469 lines
15 KiB
Python
469 lines
15 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import os
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import sys
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import unittest
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import warnings
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import onnx.backend.base
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import onnx.backend.test
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from onnx.backend.base import Device, DeviceType
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import subprocess
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import test_config
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VERBOSE = bool(os.environ.get("VERBOSE"))
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CXX = test_config.CXX_PATH
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ONNX_MLIR = os.path.join(test_config.ONNX_MLIR_BUILD_PATH, "bin/onnx-mlir")
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LLC = os.path.join(test_config.LLVM_PROJ_BUILD_PATH, "bin/llc")
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# Make lib folder under build directory visible in PYTHONPATH
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doc_check_base_dir = os.path.dirname(os.path.realpath(__file__))
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RUNTIME_DIR = os.path.join(test_config.ONNX_MLIR_BUILD_PATH, "lib")
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sys.path.append(RUNTIME_DIR)
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from PyRuntime import ExecutionSession
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def execute_commands(cmds):
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if (VERBOSE):
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print(" ".join(cmds))
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subprocess.run(cmds)
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# There are two issues, which necessitates the adoption of this endianness
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# aware wrapper around Execution Session:
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# 1. Input arrays are given sometimes in native byte order, sometime in
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# LE byte order, and as soon as the python array enters into py::array
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# C++ objects through pybind, we will no longer be able to query their
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# endianness. So we must intercept the inputs and convert them into
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# native endianness.
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# 2. Output arrays are compared with reference outputs, the comparison
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# unfortunately includes checking that our outputs and reference outputs
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# share the same endianness. So we try to figure out what is the desired
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# reference output endianness, and convert our outputs to this desired
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# endianness.
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class EndiannessAwareExecutionSession(ExecutionSession):
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def __init__(self, path, entry_point):
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super().__init__(path, entry_point)
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def is_input_le(self, inputs):
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inputs_endianness = list(map(lambda x: x.dtype.byteorder, inputs))
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endianness_is_consistent = len(set(inputs_endianness)) <= 1
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assert endianness_is_consistent, \
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"Input arrays contain a mixture of endianness configuration."
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sys_is_le = sys.byteorder == 'little'
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# To interpret character symbols indicating endianness:
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# https://numpy.org/doc/stable/reference/generated/numpy.dtype.byteorder.html
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explicitly_le = inputs_endianness[0] == "<"
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implicitly_le = (inputs_endianness[0] == "=" and sys_is_le)
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return explicitly_le or implicitly_le
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def run(self, inputs, **kwargs):
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if len(inputs):
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# Deduce desired endianness of output from inputs.
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sys_is_le = sys.byteorder == 'little'
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inp_is_le = self.is_input_le(inputs)
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if (sys_is_le != inp_is_le):
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inputs = list(
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map(lambda x: x.byteswap().newbyteorder(), inputs))
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outputs = super().run(inputs)
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if (sys_is_le != inp_is_le):
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outputs = list(
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map(lambda x: x.byteswap().newbyteorder(), outputs))
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return outputs
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else:
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# Can't deduce desired output endianess, fingers crossed.
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warnings.warn(
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"Cannot deduce desired output endianness, using native endianness by default."
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)
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return super().run(inputs)
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class DummyBackend(onnx.backend.base.Backend):
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@classmethod
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def prepare(cls, model, device='CPU', **kwargs):
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super(DummyBackend, cls).prepare(model, device, **kwargs)
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# Save model to disk as temp_model.onnx.
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onnx.save(model, "temp_model.onnx")
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# Call frontend to process temp_model.onnx, bit code will be generated.
