|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
"""PyTorch Doge model."""
|
|
|
|
import math
|
|
from typing import List, Optional, Tuple, Union
|
|
|
|
import torch
|
|
import torch.nn.functional as F
|
|
import torch.utils.checkpoint
|
|
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_outputs import (
|
|
BaseModelOutputWithPast,
|
|
CausalLMOutputWithPast,
|
|
SequenceClassifierOutputWithPast,
|
|
)
|
|
from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
|
|
from transformers.modeling_utils import PreTrainedModel
|
|
from transformers.utils import (
|
|
add_start_docstrings,
|
|
add_start_docstrings_to_model_forward,
|
|
|
|
logging,
|
|
replace_return_docstrings,
|
|
)
|
|
from .configuration_doge import DogeConfig
|
|
|
|
try:
|
|
from einx import add as einx_add
|
|
except ImportError:
|
|
einx_add = None
|
|
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
_CONFIG_FOR_DOC = "DogeConfig"
|
|
|
|
|
|
class RMSNorm(nn.Module):
|
|
def __init__(self, hidden_size, eps=1e-6):
|
|
"""
|
|
RMSNorm 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 RotaryEmbedding(nn.Module):
|
|
def __init__(self, config: Optional[DogeConfig] = None):
|
|
super().__init__()
|
|
self.rope_kwargs = {}
|
|
|
|
if config.rope_scaling is None:
|
|
self.rope_type = "default"
|
|
else:
|
|
self.rope_type = config.rope_scaling
|
|
self.max_seq_len_cached = config.max_position_embeddings
|
|
self.original_max_seq_len = config.max_position_embeddings
|
|
self.base = config.rope_theta
|
|
|
|
self.config = config
|
|
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
|
|
|
|
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, **self.rope_kwargs)
|
|
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:
|
|
inv_freq, self.attention_scaling = self.rope_init_fn(
|
|
self.config, device, seq_len=seq_len, **self.rope_kwargs
|
|
)
|
|
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
|
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:
|
|
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)
|
|
|
|
|
|
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
|
|
position_ids_expanded = position_ids[:, None, :].float()
|
|
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()
|
|
|
|
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_QK_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
|
|
|
|
|
|
class DogeInnerFuncAttn(nn.Module):
|
|
"""Inner Function 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
|
|
if layer_idx is None:
|
|
logger.warning_once(
|
|
f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
|
|
"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
|
|
"when creating this class."
|
|
)
|
|
|
|
self.hidden_dim = config.hidden_size
|
|
self.num_attention_heads = config.num_attention_heads
|
|
self.attention_dropout = config.attention_dropout
|
|
|
|
|
|
self.attention_head_dim = self.hidden_dim // self.num_attention_heads
|
|
self.num_inner_values = config.num_inner_values
|
|
self.num_inner_value_heads = config.num_inner_value_heads
|
|
self.num_value_per_head = config.num_value_per_head
|
|
self.inner_values_retrieval_dim = config.inner_values_retrieval_size
|
|
|
|
|
|
self.q_proj = nn.Linear(
|
|
self.hidden_dim,
|
|
self.num_attention_heads * self.attention_head_dim,
|
|
bias=config.hidden_bias,
|
|
)
|
|
self.k_proj = nn.Linear(
|
|
self.hidden_dim,
|
|
self.num_attention_heads * self.attention_head_dim,
|
|
bias=config.hidden_bias,
|
|
)
|
|
|
|
|
|
self.dynamic_mask = nn.Parameter(
|
|
torch.round(torch.ones(self.num_attention_heads, config.max_position_embeddings))
|
|
)
|
|
|
|
|
|
self.v_queries = nn.Linear(
|
|
self.hidden_dim,
|
|
self.num_inner_value_heads * self.inner_values_retrieval_dim,
|
|
bias=config.hidden_bias,
|
|
)
|
|
self.v_keys = nn.Parameter(
|
|
torch.zeros(
|
|
self.num_inner_value_heads,
|
|
self.inner_values_retrieval_dim,
|
|
self.num_inner_values,
|
|
)
|
|
)
|
|
|
|
|
|
self.v_embed = nn.Embedding(
|
|
self.num_inner_values,
|
|
self.hidden_dim,
|
|
)
|
|
|
|
self.o_proj = nn.Linear(
|
|
self.hidden_dim,
|
|
self.hidden_dim,
|
|
bias=config.hidden_bias,
|
|
)
|
|
|
|
def _update_causal_mask(
|
|
self,
|
|
attention_mask: torch.Tensor = None,
|
|
input_tensor: torch.Tensor = None,
|
|
cache_position: torch.Tensor = None,
|
|
past_key_values: Cache = None,
|
|
output_attentions: bool = False,
|
|
):
|
|
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
|
|
)
|
|
|
|
|
|
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position_and_dynamic_mask(
|
|
attention_mask=attention_mask,
|
|
dynamic_mask=self.dynamic_mask,
|
|
sequence_length=sequence_length,
|
|
target_length=target_length,
|
|
dtype=dtype,
|
|
device=device,
|
|
cache_position=cache_position,
|
|
batch_size=input_tensor.shape[0],
|
|
)
|
|
|
|
return causal_mask
|
|
|
|
@staticmethod
|
|
def _prepare_4d_causal_attention_mask_with_cache_position_and_dynamic_mask(
|
|
attention_mask: torch.Tensor = None,
|
|
dynamic_mask: torch.Tensor = None,
|
|
sequence_length: int = None,
|
|
target_length: int = None,
|
|
dtype: torch.dtype = None,
|
|
device: torch.device = None,
|
|
cache_position: torch.Tensor = None,
|
|
batch_size: int = None,
|
|
**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)`.
|
|
dynamic_mask (`torch.Tensor`):
|
|
A 2D dynamic mask of shape `(num_heads, max_position_embeddings)`.
|
|
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:
|
|
|
|
causal_mask = attention_mask
|
|
else:
|
|
num_heads = 1 if dynamic_mask is None else dynamic_mask.size(0)
|
|
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, num_heads, -1, -1)
|
|
if attention_mask is not None:
|
|
causal_mask = causal_mask.clone()
|
|
mask_length = attention_mask.shape[-1]
|
|
attention_mask = attention_mask[:, None, None, :].expand(-1, num_heads, 1, -1)
|
|
if dynamic_mask is not None:
|
|
dynamic_mask = dynamic_mask[None, :, None, :mask_length].expand(batch_size, -1, 1, -1)
|
|
attention_mask = attention_mask.clone() * dynamic_mask
|
|
|
|
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask
|
|
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
|
|
padding_mask == 0, min_dtype
|
|
)
|
|
|
|
return causal_mask
|
|
|
|
def inner_func(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
"""
|
|
Each value can share weights with other values to increase the expressive power
|
|
"""
|
|
bsz, seq_len, _ = hidden_states.shape
|
|
|
|
v_queries = self.v_queries(hidden_states)
|
|
v_queries = v_queries.view(bsz, seq_len, self.num_inner_value_heads, -1).transpose(1, 2)
|
|
sim = torch.matmul(v_queries, self.v_keys)
|
|
v_embed = self.v_embed(sim.topk(k=self.num_value_per_head, dim=-1).indices)
|
|
|
|
v = hidden_states * v_embed.sum(dim=-2).sum(dim=-3)
|
|
return v
|
|
|
|
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,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
|
|
**kwargs,
|
|
) -> Tuple[torch.Tensor, Optional[Cache]]:
|
|
bsz, q_len, _ = hidden_states.shape
|
|
|
|
query_states = self.q_proj(hidden_states)
|
|
key_states = self.k_proj(hidden_states)
