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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 IREE support in TorchScript e2e tests. - Add support for "expected failures" in test reporting. The new error reports look like [this](https://gist.github.com/silvasean/6ffd95e1d55302b699673da201da210d). - We will now be able to put these tests into CI, since the harness understand which tests are expected to pass and fail. - Refactor RefBackendTestConfig to NpcompBackendTestConfig which supports both RefBackend and IREE. - Add instructions for installing IREE dependencies (both from packages and for local builds of IREE) - Add `tools/torchscript_e2e_test.sh` for invoking the e2e test harness (this makes invoking a bit easier, as it doesn't rely on a loose Python invocation).
2021-07-01 05:13:21 +08:00
# This file describes the sets of tests expected to fail for each config.
# This information is deliberately kept in a side table, rather than
# in-situ on the test, as a deliberate layering decision: tests should
# have unique keys to identify them and enable side tables of various kinds
# (this includes down into lower parts of the stack, where a side table
# might be used to keep more elaborate sets of testing configurations).
Move COMMON_TORCH_MLIR_LOWERING_XFAILS into test_suite That way, downstreams don't have to duplicate this list. Also, remove "external config" feature, since it is subsumed by just importing the test suite.
2022-04-20 03:35:56 +08:00
from torch_mlir_e2e_test.test_suite import COMMON_TORCH_MLIR_LOWERING_XFAILS
Add make_fx_tosa variant to end2end tests (#2240) * Add make_fx_tosa variant to end2end tests * e2e_testing/xfail_sets.py: Add make_fx_tosa xfail for stable
2023-07-13 21:07:54 +08:00
from torch_mlir._version import torch_version_for_comparison, version
Move COMMON_TORCH_MLIR_LOWERING_XFAILS into test_suite That way, downstreams don't have to duplicate this list. Also, remove "external config" feature, since it is subsumed by just importing the test suite.
2022-04-20 03:35:56 +08:00
Advance llvm-project and stablehlo. (#2619) llvm-project: bbd2b08b95fe76bea138c1b03c1cd42ed3ee04df stablehlo: ab709fe48de88c67717abfbd7ef17425eb95ddaf These commits were chosen in order to account for an MLIR API break from https://github.com/llvm/llvm-project/commit/3dbac2c007c114a720300d2a4d79abe9ca1351e7 which required a patch to stablehlo. We integrate a bit beyond that commit to deal with some revert/reapply cycles in the intervening range which were discovered in another downstream. Further, it requires adaptation to the stablehlo API breaks introduced from https://github.com/openxla/stablehlo/pull/1872 which are along for the ride. Since some stablehlo builders were changed to directly take int64_t array refs, also traced that up some call stacks to eliminate some signed/unsigned mismatches that result. Also adds a few TOSA tests to the passing set that seem to work now.
2023-12-08 15:13:42 +08:00
print(f"TORCH_VERSION_FOR_COMPARISON =", torch_version_for_comparison())
[TOSA] Fix conversion for depthwise convolutions (#2398) * [TOSA] Fix conversion for depthwise convolutions * Add e2e tests for depthwise and grouped convolutions Co-authored-by: Lucas Camphausen <lucas.camphausen@iml.fraunhofer.de>
2023-08-18 23:15:54 +08:00
LINALG_XFAIL_SET = COMMON_TORCH_MLIR_LOWERING_XFAILS | {
# Lowering Torch Backend IR -> Linalg-on-Tensors Backend IR failed
# 'linalg.depthwise_conv_2d_nchw_chw' op inferred input/output operand #1 has shape's dimension #0 to be 4, but found 8
[Torch Dialect][stablehlo] emit aten.rand op and add converter to stablehlo (#2413) * [Torch Dialect] emit aten.rand op and add converter to stablehlo * add failed tests for torchdynamo backend * add failed test for linalg backend
2023-08-27 21:56:36 +08:00
"Conv2dWithPaddingDilationStrideStaticModule_depthwise_multiplier",
Add aten.isclose support and its torch-to-tosa lowering (#2512) Add aten.isclose op Add its torch-to-tosa lowering Update the TorchToTosa/basic.mlir tests To test e2e tosa lowering: `python -m e2e_testing.main -v -c=tosa` --------- Co-authored-by: Ze Zhang <ze.zhang@getcruise.com>
2023-10-17 00:44:53 +08:00
"IscloseStaticModule_basic",
Add aten.min.dim to linalg lowering (#2600)
2023-12-05 23:16:35 +08:00
"IscloseStaticModuleTrue_basic"
[TOSA] Fix conversion for depthwise convolutions (#2398) * [TOSA] Fix conversion for depthwise convolutions * Add e2e tests for depthwise and grouped convolutions Co-authored-by: Lucas Camphausen <lucas.camphausen@iml.fraunhofer.de>
2023-08-18 23:15:54 +08:00
}
E2E framework: Add support for list/dict/scalar values Most of the change is in the reporting code to give error messages that are useful, and adjusting TraceItem to be semantically correct w.r.t. Python's modeling of return values. This allows writing a test like `ListLiteralModule_basic` for list functionality, which we will soon be hooking up to IREE. The IR for that test currently gets this far: ``` builtin.func @forward(%arg0: f64) -> !torch.list<!torch.float> { %0 = torch.from_f64 %arg0 %1 = torch.prim.ListConstruct %0, %0 : (!torch.float, !torch.float) -> !torch.list<!torch.float> return %1 : !torch.list<!torch.float> } ``` It should be sufficient to just add a conversion of `torch.prim.ListConstruct` (+ relevant type conversion) to necessary IREE primitives. For lists of *tensors* (rather than scalar floats), it gets more complicated, as we need to deal with changing their element type to ValueTensorType first (by default, they will all be NonValueTensorType). It seems that IREE might have a type we can lower into for non-value tensors as well, TBD.
2021-08-11 07:10:31 +08:00
[torchdynamo] Initial TorchDynamo support This adds a basic e2e Config for TorchDynamo using Linalg-on-Tensors/RefBackend. But TorchDynamo is pretty orthogonal to various other pieces, so it should compose nicely with variations like: - Switching out all the backends (Linalg-on-Tensors, TOSA, MHLO) - PyTorch functionalization and decompositions - Taking the example inputs and compiling with all dynamic or all static shapes without duplicating tests. This adds it to the CI, but there are still a lot of XFAIL's. This also adds a helper `from torch_mlir.dynamo import make_simple_dynamo_backend` which simplifies some of the steps for making a Torch-MLIR-based TorchDynamo backend. We include "simple" in the name because we are going to be exploring various things next from the long-term roadmap. The next steps are: - Burn down all the XFAIL's. - Start working on the pieces from the [long-term roadmap](https://github.com/llvm/torch-mlir/blob/main/docs/long_term_roadmap.md). - Add functionalization/decompositions into the TorchDynamo flow and remove reliance on the current Torch-MLIR "frontend". - Write a pure-Python direct FX->MLIR importer. - Hook up the new PyTorch symbolic shape stuff. - Explore PrimTorch decompositions for simplifying backends.
2022-11-18 20:21:19 +08:00
TORCHDYNAMO_XFAIL_SET = {
#### General TorchDynamo/PyTorch errors
# TypeError: new_empty(): argument 'size' (position 1) must be tuple of ints, but found element of type NoneType at pos 0
# RuntimeError: Failed running call_function aten.convolution_backward(...
[torchdynamo] Update XFAIL sets with upstream bug numbers.
2022-11-24 22:36:13 +08:00
# https://github.com/pytorch/pytorch/issues/89629
[torchdynamo] Initial TorchDynamo support This adds a basic e2e Config for TorchDynamo using Linalg-on-Tensors/RefBackend. But TorchDynamo is pretty orthogonal to various other pieces, so it should compose nicely with variations like: - Switching out all the backends (Linalg-on-Tensors, TOSA, MHLO) - PyTorch functionalization and decompositions - Taking the example inputs and compiling with all dynamic or all static shapes without duplicating tests. This adds it to the CI, but there are still a lot of XFAIL's. This also adds a helper `from torch_mlir.dynamo import make_simple_dynamo_backend` which simplifies some of the steps for making a Torch-MLIR-based TorchDynamo backend. We include "simple" in the name because we are going to be exploring various things next from the long-term roadmap. The next steps are: - Burn down all the XFAIL's. - Start working on the pieces from the [long-term roadmap](https://github.com/llvm/torch-mlir/blob/main/docs/long_term_roadmap.md). - Add functionalization/decompositions into the TorchDynamo flow and remove reliance on the current Torch-MLIR "frontend". - Write a pure-Python direct FX->MLIR importer. - Hook up the new PyTorch symbolic shape stuff. - Explore PrimTorch decompositions for simplifying backends.
2022-11-18 20:21:19 +08:00
"ConvolutionBackwardModule2DPadded_basic",
"ConvolutionBackwardModule2D_basic",
build: update llvm tag to ba8b8a73f (#1856) * build: update llvm tag to ba8b8a73f * skip ConvolutionModule2DGroups_basic
2023-02-07 23:25:59 +08:00
[torchdynamo] Initial TorchDynamo support This adds a basic e2e Config for TorchDynamo using Linalg-on-Tensors/RefBackend. But TorchDynamo is pretty orthogonal to various other pieces, so it should compose nicely with variations like: - Switching out all the backends (Linalg-on-Tensors, TOSA, MHLO) - PyTorch functionalization and decompositions - Taking the example inputs and compiling with all dynamic or all static shapes without duplicating tests. This adds it to the CI, but there are still a lot of XFAIL's. This also adds a helper `from torch_mlir.dynamo import make_simple_dynamo_backend` which simplifies some of the steps for making a Torch-MLIR-based TorchDynamo backend. We include "simple" in the name because we are going to be exploring various things next from the long-term roadmap. The next steps are: - Burn down all the XFAIL's. - Start working on the pieces from the [long-term roadmap](https://github.com/llvm/torch-mlir/blob/main/docs/long_term_roadmap.md). - Add functionalization/decompositions into the TorchDynamo flow and remove reliance on the current Torch-MLIR "frontend". - Write a pure-Python direct FX->MLIR importer. - Hook up the new PyTorch symbolic shape stuff. - Explore PrimTorch decompositions for simplifying backends.
2022-11-18 20:21:19 +08:00
# RuntimeError: Index tensor must have the same number of dimensions as self tensor
# RuntimeError: Failed running call_function aten.nll_loss_backward(...
[torchdynamo] Update XFAIL sets with upstream bug numbers.
2022-11-24 22:36:13 +08:00
# https://github.com/pytorch/pytorch/issues/89630
[torchdynamo] Initial TorchDynamo support This adds a basic e2e Config for TorchDynamo using Linalg-on-Tensors/RefBackend. But TorchDynamo is pretty orthogonal to various other pieces, so it should compose nicely with variations like: - Switching out all the backends (Linalg-on-Tensors, TOSA, MHLO) - PyTorch functionalization and decompositions - Taking the example inputs and compiling with all dynamic or all static shapes without duplicating tests. This adds it to the CI, but there are still a lot of XFAIL's. This also adds a helper `from torch_mlir.dynamo import make_simple_dynamo_backend` which simplifies some of the steps for making a Torch-MLIR-based TorchDynamo backend. We include "simple" in the name because we are going to be exploring various things next from the long-term roadmap. The next steps are: - Burn down all the XFAIL's. - Start working on the pieces from the [long-term roadmap](https://github.com/llvm/torch-mlir/blob/main/docs/long_term_roadmap.md). - Add functionalization/decompositions into the TorchDynamo flow and remove reliance on the current Torch-MLIR "frontend". - Write a pure-Python direct FX->MLIR importer. - Hook up the new PyTorch symbolic shape stuff. - Explore PrimTorch decompositions for simplifying backends.
2022-11-18 20:21:19 +08:00
"NllLossModuleBackward1DMeanWeight_basic",
"NllLossModuleBackward1DMean_basic",
"NllLossModuleBackward1DSumWeight_basic",
"NllLossModuleBackward1DSum_basic",
"NllLossModuleBackward1DWeight_basic",
"NllLossModuleBackward1D_basic",
[torchdynamo] Use decompositions to support a few ops
2022-11-29 22:01:42 +08:00
# TypeError: uniform() missing 2 required keyword-only arguments: 'dtype' and 'device'
# RuntimeError: Failed running call_function aten.uniform(...
# https://github.com/pytorch/torchdynamo/issues/1954
"UniformNoCorrelationModule_basic",
[torchdynamo] Initial TorchDynamo support This adds a basic e2e Config for TorchDynamo using Linalg-on-Tensors/RefBackend. But TorchDynamo is pretty orthogonal to various other pieces, so it should compose nicely with variations like: - Switching out all the backends (Linalg-on-Tensors, TOSA, MHLO) - PyTorch functionalization and decompositions - Taking the example inputs and compiling with all dynamic or all static shapes without duplicating tests. This adds it to the CI, but there are still a lot of XFAIL's. This also adds a helper `from torch_mlir.dynamo import make_simple_dynamo_backend` which simplifies some of the steps for making a Torch-MLIR-based TorchDynamo backend. We include "simple" in the name because we are going to be exploring various things next from the long-term roadmap. The next steps are: - Burn down all the XFAIL's. - Start working on the pieces from the [long-term roadmap](https://github.com/llvm/torch-mlir/blob/main/docs/long_term_roadmap.md). - Add functionalization/decompositions into the TorchDynamo flow and remove reliance on the current Torch-MLIR "frontend". - Write a pure-Python direct FX->MLIR importer. - Hook up the new PyTorch symbolic shape stuff. - Explore PrimTorch decompositions for simplifying backends.
2022-11-18 20:21:19 +08:00
#### Torch-MLIR internal compiler errors
# These are probably due to slightly different ops being recorded by
# torchdynamo vs. torchscript.
[torchdynamo] Use decompositions to support a few ops
2022-11-29 22:01:42 +08:00
# No upstream decompositions.
[torchdynamo] Annotate the XFAIL's with more info
2022-11-25 20:33:34 +08:00
# %6:4 = torch.operator "aten._embedding_bag_forward_only"(%1, %3, %5, %false, %int0, %false, %none, %false, %int-1) : (!torch.tensor<*,f32>, !torch.tensor<*,si64>, !torch.tensor<*,si64>, !torch.bool, !torch.int, !torch.bool, !torch.none, !torch.bool, !torch.int) -> (!torch.tensor, !torch.tensor, !torch.tensor, !torch.tensor)
[torchdynamo] Use decompositions to support a few ops
2022-11-29 22:01:42 +08:00
# See also: https://github.com/pytorch/torchdynamo/issues/327
[torchdynamo] Initial TorchDynamo support This adds a basic e2e Config for TorchDynamo using Linalg-on-Tensors/RefBackend. But TorchDynamo is pretty orthogonal to various other pieces, so it should compose nicely with variations like: - Switching out all the backends (Linalg-on-Tensors, TOSA, MHLO) - PyTorch functionalization and decompositions - Taking the example inputs and compiling with all dynamic or all static shapes without duplicating tests. This adds it to the CI, but there are still a lot of XFAIL's. This also adds a helper `from torch_mlir.dynamo import make_simple_dynamo_backend` which simplifies some of the steps for making a Torch-MLIR-based TorchDynamo backend. We include "simple" in the name because we are going to be exploring various things next from the long-term roadmap. The next steps are: - Burn down all the XFAIL's. - Start working on the pieces from the [long-term roadmap](https://github.com/llvm/torch-mlir/blob/main/docs/long_term_roadmap.md). - Add functionalization/decompositions into the TorchDynamo flow and remove reliance on the current Torch-MLIR "frontend". - Write a pure-Python direct FX->MLIR importer. - Hook up the new PyTorch symbolic shape stuff. - Explore PrimTorch decompositions for simplifying backends.
2022-11-18 20:21:19 +08:00
"AtenEmbeddingBagSumExample_basic",
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
[torchdynamo] Initial TorchDynamo support This adds a basic e2e Config for TorchDynamo using Linalg-on-Tensors/RefBackend. But TorchDynamo is pretty orthogonal to various other pieces, so it should compose nicely with variations like: - Switching out all the backends (Linalg-on-Tensors, TOSA, MHLO) - PyTorch functionalization and decompositions - Taking the example inputs and compiling with all dynamic or all static shapes without duplicating tests. This adds it to the CI, but there are still a lot of XFAIL's. This also adds a helper `from torch_mlir.dynamo import make_simple_dynamo_backend` which simplifies some of the steps for making a Torch-MLIR-based TorchDynamo backend. We include "simple" in the name because we are going to be exploring various things next from the long-term roadmap. The next steps are: - Burn down all the XFAIL's. - Start working on the pieces from the [long-term roadmap](https://github.com/llvm/torch-mlir/blob/main/docs/long_term_roadmap.md). - Add functionalization/decompositions into the TorchDynamo flow and remove reliance on the current Torch-MLIR "frontend". - Write a pure-Python direct FX->MLIR importer. - Hook up the new PyTorch symbolic shape stuff. - Explore PrimTorch decompositions for simplifying backends.
2022-11-18 20:21:19 +08:00
# error: failed to legalize operation 'torch.valsem.aten.bernoulli.float' that was explicitly marked illegal
"BernoulliFloatModule_basic",
[MLIR][TORCH] Add decomposition for aten.bernoulli.p op (#1882) Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2023-02-16 01:06:29 +08:00
"BernoulliPModule_basic",
[torchdynamo] Initial TorchDynamo support This adds a basic e2e Config for TorchDynamo using Linalg-on-Tensors/RefBackend. But TorchDynamo is pretty orthogonal to various other pieces, so it should compose nicely with variations like: - Switching out all the backends (Linalg-on-Tensors, TOSA, MHLO) - PyTorch functionalization and decompositions - Taking the example inputs and compiling with all dynamic or all static shapes without duplicating tests. This adds it to the CI, but there are still a lot of XFAIL's. This also adds a helper `from torch_mlir.dynamo import make_simple_dynamo_backend` which simplifies some of the steps for making a Torch-MLIR-based TorchDynamo backend. We include "simple" in the name because we are going to be exploring various things next from the long-term roadmap. The next steps are: - Burn down all the XFAIL's. - Start working on the pieces from the [long-term roadmap](https://github.com/llvm/torch-mlir/blob/main/docs/long_term_roadmap.md). - Add functionalization/decompositions into the TorchDynamo flow and remove reliance on the current Torch-MLIR "frontend". - Write a pure-Python direct FX->MLIR importer. - Hook up the new PyTorch symbolic shape stuff. - Explore PrimTorch decompositions for simplifying backends.
2022-11-18 20:21:19 +08:00
# error: failed to legalize operation 'torch.aten.view' that was explicitly marked illegal
"ElementwiseFlattenBroadcastModule_basic",
"FlattenRank0Module_basic",
[torchdynamo] Use decompositions to support a few ops
2022-11-29 22:01:42 +08:00
"UniformModule_basic",
[Torch Dialect] add folder for aten.add (#2264) * [Torch Dialect] add folder for aten.add * update * update * update
2023-06-27 10:55:28 +08:00
"UniformStaticShapeModule_basic",
xfail ElementwisePreluModule_basic in dynamo (#1763)
2023-01-01 05:54:25 +08:00
# error: unsupported by backend contract: tensor with unknown rank
# note: see current operation: %1 = "torch.tensor_static_info_cast"(%arg0) : (!torch.vtensor<[5,4,3,2,1],f32>) -> !torch.vtensor<*,f32>
"ElementwisePreluModule_basic",
build: manually update PyTorch version Set PyTorch and TorchVision version to nightly release 2023-02-06. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2023-02-07 13:38:44 +08:00
#ERROR: value (Tensor with shape=[2, 3, 6, 10], dtype=torch.float32, min=-1.336e-32, max=+0.9152, mean=+0.4837) is not close to golden value (Tensor with shape=[2, 3, 6, 10], dtype=torch.float32, min=+0.02233, max=+0.9152, mean=+0.4777)
"UpSampleNearest2dDynamicFactor_basic",
[MLIR][TORCH] Add support for integer type input for max.dim op Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2023-02-06 19:52:04 +08:00
"ReduceMaxAlongDimUnsignedInt_basic",
Add aten.min.dim to linalg lowering (#2600)
2023-12-05 23:16:35 +08:00
"ReduceMinAlongDimUnsignedInt_basic",
build: manually update PyTorch version Set PyTorch and TorchVision version to nightly release 2023-02-20. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2023-02-20 15:40:09 +08:00
#ERROR: value (-56) is not equal to golden value (200)
"AtenIntTensorByteDtypeModule_basic",
Fix handling of non-int tensors in `getScalarValue` (#1914) The current implementation of `getScalarValue` does not check that the input to a `ValueTensorLiteralOp` is an i64 before extracting the value, and it does not check that the result type of the `PrimNumToTensorScalarOp` is also an i64. This leads to crashes or invalid IR generated when the `input` is something other than an i64 tensor or `!torch.int`. This commit addresses those issues. In addition, the function `getScalarValue` is renamed to `getScalarIntValue` to make it clear that it *only* extracts scalar integers.
2023-03-07 02:12:58 +08:00
# ERROR: assert isinstance(e, FakeTensor)
"ElementwiseAddScalar_NumToTensorFloat_Module_basic",
[Torch Dialect] add RSub, ScalarImplicit canonicalize (#1899) * add rsub, scalarimplit canonicalizer * reformat * address comments * fix bug * fix test * Update elementwise.py * resolve merge conflict * change to 3 * change to 3 * real fix * fix name * add torchdynamo fail test --------- Co-authored-by: zhekun.zhang <zhekun.zhang@bytedance.com>
2023-03-07 09:38:27 +08:00
# ERROR: assert isinstance(e, FakeTensor)
"RsubInt0d_NumToTensor_Module_basic",
[MLIR][TORCH] Add decomposition for prims::squeeze op This commit adds the decomposition for the prims.squeeze op. Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-11-16 13:57:58 +08:00
# ERROR: RuntimeError: Found a custom (non-ATen) operator that either mutates or its inputs: prims::squeeze.. Getting these operators to work with functionalization requires some extra work. For mutable ops you need to register a corresponding out-of-place variant of the op, and you also need to register a Functionalization kernel that performs some boilerplate, telling functionalization to map from the mutable op to the out-of-place op. See a more complete example of how to do this at https://gist.github.com/bdhirsh/7dadbf6296f8f7d1abcf4c482f438aaa.
"PrimsSqueezeModule_basic",
"PrimsSqueezeEmptyDimensionsModule_basic",
Lowering to linalg of prims split_dim op (#2576) Adds support for lowering to prims split_op. Similar design to collapse op lowering in https://github.com/llvm/torch-mlir/pull/2572, with some small differences, because the split_dim op (in pytorch) is view-changing whereas the collapse is not. The difference means that 1) it must be registered in the function Torch::isViewLikeOp 2) it must be be added to the "expected fail" set for the torch dynamo backend.
2023-11-21 23:56:09 +08:00
"SplitDimStaticModule_basic",
"SplitDimDynamicModule_basic",
[TorchDynamo] Add aten.squeeze op to the decomposition list Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2023-04-05 19:32:52 +08:00
[MLIR][TORCH] Add support for prims::view_of op This op does nothing and just returns the input operand as the result of the op. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2023-04-10 11:50:26 +08:00
# ERROR: RuntimeError: Found a custom (non-ATen) operator that either mutates or its inputs: prims::view_of.. Getting these operators to work with functionalization requires some extra work. For mutable ops you need to register a corresponding out-of-place variant of the op, and you also need to register a Functionalization kernel that performs some boilerplate, telling functionalization to map from the mutable op to the out-of-place op. See a more complete example of how to do this at https://gist.github.com/bdhirsh/7dadbf6296f8f7d1abcf4c482f438aaa.
