vllm.model_executor.models.jais ¶

class JAISAttention(nn.Module):
    def __init__(
        self,
        config: JAISConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.hidden_size = config.hidden_size
        total_num_heads = config.num_attention_heads
        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
        assert total_num_heads % tensor_model_parallel_world_size == 0
        self.num_heads = total_num_heads // tensor_model_parallel_world_size
        self.head_dim = self.hidden_size // total_num_heads
        if hasattr(config, "scale_qk_dot_by_d"):
            config.mup_scale_qk_dot_by_d = config.scale_qk_dot_by_d
        self.attn_scale_power = 1.0 if config.mup_scale_qk_dot_by_d else 0.5
        self.scale = self.head_dim**-self.attn_scale_power

        self.c_attn = QKVParallelLinear(
            self.hidden_size,
            self.head_dim,
            total_num_heads,
            bias=True,
            quant_config=quant_config,
        )
        self.c_proj = RowParallelLinear(
            self.hidden_size,
            self.hidden_size,
            bias=True,
            quant_config=quant_config,
        )

        tp_rank = get_tensor_model_parallel_rank()
        head_start = tp_rank * self.num_heads
        head_end = (tp_rank + 1) * self.num_heads
        alibi_slopes = _get_alibi_slopes(total_num_heads)
        alibi_slopes = alibi_slopes[head_start:head_end]
        self.attn = Attention(
            self.num_heads,
            self.head_dim,
            scale=self.scale,
            alibi_slopes=alibi_slopes,
            cache_config=cache_config,
            quant_config=quant_config,
            prefix=f"{prefix}.attn",
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        qkv, _ = self.c_attn(hidden_states)
        q, k, v = qkv.chunk(chunks=3, dim=-1)
        attn_output = self.attn(q, k, v)
        attn_output, _ = self.c_proj(attn_output)
        return attn_output

class JAISBlock(nn.Module):
    def __init__(
        self,
        config: JAISConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        hidden_size = config.hidden_size
        inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size

        self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
        self.attn = JAISAttention(
            config, cache_config, quant_config, prefix=f"{prefix}.attn"
        )
        self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
        self.mlp = JAISMLP(inner_dim, config, quant_config)

    def forward(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        residual = hidden_states
        hidden_states = self.ln_1(hidden_states)
        attn_output = self.attn(
            hidden_states=hidden_states,
        )
        # residual connection
        hidden_states = attn_output + residual

        residual = hidden_states
        hidden_states = self.ln_2(hidden_states)
        feed_forward_hidden_states = self.mlp(hidden_states)
        # residual connection
        hidden_states = residual + feed_forward_hidden_states
        return hidden_states

class JAISLMHeadModel(nn.Module, SupportsPP):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        self.config = config
        self.quant_config = quant_config
        self.transformer = JAISModel(
            vllm_config=vllm_config, prefix=maybe_prefix(prefix, "transformer")
        )
        if self.config.tie_word_embeddings:
            self.lm_head = self.transformer.wte
        else:
            self.lm_head = ParallelLMHead(
                self.config.vocab_size,
                self.config.hidden_size,
                prefix=maybe_prefix(prefix, "lm_head"),
            )
        if hasattr(config, "width_scale"):
            self.output_logits_scale = config.width_scale
        else:
            self.output_logits_scale = config.mup_output_alpha * config.mup_width_scale
        self.logits_processor = LogitsProcessor(
            vocab_size=config.vocab_size, scale=self.output_logits_scale
        )
        self.make_empty_intermediate_tensors = (
            self.transformer.make_empty_intermediate_tensors
        )

    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.transformer.get_input_embeddings(input_ids)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
    ) -> Union[IntermediateTensors, torch.Tensor]:
        hidden_states = self.transformer(
            input_ids, positions, intermediate_tensors, inputs_embeds
        )
        return hidden_states

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
    ) -> Optional[torch.Tensor]:
        logits = self.logits_processor(self.lm_head, hidden_states)
        return logits

