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llama : allow pooled embeddings on any model (ggerganov#7477)
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* create append_pooling operation; allow to specify attention_type; add last token pooling; update examples

* find result_norm/result_embd tensors properly; update output allocation logic

* only use embd output for pooling_type NONE

* get rid of old causal_attn accessor

* take out attention_type; add in llama_set_embeddings

* bypass logits when doing non-NONE pooling
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@iamlemec @NeoZhangJianyu
iamlemec authored and NeoZhangJianyu committed Jun 30, 2024
1 parent 2554c4f commit faba70f
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Showing 6 changed files with 130 additions and 70 deletions.
2 changes: 2 additions & 0 deletions common/common.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -541,6 +541,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
/**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; }
else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; }
else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; }
else if (value == "last") { params.pooling_type = LLAMA_POOLING_TYPE_LAST; }
else { invalid_param = true; }
return true;
}
Expand Down Expand Up @@ -1869,6 +1870,7 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param

options.push_back({ "backend" });
options.push_back({ "*", " --rpc SERVERS", "comma separated list of RPC servers" });

if (llama_supports_mlock()) {
options.push_back({ "*", " --mlock", "force system to keep model in RAM rather than swapping or compressing" });
}
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21 changes: 11 additions & 10 deletions examples/embedding/embedding.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -17,9 +17,10 @@ static std::vector<std::string> split_lines(const std::string & s) {
return lines;
}

static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, int seq_id) {
for (size_t i = 0; i < tokens.size(); i++) {
llama_batch_add(batch, tokens[i], i, { seq_id }, i == tokens.size() - 1);
static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, llama_seq_id seq_id) {
size_t n_tokens = tokens.size();
for (size_t i = 0; i < n_tokens; i++) {
llama_batch_add(batch, tokens[i], i, { seq_id }, true);
}
}

Expand All @@ -40,13 +41,7 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu

// try to get sequence embeddings - supported only when pooling_type is not NONE
const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
if (embd == NULL) {
embd = llama_get_embeddings_ith(ctx, i);
if (embd == NULL) {
fprintf(stderr, "%s: failed to get embeddings for token %d\n", __func__, i);
continue;
}
}
GGML_ASSERT(embd != NULL && "failed to get sequence embeddings");

float * out = output + batch.seq_id[i][0] * n_embd;
//TODO: I would also add a parameter here to enable normalization or not.
Expand Down Expand Up @@ -97,6 +92,12 @@ int main(int argc, char ** argv) {
const int n_ctx_train = llama_n_ctx_train(model);
const int n_ctx = llama_n_ctx(ctx);

const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);
if (pooling_type == LLAMA_POOLING_TYPE_NONE) {
fprintf(stderr, "%s: error: pooling type NONE not supported\n", __func__);
return 1;
}

if (n_ctx > n_ctx_train) {
fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n",
__func__, n_ctx_train, n_ctx);
Expand Down
6 changes: 4 additions & 2 deletions examples/gritlm/gritlm.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -44,6 +44,7 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve

// clear previous kv_cache values (irrelevant for embeddings)
llama_kv_cache_clear(ctx);
llama_set_embeddings(ctx, true);
llama_set_causal_attn(ctx, false);

// run model
Expand Down Expand Up @@ -98,7 +99,9 @@ static std::string generate(llama_context * ctx, const std::string & prompt, boo
llama_token eos_token = llama_token_eos(mdl);

llama_kv_cache_clear(ctx);
llama_set_embeddings(ctx, false);
llama_set_causal_attn(ctx, true);

llama_batch bat = llama_batch_init(llama_n_batch(ctx), 0, 1);

std::vector<llama_token> inputs = llama_tokenize(mdl, prompt, false, true);
Expand Down Expand Up @@ -166,8 +169,7 @@ int main(int argc, char * argv[]) {

llama_model * mdl = llama_load_model_from_file(params.model.c_str(), mparams);

// create new context - set to embedding mode
cparams.embeddings = true;
// create generation context
llama_context * ctx = llama_new_context_with_model(mdl, cparams);

// ### Embedding/Representation ###
Expand Down
13 changes: 10 additions & 3 deletions examples/retrieval/retrieval.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -73,9 +73,10 @@ static std::vector<chunk> chunk_file(const std::string & filename, int chunk_siz
return chunks;
}

static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, int seq_id) {
for (size_t i = 0; i < tokens.size(); i++) {
llama_batch_add(batch, tokens[i], i, { seq_id }, i == tokens.size() - 1);
static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, llama_seq_id seq_id) {
size_t n_tokens = tokens.size();
for (size_t i = 0; i < n_tokens; i++) {
llama_batch_add(batch, tokens[i], i, { seq_id }, true);
}
}

