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llama : rename n_embed to n_embd in rwkv6_time_mix (#9504)
This commit renames n_embed to n_embd in llm_build_rwkv6_time_mix. The motivation for this change is consistency with the other rwkv6 functions like build_rwkv6 (and other parts of the code base).
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@ -9571,7 +9571,7 @@ static struct ggml_tensor * llm_build_rwkv6_time_mix(
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struct ggml_tensor * cur,
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struct ggml_tensor * cur,
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struct ggml_tensor * x_prev,
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struct ggml_tensor * x_prev,
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struct ggml_tensor ** wkv_state) {
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struct ggml_tensor ** wkv_state) {
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size_t n_embed = cur->ne[0];
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size_t n_embd = cur->ne[0];
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size_t n_seq_tokens = cur->ne[1];
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size_t n_seq_tokens = cur->ne[1];
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size_t n_seqs = cur->ne[2];
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size_t n_seqs = cur->ne[2];
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@ -9582,8 +9582,8 @@ static struct ggml_tensor * llm_build_rwkv6_time_mix(
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struct ggml_tensor * sx = ggml_sub(ctx, x_prev, cur);
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struct ggml_tensor * sx = ggml_sub(ctx, x_prev, cur);
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sx = ggml_reshape_2d(ctx, sx, n_embed, n_tokens);
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sx = ggml_reshape_2d(ctx, sx, n_embd, n_tokens);
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cur = ggml_reshape_2d(ctx, cur, n_embed, n_tokens);
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cur = ggml_reshape_2d(ctx, cur, n_embd, n_tokens);
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struct ggml_tensor * xxx = ggml_add(ctx, ggml_mul(ctx, sx, layer->time_mix_lerp_x), cur);
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struct ggml_tensor * xxx = ggml_add(ctx, ggml_mul(ctx, sx, layer->time_mix_lerp_x), cur);
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@ -9608,11 +9608,11 @@ static struct ggml_tensor * llm_build_rwkv6_time_mix(
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xxx
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xxx
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);
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);
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struct ggml_tensor *mw = ggml_view_2d(ctx, xxx, n_embed, n_tokens, xxx->nb[1], 0);
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struct ggml_tensor *mw = ggml_view_2d(ctx, xxx, n_embd, n_tokens, xxx->nb[1], 0);
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struct ggml_tensor *mk = ggml_view_2d(ctx, xxx, n_embed, n_tokens, xxx->nb[1], n_embed * n_tokens * sizeof(float));
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struct ggml_tensor *mk = ggml_view_2d(ctx, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * sizeof(float));
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struct ggml_tensor *mv = ggml_view_2d(ctx, xxx, n_embed, n_tokens, xxx->nb[1], n_embed * n_tokens * 2 * sizeof(float));
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struct ggml_tensor *mv = ggml_view_2d(ctx, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 2 * sizeof(float));
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struct ggml_tensor *mr = ggml_view_2d(ctx, xxx, n_embed, n_tokens, xxx->nb[1], n_embed * n_tokens * 3 * sizeof(float));
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struct ggml_tensor *mr = ggml_view_2d(ctx, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 3 * sizeof(float));
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struct ggml_tensor *mg = ggml_view_2d(ctx, xxx, n_embed, n_tokens, xxx->nb[1], n_embed * n_tokens * 4 * sizeof(float));
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struct ggml_tensor *mg = ggml_view_2d(ctx, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 4 * sizeof(float));
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struct ggml_tensor * xw = ggml_add(
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struct ggml_tensor * xw = ggml_add(
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ctx,
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ctx,
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@ -9681,7 +9681,7 @@ static struct ggml_tensor * llm_build_rwkv6_time_mix(
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)
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)
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);
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);
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w = ggml_add(ctx, w, ggml_reshape_1d(ctx, layer->time_mix_decay, n_embed));
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w = ggml_add(ctx, w, ggml_reshape_1d(ctx, layer->time_mix_decay, n_embd));
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w = ggml_exp(ctx, ggml_neg(ctx, ggml_exp(ctx, w)));
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w = ggml_exp(ctx, ggml_neg(ctx, ggml_exp(ctx, w)));
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w = ggml_reshape_4d(ctx, w, 1, head_size, head_count, n_tokens);
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w = ggml_reshape_4d(ctx, w, 1, head_size, head_count, n_tokens);
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@ -9690,21 +9690,21 @@ static struct ggml_tensor * llm_build_rwkv6_time_mix(
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r = ggml_transpose(ctx, r);
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r = ggml_transpose(ctx, r);
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struct ggml_tensor * wkv_output = ggml_rwkv_wkv(ctx, k, v, r, layer->time_mix_first, w, *wkv_state);
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struct ggml_tensor * wkv_output = ggml_rwkv_wkv(ctx, k, v, r, layer->time_mix_first, w, *wkv_state);
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cur = ggml_view_1d(ctx, wkv_output, n_embed * n_tokens, 0);
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cur = ggml_view_1d(ctx, wkv_output, n_embd * n_tokens, 0);
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*wkv_state = ggml_view_1d(ctx, wkv_output, n_embed * head_size * n_seqs, n_embed * n_tokens * sizeof(float));
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*wkv_state = ggml_view_1d(ctx, wkv_output, n_embd * head_size * n_seqs, n_embd * n_tokens * sizeof(float));
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// group norm with head_count groups
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// group norm with head_count groups
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cur = ggml_reshape_3d(ctx, cur, n_embed / head_count, head_count, n_tokens);
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cur = ggml_reshape_3d(ctx, cur, n_embd / head_count, head_count, n_tokens);
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cur = ggml_norm(ctx, cur, 64e-5f);
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cur = ggml_norm(ctx, cur, 64e-5f);
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// Convert back to regular vectors.
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// Convert back to regular vectors.
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cur = ggml_reshape_2d(ctx, cur, n_embed, n_tokens);
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cur = ggml_reshape_2d(ctx, cur, n_embd, n_tokens);
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cur = ggml_add(ctx, ggml_mul(ctx, cur, layer->time_mix_ln), layer->time_mix_ln_b);
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cur = ggml_add(ctx, ggml_mul(ctx, cur, layer->time_mix_ln), layer->time_mix_ln_b);
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cur = ggml_mul(ctx, cur, g);
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cur = ggml_mul(ctx, cur, g);
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cur = llm_build_lora_mm(lctx, ctx, layer->time_mix_output, cur);
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cur = llm_build_lora_mm(lctx, ctx, layer->time_mix_output, cur);
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return ggml_reshape_3d(ctx, cur, n_embed, n_seq_tokens, n_seqs);
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return ggml_reshape_3d(ctx, cur, n_embd, n_seq_tokens, n_seqs);
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}
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}
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static struct ggml_tensor * llm_build_rwkv6_channel_mix(
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static struct ggml_tensor * llm_build_rwkv6_channel_mix(
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