def opaque_access_store(a: T.handle, c: T.handle) -> None:
A = T.match_buffer(a, (128, 128))
B = T.alloc_buffer((128, 128))
C = T.match_buffer(c, (128, 128))
for i, j in T.grid(128, 128):
with T.block("B"):
vi, vj = T.axis.remap("SS", [i, j])
B[vi, vj] = A[vi, vj] * 2.0
for i, j in T.grid(128, 128):
with T.block("C"):
vi, vj = T.axis.remap("SS", [i, j])
T.reads(B[0:128, 0:128])
T.writes(C[0:128, 0:128])
T.evaluate(B.access_ptr("r", extent=128))
T.evaluate(C.access_ptr("w", extent=128))
C[vi, vj] = B[vi, vj] + 1.0
def access_opaque_ptr_then_elemwise_inline(a: T.handle, b: T.handle) -> None:
A = T.match_buffer(a, [1024], dtype="float32")
B = T.match_buffer(b, [1024], dtype="float32")
A_cache = T.alloc_buffer([1024], dtype="float32")
with T.block("opaque"):
# annotated opaque partial access should be kept
T.reads(A[0:512])
T.writes([A_cache[0:512]])
T.evaluate(A.access_ptr("r", extent=512))
T.evaluate(A_cache.access_ptr("w", extent=512))
for i in T.serial(0, 512):
with T.block("B"):
vi = T.axis.spatial(512, i)
T.reads([A_cache[vi]])
T.writes([B[vi]])
B[vi] = A_cache[vi] * 2.0 + 1.0
def gemm_mma_m16n8k256_row_col_b1b1s32(a: T.handle, b: T.handle, c: T.handle):
T.func_attr({"global_symbol": "default_function", "tir.noalias": True})
A = T.match_buffer(a, [16, 256], dtype="int1")
B = T.match_buffer(b, [8, 256], dtype="int1")
C = T.match_buffer(c, [16, 8], dtype="int32")
brow = T.env_thread("blockIdx.y")
bcol = T.env_thread("blockIdx.x")
tx = T.env_thread("threadIdx.x")
T.launch_thread(brow, 1)
T.launch_thread(bcol, 1)
T.launch_thread(tx, 32)
MultiA = T.allocate([128], "int1", scope="local")
MultiB = T.allocate([64], "int1", scope="local")
Accum = T.allocate([4], "int32", scope="local")
for i in range(4):
Accum[i] = T.int32(0)
for mma_multi_a_col in range(128):
MultiA[mma_multi_a_col] = A[(tx % 32) // 4 +
mma_multi_a_col % 64 // 32 * 8,
(tx % 32) % 4 * 32 + mma_multi_a_col % 32 +
mma_multi_a_col // 64 * 128, ]
for mma_multi_b_col in range(16):
MultiB[mma_multi_b_col] = B[(tx % 32) // 4,
(tx % 32) % 4 * 32 + mma_multi_b_col % 32 +
mma_multi_b_col // 32 * 128, ]
T.evaluate(
T.ptx_mma(
"m16n8k256",
"row",
"col",
"int1",
"int1",
"int32",
MultiA.data,
0,
MultiB.data,
0,
Accum.data,
0,
False,
"xor",
dtype="int32",
))
for mma_accum_c_id in range(4):
C[(tx % 32) // 4 + mma_accum_c_id // 2 * 8,
(tx % 32) % 4 * 2 + mma_accum_c_id % 2, ] = Accum[mma_accum_c_id]
def gemm_mma_m16n8k8_row_col_fp16fp16fp32(a: T.handle, b: T.handle, c: T.handle):
T.func_attr({"global_symbol": "default_function", "tir.noalias": True})
A = T.match_buffer(a, [16, 8], dtype="float16")
B = T.match_buffer(b, [8, 8], dtype="float16")
C = T.match_buffer(c, [16, 8], dtype="float32")
brow = T.env_thread("blockIdx.y")
bcol = T.env_thread("blockIdx.x")
tx = T.env_thread("threadIdx.x")
T.launch_thread(brow, 1)
T.launch_thread(bcol, 1)
T.launch_thread(tx, 32)
MultiA = T.allocate([4], "float16", scope="local")