Expand Down Expand Up @@ -160,6 +161,12 @@ int main(int argc, char ** argv) {
const int n_ctx_train = llama_n_ctx_train(model);
const int n_ctx = llama_n_ctx(ctx);

const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);
if (pooling_type == LLAMA_POOLING_TYPE_NONE) {
fprintf(stderr, "%s: error: pooling type NONE not supported\n", __func__);
return 1;
}

if (n_ctx > n_ctx_train) {
fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n",
__func__, n_ctx_train, n_ctx);
Expand Down
152 changes: 98 additions & 54 deletions llama.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -7650,6 +7650,50 @@ struct llm_build_context {
return lctx.inp_s_seq;
}

struct ggml_cgraph * append_pooling(struct ggml_cgraph * gf) {
// find result_norm tensor for input
struct ggml_tensor * inp = nullptr;
for (int i = gf->n_nodes - 1; i >= 0; --i) {
inp = gf->nodes[i];
if (strcmp(inp->name, "result_norm") == 0 || strcmp(inp->name, "result_embd") == 0) {
break;
} else {
inp = nullptr;
}
}
GGML_ASSERT(inp != nullptr && "missing result_norm/result_embd tensor");

struct ggml_tensor * cur;

switch (pooling_type) {
case LLAMA_POOLING_TYPE_MEAN:
{
struct ggml_tensor * inp_mean = build_inp_mean();
cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, inp)), inp_mean);
} break;
case LLAMA_POOLING_TYPE_CLS:
case LLAMA_POOLING_TYPE_LAST:
{
struct ggml_tensor * inp_cls = build_inp_cls();
cur = ggml_get_rows(ctx0, inp, inp_cls);
} break;
case LLAMA_POOLING_TYPE_NONE:
{
cur = inp;
} break;
default:
{
GGML_ASSERT(false && "unknown pooling type");
} break;
}

cb(cur, "result_embd_pooled", -1);

ggml_build_forward_expand(gf, cur);

return gf;
}

struct ggml_cgraph * build_llama() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

Expand Down Expand Up @@ -8630,8 +8674,6 @@ struct llm_build_context {
if (model.arch != LLM_ARCH_JINA_BERT_V2) {
inp_pos = build_inp_pos();
}
struct ggml_tensor * inp_mean = build_inp_mean();
struct ggml_tensor * inp_cls = build_inp_cls();

// construct input embeddings (token, type, position)
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
Expand Down Expand Up @@ -8806,28 +8848,6 @@ struct llm_build_context {
cur = inpL;
cb(cur, "result_embd", -1);

// pooling layer
switch (pooling_type) {
case LLAMA_POOLING_TYPE_NONE:
{
// nop
} break;
case LLAMA_POOLING_TYPE_MEAN:
{
cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, cur)), inp_mean);
cb(cur, "result_embd_pooled", -1);
} break;
case LLAMA_POOLING_TYPE_CLS:
{
cur = ggml_get_rows(ctx0, cur, inp_cls);
cb(cur, "result_embd_pooled", -1);
} break;
case LLAMA_POOLING_TYPE_UNSPECIFIED:
{
GGML_ASSERT(false && "Invalid pooling type");
} break;
}

ggml_build_forward_expand(gf, cur);

return gf;
Expand Down Expand Up @@ -11912,6 +11932,11 @@ static struct ggml_cgraph * llama_build_graph(
GGML_ASSERT(false);
}

// add on pooling layer
if (lctx.cparams.embeddings) {
result = llm.append_pooling(result);
}

llm.free();

return result;
Expand Down Expand Up @@ -12001,7 +12026,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
// (!a || b) is a logical implication (a -> b)
// !hparams.causal_attn -> !cparams.causal_attn
(hparams.causal_attn || !cparams.causal_attn) &&
"causal attention with embedding models is not supported"
"causal attention is not supported by this model"
);

if (lctx.inp_KQ_mask) {
Expand Down Expand Up @@ -12133,6 +12158,37 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
}
}

if (cparams.pooling_type == LLAMA_POOLING_TYPE_LAST) {
const int64_t n_tokens = batch.n_tokens;

GGML_ASSERT(lctx.inp_cls);
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer));

uint32_t * data = (uint32_t *) lctx.inp_cls->data;
memset(lctx.inp_cls->data, 0, n_tokens * ggml_element_size(lctx.inp_cls));

std::vector<int> last_pos(n_tokens, -1);
std::vector<int> last_row(n_tokens, -1);

for (int i = 0; i < n_tokens; ++i) {
const llama_seq_id seq_id = batch.seq_id[i][0];
const llama_pos pos = batch.pos[i];

GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == LAST");

if (pos >= last_pos[seq_id]) {
last_pos[seq_id] = pos;
last_row[seq_id] = i;
}
}

for (int i = 0; i < n_tokens; ++i) {
if (last_row[i] >= 0) {
data[i] = last_row[i];
}
}
}

if (kv_self.recurrent) {
const int64_t n_kv = kv_self.n;

Expand Down Expand Up @@ -12194,8 +12250,8 @@ static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) {
const auto n_embd = hparams.n_embd;

