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llama.cpp
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Suppress logging and add error handling to allow test-backend-ops to complete

This commit is contained in:
Yu, Zijun 2026-02-13 10:29:48 +08:00
parent d5d673cde3
commit 1a54965c43
5 changed files with 76 additions and 67 deletions
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6
ggml/src/ggml-openvino/ggml-decoder.cpp
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@ -568,20 +568,20 @@ std::shared_ptr<ov::Node> GgmlOvDecoder::create_weight_node(ggml_tensor * tensor
// F16/F32/BF16 weight with shared-memory constant
auto * weight_extra = static_cast<ggml_openvino_weight_extra *>(tensor->extra);
if (weight_extra->weight_node) {
GGML_LOG_DEBUG("%s: using pre-built weight node for %s\n", __func__, tensor->name);
// GGML_LOG_DEBUG("%s: using pre-built weight node for %s\n", __func__, tensor->name);
return weight_extra->weight_node;
}
} else if (extra_base->type == ggml_openvino_extra_base::Type::QUANTIZED_WEIGHT) {
// Quantized weight with pre-extracted data
auto * quant_extra = static_cast<ggml_openvino_quantized_weight_extra *>(tensor->extra);
if (quant_extra->weight_node) {
GGML_LOG_DEBUG("%s: using pre-extracted quantized weight node for %s\n", __func__, tensor->name);
// GGML_LOG_DEBUG("%s: using pre-extracted quantized weight node for %s\n", __func__, tensor->name);
return quant_extra->weight_node;
}
}
}
GGML_LOG_DEBUG("%s: creating new weight node for %s\n", __func__, tensor->name);
// GGML_LOG_DEBUG("%s: creating new weight node for %s\n", __func__, tensor->name);
static const std::set<ggml_type> weight_types = {GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_BF16,
GGML_TYPE_Q8_0, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1,
GGML_TYPE_Q4_K, GGML_TYPE_Q5_K, GGML_TYPE_Q6_K};

2
ggml/src/ggml-openvino/ggml-openvino-extra.cpp
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@ -348,7 +348,7 @@ ggml_openvino_tensor_extra * ggml_openvino_create_tensor_extra(const ggml_tensor
element_type = ov::element::i64;
break;
default:
GGML_LOG_ERROR("%s: unsupported tensor type for ov::Tensor: %s\n", __func__, ggml_type_name(tensor->type));
// GGML_LOG_WARN("%s: unsupported tensor type for ov::Tensor: %s\n", __func__, ggml_type_name(tensor->type));
return nullptr;
}

94
ggml/src/ggml-openvino/ggml-openvino.cpp
View File

@ -223,7 +223,7 @@ static void ggml_backend_openvino_buffer_set_tensor(ggml_backend_buffer_t buffer
GGML_ASSERT(tensor != nullptr && tensor->data != nullptr);
ggml_backend_openvino_buffer_context * ctx = (ggml_backend_openvino_buffer_context *) buffer->context;
// Check if this is a weight buffer (usage is set BEFORE set_tensor is called)
// Check if this is a weight buffer (usage is set BEFORE set_tensor is called, except in test-backend-ops)
bool is_weight_buffer = (buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
// Full tensor set: offset=0, full size, not a view
bool is_full_tensor_set = (offset == 0 && size == ggml_nbytes(tensor) && tensor->view_src == nullptr);
@ -235,7 +235,7 @@ static void ggml_backend_openvino_buffer_set_tensor(ggml_backend_buffer_t buffer
auto result = process_weight_tensor(tensor, data, tensor->data);
result.weight_node->set_friendly_name(tensor->name);
const auto & layout = result.layout;
