def ldmatrix_impl(warp_handle: T.handle, shared_handle: T.handle) -> None:
s0 = T.var("int32")
s1 = T.var("int32")
shared = T.match_buffer(
shared_handle,
shmem_shape,
dtype,
align=128,
offset_factor=16,
scope=shared_scope,
strides=[s0, s1],
)
warp = T.match_buffer(warp_handle, (WARP_SIZE, local_size),
dtype,
align=128,
offset_factor=16,
scope="warp")
with T.block("root"):
T.reads(shared[0:row_dim, 0:col_dim])
T.writes(warp[0:WARP_SIZE, 0:local_size])
tx = T.env_thread("threadIdx.x")
T.launch_thread(tx, WARP_SIZE)
T.evaluate(
T.ptx_ldmatrix(
ldmatrix_col_major,
4, # Always load 4 matrices
".b16",
warp.data,
warp.elem_offset + lift(local_size) * tx,
shared.access_ptr("r"),
shared_offset(tx, s0),
dtype=dtype,
))
def ptx_ldmatrix(A: T.Buffer[(16, 16), "float16"], B: T.Buffer[(16, 16),
"float16"],
num: T.int32, trans: T.uint8) -> None:
T.func_attr({"global_symbol": "default_function", "tir.noalias": True})
bx = T.env_thread("blockIdx.x")
tx = T.env_thread("threadIdx.x")
T.launch_thread(bx, 1)
T.launch_thread(tx, 32)
with T.block():
A_shared = T.alloc_buffer([16, 16], "float16", scope="shared")
A_local = T.alloc_buffer([8], "float16", scope="local")
for i in range(8):
A_shared[i * 2 + tx // 16, tx % 16] = A[i * 2 + tx // 16, tx % 16]
T.evaluate(
T.ptx_ldmatrix(
trans,
num,
".b16",
A_local.data,
0,
A_shared.data,
16 * (tx % 16) + 8 * (tx // 16),
dtype="float16",
))
for k in range(2):
for j in range(2):
for i in range(2):
B[8 * j + tx // 4,
8 * k + (tx % 4) * 2 + i] = A_local[4 * k + 2 * j + i]
def before(A: T.Buffer[(128, 16), "float32"], n: T.int32):
i = T.env_thread("threadIdx.x")
T.launch_thread(i, 128)
for j in T.serial(16):
if i < 32:
A[i, j] = 0.0
def main(inputs: T.Buffer[(8192,), "float32"], weight: T.Buffer[(2097152,), "float32"], conv2d_transpose_nhwc: T.Buffer[(16384,), "float32"]) -> None:
# function attr dict
T.func_attr({"global_symbol": "main", "tir.noalias": True})
# var definition
threadIdx_x = T.env_thread("threadIdx.x")
blockIdx_x = T.env_thread("blockIdx.x")
T.preflattened_buffer(inputs, [1, 4, 4, 512], dtype="float32", data=inputs.data)
T.preflattened_buffer(weight, [4, 4, 512, 256], dtype="float32", data=weight.data)
T.preflattened_buffer(conv2d_transpose_nhwc, [1, 8, 8, 256], dtype="float32", data=conv2d_transpose_nhwc.data)
# body
T.launch_thread(blockIdx_x, 64)
conv2d_transpose_nhwc_local = T.allocate([8], "float32", "local")
PadInput_shared = T.allocate([768], "float32", "shared")
weight_shared = T.allocate([4096], "float32", "shared")
T.launch_thread(threadIdx_x, 32)
for i2_3_init, i1_4_init, i2_4_init in T.grid(2, 2, 2):
conv2d_transpose_nhwc_local[i1_4_init * 4 + i2_3_init * 2 + i2_4_init] = T.float32(0)
for i6_0 in T.serial(16):
for ax0_ax1_ax2_ax3_fused_0 in T.serial(24):