|
|
value_states = self.inner_func(hidden_states)
|
|
|
|
query_states = query_states.view(bsz, q_len, self.num_attention_heads, self.attention_head_dim).transpose(
|
|
1, 2
|
|
)
|
|
key_states = key_states.view(bsz, q_len, self.num_attention_heads, self.attention_head_dim).transpose(
|
|
1, 2
|
|
)
|
|
value_states = value_states.view(bsz, q_len, self.num_attention_heads, self.attention_head_dim).transpose(
|
|
1, 2
|
|
)
|
|
|
|
cos, sin = position_embeddings
|
|
query_states, query_states = apply_QK_rotary_pos_emb(query_states, query_states, cos, sin)
|
|
|
|
if past_key_value is not None:
|
|
|
|
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)
|
|
|
|
|
|
attn_weights = torch.matmul(query_states, key_states.transpose(-1, -2)) / math.sqrt(self.attention_head_dim)
|
|
|
|
|
|
causal_mask = self._update_causal_mask(attention_mask, hidden_states, cache_position, past_key_value)
|
|
causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
|
|
attn_weights = attn_weights + causal_mask
|
|
|
|
|
|
attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
|
|
attn_weights = F.dropout(attn_weights, p=self.attention_dropout, training=self.training)
|
|
|
|
|
|
attn_output = torch.matmul(attn_weights, value_states)
|
|
|
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
attn_output = attn_output.reshape(bsz, q_len, -1)
|
|
attn_output = self.o_proj(attn_output)
|
|
|
|
return attn_output, past_key_value
|
|
|
|
|
|
class DogeSdpaInnerFuncAttn(DogeInnerFuncAttn):
|
|
"""
|
|
Doge Inner Function Attention module using torch.nn.functional.scaled_dot_product_attention.
|
|
This module inherits from `DogeInnerFuncAttn` as the weights of the module stays untouched.
|
|
The only changes are on the forward pass to adapt to SDPA API.
|
|
"""
|
|
|
|
|
|
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,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
|
|
**kwargs,
|
|
) -> Tuple[torch.Tensor, Optional[Cache]]:
|
|
bsz, q_len, _ = hidden_states.shape
|
|
|
|
query_states = self.q_proj(hidden_states)
|
|
key_states = self.k_proj(hidden_states)
|
|
value_states = self.inner_func(hidden_states)
|
|
|
|
query_states = query_states.view(bsz, q_len, self.num_attention_heads, self.attention_head_dim).transpose(1, 2)
|
|
key_states = key_states.view(bsz, q_len, self.num_attention_heads, self.attention_head_dim).transpose(1, 2)
|
|
value_states = value_states.view(bsz, q_len, self.num_attention_heads, self.attention_head_dim).transpose(1, 2)
|
|
|
|
cos, sin = position_embeddings
|
|
query_states, query_states = apply_QK_rotary_pos_emb(query_states, query_states, cos, sin)
|
|
|
|
if past_key_value is not None:
|
|
|
|
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)
|
|
|
|
causal_mask = self._update_causal_mask(attention_mask, hidden_states, cache_position, past_key_value)
|
|
causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
|
|
|
|
query_states = query_states.contiguous()
|
|
key_states = key_states.contiguous()
|
|
value_states = value_states.contiguous()
|
|
|
|
attn_output = F.scaled_dot_product_attention(
|
|
query_states,
|
|
key_states,
|
|
value_states,
|
|
attn_mask=causal_mask,
|
|
dropout_p=self.attention_dropout,
|
|
)
|
|
|
|
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, past_key_value
|
|
|
|
|
|
DOGE_ATTENTION_CLASSES = {
|
|
"eager": DogeInnerFuncAttn,
|
|
"sdpa": DogeSdpaInnerFuncAttn,
|
|
}
|
|
|
|
|
|
class DogeCDMoE(nn.Module):
|
|
"""Cross-Domain Mixture of Experts from 'Wonderful Matrices' paper."""