// const auto & layout = result.layout;
ggml_openvino_extra_base * extra;
// Quantized path with extracted weight/scale/zp tensors
@ -243,24 +243,24 @@ static void ggml_backend_openvino_buffer_set_tensor(ggml_backend_buffer_t buffer
extra = new ggml_openvino_quantized_weight_extra(std::move(result.weights), std::move(result.scales),
std::move(result.zp), result.weight_node);
if (layout.is_requant) {
GGML_LOG_DEBUG("%s: requantized %s to %s (u%d, block_size=%ld)\n", __func__, tensor->name,
extra_quant_type_name(layout.requant_type.value()), layout.is_u4 ? 4 : 8,
layout.weights_per_block);
} else {
int64_t n_blocks = ggml_nelements(tensor) / layout.weights_per_block;
GGML_LOG_DEBUG("%s: extracted quantized weight node for %s (u%d, %zu weights, %ld blocks)\n",
__func__, tensor->name, layout.is_u4 ? 4 : 8, layout.weights_size, n_blocks);
}
// if (layout.is_requant) {
// GGML_LOG_DEBUG("%s: requantized %s to %s (u%d, block_size=%ld)\n", __func__, tensor->name,
// extra_quant_type_name(layout.requant_type.value()), layout.is_u4 ? 4 : 8,
// layout.weights_per_block);
// } else {
// int64_t n_blocks = ggml_nelements(tensor) / layout.weights_per_block;
// GGML_LOG_DEBUG("%s: extracted quantized weight node for %s (u%d, %zu weights, %ld blocks)\n",
// __func__, tensor->name, layout.is_u4 ? 4 : 8, layout.weights_size, n_blocks);
// }
} else {
// F16/F32/BF16 weight or F16-requant
extra = new ggml_openvino_weight_extra(std::move(result.weights), result.weight_node);
if (layout.total_size > 0) {
GGML_LOG_DEBUG("%s: requantized %s to F16\n", __func__, tensor->name);
} else {
GGML_LOG_DEBUG("%s: created shared-memory weight node for %s\n", __func__, tensor->name);
}
// if (layout.total_size > 0) {
// GGML_LOG_DEBUG("%s: requantized %s to F16\n", __func__, tensor->name);
// } else {
// GGML_LOG_DEBUG("%s: created shared-memory weight node for %s\n", __func__, tensor->name);
// }
}
ctx->tensor_extras[tensor] = extra;
@ -271,7 +271,7 @@ static void ggml_backend_openvino_buffer_set_tensor(ggml_backend_buffer_t buffer
memcpy((char *) tensor->data + offset, data, size);
}
} else {
// Non-weight tensor (KV cache, activations, etc.) - copy data
// Non-weight tensor (KV cache, activations, etc.) - copy data. test-backend-ops also goes here
if (ctx->is_remote) {
cl_command_queue queue = ggml_openvino_get_cl_queue();
auto mem_cpy_fn = ggml_openvino_get_clEnqueueMemcpyINTEL();
@ -290,7 +290,7 @@ static void ggml_backend_openvino_buffer_set_tensor(ggml_backend_buffer_t buffer
ggml_openvino_tensor_extra * extra = ggml_openvino_create_tensor_extra(tensor, ctx->is_remote);
if (extra == nullptr) {
GGML_LOG_ERROR("%s: failed to create tensor extra for %s\n", __func__, tensor->name);
// GGML_LOG_ERROR("%s: failed to create tensor extra for %s\n", __func__, tensor->name);
return;
}
@ -795,7 +795,7 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
}
case GGML_OP_SOFT_MAX: {
if (op->src[2] != nullptr) {
GGML_LOG_WARN("OpenVINO backend does not support SOFT_MAX with sinks\n");
// GGML_LOG_WARN("OpenVINO backend does not support SOFT_MAX with sinks\n");
return true;
}
float scale = 1.0f;
@ -804,14 +804,14 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
memcpy(&scale, (const float *) op_params + 0, sizeof(float));
memcpy(&max_bias, (const float *) op_params + 1, sizeof(float));
if (max_bias > 0) {
