wehub-resource-sync
425 lines
14 KiB
Python
425 lines
14 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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import tvm
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import tvm.script
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import tvm.testing
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from tvm import IRModule, relax
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from tvm.script import relax as R
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def _check(
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parsed: relax.Function | IRModule,
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expect: relax.Function | IRModule | None,
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):
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test = parsed.script(show_meta=True)
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roundtrip_mod = tvm.script.from_source(test)
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tvm.ir.assert_structural_equal(parsed, roundtrip_mod)
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if expect:
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tvm.ir.assert_structural_equal(parsed, expect)
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def test_conv1d():
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@R.function
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def foo(x: R.Tensor((2, 3, 228), "float16"), w: R.Tensor((16, 3, 5), "float16")) -> R.Tensor(
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(2, 16, 224), "float16"
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):
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gv: R.Tensor((2, 16, 224), "float16") = R.nn.conv1d(x, w, out_dtype="float16")
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return gv
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x = relax.Var("x", R.Tensor([2, 3, 228], "float16"))
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w = relax.Var("w", R.Tensor([16, 3, 5], "float16"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x, w]):
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gv = bb.emit(relax.op.nn.conv1d(x, w, out_dtype="float16"))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_conv1d_transpose():
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@R.function
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def foo(x: R.Tensor((2, 3, 228), "float16"), w: R.Tensor((3, 16, 5), "float16")) -> R.Tensor(
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(2, 16, 232), "float16"
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):
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gv: R.Tensor((2, 16, 232), "float16") = R.nn.conv1d_transpose(x, w, out_dtype="float16")
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return gv
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x = relax.Var("x", R.Tensor([2, 3, 228], "float16"))
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w = relax.Var("w", R.Tensor([3, 16, 5], "float16"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x, w]):
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gv = bb.emit(relax.op.nn.conv1d_transpose(x, w, out_dtype="float16"))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_conv2d():
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@R.function
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def foo(
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x: R.Tensor((2, 3, 228, 228), "float16"), w: R.Tensor((16, 3, 5, 5), "float16")
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) -> R.Tensor((2, 16, 224, 224), "float16"):
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gv: R.Tensor((2, 16, 224, 224), "float16") = R.nn.conv2d(x, w, out_dtype="float16")
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return gv
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x = relax.Var("x", R.Tensor([2, 3, 228, 228], "float16"))
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w = relax.Var("w", R.Tensor([16, 3, 5, 5], "float16"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x, w]):
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gv = bb.emit(relax.op.nn.conv2d(x, w, out_dtype="float16"))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_conv2d_transpose():
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@R.function
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def foo(
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x: R.Tensor((2, 3, 228, 228), "float16"), w: R.Tensor((3, 16, 5, 5), "float16")
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) -> R.Tensor((2, 16, 232, 232), "float16"):
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gv: R.Tensor((2, 16, 232, 232), "float16") = R.nn.conv2d_transpose(
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x, w, out_dtype="float16"
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)
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return gv
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x = relax.Var("x", R.Tensor([2, 3, 228, 228], "float16"))
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w = relax.Var("w", R.Tensor([3, 16, 5, 5], "float16"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x, w]):
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gv = bb.emit(relax.op.nn.conv2d_transpose(x, w, out_dtype="float16"))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_conv3d():
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@R.function
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def foo(
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x: R.Tensor((2, 3, 8, 8, 8), "float16"), w: R.Tensor((6, 3, 3, 3, 3), "float16")
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) -> R.Tensor((2, 6, 6, 6, 6), "float16"):
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gv: R.Tensor((2, 6, 6, 6, 6), "float16") = R.nn.conv3d(x, w, out_dtype="float16")
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return gv
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x = relax.Var("x", R.Tensor([2, 3, 8, 8, 8], "float16"))
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w = relax.Var("w", R.Tensor([6, 3, 3, 3, 3], "float16"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x, w]):
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gv = bb.emit(relax.op.nn.conv3d(x, w, out_dtype="float16"))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_conv3d_transpose():
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@R.function
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def foo(
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x: R.Tensor((2, 3, 8, 8, 8), "float16"), w: R.Tensor((3, 6, 3, 3, 3), "float16")
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) -> R.Tensor((2, 6, 10, 10, 10), "float16"):
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gv: R.Tensor((2, 6, 10, 10, 10), "float16") = R.nn.conv3d_transpose(
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x, w, out_dtype="float16"
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)
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return gv
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x = relax.Var("x", R.Tensor([2, 3, 8, 8, 8], "float16"))
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w = relax.Var("w", R.Tensor([3, 6, 3, 3, 3], "float16"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x, w]):
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gv = bb.emit(relax.op.nn.conv3d_transpose(x, w, out_dtype="float16"))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_max_pool2d():
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@R.function
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def foo(x: R.Tensor((1, 1, 32, 32), dtype="float32")) -> R.Tensor(
