torch-mlir/build_tools/autogen_ltc_backend.yaml

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blacklist:
# List of unsupported ops in LTC autogen because of some error
- _index_put_impl_ # Error: TODO not sure if there are other valid types to handle here
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- empty_like # Error: TODO add support for type BaseType(name=<BaseTy.MemoryFormat: 12>)
- index.Tensor # Error: TODO not sure if there are other valid types to handle here
- index_put # Error: TODO not sure if there are other valid types to handle here
- index_put_ # Error: TODO not sure if there are other valid types to handle here
- stack # Error: TODO not sure if there are other valid types to handle here
# Additional ops which autogen is supported for but don't compile yet
- _convolution
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.
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- detach
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- item
- size
- where
- copy_
E2E HuggingFace Bert using LTC Backend (#912) * Update native function definitions * Add ops to support bert lowering - Add empty_strided and as_strided - Restore zeros_like to op blacklist (Without this, tensors will be unintentionally created with a CPU device rather than lazy) - Check for composite implicit ops and add device data IR - Also fix codegen for functionalization * Add autogen to CMakeList * Remove PyTorch submodule * Reduced BERT model size * Print Mark Step status in Torch MLIR LTC debug string * Apply fixes to work with latest upstream/main - Pass importOptions into getMlirTypeFromTorchType during NodeImporter::importNode Without this, the tensor type created may have a mismatched type as ImportOptions may cause vtensor to be used instead of tensor * Update shape inference functions - Fixed compute_shape_native_batch_norm when mean and var are uninitialized Previously, the number of shapes returned would be <3 if either mean or val was didn't exist. Instead, we now initialize them with a vector matching the number of channels. - Implemented compute_shape_mul - Fixed bug in reshape shape inference error message * Get MLIR backend more consistent with TS backend - Remove LazyNativeFunctions::_unsafe_view from autogen - Blacklist ops to make JIT graph more like output of TS backend - Print graph when SSA value has mismatch of types and results - Remove normalize_index from LazyShapeInference - Fix seeds for LTC example models * Update and clean up shape inference functions - Prune shape inference functions - Add shape inference function for GenerateSlice - Add shape inference function for GenerateCopy Co-authored-by: Henry Tu <henry.tu@cerebras.net>
2022-06-08 02:38:50 +08:00
# Disabled for consistency with TS backend
- new_empty
E2E HuggingFace Bert using LTC Backend (#912) * Update native function definitions * Add ops to support bert lowering - Add empty_strided and as_strided - Restore zeros_like to op blacklist (Without this, tensors will be unintentionally created with a CPU device rather than lazy) - Check for composite implicit ops and add device data IR - Also fix codegen for functionalization * Add autogen to CMakeList * Remove PyTorch submodule * Reduced BERT model size * Print Mark Step status in Torch MLIR LTC debug string * Apply fixes to work with latest upstream/main - Pass importOptions into getMlirTypeFromTorchType during NodeImporter::importNode Without this, the tensor type created may have a mismatched type as ImportOptions may cause vtensor to be used instead of tensor * Update shape inference functions - Fixed compute_shape_native_batch_norm when mean and var are uninitialized Previously, the number of shapes returned would be <3 if either mean or val was didn't exist. Instead, we now initialize them with a vector matching the number of channels. - Implemented compute_shape_mul - Fixed bug in reshape shape inference error message * Get MLIR backend more consistent with TS backend - Remove LazyNativeFunctions::_unsafe_view from autogen - Blacklist ops to make JIT graph more like output of TS backend - Print graph when SSA value has mismatch of types and results - Remove normalize_index from LazyShapeInference - Fix seeds for LTC example models * Update and clean up shape inference functions - Prune shape inference functions - Add shape inference function for GenerateSlice - Add shape inference function for GenerateCopy Co-authored-by: Henry Tu <henry.tu@cerebras.net>
