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add exception for ibm granite, then keep using f16 kq mul for HIPBLAS only for now pending ROCM investigation re https://github.com/ggerganov/llama.cpp/pull/10015

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Concedo 2024-11-04 15:47:13 +08:00
parent 5233e8ed1d
commit c7e351bf41
1 changed files with 14 additions and 0 deletions
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14
src/llama.cpp
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@ -9733,16 +9733,30 @@ 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, 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); hparams.attn_soft_cap ? hparams.f_attn_logit_softcapping : 0.0f);
#if defined(GGML_USE_HIPBLAS) //workaround for speed regression on rocm
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) {
ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
}
#else
ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32); ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
#endif
cur = ggml_reshape_2d(ctx, cur, n_embd_head_v*n_head, n_tokens); cur = ggml_reshape_2d(ctx, cur, n_embd_head_v*n_head, n_tokens);
} else { } else {
struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q); struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
cb(kq, "kq", il); cb(kq, "kq", il);
#if defined(GGML_USE_HIPBLAS) //workaround for speed regression on rocm
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) {
// 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);
}
#else
// note: this op tends to require high floating point range // 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 // 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); ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
#endif
if (model.arch == LLM_ARCH_GROK) { if (model.arch == LLM_ARCH_GROK) {
// need to do the following: // need to do the following: