In PyTorch, the `NumberType` is equal to `Union[int, float,
complex]`. However, the abstract interpretation library was treating
the `NumberType` as `Union[int, float]`, resulting in type mismatches
when reifying certain dtype functions. This commit fixes the type
inconsistency by having the abstract interpretation functions take as
an input a `Union[int, float, complex]` for the ops that take
`!torch.number` inputs.
Single element tuples in Python need a comma after the
element. However, the `registry.py` file, which generates the expected
abstract interpretation function signatures, was not inserting the
comma. This commit changes the expected signature generator to add a
comma after the last element in any non-empty default tuple argument.
This commit adds the support for index.Tensor op when the index values
are negative. This commit wraps around the index values by checking
their values at run time.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
Set PyTorch and TorchVision version to nightly release 2023-05-16.
This commit removes the test `BaddbmmDifferentDtypesModule_basic`
since PyTorch expects all operands to have the same dtype.
Ref: 2abad0c184
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
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.
* 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>
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.
Before inlining a global slot, the users of the global slot are
checked to see if they are `ReadOnly` or `MemoryEffectFree` to make
sure that the global slot is not being mutated. Because the op
`copy.to_vtensor` currently does not have the `ReadOnly` trait, if a
global slot is passed to `copy.to_vtensor`, the pass
`InlineGlobalSlots` will fail.
The op `copy.to_vtensor` is `ReadOnly`, since it does not modify the
contents of the input tensor; it simply makes a new copy. This commit
adds the trait as well as an e2e test that generates the case of a
global slot being passed to a `copy.to_vtensor`.
* feat: split pytorch requirements into stable and nightly
* fix: add true to tests to see full output
* refactor: add comments to explain true statement
* feat: move some tests to experimental mode
* refactor: refactor pipeline into more fine grained difference
* feat: add version differentiation for some tests
* feat: activate more configs
* refactor: change implementation to use less requirement files
* refactor: remove contraints used for testing
* fix: revert some requirement file names
* refactor: remove unnecessary ninja install
* fix: fix version parsing
* refactor: remove dependency on torchvision in main requirements file
* refactor: remove index url
* style: remove unnecesary line switch
* fix: readd index url
This commit adds dtype functions for all the torch ops that did not
previously have one and removes the pass `RefineTypes`, since the
abstract interpretation library now takes care of all the dtype
propagation.
All dtype functions added are tested except for
- `aten.embedding`
- `aten._embedding_bag`
- `aten.embedding_bag`
These functions need a change to the testing framework to allow
specifying the actual data inside the tensor used for testing. I will
fix this in a follow up patch.
Co-authored-by: Jiahao Li <liplus17@163.com>
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>
When the user does not specify the `stride` value in 2d pooling ops,
`stride` is given the value of an empty list. However, the current
lowerings for pooling ops assumed that the `stride` operand would
always be a list of two ints, leading to crashes when that was not the
case. This commit fixes the crashes by setting the value of `stride`
to `kernel_size` when `stride` is the empty list, since this is the
default `stride` value specified in PyTorch docs. See:
https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html#torch.nn.MaxPool2d
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.
The current decomposition for `aten.randn.generator` does not specify
the `dtype` argument of the empty tensors created to store the random
values. This leads to invalid IR when the output type of the `randn`
op is not the default PyTorch dtype.
-- 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>
-- This commit adds e2e support for atend.sort op.
-- 1. Adds aten.sort op in torch dialect.
-- 2. Adds tm_tensor.sort op in TMTensor dialect.
-- 3. Adds lowering of aten.sort -> tm_tensor.sort.
Signed-off-by: Abhishek Varma <abhishek@nod-labs.com>
-- This commit adds e2e support for aten.randint by decomposing it into
an aten.randint.low by setting low=0.
Signed-off-by: Abhishek Varma <abhishek@nod-labs.com>
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>
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>
This commit adds the ability to specify extra abstract interpretation
functions in `torch_mlir.compile` to use during type refinement. This
allows users to easily add custom ops without having to interact with
MLIR or C++ directly.
The ops `aten.convolution_overrideable` and
`aten.convolution_backward_overrideable` are currently not e2e tested
in Torch-MLIR. Moreover, there is no way to add e2e tests for them
because the ops cannot be called using the CPU backend (this also
prevents adding tested dtype functions for these ops). Since these two
ops are not expected to ever appear in PyTorch traces obtained through
standard means (https://github.com/pytorch/pytorch/issues/97481),
Torch-MLIR should not have to worry about them.
