582 lines
24 KiB
Python
582 lines
24 KiB
Python
# coding=utf-8
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# Copyright 2024 Google Inc. HuggingFace Inc. team. All rights reserved.
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#
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Callable, Optional, Union
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import torch
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import torch.nn as nn
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import torch.utils.checkpoint
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from ...activations import ACT2FN
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from ...cache_utils import Cache, DynamicCache
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from ...configuration_utils import PretrainedConfig, layer_type_validation
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from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
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from ...modeling_flash_attention_utils import FlashAttentionKwargs
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
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from ...processing_utils import Unpack
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from ...utils import TransformersKwargs, logging
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from ...utils.deprecation import deprecate_kwarg
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from ..gemma.modeling_gemma import (
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GemmaAttention,
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GemmaForCausalLM,
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GemmaForSequenceClassification,
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GemmaForTokenClassification,
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GemmaMLP,
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GemmaModel,
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GemmaRMSNorm,
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apply_rotary_pos_emb,
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repeat_kv,
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)
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logger = logging.get_logger(__name__)
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class Gemma2Config(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Gemma2Model`]. It is used to instantiate an Gemma2
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the Gemma2-7B.
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e.g. [google/gemma2-7b](https://huggingface.co/google/gemma2-7b)
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 256000):
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Vocabulary size of the Gemma2 model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`Gemma2Model`]
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hidden_size (`int`, *optional*, defaults to 2304):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 9216):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 26):
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Number of hidden layers in the Transformer decoder.
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num_attention_heads (`int`, *optional*, defaults to 8):
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Number of attention heads for each attention layer in the Transformer decoder.
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num_key_value_heads (`int`, *optional*, defaults to 4):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details, check out [this
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paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
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`num_attention_heads`.
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head_dim (`int`, *optional*, defaults to 256):
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The attention head dimension.
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hidden_activation (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
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The non-linear activation function (function or string) in the decoder. Will default to `"gelu_pytorch_tanh"`
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if not specified. `"gelu_pytorch_tanh"` uses an approximation of the `"gelu"` activation function.
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max_position_embeddings (`int`, *optional*, defaults to 8192):
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The maximum sequence length that this model might ever be used with.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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rms_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the rms normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`.
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pad_token_id (`int`, *optional*, defaults to 0):
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Padding token id.
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eos_token_id (`int`, *optional*, defaults to 1):
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End of stream token id.
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bos_token_id (`int`, *optional*, defaults to 2):
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Beginning of stream token id.
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tie_word_embeddings (`bool`, *optional*, defaults to `True`):
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Whether to tie weight embeddings
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
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Whether to use a bias in the query, key, value and output projection layers during self-attention.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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query_pre_attn_scalar (`float`, *optional*, defaults to 256):
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scaling factor used on the attention scores
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sliding_window (`int`, *optional*, defaults to 4096):
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in Gemma2, every other layer uses sliding window attention. This is the size of the sliding window.
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layer_types (`list`, *optional*):
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Attention pattern for each layer.
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final_logit_softcapping (`float`, *optional*, defaults to 30.0):
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scaling factor when applying tanh softcapping on the logits.
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attn_logit_softcapping (`float`, *optional*, defaults to 50.0):
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scaling factor when applying tanh softcapping on the attention scores.
