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3 Commits

Author SHA1 Message Date
Xuan-Son Nguyen 2189fd3b63 mtmd : fix batch_view for m-rope (#13397)
* mtmd : fix batch_view for m-rope

* nits : fix comment
2025-05-09 11:18:02 +02:00
Xuan-Son Nguyen 3f96aeff39 llama : one-off chat template fix for Mistral-Small-2503 (#13398)
* llama : one-off chat template fix for Mistral-Small-2503

* update readme

* add mistral-v7-tekken
2025-05-09 11:17:51 +02:00
Radoslav Gerganov b486ba05bf rpc : add rpc_msg_set_tensor_hash_req (#13353)
* rpc : add rpc_msg_set_tensor_hash_req

Use a dedicated struct for the request of RPC_CMD_SET_TENSOR_HASH which
makes the code cleaner.

* fix
2025-05-09 10:31:07 +03:00
6 changed files with 51 additions and 37 deletions
+25 -27
View File
@@ -151,6 +151,12 @@ struct rpc_msg_buffer_clear_req {
uint8_t value;
};
struct rpc_msg_set_tensor_hash_req {
rpc_tensor tensor;
uint64_t offset;
uint64_t hash;
};
struct rpc_msg_set_tensor_hash_rsp {
uint8_t result;
};
@@ -548,15 +554,12 @@ static void ggml_backend_rpc_buffer_set_tensor(ggml_backend_buffer_t buffer, ggm
ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context;
rpc_tensor rpc_tensor = serialize_tensor(tensor);
if (size > HASH_THRESHOLD) {
// input serialization format: | rpc_tensor | offset (8 bytes) | hash (8 bytes)
size_t input_size = sizeof(rpc_tensor) + sizeof(uint64_t) + sizeof(uint64_t);
std::vector<uint8_t> input(input_size, 0);
uint64_t hash = fnv_hash((const uint8_t*)data, size);
memcpy(input.data(), &rpc_tensor, sizeof(rpc_tensor));
memcpy(input.data() + sizeof(rpc_tensor), &offset, sizeof(offset));
memcpy(input.data() + sizeof(rpc_tensor) + sizeof(offset), &hash, sizeof(hash));
rpc_msg_set_tensor_hash_req request;
request.tensor = rpc_tensor;
request.offset = offset;
request.hash = fnv_hash((const uint8_t*)data, size);
rpc_msg_set_tensor_hash_rsp response;
bool status = send_rpc_cmd(ctx->sock, RPC_CMD_SET_TENSOR_HASH, input.data(), input.size(), &response, sizeof(response));
bool status = send_rpc_cmd(ctx->sock, RPC_CMD_SET_TENSOR_HASH, &request, sizeof(request), &response, sizeof(response));
GGML_ASSERT(status);
if (response.result) {
// the server has the same data, no need to send it
@@ -864,7 +867,7 @@ public:
bool free_buffer(const rpc_msg_free_buffer_req & request);
bool buffer_clear(const rpc_msg_buffer_clear_req & request);
bool set_tensor(const std::vector<uint8_t> & input);
bool set_tensor_hash(const std::vector<uint8_t> & input, rpc_msg_set_tensor_hash_rsp & response);
bool set_tensor_hash(const rpc_msg_set_tensor_hash_req & request, rpc_msg_set_tensor_hash_rsp & response);
bool get_tensor(const rpc_msg_get_tensor_req & request, std::vector<uint8_t> & response);
bool copy_tensor(const rpc_msg_copy_tensor_req & request, rpc_msg_copy_tensor_rsp & response);
bool graph_compute(const std::vector<uint8_t> & input, rpc_msg_graph_compute_rsp & response);
@@ -1101,18 +1104,10 @@ bool rpc_server::get_cached_file(uint64_t hash, std::vector<uint8_t> & data) {
return true;
}
bool rpc_server::set_tensor_hash(const std::vector<uint8_t> & input, rpc_msg_set_tensor_hash_rsp & response)
bool rpc_server::set_tensor_hash(const rpc_msg_set_tensor_hash_req & request, rpc_msg_set_tensor_hash_rsp & response)
{
