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Merge commit '1c3c9674de4d455f1e571bed808252af54932767' into concedo_experimental

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# Conflicts:
#	.github/workflows/build-apple.yml
#	.github/workflows/build-vulkan.yml
#	.github/workflows/release.yml
#	docs/ops.md
#	docs/ops/Vulkan.csv
#	examples/gen-docs/gen-docs.cpp
#	ggml/CMakeLists.txt
#	ggml/src/ggml-opencl/ggml-opencl.cpp
#	ggml/src/ggml-sycl/concat.cpp
#	ggml/src/ggml-sycl/fattn-onednn.cpp
#	ggml/src/ggml-sycl/fattn.cpp
#	models/templates/deepseek-ai-DeepSeek-V4.jinja
#	scripts/sync-ggml.last
#	scripts/sync_vendor.py
#	src/CMakeLists.txt
#	src/llama-model-loader.cpp
#	tests/CMakeLists.txt
#	tests/test-arg-parser.cpp
#	tests/test-backend-sampler.cpp
#	tests/test-chat.cpp
#	tests/test-sampling.cpp
#	tools/server/README.md
This commit is contained in:
Concedo
2026-08-07 18:16:11 +08:00
parent 7098c9c53f 1c3c9674de
commit 2580b4591d
51 changed files with 2686 additions and 780 deletions
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src/llama-model-loader.cpp
+47 -52
View File
@@ -858,7 +858,11 @@ struct ggml_tensor * llama_model_loader::require_tensor_meta(const std::string &
return tensor;
}
const struct ggml_tensor * llama_model_loader::check_tensor_dims(const std::string & name, const std::vector<int64_t> & ne, bool required) const {
const struct ggml_tensor * llama_model_loader::check_tensor_dims(
const std::string & name,
const std::vector<int64_t> & ne,
bool required,
bool allow_reshape) const {
const struct ggml_tensor * cur = get_tensor_meta(name.c_str());
if (cur == NULL) {
@@ -868,21 +872,33 @@ const struct ggml_tensor * llama_model_loader::check_tensor_dims(const std::stri
throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str()));
}
{
bool is_ok = true;
bool is_ok = true;
if (allow_reshape) {
// check total number of elements only
const int64_t ncur = ggml_nelements(cur);
int64_t nexp = 1;
for (size_t i = 0; i < ne.size(); ++i) {
nexp *= ne[i];
}
if (ncur != nexp) {
is_ok = false;
}
} else {
for (size_t i = 0; i < GGML_MAX_DIMS; ++i) {
if ((i < ne.size() && ne[i] != cur->ne[i]) || (i >= ne.size() && cur->ne[i] != 1)) {
is_ok = false;
break;
}
}
if (!is_ok) {
throw std::runtime_error(
format("%s: tensor '%s' has wrong shape; expected %s, got %s",
__func__, name.c_str(),
llama_format_tensor_shape(ne).c_str(),
llama_format_tensor_shape(cur).c_str()));
}
}
if (!is_ok) {
throw std::runtime_error(
format("%s: tensor '%s' has wrong shape; expected %s, got %s",
__func__, name.c_str(),
llama_format_tensor_shape(ne).c_str(),
llama_format_tensor_shape(cur).c_str()));
}
return cur;
@@ -1247,11 +1263,25 @@ struct ggml_tensor * llama_model_loader::create_tensor(
return ret;
}
ggml_tensor * t_meta = get_tensor_meta(tn.str().c_str());
ggml_backend_buffer_type_t buft = buft_for_tensor(t_meta);
if (buft == nullptr) {
return nullptr; // return type is ggml_tensor *
LLAMA_LOG_DEBUG("%s: loading tensor %s\n", __func__, tn.str().c_str());
const struct ggml_tensor * cur = check_tensor_dims(tn.str(), ne, !(flags & TENSOR_NOT_REQUIRED), flags & TENSOR_ALLOW_RESHAPE);
if (cur == NULL) {
return NULL;
}
ggml_tensor t_meta = *cur;
if (flags & TENSOR_ALLOW_RESHAPE) {
for (size_t dim = 0; dim < GGML_MAX_DIMS; dim++) {
t_meta.ne[dim] = dim < ne.size() ? ne.begin()[dim] : 1;
t_meta.nb[dim] = dim == 0 ? ggml_type_size(t_meta.type) : t_meta.ne[dim-1]*t_meta.nb[dim-1];
}
}
ggml_backend_buffer_type_t buft = buft_for_tensor(&t_meta);
if (buft == nullptr) {
return nullptr;
}
ggml_context * ctx = ctx_for_buft(buft);
// if duplicated, check if the original tensor was allocated in the same buffer type context and avoid creating a new one
@@ -1262,20 +1292,13 @@ struct ggml_tensor * llama_model_loader::create_tensor(
}
}
// LLAMA_LOG_DEBUG("%s: loading tensor %s\n", __func__, tn.str().c_str());
const struct ggml_tensor * cur = check_tensor_dims(tn.str(), ne, !(flags & TENSOR_NOT_REQUIRED));
if (cur == NULL) {
return NULL;
}
const bool duplicated = flags & TENSOR_DUPLICATED;
struct ggml_tensor * tensor = ggml_dup_tensor(ctx, cur);
ggml_set_name(tensor, ggml_get_name(cur));
struct ggml_tensor * tensor = ggml_dup_tensor(ctx, &t_meta);
ggml_set_name(tensor, ggml_get_name(&t_meta));
if (duplicated) {
size_data += ggml_nbytes(cur);
size_data += ggml_nbytes(&t_meta);
} else {
n_created++;
}
@@ -1283,34 +1306,6 @@ struct ggml_tensor * llama_model_loader::create_tensor(
return tensor;
}
struct ggml_tensor * llama_model_loader::create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base, const std::string & name, const std::initializer_list<int64_t> & ne, size_t offset, bool required) {
const struct ggml_tensor * cur = check_tensor_dims(name, ne, required);
if (cur == NULL) {
return NULL;
}
if (cur->type != base->type) {
throw std::runtime_error(format("%s: tensor '%s' has wrong type; expected %s, got %s", __func__, name.c_str(), ggml_type_name(base->type), ggml_type_name(cur->type)));
}
std::array<int64_t, GGML_MAX_DIMS> dims;
for (size_t i = 0; i < GGML_MAX_DIMS; ++i) {
dims[i] = i < ne.size() ? ne.begin()[i] : 1;
}
struct ggml_tensor * tensor = ggml_view_4d(ctx, base,
dims[0], dims[1], dims[2], dims[3],
cur->nb[1], cur->nb[2], cur->nb[3],
offset);
ggml_set_name(tensor, name.c_str());
n_created++;
return tensor;
}
void llama_model_loader::done_getting_tensors(bool partial) const {
if (n_created > n_tensors) {
throw std::runtime_error(format("%s: too many tensors created; expected %d, got %d", __func__, n_tensors, n_created));