Flash MLA (CPU only) (#240)

* FlashMLA - it finally works (on the CPU)

* FlashMLA: allow for f16 and bf16 cache in addition to q8_0

* It works with ggml FA, not with iqk FA

* WIP

* FlashMLA: it now works with iqk

I had forgotten to divide the Q stride by sizeof(float) and
that's why, very cobfusingly, it was working for TG but not for PP.

* WIP

* FlashMLA: that should be it for now

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>

1 files changed, 81 insertions, 57 deletions

diff --git a/src/llama.cpp b/src/llama.cpp
index 3a8b54ca..5ac44055 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -3168,7 +3168,7 @@ static bool llama_kv_cache_init(
 
     // DeepSeek MLA
     cache.kv_l.reserve(n_layer);
-    if (cparams.mla_attn == 1) {
+    if (cparams.mla_attn == 1 && !cparams.flash_attn) {
         cache.kvt_l.reserve(n_layer);
     }
 
@@ -3201,14 +3201,20 @@ static bool llama_kv_cache_init(
             const uint32_t n_embd_head_qk_rope = hparams.n_rot;
             const uint32_t kv_lora_rank = hparams.n_lora_kv;
             LLAMA_LOG_INFO("%s: layer %d: n_embd_head_qk_rope = %d, kv_lora_rank = %d\n", __func__, i, n_embd_head_qk_rope, kv_lora_rank);
-            auto kv_type = cparams.mla_attn == 1 ? cache.type_k : cache.type_v;
-            ggml_tensor * kv = ggml_new_tensor_2d(ctx, kv_type, kv_lora_rank + n_embd_head_qk_rope, kv_size);
-            ggml_format_name(kv, "cache_kv_l%d", i);
-            cache.kv_l.push_back(kv);
-            if (cparams.mla_attn == 1) {
-                ggml_tensor * kvt = ggml_new_tensor_1d(ctx, cache.type_v, kv_lora_rank*kv_size);
-                ggml_format_name(kvt, "cache_kvt_l%d", i);
-                cache.kvt_l.push_back(kvt);
+            if (cparams.flash_attn) {
+                ggml_tensor * kv = ggml_new_tensor_2d(ctx, cache.type_k, kv_lora_rank + n_embd_head_qk_rope, kv_size);
+                ggml_format_name(kv, "cache_kv_l%d", i);
+                cache.kv_l.push_back(kv);
+            } else {
+                auto kv_type = cparams.mla_attn == 1 ? cache.type_k : cache.type_v;
+                ggml_tensor * kv = ggml_new_tensor_2d(ctx, kv_type, kv_lora_rank + n_embd_head_qk_rope, kv_size);
+                ggml_format_name(kv, "cache_kv_l%d", i);
+                cache.kv_l.push_back(kv);
+                if (cparams.mla_attn == 1) {
+                    ggml_tensor * kvt = ggml_new_tensor_1d(ctx, cache.type_v, kv_lora_rank*kv_size);
+                    ggml_format_name(kvt, "cache_kvt_l%d", i);
+                    cache.kvt_l.push_back(kvt);
+                }
             }
             n_mla++;
         }
@@ -13588,7 +13594,7 @@ struct llm_build_context {
 
                     ggml_tensor * kv_cache_trans;
 
-                    if (lctx.cparams.mla_attn == 1) {
+                    if (lctx.cparams.mla_attn == 1 && !lctx.cparams.flash_attn) {
                         ggml_tensor * kv_cache_trans_view = ggml_view_2d(ctx0, kv_self.kvt_l[il], n_tokens, kv_lora_rank,
                                 ggml_row_size(kv_self.kvt_l[il]->type, kv_self.size), ggml_row_size(kv_self.kvt_l[il]->type, kv_head));
                         cb(kv_cache_trans_view, "kv_cache_trans_view", il);
@@ -13630,70 +13636,88 @@ struct llm_build_context {
                     ggml_tensor * q = ggml_concat(ctx0, q_nope2, ggml_permute(ctx0, q_rope, 0, 2, 1, 3), 0);
                     cb(q, "q", il);
 
-                    if (lctx.cparams.mla_attn > 1) {
+                    ggml_tensor * kqv_compressed;
+
+                    if (lctx.cparams.flash_attn) {
                         ggml_tensor * kv_cache_lora = ggml_view_2d(ctx0, kv_self.kv_l[il],
                                 kv_lora_rank, n_kv,
                                 ggml_row_size(kv_self.kv_l[il]->type, kv_lora_rank + n_embd_head_qk_rope), 0);
-                        cb(kv_cache, "kv_cache_lora", il);
+                        cb(kv_cache_lora, "kv_cache_lora", il);
 
