aphrodite-engine/aphrodite/modeling/models/chameleon.py

from functools import cached_property
from typing import Any, Dict, Iterable, List, Optional, Tuple

import torch
import torch.nn.functional as F
from torch import nn
from transformers import ChameleonConfig

from aphrodite.attention import Attention, AttentionMetadata
from aphrodite.common.config import CacheConfig
from aphrodite.common.sequence import IntermediateTensors, SamplerOutput
from aphrodite.common.utils import print_warning_once
from aphrodite.distributed import get_tensor_model_parallel_world_size
from aphrodite.modeling.layers.activation import SiluAndMul
from aphrodite.modeling.layers.layernorm import RMSNorm
from aphrodite.modeling.layers.linear import (MergedColumnParallelLinear,
                                              QKVParallelLinear,
                                              RowParallelLinear)
from aphrodite.modeling.layers.logits_processor import LogitsProcessor
from aphrodite.modeling.layers.rotary_embedding import get_rope
from aphrodite.modeling.layers.sampler import Sampler
from aphrodite.modeling.layers.vocab_parallel_embedding import (
    ParallelLMHead, VocabParallelEmbedding)
from aphrodite.modeling.model_loader.weight_utils import default_weight_loader
from aphrodite.modeling.sampling_metadata import SamplingMetadata
from aphrodite.quantization.base_config import QuantizationConfig


class ChameleonLayerNorm(nn.LayerNorm):

    def __init__(self, hidden_size, *args, **kwargs):
        super().__init__(hidden_size, *args, **kwargs)
        self.normalized_shape = (hidden_size[-1], )

    def forward(self, hidden_states):
        hidden_states = F.layer_norm(hidden_states,
                                     self.normalized_shape,
                                     None,
                                     None,
                                     eps=1e-5)
        hidden_states = hidden_states * self.weight + self.bias
        return hidden_states


# Copied from aphrodite.modeling.models.llama.LlamaMLP -> ChameleonMLP
class ChameleonMLP(nn.Module):

    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        hidden_act: str,
        quant_config: Optional[QuantizationConfig] = None,
        bias: bool = False,
    ) -> None:
        super().__init__()
        self.gate_up_proj = MergedColumnParallelLinear(
            input_size=hidden_size,
            output_sizes=[intermediate_size] * 2,
            bias=bias,
            quant_config=quant_config)
        self.down_proj = RowParallelLinear(input_size=intermediate_size,
                                           output_size=hidden_size,
                                           bias=bias,
                                           quant_config=quant_config)
        if hidden_act != "silu":
            raise ValueError(f"Unsupported activation: {hidden_act}. "
                             "Only silu is supported for now.")
        self.act_fn = SiluAndMul()

    def forward(self, x):
        gate_up, _ = self.gate_up_proj(x)
        x = self.act_fn(gate_up)
        x, _ = self.down_proj(x)
        return x


# Modified from aphrodite.modeling.models.llama.LlamaAttention -> ChameleonAttention #noqa
class ChameleonAttention(nn.Module):

    def __init__(
        self,
        hidden_size: int,
        num_heads: int,
        num_kv_heads: int,
        rope_theta: float = 10000,
        rope_scaling: Optional[Dict[str, Any]] = None,
        max_position_embeddings: int = 4096,
        quant_config: Optional[QuantizationConfig] = None,
        bias: bool = False,
        cache_config: Optional[CacheConfig] = None,
    ) -> None:
        super().__init__()
        self.hidden_size = hidden_size
        tp_size = get_tensor_model_parallel_world_size()
        self.total_num_heads = num_heads
        assert self.total_num_heads % tp_size == 0
        self.num_heads = self.total_num_heads // tp_size
        self.total_num_kv_heads = num_kv_heads
        if self.total_num_kv_heads >= tp_size:
            # Number of KV heads is greater than TP size, so we partition
            # the KV heads across multiple tensor parallel GPUs.
            assert self.total_num_kv_heads % tp_size == 0
        else:
            # Number of KV heads is less than TP size, so we replicate
            # the KV heads across multiple tensor parallel GPUs.
            assert tp_size % self.total_num_kv_heads == 0
        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
        self.head_dim = hidden_size // self.total_num_heads
        self.q_size = self.num_heads * self.head_dim
        self.kv_size = self.num_kv_heads * self.head_dim
        self.scaling = self.head_dim**-0.5
        self.rope_theta = rope_theta
        self.max_position_embeddings = max_position_embeddings

