onnx-mlir/test/backend/test.py

473 lines
15 KiB
Python

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import os
import sys
import unittest
import warnings
import onnx.backend.base
import onnx.backend.test
from onnx.backend.base import Device, DeviceType
import subprocess
import test_config
VERBOSE = bool(os.environ.get("VERBOSE"))
CXX = test_config.CXX_PATH
ONNX_MLIR = os.path.join(test_config.ONNX_MLIR_BUILD_PATH, "bin/onnx-mlir")
LLC = os.path.join(test_config.LLVM_PROJ_BUILD_PATH, "bin/llc")
# Make lib folder under build directory visible in PYTHONPATH
doc_check_base_dir = os.path.dirname(os.path.realpath(__file__))
RUNTIME_DIR = os.path.join(test_config.ONNX_MLIR_BUILD_PATH, "lib")
sys.path.append(RUNTIME_DIR)
from PyRuntime import ExecutionSession
def execute_commands(cmds):
if (VERBOSE):
print(" ".join(cmds))
subprocess.run(cmds)
# There are two issues, which necessitates the adoption of this endianness
# aware wrapper around Execution Session:
# 1. Input arrays are given sometimes in native byte order, sometime in
# LE byte order, and as soon as the python array enters into py::array
# C++ objects through pybind, we will no longer be able to query their
# endianness. So we must intercept the inputs and convert them into
# native endianness.
# 2. Output arrays are compared with reference outputs, the comparison
# unfortunately includes checking that our outputs and reference outputs
# share the same endianness. So we try to figure out what is the desired
# reference output endianness, and convert our outputs to this desired
# endianness.
class EndiannessAwareExecutionSession(ExecutionSession):
def __init__(self, path, entry_point):
super().__init__(path, entry_point)
def is_input_le(self, inputs):
inputs_endianness = list(map(lambda x: x.dtype.byteorder, inputs))
endianness_is_consistent = len(set(inputs_endianness)) <= 1
assert endianness_is_consistent, \
"Input arrays contain a mixture of endianness configuration."
sys_is_le = sys.byteorder == 'little'
# To interpret character symbols indicating endianness:
# https://numpy.org/doc/stable/reference/generated/numpy.dtype.byteorder.html
explicitly_le = inputs_endianness[0] == "<"
implicitly_le = (inputs_endianness[0] == "=" and sys_is_le)
return explicitly_le or implicitly_le
def run(self, inputs, **kwargs):
if len(inputs):
# Deduce desired endianness of output from inputs.
sys_is_le = sys.byteorder == 'little'
inp_is_le = self.is_input_le(inputs)
if (sys_is_le != inp_is_le):
inputs = list(
map(lambda x: x.byteswap().newbyteorder(), inputs))
outputs = super().run(inputs)
if (sys_is_le != inp_is_le):
outputs = list(
map(lambda x: x.byteswap().newbyteorder(), outputs))
return outputs
else:
# Can't deduce desired output endianess, fingers crossed.
warnings.warn(
"Cannot deduce desired output endianness, using native endianness by default."
)
return super().run(inputs)
class DummyBackend(onnx.backend.base.Backend):
@classmethod
def prepare(cls, model, device='CPU', **kwargs):
super(DummyBackend, cls).prepare(model, device, **kwargs)
# Save model to disk as temp_model.onnx.
onnx.save(model, "temp_model.onnx")
