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# This file was automatically generated from src/transformers/models/doge/modular_doge.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# the file from the modular. If any change should be done, please apply the change to the
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# coding=utf-8
# Copyright 2024 Jingze Shi and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on the Wonderful Matrices paper implementation.
# The Doge family of small language models is trained by Jingze Shi.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from typing import Callable, List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
from torch import nn
from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache, StaticCache
from transformers.generation import GenerationMixin
from transformers.modeling_attn_mask_utils import AttentionMaskConverter
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
from transformers.modeling_utils import PreTrainedModel
from transformers.processing_utils import Unpack
from transformers.utils import (
LossKwargs,
add_start_docstrings,
add_start_docstrings_to_model_forward,
is_torch_flex_attn_available,
logging,
replace_return_docstrings,
)
from .configuration_doge import DogeConfig
if is_torch_flex_attn_available():
from torch.nn.attention.flex_attention import flex_attention
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "DogeConfig"
class DogeRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
DogeRMSNorm 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}"
class DogeResidual(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
def forward(self, residual_states, hidden_states):
return self.weight * residual_states + hidden_states
def extra_repr(self):
return f"{tuple(self.weight.shape)}"
class DogeRotaryEmbedding(nn.Module):
def __init__(self, config: DogeConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
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
def _dynamic_frequency_update(self, position_ids, device):
"""
dynamic RoPE layers should recompute `inv_freq` in the following situations:
1 - growing beyond the cached sequence length (allow scaling)
2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
"""
seq_len = torch.max(position_ids) + 1
if seq_len > self.max_seq_len_cached: # growth
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, seq_len=seq_len)
self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: may break with compilation
self.max_seq_len_cached = seq_len
if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset
# This .to() is needed if the model has been moved to a device after being initialized (because
# the buffer is automatically moved, but not the original copy)
self.original_inv_freq = self.original_inv_freq.to(device)
self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
self.max_seq_len_cached = self.original_max_seq_len
@torch.no_grad()
def forward(self, x, position_ids):
if "dynamic" in self.rope_type:
self._dynamic_frequency_update(position_ids, device=x.device)
# Core RoPE block
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
position_ids_expanded = position_ids[:, None, :].float()
# Force float32 (see https://github.com/huggingface/transformers/pull/29285)
device_type = x.device.type
device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
with torch.autocast(device_type=device_type, enabled=False):
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
emb = torch.cat((freqs, freqs), dim=-1)
cos = emb.cos()
sin = emb.sin()
# Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
cos = cos * self.attention_scaling
sin = sin * self.attention_scaling
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`): The query tensor.
k (`torch.Tensor`): The key tensor.
cos (`torch.Tensor`): The cosine part of the rotary embedding.
sin (`torch.Tensor`): The sine part of the rotary embedding.
position_ids (`torch.Tensor`, *optional*):
Deprecated and unused.
unsqueeze_dim (`int`, *optional*, defaults to 1):
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
"""
cos = cos.unsqueeze(unsqueeze_dim)
sin = sin.unsqueeze(unsqueeze_dim)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
"""
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
if n_rep == 1:
return hidden_states
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
def eager_attention_forward(
module: nn.Module,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attention_mask: Optional[torch.Tensor],
scaling: float,
dropout: float = 0.0,
**kwargs,
) -> Tuple[torch.Tensor, torch.Tensor]:
key_states = repeat_kv(key, module.num_key_value_groups)
value_states = repeat_kv(value, module.num_key_value_groups)
attn_weights = torch.matmul(query, key_states.transpose(-1, -2)) * scaling
if attention_mask is not None:
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
attn_weights = attn_weights + causal_mask
attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
attn_weights = F.dropout(attn_weights, p=dropout, training=module.training)
attn_output = torch.matmul(attn_weights, value_states)
attn_output = attn_output.transpose(1, 2).contiguous()
return attn_output, attn_weights
def sdpa_attention_forward(
module: nn.Module,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attention_mask: Optional[torch.Tensor],
dropout: float = 0.0,
scaling: Optional[float] = None,
is_causal: Optional[bool] = None,
**kwargs,
) -> Tuple[torch.Tensor, None]:
key = repeat_kv(key, module.num_key_value_groups)
value = repeat_kv(value, module.num_key_value_groups)
causal_mask = attention_mask
if attention_mask is not None:
causal_mask = causal_mask[:, :, :, : key.shape[-2]]
# SDPA with memory-efficient backend is bugged with non-contiguous inputs and custom attn_mask for some torch versions
# Reference: https://github.com/pytorch/pytorch/issues/112577.
query = query.contiguous()
key = key.contiguous()
value = value.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.