        self.qkv_proj = QKVParallelLinear(
            hidden_size=hidden_size,
            head_size=self.head_dim,
            total_num_heads=self.total_num_heads,
            total_num_kv_heads=self.total_num_kv_heads,
            bias=bias,
            quant_config=quant_config,
        )
        self.o_proj = RowParallelLinear(
            input_size=self.total_num_heads * self.head_dim,
            output_size=hidden_size,
            bias=bias,
            quant_config=quant_config,
        )
        self.q_norm = ChameleonLayerNorm((self.num_heads, self.head_dim))
        self.k_norm = ChameleonLayerNorm((self.num_kv_heads, self.head_dim))
        self.rotary_emb = get_rope(
            self.head_dim,
            rotary_dim=self.head_dim,
            max_position=max_position_embeddings,
            base=rope_theta,
            rope_scaling=rope_scaling,
        )

        self.attn = Attention(self.num_heads,
                              self.head_dim,
                              self.scaling,
                              num_kv_heads=self.num_kv_heads,
                              cache_config=cache_config,
                              quant_config=quant_config)

    def _apply_qk_norm(self, q: torch.Tensor,
                       k: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        # reshape for layernorm
        q = q.reshape(-1, self.num_heads, self.head_dim)
        k = k.reshape(-1, self.num_kv_heads, self.head_dim)
        q = self.q_norm(q)
        k = self.k_norm(k)
        q = q.view(*q.shape[:-2], -1)
        k = k.view(*k.shape[:-2], -1)
        return q, k

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
    ) -> torch.Tensor:
        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
        q, k = self._apply_qk_norm(q, k)

        q, k = self.rotary_emb(positions, q, k)
        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
        output, _ = self.o_proj(attn_output)
        return output


class ChameleonDecoderLayer(nn.Module):

    def __init__(
        self,
        config: ChameleonConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()
        self.hidden_size = config.hidden_size
        rope_theta = getattr(config, "rope_theta", 10000)
        rope_scaling = getattr(config, "rope_scaling", None)
        if rope_scaling is not None and getattr(
                config, "original_max_position_embeddings", None):
            rope_scaling["original_max_position_embeddings"] = (
                config.original_max_position_embeddings)
        max_position_embeddings = getattr(config, "max_position_embeddings",
                                          4096)

        self.self_attn = ChameleonAttention(
            hidden_size=self.hidden_size,
            num_heads=config.num_attention_heads,
            num_kv_heads=getattr(config, "num_key_value_heads",
                                 config.num_attention_heads),
            rope_theta=rope_theta,
            rope_scaling=rope_scaling,
            max_position_embeddings=max_position_embeddings,
            quant_config=quant_config,
            bias=False,
            cache_config=cache_config,
        )
        self.mlp = ChameleonMLP(
            hidden_size=self.hidden_size,
            intermediate_size=config.intermediate_size,
            hidden_act=config.hidden_act,
            quant_config=quant_config,
            bias=getattr(config, "mlp_bias", False),
        )
        self.input_layernorm = RMSNorm(config.hidden_size,
                                       eps=config.rms_norm_eps)
        self.post_attention_layernorm = RMSNorm(config.hidden_size,
                                                eps=config.rms_norm_eps)

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
        residual: Optional[torch.Tensor],
    ) -> Tuple[torch.Tensor, torch.Tensor]:

        if residual is None:
            residual = hidden_states
            hidden_states = self.input_layernorm(hidden_states)
        else:
            hidden_states, residual = self.input_layernorm(
                hidden_states, residual)
        hidden_states = self.self_attn(
            positions=positions,
            hidden_states=hidden_states,
            kv_cache=kv_cache,
            attn_metadata=attn_metadata,
        )