# Call frontend to process temp_model.onnx, bit code will be generated.
execute_commands([ONNX_MLIR, "temp_model.onnx"])
return EndiannessAwareExecutionSession("./temp_model.so",
"_dyn_entry_point_main_graph")
@classmethod
def supports_device(cls, device):
d = Device(device)
if d.type == DeviceType.CPU:
return True
return False
backend_test = onnx.backend.test.BackendTest(DummyBackend, __name__)
# Test directories:
# https://github.com/onnx/onnx/tree/master/onnx/backend/test/data/node
test_to_enable = [
# Abs Op:
"test_abs_cpu",
# Add Op:
"test_add_cpu",
"test_add_bcast_cpu",
# And Op:
# Sub Op:
"test_sub_cpu",
"test_sub_bcast_cpu",
"test_sub_example_cpu",
# Cosh Op:
"test_cosh_cpu",
"test_cosh_example_cpu",
# Concat
"test_concat_1d_axis_0_cpu",
"test_concat_2d_axis_0_cpu",
"test_concat_2d_axis_1_cpu",
"test_concat_3d_axis_0_cpu",
"test_concat_3d_axis_1_cpu",
"test_concat_3d_axis_2_cpu",
"test_concat_1d_axis_negative_1_cpu",
"test_concat_2d_axis_negative_1_cpu",
"test_concat_2d_axis_negative_2_cpu",
"test_concat_3d_axis_negative_1_cpu",
"test_concat_3d_axis_negative_2_cpu",
"test_concat_3d_axis_negative_3_cpu",
# Tanh:
"test_tanh_cpu",
"test_tanh_example_cpu",
# Div Op:
"test_div_cpu",
"test_div_bcast_cpu",
"test_div_example_cpu",
# Elu Op:
"test_elu_cpu",
"test_elu_default_cpu",
"test_elu_example_cpu",
# Exp Op:
"test_exp_cpu",
"test_exp_example_cpu",
# Gather Op:
"test_gather_0_cpu",
"test_gather_1_cpu",
"test_gather_negative_indices_cpu",
# Gemm Op:
"test_gemm_all_attributes_cpu",
"test_gemm_alpha_cpu",
"test_gemm_beta_cpu",
"test_gemm_default_matrix_bias_cpu",
"test_gemm_default_no_bias_cpu",
"test_gemm_default_scalar_bias_cpu",
"test_gemm_default_single_elem_vector_bias_cpu",
"test_gemm_default_vector_bias_cpu",
"test_gemm_default_zero_bias_cpu",
"test_gemm_transposeA_cpu",
"test_gemm_transposeB_cpu",
# Hard Sigmoid Op:
"test_hardsigmoid_cpu",
"test_hardsigmoid_default_cpu",
"test_hardsigmoid_example_cpu",
# Leaky Relu Op:
"test_leakyrelu_cpu",
"test_leakyrelu_default_cpu",
"test_leakyrelu_example_cpu",
# Max Op:
"test_max_example_cpu",
"test_max_one_input_cpu",
"test_max_two_inputs_cpu",
# Min Op:
"test_min_example_cpu",
"test_min_one_input_cpu",
"test_min_two_inputs_cpu",
# Mul Op:
"test_mul_cpu",
"test_mul_bcast_cpu",
"test_mul_example_cpu",
# Relu Op:
"test_relu_cpu",
# ReduceMax Op:
"test_reduce_max_default_axes_keepdim_example_cpu",
"test_reduce_max_default_axes_keepdims_random_cpu",
"test_reduce_max_do_not_keepdims_example_cpu",
"test_reduce_max_do_not_keepdims_random_cpu",
"test_reduce_max_keepdims_example_cpu",
"test_reduce_max_keepdims_random_cpu",
"test_reduce_max_negative_axes_keepdims_example_cpu",
"test_reduce_max_negative_axes_keepdims_random_cpu",
# ReduceMin Op:
"test_reduce_min_default_axes_keepdims_example_cpu",
"test_reduce_min_default_axes_keepdims_random_cpu",
"test_reduce_min_do_not_keepdims_example_cpu",
"test_reduce_min_do_not_keepdims_random_cpu",
"test_reduce_min_keepdims_example_cpu",
"test_reduce_min_keepdims_random_cpu",
"test_reduce_min_negative_axes_keepdims_example_cpu",
"test_reduce_min_negative_axes_keepdims_random_cpu",
# ReduceProd Op:
"test_reduce_prod_default_axes_keepdims_example_cpu",
"test_reduce_prod_default_axes_keepdims_random_cpu",
"test_reduce_prod_do_not_keepdims_example_cpu",
"test_reduce_prod_do_not_keepdims_random_cpu",
"test_reduce_prod_keepdims_example_cpu",
"test_reduce_prod_keepdims_random_cpu",
"test_reduce_prod_negative_axes_keepdims_example_cpu",
