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[ExecuTorch][Vulkan] Add et_vk.adamw_step kernel (on-GPU AdamW optimizer step)
Pull Request resolved: #20937 Vulkan GLSL kernel + handler for the training custom op `et_vk.adamw_step` (elementwise in-place AdamW parameter update). Mirrors `impl/BinaryOp.cpp` elementwise structure + `unary_op.glsl` push-constant scalars; param/m/v are read-modify-write (`kReadWrite`). AOT registration already exists under the shared Vulkan partitioner. ghstack-source-id: 405026087 @exported-using-ghexport Differential Revision: [D111761777](https://our.internmc.facebook.com/intern/diff/D111761777/)
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‎backends/vulkan/runtime/graph/ops/glsl/adamw_step.glsl‎

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/*
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* Copyright (c) Meta Platforms, Inc. and affiliates.
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* All rights reserved.
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*
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* This source code is licensed under the BSD-style license found in the
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* LICENSE file in the root directory of this source tree.
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*/
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#version 450 core
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#define PRECISION ${PRECISION}
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#define T ${buffer_scalar_type(DTYPE)}
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${define_active_storage_type(STORAGE)}
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${define_required_extensions(STORAGE, DTYPE)}
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layout(std430) buffer;
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${layout_declare_tensor(B, "rw", "t_param", DTYPE, STORAGE)}
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${layout_declare_tensor(B, "rw", "t_m", DTYPE, STORAGE)}
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${layout_declare_tensor(B, "rw", "t_v", DTYPE, STORAGE)}
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${layout_declare_tensor(B, "r", "t_grad", DTYPE, STORAGE)}
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layout(push_constant) uniform restrict Block {
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int numel;
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float lr;
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float beta1;
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float beta2;
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float eps;
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float weight_decay;
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float bias_correction1;
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float bias_correction2;
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};
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layout(local_size_x_id = 0, local_size_y_id = 1, local_size_z_id = 2) in;
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void main() {
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const int i = int(gl_GlobalInvocationID.x);
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if (i >= numel) {
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return;
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}
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T g = t_grad[i];
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T p = t_param[i];
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p = p - lr * weight_decay * p;
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T m = beta1 * t_m[i] + (1.0 - beta1) * g;
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T v = beta2 * t_v[i] + (1.0 - beta2) * g * g;
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t_m[i] = m;
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t_v[i] = v;
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T mhat = m / bias_correction1;
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T vhat = v / bias_correction2;
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t_param[i] = p - lr * mhat / (sqrt(vhat) + eps);
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}

‎backends/vulkan/runtime/graph/ops/glsl/adamw_step.yaml‎

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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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adamw_step:
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parameter_names_with_default_values:
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DTYPE: float
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STORAGE: buffer
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generate_variant_forall:
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STORAGE:
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- VALUE: buffer
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DTYPE:
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- VALUE: float
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shader_variants:
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- NAME: adamw_step

‎backends/vulkan/runtime/graph/ops/impl/AdamwStep.cpp‎

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/*
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* Copyright (c) Meta Platforms, Inc. and affiliates.
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* All rights reserved.
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*
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* This source code is licensed under the BSD-style license found in the
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* LICENSE file in the root directory of this source tree.
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*/
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#include <executorch/backends/vulkan/runtime/graph/ops/DynamicDispatchNode.h>
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#include <executorch/backends/vulkan/runtime/graph/ops/OperatorRegistry.h>
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#include <executorch/backends/vulkan/runtime/graph/ops/impl/Common.h>
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#include <executorch/backends/vulkan/runtime/graph/ops/utils/ShaderNameUtils.h>
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#include <array>
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namespace vkcompute {
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void check_adamw_step_args(
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ComputeGraph& graph,
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const ValueRef param,
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const ValueRef m,
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const ValueRef v,
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const ValueRef grad) {
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VK_CHECK_COND(graph.dtype_of(param) == vkapi::kFloat);
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VK_CHECK_COND(graph.dtype_of(m) == vkapi::kFloat);
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VK_CHECK_COND(graph.dtype_of(v) == vkapi::kFloat);
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VK_CHECK_COND(graph.dtype_of(grad) == vkapi::kFloat);
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const int32_t numel = graph.numel_of(param);
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VK_CHECK_COND(graph.numel_of(m) == numel);
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VK_CHECK_COND(graph.numel_of(v) == numel);
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VK_CHECK_COND(graph.numel_of(grad) == numel);
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}
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void add_adamw_step_node(
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ComputeGraph& graph,
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const ValueRef param,
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const ValueRef m,
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const ValueRef v,
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const ValueRef grad,
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const ValueRef lr,
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const ValueRef beta1,
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const ValueRef beta2,
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const ValueRef eps,
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const ValueRef weight_decay,
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const ValueRef bias_correction1,
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const ValueRef bias_correction2) {
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check_adamw_step_args(graph, param, m, v, grad);
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const float bc1 = graph.extract_scalar<float>(bias_correction1);
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const float bc2 = graph.extract_scalar<float>(bias_correction2);
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VK_CHECK_COND(bc1 != 0.0f);
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VK_CHECK_COND(bc2 != 0.0f);
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// Split into <=16-byte push-constant entries (PushConstantData.h limit).
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const std::array<float, 4> pc0 = {
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graph.extract_scalar<float>(lr),
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graph.extract_scalar<float>(beta1),
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graph.extract_scalar<float>(beta2),
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graph.extract_scalar<float>(eps)};
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const std::array<float, 3> pc1 = {
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graph.extract_scalar<float>(weight_decay), bc1, bc2};
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std::string kernel_name("adamw_step");
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add_storage_type_suffix(kernel_name, graph.storage_type_of(param));
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add_dtype_suffix(kernel_name, graph.dtype_of(param));
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graph.execute_nodes().emplace_back(new DynamicDispatchNode(
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graph,
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VK_KERNEL_FROM_STR(kernel_name),
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default_pick_global_wg_size,
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default_pick_local_wg_size,
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// Inputs and Outputs
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{{param, vkapi::kReadWrite},
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{m, vkapi::kReadWrite},
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{v, vkapi::kReadWrite},
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{grad, vkapi::kRead}},
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// Shader params buffers
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{},
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// Push Constants
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{graph.numel_pc_of(param),
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PushConstantDataInfo(pc0.data(), sizeof(pc0)),
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PushConstantDataInfo(pc1.data(), sizeof(pc1))},
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// Specialization Constants
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{},
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// Resize Args
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{},
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// Resizing Logic
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nullptr));
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}
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void adamw_step(ComputeGraph& graph, const std::vector<ValueRef>& args) {
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int arg_idx = 0;
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const ValueRef param = args[arg_idx++];
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const ValueRef m = args[arg_idx++];
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const ValueRef v = args[arg_idx++];
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const ValueRef grad = args[arg_idx++];
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const ValueRef lr = args[arg_idx++];
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const ValueRef beta1 = args[arg_idx++];
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const ValueRef beta2 = args[arg_idx++];
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const ValueRef eps = args[arg_idx++];
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const ValueRef weight_decay = args[arg_idx++];
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const ValueRef bias_correction1 = args[arg_idx++];
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const ValueRef bias_correction2 = args[arg_idx++];
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add_adamw_step_node(
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graph,
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param,
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m,
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v,
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grad,
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lr,
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beta1,
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beta2,
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eps,
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weight_decay,
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bias_correction1,
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bias_correction2);
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}
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REGISTER_OPERATORS {
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VK_REGISTER_OP(et_vk.adamw_step.default, adamw_step);
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}
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} // namespace vkcompute

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