# coding=utf-8 # Copyright 2024 Google Inc. HuggingFace Inc. team. All rights reserved. # # # 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. from typing import Optional, Tuple, Union import torch import torch.nn as nn import torch.utils.checkpoint from ...activations import ACT2FN from ...cache_utils import Cache, HybridCache from ...configuration_utils import PretrainedConfig from ...modeling_outputs import ( BaseModelOutputWithPast, CausalLMOutputWithPast, ) from ...utils import ( is_flash_attn_2_available, is_flash_attn_greater_or_equal, is_flash_attn_greater_or_equal_2_10, logging, ) from ..gemma.modeling_gemma import ( GemmaAttention, GemmaDecoderLayer, GemmaForCausalLM, GemmaForSequenceClassification, GemmaForTokenClassification, GemmaModel, GemmaPreTrainedModel, GemmaRMSNorm, apply_rotary_pos_emb, repeat_kv, ) if is_flash_attn_2_available(): from ...modeling_flash_attention_utils import _flash_attention_forward _CHECKPOINT_FOR_DOC = "google/gemma2-7b" logger = logging.get_logger(__name__) class Gemma2Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Gemma2Model`]. It is used to instantiate an Gemma2 model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Gemma2-7B. e.g. [google/gemma2-7b](https://huggingface.co/google/gemma2-7b) Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 256000): Vocabulary size of the Gemma2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Gemma2Model`] hidden_size (`int`, *optional*, defaults to 3072): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 24576): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 28): Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*, defaults to 16): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `num_attention_heads`. head_dim (`int`, *optional*, defaults to 256): The attention head dimension. hidden_activation (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`): The non-linear activation function (function or string) in the decoder. Will default to `"gelu_pytorch_tanh"` if not specified. `"gelu_pytorch_tanh"` uses an approximation of the `"gelu"` activation function. max_position_embeddings (`int`, *optional*, defaults to 8192): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the rms normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. pad_token_id (`int`, *optional*, defaults to 0): Padding token id. eos_token_id (`int`, *optional*, defaults to 1): End of stream token id. bos_token_id (`int`, *optional*, defaults to 2): Beginning of stream token id. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether to tie weight embeddings rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`): Whether to use a bias in the query, key, value and output projection layers during self-attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. query_pre_attn_scalar (`float`, *optional*, defaults to 224): scaling factor used on the attention scores sliding_window (`int`, *optional*, defaults to 4096): in Gemma2, every other layer uses sliding window attention. This is the size of the sliding window. final_logit_softcapping (`float`, *optional*, defaults to 30.0): scaling factor when applying tanh softcapping on the logits. attn_logit_softcapping (`float`, *optional*, defaults to 50.0): scaling factor when applying tanh softcapping on the attention scores. cache_implementation (`str`, *optional*, defaults to `"hybrid"`): the cache type to be used with `generate`. ```python >>> from transformers import Gemma2Model, Gemma2Config >>> # Initializing a Gemma2 gemma2-7b style configuration >>> configuration = Gemma2Config() >>> # Initializing a model from the gemma2-7b style configuration >>> model = Gemma2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "gemma2" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size=256000, hidden_size=3072, intermediate_size=24576, num_hidden_layers=28, num_attention_heads=16, num_key_value_heads=16, head_dim=256, hidden_activation="gelu_pytorch_tanh", max_position_embeddings=8192, initializer_range=0.02, rms_norm_eps=1e-6, use_cache=True, pad_token_id=0, eos_token_id=1, bos_token_id=2, tie_word_embeddings=True, rope_theta=10000.0, attention_bias=False, attention_dropout=0.0, query_pre_attn_scalar=224, sliding_window=4096, final_logit_softcapping=30.0, attn_logit_softcapping=50.0, cache_implementation="hybrid", **kwargs, ): super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs, ) self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.head_dim = head_dim self.num_key_value_heads = num_key_value_heads self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.rope_theta = rope_theta self.attention_bias = attention_bias self.attention_dropout = attention_dropout self.hidden_activation = hidden_activation self.query_pre_attn_scalar = query_pre_attn_scalar self.sliding_window = sliding_window self.final_logit_softcapping = final_logit_softcapping self.attn_logit_softcapping = attn_logit_softcapping self.cache_implementation = cache_implementation class Gemma2RMSNorm(GemmaRMSNorm): pass class Gemma2MLP(nn.Module): def __init__(self, config): super().__init__() self.config = config self.hidden_size = config.hidden_size self.intermediate_size = config.intermediate_size self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) self.act_fn = ACT2FN[config.hidden_activation] def forward(self, x): return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) class Gemma2Attention(GemmaAttention): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: Gemma2Config, layer_idx: Optional[int] = None): super().