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Merged
merged 5 commits into from
Aug 30, 2025
Merged

Sync master with upstream release b6323 #225

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f07b163
server : add documentation for `parallel_tool_calls` param (#15647)
ExtReMLapin Aug 29, 2025
38d6c3a
scripts: strip "AMD Instinct" from GPU name (#15668)
JohannesGaessler Aug 29, 2025
f9d12f7
server : removed obsolete doc (#15670)
l29ah Aug 29, 2025
3d0beb9
CANN: FIx compiler warnings (#15661)
noemotiovon Aug 30, 2025
ad5f187
vulkan: Skip syncing for prealloc_y when it is reused (#15544)
jeffbolznv Aug 30, 2025
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2 changes: 2 additions & 0 deletions docs/function-calling.md
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Expand Up @@ -21,6 +21,8 @@ Function calling is supported for all models (see https://github.com/ggml-org/ll
- Use `--chat-template-file` to override the template when appropriate (see examples below)
- Generic support may consume more tokens and be less efficient than a model's native format.

- Multiple/parallel tool calling is supported on some models but disabled by default, enable it by passing `"parallel_tool_calls": true` in the completion endpoint payload.

<details>
<summary>Show some common templates and which format handler they use</summary>

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8 changes: 4 additions & 4 deletions ggml/src/ggml-cann/ggml-cann.cpp
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Expand Up @@ -1155,7 +1155,7 @@ namespace {
* @note The workspace buffer used in this function is managed globally and reused
* across calls. This reduces overhead from repeated memory allocation and deallocation.
*/
static void weight_format_to_nz(ggml_tensor *tensor, const void *data, size_t offset) {
static void weight_format_to_nz(ggml_tensor *tensor, size_t offset) {
aclTensor* weightTransposed = ggml_cann_create_tensor(tensor, tensor->ne,
tensor->nb, 2, ACL_FORMAT_ND, offset);
uint64_t workspaceSize = 0;
Expand Down Expand Up @@ -1203,7 +1203,7 @@ static void ggml_backend_cann_buffer_set_tensor(
if (weight_to_nz && is_matmul_weight((const ggml_tensor*)tensor)) {
GGML_ASSERT(tensor->ne[2] == 1);
GGML_ASSERT(tensor->ne[3] == 1);
weight_format_to_nz(tensor, data, offset);
weight_format_to_nz(tensor, offset);
}
} else {
void *transform_buffer = malloc(size);
Expand Down Expand Up @@ -2491,7 +2491,7 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
return true;
case GGML_OP_SCALE:
float bias;
memcpy(&bias, (float*)op->op_params + 1, sizeof(float));
memcpy(&bias, (const float *)(op->op_params) + 1, sizeof(float));
return bias == 0.0f; // TODO: support bias != 0.0f
case GGML_OP_SOFT_MAX:
// TODO: support attention sinks [TAG_ATTN_SINKS]
Expand Down Expand Up @@ -2534,7 +2534,7 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
return false;
}
float logitSoftcap = 0.0f;
memcpy(&logitSoftcap, (float*)op->op_params + 2, sizeof(float));
memcpy(&logitSoftcap, (const float *)(op->op_params) + 2, sizeof(float));
if(logitSoftcap != 0.0f) {
return false;
}
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35 changes: 15 additions & 20 deletions ggml/src/ggml-vulkan/ggml-vulkan.cpp
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Expand Up @@ -5800,11 +5800,6 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub
ggml_vk_sync_buffers(ctx, subctx);
}
}
if (y_non_contig || quantize_y) {
if (ctx->prealloc_y_need_sync) {
ggml_vk_sync_buffers(ctx, subctx);
}
}

if (x_non_contig) {
ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE });
Expand All @@ -5816,6 +5811,9 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub
if (y_non_contig) {
if (ctx->prealloc_y_last_pipeline_used != to_fp16_vk_1.get() ||
ctx->prealloc_y_last_tensor_used != src1) {
if (ctx->prealloc_y_need_sync) {
ggml_vk_sync_buffers(ctx, subctx);
}
ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
ctx->prealloc_y_last_pipeline_used = to_fp16_vk_1.get();
ctx->prealloc_y_last_tensor_used = src1;
Expand All @@ -5824,6 +5822,9 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& sub
if (quantize_y) {
if (ctx->prealloc_y_last_pipeline_used != to_q8_1.get() ||
ctx->prealloc_y_last_tensor_used != src1) {
if (ctx->prealloc_y_need_sync) {
ggml_vk_sync_buffers(ctx, subctx);
}
ggml_vk_quantize_q8_1(ctx, subctx, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }, y_ne * ne12 * ne13);
ctx->prealloc_y_last_pipeline_used = to_q8_1.get();
ctx->prealloc_y_last_tensor_used = src1;
Expand Down Expand Up @@ -6008,11 +6009,6 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
ggml_vk_sync_buffers(ctx, subctx);
}
}
if (y_non_contig) {
if (ctx->prealloc_y_need_sync) {
ggml_vk_sync_buffers(ctx, subctx);
}
}

