torch-mlir/python/test/lit.cfg.py

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# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
# See https://llvm.org/LICENSE.txt for license information.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
Add pytorch interface to ATen Dialect (#30) This patch adds a pytorch interface to npcomp. This interface is modeled after pytorch_xla and exposes the MLIR-based flow as a virtual device (similar to a gpu device or the xla backend). Usage is intended to be something like: dev = torch_mlir.mlir_device() t0 = torch.randn((4,4), device=dev) t1 = torch.randn((4,4), device=dev) t2 = t0 + t1 t2_mlir = torch_mlir.get_mlir( t2 ) t2_cpu = t2.to('cpu') In this case t2_cpu would contain the result of the computation, and t2_mlir contains the mlir description of the computation. Note that this also properly returns backward paths synthesized by pytorch. There are several parts of this: 1) A tensor type (implemented by tensor.* and tensor_impl.*) 2) The device modeling (aten_mlir_bridge.*, aten_mlir_device.*, aten_mlir_type*) 3) a temporary IR (implemented by ir.cpp) There is also a reference lowering directly from the ATen dialect to C function calls consisting of two parts: 1) The driver that uses the IR to generate MLIR, run Passes and compile the result using mlir::ExecutionEngine (implemented by jit.cpp and mlir_gen.cpp) 2) A runtime library implemented by lib/aten_ops.cpp. Most of the operations are implemented by callbacks into the torch C++ libraries. Some aspects of this are known to be less than optimal, in particular: 1) There's some function definitions that don't live in the file corresponding to their declaration. 2) More aspects of this (e.g. the IR) seem like they should be automatically generated. 3) It's unclear to me how much of the 'IR' is actually necessary, or whether MLIR could be created on the fly. Note that this code is licensed in a way similar to pytorch, with the intention that eventually (when npcomp reaches some maturity) it should be pushed there. (see frontends/pytorch/LICENSE) The code is also structured much closer to the pytorch coding style than the LLVM coding style.
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import os
import platform
import re
import subprocess
import tempfile
import lit.formats
import lit.util
from lit.llvm import llvm_config
from lit.llvm.subst import ToolSubst
from lit.llvm.subst import FindTool
# Configuration file for the 'lit' test runner.
# name: The name of this test suite.
config.name = 'NPCOMP_PYTHON'
Add pytorch interface to ATen Dialect (#30) This patch adds a pytorch interface to npcomp. This interface is modeled after pytorch_xla and exposes the MLIR-based flow as a virtual device (similar to a gpu device or the xla backend). Usage is intended to be something like: dev = torch_mlir.mlir_device() t0 = torch.randn((4,4), device=dev) t1 = torch.randn((4,4), device=dev) t2 = t0 + t1 t2_mlir = torch_mlir.get_mlir( t2 ) t2_cpu = t2.to('cpu') In this case t2_cpu would contain the result of the computation, and t2_mlir contains the mlir description of the computation. Note that this also properly returns backward paths synthesized by pytorch. There are several parts of this: 1) A tensor type (implemented by tensor.* and tensor_impl.*) 2) The device modeling (aten_mlir_bridge.*, aten_mlir_device.*, aten_mlir_type*) 3) a temporary IR (implemented by ir.cpp) There is also a reference lowering directly from the ATen dialect to C function calls consisting of two parts: 1) The driver that uses the IR to generate MLIR, run Passes and compile the result using mlir::ExecutionEngine (implemented by jit.cpp and mlir_gen.cpp) 2) A runtime library implemented by lib/aten_ops.cpp. Most of the operations are implemented by callbacks into the torch C++ libraries. Some aspects of this are known to be less than optimal, in particular: 1) There's some function definitions that don't live in the file corresponding to their declaration. 2) More aspects of this (e.g. the IR) seem like they should be automatically generated. 3) It's unclear to me how much of the 'IR' is actually necessary, or whether MLIR could be created on the fly. Note that this code is licensed in a way similar to pytorch, with the intention that eventually (when npcomp reaches some maturity) it should be pushed there. (see frontends/pytorch/LICENSE) The code is also structured much closer to the pytorch coding style than the LLVM coding style.