"PrimsViewOfModule_basic",
"PrimsViewOfZeroRankModule_basic",
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
# See https://github.com/llvm/torch-mlir/pull/2040 and corresponding upstream issue
# https://github.com/pytorch/pytorch/issues/99752.
# torch._dynamo.exc.Unsupported: call_function BuiltinVariable(bool) [TensorVariable()] {}
'TensorToBoolZeroRank_basic',
'TensorToBool_basic',
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
# START tests failing due to: torch._dynamo.exc.Unsupported: call_function BuiltinVariable(float) [TensorVariable()] {}
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
'AtenSubFloatModule_basic',
'BoolFloatFalseModule_basic',
'BoolFloatTrueModule_basic',
'CeilFloatModule_basic',
'DivFloatModule_basic',
'GeFloatIntModule_basic',
'GeFloatModule_basic',
'GtFloatIntModule_basic',
'NeFloatIntModule_basic',
'SubFloatModule_basic',
'TensorToFloatZeroRank_basic',
'TensorToFloat_basic',
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
# END tests failing due to: torch._dynamo.exc.Unsupported: call_function BuiltinVariable(float) [TensorVariable()] {}
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
# START tests failing due to: torch._dynamo.exc.Unsupported: call_function BuiltinVariable(int) [TensorVariable()] {}
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
'AddIntModule_basic',
'AtenIntTensorCharDtypeModule_basic',
'BoolIntFalseModule_basic',
'BoolIntTrueModule_basic',
'DivIntModule_basic',
'EqIntModule_basic',
'GeIntModule_basic',
'GtIntModule_basic',
'MulIntModule_basic',
'NeIntModule_basic',
'SqrtIntModule_basic',
'SubIntModule_basic',
'TensorToIntZeroRank_basic',
'TensorToInt_basic',
'UnsafeViewCollapseDynamicWithAtenSizeIntModule_basic',
'ViewCollapseDynamicWithAtenSizeIntModule_basic',
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
# END tests failing due to: torch._dynamo.exc.Unsupported: call_function BuiltinVariable(int) [TensorVariable()] {}
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
# ERROR: torch._dynamo.exc.Unsupported: call_method ListVariable() sort [] {'reverse': ConstantVariable(bool)}
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
'SortIntListReverse_basic',
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
# ERROR: torch._dynamo.exc.Unsupported: call_method ListVariable() sort [] {}
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
'SortIntList_basic',
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
# START tests failing due to: torch._dynamo.exc.Unsupported: data dependent operator: aten._local_scalar_dense.default
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
'AtenFloatScalarModule_basic',
'AtenIntBoolOpModule_basic',
'QuantizedMLP_basic',
'ScalarImplicitFloatModule_basic',
'ScalarImplicitIntModule_basic',
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
# END tests failing due to: torch._dynamo.exc.Unsupported: data dependent operator: aten._local_scalar_dense.default
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
# START tests failing due to: torch._dynamo.exc.Unsupported: dynamic shape operator: aten.bincount.default
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
'BincountMinlengthModule_basic',
'BincountModule_basic',
'BincountStaticSizeModule_basic',
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
# END tests failing due to: torch._dynamo.exc.Unsupported: dynamic shape operator: aten.bincount.default
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
# ERROR: torch._dynamo.exc.Unsupported: torch.* op returned non-Tensor bool call_function aten.Bool
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
'BoolFloatConstantModule_basic',
'BoolIntConstantModule_basic',
add uncovered cases in view lowering (#2524) removes unecessary checks from empty strided
2023-11-02 10:56:44 +08:00
# ERROR: torch._dynamo.exc.Unsupported: torch.* op returned non-Tensor int call_function aten.size
"ViewSizeFromOtherTensor_basic",
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
# ERROR: torch._dynamo.exc.Unsupported: torch.* op returned non-Tensor bool call_function aten.__contains__
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
'ContainsIntList_False',
'ContainsIntList_True',
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
# ERROR: torch._dynamo.exc.Unsupported: torch.* op returned non-Tensor bool call_function aten.all
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
'AllBoolFalseModule_basic',
'AllBoolTrueModule_basic',
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
# ERROR: torch._dynamo.exc.Unsupported: torch.* op returned non-Tensor bool call_function aten.any
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
'AnyBoolFalseModule_basic',
'AnyBoolTrueModule_basic',
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
# ERROR: torch._dynamo.exc.Unsupported: torch.* op returned non-Tensor float call_function aten.sqrt
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
'SqrtIntConstantModule_basic',
[Torch-to-Linalg] Add dynamic dimension support for BroadcastTo op (#2174) -- This commit adds support for dynamic dimension in BroadcastTo op. Signed-off-by: Abhishek Varma <abhishek@nod-labs.com>
2023-07-08 01:01:51 +08:00
# ERROR: torch._dynamo.exc.Unsupported: torch.* op returned non-Tensor int call_function aten.size
'BroadcastDynamicDimModule_basic',
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
# START tests failing due to: torch._dynamo.exc.Unsupported: torch.* op returned non-Tensor int call_function aten.Int
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
'AtenIntBoolOpConstFalseModule_basic',
'AtenIntBoolOpConstTrueModule_basic',
'IntFloatModule_basic',
'PowIntFloatModule_basic',
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
# END tests failing due to: torch._dynamo.exc.Unsupported: torch.* op returned non-Tensor int call_function aten.Int
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
# ERROR: torch._dynamo.exc.Unsupported: torch.* op returned non-Tensor int call_function aten.len
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
'LenStrModule_basic',
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
# ERROR: torch._dynamo.exc.Unsupported: torch.* op returned non-Tensor int call_function aten.numel
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
'NumelModule_basic',
'NumelZeroRankModule_basic',
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
# ERROR: torch._dynamo.exc.Unsupported: torch.* op returned non-Tensor int call_function prim.max
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
'PrimMaxIntModule_basic',
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
# ERROR: torch._dynamo.exc.Unsupported: torch.* op returned non-Tensor int call_function prim.min
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
'PrimMinIntModule_basic',
[LINALG] Add dynamic support for `PrimMinIntOp`
2023-05-26 06:40:12 +08:00
'PrimMinIntDynamicModule_basic',
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
# START tests failing due to: empty graph in dynamo
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
'IsFloatingPointFloat_True',
'IsFloatingPointInt_False',
'TorchPrimLoopForLikeModule_basic',
'TorchPrimLoopWhileLikeModule_basic',
[Importer] import constant tuple (#2132) * [Importer] import constant tuple * update * update * update
2023-05-31 14:14:14 +08:00
"ScalarConstantTupleModule_basic",
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
# END tests failing due to: empty graph in dynamo
# ERROR due to: backend never runs because of empty frame
'ConstantBoolParameterModule_basic',
# START tests failing due to: 'torch.aten.mul.Tensor' op operand #1 must be Any Torch tensor type, but got '!torch.float'
"AddCDivModule_basic",
"ElementwiseMulScalarModule_basic",
"ElementwiseMulScalarModule_float",
"NativeGroupNormBackwardModule_basic",
"UpSampleNearest2dDynamicSize_basic",
"UpSampleNearest2dStaticFactor_basic",
"UpSampleNearest2dStaticSize_basic",
"UpSampleNearest2d_basic",
# END tests failing due to: 'torch.aten.mul.Tensor' op operand #1 must be Any Torch tensor type, but got '!torch.float'
# START tests failing due to: 'torch.aten.add.Tensor' op operand #1 must be Any Torch tensor type, but got '!torch.float'
"BatchNorm1DModule_basic",
"BatchNorm1DWith2DInputModule_basic",
"BatchNorm2DModule_basic",
"BatchNorm3DModule_basic",
[Stablehlo] add e2e test for aten.batch_norm (#2129)
2023-05-18 00:04:40 +08:00
"BatchNorm1DStaticShapeModule_basic",
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
"ElementwiseAddScalarFloatModule_basic",
"ElementwiseAddScalarInt64Module_basic",
"ElementwiseAddScalarIntModule_basic",
"MobilenetV3Module_basic",
"NativeBatchNorm1DModule_basic",
"NativeBatchNorm2DModule_basic",
"NativeBatchNorm3DModule_basic",
"NativeBatchNormNoneWeightModule_basic",
"NativeGroupNormModule_basic",
"ResNet18Module_basic",
"ResNet18StaticModule_basic",
# END tests failing due to: 'torch.aten.add.Tensor' op operand #1 must be Any Torch tensor type, but got '!torch.float'
# ERROR: 'torch.aten.add.Tensor' op operand #1 must be Any Torch tensor type, but got '!torch.int'
"ElementwiseAddScalar_TensorLiteralInt32_Module_basic",
"HBC_basic",
# ERROR: 'torch.aten.div.Tensor' op operand #1 must be Any Torch tensor type, but got '!torch.float'
"ElementwiseDivScalarModule_basic",
fix(TorchToTosa.cpp): adjust torch->tosa div conversion (#2200) check the return type of the division to figure out whether to use the floating point implementation of a division or to use the integer. the issue rose from the fact that the inputs are all integer but the result was casted to floating point. The conversion then chose to use the integer implementation of division which is not legal in tosa when all the inputs get casted to floating point. fix(TorchToLinalg): AtenDivScalarOp upcast self operand as well if applicable, the self operand must also be casted to float as it can be an integer.
2023-06-12 17:18:38 +08:00
# ERROR: 'torch.aten.div.Tensor' op operand #1 must be Any Torch tensor type, but got '!torch.int'
"ElementwiseAtenDivIntScalarModule_basic",
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
# ERROR: 'torch.aten.mul.Tensor' op operand #1 must be Any Torch tensor type, but got '!torch.int'
"ElementwiseMulScalarModule_int",
# ERROR: 'torch.aten.sub.Tensor' op operand #1 must be Any Torch tensor type, but got '!torch.float'
"ElementwiseSubScalarFloatModule_basic",
"ElementwiseSubScalarIntModule_basic",
# ERROR: Exception: Unsupported: missing default value for argument 0 in schema for aten.div.Tensor_mode
"ElementwiseDivRoundingModeFloorModule_basic",
"ElementwiseDivRoundingModeTruncModule_basic",
[RFC] general support for Adaptive Pooling Ops (#2661) Adaptive pooling ops can only be decomposed into their non-adaptive counterparts in trivial cases. For example, the current decomposition for AtenAdaptiveAvgPool1dOp in DecomposeComplexOps.cpp supports outSize = inSize (i.e., do literally nothing), and outSize = 1 (i.e., do a batched average). The reason adaptive pooling ops are difficult to lower to linalg is that they are not constantly strided. They are computed by taking an input tensor of shape (N, C, Hin), and an output size Hout, and computing the output tensor at position (n,c, h) in the following way: 1. compute st(h) = (h*Hin)//Hout 2. compute en(h) = 1 + ((h+1)*Hin -1)//Hout 3. apply a computation (max or avg) to the slice: INPUT[n, c, st(h):en(h)] The provided sample implementation (for ConvertAtenAdaptiveAvgPool1dOp) uses tensor.extract to access the input tensor inside the payload of a linalg generic op. This is likely an unattractive use of linalg generic ops, which is why I am asking for some more targeted feedback on the validity of this approach before attempting to support the many other adaptive pooling ops. Specifically: - Is the performance of this implementation bad enough to warrant targeting different dialects entirely? e.g. TMtensor/linalg ext/ etc. - If the provided implementation is of acceptable performance to the community, then is it permissable to remove the Adaptive pooling decompositions from DecomposeComplexOps.cpp? Based on the current structure of the -torch-decompose-complex-ops pass, it does not seem possible to only decompose the adaptive ops in special cases (it seems to get stuck in an infinite loop on a match failure). I would be happy to instead incorporate the case logic into the conversion directly, and remove the decompositions once they are rendered completely obsolete. As long as this approach is acceptable, I can clean up the implementation with some helper functions, and quickly add support for each of the remaining Adaptive pooling ops.
2024-01-10 03:14:10 +08:00
"AdaptiveAvgPool1dStaticLargerOutput_basic",
"AdaptiveAvgPool1dGeneralDynamic_basic",
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
# ERROR: Exception: Unsupported op: get_attr
"NumToTensorFloatModule_basic",
"NumToTensorIntModule_basic",
"TensorFloatModule_basic",
"TensorIntModule_basic",
# ERROR: Exception: Unsupported: missing default value for argument 0 in schema for aten.randn.generator
"RandnGeneratorF64Module_basic",
"RandnGeneratorModule_basic",
# START tests failing due to: complex floating point ops
# END tests failing due to: complex floating point ops
[Torch Op] Add unbind.int support with ListUnpack (#2058) * add unbind int * reformat * use unpack canonicalize * address comments * Empty commit, trigger test * add ltc blacklist * clean up * address comments * check permute list * erase in recompose --------- Co-authored-by: zhekun.zhang <zhekun.zhang@bytedance.com>
2023-05-19 10:07:58 +08:00
# ERROR: Exception: Unsupported: return type List[Tensor] in schema for aten.unbind.int
"UnbindIntListUnpack_Module_basic",
"UnbindIntGetItem_Module_basic",
[TM_TENSOR] Add `aten.scatter.[src|value]` op This commit adds support of `aten.scatter.src` and `aten.scatter.value` ops. Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
2022-10-16 05:46:06 +08:00
# ERROR: torch._dynamo.exc.Unsupported: call_function BuiltinVariable(float) [TensorVariable()] {}
"ScatterValueFloatModule_basic",
# ERROR: torch._dynamo.exc.Unsupported: call_function BuiltinVariable(int) [TensorVariable()] {}
"ScatterValueIntModule_basic",
[Importer] import constant tuple (#2132) * [Importer] import constant tuple * update * update * update
2023-05-31 14:14:14 +08:00
[MLIR][TORCH] Add support for aten._unsafe_index_put.hacked_twin op Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2023-07-14 15:26:54 +08:00
# AssertionError: Unregistered operation: torch.aten._unsafe_index_put
"UnsafeIndexPutHackedTwin1DFloatNonAccumulateModule_basic",
[TOSA] Fix conversion for depthwise convolutions (#2398) * [TOSA] Fix conversion for depthwise convolutions * Add e2e tests for depthwise and grouped convolutions Co-authored-by: Lucas Camphausen <lucas.camphausen@iml.fraunhofer.de>
2023-08-18 23:15:54 +08:00
# Lowering Torch Backend IR -> Linalg-on-Tensors Backend IR failed
# 'linalg.depthwise_conv_2d_nchw_chw' op inferred input/output operand #1 has shape's dimension #0 to be 4, but found 8
"Conv2dWithPaddingDilationStrideStaticModule_depthwise_multiplier",
build: manually update PyTorch version Set PyTorch and TorchVision version to nightly release 2023-08-30. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2023-08-25 14:07:30 +08:00
# Exception: Unsupported: node.meta['val'] is not a FakeTensor or list of FakeTensor's: _scaled_dot_product_flash_attention;
"ScaledDotProductAttentionSameModule_basic",
"ScaledDotProductAttentionDifferentModule_basic",
[MLIR][TORCH] Add StableHLO lowering for embedding_bag.padding_idx op Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2023-09-05 21:28:37 +08:00
# AssertionError: Unregistered operation: torch.aten._embedding_bag_forward_only
"AtenEmbeddingBagStaticModule_basic",
[MLIR][TORCH] Add support for conversion to int8 dtype Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2023-09-29 20:19:18 +08:00
# Lowering not present for this case
"ElementwiseToDtypeI64ToUI8Module_basic",
[MLIR][TORCH] Add support for int8 dtype for sub, add, and bitwise_and op Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2023-10-03 19:59:56 +08:00
# torch._dynamo.exc.TorchRuntimeError: Failed running call_function <built-in method add of type object at 0x7f4f8b05a720>(*(FakeTensor(..., size=(3, 4), dtype=torch.int8), 3, 2), **{}): Tensor with dtype torch.int64 is not the expected dtype of torch.int8!
"ElementwiseAddScalarInt8Module_basic",
build: manually update PyTorch version (#2521)
2023-10-19 22:03:00 +08:00
# ERROR: dtype (torch.int64) is not equal to golden dtype (torch.float32)
"ThresholdBackward2dMixedModule_basic",
[Torch Dialect] support AtenArangeStartOutOp in ReduceOpVariants like… (#2563) … AtenBernoulli_FloatOp It fixing case like: `%2110 = torch.aten.arange.start_out %int1, %int1517, %int1, %2109 : !torch.int, !torch.int, !torch.int, !torch.tensor -> !torch.tensor`. `aten.arange.start_out` doesn't have value semantics also, means`%2110` is an alias for %2109. So I decompose it to `aten.arange.start` + `torch.contents.overwrite`. The complex decomposition logic is target to handle cases like view and dtype cast which I add in e2e tests.
2023-11-17 00:51:55 +08:00
# ERROR: shape (torch.Size([12])) is not equal to golden shape (torch.Size([3, 4]))
"ArangeStartOutViewModule_basic",
[Torch Dialect]Add Support for AtenGroupNormOp and AtenNativeGroupNormOp (#2591) Co-authored-by: LiuYuanqiang <liuyuanqiang.yqliu@bytedance.com>
2023-12-13 11:05:12 +08:00
# ERROR: 'torch.aten.add.Tensor' op operand #1 must be Any Torch tensor type, but got '!torch.float'
"GroupNormModule_basic",
"GroupNormNoWeightAndBiasModule_basic",
[torch][quant] Support quantize and dequantize for torch (#2731) Handle both `torch.dequantize` and `torch.quantize_per_tensor` including the op based quantization parameter tracking. This includes adding `qint32` to torch types as it was missing during the initial type inclusion. For testing we only have `torch.int8` and `torch.float` types on function boundaries as the `qint8` types require passing the scale and zero point quantization information which is not supported yet.
2024-01-13 11:11:14 +08:00
# Dynamo does not support tracing quantized tensors
"ElementwiseDequantizePerTensorModule_basic",
"ElementwiseQuantizePerTensorModule_basic",
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
}
TORCHDYNAMO_CRASHING_SET = {
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
# No upstream decompositions.
# %6:4 = torch.operator "aten._embedding_bag_forward_only"(%1, %3, %5, %false, %int0, %false, %none, %false, %int-1) : (!torch.tensor<*,f32>, !torch.tensor<*,si64>, !torch.tensor<*,si64>, !torch.bool, !torch.int, !torch.bool, !torch.none, !torch.bool, !torch.int) -> (!torch.tensor, !torch.tensor, !torch.tensor, !torch.tensor)
# See also: https://github.com/pytorch/torchdynamo/issues/327
"Aten_EmbeddingBagExample_basic",
"FullModuleInt3D_basic",
"ThresholdBackward1dIntModule_basic",
"ThresholdBackward2dIntModule_basic",
"ThresholdBackward3dIntModule_basic",
# See https://github.com/llvm/torch-mlir/issues/2050
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
"ElementwiseCloneChannelsLastMemoryFormatModule_basic",
[Dynamo] fix TORCHDYNAMO_CRASHING_SET (#2082)
2023-05-02 10:35:26 +08:00
"ElementwiseCloneContiguousModule_basic",
"ElementwiseCloneModule_basic",
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
"ExpandAsFloatModule_basic",
"ExpandAsIntModule_basic",
"ExpandModule_basic",
"MoveDimIntModule_basic",
"MoveDimIntNegativeIndexModule_basic",
[Dynamo] fix TORCHDYNAMO_CRASHING_SET (#2082)
2023-05-02 10:35:26 +08:00
"NarrowVerticalTest2_basic",
"NarrowVerticalTest_basic",
[Dynamo] turn on `no_python=True` for dynamo tests (#2040)
2023-04-29 07:05:17 +08:00
"NumpyTRank2Module_basic",
"NumpyTRankNDynamicModule_basic",
"NumpyTRankNStaticModule_basic",
"PermuteModule_basic",
"PermuteNegativeIndexModule_basic",
"SelectIntNegativeDimAndIndexStaticModule_basic",
Pure-Python FX importer. (#2098) Co-authored-by: Sean Silva <silvasean@google.com>
2023-05-12 13:46:33 +08:00
"TestMultipleTensorAndPrimitiveTypesReturn_basic",
[Dynamo] fix TORCHDYNAMO_CRASHING_SET (#2082)
2023-05-02 10:35:26 +08:00
"TModuleRank2_basic",
"ToCopyModule_basic",
"TransposeIntModule_basic",
"TransposeIntNegDimsModule_basic",
[TOSA] Add aten._index_put_impl support (#2031) Add e2e support by add "tosa-to-scf"
2023-07-18 00:51:24 +08:00
"IndexPutImpl2DNoneIndexStaticModule_basic",
[torchdynamo] Initial TorchDynamo support This adds a basic e2e Config for TorchDynamo using Linalg-on-Tensors/RefBackend. But TorchDynamo is pretty orthogonal to various other pieces, so it should compose nicely with variations like: - Switching out all the backends (Linalg-on-Tensors, TOSA, MHLO) - PyTorch functionalization and decompositions - Taking the example inputs and compiling with all dynamic or all static shapes without duplicating tests. This adds it to the CI, but there are still a lot of XFAIL's. This also adds a helper `from torch_mlir.dynamo import make_simple_dynamo_backend` which simplifies some of the steps for making a Torch-MLIR-based TorchDynamo backend. We include "simple" in the name because we are going to be exploring various things next from the long-term roadmap. The next steps are: - Burn down all the XFAIL's. - Start working on the pieces from the [long-term roadmap](https://github.com/llvm/torch-mlir/blob/main/docs/long_term_roadmap.md). - Add functionalization/decompositions into the TorchDynamo flow and remove reliance on the current Torch-MLIR "frontend". - Write a pure-Python direct FX->MLIR importer. - Hook up the new PyTorch symbolic shape stuff. - Explore PrimTorch decompositions for simplifying backends.
2022-11-18 20:21:19 +08:00
}
mhlo: migrate conversion to stablehlo (#1840) This patch replaces all MHLO operations with their StableHLO counterparts and adds a validation pass to ensure that no MHLO operations remain before translating all Stablehlo operations to the MHLO dialect for further lowering to the Linalg dialect. This patch also updates all lit tests so that they refer to the `convert-torch-to-stablehlo` pass and so that they check for StableHLO operations.
2023-02-02 21:29:47 +08:00
STABLEHLO_PASS_SET = {
[Torch Dialect] emit aten.tile op and decompose it into aten.repeat (#2355)
2023-08-04 09:05:34 +08:00
"TileBigDimsSizeModule_basic",
"TileSmallDimsSizeModule_basic",
[Stablehlo] Add converter to stablehlo for aten.(Int,Float,Bool).Tensor op (#2340) [Stablehlo] Add converter to stablehlo for aten.(Int,Float,Bool).Tensor op and configure crashing e2e sets for stablehlo backend.
2023-07-29 21:55:49 +08:00
"AddIntModule_basic",
"AtenIntBoolOpModule_basic",
"AtenIntTensorByteDtypeModule_basic",
"AtenIntTensorCharDtypeModule_basic",
"BoolFloatFalseModule_basic",
"BoolFloatTrueModule_basic",
"BoolIntFalseModule_basic",
"BoolIntTrueModule_basic",
"CeilFloatModule_basic",
"DivFloatModule_basic",
"DivIntModule_basic",
"EqIntModule_basic",
"GeFloatIntModule_basic",
"GeFloatModule_basic",
"GeIntModule_basic",
"GtFloatIntModule_basic",
"GtIntModule_basic",
"MulIntModule_basic",
"NeFloatIntModule_basic",
"NeIntModule_basic",
"SqrtIntModule_basic",
"SubFloatModule_basic",
"SubIntModule_basic",
"TensorToBoolZeroRank_basic",
"TensorToIntZeroRank_basic",
"TensorToFloatZeroRank_basic",
[Torch Dialect] replace none-index in aten.Index.Tensor's param by manually generating it (#2344) * [Torch Dialect] replace none-index in aten.Index.Tensor's param by manually generating it Co-authored-by: Jiawei Wu <wujiawei.aml@bytedance.com> Co-authored-by: Jianzhe Xiao <jianzhe.xiao@bytedance.com> * minor typo fix * add new failed e2e tests for ltc * fix typo * Address comments * Add more e2e tests * add failed e2e tests for LTC * address comments * remove decomposition for AtenIndexTensorHackedTwinOp
2023-08-15 19:36:08 +08:00
"IndexTensorStaticContiguousWithNoneModule_basic",
"IndexTensorStaticNonContiguousWithNoneModule_basic",
[MLIR][TORCH] Add e2e support for aten.alias -- This commit adds e2e support for aten.alias op. Signed-off-by: Abhishek Varma <abhishek@nod-labs.com>
2023-06-21 01:14:09 +08:00
"AliasModule_basic",
[Torch-Dialect] emit aten.narrow.Tensor op and decompose it to aten.narrow op (#2297)
2023-07-20 16:46:44 +08:00
"TensorIntModule_basic",
[Stablehlo] Enable Stablehlo backend with arith dialect (#2139)
2023-05-26 22:57:57 +08:00
"AllBoolFalseModule_basic",
"AllBoolTrueModule_basic",
"AnyBoolFalseModule_basic",
"AnyBoolTrueModule_basic",
"AtenIntBoolOpConstFalseModule_basic",
"AtenIntBoolOpConstTrueModule_basic",
[stablehlo] add AtenScalarImplicitOp's reverter to stablehlo backend (#2434) * add ScalarImplicitOp's reverter to stablehlo backend * add new passed test case for stablehlo backend
2023-09-04 14:04:09 +08:00
"AtenFloatScalarModule_basic",
"ScalarImplicitFloatModule_basic",
"ScalarImplicitIntModule_basic",
[Stablehlo] Enable Stablehlo backend with arith dialect (#2139)
2023-05-26 22:57:57 +08:00
"AtenSubFloatModule_basic",
"BoolFloatConstantModule_basic",
"BoolIntConstantModule_basic",
"ContainsIntList_False",
"ContainsIntList_True",
"IntFloatModule_basic",
"IsFloatingPointFloat_True",
"IsFloatingPointInt_False",
"LenStrModule_basic",
"MeanDimAllReduceKeepdimModule_basic",
"MeanDimAllReduceModule_basic",
"MeanDimDtypeModule_basic",
"MeanDimKeepdimModule_basic",
"MeanDimModule_basic",
"MeanDimNegativeModule_basic",
"NumelZeroRankModule_basic",
"PowIntFloatModule_basic",
"PrimMaxIntModule_basic",
"PrimMinIntModule_basic",
Fix types + off-by-1 error, clamp `end` in slice+copy_ recomposition The `copy_` op being replaced by `RecomposeSliceCopy_` operates on a subset of the tensor being mutated, while the `index_put` op being used to replace the `copy_` op operates on the entire tensor being mutated. This means that the result type of the `index_put` should be the type of the input to `index_put` and we need to make sure that `copy_` does not have users before replacing to avoid type conflicts. This commit also fixes the result type used for the `AtenArangeStartStepOp`, and an off-by-1 error when creating the indices vector. Lastly, this commit also clamps the `end` value from the slice to the size of the dimension.
