# coding=utf-8 # Copyright 2024 BigCode and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch Starcoder2 model.""" from typing import Callable, List, Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from ...activations import ACT2FN from ...cache_utils import Cache, DynamicCache from ...modeling_flash_attention_utils import FlashAttentionKwargs from ...modeling_outputs import ( BaseModelOutputWithPast, ) from ...modeling_utils import ALL_ATTENTION_FUNCTIONS from ...processing_utils import Unpack from ...utils import add_start_docstrings_to_model_forward, can_return_tuple, logging from ..mistral.modeling_mistral import ( MistralAttention, MistralDecoderLayer, MistralForCausalLM, MistralForSequenceClassification, MistralForTokenClassification, MistralModel, apply_rotary_pos_emb, eager_attention_forward, ) from .configuration_starcoder2 import Starcoder2Config logger = logging.get_logger(__name__) _CONFIG_FOR_DOC = "Starcoder2Config" _CHECKPOINT_FOR_DOC = "bigcode/starcoder2-7b" class Starcoder2MLP(nn.Module): def __init__(self, config: Starcoder2Config): super().__init__() embed_dim = config.hidden_size self.c_fc = nn.Linear(embed_dim, config.intermediate_size, bias=config.use_bias) self.c_proj = nn.Linear(config.intermediate_size, embed_dim, bias=config.use_bias) self.act = ACT2FN[config.hidden_act] self.residual_dropout = config.residual_dropout def forward(self, hidden_states: Optional[Tuple[torch.FloatTensor]]) -> torch.FloatTensor: hidden_states = self.c_fc(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.c_proj(hidden_states) hidden_states = nn.functional.dropout(hidden_states, p=self.residual_dropout, training=self.training) return hidden_states class Starcoder2Attention(MistralAttention): def __init__(self, config: Starcoder2Config, layer_idx: Optional[int] = None): super().__init__() self.residual_dropout = config.residual_dropout self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.use_bias) self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.use_bias) self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.use_bias) self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.use_bias) def forward( self, hidden_states: torch.Tensor, position_embeddings: Tuple[torch.Tensor, torch.Tensor], attention_mask: Optional[torch.Tensor], past_key_value: Optional[Cache] = None, cache_position: Optional[torch.LongTensor] = None, **kwargs: Unpack[FlashAttentionKwargs], ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: input_shape = hidden_states.shape[:-1] hidden_shape = (*input_shape, -1, self.head_dim) query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) cos, sin = position_embeddings query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_value is not None: # sin and cos are specific to RoPE models; cache_position needed for the static cache cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) attention_interface: Callable = eager_attention_forward if self.config._attn_implementation != "eager": if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False): logger.warning_once( "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to " 'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' ) else: attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] attn_output, attn_weights = attention_interface( self, query_states, key_states, value_states, attention_mask, dropout=0.0 if not self.training else self.attention_dropout, scaling=self.scaling, sliding_window=getattr(self.config, "sliding_window", None), # diff with Llama **kwargs, ) attn_output = attn_output.reshape(*input_shape, -1).contiguous() attn_output = self.o_proj(attn_output) attn_output = nn.functional.dropout( attn_output, p=self.residual_dropout, training=self.training ) # diff with Llama return attn_output, attn_weights class Starcoder2DecoderLayer(MistralDecoderLayer): def __init__(self, config: Starcoder2Config, layer_idx: int): super().__init__(self) self.self_attn = Starcoder2Attention(config=config, layer_idx=layer_idx) self.mlp = Starcoder2MLP(config) self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_epsilon) self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.norm_epsilon) STARCODER2_INPUTS_DOCSTRING = None # will be automatically redefined class Starcoder2Model(MistralModel): def __init__(self, config: Starcoder2Config): super().__init__(config) self.layers = nn.ModuleList( [Starcoder2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] ) self.norm = nn.LayerNorm(config.hidden_size, eps=config.norm_epsilon) self.embedding_dropout = config.embedding_dropout @can_return_tuple @add_start_docstrings_to_model_forward(STARCODER2_INPUTS_DOCSTRING) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, **flash_attn_kwargs: Unpack[FlashAttentionKwargs], ) -> BaseModelOutputWithPast: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) use_cache = use_cache if use_cache is not None else self.config.use_cache if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if self.gradient_checkpointing and self.training and use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`." ) use_cache = False if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) if use_cache and past_key_values is None: past_key_values = DynamicCache() if cache_position is None: past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 cache_position = torch.arange( past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device ) if position_ids is None: position_ids = cache_position.unsqueeze(0) causal_mask = self._update_causal_mask( attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions ) hidden_states = inputs_embeds hidden_states = nn.functional.dropout( hidden_states, p=self.embedding_dropout, training=self.training ) # main diff with Llama # create position embeddings to be shared across the decoder layers position_embeddings = self.rotary_emb(hidden_states, position_ids) # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None for decoder_layer in self.layers[: self.config.num_hidden_layers]: if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = decoder_layer( hidden_states, attention_mask=causal_mask, position_ids=position_ids, past_key_value=past_key_values, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, **flash_attn_kwargs, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attns += (layer_outputs[1],) hidden_states = self.norm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=past_key_values if use_cache else None, hidden_states=all_hidden_states, attentions=all_self_attns, ) class Starcoder2ForCausalLM(MistralForCausalLM): pass class Starcoder2ForSequenceClassification(MistralForSequenceClassification): pass class Starcoder2ForTokenClassification(MistralForTokenClassification): pass __all__ = [ "Starcoder2ForCausalLM", "Starcoder2Model", "Starcoder2PreTrainedModel", # noqa: F822 "Starcoder2ForSequenceClassification", "Starcoder2ForTokenClassification", ]