from array import array from functools import cached_property from typing import (Any, Dict, Iterable, List, Literal, Mapping, Optional, Tuple, TypedDict) import torch import torch.nn.functional as F from PIL import Image from torch import nn from transformers import ChameleonConfig, ChameleonVQVAEConfig from vllm.attention import Attention, AttentionMetadata from vllm.config import CacheConfig, MultiModalConfig from vllm.distributed import get_tensor_model_parallel_world_size from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs from vllm.logger import init_logger from vllm.model_executor.layers.activation import SiluAndMul from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.linear import (MergedColumnParallelLinear, QKVParallelLinear, RowParallelLinear) from vllm.model_executor.layers.logits_processor import LogitsProcessor from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.rotary_embedding import get_rope from vllm.model_executor.layers.sampler import Sampler, SamplerOutput from vllm.model_executor.layers.vocab_parallel_embedding import ( ParallelLMHead, VocabParallelEmbedding) from vllm.model_executor.model_loader.weight_utils import ( default_weight_loader, row_parallel_weight_loader) from vllm.model_executor.sampling_metadata import SamplingMetadata from vllm.model_executor.utils import set_weight_attrs from vllm.multimodal import MULTIMODAL_REGISTRY from vllm.multimodal.utils import (cached_get_tokenizer, repeat_and_pad_placeholder_tokens) from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, IntermediateTensors, SequenceData) from vllm.utils import print_warning_once from .interfaces import SupportsMultiModal logger = init_logger(__name__) # These configs are not part of the model config but the preprocessor # and processor files, so we hardcode them in the model file for now. CHAMELEON_CROP_SIZE_HEIGHT = CHAMELEON_CROP_SIZE_WIDTH = 512 CHAMELEON_IMAGE_SEQ_LENGTH = 1024 CHAMELEON_IMAGE_TOKEN_ID = 8711 CHAMELEON_IMAGE_START_TOKEN_ID = 8197 CHAMELEON_IMAGE_END_TOKEN_ID = 8196 CHAMELEON_SEP_TOKEN_ID = 8710 class ChameleonImagePixelInputs(TypedDict): type: Literal["pixel_values"] data: torch.Tensor """Shape: `(batch_size * num_images, num_channels, height, width)`""" def get_max_chameleon_image_tokens(ctx: InputContext): return CHAMELEON_IMAGE_SEQ_LENGTH def dummy_seq_data_for_chameleon( seq_len: int, num_images: int, *, image_token_id: int, image_feature_size_override: Optional[int] = None, ): if image_feature_size_override is None: image_feature_size = CHAMELEON_IMAGE_SEQ_LENGTH else: image_feature_size = image_feature_size_override token_ids = array(VLLM_TOKEN_ID_ARRAY_TYPE, [image_token_id]) * image_feature_size * num_images token_ids += array(VLLM_TOKEN_ID_ARRAY_TYPE, [0]) * (seq_len - image_feature_size * num_images) return SequenceData(token_ids) def dummy_image_for_chameleon( num_images: int, *, image_width_override: Optional[int] = None, image_height_override: Optional[int] = None, ): width = CHAMELEON_CROP_SIZE_WIDTH height = CHAMELEON_CROP_SIZE_HEIGHT if image_width_override is not None: width = image_width_override if image_height_override is not None: height = image_height_override image = Image.new("RGB", (width, height), color=0) return {"image": image if num_images == 1 else [image] * num_images} def dummy_data_for_chameleon(ctx: InputContext, seq_len: int, mm_counts: Mapping[str, int]): num_images = mm_counts["image"] seq_data = dummy_seq_data_for_chameleon( seq_len, num_images, image_token_id=CHAMELEON_IMAGE_TOKEN_ID, ) mm_data = dummy_image_for_chameleon(num_images) return seq_data, mm_data def input_processor_for_chameleon(ctx: InputContext, llm_inputs: LLMInputs): """ Processing input prompt to insert required tokens for image placeholder. See https://github.com/huggingface/transformers/blob/0fdea8607d7e01eb0e38a1ebeb7feee30a22f0cf/src/transformers/models/chameleon/processing_chameleon.py#L58 """ # noqa multi_modal_data = llm_inputs.get("multi_modal_data") if multi_modal_data is None or "image" not in multi_modal_data: return llm_inputs model_config = ctx.model_config tokenizer = cached_get_tokenizer(model_config.tokenizer) new_prompt, new_token_ids = repeat_and_pad_placeholder_tokens( tokenizer, llm_inputs.get("prompt"), llm_inputs["prompt_token_ids"], placeholder_token_id=CHAMELEON_IMAGE_TOKEN_ID, repeat_count=CHAMELEON_IMAGE_SEQ_LENGTH, pad_token_left=CHAMELEON_IMAGE_START_TOKEN_ID, pad_token_right=CHAMELEON_IMAGE_END_TOKEN_ID, ) # Appending sep token for chat mode to follow default processor # behavior if new_prompt is not None: new_prompt += tokenizer.sep_token new_token_ids += [CHAMELEON_SEP_TOKEN_ID] # NOTE: Create a defensive copy of the original inputs return LLMInputs(prompt_token_ids=new_token_ids, prompt=new_prompt, multi_modal_data=multi_modal_data) class ChameleonLayerNorm(nn.LayerNorm): def __init__(self, hidden_size, *args, **kwargs): super().