graph : remove redundant scale_w parameter (#20235)
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@@ -119,9 +119,6 @@ llm_build_step35_iswa::llm_build_step35_iswa(const llama_model & model, const ll
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cb(cur, "ffn_out", il);
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} else {
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// MoE routed experts
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const bool norm_w = hparams.expert_weights_norm;
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const float w_scale = hparams.expert_weights_scale;
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const bool scale_w = w_scale != 0.0f;
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ggml_tensor * moe_out = build_moe_ffn(cur,
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model.layers[il].ffn_gate_inp,
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model.layers[il].ffn_up_exps,
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@@ -129,8 +126,8 @@ llm_build_step35_iswa::llm_build_step35_iswa(const llama_model & model, const ll
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model.layers[il].ffn_down_exps,
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model.layers[il].ffn_exp_probs_b,
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n_expert, n_expert_used,
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LLM_FFN_SILU,
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norm_w, scale_w, w_scale,
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LLM_FFN_SILU, hparams.expert_weights_norm,
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hparams.expert_weights_scale,
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(llama_expert_gating_func_type) hparams.expert_gating_func,
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il);
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cb(moe_out, "ffn_moe_out", il);
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