Sentence Similarity
Transformers
Safetensors
English
llama
feature-extraction
text-embedding
embeddings
information-retrieval
beir
text-classification
language-model
text-clustering
text-semantic-similarity
text-evaluation
text-reranking
Sentence Similarity
natural_questions
ms_marco
fever
hotpot_qa
mteb
custom_code
text-generation-inference
Inference Endpoints
File size: 7,052 Bytes
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from typing import List, Optional, Tuple, Union
import torch
from transformers import LlamaModel, LlamaPreTrainedModel, LlamaForCausalLM, AutoModel
from transformers.models.llama.modeling_llama import LlamaDecoderLayer, LlamaRMSNorm, LlamaRotaryEmbedding, LlamaConfig, LlamaMLP, LlamaAttention, LlamaFlashAttention2, LlamaSdpaAttention
from transformers.utils import logging
from torch import nn
from torch.nn import CrossEntropyLoss
import torch.nn.functional as F
from transformers.modeling_outputs import BaseModelOutputWithPast, MaskedLMOutput, CausalLMOutputWithPast, TokenClassifierOutput
from transformers.cache_utils import Cache, DynamicCache
from transformers.modeling_attn_mask_utils import AttentionMaskConverter
from transformers.cache_utils import Cache, DynamicCache, StaticCache
logger = logging.get_logger(__name__)
class ModifiedLlamaAttention(LlamaAttention):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.is_causal = False
class ModifiedLlamaFlashAttention2(LlamaFlashAttention2):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.is_causal = False
class ModifiedLlamaSdpaAttention(LlamaSdpaAttention):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.is_causal = False
LLAMA_ATTENTION_CLASSES = {
"eager": ModifiedLlamaAttention,
"flash_attention_2": ModifiedLlamaFlashAttention2,
"sdpa": ModifiedLlamaSdpaAttention,
}
class ModifiedLlamaDecoderLayer(LlamaDecoderLayer):
def __init__(self, config: LlamaConfig, layer_idx: int):
nn.Module.__init__(self)
self.hidden_size = config.hidden_size
self.self_attn = LLAMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
self.mlp = LlamaMLP(config)
self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
class LlamaEncoderModel(LlamaModel):
def __init__(self, config):
LlamaPreTrainedModel.__init__(self, config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
self.layers = nn.ModuleList(
[ModifiedLlamaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.rotary_emb = LlamaRotaryEmbedding(config=config)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool,
):
# TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
# KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
# (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
# `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and 0.0 in attention_mask:
return attention_mask
return None
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
# to infer the attention mask.
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
using_static_cache = isinstance(past_key_values, StaticCache)
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
# if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
# if AttentionMaskConverter._ignore_causal_mask_sdpa(
# attention_mask,
# inputs_embeds=input_tensor,
# past_key_values_length=past_seen_tokens,
# is_training=self.training,
# ):
# return None
dtype, device = input_tensor.dtype, input_tensor.device
min_dtype = torch.finfo(dtype).min
sequence_length = input_tensor.shape[1]
if using_static_cache:
target_length = past_key_values.get_max_length()
else:
target_length = (
attention_mask.shape[-1]
if isinstance(attention_mask, torch.Tensor)
else past_seen_tokens + sequence_length + 1
)
if attention_mask is not None and attention_mask.dim() == 4:
# in this case we assume that the mask comes already in inverted form and requires no inversion or slicing
if attention_mask.max() != 0:
raise ValueError("Custom 4D attention mask should be passed in inverted form with max==0`")
causal_mask = attention_mask
else:
causal_mask = torch.zeros(
(sequence_length, target_length), dtype=dtype, device=device
)
causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1)
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
)
if (
self.config._attn_implementation == "sdpa"
and attention_mask is not None
and attention_mask.device.type == "cuda"
and not output_attentions
):
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
# Details: https://github.com/pytorch/pytorch/issues/110213
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
return causal_mask
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