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c2taco
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C2TACO: Lifting Tensor Code to TACO
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# C2TACO: Lifting Tensor Code to TACO C2TACO is a lifting tool for synthesizing TACO, a well-known tensor DSL, from C code. We develop a guided enumerative synthesizer that uses automatically generated IO examples and program analyses to efficiently generate code. C2TACO takes as input a C function and performs source code analyses to retrieve features from the said function and use them as search aid during synthesis. Examples of features extracted are the number of tensor references and constants, the order of each tensor, and the binary operators present in the implemenetation. Using these features, C2TACO builds a reduced search space where the solution is more likely to be. We use a bottom-up enumerative synthesis algortihm to enumerate template TACO programs, i.e., TACO programs that use symbolic variables in place of all tensors and constants. We then check whether there is a valid substitution of inputs and constant literals for these symbolic variables that satisfies the specification. In our case, the specification consists of input-output examples. In case the solution is not found in the initial search space, C2TACO then tries to use a simple version of enumerative template synthesis (refered here as ETS) to find the programs. ETS consider the whole space of programs of a given size and therefore is a slower process. # Installation Clone the C2TACO repository by: ``` $ git clone https://github.com/JWesleySM/c2taco ``` C2TACO is provided as a [Docker](https://www.docker.com/) artifact. You can install Docker following the official [instructions](https://docs.docker.com/get-docker/). To build the C2TACO Docker image, run the command below within the C2TACO directory: _Important_: Depending on privilegies, you might have to run Docker as sudo or have your user added to a group that can run Docker without root access. ``` $ docker build -t c2taco-docker -f docker/Dockerfile . ``` Once the image is built, that can be executed with the command: ``` $ docker run -ti -v /root/c2taco c2taco-docker ``` This will run C2TACO Docker image and leave you inside a container with all C2TACO completely configured. Notice that, by default, C2TACO Dockerfile downloads LLVM for x86. This can be changed in case you want to run it on other architectures. # Usage All the instructions in this section assume you have executed the commands above and are inside the C2TACO container. Given an C code that performs some C tensor manipulations, C2TACO will look for an equivalent program in TACO. Correctness is done by checking behavioral equivalente. Therefore, input-output examples are also required as input to the synthesizer. IO files are specified in the JSON format. If you want to automatically generate IO samples for a C function, take a look at . Run the synthesizer by providing both the original C implementation, the IO file, and the location of the Clang compiler as input. Inside the container, the Clang compiler will be in the parent directory: ``` $ python3 c2taco.py <path-to-c-program> <path-to-IO-samples> ../llvm/bin/clang ``` For example, to run C2TACO on the  program, run: ``` $ python3 c2taco.py ./benchmarks/artificial/add_array.c ./benchmarks/artificial/add_array_io.json ../llvm/bin/clang ``` C2TACO will print the solution in the standard output, but it will also generate a lifting log at `<path-to-c-program>/name-of-c-program-lifting.log`. Log generation can be disabled by adding the `--no_log` option to the command above. The lifting log contains the following statistics of the synthesis execution: * The solution itself * Search space size, i.e., number of candidates in the search space considered * Number of candidates tried * Number of candidates discarded by type checking * Number of candidates that produced runtime errors * Time for parsing IO files * Time for enumerate candidates * Time for checking candidates * Total synthesis time If a solution is not found, the log is still produced. If C2TACO has to use ETS, that information will also appear in the log file. Some examples of programs are available in the  directory. # Example Consider the program below, taken and adapted from the UTDSP digital signal processing benchmark suite: ```c 1. void mult(int A_ROW, int A_COL, int B_ROW, int B_COL, int a_matrix[A_ROW][A_COL], 2. int b_matrix[B_ROW][B_COL], int c_matrix[C_ROW][C_COL]){ 3. for (int i = 0; i < A_ROW; i++) { 4. for (int j = 0; j < B_COL; j++) { 5. int sum = 0; 6. for (int k = 0; k < B_ROW; ++k) { 7. sum += a_matrix[i * A_ROW + k] * b_matrix[k * B_ROW + j]; 8. } 9. c_matrix[i * A_ROW + j] = sum; 10. } 11. } 12. } ``` C2TACO receives that function together