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Merge branch 'ggerganov:master' into k-shift2

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MaggotHATE 2024-10-28 09:29:39 +05:00 committed by GitHub
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1 changed files with 4 additions and 11 deletions
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@ -9618,20 +9618,16 @@ static struct ggml_tensor * llm_build_kqv(
cur = ggml_flash_attn_ext(ctx, q, k, v, kq_mask, kq_scale, hparams.f_max_alibi_bias,
hparams.attn_soft_cap ? hparams.f_attn_logit_softcapping : 0.0f);
if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX || model.arch == LLM_ARCH_GEMMA2) {
ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
}
ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
cur = ggml_reshape_2d(ctx, cur, n_embd_head_v*n_head, n_tokens);
} else {
struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
cb(kq, "kq", il);
if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX || model.arch == LLM_ARCH_QWEN2 || model.arch == LLM_ARCH_NEMOTRON || model.arch == LLM_ARCH_CHATGLM) {
// for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs
// ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847
ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
}
// note: this op tends to require high floating point range
// while for some models F16 is enough, for others it is not, so we default to F32 here
ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
if (model.arch == LLM_ARCH_GROK) {
// need to do the following:
@ -9640,9 +9636,6 @@ static struct ggml_tensor * llm_build_kqv(
// kq = 30 * tanh(kq / 30)
// before the softmax below
//try from phi2
//ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
kq = ggml_tanh(ctx, ggml_scale(ctx, kq, 0.08838834764831845f/30.0f));
kq = ggml_scale(ctx, kq, 30);
}