813 lines
35 KiB
Python
813 lines
35 KiB
Python
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/doge/modular_doge.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_doge.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 2025 Jingze Shi and the HuggingFace Inc. team. All rights reserved.
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#
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# The Doge family of small language models is trained by SmallDoge Team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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from typing import Callable, Optional, Union
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import torch
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import torch.nn.functional as F
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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
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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 ...integrations.flex_attention import compile_friendly_flex_attention
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from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
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from ...modeling_layers import GenericForSequenceClassification, GradientCheckpointingLayer
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from ...modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
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from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
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from ...modeling_utils import AttentionInterface, PreTrainedModel
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from ...processing_utils import Unpack
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from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torch_flex_attn_available
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from ...utils.generic import OutputRecorder, check_model_inputs
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from .configuration_doge import DogeConfig
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if is_torch_flex_attn_available():
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from torch.nn.attention.flex_attention import BlockMask
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@use_kernel_forward_from_hub("RMSNorm")
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class DogeRMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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"""
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DogeRMSNorm is equivalent to T5LayerNorm
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"""
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.variance_epsilon = eps
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def forward(self, hidden_states):
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input_dtype = hidden_states.dtype
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hidden_states = hidden_states.to(torch.float32)
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variance = hidden_states.pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
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return self.weight * hidden_states.to(input_dtype)
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def extra_repr(self):
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return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
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class DogeRotaryEmbedding(nn.Module):
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def __init__(self, config: DogeConfig, device=None):
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super().__init__()
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# BC: "rope_type" was originally "type"
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if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
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self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
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else:
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self.rope_type = "default"
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self.max_seq_len_cached = config.max_position_embeddings
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self.original_max_seq_len = config.max_position_embeddings
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self.config = config
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self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
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inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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self.original_inv_freq = self.inv_freq
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@torch.no_grad()
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@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
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def forward(self, x, position_ids):
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inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
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position_ids_expanded = position_ids[:, None, :].float()
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device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
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with torch.autocast(device_type=device_type, enabled=False): # Force float32
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freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
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emb = torch.cat((freqs, freqs), dim=-1)
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cos = emb.cos() * self.attention_scaling
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sin = emb.sin() * self.attention_scaling
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return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
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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 eager_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: Optional[torch.Tensor],
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scaling: float,
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dropout: float = 0.0,
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**kwargs: Unpack[TransformersKwargs],
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):
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key_states = repeat_kv(key, module.num_key_value_groups)
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value_states = repeat_kv(value, module.num_key_value_groups)
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attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
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if attention_mask is not None:
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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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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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attn_output = torch.matmul(attn_weights, value_states)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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def flex_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: Union[torch.Tensor, "BlockMask"],
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scaling: Optional[float] = None,
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softcap: Optional[float] = None,
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head_mask: Optional[torch.Tensor] = None,
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**kwargs,
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) -> tuple[torch.Tensor, torch.Tensor]:
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block_mask = None
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causal_mask = None
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if isinstance(attention_mask, BlockMask):
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block_mask = attention_mask
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else:
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causal_mask = attention_mask
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if causal_mask is not None:
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causal_mask = causal_mask[:, :, :, : key.shape[-2]]
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def score_mod(score, batch_idx, head_idx, q_idx, kv_idx):
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if softcap is not None:
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score = softcap * torch.tanh(score / softcap)
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if causal_mask is not None:
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score = score + causal_mask[batch_idx][head_idx][q_idx][kv_idx]
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if head_mask is not None:
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score = score + head_mask[batch_idx][head_idx][0][0]
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return score
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attn_output, attention_weights = compile_friendly_flex_attention(
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query,
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key,
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value,
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score_mod=score_mod,
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block_mask=block_mask,
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enable_gqa=True,
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scale=scaling,
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# Last time checked on PyTorch == 2.5.1: Flex Attention always computes the lse regardless.
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# For simplification, we thus always return it as no additional computations are introduced.
