("uint8", placeholder_d_global, 0),
32,
0,
0,
0,
0,
"NONE",
0,
0,
"NONE",
dtype="handle"))
