wehub-resource-sync
260 lines
10 KiB
Python
260 lines
10 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: F401
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import pytest
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import tvm
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import tvm.testing
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from tvm import relax, tirx
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from tvm.ir import Op
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from tvm.script import relax as R
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def test_op_correctness():
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x = relax.Var("x", R.Tensor((2, 3), "float32"))
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assert relax.op.ccl.allreduce(x).op == Op.get("relax.ccl.allreduce")
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assert relax.op.ccl.broadcast_from_worker0(x).op == Op.get("relax.ccl.broadcast_from_worker0")
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assert relax.op.ccl.allgather(x, 2).op == Op.get("relax.ccl.allgather")
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def _check_inference(bb: relax.BlockBuilder, call: relax.Call, expected_ty: relax.Type):
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ret = bb.normalize(call)
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tvm.ir.assert_structural_equal(ret.ty, expected_ty)
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def test_allreduce_infer_ty():
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bb = relax.BlockBuilder()
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x0 = relax.Var("x", R.Tensor((2, 3), "float32"))
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x1 = relax.Var("x", R.Tensor("float32", ndim=3))
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x2 = relax.Var("x", R.Tensor("float32", ndim=-1))
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x3 = relax.Var("x", R.Tensor((2, 3)))
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x4 = relax.Var("x", R.Tensor())
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x5 = relax.Var("x", R.Tensor((3, 4)))
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_check_inference(bb, relax.op.ccl.allreduce(x0), relax.TensorType((2, 3), "float32"))
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_check_inference(bb, relax.op.ccl.allreduce(x1), relax.TensorType(dtype="float32", ndim=3))
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_check_inference(bb, relax.op.ccl.allreduce(x2), relax.TensorType(dtype="float32"))
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_check_inference(bb, relax.op.ccl.allreduce(x3), relax.TensorType((2, 3), dtype=None))
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_check_inference(bb, relax.op.ccl.allreduce(x4), relax.TensorType(dtype=None))
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_check_inference(bb, relax.op.ccl.allreduce(x5), relax.TensorType((3, 4), dtype=None))
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def test_allreduce_infer_ty_shape_symbolic():
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bb = relax.BlockBuilder()
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m = tirx.Var("m", "int64")
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n = tirx.Var("n", "int64")
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x0 = relax.Var("x", R.Tensor((m, n), "float32"))
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x1 = relax.Var("x", R.Tensor((4, n), "float32"))
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_check_inference(bb, relax.op.ccl.allreduce(x0), relax.TensorType((m, n), "float32"))
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_check_inference(bb, relax.op.ccl.allreduce(x1), relax.TensorType((4, n), "float32"))
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def test_allreduce_infer_ty_shape_var():
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bb = relax.BlockBuilder()
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s0 = relax.Var("s", relax.ShapeType(ndim=2))
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s1 = relax.Var("s", relax.ShapeType())
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x0 = relax.Var("x", relax.TensorType(s0, "float32"))
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x1 = relax.Var("x", relax.TensorType(s1, "float32"))
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_check_inference(bb, relax.op.ccl.allreduce(x0), relax.TensorType(s0, "float32"))
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_check_inference(bb, relax.op.ccl.allreduce(x1), relax.TensorType(s1, "float32"))
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def test_allreduce_infer_ty_more_input_dtype():
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bb = relax.BlockBuilder()
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x0 = relax.Var("x", R.Tensor((2, 3), "float64"))
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x1 = relax.Var("x", R.Tensor((2, 3), "int8"))
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x2 = relax.Var("x", R.Tensor((2, 3), "int64"))
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_check_inference(bb, relax.op.ccl.allreduce(x0), relax.TensorType((2, 3), "float64"))
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_check_inference(bb, relax.op.ccl.allreduce(x1), relax.TensorType((2, 3), "int8"))
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_check_inference(bb, relax.op.ccl.allreduce(x2), relax.TensorType((2, 3), "int64"))
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def test_allgather_infer_ty():
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bb = relax.BlockBuilder()
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x0 = relax.Var("x", R.Tensor((2, 3), "float32"))
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x1 = relax.Var("x", R.Tensor("float32", ndim=3))
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x2 = relax.Var("x", R.Tensor("float32", ndim=-1))
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x3 = relax.Var("x", R.Tensor((2, 3)))
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x4 = relax.Var("x", R.Tensor())
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x5 = relax.Var("x", R.Tensor((3, 4)))