// serialization format: | rpc_tensor | offset (8 bytes) | hash (8 bytes) |
if (input.size() != sizeof(rpc_tensor) + 16) {
return false;
}
const rpc_tensor * in_tensor = (const rpc_tensor *)input.data();
uint64_t offset;
memcpy(&offset, input.data() + sizeof(rpc_tensor), sizeof(offset));
const uint64_t * hash = (const uint64_t *)(input.data() + sizeof(rpc_tensor) + sizeof(offset));
std::vector<uint8_t> cached_file;
if (!get_cached_file(*hash, cached_file)) {
if (!get_cached_file(request.hash, cached_file)) {
response.result = 0;
return true;
}
@@ -1125,25 +1120,28 @@ bool rpc_server::set_tensor_hash(const std::vector<uint8_t> & input, rpc_msg_set
ggml_context_ptr ctx_ptr { ggml_init(params) };
GGML_ASSERT(ctx_ptr != nullptr);
ggml_context * ctx = ctx_ptr.get();
ggml_tensor * tensor = deserialize_tensor(ctx, in_tensor);
ggml_tensor * tensor = deserialize_tensor(ctx, &request.tensor);
if (tensor == nullptr) {
GGML_LOG_ERROR("[%s] error deserializing tensor\n", __func__);
return false;
}
GGML_PRINT_DEBUG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %zu, hash: %" PRIx64 "\n", __func__, (void*)tensor->buffer, tensor->data, offset, size, *hash);
GGML_PRINT_DEBUG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %zu, hash: %" PRIx64 "\n",
__func__, (void*)tensor->buffer, tensor->data, request.offset, size, request.hash);
// sanitize tensor->data
{
const size_t p0 = (size_t) ggml_backend_buffer_get_base(tensor->buffer);
const size_t p1 = p0 + ggml_backend_buffer_get_size(tensor->buffer);
if (in_tensor->data + offset < p0 || in_tensor->data + offset >= p1 || size > (p1 - in_tensor->data - offset)) {
if (request.tensor.data + request.offset < p0
|| request.tensor.data + request.offset >= p1
|| size > (p1 - request.tensor.data - request.offset)) {
GGML_LOG_ERROR("[%s] tensor data region (data=0x%" PRIx64 ", offset=%" PRIu64 ", size=%zu, hash=0x%" PRIx64 ") out of buffer bounds [0x%zx, 0x%zx)\n",
__func__, in_tensor->data, offset, size, *hash, p0, p1);
__func__, request.tensor.data, request.offset, size, request.hash, p0, p1);
return false;
}
}
ggml_backend_tensor_set(tensor, cached_file.data(), offset, size);
ggml_backend_tensor_set(tensor, cached_file.data(), request.offset, size);
response.result = 1;
return true;
}
@@ -1503,12 +1501,12 @@ static void rpc_serve_client(ggml_backend_t backend, const char * cache_dir,
break;
}
case RPC_CMD_SET_TENSOR_HASH: {
std::vector<uint8_t> input;
if (!recv_msg(sockfd, input)) {
rpc_msg_set_tensor_hash_req request;
if (!recv_msg(sockfd, &request, sizeof(request))) {
return;
}
rpc_msg_set_tensor_hash_rsp response;
if (!server.set_tensor_hash(input, response)) {
if (!server.set_tensor_hash(request, response)) {
return;
}
if (!send_msg(sockfd, &response, sizeof(response))) {
+8 -6
View File
@@ -35,6 +35,7 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
{ "mistral-v3", LLM_CHAT_TEMPLATE_MISTRAL_V3 },
{ "mistral-v3-tekken", LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN },
{ "mistral-v7", LLM_CHAT_TEMPLATE_MISTRAL_V7 },
{ "mistral-v7-tekken", LLM_CHAT_TEMPLATE_MISTRAL_V7_TEKKEN },
{ "phi3", LLM_CHAT_TEMPLATE_PHI_3 },
{ "phi4", LLM_CHAT_TEMPLATE_PHI_4 },
{ "falcon3", LLM_CHAT_TEMPLATE_FALCON_3 },
@@ -202,19 +203,20 @@ int32_t llm_chat_apply_template(
if (add_ass) {
ss << "<|im_start|>assistant\n";
}
