ling30tiny_EPQ / results /verify_selftest.txt
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[sdq] MoE 層 23 / 共 24 層(dense lead 1):expert 權重合計 3.90 GiB
[sdq] 槽位大小:小的 1327104 B(12 層)/大的 1531904 B(11 層)
[sdq] 使用 O_DIRECT 讀取模型檔(1 個 shard,不佔用 kernel page cache)
[sdq] I/O 段平行:12 條讀取執行緒 × 640 KB 對齊緩衝(gate/up/down 同時讀,每段再切 1 塊)
[sdq] RAM 預算 1.00 GiB:已用(RSS) 0.02 GiB、預留 0.68 GiB → arena 0.30 GiB
[sdq] page-map: n_layer=24 n_expert=128 n_expert_used=8 n_embd=1536 n_ff_exp=512 | L1E0 gate=456826720/442368 up=514684768/442368 down=373606240/645120 (off/size) ne0=1536 ne1=512 d_ne0=512 d_ne1=1536
[sdq] expert pager ready: /root/app/ling/tiny/models/Ling-3.0-tiny-Q4_K_M.gguf
[sdq] 熱門 expert 釘選:0 個(無剖面,用動態 LRU/LFU)
[sdq] slot=1.46 MiB (1531904 B) slots=225 arena=0.29 GiB (RAM 實測 RSS=0.31 GiB)
[sdq] 槽位分兩種:小 1.27 MiB × 169 + 大 1.46 MiB × 56(平均 1.31 MiB)
[sdq] prefetch=off fadvise_dontneed=1
llama_model_loader: loaded meta data with 53 key-value pairs and 526 tensors from /root/app/ling/tiny/models/Ling-3.0-tiny-Q4_K_M.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = bailingmoe3
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.sampling.top_k i32 = 20
llama_model_loader: - kv 3: general.sampling.top_p f32 = 0.950000
llama_model_loader: - kv 4: general.sampling.temp f32 = 1.000000
llama_model_loader: - kv 5: general.name str = Ling-3.0-tiny
llama_model_loader: - kv 6: general.size_label str = 128x1.0B
llama_model_loader: - kv 7: general.license str = mit
llama_model_loader: - kv 8: general.tags arr[str,1] = ["text-generation"]
llama_model_loader: - kv 9: bailingmoe3.block_count u32 = 24
llama_model_loader: - kv 10: bailingmoe3.context_length u32 = 131072
llama_model_loader: - kv 11: bailingmoe3.embedding_length u32 = 1536
llama_model_loader: - kv 12: bailingmoe3.feed_forward_length u32 = 4608
llama_model_loader: - kv 13: bailingmoe3.attention.head_count u32 = 16
llama_model_loader: - kv 14: bailingmoe3.attention.head_count_kv arr[i32,24] = [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, ...
llama_model_loader: - kv 15: bailingmoe3.rope.freq_base f32 = 6000000.000000
llama_model_loader: - kv 16: bailingmoe3.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 17: bailingmoe3.expert_count u32 = 128
llama_model_loader: - kv 18: bailingmoe3.expert_used_count u32 = 8
llama_model_loader: - kv 19: bailingmoe3.expert_group_count u32 = 8
llama_model_loader: - kv 20: bailingmoe3.expert_group_used_count u32 = 4
llama_model_loader: - kv 21: bailingmoe3.expert_gating_func u32 = 2
llama_model_loader: - kv 22: bailingmoe3.attention.key_length u32 = 576
llama_model_loader: - kv 23: bailingmoe3.attention.value_length u32 = 128
llama_model_loader: - kv 24: bailingmoe3.vocab_size u32 = 157184
llama_model_loader: - kv 25: bailingmoe3.ssm.conv_kernel u32 = 4
llama_model_loader: - kv 26: bailingmoe3.kda.head_dim u32 = 128
llama_model_loader: - kv 27: bailingmoe3.kda.safe_gate bool = true
llama_model_loader: - kv 28: bailingmoe3.kda.gate_lower_bound f32 = -5.000000
llama_model_loader: - kv 29: bailingmoe3.attention.q_lora_rank u32 = 256
llama_model_loader: - kv 30: bailingmoe3.attention.kv_lora_rank u32 = 512
llama_model_loader: - kv 31: bailingmoe3.rope.dimension_count u32 = 64
llama_model_loader: - kv 32: bailingmoe3.attention.key_length_mla u32 = 192
llama_model_loader: - kv 33: bailingmoe3.attention.value_length_mla u32 = 128
llama_model_loader: - kv 34: bailingmoe3.expert_feed_forward_length u32 = 512
llama_model_loader: - kv 35: bailingmoe3.expert_shared_feed_forward_length u32 = 512
llama_model_loader: - kv 36: bailingmoe3.expert_shared_count u32 = 1
llama_model_loader: - kv 37: bailingmoe3.leading_dense_block_count u32 = 1
llama_model_loader: - kv 38: bailingmoe3.expert_weights_scale f32 = 2.500000
llama_model_loader: - kv 39: bailingmoe3.expert_weights_norm bool = true
llama_model_loader: - kv 40: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 41: tokenizer.ggml.pre str = bailingmoe2
llama_model_loader: - kv 42: tokenizer.ggml.tokens arr[str,157184] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 43: tokenizer.ggml.token_type arr[i32,157184] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 44: tokenizer.ggml.merges arr[str,156635] = ["Ġ Ġ", "Ġ t", "i n", "Ġ a", "h e...
llama_model_loader: - kv 45: tokenizer.ggml.bos_token_id u32 = 156891
llama_model_loader: - kv 46: tokenizer.ggml.eos_token_id u32 = 156895
llama_model_loader: - kv 47: tokenizer.ggml.padding_token_id u32 = 156892
llama_model_loader: - kv 48: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: - kv 49: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 50: tokenizer.chat_template str = {#- Bailing V3 chat template -#}\n{#- ...
