766 lines
34 KiB
Python
766 lines
34 KiB
Python
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/diffllama/modular_diffllama.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_diffllama.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# coding=utf-8
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# Copyright 2024 weak-kajuma and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on Llama implementations in this library and Microsoft's
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# Differential Transformer implementations.
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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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import math
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from typing import Optional, Union
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import torch
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from torch import nn
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from ...activations import ACT2FN
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from ...cache_utils import Cache, DynamicCache, StaticCache
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from ...generation import GenerationMixin
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from ...integrations import use_kernel_forward_from_hub
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from ...masking_utils import create_causal_mask
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from ...modeling_flash_attention_utils import _flash_attention_forward, flash_attn_supports_top_left_mask
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from ...modeling_layers import (
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GenericForQuestionAnswering,
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GenericForSequenceClassification,
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GenericForTokenClassification,
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GradientCheckpointingLayer,
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)
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from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
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from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
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from ...modeling_utils import PreTrainedModel
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from ...processing_utils import Unpack
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from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
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from ...utils.generic import check_model_inputs
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from .configuration_diffllama import DiffLlamaConfig
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logger = logging.get_logger(__name__)
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class DiffLlamaMLP(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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self.intermediate_size = config.intermediate_size
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self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
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self.act_fn = ACT2FN[config.hidden_act]
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def forward(self, x):
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down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
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return down_proj
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def rotate_half(x):
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"""Rotates half the hidden dims of the input."""
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x1 = x[..., : x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2 :]
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return torch.cat((-x2, x1), dim=-1)
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
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"""Applies Rotary Position Embedding to the query and key tensors.
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Args:
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q (`torch.Tensor`): The query tensor.
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k (`torch.Tensor`): The key tensor.
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cos (`torch.Tensor`): The cosine part of the rotary embedding.
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sin (`torch.Tensor`): The sine part of the rotary embedding.
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position_ids (`torch.Tensor`, *optional*):
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Deprecated and unused.
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unsqueeze_dim (`int`, *optional*, defaults to 1):
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The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
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sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
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that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
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k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
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cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
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the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
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Returns:
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`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
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"""
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cos = cos.unsqueeze(unsqueeze_dim)
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sin = sin.unsqueeze(unsqueeze_dim)
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q_embed = (q * cos) + (rotate_half(q) * sin)
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k_embed = (k * cos) + (rotate_half(k) * sin)
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return q_embed, k_embed
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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"""
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This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
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num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
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"""
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batch, num_key_value_heads, slen, head_dim = hidden_states.shape
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if n_rep == 1:
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return hidden_states
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hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
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return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
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def lambda_init_fn(layer_idx):
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return 0.8 - 0.6 * math.exp(-0.3 * layer_idx)
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class DiffLlamaAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(self, config: DiffLlamaConfig, layer_idx: Optional[int] = None):
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super().__init__()
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self.config = config
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self.layer_idx = layer_idx
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if layer_idx is None:
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logger.warning_once(
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f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
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"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
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"when creating this class."
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)
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self.attention_dropout = config.attention_dropout
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self.hidden_size = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.head_dim = getattr(config, "head_dim", self.hidden_size // self.num_heads)
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self.num_key_value_heads = config.num_key_value_heads
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self.num_key_value_groups = self.num_heads // self.num_key_value_heads
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# under this are not used
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self.max_position_embeddings = config.max_position_embeddings
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self.rope_theta = config.rope_theta
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self.is_causal = True
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self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
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self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
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self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
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self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
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self.lambda_init = lambda_init_fn(layer_idx)
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self.lambda_q1 = nn.Parameter(torch.normal(0, config.lambda_std_dev, size=(self.head_dim,)))
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self.lambda_k1 = nn.Parameter(torch.normal(0, config.lambda_std_dev, size=(self.head_dim,)))
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self.lambda_q2 = nn.Parameter(torch.normal(0, config.lambda_std_dev, size=(self.head_dim,)))
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self.lambda_k2 = nn.Parameter(torch.normal(0, config.lambda_std_dev, size=(self.head_dim,)))
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self.groupnorm = nn.RMSNorm(2 * self.head_dim, eps=config.rms_norm_eps, elementwise_affine=False)
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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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use_cache: bool = False,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs,
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
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bsz, target_len, _ = hidden_states.size()
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q_len = target_len
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).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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key_states = repeat_kv(key_states, self.num_key_value_groups)
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value_states = repeat_kv(value_states, self.num_key_value_groups)
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value_states = torch.cat(torch.chunk(value_states, 2, dim=1), dim=-1)
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value_states = value_states.repeat(1, 2, 1, 1)
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attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
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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_states.dtype)
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attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
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lambda_1 = torch.exp(torch.sum(self.lambda_q1 * self.lambda_k1, dim=-1, dtype=torch.float32)).to(
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query_states.dtype
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)
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lambda_2 = torch.exp(torch.sum(self.lambda_q2 * self.lambda_k2, dim=-1, dtype=torch.float32)).to(
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query_states.dtype
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)
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lambda_full = lambda_1 - lambda_2 + self.lambda_init
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attn_output = torch.matmul(attn_weights, value_states)
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attn_output1, attn_output2 = torch.chunk(attn_output, 2, dim=1)
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attn_output = attn_output1 - lambda_full * attn_output2
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attn_output = (1 - self.lambda_init) * self.groupnorm(attn_output)
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attn_output = attn_output.transpose(1, 2).contiguous()
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attn_output = attn_output.reshape(bsz, q_len, -1)
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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 DiffLlamaFlashAttention2(DiffLlamaAttention):
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"""
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DiffLlama flash attention module. This module inherits from `DiffLlamaAttention` as the weights of the module stays
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untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
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flash attention and deal with padding tokens in case the input contains any of them.
