[ET-VK][Ops] torchao.quantize_affine vulkan impl and shader and cleanup #13001
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Pull Request resolved: #12575 # Changes * Implement `torchao.quantize_affine` operator in Vulkan backend with comprehensive texture and buffer storage support * Add block-wise quantization mode in `quantize_texture.glsl` and `quantize_buffer.glsl` shaders for configurable tensor block quantization * Introduce comprehensive test suite in `affine_test.cpp` with multi-dimensional tensor validation and reference implementation * Extend quantization infrastructure in `Quantize.cpp` to handle affine transformations with configurable block sizes and quantization parameters BE: Improved the documentation in the shader logic which is more detailed and clear NOTE: I delegated the quantize_affine and future affine operators through a new custom test file denoted as `affine_test.cpp` as the other quantization testing framework was getting a little large, and it makes more sense to separate the namespace between torchao and quantized_decomposed. I believe the _decomposed namespace is getting phased out in favor of this affine operator so deprecation will be easier in the future. # Motivation The existing Vulkan quantization infrastructure lacked support for the `torchao.quantize_affine` operator, which is essential for enabling dynamic quantization efficiently. The `quantize_affine` operator provides flexible block-wise quantization that allows different scale and zero-point values for tensor blocks, enabling: * **Block-wise Quantization**: Applies quantization parameters to configurable tensor blocks rather than entire tensors, improving quantization accuracy for heterogeneous data distributions * **Affine Transformation**: Uses the formula `qvalue = clamp(round(value / scale) + zero_point, quant_min, quant_max)` for precise floating-point to integer mapping # Operator Description The `quantize_affine` operator converts floating-point tensor values to n-bit integer representations using pre-computed quantization parameters (scale and zero_point) applied to configurable tensor blocks. Block-wise quantization divides tensors into blocks and applies separate quantization parameters to each block, allowing fine-grained control over quantization precision. The quantization formula is: `qvalue = clamp(round(value / scale) + zero_point, quant_min, quant_max)` **Storage Requirements**: Scale and zero_point tensors must use buffer storage with width-packed layout. Input/output tensors support both buffer and texture storage with standard axis mapping. # Block-wise Quantization Implementation Block-wise quantization enables fine-grained quantization by dividing tensors into blocks and applying separate quantization parameters to each block. The implementation uses several key data structures computed in `Quantize.cpp`: * **`block_size_vec`**: WHCN-ordered block dimensions converted from PyTorch NCHW layout (e.g., [3,3,2,1] for 3×3×2×1 blocks) * **`tensor_size_whcn`**: Input tensor dimensions converted to WHCN layout using `utils::make_whcn_ivec4()` * **`num_blocks_vec`**: Number of blocks per dimension calculated as `tensor_size_whcn / block_size_vec` * **`block_stride_vec`**: Pre-computed linear strides for block grid indexing `{1, #W, #W*#H, #W*#H*#C}` to enable efficient block ID calculation The block coordinate calculation uses: `bcoord = tidx / blockSize` where `tidx` is the tensor coordinate in WHCN layout, then the linear block ID is computed as: `block_id = bcoord.x * blockStride.x + bcoord.y * blockStride.y + bcoord.z * blockStride.z + bcoord.w * blockStride.w` # Shader Algorithm Overview ## Texture Storage Implementation (`quantize_texture.glsl`) **Workgroup Configuration**: - **Global WG Size**: Default sizing based on texture dimensions - **Local WG Size**: Default with special handling for batch dimension quantization (Z dimension set to 1 for proper workgroup dispatching when `global_workgroup_size[2] > 1`) **Block-wise Mode Algorithm**: The shader processes 3D texture positions where each position represents a texel containing 4 width-packed components. For each texel at position `pos`, it calculates a base tensor index `base_tidx = ivec4(pos.x * 4, pos.y, pos.z, 0)` to account for width-packing. For each of the 4 components in the texel, it computes the actual tensor coordinate: `tidx = ivec4(base_tidx.x + i, base_tidx.y, (foldedZ % C_total), (foldedZ / C_total))` where `foldedZ = pos.z` handles batch-channel folding in 4D tensors and `C_total = numBlocks.z * blockSize.z` represents the total channel dimension. The block coordinate is calculated using integer division: `bcoord = tidx / blockSize`, then the linear block ID uses pre-computed strides: `block_id = bcoord.x * blockStride.x + bcoord.y * blockStride.y + bcoord.z * blockStride.z + bcoord.w * blockStride.w`. Each component is quantized using its corresponding block's parameters: `qvalue = quantize_val(value, t_scale[block_id], t_zero_point[block_id])` and written to the output texel. ## Buffer Storage Implementation (`quantize_buffer.glsl`) **Workgroup Configuration**: - **Global WG Size**: Default sizing based on buffer element count - **Local WG Size**: Default sizing without special constraints **Block-wise Mode Algorithm**: The shader processes linear buffer indices using `gl_GlobalInvocationID.x` as the output buffer index. It converts this to tensor coordinates using `bufi_to_tidx(out_bufi, t_out_strides, out_dim_order)` which handles the buffer-to-tensor index mapping with proper stride calculations. For each element, it computes the block coordinate directly: `bcoord = out_tidx / blockSize` where `out_tidx` is the 4D tensor coordinate in WHCN layout. The linear block ID calculation uses the same pre-computed stride approach: `block_id = bcoord.x * blockStride.x + bcoord.y * blockStride.y + bcoord.z * blockStride.z + bcoord.w * blockStride.w`. The element value is loaded using the corresponding input buffer index: `value = t_in[in_bufi]` where `in_bufi = tidx_to_bufi(out_tidx, t_in_strides)`. Quantization applies the block-specific parameters: `qvalue = quantize_val(value, t_scale[block_id], t_zero_point[block_id])`. **Future Improvements**: Dynamic workgroup sizing based on block dimensions, there is likely a better method to making it better than what it is currently. ghstack-source-id: 299473617 @exported-using-ghexport Differential Revision: [D78302195](https://our.internmc.facebook.com/intern/diff/D78302195/)
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…anup (#13002) This PR was created by the merge bot to help merge the original PR into the main branch. ghstack PR number: #12576 by @ahmtox ^ 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/ahmtox/44/base ghstack PR head: https://github.com/pytorch/executorch/tree/gh/ahmtox/44/head Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/ahmtox/43/orig Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/ahmtox/44/orig @diff-train-skip-merge cc @SS-JIA @manuelcandales @cbilgin Co-authored-by: morelos <morelos@devvm4573.ash0.facebook.com> Co-authored-by: ahmtox <69552192+ahmtox@users.noreply.github.com>
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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: #12575 by @ahmtox
^ 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/ahmtox/43/base
ghstack PR head: https://github.com/pytorch/executorch/tree/gh/ahmtox/43/head
Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/ahmtox/42/orig
Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/ahmtox/43/orig
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cc @SS-JIA @manuelcandales @cbilgin