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config.test_format = lit.formats.ShTest(not llvm_config.use_lit_shell)
if 'TEST_SRC_PATH' in os.environ:
config.environment['TEST_SRC_PATH'] = os.environ['TEST_SRC_PATH']
# path to our python operation library
config.environment['TEST_BUILD_PATH'] = os.path.join(config.npcomp_obj_root)
# suffixes: A list of file extensions to treat as test files.
config.suffixes = ['.py']
# test_source_root: The root path where tests are located.
config.test_source_root = os.path.dirname(__file__)
# test_exec_root: The root path where tests should be run.
config.test_exec_root = os.path.join(config.npcomp_obj_root, 'test')
config.substitutions.append(('%PATH%', config.environment['PATH']))
config.substitutions.append(('%shlibext', config.llvm_shlib_ext))
config.substitutions.append(('%PYTHON', config.python_executable))
Add pytorch interface to ATen Dialect (#30) This patch adds a pytorch interface to npcomp. This interface is modeled after pytorch_xla and exposes the MLIR-based flow as a virtual device (similar to a gpu device or the xla backend). Usage is intended to be something like: dev = torch_mlir.mlir_device() t0 = torch.randn((4,4), device=dev) t1 = torch.randn((4,4), device=dev) t2 = t0 + t1 t2_mlir = torch_mlir.get_mlir( t2 ) t2_cpu = t2.to('cpu') In this case t2_cpu would contain the result of the computation, and t2_mlir contains the mlir description of the computation. Note that this also properly returns backward paths synthesized by pytorch. There are several parts of this: 1) A tensor type (implemented by tensor.* and tensor_impl.*) 2) The device modeling (aten_mlir_bridge.*, aten_mlir_device.*, aten_mlir_type*) 3) a temporary IR (implemented by ir.cpp) There is also a reference lowering directly from the ATen dialect to C function calls consisting of two parts: 1) The driver that uses the IR to generate MLIR, run Passes and compile the result using mlir::ExecutionEngine (implemented by jit.cpp and mlir_gen.cpp) 2) A runtime library implemented by lib/aten_ops.cpp. Most of the operations are implemented by callbacks into the torch C++ libraries. Some aspects of this are known to be less than optimal, in particular: 1) There's some function definitions that don't live in the file corresponding to their declaration. 2) More aspects of this (e.g. the IR) seem like they should be automatically generated. 3) It's unclear to me how much of the 'IR' is actually necessary, or whether MLIR could be created on the fly. Note that this code is licensed in a way similar to pytorch, with the intention that eventually (when npcomp reaches some maturity) it should be pushed there. (see frontends/pytorch/LICENSE) The code is also structured much closer to the pytorch coding style than the LLVM coding style.
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llvm_config.with_system_environment(
['HOME', 'INCLUDE', 'LIB', 'TMP', 'TEMP'])
llvm_config.use_default_substitutions()
# excludes: A list of directories to exclude from the testsuite. The 'Inputs'
# subdirectories contain auxiliary inputs for various tests in their parent
# directories.
config.excludes = ['lit.cfg.py', 'Inputs', 'Examples', 'CMakeLists.txt', 'README.txt', 'LICENSE.txt']
# test_source_root: The root path where tests are located.
config.test_source_root = os.path.dirname(__file__)
# test_exec_root: The root path where tests should be run.
config.test_exec_root = os.path.join(config.npcomp_obj_root, 'test')
config.npcomp_tools_dir = os.path.join(config.npcomp_obj_root, 'bin')