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- rsub
- slice.Tensor # Disabled in favour of slice_copy.Tensor
# Disabled in favour of functionalized alternatives
- _reshape_alias
- expand
- permute
- select.int
- squeeze
- squeeze.dim
- t
- transpose.int
- unsqueeze
- view
# whitelist:
# List of ops to autogen even if not supported by Torch-MLIR explicitly
#- split_copy.Tensor
#- split_with_sizes_copy
#- unbind_copy.int
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# List of supported ops that we don't want to do the full codegen for
supported:
# - bernoulli
# - bernoulli_
E2E HuggingFace Bert using LTC Backend (#912) * Update native function definitions * Add ops to support bert lowering - Add empty_strided and as_strided - Restore zeros_like to op blacklist (Without this, tensors will be unintentionally created with a CPU device rather than lazy) - Check for composite implicit ops and add device data IR - Also fix codegen for functionalization * Add autogen to CMakeList * Remove PyTorch submodule * Reduced BERT model size * Print Mark Step status in Torch MLIR LTC debug string * Apply fixes to work with latest upstream/main - Pass importOptions into getMlirTypeFromTorchType during NodeImporter::importNode Without this, the tensor type created may have a mismatched type as ImportOptions may cause vtensor to be used instead of tensor * Update shape inference functions - Fixed compute_shape_native_batch_norm when mean and var are uninitialized Previously, the number of shapes returned would be <3 if either mean or val was didn't exist. Instead, we now initialize them with a vector matching the number of channels. - Implemented compute_shape_mul - Fixed bug in reshape shape inference error message * Get MLIR backend more consistent with TS backend - Remove LazyNativeFunctions::_unsafe_view from autogen - Blacklist ops to make JIT graph more like output of TS backend - Print graph when SSA value has mismatch of types and results - Remove normalize_index from LazyShapeInference - Fix seeds for LTC example models * Update and clean up shape inference functions - Prune shape inference functions - Add shape inference function for GenerateSlice - Add shape inference function for GenerateCopy Co-authored-by: Henry Tu <henry.tu@cerebras.net>
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- _to_copy
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- clone
E2E HuggingFace Bert using LTC Backend (#912) * Update native function definitions * Add ops to support bert lowering - Add empty_strided and as_strided - Restore zeros_like to op blacklist (Without this, tensors will be unintentionally created with a CPU device rather than lazy) - Check for composite implicit ops and add device data IR - Also fix codegen for functionalization * Add autogen to CMakeList * Remove PyTorch submodule * Reduced BERT model size * Print Mark Step status in Torch MLIR LTC debug string * Apply fixes to work with latest upstream/main - Pass importOptions into getMlirTypeFromTorchType during NodeImporter::importNode Without this, the tensor type created may have a mismatched type as ImportOptions may cause vtensor to be used instead of tensor * Update shape inference functions - Fixed compute_shape_native_batch_norm when mean and var are uninitialized Previously, the number of shapes returned would be <3 if either mean or val was didn't exist. Instead, we now initialize them with a vector matching the number of channels. - Implemented compute_shape_mul - Fixed bug in reshape shape inference error message * Get MLIR backend more consistent with TS backend - Remove LazyNativeFunctions::_unsafe_view from autogen - Blacklist ops to make JIT graph more like output of TS backend - Print graph when SSA value has mismatch of types and results - Remove normalize_index from LazyShapeInference - Fix seeds for LTC example models * Update and clean up shape inference functions - Prune shape inference functions - Add shape inference function for GenerateSlice - Add shape inference function for GenerateCopy Co-authored-by: Henry Tu <henry.tu@cerebras.net>
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- empty.memory_format
- empty_strided
- fill_.Scalar
- _unsafe_view
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# ops required for functionalization