MultiB = T.allocate([2], "float16", scope="local")
Accum = T.allocate([4], "float32", scope="local")
for i in range(4):
Accum[i] = T.float32(0)
for mma_multi_a_col in T.vectorized(4):
MultiA[mma_multi_a_col] = A[
(tx % 32) // 4 + mma_multi_a_col // 2 * 8, (tx % 32) % 4 * 2 + mma_multi_a_col % 2
]
for mma_multi_b_col in T.vectorized(4):
MultiB[mma_multi_b_col] = B[
(tx % 32) // 4 + mma_multi_b_col // 2 * 8, (tx % 32) % 4 * 2 + mma_multi_b_col % 2
]
T.evaluate(
T.ptx_mma(
"m16n8k8",
"row",
"col",
"fp16",
"fp16",
"fp32",
MultiA,
0,
MultiB,
0,
Accum,
0,
False,
dtype="float32",
)
)
for mma_accum_c_id in range(4):
C[
(tx % 32) // 4 + mma_accum_c_id // 2 * 8, (tx % 32) % 4 * 2 + mma_accum_c_id % 2
] = T.load("float32", Accum, mma_accum_c_id)
def opaque_access(a: T.handle, b: T.handle) -> None:
A = T.match_buffer(a, (32, 64, 128))
B = T.match_buffer(b, (64, 64, 64))
for i, j, k in T.grid(2, 64, 8):
with T.block([]):
T.reads([])
T.writes(A[i * 16 : i * 16 + 16, j, k * 16 : k * 16 + 16])
sub_A = T.match_buffer(
A[i * 16 : i * 16 + 16, j, k * 16 : k * 16 + 16],
(16, 1, 16),
strides=[8192, 128, 1],
offset_factor=1,
)
T.evaluate(
T.intrin_test(
sub_A.data,
sub_A.elem_offset,
sub_A.strides[0],
sub_A.strides[1],
sub_A.shape[0],
sub_A.shape[1],
dtype="handle",
)
)
for i, j, k in T.grid(64, 2, 8):
with T.block([]):
Bs_0 = T.var("int32")
Bs_1 = T.var("int32")
T.reads([])
T.writes(B[i, j * 32 : j * 32 + 32, k * 8 : k * 8 + 8])
sub_B = T.match_buffer(
B[i, j * 32 : j * 32 + 32, k * 8 : k * 8 + 8],
(32, 8),
strides=[Bs_0, Bs_1],
offset_factor=1,
)
T.evaluate(
T.intrin_test(
sub_B.data,
sub_B.elem_offset,
sub_B.strides[0],
sub_B.strides[1],
sub_B.shape[0],
sub_B.shape[1],
dtype="handle",
)
)
def lowered_with_block_predicate(a: T.handle, b: T.handle) -> None:
A = T.match_buffer(a, [128, 120], dtype="float32")
B = T.match_buffer(b, [128], dtype="float32")
reduce_temp0 = T.alloc_buffer([1],
dtype="float32",
strides=[1],
scope="local")
normal_reduce_temp0 = T.alloc_buffer([1],
dtype="float32",
strides=[1],
scope="local")
for i in T.serial(0, 128):
for ki in T.thread_binding(0, 32, thread="threadIdx.x"):
with T.block("B_in_thread_init"):
T.reads([])
T.writes([normal_reduce_temp0[0]])
normal_reduce_temp0[0] = T.float32(0)
for ko in T.serial(0, 4):
with T.block("B_normal_reduction"):
vi = T.axis.spatial(128, i)
vk = T.axis.reduce(120, ko * 32 + ki)
T.where(ko * 32 + ki < 120)
T.reads([A[vi, vk], normal_reduce_temp0[0]])
T.writes([normal_reduce_temp0[0]])
normal_reduce_temp0[0] = normal_reduce_temp0[0] + A[vi, vk]
with T.block("B_cross_thread_reduction"):
T.reads([normal_reduce_temp0[0]])
T.writes([reduce_temp0[0]])
T.attr(
T.comm_reducer(lambda x, y: x + y, [T.float32(0)]),
"reduce_scope",
T.reinterpret(T.uint64(0), dtype="handle"),
)
T.evaluate(
T.tvm_thread_allreduce(
T.uint32(1),
normal_reduce_temp0[0],
True,
reduce_temp0.data,
ki,
