1295 lines
52 KiB
Python
1295 lines
52 KiB
Python
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/aria/modular_aria.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_aria.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# coding=utf-8
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# Copyright 2024 The Rhymes-AI Teams Authors 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 dataclasses import dataclass
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from typing import Callable, Optional, Union
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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
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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, ModelOutput
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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 ...utils.import_utils import is_torch_available
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from ..auto import AutoModel
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from .configuration_aria import AriaConfig, AriaTextConfig
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if is_torch_available():
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import torch
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from torch import nn
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@use_kernel_forward_from_hub("RMSNorm")
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class AriaTextRMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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"""
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AriaTextRMSNorm 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 AriaProjectorMLP(nn.Module):
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"""
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Feed-Forward Network module for the Aria Projector.
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Args:
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in_features (`int`):
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Input embedding dimension.
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hidden_features (`int`):
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Hidden dimension of the feed-forward network.
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output_dim (`int`):
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Output dimension.
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"""
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def __init__(self, in_features, hidden_features, output_dim):
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super().__init__()
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self.linear_in = nn.Linear(in_features, hidden_features, bias=False)
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self.linear_out = nn.Linear(hidden_features, output_dim, bias=False)
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self.act = ACT2FN["gelu_new"]
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def forward(self, hidden_states):
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hidden_states = self.act(self.linear_in(hidden_states))
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hidden_states = self.linear_out(hidden_states)
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return hidden_states
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class AriaCrossAttention(nn.Module):
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"""
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Aria Cross-Attention module.
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Args:
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config (`AriaConfig`):
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The configuration to use.
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"""
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def __init__(self, config: AriaConfig, dropout_rate: float = 0):
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super().__init__()
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hidden_size = config.vision_config.hidden_size
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num_heads = config.vision_config.num_attention_heads
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self.num_heads = num_heads
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self.q_proj = nn.Linear(hidden_size, hidden_size, bias=False)
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self.k_proj = nn.Linear(hidden_size, hidden_size, bias=False)
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self.v_proj = nn.Linear(hidden_size, hidden_size, bias=False)
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# Original code here: https://github.com/rhymes-ai/Aria/blob/719ff4e52b727443cba3793b0e27fe64e0244fe1/aria/model/projector.py#L48
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self.multihead_attn = nn.MultiheadAttention(hidden_size, num_heads, batch_first=True)
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self.linear = nn.Linear(hidden_size, hidden_size)
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self.dropout = nn.Dropout(dropout_rate)
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self.layer_norm = nn.LayerNorm(hidden_size)
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self.layer_norm_kv = nn.LayerNorm(hidden_size)
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def forward(self, key_value_states, hidden_states, attn_mask=None):
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"""
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Forward pass of the AriaCrossAttention module.
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Args:
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key_value_states (`torch.Tensor`):
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Input tensor for key and value.
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hidden_states (`torch.Tensor`):
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Input tensor for query.
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attn_mask (`torch.Tensor`, *optional*, defaults to None):
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Attention mask.
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Returns:
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torch.Tensor:
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Output tensor after cross-attention.
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"""
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query = self.q_proj(self.layer_norm(hidden_states))
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key_value_states = self.layer_norm_kv(key_value_states)
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key = self.k_proj(key_value_states)
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value = self.v_proj(key_value_states)
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attn_output, _ = self.multihead_attn(query, key, value, attn_mask=attn_mask)
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attn_output = self.dropout(self.linear(attn_output))
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return attn_output
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class AriaProjector(nn.Module):
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"""
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Aria Projector module.
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This module projects vision features into the language model's embedding space, enabling interaction between vision and language components.
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Args:
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config (`AriaConfig`):
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Configuration object for the model.
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"""
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def __init__(
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self,
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config: AriaConfig,
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):
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super().__init__()
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self.patch_to_query_dict = config.projector_patch_to_query_dict
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self.in_features = config.vision_config.hidden_size
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self.num_heads = config.vision_config.num_attention_heads
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self.kv_dim = config.vision_config.hidden_size
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self.hidden_features = config.text_config.hidden_size
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self.output_dim = config.text_config.hidden_size
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self.query = nn.Parameter(torch.zeros(config.max_value_projector_patch_to_query_dict, self.in_features))
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self.cross_attn = AriaCrossAttention(config)
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self.layer_norm = nn.LayerNorm(self.in_features)
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self.feed_forward = AriaProjectorMLP(self.in_features, self.hidden_features, self.output_dim)
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def forward(self, key_value_states: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
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"""
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Forward pass of the Projector module.
