[SYCL] support OP OPT_STEP_ADAMW, OPT_STEP_SGD (#25268)

* fix conflict

* fix conflict of ops.md

* fix conflict of ops.md

* update the ops.md

---------

Co-authored-by: Neo Zhang Jianyu <jianyu.zhang@intel.com>

docs/ops.md

@@ -77,8 +77,8 @@ Legend:
 |                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | 🟡 | 🟡 | ❌ |
 |                              NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | 🟡 | ❌ | ❌ |
-|                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                     OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                     OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                         OUT_PROD | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 |
 |                              PAD | ❌ | 🟡 | ✅ | 🟡 | ❌ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ |
 |                   PAD_REFLECT_1D | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
@@ -98,7 +98,7 @@ Legend:
 |                        RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                            SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              SET | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
-|                         SET_ROWS | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
+|                         SET_ROWS | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ |
 |                              SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |

examples/sycl/update-ops-doc.sh

@@ -4,6 +4,6 @@
 #  Copyright (C) 2026 Intel Corporation
 #  SPDX-License-Identifier: MIT
 
-./build/bin/test-backend-ops support --output csv > docs/ops/SYCL.csv
+./build/bin/test-backend-ops -b SYCL0 support --output csv > docs/ops/SYCL.csv
 ./scripts/create_ops_docs.py
 

ggml/src/ggml-sycl/ggml-sycl.cpp

@@ -77,6 +77,7 @@
 #include "ggml-sycl/fill.hpp"
 #include "ggml-sycl/cumsum.hpp"
 #include "ggml-sycl/diag.hpp"
+#include "ggml-sycl/opt-step.hpp"
 #include "ggml-sycl/solve_tri.hpp"
 #include "ggml-sycl/gated_delta_net.hpp"
 #include "ggml-sycl/pool.hpp"
@@ -5355,6 +5356,12 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg
         case GGML_OP_GATED_DELTA_NET:
             ggml_sycl_gated_delta_net(ctx, dst);
             break;
+        case GGML_OP_OPT_STEP_ADAMW:
+            ggml_sycl_opt_step_adamw(ctx, dst);
+            break;
+        case GGML_OP_OPT_STEP_SGD:
+            ggml_sycl_opt_step_sgd(ctx, dst);
+            break;
         case GGML_OP_SSM_CONV:
             ggml_sycl_ssm_conv(ctx, dst);
             break;
@@ -6263,6 +6270,8 @@ static bool do_ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, cons
         case GGML_OP_RWKV_WKV7:
         case GGML_OP_GATED_LINEAR_ATTN:
         case GGML_OP_GATED_DELTA_NET:
+        case GGML_OP_OPT_STEP_ADAMW:
+        case GGML_OP_OPT_STEP_SGD:
             return true;
         case GGML_OP_SSM_CONV:
             return op->type == GGML_TYPE_F32 &&