2023-05-24 01:34:51 +08:00
"PrimMinIntDynamicModule_basic",
[Stablehlo] Enable Stablehlo backend with arith dialect (#2139)
2023-05-26 22:57:57 +08:00
"SortIntListReverse_basic",
"SortIntList_basic",
"SqrtIntConstantModule_basic",
"StdBiasedModule_basic",
"StdDimBiasedModule_basic",
"TestMultipleTensorAndPrimitiveTypesReturn_basic",
"VarBiasedModule_basic",
"VarDimBiasedModule_basic",
"VarMeanBiasedModule_basic",
"VarMeanDimBiasedModule_basic",
Fix import of constant bool tensor parameters (#2047) Bool tensors are represented in TorchScript as an array of `int8_t`s. However, when importing them into Torch-MLIR, the importer was assuming the array had `int32_t` elements, leading to the importer reading into memory that was out of bounds. This commit fixes the casting of the bool tensor.
2023-04-21 09:38:48 +08:00
"ConstantBoolParameterModule_basic",
[MHLO] Support AtenMaskedFillScalar (#1839) * [MHLO] Support MaskedFillScalar. * Update. * Update. * Update. --------- Co-authored-by: Ziheng Jiang <ziheng.jiang@bytedance.com>
2023-02-11 05:58:39 +08:00
"MaskedFillScalarIntValueStaticModule_basic",
"MaskedFillScalarFloatValueStaticModule_basic",
[Torch Dialect] add support for adaptive_avgpool_1d (#2342) * [MLIR][TORCH] Fix aten.cumsum lowering for int32 input (#2351) Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com> [Stablehlo] Add converter to stablehlo for aten.(Int,Float,Bool).Tensor op (#2340) [Stablehlo] Add converter to stablehlo for aten.(Int,Float,Bool).Tensor op and configure crashing e2e sets for stablehlo backend. update PyTorch version to 2.1.0.dev20230729 (#2354) - torch version: 2.1.0.dev20230729 - torch commit hash: b638df0afb83572724032c824c64e481bb4499a0 - torchvision version: 0.16.0.dev20230729 Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com> update PyTorch version to 2.1.0.dev20230730 (#2356) - torch version: 2.1.0.dev20230730 - torch commit hash: 0ff243ff350268cc98fe03fa6364375ee2824742 - torchvision version: 0.16.0.dev20230730 Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com> update PyTorch version to 2.1.0.dev20230731 (#2359) - torch version: 2.1.0.dev20230731 - torch commit hash: 6298ac688f8caafe30d71ff2ea2e20fbb32065c7 - torchvision version: 0.16.0.dev20230731 Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com> LTC->MLIR Debug Info support (#1922) * LTC->MLIR Debug Info support * SW-95317 Propagate Lazy->Jit->MLIR scope name. * Enhance location information based on op names Currently, the location information attached to the ops just considers the filename, line number and column number. Attaching operation name would help identify the type of computation by just looking at the profile of execution. * Update locations logic; updated debug-info.py test * Use {scope}/{op_name} format to track names by default --------- Co-authored-by: Gleb Kazantaev <gleb.kazantaev@cerebras.net> Co-authored-by: Mark Browning <mark@cerebras.net> Co-authored-by: Vimal Patel <vimal@polymagelabs.com> build: update llvm tag to 41895843 Summary of changes: - Update tags llvm: 41895843b5915bb78e9d02aa711fa10f7174db43 mhlo: 4726d31f7025da66de0dea709bd56c462edb83c2 Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com> update PyTorch version to 2.1.0.dev20230802 (#2366) - torch version: 2.1.0.dev20230802 - torch commit hash: c89b16917755c2abbef7b6420e340baf9ae8089e - torchvision version: 0.16.0.dev20230802 Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com> Change Python version from 3.10 to 3.11 in installation instructions (#2370) Add CITATION file (#2371) Add packaging as an install dependency (#2369) Needed by `torch_mlir._version`. Resolves #2368. [Torch Dialect] emit aten.masked_scatter and aten.masked_scatter_ op (#2358) * [Torch Dialect] emit aten.masked_scatter and aten.masked_scatter_ op update PyTorch version to 2.1.0.dev20230803 (#2372) - torch version: 2.1.0.dev20230803 - torch commit hash: f89c73be3a3e8274d025ac46a33a780853841c9e - torchvision version: 0.16.0.dev20230803 Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com> Prevent failed stable CI job from cancelling nightly jobs (#2373) The CI jobs that use stable PyTorch are currently not required to pass in order for a patch to get merged in `main`. This commit makes sure that if a CI job for stable PyTorch fails, it does not cancel the other required jobs. [Torch Dialect] emit aten.tile op and decompose it into aten.repeat (#2355) update update xfail sets update xfail_sets update fix xfail_sets update: update update: update parent 22e88d523b1970b2e904eb5421d49d987a3d255e author jianzhe.xiao <jianzhe.xiao@bytedance.com> 1691114110 +0800 committer jianzhe.xiao <jianzhe.xiao@bytedance.com> 1691114119 +0800 [Stablehlo] Add converter to stablehlo for aten.(Int,Float,Bool).Tensor op (#2340) [Stablehlo] Add converter to stablehlo for aten.(Int,Float,Bool).Tensor op and configure crashing e2e sets for stablehlo backend. update PyTorch version to 2.1.0.dev20230729 (#2354) - torch version: 2.1.0.dev20230729 - torch commit hash: b638df0afb83572724032c824c64e481bb4499a0 - torchvision version: 0.16.0.dev20230729 Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com> update PyTorch version to 2.1.0.dev20230730 (#2356) - torch version: 2.1.0.dev20230730 - torch commit hash: 0ff243ff350268cc98fe03fa6364375ee2824742 - torchvision version: 0.16.0.dev20230730 Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com> update PyTorch version to 2.1.0.dev20230731 (#2359) - torch version: 2.1.0.dev20230731 - torch commit hash: 6298ac688f8caafe30d71ff2ea2e20fbb32065c7 - torchvision version: 0.16.0.dev20230731 Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com> LTC->MLIR Debug Info support (#1922) * LTC->MLIR Debug Info support * SW-95317 Propagate Lazy->Jit->MLIR scope name. * Enhance location information based on op names Currently, the location information attached to the ops just considers the filename, line number and column number. Attaching operation name would help identify the type of computation by just looking at the profile of execution. * Update locations logic; updated debug-info.py test * Use {scope}/{op_name} format to track names by default --------- Co-authored-by: Gleb Kazantaev <gleb.kazantaev@cerebras.net> Co-authored-by: Mark Browning <mark@cerebras.net> Co-authored-by: Vimal Patel <vimal@polymagelabs.com> build: update llvm tag to 41895843 Summary of changes: - Update tags llvm: 41895843b5915bb78e9d02aa711fa10f7174db43 mhlo: 4726d31f7025da66de0dea709bd56c462edb83c2 Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com> update PyTorch version to 2.1.0.dev20230802 (#2366) - torch version: 2.1.0.dev20230802 - torch commit hash: c89b16917755c2abbef7b6420e340baf9ae8089e - torchvision version: 0.16.0.dev20230802 Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com> Change Python version from 3.10 to 3.11 in installation instructions (#2370) Add CITATION file (#2371) Add packaging as an install dependency (#2369) Needed by `torch_mlir._version`. Resolves #2368. [Torch Dialect] emit aten.masked_scatter and aten.masked_scatter_ op (#2358) * [Torch Dialect] emit aten.masked_scatter and aten.masked_scatter_ op update PyTorch version to 2.1.0.dev20230803 (#2372) - torch version: 2.1.0.dev20230803 - torch commit hash: f89c73be3a3e8274d025ac46a33a780853841c9e - torchvision version: 0.16.0.dev20230803 Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com> Prevent failed stable CI job from cancelling nightly jobs (#2373) The CI jobs that use stable PyTorch are currently not required to pass in order for a patch to get merged in `main`. This commit makes sure that if a CI job for stable PyTorch fails, it does not cancel the other required jobs. [Torch Dialect] emit aten.tile op and decompose it into aten.repeat (#2355) update update xfail sets update xfail_sets update fix xfail_sets update: update update: add support for adaptive_pool_id update xfail sets update xfail_sets update fix xfail_sets update: update: * update --------- Co-authored-by: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
2023-08-05 07:48:09 +08:00
"AdaptiveAvgPool1dNonUnitOutputSizeStaticModule_basic",
[MHLO] Evaluate RuntimeAssertOp at compile time (#1732)
2022-12-22 17:12:52 +08:00
"AdaptiveAvgPool2dNonUnitOutputSizeStaticModule_basic",
mhlo: migrate conversion to stablehlo (#1840) This patch replaces all MHLO operations with their StableHLO counterparts and adds a validation pass to ensure that no MHLO operations remain before translating all Stablehlo operations to the MHLO dialect for further lowering to the Linalg dialect. This patch also updates all lit tests so that they refer to the `convert-torch-to-stablehlo` pass and so that they check for StableHLO operations.
2023-02-02 21:29:47 +08:00
"AddSizeIntModule_basic",
"AddSizeIntNegDimModule_basic",
Add torch-to-mhlo e2e support for AtenArangeStartStepOp (#1385) Co-authored-by: Vremold <xremold@gamil.com>
2022-09-20 22:31:24 +08:00
"ArangeDtypeFloatModule_basic",
"ArangeDtypeIntModule_basic",
"ArangeFalsePinMemoryModule_basic",
"ArangeFloatModule_basic",
"ArangeIntModule_basic",
"ArangeNegativeStartFloatModule_basic",
"ArangeNegativeStartIntModule_basic",
"ArangeStartFloatModule_basic",
"ArangeStartIntModule_basic",
"ArangeStartNegativeStepFloatModule_basic",
"ArangeStartNegativeStepIntModule_basic",
"ArangeStartStepFloatModule_basic",
"ArangeStartStepIntModule_basic",
"ArangeZeroElementOutputModule_basic",
mhlo: migrate conversion to stablehlo (#1840) This patch replaces all MHLO operations with their StableHLO counterparts and adds a validation pass to ensure that no MHLO operations remain before translating all Stablehlo operations to the MHLO dialect for further lowering to the Linalg dialect. This patch also updates all lit tests so that they refer to the `convert-torch-to-stablehlo` pass and so that they check for StableHLO operations.
2023-02-02 21:29:47 +08:00
"BatchMlpLayerModule_basic",
[Stablehlo] add e2e test for aten.batch_norm (#2129)
2023-05-18 00:04:40 +08:00
"BatchNorm1DModule_basic",
"BatchNorm1DWith2DInputModule_basic",
"BatchNorm2DModule_basic",
"BatchNorm3DModule_basic",
"BatchNorm1DStaticShapeModule_basic",
"ResNet18StaticModule_basic",
[MLIR][TORCH] Add different dtype support for aten.bmm op Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2023-09-11 20:58:59 +08:00
"BmmFloatModule_basic",
"BmmIntModule_basic",
fix mhlo e2e ci crashes (#1620) * fix mhlo e2e ci crashes * add passed tests * calc dynamic positive dim
2022-11-21 21:50:35 +08:00
"BroadcastToModule_basic",
"BroadcastToSameRankStaticModule_basic",
"BroadcastZeroRankInputStaticModule_basic",
feat: support unchanged dimensions in torch.aten.broadcast_to operation. (#2204)
2023-06-12 17:17:25 +08:00
"BroadcastListConstructWithMinusOneModule_basic",
Add aten.bucketize op and its decomposition (#1834)
2023-02-03 10:20:47 +08:00
"BucketizeTensorStaticFloatModule_basic",
"BucketizeTensorStaticModule_basic",
[MHLO] Support aten.cumsum op in mhlo backend (#1825)
2023-01-30 13:38:27 +08:00
"CumsumStaticModule_basic",
"CumsumStaticNegativeDimModule_basic",
[Torch Dialect]Support aten.cosine_similarity (#2364) As title, add support for aten.cosine_similarity, support broadcast inputA/inputB to the same shape
2023-11-08 15:28:30 +08:00
"CosineSimilarityStaticModule_basic",
"CosineSimilarityStaticBroadcastModule_basic",
[Torch Dialect] fold aten.detach (#2021)
2023-04-18 23:59:14 +08:00
"DetachModule_basic",
[Torch Dialect] add support for AtenIsnanOp (#2170) * add support for mhlo * Add Test for torch.ne * fix torch.ne shape/add static test case * add support for static torch.ne --------- Co-authored-by: root <root@n31-177-039.byted.org>
2023-06-07 10:06:27 +08:00
"ElementwiseIsnanModule_basic",
Add e2e support for aten logical or/and/xor/not ops (#1761)
2023-01-04 10:11:25 +08:00
"ElementwiseAtenLogicalAndOpPromoteBroadcastStaticShapeModule_basic",
"ElementwiseAtenLogicalNotOpModule_basic",
"ElementwiseAtenLogicalNotOpPromoteModule_basic",
"ElementwiseAtenLogicalOrOpPromoteBroadcastStaticShapeModule_basic",
"ElementwiseAtenLogicalXorOpPromoteBroadcastStaticShapeModule_basic",
Add more mhlo basic converters (#1628) * Add more mhlo basic converters * remove unused pinnedMemory constraints * refine naming
2022-11-24 14:28:34 +08:00
"ElementwiseAtenWhereSelfModule_basic",
[StableHlo] Fix AtenWhereSelfOp convert rule (#2093) * fix whereself convert rule * use int to test promotion * add dynamo failing test --------- Co-authored-by: zhekun.zhang <zhekun.zhang@bytedance.com>
2023-05-06 06:21:55 +08:00
"ElementwiseWhereScalarOtherStaticModule_basic",
"ElementwiseWhereScalarSelfStaticModule_basic",
[Torch Dialect] support aten.isneginf, aten.isposinf, aten.nan_to_num (#2743)
2024-01-16 14:29:34 +08:00
"ElementwiseNanToNumModule_Basic",
[LINALG][TOSA][MHLO] Add e2e support for aten bitwise ops (#1753)
2023-01-12 06:40:03 +08:00
"ElementwiseBitwiseAndStaticShapeModule_basic",
"ElementwiseBitwiseNotInt64Module_basic",
"ElementwiseBitwiseNotInt32Module_basic",
[Torch Dialect] emit aten.__or__Tensor Op (#2437) * emit aten.__or__TensorOp * bug fix * remove convert to stablehlo * code style refinement
2023-09-06 14:21:51 +08:00
"ElementwiseOrTensorStaticShapeModule_basic",
[LINALG][TOSA][MHLO] Add e2e support for aten bitwise ops (#1753)
2023-01-12 06:40:03 +08:00
"ElementwiseBitwiseOrStaticShapeModule_basic",
"ElementwiseBitwiseXorStaticShapeModule_basic",
Add AtenClampOp conversion pattern to MHLO (#1356) Add AtenClampOp conversion pattern to MHLO
2022-09-16 15:09:21 +08:00
"ElementwiseClampModule_basic",
"ElementwiseClampMinModule_basic",
"ElementwiseClampMaxModule_basic",
Support aten.sign (#2205)
2023-06-11 02:45:35 +08:00
"ElementwiseSignModule_basic",
[StableHLO] Add support for AtenPowTensorScalar. (#1883) * [MHLO] Add support for AtenPowTensorScalar. * Update. --------- Co-authored-by: Ziheng Jiang <ziheng.jiang@bytedance.com>
2023-02-17 12:26:46 +08:00
"ElementwisePowModule_basic",
add stablehlo support for pow.tensor_tensor (#2086) Co-authored-by: zhekun.zhang <zhekun.zhang@bytedance.com>
2023-05-05 00:55:03 +08:00
"ElementwisePowTensorStaticModule_basic",
"ElementwisePowTensorBroadcastStaticModule_basic",
[MLIR][MHLO] Add AtenFrobeniusNormDimOp and add its conversion pattern to MHLO and linalg (#1306) * Add aten.frobenius_norm.dim op and init its conversion pattern to linalg and MHLO, * run symbolic-shape-optimization before hlo-legalize-to-linalg to fit more mhlo e2e tests.
2022-09-08 10:15:36 +08:00
"ElementwiseExpModule_basic",
mhlo: migrate conversion to stablehlo (#1840) This patch replaces all MHLO operations with their StableHLO counterparts and adds a validation pass to ensure that no MHLO operations remain before translating all Stablehlo operations to the MHLO dialect for further lowering to the Linalg dialect. This patch also updates all lit tests so that they refer to the `convert-torch-to-stablehlo` pass and so that they check for StableHLO operations.
2023-02-02 21:29:47 +08:00
"ElementwiseFlattenBroadcastModule_basic",
"ElementwiseLeakyReluModule_basic",
update passing test sets
2023-08-24 00:27:00 +08:00
"ElementwiseEluModule_basic",
add nondefault test case, add to illegal ops in backend contract
2023-08-25 22:42:29 +08:00
"ElementwiseEluNonDefaultModule_basic",
[torch] Add support for aten.selu (#2640) Add `aten.selu` operation to `torch` dialect.
2023-12-14 12:28:08 +08:00
"ElementwiseSeluModule_basic",
[MLIR][MHLO] Add AtenFrobeniusNormDimOp and add its conversion pattern to MHLO and linalg (#1306) * Add aten.frobenius_norm.dim op and init its conversion pattern to linalg and MHLO, * run symbolic-shape-optimization before hlo-legalize-to-linalg to fit more mhlo e2e tests.
2022-09-08 10:15:36 +08:00
"ElementwiseLogModule_basic",
"ElementwiseNegModule_basic",
[Torch][MHLO] Decompose aten.copy op. Lower aten.rsqrt & sigmoid to mhlo. (#1734)
2022-12-22 10:13:59 +08:00
"ElementwiseRsqrtModule_basic",
"ElementwiseSigmoidModule_basic",
[MLIR][MHLO] Add AtenFrobeniusNormDimOp and add its conversion pattern to MHLO and linalg (#1306) * Add aten.frobenius_norm.dim op and init its conversion pattern to linalg and MHLO, * run symbolic-shape-optimization before hlo-legalize-to-linalg to fit more mhlo e2e tests.
2022-09-08 10:15:36 +08:00
"ElementwiseSqrtModule_basic",
[MHLO] move AtenTanhOp to ConvertAtenUnaryFPOnlyPatten and add sin/cos/ceil/floor pattern (#1847)
2023-02-07 03:14:26 +08:00
"ElementwiseSinModule_basic",
"ElementwiseCosModule_basic",
"ElementwiseCeilModule_basic",
"ElementwiseFloorModule_basic",
[MLIR][MHLO] Add AtenFrobeniusNormDimOp and add its conversion pattern to MHLO and linalg (#1306) * Add aten.frobenius_norm.dim op and init its conversion pattern to linalg and MHLO, * run symbolic-shape-optimization before hlo-legalize-to-linalg to fit more mhlo e2e tests.
2022-09-08 10:15:36 +08:00
"ElementwiseUnaryModule_basic",
mhlo: migrate conversion to stablehlo (#1840) This patch replaces all MHLO operations with their StableHLO counterparts and adds a validation pass to ensure that no MHLO operations remain before translating all Stablehlo operations to the MHLO dialect for further lowering to the Linalg dialect. This patch also updates all lit tests so that they refer to the `convert-torch-to-stablehlo` pass and so that they check for StableHLO operations.
2023-02-02 21:29:47 +08:00
"ElementwiseUnsqueezeBroadcastModule_basic",
[MLIR][MHLO] Add AtenFrobeniusNormDimOp and add its conversion pattern to MHLO and linalg (#1306) * Add aten.frobenius_norm.dim op and init its conversion pattern to linalg and MHLO, * run symbolic-shape-optimization before hlo-legalize-to-linalg to fit more mhlo e2e tests.
2022-09-08 10:15:36 +08:00
"ElementwiseUnsqueezeNegDimsModule_basic",
Replace empty_like && empty_memory_format with full/full_like (#1398) * Replace empty_like && empty_memory_format with full/full_like * fix broadcast rank0 tensor
2022-09-23 10:24:36 +08:00
"ElementwiseToDtypeF32ToI64Module_basic",
Add relu6 and binary broadcasts (#1408) * Add relu6 and binary broadcasts
2022-09-23 20:39:15 +08:00
"ElementwiseAddModule_basic",
"ElementwiseAddScalarFloatModule_basic",
"ElementwiseAddScalarInt64Module_basic",
"ElementwiseAddScalarIntModule_basic",
Fix handling of non-int tensors in `getScalarValue` (#1914) The current implementation of `getScalarValue` does not check that the input to a `ValueTensorLiteralOp` is an i64 before extracting the value, and it does not check that the result type of the `PrimNumToTensorScalarOp` is also an i64. This leads to crashes or invalid IR generated when the `input` is something other than an i64 tensor or `!torch.int`. This commit addresses those issues. In addition, the function `getScalarValue` is renamed to `getScalarIntValue` to make it clear that it *only* extracts scalar integers.
2023-03-07 02:12:58 +08:00
"ElementwiseAddScalar_NumToTensorFloat_Module_basic",
"ElementwiseAddScalar_TensorLiteralInt32_Module_basic",
Add relu6 and binary broadcasts (#1408) * Add relu6 and binary broadcasts
2022-09-23 20:39:15 +08:00
"ElementwiseDivScalarModule_basic",
fix(TorchToTosa.cpp): adjust torch->tosa div conversion (#2200) check the return type of the division to figure out whether to use the floating point implementation of a division or to use the integer. the issue rose from the fact that the inputs are all integer but the result was casted to floating point. The conversion then chose to use the integer implementation of division which is not legal in tosa when all the inputs get casted to floating point. fix(TorchToLinalg): AtenDivScalarOp upcast self operand as well if applicable, the self operand must also be casted to float as it can be an integer.
2023-06-12 17:18:38 +08:00
"ElementwiseAtenDivIntScalarModule_basic",
Add relu6 and binary broadcasts (#1408) * Add relu6 and binary broadcasts
2022-09-23 20:39:15 +08:00
"ElementwiseEqDiffWidthScalarModule_basic",
"ElementwiseEqFloatScalarModule_basic",
"ElementwiseEqIntScalarModule_basic",
[Torch Dialect] add support for AtenIsnanOp (#2170) * add support for mhlo * Add Test for torch.ne * fix torch.ne shape/add static test case * add support for static torch.ne --------- Co-authored-by: root <root@n31-177-039.byted.org>
2023-06-07 10:06:27 +08:00
"ElementwiseNeFloatScalarModule_basic",
"ElementwiseNeFloatTensorStaticModule_basic",
"ElementwiseNeIntTensorStaticModule_basic",
lib/Conversion/TorchToTosa/TorchToTosa.cpp: Fix legalization of comparions where the input type is bool (#2304)
2023-07-17 15:49:04 +08:00
"ElementwiseEqBoolScalarModule_basic",
Add relu6 and binary broadcasts (#1408) * Add relu6 and binary broadcasts
2022-09-23 20:39:15 +08:00
"ElementwiseErfModule_basic",
"ElementwiseGeluModule_basic",
"ElementwiseGtFloatScalarModule_basic",
"ElementwiseGtIntScalarModule_basic",
"ElementwiseGtMixed2ScalarModule_basic",
Add more mhlo basic converters (#1628) * Add more mhlo basic converters * remove unused pinnedMemory constraints * refine naming
2022-11-24 14:28:34 +08:00
"ElementwiseGeFloatIntScalarModule_basic",
"ElementwiseGeFloatScalarModule_basic",
"ElementwiseGeIntScalarModule_basic",
"ElementwiseGeMixedIntScalarModule_basic",
Add E2E support for LeakyRelu and LeakyReluBackward ops (#1733) Co-authored-by: srirammaswamy <srirammaswamy@gmail.com>
2023-01-04 00:30:16 +08:00
"ElementwiseLeakyReluStaticModule_basic",
Add more mhlo basic converters (#1628) * Add more mhlo basic converters * remove unused pinnedMemory constraints * refine naming
2022-11-24 14:28:34 +08:00
"ElementwiseLeFloatIntScalarModule_basic",
"ElementwiseLeFloatScalarModule_basic",
"ElementwiseLeIntScalarModule_basic",
"ElementwiseLeMixedIntScalarModule_basic",
Add relu6 and binary broadcasts (#1408) * Add relu6 and binary broadcasts
2022-09-23 20:39:15 +08:00
"ElementwiseLtDiffWidthScalarModule_basic",
"ElementwiseLtFloatScalarModule_basic",
"ElementwiseLtIntScalarModule_basic",
"ElementwiseMulScalarModule_basic",
"ElementwiseMulScalarModule_float",
"ElementwiseMulScalarModule_int",
"ElementwiseNeIntScalarModule_basic",
"ElementwiseReciprocalModule_basic",
"ElementwiseRelu6Module_basic",
"ElementwiseReluModule_basic",
Add more mhlo basic converters (#1628) * Add more mhlo basic converters * remove unused pinnedMemory constraints * refine naming
2022-11-24 14:28:34 +08:00
"ElementwiseRemainderScalarModule_Bool_basic",
"ElementwiseRemainderScalarModule_Float_basic",
"ElementwiseRemainderScalarModule_Int_Float_basic",
"ElementwiseRemainderScalarModule_Int_basic",
Add relu6 and binary broadcasts (#1408) * Add relu6 and binary broadcasts
2022-09-23 20:39:15 +08:00
"ElementwiseSubScalarFloatModule_basic",
"ElementwiseSubScalarIntModule_basic",
Add more mhlo basic converters (#1628) * Add more mhlo basic converters * remove unused pinnedMemory constraints * refine naming
2022-11-24 14:28:34 +08:00
"ElementwiseWhereScalarModule_basic",
[Stablehlo] Add stablehlo support for aten.abs (#2068) Co-authored-by: AmosLewis <Amos_Lewsi@foxmail.com>
2023-05-09 13:13:00 +08:00
"ElementwiseAbsModule_basic",
fix mhlo e2e ci crashes (#1620) * fix mhlo e2e ci crashes * add passed tests * calc dynamic positive dim
2022-11-21 21:50:35 +08:00
"EmbeddingModule1DIndices_basic",
"EmbeddingModuleI32Static_basic",
"EmbeddingModuleI32_basic",
"EmbeddingModuleI64_basic",
[RefineTypes] Fix knowledge dtype for `aten.embedding` op -- The dtype of the result of `aten.embedding` should match that of the `weight` operand's (operand[0]) instead of hardcoding to f32. -- This commit aims to provide a fix for the same. Signed-off-by: Abhishek Varma <abhishek@nod-labs.com>
2022-11-30 19:14:48 +08:00
"EmbeddingModuleF16_basic",
[Stablehlo] support dynamic shape when convert aten.fill.Scalar (#2349)
2023-07-27 18:35:25 +08:00
"EmptyLikeMemoryFormatModule_basic",
"EmptyLikeModule_defaultDtype",
"EmptyLikeModule_falsePinMemory",
"EmptyLikeModule_float",
"EmptyLikeModule_int",
Replace empty_like && empty_memory_format with full/full_like (#1398) * Replace empty_like && empty_memory_format with full/full_like * fix broadcast rank0 tensor
2022-09-23 10:24:36 +08:00
"ExpandAsIntModule_basic",
[MLIR][MHLO] Add AtenFrobeniusNormDimOp and add its conversion pattern to MHLO and linalg (#1306) * Add aten.frobenius_norm.dim op and init its conversion pattern to linalg and MHLO, * run symbolic-shape-optimization before hlo-legalize-to-linalg to fit more mhlo e2e tests.
2022-09-08 10:15:36 +08:00
"ExpandModule_basic",
[Torch Dialect] Support Einsum Op (#2230) As title, support torch.aten.einsum op Right now only support Static Shape, because of the known issue, the fixed solution is here: https://github.com/llvm/torch-mlir/pull/2154 Co-authored-by: Jiawei Wu [wujiawei.aml@bytedance.com](mailto:wujiawei.aml@bytedance.com)
2023-12-10 12:30:37 +08:00
"EinsumStaticModule_basic",
"EinsumStaticFourDimensionModule_basic",
"EinsumStaticContractRhsModule_basic",
[Stablehlo] support dynamic shape when convert aten.fill.Scalar (#2349)
2023-07-27 18:35:25 +08:00
"Fill_TensorFloat64WithFloat32_basic",
"Fill_TensorFloat64WithFloat64_basic",
"Fill_TensorFloat64WithInt64_basic",
Add AtenFillScalarOp Stablehlo support (#2108) Co-authored-by: zhekun.zhang <zhekun.zhang@bytedance.com>
2023-05-12 07:41:46 +08:00
"Fill_TensorFloat64WithFloat32Static_basic",
"Fill_TensorFloat64WithInt64Static_basic",
[Stablehlo] add conversion for AtenFlipOp (#2163)
2023-06-15 10:27:34 +08:00
"FlipModuleStaticShape_basic",
"FlipNegativeIndexModule_basic",
Replace empty_like && empty_memory_format with full/full_like (#1398) * Replace empty_like && empty_memory_format with full/full_like * fix broadcast rank0 tensor
2022-09-23 10:24:36 +08:00
"FullLikeModuleDefaultDtype_basic",
"FullLikeModuleFalsePinMemory_basic",
"FullLikeModuleFloat2D_basic",
"FullLikeModuleFloat3DStatic_basic",
"FullLikeModuleFloat3D_basic",
"FullLikeModuleInt2DStatic_basic",
"FullLikeModuleInt2D_basic",
"FullLikeModuleInt3D_basic",
"FullModuleDefaultDtype_basic",
"FullModuleFalsePinMemory_basic",
"FullModuleFloat2D_basic",
"FullModuleFloat3D_basic",
"FullModuleInt2D_basic",
"FullModuleInt3D_basic",
[MLIR][TORCH] add E2E support for aten.new_full (#2425) * implement aten.new_full * remove extraneous tests
2023-09-12 22:29:08 +08:00
"NewFullModuleDefaultDtype_basic",
"NewFullModuleFalsePinMemory_basic",
"NewFullModuleFloat2D_basic",
"NewFullModuleFloat3DStatic_basic",
"NewFullModuleFloat3D_basic",
"NewFullModuleInt2DStatic_basic",
"NewFullModuleInt2D_basic",
"NewFullModuleInt3D_basic",
[Torch Dialect]Add Support for AtenGroupNormOp and AtenNativeGroupNormOp (#2591) Co-authored-by: LiuYuanqiang <liuyuanqiang.yqliu@bytedance.com>
2023-12-13 11:05:12 +08:00
"GroupNormModule_basic",
fix mhlo e2e ci crashes (#1620) * fix mhlo e2e ci crashes * add passed tests * calc dynamic positive dim
2022-11-21 21:50:35 +08:00
"GatherStaticModule_basic",
"GatherModule_basic",
"Gather2DInputModdule_basic",
"GatherRandomIndexModule_basic",
[MLIR][TORCH] Patch up Ops and their lowerings to deal with +ve `dim` -- In Python we have the concept of negative dimension indexing. -- We would want to normalize such dimensions to be +ve and within the expected range instead. -- This commit takes care of a few remaining set of Ops and their lowerings by applying `toPositiveDim` and `isValidDim` to the extracted integer `dim` value. Signed-off-by: Abhishek Varma <abhishek@nod-labs.com>
2023-04-07 19:49:35 +08:00
"GatherNegativeDimModule_basic",
Added GeluBackward: MHLO support (#1725)
2022-12-21 20:09:43 +08:00
"GeluBackwardModule_basic",
mhlo: migrate conversion to stablehlo (#1840) This patch replaces all MHLO operations with their StableHLO counterparts and adds a validation pass to ensure that no MHLO operations remain before translating all Stablehlo operations to the MHLO dialect for further lowering to the Linalg dialect. This patch also updates all lit tests so that they refer to the `convert-torch-to-stablehlo` pass and so that they check for StableHLO operations.
2023-02-02 21:29:47 +08:00
"HardswishModule_basic",
"HardswishRandomModule_basic",
fix mhlo e2e ci crashes (#1620) * fix mhlo e2e ci crashes * add passed tests * calc dynamic positive dim
2022-11-21 21:50:35 +08:00
"HardTanhIntModule_basic",
"HardTanhModule_basic",
"HardsigmoidModule_basic",
"HardsigmoidRandomModule_basic",
"IndexSelectDynamicIndexSizeModule_basic",
"IndexSelectSingleIdxModule_basic",
"IndexSelectTwoIdxModule_basic",
"IndexSelectWholeDimensionModule_basic",
"IndexSelectWholeTensorModule_basic",
[MLIR][TORCH] Patch up Ops and their lowerings to deal with +ve `dim` -- In Python we have the concept of negative dimension indexing. -- We would want to normalize such dimensions to be +ve and within the expected range instead. -- This commit takes care of a few remaining set of Ops and their lowerings by applying `toPositiveDim` and `isValidDim` to the extracted integer `dim` value. Signed-off-by: Abhishek Varma <abhishek@nod-labs.com>
2023-04-07 19:49:35 +08:00
"IndexSelectNegativeDimModule_basic",
[Stablehlo] Add `AtenIndexTensor` StableHlo support (#2107) * Add AtenIndexTensor StableHlo support * clean up * Empty commit, trigger test * try to debug hanging test * fix segfulat * fix bad include --------- Co-authored-by: zhekun.zhang <zhekun.zhang@bytedance.com>
2023-05-25 02:13:57 +08:00
"IndexTensorStaticModule_basic",
"IndexTensorMultiIndexStaticModule_basic",
[Torch][MHLO] Decompose aten.copy op. Lower aten.rsqrt & sigmoid to mhlo. (#1734)
2022-12-22 10:13:59 +08:00
"LayerNormLastDimModule_basic",
"LayerNormModule_basic",
"LayerNormNormalizeOverAllDimsModule_basic",
Add E2E support for LeakyRelu and LeakyReluBackward ops (#1733) Co-authored-by: srirammaswamy <srirammaswamy@gmail.com>
2023-01-04 00:30:16 +08:00
"LeakyReluBackwardStaticModule_basic",
[MHLO] add conversion for aten.linalg_vector_norm (#1850)
2023-03-21 05:14:27 +08:00
"LinalgVectorNormModule_basic",
"LinalgVectorNormKeepDimModule_basic",
[MLIR][MHLO] Add AtenFrobeniusNormDimOp and add its conversion pattern to MHLO and linalg (#1306) * Add aten.frobenius_norm.dim op and init its conversion pattern to linalg and MHLO, * run symbolic-shape-optimization before hlo-legalize-to-linalg to fit more mhlo e2e tests.
2022-09-08 10:15:36 +08:00
"MatmulBroadcastBatchDim_basic",
"MatmulSingleDynamicBatchDim_basic",
"Matmul_3d",
"Matmul_4d",
fix #1626 return type mismatch (#1634)
2022-11-23 15:02:41 +08:00
"MeanDimEmptyDimModule_basic",
[MLIR][MHLO] Add AtenFrobeniusNormDimOp and add its conversion pattern to MHLO and linalg (#1306) * Add aten.frobenius_norm.dim op and init its conversion pattern to linalg and MHLO, * run symbolic-shape-optimization before hlo-legalize-to-linalg to fit more mhlo e2e tests.
2022-09-08 10:15:36 +08:00
"MeanDtypeModule_basic",
fix #1626 return type mismatch (#1634)
2022-11-23 15:02:41 +08:00
"MeanDynamicSizesModule_basic",
"MeanLargeInputModule_basic",
"MeanModule_basic",
mhlo: migrate conversion to stablehlo (#1840) This patch replaces all MHLO operations with their StableHLO counterparts and adds a validation pass to ensure that no MHLO operations remain before translating all Stablehlo operations to the MHLO dialect for further lowering to the Linalg dialect. This patch also updates all lit tests so that they refer to the `convert-torch-to-stablehlo` pass and so that they check for StableHLO operations.
2023-02-02 21:29:47 +08:00
"Mlp1LayerModule_basic",
"Mlp2LayerModule_basic",
[MLIR][MHLO] Add AtenFrobeniusNormDimOp and add its conversion pattern to MHLO and linalg (#1306) * Add aten.frobenius_norm.dim op and init its conversion pattern to linalg and MHLO, * run symbolic-shape-optimization before hlo-legalize-to-linalg to fit more mhlo e2e tests.
2022-09-08 10:15:36 +08:00
"MmTanhModule_basic",
fix mhlo e2e ci crashes (#1620) * fix mhlo e2e ci crashes * add passed tests * calc dynamic positive dim
2022-11-21 21:50:35 +08:00
"Mv_basic",
[Torch][MHLO] Decompose aten.copy op. Lower aten.rsqrt & sigmoid to mhlo. (#1734)
2022-12-22 10:13:59 +08:00
"NativeLayerNormModule4D_basic",
"NativeLayerNormModule_basic",
[Torch Dialect] support for aten.one_hot (#1852)
2023-04-11 16:02:28 +08:00
"OneHotModule_basic",
fix #1626 return type mismatch (#1634)
2022-11-23 15:02:41 +08:00
"PrimsConvertElementTypeModule_basic",
[MLIR][MHLO] Add AtenFrobeniusNormDimOp and add its conversion pattern to MHLO and linalg (#1306) * Add aten.frobenius_norm.dim op and init its conversion pattern to linalg and MHLO, * run symbolic-shape-optimization before hlo-legalize-to-linalg to fit more mhlo e2e tests.
2022-09-08 10:15:36 +08:00
"ReduceFrobeniusNormKeepDimModule_basic",
fix #1626 return type mismatch (#1634)
2022-11-23 15:02:41 +08:00
"ReduceSumDimIntListElementTypeBoolModule_basic",
"ReduceSumElementTypeBoolModule_basic",
"ReduceSumDimIntListEmptyDimModule_basic",
[MLIR][MHLO] Add AtenFrobeniusNormDimOp and add its conversion pattern to MHLO and linalg (#1306) * Add aten.frobenius_norm.dim op and init its conversion pattern to linalg and MHLO, * run symbolic-shape-optimization before hlo-legalize-to-linalg to fit more mhlo e2e tests.
2022-09-08 10:15:36 +08:00
"ReduceSumDimIntListDtypeFloatModule_basic",
"ReduceSumDimIntListDtypeIntModule_basic",
"ReduceSumDimIntListKeepDimFloatModule_basic",
"ReduceSumDimIntListKeepDimIntModule_basic",
"ReduceSumDtypeFloatModule_basic",
"ReduceSumDtypeIntModule_basic",
[MHLO] add conversion for aten.linalg_vector_norm (#1850)
2023-03-21 05:14:27 +08:00
"ReduceL1NormModule_basic",
"ReduceL1NormWithDTypeModule_basic",
"ReduceL2NormModule_basic",
"ReduceL3NormAllDimsModule_basic",
"ReduceL3NormKeepDimModule_basic",
"ReduceLN3NormModule_basic",
"NormScalarOptDimKeepDimModule_basic",
"NormScalarOptDimModule_basic",
"NormalizeModule_basic",
[Importer] import constant tuple (#2132) * [Importer] import constant tuple * update * update * update
2023-05-31 14:14:14 +08:00
"ScalarConstantTupleModule_basic",
[MLIR][MHLO] Add AtenFrobeniusNormDimOp and add its conversion pattern to MHLO and linalg (#1306) * Add aten.frobenius_norm.dim op and init its conversion pattern to linalg and MHLO, * run symbolic-shape-optimization before hlo-legalize-to-linalg to fit more mhlo e2e tests.
2022-09-08 10:15:36 +08:00
"SelectIntModule_basic",
Fix empty tensor when select -1 (#1787)
2023-01-18 02:14:14 +08:00
"SelectIntNegativeDimAndIndexStaticModule_basic",
[MLIR][MHLO] Add AtenFrobeniusNormDimOp and add its conversion pattern to MHLO and linalg (#1306) * Add aten.frobenius_norm.dim op and init its conversion pattern to linalg and MHLO, * run symbolic-shape-optimization before hlo-legalize-to-linalg to fit more mhlo e2e tests.
2022-09-08 10:15:36 +08:00
"SliceSingleIdxModule_basic",
"SqueezeDimModule_dynamic",
"SqueezeDimModule_negDim",
[Torch][MHLO] Decompose aten.copy op. Lower aten.rsqrt & sigmoid to mhlo. (#1734)
2022-12-22 10:13:59 +08:00
"ToCopyBoolDTypeStaticModule_basic",
"ToCopyModule_basic",
"ToCopyWithDTypeFalsePinMemoryModule_basic",
"ToCopyWithDTypeModule_basic",
[MLIR][MHLO] Add AtenFrobeniusNormDimOp and add its conversion pattern to MHLO and linalg (#1306) * Add aten.frobenius_norm.dim op and init its conversion pattern to linalg and MHLO, * run symbolic-shape-optimization before hlo-legalize-to-linalg to fit more mhlo e2e tests.
2022-09-08 10:15:36 +08:00
"ReduceFrobeniusNormModule_basic",
[MHLO] Init end to end unit tests (#1223)
2022-08-23 16:47:21 +08:00
"FlattenStaticModule_basic",
"FlattenRank0Module_basic",
"TensorsConcatNegativeDimModule_basic",
[TORCH] Improve type refinement for aten.cat. (#1908) * [TORCH] Fix type refinement for aten.cat. * Add test. * Address comments. * Update. * Update. * Update. * Update. * Update. --------- Co-authored-by: Ziheng Jiang <ziheng.jiang@bytedance.com>
2023-03-10 08:17:35 +08:00
"TensorsConcatPromoteDTypeModule_basic",
[TOSA] lowering support for aten cat (#2039) Add support for lowering torch.aten.cat to tosa.concat * add support for aten cat to tosa --------- Co-authored-by: yifei <y.zhou@xilinx.com> Co-authored-by: Lisa Liu <lingl@xilinx.com>
2023-05-10 23:25:58 +08:00
"TensorsConcatStaticModule_basic",
"TensorsConcatNegativeDimStaticModule_basic",
Support aten.stack op and decompose it into unsqueeze & cat (#1747)
2023-03-11 09:25:25 +08:00
"TensorsStackModule_basic",
"TensorsStackNegativeDimModule_basic",
"TensorsStackPromoteDTypeModule_basic",
Replace empty_like && empty_memory_format with full/full_like (#1398) * Replace empty_like && empty_memory_format with full/full_like * fix broadcast rank0 tensor
2022-09-23 10:24:36 +08:00
"LiftFreshCopyModule_basic",
"Mlp2LayerModuleNoBias_basic",
[MHLO] Init end to end unit tests (#1223)
2022-08-23 16:47:21 +08:00
"NumelModule_basic",
mhlo: migrate conversion to stablehlo (#1840) This patch replaces all MHLO operations with their StableHLO counterparts and adds a validation pass to ensure that no MHLO operations remain before translating all Stablehlo operations to the MHLO dialect for further lowering to the Linalg dialect. This patch also updates all lit tests so that they refer to the `convert-torch-to-stablehlo` pass and so that they check for StableHLO operations.
2023-02-02 21:29:47 +08:00
"SiluModule_basic",
"SquareModule_basic",
[MHLO] Init end to end unit tests (#1223)
2022-08-23 16:47:21 +08:00
"SqueezeModule_allUnitDim",
"SqueezeDimModule_unitDim",
Implement Expand/Collapse Functionality for Aten.View (#1353)
2022-09-28 02:08:14 +08:00
"ViewCollapseOnesMiddleModule_basic",
fix mhlo e2e ci crashes (#1620) * fix mhlo e2e ci crashes * add passed tests * calc dynamic positive dim
2022-11-21 21:50:35 +08:00
"ViewDoubleMergeStaticModule_basic",
"ViewExpandDynamicDimModule_basic",
"ViewFlattenAndExpandModule_basic",
Implement Expand/Collapse Functionality for Aten.View (#1353)
2022-09-28 02:08:14 +08:00
"ViewFiveTestStaticModule_basic",
"ViewOffsetTestStaticModule_basic",
"ViewTwoFiveThreeStaticModule_basic",
"ViewTwoToThreeStaticModule_basic",
"ViewExpandOnesMiddleOppModule_basic",
"ViewOffsetBackwardTestStaticModule_basic",
[MHLO] Init end to end unit tests (#1223)
2022-08-23 16:47:21 +08:00
"NumToTensorFloatModule_basic",
"AtenToDeviceModule_basic",
[Stablehlo]Add support for AvgPool1dOp (#2268) * Add support for AvgPool1d * Update AbstractInterpLibrary * support avgpool1d in linalg * refactored code * fix nit problem
2023-07-25 14:09:53 +08:00
"AvgPool1dStaticModule_basic",
[MHLO] Init end to end unit tests (#1223)
2022-08-23 16:47:21 +08:00
"AvgPool2dStaticModule_basic",
"Conv2dWithPaddingDilationStrideStaticModule_basic",
[TOSA] Fix conversion for depthwise convolutions (#2398) * [TOSA] Fix conversion for depthwise convolutions * Add e2e tests for depthwise and grouped convolutions Co-authored-by: Lucas Camphausen <lucas.camphausen@iml.fraunhofer.de>
2023-08-18 23:15:54 +08:00
"Conv2dWithPaddingDilationStrideStaticModule_depthwise",
"Conv2dWithPaddingDilationStrideStaticModule_depthwise_multiplier",
"Conv2dWithPaddingDilationStrideStaticModule_grouped",
"Conv2dWithPaddingDilationStrideStaticModule_grouped_multiplier",
[MHLO] Init end to end unit tests (#1223)
2022-08-23 16:47:21 +08:00
"Convolution2DStaticModule_basic",
Update MHLO xfails.
2022-08-27 06:29:07 +08:00
"ConvolutionModule2DTransposeStridedStatic_basic",
[MHLO] Init end to end unit tests (#1223)
2022-08-23 16:47:21 +08:00
"ElementwiseCloneContiguousModule_basic",
[MLIR][TORCH] Add support for clone op with channels last memory format Fixes https://github.com/llvm/torch-mlir/issues/1829 Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2023-02-01 19:13:59 +08:00
"ElementwiseCloneChannelsLastMemoryFormatModule_basic",
[MHLO] Init end to end unit tests (#1223)
2022-08-23 16:47:21 +08:00
"ElementwiseCloneModule_basic",
"ElementwiseBinaryStaticShapeModule_basic",
"ReturnThreeTensorFloat32_basic",
"BoolTensorReturnFalseModule_basic",
"BoolTensorReturnTrueModule_basic",
"BoolTensorReturnMixedModule_basic",
"SqueezeModule_static",
"TModuleRank1_basic",
"TModuleRank0_basic",
"ElementwiseToDtypeIdentityModule_basic",
Revert "build: update llvm tag to 147fe9de" This reverts commit e45ad313d4395195ad8764623067aa2a645223d7.
2022-11-24 21:02:59 +08:00
"View1DFoldModule_basic",
"UnsafeView1DFoldModule_basic",
[Torch Dialect] Add Support for aten.unflatten.int (#2475) As title, Add support for aten.unflatten.int, support dim to be negative and one of the sizes' elements to be -1
2023-10-31 15:36:16 +08:00
"UnflattenStaticModule_basic",
"UnflattenIntStaticModule_basic",
"UnflattenIntNegativeOneDimStaticModule_basic",
"UnflattenIntNegativeOneSizeStaticModule_basic",
fix mhlo e2e ci crashes (#1620) * fix mhlo e2e ci crashes * add passed tests * calc dynamic positive dim
2022-11-21 21:50:35 +08:00
"RsubFloatModule_basic",
"RsubFloatModule_noalpha_basic",
"RsubIntModule_basic",
"RsubIntModule_noalpha_basic",
[Torch Dialect] add RSub, ScalarImplicit canonicalize (#1899) * add rsub, scalarimplit canonicalizer * reformat * address comments * fix bug * fix test * Update elementwise.py * resolve merge conflict * change to 3 * change to 3 * real fix * fix name * add torchdynamo fail test --------- Co-authored-by: zhekun.zhang <zhekun.zhang@bytedance.com>
2023-03-07 09:38:27 +08:00
"RsubInt0d_NumToTensor_Module_basic",
[Torch Dialect] Add support for AtenScalarTensorOp (#2085) * add scalar_tensor op * add dynamo pass test; needs PR2062 * try to fix * Empty commit, trigger test * Empty commit, trigger test * address comments * use dtype function * fix decompose rule * remove unused include * Empty commit, trigger test * fix test * disable ltc * fix dtype --------- Co-authored-by: zhekun.zhang <zhekun.zhang@bytedance.com>
2023-06-01 11:38:50 +08:00
"ScalarTensorDefaultDtypeModule_basic",
"ScalarTensorFloat32Module_basic",
"ScalarTensorInt32Module_basic",
"ScalarTensorInt64Module_basic",
[StableHLO] Support for slice_scatter (#1960) Co-authored-by: zhekun.zhang <zhekun.zhang@bytedance.com>
2023-03-23 04:41:04 +08:00
"SelectScattertModule_basic",
"SelectScattertStaticModule_basic",
fix mhlo e2e ci crashes (#1620) * fix mhlo e2e ci crashes * add passed tests * calc dynamic positive dim
2022-11-21 21:50:35 +08:00
"SliceStaticModule_basic",
[MHLO] Init end to end unit tests (#1223)
2022-08-23 16:47:21 +08:00
"SliceModule_basic",
"SliceNegIdxModule_basic",
"SliceOutOfLowerBoundStartIndexModule_basic",
"SliceOutOfUpperBoundIndexModule_basic",
Fixes for split tensor and slice (#2314) * RecomposeComplexOps: Remove dead slice op * lib/Dialect/Torch/IR/TorchOps.cpp: Fold slice ops even when they are on non-value tensors * lib/Conversion/TorchToTosa/TorchToTosa.cpp: Fix slice start/end out of range/none * lib/Dialect/Torch/IR/TorchOps.cpp: AtenSliceTensorOp::fold: Fold slices that go from 0:int_max * More tests for aten.split.Tensor
2023-07-20 15:53:54 +08:00
"SliceOutOfUpperBoundIndexStaticModule_basic",
[MHLO] Init end to end unit tests (#1223)
2022-08-23 16:47:21 +08:00
"SliceStartEqEndModule_basic",
"SliceSizeTwoStepModule_basic",
"SliceWholeTensorModule_basic",
[StableHLO] Support for slice_scatter (#1960) Co-authored-by: zhekun.zhang <zhekun.zhang@bytedance.com>
2023-03-23 04:41:04 +08:00
"SliceScatterModule_basic",
"SliceScatterNegativeDimModule_basic",
"SliceScatterNegativeEndModule_basic",
"SliceScatterStaticModule_basic",
"SliceScatterStepVariationModule_basic",
"SliceScatterZeroDimModule_basic",
fix mhlo e2e ci crashes (#1620) * fix mhlo e2e ci crashes * add passed tests * calc dynamic positive dim
2022-11-21 21:50:35 +08:00
"SqueezeDimModule_static",
"SqueezeDimModule_identity",
"SqueezeModule_broadcast",
[MHLO] Init end to end unit tests (#1223)
2022-08-23 16:47:21 +08:00
"ReturnTwoTensorF32I64_basic",
"Matmul4dStatic_basic",
"Matmul_dot",
"Matmul_2d",
"Matmul_matvec",
"Matmul_vecmat",
"MaxPool2dWithIndicesStaticModule_basic",
"MmDagModule_basic",
"MmModule_basic",
"MmModule_chained",
"MaxPool2dStaticModule_basic",
[StableHlo] Support AtenEmptyMemoryFormatOp (#2092) * add empty conversion * clean up * add tests --------- Co-authored-by: zhekun.zhang <zhekun.zhang@bytedance.com>
2023-05-19 10:07:35 +08:00
"EmptyModule_contiguous",
"EmptyModule_defaultDtype",
"EmptyModule_falsePinMemory",
"EmptyModule_int",
"EmptyModule_float",
"NewEmptyModuleDefaultDtype_basic",
"NewEmptyModuleFalsePinMemory_basic",
"NewEmptyModuleFloat2D_basic",
"NewEmptyModuleFloat3D_basic",
"NewEmptyModuleInt2D_basic",
"NewEmptyModuleInt3D_basic",
"NewEmptyModuleLayoutIntDtype_basic",
"NewEmptyModuleNonDefaultFloatDtype_basic",
"NewEmptyModuleNonDefaultIntDtype_basic",
"NewEmptyStridedModuleDefaultDtype_basic",
[MLIR][TORCH] Add E2E support for aten.empty_strided decomposition op (redo PR) (#2459) Making the same PR with #2457, as I accidentally thought the review was already made and merged it (reverted). Add decompose empty_strided op. Referring to #1776, this decomposition op only supports default stride values, because accessing the tensor or indexing over that, the indices are determined by the strides. In MLIR, this is not implicitly supported but assumes that the strides are default while iterating over the tensor.
2023-09-14 01:04:31 +08:00
"EmptyStridedModule_basic",
Rework AtenEmptyStridedOp checks (#2537) Now using Value instead of Ints. Trades compile failure for a runtime assert
2023-11-01 11:56:54 +08:00
"EmptyStridedSizeIntStrideModule_basic",
[MHLO] Init end to end unit tests (#1223)
2022-08-23 16:47:21 +08:00
"PermuteModule_basic",
"PermuteNegativeIndexModule_basic",
Replace empty_like && empty_memory_format with full/full_like (#1398) * Replace empty_like && empty_memory_format with full/full_like * fix broadcast rank0 tensor
2022-09-23 10:24:36 +08:00
"ReduceSumDimIntListKeepDimNegativeDimStaticModule_basic",
[Stablehlo] support dynamic shape when convert aten.fill.Scalar (#2349)
2023-07-27 18:35:25 +08:00
"ZeroFloat32Module_basic",
"ZeroInt32Module_basic",
"ZeroInt64Module_basic",
Replace empty_like && empty_memory_format with full/full_like (#1398) * Replace empty_like && empty_memory_format with full/full_like * fix broadcast rank0 tensor
2022-09-23 10:24:36 +08:00
"ZerosLikeModule_defaultDtype",
"ZerosLikeModule_falsePinMemory",
"ZerosLikeModule_float",
"ZerosLikeModule_int",
[MHLO] Init end to end unit tests (#1223)
2022-08-23 16:47:21 +08:00
"ZerosModuleDefaultDtype_basic",
"ZerosModuleInt2D_basic",
"ZerosModuleInt3D_basic",
"ZerosModuleFloat2D_basic",
"ZerosModuleFloat3D_basic",
"ZerosModuleFalsePinMemory_basic",
"OnesModuleDefaultDtype_basic",
"OnesModuleInt_basic",
"OnesModuleFloat_basic",
"OnesModuleFalsePinMemory_basic",
Replace empty_like && empty_memory_format with full/full_like (#1398) * Replace empty_like && empty_memory_format with full/full_like * fix broadcast rank0 tensor
2022-09-23 10:24:36 +08:00
"OnesLikeModule_defaultDtype",
"OnesLikeModule_falsePinMemory",
"OnesLikeModule_float",
"OnesLikeModule_int",
[MHLO] Init end to end unit tests (#1223)
2022-08-23 16:47:21 +08:00
"NewZerosModuleDefaultDtype_basic",
"NewZerosModuleInt2D_basic",
"NewZerosModuleInt3D_basic",
"NewZerosModuleFloat2D_basic",
"NewZerosModuleFloat3D_basic",
"NewZerosModuleFalsePinMemory_basic",
"NewOnesModuleDefaultDtype_basic",
"NewOnesModuleInt2D_basic",
"NewOnesModuleInt3D_basic",
"NewOnesModuleFloat2D_basic",
"NewOnesModuleFloat3D_basic",
"NewOnesModuleFalsePinMemory_basic",
[TOSA] Add support for AtenZerosOp 0/strided layout (#1983)
2023-03-30 22:08:20 +08:00
"NewZerosStaticModuleLayoutStrided_basic",
[MHLO] Init end to end unit tests (#1223)
2022-08-23 16:47:21 +08:00
"DropoutEvalIntModule_basic",
"DropoutEvalFloatModule_basic",
[Torch Dialect] Support aten.native_dropout (#2259) * [Torch Dialect] Support aten.native_dropout * update
2023-06-27 14:19:33 +08:00
"DropoutTrainStaticShapeModule_basic",
"NativeDropoutEvalFloatModule_basic",
"NativeDropoutTrainStaticShapeModule_basic",
[MHLO] Init end to end unit tests (#1223)
2022-08-23 16:47:21 +08:00
"ContiguousModule_basic",
"DropoutModule_basic",
"ViewCollapseModule_basic",
Revert "build: update llvm tag to 147fe9de" This reverts commit e45ad313d4395195ad8764623067aa2a645223d7.
2022-11-24 21:02:59 +08:00
"ViewCollapseInferredDimModule_basic",
[MHLO] Init end to end unit tests (#1223)
2022-08-23 16:47:21 +08:00
"ViewDynamicExpandCollapseModule_basic",
"ViewDynamicExpandModule_basic",
"ViewExpandModule_basic",
"ViewExpandOnesModule_basic",
"ViewExpandOnesBeforeAndAfterModule_basic",
"ViewExpandOnesMiddleModule_basic",
"ViewExpandCollapseModule_basic",
"ViewExpandCollapseWithOnesModule_basic",
Revert "build: update llvm tag to 147fe9de" This reverts commit e45ad313d4395195ad8764623067aa2a645223d7.
2022-11-24 21:02:59 +08:00
"ViewExpandInferredDimModule_basic",
[TOSA] Add aten.view shape -1 support (#1815)
2023-01-21 03:56:26 +08:00
"ViewNegativeStaticModule_basic",
[MHLO] Init end to end unit tests (#1223)
2022-08-23 16:47:21 +08:00
"ViewNoChangeStaticModule_basic",
"ViewNoChange1dModule_basic",
"ViewNoChange2dModule_basic",
"ViewNoChange3dModule_basic",
"UnsafeViewExpandModule_basic",
"ReduceMaxAllDims_basic",
"ReduceMaxFloatModule_basic",
"ReduceMaxSignedIntModule_basic",
"ReduceMaxUnsignedIntModule_basic",
[MLIR][TORCH] add E2E support for aten.min op (#2422) * impl aten.min op * remove extraneous test
2023-08-30 01:12:41 +08:00
"ReduceMinAllDims_basic",
"ReduceMinFloatModule_basic",
"ReduceMinSignedIntModule_basic",
"ReduceMinUnsignedIntModule_basic",
[MHLO] Init end to end unit tests (#1223)
2022-08-23 16:47:21 +08:00
"ReduceSumDimIntListFloatModule_basic",
"ReduceSumDimIntListIntModule_basic",
"ReduceSumFloatModule_basic",
"ReduceSumSignedIntModule_basic",
"ReduceSumUnsignedIntModule_basic",
"RepeatModule_basic",
"ReshapeAliasCollapseModule_basic",
"ReshapeAliasExpandModule_basic",
"ReshapeExpandModule_basic",
[Torch Dialect] emit aten.reshape_as op and add decomposition pattern. (#2553)
2023-11-05 11:38:36 +08:00
"ReshapeAsModule_basic",
[MHLO] Init end to end unit tests (#1223)
2022-08-23 16:47:21 +08:00
"TestMultipleTensorReturn_basic",
[Torch Dialect] add support for adaptive_avgpool_1d (#2342) * [MLIR][TORCH] Fix aten.cumsum lowering for int32 input (#2351) Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com> [Stablehlo] Add converter to stablehlo for aten.(Int,Float,Bool).Tensor op (#2340) [Stablehlo] Add converter to stablehlo for aten.(Int,Float,Bool).Tensor op and configure crashing e2e sets for stablehlo backend. update PyTorch version to 2.1.0.dev20230729 (#2354) - torch version: 2.1.0.dev20230729 - torch commit hash: b638df0afb83572724032c824c64e481bb4499a0 - torchvision version: 0.16.0.dev20230729 Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com> update PyTorch version to 2.1.0.dev20230730 (#2356) - torch version: 2.1.0.dev20230730 - torch commit hash: 0ff243ff350268cc98fe03fa6364375ee2824742 - torchvision version: 0.16.0.dev20230730 Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com> update PyTorch version to 2.1.0.dev20230731 (#2359) - torch version: 2.1.0.dev20230731 - torch commit hash: 6298ac688f8caafe30d71ff2ea2e20fbb32065c7 - torchvision version: 0.16.0.dev20230731 Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com> LTC->MLIR Debug Info support (#1922) * LTC->MLIR Debug Info support * SW-95317 Propagate Lazy->Jit->MLIR scope name. * Enhance location information based on op names Currently, the location information attached to the ops just considers the filename, line number and column number. Attaching operation name would help identify the type of computation by just looking at the profile of execution. * Update locations logic; updated debug-info.py test * Use {scope}/{op_name} format to track names by default --------- Co-authored-by: Gleb Kazantaev <gleb.kazantaev@cerebras.net> Co-authored-by: Mark Browning <mark@cerebras.net> Co-authored-by: Vimal Patel <vimal@polymagelabs.com> build: update llvm tag to 41895843 Summary of changes: - Update tags llvm: 41895843b5915bb78e9d02aa711fa10f7174db43 mhlo: 4726d31f7025da66de0dea709bd56c462edb83c2 Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com> update PyTorch version to 2.1.0.dev20230802 (#2366) - torch version: 2.1.0.dev20230802 - torch commit hash: c89b16917755c2abbef7b6420e340baf9ae8089e - torchvision version: 0.16.0.dev20230802 Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com> Change Python version from 3.10 to 3.11 in installation instructions (#2370) Add CITATION file (#2371) Add packaging as an install dependency (#2369) Needed by `torch_mlir._version`. Resolves #2368. [Torch Dialect] emit aten.masked_scatter and aten.masked_scatter_ op (#2358) * [Torch Dialect] emit aten.masked_scatter and aten.masked_scatter_ op update PyTorch version to 2.1.0.dev20230803 (#2372) - torch version: 2.1.0.dev20230803 - torch commit hash: f89c73be3a3e8274d025ac46a33a780853841c9e - torchvision version: 0.16.0.dev20230803 Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com> Prevent failed stable CI job from cancelling nightly jobs (#2373) The CI jobs that use stable PyTorch are currently not required to pass in order for a patch to get merged in `main`. This commit makes sure that if a CI job for stable PyTorch fails, it does not cancel the other required jobs. [Torch Dialect] emit aten.tile op and decompose it into aten.repeat (#2355) update update xfail sets update xfail_sets update fix xfail_sets update: update update: update parent 22e88d523b1970b2e904eb5421d49d987a3d255e author jianzhe.xiao <jianzhe.xiao@bytedance.com> 1691114110 +0800 committer jianzhe.xiao <jianzhe.xiao@bytedance.com> 1691114119 +0800 [Stablehlo] Add converter to stablehlo for aten.(Int,Float,Bool).Tensor op (#2340) [Stablehlo] Add converter to stablehlo for aten.(Int,Float,Bool).Tensor op and configure crashing e2e sets for stablehlo backend. update PyTorch version to 2.1.0.dev20230729 (#2354) - torch version: 2.1.0.dev20230729 - torch commit hash: b638df0afb83572724032c824c64e481bb4499a0 - torchvision version: 0.16.0.dev20230729 Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com> update PyTorch version to 2.1.0.dev20230730 (#2356) - torch version: 2.1.0.dev20230730 - torch commit hash: 0ff243ff350268cc98fe03fa6364375ee2824742 - torchvision version: 0.16.0.dev20230730 Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com> update PyTorch version to 2.1.0.dev20230731 (#2359) - torch version: 2.1.0.dev20230731 - torch commit hash: 6298ac688f8caafe30d71ff2ea2e20fbb32065c7 - torchvision version: 0.16.0.dev20230731 Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com> LTC->MLIR Debug Info support (#1922) * LTC->MLIR Debug Info support * SW-95317 Propagate Lazy->Jit->MLIR scope name. * Enhance location information based on op names Currently, the location information attached to the ops just considers the filename, line number and column number. Attaching operation name would help identify the type of computation by just looking at the profile of execution. * Update locations logic; updated debug-info.py test * Use {scope}/{op_name} format to track names by default --------- Co-authored-by: Gleb Kazantaev <gleb.kazantaev@cerebras.net> Co-authored-by: Mark Browning <mark@cerebras.net> Co-authored-by: Vimal Patel <vimal@polymagelabs.com> build: update llvm tag to 41895843 Summary of changes: - Update tags llvm: 41895843b5915bb78e9d02aa711fa10f7174db43 mhlo: 4726d31f7025da66de0dea709bd56c462edb83c2 Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com> update PyTorch version to 2.1.0.dev20230802 (#2366) - torch version: 2.1.0.dev20230802 - torch commit hash: c89b16917755c2abbef7b6420e340baf9ae8089e - torchvision version: 0.16.0.dev20230802 Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com> Change Python version from 3.10 to 3.11 in installation instructions (#2370) Add CITATION file (#2371) Add packaging as an install dependency (#2369) Needed by `torch_mlir._version`. Resolves #2368. [Torch Dialect] emit aten.masked_scatter and aten.masked_scatter_ op (#2358) * [Torch Dialect] emit aten.masked_scatter and aten.masked_scatter_ op update PyTorch version to 2.1.0.dev20230803 (#2372) - torch version: 2.1.0.dev20230803 - torch commit hash: f89c73be3a3e8274d025ac46a33a780853841c9e - torchvision version: 0.16.0.dev20230803 Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com> Prevent failed stable CI job from cancelling nightly jobs (#2373) The CI jobs that use stable PyTorch are currently not required to pass in order for a patch to get merged in `main`. This commit makes sure that if a CI job for stable PyTorch fails, it does not cancel the other required jobs. [Torch Dialect] emit aten.tile op and decompose it into aten.repeat (#2355) update update xfail sets update xfail_sets update fix xfail_sets update: update update: add support for adaptive_pool_id update xfail sets update xfail_sets update fix xfail_sets update: update: * update --------- Co-authored-by: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
2023-08-05 07:48:09 +08:00
"AdaptiveAvgPool1dUnitOutputSizeStaticModule_basic",
[MHLO] Init end to end unit tests (#1223)
2022-08-23 16:47:21 +08:00
"AdaptiveAvgPool2dUnitOutputSizeStaticModule_basic",
"BaddbmmStaticModule_basic",
"BaddbmmBroadcast1DInputModule_basic",
"BaddbmmBroadcast2DInputModule_basic",
"NarrowHorizontalTest2_basic",
"NarrowHorizontalTest_basic",
"NarrowVerticalTest2_basic",
"NarrowVerticalTest_basic",
[Torch-Dialect] emit aten.narrow.Tensor op and decompose it to aten.narrow op (#2297)
2023-07-20 16:46:44 +08:00
"NarrowTensorHorizontalModule_basic",
"NarrowTensorVerticalModule_basic",
[MHLO] Init end to end unit tests (#1223)
2022-08-23 16:47:21 +08:00
"NumToTensorIntModule_basic",
"NumpyTRank0Module_basic",
"NumpyTRank1Module_basic",
"NumpyTRank2Module_basic",
"NumpyTRankNStaticModule_basic",
"NumpyTRankNDynamicModule_basic",
"TModuleRank2_basic",
"TensorLiteralModule_basic",
"TensorsConcatModule_basic",
"TensorOpaqueLiteralModule_basic",
"TransposeIntModule_basic",
"TransposeIntNegDimsModule_basic",
Replace empty_like && empty_memory_format with full/full_like (#1398) * Replace empty_like && empty_memory_format with full/full_like * fix broadcast rank0 tensor
2022-09-23 10:24:36 +08:00
"ToDtypeBoolLayoutNoneModule_basic",
fix mhlo e2e ci crashes (#1620) * fix mhlo e2e ci crashes * add passed tests * calc dynamic positive dim
2022-11-21 21:50:35 +08:00
"ToDtypeBoolLayoutNoneStaticModule_basic",
Replace empty_like && empty_memory_format with full/full_like (#1398) * Replace empty_like && empty_memory_format with full/full_like * fix broadcast rank0 tensor
2022-09-23 10:24:36 +08:00
"ToDtypeLayoutNoneModule_basic",
"ToDtypeLayoutStridedModule_basic",
"TypeAsSameModule_basic",
[torch-dialect] fix aten.type_as op's folder (#2283) [torch-dialect] fix torch.type_as op's folder by decomposing it to prim.dtype + aten.to_dtype
2023-07-20 09:51:58 +08:00
"TypeAsDifferentModule_basic",
Replace empty_like && empty_memory_format with full/full_like (#1398) * Replace empty_like && empty_memory_format with full/full_like * fix broadcast rank0 tensor
2022-09-23 10:24:36 +08:00
"TypeConversionF32ToF64Module_basic",
"TypeConversionF64ToF32Module_basic",
"TypeConversionI1ToF32Module_basic",
"TypeConversionI1ToF64Module_basic",
"TypeConversionI1ToI32Module_basic",
"TypeConversionI1ToI64Module_basic",
"TypeConversionI32ToI64Module_basic",
"TypeConversionI64ToI32Module_basic",
fix mhlo e2e ci crashes (#1620) * fix mhlo e2e ci crashes * add passed tests * calc dynamic positive dim
2022-11-21 21:50:35 +08:00
"TypePromotionAlphaWiderModule_basic",
"TypePromotionSameCategoryZeroRankWider_basic",
"TypePromotionZeroRankHigherCategoryModule_basic",
[MHLO] Init end to end unit tests (#1223)
2022-08-23 16:47:21 +08:00
"OnesModuleCPUDevice_basic",
"Permute0RankModule_basic",
"UnsafeViewCollapseModule_basic",
"UnsafeViewDynamicExpandModule_basic",
[LINALG] Add lowering for aten::round op. -- Added the lowering for aten::round op. -- Added the folding for integer cases.
2022-10-11 17:52:01 +08:00
"AtenRoundIntModule_basic",
Add float16 support in the refbackend. This will require https://reviews.llvm.org/D139121 patch to go through.
2022-12-01 23:46:17 +08:00
"TestF16Return_basic",
mhlo: migrate conversion to stablehlo (#1840) This patch replaces all MHLO operations with their StableHLO counterparts and adds a validation pass to ensure that no MHLO operations remain before translating all Stablehlo operations to the MHLO dialect for further lowering to the Linalg dialect. This patch also updates all lit tests so that they refer to the `convert-torch-to-stablehlo` pass and so that they check for StableHLO operations.
2023-02-02 21:29:47 +08:00
"_LogSoftmaxModuleStable_basic",
[MLIR][TORCH] Add decomposition for prims::squeeze op This commit adds the decomposition for the prims.squeeze op. Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-11-16 13:57:58 +08:00
"PrimsSqueezeModule_basic",
"PrimsSqueezeEmptyDimensionsModule_basic",
[MLIR][TORCH] Add e2e support aten.movedim.int op Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2023-01-02 22:34:39 +08:00
"MoveDimIntModule_basic",
"MoveDimIntNegativeIndexModule_basic",
[MLIR][TORCH] Add support for non-unit stride for conv backward This commit also adds the support for non-unit output padding in the case of transposed convolution. Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-12-08 22:15:31 +08:00
"ConvolutionBackwardModule2DStatic_basic",
[StableHlo] Fix transposed convolution conversion (#2026) * fix conv bwd * fix * fix group case * clean up --------- Co-authored-by: zhekun.zhang <zhekun.zhang@bytedance.com>
2023-04-14 02:24:39 +08:00
"ConvolutionBackwardModule2DStrided_basic",
[MLIR][TORCH] Add support for prims::view_of op This op does nothing and just returns the input operand as the result of the op. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2023-04-10 11:50:26 +08:00
"PrimsViewOfModule_basic",
"PrimsViewOfZeroRankModule_basic",
Fix torchdynamo fail test.
2023-05-11 20:05:01 +08:00
"AtenComplex64Module_basic",
[Torch Dialect] Add split.tensor support + recompose rules (#2102) * add split.tensor support + recompose rules * add e2e test * address comments * address comments * erase op in recomposeOp --------- Co-authored-by: zhekun.zhang <zhekun.zhang@bytedance.com>
2023-05-24 03:43:33 +08:00
"SplitTensorGetItem_Module_basic",
[Torch Dialect] Support recompose aten.split.Tensor + prim.ListUnpack (#2192)
2023-06-07 01:38:04 +08:00
"SplitTensorListUnpackModule_basic",
Fixes for split tensor and slice (#2314) * RecomposeComplexOps: Remove dead slice op * lib/Dialect/Torch/IR/TorchOps.cpp: Fold slice ops even when they are on non-value tensors * lib/Conversion/TorchToTosa/TorchToTosa.cpp: Fix slice start/end out of range/none * lib/Dialect/Torch/IR/TorchOps.cpp: AtenSliceTensorOp::fold: Fold slices that go from 0:int_max * More tests for aten.split.Tensor
2023-07-20 15:53:54 +08:00
"SplitTensorNegativeDimModule_basic",
"SplitTensorLastSmallerModule_basic",
[Torch Dialect] support aten.split_with_sizes (#2431) * [Torch Dialect] support aten.split_with_sizes * update
2023-09-04 09:59:26 +08:00
"SplitWithSizesListUnpackModule_basic",
[Torch Op] Add unbind.int support with ListUnpack (#2058) * add unbind int * reformat * use unpack canonicalize * address comments * Empty commit, trigger test * add ltc blacklist * clean up * address comments * check permute list * erase in recompose --------- Co-authored-by: zhekun.zhang <zhekun.zhang@bytedance.com>
2023-05-19 10:07:58 +08:00
"UnbindIntListUnpack_Module_basic",
"UnbindIntGetItem_Module_basic",
[Torch Op] Add AtenChunkOp support (#2152) * add chunkOp support * update LTC xfail list * address comments * address comments --------- Co-authored-by: zhekun.zhang <zhekun.zhang@bytedance.com>
2023-05-26 10:05:19 +08:00
"ChunkListUnpack_Module_basic",
"ChunkListUnpackUneven_Module_basic",
[Stablehlo] Add aten.uniform lowering (#2101) * add uniform stablehlo lowering * add unit test * new line * rm redundant file * Empty commit, trigger test * fix include * address comments --------- Co-authored-by: zhekun.zhang <zhekun.zhang@bytedance.com>
2023-05-25 10:32:55 +08:00
"RandIntDtypeModule_basic",
"RandIntLowDtypeModule_basic",
"RandIntLowModule_basic",
"RandIntModule_basic",
"RandIntPinMemoryModule_basic",
[Torch Dialect][stablehlo] emit aten.rand op and add converter to stablehlo (#2413) * [Torch Dialect] emit aten.rand op and add converter to stablehlo * add failed tests for torchdynamo backend * add failed test for linalg backend
2023-08-27 21:56:36 +08:00
"RandModule_basic",
[Torch Dialect] add folder for aten.add (#2264) * [Torch Dialect] add folder for aten.add * update * update * update
2023-06-27 10:55:28 +08:00
"UniformStaticShapeModule_basic",
[Stablehlo] Add aten.uniform lowering (#2101) * add uniform stablehlo lowering * add unit test * new line * rm redundant file * Empty commit, trigger test * fix include * address comments --------- Co-authored-by: zhekun.zhang <zhekun.zhang@bytedance.com>
2023-05-25 10:32:55 +08:00
"UniformNoCorrelationModule_basic",
Fix canonicalization of `torch.prim.TupleUnpack`.
2023-07-18 22:32:26 +08:00
"TupleModule_basic",
[MLIR][TORCH] Add StableHLO lowering for embedding_bag.padding_idx op Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2023-09-05 21:28:37 +08:00
"AtenEmbeddingBagStaticModule_basic",
[MHLO] Init end to end unit tests (#1223)
2022-08-23 16:47:21 +08:00
}
[Stablehlo] Add converter to stablehlo for aten.(Int,Float,Bool).Tensor op (#2340) [Stablehlo] Add converter to stablehlo for aten.(Int,Float,Bool).Tensor op and configure crashing e2e sets for stablehlo backend.
2023-07-29 21:55:49 +08:00
STABLEHLO_CRASHING_SET = {
# These e2e tests crash because currently mlir-hlo's shape-component-analysis
# only support exact one index in tensor::ExtractOp when it's related with
# some tensors' shape. REF:
# https://github.com/tensorflow/mlir-hlo/blob/master/mhlo/analysis/shape_component_analysis.cc#L586
# FIXME if upstream mlir-hlo fix this.
"ViewCollapseDynamicWithAtenSizeIntModule_basic",
"UnsafeViewCollapseDynamicWithAtenSizeIntModule_basic",
[MLIR][TORCH] Add StableHLO lowering for embedding_bag.padding_idx op Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2023-09-05 21:28:37 +08:00
"Aten_EmbeddingBagExample_basic",
"AtenEmbeddingBagSumExample_basic"
[Stablehlo] Add converter to stablehlo for aten.(Int,Float,Bool).Tensor op (#2340) [Stablehlo] Add converter to stablehlo for aten.(Int,Float,Bool).Tensor op and configure crashing e2e sets for stablehlo backend.
2023-07-29 21:55:49 +08:00
}
Add a basic TOSA E2E backend. We lower through linalg-on-tensors and use RefBackend to run it. This adds enough support for a "tanh" op. Adding more ops should be fairly mechanical now that things are wired up. Run with: ``` ./tools/torchscript_e2e_test.sh -c tosa ``` The backend structure is very similar to linalg-on-tensors based E2E backends and is a nice parallel (see `tosa_backend.py`). Actually, this forced a nice refactoring to the layering here. We removed `torchscript-module-to-linalg-on-tensors-backend-pipeline` and instead require separately running ``` torchscript-function-to-torch-backend-pipeline,torch-backend-to-linalg-on-tensors-backend-pipeline ``` This highlights the step that lowers to the "torch backend contract" of cleaned up `torch` dialect ops is a critical step in the lowering. Going forward, that is the key load-bearing contract of the torch-mlir project, not the linalg-on-tensors backend contract. Recommended review order: - `TorchToTosa.cpp` / `TorchToTosa/basic.mlir` - `python/torch_mlir_e2e_test/torchscript/configs/tosa_backend.py` and the new `utils.py` file there. - `python/torch_mlir_e2e_test/tosa_backends/linalg_on_tensors.py` and `abc.py` in that directory for the TOSA backend e2e interface. - other misc mechanical changes
2021-10-08 10:07:03 +08:00
# Write the TOSA set as a "passing" set as it is very early in development
# and very few tests work yet.
Bump llvm-project to f1b922188ead5ca492c8d8edd47921b013a22ae0. Includes a fix to use `add_mlir_public_c_api_library` for Torch-MLIR's CAPI library, which is now required (note: upstream sample has it the right way). Disabled a TOSA test per discussion: https://github.com/llvm/torch-mlir/issues/379
2021-10-26 02:43:21 +08:00
TOSA_PASS_SET = {
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"AdaptiveAvgPool2dNonUnitOutputSizeStaticModule_basic",
"AdaptiveAvgPool2dUnitOutputSizeStaticModule_basic",
lowered addcmul and addcdiv to linalg
2021-11-25 06:01:48 +08:00
"AddCDivModule_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"AddCDiv_Module_basic",
"AddCMulModule_basic",
"AddCMul_Module_basic",
"Add_Module_basic",
"AliasModule_basic",
"ArangeDtypeFloatModule_basic",
"ArangeIntModule_basic",
"ArangeNegativeStartIntModule_basic",
"ArangeStartIntModule_basic",
"ArangeStartNegativeStepIntModule_basic",
"ArangeStartOutModule_basic",
"ArangeStartOutViewModule_basic",
"ArangeStartStepIntModule_basic",
"ArangeZeroElementOutputModule_basic",
"ArgmaxModule_keepDim",
"ArgmaxModule_with_dim",
"AtenComplex64Module_basic",
"AtenEyeMModuleCPUDevice_basic",
"AtenEyeMModuleDefaultDtype_basic",
"AtenEyeMModuleFalsePinMemory_basic",
"AtenEyeMModuleFloat2D_basic",
"AtenEyeModuleCPUDevice_basic",
"AtenEyeModuleDefaultDtype_basic",
"AtenEyeModuleFalsePinMemory_basic",
"AtenEyeModuleFloat2D_basic",
"AtenRoundIntModule_basic",
Add lowering for aten.to.device (#1107)
2022-08-11 07:24:02 +08:00
"AtenToDeviceModule_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"BaddbmmBroadcast1DInputModule_basic",
"BaddbmmBroadcast2DInputModule_basic",
"BaddbmmDynamicModule_basic",
"BaddbmmStaticModule_basic",
"BaddbmmWithAlphaBetaModule_basic",
"BaddbmmWithAlphaModule_basic",
"BaddbmmWithBetaModule_basic",
"BatchNorm1DModule_basic",
"BatchNorm1DStaticShapeModule_basic",
"BatchNorm1DWith2DInputModule_basic",
"BatchNorm2DModule_basic",
"BatchNorm3DModule_basic",
[MLIR][TORCH] Add different dtype support for aten.bmm op Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2023-09-11 20:58:59 +08:00
"BmmFloatModule_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"BoolTensorHandleSignless_basic",
"BoolTensorReturnFalseModule_basic",
"BoolTensorReturnMixedModule_basic",
"BoolTensorReturnTrueModule_basic",
"BroadcastListConstructWithMinusOneModule_basic",
"BroadcastToSameRankStaticModule_basic",
"BroadcastZeroRankInputStaticModule_basic",
"BucketizeTensorStaticFloatModule_basic",
"BucketizeTensorStaticModule_basic",
"ChunkListUnpackUneven_Module_basic",
"ChunkListUnpack_Module_basic",
"ConstantBoolParameterModule_basic",
"ConstantPad2dStaticModule_basic",
"ConstantPadNdModule_basic",
"ConstantPadNdPartialStaticModule_basic",
"ConstantPadNdStaticModule_basic",
"ContiguousModule_basic",
"Conv2dBiasNoPaddingModule_basic",
"Conv2dNoPaddingModule_basic",
"Conv2dWithPaddingDilationStrideModule_basic",
"Conv2dWithPaddingDilationStrideStaticModule_basic",
"Conv2dWithPaddingDilationStrideStaticModule_depthwise",
"Conv2dWithPaddingDilationStrideStaticModule_depthwise_multiplier",
"Conv2dWithPaddingModule_basic",
"Convolution2DStaticModule_basic",
torch-to-tosa lowering support for AtenLinalgVectorNormOp (#2734) This PR add torch-to-tosa lowering support for AtenLinalgVectorNormOp e2e test: python -m e2e_testing.main --config=tosa LIT tests: cmake --build build --target tools/torch-mlir/all --------- Co-authored-by: Ze Zhang <ze.zhang@getcruise.com>
2024-01-19 04:32:23 +08:00
"CosineSimilarityStaticModule_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"DetachModule_basic",
"DropoutEvalFloatModule_basic",
"DropoutEvalIntModule_basic",
"DropoutModule_basic",
[Torch Dialect] Support Einsum Op (#2230) As title, support torch.aten.einsum op Right now only support Static Shape, because of the known issue, the fixed solution is here: https://github.com/llvm/torch-mlir/pull/2154 Co-authored-by: Jiawei Wu [wujiawei.aml@bytedance.com](mailto:wujiawei.aml@bytedance.com)
2023-12-10 12:30:37 +08:00
"EinsumStaticContractRhsModule_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"EinsumStaticFourDimensionModule_basic",
"EinsumStaticModule_basic",
"ElementwiseAbsModule_basic",
"ElementwiseAddModule_basic",
"ElementwiseAddScalarFloatModule_basic",
"ElementwiseAddScalarInt64Module_basic",
"ElementwiseAddScalarInt8Module_basic",
"ElementwiseAddScalarIntModule_basic",
"ElementwiseAddScalar_TensorLiteralInt32_Module_basic",
"ElementwiseAtenDivIntScalarModule_basic",
lower torch.aten.isinf to linalg (#2638) Co-authored-by: Rob Suderman <rob.suderman@gmail.com>
2023-12-29 09:20:32 +08:00
"ElementwiseAtenIsinfOpModule_basic",
[Torch Dialect] support aten.isneginf, aten.isposinf, aten.nan_to_num (#2743)
2024-01-16 14:29:34 +08:00
"ElementwiseAtenIsneginfOpModule_basic",
"ElementwiseAtenIsposinfOpModule_basic",
Handle torch.none type in tosa.clamp op (#2739) This PR updates the torch-to-tosa conversion with following changes: - Support torch.none as min/max input argument for tosa.clamp op - Support negative value as start index for tosa.slice op - Add tosa.logical_or lowering support e2e test: python -m e2e_testing.main --config=tosa LIT tests: cmake --build build --target tools/torch-mlir/all --------- Co-authored-by: Ze Zhang <ze.zhang@getcruise.com>
2024-01-12 02:36:48 +08:00
"ElementwiseAtenLogicalOrOpBrodcastModule_basic",
"ElementwiseAtenLogicalOrOpDiffArgs1Module_basic",
"ElementwiseAtenLogicalOrOpDiffArgs2Module_basic",
"ElementwiseAtenLogicalOrOpDiffArgs3Module_basic",
"ElementwiseAtenLogicalOrOpModule_basic",
"ElementwiseAtenLogicalOrOpNegativeModule_basic",
"ElementwiseAtenLogicalOrOpPromoteBroadcastStaticShapeModule_basic",
"ElementwiseAtenLogicalOrOpRandomFloatModule_basic",
"ElementwiseAtenLogicalOrOpRandomModule_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"ElementwiseAtenWhereSelfModule_basic",
"ElementwiseBinaryModule_basic",
"ElementwiseBinaryStaticShapeModule_basic",
[LINALG][TOSA][MHLO] Add e2e support for aten bitwise ops (#1753)
2023-01-12 06:40:03 +08:00
"ElementwiseBitwiseAndModule_basic",
"ElementwiseBitwiseAndStaticShapeModule_basic",
"ElementwiseBitwiseNotInt32Module_basic",
"ElementwiseBitwiseNotInt64Module_basic",
"ElementwiseBitwiseOrModule_basic",
"ElementwiseBitwiseOrStaticShapeModule_basic",
"ElementwiseBitwiseXorModule_basic",
"ElementwiseBitwiseXorStaticShapeModule_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"ElementwiseCeilModule_basic",
Handle torch.none type in tosa.clamp op (#2739) This PR updates the torch-to-tosa conversion with following changes: - Support torch.none as min/max input argument for tosa.clamp op - Support negative value as start index for tosa.slice op - Add tosa.logical_or lowering support e2e test: python -m e2e_testing.main --config=tosa LIT tests: cmake --build build --target tools/torch-mlir/all --------- Co-authored-by: Ze Zhang <ze.zhang@getcruise.com>
2024-01-12 02:36:48 +08:00
"ElementwiseClampMaxModule_basic",
"ElementwiseClampMinModule_basic",
"ElementwiseClampModule_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"ElementwiseCloneChannelsLastMemoryFormatModule_basic",
"ElementwiseCloneContiguousModule_basic",
"ElementwiseCloneModule_basic",
"ElementwiseDivScalarModule_basic",
"ElementwiseEluModule_basic",
"ElementwiseEluNonDefaultModule_basic",
"ElementwiseEqBoolScalarModule_basic",
"ElementwiseEqDiffWidthScalarModule_basic",
"ElementwiseEqFloatScalarModule_basic",
"ElementwiseEqFloatTensorModule_basic",
"ElementwiseEqIntScalarModule_basic",
"ElementwiseEqIntTensorModule_basic",
"ElementwiseExpModule_basic",
"ElementwiseFlattenBroadcastModule_basic",
"ElementwiseFloorIntModule_basic",
"ElementwiseFloorModule_basic",
Support aten.sign (#2205)
2023-06-11 02:45:35 +08:00
"ElementwiseGeFloatIntScalarModule_basic",
"ElementwiseGeFloatScalarModule_basic",
"ElementwiseGeIntScalarModule_basic",
"ElementwiseGeMixedIntScalarModule_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"ElementwiseGeluModule_basic",
* [tosa] Support for AtenRsubScalarOp for scalar constants (#531) * [tosa] Support for AtenCeilOp and AtenReciprocalOp * [tosa] Support for comparator ops, Aten[Gt|Lt|Eq][Tensor|Scalar]Op with scalar constant * [tosa] Support for Scalar variants of Aten[Mul|Div|Add|Sub] Ops with scalar constants Signed-off-by: Anup Gangwar <anup.gangwar@arm.com> Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
2022-01-21 02:58:30 +08:00
"ElementwiseGtFloatScalarModule_basic",
"ElementwiseGtFloatTensorModule_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"ElementwiseGtIntScalarModule_basic",
* [tosa] Support for AtenRsubScalarOp for scalar constants (#531) * [tosa] Support for AtenCeilOp and AtenReciprocalOp * [tosa] Support for comparator ops, Aten[Gt|Lt|Eq][Tensor|Scalar]Op with scalar constant * [tosa] Support for Scalar variants of Aten[Mul|Div|Add|Sub] Ops with scalar constants Signed-off-by: Anup Gangwar <anup.gangwar@arm.com> Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
2022-01-21 02:58:30 +08:00
"ElementwiseGtIntTensorModule_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"ElementwiseGtMixed2ScalarModule_basic",
"ElementwiseIsinfModule_basic",
[Torch Dialect] support aten.isneginf, aten.isposinf, aten.nan_to_num (#2743)
2024-01-16 14:29:34 +08:00
"ElementwiseAtenIsneginfOpModule_basic",
"ElementwiseAtenIsposinfOpModule_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"ElementwiseIsnanModule_basic",
"ElementwiseLeFloatTensorModule_basic",
"ElementwiseLeIntTensorModule_basic",
"ElementwiseLeakyReluModule_basic",
"ElementwiseLeakyReluModule_basic",
"ElementwiseLeakyReluStaticModule_basic",
"ElementwiseLog2Module_basic",
"ElementwiseLogModule_basic",
* [tosa] Support for AtenRsubScalarOp for scalar constants (#531) * [tosa] Support for AtenCeilOp and AtenReciprocalOp * [tosa] Support for comparator ops, Aten[Gt|Lt|Eq][Tensor|Scalar]Op with scalar constant * [tosa] Support for Scalar variants of Aten[Mul|Div|Add|Sub] Ops with scalar constants Signed-off-by: Anup Gangwar <anup.gangwar@arm.com> Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
2022-01-21 02:58:30 +08:00
"ElementwiseLtDiffWidthScalarModule_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"ElementwiseLtFloatScalarModule_basic",
* [tosa] Support for AtenRsubScalarOp for scalar constants (#531) * [tosa] Support for AtenCeilOp and AtenReciprocalOp * [tosa] Support for comparator ops, Aten[Gt|Lt|Eq][Tensor|Scalar]Op with scalar constant * [tosa] Support for Scalar variants of Aten[Mul|Div|Add|Sub] Ops with scalar constants Signed-off-by: Anup Gangwar <anup.gangwar@arm.com> Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
2022-01-21 02:58:30 +08:00
"ElementwiseLtFloatTensorModule_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"ElementwiseLtIntScalarModule_basic",
* [tosa] Support for AtenRsubScalarOp for scalar constants (#531) * [tosa] Support for AtenCeilOp and AtenReciprocalOp * [tosa] Support for comparator ops, Aten[Gt|Lt|Eq][Tensor|Scalar]Op with scalar constant * [tosa] Support for Scalar variants of Aten[Mul|Div|Add|Sub] Ops with scalar constants Signed-off-by: Anup Gangwar <anup.gangwar@arm.com> Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
2022-01-21 02:58:30 +08:00
"ElementwiseLtIntTensorModule_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"ElementwiseMaxOtherIntModule_basic",
"ElementwiseMaxOtherModule_basic",
"ElementwiseMaximumIntModule_basic",
"ElementwiseMaximumModule_basic",
"ElementwiseMinOtherIntModule_basic",
"ElementwiseMinOtherModule_basic",
"ElementwiseMinimumIntModule_basic",
"ElementwiseMinimumModule_basic",
"ElementwiseMulScalarModule_basic",
"ElementwiseMulScalarModule_float",
"ElementwiseMulScalarModule_float",
"ElementwiseMulScalarModule_int",
"ElementwiseMulTensorIntModule_basic",
[Torch Dialect] add support for AtenIsnanOp (#2170) * add support for mhlo * Add Test for torch.ne * fix torch.ne shape/add static test case * add support for static torch.ne --------- Co-authored-by: root <root@n31-177-039.byted.org>
2023-06-07 10:06:27 +08:00
"ElementwiseNeFloatScalarModule_basic",
"ElementwiseNeFloatTensorModule_basic",
"ElementwiseNeFloatTensorStaticModule_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"ElementwiseNeIntScalarModule_basic",
[Torch Dialect] add support for AtenIsnanOp (#2170) * add support for mhlo * Add Test for torch.ne * fix torch.ne shape/add static test case * add support for static torch.ne --------- Co-authored-by: root <root@n31-177-039.byted.org>
2023-06-07 10:06:27 +08:00
"ElementwiseNeIntTensorModule_basic",
"ElementwiseNeIntTensorStaticModule_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"ElementwiseNegModule_basic",
"ElementwiseOrTensorModule_basic",
"ElementwiseOrTensorStaticShapeModule_basic",
"ElementwisePowModule_basic",
* [tosa] Support for AtenRsubScalarOp for scalar constants (#531) * [tosa] Support for AtenCeilOp and AtenReciprocalOp * [tosa] Support for comparator ops, Aten[Gt|Lt|Eq][Tensor|Scalar]Op with scalar constant * [tosa] Support for Scalar variants of Aten[Mul|Div|Add|Sub] Ops with scalar constants Signed-off-by: Anup Gangwar <anup.gangwar@arm.com> Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
2022-01-21 02:58:30 +08:00
"ElementwiseReciprocalModule_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"ElementwiseRelu6Module_basic",
"ElementwiseReluModule_basic",
"ElementwiseRemainderScalarModule_Float_basic",
"ElementwiseRemainderScalarModule_Int_Float_basic",
"ElementwiseRemainderScalarModule_Int_basic",
"ElementwiseRemainderScalarModule_Int_basic",
"ElementwiseRsqrtModule_basic",
[torch] Add support for aten.selu (#2640) Add `aten.selu` operation to `torch` dialect.
2023-12-14 12:28:08 +08:00
"ElementwiseSeluModule_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"ElementwiseSigmoidModule_basic",
"ElementwiseSignModule_basic",
"ElementwiseSqrtIntModule_basic",
"ElementwiseSqrtModule_basic",
"ElementwiseSubScalarFloatModule_basic",
[tosa] Support for some ops and fix for Issue #532 (#575) * [tosa] Support for AtenNe[Tensor|Scalar]Op, AtenLog2Op, AtenBitwiseAndTensorOp, AtenSquareOp and AtenThresholdOp * Fix for Issue #532 - Mixed input types for few ops and updated few tests to use i32 instead of i64 Signed-off-by: Anup Gangwar <anup.gangwar@arm.com> Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
2022-02-12 04:30:02 +08:00
"ElementwiseSubScalarIntModule_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"ElementwiseSubTensorInt8Module_basic",
"ElementwiseToDtypeIdentityModule_basic",
"ElementwiseUnaryModule_basic",
[MLIR][TORCH] Fix aten.unsqueeze op (#1578) The range of the unsqueeze dim is: [-input.dim() - 1, input.dim() + 1), the bug forgets to add 1.
2022-11-15 01:09:15 +08:00
"ElementwiseUnsqueezeBroadcastModule_basic",
[TOSA] Add torch.prim.NumToTensor.Scalar float support (#1802)
2023-04-19 04:36:57 +08:00
"ElementwiseWhereScalarModule_basic",
[Torch Dialect] support aten.isneginf, aten.isposinf, aten.nan_to_num (#2743)
2024-01-16 14:29:34 +08:00
"ElementwiseNanToNumModule_Basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"EmbeddingModule1DIndices_basic",
"EmbeddingModuleI32Static_basic",
"FlattenRank0Module_basic",
"FlattenStaticModule_basic",
[TOSA] Add torch.prim.NumToTensor.Scalar float support (#1802)
2023-04-19 04:36:57 +08:00
"FullLikeModuleFloat3DStatic_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"FullLikeModuleInt2DStatic_basic",
[TOSA] Add torch.prim.NumToTensor.Scalar float support (#1802)
2023-04-19 04:36:57 +08:00
"FullModuleDefaultDtype_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"FullModuleFloat2D_basic",
[TOSA] Add torch.prim.NumToTensor.Scalar float support (#1802)
2023-04-19 04:36:57 +08:00
"FullModuleFloat3D_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"FullModuleInt3D_basic",
"GatherStaticModule_basic",
"GeluBackwardModule_basic",
"GluStaticModule_basic",
"HardTanhIntModule_basic",
"HardTanhModule_basic",
"HardsigmoidModule_basic",
"HardsigmoidRandomModule_basic",
"HardswishModule_basic",
"HardswishRandomModule_basic",
"HardtanhBackward_basic",
"IndexPutImpl2DNoneIndexStaticModule_basic",
"IndexTensorMultiIndexStaticModule_basic",
"IndexTensorStaticModule_basic",
"IscloseStaticModuleTrue_basic",
"IscloseStaticModule_basic",
"LayerNormNormalizeOverAllDimsModule_basic",
"LeakyReluBackwardModule_basic",
"LeakyReluBackwardStaticModule_basic",
"LiftFreshCopyModule_basic",
torch-to-tosa lowering support for AtenLinalgVectorNormOp (#2734) This PR add torch-to-tosa lowering support for AtenLinalgVectorNormOp e2e test: python -m e2e_testing.main --config=tosa LIT tests: cmake --build build --target tools/torch-mlir/all --------- Co-authored-by: Ze Zhang <ze.zhang@getcruise.com>
2024-01-19 04:32:23 +08:00
"LinalgVectorNormKeepDimModule_basic",
"LinalgVectorNormModule_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"MaskedFillScalarDefaultModule_basic",
"MaskedFillScalarIntValueModule_basic",
"MaskedFillScalarIntValueStaticModule_basic",
"MaskedFillTensorIntValueStaticModule_basic",
"Matmul4dStatic_basic",
"Matmul_3d",
"Matmul_dot",
"MaxPool2dEmptyStrideStaticModule_basic",
"MaxPool2dStaticCeilModeTrueModule_basic",
"MaxPool2dStaticModule_basic",
"MeanModule_basic",
"MmDagModule_basic",
"MoveDimIntModule_basic",
"MoveDimIntModule_basic",
"MoveDimIntNegativeIndexModule_basic",
"MseLossNoReductionModule_basic",
"NativeLayerNormModule4D_basic",
[MLIR][TORCH] add E2E support for aten.new_full (#2425) * implement aten.new_full * remove extraneous tests
2023-09-12 22:29:08 +08:00
"NewFullModuleDefaultDtype_basic",
"NewFullModuleFalsePinMemory_basic",
"NewFullModuleFloat2D_basic",
"NewFullModuleFloat3DStatic_basic",
"NewFullModuleFloat3D_basic",
"NewFullModuleInt2DStatic_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"NewOnesModuleDefaultDtype_basic",
"NewOnesModuleFalsePinMemory_basic",
"NewOnesModuleFloat2D_basic",
"NewOnesModuleFloat3D_basic",
"NewOnesModuleInt2D_basic",
"NewOnesModuleInt3D_basic",
"NewZerosModuleDefaultDtype_basic",
"NewZerosModuleFalsePinMemory_basic",
"NewZerosModuleFloat2D_basic",
"NewZerosModuleFloat3D_basic",
"NewZerosModuleInt2D_basic",
"NewZerosModuleInt3D_basic",
"NewZerosStaticModuleLayoutStrided_basic",
torch-to-tosa lowering support for AtenLinalgVectorNormOp (#2734) This PR add torch-to-tosa lowering support for AtenLinalgVectorNormOp e2e test: python -m e2e_testing.main --config=tosa LIT tests: cmake --build build --target tools/torch-mlir/all --------- Co-authored-by: Ze Zhang <ze.zhang@getcruise.com>
2024-01-19 04:32:23 +08:00
"NormalizeModule_basic",
"NormScalarOptDimKeepDimModule_basic",
"NormScalarOptDimModule_basic",
[TOSA] Add torch.prim.NumToTensor.Scalar float support (#1802)
2023-04-19 04:36:57 +08:00
"NumToTensorFloatModule_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"NumToTensorIntModule_basic",
"NumpyTRank0Module_basic",
"NumpyTRank1Module_basic",
"NumpyTRank2Module_basic",
"NumpyTRankNDynamicModule_basic",
"NumpyTRankNStaticModule_basic",
"OnesModuleCPUDevice_basic",
"OnesModuleDefaultDtype_basic",
"OnesModuleFalsePinMemory_basic",
"OnesModuleFloat_basic",
"OnesModuleInt_basic",
"PadModule_basic",
"PadWithNoneValModule_basic",
"Permute0RankModule_basic",
"PermuteModule_basic",
"PermuteNegativeIndexModule_basic",
"PrimsSqueezeEmptyDimensionsModule_basic",
"PrimsSqueezeModule_basic",
"PrimsViewOfModule_basic",
"PrimsViewOfZeroRankModule_basic",
"ReduceAmaxKeepDim_basic",
build: update llvm tag to 4546397e (#1502) This commit makes the following changes needed to update bump LLVM: - Replace `linalg.init_tensor` with `tensor.empty` (see: https://reviews.llvm.org/D135129) - Replace `NoSideEffect` with `Pure` (see https://reviews.llvm.org/D135505) - Replace `body` region accessor for `ReduceOp` and `ReduceWindowOp` with `getBody` - Fix incorrect use of `tosa::ReduceSumOp` in `AtenNativeLayerNormOp` conversion pattern. The result type of `tosa::ReduceSumOp` must have the same rank as the input type. (see: https://www.mlplatform.org/tosa/tosa_spec.html#_reduce_sum) Co-authored-by: Ashay Rane <ashay@users.noreply.github.com> Co-authored-by: Ashay Rane <ashay@users.noreply.github.com>
2022-10-18 12:22:53 +08:00
"ReduceSumDimIntListFloatModule_basic",
"ReduceSumDimIntListIntModule_basic",
"ReduceSumDimIntListKeepDimFloatModule_basic",
"ReduceSumDimIntListKeepDimIntModule_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"ReduceSumDimIntListKeepDimNegativeDimStaticModule_basic",
build: update llvm tag to 4546397e (#1502) This commit makes the following changes needed to update bump LLVM: - Replace `linalg.init_tensor` with `tensor.empty` (see: https://reviews.llvm.org/D135129) - Replace `NoSideEffect` with `Pure` (see https://reviews.llvm.org/D135505) - Replace `body` region accessor for `ReduceOp` and `ReduceWindowOp` with `getBody` - Fix incorrect use of `tosa::ReduceSumOp` in `AtenNativeLayerNormOp` conversion pattern. The result type of `tosa::ReduceSumOp` must have the same rank as the input type. (see: https://www.mlplatform.org/tosa/tosa_spec.html#_reduce_sum) Co-authored-by: Ashay Rane <ashay@users.noreply.github.com> Co-authored-by: Ashay Rane <ashay@users.noreply.github.com>
2022-10-18 12:22:53 +08:00
"ReduceSumFloatModule_basic",
"ReduceSumSignedIntModule_basic",
"ReduceSumUnsignedIntModule_basic",
Constant pad nd to tosa (#1933) * implemented lowering torch.aten.constant_pad_nd to tosa * add constant_pad_nd e2e tests to TOSA_PASS_SET * add PadModule_basic & PadWithNoneValModule_basic to TOSA_PASS_SET --------- Co-authored-by: Lisa Liu <lingl@xilinx.com>
2023-03-15 23:42:15 +08:00
"RepeatModule_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"ResNet18StaticModule_basic",
"ReshapeAsModule_basic",
"ReshapeCollapseModule_basic",
"ReturnThreeTensorFloat32_basic",
"ReturnTwoTensorF32I64_basic",
"RsubFloatModule_basic",
"RsubFloatModule_noalpha_basic",
"RsubInt0d_NumToTensor_Module_basic",
[Torch Dialect] Add support for AtenScalarTensorOp (#2085) * add scalar_tensor op * add dynamo pass test; needs PR2062 * try to fix * Empty commit, trigger test * Empty commit, trigger test * address comments * use dtype function * fix decompose rule * remove unused include * Empty commit, trigger test * fix test * disable ltc * fix dtype --------- Co-authored-by: zhekun.zhang <zhekun.zhang@bytedance.com>
2023-06-01 11:38:50 +08:00
"ScalarTensorDefaultDtypeModule_basic",
"ScalarTensorFloat32Module_basic",
"ScalarTensorInt32Module_basic",
"ScalarTensorInt64Module_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"SelectIntNegativeDimAndIndexStaticModule_basic",
"SiluModule_basic",
"SliceOutOfUpperBoundIndexStaticModule_basic",
"SliceStaticModule_basic",
"SoftmaxIntModule_basic",
"SoftmaxIntNegDimModule_basic",
[Torch Dialect] Add split.tensor support + recompose rules (#2102) * add split.tensor support + recompose rules * add e2e test * address comments * address comments * erase op in recomposeOp --------- Co-authored-by: zhekun.zhang <zhekun.zhang@bytedance.com>
2023-05-24 03:43:33 +08:00
"SplitTensorGetItem_Module_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"SplitTensorLastSmallerModule_basic",
[Torch Dialect] Support recompose aten.split.Tensor + prim.ListUnpack (#2192)
2023-06-07 01:38:04 +08:00
"SplitTensorListUnpackModule_basic",
Fixes for split tensor and slice (#2314) * RecomposeComplexOps: Remove dead slice op * lib/Dialect/Torch/IR/TorchOps.cpp: Fold slice ops even when they are on non-value tensors * lib/Conversion/TorchToTosa/TorchToTosa.cpp: Fix slice start/end out of range/none * lib/Dialect/Torch/IR/TorchOps.cpp: AtenSliceTensorOp::fold: Fold slices that go from 0:int_max * More tests for aten.split.Tensor
2023-07-20 15:53:54 +08:00
"SplitTensorNegativeDimModule_basic",
[Torch Dialect] support aten.split_with_sizes (#2431) * [Torch Dialect] support aten.split_with_sizes * update
2023-09-04 09:59:26 +08:00
"SplitWithSizesListUnpackModule_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"SquareModule_basic",
"SqueezeDimModule_identity",
"SqueezeDimModule_static",
"SqueezeDimModule_unitDim",
"SqueezeModule_allUnitDim",
"SqueezeModule_broadcast",
"SqueezeModule_noUnitDim",
"SqueezeModule_static",
"TModuleRank0_basic",
"TModuleRank1_basic",
"TModuleRank2_basic",
"TanhBackward_basic",
"TensorLiteralModule_basic",
"TensorOpaqueLiteralModule_basic",
"TensorsConcatNegativeDimStaticModule_basic",
"TensorsConcatStaticModule_basic",
"TestF16Return_basic",
"TestMultipleTensorReturn_basic",
"Threshold1dFloatModule_basic",
"Threshold1dIntI32Module_basic",
"Threshold2dFloatModule_basic",
"Threshold3dFloatModule_basic",
"TileBigDimsSizeModule_basic",
"TileSmallDimsSizeModule_basic",
"ToCopyBoolDTypeStaticModule_basic",
"ToDtypeBoolLayoutNoneStaticModule_basic",
"TransposeIntModule_basic",
"TransposeIntNegDimsModule_basic",
Fix canonicalization of `torch.prim.TupleUnpack`.
2023-07-18 22:32:26 +08:00
"TupleModule_basic",
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"TypeAsSameModule_basic",
"TypePromotionAlphaWiderModule_basic",
"TypePromotionDifferentCategoryModule_basic",
"TypePromotionSameCategoryDifferentWidthModule_basic",
"TypePromotionSameCategoryZeroRankWider_basic",
"TypePromotionZeroRankHigherCategoryModule_basic",
"UnbindIntGetItem_Module_basic",
"UnbindIntListUnpack_Module_basic",
"UnflattenIntNegativeOneDimStaticModule_basic",
"UnflattenIntNegativeOneSizeStaticModule_basic",
"UnflattenIntStaticModule_basic",
"UnflattenStaticModule_basic",
"UnsafeView1DFoldModule_basic",
"UnsafeViewExpandModule_basic",
"View1DFoldModule_basic",
"ViewCollapseInferredDimModule_basic",
"ViewCollapseOnesMiddleModule_basic",
"ViewDoubleMergeStaticModule_basic",
"ViewExpandCollapseModule_basic",
"ViewExpandCollapseWithOnesModule_basic",
"ViewExpandInferredDimModule_basic",
"ViewExpandModule_basic",
"ViewExpandOnesBeforeAndAfterModule_basic",
"ViewExpandOnesMiddleModule_basic",
"ViewExpandOnesMiddleOppModule_basic",
"ViewExpandOnesModule_basic",
"ViewFiveTestStaticModule_basic",
"ViewNegativeStaticModule_basic",
"ViewNoChangeStaticModule_basic",
"ViewOffsetBackwardTestStaticModule_basic",
"ViewOffsetTestStaticModule_basic",
"ViewTwoFiveThreeStaticModule_basic",
"ViewTwoToThreeStaticModule_basic",
"ZerosModuleDefaultDtype_basic",
"ZerosModuleFalsePinMemory_basic",
"ZerosModuleFloat2D_basic",
"ZerosModuleFloat3D_basic",
"ZerosModuleInt2D_basic",
"ZerosModuleInt3D_basic",
"_LogSoftmaxModuleStable_basic",
build: update llvm tag to 41895843 Summary of changes: - Update tags llvm: 41895843b5915bb78e9d02aa711fa10f7174db43 mhlo: 4726d31f7025da66de0dea709bd56c462edb83c2 Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2023-07-26 15:30:13 +08:00
"_LogSoftmaxModule_basic",
"_SoftmaxModule_basic",
Bump llvm-project to f1b922188ead5ca492c8d8edd47921b013a22ae0. Includes a fix to use `add_mlir_public_c_api_library` for Torch-MLIR's CAPI library, which is now required (note: upstream sample has it the right way). Disabled a TOSA test per discussion: https://github.com/llvm/torch-mlir/issues/379
2021-10-26 02:43:21 +08:00
}
Added e2e LTC tests (#916) * Added e2e LTC Torch MLIR tests * Fix seed for reproducability * Check if computation is None before getting debug string * Updated unit tests, and added numeric tests * Print name of the model layer that fails numeric validation * Run LTC e2e test with CI/CD * Set seed in main function, instead of beginning of execution * Add comment to specify number of digits of precision * Fixed typo * Remove tests for LTC example models * Added LTC option to torchscript e2e * Implement compile and run for LTC e2e test * xfail all tests that use ops that aren't currently supported
2022-06-10 03:56:01 +08:00
Add make_fx_tosa variant to end2end tests (#2240) * Add make_fx_tosa variant to end2end tests * e2e_testing/xfail_sets.py: Add make_fx_tosa xfail for stable
2023-07-13 21:07:54 +08:00
MAKE_FX_TOSA_PASS_SET = (TOSA_PASS_SET | {
### Tests additionally passing in make_fx_tosa
Sort the TOSA passing test list (#2630) For easier tracking of issues, sort the TOSA passing list. It is still significantly smaller then the XFAIL list would be. Resolves #2620, at least until the xfail list gets smaller than the passing list. Signed-off-by: Eric Kunze <eric.kunze@arm.com>
2023-12-13 06:22:25 +08:00
"AdaptiveAvgPool1dNonUnitOutputSizeStaticModule_basic",
"AdaptiveAvgPool1dUnitOutputSizeStaticModule_basic",
[RFC] general support for Adaptive Pooling Ops (#2661) Adaptive pooling ops can only be decomposed into their non-adaptive counterparts in trivial cases. For example, the current decomposition for AtenAdaptiveAvgPool1dOp in DecomposeComplexOps.cpp supports outSize = inSize (i.e., do literally nothing), and outSize = 1 (i.e., do a batched average). The reason adaptive pooling ops are difficult to lower to linalg is that they are not constantly strided. They are computed by taking an input tensor of shape (N, C, Hin), and an output size Hout, and computing the output tensor at position (n,c, h) in the following way: 1. compute st(h) = (h*Hin)//Hout 2. compute en(h) = 1 + ((h+1)*Hin -1)//Hout 3. apply a computation (max or avg) to the slice: INPUT[n, c, st(h):en(h)] The provided sample implementation (for ConvertAtenAdaptiveAvgPool1dOp) uses tensor.extract to access the input tensor inside the payload of a linalg generic op. This is likely an unattractive use of linalg generic ops, which is why I am asking for some more targeted feedback on the validity of this approach before attempting to support the many other adaptive pooling ops. Specifically: - Is the performance of this implementation bad enough to warrant targeting different dialects entirely? e.g. TMtensor/linalg ext/ etc. - If the provided implementation is of acceptable performance to the community, then is it permissable to remove the Adaptive pooling decompositions from DecomposeComplexOps.cpp? Based on the current structure of the -torch-decompose-complex-ops pass, it does not seem possible to only decompose the adaptive ops in special cases (it seems to get stuck in an infinite loop on a match failure). I would be happy to instead incorporate the case logic into the conversion directly, and remove the decompositions once they are rendered completely obsolete. As long as this approach is acceptable, I can clean up the implementation with some helper functions, and quickly add support for each of the remaining Adaptive pooling ops.
2024-01-10 03:14:10 +08:00
"AdaptiveAvgPool1dStaticEvenMultiple_basic",
torch-to-tosa lowering support for AtenLinalgVectorNormOp (#2734) This PR add torch-to-tosa lowering support for AtenLinalgVectorNormOp e2e test: python -m e2e_testing.main --config=tosa LIT tests: cmake --build build --target tools/torch-mlir/all --------- Co-authored-by: Ze Zhang <ze.zhang@getcruise.com>
2024-01-19 04:32:23 +08:00
"CosineSimilarityModule_basic",
Add make_fx_tosa variant to end2end tests (#2240) * Add make_fx_tosa variant to end2end tests * e2e_testing/xfail_sets.py: Add make_fx_tosa xfail for stable
2023-07-13 21:07:54 +08:00
"NativeGroupNormBackwardModule_basic",
torch-to-tosa lowering support for AtenLinalgVectorNormOp (#2734) This PR add torch-to-tosa lowering support for AtenLinalgVectorNormOp e2e test: python -m e2e_testing.main --config=tosa LIT tests: cmake --build build --target tools/torch-mlir/all --------- Co-authored-by: Ze Zhang <ze.zhang@getcruise.com>
2024-01-19 04:32:23 +08:00
"ReduceFrobeniusNormKeepDimModule_basic",
"ReduceFrobeniusNormModule_basic",
Fixes for split tensor and slice (#2314) * RecomposeComplexOps: Remove dead slice op * lib/Dialect/Torch/IR/TorchOps.cpp: Fold slice ops even when they are on non-value tensors * lib/Conversion/TorchToTosa/TorchToTosa.cpp: Fix slice start/end out of range/none * lib/Dialect/Torch/IR/TorchOps.cpp: AtenSliceTensorOp::fold: Fold slices that go from 0:int_max * More tests for aten.split.Tensor
2023-07-20 15:53:54 +08:00
"SliceWholeTensorModule_basic",
Add make_fx_tosa variant to end2end tests (#2240) * Add make_fx_tosa variant to end2end tests * e2e_testing/xfail_sets.py: Add make_fx_tosa xfail for stable
2023-07-13 21:07:54 +08:00
"TensorFloatModule_basic",
"TensorIntModule_basic",
lower torch.aten.isinf to linalg (#2638) Co-authored-by: Rob Suderman <rob.suderman@gmail.com>
2023-12-29 09:20:32 +08:00
"AdaptiveAvgPool1dNonUnitOutputSizeStaticModule_basic",
"AdaptiveAvgPool1dUnitOutputSizeStaticModule_basic",
Add make_fx_tosa variant to end2end tests (#2240) * Add make_fx_tosa variant to end2end tests * e2e_testing/xfail_sets.py: Add make_fx_tosa xfail for stable
2023-07-13 21:07:54 +08:00
}) - {
### Test failing in make_fx_tosa but not in tosa
# Dynamic shape, has extra unsupported broadcast ops
"Matmul_3d",
# failed to legalize operation 'torch.aten.max_pool2d_with_indices
"MaxPool2dEmptyStrideStaticModule_basic",
"MaxPool2dStaticCeilModeTrueModule_basic",
"MaxPool2dStaticModule_basic",
"ResNet18StaticModule_basic",
# Unimplemented operator 'aten._index_put_impl_.hacked_twin'
"IndexPutImpl1DFloatNonAccumulateModule_basic",
"IndexPutImpl1DIntNonAccumulateModule_basic",
build: update llvm tag to 41895843 Summary of changes: - Update tags llvm: 41895843b5915bb78e9d02aa711fa10f7174db43 mhlo: 4726d31f7025da66de0dea709bd56c462edb83c2 Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2023-07-26 15:30:13 +08:00
# RuntimeError: The size of tensor a (7) must match the size of tensor b (3) at non-singleton dimension 1
"Add_Module_basic",
add e2e support for torch.eye operations (aten.eye, aten.eye.m) (#2478)
2023-11-02 02:23:28 +08:00
# failed to legalize operation 'torch.aten.to.dtype' that was explicitly marked illegal
"AtenEyeModuleInt2D_basic",
"AtenEyeMModuleInt2D_basic",
Add make_fx_tosa variant to end2end tests (#2240) * Add make_fx_tosa variant to end2end tests * e2e_testing/xfail_sets.py: Add make_fx_tosa xfail for stable
2023-07-13 21:07:54 +08:00
Advance llvm-project and stablehlo. (#2619) llvm-project: bbd2b08b95fe76bea138c1b03c1cd42ed3ee04df stablehlo: ab709fe48de88c67717abfbd7ef17425eb95ddaf These commits were chosen in order to account for an MLIR API break from https://github.com/llvm/llvm-project/commit/3dbac2c007c114a720300d2a4d79abe9ca1351e7 which required a patch to stablehlo. We integrate a bit beyond that commit to deal with some revert/reapply cycles in the intervening range which were discovered in another downstream. Further, it requires adaptation to the stablehlo API breaks introduced from https://github.com/openxla/stablehlo/pull/1872 which are along for the ride. Since some stablehlo builders were changed to directly take int64_t array refs, also traced that up some call stacks to eliminate some signed/unsigned mismatches that result. Also adds a few TOSA tests to the passing set that seem to work now.
2023-12-08 15:13:42 +08:00
"Conv2dBiasNoPaddingModule_basic",
"Conv2dNoPaddingModule_basic",
"Conv2dWithPaddingDilationStrideModule_basic",
"Conv2dWithPaddingModule_basic",
}
Add make_fx_tosa variant to end2end tests (#2240) * Add make_fx_tosa variant to end2end tests * e2e_testing/xfail_sets.py: Add make_fx_tosa xfail for stable
2023-07-13 21:07:54 +08:00
[LTC] Tensor[]? support operands type support using partial codegen (#2410) * Tensor[]? support operands type support using partial codegen * aten.index.Tensor support via partial codegen * Add torch.index_put tracing support * Added optional tensor list type support for LTC/TorchMLIR lowering * Added comments Co-authored-by: Gleb Kazantaev <gleb.kazantaev@cerebras.net>
2023-08-30 18:29:39 +08:00
LTC_CRASHING_SET = {
# TODO: update test to move all inputs to the lazy device. Otherwise test fails with:
# Check failed: lazy_tensor Input tensor is not a lazy tensor: CPUBoolType.
"HBC_basic",
}
Added e2e LTC tests (#916) * Added e2e LTC Torch MLIR tests * Fix seed for reproducability * Check if computation is None before getting debug string * Updated unit tests, and added numeric tests * Print name of the model layer that fails numeric validation * Run LTC e2e test with CI/CD * Set seed in main function, instead of beginning of execution * Add comment to specify number of digits of precision * Fixed typo * Remove tests for LTC example models * Added LTC option to torchscript e2e * Implement compile and run for LTC e2e test * xfail all tests that use ops that aren't currently supported
2022-06-10 03:56:01 +08:00
LTC_XFAIL_SET = {
Support for prims collapse op (lowering to linalg) (#2572) Steps taken: 1) add generator code to torch_ods_gen.py, run update_torch_ods.sh 2) add (custom) shape and type inference generator code to abstract_interp_lib_gen.py, run update_abstract_interp_lib.sh 3) Implement lowering to tensor.collapse_dims. Requires the `start` and `end` values to be constant, else lowering fails 4) Update xfail_sets.py (append to LTC_XFAIL_SET) after running /tools/e2e_test.sh --filter Collapse --verbose -c XX for all support backends (XX). Motivation: - Supporting the collapse operation will be useful for lowering of pixel_shuffle (see Issue #2559)
2023-11-16 00:34:38 +08:00
"CollapseAllDimensionsModule_basic",
"CollapseRank1DynamicModule_basic",
"CollapseStaticModule_basic",
"CollapsePartialDynamicModule_basic",
"CollapseFullDynamicModule_basic",
Lowering to linalg of prims split_dim op (#2576) Adds support for lowering to prims split_op. Similar design to collapse op lowering in https://github.com/llvm/torch-mlir/pull/2572, with some small differences, because the split_dim op (in pytorch) is view-changing whereas the collapse is not. The difference means that 1) it must be registered in the function Torch::isViewLikeOp 2) it must be be added to the "expected fail" set for the torch dynamo backend.