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Args:
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key_value_states (`torch.Tensor`):
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Input tensor of shape (batch_size, num_patches, kv_dim).
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attn_mask (`torch.Tensor`, *optional*, default is None):
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Attention mask.
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Returns:
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`torch.Tensor`: Output tensor of shape (batch_size, query_number, output_dim).
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"""
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batch_size, num_patches = key_value_states.shape[0], key_value_states.shape[1]
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if num_patches not in self.patch_to_query_dict.keys():
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raise KeyError(
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f"Number of patches {num_patches} not found in patch_to_query_dict amongst possible values {self.patch_to_query_dict.keys()}."
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)
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query_num = self.patch_to_query_dict[num_patches]
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queries = self.query[:query_num].unsqueeze(0).repeat(batch_size, 1, 1)
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if attn_mask is not None:
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attn_mask = attn_mask.repeat_interleave(self.num_heads, 0)
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attn_mask = attn_mask.unsqueeze(1).expand(-1, queries.size(1), -1)
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attention_out = self.cross_attn(key_value_states, queries, attn_mask=attn_mask)
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out = self.feed_forward(self.layer_norm(attention_out))
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return out
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class AriaSharedExpertsMLP(nn.Module):
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"""
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Shared Expert MLP for shared experts.
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Unlike routed experts, shared experts process all tokens without routing.
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This class reconfigures the intermediate size in comparison to the LlamaMLP.
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Args:
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config (`AriaTextConfig`): Configuration object for the Aria language model.
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"""
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def __init__(self, config: AriaTextConfig):
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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 * config.moe_num_shared_experts
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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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def sequential_experts_gemm(token_states, expert_weights, tokens_per_expert):
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"""
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Compute the matrix multiplication (GEMM) for each expert sequentially. This approach is computationally inefficient, especially when dealing with a large number of experts.
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Args:
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token_states (torch.Tensor): Input tensor of shape (num_tokens, in_features).
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expert_weights (torch.Tensor): Weight tensor of shape (num_experts, in_features, out_features).
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tokens_per_expert (torch.Tensor): Number of tokens assigned to each expert.
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Returns:
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torch.Tensor: Output tensor of shape (num_tokens, out_features).
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"""
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num_tokens = token_states.shape[0]
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out_features = expert_weights.shape[-1]
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output = torch.zeros(num_tokens, out_features, dtype=token_states.dtype, device=token_states.device)
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cumsum_num_tokens = torch.cumsum(tokens_per_expert, dim=0)
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# Insert zero at the beginning for offset index's convenience
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zero_tensor = torch.zeros(1, dtype=torch.long, device=cumsum_num_tokens.device)
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cumsum_num_tokens = torch.cat((zero_tensor, cumsum_num_tokens))
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for expert_num in range(expert_weights.shape[0]):
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start = cumsum_num_tokens[expert_num]
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end = cumsum_num_tokens[expert_num + 1]
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tokens = token_states[start:end]
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out = torch.matmul(tokens, expert_weights[expert_num])
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output[start:end] = out
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return output
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class AriaGroupedExpertsGemm(nn.Module):
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"""
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Grouped GEMM (General Matrix Multiplication) module for efficient expert computation.
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This module utilizes the grouped_gemm library (https://github.com/fanshiqing/grouped_gemm)
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for optimized performance. If the grouped_gemm library is not installed, it gracefully
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falls back to a sequential GEMM implementation, which may be slower but ensures
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functionality.
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Args:
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in_features (`int`):
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Number of input features.
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out_features (`int`):
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Number of output features.
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groups (`int`):
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Number of expert groups.
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"""
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def __init__(self, in_features, out_features, groups):
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super().__init__()
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self.in_features = in_features
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self.out_features = out_features
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self.groups = groups
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self.weight = nn.Parameter(torch.empty(groups, in_features, out_features))
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def forward(self, input, tokens_per_expert):
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"""
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Perform grouped matrix multiplication.
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Args:
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input (`torch.Tensor`):
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Input tensor of shape (num_tokens, in_features).
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tokens_per_expert (`torch.Tensor`):
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Number of tokens assigned to each expert.
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Returns:
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torch.Tensor: Output tensor of shape (num_tokens, out_features).
