wehub-resource-sync
148 lines
6.8 KiB
Python
148 lines
6.8 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, F841
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import tvm
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import tvm.testing
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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_allreduce():
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# fmt: off
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@tvm.script.ir_module
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class AllReduce:
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@R.function
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def main(x: R.Tensor((10, 10), "float32")) -> R.Tensor((10, 10), "float32"):
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gv0: R.Tensor((10, 10), "float32") = R.ccl.allreduce(x, "sum")
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gv1: R.Tensor((10, 10), "float32") = R.ccl.allreduce(x, "prod")
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gv2: R.Tensor((10, 10), "float32") = R.ccl.allreduce(x, "min")
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gv3: R.Tensor((10, 10), "float32") = R.ccl.allreduce(x, "max")
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gv4: R.Tensor((10, 10), "float32") = R.ccl.allreduce(x, "avg")
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return x
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@I.ir_module(s_tir=True)
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class Expected:
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@R.function
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def main(x: R.Tensor((10, 10), dtype="float32")) -> R.Tensor((10, 10), dtype="float32"):
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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"))
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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"))
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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"))
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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"))
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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"))
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return x
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# fmt: on
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mod = LegalizeOps()(AllReduce)
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tvm.ir.assert_structural_equal(mod, Expected)
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def test_allgather():
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# fmt: off
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@tvm.script.ir_module
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class AllGather:
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@R.function
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def main(x: R.Tensor((10, 10), "float32")) -> R.Tensor((10, 10), "float32"):
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gv0: R.Tensor((20, 10), "float32") = R.ccl.allgather(x, 2)
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gv1 = R.ccl.allgather(x, 2)
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return x
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@I.ir_module(s_tir=True)
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class Expected:
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@R.function
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def main(x: R.Tensor((10, 10), dtype="float32")) -> R.Tensor((10, 10), dtype="float32"):
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gv0: R.Tensor((20, 10), dtype="float32") = R.call_dps_packed("runtime.disco.allgather", [x, True], out_ty=R.Tensor((20, 10), dtype="float32"))
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gv1: R.Tensor((20, 10), dtype="float32") = R.call_dps_packed("runtime.disco.allgather", [x, True], out_ty=R.Tensor((20, 10), dtype="float32"))
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return x
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# fmt: on
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mod = LegalizeOps()(AllGather)
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tvm.ir.assert_structural_equal(mod, Expected)
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def test_broadcast_from_zero():
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# fmt: off
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@tvm.script.ir_module
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class BroadcastFromZero:
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@R.function
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def main(x: R.Tensor((10, 10), "float32")) -> R.Tensor((10, 10), "float32"):
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gv0: R.Tensor((10, 10), "float32") = R.ccl.broadcast_from_worker0(x)
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return x
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@I.ir_module(s_tir=True)
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class Expected:
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@R.function
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def main(x: R.Tensor((10, 10), dtype="float32")) -> R.Tensor((10, 10), dtype="float32"):
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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"))
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return x
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# fmt: on
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mod = LegalizeOps()(BroadcastFromZero)
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tvm.ir.assert_structural_equal(mod, Expected)
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def test_scatter_from_worker0():
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# fmt: off
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@tvm.script.ir_module
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class ScatterFromWorker0:
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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.Tensor((10,5), "float32") = R.ccl.scatter_from_worker0(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 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")):
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T.func_attr({"tirx.noalias": True})
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# with T.sblock("root"):
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for ax0, ax1, ax2 in T.grid(T.int64(10), T.int64(2), T.int64(5)):
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with T.sblock("T_reshape"):
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v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2])
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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)])
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T.writes(T_reshape[v_ax0, v_ax1, v_ax2])
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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)]
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@T.prim_func(private=True, s_tir=True)
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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")):
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T.func_attr({"tirx.noalias": True})
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# with T.sblock("root"):
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for ax0, ax1, ax2 in T.grid(T.int64(2), T.int64(10), T.int64(5)):
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with T.sblock("T_transpose"):
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v_ax0, v_ax1, v_ax2 = T.axis.remap("SSS", [ax0, ax1, ax2])
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T.reads(A[v_ax1, v_ax0, v_ax2])
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T.writes(T_transpose[v_ax0, v_ax1, v_ax2])
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T_transpose[v_ax0, v_ax1, v_ax2] = A[v_ax1, v_ax0, v_ax2]
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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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cls = Expected
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gv = R.call_tir(cls.reshape, (x,), out_ty=R.Tensor((10, 2, 5), dtype="float32"))
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gv1 = R.call_tir(cls.transpose, (gv,), out_ty=R.Tensor((2, 10, 5), dtype="float32"))
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gv0 = R.call_dps_packed("runtime.disco.scatter_from_worker0", (gv1, False), out_ty=R.Tensor((10, 5), dtype="float32"))
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return gv0
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# fmt: on
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mod = LegalizeOps()(ScatterFromWorker0)
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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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