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 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, stdout=subprocess.PIPE) 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"]) # Call llc to generate object file from bitcode. execute_commands( [LLC, "-filetype=obj", "-relocation-model=pic", "model.bc"]) # Generate shared library from object file, linking with c runtime. execute_commands([ CXX, "-shared", "-fPIC", "model.o", "-o", "model.so", "-L" + RUNTIME_DIR, "-lcruntime" ]) return ExecutionSession("./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", # 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", # 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", # Pooling "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", ] # Extract name of all test cases. import inspect 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()