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Dual license the torch-mlir project. This commit (with approval from all contributors) dual licenses the torch-mlir project under both the standard LLVM license and the standard PyTorch license. This will facilitate moving code between torch-mlir and the two upstream projects. The standard file comment is now: ``` // This file is licensed 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 // Also available under a BSD-style license. See LICENSE. ``` See `LICENSE` in the project root for the terms of both licenses.
2021-09-30 00:03:40 +08:00
# 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
# Also available under a BSD-style license. See LICENSE.
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.
Port the bulk of the remaining code to torch-mlir This leaves no real code outside torch-mlir. This also renames the "npcomp backend contract" to "linalg on tensors backend contract" as the name of the abstraction layer that RefBackend (IREE too) accepts.
2021-09-28 02:36:44 +08:00
config.name = 'TORCH_MLIR_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
Port the bulk of the remaining code to torch-mlir This leaves no real code outside torch-mlir. This also renames the "npcomp backend contract" to "linalg on tensors backend contract" as the name of the abstraction layer that RefBackend (IREE too) accepts.
2021-09-28 02:36:44 +08:00
config.environment['TEST_BUILD_PATH'] = os.path.join(config.torch_mlir_obj_root)
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.
2020-08-22 02:22:47 +08:00
# 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.
Port the bulk of the remaining code to torch-mlir This leaves no real code outside torch-mlir. This also renames the "npcomp backend contract" to "linalg on tensors backend contract" as the name of the abstraction layer that RefBackend (IREE too) accepts.
2021-09-28 02:36:44 +08:00
config.test_exec_root = os.path.join(config.torch_mlir_obj_root, 'test')
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.
2020-08-22 02:22:47 +08:00
Miscellaneous fixes for Windows builds (#1376) * test: allow spaces in path to Python executable On Windows, the path to the Python binary may contain spaces, so this patch adds quotes around the path to the python executable. Thanks to @sstamenova for suggesting the fix! * python: remove header file that causes Windows build failures Similar to https://reviews.llvm.org/D125284, we can safely remove this header file without affecting the build on either Linux. It is necessary to remove this header file on Windows builds since otherwise it causes build errors. * python: drop `TORCH_API` from function defined in Torch-MLIR `TORCH_API` should apply to functions that are either exported by libtorch.so or ones that are imported from libtorch.so by its downstream consumers (like Torch-MLIR). Neither case applies to the `importJitFunctionAsFuncOp()` function, since it is defined in Torch-MLIR (and thus outside libtorch.so). This patch fixes the problem by dropping `TORCH_API` from that function's declaration. * python: make output of class anotations deterministic The `class-annotator-repr.py` test checks for class annotations in a specific order, but prior to this patch, the order was non-deterministic, since the code iterated on an _unordered_ map. This patch makes the iteration order deterministic through two changes: 1. using a sorted map 2. using the class qualified name instead of the address of the class in memory * test: use Python3_EXECUTABLE as interpreter path for consistency This ensures that tests use the Python3 version that was detected using CMake, instead of whichever python version that happens to be in the PATH variable when invoking the test. * test: fix RUN string The parenthesis syntax does not run on Windows (the shell interprets the `(` character as part of the path). Moreover, the ODR violation in the comment no longer seems to apply. * python: port parallel test framework to Windows Since Windows does not support `fork` natively, Python's `multiprocessing` module needs to use `spawn` on Windows. However, to use `spawn`, the multiprocessing module serializes (or pickles) the worker function and its arguments. Sadly, the multiprocessing module (both the default one in Python and the one that is extended in PyTorch) is unable to serialize lambda functions (see https://stackoverflow.com/a/19985580) for detals. Unfortunately, given how our tests are structured, we require that the function under test is passed as an argument to another function, so we cannot sidestep our use of lambda functions. To resolve this problem, this patch makes use of the `multiprocess` and `dill` Python modules, which together offers a multiprocessing mechanism that can serialize lambda functions. The multiprocess module also offers a process pool, which simplifies the code for our parallel testing framework.
2022-09-30 01:07:43 +08:00
# On Windows the path to python could contains spaces in which case it needs to
# be provided in quotes. This is the equivalent of how %python is setup in
# llvm/utils/lit/lit/llvm/config.py.
if "Windows" in config.host_os:
config.python_executable = '"%s"' % (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.
2020-08-22 02:22:47 +08:00
config.substitutions.append(('%PATH%', config.environment['PATH']))
config.substitutions.append(('%shlibext', config.llvm_shlib_ext))
Update test configuration to import mlir from LLVM install location. * Also adds two lit tests to verify that all of our extensions load without fireworks, which is a good indication that the shared library deps are sane. * Bumps llvm-project version to use D89167.
2020-10-10 07:21:01 +08:00
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.
2020-08-22 02:22:47 +08:00
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']
Propagate device data names (#1157) * Propagate device data names * Address PR comment * Add example usage * Add test for device data names * Make TorchMlirComputation fields protected * Add lazy backend device data name unit tests * Disable lazy backend tests if LTC is disabled * Add comments
2022-08-16 21:30:22 +08:00
if not bool(int(os.environ.get("TORCH_MLIR_ENABLE_LTC", 0))):
config.excludes.append("lazy_backend")
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.
2020-08-22 02:22:47 +08:00
# 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.
Port the bulk of the remaining code to torch-mlir This leaves no real code outside torch-mlir. This also renames the "npcomp backend contract" to "linalg on tensors backend contract" as the name of the abstraction layer that RefBackend (IREE too) accepts.
2021-09-28 02:36:44 +08:00
config.test_exec_root = os.path.join(config.torch_mlir_obj_root, 'test')
config.torch_mlir_tools_dir = os.path.join(config.torch_mlir_obj_root, 'bin')
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.
2020-08-22 02:22:47 +08:00
# Tweak the PATH to include the tools dir.
llvm_config.with_environment('PATH', config.llvm_tools_dir, append_path=True)
Update test configuration to import mlir from LLVM install location. * Also adds two lit tests to verify that all of our extensions load without fireworks, which is a good indication that the shared library deps are sane. * Bumps llvm-project version to use D89167.
2020-10-10 07:21:01 +08:00
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.
2021-09-11 02:44:38 +08:00
os.path.join(config.torch_mlir_python_packages_dir, 'torch_mlir'),
],
Update test configuration to import mlir from LLVM install location. * Also adds two lit tests to verify that all of our extensions load without fireworks, which is a good indication that the shared library deps are sane. * Bumps llvm-project version to use D89167.
2020-10-10 07:21:01 +08:00
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.
2020-08-22 02:22:47 +08:00
Port the bulk of the remaining code to torch-mlir This leaves no real code outside torch-mlir. This also renames the "npcomp backend contract" to "linalg on tensors backend contract" as the name of the abstraction layer that RefBackend (IREE too) accepts.
2021-09-28 02:36:44 +08:00
tool_dirs = [config.torch_mlir_tools_dir, config.llvm_tools_dir]
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.
2020-08-22 02:22:47 +08:00
tools = [
Port the bulk of the remaining code to torch-mlir This leaves no real code outside torch-mlir. This also renames the "npcomp backend contract" to "linalg on tensors backend contract" as the name of the abstraction layer that RefBackend (IREE too) accepts.
2021-09-28 02:36:44 +08:00
'torch-mlir-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.
2020-08-22 02:22:47 +08:00
]
llvm_config.add_tool_substitutions(tools, tool_dirs)