__init__(config, layer_idx) self.scaling = config.query_pre_attn_scalar**-0.5 self.sliding_window = config.sliding_window if not bool(layer_idx % 2) else None def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: bool = False, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) cos, sin = self.rotary_emb(value_states, position_ids) 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, "sliding_window": self.sliding_window, "cache_position": cache_position, } key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scaling if self.config.attn_logit_softcapping is not None: attn_weights = attn_weights / self.config.attn_logit_softcapping attn_weights = torch.tanh(attn_weights) attn_weights = attn_weights * self.config.attn_logit_softcapping if attention_mask is not None: # no matter the length, we just slice it causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] attn_weights = attn_weights + causal_mask # upcast attention to fp32 attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) attn_output = torch.matmul(attn_weights, value_states) if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.view(bsz, q_len, -1) attn_output = self.o_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value class Gemma2FlashAttention2(Gemma2Attention): """ Gemma2 flash attention module. This module inherits from `Gemma2Attention` as the weights of the module stays untouched. The only required change would be on the forward pass where it needs to correctly call the public API of flash attention and deal with padding tokens in case the input contains any of them. """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: bool = False, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: output_attentions = False bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) # Flash attention requires the input to have the shape # batch_size x seq_length x head_dim x hidden_dim # therefore we just need to keep the original shape query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) cos, sin = self.rotary_emb(value_states, position_ids) 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, "sliding_window": self.sliding_window, "cache_position": cache_position, } key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) if attention_mask is not None: seq_len = attention_mask.shape[1] key_states = key_states[:, :, :seq_len] value_states = value_states[:, :, :seq_len] # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache # to be able to avoid many of these transpose/reshape/view. query_states = query_states.transpose(1, 2) key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) dropout_rate = self.attention_dropout if self.training else 0.0 # In PEFT, usually we cast the layer norms in float32 for training stability reasons # therefore the input hidden states gets silently casted in float32. Hence, we need # cast them back in the correct dtype just to be sure everything works as expected. # This might slowdown training & inference so it is recommended to not cast the LayerNorms # in fp32. (Gemma2RMSNorm handles it correctly) input_dtype = query_states.dtype if input_dtype == torch.float32: if torch.is_autocast_enabled(): target_dtype = torch.get_autocast_gpu_dtype() # Handle the case where the model is quantized elif hasattr(self.config, "_pre_quantization_dtype"): target_dtype = self.config._pre_quantization_dtype else: target_dtype = self.q_proj.weight.dtype logger.warning_once( f"The input hidden states seems to be silently casted in float32, this might be related to" f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" f" {target_dtype}." ) query_states = query_states.to(target_dtype) key_states = key_states.to(target_dtype) value_states = value_states.to(target_dtype) attn_output = _flash_attention_forward( query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate, softmax_scale=self.scaling, is_causal=self.is_causal, sliding_window=self.sliding_window, use_top_left_mask=self._flash_attn_uses_top_left_mask, softcap=self.config.attn_logit_softcapping if is_flash_attn_greater_or_equal("2.6.0") else None, ) attn_output = attn_output.reshape(bsz, q_len, -1).contiguous() attn_output = self.o_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value class Gemma2SdpaAttention(Gemma2Attention): """ Gemma2 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from `Gemma2Attention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to SDPA API. """ # Adapted from Gemma2Attention.forward def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: bool = False, use_cache: bool = False, cache_position: Optional[torch.LongTensor] = None, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: if output_attentions: # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented. logger.warning_once( "Gemma2Model is using Gemma2SdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, " 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' ) return super().forward( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, ) bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) cos, sin = self.rotary_emb(value_states, position_ids) 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, "sliding_window": self.sliding_window, "cache_position": cache_position, } key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) causal_mask = attention_mask if attention_mask is not None: causal_mask = causal_mask[:, :, :, : key_states.shape[-2]] # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask, # Reference: https://github.com/pytorch/pytorch/issues/112577. if query_states.device.type == "cuda" and causal_mask is not None: query_states = query_states.contiguous() key_states = key_states.contiguous() value_states = value_states.contiguous() # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling. is_causal = True if causal_mask is None and q_len > 1 else False attn_output = torch.nn.functional.scaled_dot_product_attention( query_states, key_states, value_states, attn_mask=causal_mask, dropout_p=self.attention_dropout if self.training else 0.0, is_causal=is_causal, scale=self.scaling, ) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.view(bsz, q_len, -1) attn_output = self.o_proj(attn_output) return attn_output, None, past_key_value class Gemma2DecoderLayer(GemmaDecoderLayer): def __init__(self, config: Gemma2Config, layer_idx: int): super().__init__(config, layer_idx) self.config = config self.is_sliding = not bool(layer_idx % 2) self.mlp = Gemma2MLP(config) self.pre_feedforward_layernorm = Gemma2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_feedforward_layernorm = Gemma2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.sliding_window = config.sliding_window def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: if self.is_sliding and attention_mask is not None: # efficient SDPA and no padding # Flash-attn is a 2D tensor if self.config._attn_implementation == "flash_attention_2": if past_key_value is not None: # when decoding attention_mask = attention_mask[:, -self.sliding_window :] else: min_dtype = torch.finfo(hidden_states.dtype).min sliding_window_mask = torch.tril( torch.ones_like(attention_mask, dtype=torch.bool), diagonal=-self.sliding_window ) attention_mask = torch.where(sliding_window_mask, min_dtype, attention_mask) if attention_mask.shape[-1] <= 1: # when decoding attention_mask = attention_mask[:, :, :, -self.sliding_window :] residual = hidden_states hidden_states = self.input_layernorm(hidden_states) # Self Attention hidden_states, self_attn_weights, present_key_value = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, ) hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.pre_feedforward_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = self.post_feedforward_layernorm(hidden_states) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if use_cache: outputs += (present_key_value,) return outputs class Gemma2PreTrainedModel(GemmaPreTrainedModel): _supports_quantized_cache = False @classmethod def _check_and_enable_sdpa(cls, config, hard_check_only: bool = False): """ Overloads `PreTrainedModel._check_and_enable_sdpa` so as to DISABLE torch SDPA by default on Gemma2 models. SDPA reduces the model performance on Gemma2 because of the logits softcapping. """ config = super()._check_and_enable_sdpa(config, hard_check_only=hard_check_only) # if using the default path -> swap sdpa by eager if not hard_check_only and config._attn_implementation == "sdpa": config._attn_implementation = "eager" return config class Gemma2Model(GemmaModel, Gemma2PreTrainedModel): def __init__(self, config: Gemma2Config): super().__init__(config) self.layers = nn.ModuleList( [Gemma2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] ) self.post_init() def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[HybridCache] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple, 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 return_dict = return_dict if return_dict is not None else self.config.use_return_dict 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 and not self.training: batch_size, seq_len, _ = inputs_embeds.shape past_key_values = HybridCache( self.config, batch_size=batch_size, max_cache_len=seq_len, device=self.device, dtype=inputs_embeds.dtype, ) 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 ) # embed positions hidden_states = inputs_embeds # normalized # Gemma2 downcasts the below to float16, causing sqrt(3072)=55.4256 to become 55.5 # See https://github.com/huggingface/transformers/pull/29402 normalizer = torch.tensor(self.config.hidden_size**0.5, dtype=hidden_states.dtype) hidden_states = hidden_states * normalizer # 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: if output_hidden_states: all_hidden_states += (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( decoder_layer.__call__, hidden_states, causal_mask, position_ids, past_key_values, output_attentions, use_cache, cache_position, ) else: 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, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attns += (layer_outputs[1],) hidden_states = self.norm(hidden_states) if output_hidden_states: all_hidden_states += (hidden_states,) next_cache = past_key_values if use_cache else None if not return_dict: return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, ) @torch.no_grad() def _update_causal_mask( self, attention_mask: torch.Tensor, input_tensor: torch.Tensor, cache_position: torch.Tensor, past_key_values: HybridCache, output_attentions: bool, ): # Flash Attention currently doesn't support static cache but Gemma2 work only with static cache. # So we will pass in attention mask as is in any case, not only when ther's padding. Then we'll use its shape # to cut out keys/values trailing 0 used in static cache. This workaround should be compile compatible # as it doesn't cause dynamic control issues. if self.config._attn_implementation == "flash_attention_2": return attention_mask dtype, device = input_tensor.dtype, input_tensor.device sequence_length = input_tensor.shape[1] if isinstance(past_key_values, HybridCache): target_length = past_key_values.get_max_cache_shape() else: target_length = attention_mask.shape[-1] if attention_mask is not None else input_tensor.shape[1] # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position( attention_mask, sequence_length=sequence_length, target_length=target_length, dtype=dtype, device=device, cache_position=cache_position, batch_size=input_tensor.shape[0], ) return causal_mask class Gemma2ForCausalLM(GemmaForCausalLM): def __init__(self, config): super().