# Tweak the PATH to include the tools dir.
npcomp_python_dir = "python" if config.npcomp_built_standalone else "tools/npcomp/python"
Add pytorch interface to ATen Dialect (#30) This patch adds a pytorch interface to npcomp. This interface is modeled after pytorch_xla and exposes the MLIR-based flow as a virtual device (similar to a gpu device or the xla backend). Usage is intended to be something like: dev = torch_mlir.mlir_device() t0 = torch.randn((4,4), device=dev) t1 = torch.randn((4,4), device=dev) t2 = t0 + t1 t2_mlir = torch_mlir.get_mlir( t2 ) t2_cpu = t2.to('cpu') In this case t2_cpu would contain the result of the computation, and t2_mlir contains the mlir description of the computation. Note that this also properly returns backward paths synthesized by pytorch. There are several parts of this: 1) A tensor type (implemented by tensor.* and tensor_impl.*) 2) The device modeling (aten_mlir_bridge.*, aten_mlir_device.*, aten_mlir_type*) 3) a temporary IR (implemented by ir.cpp) There is also a reference lowering directly from the ATen dialect to C function calls consisting of two parts: 1) The driver that uses the IR to generate MLIR, run Passes and compile the result using mlir::ExecutionEngine (implemented by jit.cpp and mlir_gen.cpp) 2) A runtime library implemented by lib/aten_ops.cpp. Most of the operations are implemented by callbacks into the torch C++ libraries. Some aspects of this are known to be less than optimal, in particular: 1) There's some function definitions that don't live in the file corresponding to their declaration. 2) More aspects of this (e.g. the IR) seem like they should be automatically generated. 3) It's unclear to me how much of the 'IR' is actually necessary, or whether MLIR could be created on the fly. Note that this code is licensed in a way similar to pytorch, with the intention that eventually (when npcomp reaches some maturity) it should be pushed there. (see frontends/pytorch/LICENSE) The code is also structured much closer to the pytorch coding style than the LLVM coding style.
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llvm_config.with_environment('PATH', config.llvm_tools_dir, append_path=True)
llvm_config.with_environment('PYTHONPATH', [
[torch-mlir earthmoving (2/N)] Python code movement. This moves the bulk of the Python code (including the Torch interop) from `frontends/pytorch` into `torch-mlir/TorchPlugin`. This also required reconciling a bunch of other Python-related stuff, like the `torch` dialects. As I did this, it was simpler to just remove all the old numpy/basicpy stuff because we were going to delete it anyway and it was faster than debugging an intermediate state that would only last O(days) anyway. torch-mlir has two top-level python packages (built into the `python_packages` directory): - `torch_mlir_dialects`: `torch` dialect Python bindings (does not depend on PyTorch). This also involves building the aggregate CAPI for `torch-mlir`. - `torch_mlir`: bindings to the part of the code that links against PyTorch (or C++ code that transitively does). Additionally, there remain two more Python packages in npcomp (but outside `torch-mlir`): - `npcomp_torch`: Contains the e2e test framework and testing configs that plug into RefBackend and IREE. - `npcomp_core`: Contains the low-level interfaces to RefBackend and IREE that `npcomp_torch` uses, along with its own `MLIR_PYTHON_PACKAGE_PREFIX=npcomp.` aggregation of the core MLIR python bindings. (all other functionality has been stripped out) After all the basicpy/numpy deletions, the `npcomp` C++ code is now very tiny. It basically just contains RefBackend and the `TorchConversion` dialect/passes (e.g. `TorchToLinalg.cpp`). Correspondingly, there are now 4 main testing targets paralleling the Python layering (which is reflective of the deeper underlying dependency structure) - `check-torch-mlir`: checks the `torch-mlir` pure MLIR C++ code. - `check-torch-mlir-plugin`: checks the code in `TorchPlugin` (e.g. TorchScript import) - `check-frontends-pytorch`: Checks the little code we have in `frontends/pytorch` -- mainly things related to the e2e framework itself. - `check-npcomp`: Checks the pure MLIR C++ code inside npcomp. There is a target `check-npcomp-all` that runs all of them. The `torch-mlir/build_standalone.sh` script does a standalone build of `torch-mlir`. The e2e tests (`tools/torchscript_e2e_test.sh`) are working too. The update_torch_ods script now lives in `torch-mlir/build_tools/update_torch_ods.sh` and expects a standalone build. This change also required a fix upstream related to cross-shlib Python dependencies, so we also update llvm-project to 8dca953dd39c0cd8c80decbeb38753f58a4de580 to get https://reviews.llvm.org/D109776 (no other fixes were needed for the integrate, thankfully). This completes most of the large source code changes. Next will be bringing the CI/packaging/examples back to life.