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"""
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return sequential_experts_gemm(
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input,
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self.weight,
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tokens_per_expert.cpu(),
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)
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class AriaGroupedExpertsMLP(nn.Module):
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"""
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Grouped MLP module for Mixture of Experts.
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Args:
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config (`AriaTextConfig`):
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Configuration object for the model.
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"""
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def __init__(self, config: AriaTextConfig) -> None:
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super().__init__()
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self.config = config
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self.fc1 = AriaGroupedExpertsGemm(config.hidden_size, config.intermediate_size * 2, config.moe_num_experts)
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self.fc2 = AriaGroupedExpertsGemm(config.intermediate_size, config.hidden_size, config.moe_num_experts)
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def forward(self, permuted_tokens, tokens_per_expert):
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"""
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Forward pass of the Grouped MLP.
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Args:
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permuted_tokens (torch.Tensor): Permuted input tokens.
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tokens_per_expert (torch.Tensor): Number of tokens assigned to each expert.
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Returns:
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torch.Tensor: Output tensor after passing through the MLP.
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"""
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fc1_output = self.fc1(permuted_tokens, tokens_per_expert)
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projection, gate = torch.chunk(fc1_output, 2, dim=-1)
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fc1_output = nn.functional.silu(projection) * gate
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fc2_output = self.fc2(fc1_output, tokens_per_expert)
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return fc2_output
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# Token permutation adapted from https://github.com/NVIDIA/Megatron-LM/blob/54f1f78529cbc2b9cddad313e7f9d96ac0420a27/megatron/core/transformer/moe/token_dispatcher.py#L291-L587
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class AriaTextMoELayer(nn.Module):
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"""
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Aria Text Mixture of Experts (MoE) Layer.
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This layer applies a gating mechanism to route input tokens to different experts.
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Args:
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config (`AriaTextConfig`):
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Configuration object for the text component of the model.
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"""
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def __init__(self, config: AriaTextConfig):
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super().__init__()
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self.router = nn.Linear(config.hidden_size, config.moe_num_experts, bias=False)
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self.experts = AriaGroupedExpertsMLP(config)
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self.shared_experts = AriaSharedExpertsMLP(config)
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self.config = config
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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"""
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Forward pass of the MoE Layer.
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Args:
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hidden_states (`torch.Tensor`):
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Input tensor of shape (batch_size, sequence_length, hidden_size).
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Returns:
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torch.Tensor: Output tensor after passing through the MoE layer.
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Process:
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1. Route tokens to experts using the router.
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2. Permute tokens based on routing decisions.
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3. Process tokens through experts.
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4. Unpermute and combine expert outputs.
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5. Add shared expert output to the final result.
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"""
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original_shape = hidden_states.shape
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hidden_states = hidden_states.view(-1, hidden_states.size(-1))
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# Top K Routing
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logits = self.router(hidden_states)
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top_logits, top_indices = torch.topk(logits, k=self.config.moe_topk, dim=1)
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scores = nn.functional.softmax(top_logits, dim=-1)
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original_dtype = top_indices.dtype
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tokens_per_expert = torch.histc(
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top_indices.flatten().to(torch.float32),
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bins=self.config.moe_num_experts,
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min=0,
