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test_tvmscript_roundtrip.py
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test_tvmscript_roundtrip.py
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import sys
import pytest
import tvm
import tvm.testing
from tvm import tir
from tvm.script import tir as T
import numpy as np
def opt_gemm_normalize():
@tvm.script.ir_module
class Module:
@T.prim_func
def mmult(A: T.handle, B: T.handle, C: T.handle) -> None:
# function attr dict
T.func_attr({"global_symbol": "mmult", "tir.noalias": True})
# buffer definition
C_global = T.buffer_decl([1024, 1024], elem_offset=0, align=128, offset_factor=1)
packedB = T.buffer_decl([32, 1024, 32], elem_offset=0, align=128, offset_factor=1)
A_1 = T.match_buffer(A, [1024, 1024], elem_offset=0, align=128, offset_factor=1)
B_1 = T.match_buffer(B, [1024, 1024], elem_offset=0, align=128, offset_factor=1)
C_1 = T.match_buffer(C, [1024, 1024], elem_offset=0, align=128, offset_factor=1)
# body
T.realize(packedB[0:32, 0:1024, 0:32], "")
for x in T.parallel(0, 32):
for y in T.serial(0, 1024):
for z in T.vectorized(0, 32):
packedB[x, y, z] = B_1[y, ((x * 32) + z)]
T.realize(C_1[0:1024, 0:1024], "")
for x_outer in T.parallel(0, 32):
for y_outer in T.serial(0, 32):
T.realize(
C_global[
(x_outer * 32) : ((x_outer * 32) + 32),
(y_outer * 32) : ((y_outer * 32) + 32),
],
"global",
)
for x_c_init in T.serial(0, 32):
for y_c_init in T.vectorized(0, 32):
C_global[
(x_c_init + (x_outer * 32)), (y_c_init + (y_outer * 32))
] = T.float32(0)
for k_outer in T.serial(0, 256):
for x_c in T.serial(0, 32):
for k_inner in T.unroll(0, 4):
for y_c in T.vectorized(0, 32):
C_global[
(x_c + (x_outer * 32)), (y_c + (y_outer * 32))
] = C_global[(x_c + (x_outer * 32)), (y_c + (y_outer * 32))] + (
A_1[(x_c + (x_outer * 32)), (k_inner + (k_outer * 4))]
* packedB[
T.floordiv((y_c + (y_outer * 32)), 32),
(k_inner + (k_outer * 4)),
T.floormod((y_c + (y_outer * 32)), 32),
]
)
for x_inner in T.serial(0, 32):
for y_inner in T.serial(0, 32):
C_1[(x_inner + (x_outer * 32)), (y_inner + (y_outer * 32))] = C_global[
(x_inner + (x_outer * 32)), (y_inner + (y_outer * 32))
]
return Module
def opt_gemm_lower():
@tvm.script.ir_module
class Module:
@T.prim_func
def mmult(A: T.handle, B: T.handle, C: T.handle) -> None:
# function attr dict
T.func_attr({"global_symbol": "mmult", "tir.noalias": True})
A_1 = T.match_buffer(A, [1024 * 1024], elem_offset=0, align=128, offset_factor=1)
B_1 = T.match_buffer(B, [1024, 1024], elem_offset=0, align=128, offset_factor=1)
C_1 = T.match_buffer(C, [1024 * 1024], elem_offset=0, align=128, offset_factor=1)
# body
packedB = T.allocate([32768], "float32", "global")
for x in T.parallel(0, 32):
for y in T.serial(0, 1024):
packedB[T.ramp(((x * 32768) + (y * 32)), 1, 32)] = B_1[y, T.ramp(x * 32, 1, 32)]
for x_outer in T.parallel(0, 32):
C_global = T.allocate([1024], "float32", "global")
for y_outer in T.serial(0, 32):
for x_c_init in T.serial(0, 32):