dtype="handle",
))
with T.block("B_write_back"):
vi = T.axis.spatial(128, i)
T.reads([reduce_temp0[0]])
T.writes([B[vi]])
B[vi] = reduce_temp0[0]
def opaque_access_fused(a: T.handle, b: T.handle) -> None:
A = T.match_buffer(a, [16, 16])
B = T.match_buffer(b, [16, 16])
for i_j_fused in T.serial(0, 256):
with T.block("A"):
vi = T.axis.S(16, T.floordiv(i_j_fused, 16))
vj = T.axis.S(16, T.floormod(i_j_fused, 16))
T.reads([])
T.writes([A[0:16, 0:16]])
A[vi, vj] = 1
for i_j_fused in T.serial(0, 256):
with T.block("B"):
vi = T.axis.S(16, T.floordiv(i_j_fused, 16))
vj = T.axis.S(16, T.floormod(i_j_fused, 16))
T.reads([])
T.writes([B[0:16, 0:16]])
T.evaluate(T.tvm_fill_fragment(B.data, 16, 16, 16, 0, ((vi * 16) + vj), dtype="handle"))
def exp_exp_opaque_access_with_tvm_access_ptr_inlined(
lookup_table: T.Buffer[(1024,), "int8"],
x: T.Buffer[(16,), "float16"],
compute: T.Buffer[(16,), "float16"],
) -> None:
for i0 in T.serial(16):
with T.block("compute_1"):
i0_1 = T.axis.spatial(16, i0)
# Do not put the opaque access to new write region when opaque access
# wrapped with a tvm_access_ptr and the access mask set to "read only"
T.reads(lookup_table[0:1024], x[i0_1])
T.writes(compute[i0_1])
T.evaluate(lookup_table.access_ptr("r"))
compute[i0_1] = T.exp(
T.exp(x[i0_1], dtype="float16"),
dtype="float16",
)
def mma_fill_impl(a: T.handle) -> None:
C_warp = T.match_buffer(a, [WARP_SIZE, local_size],
dtype=dtype,
scope="warp",
offset_factor=1)
with T.block("root"):
T.reads()
T.writes(C_warp[0:WARP_SIZE, 0:local_size])
tx = T.env_thread("threadIdx.x")
T.launch_thread(tx, WARP_SIZE)
T.evaluate(
T.mma_fill(local_size,
C_warp.data,
C_warp.elem_offset,
dtype=dtype))
def transformed_high_dim_opaque_access(a: T.handle) -> None:
A = T.match_buffer(a, (16, 32, 64))
for i, j, k in T.grid(16, 2, 4):
with T.block([]):
T.reads([])
T.writes(A[i, j * 16 : j * 16 + 16, k * 16 : k * 16 + 16])
T.evaluate(
T.intrin_test(
A.data,
i * 2048 + j * 1024 + k * 16,
64,
1,
16,
16,
dtype="handle",
)
)
def transformed_high_dim_opaque_access_with_source_strides(
a: T.handle) -> None:
A = T.match_buffer(a, (16, 32, 64), strides=[2576, 80, 1])
for i, j, k in T.grid(16, 2, 4):
with T.block():
T.reads([])
T.writes(A[i, j * 16:j * 16 + 16, k * 16:k * 16 + 16])
T.evaluate(
T.intrin_test(
A.data,
i * 2576 + j * 1280 + k * 16,
80,
1,
16,
16,
dtype="handle",
))
def opaque_access_split(a: T.handle, b: T.handle) -> None:
A = T.match_buffer(a, (16, 16))
B = T.match_buffer(b, (16, 16))
for i, j0, j1 in T.grid(16, 4, 4):
with T.block("A"):
vi = T.axis.S(16, i)
vj = T.axis.S(16, j0 * 4 + j1)
T.reads([])
T.writes([A[0:16, 0:16]])
A[vi, vj] = 1
for i, j0, j1 in T.grid(16, 4, 4):
with T.block("B"):
vi = T.axis.S(16, i)
vj = T.axis.S(16, j0 * 4 + j1)
T.reads([])
T.writes([B[0:16, 0:16]])
T.evaluate(T.tvm_fill_fragment(B.data, 16, 16, 16, 0, ((vi * 16) + vj), dtype="handle"))
def opaque_access_func() -> None:
A = T.alloc_buffer([1024])
B = T.alloc_buffer([1024])
for i in T.serial(0, 8):
with T.block():
v = T.axis.S(8, i)
T.reads([A[v * 128:v * 128 + 128]])
T.writes([B[v * 128:v * 128 + 128]])
T.evaluate(
T.call_extern("test",
B.data,
v * 128,
128,
A.data,
v * 128,
128,
dtype="float32"))
def opaque_access(a: T.handle, b: T.handle) -> None:
A = T.match_buffer(a, [16, 16], "float32")
B = T.match_buffer(b, [16, 16], "float32")
with T.block([16, 16], "A") as [vi, vj]:
T.reads([])
T.writes([A[0:16, 0:16]])
T.store(A.data, vi * 16 + vj, 1)
with T.block([16, 16], "B") as [vi, vj]:
T.reads([])
T.writes([B[0:16, 0:16]])
T.evaluate(
T.tvm_fill_fragment(B.data,
16,
16,
16,
0,
vi * 16 + vj,
dtype="handle"))
def transformed_rank0_buffer(a: T.handle, b: T.handle) -> None:
A = T.match_buffer(a, (8, 8))
B = T.match_buffer(b, (8, 8))
for i, j in T.grid(8, 8):
with T.block():
T.reads([])
T.writes([A[i, j], B[i, j]])
A[i, j] = 1
T.evaluate(
T.intrin_test(
B.data,
i * 8 + j,
0,
0,
0,
0,
dtype="handle",
))
def main(buffer2: T.Buffer[(160,), "uint8"]) -> None:
# function attr dict
T.func_attr({"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True})
buffer1 = T.buffer_decl([8192], "int8")
buffer10 = T.buffer_decl([2048], "int8")
# body
p5 = T.allocate([160], "uint8", "global")
T.evaluate(T.call_extern("ethosu_copy", buffer2[0], 160, p5[0], dtype="handle"))
T.evaluate(T.call_extern("ethosu_conv2d", "int8", 16, 16, 32, 16, 0, 16, buffer1[0], 0, 0, 0, T.float32(0.5), 10, "NHWC", 512, 32, 1, "int8", 16, 16, 2, 16, 0, 16, buffer10[0], 0, 0, 0, T.float32(0.25), 14, "NHWC", 128, 8, 1, 1, 1, 1, 1, 1, 1, p5[0], 128, 12, p5[128], 32, 0, 0, 0, 0, "NONE", 0, 0, "TFL", "NONE", 0, 0, 0, dtype="handle"))
T.evaluate(T.call_extern("ethosu_copy", buffer2[0], 160, p5[0], dtype="handle"))
T.evaluate(T.call_extern("ethosu_conv2d", "int8", 16, 16, 32, 16, 0, 16, buffer1[0], 0, 0, 0, T.float32(0.5), 10, "NHWC", 512, 32, 1, "int8", 16, 16, 2, 16, 0, 16, buffer10[0], 0, 0, 0, T.float32(0.25), 14, "NHWC", 128, 8, 1, 1, 1, 1, 1, 1, 1, p5[0], 128, 12, p5[128], 32, 0, 0, 0, 0, "NONE", 0, 0, "TFL", "NONE", 0, 0, 0, dtype="handle"))
def tir_extern(a: T.handle, b: T.handle, c: T.handle) -> None:
T.func_attr({"global_symbol": "main", "tir.noalias": True})
A = T.match_buffer(a, (128, 128))
B = T.match_buffer(b, (128, 128))
C = T.match_buffer(c, (128, 128))
# body
with T.block("C"):
T.reads([A[0:128, 0:128], B[0:128, 0:128]])
T.writes([C[0:128, 0:128]])
T.evaluate(
T.tvm_call_packed(
"tvm.contrib.cblas.matmul",
T.tvm_stack_make_array(
A.data,
T.tvm_stack_make_shape(128, 128, dtype="handle"),
0,
2,
0.0,
0,
dtype="handle",
),
T.tvm_stack_make_array(
B.data,
T.tvm_stack_make_shape(128, 128, dtype="handle"),
0,
2,
0.0,
0,
dtype="handle",
),
T.tvm_stack_make_array(
C.data,
T.tvm_stack_make_shape(128, 128, dtype="handle"),
0,
2,
0.0,
0,
dtype="handle",
),
0,
0,
dtype="int32",
)
)
