[AutoBump] Merge with 41d04a89 (Jun 12) (64) #301
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Missing types for tracing float8 types.
Linalg conversion requires mapping for f8 types
* Let `toBuiltinTensor()` reflects the original dtype of `!torch.vtensor`. * Backend handles dtype conversion themselves.
This commit adds the lowering for SequenceAt, SequenceEmpty, SequenceInsert, SequenceErase op Signed-Off By: Vivek Khandelwal<[email protected]>
Signed-Off By: Vivek Khandelwal <[email protected]>
Tests the basic constructs of registering a custom op and its abstract implementations (with FakeTensors) in python, going through TorchDynamo export, followed by importing the shape expressions in the Torch dialect. Also fixes the importer were previously the symbolic bind op insertion was not gated in one place.
this fixes the following issue: llvm#3418
half_pixel is also the default mode used by ONNX, see https://onnx.ai/onnx/operators/onnx__Resize.html
…lvm#3443) This patch fixes several failing tests in our [external test suite](https://github.com/nod-ai/SHARK-TestSuite/tree/main/iree_tests/onnx/node/generated), and addresses some of the issues discussed in llvm#3420
There is currently no int16 quantization support in torch. This patch adds a new mlir type to correspond to the missing "torch.qint16" type, and enables lowering of quantization-related onnx ops using int16 types. In follow-up patches, custom quantization logic for ops like aten.matmul/aten.mm/aten.convolution may need to be revisited to allow support for qint16. The passes in FuseQuantizedOps.cpp may also need slight modifications.
…lvm#3445) Adds the following arguments: - "--clear-domain": enabling this flag (default False) will delete the domain attribute from each node in the onnx model before importing. Shape inference does not seem to work for onnx ops in custom domains. In the rare case when these ops have a corresponding counterpart in base onnx, enabling this flag might allow shape inference to work properly. - "--opset-version": allows setting the opset version manually. This will cause the importer to attempt to update the opset_version of the onnx model before importing. Newer opset versions sometimes have more robust shape inference patterns.
Handles onnx exporters emitting default-valued attributes. Signed-off-by: Suraj Sudhir <[email protected]>
mgehre-amd
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September 11, 2024 12:08
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