diff --git a/mlir/test/Integration/Dialect/SparseTensor/python/test_SpMM.py b/mlir/test/Integration/Dialect/SparseTensor/python/test_SDDMM.py copy from mlir/test/Integration/Dialect/SparseTensor/python/test_SpMM.py copy to mlir/test/Integration/Dialect/SparseTensor/python/test_SDDMM.py --- a/mlir/test/Integration/Dialect/SparseTensor/python/test_SpMM.py +++ b/mlir/test/Integration/Dialect/SparseTensor/python/test_SDDMM.py @@ -18,59 +18,59 @@ @dsl.linalg_structured_op -def matmul_dsl( +def sddmm_dsl( A=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.K), B=dsl.TensorDef(dsl.T, dsl.S.K, dsl.S.N), + S=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.N), C=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.N, output=True)): - C[dsl.D.m, dsl.D.n] += A[dsl.D.m, dsl.D.k] * B[dsl.D.k, dsl.D.n] + C[dsl.D.m, + dsl.D.n] += S[dsl.D.m, dsl.D.n] * A[dsl.D.m, dsl.D.k] * B[dsl.D.k, dsl.D.n] -def build_SpMM(attr: st.EncodingAttr): - """Build SpMM kernel. +def build_SDDMM(attr: st.EncodingAttr): + """Build SDDMM kernel. This method generates a linalg op with for matrix multiplication using just the Python API. Effectively, a generic linalg op is constructed - that computes C(i,j) += A(i,k) * B(k,j) for annotated matrix A. + that computes C(i,j) += S(i,j) SUM_k A(i,k) B(k,j) for sparse S. """ module = ir.Module.create() f64 = ir.F64Type.get() - a = ir.RankedTensorType.get([3, 4], f64, attr) - b = ir.RankedTensorType.get([4, 2], f64) - c = ir.RankedTensorType.get([3, 2], f64) - arguments = [a, b, c] + a = ir.RankedTensorType.get([8, 8], f64) + b = ir.RankedTensorType.get([8, 8], f64) + c = ir.RankedTensorType.get([8, 8], f64) + s = ir.RankedTensorType.get([8, 8], f64, attr) + arguments = [a, b, s, c] with ir.InsertionPoint(module.body): @builtin.FuncOp.from_py_func(*arguments) - def spMxM(*args): - return matmul_dsl(args[0], args[1], outs=[args[2]]) + def sddmm(*args): + return sddmm_dsl(args[0], args[1], args[2], outs=[args[3]]) return module def boilerplate(attr: st.EncodingAttr): - """Returns boilerplate main method. - - This method sets up a boilerplate main method that takes three tensors - (a, b, c), converts the first tensor a into s sparse tensor, and then - calls the sparse kernel for matrix multiplication. For convenience, - this part is purely done as string input. - """ + """Returns boilerplate code for main driver.""" return f""" -func @main(%ad: tensor<3x4xf64>, %b: tensor<4x2xf64>, %c: tensor<3x2xf64>) -> tensor<3x2xf64> - attributes {{ llvm.emit_c_interface }} {{ - %a = sparse_tensor.convert %ad : tensor<3x4xf64> to tensor<3x4xf64, {attr}> - %0 = call @spMxM(%a, %b, %c) : (tensor<3x4xf64, {attr}>, - tensor<4x2xf64>, - tensor<3x2xf64>) -> tensor<3x2xf64> - return %0 : tensor<3x2xf64> +func @main(%a: tensor<8x8xf64>, + %b: tensor<8x8xf64>, + %c: tensor<8x8xf64>) -> tensor<8x8xf64> attributes {{ llvm.emit_c_interface }} {{ + %t = arith.constant sparse<[[0,0], [0,2], [4,1]], [1.0, 2.0, 3.0]> : tensor<8x8xf64> + %s = sparse_tensor.convert %t : tensor<8x8xf64> to tensor<8x8xf64, {attr}> + %0 = call @sddmm(%a, %b, %s, %c) : (tensor<8x8xf64>, + tensor<8x8xf64>, + tensor<8x8xf64, {attr}>, + tensor<8x8xf64>) -> tensor<8x8xf64> + return %0 : tensor<8x8xf64> }} """ -def build_compile_and_run_SpMM(attr: st.EncodingAttr, support_lib: str, - compiler): +def build_compile_and_run_SDDMMM(attr: st.EncodingAttr, opt: str, + support_lib: str, compiler): # Build. - module = build_SpMM(attr) + module = build_SDDMM(attr) func = str(module.operation.regions[0].blocks[0].operations[0].operation) module = ir.Module.parse(func + boilerplate(attr)) @@ -80,15 +80,21 @@ module, opt_level=0, shared_libs=[support_lib]) # Set up numpy input and buffer for output. - a = np.array( - [[1.1, 0.0, 0.0, 1.4], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 3.3, 0.0]], - np.float64) - b = np.array([[1.0, 2.0], [4.0, 3.0], [5.0, 6.0], [8.0, 7.0]], np.float64) - c = np.zeros((3, 2), np.float64) + a = np.array([[1.1, 2.1, 3.1, 4.1, 5.1, 6.1, 7.1, 8.1], + [1.2, 2.2, 3.2, 4.2, 5.2, 6.2, 7.2, 8.2], + [1.3, 2.3, 3.3, 4.3, 5.3, 6.3, 7.3, 8.3], + [1.4, 2.4, 3.4, 4.4, 5.4, 6.4, 