diff --git a/tests/python/unittest/test_tir_schedule_read_write_at.py b/tests/python/unittest/test_tir_schedule_read_write_at.py new file mode 100644 index 0000000000000..908e172ab9751 --- /dev/null +++ b/tests/python/unittest/test_tir_schedule_read_write_at.py @@ -0,0 +1,221 @@ +# 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. +# pylint: disable=missing-function-docstring,missing-module-docstring +import sys + +import pytest + +import tvm +from tvm import tir +from tvm.script import tir as T +from tvm.tir.schedule.testing import verify_trace_roundtrip + + +# fmt: off +# pylint: disable=no-member,invalid-name,unused-variable,line-too-long,redefined-outer-name,unexpected-keyword-arg,too-many-nested-blocks,not-callable + +@T.prim_func +def cuda_matmul(a: T.handle, b: T.handle, c: T.handle) -> None: # pylint: disable=undefined-loop-variable + A = T.match_buffer(a, [2048, 2048], "float32") + B = T.match_buffer(b, [2048, 2048], "float32") + C = T.match_buffer(c, [2048, 2048], "float32") + for by in T.thread_binding(0, 32, thread = "blockIdx.y"): + for bx in T.thread_binding(0, 32, thread = "blockIdx.x"): + for vy in T.thread_binding(0, 2, thread = "vthread.y"): + for vx in T.thread_binding(0, 2, thread = "vthread.x"): + for ty in T.thread_binding(0, 8, thread = "threadIdx.y"): + for tx in T.thread_binding(0, 8, thread = "threadIdx.x"): + for k0 in T.serial(0, 256): + for k1 in T.unroll(0, 8): + for _, i, j in T.grid(1, 4, 4): + with T.block("C"): + vi = T.axis.S(2048, by * 64 + vy * 32 + ty * 4 + i) + vj = T.axis.S(2048, bx * 64 + vx * 32 + tx * 4 + j) + vk = T.axis.R(2048, k0 * 8 + k1) + T.reads([C[vi, vj], A[vi, vk], B[vk, vj]]) + T.writes([C[vi, vj]]) + with T.init(): + C[vi, vj] = 0.0 + C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vk, vj] + + +@T.prim_func +def cuda_matmul_read_at_a(a: T.handle, b: T.handle, c: T.handle) -> None: + A = T.match_buffer(a, [2048, 2048], dtype="float32") + B = T.match_buffer(b, [2048, 2048], dtype="float32") + C = T.match_buffer(c, [2048, 2048], dtype="float32") + A_shared = T.alloc_buffer([2048, 2048], dtype="float32", scope="shared") + for by in T.thread_binding(0, 32, thread="blockIdx.y"): + for bx in T.thread_binding(0, 32, thread="blockIdx.x"): + for vy in T.thread_binding(0, 2, thread="vthread.y"): + for vx in T.thread_binding(0, 2, thread="vthread.x"): + for ty in T.thread_binding(0, 8, thread="threadIdx.y"): + for tx in T.thread_binding(0, 8, thread="threadIdx.x"): + for k0 in T.serial(0, 256): + with T.block("A_shared"): + v0 = T.axis.S(32, by) + v1 = T.axis.S(256, k0) + T.reads([A[v0 * 64 : v0 * 64 + 64, v1 * 8 : v1 * 8 + 8]]) + T.writes([A_shared[v0 * 64 : v0 * 64 + 64, v1 * 8 : v1 * 8 + 8]]) + T.block_attr({"auto_copy":1}) + for ax0, ax1 in T.grid(64, 8): + A_shared[v0 * 64 + ax0, v1 * 8 + ax1] = A[v0 * 64 + ax0, v1 * 8 + ax1] + for k1 in T.unroll(0, 8): + for v_, i, j in T.grid(1, 4, 4): + with T.block("C"): + vi = T.axis.S(2048, by * 64 + vy * 32 + ty * 4 + i) + vj = T.axis.S(2048, bx * 64 + vx * 32 + tx * 4 + j) + vk = T.axis.R(2048, k0 * 8 + k1) + T.reads([C[vi, vj], A_shared[vi, vk], B[vk, vj]]) + T.writes([C[vi, vj]]) + with T.init(): + C[vi, vj] = T.float32(0) + C[vi, vj] = C[vi, vj] + A_shared[vi, vk] * B[vk, vj] + + +@T.prim_func +def cuda_matmul_read_at_ab(a: T.handle, b: T.handle, c: T.handle) -> None: + A = T.match_buffer(a, [2048, 2048], dtype="float32") + B = T.match_buffer(b, [2048, 2048], dtype="float32") + C = T.match_buffer(c, [2048, 2048], dtype="float32") + A_shared = T.alloc_buffer([2048, 2048], dtype="float32", scope="shared") + B_shared = T.alloc_buffer([2048, 2048], dtype="float32", scope="shared") + for by in T.thread_binding(0, 32, thread="blockIdx.y"): + for bx in T.thread_binding(0, 32, thread="blockIdx.x"): + for vy in T.thread_binding(0, 2, thread="vthread.y"): + for vx in T.thread_binding(0, 2, thread="vthread.x"): + for ty in T.thread_binding(0, 8, thread="threadIdx.y"): + for tx in T.thread_binding(0, 8, thread="threadIdx.x"): + for k0 in T.serial(0, 256): + with T.block("A_shared"): + v0 = T.axis.S(32, by) + v1 = T.axis.S(256, k0) + T.reads([A[v0 * 64 : v0 * 64 + 64, v1 * 8 : v1 * 8 + 8]]) + T.writes([A_shared[v0 * 64 : v0 * 64 + 64, v1 * 8 : v1 * 8 + 8]]) + T.block_attr({"auto_copy":1}) + for ax0, ax1 in T.grid(64, 8): + A_shared[v0 * 64 + ax0, v1 * 8 + ax1] = A[v0 * 64 + ax0, v1 * 8 + ax1] + with T.block("B_shared"): + v0 = T.axis.S(256, k0) + v1 = T.axis.S(32, bx) + T.reads([B[v0 * 8 : v0 * 8 + 8, v1 * 64 : v1 * 64 + 64]]) + T.writes([B_shared[v0 * 8 : v0 * 8 + 8, v1 * 64 : v1 * 64 + 64]]) + T.block_attr({"auto_copy":1}) + for ax0, ax1 in T.grid(8, 64): + B_shared[v0 * 8 + ax0, v1 * 64 + ax1] = B[v0 * 8 + ax0, v1 * 64 + ax1] + for k1 in T.unroll(0, 8): + for v_, i, j in T.grid(1, 4, 4): + with T.block("C"): + vi = T.axis.S(2048, by * 64 + vy * 32 + ty * 4 + i) + vj = T.axis.S(2048, bx * 64 + vx * 32 + tx * 4 + j) + vk = T.axis.R(2048, k0 * 8 + k1) + T.reads([C[vi, vj], A_shared[vi, vk], B_shared[vk, vj]]) + T.writes([C[vi, vj]]) + with T.init(): + C[vi, vj] = T.float32(0) + C[vi, vj] = C[vi, vj] + A_shared[vi, vk] * B_shared[vk, vj] + +@T.prim_func +def cuda_matmul_write_at_c(a: T.handle, b: T.handle, c: T.handle) -> None: + A = T.match_buffer(a, [2048, 2048], dtype="float32") + B = T.match_buffer(b, [2048, 2048], dtype="float32") + C = T.match_buffer(c, [2048, 2048], dtype="float32") + A_shared = T.alloc_buffer([2048, 2048], dtype="float32", scope="shared") + B_shared = T.alloc_buffer([2048, 2048], dtype="float32", scope="shared") + C_shared = T.alloc_buffer([2048, 2048], dtype="float32", scope="shared") + for by in T.thread_binding(0, 32, thread="blockIdx.y"): + for bx in T.thread_binding(0, 32, thread="blockIdx.x"): + for vy in T.thread_binding(0, 2, thread="vthread.y"): + for vx in T.thread_binding(0, 2, thread="vthread.x"): + for ty in T.thread_binding(0, 8, thread="threadIdx.y"): + for tx in T.thread_binding(0, 8, thread="threadIdx.x"): + for k0 in T.serial(0, 256): + with T.block("A_shared"): + v0 = T.axis.S(32, by) + v1 = T.axis.S(256, k0) + T.reads([A[v0 * 64 : v0 * 64 + 64, v1 * 8 : v1 * 8 + 8]]) + T.writes([A_shared[v0 * 64 : v0 * 64 + 64, v1 * 8 : v1 * 8 + 8]]) + T.block_attr({"auto_copy":1}) + for ax0, ax1 in T.grid(64, 8): + A_shared[v0 * 64 + ax0, v1 * 8 + ax1] = A[v0 * 64 + ax0, v1 * 8 + ax1] + with T.block("B_shared"): + v0 = T.axis.S(256, k0) + v1 = T.axis.S(32, bx) + T.reads([B[v0 * 8 : v0 * 8 + 8, v1 * 64 : v1 * 64 + 64]]) + T.writes([B_shared[v0 * 8 : v0 * 8 + 8, v1 * 64 : v1 * 64 + 64]]) + T.block_attr({"auto_copy":1}) + for ax0, ax1 in T.grid(8, 64): + B_shared[v0 * 8 + ax0, v1 * 64 + ax1] = B[v0 * 8 + ax0, v1 * 64 + ax1] + for k1 in T.unroll(0, 8): + for v_, i, j in T.grid(1, 4, 4): + with T.block("C"): + vi = T.axis.S(2048, by * 64 + vy * 32 + ty * 4 + i) + vj = T.axis.S(2048, bx * 64 + vx * 32 + tx * 4 + j) + vk = T.axis.R(2048, k0 * 8 + k1) + T.reads([C_shared[vi, vj], A_shared[vi, vk], B_shared[vk, vj]]) + T.writes([C_shared[vi, vj]]) + with T.init(): + C_shared[vi, vj] = T.float32(0) + C_shared[vi, vj] = C_shared[vi, vj] + A_shared[vi, vk] * B_shared[vk, vj] + with T.block("C_shared"): + v0 = T.axis.S(32, by) + v1 = T.axis.S(32, bx) + T.reads([C_shared[v0 * 64 : v0 * 64 + 64, v1 * 64 : v1 * 64 + 64]]) + T.writes([C[v0 * 64 : v0 * 64 + 64, v1 * 64 : v1 * 64 + 64]]) + T.block_attr({"auto_copy":1}) + for ax0, ax1 in T.grid(64, 64): + C[v0 * 64 + ax0, v1 * 64 + ax1] = C_shared[v0 * 64 + ax0, v1 * 64 + ax1] + + +# pylint: enable=no-member,invalid-name,unused-variable,line-too-long,redefined-outer-name,unexpected-keyword-arg,too-many-nested-blocks,not-callable +# fmt: on + + +def test_read_at_global_to_shared_a(): + sch = tir.Schedule(cuda_matmul, debug_mask="all") + block = sch.get_block("C") + # pylint: disable=invalid-name + _by, _bx, _vy, _vx, _ty, _tx, k0, _k1, _, _i, _j = sch.get_loops(block) + # pylint: enable=invalid-name + sch.read_at(k0, block, 1, "shared") + tvm.ir.assert_structural_equal(sch.mod["main"], cuda_matmul_read_at_a) + verify_trace_roundtrip(sch, cuda_matmul) + + +def test_read_at_global_to_shared_ab(): + sch = tir.Schedule(cuda_matmul_read_at_a, debug_mask="all") + block = sch.get_block("C") + # pylint: disable=invalid-name + _by, _bx, _vy, _vx, _ty, _tx, k0, _k1, _, _i, _j = sch.get_loops(block) + # pylint: enable=invalid-name + sch.read_at(k0, block, 2, "shared") + tvm.ir.assert_structural_equal(sch.mod["main"], cuda_matmul_read_at_ab) + verify_trace_roundtrip(sch, cuda_matmul_read_at_a) + + +def test_read_at_local_to_shared_c(): + sch = tir.Schedule(cuda_matmul_read_at_ab, debug_mask="all") + block = sch.get_block("C") + # pylint: disable=invalid-name + _by, _bx, _vy, _vx, _ty, tx, _k0, _k1, _, _i, _j = sch.get_loops(block) + # pylint: enable=invalid-name + sch.write_at(tx, block, 0, "shared") + tvm.ir.assert_structural_equal(sch.mod["main"], cuda_matmul_write_at_c) + verify_trace_roundtrip(sch, cuda_matmul_read_at_ab) + + +if __name__ == "__main__": + sys.exit(pytest.main([__file__] + sys.argv[1:])) \ No newline at end of file