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```python
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>>> from transformers import Gemma2Model, Gemma2Config
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>>> # Initializing a Gemma2 gemma2-7b style configuration
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>>> configuration = Gemma2Config()
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>>> # Initializing a model from the gemma2-7b style configuration
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>>> model = Gemma2Model(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "gemma2"
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keys_to_ignore_at_inference = ["past_key_values"]
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base_model_tp_plan = {
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"layers.*.self_attn.q_proj": "colwise",
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"layers.*.self_attn.k_proj": "colwise",
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"layers.*.self_attn.v_proj": "colwise",
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"layers.*.self_attn.o_proj": "rowwise",
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"layers.*.mlp.gate_proj": "colwise",
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"layers.*.mlp.up_proj": "colwise",
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"layers.*.mlp.down_proj": "rowwise",
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}
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base_model_pp_plan = {
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"embed_tokens": (["input_ids"], ["inputs_embeds"]),
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"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
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"norm": (["hidden_states"], ["hidden_states"]),
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}
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def __init__(
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self,
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vocab_size=256000,
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hidden_size=2304,
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intermediate_size=9216,
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num_hidden_layers=26,
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num_attention_heads=8,
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num_key_value_heads=4,
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head_dim=256,
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hidden_activation="gelu_pytorch_tanh",
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max_position_embeddings=8192,
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initializer_range=0.02,
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rms_norm_eps=1e-6,
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use_cache=True,
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pad_token_id=0,
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eos_token_id=1,
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bos_token_id=2,
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tie_word_embeddings=True,
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rope_theta=10000.0,
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attention_bias=False,
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attention_dropout=0.0,
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query_pre_attn_scalar=256,
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sliding_window=4096,
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layer_types=None,
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final_logit_softcapping=30.0,
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attn_logit_softcapping=50.0,
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**kwargs,
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):
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.head_dim = head_dim
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self.num_key_value_heads = num_key_value_heads
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.hidden_activation = hidden_activation
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self.query_pre_attn_scalar = query_pre_attn_scalar
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self.sliding_window = sliding_window
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self.final_logit_softcapping = final_logit_softcapping
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self.attn_logit_softcapping = attn_logit_softcapping
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self.layer_types = layer_types
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if self.layer_types is None:
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self.layer_types = [
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"sliding_attention" if bool((i + 1) % 2) else "full_attention" for i in range(self.num_hidden_layers)
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]
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layer_type_validation(self.layer_types)
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class Gemma2RMSNorm(GemmaRMSNorm):
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pass
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class Gemma2MLP(GemmaMLP):
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def __init__(self, config):
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super().__init__()
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self.act_fn = ACT2FN[config.hidden_activation]
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def eager_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: Optional[torch.Tensor],
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dropout: float = 0.0,
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scaling: Optional[float] = None,
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softcap: Optional[float] = None,
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**kwargs,
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) -> tuple[torch.Tensor, torch.Tensor]:
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if scaling is None:
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scaling = module.head_dim**-0.5
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key_states = repeat_kv(key, module.num_key_value_groups)
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value_states = repeat_kv(value, module.num_key_value_groups)
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attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
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if softcap is not None:
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attn_weights = attn_weights / softcap
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attn_weights = torch.tanh(attn_weights)
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attn_weights = attn_weights * softcap
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if attention_mask is not None: # no matter the length, we just slice it
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causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
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attn_weights = attn_weights + causal_mask
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# upcast attention to fp32
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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attn_output = torch.matmul(attn_weights, value_states)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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class Gemma2Attention(GemmaAttention):
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def __init__(self, config: Gemma2Config, layer_idx: int):
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super().__init__(config, layer_idx)
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self.attn_logit_softcapping = self.config.attn_logit_softcapping
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self.attention_dropout = self.config.attention_dropout
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self.is_causal = True
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self.scaling = config.query_pre_attn_scalar**-0.5
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self.sliding_window = config.sliding_window if config.layer_types[layer_idx] == "sliding_attention" else None
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: tuple[torch.Tensor, torch.Tensor],
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attention_mask: Optional[torch.Tensor],
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past_key_value: Optional[Cache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.head_dim)
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query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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cos, sin = position_embeddings
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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if past_key_value is not None:
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# sin and cos are specific to RoPE models; cache_position needed for the static cache
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
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attention_interface: Callable = eager_attention_forward
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if self.config._attn_implementation != "eager":
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attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
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attn_output, attn_weights = attention_interface(
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self,
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query_states,
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key_states,
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value_states,
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attention_mask,
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dropout=self.attention_dropout if self.training else 0.0,
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scaling=self.scaling,
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sliding_window=self.sliding_window,