def main(buffer2: T.Buffer[(80,), "uint8"], buffer3: T.Buffer[(64,), "uint8"]) -> None:
T.func_attr({"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True})
buffer0 = T.buffer_decl([390336], "int8")
buffer1 = T.buffer_decl([97156], "int8")
buffer6 = T.buffer_decl([390336], "int8")
# body
p2 = T.allocate([80], "uint8", "global")
p3 = T.allocate([64], "uint8", "global")
T.evaluate(T.call_extern("ethosu_pooling", "int8", 214, 227, 2, 214, 0, 227, buffer1[0], 0, 0, 0, T.float32(1), 0, "NHWC", 454, 2, 1, "int8", 214, 114, 2, 214, 0, 114, buffer0[0], 0, 0, 0, T.float32(1), 0, "NHCWB16", 1824, 16, 1, "MAX", 2, 1, 2, 1, 1, 1, 0, 0, 0, 1, "NONE", 0, 0, "TFL", "NONE", 0, 0, 0, dtype="handle"))
T.evaluate(T.call_extern("ethosu_copy", buffer2[0], 80, p2[0], dtype="handle"))
T.evaluate(T.call_extern("ethosu_copy", buffer3[0], 64, p3[0], dtype="handle"))
T.evaluate(T.call_extern("ethosu_conv2d", "int8", 214, 114, 2, 214, 0, 114, buffer0[0], 0, 0, 0, T.float32(0.00392157), -128, "NHCWB16", 1824, 16, 1, "int8", 214, 114, 5, 214, 0, 114, buffer6[0], 0, 0, 0, T.float32(0.0174839), -128, "NHCWB16", 1824, 16, 1, 3, 1, 1, 1, 1, 2, p2[0], 80, 0, p3[0], 64, 0, 1, 0, 1, "NONE", 0, 0, "TFL", "NONE", 0, 0, 0, dtype="handle"))
def match_buffer_func(a: T.handle, b: T.handle) -> None:
A = T.match_buffer(a, (128, 128), "float32")
B = T.match_buffer(b, (128, 128), "float32")
for i, j in T.grid(8, 8):
with T.block("block"):
vi, vj = T.axis.remap("SS", [i, j])
T.reads(B[vi * 16 + 2:vi * 16 + 12, vj * 16 + 2:vj * 16 + 16])
T.writes(A[vi * 16:vi * 16 + 16, vj * 16:vj * 16 + 16])
B0 = T.match_buffer(
B[vi * 16 + 2:vi * 16 + 6, vj * 16 + 2:vj * 16 + 6], (4, 4))
B1 = T.match_buffer(
B[vi * 16 + 8:vi * 16 + 12, vj * 16 + 8:vj * 16 + 16], (4, 8))
for ii, jj in T.grid(16, 16):
with T.block("AAA"):
vii, vjj = T.axis.remap("SS", [ii, jj])
AA = T.match_buffer(A[vii, vjj], ())
AA[()] = 1.0
T.evaluate(B0.data)
T.evaluate(B1.data)
def dot_product_intrin(a: T.handle, b: T.handle, c: T.handle) -> None:
A = T.match_buffer(a, (4, ), offset_factor=1)
B = T.match_buffer(b, (4, ), offset_factor=1)
C = T.match_buffer(c, (), offset_factor=1)
with T.block("root"):
T.reads(C[()], A[0:4], B[0:4])
T.writes(C[()])
T.evaluate(
T.call_extern(
"vec4add",
C.data,
C.elem_offset,
A.data,
A.elem_offset,
B.data,
B.elem_offset,
dtype="int32",
))
def gemm_mma_m8n8k32_row_col_s4u4s32(a: T.handle, b: T.handle, c: T.handle):
T.func_attr({"global_symbol": "default_function", "tir.noalias": True})
A = T.match_buffer(a, [8, 32], dtype="int4")
B = T.match_buffer(b, [8, 32], dtype="uint4")
C = T.match_buffer(c, [8, 8], dtype="int32")
brow = T.env_thread("blockIdx.y")
bcol = T.env_thread("blockIdx.x")
tx = T.env_thread("threadIdx.x")
T.launch_thread(brow, 1)
T.launch_thread(bcol, 1)
T.launch_thread(tx, 32)
MultiA = T.allocate([8], "int4", scope="local")
MultiB = T.allocate([8], "uint4", scope="local")