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"""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
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# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, 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.
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# 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).
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self._flash_attn_uses_top_left_mask = flash_attn_supports_top_left_mask()
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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.LongTensor] = 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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use_cache: bool = False,
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cache_position: Optional[torch.LongTensor] = None,
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
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if isinstance(past_key_value, StaticCache):
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raise ValueError(
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"`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` "
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"make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers"
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)
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bsz, q_len, _ = hidden_states.size()
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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# Flash attention requires the input to have the shape
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# batch_size x seq_length x head_dim x hidden_dim
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# therefore we just need to keep the original shape
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query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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if position_embeddings is None:
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logger.warning_once(
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"The attention layers in this model are transitioning from computing the RoPE embeddings internally "
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"through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
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"`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be "
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"removed and `position_embeddings` will be mandatory."
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)
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cos, sin = self.rotary_emb(value_states, position_ids)
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else:
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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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# 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
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# to be able to avoid many of these transpose/reshape/view.
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query_states = query_states.transpose(1, 2)
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key_states = key_states.transpose(1, 2)
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value_states = value_states.transpose(1, 2)
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dropout_rate = self.attention_dropout if self.training else 0.0
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# In PEFT, usually we cast the layer norms in float32 for training stability reasons
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# therefore the input hidden states gets silently casted in float32. Hence, we need
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# cast them back in the correct dtype just to be sure everything works as expected.
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# This might slowdown training & inference so it is recommended to not cast the LayerNorms
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# in fp32. (DiffLlamaRMSNorm handles it correctly)
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input_dtype = query_states.dtype
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device_type = query_states.device.type if query_states.device.type != "mps" else "cpu"
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if input_dtype == torch.float32:
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if torch.is_autocast_enabled():
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target_dtype = (
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torch.get_autocast_dtype(device_type)
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if hasattr(torch, "get_autocast_dtype")
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else torch.get_autocast_gpu_dtype()
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)
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# Handle the case where the model is quantized
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elif hasattr(self.config, "_pre_quantization_dtype"):
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target_dtype = self.config._pre_quantization_dtype
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else:
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target_dtype = self.q_proj.weight.dtype
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logger.warning_once(
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f"The input hidden states seems to be silently casted in float32, this might be related to"
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f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
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f" {target_dtype}."
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)
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query_states = query_states.to(target_dtype)
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key_states = key_states.to(target_dtype)
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value_states = value_states.to(target_dtype)
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value_states1, value_states2 = torch.chunk(value_states, 2, dim=2)
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value_states1 = value_states1.repeat(1, 1, 2, 1)
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value_states2 = value_states2.repeat(1, 1, 2, 1)
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attn_output1 = _flash_attention_forward(
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query_states,
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key_states,
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value_states1,
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attention_mask,
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q_len,
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position_ids=position_ids,
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dropout=dropout_rate,
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sliding_window=getattr(self, "sliding_window", None),
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use_top_left_mask=self._flash_attn_uses_top_left_mask,
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is_causal=self.is_causal,
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)
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attn_output2 = _flash_attention_forward(
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query_states,
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key_states,
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value_states2,
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attention_mask,
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q_len,
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position_ids=position_ids,
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dropout=dropout_rate,
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sliding_window=getattr(self, "sliding_window", None),
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use_top_left_mask=self._flash_attn_uses_top_left_mask,
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is_causal=self.is_causal,
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)
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attn_output = torch.cat([attn_output1, attn_output2], dim=-1)
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attn_output1, attn_output2 = torch.chunk(attn_output, 2, dim=2)
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lambda_1 = torch.exp(torch.sum(self.lambda_q1 * self.lambda_k1, dim=-1, dtype=torch.float32)).to(
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query_states.dtype
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)
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lambda_2 = torch.exp(torch.sum(self.lambda_q2 * self.lambda_k2, dim=-1, dtype=torch.float32)).to(
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query_states.dtype
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)
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lambda_full = lambda_1 - lambda_2 + self.lambda_init
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attn_output = attn_output1 - lambda_full * attn_output2
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attn_output = (1 - self.lambda_init) * self.groupnorm(attn_output)
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attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
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attn_output = self.o_proj(attn_output)
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return attn_output, None
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class DiffLlamaSdpaAttention(DiffLlamaAttention):
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"""
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DiffLlama attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
|
|
`DiffLlamaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
|
|
SDPA API.