2023-11-21 23:56:09 +08:00
"SplitDimStaticModule_basic",
"SplitDimDynamicModule_basic",
Decomposition of aten.pixel_shuffle with static input shape (#2550) For static tests (that is when the shape is know) for example: ``` @annotate_args([None, ([3, 18, 2, 2], torch.float32, True)]) ``` The e2e passes. But only if the replacement op's return type is set as undefined (optional shape and type must be explicitly made unset), otherwise there's a error about the function return type. For dynamic cases, for example if the above is replaced with ``` @annotate_args([None, ([-1, -1, -1, -1], torch.float32, True)]) ``` There is a failure to lower to linalg from torch ("view op explicitly labelled as illegal"). This seems to be because the support for lowering from torch to linalg with dynamic shapes is limited.
2023-11-08 21:52:44 +08:00
"PixelShuffleModuleStaticRank3Int64_basic",
"PixelShuffleModuleStaticRank4Float32_basic",
Improve decomposition of pixel_shuffle (support dynamic shapes) (#2590) The aten.reshape ops in the decomposition are replaced with prims.collapse and prims.split_dim ops, which means that the cases where the lowering of reshape from torch to linalg which are not supported, are avoided. Essentially, by using the collapse and split_dim ops instead of the reshape ops, we are not "losing" the information that the reshapes do not arbitrarily mix dimensions. Which makes lowering easy. 3 additional tests added: - fully dynamic, - dynamic only the spatial dimensions, - dynamic only in the non-spatial dimensions.
2023-11-23 04:31:06 +08:00
"PixelShuffleModuleFullDynamic_basic",
"PixelShuffleModuleSpatiallyDynamic_basic",
"PixelShuffleModuleSpatiallyStatic_basic",
Blacklist _convolution op (#1048) * Blacklist _convolution op in LTC * Removed duplicate Torch_AtenSelectScatterOp instance from autogen .td * Removed duplicate Torch_AtenSliceScatterOp instance from autogen .td
2022-07-14 01:28:05 +08:00
"_Convolution2DAllFalseModule_basic",
"_Convolution2DBenchmarkModule_basic",
"_Convolution2DCudnnModule_basic",
"_Convolution2DDeterministicModule_basic",
"_Convolution2DTF32Module_basic",
[MLIR][TORCH] Fix CI failure due to failing tests Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-08-22 13:44:44 +08:00
"_ConvolutionDeprecated2DAllFalseModule_basic",
"_ConvolutionDeprecated2DBenchmarkModule_basic",
Fix as_strided/slice symint (#1401) * Fix as_strided symint * Re-enable LTC tests * Re-enable LTC * Add hardtanh shape inference function * Fix slice symint
2022-09-27 00:16:49 +08:00
"_ConvolutionDeprecated2DCudnnModule_basic",
[MLIR][TORCH] Fix CI failure due to failing tests Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-08-22 13:44:44 +08:00
"_ConvolutionDeprecated2DDeterministicModule_basic",
Added e2e LTC tests (#916) * Added e2e LTC Torch MLIR tests * Fix seed for reproducability * Check if computation is None before getting debug string * Updated unit tests, and added numeric tests * Print name of the model layer that fails numeric validation * Run LTC e2e test with CI/CD * Set seed in main function, instead of beginning of execution * Add comment to specify number of digits of precision * Fixed typo * Remove tests for LTC example models * Added LTC option to torchscript e2e * Implement compile and run for LTC e2e test * xfail all tests that use ops that aren't currently supported
2022-06-10 03:56:01 +08:00
"AddIntModule_basic",
[Torch Dialect] support AtenArangeStartOutOp in ReduceOpVariants like… (#2563) … AtenBernoulli_FloatOp It fixing case like: `%2110 = torch.aten.arange.start_out %int1, %int1517, %int1, %2109 : !torch.int, !torch.int, !torch.int, !torch.tensor -> !torch.tensor`. `aten.arange.start_out` doesn't have value semantics also, means`%2110` is an alias for %2109. So I decompose it to `aten.arange.start` + `torch.contents.overwrite`. The complex decomposition logic is target to handle cases like view and dtype cast which I add in e2e tests.
2023-11-17 00:51:55 +08:00
"ArangeStartOutViewModule_basic",
Fix empty tensor when select -1 (#1787)
2023-01-18 02:14:14 +08:00
"AtenIntBoolOpModule_basic",
Added e2e LTC tests (#916) * Added e2e LTC Torch MLIR tests * Fix seed for reproducability * Check if computation is None before getting debug string * Updated unit tests, and added numeric tests * Print name of the model layer that fails numeric validation * Run LTC e2e test with CI/CD * Set seed in main function, instead of beginning of execution * Add comment to specify number of digits of precision * Fixed typo * Remove tests for LTC example models * Added LTC option to torchscript e2e * Implement compile and run for LTC e2e test * xfail all tests that use ops that aren't currently supported
2022-06-10 03:56:01 +08:00
"BernoulliTensorModule_basic",
"BincountMinlengthModule_basic",
"BincountModule_basic",
"BincountStaticSizeModule_basic",
"BoolFloatFalseModule_basic",
"BoolFloatTrueModule_basic",
"BoolIntFalseModule_basic",
"BoolIntTrueModule_basic",
"CeilFloatModule_basic",
"DivFloatModule_basic",
"EqIntModule_basic",
Implement lowering of torch.aten.exponential (#2680) https://github.com/llvm/torch-mlir/issues/2646 Decompose aten.exponential() into: -exp(1-x)/lambda
2023-12-28 12:33:18 +08:00
"ExponentialModule_basic",
Added e2e LTC tests (#916) * Added e2e LTC Torch MLIR tests * Fix seed for reproducability * Check if computation is None before getting debug string * Updated unit tests, and added numeric tests * Print name of the model layer that fails numeric validation * Run LTC e2e test with CI/CD * Set seed in main function, instead of beginning of execution * Add comment to specify number of digits of precision * Fixed typo * Remove tests for LTC example models * Added LTC option to torchscript e2e * Implement compile and run for LTC e2e test * xfail all tests that use ops that aren't currently supported
2022-06-10 03:56:01 +08:00
"GeFloatIntModule_basic",
"GeFloatModule_basic",
Resolve remaining LTC CI failures (#1110) * Replace CHECK_EQ with TORCH_CHECK_EQ * Check value of TORCH_MLIR_USE_INSTALLED_PYTORCH during LTC build * Update LTC XFAIL with NewZerosModule ops * Explicitly blacklist _like ops * Automatically blacklist new_/_like ops * Prune away unused Python dependencies from LTC * Add flag to disable LTC * Autogen dummy _REFERENCE_LAZY_BACKEND library when LTC is disabled * Implement compute_shape_var * Removed Var tests from XFAIL Set * XFAIL tests using _local_scalar_dense or index.Tensor * Add StdDim tests to XFAIL set * Autogen aten::cat
2022-07-30 17:54:40 +08:00
"GeIntModule_basic",
Added e2e LTC tests (#916) * Added e2e LTC Torch MLIR tests * Fix seed for reproducability * Check if computation is None before getting debug string * Updated unit tests, and added numeric tests * Print name of the model layer that fails numeric validation * Run LTC e2e test with CI/CD * Set seed in main function, instead of beginning of execution * Add comment to specify number of digits of precision * Fixed typo * Remove tests for LTC example models * Added LTC option to torchscript e2e * Implement compile and run for LTC e2e test * xfail all tests that use ops that aren't currently supported
2022-06-10 03:56:01 +08:00
"GtFloatIntModule_basic",
"GtIntModule_basic",
"IndexPutImpl1DFloatAccumulateModule_basic",
"IndexPutImpl1DFloatNonAccumulateModule_basic",
"IndexPutImpl1DIntAccumulateModule_basic",
"IndexPutImpl1DIntNonAccumulateModule_basic",
"IndexPutImpl2DFloatAccumulateModule_basic",
"IndexPutImpl2DFloatNonAccumulateModule_basic",
[MLIR][TORCH] Extend implementation of aten._index_put_impl op. This commits adds the support for cases for index_put_op: 1.) where index is a 2-d tensor. 2.) where indices is a list of tensors and none, with exactly 2 non none tensors along the consecutive dimensions. This commit also adds a utility to compute the broadcast shape given the two input tensors. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-12-08 13:46:54 +08:00
"IndexPutImpl2DIndexModule_basic",
[TOSA] Add aten._index_put_impl support (#2031) Add e2e support by add "tosa-to-scf"
2023-07-18 00:51:24 +08:00
"IndexPutImpl2DNoneIndexStaticModule_basic",
Added e2e LTC tests (#916) * Added e2e LTC Torch MLIR tests * Fix seed for reproducability * Check if computation is None before getting debug string * Updated unit tests, and added numeric tests * Print name of the model layer that fails numeric validation * Run LTC e2e test with CI/CD * Set seed in main function, instead of beginning of execution * Add comment to specify number of digits of precision * Fixed typo * Remove tests for LTC example models * Added LTC option to torchscript e2e * Implement compile and run for LTC e2e test * xfail all tests that use ops that aren't currently supported
2022-06-10 03:56:01 +08:00
"IndexPutImpl3DFloatAccumulateModule_basic",
"IndexPutImpl3DFloatNonAccumulateModule_basic",
[MLIR][TORCH] Extend implementation of aten._index_put_impl op. This commits adds the support for cases for index_put_op: 1.) where index is a 2-d tensor. 2.) where indices is a list of tensors and none, with exactly 2 non none tensors along the consecutive dimensions. This commit also adds a utility to compute the broadcast shape given the two input tensors. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-12-08 13:46:54 +08:00
"IndexPutImplIndexWithNoneModule_basic",
Added e2e LTC tests (#916) * Added e2e LTC Torch MLIR tests * Fix seed for reproducability * Check if computation is None before getting debug string * Updated unit tests, and added numeric tests * Print name of the model layer that fails numeric validation * Run LTC e2e test with CI/CD * Set seed in main function, instead of beginning of execution * Add comment to specify number of digits of precision * Fixed typo * Remove tests for LTC example models * Added LTC option to torchscript e2e * Implement compile and run for LTC e2e test * xfail all tests that use ops that aren't currently supported
2022-06-10 03:56:01 +08:00
"Matmul_dot",
"MulIntModule_basic",
[MLIR][TORCH] Add E2E support for `aten.[div.int|bitwise_or.Tensor]` ops This commit adds lowering of `aten.div.int` and `aten.bitwise_or.Tensor` ops. Both these ops are required in order to support bloom_560m model. Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
2022-10-06 21:11:52 +08:00
"DivIntModule_basic",
Added e2e LTC tests (#916) * Added e2e LTC Torch MLIR tests * Fix seed for reproducability * Check if computation is None before getting debug string * Updated unit tests, and added numeric tests * Print name of the model layer that fails numeric validation * Run LTC e2e test with CI/CD * Set seed in main function, instead of beginning of execution * Add comment to specify number of digits of precision * Fixed typo * Remove tests for LTC example models * Added LTC option to torchscript e2e * Implement compile and run for LTC e2e test * xfail all tests that use ops that aren't currently supported
2022-06-10 03:56:01 +08:00
"NeFloatIntModule_basic",
"NeIntModule_basic",
"QuantizedMLP_basic",
"ScalarImplicitFloatModule_basic",
"ScalarImplicitIntModule_basic",
"SliceEndSleStartModule_basic",
"SliceOutOfUpperBoundIndexModule_basic",
Fixes for split tensor and slice (#2314) * RecomposeComplexOps: Remove dead slice op * lib/Dialect/Torch/IR/TorchOps.cpp: Fold slice ops even when they are on non-value tensors * lib/Conversion/TorchToTosa/TorchToTosa.cpp: Fix slice start/end out of range/none * lib/Dialect/Torch/IR/TorchOps.cpp: AtenSliceTensorOp::fold: Fold slices that go from 0:int_max * More tests for aten.split.Tensor
2023-07-20 15:53:54 +08:00
"SliceOutOfUpperBoundIndexStaticModule_basic",
Added e2e LTC tests (#916) * Added e2e LTC Torch MLIR tests * Fix seed for reproducability * Check if computation is None before getting debug string * Updated unit tests, and added numeric tests * Print name of the model layer that fails numeric validation * Run LTC e2e test with CI/CD * Set seed in main function, instead of beginning of execution * Add comment to specify number of digits of precision * Fixed typo * Remove tests for LTC example models * Added LTC option to torchscript e2e * Implement compile and run for LTC e2e test * xfail all tests that use ops that aren't currently supported
2022-06-10 03:56:01 +08:00
"SliceStartEqEndModule_basic",
"SqrtIntModule_basic",
"SubFloatModule_basic",
"SubIntModule_basic",
Support aten.stack op and decompose it into unsqueeze & cat (#1747)
2023-03-11 09:25:25 +08:00
"TensorsStackPromoteDTypeModule_basic",
Added e2e LTC tests (#916) * Added e2e LTC Torch MLIR tests * Fix seed for reproducability * Check if computation is None before getting debug string * Updated unit tests, and added numeric tests * Print name of the model layer that fails numeric validation * Run LTC e2e test with CI/CD * Set seed in main function, instead of beginning of execution * Add comment to specify number of digits of precision * Fixed typo * Remove tests for LTC example models * Added LTC option to torchscript e2e * Implement compile and run for LTC e2e test * xfail all tests that use ops that aren't currently supported
2022-06-10 03:56:01 +08:00
"TensorToBoolZeroRank_basic",
"TensorToBool_basic",
"TensorToFloatZeroRank_basic",
"TensorToFloat_basic",
"TensorToIntZeroRank_basic",
"TensorToInt_basic",
"UniformModule_basic",
"UnsafeViewCollapseDynamicWithAtenSizeIntModule_basic",
"ViewCollapseDynamicWithAtenSizeIntModule_basic",
Add embedding Bag e2e case in xfail set (#1130)
2022-08-02 06:23:45 +08:00
"AtenEmbeddingBagSumExample_basic",
Add decomposition and E2E support for Aten_EmbeddingBag (#1137) * Add decomposition and E2E support for Aten_EmbeddingBag
2022-08-09 06:56:49 +08:00
"Aten_EmbeddingBagExample_basic",
Implement lowering of torch.aten.logit (#2697) Closes nod-ai/SHARK-Turbine#290
2024-01-11 22:55:42 +08:00
"ElementwiseLogitModule_basic",
E2E support for AtenRemainderScalarOp (#1200)
2022-08-11 08:02:06 +08:00
"ElementwiseRemainderScalarModule_Int_Float_basic",
"ElementwiseRemainderScalarModule_Bool_basic",
[MLIR][TORCH] Add Byte and Char Dtype support
2022-09-20 02:50:51 +08:00
"AtenIntTensorByteDtypeModule_basic",
"AtenIntTensorCharDtypeModule_basic",
[MLIR][TORCH] Add E2E support for aten.upsample_nearest2d_backward.vec op Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-11-01 21:08:04 +08:00
"UpSampleNearest2dBackwardVec_basic",
"UpSampleNearest2dBackwardOutputSizeNone_basic",
[MLIR][TORCH] Add E2E support aten.convolution_backward op This commit adds the decomposition for the `aten.convolution_backward` and `aten.convolution_backward_overrideable` op.
2022-11-04 15:57:29 +08:00
"ConvolutionBackwardModule2D_basic",
"ConvolutionBackwardModule2DPadded_basic",
[MLIR][TORCH] Add decomposition for aten.var_mean.correction op Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-11-15 22:39:40 +08:00
"VarMeanCorrectionModule_basic",
[MLIR][TORCH] Add E2E support for prims.convert_element_type op Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-11-21 16:38:47 +08:00
"VarMeanCorrectionNoneModule_basic",
Prelu lowering to linalg (#1712) Prelu lowering to linalg
2022-12-28 11:21:33 +08:00
"ElementwisePreluModule_basic",
[MLIR][TORCH] Add e2e support for aten.var_mean op Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-12-09 23:22:26 +08:00
"VarMeanBiasedModule_basic",
"VarMeanUnbiasedModule_basic",
[MLIR][TORCH] Add decomposition for aten.randn_like op This commit decomposes aten.randn_like op into aten.randn.generator op. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2023-01-16 19:40:21 +08:00
"RandnLikeModule_basic",
"RandnLikeDtypeModule_basic",
Decompose AtenNormalFunctionalOp into AtenRandn* and other arithmetic. (#2737)
2024-01-16 14:49:29 +08:00
"NormalFunctionalModule_basic",
build: manually update PyTorch version Set PyTorch and TorchVision version to nightly release 2023-02-27. This commit also adds the lowering for aten.add and aten.Float.Scalar op. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2023-02-28 10:32:21 +08:00
"BernoulliFloatModule_basic",
"BernoulliModule_basic",
"BernoulliPModule_basic",
"DropoutTrainModule_basic",
[Torch Dialect] Support aten.native_dropout (#2259) * [Torch Dialect] Support aten.native_dropout * update
2023-06-27 14:19:33 +08:00
"DropoutTrainStaticShapeModule_basic",
"NativeDropoutTrainModule_basic",
"NativeDropoutTrainStaticShapeModule_basic",
build: manually update PyTorch version Set PyTorch and TorchVision version to nightly release 2023-02-27. This commit also adds the lowering for aten.add and aten.Float.Scalar op. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2023-02-28 10:32:21 +08:00
"StdCorrectionKeepDimModule_basic",
"StdCorrectionNoneModule_basic",
"VarCorrectionKeepDimModule_basic",
"VarCorrectionNoneModule_basic",
[MLIR][TORCH] Add decomposition for prims::squeeze op This commit adds the decomposition for the prims.squeeze op. Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-11-16 13:57:58 +08:00
"AtenFloatScalarModule_basic",
"PrimsSqueezeModule_basic",
"PrimsSqueezeEmptyDimensionsModule_basic",
[MLIR][TORCH] Add support for prims::view_of op This op does nothing and just returns the input operand as the result of the op. Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2023-04-10 11:50:26 +08:00
"PrimsViewOfModule_basic",
"PrimsViewOfZeroRankModule_basic",
[Torch Dialect] support for aten.one_hot (#1852)
2023-04-11 16:02:28 +08:00
"OneHotModule_basic",
[MLIR][TORCH] Add E2E support for aten.var_mean.dim op This commit adds the decomposition for the aten.var_mean.dim op. Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2023-04-26 15:14:06 +08:00
"VarMeanDimModule_basic",
"VarMeanDimBiasedModule_basic",
Add complex types support with basic complex ops. Add complex types support with basic complex types. Add aten.imag and aten.real op lowering via linalg_backend.
2023-04-20 00:22:57 +08:00
"AtenComplexImagModule_basic",
"AtenComplexRealModule_basic",
[Torch Op] Add unbind.int support with ListUnpack (#2058) * add unbind int * reformat * use unpack canonicalize * address comments * Empty commit, trigger test * add ltc blacklist * clean up * address comments * check permute list * erase in recompose --------- Co-authored-by: zhekun.zhang <zhekun.zhang@bytedance.com>
2023-05-19 10:07:58 +08:00
"AtenComplexViewModule_basic",
[MLIR][TORCH] Add E2E support for view_as_real op (#2419) * view_as_real test case, allow dtype in testutils.randn * abstract python upstream func implemented * fixed upstream dtype func, implemented view_as_real backend op * formatted AtenViewAsRealOp, removed change in e2etest/framework * removed test suit from reshape_like.py, because it's moved to basic.py * implemented C-API wrapper for mlirComplexF128 type * fixed torch.complex dtype width in MLIR and Torch MLIR, deleted float16 dtype dict * Changed IR input of aten fft_fft unit test * code refactored * code refactored and fixed ci test * refactored: removed white spaces, and rolled back to having both input/output affine expr * refactored: deleted output affine expr to reduce redundancy * xfail ltc backend * removed ComplexImag and ComplexReal from torchdynamo xfail set * copied and pasted from main branch as there's no change to be made in this file * refactored abstract_interp_lib_gen.py * refactored: torchtypes.td, formatted, removed commented out code
2023-09-02 12:12:01 +08:00
"AtenRealView128Module_basic",
"AtenRealView64Module_basic",
[TM_TENSOR] Add `aten.scatter.[src|value]` op This commit adds support of `aten.scatter.src` and `aten.scatter.value` ops. Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
2022-10-16 05:46:06 +08:00
"ScatterValueFloatModule_basic",
"ScatterValueIntModule_basic",
[Torch Dialect] add folder for aten.add (#2264) * [Torch Dialect] add folder for aten.add * update * update * update
2023-06-27 10:55:28 +08:00
"UniformStaticShapeModule_basic",
[MLIR][TORCH] Add StableHLO lowering for embedding_bag.padding_idx op Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2023-09-05 21:28:37 +08:00
"AtenEmbeddingBagStaticModule_basic",
[MLIR][TORCH] Add E2E support for aten.empty_strided decomposition op (redo PR) (#2459) Making the same PR with #2457, as I accidentally thought the review was already made and merged it (reverted). Add decompose empty_strided op. Referring to #1776, this decomposition op only supports default stride values, because accessing the tensor or indexing over that, the indices are determined by the strides. In MLIR, this is not implicitly supported but assumes that the strides are default while iterating over the tensor.
2023-09-14 01:04:31 +08:00
"EmptyStridedModule_basic",
Rework AtenEmptyStridedOp checks (#2537) Now using Value instead of Ints. Trades compile failure for a runtime assert
2023-11-01 11:56:54 +08:00
"EmptyStridedSizeIntStrideModule_basic",
[MLIR][TORCH] Add support for bitwise_right_shit and bitwise_and.Scalar op Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2023-09-28 20:53:02 +08:00
"ElementwiseBitwiseAndScalarInt64Module_basic",
"ElementwiseBitwiseAndScalarInt32Module_basic",
[MLIR][TORCH] Add support for int8 dtype for sub, add, and bitwise_and op Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2023-10-03 19:59:56 +08:00
"ElementwiseBitwiseAndScalarInt8Module_basic",
[Torch Dialect] support aten.isneginf, aten.isposinf, aten.nan_to_num (#2743)
2024-01-16 14:29:34 +08:00
"ElementwiseNanToNumModule_Basic",
[torch][quant] Support quantize and dequantize for torch (#2731) Handle both `torch.dequantize` and `torch.quantize_per_tensor` including the op based quantization parameter tracking. This includes adding `qint32` to torch types as it was missing during the initial type inclusion. For testing we only have `torch.int8` and `torch.float` types on function boundaries as the `qint8` types require passing the scale and zero point quantization information which is not supported yet.
2024-01-13 11:11:14 +08:00
"ElementwiseQuantizePerTensorModule_basic",
"ElementwiseDequantizePerTensorModule_basic"
Added e2e LTC tests (#916) * Added e2e LTC Torch MLIR tests * Fix seed for reproducability * Check if computation is None before getting debug string * Updated unit tests, and added numeric tests * Print name of the model layer that fails numeric validation * Run LTC e2e test with CI/CD * Set seed in main function, instead of beginning of execution * Add comment to specify number of digits of precision * Fixed typo * Remove tests for LTC example models * Added LTC option to torchscript e2e * Implement compile and run for LTC e2e test * xfail all tests that use ops that aren't currently supported
2022-06-10 03:56:01 +08:00
}