Accum = T.allocate([2], "int32", scope="local")
for i in range(2):
Accum[i] = T.int32(0)
for mma_multi_a_col in T.vectorized(8):
MultiA[mma_multi_a_col] = A[(tx % 32) // 4, mma_multi_a_col + (tx % 32) % 4 * 8]
for mma_multi_b_col in T.vectorized(8):
MultiB[mma_multi_b_col] = B[(tx % 32) // 4, mma_multi_b_col + (tx % 32) % 4 * 8]
T.evaluate(
T.ptx_mma(
"m8n8k32",
"row",
"col",
"int4",
"uint4",
"int32",
MultiA,
0,
MultiB,
0,
Accum,
0,
False,
dtype="int32",
)
)
for mma_accum_c_id in range(2):
C[(tx % 32) // 4, (tx % 32) % 4 * 2 + mma_accum_c_id] = T.load(
"int32", Accum, mma_accum_c_id
)
def access_opaque_ptr_then_elemwise(a: T.handle, b: T.handle) -> None:
A = T.match_buffer(a, [1024])
B = T.match_buffer(b, [1024])
A_cache = T.alloc_buffer([1024])
BB = T.alloc_buffer([1024])
with T.block("opaque"):
# annotated opaque partial access
T.reads(A[0:512])
T.writes(A_cache[0:512])
T.evaluate(A.access_ptr("r", extent=512))
T.evaluate(A_cache.access_ptr("w", extent=512))
for i in range(512):
with T.block("BB"):
vi = T.axis.remap("S", [i])
BB[vi] = A_cache[vi] * 2.0
for i in range(512):
with T.block("B"):
vi = T.axis.remap("S", [i])
B[vi] = BB[vi] + 1.0
def compacted_opaque_access_annotated_func(a: T.handle) -> None:
A = T.match_buffer(a, (1024,), "float32")
with T.block():
B = T.alloc_buffer((1024,), dtypes="float32")
C = T.alloc_buffer((520,), dtypes="float32")
for i in range(0, 512):
with T.block():
# no annotation, opaque access will cover full region
T.reads([])
T.writes([])
T.evaluate(T.call_extern("opaque_extern_function", A.data, B.data, dtype="int32"))
B[i] = A[i]
with T.block():
# treat opaque access only access annotated regions, even if
# they are not compatible with actual buffer accesses.
T.reads([B[i]])
T.writes([C[i : i + 9]])
T.evaluate(T.call_extern("opaque_extern_function", B.data, C.data, dtype="int32"))
C[i] = B[i]
def func() -> None:
A = T.alloc_buffer((128, 128), "float32")
B = T.alloc_buffer((128, 128), "float32")
C = T.alloc_buffer((128, 128), "float32")
D = T.alloc_buffer((128, 128), "float32")
with T.block():
# Need add read/write region manually to avoid triggering block access region detector
T.reads([B[0, 0], C[0:16, 0:16], A[4:12, 4:12]])
T.writes([A[0:12, 0:12]])
for i, j in T.grid(8, 8):
A[i, j] = B[0, 0] + C[0, 0]
for i, j in T.grid(2, 2):
with T.block():
vi, vj = T.axis.remap("SS", [i, j])
T.reads([A[vi * 4 + 4 : vi * 4 + 8, vj * 4 + 4 : vj * 4 + 8], C[12:16, 12:16]])
T.writes([A[vi * 4 + 4 : vi * 4 + 8, vj * 4 + 4 : vj * 4 + 8]])
for i, j in T.grid(4, 4):
A[vi * 4 + 4 + i, vj * 4 + 4 + j] += C[i + 12, j + 12]
T.evaluate(D.data)
def transformed_trivial_pipeline(A: T.Buffer[(16, 1), "float32"],
C: T.Buffer[(16, 1), "float32"]) -> None:
for tx in T.thread_binding(16, thread="threadIdx.x"):
with T.block():
T.reads(A[tx, 0])
T.writes(C[tx, 0])
B = T.alloc_buffer([2, 16, 1], dtype="float32", scope="shared")
with T.block():
T.reads(A[tx, 0])
