wehub-resource-sync
197 lines
7.7 KiB
Python
197 lines
7.7 KiB
Python
#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import subprocess as sp
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import numpy as np
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import pytest
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import tensorrt as trt
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from polygraphy import util, mod
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from polygraphy.backend.onnx import GsFromOnnx, OnnxFromBytes
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from polygraphy.backend.onnxrt import OnnxrtRunner, SessionFromOnnx
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from polygraphy.backend.pluginref import PluginRefRunner
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from polygraphy.backend.trt import (
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EngineFromNetwork,
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NetworkFromOnnxBytes,
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TrtRunner,
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network_from_onnx_bytes,
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)
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from polygraphy.backend.trt.util import get_all_tensors
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from polygraphy.comparator import (
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Comparator,
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CompareFunc,
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DataLoader,
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IterationResult,
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PostprocessFunc,
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RunResults,
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)
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from polygraphy.exception import PolygraphyException
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from tests.models.meta import ONNX_MODELS
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build_torch = lambda a, **kwargs: util.array.to_torch(np.array(a, **kwargs))
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class TestComparator:
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def test_warmup_runs(self):
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onnx_loader = ONNX_MODELS["identity"].loader
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runner = OnnxrtRunner(SessionFromOnnx(onnx_loader))
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run_results = Comparator.run([runner], warm_up=2)
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assert len(run_results[runner.name]) == 1
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def test_list_as_data_loader(self):
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onnx_loader = ONNX_MODELS["identity"].loader
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runner = OnnxrtRunner(SessionFromOnnx(onnx_loader), name="onnx_runner")
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data = [{"x": np.ones((1, 1, 2, 2), dtype=np.float32)}] * 2
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run_results = Comparator.run([runner], data_loader=data)
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iter_results = run_results["onnx_runner"]
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assert len(iter_results) == 2
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for actual, expected in zip(iter_results, data):
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assert np.all(actual["y"] == expected["x"])
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def test_generator_as_data_loader(self):
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onnx_loader = ONNX_MODELS["identity"].loader
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runner = OnnxrtRunner(SessionFromOnnx(onnx_loader), name="onnx_runner")
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def data():
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for feed_dict in [{"x": np.ones((1, 1, 2, 2), dtype=np.float32)}] * 2:
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yield feed_dict
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run_results = Comparator.run([runner], data_loader=data())
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iter_results = run_results["onnx_runner"]
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assert len(iter_results) == 2
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for actual, expected in zip(iter_results, data()):
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assert np.all(actual["y"] == expected["x"])
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def test_multiple_runners(self):
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onnx_bytes = ONNX_MODELS["identity"].loader()
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build_onnxrt_session = SessionFromOnnx(onnx_bytes)
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load_engine = EngineFromNetwork(NetworkFromOnnxBytes(onnx_bytes))
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gs_graph = GsFromOnnx(OnnxFromBytes(onnx_bytes))
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runners = [
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OnnxrtRunner(build_onnxrt_session),
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PluginRefRunner(gs_graph),
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TrtRunner(load_engine),
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]
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run_results = Comparator.run(runners)
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compare_func = CompareFunc.simple(check_shapes=True)
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assert bool(Comparator.compare_accuracy(run_results, compare_func=compare_func))
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assert len(list(run_results.values())[0]) == 1 # Default number of iterations
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def test_postprocess(self):
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onnx_loader = ONNX_MODELS["identity"].loader
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run_results = Comparator.run([OnnxrtRunner(SessionFromOnnx(onnx_loader))])
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# Output shape is (1, 1, 2, 2)
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postprocessed = Comparator.postprocess(
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run_results, postprocess_func=PostprocessFunc.top_k(k=(1, -1))
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)
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for _, results in postprocessed.items():
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for result in results:
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for _, output in result.items():
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assert output.shape == (1, 1, 2, 1)
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def test_errors_do_not_hang(self):
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# Should error because interface is not implemented correctly.
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class FakeRunner:
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def __init__(self):
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self.name = "fake"
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runners = [FakeRunner()]
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with pytest.raises(PolygraphyException):
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Comparator.run(runners, use_subprocess=True, subprocess_polling_interval=1)
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def test_segfault_does_not_hang(self):
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def raise_called_process_error():
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class FakeSegfault(sp.CalledProcessError):
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pass
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raise FakeSegfault(-11, ["simulate", "segfault"])
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runners = [TrtRunner(EngineFromNetwork(raise_called_process_error))]
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with pytest.raises(PolygraphyException):
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Comparator.run(runners, use_subprocess=True, subprocess_polling_interval=1)
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def test_multirun_outputs_are_different(self):
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onnx_loader = ONNX_MODELS["identity"].loader
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runner = TrtRunner(EngineFromNetwork(NetworkFromOnnxBytes(onnx_loader)))
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run_results = Comparator.run([runner], data_loader=DataLoader(iterations=2))
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iteration0 = run_results[runner.name][0]
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iteration1 = run_results[runner.name][1]
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for name in iteration0.keys():
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assert util.array.any(iteration0[name] != iteration1[name])
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@pytest.mark.parametrize("array_type", [np.array, build_torch])
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def test_validate_nan(self, array_type):
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run_results = RunResults()
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run_results["fake-runner"] = [
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IterationResult(outputs={"x": array_type(np.nan)})
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]
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assert not Comparator.validate(run_results)
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@pytest.mark.parametrize("array_type", [np.array, build_torch])
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def test_validate_inf(self, array_type):
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run_results = RunResults()
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run_results["fake-runner"] = [
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IterationResult(outputs={"x": array_type(np.inf)})
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]
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assert not Comparator.validate(run_results, check_inf=True)
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def test_dim_param_trt_onnxrt(self):
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load_onnx_bytes = ONNX_MODELS["dim_param"].loader
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build_onnxrt_session = SessionFromOnnx(load_onnx_bytes)
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load_engine = EngineFromNetwork(NetworkFromOnnxBytes(load_onnx_bytes))
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runners = [
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OnnxrtRunner(build_onnxrt_session),
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TrtRunner(load_engine),
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]
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run_results = Comparator.run(runners)
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compare_func = CompareFunc.simple(check_shapes=True)
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assert bool(Comparator.compare_accuracy(run_results, compare_func=compare_func))
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assert len(list(run_results.values())[0]) == 1 # Default number of iterations
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@pytest.mark.skipif(
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mod.version(trt.__version__) < mod.version("10.0"),
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reason="Feature not present before 10.0",
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)
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def test_debug_tensors(self):
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model = ONNX_MODELS["identity"]
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builder, network, parser = network_from_onnx_bytes(model.loader)
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tensor_map = get_all_tensors(network)
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network.mark_debug(tensor_map["x"])
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load_engine = EngineFromNetwork((builder, network, parser))
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runners = [TrtRunner(load_engine)]
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data = [{"x": np.ones((1, 1, 2, 2), dtype=np.float32)}]
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run_results = Comparator.run(runners, data_loader=data)
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for iteration_list in run_results.values():
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# There should be 2 outputs, debug tensor "x" and output "y"
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assert len(list(iteration_list[0].items())) == 2
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run_results["fake-runner"] = [
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IterationResult(
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outputs={
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"x": np.ones((1, 1, 2, 2), dtype=np.float32),
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"y": np.ones((1, 1, 2, 2), dtype=np.float32),
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}
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)
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]
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compare_func = CompareFunc.simple(check_shapes=True)
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assert bool(Comparator.compare_accuracy(run_results, compare_func=compare_func))
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