-                        kv_cache_trans = ggml_cont(ctx0, ggml_transpose(ctx0, kv_cache_lora));
-                        cb(kv_cache_trans, "kv_cache_trans", il);
-                    }
+                        //ggml_tensor * v = ggml_cont(ctx0, kv_cache_lora);
+                        //kqv_compressed = ggml_flash_attn_ext(ctx0, q, kv_cache, v, KQ_mask, kq_scale, hparams.f_max_alibi_bias, 0.f);
 
-                    ggml_tensor * kqv_compressed;
-                    auto kq_size = kv_cache->ne[1]*q->ne[1]*q->ne[2]*sizeof(float)/(1024*1024); // K*Q in MiB
-                    if (lctx.cparams.attn_max_batch <= 0 || lctx.cparams.attn_max_batch >= kq_size) {
-                        if (!pp_opt) {
-                            q = ggml_permute(ctx0, q, 0, 2, 1, 3);
-                            cb(q, "q_perm", il);
+                        kqv_compressed = ggml_flash_attn_ext(ctx0, q, kv_cache, kv_cache_lora, KQ_mask, kq_scale, hparams.f_max_alibi_bias, 0.f);
+                        cb(kqv_compressed, "kqv_compressed", il);
+
+                        kqv_compressed = ggml_permute(ctx0, kqv_compressed, 0, 2, 1, 3);
+                        cb(kqv_compressed, "kqv_compressed_perm", il);
+                    }
+                    else {
+                        if (lctx.cparams.mla_attn > 1) {
+                            ggml_tensor * kv_cache_lora = ggml_view_2d(ctx0, kv_self.kv_l[il],
+                                    kv_lora_rank, n_kv,
+                                    ggml_row_size(kv_self.kv_l[il]->type, kv_lora_rank + n_embd_head_qk_rope), 0);
+                            cb(kv_cache, "kv_cache_lora", il);
+
+                            kv_cache_trans = ggml_cont(ctx0, ggml_transpose(ctx0, kv_cache_lora));
+                            cb(kv_cache_trans, "kv_cache_trans", il);
                         }
 
-                        ggml_tensor * kq = ggml_mul_mat(ctx0, kv_cache, q);
-                        cb(kq, "kq", il);
+                        auto kq_size = kv_cache->ne[1]*q->ne[1]*q->ne[2]*sizeof(float)/(1024*1024); // K*Q in MiB
+                        if (lctx.cparams.attn_max_batch <= 0 || lctx.cparams.attn_max_batch >= kq_size) {
+                            if (!pp_opt) {
+                                q = ggml_permute(ctx0, q, 0, 2, 1, 3);
+                                cb(q, "q_perm", il);
+                            }
 
-                        if (!pp_opt) {
-                            kq = ggml_cont(ctx0, ggml_permute(ctx0, kq, 0, 2, 1, 3));
-                            cb(kq, "kq_perm", il);
-                        }
+                            ggml_tensor * kq = ggml_mul_mat(ctx0, kv_cache, q);
+                            cb(kq, "kq", il);
 
-                        kq = ggml_soft_max_ext(ctx0, kq, KQ_mask, kq_scale, hparams.f_max_alibi_bias);
-                        cb(kq, "kq_soft_max_ext", il);
+                            if (!pp_opt) {
+                                kq = ggml_cont(ctx0, ggml_permute(ctx0, kq, 0, 2, 1, 3));
+                                cb(kq, "kq_perm", il);
+                            }
 
-                        if (!pp_opt) {
-                            kq = ggml_permute(ctx0, kq, 0, 2, 1, 3);
-                            cb(kq, "kq_soft_max_ext_perm", il);
-                        }
+                            kq = ggml_soft_max_ext(ctx0, kq, KQ_mask, kq_scale, hparams.f_max_alibi_bias);
+                            cb(kq, "kq_soft_max_ext", il);
 
-                        kqv_compressed = ggml_mul_mat(ctx0, kv_cache_trans, kq);
-                        cb(kqv_compressed, "kqv_compressed", il);
+                            if (!pp_opt) {
+                                kq = ggml_permute(ctx0, kq, 0, 2, 1, 3);
+                                cb(kq, "kq_soft_max_ext_perm", il);
+                            }
 
