def lowered_zero_rank_buffer(a: T.handle, b: T.handle) -> None:
A = T.match_buffer(a, [128], dtype="float32")
B = T.match_buffer(b, [], dtype="float32")
reduce_temp0 = T.alloc_buffer([1],
dtype="float32",
strides=[1],
scope="local")
for k in T.thread_binding(0, 128, thread="threadIdx.x"):
with T.block("B_cross_thread_reduction"):
vk = T.axis.reduce(128, k)
T.reads([A[vk]])
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),
A[vk],
True,
reduce_temp0.data,
k,
dtype="handle"))
with T.block("B_write_back"):
T.reads([reduce_temp0[0]])
T.writes([B[()]])
B[()] = reduce_temp0[0]
def lowered_reducer_max(a: T.handle, b: T.handle) -> None:
A = T.match_buffer(a, [128, 128], dtype="float32")
B = T.match_buffer(b, [128], dtype="float32")
reduce_temp0 = T.alloc_buffer([1],
dtype="float32",
strides=[1],
scope="local")
for i in T.serial(0, 128):
for k in T.thread_binding(0, 128, thread="threadIdx.x"):
with T.block("B_cross_thread_reduction"):
vi, vk = T.axis.remap("SR", [i, k])
T.reads([A[vi, vk]])
T.writes([reduce_temp0[0]])
T.attr(
T.comm_reducer(lambda x, y: T.max(x, y),
[T.min_value("float32")]),
"reduce_scope",
T.reinterpret(T.uint64(0), dtype="handle"),
)
T.evaluate(
T.tvm_thread_allreduce(T.uint32(1),
A[vi, vk],
True,
reduce_temp0.data,
k,
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 lowered_two_bound_loops(a: T.handle, b: T.handle) -> None:
A = T.match_buffer(a, [128, 128], dtype="float32")
B = T.match_buffer(b, [128], dtype="float32")
reduce_temp0 = T.alloc_buffer([1],
dtype="float32",
strides=[1],
scope="local")
for i in T.serial(0, 128):
for ko in T.thread_binding(0, 4, thread="threadIdx.x"):
for ki in T.thread_binding(0, 32, thread="threadIdx.y"):
with T.block("B_cross_thread_reduction"):
vi = T.axis.spatial(128, i)
vk = T.axis.reduce(128, ko * 32 + ki)
T.reads([A[vi, vk]])
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),
A[vi, vk],
True,
reduce_temp0.data,
ko,
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 lowered_multiple_blocks_under_reduction_loop(a: T.handle,
b: T.handle) -> None:
A = T.match_buffer(a, [16, 16, 16], dtype="float32")
B = T.match_buffer(b, [16], dtype="float32")
B_rf_local = T.alloc_buffer([16, 16], dtype="float32", scope="local")
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.thread_binding(0, 16, thread="blockIdx.x"):
for k0o in T.thread_binding(0, 4, 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 k0i0, k1 in T.grid(4, 16):
with T.block("B_rf"):
vk0 = T.axis.spatial(16, k0o * 4 + k0i0)
vi, vk1 = T.axis.remap("SR", [i, k1])
T.reads([B_rf_local[vk0, vi], A[vi, vk0, vk1]])
T.writes([B_rf_local[vk0, vi]])
with T.init():
B_rf_local[vk0, vi] = T.float32(0)
B_rf_local[vk0, vi] = B_rf_local[vk0, vi] + A[vi, vk0, vk1]
for k0i1 in T.serial(0, 4):
with T.block("B_normal_reduction"):
vk0 = T.axis.reduce(16, k0o * 4 + k0i1)
vi = T.axis.spatial(16, i)
T.reads([B_rf_local[vk0, vi], normal_reduce_temp0[0]])
T.writes([normal_reduce_temp0[0]])
normal_reduce_temp0[
0] = normal_reduce_temp0[0] + B_rf_local[vk0, vi]
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,
k0o,
dtype="handle",
))
with T.block("B_write_back"):
vi = T.axis.spatial(16, i)
T.reads([reduce_temp0[0]])
T.writes([B[vi]])
B[vi] = reduce_temp0[0]
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[0],
