gistfile1.txt

0 -> ('', _VllmRunner(
  (vllm_model): Qwen2ForCausalLM(
    (model): Qwen2Model(
      (embed_tokens): VocabParallelEmbedding(num_embeddings=151936, embedding_dim=2048, org_vocab_size=151936, num_embeddings_padded=151936, tp_size=1)
      (layers): ModuleList(
        (0-35): 36 x Qwen2DecoderLayer(
          (self_attn): Qwen2Attention(
            (qkv_proj): MergedQKVParallelLinearWithLoRA(
              (base_layer): QKVParallelLinear(in_features=2048, output_features=2560, bias=True, tp_size=1, gather_output=False)
            )
            (o_proj): RowParallelLinearWithLoRA(
              (base_layer): RowParallelLinear(input_features=2048, output_features=2048, bias=False, tp_size=1, reduce_results=True)
            )
            (rotary_emb): RotaryEmbedding(head_size=128, rotary_dim=128, max_position_embeddings=32768, base=1000000.0, is_neox_style=True)
            (attn): Attention(head_size=128, num_heads=16, num_kv_heads=2, scale=0.08838834764831845, backend=PallasAttentionBackendImpl)
          )
          (mlp): Qwen2MLP(
            (gate_up_proj): MergedColumnParallelLinearWithLoRA(
              (base_layer): MergedColumnParallelLinear(in_features=2048, output_features=22016, bias=False, tp_size=1, gather_output=False)
            )
            (down_proj): RowParallelLinearWithLoRA(
              (base_layer): RowParallelLinear(input_features=11008, output_features=2048, bias=False, tp_size=1, reduce_results=True)
            )
            (act_fn): SiluAndMul()
          )
          (input_layernorm): RMSNorm(hidden_size=2048, eps=1e-06)
          (post_attention_layernorm): RMSNorm(hidden_size=2048, eps=1e-06)
        )
      )
      (norm): RMSNorm(hidden_size=2048, eps=1e-06)
    )
    (lm_head): VocabParallelEmbedding(num_embeddings=151936, embedding_dim=2048, org_vocab_size=151936, num_embeddings_padded=151936, tp_size=1)
    (logits_processor): LogitsProcessor(vocab_size=151936, org_vocab_size=151936, scale=1.0, logits_as_input=False)
  )
))
1 -> ('vllm_model', Qwen2ForCausalLM(
  (model): Qwen2Model(
    (embed_tokens): VocabParallelEmbedding(num_embeddings=151936, embedding_dim=2048, org_vocab_size=151936, num_embeddings_padded=151936, tp_size=1)
    (layers): ModuleList(
      (0-35): 36 x Qwen2DecoderLayer(
        (self_attn): Qwen2Attention(
          (qkv_proj): MergedQKVParallelLinearWithLoRA(
            (base_layer): QKVParallelLinear(in_features=2048, output_features=2560, bias=True, tp_size=1, gather_output=False)
          )
          (o_proj): RowParallelLinearWithLoRA(
            (base_layer): RowParallelLinear(input_features=2048, output_features=2048, bias=False, tp_size=1, reduce_results=True)
          )
          (rotary_emb): RotaryEmbedding(head_size=128, rotary_dim=128, max_position_embeddings=32768, base=1000000.0, is_neox_style=True)
          (attn): Attention(head_size=128, num_heads=16, num_kv_heads=2, scale=0.08838834764831845, backend=PallasAttentionBackendImpl)
        )
        (mlp): Qwen2MLP(
          (gate_up_proj): MergedColumnParallelLinearWithLoRA(
            (base_layer): MergedColumnParallelLinear(in_features=2048, output_features=22016, bias=False, tp_size=1, gather_output=False)
          )
          (down_proj): RowParallelLinearWithLoRA(
            (base_layer): RowParallelLinear(input_features=11008, output_features=2048, bias=False, tp_size=1, reduce_results=True)
          )
          (act_fn): SiluAndMul()
        )
        (input_layernorm): RMSNorm(hidden_size=2048, eps=1e-06)
        (post_attention_layernorm): RMSNorm(hidden_size=2048, eps=1e-06)
      )
    )
    (norm): RMSNorm(hidden_size=2048, eps=1e-06)
  )
  (lm_head): VocabParallelEmbedding(num_embeddings=151936, embedding_dim=2048, org_vocab_size=151936, num_embeddings_padded=151936, tp_size=1)
  (logits_processor): LogitsProcessor(vocab_size=151936, org_vocab_size=151936, scale=1.0, logits_as_input=False)
))