PadInput_shared[ax0_ax1_ax2_ax3_fused_0 * 32 + threadIdx_x] = T.if_then_else(4 <= ax0_ax1_ax2_ax3_fused_0 and ax0_ax1_ax2_ax3_fused_0 < 20 and 1 <= blockIdx_x // 32 * 2 + ax0_ax1_ax2_ax3_fused_0 % 4 and blockIdx_x // 32 * 2 + ax0_ax1_ax2_ax3_fused_0 % 4 < 5, inputs[blockIdx_x // 32 * 1024 + ax0_ax1_ax2_ax3_fused_0 * 512 + i6_0 * 32 + threadIdx_x - 2560], T.float32(0), dtype="float32")
for ax0_ax1_ax2_ax3_fused_0 in T.serial(32):
weight_shared[T.ramp(ax0_ax1_ax2_ax3_fused_0 * 128 + threadIdx_x * 4, 1, 4)] = weight[T.ramp(ax0_ax1_ax2_ax3_fused_0 // 2 * 131072 + i6_0 * 8192 + ax0_ax1_ax2_ax3_fused_0 % 2 * 4096 + threadIdx_x // 2 * 256 + blockIdx_x % 32 * 8 + threadIdx_x % 2 * 4, 1, 4)]
for i6_1, i2_3, i4_2, i5_2, i6_2, i1_4, i2_4 in T.grid(4, 2, 4, 4, 8, 2, 2):
conv2d_transpose_nhwc_local[i1_4 * 4 + i2_3 * 2 + i2_4] = conv2d_transpose_nhwc_local[i1_4 * 4 + i2_3 * 2 + i2_4] + T.if_then_else((i1_4 + i4_2) % 2 == 0 and (i2_4 + i5_2) % 2 == 0, PadInput_shared[threadIdx_x // 8 * 128 + (i1_4 + i4_2) // 2 * 128 + (i2_4 + i5_2) // 2 * 32 + i2_3 * 32 + i6_1 * 8 + i6_2], T.float32(0), dtype="float32") * weight_shared[i6_1 * 64 + i6_2 * 8 + threadIdx_x % 8 + 3840 - i5_2 * 256 - i4_2 * 1024]
for ax1, ax2 in T.grid(2, 4):
conv2d_transpose_nhwc[threadIdx_x // 8 * 4096 + ax1 * 2048 + blockIdx_x // 32 * 1024 + ax2 * 256 + blockIdx_x % 32 * 8 + threadIdx_x % 8] = conv2d_transpose_nhwc_local[ax1 * 4 + ax2]
def ptx_cp_async(A: T.Buffer[(32, 128), "float16"],
B: T.Buffer[(32, 128), "float16"]) -> None:
T.func_attr({"global_symbol": "default_function", "tir.noalias": True})
bx = T.env_thread("blockIdx.x")
tx = T.env_thread("threadIdx.x")
T.launch_thread(bx, 1)
T.launch_thread(tx, 32)
with T.block():
A_shared = T.alloc_buffer([32, 128], "float16", scope="shared")
T.reads(A[0:32, 0:128])
T.writes(B[0:32, 0:128])
for i in range(16):
T.evaluate(
T.ptx_cp_async(A_shared.data,
tx * 128 + 8 * i,
A.data,
tx * 128 + 8 * i,
16,
dtype="float16"))
# TODO(masahi): Remove dtype requirement from TVMScript parser
T.evaluate(T.ptx_commit_group(dtype="float16"))
T.evaluate(T.ptx_wait_group(0, dtype="float16"))
for i in range(128):
B[tx, i] = A_shared[tx, i]
def ptx_global_to_shared_dyn_copy_fp16x8(
A: T.Buffer[(32, 128), "float16"],
B: T.Buffer[(32, 128), "float16"],
C: T.Buffer[(32, 128), "float16"],
) -> None:
T.func_attr({"global_symbol": "main", "tir.noalias": True})
bx = T.env_thread("blockIdx.x")
tx = T.env_thread("threadIdx.x")
T.launch_thread(bx, 1)
T.launch_thread(tx, 32)
with T.block():
A_shared = T.alloc_buffer([32, 128], "float16", scope="shared.dyn")
B_shared = T.alloc_buffer([32, 128], "float16", scope="shared.dyn")
T.reads(A[0:32, 0:128], B[0:32, 0:128])
T.writes(C[0:32, 0:128])
T.attr("default", "async_scope", 1)
for i in T.serial(16):
for j in T.vectorized(8):
A_shared[tx, i * 8 + j] = A[tx, i * 8 + j]
B_shared[tx, i * 8 + j] = B[tx, i * 8 + j]
T.evaluate(T.ptx_commit_group(dtype=""))
T.evaluate(T.ptx_wait_group(0, dtype=""))
for i in range(128):
C[tx, i] = A_shared[tx, i] + B_shared[tx, i]