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execute_commands([ONNX_MLIR, "temp_model.onnx"])
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return EndiannessAwareExecutionSession("./temp_model.so",
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"_dyn_entry_point_main_graph")
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@classmethod
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def supports_device(cls, device):
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d = Device(device)
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if d.type == DeviceType.CPU:
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return True
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return False
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backend_test = onnx.backend.test.BackendTest(DummyBackend, __name__)
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# Test directories:
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# https://github.com/onnx/onnx/tree/master/onnx/backend/test/data/node
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test_to_enable = [
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# Abs Op:
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"test_abs_cpu",
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# Add Op:
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"test_add_cpu",
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"test_add_bcast_cpu",
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# And Op:
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# Sub Op:
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"test_sub_cpu",
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"test_sub_bcast_cpu",
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"test_sub_example_cpu",
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# Cosh Op:
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"test_cosh_cpu",
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"test_cosh_example_cpu",
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# Concat
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"test_concat_1d_axis_0_cpu",
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"test_concat_2d_axis_0_cpu",
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"test_concat_2d_axis_1_cpu",
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"test_concat_3d_axis_0_cpu",
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"test_concat_3d_axis_1_cpu",
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"test_concat_3d_axis_2_cpu",
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"test_concat_1d_axis_negative_1_cpu",
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"test_concat_2d_axis_negative_1_cpu",
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"test_concat_2d_axis_negative_2_cpu",
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"test_concat_3d_axis_negative_1_cpu",
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"test_concat_3d_axis_negative_2_cpu",
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"test_concat_3d_axis_negative_3_cpu",
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# Tanh:
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"test_tanh_cpu",
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"test_tanh_example_cpu",
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# Div Op:
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"test_div_cpu",
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"test_div_bcast_cpu",
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"test_div_example_cpu",
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# Elu Op:
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"test_elu_cpu",
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"test_elu_default_cpu",
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"test_elu_example_cpu",
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# Exp Op:
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"test_exp_cpu",
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"test_exp_example_cpu",
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# Gather Op:
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#"test_gather_0",
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#"test_gather_1",
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#"test_gather_negative_indices",
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# Gemm Op:
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"test_gemm_all_attributes_cpu",
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"test_gemm_alpha_cpu",
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"test_gemm_beta_cpu",
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"test_gemm_default_matrix_bias_cpu",
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"test_gemm_default_no_bias_cpu",
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"test_gemm_default_scalar_bias_cpu",
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"test_gemm_default_single_elem_vector_bias_cpu",
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"test_gemm_default_vector_bias_cpu",
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"test_gemm_default_zero_bias_cpu",
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"test_gemm_transposeA_cpu",
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"test_gemm_transposeB_cpu",
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# Hard Sigmoid Op:
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"test_hardsigmoid_cpu",
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"test_hardsigmoid_default_cpu",
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"test_hardsigmoid_example_cpu",
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# Leaky Relu Op:
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"test_leakyrelu_cpu",
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"test_leakyrelu_default_cpu",
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"test_leakyrelu_example_cpu",
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# Max Op:
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"test_max_example_cpu",
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"test_max_one_input_cpu",
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"test_max_two_inputs_cpu",
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# Min Op:
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"test_min_example_cpu",
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"test_min_one_input_cpu",
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"test_min_two_inputs_cpu",
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# Mul Op:
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"test_mul_cpu",
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"test_mul_bcast_cpu",
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"test_mul_example_cpu",
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# Relu Op:
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"test_relu_cpu",
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# ReduceMax Op:
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"test_reduce_max_default_axes_keepdim_example_cpu",
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"test_reduce_max_default_axes_keepdims_random_cpu",
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"test_reduce_max_do_not_keepdims_example_cpu",
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"test_reduce_max_do_not_keepdims_random_cpu",
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"test_reduce_max_keepdims_example_cpu",
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"test_reduce_max_keepdims_random_cpu",
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"test_reduce_max_negative_axes_keepdims_example_cpu",
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"test_reduce_max_negative_axes_keepdims_random_cpu",
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# ReduceMin Op:
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"test_reduce_min_default_axes_keepdims_example_cpu",
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"test_reduce_min_default_axes_keepdims_random_cpu",
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"test_reduce_min_do_not_keepdims_example_cpu",
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"test_reduce_min_do_not_keepdims_random_cpu",
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"test_reduce_min_keepdims_example_cpu",
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"test_reduce_min_keepdims_random_cpu",
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"test_reduce_min_negative_axes_keepdims_example_cpu",
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"test_reduce_min_negative_axes_keepdims_random_cpu",
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# ReduceProd Op:
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"test_reduce_prod_default_axes_keepdims_example_cpu",
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"test_reduce_prod_default_axes_keepdims_random_cpu",