C_global[T.ramp((x_c_init * 32), 1, 32)] = T.broadcast(T.float32(0), 32)
for k_outer in T.serial(0, 256):
for x_c in T.serial(0, 32):
C_global[T.ramp((x_c * 32), 1, 32)] = C_global[
T.ramp((x_c * 32), 1, 32)
] + (
T.broadcast(
A_1[
(((x_outer * 32768) + (x_c * 1024)) + (k_outer * 4)),
],
32,
)
* packedB[T.ramp(((y_outer * 32768) + (k_outer * 128)), 1, 32)]
)
C_global[T.ramp((x_c * 32), 1, 32)] = C_global[
T.ramp((x_c * 32), 1, 32)
] + (
T.broadcast(
A_1[
((((x_outer * 32768) + (x_c * 1024)) + (k_outer * 4)) + 1),
],
32,
)
* packedB[
T.ramp((((y_outer * 32768) + (k_outer * 128)) + 32), 1, 32)
]
)
C_global[T.ramp((x_c * 32), 1, 32)] = C_global[
T.ramp((x_c * 32), 1, 32)
] + (
T.broadcast(
A_1[
((((x_outer * 32768) + (x_c * 1024)) + (k_outer * 4)) + 2),
],
32,
)
* packedB[
T.ramp((((y_outer * 32768) + (k_outer * 128)) + 64), 1, 32)
]
)
C_global[T.ramp((x_c * 32), 1, 32)] = C_global[
T.ramp((x_c * 32), 1, 32)
] + (
T.broadcast(
A_1[
((((x_outer * 32768) + (x_c * 1024)) + (k_outer * 4)) + 3),
],
32,
)
* packedB[
T.ramp((((y_outer * 32768) + (k_outer * 128)) + 96), 1, 32)
]
)
for x_inner in T.serial(0, 32):
for y_inner in T.serial(0, 32):
C_1[
(
(((x_outer * 32768) + (x_inner * 1024)) + (y_outer * 32))
+ y_inner
)
] = C_global[((x_inner * 32) + y_inner)]
return Module
def opt_gemm_mod_host():
@tvm.script.ir_module
class Module:
@T.prim_func
def mmult(
args: T.handle,
arg_type_ids: T.handle,
num_args: T.int32,
out_ret_value: T.handle,
out_ret_tcode: T.handle,
) -> T.int32:
# function attr dict
T.func_attr(
{
"tir.noalias": True,
"global_symbol": "mmult",
"tir.is_entry_func": True,
"calling_conv": 1,
}
)
# buffer definition
buf_type_ids = T.match_buffer(arg_type_ids, [3], dtype="int32")
packedB = T.buffer_decl([32768], dtype="float32")
C_global = T.buffer_decl([1024], dtype="float32")
# var definition
# C_global = T.buffer_var("float32", "global")
# packedB = T.buffer_var("float32", "global")
# body
assert num_args == 3, "mmult: num_args should be 3"
arg0: T.handle = T.tvm_struct_get(args, 0, 12, dtype="handle")
arg0_code: T.int32 = buf_type_ids[0]
arg1: T.handle = T.tvm_struct_get(args, 1, 12, dtype="handle")
arg1_code: T.int32 = buf_type_ids[1]
arg2: T.handle = T.tvm_struct_get(args, 2, 12, dtype="handle")
arg2_code: T.int32 = buf_type_ids[2]
A_data: T.Ptr[T.int32] = T.tvm_struct_get(arg0, 0, 1, dtype="handle")
T.attr(A_data, "storage_alignment", 128)
A: T.Buffer = T.buffer_decl([1024 * 1024], dtype="int32", data=A_data)
buf0_shape_data: T.Ptr[T.int32] = T.tvm_struct_get(arg0, 0, 2, dtype="handle")
buf0_shape: T.Buffer = T.buffer_decl([2], dtype="int32", data=buf0_shape_data)
buf0_strides_data: T.Ptr[T.int32] = T.tvm_struct_get(arg0, 0, 3, dtype="handle")
buf0_strides: T.Buffer = T.buffer_decl([2], dtype="int32", data=buf0_strides_data)
dev_id: T.int32 = T.tvm_struct_get(arg0, 0, 9, dtype="int32")
B_data: T.Ptr[T.int32] = T.tvm_struct_get(arg1, 0, 1, dtype="handle")
T.attr(B_data, "storage_alignment", 128)