def unified_element_wise_thread_x(a: T.handle, b: T.handle,
c: T.handle) -> None:
thread_x = T.env_thread("threadIdx.x")
block_x = T.env_thread("blockIdx.x")
A = T.match_buffer(a, [128, 128])
B = T.match_buffer(b, [128, 128])
C = T.match_buffer(c, [128, 128])
T.launch_thread(block_x, 128)
with T.launch_thread(thread_x, 4):
for j0_1 in T.serial(0, 32):
T.store(
B.data,
block_x * 128 + thread_x * 32 + j0_1,
T.load("float32", A.data, block_x * 128 + thread_x * 32 + j0_1)
* 2.0,
True,
)
T.launch_thread(thread_x, 4)
for j1_1 in T.serial(0, 32):
T.store(
C.data,
block_x * 128 + thread_x * 32 + j1_1,
T.load("float32", A.data, block_x * 128 + thread_x * 32 + j1_1) +
1.0,
True,
)
def main(a: T.handle, b: T.handle) -> None:
# function attr dict
T.func_attr({"global_symbol": "main", "T.noalias": True})
# var definition
threadIdx_x = T.env_thread("threadIdx.x")
threadIdx_y = T.env_thread("threadIdx.y")
blockIdx_x = T.env_thread("blockIdx.x")
blockIdx_y = T.env_thread("blockIdx.y")
blockIdx_z = T.env_thread("blockIdx.z")
A = T.match_buffer(a, [14, 14, 256, 256], dtype="float32")
B = T.match_buffer(b, [14, 14, 512, 256], dtype="float32")
# body
T.launch_thread(blockIdx_z, 196)
B_local = T.allocate([6400000], "float32", "local")
Apad_shared = T.allocate([512], "float32", "shared")
Apad_shared_local = T.allocate([8], "float32", "local")
T.launch_thread(blockIdx_y, 8)
T.launch_thread(blockIdx_x, 4)
T.launch_thread(threadIdx_y, 8)
T.launch_thread(threadIdx_x, 8)
for ff_c_init, nn_c_init in T.grid(8, 8):
T.store(B_local, ff_c_init * 8 + nn_c_init, T.float32(0), True)
for rc_outer, ry, rx in T.grid(32, 3, 3):
for ax3_inner_outer in T.serial(0, 2):
T.store(Apad_shared, T.ramp(threadIdx_y * 64 + threadIdx_x * 8 + ax3_inner_outer * 4, 1, 4), T.if_then_else(1 <= blockIdx_z // 14 + ry and blockIdx_z // 14 + ry < 15 and 1 <= rx + blockIdx_z % 14 and rx + blockIdx_z % 14 < 15, T.load("float32x4", A.data, T.ramp(ry * 917504 + blockIdx_z * 65536 + rx * 65536 + rc_outer * 2048 + threadIdx_y * 256 + blockIdx_x * 64 + threadIdx_x * 8 + ax3_inner_outer * 4 - 983040, 1, 4), T.broadcast(True, 4)), T.broadcast(T.float32(0), 4), dtype="float32x4"), T.broadcast(True, 4))
for rc_inner in T.serial(0, 8):
for ax3 in T.serial(0, 8):
T.store(Apad_shared_local, ax3, T.load("float32", Apad_shared, rc_inner * 64 + threadIdx_x * 8 + ax3), True)
for ff_c, nn_c in T.grid(8, 8):
T.store(B_local, ff_c * 8 + nn_c, T.load("float32", B_local, ff_c * 8 + nn_c) + T.load("float32", Apad_shared_local, nn_c), True)
for ff_inner_inner_inner, nn_inner_inner_inner in T.grid(8, 8):
T.store(B.data, blockIdx_z * 131072 + blockIdx_y * 16384 + threadIdx_y * 2048 + ff_inner_inner_inner * 256 + blockIdx_x * 64 + threadIdx_x * 8 + nn_inner_inner_inner, T.load("float32", B_local, ff_inner_inner_inner * 8 + nn_inner_inner_inner), True)# fmt: on
def mma_store_impl(a: T.handle, c: T.handle) -> None:
s0 = T.var("int32")
s1 = T.var("int32")
C_warp = T.match_buffer(a, [WARP_SIZE, local_size],
dtype=dtype,
scope="warp",
offset_factor=1)
C = T.match_buffer(c, [M_DIM, N_DIM],
dtype=dtype,
scope="global",
offset_factor=1,
strides=[s0, s1])
with T.block("root"):
T.reads(C_warp[0:WARP_SIZE, 0:local_size])
T.writes(C[0:M_DIM, 0:N_DIM])
tx = T.env_thread("threadIdx.x")
T.launch_thread(tx, WARP_SIZE)
T.evaluate(
T.mma_store(
M_DIM,
N_DIM,
C.access_ptr("w"),
C_warp.data,
C_warp.elem_offset,
s0,
dtype=dtype,
))
def before(A: T.Buffer[(128, 16), "float32"], n: T.int32):
thread_x = T.env_thread("threadIdx.x")
T.launch_thread(thread_x, 128)
for i in T.thread_binding(0, 128, thread="threadIdx.x"):
if i < 32:
for j in T.serial(16):
A[i, j] = 0.0
def expected(A: T.Buffer[(128, 16), "float32"], n: T.int32):
thread_x = T.env_thread("threadIdx.x")
T.launch_thread(thread_x, 128)
if n == 0:
for i in T.thread_binding(0, 128, thread="threadIdx.x"):
for j in T.serial(16):
A[i, j] = 0.0
def mma_sync_impl(a: T.handle, b: T.handle, c: T.handle) -> None:
A = T.match_buffer(
a, (WARP_SIZE, local_size), in_dtype, align=128, offset_factor=16, scope="warp"
)
B = T.match_buffer(
b, (WARP_SIZE, local_size), in_dtype, align=128, offset_factor=16, scope="warp"
)
C = T.match_buffer(
c, (WARP_SIZE, local_size_out), out_dtype, align=128, offset_factor=16, scope="warp"
)
with T.block("root"):
T.reads(
C[0:WARP_SIZE, 0:local_size_out],
A[0:WARP_SIZE, 0:local_size],
B[0:WARP_SIZE, 0:local_size],
)
T.writes(C[0:WARP_SIZE, 0:local_size_out])
tx = T.env_thread("threadIdx.x")
T.launch_thread(tx, WARP_SIZE)
T.evaluate(
T.ptx_mma(
mma_prefix,
"row",
"col",
in_dtype_abbrv,
in_dtype_abbrv,
out_dtype_abbrv,
A.data,
A.elem_offset + tx * lift(local_size),
B.data,
B.elem_offset + tx * lift(local_size),
C.data,
C.elem_offset + tx * lift(local_size_out),
False,
dtype=out_dtype,
)
)
T.evaluate(
T.ptx_mma(
mma_prefix,
"row",
"col",
in_dtype_abbrv,
in_dtype_abbrv,
out_dtype_abbrv,
A.data,
A.elem_offset + tx * lift(local_size),
B.data,
B.elem_offset + tx * lift(local_size) + lift(local_size) // 2,
C.data,
C.elem_offset + tx * lift(local_size_out) + lift(local_size_out) // 2,
False,
dtype=out_dtype,
)
)
def main(a: T.handle, b: T.handle) -> None:
# function attr dict
T.func_attr({"global_symbol": "main", "T.noalias": True})
# var definition
threadIdx_x = T.env_thread("threadIdx.x")
threadIdx_y = T.env_thread("threadIdx.y")
blockIdx_x = T.env_thread("blockIdx.x")
blockIdx_y = T.env_thread("blockIdx.y")
blockIdx_z = T.env_thread("blockIdx.z")
A = T.match_buffer(a, [14 * 14 * 256 * 256], dtype="float32")
B = T.match_buffer(b, [14 * 14 * 512 * 256], dtype="float32")
# body
T.launch_thread(blockIdx_z, 196)
B_local = T.allocate([64], "float32", "local")
Apad_shared = T.allocate([512000], "float32", "shared")
Apad_shared_local = T.allocate([8], "float32", "local")
T.launch_thread(blockIdx_y, 8)
T.launch_thread(blockIdx_x, 4)
T.launch_thread(threadIdx_y, 8)
T.launch_thread(threadIdx_x, 8)
for ff_c_init, nn_c_init in T.grid(8, 8):
B_local[ff_c_init * 8 + nn_c_init] = T.float32(0)
for rc_outer, ry, rx in T.grid(32, 3, 3):
for ax3_inner_outer in T.serial(0, 2):
Apad_shared[T.ramp(
threadIdx_y * 64 + threadIdx_x * 8 + ax3_inner_outer * 4,
1, 4)] = T.if_then_else(
1 <= blockIdx_z // 14 + ry
and blockIdx_z // 14 + ry < 15
and 1 <= rx + blockIdx_z % 14
and rx + blockIdx_z % 14 < 15,
A[T.ramp(
ry * 917504 + blockIdx_z * 65536 + rx * 65536 +
rc_outer * 2048 + threadIdx_y * 256 +
blockIdx_x * 64 + threadIdx_x * 8 +
ax3_inner_outer * 4 - 983040, 1, 4)],
T.broadcast(T.float32(0), 4),
dtype="float32x4",
)
# Access of the last element of Apad_shared prevents
# buffer compacting from reducing the amount of shared
# memory used.
Apad_shared[512000 - 1] = 0.0
for rc_inner in T.serial(0, 8):
for ax3 in T.serial(0, 8):
Apad_shared_local[ax3] = Apad_shared[rc_inner * 64 +
threadIdx_x * 8 + ax3]
for ff_c, nn_c in T.grid(8, 8):
B_local[ff_c * 8 +
nn_c] = B_local[ff_c * 8 +
nn_c] + Apad_shared_local[nn_c]
for ff_inner_inner_inner, nn_inner_inner_inner in T.grid(8, 8):
B[blockIdx_z * 131072 + blockIdx_y * 16384 + threadIdx_y * 2048 +
ff_inner_inner_inner * 256 + blockIdx_x * 64 + threadIdx_x * 8 +
nn_inner_inner_inner] = B_local[ff_inner_inner_inner * 8 +
nn_inner_inner_inner] # fmt: on
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 main(A_param: T.handle, C_param: T.handle):
A = T.match_buffer(A_param, (400,), "float32", strides=[1])
C = T.match_buffer(C_param, (4,), "float32", strides=[1])
T.func_attr({"from_legacy_te_schedule": True})
threadIdx_x = T.env_thread("threadIdx.x")
T.launch_thread(threadIdx_x, 1)
for i in T.serial(0, 100):
B = T.allocate([4], "float32", scope="shared", strides=[1])
with T.attr(B.data, "double_buffer_scope", 1):
for j in T.serial(0, 4):
B[j] = A[4 * i + j]
for j in T.serial(0, 4):
C[j] = B[j] + 1.0
def element_wise_two_thread_x_in_same_kernel_not_equal(a: T.handle,
b: T.handle,
c: T.handle) -> None:
i = T.env_thread("blockIdx.x")
j0 = T.env_thread("threadIdx.x")
j1 = T.env_thread("threadIdx.x")
A = T.match_buffer(a, [128, 128])
B = T.match_buffer(b, [128, 128])
C = T.match_buffer(c, [128, 64])
T.launch_thread(i, 128)
with T.launch_thread(j0, 128):
T.store(B.data, i * 64 + j0,
T.load("float32", A.data, i * 128 + j0) * 2.0, True)
T.launch_thread(j1, 64)
T.store(C.data, i * 64 + j1,
T.load("float32", A.data, i * 128 + j1) + 1.0, True)
def unified_element_wise_kernels_with_different_size(a: T.handle, b: T.handle,
c: T.handle,
d: T.handle) -> None:
block_x = T.env_thread("blockIdx.x")
thread_x = T.env_thread("threadIdx.x")
block_x_1 = T.env_thread("blockIdx.x")
thread_x_1 = T.env_thread("threadIdx.x")
A = T.match_buffer(a, [128, 128])
B = T.match_buffer(b, [128, 128])
C = T.match_buffer(c, [256, 256])