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return_lse=True,
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)
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# lse is returned in float32
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attention_weights = attention_weights.to(value.dtype)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attention_weights
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ALL_ATTENTION_FUNCTIONS = AttentionInterface()
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ALL_ATTENTION_FUNCTIONS["doge_flex_attention"] = flex_attention_forward
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class DogeAttention(nn.Module):
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def __init__(self, config: DogeConfig, 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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self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
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self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
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self.scaling = self.head_dim**-0.5
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self.attention_dropout = config.attention_dropout
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self.keep_window_size = config.keep_window_size
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self.q_proj = nn.Linear(
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config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
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)
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self.k_proj = nn.Linear(
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config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
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)
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self.v_proj = nn.Linear(
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config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
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)
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# dynamic mask for the QK^T attention weights matrix
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self.A = nn.Parameter(torch.zeros(config.num_key_value_heads))
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self.dt_proj = nn.Linear(
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config.num_key_value_heads * self.head_dim, config.num_key_value_heads, bias=config.attention_bias
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)
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self.o_proj = nn.Linear(
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config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
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)
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self.q_norm = DogeRMSNorm(self.head_dim, eps=config.rms_norm_eps)
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self.k_norm = DogeRMSNorm(self.head_dim, eps=config.rms_norm_eps)
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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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past_key_value: Optional[Cache] = None,
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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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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.head_dim)
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query_states = self.q_norm(self.q_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
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key_states = self.k_norm(self.k_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
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value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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cos, sin = position_embeddings
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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if past_key_value is not None:
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# sin and cos are specific to RoPE models; cache_position needed for the static cache
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
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# calculate dynamic mask from value_states
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dt_states = self.dt_proj(
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value_states.transpose(1, 2).reshape(value_states.shape[0], value_states.shape[-2], -1)
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)
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dt_states = torch.exp(self.A * F.softplus(dt_states)).transpose(-1, -2)
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attn_mask = self.prepare_dynamic_mask(
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hidden_states=hidden_states,
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dt_states=dt_states,
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keep_window_size=self.keep_window_size,
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attention_mask=attention_mask,
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)
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attn_mask = repeat_kv(attn_mask, self.num_key_value_groups)
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attention_interface: Callable = eager_attention_forward
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if self.config._attn_implementation != "eager":
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attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
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attn_output, attn_weights = attention_interface(
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self,
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query_states,
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key_states,
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value_states,
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attention_mask=attn_mask,
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dropout=0.0 if not self.training else self.attention_dropout,
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scaling=self.scaling,
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**kwargs,
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)
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attn_output = attn_output.reshape(*input_shape, -1).contiguous()
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attn_output = self.o_proj(attn_output)
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return attn_output, attn_weights
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def prepare_dynamic_mask(
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self,
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hidden_states: torch.Tensor,
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dt_states: torch.Tensor,
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keep_window_size: int = 2048,
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attention_mask: Optional[torch.Tensor] = None,
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):
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"""
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The core idea of DMA is to calculate the dynamic attention mask to mask the tokens that should be masked, so as to form sparse attention.
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Combine `dt_states` with `attention_mask` to generate the final `attn_mask`.
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Args:
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hidden_states (`torch.Tensor`): The input hidden_states, used to determine the minimum value of the current input precision.
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dt_states (`torch.Tensor`): dt_states of shape `(batch_size, num_heads, key_sequence_length)`.
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keep_window_size (`int`): The window size of tokens that are not dynamically masked, and dynamic masking is only performed when the sequence length exceeds this value.
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attention_mask (`torch.Tensor`, *optional*): attention mask of shape `(batch_size, 1, query_sequence_length, key_sequence_length)`.