- lift
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- lift_fresh
# Below are all operators that are "composite" in core,
# but require us to explicitly re-enable functionalization in order to use them.
# Why? These operators are all CompositeExplicitAutograd, which mean that they run
# after functionalization,
# but their implementations call view operators (which we need to functionalize away).
- block_diag
- new_empty_strided
- narrow_copy
- pixel_shuffle
- pixel_unshuffle
- select_backward
- slice_backward
- diagonal_backward
- _trilinear
- linalg_inv_ex
- linalg_pinv.atol_rtol_tensor
- logsumexp.out
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additional_ops:
# Additional ops to support that are not supported by Torch-MLIR explicitly
- _copy_from
- _copy_from_and_resize
# List of non native ops that we only want to do IR node class generation for
non_native:
E2E HuggingFace Bert using LTC Backend (#912) * Update native function definitions * Add ops to support bert lowering - Add empty_strided and as_strided - Restore zeros_like to op blacklist (Without this, tensors will be unintentionally created with a CPU device rather than lazy) - Check for composite implicit ops and add device data IR - Also fix codegen for functionalization * Add autogen to CMakeList * Remove PyTorch submodule * Reduced BERT model size * Print Mark Step status in Torch MLIR LTC debug string * Apply fixes to work with latest upstream/main - Pass importOptions into getMlirTypeFromTorchType during NodeImporter::importNode Without this, the tensor type created may have a mismatched type as ImportOptions may cause vtensor to be used instead of tensor * Update shape inference functions - Fixed compute_shape_native_batch_norm when mean and var are uninitialized Previously, the number of shapes returned would be <3 if either mean or val was didn't exist. Instead, we now initialize them with a vector matching the number of channels. - Implemented compute_shape_mul - Fixed bug in reshape shape inference error message * Get MLIR backend more consistent with TS backend - Remove LazyNativeFunctions::_unsafe_view from autogen - Blacklist ops to make JIT graph more like output of TS backend - Print graph when SSA value has mismatch of types and results - Remove normalize_index from LazyShapeInference - Fix seeds for LTC example models * Update and clean up shape inference functions - Prune shape inference functions - Add shape inference function for GenerateSlice - Add shape inference function for GenerateCopy Co-authored-by: Henry Tu <henry.tu@cerebras.net>
2022-06-08 02:38:50 +08:00
- func: scalar(Scalar value, ScalarType type) -> Tensor
opkind: at::prim::Constant
E2E HuggingFace Bert using LTC Backend (#912) * Update native function definitions * Add ops to support bert lowering - Add empty_strided and as_strided - Restore zeros_like to op blacklist (Without this, tensors will be unintentionally created with a CPU device rather than lazy) - Check for composite implicit ops and add device data IR - Also fix codegen for functionalization * Add autogen to CMakeList * Remove PyTorch submodule * Reduced BERT model size * Print Mark Step status in Torch MLIR LTC debug string * Apply fixes to work with latest upstream/main - Pass importOptions into getMlirTypeFromTorchType during NodeImporter::importNode Without this, the tensor type created may have a mismatched type as ImportOptions may cause vtensor to be used instead of tensor * Update shape inference functions - Fixed compute_shape_native_batch_norm when mean and var are uninitialized Previously, the number of shapes returned would be <3 if either mean or val was didn't exist. Instead, we now initialize them with a vector matching the number of channels. - Implemented compute_shape_mul - Fixed bug in reshape shape inference error message * Get MLIR backend more consistent with TS backend - Remove LazyNativeFunctions::_unsafe_view from autogen - Blacklist ops to make JIT graph more like output of TS backend - Print graph when SSA value has mismatch of types and results - Remove normalize_index from LazyShapeInference - Fix seeds for LTC example models * Update and clean up shape inference functions - Prune shape inference functions - Add shape inference function for GenerateSlice - Add shape inference function for GenerateCopy Co-authored-by: Henry Tu <henry.tu@cerebras.net>
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properties:
- ShapeCompute
- TreatScalarsAsConstants
- func: cast(Tensor input, ScalarType dtype, ScalarType? stype) -> Tensor
opkind: ltc_cast
E2E HuggingFace Bert using LTC Backend (#912) * Update native function definitions * Add ops to support bert lowering - Add empty_strided and as_strided - Restore zeros_like to op blacklist (Without this, tensors will be unintentionally created with a CPU device rather than lazy) - Check for composite implicit ops and add device data IR - Also fix codegen for functionalization * Add autogen to CMakeList * Remove PyTorch submodule * Reduced BERT model size * Print Mark Step status in Torch MLIR LTC debug string * Apply fixes to work with latest upstream/main - Pass importOptions into getMlirTypeFromTorchType during NodeImporter::importNode Without this, the tensor type created may have a mismatched type as ImportOptions may cause vtensor to be used instead of tensor * Update shape inference functions - Fixed compute_shape_native_batch_norm when mean and var are uninitialized Previously, the number of shapes returned would be <3 if either mean or val was didn't exist. Instead, we now initialize them with a vector matching the number of channels. - Implemented compute_shape_mul - Fixed bug in reshape shape inference error message * Get MLIR backend more consistent with TS backend - Remove LazyNativeFunctions::_unsafe_view from autogen - Blacklist ops to make JIT graph more like output of TS backend - Print graph when SSA value has mismatch of types and results - Remove normalize_index from LazyShapeInference - Fix seeds for LTC example models * Update and clean up shape inference functions - Prune shape inference functions - Add shape inference function for GenerateSlice - Add shape inference function for GenerateCopy Co-authored-by: Henry Tu <henry.tu@cerebras.net>
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properties:
- ShapeCompute