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_check_inference(bb, relax.op.ccl.allgather(x0, 2), relax.TensorType((4, 3), "float32"))
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_check_inference(bb, relax.op.ccl.allgather(x1, 2), relax.TensorType(dtype="float32", ndim=3))
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_check_inference(bb, relax.op.ccl.allgather(x2, 2), relax.TensorType(dtype="float32"))
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_check_inference(bb, relax.op.ccl.allgather(x3, 2), relax.TensorType((4, 3), dtype=None))
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_check_inference(bb, relax.op.ccl.allgather(x4, 2), relax.TensorType(dtype=None))
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_check_inference(bb, relax.op.ccl.allgather(x5, 2), relax.TensorType((6, 4), dtype=None))
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def test_allgather_infer_ty_shape_symbolic():
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bb = relax.BlockBuilder()
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m = tirx.Var("m", "int64")
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n = tirx.Var("n", "int64")
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x0 = relax.Var("x", R.Tensor((m, n), "float32"))
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x1 = relax.Var("x", R.Tensor((4, n), "float32"))
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_check_inference(bb, relax.op.ccl.allgather(x0, 2), relax.TensorType((m * 2, n), "float32"))
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_check_inference(bb, relax.op.ccl.allgather(x1, 2), relax.TensorType((8, n), "float32"))
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def test_allgather_infer_ty_shape_var():
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bb = relax.BlockBuilder()
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s0 = relax.Var("s", relax.ShapeType(ndim=2))
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s1 = relax.Var("s", relax.ShapeType())
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x0 = relax.Var("x", relax.TensorType(s0, "float32"))
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x1 = relax.Var("x", relax.TensorType(s1, "float32"))
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_check_inference(bb, relax.op.ccl.allgather(x0, 2), relax.TensorType(s0, "float32"))
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_check_inference(bb, relax.op.ccl.allgather(x1, 2), relax.TensorType(s1, "float32"))
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def test_allgather_infer_ty_more_input_dtype():
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bb = relax.BlockBuilder()
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x0 = relax.Var("x", R.Tensor((2, 3), "float64"))
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x1 = relax.Var("x", R.Tensor((2, 3), "int8"))
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x2 = relax.Var("x", R.Tensor((2, 3), "int64"))
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_check_inference(bb, relax.op.ccl.allgather(x0, 2), relax.TensorType((4, 3), "float64"))
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_check_inference(bb, relax.op.ccl.allgather(x1, 2), relax.TensorType((4, 3), "int8"))
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_check_inference(bb, relax.op.ccl.allgather(x2, 2), relax.TensorType((4, 3), "int64"))
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def test_broadcast_from_worker0_infer_ty():
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bb = relax.BlockBuilder()
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x0 = relax.Var("x", R.Tensor((2, 3), "float32"))
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x1 = relax.Var("x", R.Tensor("float32", ndim=3))
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x2 = relax.Var("x", R.Tensor("float32", ndim=-1))
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x3 = relax.Var("x", R.Tensor((2, 3)))
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x4 = relax.Var("x", R.Tensor())
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x5 = relax.Var("x", R.Tensor((3, 4)))
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_check_inference(
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bb, relax.op.ccl.broadcast_from_worker0(x0), relax.TensorType((2, 3), "float32")
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)
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_check_inference(
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bb, relax.op.ccl.broadcast_from_worker0(x1), relax.TensorType(dtype="float32", ndim=3)
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)
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_check_inference(bb, relax.op.ccl.broadcast_from_worker0(x2), relax.TensorType(dtype="float32"))
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_check_inference(
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bb, relax.op.ccl.broadcast_from_worker0(x3), relax.TensorType((2, 3), dtype=None)
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)
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_check_inference(bb, relax.op.ccl.broadcast_from_worker0(x4), relax.TensorType(dtype=None))
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_check_inference(
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bb, relax.op.ccl.broadcast_from_worker0(x5), relax.TensorType((3, 4), dtype=None)
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)
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def test_broadcast_from_worker0_infer_ty_shape_symbolic():
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bb = relax.BlockBuilder()
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m = tirx.Var("m", "int64")
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n = tirx.Var("n", "int64")
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x0 = relax.Var("x", R.Tensor((m, n), "float32"))
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x1 = relax.Var("x", R.Tensor((4, n), "float32"))
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_check_inference(
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bb, relax.op.ccl.broadcast_from_worker0(x0), relax.TensorType((m, n), "float32")
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)
