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vulkan : added the PAD_REFLECT_1D operation (#26586)

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* vulkan : added PAD_REFLECT_1D operation

Implemented the GGML_OP_PAD_REFLECT_1D operation for the Vulkan backend

Changes:
- pad_reflect_1d.comp: implemented the GLSL compute shader with reflection logic
- vulkan-shaders-gen.cpp: register the shader for SPIR-V compilation
- ggml-vulkan.cpp: pushed constants struct, pipeline creation,
  supports_op, dispatch function, compute switch and debug validation

Tested the PAD_REFLECT_1D on Intel Iris Xe (Vulkan 1.4, Mesa 25.2.8):

Correctness:
  PAD_REFLECT_1D(type=f32,ne_a=[512,34,2,1],pad_0=10,pad_1=9) = Pass
  PAD_REFLECT_1D(type=f32,ne_a=[3000,384,4,1],pad_0=10,pad_1=9) = Pass
  2/2 tests passed
 - All test are passed

Performance:
  ne_a=[512,34,2,1] -> 5.38 us/run, 24.55 GB/s
  ne_a=[3000,80,1,1] -> 30.09 us/run, 59.62 GB/s
  ne_a=[3000,384,4,1] -> 158.31 us/run, 54.39 GB/s

* Update ggml/src/ggml-vulkan/vulkan-shaders/pad_reflect_1d.comp

Co-authored-by: Jeff Bolz <jbolz@nvidia.com>

---------

Co-authored-by: Jeff Bolz <jbolz@nvidia.com>
This commit is contained in:
Safi Ullah
2026-08-22 14:42:20 -05:00
committed by GitHub
co-authored by Jeff Bolz
parent b21e4de745
commit 3f545becce
3 changed files with 70 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files
ggml/src/ggml-vulkan/ggml-vulkan.cpp
+26
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@@ -955,6 +955,7 @@ struct vk_device_struct {
vk_pipeline pipeline_diag[2];
vk_pipeline pipeline_clamp[2];
vk_pipeline pipeline_pad_f32;
vk_pipeline pipeline_pad_reflect_1d_f32;
vk_pipeline pipeline_roll_f32;
vk_pipeline pipeline_repeat_i32, pipeline_repeat_back_f32;
vk_pipeline pipeline_repeat_i16;
@@ -5630,6 +5631,7 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
ggml_vk_create_pipeline(device, device->pipeline_diag[1], "diag_f16", diag_f16_len, diag_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
ggml_vk_create_pipeline(device, device->pipeline_pad_f32, "pad_f32", pad_f32_len, pad_f32_data, "main", 2, sizeof(vk_op_pad_push_constants), {512, 1, 1}, {}, 1);
ggml_vk_create_pipeline(device, device->pipeline_pad_reflect_1d_f32, "pad_reflect_1d_f32", pad_reflect_1d_f32_len, pad_reflect_1d_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
ggml_vk_create_pipeline(device, device->pipeline_roll_f32, "roll_f32", roll_f32_len, roll_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
@@ -11336,6 +11338,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
return ctx->device->pipeline_pad_f32;
}
return nullptr;
case GGML_OP_PAD_REFLECT_1D:
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
return ctx->device->pipeline_pad_reflect_1d_f32;
}
return nullptr;
case GGML_OP_ROLL:
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
return ctx->device->pipeline_roll_f32;
@@ -12239,6 +12246,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
case GGML_OP_CLAMP:
case GGML_OP_LEAKY_RELU:
case GGML_OP_PAD:
case GGML_OP_PAD_REFLECT_1D:
case GGML_OP_ROLL:
case GGML_OP_REPEAT:
case GGML_OP_REPEAT_BACK:
@@ -13111,6 +13119,17 @@ static void ggml_vk_pad(ggml_backend_vk_context * ctx, vk_context& subctx, const
ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_PAD, std::move(p));
}
static void ggml_vk_pad_reflect_1d(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
const uint32_t p0 = (uint32_t)dst->op_params[0];
const uint32_t p1 = (uint32_t)dst->op_params[1];
vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst, ggml_nelements(dst));
memcpy(&p.param1, &p0, sizeof(float));
memcpy(&p.param2, &p1, sizeof(float));
ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_PAD_REFLECT_1D, std::move(p));
}
static void ggml_vk_roll(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
const int32_t s0 = ggml_get_op_params_i32(dst, 0);
const int32_t s1 = ggml_get_op_params_i32(dst, 1);
@@ -15520,6 +15539,10 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
case GGML_OP_PAD:
ggml_vk_pad(ctx, compute_ctx, src0, node);
break;
case GGML_OP_PAD_REFLECT_1D:
ggml_vk_pad_reflect_1d(ctx, compute_ctx, src0, node);
break;
case GGML_OP_ROLL:
ggml_vk_roll(ctx, compute_ctx, src0, node);
@@ -18446,6 +18469,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
case GGML_OP_SCALE:
return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
case GGML_OP_PAD:
case GGML_OP_PAD_REFLECT_1D:
case GGML_OP_ROLL:
return op->src[0]->type == GGML_TYPE_F32;
case GGML_OP_DIAG_MASK_INF:
@@ -19228,6 +19252,8 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
} else if (tensor->op == GGML_OP_PAD) {
tensor_clone = ggml_pad_ext(ggml_ctx, src_clone[0], tensor->op_params[0], tensor->op_params[1], tensor->op_params[2], tensor->op_params[3],
tensor->op_params[4], tensor->op_params[5], tensor->op_params[6], tensor->op_params[7]);
} else if (tensor->op == GGML_OP_PAD_REFLECT_1D) {
tensor_clone = ggml_pad_reflect_1d(ggml_ctx, src_clone[0], tensor->op_params[0], tensor->op_params[1]);
} else if (tensor->op == GGML_OP_REPEAT) {
tensor_clone = ggml_repeat(ggml_ctx, src_clone[0], tensor);
} else if (tensor->op == GGML_OP_REPEAT_BACK) {