vbatts/llama.cpp
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llama : rename batch to ubatch

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This commit renames the member field batch in llm_build_context to
ubatch, and also the parameter batch in llama_build_graph, and
llama_set_inputs to ubatch.

The motivation for this change is to make the code more readable
(considering there are the structs llama_batch and llama_sbatch), and
consistent with other parts of the code base where parameters/fields of
type llama_ubatch are named ubatch.
This commit is contained in:
Daniel Bevenius 2024-10-19 06:13:20 +02:00
parent cda0e4b648
commit 761384ac80
1 changed files with 109 additions and 109 deletions
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218
src/llama.cpp
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@ -10030,7 +10030,7 @@ struct llm_build_context {
llama_context & lctx;
const llama_hparams & hparams;
const llama_cparams & cparams;
const llama_ubatch & batch;
const llama_ubatch & ubatch;
const llama_kv_cache & kv_self;
const int64_t n_embd;
@ -10076,14 +10076,14 @@ struct llm_build_context {
// TODO: consider making the entire interface noexcept
llm_build_context(
llama_context & lctx,
const llama_ubatch & batch,
const llama_ubatch & ubatch,
const llm_build_cb & cb,
bool worst_case) :
model (lctx.model),
lctx (lctx),
hparams (model.hparams),
cparams (lctx.cparams),
batch (batch),
ubatch (ubatch),
kv_self (lctx.kv_self),
n_embd (hparams.n_embd),
n_layer (hparams.n_layer),
@ -10105,7 +10105,7 @@ struct llm_build_context {
beta_slow (cparams.yarn_beta_slow),
norm_eps (hparams.f_norm_eps),
norm_rms_eps (hparams.f_norm_rms_eps),
n_tokens (batch.n_tokens),
n_tokens (ubatch.n_tokens),
n_kv (worst_case ? kv_self.size : kv_self.n),
n_outputs (worst_case ? n_tokens : lctx.n_outputs),
n_outputs_enc (worst_case ? n_tokens : lctx.embd_enc.size() / hparams.n_embd),
@ -10474,7 +10474,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -10634,7 +10634,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = model.type == MODEL_7B ? build_inp_pos() : nullptr;
@ -10749,7 +10749,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -10853,7 +10853,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -10975,7 +10975,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// multiply by embedding_multiplier_scale of 78.38367176906169
inpL = ggml_scale(ctx0, inpL, 78.38367176906169f);
@ -11133,7 +11133,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -11255,7 +11255,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -11358,7 +11358,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
@ -11460,7 +11460,7 @@ struct llm_build_context {
}
// construct input embeddings (token, type, position)
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// token types are hardcoded to zero ("Sentence A")
struct ggml_tensor * type_row0 = ggml_view_1d(ctx0, model.type_embd, n_embd, 0);
@ -11647,7 +11647,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
@ -11749,7 +11749,7 @@ struct llm_build_context {
struct ggml_tensor * pos;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
@ -11887,7 +11887,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -12037,7 +12037,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -12150,7 +12150,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -12265,7 +12265,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -12410,7 +12410,7 @@ struct llm_build_context {
struct ggml_tensor * ffn_output;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -12529,7 +12529,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -12657,7 +12657,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -12762,7 +12762,7 @@ struct llm_build_context {