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_check_inference(
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bb, relax.op.ccl.broadcast_from_worker0(x1), relax.TensorType((4, n), "float32")
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)
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def test_broadcast_from_worker0_infer_ty_shape_var():
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bb = relax.BlockBuilder()
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s0 = relax.Var("s", relax.ShapeType(ndim=2))
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s1 = relax.Var("s", relax.ShapeType())
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x0 = relax.Var("x", relax.TensorType(s0, "float32"))
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x1 = relax.Var("x", relax.TensorType(s1, "float32"))
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_check_inference(bb, relax.op.ccl.broadcast_from_worker0(x0), relax.TensorType(s0, "float32"))
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_check_inference(bb, relax.op.ccl.broadcast_from_worker0(x1), relax.TensorType(s1, "float32"))
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def test_broadcast_from_worker0_infer_ty_more_input_dtype():
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bb = relax.BlockBuilder()
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x0 = relax.Var("x", R.Tensor((2, 3), "float64"))
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x1 = relax.Var("x", R.Tensor((2, 3), "int8"))
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x2 = relax.Var("x", R.Tensor((2, 3), "int64"))
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_check_inference(
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bb, relax.op.ccl.broadcast_from_worker0(x0), relax.TensorType((2, 3), "float64")
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)
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_check_inference(bb, relax.op.ccl.broadcast_from_worker0(x1), relax.TensorType((2, 3), "int8"))
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_check_inference(bb, relax.op.ccl.broadcast_from_worker0(x2), relax.TensorType((2, 3), "int64"))
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def test_scatter_from_worker0_infer_ty():
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bb = relax.BlockBuilder()
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x0 = relax.Var("x", R.Tensor((2, 3), "float32"))
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x1 = relax.Var("x", R.Tensor((3, 4, 5)))
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_check_inference(
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bb, relax.op.ccl.scatter_from_worker0(x0, 2), relax.TensorType((1, 3), "float32")
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)
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_check_inference(
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bb, relax.op.ccl.scatter_from_worker0(x1, 3), relax.TensorType((1, 4, 5), dtype=None)
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)
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def test_scatter_from_worker0_infer_ty_shape_symbolic():
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bb = relax.BlockBuilder()
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m = tirx.Var("m", "int64")
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n = tirx.Var("n", "int64")
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x0 = relax.Var("x", R.Tensor((m, n), "float32"))
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x1 = relax.Var("x", R.Tensor((4, n), "float32"))
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_check_inference(
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bb,
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relax.op.ccl.scatter_from_worker0(x0, 2),
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relax.TensorType((tirx.div(m, 2), n), "float32"),
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)
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_check_inference(
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bb, relax.op.ccl.scatter_from_worker0(x1, 2), relax.TensorType((2, n), "float32")
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)
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def test_scatter_from_worker0_infer_ty_shape_var():
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bb = relax.BlockBuilder()
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s0 = relax.Var("s", relax.ShapeType((2, 4, 8)))
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x0 = relax.Var("x", relax.TensorType(s0, "float32"))
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_check_inference(
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bb, relax.op.ccl.scatter_from_worker0(x0, 2), relax.TensorType((1, 4, 8), "float32")
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)
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def test_scatter_from_worker0_infer_ty_more_input_dtype():
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bb = relax.BlockBuilder()
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x0 = relax.Var("x", R.Tensor((2, 3), "float64"))
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x1 = relax.Var("x", R.Tensor((2, 3), "int8"))
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x2 = relax.Var("x", R.Tensor((2, 3), "int64"))
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_check_inference(
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bb, relax.op.ccl.scatter_from_worker0(x0, 2), relax.TensorType((1, 3), "float64")
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)
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_check_inference(bb, relax.op.ccl.scatter_from_worker0(x1, 2), relax.TensorType((1, 3), "int8"))
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_check_inference(
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bb, relax.op.ccl.scatter_from_worker0(x2, 2), relax.TensorType((1, 3), "int64")
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)
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if __name__ == "__main__":
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tvm.testing.main()
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