# 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, F401, F841 import tvm import tvm.testing from tvm import relax 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_redistribute_replica_to_shard(): # fmt: off @tvm.script.ir_module class Before: @R.function def main(x: R.Tensor((10, 10), "float32")) -> R.Tensor((10, 5), "float32"): gv0 = R.dist.redistribute_replica_to_shard(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 strided_slice(A: T.Buffer((T.int64(10), T.int64(10)), "float32"), redistribute_replica_to_shard: T.Buffer((T.int64(10), T.int64(5)), "float32"), worker_id: T.int64): T.func_attr({"tirx.noalias": True}) # with T.sblock("root"): for i0, i1 in T.grid(T.int64(10), T.int64(5)): with T.sblock("redistribute_replica_to_shard"): v_i0, v_i1 = T.axis.remap("SS", [i0, i1]) T.reads(A[v_i0, worker_id * T.int64(5) + v_i1]) T.writes(redistribute_replica_to_shard[v_i0, v_i1]) redistribute_replica_to_shard[v_i0, v_i1] = A[v_i0, worker_id * T.int64(5) + v_i1] @R.function def main(x: R.Tensor((10, 10), dtype="float32")) -> R.Tensor((10, 5), dtype="float32"): worker_id = T.int64() cls = Expected gv: R.Shape(ndim=-1) = R.call_pure_packed("runtime.disco.worker_id", ty_args=(R.Shape(ndim=-1),)) gv1: R.Shape([worker_id]) = R.match_cast(gv, R.Shape([worker_id])) gv0 = R.call_tir(cls.strided_slice, (x,), out_ty=R.Tensor((10, 5), dtype="float32"), tir_vars=R.shape([worker_id])) return gv0 # fmt: on mod = LegalizeOps()(Before) tvm.ir.assert_structural_equal(mod, Expected) if __name__ == "__main__": tvm.testing.main()