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Signed-off-by: Max de Bayser <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,166 @@ | ||
| # -*- coding: utf-8 -*- | ||
| from typing import List, Optional, Tuple, Union | ||
|
|
||
| import torch | ||
| from torch import nn | ||
| from transformers import PretrainedConfig | ||
|
|
||
| from vllm.attention import AttentionMetadata | ||
| from vllm.config import CacheConfig | ||
| from vllm.distributed import get_pp_group | ||
| from vllm.model_executor.layers.layernorm import RMSNorm | ||
| from vllm.model_executor.layers.quantization import QuantizationConfig | ||
| from vllm.model_executor.models.internlm2 import (InternLM2Attention, | ||
| InternLM2ForCausalLM, | ||
| InternLM2MLP, InternLM2Model) | ||
| from vllm.sequence import IntermediateTensors | ||
|
|
||
| from .utils import make_layers | ||
|
|
||
|
|
||
| class InternLM2VEDecoderLayer(nn.Module): | ||
|
|
||
| def __init__( | ||
| self, | ||
| config: PretrainedConfig, | ||
| 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) | ||
| max_position_embeddings = getattr(config, "max_position_embeddings", | ||
| 8192) | ||
| self.attention = InternLM2Attention( | ||
| hidden_size=self.hidden_size, | ||
| num_heads=config.num_attention_heads, | ||
| num_kv_heads=config.num_key_value_heads, | ||
| rope_theta=rope_theta, | ||
| rope_scaling=rope_scaling, | ||
| max_position_embeddings=max_position_embeddings, | ||
| cache_config=cache_config, | ||
| quant_config=quant_config, | ||
| ) | ||
| self.feed_forward = InternLM2MLP( | ||
| hidden_size=self.hidden_size, | ||
| intermediate_size=config.intermediate_size, | ||
| hidden_act=config.hidden_act, | ||
| quant_config=quant_config, | ||
| ) | ||
| self.feed_forward_ve = InternLM2MLP( | ||
| hidden_size=self.hidden_size, | ||
| intermediate_size=config.intermediate_size, | ||
| hidden_act=config.hidden_act, | ||
| quant_config=quant_config, | ||
| ) | ||
| self.attention_norm = RMSNorm(config.hidden_size, | ||
| eps=config.rms_norm_eps) | ||
| self.ffn_norm = 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], | ||
| visual_token_mask: Optional[torch.Tensor] = None, | ||
| ) -> Tuple[torch.Tensor, torch.Tensor]: | ||
| # Self Attention | ||
| if residual is None: | ||
| residual = hidden_states | ||
| hidden_states = self.attention_norm(hidden_states) | ||
| else: | ||
| hidden_states, residual = self.attention_norm( | ||
| hidden_states, residual) | ||
| hidden_states = self.attention( | ||
| positions=positions, | ||
| hidden_states=hidden_states, | ||
| kv_cache=kv_cache, | ||
| attn_metadata=attn_metadata, | ||
| ) | ||
|
|
||
| # Fully Connected | ||
| hidden_states, residual = self.ffn_norm(hidden_states, residual) | ||
| if visual_token_mask is not None and visual_token_mask.any(): | ||
| visual_token_mask = visual_token_mask.repeat( | ||
| 1, self.hidden_size).bool() | ||
| text_token_mask = ~visual_token_mask | ||
| hidden_states[visual_token_mask] = self.feed_forward_ve( | ||
| hidden_states[visual_token_mask].reshape( | ||
| -1, self.hidden_size)).flatten() | ||
| if text_token_mask.any(): | ||
| hidden_states[text_token_mask] = self.feed_forward( | ||
| hidden_states[text_token_mask].reshape( | ||
| -1, self.hidden_size)).flatten() | ||
| else: | ||
| hidden_states = self.feed_forward(hidden_states) | ||
| return hidden_states, residual | ||
|
|
||
|
|
||
| class InternLM2VEModel(InternLM2Model): | ||
|
|
||
| def __init__( | ||
| self, | ||
| config: PretrainedConfig, | ||
| cache_config: Optional[CacheConfig] = None, | ||
| quant_config: Optional[QuantizationConfig] = None, | ||
| prefix: str = "", | ||
| ) -> None: | ||
| super().__init__(config, cache_config, quant_config) | ||
| self.start_layer, self.end_layer, self.layers = make_layers( | ||
| config.num_hidden_layers, | ||
| lambda prefix: InternLM2VEDecoderLayer(config, cache_config, | ||
| quant_config), | ||
| prefix=f"{prefix}.layers") | ||
|
|
||
| def forward( | ||
| self, | ||
| input_ids: torch.Tensor, | ||
| positions: torch.Tensor, | ||
| kv_caches: List[torch.Tensor], | ||
| attn_metadata: AttentionMetadata, | ||
| intermediate_tensors: Optional[IntermediateTensors] = None, | ||
| inputs_embeds: Optional[torch.Tensor] = None, | ||
| visual_token_mask: 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.tok_embeddings(input_ids) | ||
| residual = None | ||
| else: | ||
| assert intermediate_tensors is not None | ||
| hidden_states = intermediate_tensors["hidden_states"] | ||
| residual = intermediate_tensors["residual"] | ||
| for i in range(self.start_layer, self.end_layer): | ||
| layer = self.layers[i] | ||
| hidden_states, residual = layer( | ||
| positions, | ||
| hidden_states, | ||
| kv_caches[i - self.start_layer], | ||
| attn_metadata, | ||
| residual, | ||
| visual_token_mask=visual_token_mask, | ||
| ) | ||
| if not get_pp_group().is_last_rank: | ||
| return IntermediateTensors({ | ||
| "hidden_states": hidden_states, | ||
| "residual": residual | ||
| }) | ||
| hidden_states, _ = self.norm(hidden_states, residual) | ||
| return hidden_states | ||
|
|
||
|
|
||
| class InternLM2VEForCausalLM(InternLM2ForCausalLM): | ||
|
|
||
| def __init__( | ||
| self, | ||
| config: PretrainedConfig, | ||
| cache_config: Optional[CacheConfig] = None, | ||
| quant_config: Optional[QuantizationConfig] = None, | ||
| ) -> None: | ||
| super().__init__(config, cache_config, quant_config) | ||
| self.model = InternLM2VEModel(config, cache_config, quant_config) |
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