555 lines
21 KiB
Python
555 lines
21 KiB
Python
from functools import cached_property
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from typing import Any, Dict, Iterable, List, Optional, Tuple
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import torch
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import torch.nn.functional as F
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from torch import nn
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from vllm.attention import Attention, AttentionMetadata
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from vllm.config import CacheConfig
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from vllm.distributed import get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig)
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.sampler import Sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors, SamplerOutput
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from vllm.transformers_utils.configs import ChameleonConfig
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from vllm.utils import print_warning_once
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class ChameleonLayerNorm(nn.LayerNorm):
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def __init__(self, hidden_size, *args, **kwargs):
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super().__init__(hidden_size, *args, **kwargs)
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self.normalized_shape = (hidden_size[-1], )
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def forward(self, hidden_states):
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hidden_states = F.layer_norm(hidden_states,
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self.normalized_shape,
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None,
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None,
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eps=1e-5)
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hidden_states = hidden_states * self.weight + self.bias
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return hidden_states
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# Copied from vllm.model_executor.models.llama.LlamaMLP -> ChameleonMLP
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class ChameleonMLP(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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hidden_act: str,
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quant_config: Optional[QuantizationConfig] = None,
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bias: bool = False,
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) -> None:
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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input_size=hidden_size,
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output_sizes=[intermediate_size] * 2,
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bias=bias,
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quant_config=quant_config)
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self.down_proj = RowParallelLinear(input_size=intermediate_size,
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output_size=hidden_size,
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bias=bias,
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quant_config=quant_config)
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if hidden_act != "silu":
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raise ValueError(f"Unsupported activation: {hidden_act}. "
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"Only silu is supported for now.")
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self.act_fn = SiluAndMul()
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def forward(self, x):
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gate_up, _ = self.gate_up_proj(x)
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x = self.act_fn(gate_up)
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x, _ = self.down_proj(x)
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return x
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# Modified from vllm.model_executor.models.llama.LlamaAttention -> ChameleonAttention #noqa
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class ChameleonAttention(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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rope_theta: float = 10000,
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rope_scaling: Optional[Dict[str, Any]] = None,
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max_position_embeddings: int = 4096,
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quant_config: Optional[QuantizationConfig] = None,
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bias: bool = False,
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cache_config: Optional[CacheConfig] = None,
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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self.head_dim = hidden_size // self.total_num_heads
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.head_dim**-0.5
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self.rope_theta = rope_theta
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self.max_position_embeddings = max_position_embeddings
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self.qkv_proj = QKVParallelLinear(
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hidden_size=hidden_size,
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head_size=self.head_dim,
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total_num_heads=self.total_num_heads,
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total_num_kv_heads=self.total_num_kv_heads,
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bias=bias,
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quant_config=quant_config,
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)
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self.o_proj = RowParallelLinear(
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input_size=self.total_num_heads * self.head_dim,
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output_size=hidden_size,
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bias=bias,
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quant_config=quant_config,
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)
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self.q_norm = ChameleonLayerNorm((self.num_heads, self.head_dim))
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self.k_norm = ChameleonLayerNorm((self.num_kv_heads, self.head_dim))
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=max_position_embeddings,
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base=rope_theta,
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rope_scaling=rope_scaling,
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)
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self.attn = Attention(self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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cache_config=cache_config,
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quant_config=quant_config)
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def _apply_qk_norm(self, q: torch.Tensor,
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k: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
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# reshape for layernorm
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q = q.reshape(-1, self.num_heads, self.head_dim)
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k = k.reshape(-1, self.num_kv_heads, self.head_dim)
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q = self.q_norm(q)
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k = self.k_norm(k)
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q = q.view(*q.shape[:-2], -1)
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k = k.view(*k.shape[:-2], -1)
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return q, k
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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q, k = self._apply_qk_norm(q, k)
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q, k = self.rotary_emb(positions, q, k)
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attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
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output, _ = self.o_proj(attn_output)
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return output
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class ChameleonDecoderLayer(nn.Module):
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def __init__(
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self,
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config: ChameleonConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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rope_theta = getattr(config, "rope_theta", 10000)
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rope_scaling = getattr(config, "rope_scaling", None)
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if rope_scaling is not None and getattr(
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config, "original_max_position_embeddings", None):
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rope_scaling["original_max_position_embeddings"] = (
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config.original_max_position_embeddings)
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max_position_embeddings = getattr(config, "max_position_embeddings",
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4096)
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self.self_attn = ChameleonAttention(
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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num_kv_heads=getattr(config, "num_key_value_heads",
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config.num_attention_heads),
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rope_theta=rope_theta,
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rope_scaling=rope_scaling,
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max_position_embeddings=max_position_embeddings,
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quant_config=quant_config,
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bias=False,
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cache_config=cache_config,
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)
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self.mlp = ChameleonMLP(
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hidden_size=self.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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bias=getattr(config, "mlp_bias", False),
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)
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self.input_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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residual: Optional[torch.Tensor],
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) -> Tuple[torch.Tensor, torch.Tensor]:
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if residual is None:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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else:
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hidden_states, residual = self.input_layernorm(
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hidden_states, residual)
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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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kv_cache=kv_cache,
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attn_metadata=attn_metadata,
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)
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# Fully Connected
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hidden_states, residual = self.post_attention_layernorm(
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hidden_states, residual)
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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual
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class ChameleonSwinDecoderLayer(nn.Module):
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def __init__(
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self,
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config: ChameleonConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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rope_theta = getattr(config, "rope_theta", 10000)
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rope_scaling = getattr(config, "rope_scaling", None)
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if rope_scaling is not None and getattr(
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config, "original_max_position_embeddings", None):
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rope_scaling["original_max_position_embeddings"] = (
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config.original_max_position_embeddings)
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max_position_embeddings = getattr(config, "max_position_embeddings",
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4096)
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self.self_attn = ChameleonAttention(
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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num_kv_heads=getattr(config, "num_key_value_heads",
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config.num_attention_heads),
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rope_theta=rope_theta,
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rope_scaling=rope_scaling,
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max_position_embeddings=max_position_embeddings,
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quant_config=quant_config,
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bias=False,
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cache_config=cache_config,
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)
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self.mlp = ChameleonMLP(
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hidden_size=self.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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bias=getattr(config, "mlp_bias", False),
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)
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self.input_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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residual: Optional[torch.Tensor],
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) -> Tuple[torch.Tensor, torch.Tensor]:
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residual = hidden_states
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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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kv_cache=kv_cache,
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attn_metadata=attn_metadata,
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)
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hidden_states = self.input_layernorm(hidden_states)
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hidden_states = hidden_states + residual
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# Fully Connected
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residual = hidden_states
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hidden_states = self.mlp(hidden_states)
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hidden_states = self.post_attention_layernorm(hidden_states)
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hidden_states = residual + hidden_states
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return hidden_states, residual
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class ChameleonImageVocabularyMapping:
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"""
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A class for mapping discrete image tokens from VQGAN to BPE tokens.