2 -> ('vllm_model.model', Qwen2Model(
  (embed_tokens): VocabParallelEmbedding(num_embeddings=151936, embedding_dim=2048, org_vocab_size=151936, num_embeddings_padded=151936, tp_size=1)
  (layers): ModuleList(
    (0-35): 36 x Qwen2DecoderLayer(
      (self_attn): Qwen2Attention(
        (qkv_proj): MergedQKVParallelLinearWithLoRA(
          (base_layer): QKVParallelLinear(in_features=2048, output_features=2560, bias=True, tp_size=1, gather_output=False)
        )
        (o_proj): RowParallelLinearWithLoRA(
          (base_layer): RowParallelLinear(input_features=2048, output_features=2048, bias=False, tp_size=1, reduce_results=True)
        )
        (rotary_emb): RotaryEmbedding(head_size=128, rotary_dim=128, max_position_embeddings=32768, base=1000000.0, is_neox_style=True)
        (attn): Attention(head_size=128, num_heads=16, num_kv_heads=2, scale=0.08838834764831845, backend=PallasAttentionBackendImpl)
      )
      (mlp): Qwen2MLP(
        (gate_up_proj): MergedColumnParallelLinearWithLoRA(
          (base_layer): MergedColumnParallelLinear(in_features=2048, output_features=22016, bias=False, tp_size=1, gather_output=False)
        )
        (down_proj): RowParallelLinearWithLoRA(
          (base_layer): RowParallelLinear(input_features=11008, output_features=2048, bias=False, tp_size=1, reduce_results=True)
        )
        (act_fn): SiluAndMul()
      )
      (input_layernorm): RMSNorm(hidden_size=2048, eps=1e-06)
      (post_attention_layernorm): RMSNorm(hidden_size=2048, eps=1e-06)
    )
  )
  (norm): RMSNorm(hidden_size=2048, eps=1e-06)
))
3 -> ('vllm_model.model.embed_tokens', VocabParallelEmbedding(num_embeddings=151936, embedding_dim=2048, org_vocab_size=151936, num_embeddings_padded=151936, tp_size=1))
4 -> ('vllm_model.model.layers', ModuleList(
  (0-35): 36 x Qwen2DecoderLayer(
    (self_attn): Qwen2Attention(
      (qkv_proj): MergedQKVParallelLinearWithLoRA(
        (base_layer): QKVParallelLinear(in_features=2048, output_features=2560, bias=True, tp_size=1, gather_output=False)
      )
      (o_proj): RowParallelLinearWithLoRA(
        (base_layer): RowParallelLinear(input_features=2048, output_features=2048, bias=False, tp_size=1, reduce_results=True)
      )
      (rotary_emb): RotaryEmbedding(head_size=128, rotary_dim=128, max_position_embeddings=32768, base=1000000.0, is_neox_style=True)
      (attn): Attention(head_size=128, num_heads=16, num_kv_heads=2, scale=0.08838834764831845, backend=PallasAttentionBackendImpl)
    )
    (mlp): Qwen2MLP(
      (gate_up_proj): MergedColumnParallelLinearWithLoRA(
        (base_layer): MergedColumnParallelLinear(in_features=2048, output_features=22016, bias=False, tp_size=1, gather_output=False)
      )
      (down_proj): RowParallelLinearWithLoRA(
        (base_layer): RowParallelLinear(input_features=11008, output_features=2048, bias=False, tp_size=1, reduce_results=True)
      )
      (act_fn): SiluAndMul()
    )
    (input_layernorm): RMSNorm(hidden_size=2048, eps=1e-06)
    (post_attention_layernorm): RMSNorm(hidden_size=2048, eps=1e-06)
  )
))
5 -> ('vllm_model.model.layers.0', Qwen2DecoderLayer(
  (self_attn): Qwen2Attention(
    (qkv_proj): MergedQKVParallelLinearWithLoRA(
      (base_layer): QKVParallelLinear(in_features=2048, output_features=2560, bias=True, tp_size=1, gather_output=False)
    )
    (o_proj): RowParallelLinearWithLoRA(
      (base_layer): RowParallelLinear(input_features=2048, output_features=2048, bias=False, tp_size=1, reduce_results=True)
    )
    (rotary_emb): RotaryEmbedding(head_size=128, rotary_dim=128, max_position_embeddings=32768, base=1000000.0, is_neox_style=True)
    (attn): Attention(head_size=128, num_heads=16, num_kv_heads=2, scale=0.08838834764831845, backend=PallasAttentionBackendImpl)
  )
  (mlp): Qwen2MLP(
    (gate_up_proj): MergedColumnParallelLinearWithLoRA(
      (base_layer): MergedColumnParallelLinear(in_features=2048, output_features=22016, bias=False, tp_size=1, gather_output=False)
    )
    (down_proj): RowParallelLinearWithLoRA(
      (base_layer): RowParallelLinear(input_features=11008, output_features=2048, bias=False, tp_size=1, reduce_results=True)
    )
    (act_fn): SiluAndMul()
  )
  (input_layernorm): RMSNorm(hidden_size=2048, eps=1e-06)
  (post_attention_layernorm): RMSNorm(hidden_size=2048, eps=1e-06)
))
6 -> ('vllm_model.model.layers.0.self_attn', Qwen2Attention(
  (qkv_proj): MergedQKVParallelLinearWithLoRA(
    (base_layer): QKVParallelLinear(in_features=2048, output_features=2560, bias=True, tp_size=1, gather_output=False)
  )
  (o_proj): RowParallelLinearWithLoRA(
    (base_layer): RowParallelLinear(input_features=2048, output_features=2048, bias=False, tp_size=1, reduce_results=True)
  )
  (rotary_emb): RotaryEmbedding(head_size=128, rotary_dim=128, max_position_embeddings=32768, base=1000000.0, is_neox_style=True)
  (attn): Attention(head_size=128, num_heads=16, num_kv_heads=2, scale=0.08838834764831845, backend=PallasAttentionBackendImpl)
))
7 -> ('vllm_model.model.layers.0.self_attn.qkv_proj', MergedQKVParallelLinearWithLoRA(
  (base_layer): QKVParallelLinear(in_features=2048, output_features=2560, bias=True, tp_size=1, gather_output=False)
))
8 -> ('vllm_model.model.layers.0.self_attn.qkv_proj.base_layer', QKVParallelLinear(in_features=2048, output_features=2560, bias=True, tp_size=1, gather_output=False))
9 -> ('vllm_model.model.layers.0.self_attn.o_proj', RowParallelLinearWithLoRA(
  (base_layer): RowParallelLinear(input_features=2048, output_features=2048, bias=False, tp_size=1, reduce_results=True)
))
10 -> ('vllm_model.model.layers.0.self_attn.o_proj.base_layer', RowParallelLinear(input_features=2048, output_features=2048, bias=False, tp_size=1, reduce_results=True))
11 -> ('vllm_model.model.layers.0.self_attn.rotary_emb', RotaryEmbedding(head_size=128, rotary_dim=128, max_position_embeddings=32768, base=1000000.0, is_neox_style=True))
12 -> ('vllm_model.model.layers.0.self_attn.attn', Attention(head_size=128, num_heads=16, num_kv_heads=2, scale=0.08838834764831845, backend=PallasAttentionBackendImpl))
13 -> ('vllm_model.model.layers.0.mlp', Qwen2MLP(
  (gate_up_proj): MergedColumnParallelLinearWithLoRA(
    (base_layer): MergedColumnParallelLinear(in_features=2048, output_features=22016, bias=False, tp_size=1, gather_output=False)
  )
  (down_proj): RowParallelLinearWithLoRA(
    (base_layer): RowParallelLinear(input_features=11008, output_features=2048, bias=False, tp_size=1, reduce_results=True)
  )
  (act_fn): SiluAndMul()
))
14 -> ('vllm_model.model.layers.0.mlp.gate_up_proj', MergedColumnParallelLinearWithLoRA(
  (base_layer): MergedColumnParallelLinear(in_features=2048, output_features=22016, bias=False, tp_size=1, gather_output=False)
))
15 -> ('vllm_model.model.layers.0.mlp.gate_up_proj.base_layer', MergedColumnParallelLinear(in_features=2048, output_features=22016, bias=False, tp_size=1, gather_output=False))
16 -> ('vllm_model.model.layers.0.mlp.down_proj', RowParallelLinearWithLoRA(
  (base_layer): RowParallelLinear(input_features=11008, output_features=2048, bias=False, tp_size=1, reduce_results=True)
))
17 -> ('vllm_model.model.layers.0.mlp.down_proj.base_layer', RowParallelLinear(input_features=11008, output_features=2048, bias=False, tp_size=1, reduce_results=True))
18 -> ('vllm_model.model.layers.0.mlp.act_fn', SiluAndMul())
19 -> ('vllm_model.model.layers.0.input_layernorm', RMSNorm(hidden_size=2048, eps=1e-06))
20 -> ('vllm_model.model.layers.0.post_attention_layernorm', RMSNorm(hidden_size=2048, eps=1e-06))
21 -> ('vllm_model.model.layers.1', Qwen2DecoderLayer(
...
545 -> ('vllm_model.model.layers.35.post_attention_layernorm', RMSNorm(hidden_size=2048, eps=1e-06))
546 -> ('vllm_model.model.norm', RMSNorm(hidden_size=2048, eps=1e-06))
547 -> ('vllm_model.logits_processor', LogitsProcessor(vocab_size=151936, org_vocab_size=151936, scale=1.0, logits_as_input=False))