vllm.model_executor.models.bloom

class BloomAttention(nn.Module):

    def __init__(
        self,
        config: BloomConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.total_num_heads = config.n_head
        self.head_dim = self.hidden_size // self.total_num_heads
        assert self.head_dim * self.total_num_heads == self.hidden_size

        tp_world_size = get_tensor_model_parallel_world_size()
        assert self.total_num_heads % tp_world_size == 0
        self.num_heads = self.total_num_heads // tp_world_size

        self.query_key_value = QKVParallelLinear(
            self.hidden_size,
            self.head_dim,
            self.total_num_heads,
            bias=True,
            quant_config=quant_config,
        )
        self.dense = RowParallelLinear(
            self.hidden_size,
            self.hidden_size,
            bias=True,
            quant_config=quant_config,
        )

        # Create the alibi slopes and slice them.
        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(self.total_num_heads)
        alibi_slopes = alibi_slopes[head_start:head_end].tolist()

        scaling = self.head_dim**-0.5
        self.attn = Attention(self.num_heads,
                              self.head_dim,
                              scaling,
                              alibi_slopes=alibi_slopes,
                              cache_config=cache_config,
                              quant_config=quant_config,
                              prefix=f"{prefix}.attn")

    def forward(
        self,
        position_ids: torch.Tensor,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        del position_ids  # Unused.
        qkv, _ = self.query_key_value(hidden_states)
        q, k, v = qkv.chunk(chunks=3, dim=-1)
        attn_output = self.attn(q, k, v)
        output, _ = self.dense(attn_output)
        return output

class BloomBlock(nn.Module):

    def __init__(
        self,
        config: BloomConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        hidden_size = config.hidden_size

        self.input_layernorm = nn.LayerNorm(hidden_size,
                                            eps=config.layer_norm_epsilon)
        self.self_attention = BloomAttention(config,
                                             cache_config,
                                             quant_config,
                                             prefix=f"{prefix}.self_attention")
        self.post_attention_layernorm = nn.LayerNorm(
            hidden_size, eps=config.layer_norm_epsilon)
        self.mlp = BloomMLP(config, quant_config)
        self.apply_residual_connection_post_layernorm = (
            config.apply_residual_connection_post_layernorm)

    def forward(
        self,
        position_ids: torch.Tensor,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        # Layer norm at the beginning of the transformer layer.
        layernorm_output = self.input_layernorm(hidden_states)

        # Layer norm post the self attention.
        if self.apply_residual_connection_post_layernorm:
            residual = layernorm_output
        else:
            residual = hidden_states

        # Self attention.
        attention_output = self.self_attention(
            position_ids=position_ids,
            hidden_states=layernorm_output,
        )
        attention_output = attention_output + residual
        layernorm_output = self.post_attention_layernorm(attention_output)

        # Get residual
        if self.apply_residual_connection_post_layernorm:
            residual = layernorm_output
        else:
            residual = attention_output

        # MLP.
        output = self.mlp(layernorm_output) + residual
        return output

class BloomForCausalLM(nn.Module, SupportsPP, SupportsQuant):

    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 = BloomModel(vllm_config=vllm_config,
                                      prefix=maybe_prefix(
                                          prefix, "transformer"))
        if self.config.tie_word_embeddings:
            self.lm_head = self.transformer.word_embeddings
        else:
            self.lm_head = ParallelLMHead(self.config.vocab_size,
                                          self.config.hidden_size)

        self.logits_processor = LogitsProcessor(config.vocab_size)
        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[torch.Tensor, IntermediateTensors]:
        hidden_states = self.transformer(input_ids, positions,
                                         intermediate_tensors, inputs_embeds)
        return hidden_states

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

    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
        loader = AutoWeightsLoader(self, skip_prefixes=["lm_head.weight"])
        weights = _add_transformer_prefix(weights)
        return loader.load_weights(weights)

class BloomMLP(nn.Module):

    def __init__(
        self,
        config: BloomConfig,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        hidden_size = config.hidden_size
        self.dense_h_to_4h = ColumnParallelLinear(
            hidden_size,
            4 * hidden_size,
            quant_config=quant_config,
        )
        self.gelu_impl = get_act_fn("gelu")
        self.dense_4h_to_h = RowParallelLinear(
            4 * hidden_size,
            hidden_size,
            quant_config=quant_config,
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x, _ = self.dense_h_to_4h(x)
        x = self.gelu_impl(x)
        x, _ = self.dense_4h_to_h(x)
        return x

@support_torch_compile
class BloomModel(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

        self.embed_dim = config.hidden_size

        # Embedding + LN Embedding
        self.word_embeddings = VocabParallelEmbedding(
            config.vocab_size,
            self.embed_dim,
        )
        self.word_embeddings_layernorm = nn.LayerNorm(
            self.embed_dim, eps=config.layer_norm_epsilon)

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

        # Final Layer Norm
        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.hidden_size))

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

    def forward(
        self,
        input_ids: torch.Tensor,
        position_ids: torch.Tensor,
        intermediate_tensors: Optional[IntermediateTensors],
        inputs_embeds: Optional[torch.Tensor] = None,
    ) -> Union[torch.Tensor, IntermediateTensors]:
        if get_pp_group().is_first_rank:
            if inputs_embeds is not None:
                hidden_states = inputs_embeds
            else:
                hidden_states = self.get_input_embeddings(input_ids)
        else:
            assert intermediate_tensors is not None
            hidden_states = intermediate_tensors["hidden_states"]
        for layer in self.h[self.start_layer:self.end_layer]:
            hidden_states = layer(position_ids, 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

    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 is_pp_missing_parameter(name, self):
                continue
            param = params_dict[name]

            if "query_key_value" in name:
                # NOTE: BLOOM's fused QKV's output_dim has the shape of
                # (num_heads * 3 * head_size), while the
                # required shape is (3 * num_heads * head_size).
                # Thus, we need weight conversion.
                output_dim = getattr(param, "output_dim", None)
                num_heads = self.config.num_attention_heads
                if output_dim is not None:
                    loaded_weight_shape = loaded_weight.shape
                    loaded_weight = loaded_weight.view(
                        loaded_weight_shape[:output_dim] + (num_heads, 3, -1) +
                        loaded_weight_shape[output_dim + 1:])
                    loaded_weight = loaded_weight.transpose(
                        output_dim, output_dim + 1)
                    loaded_weight = loaded_weight.reshape(loaded_weight_shape)

            weight_loader = getattr(param, "weight_loader",
                                    default_weight_loader)
            weight_loader(param, loaded_weight)
            loaded_params.add(name)

        return loaded_params