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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- arange # Error: Code below assumes there is at least one tensor arg
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- contiguous # Error: TODO add support for type BaseType(name=<BaseTy.MemoryFormat: 12>)
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- empty_like # Error: TODO add support for type BaseType(name=<BaseTy.MemoryFormat: 12>)
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- full # Error: Code below assumes there is at least one tensor arg
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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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- _index_put_impl_ # Error: TODO not sure if there are other valid types to handle here
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- ones # Error: Code below assumes there is at least one tensor arg
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- ones_like # Error: TODO add support for type BaseType(name=<BaseTy.MemoryFormat: 12>)
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- resize_ # Error: TODO add support for type BaseType(name=<BaseTy.MemoryFormat: 12>)
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- stack # Error: TODO not sure if there are other valid types to handle here
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- to.dtype # Error: TODO add support for type BaseType(name=<BaseTy.MemoryFormat: 12>)
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- to.other # Error: TODO add support for type BaseType(name=<BaseTy.MemoryFormat: 12>)
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- uniform_ # Error: TODO add support for type BaseType(name=<BaseTy.MemoryFormat: 12>)
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- zeros # Error: Code below assumes there is at least one tensor arg
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- zeros_like # Error: TODO add support for type BaseType(name=<BaseTy.MemoryFormat: 12>)
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# Additional ops which autogen is supported for but don't compile yet
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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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- where
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- copy_
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- _to_copy
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- log_softmax # Not inherently differentiable. Needs to be decomposed.
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- linear # Not inherently differentiable. Needs to be decomposed.
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# List of supported ops that we don't want to do the full codegen for
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# primarily view ops
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supported:
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# - bernoulli
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# - bernoulli_
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- cat
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- clone
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- empty
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- expand
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- fill_
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2022-05-03 21:35:44 +08:00
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- native_batch_norm_backward
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2022-03-24 22:15:43 +08:00
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- permute
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- squeeze
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- t
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- unsqueeze
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- view
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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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# - native_batch_norm_backward
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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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- func: device_data(std::shared_ptr<BackendData> data) -> Tensor
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opkind: ltc_device_data
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cache_shape: false
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- func: scalar(at::Scalar value, at::ScalarType type) -> Tensor
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opkind: at::prim::Constant
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cache_shape: false
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- func: expand(Tensor input, std::vector<int64_t> size, bool is_scalar_expand) -> Tensor
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- func: view(Tensor input, std::vector<int64_t> output_size) -> Tensor
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cache_shape: false
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- func: cast(Tensor input, at::ScalarType dtype, optional<at::ScalarType> stype) -> Tensor
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opkind: ltc_cast
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cache_shape: false
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# View ops only required until proper functionalization pass is introduced into LTC
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- func: as_strided_view_update(Tensor target, Tensor input, std::vector<int64_t> size, std::vector<int64_t> stride, int64_t storage_offset) -> Tensor
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opkind: ltc_as_strided_view_update
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- func: as_strided(Tensor input, std::vector<int64_t> size, std::vector<int64_t> stride, int64_t storage_offset) -> Tensor
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- func: diagonal_view_update(Tensor target, Tensor input, int64_t offset, int64_t dim1, int64_t dim2) -> Tensor
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opkind: ltc_diagonal_view_update
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cache_shape: false
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- func: diagonal(Tensor input, int64_t offset, int64_t dim1, int64_t dim2) -> Tensor
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- func: narrow_view_update(Tensor input, Tensor source, std::vector<int64_t> base_indices) -> Tensor
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opkind: ltc_narrow_view_update
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- func: narrow(Tensor input, std::vector<int64_t> base_indices, std::vector<int64_t> sizes) -> Tensor
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- func: permute(Tensor input, std::vector<int64_t> dims) -> Tensor
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- func: resize(Tensor input, std::vector<int64_t> size) -> Tensor
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- func: select_view_update(Tensor target, Tensor source, int64_t dim, int64_t start, int64_t end, int64_t stride) -> Tensor
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opkind: ltc_select_view_update
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cache_shape: false
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- func: select(Tensor input, int64_t dim, int64_t start, int64_t end, int64_t stride) -> Tensor
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- func: squeeze(Tensor input, int dim) -> Tensor
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- func: unsqueeze(Tensor input, int dim) -> Tensor
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