"test_reduce_prod_negative_axes_keepdims_random_cpu",
# ReduceSum Op:
"test_reduce_sum_default_axes_keepdims_example_cpu",
"test_reduce_sum_default_axes_keepdims_random_cpu",
"test_reduce_sum_do_not_keepdims_example_cpu",
"test_reduce_sum_do_not_keepdims_random_cpu",
"test_reduce_sum_keepdims_example_cpu",
"test_reduce_sum_keepdims_random_cpu",
"test_reduce_sum_negative_axes_keepdims_example_cpu",
"test_reduce_sum_negative_axes_keepdims_random_cpu",
# ReduceL1
"test_reduce_l1_default_axes_keepdims_example_cpu",
"test_reduce_l1_default_axes_keepdims_random_cpu",
"test_reduce_l1_do_not_keepdims_example_cpu",
"test_reduce_l1_do_not_keepdims_random_cpu",
"test_reduce_l1_keep_dims_example_cpu",
"test_reduce_l1_keep_dims_random_cpu",
"test_reduce_l1_negative_axes_keep_dims_example_cpu",
"test_reduce_l1_negative_axes_keep_dims_random_cpu",
# ReduceL2
"test_reduce_l2_default_axes_keepdims_example_cpu",
"test_reduce_l2_default_axes_keepdims_random_cpu",
"test_reduce_l2_do_not_keepdims_example_cpu",
"test_reduce_l2_do_not_keepdims_random_cpu",
"test_reduce_l2_keep_dims_example_cpu",
"test_reduce_l2_keep_dims_random_cpu",
"test_reduce_l2_negative_axes_keep_dims_example_cpu",
"test_reduce_l2_negative_axes_keep_dims_random_cpu",
# ReduceLogSum
"test_reduce_log_sum_asc_axes_cpu",
"test_reduce_log_sum_cpu",
"test_reduce_log_sum_default_cpu",
"test_reduce_log_sum_desc_axes_cpu",
# ReduceLogSumExp
"test_reduce_log_sum_exp_default_axes_keepdims_example_cpu",
"test_reduce_log_sum_exp_default_axes_keepdims_random_cpu",
"test_reduce_log_sum_exp_do_not_keepdims_example_cpu",
"test_reduce_log_sum_exp_do_not_keepdims_random_cpu",
"test_reduce_log_sum_exp_keepdims_example_cpu",
"test_reduce_log_sum_exp_keepdims_random_cpu",
"test_reduce_log_sum_exp_negative_axes_keepdims_example_cpu",
"test_reduce_log_sum_exp_negative_axes_keepdims_random_cpu",
"test_reduce_log_sum_negative_axes_cpu",
# ReduceSumSquare
"test_reduce_sum_square_default_axes_keepdims_example_cpu",
"test_reduce_sum_square_default_axes_keepdims_random_cpu",
"test_reduce_sum_square_do_not_keepdims_example_cpu",
"test_reduce_sum_square_do_not_keepdims_random_cpu",
"test_reduce_sum_square_keepdims_example_cpu",
"test_reduce_sum_square_keepdims_random_cpu",
"test_reduce_sum_square_negative_axes_keepdims_example_cpu",
"test_reduce_sum_square_negative_axes_keepdims_random_cpu",
# Selu Op:
"test_selu_cpu",
"test_selu_default_cpu",
"test_selu_example_cpu",
# Sigmoid Op:
"test_sigmoid_cpu",
"test_sigmoid_example_cpu",
# Softmax Op:
"test_softmax_axis_0_cpu",
"test_softmax_axis_1_cpu",
"test_softmax_axis_2_cpu",
"test_softmax_default_axis_cpu",
"test_softmax_example_cpu",
"test_softmax_large_number_cpu",
# Sqrt Op:
"test_sqrt_cpu",
"test_sqrt_example_cpu",
# Sum Op:
"test_sum_example_cpu",
"test_sum_one_input_cpu",
"test_sum_two_inputs_cpu",
# Unsqueeze Op:
"test_unsqueeze_axis_0_cpu",
"test_unsqueeze_axis_1_cpu",
"test_unsqueeze_axis_2_cpu",
"test_unsqueeze_axis_3_cpu",
"test_unsqueeze_negative_axes_cpu",
"test_unsqueeze_three_axes_cpu",
"test_unsqueeze_two_axes_cpu",
# "test_unsqueeze_unsorted_axes_cpu",
# Reciprocal Op:
"test_reciprocal_cpu",
"test_reciprocal_example_cpu",
# SoftplusOp:
"test_softplus_cpu",
"test_softplus_example_cpu",
# SoftsignOp:
"test_softsign_cpu",
"test_softsign_example_cpu",
# ReshapeOp:
"test_reshape_extended_dims_cpu",
"test_reshape_negative_dim_cpu",
"test_reshape_negative_extended_dims_cpu",
"test_reshape_one_dim_cpu",
"test_reshape_reduced_dims_cpu",
"test_reshape_reordered_all_dims_cpu",