} else if (tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V7) {
} else if (tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V7 || tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V7_TEKKEN) {
// Official mistral 'v7' template
// See: https://huggingface.co/mistralai/Mistral-Large-Instruct-2411#basic-instruct-template-v7
// https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503#basic-instruct-template-v7-tekken
const char * trailing_space = tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V7 ? " " : "";
for (auto message : chat) {
std::string role(message->role);
std::string content(message->content);
if (role == "system") {
ss << "[SYSTEM_PROMPT] " << content << "[/SYSTEM_PROMPT]";
ss << "[SYSTEM_PROMPT]" << trailing_space << content << "[/SYSTEM_PROMPT]";
} else if (role == "user") {
ss << "[INST] " << content << "[/INST]";
}
else {
ss << " " << content << "</s>";
ss << "[INST]" << trailing_space << content << "[/INST]";
} else {
ss << trailing_space << content << "</s>";
}
}
} else if (tmpl == LLM_CHAT_TEMPLATE_MISTRAL_V1
+1
View File
@@ -14,6 +14,7 @@ enum llm_chat_template {
LLM_CHAT_TEMPLATE_MISTRAL_V3,
LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN,
LLM_CHAT_TEMPLATE_MISTRAL_V7,
LLM_CHAT_TEMPLATE_MISTRAL_V7_TEKKEN,
LLM_CHAT_TEMPLATE_PHI_3,
LLM_CHAT_TEMPLATE_PHI_4,
LLM_CHAT_TEMPLATE_FALCON_3,
+8
View File
@@ -13387,6 +13387,14 @@ const char * llama_model_chat_template(const llama_model * model, const char * n
: LLM_KV(model->arch)(LLM_KV_TOKENIZER_CHAT_TEMPLATE);
const auto & it = model->gguf_kv.find(key);
if (it == model->gguf_kv.end()) {
// one-off fix for very popular models (so we are not flooded with issues)
// do not extend this list unless absolutely necessary
// Mistral-Small-2503 does not have built-in chat template
llama_vocab_pre_type pre_type = model->vocab.get_pre_type();
if (pre_type == LLAMA_VOCAB_PRE_TYPE_TEKKEN && model->layers.size() == 40) {
return "mistral-v7-tekken";
}
return nullptr;
}
+1 -1
View File
@@ -46,7 +46,7 @@ llama-mtmd-cli -hf ggml-org/Qwen2.5-VL-32B-Instruct-GGUF
llama-mtmd-cli -hf ggml-org/Qwen2.5-VL-72B-Instruct-GGUF
# Mistral Small 3.1 24B (IQ2_M quantization)
llama-mtmd-cli -hf ggml-org/Mistral-Small-3.1-24B-Instruct-2503-GGUF --chat-template mistral-v7
llama-mtmd-cli -hf ggml-org/Mistral-Small-3.1-24B-Instruct-2503-GGUF
```
## How it works and what is `mmproj`?
+8 -3
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@@ -554,14 +554,19 @@ struct decode_embd_batch {
llama_batch get_view(int offset, int n_tokens) {
llama_pos * pos_ptr;
pos_view.clear();
pos_view.resize(n_tokens * n_pos_per_embd);
pos_view.reserve(n_tokens * n_pos_per_embd);
if (n_pos_per_embd > 1) {
// mrope
// for example, with layout of src: 1234...1234...1234...1234...
// offset 2 will give us dst: 34...34...34...34...
for (int i = 0; i < n_pos_per_embd; i++) {
auto src = pos.begin() + i * batch.n_tokens + offset;
pos_view.insert(pos_view.end(), src, src + n_tokens);
// assume n_tokens is less than or equal to batch.n_tokens
// batch.n_tokens is number of **total** tokens
// n_tokens is number of viewed token
size_t src_idx = i * batch.n_tokens + offset;
pos_view.insert(pos_view.end(),
pos.data() + src_idx,
pos.data() + src_idx + n_tokens);
}
pos_ptr = pos_view.data();
} else {