B: T.Buffer = T.buffer_decl([1024 * 1024], dtype="int32", data=B_data)
buf1_shape_data: T.Ptr[T.int32] = T.tvm_struct_get(arg1, 0, 2, dtype="handle")
buf1_shape: T.Buffer = T.buffer_decl([2], dtype="int32", data=buf1_shape_data)
buf1_strides_data: T.Ptr[T.int32] = T.tvm_struct_get(arg1, 0, 3, dtype="handle")
buf1_strides: T.Buffer = T.buffer_decl([2], dtype="int32", data=buf1_strides_data)
C_data: T.Ptr[T.int32] = T.tvm_struct_get(arg2, 0, 1, dtype="handle")
T.attr(C_data, "storage_alignment", 128)
C: T.Buffer = T.buffer_decl([1024 * 1024], dtype="int32", data=C_data)
buf2_shape_data: T.Ptr[T.int32] = T.tvm_struct_get(arg2, 0, 2, dtype="handle")
buf2_shape: T.Buffer = T.buffer_decl([2], dtype="int32", data=buf2_shape_data)
buf2_strides_data: T.Ptr[T.int32] = T.tvm_struct_get(arg2, 0, 3, dtype="handle")
buf2_strides: T.Buffer = T.buffer_decl([2], dtype="int32", data=buf2_strides_data)
assert (((arg0_code == 3) or (arg0_code == 13)) or (arg0_code == 7)) or (
arg0_code == 4
), "mmult: Expect arg[0] to be pointer"
assert (((arg1_code == 3) or (arg1_code == 13)) or (arg1_code == 7)) or (
arg1_code == 4
), "mmult: Expect arg[1] to be pointer"
assert (((arg2_code == 3) or (arg2_code == 13)) or (arg2_code == 7)) or (
arg2_code == 4
), "mmult: Expect arg[2] to be pointer"
assert 2 == T.tvm_struct_get(
arg0, 0, 4, dtype="int32"
), "arg0.ndim is expected to equal 2"
assert 2 == T.tvm_struct_get(
arg0, 0, 4, dtype="int32"
), "arg0.ndim is expected to equal 2"
assert (
(T.tvm_struct_get(arg0, 0, 5, dtype="uint8") == T.uint8(2))
and (T.tvm_struct_get(arg0, 0, 6, dtype="uint8") == T.uint8(32))
) and (
T.tvm_struct_get(arg0, 0, 7, dtype="uint16") == T.uint16(1)
), "arg0.dtype is expected to be float32"
assert 1024 == T.cast(
buf0_shape[0], "int32"
), "Argument arg0.shape[0] has an unsatisfied constraint"
assert 1024 == T.cast(
buf0_shape[1], "int32"
), "Argument arg0.shape[1] has an unsatisfied constraint"
if not (T.isnullptr(buf0_strides.data, dtype="bool")):
assert (1 == T.cast(buf0_strides[1], "int32")) and (
1024 == T.cast(buf0_strides[0], "int32")
), "arg0.strides: expected to be compact array"
T.evaluate(0)
assert T.uint64(0) == T.tvm_struct_get(
arg0, 0, 8, dtype="uint64"
), "Argument arg0.byte_offset has an unsatisfied constraint"
assert 1 == T.tvm_struct_get(
arg0, 0, 10, dtype="int32"
), "Argument arg0.device_type has an unsatisfied constraint"
assert 2 == T.tvm_struct_get(
arg1, 0, 4, dtype="int32"
), "arg1.ndim is expected to equal 2"
assert 2 == T.tvm_struct_get(
arg1, 0, 4, dtype="int32"
), "arg1.ndim is expected to equal 2"
assert (
(T.tvm_struct_get(arg1, 0, 5, dtype="uint8") == T.uint8(2))
and (T.tvm_struct_get(arg1, 0, 6, dtype="uint8") == T.uint8(32))
) and (
T.tvm_struct_get(arg1, 0, 7, dtype="uint16") == T.uint16(1)
), "arg1.dtype is expected to be float32"
assert 1024 == T.cast(
buf1_shape[0], "int32"
), "Argument arg1.shape[0] has an unsatisfied constraint"
assert 1024 == T.cast(
buf1_shape[1], "int32"
), "Argument arg1.shape[1] has an unsatisfied constraint"