__init__(hidden_size, *args, **kwargs) self.normalized_shape = (hidden_size[-1], ) set_weight_attrs(self.weight, {"weight_loader": row_parallel_weight_loader}) set_weight_attrs(self.bias, {"weight_loader": row_parallel_weight_loader}) 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 vllm.model_executor.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 vllm.model_executor.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 # Copied from transformers.models.chameleon.modeling_chameleon.ChameleonVQVAEVectorQuantizer #noqa class ChameleonVQVAEVectorQuantizer(nn.Module): def __init__(self, config: ChameleonVQVAEConfig): super().__init__() self.num_embeddings = config.num_embeddings self.embedding_dim = config.embed_dim self.beta = getattr(config, "beta", 0.25) self.embedding = nn.Embedding(self.num_embeddings, self.embedding_dim) self.re_embed = self.num_embeddings def forward(self, hidden_state: torch.Tensor): hidden_state = hidden_state.permute(0, 2, 3, 1).contiguous() hidden_state_flattened = hidden_state.view(-1, self.embedding_dim) # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z distances = ( torch.sum(hidden_state_flattened**2, dim=1, keepdim=True) + torch.sum(self.embedding.weight**2, dim=1) - 2 * torch.einsum("bd,dn->bn", hidden_state_flattened, self.embedding.weight.transpose(0, 1))) min_encoding_indices = torch.argmin(distances, dim=1) hidden_state_quant = self.embedding(min_encoding_indices).view( hidden_state.shape) # compute loss for embedding loss = torch.mean((hidden_state_quant.detach() - hidden_state)** 2) + self.beta * torch.mean( (hidden_state_quant - hidden_state.detach())**2) # preserve gradients hidden_state_quant = hidden_state + (hidden_state_quant - hidden_state).detach() # reshape back to match original input shape hidden_state_quant = hidden_state_quant.permute(0, 3, 1, 2).contiguous() return hidden_state_quant, loss, min_encoding_indices # Copied from transformers.models.chameleon.modeling_chameleon.ChameleonVQVAEEncoderConvDownsample #noqa class ChameleonVQVAEEncoderConvDownsample(nn.Module): def __init__(self, in_channels: int): super().__init__() self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) def forward(self, hidden_states: torch.Tensor): # no asymmetric padding in torch conv, must do it ourselves hidden_states = F.pad(hidden_states, pad=(0, 1, 0, 1), mode="constant", value=0) hidden_states = self.conv(hidden_states) return hidden_states # Copied from transformers.models.chameleon.modeling_chameleon.ChameleonVQVAEEncoderResnetBlock #noqa class ChameleonVQVAEEncoderResnetBlock(nn.Module): def __init__( self, config: ChameleonVQVAEConfig, in_channels: int, out_channels=None, conv_shortcut=False, ): super().__init__() self.in_channels = in_channels self.out_channels = in_channels if out_channels is None \ else out_channels self.use_conv_shortcut = conv_shortcut self.norm1 = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) self.norm2 = torch.nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=1e-6, affine=True) self.dropout = torch.nn.Dropout(config.dropout) self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) if self.in_channels != self.out_channels: if self.use_conv_shortcut: self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) else: self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) def forward(self, hidden_states: torch.Tensor): residual = hidden_states hidden_states = self.norm1(hidden_states) hidden_states *= torch.sigmoid(hidden_states) hidden_states = self.conv1(hidden_states) hidden_states = self.norm2(hidden_states) hidden_states *= torch.sigmoid(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.conv2(hidden_states) if self.in_channels != self.out_channels: if self.use_conv_shortcut: residual = self.conv_shortcut(residual) else: residual = self.nin_shortcut(residual) return residual + hidden_states # Copied from transformers.models.chameleon.modeling_chameleon.ChameleonVQVAEEncoderAttnBlock #noqa class ChameleonVQVAEEncoderAttnBlock(nn.Module): def __init__(self, in_channels: int): super().