llama : fix kq_scale for the attention layers of PLaMo2 (#14892)

* Fix dimensions for expand

* Change dimensions to copy states to cache

* Fix the default value for plamo2 conversion

* Fix scale given to build_attn

* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <redacted>
* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <redacted>
* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <redacted>
---------

Co-authored-by: Sigbjørn Skjæret <redacted>

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index e12c922bd9ab489b4dfcf034467ecefdbdccf16d..2c51b6c42d4c595f779ad22140abd73526af7956 100755 (executable)
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -3791,7 +3791,7 @@ class Plamo2Model(TextModel):
         self.gguf_writer.add_block_count(block_count)
         self.gguf_writer.add_head_count(hparams.get("num_attention_heads", 32))
         self.gguf_writer.add_layer_norm_rms_eps(hparams.get("rms_norm_eps", 1e-06))
-        self.gguf_writer.add_rope_freq_base(hparams.get("rope_theta", 1000000.0))
+        self.gguf_writer.add_rope_freq_base(hparams.get("rope_theta", 10000))
 
         # Mamba parameters
         self.gguf_writer.add_ssm_state_size(hparams.get("mamba_d_state", 64))
@@ -3802,7 +3802,7 @@ class Plamo2Model(TextModel):
         self.gguf_writer.add_ssm_group_count(0)
 
         # MLP feed forward parameters (for attention layers)
-        self.gguf_writer.add_feed_forward_length(hparams.get("intermediate_size", 16384))
+        self.gguf_writer.add_feed_forward_length(hparams.get("intermediate_size", 13312))
         self.gguf_writer.add_file_type(self.ftype)
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:

diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index a997a1e80f8cfc580de1c0bdc419c201af9d216a..f16789c2a77e927803a76014e121326473579829 100644 (file)
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -16191,7 +16191,7 @@ private:
         {
             // PLaMo-2 uses combined QKV tensor
             ggml_tensor * qkv = build_lora_mm(model.layers[il].wqkv, cur);
-            cb(qkv, "qkv", il);
+            cb(qkv, "wqkv", il);
 
             // split QKV tensor into Q, K, V
             const int64_t n_embd_head_q = hparams.n_embd_head_k;
@@ -16231,7 +16231,7 @@ private:
                     ext_factor, attn_factor, beta_fast, beta_slow
                     );
 
-            cur = build_attn(inp, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, NULL, NULL, 1.0f, il);
+            cur = build_attn(inp, model.layers[il].wo, NULL, Qcur, Kcur, Vcur, NULL, NULL, 1.0f/sqrtf(float(n_embd_head_v)), il);
         }
 
         cb(cur, "attn_out", il);
@@ -16306,8 +16306,9 @@ private:
             ggml_build_forward_expand(gf,
                 ggml_cpy(ctx0, last_conv,
                     ggml_view_1d(ctx0, conv_states_all,
-                        (d_conv - 1)*(d_inner)*(n_seqs),
-                        kv_head*(d_conv - 1)*(d_inner)*ggml_element_size(conv_states_all))));
+                        (d_conv - 1)*(d_inner + 2*n_group*d_state)*(n_seqs),
+                        kv_head*(d_conv - 1)*(d_inner + 2*n_group*d_state)*ggml_element_size(conv_states_all))));
+            cb(conv_states_all, "mamba_conv1d_state", il);
 
             // 1D convolution
             x = ggml_ssm_conv(ctx0, conv_x, model.layers[il].ssm_conv1d);
@@ -16370,9 +16371,9 @@ private:
             // store last states
             ggml_build_forward_expand(gf,
                 ggml_cpy(ctx0,
-                    ggml_view_1d(ctx0, y_ssm, d_state*d_inner*n_seqs, x->nb[3]*x->ne[3]),
-                    ggml_view_1d(ctx0, ssm_states_all, d_state*d_inner*n_seqs,
-                            kv_head*d_state*d_inner*ggml_element_size(ssm_states_all))));
+                    ggml_view_1d(ctx0, y_ssm, n_heads*head_dim*d_state*n_seqs, n_heads*head_dim*n_seq_tokens*n_seqs*ggml_element_size(y_ssm)),
+                    ggml_view_1d(ctx0, ssm_states_all, n_heads*head_dim*d_state*n_seqs, kv_head*n_seqs*n_heads*head_dim*d_state*ggml_element_size(ssm_states_all))));
+            cb(ssm_states_all, "mamba_ssm_states", il);
 
             ggml_tensor * y = ggml_view_4d(ctx0, y_ssm, head_dim, n_heads, n_seq_tokens, n_seqs, head_dim * ggml_element_size(x), head_dim * n_heads * ggml_element_size(x), head_dim * n_heads * n_seq_tokens * ggml_element_size(x), 0);
             cb(y, "mamba_y_view", il);