if not (T.isnullptr(buf1_strides.data, dtype="bool")):
assert (1 == T.cast(buf1_strides[1], "int32")) and (
1024 == T.cast(buf1_strides[0], "int32")
), "arg1.strides: expected to be compact array"
T.evaluate(0)
assert T.uint64(0) == T.tvm_struct_get(
arg1, 0, 8, dtype="uint64"
), "Argument arg1.byte_offset has an unsatisfied constraint"
assert 1 == T.tvm_struct_get(
arg1, 0, 10, dtype="int32"
), "Argument arg1.device_type has an unsatisfied constraint"
assert dev_id == T.tvm_struct_get(
arg1, 0, 9, dtype="int32"
), "Argument arg1.device_id has an unsatisfied constraint"
assert 2 == T.tvm_struct_get(
arg2, 0, 4, dtype="int32"
), "arg2.ndim is expected to equal 2"
assert 2 == T.tvm_struct_get(
arg2, 0, 4, dtype="int32"
), "arg2.ndim is expected to equal 2"
assert (
(T.tvm_struct_get(arg2, 0, 5, dtype="uint8") == T.uint8(2))
and (T.tvm_struct_get(arg2, 0, 6, dtype="uint8") == T.uint8(32))
) and (
T.tvm_struct_get(arg2, 0, 7, dtype="uint16") == T.uint16(1)
), "arg2.dtype is expected to be float32"
assert 1024 == T.cast(
buf2_shape[0], "int32"
), "Argument arg2.shape[0] has an unsatisfied constraint"
assert 1024 == T.cast(
buf2_shape[1], "int32"
), "Argument arg2.shape[1] has an unsatisfied constraint"
if not (T.isnullptr(buf2_strides.data, dtype="bool")):
assert (1 == T.cast(buf2_strides[1], "int32")) and (
1024 == T.cast(buf2_strides[0], "int32")
), "arg2.strides: expected to be compact array"
T.evaluate(0)
assert T.uint64(0) == T.tvm_struct_get(
arg2, 0, 8, dtype="uint64"
), "Argument arg2.byte_offset has an unsatisfied constraint"
assert 1 == T.tvm_struct_get(
arg2, 0, 10, dtype="int32"
), "Argument arg2.device_type has an unsatisfied constraint"
assert dev_id == T.tvm_struct_get(
arg2, 0, 9, dtype="int32"
), "Argument arg2.device_id has an unsatisfied constraint"
T.attr(0, "compute_scope", "mmult_compute_")
T.attr(packedB.data, "storage_scope", "global")
T.attr(packedB.data, "storage_alignment", 128)
with T.let(
packedB.data,
T.TVMBackendAllocWorkspace(1, dev_id, T.uint64(4194304), 2, 32, dtype="handle"),
):
if T.isnullptr(packedB.data, dtype="bool"):
T.evaluate(T.tvm_throw_last_error(dtype="int32"))
for x in T.parallel(0, 32):
for y in T.serial(0, 1024):
packedB[T.ramp(((x * 32768) + (y * 32)), 1, 32)] = B[
T.ramp(((y * 1024) + (x * 32)), 1, 32)
]
for x_outer in T.parallel(0, 32):
T.attr(C_global.data, "storage_scope", "global")
T.attr(C_global.data, "storage_alignment", 128)
with T.let(
C_global.data,
T.TVMBackendAllocWorkspace(
1, dev_id, T.uint64(4096), 2, 32, dtype="handle"
),
):
if T.isnullptr(C_global.data, dtype="bool"):
T.evaluate(T.tvm_throw_last_error(dtype="int32"))
for y_outer in T.serial(0, 32):
for x_c_init in T.serial(0, 32):
C_global[T.ramp((x_c_init * 32), 1, 32)] = T.broadcast(
T.float32(0), 32
)
for k_outer in T.serial(0, 256):
for x_c in T.serial(0, 32):
C_global[T.ramp((x_c * 32), 1, 32)] = T.call_llvm_pure_intrin(
T.uint32(97),
T.uint32(3),
T.broadcast(
A[
(
((x_outer * 32768) + (x_c * 1024))
