wehub-resource-sync
66 lines
2.7 KiB
Python
66 lines
2.7 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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# ruff: noqa: E501, F401, F841
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import tvm
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import tvm.testing
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from tvm import relax
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from tvm.relax.transform import LegalizeOps
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from tvm.script import ir as I
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from tvm.script import relax as R
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from tvm.script import tirx as T
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def test_redistribute_replica_to_shard():
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# fmt: off
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@tvm.script.ir_module
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class Before:
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@R.function
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def main(x: R.Tensor((10, 10), "float32")) -> R.Tensor((10, 5), "float32"):
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gv0 = R.dist.redistribute_replica_to_shard(x, num_workers=2, axis=1)
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return gv0
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@I.ir_module(s_tir=True)
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class Expected:
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@T.prim_func(private=True, s_tir=True)
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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):
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T.func_attr({"tirx.noalias": True})
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# with T.sblock("root"):
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for i0, i1 in T.grid(T.int64(10), T.int64(5)):
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with T.sblock("redistribute_replica_to_shard"):
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v_i0, v_i1 = T.axis.remap("SS", [i0, i1])
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T.reads(A[v_i0, worker_id * T.int64(5) + v_i1])
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T.writes(redistribute_replica_to_shard[v_i0, v_i1])
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redistribute_replica_to_shard[v_i0, v_i1] = A[v_i0, worker_id * T.int64(5) + v_i1]
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@R.function
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def main(x: R.Tensor((10, 10), dtype="float32")) -> R.Tensor((10, 5), dtype="float32"):
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worker_id = T.int64()
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cls = Expected
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gv: R.Shape(ndim=-1) = R.call_pure_packed("runtime.disco.worker_id", ty_args=(R.Shape(ndim=-1),))
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gv1: R.Shape([worker_id]) = R.match_cast(gv, R.Shape([worker_id]))
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gv0 = R.call_tir(cls.strided_slice, (x,), out_ty=R.Tensor((10, 5), dtype="float32"), tir_vars=R.shape([worker_id]))
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return gv0
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# fmt: on
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mod = LegalizeOps()(Before)
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tvm.ir.assert_structural_equal(mod, Expected)
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if __name__ == "__main__":
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tvm.testing.main()
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