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"""
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min_dtype = torch.finfo(hidden_states.dtype).min
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dtype = hidden_states.dtype
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attn_mask = dt_states[:, :, None, :].expand(
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-1, -1, hidden_states.shape[1], -1
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) # [batch_size, num_heads, query_len, key_len]
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if attention_mask is not None and not isinstance(attention_mask, BlockMask):
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if attention_mask.dtype == torch.bool:
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dtype = hidden_states.dtype
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attention_mask = torch.where(
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attention_mask, torch.tensor(0.0, device=attention_mask.device, dtype=dtype), min_dtype
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)
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attn_mask = attn_mask.masked_fill(attention_mask[:, :, :, : attn_mask.shape[-1]] != 0, min_dtype)
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if attn_mask.shape[-1] > keep_window_size:
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active_mask = torch.zeros_like(attn_mask, dtype=dtype, device=attn_mask.device)
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topk_indices = torch.topk(attn_mask, keep_window_size, dim=-1, largest=True, sorted=False).indices
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active_mask = active_mask.scatter(-1, topk_indices, 1.0)
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attn_mask = attn_mask.masked_fill(active_mask == 0.0, min_dtype)
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return attn_mask
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class DogeMLP(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=config.mlp_bias)
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self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
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self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
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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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class DogeCDMoE(nn.Module):
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def __init__(self, config: DogeConfig):
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super().__init__()
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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.act_fn = ACT2FN[config.hidden_act]
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self.num_experts = config.num_experts
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self.num_keys = math.floor(math.sqrt(self.num_experts))
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self.top_k = config.num_experts_per_tok
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self.norm_topk_prob = config.norm_topk_prob
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# shared expert
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self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
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self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
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self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
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# router gate for retrieval experts
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self.router_gate = nn.Linear(self.hidden_size, self.num_keys * 2, bias=False)
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# routed experts
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self.down_embed = nn.Embedding(self.num_experts, self.hidden_size)
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self.up_embed = nn.Embedding(self.num_experts, self.hidden_size)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
**kwargs,
|
|
) -> torch.Tensor:
|
|
bsz, seq_len, _ = hidden_states.shape
|
|
|
|
# get routing logits with router gate
|
|
router_logits = self.router_gate(hidden_states).view(2, bsz * seq_len, -1)
|
|
|
|
# get experts with the highest routing logits
|
|
(scores_x, scores_y), (indices_x, indices_y) = router_logits.topk(self.num_keys, dim=-1)
|
|
all_scores = scores_x.unsqueeze(-1) + scores_y.unsqueeze(-2)
|
|
all_indices = indices_x.unsqueeze(-1) * self.num_keys + indices_y.unsqueeze(-2)
|
|
all_scores = all_scores.view(*all_scores.shape[:-2], -1)
|
|
all_indices = all_indices.view(*all_indices.shape[:-2], -1)
|
|
scores, position_indices = all_scores.topk(self.top_k, dim=-1)
|
|
indices = all_indices.gather(-1, position_indices)
|
|
routing_weights = F.softmax(scores, dim=-1)
|
|
if self.norm_topk_prob:
|
|
routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
|
|
|
|
# mix routed experts states with shared expert states
|
|
down_embed = self.down_embed(indices)
|
|
up_embed = self.up_embed(indices)
|
|
experts_weights = torch.matmul(down_embed, hidden_states.view(bsz * seq_len, -1, 1)).view(bsz * seq_len, -1)
|
|
experts_weights = self.act_fn(experts_weights) * routing_weights
|
|