if is_causal is None:
is_causal = causal_mask is None and query.shape[2] > 1
# Shapes (e.g. query.shape[2]) are tensors during jit tracing, resulting in `is_causal` being a tensor.
# We convert it to a bool for the SDPA kernel that only accepts bools.
if torch.jit.is_tracing() and isinstance(is_causal, torch.Tensor):
is_causal = is_causal.item()
# NOTE: As of pytorch 2.5.1, SDPA backward pass of cuDNN is still incorrect, so we disable cuDNN SDPA (see https://github.com/pytorch/pytorch/issues/138581)
torch.backends.cuda.enable_cudnn_sdp(False)
attn_output = F.scaled_dot_product_attention(
query=query,
key=key,
value=value,
attn_mask=causal_mask,
dropout_p=dropout,
scale=scaling,
is_causal=is_causal,
)
attn_output = attn_output.transpose(1, 2).contiguous()
return attn_output, None
def flex_attention_forward(
module: nn.Module,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attention_mask: Optional[torch.Tensor],
scaling: Optional[float] = None,
is_causal: Optional[bool] = None,
softcap: Optional[float] = None,
head_mask: Optional[torch.Tensor] = None,
**kwargs,
) -> Tuple[torch.Tensor, torch.Tensor]:
causal_mask = attention_mask
if attention_mask is not None:
causal_mask = causal_mask[:, :, :, : key.shape[-2]]
if is_causal is None:
is_causal = causal_mask is None and query.shape[2] > 1
def causal_mod(score, batch, head, q_idx, kv_idx):
if softcap is not None:
score = softcap * torch.tanh(score / softcap)
if causal_mask is not None:
score = score + causal_mask[batch][0][q_idx][kv_idx]
if head_mask is not None:
score = score + head_mask[batch][head][0][0]
return score
def dynamic_mod(score, batch, head, q_idx, kv_idx):
if softcap is not None:
score = softcap * torch.tanh(score / softcap)
if causal_mask is not None:
score = score + causal_mask[batch][head][q_idx][kv_idx]
if head_mask is not None:
score = score + head_mask[batch][head][0][0]
return score
# TODO: flex_attention: As of pytorch 2.5.1, captured buffers that require grad are not yet supported.
# NOTE: So we only use flex_attention in inference mode.
mask_mod = causal_mod if is_causal or module.training else dynamic_mod
attn_output, attention_weights = flex_attention(
query=query,
key=key,
value=value,
score_mod=mask_mod,
enable_gqa=True,
scale=scaling,
# Last time checked on PyTorch == 2.5.1: Flex Attention always computes the lse regardless.
# For simplification, we thus always return it as no additional computations are introduced.
return_lse=True,
)
# lse is returned in float32
attention_weights = attention_weights.to(value.dtype)
attn_output = attn_output.transpose(1, 2).contiguous()
return attn_output, attention_weights
ALL_ATTENTION_FUNCTIONS = {
"eager": eager_attention_forward,
"sdpa": sdpa_attention_forward,
"flex_attention": flex_attention_forward,
}
class DogeDynamicMaskAttention(nn.Module):
"""Dynamic Mask Attention from 'Wonderful Matrices' paper."""
def __init__(self, config: DogeConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.attention_dropout = config.attention_dropout
self.dynamic_mask_ratio = config.dynamic_mask_ratio
self.q_proj = nn.Linear(
config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.hidden_bias
)
self.k_proj = nn.Linear(
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.hidden_bias
)
self.v_proj = nn.Linear(
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.hidden_bias
)
# dynamic mask for the QK^T attention weights matrix
self.A = nn.Parameter(torch.zeros(config.num_attention_heads))
self.dt_proj = nn.Linear(
config.num_key_value_heads * self.head_dim, config.num_attention_heads, bias=config.hidden_bias
)
self.o_proj = nn.Linear(
config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.hidden_bias
)
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: Tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Cache] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
input_shape = hidden_states.shape[:-1]
hidden_shape = (*input_shape, -1, self.head_dim)
query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
cos, sin = position_embeddings
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
# calculate dynamic mask from value_states
dt_states = self.dt_proj(
value_states.transpose(1, 2).reshape(value_states.shape[0], value_states.shape[-2], -1)
)
dynamic_mask = torch.exp(self.A * F.softplus(dt_states)).transpose(-1, -2)
attn_mask = self.prepare_dynamic_mask(
hidden_states=hidden_states,
dynamic_mask=dynamic_mask,
dynamic_mask_ratio=self.dynamic_mask_ratio,
attention_mask=attention_mask,
)
attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != "eager":
if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
logger.warning_once(
"`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
else:
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask=attn_mask,
dropout=0.0 if not self.training else self.attention_dropout,
scaling=self.scaling,
**kwargs,
)
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
attn_output = self.o_proj(attn_output)
return attn_output, attn_weights
def prepare_dynamic_mask(
self,
hidden_states: torch.Tensor,
dynamic_mask: torch.Tensor,
dynamic_mask_ratio: float = 0.0,
attention_mask: Optional[torch.Tensor] = None,
):
"""
Combine `dynamic_mask` with `attention_mask` to generate the final `attn_mask`.
