popov/neuron-tai
1
0
Fork 0
Code Issues 42 Pull Requests Actions Packages Projects Releases Wiki Activity

feat: implement numbered patch-stack apply/verify enforcement (DGR-028)

Browse Source 
Split the range-loader patch into single-concern patches 0002-0005 (loader,
filtered state report, boundary I/O endpoint guard, worker range-report
hook), add UPSTREAM-ASSUMPTIONS.json describing each patch's assumptions,
and enforce control-plane/license boundary checks plus first-incompatible-
patch reporting in scripts/llama_cpp_dependency.py apply/reverse/verify.

7 passed in tests/test_llama_cpp_dependency.py; SHA256SUMS verified against
all five patches; focused native CTest (test-meshnet-range-ownership 1/1)
recorded in evidence README (build/ dir not present in this environment to
independently reverify).
This commit is contained in:
Dobromir Popov
2026-07-21 13:22:55 +03:00
parent 902ecde363
commit 7da90ef475
14 changed files with 1027 additions and 186 deletions
Download Patch File Download Diff File Expand all files Collapse all files

122
packages/node/native/llama/patches/0002-dense-llama-owned-range-loading.patch Normal file
View File

@@ -0,0 +1,122 @@
From: Meshnet <meshnet@invalid>
Subject: [PATCH] llama: add dense owned-range tensor loading
Concern: range loading. Adds meshnet_owned_layer_start/end model params,
validates the half-open range against the GGUF block count for dense Llama
only, filters per-layer tensor registration and the optional per-layer scale
pass to the owned range, and keeps endpoint tensors with their owning
endpoints (head: token embeddings; tail: final norm and output head).
Stock zero/zero params preserve whole-model loading.
---
diff --git a/include/llama.h b/include/llama.h
index a311ac202..229946ede 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -319,6 +319,12 @@ extern "C" {
// override key-value pairs of the model meta data
const struct llama_model_kv_override * kv_overrides;
+ // Project-owned dense-Llama owned range [start, end). A zero/zero
+ // pair preserves stock whole-model loading; any other pair is
+ // validated against the GGUF block count before tensor registration.
+ int32_t meshnet_owned_layer_start;
+ int32_t meshnet_owned_layer_end;
+
// Keep the booleans together to avoid misalignment during copy-by-value.
bool vocab_only; // only load the vocabulary, no weights
bool use_mmap; // use mmap if possible
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index d87481381..05b8b9c91 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1236,6 +1236,19 @@ bool llama_model_base::load_tensors(llama_model_loader & ml) {
const bool use_mmap_buffer = true;
+ const bool meshnet_range_requested = params.meshnet_owned_layer_start != 0 || params.meshnet_owned_layer_end != 0;
+ const int meshnet_start = params.meshnet_owned_layer_start;
+ const int meshnet_end = params.meshnet_owned_layer_end;
+ if (meshnet_range_requested) {
+ if (arch != LLM_ARCH_LLAMA) {
+ throw std::runtime_error("Meshnet owned range currently supports dense Llama only");
+ }
+ if (meshnet_start < 0 || meshnet_end <= meshnet_start || meshnet_end > static_cast<int>(hparams.n_layer())) {
+ throw std::runtime_error(format("invalid Meshnet owned range [%d, %d) for GGUF block count %d",
+ meshnet_start, meshnet_end, hparams.n_layer()));
+ }
+ }
+
this->ml = &ml; // to be used by create_tensor() and load_arch_tensors()
LLAMA_LOG_INFO("%s: loading model tensors, this can take a while... (mmap = %s, direct_io = %s)\n",
@@ -1336,7 +1349,9 @@ bool llama_model_base::load_tensors(llama_model_loader & ml) {
// generic pass: load optional per-tensor/per-expert ".scale" tensors (e.g. NVFP4 scale2)
// this avoids having to add scale loading to every architecture
- for (int i = 0; i < n_layer_all; ++i) {
+ const int optional_scale_start = meshnet_range_requested ? meshnet_start : 0;
+ const int optional_scale_end = meshnet_range_requested ? meshnet_end : n_layer_all;
+ for (int i = optional_scale_start; i < optional_scale_end; ++i) {
auto & layer = layers[i];
// attention weight scales (per-tensor, shape {1})
@@ -1487,7 +1502,7 @@ bool llama_model_base::load_tensors(llama_model_loader & ml) {
}
}
}
- ml.done_getting_tensors();
+ ml.done_getting_tensors(meshnet_range_requested);
// Tied NVFP4 output is valid when no separate LM-head scale tensors are present.
// If sidecar scales exist, the output weight must be an actual output tensor.
@@ -2308,6 +2323,8 @@ llama_model_params llama_model_default_params() {
/*.progress_callback =*/ nullptr,
/*.progress_callback_user_data =*/ nullptr,
/*.kv_overrides =*/ nullptr,
+ /*.meshnet_owned_layer_start =*/ 0,
+ /*.meshnet_owned_layer_end =*/ 0,
/*.vocab_only =*/ false,
/*.use_mmap =*/ true,
/*.use_direct_io =*/ false,
diff --git a/src/models/llama.cpp b/src/models/llama.cpp
index 4bfebc884..c3092763b 100644
--- a/src/models/llama.cpp
+++ b/src/models/llama.cpp
@@ -34,18 +34,29 @@ void llama_model_llama::load_arch_hparams(llama_model_loader & ml) {
void llama_model_llama::load_arch_tensors(llama_model_loader &) {
LLAMA_LOAD_LOCALS;
- tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+ const bool meshnet_range_requested = params.meshnet_owned_layer_start != 0 || params.meshnet_owned_layer_end != 0;
+ const int meshnet_start = meshnet_range_requested ? params.meshnet_owned_layer_start : 0;
+ const int meshnet_end = meshnet_range_requested ? params.meshnet_owned_layer_end : n_layer;
+
+ // Endpoint ownership: only the head shard (start == 0) owns the token
+ // embeddings and only the tail shard (end == n_layer) owns the final norm
+ // and output head. Middle ranges register per-layer tensors only.
+ if (!meshnet_range_requested || meshnet_start == 0) {
+ tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+ }
- // output
- output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
- output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+ if (!meshnet_range_requested || meshnet_end == n_layer) {
+ // output
+ output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+ output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
- // if output is NULL, init from the input tok embed
- if (output == NULL) {
- output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+ // if output is NULL, init from the input tok embed
+ if (output == NULL) {
+ output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+ }
}
- for (int i = 0; i < n_layer; ++i) {
+ for (int i = meshnet_start; i < meshnet_end; ++i) {
auto & layer = layers[i];
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);