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os.path.join(config.npcomp_python_packages_dir, 'npcomp_core'),
os.path.join(config.torch_mlir_python_packages_dir, 'torch_mlir'),
],
append_path=True)
Add pytorch interface to ATen Dialect (#30) This patch adds a pytorch interface to npcomp. This interface is modeled after pytorch_xla and exposes the MLIR-based flow as a virtual device (similar to a gpu device or the xla backend). Usage is intended to be something like: dev = torch_mlir.mlir_device() t0 = torch.randn((4,4), device=dev) t1 = torch.randn((4,4), device=dev) t2 = t0 + t1 t2_mlir = torch_mlir.get_mlir( t2 ) t2_cpu = t2.to('cpu') In this case t2_cpu would contain the result of the computation, and t2_mlir contains the mlir description of the computation. Note that this also properly returns backward paths synthesized by pytorch. There are several parts of this: 1) A tensor type (implemented by tensor.* and tensor_impl.*) 2) The device modeling (aten_mlir_bridge.*, aten_mlir_device.*, aten_mlir_type*) 3) a temporary IR (implemented by ir.cpp) There is also a reference lowering directly from the ATen dialect to C function calls consisting of two parts: 1) The driver that uses the IR to generate MLIR, run Passes and compile the result using mlir::ExecutionEngine (implemented by jit.cpp and mlir_gen.cpp) 2) A runtime library implemented by lib/aten_ops.cpp. Most of the operations are implemented by callbacks into the torch C++ libraries. Some aspects of this are known to be less than optimal, in particular: 1) There's some function definitions that don't live in the file corresponding to their declaration. 2) More aspects of this (e.g. the IR) seem like they should be automatically generated. 3) It's unclear to me how much of the 'IR' is actually necessary, or whether MLIR could be created on the fly. Note that this code is licensed in a way similar to pytorch, with the intention that eventually (when npcomp reaches some maturity) it should be pushed there. (see frontends/pytorch/LICENSE) The code is also structured much closer to the pytorch coding style than the LLVM coding style.
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tool_dirs = [config.npcomp_tools_dir, config.llvm_tools_dir]
tools = [
'npcomp-opt',
Add pytorch interface to ATen Dialect (#30) This patch adds a pytorch interface to npcomp. This interface is modeled after pytorch_xla and exposes the MLIR-based flow as a virtual device (similar to a gpu device or the xla backend). Usage is intended to be something like: dev = torch_mlir.mlir_device() t0 = torch.randn((4,4), device=dev) t1 = torch.randn((4,4), device=dev) t2 = t0 + t1 t2_mlir = torch_mlir.get_mlir( t2 ) t2_cpu = t2.to('cpu') In this case t2_cpu would contain the result of the computation, and t2_mlir contains the mlir description of the computation. Note that this also properly returns backward paths synthesized by pytorch. There are several parts of this: 1) A tensor type (implemented by tensor.* and tensor_impl.*) 2) The device modeling (aten_mlir_bridge.*, aten_mlir_device.*, aten_mlir_type*) 3) a temporary IR (implemented by ir.cpp) There is also a reference lowering directly from the ATen dialect to C function calls consisting of two parts: 1) The driver that uses the IR to generate MLIR, run Passes and compile the result using mlir::ExecutionEngine (implemented by jit.cpp and mlir_gen.cpp) 2) A runtime library implemented by lib/aten_ops.cpp. Most of the operations are implemented by callbacks into the torch C++ libraries. Some aspects of this are known to be less than optimal, in particular: 1) There's some function definitions that don't live in the file corresponding to their declaration. 2) More aspects of this (e.g. the IR) seem like they should be automatically generated. 3) It's unclear to me how much of the 'IR' is actually necessary, or whether MLIR could be created on the fly. Note that this code is licensed in a way similar to pytorch, with the intention that eventually (when npcomp reaches some maturity) it should be pushed there. (see frontends/pytorch/LICENSE) The code is also structured much closer to the pytorch coding style than the LLVM coding style.
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]
llvm_config.add_tool_substitutions(tools, tool_dirs)