-                        if (!pp_opt) {
-                            kqv_compressed = ggml_permute(ctx0, kqv_compressed, 0, 2, 1, 3);
-                            cb(kqv_compressed, "kqv_compressed_perm", il);
-                        }
+                            kqv_compressed = ggml_mul_mat(ctx0, kv_cache_trans, kq);
+                            cb(kqv_compressed, "kqv_compressed", il);
 
-                    } else {
+                            if (!pp_opt) {
+                                kqv_compressed = ggml_permute(ctx0, kqv_compressed, 0, 2, 1, 3);
+                                cb(kqv_compressed, "kqv_compressed_perm", il);
+                            }
 
-                        int n_step = (kq_size + lctx.cparams.attn_max_batch - 1)/lctx.cparams.attn_max_batch;
-                        n_step = std::min(n_step, int(q->ne[2]));
-                        int n_per_step = (q->ne[2] + n_step - 1)/n_step;
+                        } else {
 
-                        //printf("kq size would be %ld MiB -> splitting kqv computation into %d steps\n", kq_size, n_step);
+                            int n_step = (kq_size + lctx.cparams.attn_max_batch - 1)/lctx.cparams.attn_max_batch;
+                            n_step = std::min(n_step, int(q->ne[2]));
+                            int n_per_step = (q->ne[2] + n_step - 1)/n_step;
 
-                        for (int i_head = 0; i_head < q->ne[2]; i_head += n_per_step) {
-                            int this_ne12 = i_head + n_per_step <= q->ne[2] ? n_per_step : q->ne[2] - i_head;
-                            ggml_tensor * q_i = ggml_view_3d(ctx0, q, q->ne[0], q->ne[1], this_ne12, q->nb[1], q->nb[2], q->nb[2]*i_head);
-                            ggml_tensor * kq_i = ggml_mul_mat(ctx0, kv_cache, q_i);
-                            kq_i = ggml_soft_max_ext(ctx0, kq_i, KQ_mask, kq_scale, hparams.f_max_alibi_bias);
-                            ggml_tensor * kqv_i = ggml_mul_mat(ctx0, kv_cache_trans, kq_i);
-                            if (i_head == 0) {
-                                kqv_compressed = kqv_i;
-                            } else {
-                                kqv_compressed = ggml_concat(ctx0, kqv_compressed, kqv_i, 2);
+                            //printf("kq size would be %ld MiB -> splitting kqv computation into %d steps\n", kq_size, n_step);
+
+                            for (int i_head = 0; i_head < q->ne[2]; i_head += n_per_step) {
+                                int this_ne12 = i_head + n_per_step <= q->ne[2] ? n_per_step : q->ne[2] - i_head;
+                                ggml_tensor * q_i = ggml_view_3d(ctx0, q, q->ne[0], q->ne[1], this_ne12, q->nb[1], q->nb[2], q->nb[2]*i_head);
+                                ggml_tensor * kq_i = ggml_mul_mat(ctx0, kv_cache, q_i);
+                                kq_i = ggml_soft_max_ext(ctx0, kq_i, KQ_mask, kq_scale, hparams.f_max_alibi_bias);
+                                ggml_tensor * kqv_i = ggml_mul_mat(ctx0, kv_cache_trans, kq_i);
+                                if (i_head == 0) {
+                                    kqv_compressed = kqv_i;
+                                } else {
+                                    kqv_compressed = ggml_concat(ctx0, kqv_compressed, kqv_i, 2);
+                                }
+                                ggml_build_forward_expand(gf, kqv_compressed);
                             }
-                            ggml_build_forward_expand(gf, kqv_compressed);
+                            cb(kqv_compressed, "kqv_compressed", il);
                         }
-                        cb(kqv_compressed, "kqv_compressed", il);
                     }
 
                     struct ggml_tensor * wv_b = ggml_view_3d(ctx0, model.layers[il].wv_b, kv_lora_rank, n_embd_head_v, n_head,
@@ -18226,7 +18250,7 @@ struct llama_context * llama_new_context_with_model(
             }
 
             if (memory_size_kv + memory_size_kvt > 0) {
-                if (cparams.mla_attn == 1) {
+                if (cparams.mla_attn == 1 && !cparams.flash_attn) {
                     LLAMA_LOG_INFO("%s: KV self size  = %7.2f MiB, c^KV (%s): %7.2f MiB, kv^T (%s): %7.2f MiB\n", __func__,
                             (float)(memory_size_kv + memory_size_kvt) / (1024.0f * 1024.0f),
                             ggml_type_name(kv_type), (float)memory_size_kv / (1024.0f * 1024.0f),