@@ -9733,16 +9733,30 @@ static struct ggml_tensor * llm_build_kqv(
97339733 cur = ggml_flash_attn_ext(ctx, q, k, v, kq_mask, kq_scale, hparams.f_max_alibi_bias,
97349734 hparams.attn_soft_cap ? hparams.f_attn_logit_softcapping : 0.0f);
97359735
9736+ #if defined(GGML_USE_HIPBLAS) //workaround for speed regression on rocm
9737+ if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX || model.arch == LLM_ARCH_GEMMA2 || model.arch == LLM_ARCH_GRANITE || model.arch == LLM_ARCH_GRANITE_MOE) {
9738+ ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
9739+ }
9740+ #else
97369741 ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
9742+ #endif
97379743
97389744 cur = ggml_reshape_2d(ctx, cur, n_embd_head_v*n_head, n_tokens);
97399745 } else {
97409746 struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
97419747 cb(kq, "kq", il);
97429748
9749+ #if defined(GGML_USE_HIPBLAS) //workaround for speed regression on rocm
9750+ 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 || model.arch == LLM_ARCH_GRANITE || model.arch == LLM_ARCH_GRANITE_MOE) {
9751+ // for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs
9752+ // ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847
9753+ ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
9754+ }
9755+ #else
97439756 // note: this op tends to require high floating point range
97449757 // while for some models F16 is enough, for others it is not, so we default to F32 here
97459758 ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
9759+ #endif
97469760
97479761 if (model.arch == LLM_ARCH_GROK) {
97489762 // need to do the following:
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