if (x_non_contig) {
GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment));
Expand All @@ -6022,6 +6018,9 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
GGML_ASSERT(y_sz == ggml_type_size(src1->type) * y_ne);
if (ctx->prealloc_y_last_pipeline_used != to_fp16_vk_1.get() ||
ctx->prealloc_y_last_tensor_used != src1) {
if (ctx->prealloc_y_need_sync) {
ggml_vk_sync_buffers(ctx, subctx);
}
ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
ctx->prealloc_y_last_pipeline_used = to_fp16_vk_1.get();
ctx->prealloc_y_last_tensor_used = src1;
Expand Down Expand Up @@ -6454,11 +6453,6 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context&
ggml_vk_sync_buffers(ctx, subctx);
}
}
if (y_non_contig) {
if (ctx->prealloc_y_need_sync) {
ggml_vk_sync_buffers(ctx, subctx);
}
}

if (x_non_contig) {
ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE });
Expand All @@ -6471,6 +6465,9 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context&
if (y_non_contig) {
if (ctx->prealloc_y_last_pipeline_used != to_fp16_vk_1.get() ||
ctx->prealloc_y_last_tensor_used != src1) {
if (ctx->prealloc_y_need_sync) {
ggml_vk_sync_buffers(ctx, subctx);
}
ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
ctx->prealloc_y_last_pipeline_used = to_fp16_vk_1.get();
ctx->prealloc_y_last_tensor_used = src1;
Expand Down Expand Up @@ -6668,11 +6665,6 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte
ggml_vk_sync_buffers(ctx, subctx);
}
}
if (y_non_contig) {
if (ctx->prealloc_y_need_sync) {
ggml_vk_sync_buffers(ctx, subctx);
}
}

if (x_non_contig) {
GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment));
Expand All @@ -6682,6 +6674,9 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte
GGML_ASSERT(y_sz == ggml_type_size(src1->type) * y_ne);
if (ctx->prealloc_y_last_pipeline_used != to_fp16_vk_1.get() ||
ctx->prealloc_y_last_tensor_used != src1) {
if (ctx->prealloc_y_need_sync) {
ggml_vk_sync_buffers(ctx, subctx);
}
ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE });
ctx->prealloc_y_last_pipeline_used = to_fp16_vk_1.get();
ctx->prealloc_y_last_tensor_used = src1;
Expand Down
2 changes: 1 addition & 1 deletion scripts/compare-llama-bench.py
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Expand Up @@ -96,7 +96,7 @@
DEFAULT_SHOW_TEST_BACKEND_OPS = ["backend_name", "op_name"] # Always show these properties by default.
DEFAULT_HIDE_TEST_BACKEND_OPS = ["error_message"] # Always hide these properties by default.

GPU_NAME_STRIP = ["NVIDIA GeForce ", "Tesla ", "AMD Radeon "] # Strip prefixes for smaller tables.
GPU_NAME_STRIP = ["NVIDIA GeForce ", "Tesla ", "AMD Radeon ", "AMD Instinct "] # Strip prefixes for smaller tables.
MODEL_SUFFIX_REPLACE = {" - Small": "_S", " - Medium": "_M", " - Large": "_L"}

DESCRIPTION = """Creates tables from llama-bench or test-backend-ops data written to multiple JSON/CSV files, a single JSONL file or SQLite database. Example usage (Linux):
Expand Down
3 changes: 2 additions & 1 deletion tools/server/README.md
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Expand Up @@ -62,7 +62,6 @@ The project is under active development, and we are [looking for feedback and co
| `--yarn-attn-factor N` | YaRN: scale sqrt(t) or attention magnitude (default: 1.0)<br/>(env: LLAMA_ARG_YARN_ATTN_FACTOR) |
| `--yarn-beta-slow N` | YaRN: high correction dim or alpha (default: 1.0)<br/>(env: LLAMA_ARG_YARN_BETA_SLOW) |
| `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: 32.0)<br/>(env: LLAMA_ARG_YARN_BETA_FAST) |
| `-dkvc, --dump-kv-cache` | verbose print of the KV cache |
| `-nkvo, --no-kv-offload` | disable KV offload<br/>(env: LLAMA_ARG_NO_KV_OFFLOAD) |
| `-ctk, --cache-type-k TYPE` | KV cache data type for K<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_K) |
| `-ctv, --cache-type-v TYPE` | KV cache data type for V<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V) |
Expand Down Expand Up @@ -1143,6 +1142,8 @@ The `response_format` parameter supports both plain JSON output (e.g. `{"type":

`parse_tool_calls`: Whether to parse the generated tool call.

`parallel_tool_calls` : Whether to enable parallel/multiple tool calls (only supported on some models, verification is based on jinja template).

*Examples:*

You can use either Python `openai` library with appropriate checkpoints:
Expand Down