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 lowered_softmax(var_A: T.handle, var_T_softmax_norm: T.handle) -> None:
A = T.match_buffer(var_A, [256, 256], dtype="float32")
T_softmax_norm = T.match_buffer(var_T_softmax_norm, [256, 256],
dtype="float32")
T_softmax_maxelem_shared = T.alloc_buffer([256],
dtype="float32",
scope="shared")
T_softmax_expsum_shared = T.alloc_buffer([256],
dtype="float32",
scope="shared")
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")
reduce_temp1 = T.alloc_buffer([1],
dtype="float32",
strides=[1],
scope="local")
normal_reduce_temp1 = T.alloc_buffer([1],
dtype="float32",
strides=[1],
scope="local")
for i0 in T.thread_binding(0, 256, thread="blockIdx.x"):
for ax0_1 in T.thread_binding(0, 32, thread="threadIdx.x"):
with T.block("T_softmax_maxelem_normal_reduction_init"):
T.reads([])
T.writes([normal_reduce_temp0[0]])
normal_reduce_temp0[0] = T.min_value("float32")
for ax0_0 in T.serial(0, 8):
with T.block("T_softmax_maxelem_normal_reduction"):
i0_1 = T.axis.spatial(256, i0)
k = T.axis.reduce(256, ax0_0 * 32 + ax0_1)
T.reads([A[i0_1, k], normal_reduce_temp0[0]])
T.writes([normal_reduce_temp0[0]])
normal_reduce_temp0[0] = T.max(normal_reduce_temp0[0],
A[i0_1, k])
with T.block("T_softmax_maxelem_cross_thread_reduction"):
T.reads([normal_reduce_temp0[0]])
T.writes([reduce_temp0[0]])
T.attr(
T.comm_reducer(lambda x, y: T.max(x, y),
[T.min_value("float32")]),
"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,
ax0_1,
dtype="handle",
))
with T.block("T_softmax_maxelem_write_back"):
i0_2 = T.axis.spatial(256, i0)
T.reads([reduce_temp0[0]])
T.writes([T_softmax_maxelem_shared[i0_2]])
T_softmax_maxelem_shared[i0_2] = reduce_temp0[0]
for ax0_1 in T.thread_binding(0, 32, thread="threadIdx.x"):
with T.block("T_softmax_expsum_normal_reduction_init"):
T.reads([])
T.writes([normal_reduce_temp1[0]])
normal_reduce_temp1[0] = T.float32(0)
for ax0_0 in T.serial(0, 8):
with T.block("T_softmax_expsum_normal_reduction"):
i0_3 = T.axis.spatial(256, i0)
k = T.axis.reduce(256, ax0_0 * 32 + ax0_1)
T.reads([
A[i0_3, k],
T_softmax_maxelem_shared[i0_3],
normal_reduce_temp1[0],
])
T.writes([normal_reduce_temp1[0]])
normal_reduce_temp1[0] = normal_reduce_temp1[0] + T.exp(
A[i0_3, k] - T_softmax_maxelem_shared[i0_3],
dtype="float32")
with T.block("T_softmax_expsum_cross_thread_reduction"):
T.reads([normal_reduce_temp1[0]])
T.writes([reduce_temp1[0]])
T.attr(
T.comm_reducer(lambda x_1, y_1: x_1 + y_1, [T.float32(0)]),
"reduce_scope",
T.reinterpret(T.uint64(0), dtype="handle"),
)
T.evaluate(
T.tvm_thread_allreduce(
T.uint32(1),
normal_reduce_temp1[0],
True,
reduce_temp1.data,
ax0_1,
dtype="handle",
))
with T.block("T_softmax_expsum_write_back"):
i0_4 = T.axis.spatial(256, i0)
T.reads([reduce_temp1[0]])
T.writes([T_softmax_expsum_shared[i0_4]])
T_softmax_expsum_shared[i0_4] = reduce_temp1[0]
for i1_0 in T.serial(0, 8):
for i1_1 in T.thread_binding(0, 32, thread="threadIdx.x"):
with T.block("T_softmax_norm"):
i0_5 = T.axis.spatial(256, i0)
i1 = T.axis.spatial(256, i1_0 * 32 + i1_1)
T.reads([
A[i0_5, i1],
T_softmax_maxelem_shared[i0_5],
T_softmax_expsum_shared[i0_5],
])
T.writes([T_softmax_norm[i0_5, i1]])
T.block_attr({"axis": 1})
T_softmax_norm[i0_5, i1] = (T.exp(
A[i0_5, i1] - T_softmax_maxelem_shared[i0_5],
dtype="float32",