with IO samples obtained by running `mult`. Then, it perform three different static code analyses on this program. * program length: this analysis will determine that there are 3 references for non-local tensor variables on the program, `a_matrix` and `b_matrix` at line 7 and `c_matrix` at line 9. * tensor orders/dimensions: by performing a combination of array recover and delinearization, this analysis points out that each of the tensors involded in the computation have order = 2, i.e., they are matrices. * operators: this analysis accounts for arithmetic operators in relevant computations in the program. For `mult`, there is a multiplication (`*`) operator at line 7. Using the features extraced via the analyses, C2TACO starts the synthesis process searching specifically for TACO programs with 3 tensors, all of them having order = 2 and containing multiplication operators. The result of synthesis is a program written in the TACO index notation as shown below: ``` a(i,j) = b(i,k) * c(k,j) ``` which corresponds to matrix-matrix product. The program can then be passed as input to the TACO compiler, which can generate eihter high-performance C when targeting CPUs or CUDA code for GPUs. C version: ```c int compute(taco_tensor_t *a, taco_tensor_t *b, taco_tensor_t *c) { int a1_dimension = (int)(a->dimensions[0]); int a2_dimension = (int)(a->dimensions[1]); int32_t* restrict a_vals = (int32_t*)(a->vals); int b1_dimension = (int)(b->dimensions[0]); int b2_dimension = (int)(b->dimensions[1]); int32_t* restrict b_vals = (int32_t*)(b->vals); int c1_dimension = (int)(c->dimensions[0]); int c2_dimension = (int)(c->dimensions[1]); int32_t* restrict c_vals = (int32_t*)(c->vals); #pragma omp parallel for schedule(static) for (int32_t pa = 0; pa < (a1_dimension * a2_dimension); pa++) { a_vals[pa] = 0; } #pragma omp parallel for schedule(runtime) for (int32_t i = 0; i < b1_dimension; i++) { for (int32_t k = 0; k < c1_dimension; k++) { int32_t kb = i * b2_dimension + k; for (int32_t j = 0; j < c2_dimension; j++) { int32_t ja = i * a2_dimension + j; int32_t jc = k * c2_dimension + j; a_vals[ja] = a_vals[ja] + b_vals[kb] * c_vals[jc]; } } } return 0; } ``` CUDA version: ```cuda void computeDeviceKernel0(taco_tensor_t * __restrict__ a, taco_tensor_t * __restrict__ b, taco_tensor_t * __restrict__ c){ int32_t i78 = blockIdx.x; int32_t i79 = (threadIdx.x % (256)); if (threadIdx.x >= 256) { return; } int32_t i = i78 * 256 + i79; if (i >= b1_dimension) return; for (int32_t k = 0; k < c1_dimension; k++) { int32_t kb = i * b2_dimension + k; for (int32_t j = 0; j < c2_dimension; j++) { int32_t ja = i * a2_dimension + j; int32_t jc = k * c2_dimension + j; a_vals[ja] = a_vals[ja] + b_vals[kb] * c_vals[jc]; } } } ``` If you want to reproduce this example from inside the container, run: ``` $ python3 c2taco.py ./benchmarks/real/utdsp/mult.c ./benchmarks/real/utdsp/mult_io.json ../llvm/bin/clang ``` # How to Cite If you use C2TACO, please refer the reference [paper](https://dl.acm.org/doi/10.1145/3624007.3624053): ```bibtex @inproceedings{10.1145/3624007.3624053, author = {Magalh\~{a}es, Jos\'{e} Wesley de Souza and Woodruff, Jackson and Polgreen, Elizabeth and O'Boyle, Michael F. P.}, title = {C2TACO: Lifting Tensor Code to TACO}, year = {2023}, isbn = {9798400704062}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3624007.3624053}, doi = {10.1145/3624007.3624053}, abstract = {Domain-specific languages (DSLs) promise a significant performance and portability advantage over traditional languages. DSLs are designed to be high-level and platform-independent, allowing an optimizing compiler significant leeway when targeting a particular device. Such languages are particularly popular with emerging tensor algebra workloads. However, DSLs present their own challenge: they require programmers to learn new programming languages and put in significant effort to migrate legacy code. We present C2TACO, a synthesis tool for synthesizing TACO, a well-known tensor DSL, from C code. We develop a smart, enumerative synthesizer that uses automatically generated IO examples and source-code analysis to efficiently generate code. C2TACO is able to synthesize 95\% bench marks from a tensor benchmark suite, out-performing an alternative neural machine translation technique, and demonstrates substantially higher levels of accuracy when evaluated against two state-of-the-art existing schemes, TF-Coder and ChatGPT. Our synthesized TACO programs are, by design, portable achieving significant performance improvement when evaluated on a multi-core and GPU platform.}, booktitle = {Proceedings of the 22nd ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences}, pages = {42–56}, numpages = {15}, keywords = {Program Lifting, TACO, Synthesis, Tensor Algebra}, location = {Cascais, Portugal}, series = {GPCE 2023} } ```