struct ggml_tensor * pos;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -12867,7 +12867,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -12977,7 +12977,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -13095,7 +13095,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -13222,7 +13222,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// scale the input embeddings
inpL = ggml_scale(ctx0, inpL, scale_embd);
@ -13366,7 +13366,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// scale the input embeddings
inpL = ggml_scale(ctx0, inpL, scale_embd);
@ -13567,7 +13567,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
cb(inpL, "inp_scaled", -1);
@ -13675,7 +13675,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
cb(inpL, "inp_scaled", -1);
@ -13813,7 +13813,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -13929,7 +13929,7 @@ struct llm_build_context {
struct ggml_tensor * inpL;
// {n_embd, n_tokens}
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
struct ggml_tensor * state_copy = build_inp_s_copy();
struct ggml_tensor * state_mask = build_inp_s_mask();
@ -13941,7 +13941,7 @@ struct llm_build_context {
LLM_NORM_RMS, cb, il);
cb(cur, "attn_norm", il);
cur = llm_build_mamba(ctx0, lctx, batch, gf, cur,
cur = llm_build_mamba(ctx0, lctx, ubatch, gf, cur,
state_copy, state_mask,
kv_head, n_kv, cb, il);
@ -13987,7 +13987,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -14144,7 +14144,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -14272,7 +14272,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -14391,7 +14391,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -14518,7 +14518,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -14663,7 +14663,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -14804,7 +14804,7 @@ struct llm_build_context {
struct ggml_tensor * inpL;
// {n_embd, n_tokens}
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -15019,7 +15019,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -15173,7 +15173,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
GGML_ASSERT(lctx.is_encoding);
struct ggml_tensor * pos_bucket_enc = llm_build_pos_bucket(false);
@ -15305,7 +15305,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
GGML_ASSERT(!lctx.is_encoding);
GGML_ASSERT(n_outputs_enc > 0 && "call llama_encode() first");
@ -15507,7 +15507,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
@ -15599,7 +15599,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -15713,7 +15713,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -15837,7 +15837,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -15957,11 +15957,11 @@ struct llm_build_context {
// Token shift state dimensions should be 2 * n_emb
GGML_ASSERT(n_embd == hparams.n_embd_k_s() / 2);
const int64_t n_seqs = batch.n_seqs;
const int64_t n_seq_tokens = batch.n_seq_tokens;
const int64_t n_tokens = batch.n_tokens;
const int64_t n_seqs = ubatch.n_seqs;
const int64_t n_seq_tokens = ubatch.n_seq_tokens;
const int64_t n_tokens = ubatch.n_tokens;
GGML_ASSERT(n_seqs != 0);
GGML_ASSERT(batch.equal_seqs);
GGML_ASSERT(ubatch.equal_seqs);
GGML_ASSERT(n_tokens == n_seq_tokens * n_seqs);
struct ggml_tensor * cur;
@ -15969,7 +15969,7 @@ struct llm_build_context {
struct ggml_tensor * state_copy = build_inp_s_copy();
struct ggml_tensor * state_mask = build_inp_s_mask();
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
inpL = llm_build_norm(ctx0, inpL, hparams, model.tok_norm, model.tok_norm_b, LLM_NORM, cb, -1);
for (int il = 0; il < n_layer; ++il) {
@ -16083,7 +16083,7 @@ struct llm_build_context {
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
@ -16279,7 +16279,7 @@ static struct ggml_cgraph * llama_build_graph_k_shift(llama_context & lctx) {
static struct ggml_cgraph * llama_build_graph(
llama_context & lctx,
const llama_ubatch & batch,
const llama_ubatch & ubatch,
bool worst_case) {
const auto & model = lctx.model;
@ -16301,7 +16301,7 @@ static struct ggml_cgraph * llama_build_graph(
// norm may be automatically assigned to the backend of the previous layer, increasing data transfer between backends
// FIXME: fix in ggml_backend_sched
const bool full_offload = lctx.model.n_gpu_layers > (int)lctx.model.hparams.n_layer;
if (batch.n_tokens < 32 || full_offload) {
if (ubatch.n_tokens < 32 || full_offload) {
if (il != -1 && strcmp(name, "norm") == 0) {
for (auto * backend : lctx.backends) {
if (ggml_backend_supports_buft(backend, lctx.model.buft_layer[il].buft) &&
@ -16316,7 +16316,7 @@ static struct ggml_cgraph * llama_build_graph(
struct ggml_cgraph * result = NULL;
struct llm_build_context llm(lctx, batch, cb, worst_case);
struct llm_build_context llm(lctx, ubatch, cb, worst_case);
llm.init();
@ -16567,7 +16567,7 @@ static int32_t llama_relative_position_bucket(llama_pos x, llama_pos y, uint64_t
return relative_bucket;
}
static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
static void llama_set_inputs(llama_context & lctx, const llama_ubatch & ubatch) {
//
// set input data
//
@ -16576,28 +16576,28 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
const auto & cparams = lctx.cparams;
const auto & kv_self = lctx.kv_self;
if (batch.token) {
const int64_t n_tokens = batch.n_tokens;
if (ubatch.token) {
const int64_t n_tokens = ubatch.n_tokens;
ggml_backend_tensor_set(lctx.inp_tokens, batch.token, 0, n_tokens*ggml_element_size(lctx.inp_tokens));
ggml_backend_tensor_set(lctx.inp_tokens, ubatch.token, 0, n_tokens*ggml_element_size(lctx.inp_tokens));
}
if (batch.embd) {
if (ubatch.embd) {
const int64_t n_embd = hparams.n_embd;
const int64_t n_tokens = batch.n_tokens;
const int64_t n_tokens = ubatch.n_tokens;
ggml_backend_tensor_set(lctx.inp_embd, batch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd));
ggml_backend_tensor_set(lctx.inp_embd, ubatch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd));
}
if (batch.pos && lctx.inp_pos) {
const int64_t n_tokens = batch.n_tokens;
if (ubatch.pos && lctx.inp_pos) {
const int64_t n_tokens = ubatch.n_tokens;
ggml_backend_tensor_set(lctx.inp_pos, batch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos));
ggml_backend_tensor_set(lctx.inp_pos, ubatch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos));
}
if (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE) {
GGML_ASSERT(lctx.inp_out_ids && "every model that can must skip unused outputs");
const int64_t n_tokens = batch.n_tokens;
const int64_t n_tokens = ubatch.n_tokens;
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_out_ids->buffer));
int32_t * data = (int32_t *) lctx.inp_out_ids->data;
@ -16606,10 +16606,10 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
for (int i = 0; i < n_tokens; ++i) {
data[i] = i;
}
} else if (batch.output) {
} else if (ubatch.output) {
int32_t n_outputs = 0;
for (int i = 0; i < n_tokens; ++i) {
if (batch.output[i]) {
if (ubatch.output[i]) {
data[n_outputs++] = i;
}
}
@ -16634,9 +16634,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
// NOTE: hparams.causal_attn indicates the model is capable of generation and uses the kv cache.
if (cparams.causal_attn && !lctx.is_encoding) {
const int64_t n_kv = kv_self.n;
const int64_t n_tokens = batch.n_tokens;
const int64_t n_seq_tokens = batch.n_seq_tokens;
const int64_t n_seqs = batch.n_seqs;
const int64_t n_tokens = ubatch.n_tokens;
const int64_t n_seq_tokens = ubatch.n_seq_tokens;
const int64_t n_seqs = ubatch.n_seqs;
float * data = nullptr;
@ -16653,14 +16653,14 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
}
// For causal attention, use only the previous KV cells
// of the correct sequence for each token of the batch.
// of the correct sequence for each token of the ubatch.
// It's assumed that if a token in the batch has multiple sequences, they are equivalent.
