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ghstack-source-id: 7ea1be26e094c152e9e2f4e329c8717e00bf46d3 Pull Request resolved: #265
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| Original file line number | Diff line number | Diff line change |
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| # Copyright (c) Meta Platforms, Inc. and affiliates. | ||
| # All rights reserved. | ||
| # | ||
| # This source code is licensed under the BSD-style license found in the | ||
| # LICENSE file in the root directory of this source tree. | ||
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| import unittest | ||
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| import torch | ||
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| class TestCompat(unittest.TestCase): | ||
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| def test_torchdynamo(self): | ||
| resnet_path = "multipy/runtime/example/generated/resnet"; | ||
| resnet_jit_path = "multipy/runtime/example/generated/resnet_jit"; | ||
| importer_dynamo = torch.package.PackageImporter(resnet_path) | ||
| model_dynamo = importer_dynamo.load_pickle("model", "model.pkl") | ||
| eg_dynamo = importer_dynamo.load_pickle("model", "example.pkl") | ||
| model_jit = torch.jit.load(resnet_jit_path) | ||
| model_dynamo.forward(eg_dynamo[0]) | ||
| print(model_dynamo(eg_dynamo[0]), model_jit(eg_dynamo[0])) | ||
| self.assertTrue( | ||
| torch.allclose(model_dynamo(eg_dynamo[0]), model_jit(eg_dynamo[0])) | ||
| ) | ||
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| if __name__ == "__main__": | ||
| unittest.main() |
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| @@ -0,0 +1,105 @@ | ||
| // Copyright (c) Meta Platforms, Inc. and affiliates. | ||
| // All rights reserved. | ||
| // | ||
| // This source code is licensed under the BSD-style license found in the | ||
| // LICENSE file in the root directory of this source tree. | ||
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| #include <ATen/Parallel.h> | ||
| #include <gtest/gtest.h> | ||
| #include <libgen.h> | ||
| #include <cstring> | ||
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| #include <c10/util/irange.h> | ||
| #include <libgen.h> | ||
| #include <multipy/runtime/deploy.h> | ||
| #include <torch/script.h> | ||
| #include <torch/torch.h> | ||
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| #include <future> | ||
| #include <iostream> | ||
| #include <string> | ||
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| void compare_torchpy_jit(const char* model_filename, const char* jit_filename) { | ||
| // Test | ||
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| torch::deploy::InterpreterManager m(2); | ||
| torch::deploy::Package p = m.loadPackage(model_filename); | ||
| auto model = p.loadPickle("model", "model.pkl"); | ||
| at::IValue eg; | ||
| { | ||
| auto I = p.acquireSession(); | ||
| eg = I.self.attr("load_pickle")({"model", "example.pkl"}).toIValue(); | ||
| } | ||
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| at::Tensor output = model(eg.toTupleRef().elements()).toTensor(); | ||
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| // Reference | ||
| auto ref_model = torch::jit::load(jit_filename); | ||
| at::Tensor ref_output = | ||
| ref_model.forward(eg.toTupleRef().elements()).toTensor(); | ||
| ASSERT_TRUE(ref_output.allclose(output, 1e-03, 1e-05)); | ||
| } | ||
| const char* resnet_path = "multipy/runtime/example/generated/resnet"; | ||
| // const char* resnet_path = "multipy/runtime/example/generated/resnet_dynamo"; | ||
| const char* resnet_jit_path = "multipy/runtime/example/generated/resnet_jit"; | ||
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| const char* path(const char* envname, const char* path) { | ||
| const char* e = getenv(envname); | ||
| return e ? e : path; | ||
| } | ||
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| TEST(TorchpyTest, ResNetWithDynamo) { | ||
| compare_torchpy_jit( | ||
| path("RESNET", resnet_path), | ||
| path("RESNET_JIT", resnet_jit_path)); | ||
| } | ||
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| TEST(TorchpyTest, ThreadedResnetModelWithDynamo) { | ||
| size_t nthreads = 3; | ||
| torch::deploy::InterpreterManager manager(nthreads); | ||
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| torch::deploy::Package p = manager.loadPackage(path("RESNET", resnet_path)); | ||
| auto model = p.loadPickle("model", "model.pkl"); | ||
| auto ref_model = torch::jit::load(path("RESNET_JIT", resnet_jit_path)); | ||
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| auto input = torch::ones({10, 20}); | ||
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| std::vector<at::Tensor> outputs; | ||
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| std::vector<std::future<at::Tensor>> futures; | ||
| for (const auto i : c10::irange(nthreads)) { | ||
| (void)i; | ||
| futures.push_back(std::async(std::launch::async, [&model]() { | ||
| auto input = torch::ones({10, 10, 10}); | ||
| for (const auto j : c10::irange(100)) { | ||
| (void)j; | ||
| model({input.alias()}).toTensor(); | ||
| } | ||
| auto result = model({input.alias()}).toTensor(); | ||
| return result; | ||
| })); | ||
| } | ||
| for (const auto i : c10::irange(nthreads)) { | ||
| outputs.push_back(futures[i].get()); | ||
| } | ||
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| // Generate reference | ||
| auto ref_output = ref_model.forward({input.alias()}).toTensor(); | ||
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| // Compare all to reference | ||
| for (const auto i : c10::irange(nthreads)) { | ||
| ASSERT_TRUE(ref_output.equal(outputs[i])); | ||
| } | ||
| } | ||
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| int main(int argc, char* argv[]) { | ||
| ::testing::InitGoogleTest(&argc, argv); | ||
| char tempeh[256]; | ||
| getcwd(tempeh, 256); | ||
| std::cout << "Current working directory: " << tempeh << std::endl; | ||
| int rc = RUN_ALL_TESTS(); | ||
| char tmp[256]; | ||
| getcwd(tmp, 256); | ||
| std::cout << "Current working directory: " << tmp << std::endl; | ||
| return rc; | ||
| } |