179 lines · python
1# RUN: env SUPPORT_LIB=%mlir_c_runner_utils \2# RUN: %PYTHON %s | FileCheck %s3 4import ctypes5import numpy as np6import os7import sys8 9from mlir import ir10from mlir import runtime as rt11 12from mlir.dialects import sparse_tensor as st13from mlir.dialects import builtin14from mlir.dialects import func15from mlir.dialects.linalg.opdsl import lang as dsl16 17_SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__))18sys.path.append(_SCRIPT_PATH)19from tools import sparsifier20 21 22@dsl.linalg_structured_op23def sddmm_dsl(24 A=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.K),25 B=dsl.TensorDef(dsl.T, dsl.S.K, dsl.S.N),26 S=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.N),27 C=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.N, output=True),28):29 C[dsl.D.m, dsl.D.n] += (30 S[dsl.D.m, dsl.D.n] * A[dsl.D.m, dsl.D.k] * B[dsl.D.k, dsl.D.n]31 )32 33 34def build_SDDMM(attr: st.EncodingAttr):35 """Build SDDMM kernel.36 37 This method generates a linalg op with for matrix multiplication using38 just the Python API. Effectively, a generic linalg op is constructed39 that computes C(i,j) += S(i,j) SUM_k A(i,k) B(k,j) for sparse S.40 """41 module = ir.Module.create()42 f64 = ir.F64Type.get()43 a = ir.RankedTensorType.get([8, 8], f64)44 b = ir.RankedTensorType.get([8, 8], f64)45 c = ir.RankedTensorType.get([8, 8], f64)46 s = ir.RankedTensorType.get([8, 8], f64, attr)47 arguments = [a, b, s, c]48 with ir.InsertionPoint(module.body):49 50 @func.FuncOp.from_py_func(*arguments)51 def sddmm(*args):52 return sddmm_dsl(args[0], args[1], args[2], outs=[args[3]])53 54 return module55 56 57def boilerplate(attr: st.EncodingAttr):58 """Returns boilerplate code for main driver."""59 return f"""60func.func @main(%a: tensor<8x8xf64>,61 %b: tensor<8x8xf64>,62 %c: tensor<8x8xf64>) -> tensor<8x8xf64> attributes {{ llvm.emit_c_interface }} {{63 %t = arith.constant sparse<[[0,0], [0,2], [4,1]], [1.0, 2.0, 3.0]> : tensor<8x8xf64>64 %s = sparse_tensor.convert %t : tensor<8x8xf64> to tensor<8x8xf64, {attr}>65 %0 = call @sddmm(%a, %b, %s, %c) : (tensor<8x8xf64>,66 tensor<8x8xf64>,67 tensor<8x8xf64, {attr}>,68 tensor<8x8xf64>) -> tensor<8x8xf64>69 return %0 : tensor<8x8xf64>70}}71"""72 73 74def build_compile_and_run_SDDMMM(attr: st.EncodingAttr, compiler):75 # Build.76 module = build_SDDMM(attr)77 func = str(module.operation.regions[0].blocks[0].operations[0].operation)78 module = ir.Module.parse(func + boilerplate(attr))79 80 # Compile.81 engine = compiler.compile_and_jit(module)82 83 # Set up numpy input and buffer for output.84 a = np.array(85 [86 [1.1, 2.1, 3.1, 4.1, 5.1, 6.1, 7.1, 8.1],87 [1.2, 2.2, 3.2, 4.2, 5.2, 6.2, 7.2, 8.2],88 [1.3, 2.3, 3.3, 4.3, 5.3, 6.3, 7.3, 8.3],89 [1.4, 2.4, 3.4, 4.4, 5.4, 6.4, 7.4, 8.4],90 [1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5],91 [1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6],92 [1.7, 2.7, 3.7, 4.7, 5.7, 6.7, 7.7, 8.7],93 [1.8, 2.8, 3.8, 4.8, 5.8, 6.8, 7.8, 8.8],94 ],95 np.float64,96 )97 b = np.ones((8, 8), np.float64)98 c = np.zeros((8, 8), np.float64)99 100 mem_a = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(a)))101 mem_b = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(b)))102 mem_c = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(c)))103 104 # Allocate a MemRefDescriptor to receive the output tensor.105 # The buffer itself is allocated inside the MLIR code generation.106 ref_out = rt.make_nd_memref_descriptor(2, ctypes.c_double)()107 mem_out = ctypes.pointer(ctypes.pointer(ref_out))108 109 # Invoke the kernel and get numpy output.110 # Built-in bufferization uses in-out buffers.111 engine.invoke("main", mem_out, mem_a, mem_b, mem_c)112 113 # Sanity check on computed result. Only a few elements114 # are sampled from the full dense matrix multiplication.115 full_matmul = np.matmul(a, b)116 expected = np.zeros((8, 8), np.float64)117 expected[0, 0] = 1.0 * full_matmul[0, 0]118 expected[0, 2] = 2.0 * full_matmul[0, 2]119 expected[4, 1] = 3.0 * full_matmul[4, 1]120 c = rt.ranked_memref_to_numpy(mem_out[0])121 if np.allclose(c, expected):122 pass123 else:124 quit(f"FAILURE")125 126 127def main():128 support_lib = os.getenv("SUPPORT_LIB")129 assert support_lib is not None, "SUPPORT_LIB is undefined"130 if not os.path.exists(support_lib):131 raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), support_lib)132 133 # CHECK-LABEL: TEST: testSDDMMM134 print("\nTEST: testSDDMMM")135 count = 0136 with ir.Context() as ctx, ir.Location.unknown():137 # Loop over various ways to compile and annotate the SDDMM kernel with138 # a *single* sparse tensor. Note that we deliberate do not exhaustively139 # search the full state space to reduce runtime of the test. It is140 # straightforward to adapt the code below to explore more combinations.141 # For these simple orderings, dim2lvl and lvl2dim are the same.142 builder = st.EncodingAttr.build_level_type143 fmt = st.LevelFormat144 prop = st.LevelProperty145 levels = [146 [builder(fmt.compressed, [prop.non_unique]), builder(fmt.singleton)],147 [builder(fmt.dense), builder(fmt.dense)],148 [builder(fmt.dense), builder(fmt.compressed)],149 [builder(fmt.compressed), builder(fmt.dense)],150 [builder(fmt.compressed), builder(fmt.compressed)],151 ]152 orderings = [153 ir.AffineMap.get_permutation([0, 1]),154 ir.AffineMap.get_permutation([1, 0]),155 ]156 for level in levels:157 for ordering in orderings:158 for pwidth in [32]:159 for iwidth in [32]:160 for e in [True]:161 attr = st.EncodingAttr.get(162 level, ordering, ordering, pwidth, iwidth163 )164 opt = f"parallelization-strategy=none"165 compiler = sparsifier.Sparsifier(166 extras="",167 options=opt,168 opt_level=0,169 shared_libs=[support_lib],170 )171 build_compile_and_run_SDDMMM(attr, compiler)172 count = count + 1173 # CHECK: Passed 10 tests174 print("Passed ", count, "tests")175 176 177if __name__ == "__main__":178 main()179