        # Fully Connected
        hidden_states, residual = self.post_attention_layernorm(
            hidden_states, residual)
        hidden_states = self.mlp(hidden_states)

        return hidden_states, residual


class ChameleonSwinDecoderLayer(nn.Module):

    def __init__(
        self,
        config: ChameleonConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()
        self.hidden_size = config.hidden_size
        rope_theta = getattr(config, "rope_theta", 10000)
        rope_scaling = getattr(config, "rope_scaling", None)
        if rope_scaling is not None and getattr(
                config, "original_max_position_embeddings", None):
            rope_scaling["original_max_position_embeddings"] = (
                config.original_max_position_embeddings)
        max_position_embeddings = getattr(config, "max_position_embeddings",
                                          4096)

        self.self_attn = ChameleonAttention(
            hidden_size=self.hidden_size,
            num_heads=config.num_attention_heads,
            num_kv_heads=getattr(config, "num_key_value_heads",
                                 config.num_attention_heads),
            rope_theta=rope_theta,
            rope_scaling=rope_scaling,
            max_position_embeddings=max_position_embeddings,
            quant_config=quant_config,
            bias=False,
            cache_config=cache_config,
        )
        self.mlp = ChameleonMLP(
            hidden_size=self.hidden_size,
            intermediate_size=config.intermediate_size,
            hidden_act=config.hidden_act,
            quant_config=quant_config,
            bias=getattr(config, "mlp_bias", False),
        )
        self.input_layernorm = RMSNorm(config.hidden_size,
                                       eps=config.rms_norm_eps)
        self.post_attention_layernorm = RMSNorm(config.hidden_size,
                                                eps=config.rms_norm_eps)

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
        residual: Optional[torch.Tensor],
    ) -> Tuple[torch.Tensor, torch.Tensor]:

        residual = hidden_states
        hidden_states = self.self_attn(
            positions=positions,
            hidden_states=hidden_states,
            kv_cache=kv_cache,
            attn_metadata=attn_metadata,
        )

        hidden_states = self.input_layernorm(hidden_states)
        hidden_states = hidden_states + residual

        # Fully Connected
        residual = hidden_states
        hidden_states = self.mlp(hidden_states)
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = residual + hidden_states

        return hidden_states, residual


class ChameleonImageVocabularyMapping:
    """
    A class for mapping discrete image tokens from VQGAN to BPE tokens.
    """

    def __init__(self, vocab_map):
        self.vocab_map = vocab_map
        self.image_token_id = vocab_map.get("<image>")

    @cached_property
    def val2name(self):
        return {v: k for k, v in self.vocab_map.items()}

    @cached_property
    def image_tokens(self):
        return sorted([
            val for name, val in self.vocab_map.items()
            if name.startswith("IMGIMG")
        ])

    @cached_property
    def bpe2img(self):
        img_tkn_chr_mapping = {chr(ord("A") + i): str(i) for i in range(10)}

        def remap(old_name: str) -> str:
            return "".join(
                img_tkn_chr_mapping.get(c, c)
                for c in old_name[len("IMGIMG"):-1])

        return {
            tok: int(remap(self.val2name[tok]))
            for tok in self.image_tokens
        }

    @cached_property
    def img2bpe(self):
        return {v: k for k, v in self.bpe2img.items()}

    @cached_property
    def bpe2img_search_tensors(self):
        return torch.tensor(sorted(self.bpe2img.keys())), torch.tensor(
            sorted(self.bpe2img.values()))