D = T.match_buffer(d, [256, 256])
with T.launch_thread(block_x, 128):
T.launch_thread(thread_x, 128)
T.store(
B.data,
block_x * 128 + thread_x,
T.load("float32", A.data, block_x * 128 + thread_x) * 2.0,
True,
)
T.launch_thread(block_x_1, 256)
T.launch_thread(thread_x_1, 256)
T.store(
D.data,
block_x_1 * 256 + thread_x_1,
T.load("float32", C.data, block_x_1 * 256 + thread_x_1) + 1.0,
True,
)
def ptx_global_to_shared_copy_fp32x1(
A: T.Buffer[(32, 128), "float32"], B: T.Buffer[(32, 128),
"float32"]) -> None:
T.func_attr({"global_symbol": "main", "tir.noalias": True})
bx = T.env_thread("blockIdx.x")
tx = T.env_thread("threadIdx.x")
T.launch_thread(bx, 1)
T.launch_thread(tx, 32)
with T.block():
A_shared = T.alloc_buffer([32, 128], "float32", scope="shared")
T.reads(A[0:32, 0:128])
T.writes(B[0:32, 0:128])
T.attr("default", "async_scope", 1)
for i in T.serial(128):
A_shared[tx, i] = A[tx, i]
T.evaluate(T.ptx_commit_group(dtype=""))
T.evaluate(T.ptx_wait_group(0, dtype=""))
for i in range(128):
B[tx, i] = A_shared[tx, i]
def gemm_mma_m8n8k4_row_row_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, 4], dtype="float16")
B = T.match_buffer(b, [4, 16], dtype="float16")
C = T.match_buffer(c, [16, 16], 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([4], "float16", scope="local")
Accum = T.allocate([8], "float32", scope="local")
for i in range(8):
Accum[i] = T.float32(0)
for mma_multi_a_col in T.vectorized(4):
MultiA[mma_multi_a_col] = A[
((tx % 32) % 4) + (4 * ((((tx % 32) // 16 + (tx % 32) % 16 // 4 * 2)) % 4)),
mma_multi_a_col,
]
for mma_multi_b_col in T.vectorized(4):
MultiB[mma_multi_b_col] = B[
(tx % 32) % 4,
mma_multi_b_col + (4 * ((tx % 32) // 8)),
]
T.evaluate(
T.ptx_mma(
"m8n8k4",
"row",
"row",
"fp16",
"fp16",
"fp32",
MultiA,
0,
MultiB,
0,
Accum,
0,
False,
dtype="float32",
)
)
for mma_accum_c_id in range(8):
C[
((tx % 32) % 2)
+ ((mma_accum_c_id // 2 % 2) * 2)
+ 4 * ((tx % 32) // 16)
+ ((tx % 32) % 16 // 4) % 2 * 8,
(tx % 32) % 4 // 2 * 2
+ (tx % 32) % 16 // 8 * 4
+ mma_accum_c_id % 2
+ mma_accum_c_id // 4 * 8,
] = T.load("float32", Accum, mma_accum_c_id)
def element_wise_thread_x(a: T.handle, b: T.handle, c: T.handle) -> None:
j1_0 = T.env_thread("threadIdx.x")
j0_0 = T.env_thread("threadIdx.x")
i = T.env_thread("blockIdx.x")
A = T.match_buffer(a, [128, 128])
B = T.match_buffer(b, [128, 128])
C = T.match_buffer(c, [128, 128])
T.launch_thread(i, 128)
with T.launch_thread(j0_0, 4):
for j0_1 in T.serial(0, 32):
T.store(
B.data,
i * 128 + j0_0 * 32 + j0_1,
T.load("float32", A.data, i * 128 + j0_0 * 32 + j0_1) * 2.0,
True,
)
T.launch_thread(j1_0, 4)
for j1_1 in T.serial(0, 32):
T.store(
C.data,
i * 128 + j1_0 * 32 + j1_1,
T.load("float32", A.data, i * 128 + j1_0 * 32 + j1_1) + 1.0,
True,
)
def mma_sp_m16n8k16_f16f16f32(a: T.handle, b: T.handle, c: T.handle, _metadata: 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, [16, 8], dtype="float16")
