Commit Graph

16 Commits (6b3a7d07c2c76f5e8437ff4e88110899621557b9)

Author SHA1 Message Date
Andreas Falkenberg 55dc8deb92
[torch] GridSample TorchToLinalg lowering (#2883)
Lowers `torch.grid_sample` to the equilvalent `linalg` representation.
2024-02-23 09:14:38 -08:00
Aart Bik 0aed231e21
[torch-mlir][conversion-test] cleanup trailing whitespace in mlir files (#2807) 2024-01-25 14:24:28 -08:00
John Wu 704cfdaf08
Add aten.pool_max3d support to torch-to-linalg (#2735)
Added verification logic to the abstract_interpreter_lib_gen.py

Also made some unit tests

Initially, I thought we can use `linalg::pooling_ndhwc_max` to help
implement this problem. However, on a 5-dimensional matrix it does the
pooling on dimensions (2, 3, 4) which is not what we want. We want
pooling on dimensions (3, 4, 5).

To achieve this, we would need to lower our code using the `linalg`
dialect.


Turns out the pooling code in `linalg` looks like this.

```
func @max_pooling_ncdhw(%I: memref<?x?x?x?x?xf32>, %K: memref<3xindex>, %O: memref<?x?x?x?x?xf32>,
                        %strides: memref<3xindex>, %dilations: memref<3xindex>) {
    %c0 = arith.constant 0 : index
    %c1 = arith.constant 1 : index
    %N = memref.dim %I, %c0 : memref<?x?x?x?x?xf32>
    %C = memref.dim %I, %c1 : memref<?x?x?x?x?xf32>
    %D = memref.dim %I, 2 : memref<?x?x?x?x?xf32>
    %H = memref.dim %I, 3 : memref<?x?x?x?x?xf32>
    %W = memref.dim %I, 4 : memref<?x?x?x?x?xf32>

    %kernel_d = memref.load %K[%c0] : memref<3xindex>
    %kernel_h = memref.load %K[%c1] : memref<3xindex>
    %kernel_w = memref.load %K[2] : memref<3xindex>
    %stride_d = memref.load %strides[%c0] : memref<3xindex>
    %stride_h = memref.load %strides[%c1] : memref<3xindex>
    %stride_w = memref.load %strides[2] : memref<3xindex>
    %dilation_d = memref.load %dilations[%c0] : memref<3xindex>
    %dilation_h = memref.load %dilations[%c1] : memref<3xindex>
    %dilation_w = memref.load %dilations[2] : memref<3xindex>

    linalg.generic {
        indexing_maps = [
            affine_map<(n, c, d, h, w, kd, kh, kw) -> (n, c, d * %stride_d + kd * %dilation_d, h * %stride_h + kh * %dilation_h, w * %stride_w + kw * %dilation_w)>,  // Map for input tensor
            affine_map<(n, c, d, h, w, kd, kh, kw) -> (kd, kh, kw)>,                                              // Map for kernel tensor
            affine_map<(n, c, d, h, w, kd, kh, kw) -> (n, c, d, h, w)>                                            // Map for output tensor
        ],
        iterator_types = ["parallel", "parallel", "parallel", "parallel", "parallel", "reduction", "reduction", "reduction"],
        doc = "3D Max Pooling NCDHW with Strides, Dilations, and Kernel Size"
    } ins(%I, %K : memref<?x?x?x?x?xf32>, memref<3xindex>) outs(%O : memref<?x?x?x?x?xf32>) {
        ^bb0(%input_elem: f32, %kernel_elem: index, %output_elem: f32):
            %max_val = arith.maxf %input_elem, %output_elem : f32
            linalg.yield %max_val : f32
    }
    return
}

```

This was implemented based on it's source code with the adjustments
mentioned above:

4ca1b5e094/mlir/include/mlir/Dialect/Linalg/IR/LinalgNamedStructuredOps.yaml (L5647)

Issues related to this can be found here

https://github.com/nod-ai/SHARK-Turbine/issues/324
2024-01-19 21:09:46 +05:30
Yuanqiang Liu ef6dae6ae2
[Linalg] fix lowering reduce max with -inf (#2097) 2023-05-08 09:17:49 -07:00
Ramiro Leal-Cavazos 82a3860e25
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 04:22:53 +00:00
Vivek Khandelwal 6f548fc3ad [MLIR][TORCH] Add decomposition of aten.adaptive_avg_pool2d op
This commit adds the decomposition of `aten.adaptive_avg_pool2d` op into
`aten.avg_pool2d` op. The current decomposition only supports cases where
input size is equal to the output size.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-05-27 07:56:37 +05:30
Ashay Rane 9208bf0eb6
llvm: bump tag to e1318078 (#781)
The updated LLVM code includes a patch to create bfloat16 array
attributes, thus enabling a different patch to torch-mlir to flesh out
support for the bfloat16 type.
2022-04-26 12:27:51 -07:00
Vivek Khandelwal 769f3a8870 [MLIR][TORCH] Add E2E support for max_pool2d_with_indices op
This commit adds lowering of `max_pool2d_with_indices` op.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-04-18 21:05:19 +05:30
Vigilans 63fb1e5aad Bump LLVM at 8361c5da30588d3d4a48eae648f53be1feb5cfad 2022-03-18 13:16:14 -04:00
Sean Silva 84a9693006 Elide `!torch.` prefix in nested dialect types.
This leads to much more succinct types in many cases:

```
!torch.list<!torch.int>
!torch.list<int>

!torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>>
!torch.tuple<list<int>, list<int>>

!torch.optional<!torch.list<!torch.int>>
!torch.optional<list<int>>

!torch.list<list<list<tensor>>>
!torch.list<!torch.list<!torch.list<!torch.tensor>>>
```