    @cached_property
    def img2bpe_mapping_tensor(self):
        mapping = torch.zeros(max(self.img2bpe.keys()) + 1, dtype=torch.int)
        for k, v in self.img2bpe.items():
            mapping[k] = v
        return mapping

    def convert_img2bpe(self, img_batch: torch.Tensor) -> torch.Tensor:
        device = img_batch.device
        img_tokens = self.img2bpe_mapping_tensor[img_batch.to("cpu")]
        return img_tokens.to(device)


class ChameleonModel(nn.Module):

    def __init__(
        self,
        config: ChameleonConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()
        self.config = config
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size
        self.embed_tokens = VocabParallelEmbedding(
            self.vocab_size,
            config.hidden_size,
        )
        self.vocabulary_mapping = ChameleonImageVocabularyMapping(
            config.vocabulary_map)
        decoder_layer = ChameleonDecoderLayer if not self.config.swin_norm \
            else ChameleonSwinDecoderLayer
        self.layers = nn.ModuleList([
            decoder_layer(config=config,
                          cache_config=cache_config,
                          quant_config=quant_config)
            for _ in range(config.num_hidden_layers)
        ])
        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

        # TODO: Support image input
        # self.vqmodel = ChameleonVQModel(config.vq_config)

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

    def forward(
        self,
        input_ids: Optional[torch.Tensor],
        positions: torch.Tensor,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
        inputs_embeds: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        if inputs_embeds is not None:
            hidden_states = inputs_embeds
        else:
            hidden_states = self.get_input_embeddings(input_ids)
        residual = None
        for i in range(len(self.layers)):
            layer = self.layers[i]
            hidden_states, residual = layer(
                positions,
                hidden_states,
                kv_caches[i],
                attn_metadata,
                residual,
            )
        hidden_states, _ = self.norm(hidden_states, residual)
        return hidden_states


class ChameleonForConditionalGeneration(nn.Module):

    def __init__(
        self,
        config: ChameleonConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()
        self.config = config
        self.model = ChameleonModel(config, cache_config, quant_config)
        self.unpadded_vocab_size = config.vocab_size
        self.lm_head = ParallelLMHead(
            self.unpadded_vocab_size,
            config.hidden_size,
        )
        if config.tie_word_embeddings:
            self.lm_head.weight = self.model.embed_tokens.weight

        logit_scale = getattr(config, "logit_scale", 1.0)
        self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
                                                config.vocab_size, logit_scale)
        self.sampler = Sampler()

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        **kwargs,
    ) -> torch.Tensor:

        # TODO: Support image input
        # image_tokens = self.process_image_input(**kwargs)
        # image_mask = input_ids == self.vocabulary_mapping.image_token_id
        # input_ids[special_image_mask] = image_tokens.flatten().to(input_ids.dtype) #noqa

        hidden_states = self.model(input_ids, positions, kv_caches,
                                   attn_metadata)
        return hidden_states

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

        # Disallow image tokens which does not include special
        # begin-image and end-image tokens
        image_tokens = self.model.vocabulary_mapping.image_tokens
        logits[:, image_tokens] = torch.finfo(logits.dtype).min

        return logits

    def sample(
        self,
        logits: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[SamplerOutput]:
        next_tokens = self.sampler(logits, sampling_metadata)
        return next_tokens

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            (".qkv_proj", ".q_proj", "q"),
            (".qkv_proj", ".k_proj", "k"),
            (".qkv_proj", ".v_proj", "v"),
            (".gate_up_proj", ".gate_proj", 0),
            (".gate_up_proj", ".up_proj", 1),
        ]
        params_dict = dict(self.named_parameters())
        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue

            # Skip loading vqgan
            # TODO: add support for the vision model
            if "vqmodel" in name:
                continue
            if ("rotary_emb.cos_cached" in name
                    or "rotary_emb.sin_cached" in name):
                # Models trained using ColossalAI may include these tensors in
                # the checkpoint. Skip them.
                continue
            for (param_name, weight_name, shard_id) in stacked_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                # Remapping the name of FP8 kv-scale.
                if name.endswith("kv_scale"):
                    remapped_kv_scale_name = name.replace(
                        ".kv_scale", ".attn.kv_scale")
                    if remapped_kv_scale_name not in params_dict:
                        print_warning_once(
                            f"Found kv scale in the checkpoint (e.g. {name}), "
                            "but not found the expected name in the model "
                            f"(e.g. {remapped_kv_scale_name}). kv-scale is "
                            "not loaded.")
                        continue
                    else:
                        name = remapped_kv_scale_name
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader",
                                        default_weight_loader)
                weight_loader(param, loaded_weight)