614 lines
27 KiB
Python
614 lines
27 KiB
Python
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/dots1/modular_dots1.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_dots1.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 The rednote-hilab team and the HuggingFace Inc. team. All rights reserved.
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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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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 ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
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from ...modeling_flash_attention_utils import FlashAttentionKwargs
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
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from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from ...processing_utils import Unpack
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from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
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from ...utils.generic import check_model_inputs
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from .configuration_dots1 import Dots1Config
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@use_kernel_forward_from_hub("RMSNorm")
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class Dots1RMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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"""
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Dots1RMSNorm 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 Dots1RotaryEmbedding(nn.Module):
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def __init__(self, config: Dots1Config, 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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class Dots1Attention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(self, config: Dots1Config, layer_idx: int):
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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.is_causal = True
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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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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 = Dots1RMSNorm(self.head_dim, eps=config.rms_norm_eps) # unlike olmo, only on the head dim!
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self.k_norm = Dots1RMSNorm(self.head_dim, eps=config.rms_norm_eps) # thus post q_norm does not need reshape
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self.sliding_window = config.sliding_window if config.layer_types[layer_idx] == "sliding_attention" else None
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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],
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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: Unpack[FlashAttentionKwargs],
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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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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,
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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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sliding_window=self.sliding_window, # diff with Llama
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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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class Dots1MLP(nn.Module):
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def __init__(self, config, hidden_size=None, intermediate_size=None):
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size if hidden_size is None else hidden_size
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self.intermediate_size = config.intermediate_size if intermediate_size is None else intermediate_size
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self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
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self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
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self.act_fn = ACT2FN[config.hidden_act]
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def forward(self, x):
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down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
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return down_proj
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class Dots1MoE(nn.Module):
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"""
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A mixed expert module containing shared experts.
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"""
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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.experts = nn.ModuleList(
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[Dots1MLP(config, intermediate_size=config.moe_intermediate_size) for _ in range(config.n_routed_experts)]
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)
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self.gate = Dots1TopkRouter(config)
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self.shared_experts = Dots1MLP(
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config=config, intermediate_size=config.moe_intermediate_size * config.n_shared_experts
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)
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def moe(self, hidden_states: torch.Tensor, topk_indices: torch.Tensor, topk_weights: torch.Tensor):
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r"""
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CALL FOR CONTRIBUTION! I don't have time to optimise this right now, but expert weights need to be fused
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to not have to do a loop here (deepseek has 256 experts soooo yeah).
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"""
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final_hidden_states = torch.zeros_like(hidden_states, dtype=topk_weights.dtype)
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expert_mask = torch.nn.functional.one_hot(topk_indices, num_classes=len(self.experts))
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expert_mask = expert_mask.permute(2, 0, 1)
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for expert_idx in range(len(self.experts)):
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expert = self.experts[expert_idx]
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mask = expert_mask[expert_idx]
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token_indices, weight_indices = torch.where(mask)
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if token_indices.numel() > 0:
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expert_weights = topk_weights[token_indices, weight_indices]
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expert_input = hidden_states[token_indices]
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expert_output = expert(expert_input)
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weighted_output = expert_output * expert_weights.unsqueeze(-1)
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final_hidden_states.index_add_(0, token_indices, weighted_output)
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# in original deepseek, the output of the experts are gathered once we leave this module
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# thus the moe module is itelsf an IsolatedParallel module
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# and all expert are "local" meaning we shard but we don't gather
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return final_hidden_states.type(hidden_states.dtype)
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def forward(self, hidden_states):
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residuals = hidden_states
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orig_shape = hidden_states.shape
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topk_indices, topk_weights = self.gate(hidden_states)
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hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
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hidden_states = self.moe(hidden_states, topk_indices, topk_weights).view(*orig_shape)
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hidden_states = hidden_states + self.shared_experts(residuals)
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return hidden_states
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class Dots1TopkRouter(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.top_k = config.num_experts_per_tok
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self.n_routed_experts = config.n_routed_experts
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self.routed_scaling_factor = config.routed_scaling_factor
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self.n_group = config.n_group
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self.topk_group = config.topk_group