llama_model_loader: - kv 51: general.quantization_version u32 = 2
llama_model_loader: - kv 52: general.file_type u32 = 15
llama_model_loader: - type f32: 215 tensors
llama_model_loader: - type q5_0: 6 tensors
llama_model_loader: - type q4_K: 272 tensors
llama_model_loader: - type q6_K: 33 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q4_K - Medium
print_info: file size = 4.49 GiB (4.88 BPW)
init_tokenizer: initializing tokenizer for type 2
load: 0 unused tokens
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load: control token: 156894 '[gMASK]' is not marked as EOG
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load: control token: 157037 '<|reserved_token_142|>' is not marked as EOG
load: control token: 156913 '<|reserved_token_18|>' is not marked as EOG
load: control token: 157136 '<|reserved_token_241|>' is not marked as EOG
load: control token: 156933 '<|reserved_token_38|>' is not marked as EOG
load: control token: 157130 '<|reserved_token_235|>' is not marked as EOG
load: control token: 157084 '<|reserved_token_189|>' is not marked as EOG
load: control token: 157018 '<|reserved_token_123|>' is not marked as EOG
load: control token: 157054 '<|reserved_token_159|>' is not marked as EOG
load: control token: 157121 '<|reserved_token_226|>' is not marked as EOG
load: control token: 157021 '<|reserved_token_126|>' is not marked as EOG
load: control token: 157115 '<|reserved_token_220|>' is not marked as EOG
load: control token: 156901 '<|fim_hole|>' is not marked as EOG
load: control token: 157137 '<|reserved_token_242|>' is not marked as EOG
load: control token: 157147 '<|reserved_token_252|>' is not marked as EOG
load: control token: 157029 '<|reserved_token_134|>' is not marked as EOG
load: control token: 157032 '<|reserved_token_137|>' is not marked as EOG
load: control token: 156911 '<|reserved_token_16|>' is not marked as EOG
load: control token: 157078 '<|reserved_token_183|>' is not marked as EOG
load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
load: printing all EOG tokens:
load: - 156892 ('<|endoftext|>')
load: - 156895 ('<|role_end|>')
load: special tokens cache size = 262
load: token to piece cache size = 1.0009 MB
print_info: arch = bailingmoe3
print_info: vocab_only = 0
print_info: no_alloc = 0
print_info: n_ctx_train = 131072
print_info: n_embd_inp = 1536
print_info: n_embd = 1536
print_info: n_embd_out = 1536
print_info: n_layer = 24
print_info: n_layer_all = 24
print_info: n_head = 16
print_info: n_head_kv = [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1]
print_info: n_rot = 64
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: non_causal_type = 0
print_info: n_embd_head_k = 576
print_info: n_embd_head_v = 128
print_info: n_gqa = [0, 0, 0, 16, 0, 0, 0, 16, 0, 0, 0, 16, 0, 0, 0, 16, 0, 0, 0, 16, 0, 0, 0, 16]
print_info: n_embd_k_gqa = [0, 0, 0, 576, 0, 0, 0, 576, 0, 0, 0, 576, 0, 0, 0, 576, 0, 0, 0, 576, 0, 0, 0, 576]
print_info: n_embd_v_gqa = [0, 0, 0, 128, 0, 0, 0, 128, 0, 0, 0, 128, 0, 0, 0, 128, 0, 0, 0, 128, 0, 0, 0, 128]
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 0.0e+00
print_info: f_attn_value_scale = 0.0000
print_info: n_ff = 4608
print_info: n_expert = 128
print_info: n_expert_used = 8
print_info: n_expert_groups = 8
print_info: n_group_used = 4
print_info: causal attn = 1
print_info: pooling type = -1
print_info: rope type = 0
print_info: rope scaling = linear
print_info: freq_base_train = 6000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 131072
print_info: rope_yarn_log_mul = 0.0000
print_info: rope_finetuned = unknown
print_info: model type = 7.9B.A1.3B
print_info: model params = 7.89 B
print_info: general.name = Ling-3.0-tiny
print_info: n_layer_dense_lead = 1
print_info: n_ff_exp = 512
print_info: n_ff_shexp = 512
print_info: n_expert_shared = 1
print_info: expert_weights_scale = 2.5
print_info: expert_weights_norm = 1
print_info: expert_gating_func = sigmoid
print_info: n_layer_nextn = 0
print_info: vocab type = BPE
print_info: n_vocab = 157184
print_info: n_merges = 156635
print_info: BOS token = 156891 '<|startoftext|>'
print_info: EOS token = 156895 '<|role_end|>'
print_info: EOT token = 156892 '<|endoftext|>'
print_info: PAD token = 156892 '<|endoftext|>'
print_info: LF token = 198 'Ċ'
print_info: EOG token = 156892 '<|endoftext|>'
print_info: EOG token = 156895 '<|role_end|>'
print_info: max token length = 154
load_tensors: loading model tensors, this can take a while... (load_mode = mmap)
load_tensors: layer 0 assigned to device CPU, is_swa = 0
load_tensors: layer 1 assigned to device CPU, is_swa = 0
load_tensors: layer 2 assigned to device CPU, is_swa = 0
load_tensors: layer 3 assigned to device CPU, is_swa = 0
load_tensors: layer 4 assigned to device CPU, is_swa = 0
load_tensors: layer 5 assigned to device CPU, is_swa = 0
load_tensors: layer 6 assigned to device CPU, is_swa = 0
load_tensors: layer 7 assigned to device CPU, is_swa = 0
load_tensors: layer 8 assigned to device CPU, is_swa = 0
load_tensors: layer 9 assigned to device CPU, is_swa = 0
load_tensors: layer 10 assigned to device CPU, is_swa = 0
load_tensors: layer 11 assigned to device CPU, is_swa = 0
load_tensors: layer 12 assigned to device CPU, is_swa = 0
load_tensors: layer 13 assigned to device CPU, is_swa = 0
load_tensors: layer 14 assigned to device CPU, is_swa = 0
load_tensors: layer 15 assigned to device CPU, is_swa = 0
load_tensors: layer 16 assigned to device CPU, is_swa = 0
load_tensors: layer 17 assigned to device CPU, is_swa = 0
load_tensors: layer 18 assigned to device CPU, is_swa = 0
load_tensors: layer 19 assigned to device CPU, is_swa = 0
load_tensors: layer 20 assigned to device CPU, is_swa = 0
load_tensors: layer 21 assigned to device CPU, is_swa = 0
load_tensors: layer 22 assigned to device CPU, is_swa = 0
load_tensors: layer 23 assigned to device CPU, is_swa = 0
load_tensors: layer 24 assigned to device CPU, is_swa = 0
create_tensor: loading tensor token_embd.weight
create_tensor: loading tensor output_norm.weight
create_tensor: loading tensor output.weight
create_tensor: loading tensor blk.0.attn_norm.weight
create_tensor: loading tensor blk.0.ssm_conv1d_q.weight
create_tensor: loading tensor blk.0.ssm_conv1d_k.weight
create_tensor: loading tensor blk.0.ssm_conv1d_v.weight
create_tensor: loading tensor blk.0.attn_qkv.weight
create_tensor: loading tensor blk.0.attn_q.weight
create_tensor: loading tensor blk.0.attn_k.weight
create_tensor: loading tensor blk.0.attn_v.weight
create_tensor: loading tensor blk.0.attn_q.bias
create_tensor: loading tensor blk.0.attn_k.bias
create_tensor: loading tensor blk.0.attn_v.bias