+ (k_outer * 4)
),
],
32,
),
packedB[
T.ramp(((y_outer * 32768) + (k_outer * 128)), 1, 32)
],
C_global[T.ramp((x_c * 32), 1, 32)],
dtype="float32x32",
)
C_global[T.ramp((x_c * 32), 1, 32)] = T.call_llvm_pure_intrin(
T.uint32(97),
T.uint32(3),
T.broadcast(
A[
(
(
((x_outer * 32768) + (x_c * 1024))
+ (k_outer * 4)
)
+ 1
),
],
32,
),
packedB[
T.ramp(
(((y_outer * 32768) + (k_outer * 128)) + 32), 1, 32
)
],
C_global[T.ramp((x_c * 32), 1, 32)],
dtype="float32x32",
)
C_global[T.ramp((x_c * 32), 1, 32)] = T.call_llvm_pure_intrin(
T.uint32(97),
T.uint32(3),
T.broadcast(
A[
(
(
((x_outer * 32768) + (x_c * 1024))
+ (k_outer * 4)
)
+ 2
),
],
32,
),
packedB[
T.ramp(
(((y_outer * 32768) + (k_outer * 128)) + 64), 1, 32
)
],
C_global[T.ramp((x_c * 32), 1, 32)],
dtype="float32x32",
)
C_global[T.ramp((x_c * 32), 1, 32)] = T.call_llvm_pure_intrin(
T.uint32(97),
T.uint32(3),
T.broadcast(
A[
(
(
((x_outer * 32768) + (x_c * 1024))
+ (k_outer * 4)
)
+ 3
),
],
32,
),
packedB[
T.ramp(
(((y_outer * 32768) + (k_outer * 128)) + 96), 1, 32
)
],
C_global[T.ramp((x_c * 32), 1, 32)],
dtype="float32x32",
)
for x_inner in T.serial(0, 32):
for y_inner in T.serial(0, 32):
C[
(
(
((x_outer * 32768) + (x_inner * 1024))
+ (y_outer * 32)
)
+ y_inner
)
] = C_global[((x_inner * 32) + y_inner)]
if T.TVMBackendFreeWorkspace(1, dev_id, C_global.data, dtype="int32") != 0:
T.evaluate(T.tvm_throw_last_error(dtype="int32"))
if T.TVMBackendFreeWorkspace(1, dev_id, packedB.data, dtype="int32") != 0:
T.evaluate(T.tvm_throw_last_error(dtype="int32"))
return Module
def opt_conv_tensorcore_normalize():
@T.prim_func
def func(A: T.handle, W: T.handle, Conv: T.handle) -> None:
# function attr dict
T.func_attr({"global_symbol": "default_function", "tir.noalias": True})
# var definition
bx = T.env_thread("blockIdx.x")
by = T.env_thread("blockIdx.y")
bz = T.env_thread("blockIdx.z")
tx = T.env_thread("threadIdx.x")
ty = T.env_thread("threadIdx.y")
tz = T.env_thread("threadIdx.z")
# buffer definition
Apad_shared = T.buffer_decl(
[16, 16, 16, 16, 16, 16], dtype="float16", elem_offset=0, align=128, offset_factor=1
)
Apad_shared_wmma_matrix_a = T.buffer_decl(
[16, 16, 16, 16, 16, 16], dtype="float16", elem_offset=0, align=128, offset_factor=1
)
BA = T.buffer_decl(
[16, 16], dtype="float16", scope="wmma.matrix_a", align=32, offset_factor=256
)
BB = T.buffer_decl(
[16, 16], dtype="float16", scope="wmma.matrix_b", align=32, offset_factor=256
)
BC = T.buffer_decl([16, 16], scope="wmma.accumulator", align=32, offset_factor=256)
Conv_wmma_accumulator = T.buffer_decl(
[16, 14, 14, 32, 16, 16], elem_offset=0, align=128, offset_factor=1
)
W_shared = T.buffer_decl(
[3, 3, 16, 32, 16, 16], dtype="float16", elem_offset=0, align=128, offset_factor=1
)
W_shared_wmma_matrix_b = T.buffer_decl(
[3, 3, 16, 32, 16, 16], dtype="float16", elem_offset=0, align=128, offset_factor=1
)
buffer = T.buffer_decl(
[16, 16], dtype="float16", scope="shared", align=32, offset_factor=256
)
buffer_1 = T.buffer_decl(
