Kimi K2 · Logits · OpenModelAtlas

Logits

tensorlogits · 1616

Unnormalized vocabulary scores for the token following each sequence position.

shape: $L$ [B batch, T positions, 163840 vocabulary]

Sources

Contextual evidence from Vocabulary projection

The model embeds tokens, numbers positions from the cached length, builds a 4D causal mask, applies all blocks, normalizes and projects to vocabulary logits with an untied head, computing every position’s logits in float32.

transformer · moonshotai/Kimi-K2-Instruct / modeling_deepseek.py
Pinned revision fd1984e2b7a3 ↗ · lines 1360–1616

Read code excerpt

        self.embed_tokens = nn.Embedding(
            config.vocab_size, config.hidden_size, self.padding_idx
        )
        self.layers = nn.ModuleList(
            [
                DeepseekV3DecoderLayer(config, layer_idx)
                for layer_idx in range(config.num_hidden_layers)
            ]
        )
        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
        self.norm = DeepseekV3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

… lines 1371–1422 omitted …

        past_key_values_length = 0
        if use_cache:
            use_legacy_cache = not isinstance(past_key_values, Cache)
            if use_legacy_cache:
                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
            past_key_values_length = past_key_values.get_usable_length(seq_length)

        if position_ids is None:
            device = input_ids.device if input_ids is not None else inputs_embeds.device
            position_ids = torch.arange(
                past_key_values_length,
                seq_length + past_key_values_length,
                dtype=torch.long,
                device=device,
            )
            position_ids = position_ids.unsqueeze(0)

… lines 1439–1442 omitted …

        if self._use_flash_attention_2:
            # 2d mask is passed through the layers
            attention_mask = (
                attention_mask
                if (attention_mask is not None and 0 in attention_mask)
                else None
            )
        else:
            # 4d mask is passed through the layers
            attention_mask = _prepare_4d_causal_attention_mask(
                attention_mask,
                (batch_size, seq_length),
                inputs_embeds,
                past_key_values_length,
            )

… lines 1458–1521 omitted …

        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

… lines 1523–1613 omitted …

        hidden_states = outputs[0]
        logits = self.lm_head(hidden_states)
        logits = logits.float()

Contextual evidence from Vocabulary projection

The released configuration: width, layers, dense layers, heads, routed and shared experts, routing (sigmoid scores, one expert group, top-8 of 384, route scale 2.827), MLA ranks and head widths, vocabulary, YaRN RoPE.

config · moonshotai/Kimi-K2-Instruct / config.json
Pinned revision fd1984e2b7a3 ↗ · lines 1–68

Read code excerpt

{
  "architectures": [
    "DeepseekV3ForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "auto_map": {
    "AutoConfig": "configuration_deepseek.DeepseekV3Config",
    "AutoModel": "modeling_deepseek.DeepseekV3Model",
    "AutoModelForCausalLM": "modeling_deepseek.DeepseekV3ForCausalLM"
  },
  "aux_loss_alpha": 0.001,
  "bos_token_id": 163584,
  "eos_token_id": 163585,
  "first_k_dense_replace": 1,
  "hidden_act": "silu",
  "hidden_size": 7168,
  "initializer_range": 0.02,
  "intermediate_size": 18432,
  "kv_lora_rank": 512,
  "max_position_embeddings": 131072,
  "model_type": "kimi_k2",
  "moe_intermediate_size": 2048,
  "moe_layer_freq": 1,
  "n_group": 1,
  "n_routed_experts": 384,
  "n_shared_experts": 1,
  "norm_topk_prob": true,
  "num_attention_heads": 64,
  "num_experts_per_tok": 8,
  "num_hidden_layers": 61,
  "num_key_value_heads": 64,
  "num_nextn_predict_layers": 0,
  "pretraining_tp": 1,
  "q_lora_rank": 1536,
  "qk_nope_head_dim": 128,
  "qk_rope_head_dim": 64,
  "quantization_config": {
    "activation_scheme": "dynamic",
    "fmt": "e4m3",
    "quant_method": "fp8",
    "weight_block_size": [
      128,
      128
    ]
  },
  "rms_norm_eps": 1e-06,
  "rope_theta": 50000.0,
  "routed_scaling_factor": 2.827,
  "rope_scaling": {
    "beta_fast": 1.0,
    "beta_slow": 1.0,
    "factor": 32.0,
    "mscale": 1.0,
    "mscale_all_dim": 1.0,
    "original_max_position_embeddings": 4096,
    "type": "yarn"
  },
  "scoring_func": "sigmoid",
  "seq_aux": true,
  "tie_word_embeddings": false,
  "topk_group": 1,
  "topk_method": "noaux_tc",
  "torch_dtype": "bfloat16",
  "transformers_version": "4.48.3",
  "use_cache": true,
  "v_head_dim": 128,
  "vocab_size": 163840