GGML_LOG_WARN("OpenVINO backend does not support SOFT_MAX with max_bias > 0\n");
// GGML_LOG_WARN("OpenVINO backend does not support SOFT_MAX with max_bias > 0\n");
return true;
}
break;
}
case GGML_OP_FLASH_ATTN_EXT: {
if (op->src[4] != nullptr) {
GGML_LOG_WARN("OpenVINO backend does not support FLASH_ATTN_EXT with sinks\n");
// GGML_LOG_WARN("OpenVINO backend does not support FLASH_ATTN_EXT with sinks\n");
return true;
}
float scale = 1.0f;
@ -822,11 +822,11 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
memcpy(&max_bias, (const float *) op_params + 1, sizeof(float));
memcpy(&logit_softcap, (const float *) op_params + 2, sizeof(float));
if (max_bias > 0) {
GGML_LOG_WARN("OpenVINO backend does not support FLASH_ATTN_EXT with max_bias > 0\n");
// GGML_LOG_WARN("OpenVINO backend does not support FLASH_ATTN_EXT with max_bias > 0\n");
return true;
}
if (logit_softcap != 0) {
GGML_LOG_WARN("OpenVINO backend does not support FLASH_ATTN_EXT with logit_softcap != 0\n");
// GGML_LOG_WARN("OpenVINO backend does not support FLASH_ATTN_EXT with logit_softcap != 0\n");
return true;
}
break;
@ -834,14 +834,14 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
case GGML_OP_PERMUTE: {
if (op->type == GGML_TYPE_BF16) {
// err msg: [GPU] Could not find a suitable kernel for transpose
GGML_LOG_WARN("OpenVINO backend does not support PERMUTE with BF16 type\n");
// GGML_LOG_WARN("OpenVINO backend does not support PERMUTE with BF16 type\n");
return true;
}
break;
}
case GGML_OP_CPY: {
if (op->src[1] != op) {
GGML_LOG_WARN("OpenVINO backend only supports CPY that is a cast\n");
// GGML_LOG_WARN("OpenVINO backend only supports CPY that is a cast\n");
return true;
}
break;
@ -849,7 +849,7 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
case GGML_OP_MUL_MAT: {
if (op->src[0]->type == GGML_TYPE_F16 && op->src[1]->type == GGML_TYPE_F16) {
// Has accuracy issue, try enabling this and see `test-backend-ops -o "MUL_MAT"`
GGML_LOG_WARN("OpenVINO backend does not support MUL_MAT with two F16 tensors\n");
// GGML_LOG_WARN("OpenVINO backend does not support MUL_MAT with two F16 tensors\n");
return true;
}
break;
@ -858,17 +858,17 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
const int32_t * op_params = op->op_params;
const int n_dims = op_params[1];
const int mode = op_params[2];
if (mode == GGML_ROPE_TYPE_MROPE || mode == GGML_ROPE_TYPE_VISION) {
GGML_LOG_WARN("OpenVINO backend does not support ROPE with mode %d\n", mode);
if (mode != GGML_ROPE_TYPE_NORMAL && mode != GGML_ROPE_TYPE_NEOX) {
// GGML_LOG_WARN("OpenVINO backend does not support ROPE with mode %d\n", mode);
return true;
}
if (n_dims != 0.0f && n_dims != op->src[0]->ne[0]) {
GGML_LOG_WARN("OpenVINO backend does not support ROPE with n_dims %d != src[0]->ne[0] %ld\n", n_dims,
op->src[0]->ne[0]);
// GGML_LOG_WARN("OpenVINO backend does not support ROPE with n_dims %d != src[0]->ne[0] %ld\n", n_dims,
// op->src[0]->ne[0]);
return true;
}
if (op->type != GGML_TYPE_F32) {
GGML_LOG_WARN("OpenVINO backend does not support ROPE with type %s\n", ggml_type_name(op->type));
// GGML_LOG_WARN("OpenVINO backend does not support ROPE with type %s\n", ggml_type_name(op->type));
return true;
}
float freq_scale;
@ -876,15 +876,15 @@ static bool is_op_unsupported_case(const ggml_tensor * op) {
memcpy(&freq_scale, op_params + 6, sizeof(float));
memcpy(&ext_factor, op_params + 7, sizeof(float));