create_tensor: loading tensor blk.0.ssm_f_a.weight
create_tensor: loading tensor blk.0.ssm_beta.weight
create_tensor: loading tensor blk.0.ssm_a
create_tensor: loading tensor blk.0.ssm_dt.bias
create_tensor: loading tensor blk.0.ssm_g_a.weight
create_tensor: loading tensor blk.0.ssm_norm.weight
create_tensor: loading tensor blk.0.attn_output.weight
create_tensor: loading tensor blk.0.ffn_norm.weight
create_tensor: loading tensor blk.0.ffn_gate.weight
create_tensor: loading tensor blk.0.ffn_up.weight
create_tensor: loading tensor blk.0.ffn_down.weight
create_tensor: loading tensor blk.1.attn_norm.weight
create_tensor: loading tensor blk.1.ssm_conv1d_q.weight
create_tensor: loading tensor blk.1.ssm_conv1d_k.weight
create_tensor: loading tensor blk.1.ssm_conv1d_v.weight
create_tensor: loading tensor blk.1.attn_qkv.weight
create_tensor: loading tensor blk.1.attn_q.weight
create_tensor: loading tensor blk.1.attn_k.weight
create_tensor: loading tensor blk.1.attn_v.weight
create_tensor: loading tensor blk.1.attn_q.bias
create_tensor: loading tensor blk.1.attn_k.bias
create_tensor: loading tensor blk.1.attn_v.bias
create_tensor: loading tensor blk.1.ssm_f_a.weight
create_tensor: loading tensor blk.1.ssm_beta.weight
create_tensor: loading tensor blk.1.ssm_a
create_tensor: loading tensor blk.1.ssm_dt.bias
create_tensor: loading tensor blk.1.ssm_g_a.weight
create_tensor: loading tensor blk.1.ssm_norm.weight
create_tensor: loading tensor blk.1.attn_output.weight
create_tensor: loading tensor blk.1.ffn_norm.weight
create_tensor: loading tensor blk.1.ffn_gate_inp.weight
create_tensor: loading tensor blk.1.exp_probs_b.bias
create_tensor: loading tensor blk.1.ffn_gate_exps.weight
tensor blk.1.ffn_gate_exps.weight (54 MiB q4_K) buffer type overridden to SDQ_ExpertSSD
create_tensor: loading tensor blk.1.ffn_up_exps.weight
tensor blk.1.ffn_up_exps.weight (54 MiB q4_K) buffer type overridden to SDQ_ExpertSSD
create_tensor: loading tensor blk.1.ffn_down_exps.weight
tensor blk.1.ffn_down_exps.weight (78 MiB q6_K) buffer type overridden to SDQ_ExpertSSD
create_tensor: loading tensor blk.1.ffn_gate_shexp.weight
create_tensor: loading tensor blk.1.ffn_up_shexp.weight
create_tensor: loading tensor blk.1.ffn_down_shexp.weight
create_tensor: loading tensor blk.2.attn_norm.weight
create_tensor: loading tensor blk.2.ssm_conv1d_q.weight
create_tensor: loading tensor blk.2.ssm_conv1d_k.weight
create_tensor: loading tensor blk.2.ssm_conv1d_v.weight
create_tensor: loading tensor blk.2.attn_qkv.weight
create_tensor: loading tensor blk.2.attn_q.weight
create_tensor: loading tensor blk.2.attn_k.weight
create_tensor: loading tensor blk.2.attn_v.weight
create_tensor: loading tensor blk.2.attn_q.bias
create_tensor: loading tensor blk.2.attn_k.bias
create_tensor: loading tensor blk.2.attn_v.bias
create_tensor: loading tensor blk.2.ssm_f_a.weight
create_tensor: loading tensor blk.2.ssm_beta.weight
create_tensor: loading tensor blk.2.ssm_a
create_tensor: loading tensor blk.2.ssm_dt.bias
create_tensor: loading tensor blk.2.ssm_g_a.weight
create_tensor: loading tensor blk.2.ssm_norm.weight
create_tensor: loading tensor blk.2.attn_output.weight
create_tensor: loading tensor blk.2.ffn_norm.weight
create_tensor: loading tensor blk.2.ffn_gate_inp.weight
create_tensor: loading tensor blk.2.exp_probs_b.bias
create_tensor: loading tensor blk.2.ffn_gate_exps.weight
tensor blk.2.ffn_gate_exps.weight (54 MiB q4_K) buffer type overridden to SDQ_ExpertSSD
create_tensor: loading tensor blk.2.ffn_up_exps.weight
tensor blk.2.ffn_up_exps.weight (54 MiB q4_K) buffer type overridden to SDQ_ExpertSSD
create_tensor: loading tensor blk.2.ffn_down_exps.weight
tensor blk.2.ffn_down_exps.weight (78 MiB q6_K) buffer type overridden to SDQ_ExpertSSD
create_tensor: loading tensor blk.2.ffn_gate_shexp.weight
create_tensor: loading tensor blk.2.ffn_up_shexp.weight
create_tensor: loading tensor blk.2.ffn_down_shexp.weight
create_tensor: loading tensor blk.3.attn_norm.weight
create_tensor: loading tensor blk.3.attn_q_a.weight
create_tensor: loading tensor blk.3.attn_q_a_norm.weight
create_tensor: loading tensor blk.3.attn_q_b.weight
create_tensor: loading tensor blk.3.attn_kv_a_mqa.weight
create_tensor: loading tensor blk.3.attn_kv_a_norm.weight
create_tensor: loading tensor blk.3.attn_k_b.weight
create_tensor: loading tensor blk.3.attn_v_b.weight
create_tensor: loading tensor blk.3.attn_gate.weight
create_tensor: loading tensor blk.3.attn_output.weight
create_tensor: loading tensor blk.3.ffn_norm.weight
create_tensor: loading tensor blk.3.ffn_gate_inp.weight
create_tensor: loading tensor blk.3.exp_probs_b.bias
create_tensor: loading tensor blk.3.ffn_gate_exps.weight
tensor blk.3.ffn_gate_exps.weight (54 MiB q4_K) buffer type overridden to SDQ_ExpertSSD
create_tensor: loading tensor blk.3.ffn_up_exps.weight
tensor blk.3.ffn_up_exps.weight (54 MiB q4_K) buffer type overridden to SDQ_ExpertSSD
create_tensor: loading tensor blk.3.ffn_down_exps.weight
tensor blk.3.ffn_down_exps.weight (54 MiB q4_K) buffer type overridden to SDQ_ExpertSSD
create_tensor: loading tensor blk.3.ffn_gate_shexp.weight
create_tensor: loading tensor blk.3.ffn_up_shexp.weight
create_tensor: loading tensor blk.3.ffn_down_shexp.weight
create_tensor: loading tensor blk.4.attn_norm.weight
create_tensor: loading tensor blk.4.ssm_conv1d_q.weight
create_tensor: loading tensor blk.4.ssm_conv1d_k.weight
create_tensor: loading tensor blk.4.ssm_conv1d_v.weight
create_tensor: loading tensor blk.4.attn_qkv.weight
create_tensor: loading tensor blk.4.attn_q.weight
create_tensor: loading tensor blk.4.attn_k.weight
create_tensor: loading tensor blk.4.attn_v.weight
create_tensor: loading tensor blk.4.attn_q.bias
create_tensor: loading tensor blk.4.attn_k.bias
create_tensor: loading tensor blk.4.attn_v.bias
create_tensor: loading tensor blk.4.ssm_f_a.weight
create_tensor: loading tensor blk.4.ssm_beta.weight
create_tensor: loading tensor blk.4.ssm_a
create_tensor: loading tensor blk.4.ssm_dt.bias
create_tensor: loading tensor blk.4.ssm_g_a.weight
create_tensor: loading tensor blk.4.ssm_norm.weight
create_tensor: loading tensor blk.4.attn_output.weight
create_tensor: loading tensor blk.4.ffn_norm.weight
create_tensor: loading tensor blk.4.ffn_gate_inp.weight
create_tensor: loading tensor blk.4.exp_probs_b.bias
create_tensor: loading tensor blk.4.ffn_gate_exps.weight
tensor blk.4.ffn_gate_exps.weight (54 MiB q4_K) buffer type overridden to SDQ_ExpertSSD
create_tensor: loading tensor blk.4.ffn_up_exps.weight
tensor blk.4.ffn_up_exps.weight (54 MiB q4_K) buffer type overridden to SDQ_ExpertSSD