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max=self.config.moe_num_experts - 1,
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).to(original_dtype)
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indices = top_indices
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# Token permutation
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flatten_indices = indices.view(-1)
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sorted_indices = torch.argsort(flatten_indices)
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permuted_tokens = hidden_states.index_select(0, sorted_indices // self.config.moe_topk)
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# Process through experts
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expert_output = self.experts(permuted_tokens, tokens_per_expert)
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# Token unpermutation
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unpermuted_tokens = torch.zeros(
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(scores.shape[0] * self.config.moe_topk, expert_output.size(1)),
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dtype=expert_output.dtype,
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device=expert_output.device,
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)
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unpermuted_tokens.index_copy_(0, sorted_indices, expert_output)
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unpermuted_tokens = unpermuted_tokens.view(-1, self.config.moe_topk, expert_output.size(1))
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output = (unpermuted_tokens * scores.unsqueeze(-1)).sum(dim=1).view(original_shape)
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# Add shared expert output
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shared_expert_output = self.shared_experts(hidden_states.view(original_shape))
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return output + shared_expert_output
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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
|
|
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
|
|
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
|
|
Returns:
|
|
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
|
|
"""
|
|
cos = cos.unsqueeze(unsqueeze_dim)
|
|
sin = sin.unsqueeze(unsqueeze_dim)
|
|
q_embed = (q * cos) + (rotate_half(q) * sin)
|
|
k_embed = (k * cos) + (rotate_half(k) * sin)
|
|
return q_embed, k_embed
|
|
|
|
|
|
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
|
|
"""
|
|
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
|
|
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
|
|
"""
|
|
batch, num_key_value_heads, slen, head_dim = hidden_states.shape
|
|
if n_rep == 1:
|
|
return hidden_states
|
|
hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
|
|
return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
|
|
|
|
|
|
def eager_attention_forward(
|
|
module: nn.Module,
|
|
query: torch.Tensor,
|
|
key: torch.Tensor,
|
|
value: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor],
|
|
scaling: float,
|
|
dropout: float = 0.0,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
):
|
|
key_states = repeat_kv(key, module.num_key_value_groups)
|
|
value_states = repeat_kv(value, module.num_key_value_groups)
|
|
|
|
attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
|
|
if attention_mask is not None:
|
|
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
|
|
attn_weights = attn_weights + causal_mask
|
|
|
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
|
|
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
|
|
attn_output = torch.matmul(attn_weights, value_states)
|
|
attn_output = attn_output.transpose(1, 2).contiguous()
|
|
|
|
return attn_output, attn_weights
|
|
|
|
|
|
class AriaTextAttention(nn.Module):
|
|
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
|
|
|
def __init__(self, config: AriaTextConfig, layer_idx: int):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layer_idx = layer_idx
|
|
self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
|
|
self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
|
|
self.scaling = self.head_dim**-0.5
|
|
self.attention_dropout = config.attention_dropout
|
|
self.is_causal = True
|
|
|
|
self.q_proj = nn.Linear(
|
|
config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
|
|
)
|
|
self.k_proj = nn.Linear(
|
|
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
|
|
)
|
|
self.v_proj = nn.Linear(
|
|
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
|
|
)
|
|
self.o_proj = nn.Linear(
|
|
config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
position_embeddings: tuple[torch.Tensor, torch.Tensor],
|
|
attention_mask: Optional[torch.Tensor],
|
|
past_key_value: Optional[Cache] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
|
input_shape = hidden_states.shape[:-1]
|
|
hidden_shape = (*input_shape, -1, self.head_dim)
|
|
|
|
query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
|
key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
|
value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
|
|
|
|
cos, sin = position_embeddings
|
|
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
|
|
|
if past_key_value is not None:
|
|
# sin and cos are specific to RoPE models; cache_position needed for the static cache
|
|
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
|
|
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
|
|
|
attention_interface: Callable = eager_attention_forward
|
|
if self.config._attn_implementation != "eager":
|
|
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
|
|
|
|
attn_output, attn_weights = attention_interface(
|
|
self,
|
|
query_states,
|
|
key_states,
|
|
value_states,
|
|
attention_mask,
|
|
dropout=0.0 if not self.training else self.attention_dropout,
|
|
scaling=self.scaling,
|
|
**kwargs,
|
|
)
|
|
|
|
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
|
|
attn_output = self.o_proj(attn_output)
|
|
return attn_output, attn_weights
|
|
|
|
|
|
class AriaTextDecoderLayer(GradientCheckpointingLayer):
|
|
"""
|
|
Aria Text Decoder Layer.
|
|
|
|
This class defines a single decoder layer in the language model, incorporating self-attention and Mixture of Experts (MoE) feed-forward network.
|
|
|
|
Args:
|
|
config (`AriaTextConfig`):
|
|
Configuration object for the text component of the model.
|
|
layer_idx (`int`):
|
|
Index of the layer.