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(1, 1, 30, 30), dtype="float32"
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):
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gv: R.Tensor((1, 1, 30, 30), dtype="float32") = R.nn.max_pool2d(x, pool_size=(3,))
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return gv
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x = relax.Var("x", R.Tensor([1, 1, 32, 32], "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x]):
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gv = bb.emit(relax.op.nn.max_pool2d(x, pool_size=(3,)))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_avg_pool2d():
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@R.function
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def foo(x: R.Tensor((1, 1, 32, 32), dtype="float32")) -> R.Tensor(
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(1, 1, 30, 30), dtype="float32"
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):
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gv: R.Tensor((1, 1, 30, 30), dtype="float32") = R.nn.avg_pool2d(x, pool_size=(3,))
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return gv
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x = relax.Var("x", R.Tensor([1, 1, 32, 32], "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x]):
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gv = bb.emit(relax.op.nn.avg_pool2d(x, pool_size=(3,)))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_adaptive_avg_pool2d():
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@R.function
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def foo(x: R.Tensor((2, 64, 8, 9), "float32")) -> R.Tensor((2, 64, 7, 7), "float32"):
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gv: R.Tensor((2, 64, 7, 7), "float32") = R.nn.adaptive_avg_pool2d(x, output_size=(7, 7))
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return gv
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x = relax.Var("x", R.Tensor((2, 64, 8, 9), dtype="float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x]):
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gv = bb.emit(relax.op.nn.adaptive_avg_pool2d(x, output_size=(7, 7)))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_gelu():
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@R.function
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def foo(x: R.Tensor((2, 3), "float32")) -> R.Tensor((2, 3), "float32"):
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gv: R.Tensor((2, 3), "float32") = R.nn.gelu(x)
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return gv
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x = relax.Var("x", R.Tensor((2, 3), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x]):
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gv = bb.emit(relax.op.nn.gelu(x))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_softmax():
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@R.function
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def foo(x: R.Tensor((2, 3), "float32")) -> R.Tensor((2, 3), "float32"):
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gv: R.Tensor((2, 3), "float32") = R.nn.softmax(x)
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return gv
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x = relax.Var("x", R.Tensor((2, 3), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x]):
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gv = bb.emit(relax.op.nn.softmax(x))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_log_softmax():
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@R.function
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def foo(x: R.Tensor((2, 3), "float32")) -> R.Tensor((2, 3), "float32"):
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gv: R.Tensor((2, 3), "float32") = R.nn.log_softmax(x)
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return gv
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x = relax.Var("x", R.Tensor((2, 3), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x]):
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gv = bb.emit(relax.op.nn.log_softmax(x))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_batch_norm():
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@R.function
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def foo(
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x: R.Tensor((2, 4, 3, 3), dtype="float32"),
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gamma: R.Tensor((4,), dtype="float32"),
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beta: R.Tensor((4,), dtype="float32"),
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moving_mean: R.Tensor((4,), dtype="float32"),
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moving_var: R.Tensor((4,), dtype="float32"),
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) -> R.Tuple(
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R.Tensor((2, 4, 3, 3), dtype="float32"),
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R.Tensor((4,), dtype="float32"),
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R.Tensor((4,), dtype="float32"),
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):
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gv: R.Tuple(
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R.Tensor((2, 4, 3, 3), dtype="float32"),
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R.Tensor((4,), dtype="float32"),
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R.Tensor((4,), dtype="float32"),
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) = R.nn.batch_norm(x, gamma, beta, moving_mean, moving_var, axis=1)
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return gv
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x = relax.Var("x", R.Tensor((2, 4, 3, 3), "float32"))
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gamma = relax.Var("gamma", R.Tensor((4,), "float32"))
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beta = relax.Var("beta", R.Tensor((4,), "float32"))
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moving_mean = relax.Var("moving_mean", R.Tensor((4,), "float32"))
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moving_var = relax.Var("moving_var", R.Tensor((4,), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x, gamma, beta, moving_mean, moving_var]):
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gv = bb.emit(relax.op.nn.batch_norm(x, gamma, beta, moving_mean, moving_var, axis=1))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_layer_norm():
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@R.function
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def foo(
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x: R.Tensor((2, 3, 4, 5), "float32"),
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gamma: R.Tensor((4, 5), "float32"),
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beta: R.Tensor((4, 5), "float32"),
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) -> R.Tensor((2, 3, 4, 5), "float32"):
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gv: R.Tensor((2, 3, 4, 5), "float32") = R.nn.layer_norm(x, gamma, beta, axes=[-2, -1])
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return gv
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x = relax.Var("x", R.Tensor((2, 3, 4, 5), "float32"))
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gamma = relax.Var("gamma", R.Tensor((4, 5), "float32"))
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beta = relax.Var("beta", R.Tensor((4, 5), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x, gamma, beta]):
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gv = bb.emit(relax.op.nn.layer_norm(x, gamma, beta, axes=[-2, -1]))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_group_norm():