|
|
|
|
def __init__(self, config: DogeConfig):
|
|
super().__init__()
|
|
self.hidden_dim = config.hidden_size
|
|
self.act_fn = ACT2FN[config.hidden_act]
|
|
self.intermediate_dim = config.intermediate_size
|
|
|
|
self.private_expert_retrieval_dim = config.private_expert_retrieval_size
|
|
self.num_cdmmoe_experts = config.num_cdmmoe_experts
|
|
self.num_cdmmoe_heads = config.num_cdmmoe_heads
|
|
self.num_cdmmoe_experts_per_head = config.num_cdmmoe_experts_per_head
|
|
|
|
|
|
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,
|
|
)
|
|
|
|
|
|
self.queries = nn.Linear(
|
|
self.hidden_dim,
|
|
self.num_cdmmoe_heads * self.private_expert_retrieval_dim,
|
|
bias=False,
|
|
)
|
|
self.num_keys = int(math.sqrt(self.num_cdmmoe_experts))
|
|
self.keys = nn.Parameter(
|
|
torch.zeros(
|
|
self.num_cdmmoe_heads,
|
|
self.num_keys,
|
|
2,
|
|
self.private_expert_retrieval_dim // 2,
|
|
)
|
|
)
|
|
|
|
|
|
self.down_embed = nn.Embedding(
|
|
self.num_cdmmoe_experts,
|
|
self.hidden_dim,
|
|
)
|
|
self.up_embed = nn.Embedding(
|
|
self.num_cdmmoe_experts,
|
|
self.hidden_dim,
|
|
)
|
|
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
**kwargs,
|
|
) -> torch.Tensor:
|
|
bsz, seq_len, _ = hidden_states.shape
|
|
|
|
|
|
queries = self.queries(hidden_states)
|
|
queries = queries.view(bsz, seq_len, 2, self.num_cdmmoe_heads, -1).permute(2, 0, 1, 3, 4)
|
|
sim = torch.einsum("p b t h n, h k p n -> p b t h k", queries, self.keys)
|
|
|
|
|
|
(scores_x, scores_y), (indices_x, indices_y) = sim.topk(self.num_cdmmoe_experts_per_head, dim=-1)
|
|
if einx_add is not None:
|
|
all_scores = einx_add("... i, ... j -> ... (i j)", scores_x, scores_y)
|
|
all_indices = einx_add("... i, ... j -> ... (i j)", indices_x * self.num_keys, indices_y)
|
|
else:
|
|
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_cdmmoe_experts_per_head, dim=-1)
|
|
indices = all_indices.gather(-1, pk_indices)
|
|
|
|
|
|
down_embed = self.down_embed(indices)
|
|
up_embed = self.up_embed(indices)
|
|
|
|
|
|
experts_weights = self.act_fn(torch.einsum("b t d, b t h k d -> b t h k", hidden_states, down_embed) * scores.softmax(dim=-1))
|
|
experts_states = torch.einsum("b t h k, b t h k d -> b t d", experts_weights, up_embed)
|
|
|
|
|
|
hidden_states = self.down_proj(self.act_fn(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.in_attn_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.attn = DOGE_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
|
|
self.in_ff_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.feed_forward = DogeCDMoE(config)
|
|
|
|
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,
|
|
**kwargs,
|
|
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
|
"""
|
|
Args:
|
|
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
|
attention_mask (`torch.FloatTensor`, *optional*):
|
|
attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
|
|
query_sequence_length, key_sequence_length)` if default attention is used.
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
|
returned tensors for more detail.
|
|
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`).
|
|
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
|
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
|
|
Indices depicting the position of the input sequence tokens in the sequence
|
|
position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
|
|
Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
|
|
with `head_dim` being the embedding dimension of each attention head.
|
|
kwargs (`dict`, *optional*):
|
|
Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
|
|
into the model
|
|
"""
|
|
|
|
|
|
residual = hidden_states
|
|
hidden_states = self.in_attn_layernorm(hidden_states)
|
|
hidden_states, present_key_value = self.attn(
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_value,
|
|
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 = residual + hidden_states
|
|
|
|
|
|
residual = hidden_states
|
|
hidden_states = self.in_ff_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 = residual + hidden_states
|
|
|
|
outputs = (hidden_states,)
|
|
|
|
if output_attentions:
|
|
outputs += (self_attn_weights,)
|
|
|
|
if use_cache:
|
|
outputs += (present_key_value,)
|
|
|
|
return outputs
|
|
|
|
|
|
@add_start_docstrings("The bare Doge Model outputting raw hidden-states without any specific head on top.")
|
|
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_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.")
