1# RUN: SUPPORT_LIB=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext \
2# RUN:   %PYTHON %s | FileCheck %s
3
4import ctypes
5import numpy as np
6import os
7
8import mlir.all_passes_registration
9
10from mlir import ir
11from mlir import runtime as rt
12from mlir import execution_engine
13from mlir import passmanager
14
15from mlir.dialects import sparse_tensor as st
16from mlir.dialects import builtin
17from mlir.dialects.linalg.opdsl import lang as dsl
18
19
20@dsl.linalg_structured_op
21def sddmm_dsl(
22    A=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.K),
23    B=dsl.TensorDef(dsl.T, dsl.S.K, dsl.S.N),
24    S=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.N),
25    C=dsl.TensorDef(dsl.T, dsl.S.M, dsl.S.N, output=True)):
26  C[dsl.D.m,
27    dsl.D.n] += S[dsl.D.m, dsl.D.n] * A[dsl.D.m, dsl.D.k] * B[dsl.D.k, dsl.D.n]
28
29
30def build_SDDMM(attr: st.EncodingAttr):
31  """Build SDDMM kernel.
32
33  This method generates a linalg op with for matrix multiplication using
34  just the Python API. Effectively, a generic linalg op is constructed
35  that computes C(i,j) += S(i,j) SUM_k A(i,k) B(k,j) for sparse S.
36  """
37  module = ir.Module.create()
38  f64 = ir.F64Type.get()
39  a = ir.RankedTensorType.get([8, 8], f64)
40  b = ir.RankedTensorType.get([8, 8], f64)
41  c = ir.RankedTensorType.get([8, 8], f64)
42  s = ir.RankedTensorType.get([8, 8], f64, attr)
43  arguments = [a, b, s, c]
44  with ir.InsertionPoint(module.body):
45
46    @builtin.FuncOp.from_py_func(*arguments)
47    def sddmm(*args):
48      return sddmm_dsl(args[0], args[1], args[2], outs=[args[3]])
49
50  return module
51
52
53def boilerplate(attr: st.EncodingAttr):
54  """Returns boilerplate code for main driver."""
55  return f"""
56func @main(%a: tensor<8x8xf64>,
57           %b: tensor<8x8xf64>,
58           %c: tensor<8x8xf64>) -> tensor<8x8xf64> attributes {{ llvm.emit_c_interface }} {{
59  %t = arith.constant sparse<[[0,0], [0,2], [4,1]], [1.0, 2.0, 3.0]> : tensor<8x8xf64>
60  %s = sparse_tensor.convert %t : tensor<8x8xf64> to tensor<8x8xf64, {attr}>
61  %0 = call @sddmm(%a, %b, %s, %c) : (tensor<8x8xf64>,
62                                      tensor<8x8xf64>,
63                                      tensor<8x8xf64, {attr}>,
64                                      tensor<8x8xf64>) -> tensor<8x8xf64>
65  return %0 : tensor<8x8xf64>
66}}
67"""
68
69
70def build_compile_and_run_SDDMMM(attr: st.EncodingAttr, opt: str,
71                                 support_lib: str, compiler):
72  # Build.
73  module = build_SDDMM(attr)
74  func = str(module.operation.regions[0].blocks[0].operations[0].operation)
75  module = ir.Module.parse(func + boilerplate(attr))
76
77  # Compile.
78  compiler(module)
79  engine = execution_engine.ExecutionEngine(
80      module, opt_level=0, shared_libs=[support_lib])
81
82  # Set up numpy input and buffer for output.
83  a = np.array([[1.1, 2.1, 3.1, 4.1, 5.1, 6.1, 7.1, 8.1],
84                [1.2, 2.2, 3.2, 4.2, 5.2, 6.2, 7.2, 8.2],
85                [1.3, 2.3, 3.3, 4.3, 5.3, 6.3, 7.3, 8.3],
86                [1.4, 2.4, 3.4, 4.4, 5.4, 6.4, 7.4, 8.4],
87                [1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5],
88                [1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6],
89                [1.7, 2.7, 3.7, 4.7, 5.7, 6.7, 7.7, 8.7],
90                [1.8, 2.8, 3.8, 4.8, 5.8, 6.8, 7.8, 8.8]], np.float64)
91  b = np.ones((8, 8), np.float64)
92  c = np.zeros((8, 8), np.float64)
93
94  mem_a = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(a)))
95  mem_b = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(b)))
96  mem_c = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(c)))
97
98  # Allocate a MemRefDescriptor to receive the output tensor.