Args:
hidden_states (`torch.Tensor`): The input hidden_states, used to determine the minimum value of the current input precision.
dynamic_mask (`torch.Tensor`): dynamic mask of shape `(batch_size, num_heads, key_sequence_length)`.
dynamic_mask_ratio (`float`, *optional*): Ratio from 0.0 to 1.0 used to control the proportion of the dynamic mask filled with the minimum value.
attention_mask (`torch.Tensor`, *optional*): attention mask of shape `(batch_size, 1, query_sequence_length, key_sequence_length)`.
"""
attn_mask = None
if dynamic_mask is not None:
attn_mask = dynamic_mask[:, :, None, :]
if 0.0 < dynamic_mask_ratio < 1.0:
min_type = torch.finfo(hidden_states.dtype).min
num_dynamic_mask = int(attn_mask.shape[-1] * dynamic_mask_ratio)
if num_dynamic_mask > 0:
rate_value = torch.kthvalue(attn_mask, num_dynamic_mask, dim=-1, keepdim=True).values
attn_mask = attn_mask.masked_fill(attn_mask < rate_value, min_type)
if attention_mask is not None:
attn_mask = attn_mask + attention_mask[:, :, :, : attn_mask.shape[-1]]
else:
attn_mask = attention_mask
return attn_mask
class DogeMLP(nn.Module):
def __init__(self, config: DogeConfig):
super().__init__()
self.hidden_dim = config.hidden_size
self.intermediate_dim = config.intermediate_size
self.act_fn = ACT2FN[config.hidden_act]
self.gate_proj = nn.Linear(self.hidden_dim, self.intermediate_dim, bias=config.hidden_bias)
self.up_proj = nn.Linear(self.hidden_dim, self.intermediate_dim, bias=config.hidden_bias)
self.down_proj = nn.Linear(self.intermediate_dim, self.hidden_dim, bias=config.hidden_bias)
def forward(
self,
hidden_states: torch.Tensor,
**kwargs,
) -> torch.Tensor:
hidden_states = self.down_proj(self.act_fn(self.gate_proj(hidden_states)) * self.up_proj(hidden_states))
return hidden_states
class DogeCDMoE(DogeMLP):
"""Cross Domain Mixture of Experts from 'Wonderful Matrices' paper."""
def __init__(self, config: DogeConfig):
super().__init__(config)
self.hidden_dim = config.hidden_size
self.act_fn = ACT2FN[config.hidden_act]
self.expert_retrieval_dim = config.expert_retrieval_size
self.num_cdmoe_experts = config.num_cdmoe_experts
self.num_cdmoe_heads = config.num_cdmoe_heads
self.num_cdmoe_experts_per_head = config.num_cdmoe_experts_per_head
self.num_keys = int(math.sqrt(self.num_cdmoe_experts))
# queries and keys for retrieval experts
self.queries_proj = nn.Linear(self.hidden_dim, self.num_cdmoe_heads * self.expert_retrieval_dim, bias=False)
self.keys = nn.Parameter(torch.zeros(self.num_cdmoe_heads, self.expert_retrieval_dim, self.num_keys))
# experts
self.down_embed = nn.Embedding(self.num_cdmoe_experts, self.hidden_dim)
self.up_embed = nn.Embedding(self.num_cdmoe_experts, self.hidden_dim)
def forward(
self,
hidden_states: torch.Tensor,
**kwargs,
) -> torch.Tensor:
bsz, seq_len, _ = hidden_states.shape
# get routing weights with queries and keys
queries = self.queries_proj(hidden_states)
queries = queries.view(2, self.num_cdmoe_heads, bsz * seq_len, -1)
keys = self.keys.view(2, self.num_cdmoe_heads, -1, self.num_keys)
routing_weights = torch.matmul(queries, keys)
routing_weights = routing_weights.transpose(-2, -3).view(2, bsz, seq_len, self.num_cdmoe_heads, self.num_keys)
# get experts with the highest routing weights
(scores_x, scores_y), (indices_x, indices_y) = routing_weights.topk(self.num_cdmoe_experts_per_head, dim=-1)
all_scores = scores_x.unsqueeze(-1) + scores_y.unsqueeze(-2)
all_scores = all_scores.view(*scores_x.shape[:-1], -1)
all_indices = (indices_x.unsqueeze(-1) * self.num_keys) + indices_y.unsqueeze(-2)
all_indices = all_indices.view(*indices_x.shape[:-1], -1)
scores, pk_indices = all_scores.topk(self.num_cdmoe_experts_per_head, dim=-1)