|
|
class DogeModel(DogePreTrainedModel):
|
|
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 = RotaryEmbedding(config)
|
|
self.layers = nn.ModuleList(
|
|
[DogeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
|
)
|
|
self.final_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.gradient_checkpointing = False
|
|
|
|
|
|
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,
|
|
) -> 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)
|
|
|
|
|
|
return_legacy_cache = False
|
|
if use_cache and not isinstance(past_key_values, Cache):
|
|
return_legacy_cache = True
|
|
if past_key_values is None:
|
|
past_key_values = DynamicCache()
|
|
else:
|
|
past_key_values = DynamicCache.from_legacy_cache(past_key_values)
|
|
logger.warning_once(
|
|
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
|
|
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
|
|
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
|
|
)
|
|
|
|
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)
|
|
|
|
|
|
|
|
|
|
hidden_states = inputs_embeds
|
|
|
|
|
|
position_embeddings = self.rotary_emb(hidden_states, position_ids)
|
|
|
|
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attns = () if output_attentions else None
|
|
|
|
for decoder_layer in self.layers:
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
layer_outputs = self._gradient_checkpointing_func(
|
|
decoder_layer.__call__,
|
|
hidden_states,
|
|
attention_mask,
|
|
position_ids,
|
|
past_key_values,
|
|
output_attentions,
|
|
use_cache,
|
|
cache_position,
|
|
position_embeddings,
|
|
)
|
|
else:
|
|
layer_outputs = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=attention_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,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if use_cache:
|
|
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
|
|
|
|
if output_attentions:
|
|
all_self_attns += (layer_outputs[1],)
|
|
|
|
hidden_states = self.final_layernorm(hidden_states)
|
|
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
next_cache = next_decoder_cache if use_cache else None
|
|
if return_legacy_cache:
|
|
next_cache = next_cache.to_legacy_cache()
|
|
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
|
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=next_cache,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attns,
|
|
)
|
|
|
|
"""Move to DogeInnerFuncAttn"""
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class DogeForCausalLM(DogePreTrainedModel, GenerationMixin):
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
|
|
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)
|
|
|
|
|
|
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 set_decoder(self, decoder):
|
|
self.model = decoder
|
|
|
|
def get_decoder(self):
|
|
return self.model
|
|
|
|
@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[torch.Tensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
num_logits_to_keep: int = 0,
|
|
**loss_kwargs,
|
|
) -> Union[Tuple, CausalLMOutputWithPast]:
|
|
r"""
|
|
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]`.
|
|
|
|
num_logits_to_keep (`int`, *optional*):
|
|
Calculate logits for the last `num_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.
|
|
|
|
Returns:
|
|
"""
|
|
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
|
|
|
|
|
|
outputs = self.model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
cache_position=cache_position,
|
|
)
|
|
|
|
hidden_states = outputs[0]
|
|
|
|
|
|
logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.vocab_size, **loss_kwargs)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[1:]
|
|
return (loss,) + output if loss is not None else output
|
|
|
|
return CausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@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).
|
|
"""
|
|
)
|
|
class DogeForSequenceClassification(DogePreTrainedModel):
|
|
def __init__(self, config: DogeConfig):
|
|
super().__init__(config)
|
|
self.config = config
|
|
self.num_labels = config.num_labels
|
|
|
|
self.model = DogeModel(config)
|
|
self.classifier = nn.Linear(config.hidden_size, self.num_labels, bias=False)
|
|
|
|
|
|
self.init_weights()
|
|
|
|
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
|
|
|
|
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,
|
|
)
|
|
hidden_states = outputs[0]
|
|
logits = self.classifier(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:
|
|
sequence_lengths = -1
|
|
else:
|
|
if input_ids is not None:
|
|
|
|
sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
|
|
sequence_lengths = sequence_lengths % input_ids.shape[-1]
|
|
sequence_lengths = sequence_lengths.to(logits.device)
|
|
else:
|
|
sequence_lengths = -1
|
|
|
|
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
|
|
|
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,) + outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return SequenceClassifierOutputWithPast(
|
|
loss=loss,
|
|
logits=pooled_logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|