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
        params_dict = dict(self.named_parameters(remove_duplicate=False))
        loaded_params: set[str] = set()
        for name, loaded_weight in weights:
            if "lm_head.weight" in name:
                # GPT-2 ties the weights of the embedding layer and the final
                # linear layer.
                continue
            if ".attn.bias" in name or ".attn.masked_bias" in name:
                # Skip attention mask.
                # NOTE: "c_attn.bias" should not be skipped.
                continue
            if "relative_pe" in name:
                continue
            if not name.startswith("transformer."):
                name = "transformer." + name

            if is_pp_missing_parameter(name, self):
                continue

            param = params_dict[name]
            # The HF's GPT-2 implementation uses Conv1D instead of Linear.
            # Because of this, we need to transpose the weights.
            # Note(zhuohan): the logic below might break quantized models.
            for conv1d_weight_name in ["c_attn", "c_proj", "c_fc"]:
                if conv1d_weight_name not in name:
                    continue
                if not name.endswith(".weight"):
                    continue
                loaded_weight = loaded_weight.t()
            weight_loader = getattr(param, "weight_loader", default_weight_loader)
            weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params

class JAISMLP(nn.Module):
    def __init__(
        self,
        intermediate_size: int,
        config: JAISConfig,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        hidden_size = config.hidden_size
        self.swiglu = config.activation_function == "swiglu"
        self.c_fc = ColumnParallelLinear(
            hidden_size,
            intermediate_size,
            bias=True,
            quant_config=quant_config,
        )
        self.c_fc2 = (
            ColumnParallelLinear(
                hidden_size,
                intermediate_size,
                bias=True,
                quant_config=quant_config,
            )
            if self.swiglu
            else None
        )
        self.c_proj = RowParallelLinear(
            intermediate_size,
            hidden_size,
            bias=True,
            quant_config=quant_config,
        )

        self.act = SwiGLUActivation()

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        if self.swiglu:
            hidden_states2, _ = self.c_fc2(hidden_states)
        hidden_states, _ = self.c_fc(hidden_states)
        hidden_states = (
            self.act(hidden_states, hidden_states2)
            if self.swiglu
            else self.act(hidden_states)
        )
        hidden_states, _ = self.c_proj(hidden_states)
        return hidden_states

@support_torch_compile
class JAISModel(nn.Module):
    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()

        config = vllm_config.model_config.hf_config
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config

        self.config = config
        assert not config.add_cross_attention
        assert not config.scale_attn_by_inverse_layer_idx
        assert not config.reorder_and_upcast_attn
        self.embed_dim = config.hidden_size
        self.wte = VocabParallelEmbedding(config.vocab_size, self.embed_dim)
        self.wpe = (
            nn.Embedding(config.max_position_embeddings, self.embed_dim)
            if config.position_embedding_type != "alibi"
            else None
        )
        if hasattr(config, "embeddings_scale"):
            self.embeddings_scale = config.embeddings_scale
        else:
            self.embeddings_scale = config.mup_embeddings_scale

        self.start_layer, self.end_layer, self.h = make_layers(
            config.num_hidden_layers,
            lambda prefix: JAISBlock(
                config=config,
                cache_config=cache_config,
                quant_config=quant_config,
                prefix=prefix,
            ),
            prefix=f"{prefix}.h",
        )

        self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
        self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
            ["hidden_states"], config.n_embd
        )

    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.wte(input_ids)

    def forward(
        self,
        input_ids: torch.Tensor,
        position_ids: torch.Tensor,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
    ) -> Union[IntermediateTensors, torch.Tensor]:
        if get_pp_group().is_first_rank:
            if inputs_embeds is None:
                inputs_embeds = self.get_input_embeddings(input_ids)
            if self.wpe is not None:
                position_embeds = self.wpe(position_ids)
                hidden_states = inputs_embeds + position_embeds
            else:
                hidden_states = inputs_embeds
            hidden_states *= torch.tensor(
                float(self.embeddings_scale), dtype=hidden_states.dtype
            )
        else:
            assert intermediate_tensors is not None
            hidden_states = intermediate_tensors["hidden_states"]

        for layer in islice(self.h, self.start_layer, self.end_layer):
            hidden_states = layer(hidden_states)

        if not get_pp_group().is_last_rank:
            return IntermediateTensors({"hidden_states": hidden_states})

        hidden_states = self.ln_f(hidden_states)
        return hidden_states

class SwiGLUActivation(nn.Module):
    def forward(self, x1: torch.Tensor, x2: torch.Tensor) -> torch.Tensor:
        return x1 * nn.functional.silu(x2)