[ET-VK] Add fused q8ta_relu unary operator for int8x4 tensors#17576
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Pull Request resolved: #17507 This adds a fused quantized unary operator (ReLU) that operates directly on int8x4 packed buffer tensors, avoiding the overhead of separate dequantize-relu-requantize dispatches. The implementation follows the same pattern as q8ta_binary: a single GLSL compute shader dequantizes int8x4 blocks to float, applies the unary operation, and requantizes back to int8x4 in one dispatch. The shader uses the OPERATOR macro for parameterization so additional unary ops can be added as YAML variants without new shader code. Components added: - GLSL shader (q8ta_unary.glsl) and YAML config with relu variant - C++ operator implementation (Q8taUnary.cpp/h) registering et_vk.q8ta_relu.default - Export graph fusion pattern (quantized_unary.py) that detects dequant->relu->quant sequences and replaces them with the fused op - Custom op definition (q8ta_relu in custom_ops_lib.py) for the export pipeline - Test harness (TestQ8taUnary.cpp, test_q8ta_unary.cpp) with reference implementation and coverage across multiple shapes and quantized layouts This diff was authored with Claude. ghstack-source-id: 342806073 @exported-using-ghexport Differential Revision: [D93511629](https://our.internmc.facebook.com/intern/diff/D93511629/)
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/17576
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Pull Request resolved: #17508 The batch norm operator registration had a check_batch_norm_node guard that restricted partitioning to 4D input tensors only. Since batch norm is always fused with adjacent operations during graph compilation, this restriction is unnecessary and prevents valid models from being partitioned to the Vulkan backend. Remove the guard so batch norm is always eligible for Vulkan partitioning regardless of input dimensionality. ghstack-source-id: 342806074 @exported-using-ghexport Differential Revision: [D93511630](https://our.internmc.facebook.com/intern/diff/D93511630/)
Previously, the q8ta_binary operator required both inputs to use the same memory layout. This was enforced by using a single `in_layout` specialization constant for both input buffers. However, some models may have inputs with different layouts (e.g., 4W4C and 4C1W) that share the same packed dimension and block size, which should be compatible for binary operations. This change introduces a separate `other_layout` specialization constant for the second input, allowing the shader to correctly load from input_b using its actual layout while input_a continues to use `in_layout`. The C++ side now passes both layout hashes as separate specialization constants to the shader. Differential Revision: [D93768638](https://our.internmc.facebook.com/intern/diff/D93768638/) ghstack-source-id: 342806076 Pull Request resolved: #17563
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Feb 20, 2026
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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: #17507 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/435/base
ghstack PR head: https://github.com/pytorch/executorch/tree/gh/SS-JIA/435/head
Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/SS-JIA/434/orig
Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/SS-JIA/435/orig
Differential Revision: D93511629
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