C = T.match_buffer(c, [16, 8], dtype="float32")
metadata = T.match_buffer(_metadata, [8], dtype="uint32")
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)
multi_a = T.allocate([4], "float16", scope="local")
multi_b = T.allocate([4], "float16", scope="local")
accum = T.allocate([4], "float32", scope="local")
meta_local = T.allocate([1], "uint32", scope="local")
for i in range(4):
accum[i] = T.float16(0)
for i in range(4):
multi_a[i] = A[tx // 4 + i // 2 * 8, tx % 4 * 2 + i % 2]
for i in range(4):
multi_b[i] = B[tx % 4 * 2 + i % 2 + i // 2 * 8, tx // 4]
meta_local[0] = metadata[tx // 4]
T.evaluate(
T.ptx_mma_sp(
"m16n8k16",
"row",
"col",
"fp16",
"fp16",
"fp32",
multi_a.data,
0,
multi_b.data,
0,
accum.data,
0,
meta_local.data,
0,
0,
False,
dtype="float32",
)
)
for i in range(4):
C[i // 2 * 8 + tx // 4, tx % 4 * 2 + i % 2] = accum[i]
def gemm_mma_m16n8k32_row_col_s8s8s32(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, 32], dtype="int8")
B = T.match_buffer(b, [8, 32], dtype="int8")
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([16], "int8", scope="local")
MultiB = T.allocate([8], "int8", 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(16):
MultiA[mma_multi_a_col] = A[
(tx % 32) // 4 + mma_multi_a_col % 8 // 4 * 8,
(tx % 32) % 4 * 4 + mma_multi_a_col % 4 + mma_multi_a_col // 8 * 16,
]
for mma_multi_b_col in range(8):
MultiB[mma_multi_b_col] = B[
(tx % 32) // 4,
(tx % 32) % 4 * 4 + mma_multi_b_col % 4 + mma_multi_b_col // 4 * 16,
]
T.evaluate(
T.ptx_mma(
"m16n8k32",
"row",
"col",
"int8",
"int8",
"int32",
MultiA,
0,
MultiB,
0,
Accum,
0,
False,
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,
] = T.load("int32", Accum, mma_accum_c_id)
def gemm_mma_m16n8k16_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, 16], dtype="float16")
B = T.match_buffer(b, [8, 16], 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([8], "float16", scope="local")
MultiB = T.allocate([4], "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 range(8):
MultiA[mma_multi_a_col] = A[
(tx % 32) // 4 + mma_multi_a_col % 4 // 2 * 8,
(tx % 32) % 4 * 2 + mma_multi_a_col % 2 + mma_multi_a_col // 4 * 8,
]
for mma_multi_b_col in T.vectorized(4):
MultiB[mma_multi_b_col] = B[
(tx % 32) // 4,
(tx % 32) % 4 * 2 + mma_multi_b_col % 2 + mma_multi_b_col // 2 * 8,
]
T.evaluate(
T.ptx_mma(
"m16n8k16",
"row",
"col",
"fp16",
"fp16",
"fp32",
MultiA.data,
0,
MultiB.data,
0,
Accum.data,
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,
] = Accum[mma_accum_c_id]
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,
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 element_wise_kernels_with_different_size(a: T.handle, b: T.handle,
c: T.handle, d: T.handle) -> None:
i0 = T.env_thread("blockIdx.x")
j0 = T.env_thread("threadIdx.x")
i1 = T.env_thread("blockIdx.x")
j1 = T.env_thread("threadIdx.x")
A = T.match_buffer(a, [128, 128])