create_tensor: loading tensor blk.4.ffn_down_exps.weight
tensor blk.4.ffn_down_exps.weight (54 MiB q4_K) buffer type overridden to SDQ_ExpertSSD
create_tensor: loading tensor blk.4.ffn_gate_shexp.weight
create_tensor: loading tensor blk.4.ffn_up_shexp.weight
create_tensor: loading tensor blk.4.ffn_down_shexp.weight
create_tensor: loading tensor blk.5.attn_norm.weight
create_tensor: loading tensor blk.5.ssm_conv1d_q.weight
create_tensor: loading tensor blk.5.ssm_conv1d_k.weight
create_tensor: loading tensor blk.5.ssm_conv1d_v.weight
create_tensor: loading tensor blk.5.attn_qkv.weight
create_tensor: loading tensor blk.5.attn_q.weight
create_tensor: loading tensor blk.5.attn_k.weight
create_tensor: loading tensor blk.5.attn_v.weight
create_tensor: loading tensor blk.5.attn_q.bias
create_tensor: loading tensor blk.5.attn_k.bias
create_tensor: loading tensor blk.5.attn_v.bias
create_tensor: loading tensor blk.5.ssm_f_a.weight
create_tensor: loading tensor blk.5.ssm_beta.weight
create_tensor: loading tensor blk.5.ssm_a
create_tensor: loading tensor blk.5.ssm_dt.bias
create_tensor: loading tensor blk.5.ssm_g_a.weight
create_tensor: loading tensor blk.5.ssm_norm.weight
create_tensor: loading tensor blk.5.attn_output.weight
create_tensor: loading tensor blk.5.ffn_norm.weight
create_tensor: loading tensor blk.5.ffn_gate_inp.weight
create_tensor: loading tensor blk.5.exp_probs_b.bias
create_tensor: loading tensor blk.5.ffn_gate_exps.weight
tensor blk.5.ffn_gate_exps.weight (54 MiB q4_K) buffer type overridden to SDQ_ExpertSSD
create_tensor: loading tensor blk.5.ffn_up_exps.weight
tensor blk.5.ffn_up_exps.weight (54 MiB q4_K) buffer type overridden to SDQ_ExpertSSD
create_tensor: loading tensor blk.5.ffn_down_exps.weight
tensor blk.5.ffn_down_exps.weight (78 MiB q6_K) buffer type overridden to SDQ_ExpertSSD
create_tensor: loading tensor blk.5.ffn_gate_shexp.weight
create_tensor: loading tensor blk.5.ffn_up_shexp.weight
create_tensor: loading tensor blk.5.ffn_down_shexp.weight
create_tensor: loading tensor blk.6.attn_norm.weight
create_tensor: loading tensor blk.6.ssm_conv1d_q.weight
create_tensor: loading tensor blk.6.ssm_conv1d_k.weight
create_tensor: loading tensor blk.6.ssm_conv1d_v.weight
create_tensor: loading tensor blk.6.attn_qkv.weight
create_tensor: loading tensor blk.6.attn_q.weight
create_tensor: loading tensor blk.6.attn_k.weight
create_tensor: loading tensor blk.6.attn_v.weight
create_tensor: loading tensor blk.6.attn_q.bias
create_tensor: loading tensor blk.6.attn_k.bias
create_tensor: loading tensor blk.6.attn_v.bias
create_tensor: loading tensor blk.6.ssm_f_a.weight
create_tensor: loading tensor blk.6.ssm_beta.weight
create_tensor: loading tensor blk.6.ssm_a
create_tensor: loading tensor blk.6.ssm_dt.bias
create_tensor: loading tensor blk.6.ssm_g_a.weight
create_tensor: loading tensor blk.6.ssm_norm.weight
create_tensor: loading tensor blk.6.attn_output.weight
create_tensor: loading tensor blk.6.ffn_norm.weight
create_tensor: loading tensor blk.6.ffn_gate_inp.weight
create_tensor: loading tensor blk.6.exp_probs_b.bias
create_tensor: loading tensor blk.6.ffn_gate_exps.weight
tensor blk.6.ffn_gate_exps.weight (54 MiB q4_K) buffer type overridden to SDQ_ExpertSSD
create_tensor: loading tensor blk.6.ffn_up_exps.weight
tensor blk.6.ffn_up_exps.weight (54 MiB q4_K) buffer type overridden to SDQ_ExpertSSD
create_tensor: loading tensor blk.6.ffn_down_exps.weight
tensor blk.6.ffn_down_exps.weight (54 MiB q4_K) buffer type overridden to SDQ_ExpertSSD
create_tensor: loading tensor blk.6.ffn_gate_shexp.weight
create_tensor: loading tensor blk.6.ffn_up_shexp.weight
create_tensor: loading tensor blk.6.ffn_down_shexp.weight
create_tensor: loading tensor blk.7.attn_norm.weight
create_tensor: loading tensor blk.7.attn_q_a.weight
create_tensor: loading tensor blk.7.attn_q_a_norm.weight
create_tensor: loading tensor blk.7.attn_q_b.weight
create_tensor: loading tensor blk.7.attn_kv_a_mqa.weight
create_tensor: loading tensor blk.7.attn_kv_a_norm.weight
create_tensor: loading tensor blk.7.attn_k_b.weight
create_tensor: loading tensor blk.7.attn_v_b.weight
create_tensor: loading tensor blk.7.attn_gate.weight
create_tensor: loading tensor blk.7.attn_output.weight
create_tensor: loading tensor blk.7.ffn_norm.weight
create_tensor: loading tensor blk.7.ffn_gate_inp.weight
create_tensor: loading tensor blk.7.exp_probs_b.bias
create_tensor: loading tensor blk.7.ffn_gate_exps.weight
tensor blk.7.ffn_gate_exps.weight (54 MiB q4_K) buffer type overridden to SDQ_ExpertSSD
create_tensor: loading tensor blk.7.ffn_up_exps.weight
tensor blk.7.ffn_up_exps.weight (54 MiB q4_K) buffer type overridden to SDQ_ExpertSSD
create_tensor: loading tensor blk.7.ffn_down_exps.weight
tensor blk.7.ffn_down_exps.weight (54 MiB q4_K) buffer type overridden to SDQ_ExpertSSD
create_tensor: loading tensor blk.7.ffn_gate_shexp.weight
create_tensor: loading tensor blk.7.ffn_up_shexp.weight
create_tensor: loading tensor blk.7.ffn_down_shexp.weight
create_tensor: loading tensor blk.8.attn_norm.weight
create_tensor: loading tensor blk.8.ssm_conv1d_q.weight
create_tensor: loading tensor blk.8.ssm_conv1d_k.weight
create_tensor: loading tensor blk.8.ssm_conv1d_v.weight
create_tensor: loading tensor blk.8.attn_qkv.weight
create_tensor: loading tensor blk.8.attn_q.weight
create_tensor: loading tensor blk.8.attn_k.weight
create_tensor: loading tensor blk.8.attn_v.weight
create_tensor: loading tensor blk.8.attn_q.bias
create_tensor: loading tensor blk.8.attn_k.bias
create_tensor: loading tensor blk.8.attn_v.bias
create_tensor: loading tensor blk.8.ssm_f_a.weight
create_tensor: loading tensor blk.8.ssm_beta.weight
create_tensor: loading tensor blk.8.ssm_a
create_tensor: loading tensor blk.8.ssm_dt.bias
create_tensor: loading tensor blk.8.ssm_g_a.weight
create_tensor: loading tensor blk.8.ssm_norm.weight
create_tensor: loading tensor blk.8.attn_output.weight
create_tensor: loading tensor blk.8.ffn_norm.weight
create_tensor: loading tensor blk.8.ffn_gate_inp.weight
create_tensor: loading tensor blk.8.exp_probs_b.bias
create_tensor: loading tensor blk.8.ffn_gate_exps.weight
tensor blk.8.ffn_gate_exps.weight (54 MiB q4_K) buffer type overridden to SDQ_ExpertSSD
create_tensor: loading tensor blk.8.ffn_up_exps.weight
tensor blk.8.ffn_up_exps.weight (54 MiB q4_K) buffer type overridden to SDQ_ExpertSSD
create_tensor: loading tensor blk.8.ffn_down_exps.weight
tensor blk.8.ffn_down_exps.weight (78 MiB q6_K) buffer type overridden to SDQ_ExpertSSD
create_tensor: loading tensor blk.8.ffn_gate_shexp.weight
create_tensor: loading tensor blk.8.ffn_up_shexp.weight
