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Added isnan, isfinite and isinf for SYCL device. Plus test for that.
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@@ -98,9 +98,36 @@ template <typename T> T inverse(T x) { return 1 / x; }
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TEST_UNARY_BUILTINS_FOR_SCALAR(round, SCALAR, OPERATOR) \
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TEST_UNARY_BUILTINS_FOR_SCALAR(log1p, SCALAR, OPERATOR)
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#define TEST_IS_THAT_RETURNS_BOOL(SCALAR, FUNC) \
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{ \
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/* out OPERATOR in.FUNC() */ \
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Tensor<SCALAR, 3> in(tensorRange); \
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Tensor<bool, 3> out(tensorRange); \
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in = in.random() + static_cast<SCALAR>(0.01); \
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SCALAR *gpu_data = static_cast<SCALAR *>( \
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sycl_device.allocate(in.size() * sizeof(SCALAR))); \
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bool *gpu_data_out = \
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static_cast<bool *>(sycl_device.allocate(out.size() * sizeof(bool))); \
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TensorMap<Tensor<SCALAR, 3>> gpu(gpu_data, tensorRange); \
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TensorMap<Tensor<bool, 3>> gpu_out(gpu_data_out, tensorRange); \
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sycl_device.memcpyHostToDevice(gpu_data, in.data(), \
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(in.size()) * sizeof(SCALAR)); \
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gpu_out.device(sycl_device) = gpu.FUNC(); \
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sycl_device.memcpyDeviceToHost(out.data(), gpu_data_out, \
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(out.size()) * sizeof(bool)); \
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for (int i = 0; i < out.size(); ++i) { \
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VERIFY_IS_EQUAL(out(i), std::FUNC(in(i))); \
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} \
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sycl_device.deallocate(gpu_data); \
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sycl_device.deallocate(gpu_data_out); \
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}
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#define TEST_UNARY_BUILTINS(SCALAR) \
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TEST_UNARY_BUILTINS_OPERATOR(SCALAR, += ) \
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TEST_UNARY_BUILTINS_OPERATOR(SCALAR, = )
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TEST_UNARY_BUILTINS_OPERATOR(SCALAR, = ) \
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TEST_IS_THAT_RETURNS_BOOL(SCALAR, isnan) \
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TEST_IS_THAT_RETURNS_BOOL(SCALAR, isfinite) \
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TEST_IS_THAT_RETURNS_BOOL(SCALAR, isinf)
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static void test_builtin_unary_sycl(const Eigen::SyclDevice &sycl_device) {
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int sizeDim1 = 10;
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