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"test_reduce_prod_do_not_keepdims_example_cpu",
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"test_reduce_prod_do_not_keepdims_random_cpu",
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"test_reduce_prod_keepdims_example_cpu",
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"test_reduce_prod_keepdims_random_cpu",
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"test_reduce_prod_negative_axes_keepdims_example_cpu",
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"test_reduce_prod_negative_axes_keepdims_random_cpu",
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# ReduceSum Op:
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"test_reduce_sum_default_axes_keepdims_example_cpu",
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"test_reduce_sum_default_axes_keepdims_random_cpu",
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"test_reduce_sum_do_not_keepdims_example_cpu",
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"test_reduce_sum_do_not_keepdims_random_cpu",
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"test_reduce_sum_keepdims_example_cpu",
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"test_reduce_sum_keepdims_random_cpu",
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"test_reduce_sum_negative_axes_keepdims_example_cpu",
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"test_reduce_sum_negative_axes_keepdims_random_cpu",
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# ReduceL1
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"test_reduce_l1_default_axes_keepdims_example_cpu",
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"test_reduce_l1_default_axes_keepdims_random_cpu",
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"test_reduce_l1_do_not_keepdims_example_cpu",
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"test_reduce_l1_do_not_keepdims_random_cpu",
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"test_reduce_l1_keep_dims_example_cpu",
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"test_reduce_l1_keep_dims_random_cpu",
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"test_reduce_l1_negative_axes_keep_dims_example_cpu",
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"test_reduce_l1_negative_axes_keep_dims_random_cpu",
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# ReduceL2
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"test_reduce_l2_default_axes_keepdims_example_cpu",
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"test_reduce_l2_default_axes_keepdims_random_cpu",
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"test_reduce_l2_do_not_keepdims_example_cpu",
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"test_reduce_l2_do_not_keepdims_random_cpu",
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"test_reduce_l2_keep_dims_example_cpu",
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"test_reduce_l2_keep_dims_random_cpu",
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"test_reduce_l2_negative_axes_keep_dims_example_cpu",
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"test_reduce_l2_negative_axes_keep_dims_random_cpu",
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# ReduceLogSum
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"test_reduce_log_sum_asc_axes_cpu",
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"test_reduce_log_sum_cpu",
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"test_reduce_log_sum_default_cpu",
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"test_reduce_log_sum_desc_axes_cpu",
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# ReduceLogSumExp
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"test_reduce_log_sum_exp_default_axes_keepdims_example_cpu",
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"test_reduce_log_sum_exp_default_axes_keepdims_random_cpu",
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"test_reduce_log_sum_exp_do_not_keepdims_example_cpu",
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"test_reduce_log_sum_exp_do_not_keepdims_random_cpu",
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"test_reduce_log_sum_exp_keepdims_example_cpu",
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"test_reduce_log_sum_exp_keepdims_random_cpu",
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"test_reduce_log_sum_exp_negative_axes_keepdims_example_cpu",
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"test_reduce_log_sum_exp_negative_axes_keepdims_random_cpu",
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"test_reduce_log_sum_negative_axes_cpu",
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# ReduceSumSquare
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"test_reduce_sum_square_default_axes_keepdims_example_cpu",
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"test_reduce_sum_square_default_axes_keepdims_random_cpu",
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"test_reduce_sum_square_do_not_keepdims_example_cpu",
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"test_reduce_sum_square_do_not_keepdims_random_cpu",
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"test_reduce_sum_square_keepdims_example_cpu",
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"test_reduce_sum_square_keepdims_random_cpu",
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"test_reduce_sum_square_negative_axes_keepdims_example_cpu",
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"test_reduce_sum_square_negative_axes_keepdims_random_cpu",
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# Selu Op:
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"test_selu_cpu",
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"test_selu_default_cpu",
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"test_selu_example_cpu",
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# Sigmoid Op:
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"test_sigmoid_cpu",
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"test_sigmoid_example_cpu",
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# Softmax Op:
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"test_softmax_axis_0_cpu",
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"test_softmax_axis_1_cpu",
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"test_softmax_axis_2_cpu",
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"test_softmax_default_axis_cpu",
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"test_softmax_example_cpu",
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"test_softmax_large_number_cpu",
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# Sqrt Op:
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"test_sqrt_cpu",
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"test_sqrt_example_cpu",
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# Sum Op:
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"test_sum_example_cpu",
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"test_sum_one_input_cpu",
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"test_sum_two_inputs_cpu",
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# Unsqueeze Op:
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"test_unsqueeze_axis_0_cpu",
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"test_unsqueeze_axis_1_cpu",
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"test_unsqueeze_axis_2_cpu",
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"test_unsqueeze_axis_3_cpu",
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"test_unsqueeze_negative_axes_cpu",
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"test_unsqueeze_three_axes_cpu",
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"test_unsqueeze_two_axes_cpu",
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# "test_unsqueeze_unsorted_axes_cpu",
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# Reciprocal Op:
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"test_reciprocal_cpu",
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"test_reciprocal_example_cpu",
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# SoftplusOp:
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"test_softplus_cpu",
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"test_softplus_example_cpu",
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# SoftsignOp:
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"test_softsign_cpu",
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"test_softsign_example_cpu",
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# ReshapeOp:
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"test_reshape_extended_dims_cpu",
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"test_reshape_negative_dim_cpu",
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"test_reshape_negative_extended_dims_cpu",
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"test_reshape_one_dim_cpu",
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"test_reshape_reduced_dims_cpu",