7.4, 8.4], + [1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5], + [1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6], + [1.7, 2.7, 3.7, 4.7, 5.7, 6.7, 7.7, 8.7], + [1.8, 2.8, 3.8, 4.8, 5.8, 6.8, 7.8, 8.8]], np.float64) + b = np.ones((8, 8), np.float64) + c = np.zeros((8, 8), np.float64) mem_a = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(a))) mem_b = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(b))) mem_c = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(c))) + # Allocate a MemRefDescriptor to receive the output tensor. # The buffer itself is allocated inside the MLIR code generation. ref_out = rt.make_nd_memref_descriptor(2, ctypes.c_double)() @@ -99,8 +105,13 @@ # TODO: replace with inplace comprehensive bufferization. engine.invoke('main', mem_out, mem_a, mem_b, mem_c) - # Sanity check on computed result. - expected = np.matmul(a, b); + # Sanity check on computed result. Only a few elements + # are sampled from the full dense matrix multiplication. + full_matmul = np.matmul(a, b) + expected = np.zeros((8, 8), np.float64) + expected[0, 0] = 1.0 * full_matmul[0, 0] + expected[0, 2] = 2.0 * full_matmul[0, 2] + expected[4, 1] = 3.0 * full_matmul[4, 1] c = rt.ranked_memref_to_numpy(mem_out[0]) if np.allclose(c, expected): pass @@ -113,7 +124,6 @@ def __init__(self, options: str): pipeline = ( - f'builtin.func(linalg-generalize-named-ops,linalg-fuse-elementwise-ops),' f'sparsification{{{options}}},' f'sparse-tensor-conversion,' f'builtin.func(linalg-bufferize,convert-linalg-to-loops,convert-vector-to-scf),' @@ -136,23 +146,17 @@ support_lib = os.getenv('SUPPORT_LIB') assert support_lib is not None, 'SUPPORT_LIB is undefined' if not os.path.exists(support_lib): - raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), support_lib) + raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), + support_lib) - # CHECK-LABEL: TEST: testSpMM - print('\nTEST: testSpMM') + # CHECK-LABEL: TEST: testSDDMMM + print('\nTEST: testSDDMMM') with ir.Context() as ctx, ir.Location.unknown(): count = 0 - # Loop over various ways to compile and annotate the SpMM kernel with + # Loop over various ways to compile and annotate the SDDMM kernel with # a *single* sparse tensor. Note that we deliberate do not exhaustively # search the full state space to reduce runtime of the test. It is # straightforward to adapt the code below to explore more combinations. - par = 0 - vec = 0 - vl = 1 - e = False - opt = (f'parallelization-strategy={par} ' - f'vectorization-strategy={vec} ' - f'vl={vl} enable-simd-index32={e}') levels = [[st.DimLevelType.dense, st.DimLevelType.dense], [st.DimLevelType.dense, st.DimLevelType.compressed], [st.DimLevelType.compressed, st.DimLevelType.dense], @@ -161,17 +165,24 @@ ir.AffineMap.get_permutation([0, 1]), ir.AffineMap.get_permutation([1, 0]) ] - bitwidths = [0] for level in levels: for ordering in orderings: - for pwidth in bitwidths: - for iwidth in bitwidths: - attr = st.EncodingAttr.get(level, ordering, pwidth, iwidth) - compiler = SparseCompiler(options=opt) - build_compile_and_run_SpMM(attr, support_lib, compiler) - count = count + 1 - # CHECK: Passed 8 tests - print('Passed ', count, 'tests') + for pwidth in [32]: + for iwidth in [32]: + for par in [0]: + for vec in [0, 1]: + for e in [True]: + vl = 1 if vec == 0 else 16 + attr = st.EncodingAttr.get(level, ordering, pwidth, iwidth) + opt = (f'parallelization-strategy={par} ' + f'vectorization-strategy={vec} ' + f'vl={vl} enable-simd-index32={e}') + compiler = SparseCompiler(options=opt) + build_compile_and_run_SDDMMM(attr, opt, support_lib, compiler) + count = count + 1 + # CHECK: Passed 16 tests + print('Passed ', count, 'tests') + if __name__ == '__main__': main() diff --git a/mlir/test/Integration/Dialect/SparseTensor/python/test_SpMM.py b/mlir/test/Integration/Dialect/SparseTensor/python/test_SpMM.py --- a/mlir/test/Integration/Dialect/SparseTensor/python/test_SpMM.py +++ b/mlir/test/Integration/Dialect/SparseTensor/python/test_SpMM.py @@ -173,5 +173,6 @@ # CHECK: Passed 8 tests print('Passed ', count, 'tests') + if __name__ == '__main__': main()