def build_compile_and_run_SpMM(attr: st.EncodingAttr, support_lib: str,
compiler):
# Build.
module = build_SpMM(attr)
func = str(module.operation.regions[0].blocks[0].operations[0].operation)
module = ir.Module.parse(func + boilerplate(attr))
# Compile.
compiler(module)
engine = execution_engine.ExecutionEngine(module,
opt_level=0,
shared_libs=[support_lib])
# Set up numpy input, invoke the kernel, and get numpy output.
# Built-in bufferization uses in-out buffers.
# TODO: replace with inplace comprehensive bufferization.
Cin = np.zeros((3, 2), np.double)
Cout = np.zeros((3, 2), np.double)
Cin_memref_ptr = ctypes.pointer(
ctypes.pointer(rt.get_ranked_memref_descriptor(Cin)))
Cout_memref_ptr = ctypes.pointer(
ctypes.pointer(rt.get_ranked_memref_descriptor(Cout)))
engine.invoke('main', Cout_memref_ptr, Cin_memref_ptr)
Cresult = rt.ranked_memref_to_numpy(Cout_memref_ptr[0])
# Sanity check on computed result.
expected = [[12.3, 12.0], [0.0, 0.0], [16.5, 19.8]]
if np.allclose(Cresult, expected):
pass
else:
quit(f'FAILURE')
def build_compile_and_run_SDDMMM(attr: st.EncodingAttr, opt: str,
support_lib: str, compiler):
# Build.
module = build_SDDMM(attr)
func = str(module.operation.regions[0].blocks[0].operations[0].operation)
module = ir.Module.parse(func + boilerplate(attr))
# Compile.
compiler(module)
engine = execution_engine.ExecutionEngine(module,
opt_level=0,
shared_libs=[support_lib])
# Set up numpy input and buffer for output.
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)()
mem_out = ctypes.pointer(ctypes.pointer(ref_out))
# Invoke the kernel and get numpy output.
# Built-in bufferization uses in-out buffers.
# TODO: replace with inplace comprehensive bufferization.
engine.invoke('main', mem_out, mem_a, mem_b, mem_c)
# 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
else:
quit(f'FAILURE')
def run(self, np_arg0: np.ndarray) -> np.ndarray:
"""Runs the test on the given numpy array, and returns the resulting
numpy array."""
assert self._engine is not None, \
'StressTest: must call compile() before run()'
self._assertEqualsRoundtripTp(
self._tyconv.get_RankedTensorType_of_nparray(np_arg0))
np_out = np.zeros(np_arg0.shape, dtype=np_arg0.dtype)
self._assertEqualsRoundtripTp(
self._tyconv.get_RankedTensorType_of_nparray(np_out))
mem_arg0 = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(np_arg0)))
mem_out = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(np_out)))
self._engine.invoke('main', mem_out, mem_arg0)
return rt.ranked_memref_to_numpy(mem_out[0])
def build_compile_and_run_SpMM(attr: st.EncodingAttr, support_lib: str,
compiler):
# Build.
module = build_SpMM(attr)
func = str(module.operation.regions[0].blocks[0].operations[0].operation)
module = ir.Module.parse(func + boilerplate(attr))
# Compile.
compiler(module)
engine = execution_engine.ExecutionEngine(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)
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)()
mem_out = ctypes.pointer(ctypes.pointer(ref_out))
# Invoke the kernel and get numpy output.
# Built-in bufferization uses in-out buffers.
# 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)
c = rt.ranked_memref_to_numpy(mem_out[0])
if np.allclose(c, expected):
pass
else:
quit(f'FAILURE')
def create_sparse_tensor(
filename: str, sparsity: Sequence[sparse_tensor.DimLevelType]
) -> Tuple[ctypes.c_void_p, np.ndarray]:
"""Creates an MLIR sparse tensor from the input file.
Args:
filename: A string for the name of the file that contains the tensor data in
a COO-flavored format.
sparsity: A sequence of DimLevelType values, one for each dimension of the
tensor.
Returns:
A Tuple containing the following values:
storage: A ctypes.c_void_p for the MLIR sparse tensor storage.
shape: A 1D numpy array of integers, for the shape of the tensor.
Raises:
OSError: If there is any problem in loading the supporting C shared library.
ValueError: If the shared library doesn't contain the needed routine.
"""
with ir.Context() as ctx, ir.Location.unknown():
module = _get_create_sparse_tensor_kernel(sparsity)
module = ir.Module.parse(module)
engine = compile_and_build_engine(module)
# A sparse tensor descriptor to receive the kernel result.
c_tensor_desc = _SparseTensorDescriptor()
# Convert the filename to a byte stream.
c_filename = ctypes.c_char_p(bytes(filename, "utf-8"))
arg_pointers = [
ctypes.byref(ctypes.pointer(c_tensor_desc)),
ctypes.byref(c_filename)
]
# Invoke the execution engine to run the module and return the result.
engine.invoke(_ENTRY_NAME, *arg_pointers)
shape = runtime.ranked_memref_to_numpy(ctypes.pointer(c_tensor_desc.shape))
return c_tensor_desc.storage, shape