create_tensor: loading tensor blk.8.ffn_down_shexp.weight
create_tensor: loading tensor blk.9.attn_norm.weight
create_tensor: loading tensor blk.9.ssm_conv1d_q.weight
create_tensor: loading tensor blk.9.ssm_conv1d_k.weight
create_tensor: loading tensor blk.9.ssm_conv1d_v.weight
create_tensor: loading tensor blk.9.attn_qkv.weight
create_tensor: loading tensor blk.9.attn_q.weight
create_tensor: loading tensor blk.9.attn_k.weight
create_tensor: loading tensor blk.9.attn_v.weight
create_tensor: loading tensor blk.9.attn_q.bias
create_tensor: loading tensor blk.9.attn_k.bias
create_tensor: loading tensor blk.9.attn_v.bias
create_tensor: loading tensor blk.9.ssm_f_a.weight
create_tensor: loading tensor blk.9.ssm_beta.weight
create_tensor: loading tensor blk.9.ssm_a
create_tensor: loading tensor blk.9.ssm_dt.bias
create_tensor: loading tensor blk.9.ssm_g_a.weight
create_tensor: loading tensor blk.9.ssm_norm.weight
create_tensor: loading tensor blk.9.attn_output.weight
create_tensor: loading tensor blk.9.ffn_norm.weight
create_tensor: loading tensor blk.9.ffn_gate_inp.weight
create_tensor: loading tensor blk.9.exp_probs_b.bias
create_tensor: loading tensor blk.9.ffn_gate_exps.weight
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create_tensor: loading tensor blk.16.ffn_down_exps.input_scale
create_tensor: loading tensor blk.16.ffn_up_exps.input_scale
create_tensor: loading tensor blk.16.ffn_gate_shexp.input_scale
create_tensor: loading tensor blk.16.ffn_down_shexp.input_scale
create_tensor: loading tensor blk.16.ffn_up_shexp.input_scale
create_tensor: loading tensor blk.16.ssm_beta.input_scale
create_tensor: loading tensor blk.17.attn_q.scale
create_tensor: loading tensor blk.17.attn_k.scale
create_tensor: loading tensor blk.17.attn_v.scale
create_tensor: loading tensor blk.17.attn_output.scale
create_tensor: loading tensor blk.17.ffn_gate_shexp.scale
create_tensor: loading tensor blk.17.ffn_down_shexp.scale
create_tensor: loading tensor blk.17.ffn_up_shexp.scale
create_tensor: loading tensor blk.17.ffn_gate_exps.scale
create_tensor: loading tensor blk.17.ffn_down_exps.scale
create_tensor: loading tensor blk.17.ffn_up_exps.scale
create_tensor: loading tensor blk.17.ssm_beta.scale
create_tensor: loading tensor blk.17.attn_q.input_scale
create_tensor: loading tensor blk.17.attn_k.input_scale
create_tensor: loading tensor blk.17.attn_v.input_scale
create_tensor: loading tensor blk.17.attn_output.input_scale
create_tensor: loading tensor blk.17.ffn_gate_exps.input_scale
create_tensor: loading tensor blk.17.ffn_down_exps.input_scale
create_tensor: loading tensor blk.17.ffn_up_exps.input_scale
create_tensor: loading tensor blk.17.ffn_gate_shexp.input_scale
create_tensor: loading tensor blk.17.ffn_down_shexp.input_scale
create_tensor: loading tensor blk.17.ffn_up_shexp.input_scale
create_tensor: loading tensor blk.17.ssm_beta.input_scale
create_tensor: loading tensor blk.18.attn_q.scale
create_tensor: loading tensor blk.18.attn_k.scale
create_tensor: loading tensor blk.18.attn_v.scale
create_tensor: loading tensor blk.18.attn_output.scale
create_tensor: loading tensor blk.18.ffn_gate_shexp.scale
create_tensor: loading tensor blk.18.ffn_down_shexp.scale
create_tensor: loading tensor blk.18.ffn_up_shexp.scale
create_tensor: loading tensor blk.18.ffn_gate_exps.scale
create_tensor: loading tensor blk.18.ffn_down_exps.scale
create_tensor: loading tensor blk.18.ffn_up_exps.scale
create_tensor: loading tensor blk.18.ssm_beta.scale
create_tensor: loading tensor blk.18.attn_q.input_scale
create_tensor: loading tensor blk.18.attn_k.input_scale
create_tensor: loading tensor blk.18.attn_v.input_scale
create_tensor: loading tensor blk.18.attn_output.input_scale
create_tensor: loading tensor blk.18.ffn_gate_exps.input_scale
create_tensor: loading tensor blk.18.ffn_down_exps.input_scale
create_tensor: loading tensor blk.18.ffn_up_exps.input_scale
create_tensor: loading tensor blk.18.ffn_gate_shexp.input_scale
create_tensor: loading tensor blk.18.ffn_down_shexp.input_scale
create_tensor: loading tensor blk.18.ffn_up_shexp.input_scale
create_tensor: loading tensor blk.18.ssm_beta.input_scale
create_tensor: loading tensor blk.19.attn_output.scale
create_tensor: loading tensor blk.19.attn_gate.scale
create_tensor: loading tensor blk.19.ffn_gate_shexp.scale
create_tensor: loading tensor blk.19.ffn_down_shexp.scale
create_tensor: loading tensor blk.19.ffn_up_shexp.scale
create_tensor: loading tensor blk.19.ffn_gate_exps.scale
create_tensor: loading tensor blk.19.ffn_down_exps.scale
create_tensor: loading tensor blk.19.ffn_up_exps.scale
create_tensor: loading tensor blk.19.attn_output.input_scale
create_tensor: loading tensor blk.19.attn_gate.input_scale
create_tensor: loading tensor blk.19.ffn_gate_exps.input_scale
create_tensor: loading tensor blk.19.ffn_down_exps.input_scale
create_tensor: loading tensor blk.19.ffn_up_exps.input_scale
create_tensor: loading tensor blk.19.ffn_gate_shexp.input_scale
create_tensor: loading tensor blk.19.ffn_down_shexp.input_scale
create_tensor: loading tensor blk.19.ffn_up_shexp.input_scale
create_tensor: loading tensor blk.20.attn_q.scale
create_tensor: loading tensor blk.20.attn_k.scale
create_tensor: loading tensor blk.20.attn_v.scale
create_tensor: loading tensor blk.20.attn_output.scale
create_tensor: loading tensor blk.20.ffn_gate_shexp.scale
create_tensor: loading tensor blk.20.ffn_down_shexp.scale
create_tensor: loading tensor blk.20.ffn_up_shexp.scale
create_tensor: loading tensor blk.20.ffn_gate_exps.scale
create_tensor: loading tensor blk.20.ffn_down_exps.scale
create_tensor: loading tensor blk.20.ffn_up_exps.scale
create_tensor: loading tensor blk.20.ssm_beta.scale
create_tensor: loading tensor blk.20.attn_q.input_scale
create_tensor: loading tensor blk.20.attn_k.input_scale
create_tensor: loading tensor blk.20.attn_v.input_scale
create_tensor: loading tensor blk.20.attn_output.input_scale
create_tensor: loading tensor blk.20.ffn_gate_exps.input_scale
create_tensor: loading tensor blk.20.ffn_down_exps.input_scale
create_tensor: loading tensor blk.20.ffn_up_exps.input_scale
create_tensor: loading tensor blk.20.ffn_gate_shexp.input_scale
create_tensor: loading tensor blk.20.ffn_down_shexp.input_scale
create_tensor: loading tensor blk.20.ffn_up_shexp.input_scale
create_tensor: loading tensor blk.20.ssm_beta.input_scale
create_tensor: loading tensor blk.21.attn_q.scale
create_tensor: loading tensor blk.21.attn_k.scale
create_tensor: loading tensor blk.21.attn_v.scale
create_tensor: loading tensor blk.21.attn_output.scale
create_tensor: loading tensor blk.21.ffn_gate_shexp.scale