|
|
"""
|
|
|
|
# Adapted from DiffLlamaAttention.forward
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
position_embeddings: tuple[torch.Tensor, torch.Tensor],
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_value: Optional[Cache] = None,
|
|
use_cache: bool = False,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
**kwargs,
|
|
) -> 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 = 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)
|
|
|
|
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
|
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
|
value_states = torch.cat(torch.chunk(value_states, 2, dim=1), dim=-1)
|
|
value_states = value_states.repeat(1, 2, 1, 1)
|
|
|
|
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,
|
|
)
|
|
|
|
attn_output1, attn_output2 = torch.chunk(attn_output, 2, dim=1)
|
|
|
|
lambda_1 = torch.exp(torch.sum(self.lambda_q1 * self.lambda_k1, dim=-1, dtype=torch.float32)).to(
|
|
query_states.dtype
|
|
)
|
|
lambda_2 = torch.exp(torch.sum(self.lambda_q2 * self.lambda_k2, dim=-1, dtype=torch.float32)).to(
|
|
query_states.dtype
|
|
)
|
|
lambda_full = lambda_1 - lambda_2 + self.lambda_init
|
|
|
|
attn_output = attn_output1 - lambda_full * attn_output2
|
|
attn_output = (1 - self.lambda_init) * self.groupnorm(attn_output)
|
|
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
|
|
|
|
|
|
@use_kernel_forward_from_hub("RMSNorm")
|
|
class DiffLlamaRMSNorm(nn.Module):
|
|
def __init__(self, hidden_size, eps=1e-6):
|
|
"""
|
|
DiffLlamaRMSNorm is equivalent to T5LayerNorm
|
|
"""
|
|
super().__init__()
|
|
self.weight = nn.Parameter(torch.ones(hidden_size))
|
|
self.variance_epsilon = eps
|
|
|
|
def forward(self, hidden_states):
|
|
input_dtype = hidden_states.dtype
|
|
hidden_states = hidden_states.to(torch.float32)
|
|
variance = hidden_states.pow(2).mean(-1, keepdim=True)
|
|
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
|
return self.weight * hidden_states.to(input_dtype)
|
|
|
|
def extra_repr(self):
|
|
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
|
|
|
|
|
|
DIFFLLAMA_ATTENTION_CLASSES = {
|
|
"eager": DiffLlamaAttention,
|
|
"flash_attention_2": DiffLlamaFlashAttention2,
|
|
"sdpa": DiffLlamaSdpaAttention,
|
|
}
|
|
|
|
|
|
class DiffLlamaDecoderLayer(GradientCheckpointingLayer):
|
|
def __init__(self, config: DiffLlamaConfig, layer_idx: int):
|
|
super().__init__()
|
|
self.hidden_size = config.hidden_size
|
|
|
|
self.self_attn = DIFFLLAMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
|
|
|
|
self.mlp = DiffLlamaMLP(config)
|
|
self.input_layernorm = DiffLlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.post_attention_layernorm = DiffLlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
|
|
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,
|
|
use_cache: Optional[bool] = False,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple[torch.Tensor]:
|
|
residual = hidden_states
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
# Self Attention
|
|
hidden_states, _ = self.self_attn(
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_value,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
position_embeddings=position_embeddings,
|
|
**kwargs,
|
|
)
|
|
hidden_states = residual + hidden_states
|
|
|
|
# Fully Connected
|
|
residual = hidden_states
|
|
hidden_states = self.post_attention_layernorm(hidden_states)
|
|
hidden_states = self.mlp(hidden_states)
|
|
hidden_states = residual + hidden_states
|
|
return hidden_states
|
|
|
|
|
|
@auto_docstring
|
|
class DiffLlamaPreTrainedModel(PreTrainedModel):
|
|
config: DiffLlamaConfig
|
|
base_model_prefix = "model"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["DiffLlamaDecoderLayer"]
|
|
_skip_keys_device_placement = ["past_key_values"]
|
|
_supports_flash_attn = True
|
|
_supports_sdpa = True
|
|
_supports_flex_attn = False
|
|
|
|
_can_compile_fullgraph = True
|
|
_supports_attention_backend = False
|
|
_can_record_outputs = {
|
|
"hidden_states": DiffLlamaDecoderLayer,
|
|
"attentions": DiffLlamaAttention,
|
|
}
|
|
|
|
def _init_weights(self, module):
|
|
super()._init_weights(module)
|
|
if isinstance(module, DiffLlamaAttention):
|
|
module.lambda_q1.data.normal_(0, self.config.lambda_std_dev)
|
|
module.lambda_k1.data.normal_(0, self.config.lambda_std_dev)
|
|
module.lambda_q2.data.normal_(0, self.config.lambda_std_dev)
|
|
module.lambda_k2.data.normal_(0, self.config.lambda_std_dev)
|
|
|
|
|
|
class DiffLlamaRotaryEmbedding(nn.Module):
|
|
def __init__(self, config: DiffLlamaConfig, device=None):
|
|
super().__init__()
|
|
# BC: "rope_type" was originally "type"
|
|
if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
|
|
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
|
|
else:
|
|
self.rope_type = "default"
|
|
self.max_seq_len_cached = config.max_position_embeddings
|
|
self.original_max_seq_len = config.max_position_embeddings
|
|
|
|
self.config = config
|
|
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
|
|
|
|
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
|
|
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
|
self.original_inv_freq = self.inv_freq
|
|
|
|
@torch.no_grad()
|
|
@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
|
|
def forward(self, x, position_ids):
|
|
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
|
|
position_ids_expanded = position_ids[:, None, :].float()
|
|
|
|
device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
|
|
with torch.autocast(device_type=device_type, enabled=False): # Force float32
|
|
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
|
|
emb = torch.cat((freqs, freqs), dim=-1)
|
|
cos = emb.cos() * self.attention_scaling
|
|
sin = emb.sin() * self.attention_scaling
|
|
|
|
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
|
|
|
|
|
|
@auto_docstring
|
|
class DiffLlamaModel(DiffLlamaPreTrainedModel):
|
|
def __init__(self, config: DiffLlamaConfig):
|
|
super().__init__(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(
|
|
[DiffLlamaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
|
)
|
|
self.norm = DiffLlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.rotary_emb = DiffLlamaRotaryEmbedding(config=config)
|
|
self.gradient_checkpointing = False
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@check_model_inputs
|
|
@auto_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[Cache] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> BaseModelOutputWithPast:
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds: torch.Tensor = 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.Tensor = 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 = create_causal_mask(
|
|
config=self.config,
|
|
input_embeds=inputs_embeds,
|
|
attention_mask=attention_mask,
|
|
cache_position=cache_position,
|
|
past_key_values=past_key_values,
|
|
position_ids=position_ids,
|
|
)
|
|
|
|
hidden_states = inputs_embeds
|
|
position_embeddings = self.rotary_emb(hidden_states, position_ids)
|
|
|
|
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
|
|
hidden_states = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=causal_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_values,
|
|
cache_position=cache_position,
|
|
position_embeddings=position_embeddings,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class DiffLlamaForCausalLM(DiffLlamaPreTrainedModel, GenerationMixin):
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
_tp_plan = {"lm_head": "colwise_rep"}
|
|
_pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.model = DiffLlamaModel(config)
|
|
self.vocab_size = config.vocab_size
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def set_decoder(self, decoder):
|
|
self.model = decoder
|
|
|
|
def get_decoder(self):
|
|
return self.model
|
|
|
|
@can_return_tuple
|
|
@auto_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[Cache] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
logits_to_keep: Union[int, torch.Tensor] = 0,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> CausalLMOutputWithPast:
|
|
r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, DiffLlamaForCausalLM
|
|
|
|
>>> model = DiffLlamaForCausalLM.from_pretrained("google/diffllama-7b")
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("google/diffllama-7b")
|
|
|
|
>>> 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?"
|
|
```"""
|
|
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,
|
|
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, :])
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.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 DiffLlamaForSequenceClassification(GenericForSequenceClassification, DiffLlamaPreTrainedModel):
|
|
pass
|
|
|
|
|
|
class DiffLlamaForQuestionAnswering(GenericForQuestionAnswering, DiffLlamaPreTrainedModel):
|
|
base_model_prefix = "transformer" # For BC, where `transformer` was used instead of `model`
|
|
|
|
|
|
class DiffLlamaForTokenClassification(GenericForTokenClassification, DiffLlamaPreTrainedModel):
|
|
pass
|
|
|
|
|
|
__all__ = [
|
|
"DiffLlamaPreTrainedModel",
|
|
"DiffLlamaModel",
|
|
"DiffLlamaForCausalLM",
|
|
"DiffLlamaForSequenceClassification",
|
|
"DiffLlamaForQuestionAnswering",
|
|
"DiffLlamaForTokenClassification",
|
|
]
|