indices = all_indices.gather(-1, pk_indices)
down_embed = self.down_embed(indices)
up_embed = self.up_embed(indices)
# mix experts states with cross domain states
experts_weights = torch.sum(hidden_states[:, :, None, None, :] * down_embed, dim=-1)
experts_weights = self.act_fn(experts_weights) * scores.softmax(dim=-1)
experts_states = torch.sum(experts_weights[:, :, :, :, None] * up_embed, dim=(-2, -3))
hidden_states = self.down_proj(self.act_fn(self.gate_proj(hidden_states)) * self.up_proj(hidden_states))
hidden_states = hidden_states + experts_states
return hidden_states
class DogeDecoderLayer(nn.Module):
def __init__(self, config: DogeConfig, layer_idx: Optional[int] = None):
super().__init__()
self.hidden_dropout = config.hidden_dropout
self.pre_layernorm = DogeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.self_attn = DogeDynamicMaskAttention(config=config, layer_idx=layer_idx)
self.pre_residual = DogeResidual(config.hidden_size)
self.post_layernorm = DogeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.feed_forward = DogeMLP(config) if not config.is_moe else DogeCDMoE(config)
self.post_residual = DogeResidual(config.hidden_size)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
**kwargs,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
# sequence transformation
residual = hidden_states
hidden_states = self.pre_layernorm(hidden_states)
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
**kwargs,
)
self_attn_weights = None
hidden_states = F.dropout(hidden_states, p=self.hidden_dropout, training=self.training)
hidden_states = self.pre_residual(residual, hidden_states)
# state transformation
residual = hidden_states
hidden_states = self.post_layernorm(hidden_states)
hidden_states = self.feed_forward(hidden_states)
hidden_states = F.dropout(hidden_states, p=self.hidden_dropout, training=self.training)
hidden_states = self.post_residual(residual, hidden_states)
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
return outputs
DOGE_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`DogeConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
@add_start_docstrings(
"The bare Doge Model outputting raw hidden-states without any specific head on top.",
DOGE_START_DOCSTRING,
)
class DogePreTrainedModel(PreTrainedModel):
config_class = DogeConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["DogeDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_sdpa = True
# _supports_flex_attn = True # TODO: enable this when flex_attention is fully supported
_supports_cache_class = True
_supports_quantized_cache = True
_supports_static_cache = True
def _init_weights(self, module):
std = self.config.initializer_range
if isinstance(module, (nn.Linear)):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
DOGE_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
`past_key_values`).
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
information on the default strategy.
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.n_positions - 1]`.
[What are position IDs?](../glossary#position-ids)
past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
Two formats are allowed:
- a [`~cache_utils.Cache`] instance, see our
[kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache);
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
cache format.
The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
legacy cache format will be returned.
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
model's internal embedding lookup matrix.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
the complete sequence length.