experts_states = torch.matmul(experts_weights.view(bsz * seq_len, 1, -1), up_embed).view(bsz, seq_len, -1)
|
|
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, router_logits
|
|
|
|
|
|
class DogeDecoderLayer(GradientCheckpointingLayer):
|
|
def __init__(self, config: DogeConfig, layer_idx: Optional[int] = None):
|
|
super().__init__()
|
|
self.hidden_dropout = config.hidden_dropout
|
|
|
|
self.input_layernorm = DogeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.self_attn = DogeAttention(config=config, layer_idx=layer_idx)
|
|
self.input_residual = nn.Parameter(torch.ones(config.hidden_size))
|
|
|
|
self.post_attention_layernorm = DogeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.mlp = DogeMLP(config) if not config.is_moe else DogeCDMoE(config)
|
|
self.post_attention_residual = nn.Parameter(torch.ones(config.hidden_size))
|
|
|
|
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[tuple[torch.Tensor]] = None,
|
|
use_cache: Optional[bool] = False,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
|
# sequence transformation
|
|
residual = hidden_states
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
hidden_states, self_attn_weights = self.self_attn(
|
|
hidden_states=hidden_states,
|
|
position_embeddings=position_embeddings,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_value,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
hidden_states = F.dropout(hidden_states, p=self.hidden_dropout, training=self.training)
|
|
hidden_states = self.input_residual * residual + hidden_states
|
|
|
|
# state transformation
|
|
residual = hidden_states
|
|
hidden_states = self.post_attention_layernorm(hidden_states)
|
|
hidden_states = self.mlp(hidden_states)
|
|
hidden_states = F.dropout(hidden_states, p=self.hidden_dropout, training=self.training)
|
|
hidden_states = self.post_attention_residual * residual + hidden_states
|
|
|
|
return hidden_states
|
|
|
|
|
|
@auto_docstring
|
|
class DogePreTrainedModel(PreTrainedModel):
|
|
config: DogeConfig
|
|
base_model_prefix = "model"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["DogeDecoderLayer"]
|
|
_skip_keys_device_placement = ["past_key_values"]
|
|
_supports_flash_attn = False
|
|
_supports_sdpa = True
|
|
_supports_flex_attn = True
|
|
_can_compile_fullgraph = False
|
|
_supports_attention_backend = True
|
|
_can_record_outputs = {
|
|
"router_logits": OutputRecorder(DogeCDMoE, index=1),
|
|
"hidden_states": DogeDecoderLayer,
|
|
"attentions": DogeAttention,
|
|
}
|
|
|
|
def _init_weights(self, module):
|
|
"""Initialize the weights"""
|
|
super()._init_weights(module)
|
|
if isinstance(module, DogeAttention):
|
|
if hasattr(module, "A"):
|
|
module.A.data.zero_()
|
|
elif isinstance(module, DogeDecoderLayer):
|
|
if hasattr(module, "input_residual"):
|
|
module.input_residual.data.fill_(1.0)
|
|
if hasattr(module, "post_attention_residual"):
|
|
module.post_attention_residual.data.fill_(1.0)
|
|
|
|
|
|
@auto_docstring
|
|
class DogeModel(DogePreTrainedModel):
|
|
def __init__(self, config: DogeConfig):
|
|
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(
|
|
[DogeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
|
)
|
|
self.norm = DogeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.rotary_emb = DogeRotaryEmbedding(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,
|
|
use_cache: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> MoeModelOutputWithPast:
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
|
|
if use_cache and past_key_values is None:
|
|
past_key_values = DynamicCache()
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
|
|
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)
|
|
|
|
mask_function = create_causal_mask if self.config.sliding_window is None else create_sliding_window_causal_mask
|
|
causal_mask = mask_function(
|
|
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
|
|
|
|
# create position embeddings to be shared across the decoder layers
|
|
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,
|
|
position_embeddings=position_embeddings,
|
|
attention_mask=causal_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_values,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
|
|
return MoeModelOutputWithPast( # only diff with Mistral is the output type, we need MoE
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values,
|
|
)
|
|
|
|
|
|
def load_balancing_loss_func(
|
|
gate_logits: Union[torch.Tensor, tuple[torch.Tensor], None],
|
|
num_experts: Optional[int] = None,
|
|
num_keys: Optional[int] = None,
|
|
top_k: int = 2,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
) -> Union[torch.Tensor, int]:
|
|
r"""
|
|
Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
|
|
|
|
See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss
|
|
function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
|
|
experts is too unbalanced.
|
|
|
|
Args:
|
|
gate_logits:
|
|
Logits from the `router_gate`, should be a tuple of model.config.num_hidden_layers tensors of
|
|
shape [2, batch_size * sequence_length, num_keys].
|
|
num_experts:
|
|
Number of experts
|
|
num_keys:
|
|
Number of keys
|
|
top_k:
|
|
The number of experts to route per-token, can be also interpreted as the `top-k` routing
|
|
parameter.
|
|
attention_mask (`torch.Tensor`, *optional*):
|
|
The attention_mask used in forward function
|
|
shape [batch_size X sequence_length] if not None.
|
|
|
|
Returns:
|
|
The auxiliary loss.