// TODO: use a per-batch flag for logits presence instead
const bool has_logits = cparams.causal_attn;
const bool has_embd = cparams.embeddings && (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE);
const bool has_logits = !cparams.embeddings;
const bool has_embd = cparams.embeddings && (cparams.pooling_type == LLAMA_POOLING_TYPE_NONE);

const size_t logits_size = has_logits ? n_vocab*n_outputs_max : 0;
const size_t embd_size = has_embd ? n_embd*n_outputs_max : 0;
Expand Down Expand Up @@ -12325,11 +12381,13 @@ static int llama_decode_internal(
std::vector<std::vector<llama_seq_id>> seq_id;

// count outputs
if (batch_all.logits) {
if (cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE) {
n_outputs = n_tokens_all;
} else if (batch_all.logits) {
for (uint32_t i = 0; i < n_tokens_all; ++i) {
n_outputs += batch_all.logits[i] != 0;
}
} else if (lctx.logits_all || (cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE)) {
} else if (lctx.logits_all) {
n_outputs = n_tokens_all;
} else {
// keep last output only
Expand Down Expand Up @@ -12460,30 +12518,13 @@ static int llama_decode_internal(
// no output
res = nullptr;
embd = nullptr;
} else if (!hparams.causal_attn) {
res = nullptr; // do not extract logits for embedding models such as BERT

// token or sequence embeddings
embd = gf->nodes[gf->n_nodes - 1];

GGML_ASSERT(strcmp(embd->name, "result_embd") == 0 || strcmp(embd->name, "result_embd_pooled") == 0);
} else if (cparams.embeddings) {
// the embeddings could be in the second to last tensor, or any of the previous tensors
int i_embd = gf->n_nodes - 2;
for (int i = 3; strcmp(embd->name, "result_norm") != 0; ++i) {
i_embd = gf->n_nodes - i;
if (i_embd < 0) { break; }
embd = gf->nodes[i_embd];
}
GGML_ASSERT(i_embd >= 0 && "missing result_norm tensor");

// TODO: use a per-batch flag to know when to skip logits while keeping embeddings
if (!cparams.causal_attn) {
res = nullptr; // do not extract logits when not needed
// skip computing logits
// TODO: is this safe?
gf->n_nodes = i_embd + 1;
res = nullptr; // do not extract logits for embedding case
embd = gf->nodes[gf->n_nodes - 1];
if (strcmp(embd->name, "result_embd_pooled") != 0) {
embd = gf->nodes[gf->n_nodes - 2];
}
GGML_ASSERT(strcmp(embd->name, "result_embd_pooled") == 0 && "missing embeddings tensor");
} else {
embd = nullptr; // do not extract embeddings when not needed
GGML_ASSERT(strcmp(res->name, "result_output") == 0 && "missing result_output tensor");
Expand Down Expand Up @@ -12552,11 +12593,10 @@ static int llama_decode_internal(
ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_outputs_new*n_embd*sizeof(float));
}
} break;
case LLAMA_POOLING_TYPE_CLS:
case LLAMA_POOLING_TYPE_MEAN:
case LLAMA_POOLING_TYPE_CLS:
case LLAMA_POOLING_TYPE_LAST:
{
GGML_ASSERT(strcmp(embd->name, "result_embd_pooled") == 0);

// extract sequence embeddings
auto & embd_seq_out = lctx.embd_seq;
embd_seq_out.clear();
Expand Down Expand Up @@ -18112,6 +18152,10 @@ void llama_set_abort_callback(struct llama_context * ctx, bool (*abort_callback)
ctx->abort_callback_data = abort_callback_data;
}

void llama_set_embeddings(struct llama_context * ctx, bool embeddings) {
ctx->cparams.embeddings = embeddings;
}

void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn) {
ctx->cparams.causal_attn = causal_attn;
}
Expand Down
6 changes: 5 additions & 1 deletion llama.h
View file
Original file line number Diff line number Diff line change
Expand Up @@ -174,6 +174,7 @@ extern "C" {
LLAMA_POOLING_TYPE_NONE = 0,
LLAMA_POOLING_TYPE_MEAN = 1,
LLAMA_POOLING_TYPE_CLS = 2,
LLAMA_POOLING_TYPE_LAST = 3,
};

enum llama_split_mode {
Expand Down Expand Up @@ -293,7 +294,6 @@ extern "C" {

enum llama_rope_scaling_type rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
enum llama_pooling_type pooling_type; // whether to pool (sum) embedding results by sequence id
// (ignored if no pooling layer)

// ref: https://github.com/ggerganov/llama.cpp/pull/2054
float rope_freq_base; // RoPE base frequency, 0 = from model
Expand Down Expand Up @@ -786,6 +786,10 @@ extern "C" {
// Get the number of threads used for prompt and batch processing (multiple token).
LLAMA_API uint32_t llama_n_threads_batch(struct llama_context * ctx);

// Set whether the model is in embeddings model or not
// If true, embeddings will be returned but logits will not
LLAMA_API void llama_set_embeddings(struct llama_context * ctx, bool embeddings);

// Set whether to use causal attention or not
// If set to true, the model will only attend to the past tokens
LLAMA_API void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn);
Expand Down

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