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@R.function
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def foo(
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x: R.Tensor((2, 4, 4, 5), "float32"),
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gamma: R.Tensor((4,), "float32"),
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beta: R.Tensor((4,), "float32"),
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) -> R.Tensor((2, 4, 4, 5), "float32"):
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gv: R.Tensor((2, 4, 4, 5), "float32") = R.nn.group_norm(
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x, gamma, beta, num_groups=2, channel_axis=1, axes=[2, 3]
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)
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return gv
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x = relax.Var("x", R.Tensor((2, 4, 4, 5), "float32"))
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gamma = relax.Var("gamma", R.Tensor((4,), "float32"))
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beta = relax.Var("beta", R.Tensor((4,), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x, gamma, beta]):
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gv = bb.emit(
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relax.op.nn.group_norm(x, gamma, beta, num_groups=2, channel_axis=1, axes=[2, 3])
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)
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_dropout():
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@R.function
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def foo(x: R.Tensor((2, 3), "float32")) -> R.Tuple(
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R.Tensor((2, 3), "float32"), R.Tensor((2, 3), "float32")
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):
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gv: R.Tuple(R.Tensor((2, 3), "float32"), R.Tensor((2, 3), "float32")) = R.nn.dropout(
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x, rate=0.5
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)
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return gv
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x = relax.Var("x", R.Tensor((2, 3), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x]):
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gv = bb.emit(relax.op.nn.dropout(x))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_cross_entropy_with_logits():
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@R.function
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def foo(
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predictions: R.Tensor((2, 3), "float32"), labels: R.Tensor((2, 3), "float32")
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) -> R.Tensor((), "float32"):
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gv: R.Tensor((), "float32") = R.nn.cross_entropy_with_logits(predictions, labels)
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return gv
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predictions = relax.Var("predictions", R.Tensor((2, 3), "float32"))
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labels = relax.Var("labels", R.Tensor((2, 3), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [predictions, labels]):
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gv = bb.emit(relax.op.nn.cross_entropy_with_logits(predictions, labels))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_nll_loss():
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@R.function
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def foo(
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predictions: R.Tensor((3, 5, 10, 10), dtype="float32"),
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targets: R.Tensor((3, 10, 10), dtype="int64"),
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weights: R.Tensor((5,), dtype="float32"),
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) -> R.Tensor((), dtype="float32"):
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gv: R.Tensor((), dtype="float32") = R.nn.nll_loss(predictions, targets, weights, "mean", -1)
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return gv
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predictions = relax.Var("predictions", R.Tensor((3, 5, 10, 10), "float32"))
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targets = relax.Var("targets", R.Tensor((3, 10, 10), "int64"))
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weights = relax.Var("weights", R.Tensor((5,), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [predictions, targets, weights]):
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gv = bb.emit(
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relax.op.nn.nll_loss(predictions, targets, weights, reduction="mean", ignore_index=-1)
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)
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_nll_loss_no_weights():
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@R.function
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def foo(
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predictions: R.Tensor((3, 5, 10, 10), dtype="float32"),
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targets: R.Tensor((3, 10, 10), dtype="int64"),
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) -> R.Tensor((), dtype="float32"):
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gv: R.Tensor((), dtype="float32") = R.nn.nll_loss(
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predictions, targets, reduction="mean", ignore_index=-1
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)
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return gv
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predictions = relax.Var("predictions", R.Tensor((3, 5, 10, 10), "float32"))
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targets = relax.Var("targets", R.Tensor((3, 10, 10), "int64"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [predictions, targets]):
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gv = bb.emit(relax.op.nn.nll_loss(predictions, targets, reduction="mean", ignore_index=-1))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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def test_prelu():
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@R.function
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def foo(
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x: R.Tensor((2, 4, 4, 5), "float32"),
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alpha: R.Tensor((1,), "float32"),
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) -> R.Tensor((2, 4, 4, 5), "float32"):
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gv: R.Tensor((2, 4, 4, 5), "float32") = R.nn.prelu(x, alpha)
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return gv
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x = relax.Var("x", R.Tensor((2, 4, 4, 5), "float32"))
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alpha = relax.Var("alpha", R.Tensor((1,), "float32"))
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bb = relax.BlockBuilder()
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with bb.function("foo", [x, alpha]):
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gv = bb.emit(relax.op.nn.prelu(x, alpha))
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bb.emit_func_output(gv)
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_check(foo, bb.get()["foo"])
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if __name__ == "__main__":
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tvm.testing.main()
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