for (int h = 0; h < 1; ++h) {
for (int s = 0; s < n_seqs; ++s) {
const llama_seq_id seq_id = batch.seq_id[s][0];
const llama_seq_id seq_id = ubatch.seq_id[s][0];
for (int j = 0; j < n_seq_tokens; ++j) {
const llama_pos pos = batch.pos[s*n_seq_tokens + j];
const llama_pos pos = ubatch.pos[s*n_seq_tokens + j];
for (int i = 0; i < n_kv; ++i) {
float f;
@ -16706,9 +16706,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
}
}
} else {
const int64_t n_tokens = batch.n_tokens;
const int64_t n_seq_tokens = batch.n_seq_tokens;
const int64_t n_seqs = batch.n_seqs;
const int64_t n_tokens = ubatch.n_tokens;
const int64_t n_seq_tokens = ubatch.n_seq_tokens;
const int64_t n_seqs = ubatch.n_seqs;
// when using kv cache, the mask needs to match the kv cache size
const int64_t n_stride = hparams.causal_attn && !lctx.is_encoding ? kv_self.n : n_tokens;
@ -16718,7 +16718,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
for (int h = 0; h < 1; ++h) {
for (int s1 = 0; s1 < n_seqs; ++s1) {
const llama_seq_id seq_id = batch.seq_id[s1][0];
const llama_seq_id seq_id = ubatch.seq_id[s1][0];
for (int j = 0; j < n_seq_tokens; ++j) {
const int32_t tj = s1*n_seq_tokens + j;
@ -16728,10 +16728,10 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
const int32_t ti = s0*n_seq_tokens + i;
float f = -INFINITY;
for (int s = 0; s < batch.n_seq_id[s0]; ++s) {
if (batch.seq_id[s0][s] == seq_id) {
for (int s = 0; s < ubatch.n_seq_id[s0]; ++s) {
if (ubatch.seq_id[s0][s] == seq_id) {
if (hparams.use_alibi) {
f = -std::abs(batch.pos[ti] - batch.pos[tj]);
f = -std::abs(ubatch.pos[ti] - ubatch.pos[tj]);
} else {
f = 0.0f;
}
@ -16753,9 +16753,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
}
if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) {
const int64_t n_tokens = batch.n_tokens;
const int64_t n_seq_tokens = batch.n_seq_tokens;
const int64_t n_seqs = batch.n_seqs;
const int64_t n_tokens = ubatch.n_tokens;
const int64_t n_seq_tokens = ubatch.n_seq_tokens;
const int64_t n_seqs = ubatch.n_seqs;
GGML_ASSERT(lctx.inp_mean);
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer));
@ -16766,12 +16766,12 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
std::vector<uint64_t> sum(n_tokens, 0);
for (int s = 0; s < n_seqs; ++s) {
const llama_seq_id seq_id = batch.seq_id[s][0];
const llama_seq_id seq_id = ubatch.seq_id[s][0];
// TODO: adapt limits to n_seqs when batch.equal_seqs is true
// TODO: adapt limits to n_seqs when ubatch.equal_seqs is true
GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == MEAN");
sum[seq_id] += batch.n_seq_tokens;
sum[seq_id] += ubatch.n_seq_tokens;
}
std::vector<float> div(n_tokens, 0.0f);
@ -16783,7 +16783,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
}
for (int s = 0; s < n_seqs; ++s) {
const llama_seq_id seq_id = batch.seq_id[s][0];
const llama_seq_id seq_id = ubatch.seq_id[s][0];
for (int i = 0; i < n_seq_tokens; ++i) {
data[seq_id*n_tokens + s*n_seq_tokens + i] = div[seq_id];
@ -16794,9 +16794,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
if (cparams.embeddings && (
cparams.pooling_type == LLAMA_POOLING_TYPE_CLS ||
cparams.pooling_type == LLAMA_POOLING_TYPE_RANK)) {
const int64_t n_tokens = batch.n_tokens;
const int64_t n_seq_tokens = batch.n_seq_tokens;
const int64_t n_seqs = batch.n_seqs;
const int64_t n_tokens = ubatch.n_tokens;
const int64_t n_seq_tokens = ubatch.n_seq_tokens;
const int64_t n_seqs = ubatch.n_seqs;
GGML_ASSERT(lctx.inp_cls);
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer));
@ -16805,13 +16805,13 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
memset(lctx.inp_cls->data, 0, n_tokens * ggml_element_size(lctx.inp_cls));
for (int s = 0; s < n_seqs; ++s) {
const llama_seq_id seq_id = batch.seq_id[s][0];
const llama_seq_id seq_id = ubatch.seq_id[s][0];
// TODO: adapt limits to n_seqs when batch.equal_seqs is true
// TODO: adapt limits to n_seqs when ubatch.equal_seqs is true
GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS or RANK");
for (int i = 0; i < n_seq_tokens; ++i) {
const llama_pos pos = batch.pos[s*n_seq_tokens + i];
const llama_pos pos = ubatch.pos[s*n_seq_tokens + i];
if (pos == 0) {
data[seq_id] = s*n_seq_tokens + i;
@ -16821,9 +16821,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
}
if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_LAST) {
const int64_t n_tokens = batch.n_tokens;
const int64_t n_seq_tokens = batch.n_seq_tokens;
const int64_t n_seqs = batch.n_seqs;
const int64_t n_tokens = ubatch.n_tokens;
const int64_t n_seq_tokens = ubatch.n_seq_tokens;
const int64_t n_seqs = ubatch.n_seqs;
GGML_ASSERT(lctx.inp_cls);
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer));
@ -16835,13 +16835,13 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
std::vector<int> last_row(n_tokens, -1);
for (int s = 0; s < n_seqs; ++s) {
const llama_seq_id seq_id = batch.seq_id[s][0];
const llama_seq_id seq_id = ubatch.seq_id[s][0];
// TODO: adapt limits to n_seqs when batch.equal_seqs is true
// TODO: adapt limits to n_seqs when ubatch.equal_seqs is true
GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == LAST");
for (int i = 0; i < n_seq_tokens; ++i) {
const llama_pos pos = batch.pos[s*n_seq_tokens + i];
const llama_pos pos = ubatch.pos[s*n_seq_tokens + i];
if (pos >= last_pos[seq_id]) {
last_pos[seq_id] = pos;
@ -16903,10 +16903,10 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
}
if (lctx.inp_pos_bucket) {
const int64_t n_tokens = batch.n_tokens;
const int64_t n_tokens = ubatch.n_tokens;
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_pos_bucket->buffer));
GGML_ASSERT(!batch.equal_seqs); // TODO: use batch.n_seqs instead of failing
GGML_ASSERT(!ubatch.equal_seqs); // TODO: use ubatch.n_seqs instead of failing
int32_t * data = (int32_t *) lctx.inp_pos_bucket->data;
@ -16915,7 +16915,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
for (int h = 0; h < 1; ++h) {
for (int j = 0; j < n_tokens; ++j) {
for (int i = 0; i < n_kv; ++i) {
data[h*(n_kv*n_tokens) + j*n_kv + i] = llama_relative_position_bucket(lctx.kv_self.cells[i].pos, batch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding);
data[h*(n_kv*n_tokens) + j*n_kv + i] = llama_relative_position_bucket(lctx.kv_self.cells[i].pos, ubatch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding);
}
}
}
@ -16923,7 +16923,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
for (int h = 0; h < 1; ++h) {
for (int j = 0; j < n_tokens; ++j) {
for (int i = 0; i < n_tokens; ++i) {
data[h*(n_tokens*n_tokens) + j*n_tokens + i] = llama_relative_position_bucket(batch.pos[i], batch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding);
data[h*(n_tokens*n_tokens) + j*n_tokens + i] = llama_relative_position_bucket(ubatch.pos[i], ubatch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding);
}
}
}
@ -16939,10 +16939,10 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
if (!lctx.is_encoding && lctx.inp_KQ_mask_cross) {
const int64_t n_output_enc = lctx.embd_enc.size() / hparams.n_embd;
const int64_t n_tokens = batch.n_tokens;
const int64_t n_tokens = ubatch.n_tokens;
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask_cross->buffer));
GGML_ASSERT(!batch.equal_seqs); // TODO: use batch.n_seqs instead of failing
GGML_ASSERT(!ubatch.equal_seqs); // TODO: use ubatch.n_seqs instead of failing
float * data = (float *) lctx.inp_KQ_mask_cross->data;
@ -16950,8 +16950,8 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
for (int j = 0; j < n_tokens; ++j) {
for (int i = 0; i < n_output_enc; ++i) {
float f = -INFINITY;
for (int s = 0; s < batch.n_seq_id[j]; ++s) {
const llama_seq_id seq_id = batch.seq_id[j][s];
for (int s = 0; s < ubatch.n_seq_id[j]; ++s) {
const llama_seq_id seq_id = ubatch.seq_id[j][s];
if (lctx.seq_ids_enc[i].find(seq_id) != lctx.seq_ids_enc[i].end()) {
f = 0.0f;
}