"test_reshape_reordered_last_dims_cpu",
"test_reshape_zero_and_negative_dim_cpu",
"test_reshape_zero_dim_cpu",
# Transpose
"test_transpose_default_cpu",
"test_transpose_all_permutations_0_cpu",
"test_transpose_all_permutations_1_cpu",
"test_transpose_all_permutations_2_cpu",
"test_transpose_all_permutations_3_cpu",
"test_transpose_all_permutations_4_cpu",
"test_transpose_all_permutations_5_cpu",
# Conv
"test_basic_conv_without_padding_cpu",
"test_conv_with_strides_no_padding_cpu",
# Sign Op:
"test_sign_cpu",
# MatmulOp
"test_matmul_2d_cpu",
"test_matmul_3d_cpu",
"test_matmul_4d_cpu",
# BatchNormalization (test mode)
"test_batchnorm_epsilon_cpu",
"test_batchnorm_example_cpu",
# MaxPoolSingleOut
"test_maxpool_1d_default_cpu",
"test_maxpool_2d_ceil_cpu",
"test_maxpool_2d_default_cpu",
"test_maxpool_2d_dilations_cpu",
"test_maxpool_2d_pads_cpu",
"test_maxpool_2d_precomputed_pads_cpu",
"test_maxpool_2d_precomputed_same_upper_cpu",
"test_maxpool_2d_precomputed_strides_cpu",
"test_maxpool_2d_same_lower_cpu",
"test_maxpool_2d_same_upper_cpu",
"test_maxpool_2d_strides_cpu",
"test_maxpool_3d_default_cpu",
# AveragePool
"test_averagepool_1d_default_cpu",
"test_averagepool_2d_ceil_cpu",
"test_averagepool_2d_default_cpu",
"test_averagepool_2d_pads_count_include_pad_cpu",
"test_averagepool_2d_pads_cpu",
"test_averagepool_2d_precomputed_pads_count_include_pad_cpu",
"test_averagepool_2d_precomputed_pads_cpu",
"test_averagepool_2d_precomputed_same_upper_cpu",
"test_averagepool_2d_precomputed_strides_cpu",
"test_averagepool_2d_same_lower_cpu",
"test_averagepool_2d_same_upper_cpu",
"test_averagepool_2d_strides_cpu",
"test_averagepool_3d_default_cpu",
# LSTM
"test_lstm_defaults_cpu",
"test_lstm_with_initial_bias_cpu",
"test_lstm_with_peepholes_cpu",
# Squeeze
"test_squeeze_cpu",
"test_squeeze_negative_axes_cpu",
# Split
"test_split_equal_parts_1d_cpu",
"test_split_equal_parts_2d_cpu",
"test_split_equal_parts_default_axis_cpu",
"test_split_variable_parts_1d_cpu",
"test_split_variable_parts_2d_cpu",
"test_split_variable_parts_default_axis_cpu",
# Tile
"test_tile_cpu",
"test_tile_precomputed_cpu",
# ConstantOfShape
"test_constantofshape_float_ones_cpu",
# Size
# TODO(tjingrant): fix unit test for size ops.
# "test_size_cpu",
# "test_size_example_cpu",
# Error:
# Items are not equal:
# ACTUAL: dtype('int32')
# DESIRED: dtype('uint8')
# In this test, 'int32' was specified for value attribute as in
# onnx/onnx/backend/test/case/node/constantofshape.py
# and onnx-mlir correctly imported and converted the model.
# It is unknown why 'uint8' came from.
#"test_constantofshape_int_zeros_cpu",
# Model
"test_resnet50_cpu",
"test_vgg19_cpu",
"test_shufflenet_cpu",
]
# Extract name of all test cases.
import inspect
all_tests = []
all_tests += inspect.getmembers(
backend_test.test_cases["OnnxBackendRealModelTest"])
all_tests += inspect.getmembers(
backend_test.test_cases["OnnxBackendNodeModelTest"])
all_test_names = list(map(lambda x: x[0], all_tests))
# Ensure that test names specified in test_to_enable actually exist.
for test_name in test_to_enable:
assert test_name in all_test_names, """test name {} not found, it is likely
that you may have misspelled the test name or the specified test does not
exist in the version of onnx package you installed.""".format(test_name)
backend_test.include(r"^{}$".format(test_name))
# import all test cases at global scope to make them visible to python.unittest
globals().update(backend_test.test_cases)
if __name__ == '__main__':
unittest.main()