if (ext_factor != 0.0f) {
GGML_LOG_WARN("OpenVINO backend does not support ROPE with ext_factor %f != 0.0f\n", ext_factor);
// GGML_LOG_WARN("OpenVINO backend does not support ROPE with ext_factor %f != 0.0f\n", ext_factor);
return true;
}
if (op->src[0]->op == GGML_OP_VIEW) {
if (op->src[0]->view_src->ne[1] != op->src[0]->ne[2]) {
GGML_LOG_WARN(
"OpenVINO backend does not support ROPE with src[0]->view_src->ne[1] %ld != src[0]->ne[2] "
"%ld\n",
op->src[0]->view_src->ne[1], op->src[0]->ne[2]);
// GGML_LOG_WARN(
// "OpenVINO backend does not support ROPE with src[0]->view_src->ne[1] %ld != src[0]->ne[2] "
// "%ld\n",
// op->src[0]->view_src->ne[1], op->src[0]->ne[2]);
return true;
}
}
@ -921,12 +921,12 @@ static bool ggml_backend_openvino_device_supports_op(ggml_backend_dev_t dev, con
case GGML_OP_UNARY: {
auto supported = supported_unary_ops.find(ggml_get_unary_op(op)) != supported_unary_ops.end();
if (!supported) {
GGML_LOG_WARN("OpenVINO backend does not support unary op %s\n", ggml_unary_op_name(ggml_get_unary_op(op)));
// GGML_LOG_WARN("OpenVINO backend does not support unary op %s\n", ggml_unary_op_name(ggml_get_unary_op(op)));
return false;
}
if (has_view_input(op)) {
GGML_LOG_WARN("OpenVINO backend does not support unary op %s with view input\n",
ggml_unary_op_name(ggml_get_unary_op(op)));
// GGML_LOG_WARN("OpenVINO backend does not support unary op %s with view input\n",
// ggml_unary_op_name(ggml_get_unary_op(op)));
return false;
}
break;
@ -934,12 +934,12 @@ static bool ggml_backend_openvino_device_supports_op(ggml_backend_dev_t dev, con
case GGML_OP_GLU: {
auto supported = supported_glu_ops.find(ggml_get_glu_op(op)) != supported_glu_ops.end();
if (!supported) {
GGML_LOG_WARN("OpenVINO backend does not support GLU op %s\n", ggml_glu_op_name(ggml_get_glu_op(op)));
// GGML_LOG_WARN("OpenVINO backend does not support GLU op %s\n", ggml_glu_op_name(ggml_get_glu_op(op)));
return false;
}
if (has_view_input(op)) {
GGML_LOG_WARN("OpenVINO backend does not support unary op %s with view input\n",
ggml_glu_op_name(ggml_get_glu_op(op)));
// GGML_LOG_WARN("OpenVINO backend does not support unary op %s with view input\n",
// ggml_glu_op_name(ggml_get_glu_op(op)));
return false;
}
break;
@ -947,7 +947,7 @@ static bool ggml_backend_openvino_device_supports_op(ggml_backend_dev_t dev, con
default: {
auto supported = supported_ops.find(op->op) != supported_ops.end();
if (!supported) {
GGML_LOG_WARN("OpenVINO backend does not support op %s\n", ggml_op_name(op->op));
// GGML_LOG_WARN("OpenVINO backend does not support op %s\n", ggml_op_name(op->op));
return false;
}
static std::set<ggml_op> ops_not_support_view_input{
@ -955,14 +955,14 @@ static bool ggml_backend_openvino_device_supports_op(ggml_backend_dev_t dev, con
GGML_OP_RMS_NORM,
};
if (ops_not_support_view_input.find(op->op) != ops_not_support_view_input.end() && has_view_input(op)) {
GGML_LOG_WARN("OpenVINO backend does not support op %s with view input\n", ggml_op_name(op->op));
// GGML_LOG_WARN("OpenVINO backend does not support op %s with view input\n", ggml_op_name(op->op));
return false;
}
}
}
if (supported_types.find(op->type) == supported_types.end()) {
GGML_LOG_WARN("OpenVINO backend does not support tensor type %s\n", ggml_type_name(op->type));
// GGML_LOG_WARN("OpenVINO backend does not support tensor type %s\n", ggml_type_name(op->type));
