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Fix kq_scale for the attention layers of PLaMo2 #14892

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Merged
merged 7 commits into from
Jul 27, 2025
Merged

Fix kq_scale for the attention layers of PLaMo2 #14892

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7baf4fd
Fix dimensions for expand
mitmul Jul 18, 2025
e39bc09
Change dimensions to copy states to cache
mitmul Jul 26, 2025
bd4d2e1
Fix the default value for plamo2 conversion
mitmul Jul 26, 2025
c475203
Fix scale given to build_attn
mitmul Jul 26, 2025
75f0a0d
Update src/llama-model.cpp
mitmul Jul 27, 2025
429639d
Update src/llama-model.cpp
mitmul Jul 27, 2025
60a705d
Update src/llama-model.cpp
mitmul Jul 27, 2025
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4 changes: 2 additions & 2 deletions convert_hf_to_gguf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3788,7 +3788,7 @@ def set_gguf_parameters(self):
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))
Expand All @@ -3799,7 +3799,7 @@ def set_gguf_parameters(self):
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]]:
Expand Down
19 changes: 9 additions & 10 deletions src/llama-model.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -15988,7 +15988,7 @@ struct llm_build_plamo2 : public llm_graph_context_mamba {
{
// 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;
Expand All @@ -16012,7 +16012,6 @@ struct llm_build_plamo2 : public llm_graph_context_mamba {

Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
cb(Qcur, "Qcur_normed", il);

Qcur = ggml_rope_ext(
ctx0, Qcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
Expand All @@ -16021,17 +16020,16 @@ struct llm_build_plamo2 : public llm_graph_context_mamba {

Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
cb(Kcur, "Kcur_normed", il);

Kcur = ggml_rope_ext(
ctx0, Kcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
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);
cb(cur, "attn_output", il);

return cur;
}
Expand Down Expand Up @@ -16103,8 +16101,9 @@ struct llm_build_plamo2 : public llm_graph_context_mamba {
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);
Expand Down Expand Up @@ -16167,9 +16166,9 @@ struct llm_build_plamo2 : public llm_graph_context_mamba {
// 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))));
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Mind commenting on these changes?

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These changes actually replace d_inner with n_heads*head_dim for both of y_ssm and ssm_states_all. This is because I thought it's more natural because the ssm state created in get_ssm_rows() has the shape of (d_state , head_dim, n_heads, n_seqs) here:

llama.cpp/src/llama-model.cpp

Line 16359 in 446595b

ggml_tensor * ssm = ggml_reshape_4d(ctx, states, d_state, head_dim, n_heads, mctx_cur->get_size());

But, if d_inner is always the same as n_heads*head_dim, I'm happy to revert this change cause it's unnecessary.

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);
Expand Down
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