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softcap=self.attn_logit_softcapping,
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**kwargs,
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)
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attn_output = attn_output.reshape(*input_shape, -1).contiguous()
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attn_output = self.o_proj(attn_output)
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return attn_output, attn_weights
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class Gemma2DecoderLayer(GradientCheckpointingLayer):
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def __init__(self, config: Gemma2Config, layer_idx: int):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.config = config
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self.attention_type = config.layer_types[layer_idx]
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self.self_attn = Gemma2Attention(config=config, layer_idx=layer_idx)
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self.mlp = Gemma2MLP(config)
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self.input_layernorm = Gemma2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = Gemma2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.pre_feedforward_layernorm = Gemma2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_feedforward_layernorm = Gemma2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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@deprecate_kwarg("last_cache_position", version="4.53.0")
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: tuple[torch.Tensor, torch.Tensor],
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Cache] = None,
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output_attentions: Optional[bool] = False,
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use_cache: Optional[bool] = False,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs,
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) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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# Self Attention
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hidden_states, self_attn_weights = self.self_attn(
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hidden_states=hidden_states,
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position_embeddings=position_embeddings,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_value=past_key_value,
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output_attentions=output_attentions,
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use_cache=use_cache,
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cache_position=cache_position,
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**kwargs,
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)
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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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residual = hidden_states
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hidden_states = self.pre_feedforward_layernorm(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = self.post_feedforward_layernorm(hidden_states)
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hidden_states = residual + hidden_states
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outputs = (hidden_states,)
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if output_attentions:
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outputs += (self_attn_weights,)
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return outputs
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class Gemma2Model(GemmaModel):
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def __init__(self, config: Gemma2Config):
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super().__init__(config)
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self.layers = nn.ModuleList(
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[Gemma2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
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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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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[Cache] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> BaseModelOutputWithPast:
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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if (input_ids is None) ^ (inputs_embeds is not None):
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raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
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if self.gradient_checkpointing and self.training and use_cache:
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logger.warning_once(
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"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
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)
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use_cache = False
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if inputs_embeds is None:
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inputs_embeds = self.embed_tokens(input_ids)
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if use_cache and past_key_values is None and not self.training:
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past_key_values = DynamicCache()
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if cache_position is None:
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past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
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cache_position = torch.arange(
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past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
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)
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if position_ids is None:
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position_ids = cache_position.unsqueeze(0)
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# It may already have been prepared by e.g. `generate`
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if not isinstance(causal_mask_mapping := attention_mask, dict):
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# Prepare mask arguments
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mask_kwargs = {
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"config": self.config,
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"input_embeds": inputs_embeds,
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"attention_mask": attention_mask,
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"cache_position": cache_position,
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"past_key_values": past_key_values,
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"position_ids": position_ids,
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}
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# Create the masks
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causal_mask_mapping = {
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"full_attention": create_causal_mask(**mask_kwargs),
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"sliding_attention": create_sliding_window_causal_mask(**mask_kwargs),
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}
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# embed positions
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|
hidden_states = inputs_embeds
|
|
|
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# create position embeddings to be shared across the decoder layers
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|
position_embeddings = self.rotary_emb(hidden_states, position_ids)
|
|
|
|
# normalized
|
|
# Gemma2 downcasts the below to float16, causing sqrt(3072)=55.4256 to become 55.5
|
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# See https://github.com/huggingface/transformers/pull/29402
|
|
normalizer = torch.tensor(self.config.hidden_size**0.5, dtype=hidden_states.dtype)
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|
hidden_states = hidden_states * normalizer
|
|
|
|
# decoder layers
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|
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,
|
|
position_embeddings=position_embeddings,
|
|
attention_mask=causal_mask_mapping[decoder_layer.attention_type],
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_values,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
|
|
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,)
|
|
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attns,
|
|
)
|
|
|
|
|
|
class Gemma2ForCausalLM(GemmaForCausalLM):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.model = Gemma2Model(config)
|
|
self.post_init()
|
|
|
|
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[Cache] = 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,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
logits_to_keep: Union[int, torch.Tensor] = 0,
|
|
**kwargs,
|
|
) -> CausalLMOutputWithPast:
|
|
r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, Gemma2ForCausalLM
|
|
|
|
>>> model = Gemma2ForCausalLM.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('<path-to-checkpoint>', 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
|
|
)
|
|
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
|
outputs: BaseModelOutputWithPast = 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,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = outputs.last_hidden_state
|
|
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
|
|
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
|
logits = self.lm_head(hidden_states[:, slice_indices, :])
|
|
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, **kwargs)
|
|
|
|
return CausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class Gemma2ForSequenceClassification(GemmaForSequenceClassification):
|
|
pass
|
|
|
|
|
|
class Gemma2ForTokenClassification(GemmaForTokenClassification):
|
|
pass
|
|
|
|
|
|
__all__ = [
|
|
"Gemma2Config",
|
|
"Gemma2ForCausalLM",
|
|
"Gemma2Model",
|
|
"Gemma2PreTrainedModel", # noqa: F822
|
|
"Gemma2ForSequenceClassification",
|
|
"Gemma2ForTokenClassification",
|
|
]
|