create_tensor: loading tensor blk.21.ffn_down_shexp.scale
create_tensor: loading tensor blk.21.ffn_up_shexp.scale
create_tensor: loading tensor blk.21.ffn_gate_exps.scale
create_tensor: loading tensor blk.21.ffn_down_exps.scale
create_tensor: loading tensor blk.21.ffn_up_exps.scale
create_tensor: loading tensor blk.21.ssm_beta.scale
create_tensor: loading tensor blk.21.attn_q.input_scale
create_tensor: loading tensor blk.21.attn_k.input_scale
create_tensor: loading tensor blk.21.attn_v.input_scale
create_tensor: loading tensor blk.21.attn_output.input_scale
create_tensor: loading tensor blk.21.ffn_gate_exps.input_scale
create_tensor: loading tensor blk.21.ffn_down_exps.input_scale
create_tensor: loading tensor blk.21.ffn_up_exps.input_scale
create_tensor: loading tensor blk.21.ffn_gate_shexp.input_scale
create_tensor: loading tensor blk.21.ffn_down_shexp.input_scale
create_tensor: loading tensor blk.21.ffn_up_shexp.input_scale
create_tensor: loading tensor blk.21.ssm_beta.input_scale
create_tensor: loading tensor blk.22.attn_q.scale
create_tensor: loading tensor blk.22.attn_k.scale
create_tensor: loading tensor blk.22.attn_v.scale
create_tensor: loading tensor blk.22.attn_output.scale
create_tensor: loading tensor blk.22.ffn_gate_shexp.scale
create_tensor: loading tensor blk.22.ffn_down_shexp.scale
create_tensor: loading tensor blk.22.ffn_up_shexp.scale
create_tensor: loading tensor blk.22.ffn_gate_exps.scale
create_tensor: loading tensor blk.22.ffn_down_exps.scale
create_tensor: loading tensor blk.22.ffn_up_exps.scale
create_tensor: loading tensor blk.22.ssm_beta.scale
create_tensor: loading tensor blk.22.attn_q.input_scale
create_tensor: loading tensor blk.22.attn_k.input_scale
create_tensor: loading tensor blk.22.attn_v.input_scale
create_tensor: loading tensor blk.22.attn_output.input_scale
create_tensor: loading tensor blk.22.ffn_gate_exps.input_scale
create_tensor: loading tensor blk.22.ffn_down_exps.input_scale
create_tensor: loading tensor blk.22.ffn_up_exps.input_scale
create_tensor: loading tensor blk.22.ffn_gate_shexp.input_scale
create_tensor: loading tensor blk.22.ffn_down_shexp.input_scale
create_tensor: loading tensor blk.22.ffn_up_shexp.input_scale
create_tensor: loading tensor blk.22.ssm_beta.input_scale
create_tensor: loading tensor blk.23.attn_output.scale
create_tensor: loading tensor blk.23.attn_gate.scale
create_tensor: loading tensor blk.23.ffn_gate_shexp.scale
create_tensor: loading tensor blk.23.ffn_down_shexp.scale
create_tensor: loading tensor blk.23.ffn_up_shexp.scale
create_tensor: loading tensor blk.23.ffn_gate_exps.scale
create_tensor: loading tensor blk.23.ffn_down_exps.scale
create_tensor: loading tensor blk.23.ffn_up_exps.scale
create_tensor: loading tensor blk.23.attn_output.input_scale
create_tensor: loading tensor blk.23.attn_gate.input_scale
create_tensor: loading tensor blk.23.ffn_gate_exps.input_scale
create_tensor: loading tensor blk.23.ffn_down_exps.input_scale
create_tensor: loading tensor blk.23.ffn_up_exps.input_scale
create_tensor: loading tensor blk.23.ffn_gate_shexp.input_scale
create_tensor: loading tensor blk.23.ffn_down_shexp.input_scale
create_tensor: loading tensor blk.23.ffn_up_shexp.input_scale
done_getting_tensors: tensor 'blk.1.ffn_gate_exps.weight' (q4_K) (and 68 others) cannot be used with preferred buffer type CPU, using SDQ_ExpertSSD instead
load_tensors: CPU_Mapped model buffer size = 4594.21 MiB
load_tensors: SDQ_ExpertSSD model buffer size = 0.27 MiB
.............................................................................
llama_context: constructing llama_context
llama_context: n_seq_max = 1
llama_context: n_ctx = 256
llama_context: n_ctx_seq = 256
llama_context: n_batch = 256
llama_context: n_ubatch = 32
llama_context: causal_attn = 1
llama_context: flash_attn = auto
llama_context: kv_unified = false
llama_context: freq_base = 6000000.0
llama_context: freq_scale = 1
llama_context: n_rs_seq = 0
llama_context: n_outputs_max = 256
llama_context: n_outputs_max_per_seq = 1
llama_context: n_ctx_seq (256) < n_ctx_train (131072) -- the full capacity of the model will not be utilized
set_abort_callback: call
llama_context: CPU output buffer size = 0.60 MiB
llama_kv_cache: layer 0: filtered
llama_kv_cache: layer 1: filtered
llama_kv_cache: layer 2: filtered
llama_kv_cache: layer 3: dev = CPU
llama_kv_cache: layer 4: filtered
llama_kv_cache: layer 5: filtered
llama_kv_cache: layer 6: filtered
llama_kv_cache: layer 7: dev = CPU
llama_kv_cache: layer 8: filtered
llama_kv_cache: layer 9: filtered
llama_kv_cache: layer 10: filtered
llama_kv_cache: layer 11: dev = CPU
llama_kv_cache: layer 12: filtered
llama_kv_cache: layer 13: filtered
llama_kv_cache: layer 14: filtered
llama_kv_cache: layer 15: dev = CPU
llama_kv_cache: layer 16: filtered
llama_kv_cache: layer 17: filtered
llama_kv_cache: layer 18: filtered
llama_kv_cache: layer 19: dev = CPU
llama_kv_cache: layer 20: filtered
llama_kv_cache: layer 21: filtered
llama_kv_cache: layer 22: filtered
llama_kv_cache: layer 23: dev = CPU
llama_kv_cache: CPU KV buffer size = 1.69 MiB
llama_kv_cache: size = 1.69 MiB ( 256 cells, 6 layers, 1/1 seqs), K (f16): 1.69 MiB, V (f16): 0.00 MiB
llama_kv_cache: n_rot_k = 0, n_embd_head_k_all = 576
llama_kv_cache: n_rot_v = 0, n_embd_head_k_all = 0
llama_memory_recurrent, layer 0: dev = CPU
llama_memory_recurrent, layer 1: dev = CPU
llama_memory_recurrent, layer 2: dev = CPU
llama_memory_recurrent: layer 3: skipped
llama_memory_recurrent, layer 4: dev = CPU
llama_memory_recurrent, layer 5: dev = CPU
llama_memory_recurrent, layer 6: dev = CPU
llama_memory_recurrent: layer 7: skipped
llama_memory_recurrent, layer 8: dev = CPU
llama_memory_recurrent, layer 9: dev = CPU
llama_memory_recurrent, layer 10: dev = CPU
llama_memory_recurrent: layer 11: skipped
llama_memory_recurrent, layer 12: dev = CPU
llama_memory_recurrent, layer 13: dev = CPU
llama_memory_recurrent, layer 14: dev = CPU
llama_memory_recurrent: layer 15: skipped
llama_memory_recurrent, layer 16: dev = CPU
llama_memory_recurrent, layer 17: dev = CPU
llama_memory_recurrent, layer 18: dev = CPU
llama_memory_recurrent: layer 19: skipped
llama_memory_recurrent, layer 20: dev = CPU
llama_memory_recurrent, layer 21: dev = CPU
llama_memory_recurrent, layer 22: dev = CPU
llama_memory_recurrent: layer 23: skipped
llama_memory_recurrent: CPU RS buffer size = 19.27 MiB
llama_memory_recurrent: size = 19.27 MiB ( 1 cells, 24 layers, 1 seqs 0 rs_seq), R (f32): 1.27 MiB, S (f32): 18.00 MiB, P (f32): 0.00 MiB
llama_context: enumerating backends
llama_context: backend_ptrs.size() = 1
sched_reserve: reserving ...
