mirror of https://github.com/llvm/torch-mlir
d9aee0d7a7
* 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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| .. | ||
| python_deploy | ||
| torchscript_e2e_heavydep_tests | ||
| autogen_ltc_backend.py | ||
| autogen_ltc_backend.yaml | ||
| build_libtorch.sh | ||
| build_python_wheels.sh | ||
| build_standalone.sh | ||
| update_shape_lib.sh | ||
| update_torch_ods.sh | ||
| write_env_file.sh | ||