B = T.match_buffer(b, [128, 128])
C = T.match_buffer(c, [256, 256])
D = T.match_buffer(d, [256, 256])
with T.launch_thread(i0, 128):
T.launch_thread(j0, 128)
T.store(B.data, i0 * 128 + j0,
T.load("float32", A.data, i0 * 128 + j0) * 2.0, True)
T.launch_thread(i1, 256)
T.launch_thread(j1, 256)
T.store(D.data, i1 * 256 + j1,
T.load("float32", C.data, i1 * 256 + j1) + 1.0, True)
def gpu_func(a: T.handle, c: T.handle) -> None:
A = T.match_buffer(a, (16, 16), "float32")
C = T.match_buffer(c, (16, 16), "float32")
i0 = T.env_thread("blockIdx.x")
i1 = T.env_thread("threadIdx.x")
i2 = T.env_thread("vthread")
T.launch_thread(i0, 4)
T.launch_thread(i1, 2)
T.launch_thread(i2, 2)
B = T.allocate([1, 16], "float32", "local")
for j in range(0, 16):
B[0, j] = A[i0 * 4 + i1 * 2 + i2, j] + 1.0
for j in range(0, 16):
C[i0 * 4 + i1 * 2 + i2, j] = B[0, j] * 2.0
def flattened_gpu_func(a: T.handle, c: T.handle) -> None:
A = T.match_buffer(a, (16, 16), "float32")
C = T.match_buffer(c, (16, 16), "float32")
i0 = T.env_thread("blockIdx.x")
i1 = T.env_thread("threadIdx.x")
i2 = T.env_thread("vthread")
T.launch_thread(i0, 4)
T.launch_thread(i1, 2)
T.launch_thread(i2, 2)
B = T.allocate([16], "float32", "local")
for j in range(0, 16):
B[j] = T.load("float32", A.data, i0 * 64 + i1 * 32 + i2 * 16 + j) + 1.0
for j in range(0, 16):
C.data[i0 * 64 + i1 * 32 + i2 * 16 + j] = T.load("float32", B, j) * 2.0
def unified_element_wise_vthread_x(a: T.handle, b: T.handle) -> None:
vthread_x = T.env_thread("vthread.x")
thread_x = T.env_thread("threadIdx.x")
A = T.match_buffer(a, [128, 128])
B = T.match_buffer(b, [128, 128])
T.launch_thread(vthread_x, 2)
T.launch_thread(thread_x, 64)
T.launch_thread(vthread_x, 2)
for j_1 in T.serial(0, 64):
T.store(
B.data,
vthread_x * 8256 + thread_x * 128 + j_1,
T.load("float32", A.data, vthread_x * 8256 + thread_x * 128 + j_1)
* 2.0,
True,
)
def element_wise_vthread_x(a: T.handle, b: T.handle) -> None:
i_0 = T.env_thread("vthread.x")
i_1 = T.env_thread("threadIdx.x")
j_0 = T.env_thread("vthread.x")
A = T.match_buffer(a, [128, 128])
B = T.match_buffer(b, [128, 128])
T.launch_thread(i_0, 2)
T.launch_thread(i_1, 64)
T.launch_thread(j_0, 2)
for j_1 in T.serial(0, 64):
T.store(
B.data,
i_0 * 8192 + i_1 * 128 + j_0 * 64 + j_1,
T.load("float32", A.data, i_0 * 8192 + i_1 * 128 + j_0 * 64 + j_1)
* 2.0,
True,
)
def element_wise_env_thread_x(a: T.handle, b: T.handle, c: T.handle) -> None:
j1_0 = T.env_thread("threadIdx.x")
j0_0 = T.env_thread("threadIdx.x")
i = T.env_thread("blockIdx.x")
A = T.match_buffer(a, [128, 128])
B = T.match_buffer(b, [128, 128])
C = T.match_buffer(c, [128, 128])
T.launch_thread(i, 128)
T.launch_thread(j0_0, 4)
T.launch_thread(j1_0, 4)
for j0_1 in T.serial(0, 32):
with T.block(""):
B[i, j0_0 * 32 + j0_1] = A[i, j0_0 * 32 + j0_1] * 2.0
for j1_1 in T.serial(0, 32):
with T.block(""):
C[i, j1_0 * 32 + j1_1] = B[i, j1_0 * 32 + j1_1] + 1.0