"""
@add_start_docstrings(
"The bare Doge Model outputting raw hidden-states without any specific head on top.",
DOGE_START_DOCSTRING,
)
class DogeModel(DogePreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DogeDecoderLayer`]
Args:
config: DogeConfig
"""
def __init__(self, config: DogeConfig):
super().__init__(config)
self.config = config
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.word_embed = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.rotary_emb = DogeRotaryEmbedding(config)
self.layers = nn.ModuleList(
[DogeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.final_layernorm = DogeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.word_embed
def set_input_embeddings(self, value):
self.word_embed = value
@add_start_docstrings_to_model_forward(DOGE_INPUTS_DOCSTRING)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs,
) -> Union[Tuple, BaseModelOutputWithPast]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You cannot specify both input_ids and inputs_embeds")
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
if inputs_embeds is None:
inputs_embeds = self.word_embed(input_ids)
if use_cache and past_key_values is None:
past_key_values = DynamicCache()
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
hidden_states = inputs_embeds
# create position embeddings to be shared across the decoder layers
position_embeddings = self.rotary_emb(hidden_states, position_ids)
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
causal_mask,
position_ids,
past_key_values,
output_attentions,
use_cache,
cache_position,
position_embeddings,
)
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=causal_mask,
position_ids=position_ids,
past_key_value=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
**kwargs,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.final_layernorm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
output = BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=past_key_values if use_cache else None,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
return output if return_dict else output.to_tuple()
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool,
):
# We have to provide attention_mask for dynamic mask computation
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)
dtype, device = input_tensor.dtype, input_tensor.device
sequence_length = input_tensor.shape[1]
if using_static_cache:
target_length = past_key_values.get_max_cache_shape()
else:
target_length = (
attention_mask.shape[-1]
if isinstance(attention_mask, torch.Tensor)
else past_seen_tokens + sequence_length + 1
)
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
attention_mask,
sequence_length=sequence_length,
target_length=target_length,
dtype=dtype,
device=device,
cache_position=cache_position,
batch_size=input_tensor.shape[0],
)
if (
self.config._attn_implementation == "sdpa"
and attention_mask is not None
and attention_mask.device.type in ["cuda", "xpu"]
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
min_dtype = torch.finfo(dtype).min
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
return causal_mask
@staticmethod
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype: torch.dtype,
device: torch.device,
cache_position: torch.Tensor,
batch_size: int,
**kwargs,
):
"""
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
`(batch_size, 1, query_length, key_value_length)`.
sequence_length (`int`):
The sequence length being processed.
target_length (`int`):
The target length: when generating with static cache, the mask should be as long as the static cache,
to account for the 0 padding, the part of the cache that is not filled yet.
dtype (`torch.dtype`):
The dtype to use for the 4D attention mask.
device (`torch.device`):
The device to plcae the 4D attention mask on.
cache_position (`torch.Tensor`):
Indices depicting the position of the input sequence tokens in the sequence.
batch_size (`torch.Tensor`):
Batch size.
"""
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.
causal_mask = attention_mask
else:
min_dtype = torch.finfo(dtype).min
causal_mask = torch.full(
(sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
)
if sequence_length != 1:
causal_mask = torch.triu(causal_mask, diagonal=1)
causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 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
)
return causal_mask
class DogeForCausalLM(DogePreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
def __init__(self, config: DogeConfig):
super().__init__(config)
self.config = config
self.model = DogeModel(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 get_input_embeddings(self):
return self.model.word_embed
def set_input_embeddings(self, value):
self.model.word_embed = value
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def get_decoder(self):
return self.model
def set_decoder(self, decoder):
self.model = decoder
@add_start_docstrings_to_model_forward(DOGE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
logits_to_keep: Union[int, torch.Tensor] = 0,
**kwargs: Unpack[LossKwargs],
) -> Union[Tuple, CausalLMOutputWithPast]:
r"""
Args:
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
logits_to_keep (`int`, *optional*):
If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
This is useful when using packed tensor format (single dimension for batch and sequence length).
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> model = AutoModelForCausalLM.from_pretrained("SmallDoge/Doge-20M")
>>> tokenizer = AutoTokenizer.from_pretrained("SmallDoge/Doge-20M")
>>> prompt = "Hey, are you conscious? Can you talk to me?"
>>> 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]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder output consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
cache_position=cache_position,
**kwargs,
)
hidden_states = outputs[0]
# 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.vocab_size, **kwargs)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
The Doge Model transformer with a sequence classification head on top (linear layer).
[`DogeForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-2) do.
Since it does classification on the last token, it requires to know the position of the last token. If a
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
each row of the batch).
""",
DOGE_START_DOCSTRING,
)
class DogeForSequenceClassification(DogePreTrainedModel):
def __init__(self, config: DogeConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.model = DogeModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
self.config = config
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.word_embed
def set_input_embeddings(self, value):
self.model.word_embed = value
@add_start_docstrings_to_model_forward(DOGE_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
logits = self.score(hidden_states)
if input_ids is not None:
batch_size = input_ids.shape[0]
else:
batch_size = inputs_embeds.shape[0]
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
if self.config.pad_token_id is None:
last_non_pad_token = -1
elif input_ids is not None:
# To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
token_indices = torch.arange(input_ids.shape[-1], device=logits.device)
last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
else:
last_non_pad_token = -1
logger.warning_once(
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
)
pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
loss = None
if labels is not None:
loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
if not return_dict:
output = (pooled_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
__all__ = ["DogeForCausalLM", "DogeModel", "DogePreTrainedModel", "DogeForSequenceClassification"]