ggml/src/ggml-sycl/opt-step.cpp

@@ -0,0 +1,131 @@
+#include "opt-step.hpp"
+
+#define SYCL_OPT_STEP_BLOCK_SIZE 256
+
+template <typename T>
+static void opt_step_adamw_f32_kernel(
+    T * __restrict__ x,
+    const T * __restrict__ g,
+    T * __restrict__ g_m,
+    T * __restrict__ g_v,
+    const T * __restrict__ pars,
+    const int64_t k,
+    const sycl::nd_item<1> & item) {
+
+    const int64_t i = (int64_t) item.get_global_id(0);
+    if (i >= k) {
+        return;
+    }
+
+    const float alpha  = pars[0];
+    const float beta1  = pars[1];
+    const float beta2  = pars[2];
+    const float eps    = pars[3];
+    const float wd     = pars[4];
+    const float beta1h = pars[5];
+    const float beta2h = pars[6];
+
+    const float gi  = g[i];
+    const float gmi = g_m[i] * beta1 +      gi * (1.0f - beta1);
+    const float gvi = g_v[i] * beta2 + gi * gi * (1.0f - beta2);
+
+    g_m[i] = gmi;
+    g_v[i] = gvi;
+
+    const float mh = gmi * beta1h;
+    const float vh = sycl::sqrt(gvi * beta2h) + eps;
+
+    x[i] = x[i] * (1.0f - alpha * wd) - alpha * mh / vh;
+}
+
+template <typename T>
+static void opt_step_sgd_f32_kernel(
+    T * __restrict__ x,
+    const T * __restrict__ g,
+    const T * __restrict__ pars,
+    const int64_t k,
+    const sycl::nd_item<1> & item) {
+
+    const int64_t i = (int64_t) item.get_global_id(0);
+    if (i >= k) {
+        return;
+    }
+
+    x[i] = x[i] * (1.0f - pars[0] * pars[1]) - pars[0] * g[i];
+}
+
+void ggml_sycl_opt_step_adamw(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/5);
+
+    const ggml_tensor * src0         = dst->src[0];
+    const ggml_tensor * src0_grad    = dst->src[1];
+    const ggml_tensor * src0_grad_m  = dst->src[2];
+    const ggml_tensor * src0_grad_v  = dst->src[3];
+    const ggml_tensor * adamw_params = dst->src[4];
+
+    GGML_ASSERT(src0->type         == GGML_TYPE_F32);
+    GGML_ASSERT(src0_grad->type    == GGML_TYPE_F32);
+    GGML_ASSERT(src0_grad_m->type  == GGML_TYPE_F32);
+    GGML_ASSERT(src0_grad_v->type  == GGML_TYPE_F32);
+    GGML_ASSERT(adamw_params->type == GGML_TYPE_F32);
+    GGML_ASSERT(ggml_is_contiguous(src0));
+    GGML_ASSERT(ggml_is_contiguous(src0_grad));
+    GGML_ASSERT(ggml_is_contiguous(src0_grad_m));
+    GGML_ASSERT(ggml_is_contiguous(src0_grad_v));
+    GGML_ASSERT(ggml_is_contiguous(adamw_params));
+    GGML_ASSERT(ggml_are_same_shape(src0, src0_grad));
+    GGML_ASSERT(ggml_are_same_shape(src0, src0_grad_m));
+    GGML_ASSERT(ggml_are_same_shape(src0, src0_grad_v));
+    GGML_ASSERT(ggml_nelements(adamw_params) == 7);
+
+    dpct::queue_ptr stream = ctx.stream();
+    SYCL_CHECK(ggml_sycl_set_device(ctx.device));
+
+    float       * src0_d         = (float       *) src0->data;
+    const float * src0_grad_d    = (const float *) src0_grad->data;
+    float       * src0_grad_m_d  = (float       *) src0_grad_m->data;
+    float       * src0_grad_v_d  = (float       *) src0_grad_v->data;
+    const float * adamw_params_d = (const float *) adamw_params->data;
+
+    const int64_t ne = ggml_nelements(src0);
+    const int64_t num_blocks = (ne + SYCL_OPT_STEP_BLOCK_SIZE - 1) / SYCL_OPT_STEP_BLOCK_SIZE;
+
+    stream->parallel_for(
+        sycl::nd_range<1>(num_blocks * SYCL_OPT_STEP_BLOCK_SIZE, SYCL_OPT_STEP_BLOCK_SIZE),
+        [=](sycl::nd_item<1> item) {
+            opt_step_adamw_f32_kernel(src0_d, src0_grad_d, src0_grad_m_d, src0_grad_v_d, adamw_params_d, ne, item);
+        });
+}
+
+void ggml_sycl_opt_step_sgd(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/3);
+
+    const ggml_tensor * src0       = dst->src[0];
+    const ggml_tensor * src0_grad  = dst->src[1];
+    const ggml_tensor * sgd_params = dst->src[2];
+
+    GGML_ASSERT(src0->type      == GGML_TYPE_F32);
+    GGML_ASSERT(src0_grad->type == GGML_TYPE_F32);
+    GGML_ASSERT(sgd_params->type == GGML_TYPE_F32);
+    GGML_ASSERT(ggml_is_contiguous(src0));
+    GGML_ASSERT(ggml_is_contiguous(src0_grad));
+    GGML_ASSERT(ggml_is_contiguous(sgd_params));
+    GGML_ASSERT(ggml_are_same_shape(src0, src0_grad));
+    GGML_ASSERT(ggml_nelements(sgd_params) == 2);
+
+    dpct::queue_ptr stream = ctx.stream();
+    SYCL_CHECK(ggml_sycl_set_device(ctx.device));
+
+    float       * src0_d      = (float       *) src0->data;
+    const float * src0_grad_d = (const float *) src0_grad->data;
+    const float * sgd_params_d = (const float *) sgd_params->data;
+
+    const int64_t ne = ggml_nelements(src0);
+    const int64_t num_blocks = (ne + SYCL_OPT_STEP_BLOCK_SIZE - 1) / SYCL_OPT_STEP_BLOCK_SIZE;
+
+    stream->parallel_for(
+        sycl::nd_range<1>(num_blocks * SYCL_OPT_STEP_BLOCK_SIZE, SYCL_OPT_STEP_BLOCK_SIZE),
+        [=](sycl::nd_item<1> item) {
+            opt_step_sgd_f32_kernel(src0_d, src0_grad_d, sgd_params_d, ne, item);
+        });
+}

ggml/src/ggml-sycl/opt-step.hpp

@@ -0,0 +1,6 @@
+#pragma once
+
+#include "common.hpp"
+
+void ggml_sycl_opt_step_adamw(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
+void ggml_sycl_opt_step_sgd(ggml_backend_sycl_context & ctx, ggml_tensor * dst);