sched_reserve: max_nodes = 16832
sched_reserve: reserving full memory module
sched_reserve: worst-case: n_tokens = 32, n_seqs = 1, n_outputs = 1
graph_reserve: reserving a graph for ubatch with n_tokens = 1, n_seqs = 1, n_outputs = 1
llama_graph_n_input_tensors: input tensor ' out_ids' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2662' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' out_ids' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2663' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l3 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l7 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l11 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l15 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l19 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l23 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_361' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_369' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_815' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_823' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1269' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1277' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1723' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1731' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2177' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2185' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2631' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2639' (ROPE)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_7' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_41' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_84' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_137' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_222' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_262' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_426' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_466' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_551' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_591' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_676' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_716' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_880' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_920' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1005' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1045' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1130' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1170' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1334' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1374' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1459' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1499' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1584' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1624' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1788' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1828' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1913' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1953' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2038' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2078' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2242' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2282' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2367' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2407' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2492' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2532' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' inp_mixed_slots' [i64, ne = { 1, 1, 1, 1 }] is used by node ' (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' inp_mixed_embd' [f32, ne = { 1536, 1, 1, 1 }] is used by node 'node_2' (DUP)
llama_graph_n_input_tensors: input tensor ' inp_mixed_tokens' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_379' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_833' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_1287' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_1741' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_2195' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_2649' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' inp_tokens' [i32, ne = { 1, 1, 1, 1 }] is used by node 'model.input_embed' (GET_ROWS)
resolve_fused_ops: Flash Attention enabled
resolve_fused_ops: resolving fused DeepSeek V4 HC support:
graph_reserve: reserving a graph for ubatch with n_tokens = 1, n_seqs = 1, n_outputs = 1
llama_graph_n_input_tensors: input tensor ' out_ids' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2662' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' out_ids' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2663' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l3 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l7 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l11 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l15 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l19 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l23 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_361' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_369' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_815' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_823' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1269' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1277' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1723' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1731' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2177' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2185' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2631' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2639' (ROPE)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_7' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_41' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_84' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_137' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_222' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_262' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_426' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_466' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_551' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_591' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_676' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_716' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_880' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_920' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1005' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1045' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1130' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1170' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1334' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1374' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1459' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1499' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1584' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1624' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1788' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1828' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1913' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1953' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2038' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2078' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2242' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2282' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2367' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2407' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2492' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2532' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' inp_mixed_slots' [i64, ne = { 1, 1, 1, 1 }] is used by node ' (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' inp_mixed_embd' [f32, ne = { 1536, 1, 1, 1 }] is used by node 'node_2' (DUP)
llama_graph_n_input_tensors: input tensor ' inp_mixed_tokens' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_379' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_833' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_1287' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_1741' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_2195' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_2649' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' inp_tokens' [i32, ne = { 1, 1, 1, 1 }] is used by node 'model.input_embed' (GET_ROWS)
resolve_fused_ops: fused DeepSeek V4 HC pre enabled
graph_reserve: reserving a graph for ubatch with n_tokens = 1, n_seqs = 1, n_outputs = 1
llama_graph_n_input_tensors: input tensor ' out_ids' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2662' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' out_ids' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2663' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l3 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l7 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l11 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l15 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l19 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l23 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_361' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_369' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_815' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_823' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1269' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1277' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1723' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1731' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2177' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2185' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2631' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2639' (ROPE)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_7' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_41' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_84' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_137' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_222' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_262' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_426' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_466' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_551' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_591' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_676' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_716' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_880' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_920' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1005' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1045' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1130' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1170' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1334' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1374' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1459' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1499' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1584' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1624' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1788' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1828' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1913' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1953' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2038' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2078' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2242' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2282' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2367' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2407' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2492' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2532' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' inp_mixed_slots' [i64, ne = { 1, 1, 1, 1 }] is used by node ' (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' inp_mixed_embd' [f32, ne = { 1536, 1, 1, 1 }] is used by node 'node_2' (DUP)
llama_graph_n_input_tensors: input tensor ' inp_mixed_tokens' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_379' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_833' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_1287' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_1741' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_2195' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_2649' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' inp_tokens' [i32, ne = { 1, 1, 1, 1 }] is used by node 'model.input_embed' (GET_ROWS)
resolve_fused_ops: fused DeepSeek V4 HC comb enabled
graph_reserve: reserving a graph for ubatch with n_tokens = 1, n_seqs = 1, n_outputs = 1
llama_graph_n_input_tensors: input tensor ' out_ids' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2662' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' out_ids' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2663' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l3 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l7 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l11 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l15 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l19 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l23 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_361' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_369' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_815' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_823' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1269' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1277' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1723' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1731' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2177' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2185' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2631' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2639' (ROPE)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_7' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_41' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_84' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_137' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_222' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_262' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_426' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_466' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_551' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_591' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_676' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_716' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_880' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_920' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1005' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1045' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1130' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1170' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1334' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1374' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1459' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1499' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1584' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1624' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1788' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1828' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1913' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1953' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2038' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2078' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2242' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2282' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2367' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2407' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2492' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2532' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' inp_mixed_slots' [i64, ne = { 1, 1, 1, 1 }] is used by node ' (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' inp_mixed_embd' [f32, ne = { 1536, 1, 1, 1 }] is used by node 'node_2' (DUP)
llama_graph_n_input_tensors: input tensor ' inp_mixed_tokens' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_379' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_833' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_1287' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_1741' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_2195' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_2649' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' inp_tokens' [i32, ne = { 1, 1, 1, 1 }] is used by node 'model.input_embed' (GET_ROWS)
resolve_fused_ops: fused DeepSeek V4 HC post enabled
graph_reserve: reserving a graph for ubatch with n_tokens = 32, n_seqs = 1, n_outputs = 32
llama_graph_n_input_tensors: input tensor ' out_ids' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_2690' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' out_ids' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_2691' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 32, 1, 1, 1 }] is used by node 'cache_k_l3 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 32, 1, 1, 1 }] is used by node 'cache_k_l7 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 32, 1, 1, 1 }] is used by node 'cache_k_l11 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 32, 1, 1, 1 }] is used by node 'cache_k_l15 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 32, 1, 1, 1 }] is used by node 'cache_k_l19 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 32, 1, 1, 1 }] is used by node 'cache_k_l23 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_363' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_371' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_822' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_830' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_1281' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_1289' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_1740' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_1748' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_2199' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_2207' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_2658' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_2666' (ROPE)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_7' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_41' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_84' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_137' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_223' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_263' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_430' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_470' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_556' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_596' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_682' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_722' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_889' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_929' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1015' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1055' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1141' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1181' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1348' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1388' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1474' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1514' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1600' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1640' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1807' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1847' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1933' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1973' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2059' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2099' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2266' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2306' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2392' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2432' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2518' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2558' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' inp_mixed_slots' [i64, ne = { 32, 1, 1, 1 }] is used by node ' (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' inp_mixed_embd' [f32, ne = { 1536, 32, 1, 1 }] is used by node 'node_2' (DUP)
llama_graph_n_input_tensors: input tensor ' inp_mixed_tokens' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_1' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 32, 1, 1 }] is used by node 'node_381' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 32, 1, 1 }] is used by node 'node_840' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 32, 1, 1 }] is used by node 'node_1299' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 32, 1, 1 }] is used by node 'node_1758' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 32, 1, 1 }] is used by node 'node_2217' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 32, 1, 1 }] is used by node 'node_2676' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' inp_tokens' [i32, ne = { 32, 1, 1, 1 }] is used by node 'model.input_embed' (GET_ROWS)
graph_reserve: reserving a graph for ubatch with n_tokens = 1, n_seqs = 1, n_outputs = 1
llama_graph_n_input_tensors: input tensor ' out_ids' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2662' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' out_ids' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2663' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l3 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l7 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l11 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l15 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l19 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 1, 1, 1, 1 }] is used by node 'cache_k_l23 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_361' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_369' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_815' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_823' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1269' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1277' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1723' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1731' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2177' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2185' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2631' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2639' (ROPE)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_7' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_41' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_84' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_137' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_222' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_262' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_426' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_466' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_551' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_591' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_676' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_716' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_880' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_920' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1005' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1045' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1130' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1170' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1334' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1374' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1459' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1499' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1584' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1624' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1788' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1828' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1913' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1953' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2038' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2078' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2242' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2282' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2367' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2407' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2492' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2532' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' inp_mixed_slots' [i64, ne = { 1, 1, 1, 1 }] is used by node ' (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' inp_mixed_embd' [f32, ne = { 1536, 1, 1, 1 }] is used by node 'node_2' (DUP)
llama_graph_n_input_tensors: input tensor ' inp_mixed_tokens' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_379' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_833' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_1287' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_1741' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_2195' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 1, 1, 1 }] is used by node 'node_2649' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' inp_tokens' [i32, ne = { 1, 1, 1, 1 }] is used by node 'model.input_embed' (GET_ROWS)
graph_reserve: reserving a graph for ubatch with n_tokens = 32, n_seqs = 1, n_outputs = 32
llama_graph_n_input_tensors: input tensor ' out_ids' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_2690' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' out_ids' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_2691' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 32, 1, 1, 1 }] is used by node 'cache_k_l3 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 32, 1, 1, 1 }] is used by node 'cache_k_l7 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 32, 1, 1, 1 }] is used by node 'cache_k_l11 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 32, 1, 1, 1 }] is used by node 'cache_k_l15 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 32, 1, 1, 1 }] is used by node 'cache_k_l19 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_k_idxs' [i64, ne = { 32, 1, 1, 1 }] is used by node 'cache_k_l23 (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_363' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_371' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_822' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_830' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_1281' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_1289' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_1740' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_1748' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_2199' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_2207' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_2658' (ROPE)
llama_graph_n_input_tensors: input tensor ' inp_pos' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_2666' (ROPE)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_7' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_41' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_84' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_137' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_223' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_263' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_430' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_470' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_556' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_596' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_682' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_722' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_889' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_929' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1015' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1055' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1141' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1181' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1348' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1388' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1474' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1514' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1600' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1640' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1807' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1847' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1933' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_1973' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2059' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2099' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2266' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2306' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2392' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2432' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2518' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' rs_s_copy' [i32, ne = { 1, 1, 1, 1 }] is used by node 'node_2558' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' inp_mixed_slots' [i64, ne = { 32, 1, 1, 1 }] is used by node ' (view)' (SET_ROWS)
llama_graph_n_input_tensors: input tensor ' inp_mixed_embd' [f32, ne = { 1536, 32, 1, 1 }] is used by node 'node_2' (DUP)
llama_graph_n_input_tensors: input tensor ' inp_mixed_tokens' [i32, ne = { 32, 1, 1, 1 }] is used by node 'node_1' (GET_ROWS)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 32, 1, 1 }] is used by node 'node_381' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 32, 1, 1 }] is used by node 'node_840' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 32, 1, 1 }] is used by node 'node_1299' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 32, 1, 1 }] is used by node 'node_1758' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 32, 1, 1 }] is used by node 'node_2217' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' attn_inp_kq_mask' [f16, ne = { 256, 32, 1, 1 }] is used by node 'node_2676' (FLASH_ATTN_EXT)
llama_graph_n_input_tensors: input tensor ' inp_tokens' [i32, ne = { 32, 1, 1, 1 }] is used by node 'model.input_embed' (GET_ROWS)
sched_reserve: CPU compute buffer size = 20.33 MiB
sched_reserve: graph (pp bs=32, tg bs=1): nodes = 2733 / 2704, splits = 1 / 1, input objects = 4 / 4, input tensors = 9 / 9
sched_reserve: reserve took 10.18 ms, sched copies = 1
[sdq] 記憶體(載入 + pager 後):匿名 RSS 382 MB / 檔案對映 RSS 7 MB / 總 RSS 390 MB / 峰值 390 MB / swap 0 MB
[sdq-selftest] L20 E17 n_tok=4 計算完成(gate q4_K / up q4_K / down q6_K)
[sdq] page-map: n_layer=24 n_expert=128 n_expert_used=8 n_embd=1536 n_ff_exp=512 | L1E0 gate=456826720/442368 up=514684768/442368 down=373606240/645120 (off/size) ne0=1536 ne1=512 d_ne0=512 d_ne1=1536
[sdq] expert pager ready: /root/app/ling/tiny/models/Ling-3.0-tiny-Q4_K_M.gguf
[sdq] 熱門 expert 釘選:0 個(無剖面,用動態 LRU/LFU)
[sdq] slot=1.46 MiB (1531904 B) slots=225 arena=0.29 GiB (RAM 實測 RSS=0.38 GiB)
[sdq] 槽位分兩種:小 1.27 MiB × 169 + 大 1.46 MiB × 56(平均 1.31 MiB)
[sdq] prefetch=off fadvise_dontneed=1
~llama_context: CPU compute buffer size is 20.3291 MiB, matches expectation of 20.3291 MiB
[sdq] 載入模型:/root/app/ling/tiny/models/Ling-3.0-tiny-Q4_K_M.gguf
[sdq] ctx=256 threads=4 ubatch=32 RAM(RSS)=390 MB 模型載入 0.5s
[sdq] selftest 結果已寫到 results/selftest_l20e17.bin(layer=20 expert=17 seed=1234 n_embd=1536 n_ff=512)