[16, 16], dtype="float16", scope="wmma.matrix_a", align=32, offset_factor=256
)
buffer_2 = T.buffer_decl(
[16, 16], dtype="float16", scope="shared", align=32, offset_factor=256
)
buffer_3 = T.buffer_decl(
[16, 16], dtype="float16", scope="wmma.matrix_b", align=32, offset_factor=256
)
buffer_4 = T.buffer_decl([16, 16], scope="wmma.accumulator", align=32, offset_factor=256)
buffer_5 = T.buffer_decl([16, 16], align=32, offset_factor=256)
A_1 = T.match_buffer(
A, [16, 14, 14, 16, 16, 16], dtype="float16", elem_offset=0, align=128, offset_factor=1
)
W_1 = T.match_buffer(
W, [3, 3, 16, 32, 16, 16], dtype="float16", elem_offset=0, align=128, offset_factor=1
)
Conv_1 = T.match_buffer(
Conv, [16, 14, 14, 32, 16, 16], elem_offset=0, align=128, offset_factor=1
)
# body
T.realize(Conv_1[0:16, 0:14, 0:14, 0:32, 0:16, 0:16], "")
T.launch_thread(bz, 196)
T.launch_thread(bx, 2)
T.launch_thread(by, 4)
T.launch_thread(ty, 4)
T.launch_thread(tz, 2)
T.realize(
Conv_wmma_accumulator[
((bx * 8) + (ty * 2)) : (((bx * 8) + (ty * 2)) + 2),
T.floordiv(bz, 14) : (T.floordiv(bz, 14) + 1),
T.floormod(bz, 14) : (T.floormod(bz, 14) + 1),
((by * 8) + (tz * 4)) : (((by * 8) + (tz * 4)) + 4),
0:16,
0:16,
],
"wmma.accumulator",
)
for n_c_init in T.serial(0, 2):
for o_c_init in T.serial(0, 4):
T.attr(
[BC, Conv_wmma_accumulator],
"buffer_bind_scope",
T.tvm_tuple(
(n_c_init + ((bx * 8) + (ty * 2))),
1,
T.floordiv(bz, 14),
1,
T.floormod(bz, 14),
1,
(o_c_init + ((by * 8) + (tz * 4))),
1,
0,
16,
0,
16,
dtype="handle",
),
)
T.evaluate(
T.tvm_fill_fragment(
BC.data,
16,
16,
16,
T.floordiv(BC.elem_offset, 256),
T.float32(0),
dtype="handle",
)
)
for ic_outer in T.serial(0, 8):
for kh in T.serial(0, 3):
T.realize(
Apad_shared[
(bx * 8) : ((bx * 8) + 8),
(T.floordiv(bz, 14) + kh) : ((T.floordiv(bz, 14) + kh) + 1),
T.floormod(bz, 14) : (T.floormod(bz, 14) + 3),
(ic_outer * 2) : ((ic_outer * 2) + 2),
0:16,
0:16,
],
"shared",
)
for ax2 in T.serial(0, 3):
for ax3 in T.serial(0, 2):
for ax4_ax5_fused_outer in T.serial(0, 8):
T.launch_thread(tx, 32)
Apad_shared[
((tz + (ty * 2)) + (bx * 8)),
(T.floordiv(bz, 14) + kh),
(ax2 + T.floormod(bz, 14)),
(ax3 + (ic_outer * 2)),
T.floordiv((tx + (ax4_ax5_fused_outer * 32)), 16),
T.floormod((tx + (ax4_ax5_fused_outer * 32)), 16),
] = T.if_then_else(
(
(
(
((T.floordiv(bz, 14) + kh) >= 1)
and (((T.floordiv(bz, 14) + kh) - 1) < 14)
)
and ((ax2 + T.floormod(bz, 14)) >= 1)
)
and (((ax2 + T.floormod(bz, 14)) - 1) < 14)
),
A_1[
((tz + (ty * 2)) + (bx * 8)),
((T.floordiv(bz, 14) + kh) - 1),
((ax2 + T.floormod(bz, 14)) - 1),
(ax3 + (ic_outer * 2)),
T.floordiv((tx + (ax4_ax5_fused_outer * 32)), 16),
T.floormod((tx + (ax4_ax5_fused_outer * 32)), 16),
],
T.float16(0),
dtype="float16",
)
T.realize(
W_shared[
kh : (kh + 1),
0:3,
(ic_outer * 2) : ((ic_outer * 2) + 2),
(by * 8) : ((by * 8) + 8),
0:16,
0:16,
],
"shared",
)
for ax1 in T.serial(0, 3):
for ax2_1 in T.serial(0, 2):
T.launch_thread(tx, 32)
for ax4_ax5_fused_inner in T.vectorized(0, 8):
