We can map to `tensor.reshape` for handling multiple output dynamic
shapes. Later we can perform a more complex analysis for indentifying
expand/collapse cases from the tensor.reshape.
Initially we planned to handle this identification at the `torch` level
however it will be easier to handle once converted to core
mlir-dialects.
Decomposition RepeatInterleaveSelfInt with following ops:
```python
def my_repeat_interleave(input, repeats, dim=None):
if dim is None:
# Flatten the input and then repeat
return input.flatten().unsqueeze(-1).tile((1, repeats)).flatten()
else:
# Calculate the shape after repeat
expanded_shape = list(input.shape)
expanded_shape[dim] *= repeats
# Repeat the tensor along the specified dimension
repeat_shape = [1] * (input.dim() + 1)
repeat_shape[dim + 1] = repeats
input = input.unsqueeze(-1)
# Tile and then reshape
tiled = torch.tile(input, repeat_shape)
# Rearrange and reshape
repeated = tiled.reshape(*expanded_shape)
return repeated
```
I passed the tests of stablehlo and linalg. When testing onnx, strange
things happened.
In torch-mlir's CI **torch_nightly** and my own
environment(torch==2.4.0.dev20240318+cpu), it can **pass the pass**.
In torch-mlir's CI **torch_stable**, it **failed**.
The test case is `RepeatInterleaveSelfIntNoDimModule_basic`, the result
shape should be [120].
```python
class RepeatInterleaveSelfIntNoDimModule(torch.nn.Module):
def __init__(self):
super().__init__()
@export
@annotate_args([
None,
([3, 4, 5], torch.float32, True),
])
def forward(self, x):
return x.repeat_interleave(2)
@register_test_case(module_factory=lambda: RepeatInterleaveSelfIntNoDimModule())
def RepeatInterleaveSelfIntNoDimModule_basic(module, tu: TestUtils):
module.forward(tu.rand(3, 4, 5))
```
The error log is as follows:
```
Unexpected outcome summary: (onnx)
****** Failed tests - 1 tests
FAIL - "RepeatInterleaveSelfIntNoDimModule_basic"
@ trace item #0 - call to "forward"
@ output of call to "forward"
ERROR: shape (torch.Size([6, 4, 5])) is not equal to golden shape (torch.Size([120]))
```
@rsuderman
Would you please help me check what's wrong with my PR? Thanks a lot.
weights and biases and other model parameters appear as a separate data
structure to the traced graph, but are needed when running the MLIR
compiled code; this PR implements that extended functionality
Align corner modes which select what the corners mean.
Either the center of the corner points or the edges of the edge points.
---------
Co-authored-by: Rob Suderman <rob.suderman@gmail.com>
The new cases added for quantized matmuls are:
1. vec-vec
2. vec-mat
3. mat-vec
each of which are now lowered to expand(s), quantized_matmul, and
collapse.
1. onnx.MatMulInteger now converts to aten.matmul instead of aten.mm
2. aten.matmul, for ranks >=2, now allows quantized inputs and will
lower to linalg::quantized_matmul or linalg::quantized_batch_matmul.
3. added AtenMatmulOp to the FuseQuantizeOps rewrite patters
QuantizeOperands, QuantizeTransposedOperands, and QuantizeAccumulator
4. added several tests, including some to test AtenMmOp with varying
quantization signed-ness.
5. a quantized matmul mat-vec test is added to verify the failure to
lower to linalg; cleaned of out-of-date code related to common
torch-mlir lowering xfails.
6. in debugging a real model with quantized matmuls, I found a bug on
the scalarize-shapes pass which resulted from the aten.full op folder
returning an incompatible result type. This is fixed by the small change
here to
[lib/Dialect/Torch/IR/TorchOps.cpp](https://github.com/llvm/torch-mlir/compare/main...zjgarvey:torch-mlir:MatMulIntegerFix?expand=1#diff-dc8ed165c207918e606490eee3984b1ad51d7034e6aac36fc046bf47f6f03f4f).
- Added linalg lowering for `AtenFloorDivideScalarOp`
- Needed `AtenDivScalarModeOp` for the decomp.
- Added linalg lowering for `AtenDivScalarModeOp`
- Moved linalg payload logic to `createDivModePayload()` since the logic
was nearly identical for both `AtenDivScalarModeOp` and
`AtenDivTensorModeOp`. Just a template function
- Added `AtenDivScalarModeOp` lowering for stablehlo
Pytorch's
[`torch.floor_divide()`](https://pytorch.org/docs/stable/generated/torch.floor_divide.html)
in a previous version (for a reason unknown to me) preformed a
truncation instead of "floor". The already implemented op
`AtenFloorDivideTensorOp` was done before this change. However, this
wasn't caught because our testcases only tested positive floor division.