99  # The buffer itself is allocated inside the MLIR code generation.
100  ref_out = rt.make_nd_memref_descriptor(2, ctypes.c_double)()
101  mem_out = ctypes.pointer(ctypes.pointer(ref_out))
102
103  # Invoke the kernel and get numpy output.
104  # Built-in bufferization uses in-out buffers.
105  # TODO: replace with inplace comprehensive bufferization.
106  engine.invoke('main', mem_out, mem_a, mem_b, mem_c)
107
108  # Sanity check on computed result. Only a few elements
109  # are sampled from the full dense matrix multiplication.
110  full_matmul = np.matmul(a, b)
111  expected = np.zeros((8, 8), np.float64)
112  expected[0, 0] = 1.0 * full_matmul[0, 0]
113  expected[0, 2] = 2.0 * full_matmul[0, 2]
114  expected[4, 1] = 3.0 * full_matmul[4, 1]
115  c = rt.ranked_memref_to_numpy(mem_out[0])
116  if np.allclose(c, expected):
117    pass
118  else:
119    quit(f'FAILURE')
120
121
122class SparseCompiler:
123  """Sparse compiler passes."""
124
125  def __init__(self, options: str):
126    pipeline = (
127        f'sparsification{{{options}}},'
128        f'sparse-tensor-conversion,'
129        f'builtin.func(linalg-bufferize,convert-linalg-to-loops,convert-vector-to-scf),'
130        f'convert-scf-to-std,'
131        f'func-bufferize,'
132        f'tensor-constant-bufferize,'
133        f'builtin.func(tensor-bufferize,std-bufferize,finalizing-bufferize),'
134        f'convert-vector-to-llvm{{reassociate-fp-reductions=1 enable-index-optimizations=1}},'
135        f'lower-affine,'
136        f'convert-memref-to-llvm,'
137        f'convert-std-to-llvm,'
138        f'reconcile-unrealized-casts')
139    self.pipeline = pipeline
140
141  def __call__(self, module: ir.Module):
142    passmanager.PassManager.parse(self.pipeline).run(module)
143
144
145def main():
146  support_lib = os.getenv('SUPPORT_LIB')
147  assert support_lib is not None, 'SUPPORT_LIB is undefined'
148  if not os.path.exists(support_lib):
149    raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT),
150                            support_lib)
151
152  # CHECK-LABEL: TEST: testSDDMMM
153  print('\nTEST: testSDDMMM')
154  with ir.Context() as ctx, ir.Location.unknown():
155    count = 0
156    # Loop over various ways to compile and annotate the SDDMM kernel with
157    # a *single* sparse tensor. Note that we deliberate do not exhaustively
158    # search the full state space to reduce runtime of the test. It is
159    # straightforward to adapt the code below to explore more combinations.
160    levels = [[st.DimLevelType.dense, st.DimLevelType.dense],
161              [st.DimLevelType.dense, st.DimLevelType.compressed],
162              [st.DimLevelType.compressed, st.DimLevelType.dense],
163              [st.DimLevelType.compressed, st.DimLevelType.compressed]]
164    orderings = [
165        ir.AffineMap.get_permutation([0, 1]),
166        ir.AffineMap.get_permutation([1, 0])
167    ]
168    for level in levels:
169      for ordering in orderings:
170        for pwidth in [32]:
171          for iwidth in [32]:
172            for par in [0]:
173              for vec in [0, 1]:
174                for e in [True]:
175                  vl = 1 if vec == 0 else 16
176                  attr = st.EncodingAttr.get(level, ordering, pwidth, iwidth)
177                  opt = (f'parallelization-strategy={par} '
178                         f'vectorization-strategy={vec} '
179                         f'vl={vl} enable-simd-index32={e}')
180                  compiler = SparseCompiler(options=opt)
181                  build_compile_and_run_SDDMMM(attr, opt, support_lib, compiler)
182                  count = count + 1
183  # CHECK: Passed 16 tests
184  print('Passed ', count, 'tests')
185
186
187if __name__ == '__main__':
188  main()
189