2022-03-24 22:15:43 +08:00
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blacklist:
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# List of unsupported ops in LTC autogen because of some error
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2022-07-14 01:28:05 +08:00
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- _index_put_impl_ # Error: TODO not sure if there are other valid types to handle here
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2022-10-28 23:06:11 +08:00
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- _index_put_impl # Error: TODO not sure if there are other valid types to handle here
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2022-03-24 22:15:43 +08:00
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- empty_like # Error: TODO add support for type BaseType(name=<BaseTy.MemoryFormat: 12>)
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- index.Tensor # Error: TODO not sure if there are other valid types to handle here
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- index_put # Error: TODO not sure if there are other valid types to handle here
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- index_put_ # Error: TODO not sure if there are other valid types to handle here
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2023-05-19 10:07:58 +08:00
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# Ops with list of tensors output
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2023-05-24 03:43:33 +08:00
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- split.Tensor
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2023-05-19 10:07:58 +08:00
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- unbind.int
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2023-05-26 10:05:19 +08:00
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- chunk
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2023-05-19 10:07:58 +08:00
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2022-03-24 22:15:43 +08:00
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# Additional ops which autogen is supported for but don't compile yet
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2022-07-14 01:28:05 +08:00
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- _convolution
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Enable support for LTC Input/Output Mapping (#764)
* Save InputOutputAliases to TorchMlirComputation
* Implement GetResultShape for TorchMlirLoweringContext
* Use optional return type for GetResultShape
* Remove support for aten::detach
With this op enabled, tensors were being copied, which resulted in incorrect aliasing.
* Add newline before printing I/O alias mapping
* Changed printout to use "Input param" as label instead of "Input"
* Remote shape inference function for aten::detach
* Moved implementation of SetUpAlias to MlirLoweringContext
As part of this change, TorchMlirComputation has been moved to the end of mlir_lowering_context.h so that it can access some new structs in TorchMlirLoweringContext
* Use updated PyTorch API
* Remove GetResultShape
Complements this upstream PyTorch PR: pytorch/pytorch#75828
This PR adds support for mapping input and output tensors which alias each other. (e.g. maps input weight tensor in parameter to the same tensor in output after a training iteration)
MLIR:
func @graph(%arg0: !torch.vtensor<[1,5],f32>, %arg1: !torch.vtensor<[1],si64>, ..., %arg6: !torch.vtensor<[10,5],f32>, %arg7: !torch.vtensor<[10],f32>, ...) {
...
return %arg0, %arg1, %17, %23, ... : !torch.vtensor<[1,5],f32>, !torch.vtensor<[1],si64>, !torch.vtensor<[10,5],f32>, !torch.vtensor<[10],f32>, ...
}
Input/Output Alias Mapping:
Output: 0 -> Input: 0
Output: 1 -> Input: 1
Output: 2 -> Input: 6
Output: 3 -> Input: 7
The aten::detach op has also been disabled in this PR to fix the issue of tensors not aliasing properly due to copying.
2022-04-28 01:48:04 +08:00
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- detach
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2022-03-24 22:15:43 +08:00
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- item
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- size
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- copy_
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2022-06-08 02:38:50 +08:00
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# Disabled for consistency with TS backend
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2022-09-20 22:16:04 +08:00
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- lift_fresh_copy
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2022-07-01 03:19:05 +08:00
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- new_empty
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2022-06-08 02:38:50 +08:00
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- rsub
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2022-07-01 03:19:05 +08:00
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- slice.Tensor # Disabled in favour of slice_copy.Tensor
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2022-08-27 04:13:28 +08:00
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- zeros
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2022-10-14 22:28:21 +08:00
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- ones
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- arange
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- arange.start
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- arange.start_step
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2022-10-28 23:06:11 +08:00
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- fill.Scalar
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2023-06-01 11:38:50 +08:00
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- scalar_tensor
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2022-07-01 03:19:05 +08:00
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# Disabled in favour of functionalized alternatives
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- _reshape_alias
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- expand
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- permute
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- select.int
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- squeeze
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- squeeze.dim
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- t
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- transpose.int
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- unsqueeze
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- view
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2022-10-14 22:28:21 +08:00
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whitelist:
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# Enabled for consistency with TS backend
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- arange.start_out
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2022-07-01 03:19:05 +08:00
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# List of ops to autogen even if not supported by Torch-MLIR explicitly
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#- split_copy.Tensor
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#- split_with_sizes_copy
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#- unbind_copy.int
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2022-03-24 22:15:43 +08:00
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# List of supported ops that we don't want to do the full codegen for
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supported:
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# - bernoulli
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# - bernoulli_
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2022-06-08 02:38:50 +08:00
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- _to_copy
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2022-03-24 22:15:43 +08:00
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- clone
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2022-06-08 02:38:50 +08:00
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- empty.memory_format
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- empty_strided
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- fill_.Scalar
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- _unsafe_view
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2022-03-24 22:15:43 +08:00
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2022-07-01 03:19:05 +08:00
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# ops required for functionalization
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- lift
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2022-07-26 03:20:17 +08:00
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- lift_fresh
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2022-07-01 03:19:05 +08:00
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# Below are all operators that are "composite" in core,
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# but require us to explicitly re-enable functionalization in order to use them.
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# Why? These operators are all CompositeExplicitAutograd, which mean that they run
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# after functionalization,
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# but their implementations call view operators (which we need to functionalize away).
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- block_diag
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- new_empty_strided
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2022-09-20 22:16:04 +08:00
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- narrow_copy
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2022-07-01 03:19:05 +08:00
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- pixel_shuffle
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- pixel_unshuffle
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- select_backward
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- slice_backward
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- diagonal_backward
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- _trilinear
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- linalg_pinv.atol_rtol_tensor
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- logsumexp.out
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2022-09-20 22:16:04 +08:00
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# List of ops that will take in symints for the size instead of ints
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symint:
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- empty.memory_format
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- new_empty_strided
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- expand_copy
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- narrow_copy
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2022-09-27 00:16:49 +08:00
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- slice_backward
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- slice_copy.Tensor
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- slice_scatter
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2022-09-20 22:16:04 +08:00
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- view
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- view_copy
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2022-09-27 00:16:49 +08:00
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- as_strided_copy
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- as_strided_scatter
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2022-09-20 22:16:04 +08:00
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2022-07-01 03:19:05 +08:00
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2022-03-24 22:15:43 +08:00
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additional_ops:
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# Additional ops to support that are not supported by Torch-MLIR explicitly
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- _copy_from
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- _copy_from_and_resize
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2022-05-03 21:35:44 +08:00
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# List of non native ops that we only want to do IR node class generation for
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non_native:
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2022-06-08 02:38:50 +08:00
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- func: scalar(Scalar value, ScalarType type) -> Tensor
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2022-05-03 21:35:44 +08:00
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opkind: at::prim::Constant
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2022-06-08 02:38:50 +08:00
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properties:
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- ShapeCompute
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- TreatScalarsAsConstants
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2022-10-14 22:28:21 +08:00
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- func: expand(Tensor input, int[] size, bool is_scalar_expand) -> Tensor
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2022-06-08 02:38:50 +08:00
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- func: cast(Tensor input, ScalarType dtype, ScalarType? stype) -> Tensor
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2022-05-03 21:35:44 +08:00
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opkind: ltc_cast
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2022-06-08 02:38:50 +08:00
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properties:
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- ShapeCompute
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