|
|
"""
|
|
if gate_logits is None or not isinstance(gate_logits, tuple):
|
|
return 0
|
|
|
|
compute_dtype = gate_logits[0].dtype
|
|
compute_device = gate_logits[0].device
|
|
all_expert_indices = []
|
|
all_routing_weights = []
|
|
|
|
for layer_gate_logits in gate_logits:
|
|
layer_gate_logits = layer_gate_logits.to(compute_device)
|
|
|
|
(scores_x, scores_y), (indices_x, indices_y) = layer_gate_logits.topk(num_keys, dim=-1)
|
|
|
|
all_scores = scores_x.unsqueeze(-1) + scores_y.unsqueeze(-2)
|
|
all_indices = indices_x.unsqueeze(-1) * num_keys + indices_y.unsqueeze(-2)
|
|
all_scores = all_scores.view(*all_scores.shape[:-2], -1)
|
|
all_indices = all_indices.view(*all_indices.shape[:-2], -1)
|
|
|
|
_, position_indices = all_scores.topk(top_k, dim=-1)
|
|
expert_indices = all_indices.gather(-1, position_indices)
|
|
|
|
routing_weights = F.softmax(all_scores, dim=-1)
|
|
|
|
all_expert_indices.append(expert_indices)
|
|
all_routing_weights.append(routing_weights)
|
|
all_expert_indices = torch.cat(all_expert_indices, dim=0)
|
|
all_routing_weights = torch.cat(all_routing_weights, dim=0)
|
|
|
|
if attention_mask is None:
|
|
# Compute the percentage of tokens routed to each experts
|
|
all_expert_indices = all_expert_indices.view(-1)
|
|
tokens_per_expert = torch.zeros(num_experts, dtype=compute_dtype, device=compute_device)
|
|
pad = torch.ones_like(all_expert_indices, dtype=compute_dtype, device=compute_device)
|
|
tokens_per_expert = tokens_per_expert.scatter_add_(0, all_expert_indices, pad) / all_expert_indices.shape[0]
|
|
|
|
# Compute the average probability of routing to these experts
|
|
router_prob_per_expert = torch.mean(all_routing_weights, dim=0)
|
|
else:
|
|
batch_size, sequence_length = attention_mask.shape
|
|
num_hidden_layers = len(gate_logits)
|
|
|
|
# Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
|
|
expert_attention_mask = (
|
|
attention_mask[None, :, :, None]
|
|
.expand((num_hidden_layers, batch_size, sequence_length, top_k))
|
|
.reshape(-1)
|
|
.to(compute_device)
|
|
)
|
|
all_expert_indices = all_expert_indices.view(-1)[expert_attention_mask.bool()]
|
|
|
|
# Compute the percentage of tokens routed to each experts
|
|
tokens_per_expert = torch.zeros(num_experts, dtype=compute_dtype, device=compute_device)
|
|
pad = torch.ones_like(all_expert_indices, dtype=compute_dtype, device=compute_device)
|
|
tokens_per_expert = tokens_per_expert.scatter_add_(0, all_expert_indices, pad) / torch.sum(
|
|
expert_attention_mask
|
|
)
|
|
|
|
# Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
|
|
router_per_expert_attention_mask = (
|
|
attention_mask[None, :, :, None]
|
|
.expand((num_hidden_layers, batch_size, sequence_length, num_experts))
|
|
.reshape(-1, num_experts)
|
|
.to(compute_device)
|
|
)
|
|
|
|
# Compute the average probability of routing to these experts
|
|
router_prob_per_expert = torch.sum(all_routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
|
|
router_per_expert_attention_mask, dim=0
|
|
)
|
|
|
|
overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert)
|
|
return overall_loss * num_experts
|
|
|
|
|
|
@auto_docstring
|
|
class DogeForCausalLM(DogePreTrainedModel, 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 = DogeModel(config)
|
|
self.vocab_size = config.vocab_size
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
self.router_aux_loss_coef = config.router_aux_loss_coef
|
|
self.num_experts = config.num_experts
|
|
self.num_experts_per_tok = config.num_experts_per_tok
|
|
|
|
# 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[list[torch.FloatTensor]] = 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,
|
|
output_router_logits: Optional[bool] = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> MoeCausalLMOutputWithPast:
|
|
r"""
|
|
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]`.
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, DogeForCausalLM
|
|
|
|
>>> model = DogeForCausalLM.from_pretrained("SmallDoge/Doge-320M")
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("SmallDoge/Doge-320M")
|
|
|
|
>>> 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_router_logits = (
|
|
output_router_logits if output_router_logits is not None else self.config.output_router_logits
|
|
)
|
|
|
|
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
|
outputs: MoeModelOutputWithPast = 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, labels, self.vocab_size, **kwargs)
|
|
|
|
aux_loss = None
|
|
if output_router_logits:
|
|
aux_loss = load_balancing_loss_func(
|
|
outputs.router_logits,
|
|
self.num_experts,
|
|
math.floor(math.sqrt(self.num_experts)),
|
|
self.num_experts_per_tok,
|
|
attention_mask,
|
|
)
|
|
if labels is not None:
|
|
loss += self.router_aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device
|
|
|
|
return MoeCausalLMOutputWithPast(
|
|
loss=loss,
|
|
aux_loss=aux_loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
router_logits=outputs.router_logits,
|
|
)
|
|
|
|
|
|
class DogeForSequenceClassification(GenericForSequenceClassification, DogePreTrainedModel):
|
|
pass
|
|
|
|
|
|
__all__ = ["DogeForCausalLM", "DogeModel", "DogePreTrainedModel", "DogeForSequenceClassification"]
|