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self.norm_topk_prob = config.norm_topk_prob
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self.weight = nn.Parameter(torch.empty((self.n_routed_experts, config.hidden_size)))
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self.register_buffer("e_score_correction_bias", torch.zeros(self.n_routed_experts))
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@torch.no_grad()
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def get_topk_indices(self, scores):
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scores_for_choice = scores.view(-1, self.n_routed_experts) + self.e_score_correction_bias.unsqueeze(0)
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group_scores = (
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scores_for_choice.view(-1, self.n_group, self.n_routed_experts // self.n_group)
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.topk(2, dim=-1)[0]
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.sum(dim=-1)
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)
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group_idx = torch.topk(group_scores, k=self.topk_group, dim=-1, sorted=False)[1]
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group_mask = torch.zeros_like(group_scores)
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group_mask.scatter_(1, group_idx, 1)
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score_mask = (
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group_mask.unsqueeze(-1)
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.expand(-1, self.n_group, self.n_routed_experts // self.n_group)
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.reshape(-1, self.n_routed_experts)
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)
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scores_for_choice = scores_for_choice.masked_fill(~score_mask.bool(), 0.0)
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topk_indices = torch.topk(scores_for_choice, k=self.top_k, dim=-1, sorted=False)[1]
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return topk_indices
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def forward(self, hidden_states):
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hidden_states = hidden_states.view(-1, self.config.hidden_size)
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router_logits = F.linear(hidden_states.type(torch.float32), self.weight.type(torch.float32))
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scores = router_logits.sigmoid()
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topk_indices = self.get_topk_indices(scores)
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topk_weights = scores.gather(1, topk_indices)
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if self.norm_topk_prob:
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denominator = topk_weights.sum(dim=-1, keepdim=True) + 1e-20
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topk_weights /= denominator
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topk_weights = topk_weights * self.routed_scaling_factor
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return topk_indices, topk_weights
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class Dots1DecoderLayer(GradientCheckpointingLayer):
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def __init__(self, config: Dots1Config, layer_idx: int):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.self_attn = Dots1Attention(config=config, layer_idx=layer_idx)
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if layer_idx >= config.first_k_dense_replace:
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self.mlp = Dots1MoE(config)
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else:
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self.mlp = Dots1MLP(config)
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self.input_layernorm = Dots1RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = Dots1RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.attention_type = config.layer_types[layer_idx]
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Cache] = None,
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use_cache: Optional[bool] = False,
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cache_position: Optional[torch.LongTensor] = None,
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position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
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|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple[torch.Tensor]:
|
|
residual = hidden_states
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
# Self Attention
|
|
hidden_states, _ = self.self_attn(
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_value,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
position_embeddings=position_embeddings,
|
|
**kwargs,
|
|
)
|
|
hidden_states = residual + hidden_states
|
|
|
|
# Fully Connected
|
|
residual = hidden_states
|
|
hidden_states = self.post_attention_layernorm(hidden_states)
|
|
hidden_states = self.mlp(hidden_states)
|
|
hidden_states = residual + hidden_states
|
|
return hidden_states
|
|
|
|
|
|
@auto_docstring
|
|
class Dots1PreTrainedModel(PreTrainedModel):
|
|
config: Dots1Config
|
|
base_model_prefix = "model"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["Dots1DecoderLayer"]
|
|
_skip_keys_device_placement = ["past_key_values"]
|
|
_supports_flash_attn = True
|
|
_supports_sdpa = True
|
|
_supports_flex_attn = True
|
|
|
|
_can_compile_fullgraph = True
|
|
_supports_attention_backend = True
|
|
_can_record_outputs = {
|
|
"hidden_states": Dots1DecoderLayer,
|
|
"attentions": Dots1Attention,
|
|
}
|
|
|
|
def _init_weights(self, module):
|
|
super()._init_weights(module)
|
|
if isinstance(module, Dots1TopkRouter):
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
|
|
|
|
@auto_docstring
|
|
class Dots1Model(Dots1PreTrainedModel):
|
|
def __init__(self, config: Dots1Config):
|
|
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(
|
|
[Dots1DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
|
)
|
|
self.norm = Dots1RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.rotary_emb = Dots1RotaryEmbedding(config=config)
|
|
self.gradient_checkpointing = False
|
|
self.has_sliding_layers = "sliding_attention" in self.config.layer_types
|
|
|
|
# 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],
|
|
) -> BaseModelOutputWithPast:
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
|
|
if use_cache and past_key_values is None:
|
|
past_key_values = DynamicCache()
|
|
|
|
if cache_position is None:
|
|
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
cache_position = torch.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)
|
|
|
|
# It may already have been prepared by e.g. `generate`
|
|
if not isinstance(causal_mask_mapping := attention_mask, dict):
|
|
# Prepare mask arguments
|
|
mask_kwargs = {
|
|
"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,
|
|
}
|
|
# Create the masks
|
|
causal_mask_mapping = {
|
|
"full_attention": create_causal_mask(**mask_kwargs),
|
|
}
|
|
# The sliding window alternating layers are not always activated depending on the config
|
|
if self.has_sliding_layers:
|
|
causal_mask_mapping["sliding_attention"] = create_sliding_window_causal_mask(**mask_kwargs)
|
|
|
|
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,
|
|
attention_mask=causal_mask_mapping[decoder_layer.attention_type],
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_values,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
position_embeddings=position_embeddings,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values if use_cache else None,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class Dots1ForCausalLM(Dots1PreTrainedModel, 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 = Dots1Model(config)
|
|
self.vocab_size = config.vocab_size
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def set_decoder(self, decoder):
|
|
self.model = decoder
|
|
|
|
def get_decoder(self):
|
|
return self.model
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Cache] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
logits_to_keep: Union[int, torch.Tensor] = 0,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> CausalLMOutputWithPast:
|
|
r"""
|
|
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, Dots1ForCausalLM
|
|
|
|
>>> model = Dots1ForCausalLM.from_pretrained("rednote-hilab/dots1.llm1.inst")
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("rednote-hilab/dots1.llm1.inst")
|
|
|
|
>>> 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."
|
|
```"""
|
|
outputs: BaseModelOutputWithPast = self.model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = outputs.last_hidden_state
|
|
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
|
|
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
|
logits = self.lm_head(hidden_states[:, slice_indices, :])
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
|
|
|
|
return CausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
__all__ = ["Dots1PreTrainedModel", "Dots1Model", "Dots1ForCausalLM"]
|