) / T_softmax_expsum_shared[i0_5])
def lowered_single_reduction_loop_with_block_predicate(
A: T.Buffer[(256, 256), "float32"],
T_softmax_norm: T.Buffer[(256, 256), "float32"]) -> None:
T_softmax_maxelem_shared = T.alloc_buffer([256],
dtype="float32",
scope="shared")
T_softmax_expsum_shared = T.alloc_buffer([256],
dtype="float32",
scope="shared")
cross_thread_0 = T.alloc_buffer([1],
dtype="float32",
strides=[1],
scope="local")
in_thread_0 = T.alloc_buffer([1],
dtype="float32",
strides=[1],
scope="local")
cross_thread_1 = T.alloc_buffer([1],
dtype="float32",
strides=[1],
scope="local")
in_thread_1 = T.alloc_buffer([1],
dtype="float32",
strides=[1],
scope="local")
for i0 in T.serial(256):
for ax0, ax1_0 in T.grid(1, 1):
for ax1_1 in T.thread_binding(512, thread="threadIdx.x"):
with T.block("T_softmax_maxelem_in_thread_init"):
T.reads()
T.writes(in_thread_0[0])
in_thread_0[0] = T.float32(-3.4028234663852886e38)
with T.block("T_softmax_maxelem_in_thread"):
i0_1 = T.axis.spatial(256, i0)
k = T.axis.reduce(256, ax1_1)
T.where(ax1_0 * 512 + ax1_1 < 256)
T.reads(A[i0_1, k], in_thread_0[0])
T.writes(in_thread_0[0])
in_thread_0[0] = T.max(in_thread_0[0], A[i0_1, k])
with T.block("T_softmax_maxelem_cross_thread"):
T.reads(in_thread_0[0])
T.writes(cross_thread_0[0])
T.attr(
T.comm_reducer(lambda x, y: T.max(x, y),
[T.float32(-3.4028234663852886e38)]),
"reduce_scope",
T.reinterpret(T.uint64(0), dtype="handle"),
)
T.evaluate(
T.tvm_thread_allreduce(
T.uint32(1),
in_thread_0[0],
True,
cross_thread_0.data,
ax1_1,
dtype="handle",
))
with T.block("T_softmax_maxelem_write_back"):
i0_2 = T.axis.spatial(256, i0)
T.reads(cross_thread_0[0])
T.writes(T_softmax_maxelem_shared[i0_2])
T_softmax_maxelem_shared[i0_2] = cross_thread_0[0]
for ax0, ax1_0 in T.grid(1, 1):
for ax1_1 in T.thread_binding(512, thread="threadIdx.x"):
with T.block("T_softmax_expsum_in_thread_init"):
T.reads()
T.writes(in_thread_1[0])
in_thread_1[0] = T.float32(0)
with T.block("T_softmax_expsum_in_thread"):
i0_3 = T.axis.spatial(256, i0)
k = T.axis.reduce(256, ax1_1)
T.where(ax1_0 * 512 + ax1_1 < 256)
T.reads(A[i0_3, k], T_softmax_maxelem_shared[i0_3],
in_thread_1[0])
T.writes(in_thread_1[0])
in_thread_1[0] = in_thread_1[0] + T.exp(
A[i0_3, k] - T_softmax_maxelem_shared[i0_3],
dtype="float32")
with T.block("T_softmax_expsum_cross_thread"):
T.reads(in_thread_1[0])
T.writes(cross_thread_1[0])
T.attr(
T.comm_reducer(lambda x_1, y_1: x_1 + y_1,
[T.float32(0)]),
"reduce_scope",
T.reinterpret(T.uint64(0), dtype="handle"),
)
T.evaluate(
T.tvm_thread_allreduce(
T.uint32(1),
in_thread_1[0],
True,
cross_thread_1.data,
ax1_1,
dtype="handle",
))
with T.block("T_softmax_expsum_write_back"):
i0_4 = T.axis.spatial(256, i0)
T.reads(cross_thread_1[0])
T.writes(T_softmax_expsum_shared[i0_4])
T_softmax_expsum_shared[i0_4] = cross_thread_1[0]
for i1_0 in T.serial(1):
for i1_1 in T.thread_binding(512, thread="threadIdx.x"):
with T.block("T_softmax_norm"):
i0_5 = T.axis.spatial(256, i0)
i1 = T.axis.spatial(256, i1_1)
T.where(i1_0 * 512 + i1_1 < 256)
T.reads(A[i0_5, i1], T_softmax_maxelem_shared[i0_5],
T_softmax_expsum_shared[i0_5])
T.writes(T_softmax_norm[i0_5, i1])
T.block_attr({"axis": 1})
T_softmax_norm[i0_5, i1] = (
T.exp(A[i0_5, i1] - T_softmax_maxelem_shared[i0_5],
dtype="float32") / T_softmax_expsum_shared[i0_5])