I would like to take this further and allow omitting the `!torch.`
prefix in all cases, but that's harder -- for example, we currently use
`FuncOp` for functions, and so I don't think we can customize the
printing there. It seems like it will be a longer road to getting that
level of customization.
2022-03-15 17:24:08 -07:00
stephenneuendorffer 3fd9b7789e
Bump LLVM to 881ff4e4ebe8cc0cc045c7c167cffb01f94f27f8 (#539) 2022-01-25 22:16:30 -08:00
Yi Zhang 0902438882 Update llvm-project to a54f4eae0e1d0ef5adccdcf9f6c2b518dc1101aa
This brings in https://reviews.llvm.org/D110797. PRs that are in
progress will need to use scripts provided by
https://llvm.discourse.group/t/psa-removed-arithmetic-ops-from-standard/4455.
2021-10-18 13:36:42 -04:00
Sean Silva 4fad753073 Move external/torch-mlir to the root of the repo. 2021-09-27 17:11:08 -07:00
Sean Silva a99cbeeb7e Move TorchConversion dialect and TorchTo* into torch-mlir 2021-09-23 21:39:31 -07:00
Sean Silva 1dec561cfd Update llvm-project to 830c0b9023cd0cf91955900e0d96283e7a8c3711
- builder.getSymbolRefAttr is gone.
- OpAsmOpInterface's getAsmResultNames method needs explicit override
- a bunch of churn for builtin.func needing to be made explicit (and
  sometimes implicit?)
- operation printers no longer need to print the operation name
  themselves.
- snuck in beneficial trivial addition to TmpDeleteDeadIREEListsPass to
  test a particular upstream change e2e with my local patchset.
2021-09-03 14:16:38 -07:00
Sean Silva cab8d922ec Add TorchToIREE and factor out TorchConversion dialect.
This converts a basic list op (torch.prim.ListConstruct) to the IREE
dialect.

```
    def forward(self, x: float):
            return [x, x]
```

turns into:

```
builtin.func @forward(%arg0: !torch.float) -> !torch.list<!torch.float> {
  %0 = torch.prim.ListConstruct %arg0, %arg0 : (!torch.float, !torch.float) -> !torch.list<!torch.float>
  return %0 : !torch.list<!torch.float>
}
```

which turns into:

```
builtin.func @forward(%arg0: f64) -> !iree.list<f64> {
  %c1 = constant 1 : index
  %c0 = constant 0 : index
  %c2 = constant 2 : index
  %0 = iree.list.create %c2 : !iree.list<f64>
  iree.list.set %0[%c0], %arg0 : !iree.list<f64>, f64
  iree.list.set %0[%c1], %arg0 : !iree.list<f64>, f64
  return %0 : !iree.list<f64>
}
```

As part of doing this, I realized that it was time to formalize the IR
form that we reach right before running TorchTo{Linalg,Std,...}. We now
call it the "Torch backend contract". We then lower the "Torch backend
contract" to the "npcomp backend contract", which involves the new
TorchConversion (`torch_c`) dialect, which holds ops that need to
operate on both the npcomp backend types (e.g. builtin tensors, i1, IREE
list, etc.) and the `!torch` types.

This made more sense, as I realized that if I didn't factor out
`torch_c` then the Torch dialect would have a dependency on IREE
dialect (we previously didn't notice this was an issue because we only
depended on `builtin` types), which seemed wrong to me.

Recommended review order:
- TorchToIREE.cpp / `TorchToIREE/basic.mlir`
- Look at the new structure of createTorchScriptToNpcompBackendPipeline.
  It now lives in TorchConversion/Transforms/Passes.cpp and cleanly
  calls into `Torch::createTorchScriptToTorchBackendPipeline` for the
  frontend lowering to the Torch backend contract.
- Mechanical change extracting
  `torch_c.{to,from}_{i1,i64,f64,builtin_tensor,iree_list}` into a new
  TorchConversion dialect, and a few passes specific to the lowering
  from the Torch backend contract to the npcomp backend contract.
- Minor fixes to TorchToLinalg.cpp to use unconverted operands (now that
  we convert lists as part of operand materialization, we need to use
  the original operands). Also added test for AtenMaxPool2dOp and fixed
  m_TorchConstantIntList.
- TmpDeleteDeadIREELists pass. Temporary pass for deleting dead IREE lists that
  are created as part of operand materialization for conv/max pool/avg pool ops
  in TorchToLinalg.
2021-08-16 15:01:58 -07:00