W_shared[
kh,
ax1,
(ax2_1 + (ic_outer * 2)),
((tz + (ty * 2)) + (by * 8)),
T.floordiv((ax4_ax5_fused_inner + (tx * 8)), 16),
T.floormod((ax4_ax5_fused_inner + (tx * 8)), 16),
] = W_1[
kh,
ax1,
(ax2_1 + (ic_outer * 2)),
((tz + (ty * 2)) + (by * 8)),
T.floordiv((ax4_ax5_fused_inner + (tx * 8)), 16),
T.floormod((ax4_ax5_fused_inner + (tx * 8)), 16),
]
for ic_inner in T.serial(0, 2):
for kw in T.serial(0, 3):
T.realize(
Apad_shared_wmma_matrix_a[
((bx * 8) + (ty * 2)) : (((bx * 8) + (ty * 2)) + 2),
(T.floordiv(bz, 14) + kh) : ((T.floordiv(bz, 14) + kh) + 1),
(kw + T.floormod(bz, 14)) : ((kw + T.floormod(bz, 14)) + 1),
((ic_outer * 2) + ic_inner) : (((ic_outer * 2) + ic_inner) + 1),
0:16,
0:16,
],
"wmma.matrix_a",
)
for ax0 in T.serial(0, 2):
T.attr(
[buffer, Apad_shared],
"buffer_bind_scope",
T.tvm_tuple(
(ax0 + ((bx * 8) + (ty * 2))),
1,
(T.floordiv(bz, 14) + kh),
1,
(kw + T.floormod(bz, 14)),
1,
((ic_outer * 2) + ic_inner),
1,
0,
16,
0,
16,
dtype="handle",
),
)
T.attr(
[buffer_1, Apad_shared_wmma_matrix_a],
"buffer_bind_scope",
T.tvm_tuple(
(ax0 + ((bx * 8) + (ty * 2))),
1,
(T.floordiv(bz, 14) + kh),
1,
(kw + T.floormod(bz, 14)),
1,
((ic_outer * 2) + ic_inner),
1,
0,
16,
0,
16,
dtype="handle",
),
)
T.evaluate(
T.tvm_load_matrix_sync(
buffer_1.data,
16,
16,
16,
T.floordiv(buffer_1.elem_offset, 256),
T.tvm_access_ptr(
T.type_annotation(dtype="float16"),
buffer.data,
buffer.elem_offset,
256,
1,
dtype="handle",
),
16,
"row_major",
dtype="handle",
)
)
T.realize(
W_shared_wmma_matrix_b[
kh : (kh + 1),
kw : (kw + 1),
((ic_outer * 2) + ic_inner) : (((ic_outer * 2) + ic_inner) + 1),
((by * 8) + (tz * 4)) : (((by * 8) + (tz * 4)) + 4),
0:16,
0:16,
],
"wmma.matrix_b",
)
for ax3_1 in T.serial(0, 4):
T.attr(
[buffer_2, W_shared],
"buffer_bind_scope",
T.tvm_tuple(
kh,
1,
kw,
1,
((ic_outer * 2) + ic_inner),
1,
(ax3_1 + ((by * 8) + (tz * 4))),
1,
0,
16,
0,
16,
dtype="handle",
),
)
T.attr(
[buffer_3, W_shared_wmma_matrix_b],
"buffer_bind_scope",
T.tvm_tuple(
kh,
1,
kw,
1,
((ic_outer * 2) + ic_inner),
1,
(ax3_1 + ((by * 8) + (tz * 4))),
1,
0,
16,
0,
16,
dtype="handle",
),
)
T.evaluate(
T.tvm_load_matrix_sync(
buffer_3.data,
16,
16,
16,
T.floordiv(buffer_3.elem_offset, 256),
T.tvm_access_ptr(
T.type_annotation(dtype="float16"),
buffer_2.data,
buffer_2.elem_offset,
256,
1,
dtype="handle",
),
16,
"row_major",
dtype="handle",
)
)
for n_c in T.serial(0, 2):
for o_c in T.serial(0, 4):
T.attr(
[BA, Apad_shared_wmma_matrix_a],
"buffer_bind_scope",
T.tvm_tuple(
(n_c + ((bx * 8) + (ty * 2))),
1,
(T.floordiv(bz, 14) + kh),
1,
(T.floormod(bz, 14) + kw),
1,
((ic_outer * 2) + ic_inner),
1,
0,
16,
0,
16,
dtype="handle",
),
)
T.attr(
[BB, W_shared_wmma_matrix_b],
"buffer_bind_scope",
T.tvm_tuple(
kh,
1,
kw,
1,
((ic_outer * 2) + ic_inner),
1,
(o_c + ((by * 8) + (tz * 4))),
1,
0,
16,
0,
16,
dtype="handle",
),
)
T.attr(
[BC, Conv_wmma_accumulator],
"buffer_bind_scope",
T.tvm_tuple(
(n_c + ((bx * 8) + (ty * 2))),
1,
T.floordiv(bz, 14),
1,
T.floormod(bz, 14),
1,