T.writes(B[0, tx, 0])
B[0, tx, 0] = A[tx, 0] * T.float32(2)
with T.block():
T.reads()
T.writes()
T.evaluate(0)
with T.block():
T.reads(B[0, tx, 0])
T.writes(C[tx, 0])
C[tx, 0] = B[0, tx, 0] + T.float32(1)
def wmma_fill_impl(c: T.handle) -> None:
C = T.match_buffer(c, (m_dim, n_dim),
dtype,
align=128,
offset_factor=16,
scope="wmma.accumulator")
with T.block("root"):
T.reads()
T.writes(C[0:m_dim, 0:n_dim])
T.evaluate(
T.tvm_fill_fragment(
C.data,
m_dim,
n_dim,
k_dim,
get_wmma_fragment_index(C, m_dim, n_dim),
T.float32(0),
dtype="handle",
))
def tir_extern(a: T.handle, b: T.handle, c: T.handle) -> None:
A = T.match_buffer(a, (128, 128))
B = T.match_buffer(b, (128, 128))
C = T.match_buffer(c, (128, 128))
# body
with T.block([], "C"):
T.reads([A[0:128, 0:128], B[0:128, 0:128]])
T.writes([C[0:128, 0:128]])
T.evaluate(
T.tvm_call_packed(
"tvm.contrib.cblas.matmul",
T.tvm_stack_make_array(
A.data,
T.tvm_stack_make_shape(128, 128, dtype="handle"),
0,
2,
0.0,
0,
dtype="handle",
),
T.tvm_stack_make_array(
B.data,
T.tvm_stack_make_shape(128, 128, dtype="handle"),
0,
2,
0.0,
0,
dtype="handle",
),
T.tvm_stack_make_array(
C.data,
T.tvm_stack_make_shape(128, 128, dtype="handle"),
0,
2,
0.0,
0,
dtype="handle",
),
0,
0,
dtype="int32",
))
def transformed_recursive_match(a: T.handle, b: T.handle) -> None:
A = T.match_buffer(a, (64, 64, 64))
B = T.match_buffer(b, (64, 64, 64))
for i, j, k in T.grid(64, 4, 4):
with T.block([]):
T.reads([])
T.writes(
[
A[i, j * 16 : j * 16 + 16, k * 16 : k * 16 + 16],
B[i, j * 16 : j * 16 + 16, k * 16 : k * 16 + 16],
]
)
for jj, kk in T.grid(4, 4):
with T.block([]):
T.reads([])
T.writes(
[
A[
i,
j * 16 + jj * 4 : j * 16 + jj * 4 + 4,
k * 16 + kk * 4 : k * 16 + kk * 4 + 4,
],
B[
i,
j * 16 + jj * 4 : j * 16 + jj * 4 + 4,
k * 16 + kk * 4 : k * 16 + kk * 4 + 4,
],
]
)
T.evaluate(
T.intrin_test(
A.data,
i * 4096 + j * 1024 + jj * 256 + k * 16 + kk * 4,
64,
1,
4,
4,
dtype="handle",
)
)
for jjj, kkk in T.grid(4, 4):
B[i, j * 16 + jj * 4 + jjj, k * 16 + kk * 4 + kkk] = 1
def mma_intrin(a: T.handle, b: T.handle, c: T.handle) -> None:
A = T.match_buffer(a, (16, 16), align=128, offset_factor=1)
B = T.match_buffer(b, (16, 16), align=128, offset_factor=1)
C = T.match_buffer(c, (16, 16), align=128, offset_factor=1)
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])
T.evaluate(
T.tvm_mma_sync(
C.data,
C.elem_offset // 256,
A.data,
A.elem_offset // 256,
B.data,
B.elem_offset // 256,
C.data,
C.elem_offset // 256,
dtype="handle",
))
def rank0_buffer(a: T.handle, b: T.handle) -> None:
A = T.match_buffer(a, (8, 8))
B = T.match_buffer(b, (8, 8))
for i, j in T.grid(8, 8):
with T.block():
T.reads([])
T.writes([A[i, j], B[i, j]])
sub_A = T.match_buffer(A[i, j], (), offset_factor=1)
sub_B = T.match_buffer(B[i, j], (), offset_factor=1)
sub_A[()] = 1
T.evaluate(
T.intrin_test(
sub_B.data,
sub_B.elem_offset,
0,
0,
0,
0,
dtype="handle",
))