__init__() self.in_channels = in_channels self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.proj_out = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) def forward(self, hidden_states: torch.Tensor): residual = hidden_states hidden_states = self.norm(hidden_states) query_states = self.q(hidden_states) key_states = self.k(hidden_states) value_states = self.v(hidden_states) # compute attention batch_size, channels, height, width = query_states.shape query_states = query_states.reshape(batch_size, channels, height * width).permute(0, 2, 1) key_states = key_states.reshape(batch_size, channels, height * width) attn_weights = torch.bmm(query_states, key_states) attn_weights = attn_weights * (int(channels)**(-0.5)) attn_weights = F.softmax(attn_weights, dim=2) # attend to values value_states = value_states.reshape(batch_size, channels, height * width) attn_weights = attn_weights.permute(0, 2, 1) attn_output = torch.bmm(value_states, attn_weights).reshape(batch_size, channels, height, width) attn_output = self.proj_out(attn_output) return residual + attn_output # Copied from transformers.models.chameleon.modeling_chameleon.ChameleonVQVAEEncoder #noqa class ChameleonVQVAEEncoder(nn.Module): def __init__(self, config: ChameleonVQVAEConfig): super().__init__() self.num_resolutions = len(config.channel_multiplier) self.num_res_blocks = config.num_res_blocks base_channels = config.base_channels resolution = config.resolution in_channels = config.in_channels double_latent = config.double_latent latent_channels = config.latent_channels channel_multiplier = config.channel_multiplier self.conv_in = torch.nn.Conv2d(in_channels, base_channels, kernel_size=3, stride=1, padding=1) curr_res = resolution in_channel_multiplier = (1, ) + tuple(channel_multiplier) self.in_channel_multiplier = in_channel_multiplier self.down = nn.ModuleList() for i_level in range(self.num_resolutions): block = nn.ModuleList() attn = nn.ModuleList() block_in = base_channels * in_channel_multiplier[i_level] block_out = base_channels * channel_multiplier[i_level] for i_block in range(self.num_res_blocks): block.append( ChameleonVQVAEEncoderResnetBlock( config=config, in_channels=block_in, out_channels=block_out, )) block_in = block_out if (config.attn_resolutions is not None and curr_res in config.attn_resolutions and config.attn_type == "vanilla"): attn.append(ChameleonVQVAEEncoderAttnBlock(block_in)) down = nn.Module() down.block = block down.attn = attn if i_level != self.num_resolutions - 1: down.downsample = ChameleonVQVAEEncoderConvDownsample(block_in) curr_res = curr_res // 2 self.down.append(down) self.mid = nn.Module() self.mid.block_1 = ChameleonVQVAEEncoderResnetBlock( config=config, in_channels=block_in, out_channels=block_in, ) self.mid.attn_1 = ChameleonVQVAEEncoderAttnBlock( block_in) if config.attn_type == "vanilla" else nn.Identity() self.mid.block_2 = ChameleonVQVAEEncoderResnetBlock( config=config, in_channels=block_in, out_channels=block_in, ) self.norm_out = torch.nn.GroupNorm(num_groups=32, num_channels=block_in, eps=1e-6, affine=True) self.conv_out = torch.nn.Conv2d( block_in, 2 * latent_channels if double_latent else latent_channels, kernel_size=3, stride=1, padding=1, ) def forward(self, pixel_values: torch.Tensor): pixel_values = pixel_values.to(self.conv_in.weight.dtype) # downsampling hidden_states = [self.conv_in(pixel_values)] for i_level in range(self.num_resolutions): for i_block in range(self.num_res_blocks): hidden_state = self.down[i_level].block[i_block]( hidden_states[-1], ) if len(self.down[i_level].attn) > 0: hidden_state = self.down[i_level].attn[i_block]( hidden_state) hidden_states.append(hidden_state) if i_level != self.num_resolutions - 1: hidden_states.append(self.down[i_level].downsample( hidden_states[-1])) # middle last_hidden_state = hidden_states[-1] last_hidden_state = self.mid.block_1(last_hidden_state) last_hidden_state = self.mid.attn_1(last_hidden_state) last_hidden_state = self.mid.block_2(last_hidden_state) # end last_hidden_state = self.norm_out(last_hidden_state) last_hidden_state *= torch.sigmoid(last_hidden_state) last_hidden_state = self.conv_out(last_hidden_state) return last_hidden_state # Adapted from transformers.models.chameleon.modeling_chameleon.ChameleonVQVAE #noqa class ChameleonVQVAE(nn.Module): def __init__(self, config: ChameleonVQVAEConfig): super().