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"""
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def __init__(self, vocab_map):
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self.vocab_map = vocab_map
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self.image_token_id = vocab_map.get("<image>")
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@cached_property
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def val2name(self):
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return {v: k for k, v in self.vocab_map.items()}
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@cached_property
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def image_tokens(self):
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return sorted([
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val for name, val in self.vocab_map.items()
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if name.startswith("IMGIMG")
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])
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@cached_property
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def bpe2img(self):
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img_tkn_chr_mapping = {chr(ord("A") + i): str(i) for i in range(10)}
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def remap(old_name: str) -> str:
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return "".join(
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img_tkn_chr_mapping.get(c, c)
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for c in old_name[len("IMGIMG"):-1])
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return {
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tok: int(remap(self.val2name[tok]))
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for tok in self.image_tokens
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}
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@cached_property
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def img2bpe(self):
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return {v: k for k, v in self.bpe2img.items()}
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@cached_property
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def bpe2img_search_tensors(self):
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return torch.tensor(sorted(self.bpe2img.keys())), torch.tensor(
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sorted(self.bpe2img.values()))
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@cached_property
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def img2bpe_mapping_tensor(self):
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mapping = torch.zeros(max(self.img2bpe.keys()) + 1, dtype=torch.int)
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for k, v in self.img2bpe.items():
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mapping[k] = v
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return mapping
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def convert_img2bpe(self, img_batch: torch.Tensor) -> torch.Tensor:
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device = img_batch.device
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img_tokens = self.img2bpe_mapping_tensor[img_batch.to("cpu")]
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return img_tokens.to(device)
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class ChameleonModel(nn.Module):
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def __init__(
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self,
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config: ChameleonConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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) -> None:
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super().__init__()
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self.config = config
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self.padding_idx = config.pad_token_id
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self.vocab_size = config.vocab_size
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self.embed_tokens = VocabParallelEmbedding(
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self.vocab_size,
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config.hidden_size,
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)
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self.vocabulary_mapping = ChameleonImageVocabularyMapping(
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config.vocabulary_map)
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decoder_layer = ChameleonDecoderLayer if not self.config.swin_norm \
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else ChameleonSwinDecoderLayer
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self.layers = nn.ModuleList([
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decoder_layer(config=config,
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cache_config=cache_config,
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quant_config=quant_config)
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for _ in range(config.num_hidden_layers)
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])
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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# TODO: Support image input
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# self.vqmodel = ChameleonVQModel(config.vq_config)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(input_ids)
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def forward(
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self,
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input_ids: Optional[torch.Tensor],
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positions: torch.Tensor,
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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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inputs_embeds: Optional[torch.Tensor] = None,
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) -> torch.Tensor:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.get_input_embeddings(input_ids)
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residual = None
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for i in range(len(self.layers)):
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layer = self.layers[i]
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hidden_states, residual = layer(
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positions,
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hidden_states,
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kv_caches[i],
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attn_metadata,
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residual,
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)
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hidden_states, _ = self.norm(hidden_states, residual)
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return hidden_states
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class ChameleonForConditionalGeneration(nn.Module):
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def __init__(
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self,
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config: ChameleonConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None,
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) -> None:
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super().__init__()
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self.config = config
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self.model = ChameleonModel(config, cache_config, quant_config)
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self.unpadded_vocab_size = config.vocab_size
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self.lm_head = ParallelLMHead(
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self.unpadded_vocab_size,
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config.hidden_size,
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)
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if config.tie_word_embeddings:
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self.lm_head.weight = self.model.embed_tokens.weight
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logit_scale = getattr(config, "logit_scale", 1.0)
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self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
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config.vocab_size, logit_scale)
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self.sampler = Sampler()
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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**kwargs,
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) -> torch.Tensor:
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# TODO (ywang96): Support image input
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# image_tokens = self.process_image_input(**kwargs)
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# image_mask = input_ids == self.vocabulary_mapping.image_token_id
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# input_ids[special_image_mask] = image_tokens.flatten().to(input_ids.dtype) #noqa
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hidden_states = self.model(input_ids, positions, kv_caches,
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attn_metadata)
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return hidden_states
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def compute_logits(self, hidden_states: torch.Tensor,
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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)
|