wehub-resource-sync
154 lines
5.2 KiB
Python
154 lines
5.2 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
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import tvm.testing
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from tvm.ir import Range
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from tvm.relax import TensorType
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from tvm.relax.distributed import DeviceMesh, DTensorType, Placement
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from tvm.script.parser import ir as I
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from tvm.script.parser import relax as R
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from tvm.script.parser import tirx as T
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def _assert_print(obj, expected):
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if not isinstance(obj, str):
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obj = obj.script(verbose_expr=True)
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obj = obj.strip()
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assert obj == expected.strip(), "\n" + obj
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def test_constant():
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constant = R.dist.const(
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1,
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ty=R.DTensor((), "float32", device_mesh=DeviceMesh((2, 2), Range(0, 4)), placement="R, R"),
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)
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assert (
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constant.__str__()
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== """R.dist.const(1.0, R.DTensor((), "float32", R.device_mesh((2, 2), R.Range(0, 4)), "R, R"))"""
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)
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def test_dtensor_type():
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tensor_ty1 = TensorType((32, 32), "float32")
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tensor_ty2 = TensorType((32, 32), None)
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obj0 = DTensorType(tensor_ty1, DeviceMesh((2, 2), Range(0, 4)), Placement.from_text("S[1], R"))
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assert (
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obj0.__str__()
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== """R.DTensor((32, 32), "float32", R.device_mesh((2, 2), R.Range(0, 4)), "S[1], R")"""
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)
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obj1 = DTensorType(tensor_ty2, DeviceMesh((2, 2), Range(0, 4)), Placement.from_text("S[1], R"))
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assert (
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obj1.__str__()
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== """R.DTensor((32, 32), device_mesh=R.device_mesh((2, 2), R.Range(0, 4)), placement="S[1], R")"""
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)
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obj2 = DTensorType(tensor_ty2, DeviceMesh((2, 2), [0, 1, 2, 3]), Placement.from_text("S[1], R"))
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assert (
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obj2.__str__()
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== """R.DTensor((32, 32), device_mesh=R.device_mesh((2, 2), [0, 1, 2, 3]), placement="S[1], R")"""
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)
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@I.ir_module(s_tir=True)
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class TestModule:
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I.module_attrs({"device_num": 10})
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I.module_global_infos(
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{
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"mesh": [
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R.device_mesh((2, 2), I.Range(0, 4)), # mesh[0]
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R.device_mesh((1,), I.Range(4, 5)), # mesh[1]
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]
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}
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)
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@T.prim_func(s_tir=True)
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def tir_func(
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x: T.Buffer((T.int64(128), T.int64(128)), "float32"),
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y: T.Buffer((T.int64(128), T.int64(128)), "float32"),
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):
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T.func_attr({"tirx.noalias": True})
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for i, j in T.grid(T.int64(128), T.int64(128)):
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with T.sblock():
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vi, vj = T.axis.remap("SS", [i, j])
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y[vi, vj] = x[vi, vj] + 1.0
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@R.function
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def foo(
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x: R.DTensor((128, 128), "float32", device_mesh="mesh[0]", placement="S[0], R"),
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) -> R.DTensor((128, 128), "float32", device_mesh="mesh[0]", placement="S[0], R"):
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gv0 = R.dist.call_tir(
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TestModule.tir_func,
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x,
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R.DTensor(
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shape=(128, 128), dtype="float32", device_mesh="mesh[0]", placement="S[0], R"
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),
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)
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return gv0
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def test_func():
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_assert_print(
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TestModule["foo"],
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"""
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# from tvm.script import relax as R
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@R.function
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def foo(x: R.DTensor((128, 128), "float32", R.device_mesh((2, 2), R.Range(0, 4)), "S[0], R")) -> R.DTensor((128, 128), "float32", R.device_mesh((2, 2), R.Range(0, 4)), "S[0], R"):
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gv0 = R.dist.call_tir(tir_func, (x,), out_ty=R.DTensor((128, 128), "float32", R.device_mesh((2, 2), R.Range(0, 4)), "S[0], R"))
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return gv0
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""",
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)
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def test_module():
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_assert_print(
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TestModule,
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"""
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# from tvm.script import ir as I
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# from tvm.script import tirx as T
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# from tvm.tirx.layout import Axis
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# from tvm.script import relax as R
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@I.ir_module
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class Module:
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I.module_attrs({"device_num": 10})
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I.module_global_infos({"mesh": [R.device_mesh((2, 2), I.Range(0, 4)), R.device_mesh((1,), I.Range(4, 5))]})
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@T.prim_func(s_tir=True)
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def tir_func(x: T.Buffer((T.int64(128), T.int64(128)), "float32"), y: T.Buffer((T.int64(128), T.int64(128)), "float32")):
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T.func_attr({"tirx.noalias": True})
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# with T.sblock("root"):
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for i, j in T.grid(T.int64(128), T.int64(128)):
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with T.sblock(""):
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vi, vj = T.axis.remap("SS", [i, j])
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T.reads(x[vi, vj])
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T.writes(y[vi, vj])
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y[vi, vj] = x[vi, vj] + T.float32(1.0)
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@R.function
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def foo(x: R.DTensor((128, 128), "float32", "mesh[0]", "S[0], R")) -> R.DTensor((128, 128), "float32", "mesh[0]", "S[0], R"):
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cls = Module
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gv0 = R.dist.call_tir(cls.tir_func, (x,), out_ty=R.DTensor((128, 128), "float32", "mesh[0]", "S[0], R"))
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return gv0
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""",
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)
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if __name__ == "__main__":
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tvm.testing.main()
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