# 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. # ruff: noqa: E501, F841 import tvm import tvm.testing from tvm.relax.transform import LegalizeOps from tvm.script import ir as I from tvm.script import relax as R from tvm.script import tirx as T def test_allreduce(): # fmt: off @tvm.script.ir_module class AllReduce: @R.function def main(x: R.Tensor((10, 10), "float32")) -> R.Tensor((10, 10), "float32"): gv0: R.Tensor((10, 10), "float32") = R.ccl.allreduce(x, "sum") gv1: R.Tensor((10, 10), "float32") = R.ccl.allreduce(x, "prod") gv2: R.Tensor((10, 10), "float32") = R.ccl.allreduce(x, "min") gv3: R.Tensor((10, 10), "float32") = R.ccl.allreduce(x, "max") gv4: R.Tensor((10, 10), "float32") = R.ccl.allreduce(x, "avg") return x @I.ir_module(s_tir=True) class Expected: @R.function def main(x: R.Tensor((10, 10), dtype="float32")) -> R.Tensor((10, 10), dtype="float32"): gv0: R.Tensor((10, 10), dtype="float32") = R.call_dps_packed("runtime.disco.allreduce", [x, R.shape([0]), True], out_ty=R.Tensor((10, 10), dtype="float32")) gv1: R.Tensor((10, 10), dtype="float32") = R.call_dps_packed("runtime.disco.allreduce", [x, R.shape([1]), True], out_ty=R.Tensor((10, 10), dtype="float32")) gv2: R.Tensor((10, 10), dtype="float32") = R.call_dps_packed("runtime.disco.allreduce", [x, R.shape([2]), True], out_ty=R.Tensor((10, 10), dtype="float32")) gv3: R.Tensor((10, 10), dtype="float32") = R.call_dps_packed("runtime.disco.allreduce", [x, R.shape([3]), True], out_ty=R.Tensor((10, 10), dtype="float32")) gv4: R.Tensor((10, 10), dtype="float32") = R.call_dps_packed("runtime.disco.allreduce", [x, R.shape([4]), True], out_ty=R.Tensor((10, 10), dtype="float32")) return x # fmt: on mod = LegalizeOps()(AllReduce) tvm.ir.assert_structural_equal(mod, Expected) def test_allgather(): # fmt: off @tvm.script.ir_module class AllGather: @R.function def main(x: R.Tensor((10, 10), "float32")) -> R.Tensor((10, 10), "float32"): gv0: R.Tensor((20, 10), "float32") = R.ccl.allgather(x, 2) gv1 = R.ccl.allgather(x, 2) return x @I.ir_module(s_tir=True) class Expected: @R.function def main(x: R.Tensor((10, 10), dtype="float32")) -> R.Tensor((10, 10), dtype="float32"): gv0: R.Tensor((20, 10), dtype="float32") = R.call_dps_packed("runtime.disco.allgather", [x, True], out_ty=R.Tensor((20, 10), dtype="float32")) gv1: R.Tensor((20, 10), dtype="float32") = R.call_dps_packed("runtime.disco.allgather", [x, True], out_ty=R.Tensor((20, 10), dtype="float32")) return x # fmt: on mod = LegalizeOps()(AllGather) tvm.ir.assert_structural_equal(mod, Expected) def test_broadcast_from_zero(): # fmt: off @tvm.script.ir_module class BroadcastFromZero: @R.function def main(x: R.Tensor((10, 10), "float32")) -> R.Tensor((10, 10), "float32"): gv0: R.Tensor((10, 10), "float32") = R.ccl.broadcast_from_worker0(x) return x @I.ir_module(s_tir=True) class Expected: @R.function def main(x: R.Tensor((10, 10), dtype="float32")) -> R.Tensor((10, 10), dtype="float32"): gv0: R.Tensor((10, 10), dtype="float32") = R.call_dps_packed("runtime.disco.broadcast_from_worker0", [x, False], out_ty=R.Tensor((10, 10), dtype="float32")) return x # fmt: on mod = LegalizeOps()(BroadcastFromZero) tvm.ir.assert_structural_equal(mod, Expected) def test_scatter_from_worker0(): # fmt: off @tvm.script.ir_module class ScatterFromWorker0: @R.function def main(x: R.Tensor((10, 10), "float32")) -> R.Tensor((10,5), "float32"): gv0: R.Tensor((10,5), "float32") = R.ccl.scatter_from_worker0(x, num_workers=2, axis=1) return gv0 @I.ir_module(s_tir=True) class Expected: @T.prim_func(private=True, s_tir=True) def reshape(A: T.Buffer((T.int64(10), T.int64(10)), "float32"), T_reshape: T.Buffer((T.int64(10), T.int64(2), T.int64(5)), "float32")): T.func_attr({"tirx.noalias": True}) # with T.sblock("root"): for ax0, ax1, ax2 in T.grid(T.int64(10), T.int64(2), T.int64(5)): with T.sblock("T_reshape"): v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) T.reads(A[((v_ax1 * T.int64(5) + v_ax2) // T.int64(10) + v_ax0) % T.int64(10), (v_ax1 * T.int64(5) + v_ax2) % T.int64(10)]) T.writes(T_reshape[v_ax0, v_ax1, v_ax2]) T_reshape[v_ax0, v_ax1, v_ax2] = A[((v_ax1 * T.int64(5) + v_ax2) // T.int64(10) + v_ax0) % T.int64(10), (v_ax1 * T.int64(5) + v_ax2) % T.int64(10)] @T.prim_func(private=True, s_tir=True) def transpose(A: T.Buffer((T.int64(10), T.int64(2), T.int64(5)), "float32"), T_transpose: T.Buffer((T.int64(2), T.int64(10), T.int64(5)), "float32")): T.func_attr({"tirx.noalias": True}) # with T.sblock("root"): for ax0, ax1, ax2 in T.grid(T.int64(2), T.int64(10), T.int64(5)): with T.sblock("T_transpose"): v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2]) T.reads(A[v_ax1, v_ax0, v_ax2]) T.writes(T_transpose[v_ax0, v_ax1, v_ax2]) T_transpose[v_ax0, v_ax1, v_ax2] = A[v_ax1, v_ax0, v_ax2] @R.function def main(x: R.Tensor((10, 10), dtype="float32")) -> R.Tensor((10, 5), dtype="float32"): cls = Expected gv = R.call_tir(cls.reshape, (x,), out_ty=R.Tensor((10, 2, 5), dtype="float32")) gv1 = R.call_tir(cls.transpose, (gv,), out_ty=R.Tensor((2, 10, 5), dtype="float32")) gv0 = R.call_dps_packed("runtime.disco.scatter_from_worker0", (gv1, False), out_ty=R.Tensor((10, 5), dtype="float32")) return gv0 # fmt: on mod = LegalizeOps()(ScatterFromWorker0) tvm.ir.assert_structural_equal(mod, Expected) if __name__ == "__main__": tvm.testing.main()