@@ -9761,20 +9761,16 @@ static struct ggml_tensor * llm_build_kqv(
97619761 cur = ggml_flash_attn_ext(ctx, q, k, v, kq_mask, kq_scale, hparams.f_max_alibi_bias,
97629762 hparams.attn_soft_cap ? hparams.f_attn_logit_softcapping : 0.0f);
97639763
9764- if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX || model.arch == LLM_ARCH_GEMMA2) {
9765- ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
9766- }
9764+ ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
97679765
97689766 cur = ggml_reshape_2d(ctx, cur, n_embd_head_v*n_head, n_tokens);
97699767 } else {
97709768 struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
97719769 cb(kq, "kq", il);
97729770
9773- 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) {
9774- // for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs
9775- // ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847
9776- ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
9777- }
9771+ // note: this op tends to require high floating point range
9772+ // while for some models F16 is enough, for others it is not, so we default to F32 here
9773+ ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
97789774
97799775 if (model.arch == LLM_ARCH_GROK) {
97809776 // need to do the following:
@@ -9783,9 +9779,6 @@ static struct ggml_tensor * llm_build_kqv(
97839779 // kq = 30 * tanh(kq / 30)
97849780 // before the softmax below
97859781
9786- //try from phi2
9787- //ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
9788-
97899782 kq = ggml_tanh(ctx, ggml_scale(ctx, kq, 0.08838834764831845f/30.0f));
97909783 kq = ggml_scale(ctx, kq, 30);
97919784 }
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