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llama : rename batch to ubatch (#9950)

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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 2024-10-22 15:31:06 +02:00 committed by GitHub
parent 11d47057a5
commit 19d900a756
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GPG Key ID: B5690EEEBB952194
1 changed files with 109 additions and 109 deletions
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src/llama.cpp
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@ -10017,7 +10017,7 @@ 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;
@ -10063,14 +10063,14 @@ 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),
@ -10092,7 +10092,7 @@ 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),
@ -10461,7 +10461,7 @@ 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();
@ -10621,7 +10621,7 @@ 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;
@ -10736,7 +10736,7 @@ 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();
@ -10840,7 +10840,7 @@ 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();
@ -10962,7 +10962,7 @@ 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);
@ -11120,7 +11120,7 @@ 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();
@ -11242,7 +11242,7 @@ 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();
@ -11345,7 +11345,7 @@ 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();
@ -11447,7 +11447,7 @@ 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);
@ -11634,7 +11634,7 @@ 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();
@ -11736,7 +11736,7 @@ 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();
@ -11874,7 +11874,7 @@ 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();
@ -12024,7 +12024,7 @@ 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();
@ -12137,7 +12137,7 @@ 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();
@ -12252,7 +12252,7 @@ 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();
@ -12397,7 +12397,7 @@ 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();
@ -12516,7 +12516,7 @@ 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();
@ -12644,7 +12644,7 @@ 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();
@ -12749,7 +12749,7 @@ 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();
@ -12854,7 +12854,7 @@ 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();
@ -12964,7 +12964,7 @@ 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();
@ -13082,7 +13082,7 @@ 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();
@ -13209,7 +13209,7 @@ 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);
@ -13353,7 +13353,7 @@ 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);
@ -13554,7 +13554,7 @@ 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);
@ -13662,7 +13662,7 @@ 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);
@ -13800,7 +13800,7 @@ 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();
@ -13916,7 +13916,7 @@ 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();
@ -13928,7 +13928,7 @@ 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);
@ -13974,7 +13974,7 @@ 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();
@ -14131,7 +14131,7 @@ 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();
@ -14259,7 +14259,7 @@ 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();
@ -14378,7 +14378,7 @@ 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();
@ -14505,7 +14505,7 @@ 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();
@ -14650,7 +14650,7 @@ 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();
@ -14791,7 +14791,7 @@ 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();
@ -15006,7 +15006,7 @@ 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();
@ -15160,7 +15160,7 @@ 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);
@ -15292,7 +15292,7 @@ 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");
@ -15494,7 +15494,7 @@ 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();
@ -15586,7 +15586,7 @@ 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();
@ -15700,7 +15700,7 @@ 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();
@ -15824,7 +15824,7 @@ 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();
@ -15944,11 +15944,11 @@ 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;
@ -15956,7 +15956,7 @@ 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) {
@ -16070,7 +16070,7 @@ 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();
@ -16266,7 +16266,7 @@ 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;
@ -16288,7 +16288,7 @@ 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) &&
@ -16303,7 +16303,7 @@ 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();
@ -16554,7 +16554,7 @@ 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
//
@ -16563,28 +16563,28 @@ 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;
@ -16593,10 +16593,10 @@ 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;
}
}
@ -16621,9 +16621,9 @@ 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;
@ -16640,14 +16640,14 @@ 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;
@ -16693,9 +16693,9 @@ 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;
@ -16705,7 +16705,7 @@ 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;
@ -16715,10 +16715,10 @@ 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;
}
@ -16740,9 +16740,9 @@ 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));
@ -16753,12 +16753,12 @@ 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);
@ -16770,7 +16770,7 @@ 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];
@ -16781,9 +16781,9 @@ 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));
@ -16792,13 +16792,13 @@ 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;
@ -16808,9 +16808,9 @@ 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));
@ -16822,13 +16822,13 @@ 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;
@ -16890,10 +16890,10 @@ 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;
@ -16902,7 +16902,7 @@ 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);
}
}
}
@ -16910,7 +16910,7 @@ 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);
}
}
}
@ -16926,10 +16926,10 @@ 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;
@ -16937,8 +16937,8 @@ 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;
}