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"test_reshape_reordered_all_dims_cpu",
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"test_reshape_reordered_last_dims_cpu",
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"test_reshape_zero_and_negative_dim_cpu",
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"test_reshape_zero_dim_cpu",
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# Transpose
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"test_transpose_default_cpu",
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"test_transpose_all_permutations_0_cpu",
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"test_transpose_all_permutations_1_cpu",
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"test_transpose_all_permutations_2_cpu",
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"test_transpose_all_permutations_3_cpu",
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"test_transpose_all_permutations_4_cpu",
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"test_transpose_all_permutations_5_cpu",
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# Conv
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"test_basic_conv_without_padding_cpu",
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"test_conv_with_strides_no_padding_cpu",
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# Sign Op:
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"test_sign_cpu",
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# MatmulOp
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"test_matmul_2d_cpu",
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"test_matmul_3d_cpu",
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"test_matmul_4d_cpu",
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# BatchNormalization (test mode)
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"test_batchnorm_epsilon_cpu",
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"test_batchnorm_example_cpu",
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# MaxPoolSingleOut
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"test_maxpool_1d_default_cpu",
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"test_maxpool_2d_ceil_cpu",
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"test_maxpool_2d_default_cpu",
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"test_maxpool_2d_dilations_cpu",
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"test_maxpool_2d_pads_cpu",
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"test_maxpool_2d_precomputed_pads_cpu",
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"test_maxpool_2d_precomputed_same_upper_cpu",
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"test_maxpool_2d_precomputed_strides_cpu",
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"test_maxpool_2d_same_lower_cpu",
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"test_maxpool_2d_same_upper_cpu",
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"test_maxpool_2d_strides_cpu",
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"test_maxpool_3d_default_cpu",
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# AveragePool
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"test_averagepool_1d_default_cpu",
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"test_averagepool_2d_ceil_cpu",
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"test_averagepool_2d_default_cpu",
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"test_averagepool_2d_pads_count_include_pad_cpu",
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"test_averagepool_2d_pads_cpu",
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"test_averagepool_2d_precomputed_pads_count_include_pad_cpu",
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"test_averagepool_2d_precomputed_pads_cpu",
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"test_averagepool_2d_precomputed_same_upper_cpu",
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"test_averagepool_2d_precomputed_strides_cpu",
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"test_averagepool_2d_same_lower_cpu",
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"test_averagepool_2d_same_upper_cpu",
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"test_averagepool_2d_strides_cpu",
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"test_averagepool_3d_default_cpu",
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# LSTM
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"test_lstm_defaults_cpu",
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"test_lstm_with_initial_bias_cpu",
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"test_lstm_with_peepholes_cpu",
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# Squeeze
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"test_squeeze_cpu",
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"test_squeeze_negative_axes_cpu",
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# Split
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"test_split_equal_parts_1d_cpu",
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"test_split_equal_parts_2d_cpu",
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"test_split_equal_parts_default_axis_cpu",
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"test_split_variable_parts_1d_cpu",
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"test_split_variable_parts_2d_cpu",
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"test_split_variable_parts_default_axis_cpu",
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# ConstantOfShape
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"test_constantofshape_float_ones_cpu",
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# Size
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# TODO(tjingrant): fix unit test for size ops.
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# "test_size_cpu",
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# "test_size_example_cpu",
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# Error:
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# Items are not equal:
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# ACTUAL: dtype('int32')
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# DESIRED: dtype('uint8')
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# In this test, 'int32' was specified for value attribute as in
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# onnx/onnx/backend/test/case/node/constantofshape.py
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# and onnx-mlir correctly imported and converted the model.
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# It is unknown why 'uint8' came from.
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#"test_constantofshape_int_zeros_cpu",
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# Model
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"test_resnet50_cpu",
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"test_vgg19_cpu",
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"test_shufflenet_cpu",
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]
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# Extract name of all test cases.
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import inspect
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all_tests = []
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all_tests += inspect.getmembers(
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backend_test.test_cases["OnnxBackendRealModelTest"])
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all_tests += inspect.getmembers(
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backend_test.test_cases["OnnxBackendNodeModelTest"])
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all_test_names = list(map(lambda x: x[0], all_tests))
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# Ensure that test names specified in test_to_enable actually exist.
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for test_name in test_to_enable:
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assert test_name in all_test_names, """test name {} not found, it is likely
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that you may have misspelled the test name or the specified test does not
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|
exist in the version of onnx package you installed.""".format(test_name)
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backend_test.include(r"^{}$".format(test_name))
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|
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# import all test cases at global scope to make them visible to python.unittest
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|
globals().update(backend_test.test_cases)
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|
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if __name__ == '__main__':
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unittest.main()
|