(o_c + ((by * 8) + (tz * 4))),
1,
0,
16,
0,
16,
dtype="handle",
),
)
T.evaluate(
T.tvm_mma_sync(
BC.data,
T.floordiv(BC.elem_offset, 256),
BA.data,
T.floordiv(BA.elem_offset, 256),
BB.data,
T.floordiv(BB.elem_offset, 256),
BC.data,
T.floordiv(BC.elem_offset, 256),
dtype="handle",
)
)
for n_inner in T.serial(0, 2):
for o_inner in T.serial(0, 4):
T.attr(
[buffer_4, Conv_wmma_accumulator],
"buffer_bind_scope",
T.tvm_tuple(
((((bx * 4) + ty) * 2) + n_inner),
1,
T.floordiv(bz, 14),
1,
T.floormod(bz, 14),
1,
((((by * 2) + tz) * 4) + o_inner),
1,
0,
16,
0,
16,
dtype="handle",
),
)
T.attr(
[buffer_5, Conv_1],
"buffer_bind_scope",
T.tvm_tuple(
((((bx * 4) + ty) * 2) + n_inner),
1,
T.floordiv(bz, 14),
1,
T.floormod(bz, 14),
1,
((((by * 2) + tz) * 4) + o_inner),
1,
0,
16,
0,
16,
dtype="handle",
),
)
T.evaluate(
T.tvm_store_matrix_sync(
buffer_4.data,
16,
16,
16,
T.floordiv(buffer_4.elem_offset, 256),
T.tvm_access_ptr(
T.type_annotation(dtype="float32"),
buffer_5.data,
buffer_5.elem_offset,
256,
2,
dtype="handle",
),
16,
"row_major",
dtype="handle",
)
)
return func
def opt_conv_tensorcore_lower():
@T.prim_func
def func(
A: T.Buffer[(16, 14, 14, 16, 16, 16), "float16"],
W: T.Buffer[(3, 3, 16, 32, 16, 16), "float16"],
Conv: T.Buffer[(16, 14, 14, 32, 16, 16), "float32"],
) -> None:
# function attr dict
T.func_attr({"global_symbol": "default_function", "tir.noalias": True})
# body
A_1 = T.buffer_decl([12845056], dtype="float16", data=A.data)
W_1 = T.buffer_decl([1179648], dtype="float16", data=W.data)
Conv_1 = T.buffer_decl([25690112], data=Conv.data)
bx = T.env_thread("blockIdx.x")
by = T.env_thread("blockIdx.y")
bz = T.env_thread("blockIdx.z")
tx = T.env_thread("threadIdx.x")
ty = T.env_thread("threadIdx.y")
tz = T.env_thread("threadIdx.z")
T.launch_thread(bz, 196)
Conv_wmma_accumulator = T.allocate([2048], "float32", "wmma.accumulator")
Apad_shared = T.allocate([12288], "float16", "shared")
W_shared = T.allocate([12288], "float16", "shared")
Apad_shared_wmma_matrix_a = T.allocate([512], "float16", "wmma.matrix_a")
W_shared_wmma_matrix_b = T.allocate([1024], "float16", "wmma.matrix_b")
T.launch_thread(bx, 2)
T.launch_thread(by, 4)
T.launch_thread(ty, 4)
T.launch_thread(tz, 2)
T.evaluate(
T.tvm_fill_fragment(
Conv_wmma_accumulator.data, 16, 16, 16, 0, T.float32(0), dtype="handle"
)
)
T.evaluate(
T.tvm_fill_fragment(
Conv_wmma_accumulator.data, 16, 16, 16, 1, T.float32(0), dtype="handle"
)
)
T.evaluate(
T.tvm_fill_fragment(
Conv_wmma_accumulator.data, 16, 16, 16, 2, T.float32(0), dtype="handle"
)
)
T.evaluate(
T.tvm_fill_fragment(
Conv_wmma_accumulator.data, 16, 16, 16, 3, T.float32(0), dtype="handle"
)
)
T.evaluate(
T.tvm_fill_fragment(
Conv_wmma_accumulator.data, 16, 16, 16, 4, T.float32(0), dtype="handle"
)
)
T.evaluate(
T.tvm_fill_fragment(
Conv_wmma_accumulator.data, 16, 16, 16, 5, T.float32(0), dtype="handle"
)
)
T.evaluate(
T.tvm_fill_fragment(
Conv_wmma_accumulator.data, 16, 16, 16, 6, T.float32(0), dtype="handle"
)
)
T.evaluate(