__init__(config) self.model = Gemma2Model(config) self.post_init() def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[HybridCache] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, num_logits_to_keep: int = 0, **loss_kwargs, ) -> Union[Tuple, CausalLMOutputWithPast]: r""" ```python >>> from transformers import AutoTokenizer, GemmaForCausalLM >>> model = GemmaForCausalLM.from_pretrained("google/gemma-2-9b") >>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b") >>> prompt = "What is your favorite condiment?" >>> inputs = tokenizer(prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "What is your favorite condiment?" ```""" if self.training and self.config._attn_implementation != "eager": logger.warning_once( "It is strongly recommended to train Gemma2 models with the `eager` attention implementation " f"instead of `{self.config._attn_implementation}`. Use `eager` with `AutoModelForCausalLM.from_pretrained('', attn_implementation='eager')`." ) 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 ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, cache_position=cache_position, ) hidden_states = outputs[0] # Only compute necessary logits, and do not upcast them to float if we are not computing the loss logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]) if self.config.final_logit_softcapping is not None: logits = logits / self.config.final_logit_softcapping logits = torch.tanh(logits) logits = logits * self.config.final_logit_softcapping loss = None if labels is not None: loss = self.loss_function(logits, labels, self.vocab_size, **loss_kwargs) if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def prepare_inputs_for_generation( self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, cache_position=None, position_ids=None, use_cache=True, num_logits_to_keep=None, **kwargs, ): # Overwritten: has a special cache type, `HybridCache` # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens # Exception 1: when passing input_embeds, input_ids may be missing entries # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here if past_key_values is not None: if inputs_embeds is not None: # Exception 1 input_ids = input_ids[:, -cache_position.shape[0] :] elif input_ids.shape[1] != cache_position.shape[0]: # Default case (the "else", a no op, is Exception 2) input_ids = input_ids[:, cache_position] if attention_mask is not None and position_ids is None: # create position_ids on the fly for batch generation position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) if past_key_values: position_ids = position_ids[:, -input_ids.shape[1] :] # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s # `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride # during the decoding. Here, simply using `.contiguous()` is not sufficient as in the # batch size = 1 case, `position_ids` is already contiguous but with varying stride # which retriggers a capture. position_ids = position_ids.clone(memory_format=torch.contiguous_format) # if `inputs_embeds` are passed, we only want to use them in the 1st generation step if inputs_embeds is not None and cache_position[0] == 0: model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None} else: # The clone here is for the same reason as for `position_ids`. model_inputs = {"input_ids": input_ids.clone(memory_format=torch.contiguous_format), "inputs_embeds": None} if ( isinstance(past_key_values, HybridCache) and attention_mask.ndim == 2 and not self.config._attn_implementation == "flash_attention_2" ): if model_inputs["inputs_embeds"] is not None: batch_size, sequence_length, _ = model_inputs["inputs_embeds"].shape device = model_inputs["inputs_embeds"].device else: batch_size, sequence_length = model_inputs["input_ids"].shape device = model_inputs["input_ids"].device attention_mask = self.model._prepare_4d_causal_attention_mask_with_cache_position( attention_mask, sequence_length=sequence_length, target_length=past_key_values.get_max_cache_shape(), dtype=self.lm_head.weight.dtype, device=device, cache_position=cache_position, batch_size=batch_size, ) if num_logits_to_keep is not None: model_inputs["num_logits_to_keep"] = num_logits_to_keep model_inputs.update( { "position_ids": position_ids, "cache_position": cache_position, "past_key_values": past_key_values, "use_cache": use_cache, "attention_mask": attention_mask, } ) return model_inputs class Gemma2ForSequenceClassification(GemmaForSequenceClassification): def __init__(self, config): super().__init__(config) self.model = Gemma2Model(config) self.post_init() class Gemma2ForTokenClassification(GemmaForTokenClassification): def __init__(self, config): super().__init__(config) self.model = Gemma2Model(config) self.post_init()