Kimi K2 · Construct causal mask

Construct causal mask

no parameters_prepare_4d_causal_attention_mask

Identify the key positions permitted for every query position.

{\htmlData{light=causal-mask model-mask}{{\htmlClass{vl-stream-context}{M_{t,s}}}}}=\begin{cases}0&s\le t\\-\infty&s>t\end{cases}

in: $p$ [T positions]
out: $M$ [T queries, T keys]

A four-dimensional additive mask on the eager path (modeling_deepseek.py 1450–1457); the flash-attention path keeps the two-dimensional padding mask (1443–1449).

Explanation

Row $t$ is the query position, column $s$ the key position.

Codemodeling_deepseek.py 1360–1616

modeling_deepseek.py 1360–1370

        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)

modeling_deepseek.py 1423–1438

        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)

modeling_deepseek.py 1443–1457

        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,
            )

modeling_deepseek.py 1522

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

modeling_deepseek.py 1614–1616

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

Sources1

Direct evidence

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()