mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-09-11 20:34:54 +00:00
Removing some unused code.
This commit is contained in:
Nicolas Patry
parent
3b5d93e68d
commit
a013c3ae68
@ -589,7 +589,7 @@ pub(crate) struct CompletionCompleteChunk {
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pub system_fingerprint: String,
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}
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#[derive(Clone, Deserialize, Serialize, ToSchema)]
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#[derive(Clone, Serialize, ToSchema)]
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pub(crate) struct ChatCompletionChunk {
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pub id: String,
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pub object: String,
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@ -601,7 +601,7 @@ pub(crate) struct ChatCompletionChunk {
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pub choices: Vec<ChatCompletionChoice>,
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}
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#[derive(Clone, Deserialize, Serialize, ToSchema)]
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#[derive(Clone, Serialize, ToSchema)]
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pub(crate) struct ChatCompletionChoice {
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pub index: u32,
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pub delta: ChatCompletionDelta,
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@ -616,7 +616,8 @@ pub struct ToolCallDelta {
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tool_calls: DeltaToolCall,
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}
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#[derive(Clone, Debug, Deserialize, Serialize, ToSchema)]
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#[derive(Clone, Debug, Serialize, ToSchema)]
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#[serde(untagged)]
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enum ChatCompletionDelta {
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Chat(TextMessage),
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Tool(ToolCallDelta),
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@ -64,45 +64,6 @@ class SiglipVisionEmbeddings(nn.Module):
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return embeddings
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class SiglipTextEmbeddings(nn.Module):
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def __init__(self, config: SiglipTextConfig):
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super().__init__()
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embed_dim = config.hidden_size
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self.token_embedding = nn.Embedding(config.vocab_size, embed_dim)
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self.position_embedding = nn.Embedding(
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config.max_position_embeddings, embed_dim
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)
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# position_ids (1, len position emb) is contiguous in memory and exported when serialized
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self.register_buffer(
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"position_ids",
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torch.arange(config.max_position_embeddings).expand((1, -1)),
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persistent=False,
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)
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def forward(
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self,
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input_ids: Optional[torch.LongTensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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) -> torch.Tensor:
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seq_length = (
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input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
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)
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if position_ids is None:
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position_ids = self.position_ids[:, :seq_length]
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if inputs_embeds is None:
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inputs_embeds = self.token_embedding(input_ids)
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position_embeddings = self.position_embedding(position_ids)
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embeddings = inputs_embeds + position_embeddings
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return embeddings
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class SiglipAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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@ -147,7 +108,6 @@ class SiglipAttention(nn.Module):
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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output_attentions: Optional[bool] = False,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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"""Input shape: Batch x Time x Channel"""
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@ -243,32 +203,18 @@ class SiglipEncoderLayer(nn.Module):
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self,
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hidden_states: torch.Tensor,
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attention_mask: torch.Tensor,
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output_attentions: Optional[bool] = False,
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) -> Tuple[torch.FloatTensor]:
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"""
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Args:
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hidden_states (`torch.FloatTensor`):
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Input to the layer of shape `(batch, seq_len, embed_dim)`.
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attention_mask (`torch.FloatTensor`):
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Attention mask of shape `(batch, 1, q_len, k_v_seq_len)` where padding elements are indicated by very large negative values.
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output_attentions (`bool`, *optional*, defaults to `False`):
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Whether or not to return the attentions tensors of all attention layers. See `attentions` under
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returned tensors for more detail.
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"""
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residual = hidden_states
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hidden_states = self.layer_norm1(hidden_states)
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hidden_states, attn_weights = self.self_attn(
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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output_attentions=output_attentions,
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)
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hidden_states = residual + hidden_states
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residual = hidden_states
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hidden_states = self.layer_norm2(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = residual + hidden_states
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if output_attentions:
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return hidden_states, attn_weights
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return hidden_states, None
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@ -406,58 +352,6 @@ def default_flax_embed_init(tensor):
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from transformers import PreTrainedModel
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class SiglipPreTrainedModel(PreTrainedModel):
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"""
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An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
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models.
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"""
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config_class = SiglipConfig
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base_model_prefix = "siglip"
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supports_gradient_checkpointing = True
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def _init_weights(self, module):
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"""Initialize the weights"""
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if isinstance(module, SiglipVisionEmbeddings):
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width = (
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self.config.vision_config.hidden_size
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if isinstance(self.config, SiglipConfig)
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else self.config.hidden_size
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)
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nn.init.normal_(module.position_embedding.weight, std=1 / np.sqrt(width))
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elif isinstance(module, nn.Embedding):
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default_flax_embed_init(module.weight)
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elif isinstance(module, SiglipAttention):
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nn.init.xavier_uniform_(module.q_proj.weight)
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nn.init.xavier_uniform_(module.k_proj.weight)
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nn.init.xavier_uniform_(module.v_proj.weight)
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nn.init.xavier_uniform_(module.out_proj.weight)
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nn.init.zeros_(module.q_proj.bias)
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nn.init.zeros_(module.k_proj.bias)
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nn.init.zeros_(module.v_proj.bias)
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nn.init.zeros_(module.out_proj.bias)
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elif isinstance(module, SiglipMLP):
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nn.init.xavier_uniform_(module.fc1.weight)
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nn.init.xavier_uniform_(module.fc2.weight)
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nn.init.normal_(module.fc1.bias, std=1e-6)
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nn.init.normal_(module.fc2.bias, std=1e-6)
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elif isinstance(module, SiglipMultiheadAttentionPoolingHead):
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nn.init.xavier_uniform_(module.probe.data)
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nn.init.xavier_uniform_(module.attention.in_proj_weight.data)
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nn.init.zeros_(module.attention.in_proj_bias.data)
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elif isinstance(module, SiglipModel):
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logit_scale_init = torch.log(torch.tensor(1.0))
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module.logit_scale.data.fill_(logit_scale_init)
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module.logit_bias.data.zero_()
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elif isinstance(module, (nn.Linear, nn.Conv2d)):
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lecun_normal_(module.weight)
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if module.bias is not None:
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nn.init.zeros_(module.bias)
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elif isinstance(module, nn.LayerNorm):
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module.bias.data.zero_()
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module.weight.data.fill_(1.0)
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class SiglipEncoder(nn.Module):
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"""
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Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
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@ -483,36 +377,13 @@ class SiglipEncoder(nn.Module):
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self,
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inputs_embeds,
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attention_mask: Optional[torch.Tensor] = None,
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output_attentions: Optional[torch.Tensor] = None,
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):
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r"""
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Args:
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inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
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Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
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This is useful if you want more control over how to convert `input_ids` indices into associated vectors
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than the model's internal embedding lookup matrix.
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attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
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Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
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- 1 for tokens that are **not masked**,
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- 0 for tokens that are **masked**.
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[What are attention masks?](../glossary#attention-mask)
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causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
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Causal mask for the text model. Mask values selected in `[0, 1]`:
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- 1 for tokens that are **not masked**,
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- 0 for tokens that are **masked**.
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[What are attention masks?](../glossary#attention-mask)
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"""
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hidden_states = inputs_embeds
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for idx, encoder_layer in enumerate(self.layers):
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hidden_states, _ = encoder_layer(
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hidden_states,
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attention_mask,
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output_attentions=output_attentions,
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)
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return hidden_states
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