I changed this to floor as well as adding a few test cases.
As this
issuecomment(https://github.com/llvm/torch-mlir/pull/3021#issuecomment-2031248199)
suggests, `setup.py` should only be used for building Python packages,
so:
* disabled the develop command
* refactor the environment variable parameters
* add more doc for the usage and env var of setup.py
If there is only a single value scattered there can be an implicit batch
dimension. This includes a check for the implicit batch dimension when
reshaping the update tensor. It includes an e2e test to verify
correctness.
Fix the case PrimListUnpackOp's result num is not equal to PrimList
length.
See the following example:
```python
def forward(self, x):
if len(x.shape) == 5:
b0, t, c0, h0, w0 = x.shape
b, c, h, w = torch.mul(b0, t), c0, h0, w0
else:
b1, c1, h1, w1 = x.shape
b, c, h, w = b1, c1, h1, w1
res = torch.reshape(x, [b, c, h, w])
return res
```
Without this fix, the following error message will occur：
```
/root/torch-mlir/externals/llvm-project/mlir/lib/IR/PatternMatch.cpp:118: virtual void mlir::RewriterBase::replaceOp(mlir::Operation *, mlir::ValueRange): Assertion `op->getNumResults() == newValues.size() && "incorrect # of replacement values"' failed.
```
Previously, it could only handle the situations where outputsize == (1,
1) or outputsize == (input_H, input_W). Now it supports all situations
where input_H % output_H== 0 && input_W % output_W == 0
1. Changes the linalg lowering for dequantization ops to always sign
cast to float to prevent misrepresenting uint32 overflow on subtraction
with zero point.
2. Adds a basic quantized model test which only quantizes and
dequantizes and now passes with these changes in linalg and onnx
configs.
3. Changes the aten.mm lowering to allow mismatched quantized types.
4. If a quantized matmul arg is uint8, we shift by 128 to faithfully
represent the quantization as a signed i8 quantization. This worked fine
in the AtenMmOp lowering, but I'd be happy to move it to a rewrite in
FuseQuantizedOps.cpp instead if that seems more appropriate.
With the changes 3 and 4, the QuantizedMLP_basic and
QuantizedSingleLayer_basic e2e tests now passes with the onnx config.
They replaced all `c10::optional` usages with `std::optional` in
torchgen'd code in
fb90b4d4b2
causing the LTC build to break.
Replacing all usages of `c10::optional` with `std::optional` in
`projects/ltc` has fixed the issue
Issue: #3120
Added error message when adding new torch op to
[torch_ods_gen.py](https://github.com/llvm/torch-mlir/compare/main...IanWood1:torch-mlir:ods_gen_error_message?expand=1#diff-889b60b904ed67a5065a14e8de6fc89e00e199577e4d2bfa134ac4d1c89832d2).
New message displays which op key is failing and possible matches in the
torch `Registry`.
```Op does not match any Torch ops in Registry
Given op:
"aten::hardtanh_wrong : (Tensor, Scalar) -> (Tensor)"
Possible matches:
"aten::hardshrink : (Tensor, Scalar) -> (Tensor)"
"aten::hardtanh_ : (Tensor, Scalar, Scalar) -> (Tensor)"
"aten::hardtanh : (Tensor, Scalar, Scalar) -> (Tensor)"
"aten::clamp_min : (Tensor, Scalar) -> (Tensor)"
"aten::linalg_cond : (Tensor, Scalar?) -> (Tensor)"```
Also, ran black formatting on file. Based on LLVM style guides this seems to be correct, but I can revert the formatting if needed.
…ute_reshape_shape
as that `aten.view` support at most one `-1` in dim list. The original
calculation of `numel` is wrong when there is a `-1` in dim list.
This tests COO for more than 2-dim. Note that sparsity should really
propagate into the relu activation and the output, but such cleverness
needs to wait for the pending work in the PyTorch tree.
This PR only performs a lit test. In lieu of an e2e test, https://github.com/nod-ai/SHARK-TestSuite/pull/142 makede sure that the lowering works & the numbers check out.
Co-authored-by: Xida Ren <xida.ren.dev@gmail.com>
* as that `TorchMLIRTorchConversionPasses` missing dependencies of
`TorchMLIRTorchToStablehlo` and `TorchMLIRTorchToTensor`.
* use `TorchMLIRConversionPasses` instead of scattered targets.
penguin_wwy
Rob Suderman
Vivek Khandelwal
Xinyu Yang
Aart Bik
Andreas Falkenberg
zjgarvey
Vinayak Dev
IanWood1
jinchen
Xida Ren (Cedar)
Xinan Jiang(姜曦楠)
Yuanqiang Liu
Jae Hoon (Antonio) Kim
Sambhav Jain