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 _run_test(support_lib, kernel):
"""Compiles, runs and checks results."""
compiler = sparse_compiler.SparseCompiler(
options='', opt_level=2, shared_libs=[support_lib])
module = ir.Module.parse(kernel)
engine = compiler.compile_and_jit(module)
# Set up numpy inputs and buffer for output.
a = np.array(
[[1.1, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 6.6, 0.0]],
np.float64)
b = np.array(
[[1.1, 0.0, 0.0, 2.8], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0]],
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)))
# The sparse tensor output is a pointer to pointer of char.
out = ctypes.c_char(0)
mem_out = ctypes.pointer(ctypes.pointer(out))
# Invoke the kernel.
engine.invoke('main', mem_a, mem_b, mem_out)
# Retrieve and check the result.
rank, nse, shape, values, indices = test_tools.sparse_tensor_to_coo_tensor(
support_lib, mem_out[0], np.float64)
# CHECK: PASSED
if np.allclose(values, [2.2, 2.8, 6.6]) and np.allclose(
indices, [[0, 0], [0, 3], [2, 2]]):
print('PASSED')
else:
quit('FAILURE')
def runner(engine_invoke):
compiled_program_args = []
for argument_type in [
result_type, param1_type, param2_type, result_type
]:
argument_type_str = str(argument_type)
dimensions_str = re.sub("<|>|tensor", "", argument_type_str)
dimensions = [int(dim) for dim in dimensions_str.split("x")[:-1]]
if argument_type == result_type:
argument = np.zeros(dimensions, np.float64)
else:
argument = create_sparse_np_tensor(dimensions, 1000)
compiled_program_args.append(
ctypes.pointer(
ctypes.pointer(rt.get_ranked_memref_descriptor(argument))
)
)
np_timers_ns = np.array([0], dtype=np.int64)
compiled_program_args.append(
ctypes.pointer(
ctypes.pointer(rt.get_ranked_memref_descriptor(np_timers_ns))
)
)
engine_invoke("main", *compiled_program_args)
return int(np_timers_ns[0])
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')