__init__() self.encoder = ChameleonVQVAEEncoder(config) self.quantize = ChameleonVQVAEVectorQuantizer(config) self.quant_conv = torch.nn.Conv2d(config.latent_channels, config.embed_dim, 1) self.post_quant_conv = torch.nn.Conv2d(config.embed_dim, config.latent_channels, 1) self.eval() # Chameleon's VQ model is frozen def encode( self, pixel_values: torch.Tensor ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: hidden_states = self.encoder(pixel_values) hidden_states = self.quant_conv(hidden_states) quant, emb_loss, indices = self.quantize(hidden_states) return quant, emb_loss, indices # Copied from transformers.models.chameleon.modeling_chameleon.ChameleonImageVocabularyMapping #noqa class ChameleonImageVocabularyMapping: """ A class for mapping discrete image tokens from VQGAN to BPE tokens. """ def __init__(self, vocab_map: Dict[str, int]): self.vocab_map = vocab_map self.image_token_id = vocab_map.get("") @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) self.vqmodel = ChameleonVQVAE(config.vq_config) def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: return self.embed_tokens(input_ids) def get_image_tokens(self, pixel_values: torch.Tensor) -> torch.Tensor: """ Tokenizes images into discrete tokens with VQGAN module. Converts obtained image tokens into BPE tokens and wraps with "boi" and "eoi" special tokens. """ batch_size = pixel_values.shape[0] _, _, image_toks = self.vqmodel.encode(pixel_values) bpe_toks = self.vocabulary_mapping.convert_img2bpe(image_toks) bpe_toks = bpe_toks.view(batch_size, -1) return bpe_toks 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 @MULTIMODAL_REGISTRY.register_image_input_mapper() @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_chameleon_image_tokens) @INPUT_REGISTRY.register_dummy_data(dummy_data_for_chameleon) @INPUT_REGISTRY.register_input_processor(input_processor_for_chameleon) class ChameleonForConditionalGeneration(nn.Module, SupportsMultiModal): def __init__( self, config: ChameleonConfig, multimodal_config: MultiModalConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, ) -> None: super().__init__() self.config = config self.multimodal_config = multimodal_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 _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor: expected_dims = (3, CHAMELEON_CROP_SIZE_HEIGHT, CHAMELEON_CROP_SIZE_WIDTH) actual_dims = tuple(data.shape[1:]) if actual_dims != expected_dims: expected_expr = ("batch_size", *map(str, expected_dims)) raise ValueError( f"The expected shape of pixel values is {expected_expr}. " f"You supplied {tuple(data.shape)}.") return data def _parse_and_validate_image_input( self, **kwargs: object) -> Optional[ChameleonImagePixelInputs]: pixel_values = kwargs.pop("pixel_values", None) if pixel_values is None: return None if not isinstance(pixel_values, torch.Tensor): raise ValueError("Incorrect type of pixel values. " f"Got type: {type(pixel_values)}") # Remove the N dimension until multiple images are supported. pixel_values = pixel_values.squeeze(1) return ChameleonImagePixelInputs( type="pixel_values", data=self._validate_pixel_values(pixel_values), ) 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: image_input = self._parse_and_validate_image_input(**kwargs) if image_input is not None: assert self.model.vqmodel is not None image_tokens = self.model.get_image_tokens(image_input["data"].to( self.config.torch_dtype)) image_token_id = self.model.vocabulary_mapping.image_token_id special_image_mask = input_ids == image_token_id image_tokens = image_tokens.to(input_ids.device, input_ids.dtype) input_ids = input_ids.masked_scatter(special_image_mask, image_tokens) 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, ) -> Optional[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 if logits is not None: 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 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 # With tie_word_embeddings, we can skip lm_head.weight # The weight might appear unnecessarily in the files if the model is # processed with quantization, LoRA, fine-tuning, etc. if self.config.tie_word_embeddings and "lm_head.weight" in name: continue use_default_weight_loading = False if "vqmodel" in name: if self.model.vqmodel is not None: # We only do sharding for language model and # not vqvae for now. use_default_weight_loading = True else: 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( "Found kv scale in the checkpoint (e.g. " f"{name}), but not found the expected name in " f"the model (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) if use_default_weight_loading and name in params_dict: param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight)