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[ET-VK][conv2d] Fuse residual and activation epilogues into conv2d shaders - #24822
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…aders Pull Request resolved: #24807 ## Ulterior Motive Closing the DSR FP16 gap to LiteRT. DSR has 83 skip connections of the form `conv(...) + x * scale`. ET-VK runs each as a separate `mul` and `add` (168 dispatches, 347 ms per inference on Galaxy S25 against LiteRT's 148 ms). This commit adds runtime support for fusing them into the preceding convolution; the next commit fuses them during lowering. ## Rationale **What**: Every conv2d shader (direct, pointwise, depthwise, im2col GEMM and transposed) gains residual and scale bindings and applies an epilogue before storing: ``` out = activation(residual_op(conv + bias, residual * scale, alpha)) ``` Specialization constants select each stage. A new `et_vk.conv2d.with_epilogue` op exposes it. **Why**: One dispatch replaces conv, `mul` and `add`, and the conv output and scaled residual are never written to memory. Because every route applies the epilogue itself, the op never falls back to separate element-wise dispatches. ## Details - **Epilogue** (`conv2d_epilogue.glslh`): calls `apply_binary_op()` for the residual op and `apply_unary_op()` for the activation. - The C++ side accepts add, sub and mul residual ops and clamp, relu and leaky_relu activations. - Inlining the `apply_unary_op()` switch changes Adreno's code generation for the 2x2 tiled direct shaders, even with the activation specialized away: AOC instruction counts rise 6–25% (`conv2d_2x2x8_border_float`: 532 → 660) with unchanged register counts. - Against a switch limited to the three supported activations, large FP16 3x3 and 5x5 direct convolutions are 2.1–2.9% slower on Pixel 8 Pro, and small 3x3 direct convolutions 4.4–5.7% slower on Galaxy S23. Calling `apply_unary_op()` avoids a second activation dispatcher. - **Absent operands**: The residual slot binds the convolution input and the scale slot binds the packed bias, which have the slots' storage types. `has_residual` / `has_scale` gate the reads. - **Scale**: A constant `[1, C, 1, 1]` or `[C, 1, 1]` residual scale. Each route prepacks it like the bias (texture2d, using the transposed weight layout for transposed convolutions) with `prepack_conv2d_residual_scale()`, next to the bias prepack, before building the bindings (`Conv2dEpilogueArgs`). - **Validation**: `conv2d_epilogue_from_args()` throws at graph build when: - the residual is not texture3d or does not match the output's sizes, dtype and layout; - the scale is not a constant, is given without a residual, does not have the weight's dtype, or is not `[1, C, 1, 1]` or `[C, 1, 1]`. Other shapes with C values, such as `[C]`, broadcast along another dim in PyTorch; - the residual op or activation is unsupported. conv2d nodes pass the residual as their last resize argument, and their resize functions check that it still matches the resized output's sizes. - **Clamp** - **Before**: Each shader family selected its clamp differently: a `clamp_out` spec constant and an `out_min` / `out_max` UBO (direct); an `activation_type` 0/1/2 spec constant (pointwise and im2col GEMM); separately generated `_clamp` shader variants (depthwise and transposed). - **After**: All use `has_activation` + `activation_op` with `activation_a` / `activation_b`. The `_clamp` variants are gone. `conv_with_clamp` builds a clamp epilogue. - **Bindings and constants** - `conv2d_tiled`, `conv2d_dw` and `conv_transpose2d`: residual at 4, scale at 5, UBOs move from 4–8 to 6–10. UBO 10 holds `activation_a`, `activation_b` and `residual_alpha`. - `conv2d_pw_tiled`, `conv2d_gemm` and the depthwise output-tile shaders: residual and scale follow the bias; the clamp push constants become `activation_a`, `activation_b` and `residual_alpha`. - The epilogue specialization constants `has_activation`, `activation_op`, `has_residual`, `has_scale` and `residual_op` are contiguous in every shader, and `append_conv2d_epilogue_spec_vars()` appends all five. In `conv2d_tiled`, `has_bias` moves from ID 7 to 6, the group channel counts from 8–9 to 7–8, and `has_activation` (formerly `clamp_out`, 6) takes 9, ahead of the other epilogue constants at 10–13. - The unused `ngroups` (`conv2d_dw`) and `packed_dim` (`conv_transpose2d`) spec constants are removed. The transposed convolution no longer needs the `has_activation` resize argument that picked its `_clamp` variant. - **Entry point**: `conv2d_with_epilogue_impl()` replaces `conv2d_impl()` and implements `aten.convolution`, `conv_with_clamp` and `conv2d.with_epilogue` for 4D inputs. - **Depthwise tile override**: `conv2d_dw_impl` takes an optional `b1x1_override`. The benchmark's depthwise `b1x1` / `b4x2` routes call it instead of carrying a copy of the depthwise dispatch. The `b1x1` route now also selects the 1x1 tile for 5x5 kernels, as the Mali default already does. - **Benchmark** - Adds a `conv2d_with_epilogue` reference and a transposed convolution reference; the conv2d reference previously rejected transposed convolutions. The epilogue reference rejects the scale shapes and the scale without a residual that the runtime rejects. - `et_vk.conv2d.with_epilogue` accepts the `direct`, `im2col`, `b1x1` and `b4x2` routes and reports throughput. - Adds 204 correctness cases. 200 cover 5 epilogues × FP32/FP16 on 20 shape and route combinations: - one shape per route: direct, pointwise, im2col, the three depthwise shaders, transposed; - partial output texels or tiles on the direct, pointwise, im2col, depthwise tile and transposed routes; - a strided and dilated direct conv and a grouped direct conv. - The other 4 run a pointwise, direct, im2col and depthwise epilogue through a full → shrunk → full resize. - Adds the `operator_dynamic_resize` case check. It builds an operator case, which must expand to one case, once, then for each step resizes the staged inputs, checks the output sizes and validates the output. The q8ta streaming-shrink check now uses it instead of its own copy. - The depthwise `b1x1` / `b4x2` routes report batch > 1 as unsupported (SKIP), since the depthwise shaders support batch 1 only. - The route inspector now recognizes the tiled direct shader names. - `conv2d_gemm_sustained.json` binds the new GEMM slots. Usage: ```cpp // Parses and validates the epilogue arguments of a conv2d op const Conv2dEpilogue epilogue = conv2d_epilogue_from_args(graph, args); conv2d_direct_impl(graph, in, weight, bias, stride, padding, dilation, transposed, output_padding, groups, out, epilogue); // Inside a route const ValueRef packed_scale = prepack_conv2d_residual_scale(graph, epilogue, weight, transposed); const Conv2dEpilogueArgs epilogue_args(epilogue, in, packed_bias, packed_scale); // Bind epilogue_args.residual and epilogue_args.scale, append the epilogue's // specialization constants, and pass epilogue.residual as the last resize arg. append_conv2d_epilogue_spec_vars(spec_vars, epilogue); ``` ghstack-source-id: 469036183 @exported-using-ghexport Differential Revision: [D130216287](https://our.internmc.facebook.com/intern/diff/D130216287/)
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/24822
Note: Links to docs will display an error until the docs builds have been completed. ⏳ No Failures, 19 PendingAs of commit cbc00c0 with merge base e5ffebe ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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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: #24807 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/698/base
ghstack PR head: https://github.com/pytorch/executorch/tree/gh/SS-JIA/698/head
Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/SS-JIA/697/orig
Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/SS-JIA/698/orig
Differential Revision: D130216287
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