b1fa5ac3ba
This is a simple datatype with a few JSON utilities, and is independent of the underlying executor. The main motivation is to allow taking a dependency on it on the AOT side, and allow us build a correctly-sized buffer in the cases when the requested feature isn't supported by the model. This, in turn, allows us to grow the feature set supported by the compiler in a backward-compatible way; and also collect traces exposing the new features, but starting off the older model, and continue training from those new traces. Differential Revision: https://reviews.llvm.org/D124417
62 lines
2.0 KiB
C++
62 lines
2.0 KiB
C++
//===- TensorSpecTest.cpp - test for TensorSpec ---------------------------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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#include "llvm/Analysis/TensorSpec.h"
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#include "llvm/Support/Path.h"
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#include "llvm/Support/SourceMgr.h"
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#include "llvm/Testing/Support/SupportHelpers.h"
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#include "gtest/gtest.h"
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using namespace llvm;
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extern const char *TestMainArgv0;
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TEST(TensorSpecTest, JSONParsing) {
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auto Value = json::parse(
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R"({"name": "tensor_name",
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"port": 2,
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"type": "int32_t",
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"shape":[1,4]
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})");
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EXPECT_TRUE(!!Value);
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LLVMContext Ctx;
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Optional<TensorSpec> Spec = getTensorSpecFromJSON(Ctx, *Value);
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EXPECT_TRUE(Spec.hasValue());
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EXPECT_EQ(*Spec, TensorSpec::createSpec<int32_t>("tensor_name", {1, 4}, 2));
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}
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TEST(TensorSpecTest, JSONParsingInvalidTensorType) {
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auto Value = json::parse(
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R"(
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{"name": "tensor_name",
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"port": 2,
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"type": "no such type",
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"shape":[1,4]
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}
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)");
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EXPECT_TRUE(!!Value);
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LLVMContext Ctx;
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auto Spec = getTensorSpecFromJSON(Ctx, *Value);
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EXPECT_FALSE(Spec.hasValue());
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}
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TEST(TensorSpecTest, TensorSpecSizesAndTypes) {
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auto Spec1D = TensorSpec::createSpec<int16_t>("Hi1", {1});
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auto Spec2D = TensorSpec::createSpec<int16_t>("Hi2", {1, 1});
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auto Spec1DLarge = TensorSpec::createSpec<float>("Hi3", {10});
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auto Spec3DLarge = TensorSpec::createSpec<float>("Hi3", {2, 4, 10});
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EXPECT_TRUE(Spec1D.isElementType<int16_t>());
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EXPECT_FALSE(Spec3DLarge.isElementType<double>());
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EXPECT_EQ(Spec1D.getElementCount(), 1U);
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EXPECT_EQ(Spec2D.getElementCount(), 1U);
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EXPECT_EQ(Spec1DLarge.getElementCount(), 10U);
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EXPECT_EQ(Spec3DLarge.getElementCount(), 80U);
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EXPECT_EQ(Spec3DLarge.getElementByteSize(), sizeof(float));
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EXPECT_EQ(Spec1D.getElementByteSize(), sizeof(int16_t));
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}
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