[ET-VK] Introduce generic export pass for fusing Q/DQ nodes #10771
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Pull Request resolved: #10525 ## Context When quantizing models with the PT2E quantization flow, quantize/dequantize nodes will be inserted into the graph. However, these quantize/dequantize nodes must be fused with operators such as `aten.linear.default` to produce nodes corresponding to quantized operators (e.g. `weight_int8pack_mm`) in order for quantized operator implementations to be called at runtime. Currently, the op fusion is done by the `fuse_dequant_linear.py` pass, however, this only handles one specific fusion pattern to generate a `weight_int8pack_mm` operator. As more quantized operators are to be supported in ET-VK via the PT2E quantization flow, a more generic fusion pass is needed that can handle a variety of fusion patterns. ## Changes Introduce the `FuseQuantizedOpsTransform()` pass. I elected to introduce a new pass under the `backends/vulkan/_passes` directory, as opposed to modifying the existing pass because I anticipate the majority of the fusion patterns to be specific to ET-VK. Remove the existing `FuseDequantLinearPass()` Switch to using the `FuseQuantizedOpsTransform` pass instead of the old `FuseDequantLinear` pass. Add `test_vulkan_passes` Python test to test export passes. Added some refactors to `test_vulkan_delegate` Python test to improve code organization. Introduce the `linear_qcsnw` nomenclature: * q - quantized * c - per-channel / channelswise * s - symmetric * n - number of bits (qcs4w for 4-bit quant, qcs8w for 8-bit quant) * w - weight quantized Added custom op for `linear_qcs4w` for 4-bit weight quantized linear and add the ability for the quantized op fusion pass to produce this op. Slight renaming/refactoring of quantization config retrieval functions in the `VulkanQuantizer` to improve clarity and API flexibility. ghstack-source-id: 282688199 @exported-using-ghexport Differential Revision: [D73794042](https://our.internmc.facebook.com/intern/diff/D73794042/)
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/10771
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…10771) ## Context When quantizing models with the PT2E quantization flow, quantize/dequantize nodes will be inserted into the graph. However, these quantize/dequantize nodes must be fused with operators such as `aten.linear.default` to produce nodes corresponding to quantized operators (e.g. `weight_int8pack_mm`) in order for quantized operator implementations to be called at runtime. Currently, the op fusion is done by the `fuse_dequant_linear.py` pass, however, this only handles one specific fusion pattern to generate a `weight_int8pack_mm` operator. As more quantized operators are to be supported in ET-VK via the PT2E quantization flow, a more generic fusion pass is needed that can handle a variety of fusion patterns. ## Changes Introduce the `FuseQuantizedOpsTransform()` pass. I elected to introduce a new pass under the `backends/vulkan/_passes` directory, as opposed to modifying the existing pass because I anticipate the majority of the fusion patterns to be specific to ET-VK. Remove the existing `FuseDequantLinearPass()` Switch to using the `FuseQuantizedOpsTransform` pass instead of the old `FuseDequantLinear` pass. Add `test_vulkan_passes` Python test to test export passes. Some small refactors to `test_vulkan_delegate` Python test to improve code organizations. Differential Revision: [D73794042](https://our.internmc.facebook.com/intern/diff/D73794042/)
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This PR was created by the merge bot to help merge the original PR into the main branch.
ghstack PR number: #10525 by @SS-JIA
^ Please use this as the source of truth for the PR details, comments, and reviews
ghstack PR base: https://github.com/pytorch/executorch/tree/gh/SS-JIA/220/base
ghstack PR head: https://github.com/pytorch/executorch/tree/gh/SS-JIA/220/head
Merge bot PR base: https://github.com/pytorch/executorch/tree/main
Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/SS-JIA/220/orig
@diff-train-skip-merge
cc @SS-JIA @manuelcandales @cbilgin