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1# RUN: env SUPPORT_LIB=%mlir_c_runner_utils \2# RUN:   %PYTHON %s | FileCheck %s3 4import ctypes5import os6import sys7import tempfile8 9from mlir import ir10from mlir import runtime as rt11from mlir.dialects import builtin12from mlir.dialects import sparse_tensor as st13import numpy as np14 15_SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__))16sys.path.append(_SCRIPT_PATH)17from tools import sparsifier18 19 20def boilerplate():21    """Returns boilerplate main method."""22    return """23#Dense = #sparse_tensor.encoding<{24  map = (i, j) -> (i: dense, j: dense)25}>26 27#map = affine_map<(d0, d1) -> (d0, d1)>28func.func @add(%st_0 : tensor<3x4xf64, #Dense>,29               %st_1 : tensor<3x4xf64, #Dense>) attributes { llvm.emit_c_interface } {30  %out_st = tensor.empty() : tensor<3x4xf64, #Dense>31  %res = linalg.generic {indexing_maps = [#map, #map, #map],32                         iterator_types = ["parallel", "parallel"]}33                         ins(%st_0, %st_1 : tensor<3x4xf64, #Dense>, tensor<3x4xf64, #Dense>)34                         outs(%out_st : tensor<3x4xf64, #Dense>) {35  ^bb0(%in_0: f64, %in_1: f64, %out: f64):36    %2 = sparse_tensor.binary %in_0, %in_1 : f64, f64 to f6437    overlap = {38      ^bb0(%arg1: f64, %arg2: f64):39        %3 = arith.addf %arg1, %arg2 : f6440        sparse_tensor.yield %3 : f6441    }42    left = {43      ^bb0(%arg1: f64):44        sparse_tensor.yield %arg1 : f6445    }46    right = {47      ^bb0(%arg1: f64):48        sparse_tensor.yield %arg1 : f6449    }50    linalg.yield %2 : f6451  } -> tensor<3x4xf64, #Dense>52  sparse_tensor.print %res : tensor<3x4xf64, #Dense>53  return54}55"""56 57 58def main():59    support_lib = os.getenv("SUPPORT_LIB")60    assert support_lib is not None, "SUPPORT_LIB is undefined"61    if not os.path.exists(support_lib):62        raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), support_lib)63 64    # CHECK-LABEL: TEST: all dense65    # CHECK: ---- Sparse Tensor ----66    # CHECK: nse = 1267    # CHECK: dim = ( 3, 4 )68    # CHECK: lvl = ( 3, 4 )69    # CHECK: values : ( 1, 1, 0, 1, 0, 6, 2, 3, 0, 0, 0, 2 )70    # CHECK: ----71    print("\nTEST: all dense")72    with ir.Context() as ctx, ir.Location.unknown():73        compiler = sparsifier.Sparsifier(74            extras="sparse-assembler,",75            options="enable-runtime-library=false",76            opt_level=2,77            shared_libs=[support_lib],78        )79        module = ir.Module.parse(boilerplate())80        engine = compiler.compile_and_jit(module)81        print(module)82 83        a = np.array([1, 0, 0, 1, 0, 2, 2, 0, 0, 0, 0, 1], dtype=np.float64)84        b = np.array([0, 1, 0, 0, 0, 4, 0, 3, 0, 0, 0, 1], dtype=np.float64)85        mem_a = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(a)))86        mem_b = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(b)))87 88        # Invoke the kernel and get numpy output.89        # Built-in bufferization uses in-out buffers.90        engine.invoke("add", mem_a, mem_b)91 92 93if __name__ == "__main__":94    main()95