There are several ops that have their shape function upstream and had
not been updated in Torch-MLIR to use the upstream version. This
commit updates those shape function. In addition, TODOs have been
added for shape functions that should be upstream but are not.
The original design for the dtype functions outlined in
https://github.com/llvm/torch-mlir/issues/1462 was unable to properly
handle ops that take optional tensors as an input when the optional
tensor has a value of None. By the time the op gets imported into
torch-mlir, if an optional value is None, all information about the
original type is lost from the op type signature, preventing
torch-mlir from knowing if a value of None was from an optional tensor
or not, which was crucial in the original design since each tensor
argument must be turned into two separate arguments for the dtype
function.
This commit changes the interface to dtype functions such that each
tensor turns into a tuple of two ints, the first representing the rank
of the tensor and the second the dtype of the tensor. Since now there
is a one-to-one correspondence between the operands of an op and the
operands of its dtype function, there is no ambiguity about which
operand of the op corresponds with which operand of the dtype
function.
To test the implementation, this commit defines dtype function for
convolution op, which takes one optional tensor as an argument.
* implemented ceil_mode== true support for lowering aten.max_pool2d to tosa
* add e2e test for lowering aten.max_pool2d to tosa with ceil_mode=true
---------
Co-authored-by: Lisa Liu <lingl@xilinx.com>
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.
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>
Random tensors used in e2e tests should be created using the
`TestUtils` object passed to the registered test case to ensure that
the compiled module and the golden trace receive the same tensors as
input. This commit changes all the cases of `torch.rand` and
`torch.randn` to use the `TestUtils` instead.
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.
Week of 01/30/2023:
Green LLVM commit: e31ee6417c33a6e2f0e8440b1a86d5365279ad68
Green MHLO commit: c2a6f4064d426567b9ef7b0d29d5ab86dc7b2b02 (branch greencommit/2023-01-30-e31ee641)
This commit replaces the `tanh` dtype function, which was being used
to test the implementation of dtype functions in
a710237437, with a dtype function for
`expm1`. The dtype function for `expm1` is identical to the `tanh`
one, so the same level of testing is maintained.
Currently, there are ops getting dtype information from the
`RefineTypes` pass and ops getting dtype information from the
`TorchDtypeRefinementPipeline`. Since each pass can only propagete
dtype information for the ops it knows how to handle, some models with
many ops handled in both passes require the two dtype propagation
passes to execute many times, reaching the iteration limit set in the
`LowerToBackendContractPass`. To temporarily avoid this issue while
the migration to `TorchDtypeRefinementPipeline` is finished, this
commit switches `tanh` to `expm1`, since the latter is used a lot less
in large models.
This reverts commit eaab9be207, since it
is causing the post-merge CI tests to fail, causing subsequent PRs to be
blocked. Specifically, the tests
`ElementwiseAtenLogicalAndOpPromoteBroadcastModule_basic` and
`ElementwiseAtenLogicalXorOpPromoteBroadcastModule_basic` fail because
the oracle does not match the computed result. This patch reverts the
commit to make the post-merge builds green again.
This commit adds support for passing to `torch_mlir.compile` the
result of running `torch.jit.trace` on a model by relaxing the
condition that checks if the model is already in JIT IR to allow any
`torch.jit.ScriptModule`.
Fixes https://github.com/llvm/torch-mlir/issues/1739
max
Jiawei Wu
Ramiro Leal-Cavazos
Chi_Liu
Matthias Gehre
nithinsubbiah
Abhishek Varma
Jiawei Wu
Yuanqiang Liu
Vivek Khandelwal
Christopher McGirr
Tiago Trevisan Jost
JianzheXiao
Zhekun Zhang
maxbartel
Gaurav Shukla
Prateek Gupta
gpetters94
David Gens
Prashant Kumar
rahul shrivastava
yifei410
Ze Zhang
Abhishek Varma
rahuls-cerebras
lisaliu1
Sean Silva
Jiahao Li
Ziheng Jiang
Eric Kunze
Priya Savithiri
Prateek Gupta
Zachary Cetinic
Zachary Cetinic
Ashay Rane
Gleb Kazantaev
Srirammaswamy
Shivam Gupta
Chi_Liu
Jae Hoon (Antonio) Kim