return false;
}
for (int i = 0; i < GGML_MAX_SRC; i++) {
@ -971,11 +971,11 @@ static bool ggml_backend_openvino_device_supports_op(ggml_backend_dev_t dev, con
break;
}
if (supported_types.find(src->type) == supported_types.end()) {
GGML_LOG_WARN("OpenVINO backend does not support tensor type %s\n", ggml_type_name(src->type));
// GGML_LOG_WARN("OpenVINO backend does not support tensor type %s\n", ggml_type_name(src->type));
return false;
}
if (ggml_is_quantized(src->type) && src->ne[2] != 1) {
GGML_LOG_WARN("OpenVINO backend does not support 3D quantized tensors\n");
// GGML_LOG_WARN("OpenVINO backend does not support 3D quantized tensors\n");
return false;
}
}

4
ggml/src/ggml-openvino/openvino/op/rope.cpp
View File

@ -66,10 +66,10 @@ OutputVector translate_rope(const NodeContext & context) {
}
const int mode = op_params[2];
constexpr int ROPE_TYPE_NORMAL = 0;
constexpr int ROPE_TYPE_NEOX = 2;
constexpr int ROPE_TYPE_NORM = 0;
if (mode == ROPE_TYPE_NORM) {
if (mode == ROPE_TYPE_NORMAL) {
auto neg_one = ov::op::v0::Constant::create(ov::element::i64, {1}, {-1});
auto zero = ov::op::v0::Constant::create(ov::element::i64, {1}, {0});
auto one = ov::op::v0::Constant::create(ov::element::i64, {1}, {1});

37
ggml/src/ggml-openvino/utils.cpp
View File

@ -38,19 +38,31 @@
#pragma GCC diagnostic ignored "-Wdeprecated-declarations"
enum ggml_status ov_graph_compute(ggml_cgraph * cgraph) {
if (getenv("GGML_OPENVINO_DUMP_CGRAPH")) {
std::string filename = "cgraph_ov.txt";
GgmlOvDecoder::dump_cgraph(cgraph, filename);
}
try {
if (getenv("GGML_OPENVINO_DUMP_CGRAPH")) {
std::string filename = "cgraph_ov.txt";
GgmlOvDecoder::dump_cgraph(cgraph, filename);
}
// Use device from singleton (initialized during backend init)
const auto & device = ggml_openvino_get_device_name();
const auto is_static = ggml_openvino_is_npu();
bool stateful = false;
if (getenv("GGML_OPENVINO_STATEFUL_EXECUTION") && !is_static) {
stateful = true;
// Use device from singleton (initialized during backend init)
const auto & device = ggml_openvino_get_device_name();
const auto is_static = ggml_openvino_is_npu();
bool stateful = false;
if (getenv("GGML_OPENVINO_STATEFUL_EXECUTION") && !is_static) {
stateful = true;
}
return is_static ? ov_graph_compute_static(cgraph) : ov_graph_compute_dynamic(cgraph, device, stateful);
} catch (const ov::Exception & e) {
// GGML_LOG_ERROR("GGML OpenVINO backend ov::Exception: %s\n", e.what());
return GGML_STATUS_FAILED;
} catch (const std::exception & e) {
// GGML_LOG_ERROR("GGML OpenVINO backend std::exception: %s\n", e.what());
return GGML_STATUS_FAILED;
} catch (...) {
// GGML_LOG_ERROR("GGML OpenVINO backend unknown exception\n");
return GGML_STATUS_FAILED;
}
return is_static ? ov_graph_compute_static(cgraph) : ov_graph_compute_dynamic(cgraph, device, stateful);
}
enum ggml_status ov_graph_compute_dynamic(ggml_cgraph * cgraph, const std::string & device, bool stateful) {
@ -454,9 +466,6 @@ enum ggml_status naive_compute(ggml_cgraph * cgraph,
if (cgraph->n_nodes == 1 && (cgraph->nodes[0]->op == GGML_OP_NONE || cgraph->nodes[0]->op == GGML_OP_VIEW)) {
return GGML_STATUS_SUCCESS;
}
if (cgraph->nodes[0]->op == GGML_OP_FLASH_ATTN_EXT) {
return GGML_STATUS_FAILED;
}
auto model_weights = GgmlOvDecoder::create_weight_nodes(cgraph);
auto decoder = std::make_shared<GgmlOvDecoder>(cgraph, model_weights);