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77 lines
4.2 KiB
77 lines
4.2 KiB
import unittest, onnx, tempfile
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from tinygrad import dtypes
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from tinygrad.frontend.onnx import OnnxRunner, onnx_load
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from tinygrad.device import is_dtype_supported
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from extra.onnx import data_types
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from hypothesis import given, settings, strategies as st
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import numpy as np
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data_types.pop(16) # TODO: this is bf16, need to support double parsing first.
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device_supported_dtypes = [odt for odt, dtype in data_types.items() if is_dtype_supported(dtype)]
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device_unsupported_dtypes = [odt for odt, dtype in data_types.items() if not is_dtype_supported(dtype)]
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class TestOnnxRunnerDtypes(unittest.TestCase):
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def _test_input_spec_dtype(self, onnx_data_type, tinygrad_dtype):
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input_tensor = onnx.helper.make_tensor_value_info('input', onnx_data_type, ())
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output_tensor = onnx.helper.make_tensor_value_info('output', onnx_data_type, ())
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node = onnx.helper.make_node('Identity', inputs=['input'], outputs=['output'])
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graph = onnx.helper.make_graph([node], 'identity_test', [input_tensor], [output_tensor])
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model = onnx.helper.make_model(graph)
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tmp = tempfile.NamedTemporaryFile(suffix='.onnx')
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onnx.save(model, tmp.name)
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tmp.flush()
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model = onnx_load(tmp.name)
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runner = OnnxRunner(model)
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self.assertEqual(len(runner.graph_inputs), 1)
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self.assertEqual(runner.graph_inputs['input'].dtype, tinygrad_dtype)
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def _test_initializer_dtype(self, onnx_data_type, tinygrad_dtype):
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arr = np.array([0, 1], dtype=onnx.helper.tensor_dtype_to_np_dtype(onnx_data_type))
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initializer = onnx.helper.make_tensor('initializer', onnx_data_type, arr.shape, arr.tobytes(), raw=True)
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input_tensor = onnx.helper.make_tensor_value_info('input', onnx_data_type, ())
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output_tensor = onnx.helper.make_tensor_value_info('output', onnx_data_type, ())
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node = onnx.helper.make_node('Identity', inputs=['input'], outputs=['output'])
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graph = onnx.helper.make_graph([node], 'identity_test', [input_tensor], [output_tensor], [initializer])
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model = onnx.helper.make_model(graph)
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tmp = tempfile.NamedTemporaryFile(suffix='.onnx')
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onnx.save(model, tmp.name)
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tmp.flush()
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model = onnx_load(tmp.name)
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runner = OnnxRunner(model)
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self.assertEqual(len(runner.graph_inputs), 1)
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self.assertEqual(runner.graph_values['initializer'].dtype, tinygrad_dtype)
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def _test_node_attribute_dtype(self, onnx_data_type, tinygrad_dtype):
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arr = np.array([0, 1], dtype=onnx.helper.tensor_dtype_to_np_dtype(onnx_data_type))
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output_tensor = onnx.helper.make_tensor_value_info('output', onnx_data_type, arr.shape)
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value_tensor = onnx.helper.make_tensor('value', onnx_data_type, arr.shape, arr.tobytes(), raw=True)
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node = onnx.helper.make_node('Constant', inputs=[], outputs=['output'], value=value_tensor)
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graph = onnx.helper.make_graph([node], 'attribute_test', [], [output_tensor])
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model = onnx.helper.make_model(graph)
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tmp = tempfile.NamedTemporaryFile(suffix='.onnx')
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tmp.flush()
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onnx.save(model, tmp.name)
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model = onnx_load(tmp.name)
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runner = OnnxRunner(model)
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self.assertEqual(runner.graph_nodes[0].opts['value'].dtype, tinygrad_dtype)
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@settings(deadline=1000) # TODO investigate unreliable timing
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@given(onnx_data_type=st.sampled_from(device_supported_dtypes))
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def test_supported_dtype_spec(self, onnx_data_type):
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tinygrad_dtype = data_types[onnx_data_type]
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self._test_input_spec_dtype(onnx_data_type, tinygrad_dtype)
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self._test_initializer_dtype(onnx_data_type, tinygrad_dtype)
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self._test_node_attribute_dtype(onnx_data_type, tinygrad_dtype)
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@unittest.skipUnless(device_unsupported_dtypes, "No unsupported dtypes for this device to test.")
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@settings(deadline=1000) # TODO investigate unreliable timing
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@given(onnx_data_type=st.sampled_from(device_unsupported_dtypes))
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def test_unsupported_dtype_spec(self, onnx_data_type):
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tinygrad_dtype = dtypes.default_int if dtypes.is_int(data_types[onnx_data_type]) else dtypes.default_float
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# TODO: maybe unsupported input spec dtype parsing shouldn't default to a dtype
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self._test_input_spec_dtype(onnx_data_type, tinygrad_dtype)
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self._test_initializer_dtype(onnx_data_type, tinygrad_dtype)
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self._test_node_attribute_dtype(onnx_data_type, tinygrad_dtype)
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if __name__ == '__main__':
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unittest.main() |