|
|
"""
|
|
|
|
def __init__(self, config: AriaTextConfig, layer_idx: int):
|
|
super().__init__()
|
|
self.hidden_size = config.hidden_size
|
|
|
|
self.self_attn = AriaTextAttention(config=config, layer_idx=layer_idx)
|
|
self.mlp = AriaTextMoELayer(config)
|
|
self.input_layernorm = AriaTextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.post_attention_layernorm = AriaTextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_value: Optional[Cache] = None,
|
|
use_cache: Optional[bool] = False,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple[torch.Tensor]:
|
|
residual = hidden_states
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
# Self Attention
|
|
hidden_states, _ = self.self_attn(
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_value,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
position_embeddings=position_embeddings,
|
|
**kwargs,
|
|
)
|
|
hidden_states = residual + hidden_states
|
|
|
|
# Fully Connected
|
|
residual = hidden_states
|
|
hidden_states = self.post_attention_layernorm(hidden_states)
|
|
hidden_states = self.mlp(hidden_states)
|
|
hidden_states = residual + hidden_states
|
|
return hidden_states
|
|
|
|
|
|
@auto_docstring
|
|
class AriaTextPreTrainedModel(PreTrainedModel):
|
|
config: AriaTextConfig
|
|
base_model_prefix = "model"
|
|
_no_split_modules = ["AriaTextDecoderLayer", "AriaGroupedExpertsGemm"]
|
|
supports_gradient_checkpointing = True
|
|
_skip_keys_device_placement = "past_key_values"
|
|
_supports_flash_attn = False
|
|
_supports_sdpa = True
|
|
|
|
_supports_attention_backend = True
|
|
_can_record_outputs = {
|
|
"hidden_states": AriaTextDecoderLayer,
|
|
"attentions": AriaTextAttention,
|
|
}
|
|
|
|
def _init_weights(self, module):
|
|
super()._init_weights(module)
|
|
if isinstance(module, AriaGroupedExpertsGemm):
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
|
|
|
|
@auto_docstring
|
|
class AriaPreTrainedModel(PreTrainedModel):
|
|
config: AriaConfig
|
|
base_model_prefix = ""
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["AriaDecoderLayer"]
|
|
_skip_keys_device_placement = ["past_key_values"]
|
|
_supports_flash_attn = True
|
|
_supports_sdpa = True
|
|
_supports_flex_attn = True
|
|
_can_compile_fullgraph = False # MoE models don't work with torch.compile (dynamic slicing)
|
|
_supports_attention_backend = True
|
|
_can_record_outputs = {
|
|
"hidden_states": AriaTextDecoderLayer,
|
|
"attentions": AriaTextAttention,
|
|
}
|
|
|
|
def _init_weights(self, module):
|
|
super()._init_weights(module)
|
|
if isinstance(module, AriaProjector):
|
|
nn.init.trunc_normal_(module.query, std=self.config.initializer_range)
|
|
|
|
|
|
class AriaTextRotaryEmbedding(nn.Module):
|
|
def __init__(self, config: AriaTextConfig, device=None):
|
|
super().__init__()
|
|
# BC: "rope_type" was originally "type"
|
|
if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
|
|
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
|
|
else:
|
|
self.rope_type = "default"
|
|
self.max_seq_len_cached = config.max_position_embeddings
|
|
self.original_max_seq_len = config.max_position_embeddings
|
|
|
|
self.config = config
|
|
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
|
|
|
|
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
|
|
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
|
self.original_inv_freq = self.inv_freq
|
|
|
|
@torch.no_grad()
|
|
@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
|
|
def forward(self, x, position_ids):
|
|
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
|
|
position_ids_expanded = position_ids[:, None, :].float()
|
|
|
|
device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
|
|
with torch.autocast(device_type=device_type, enabled=False): # Force float32
|
|
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
|
|
emb = torch.cat((freqs, freqs), dim=-1)
|
|
cos = emb.cos() * self.attention_scaling
|
|
sin = emb.sin() * self.attention_scaling
|
|
|
|
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
|
|
|
|
|
|
@auto_docstring
|
|
class AriaTextModel(AriaTextPreTrainedModel):
|
|
def __init__(self, config: AriaTextConfig):
|
|
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(
|
|
[AriaTextDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
|
)
|
|
self.norm = AriaTextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.rotary_emb = AriaTextRotaryEmbedding(config=config)
|
|
self.gradient_checkpointing = False
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@check_model_inputs
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Cache] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> BaseModelOutputWithPast:
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds: torch.Tensor = self.embed_tokens(input_ids)
|
|
|
|
if use_cache and past_key_values is None:
|
|
past_key_values = DynamicCache()
|
|
|
|
if cache_position is None:
|
|
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
cache_position: torch.Tensor = torch.arange(
|
|
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
|
|
)
|
|
|
|
if position_ids is None:
|
|
position_ids = cache_position.unsqueeze(0)
|
|
|
|
causal_mask = create_causal_mask(
|
|
config=self.config,
|
|
input_embeds=inputs_embeds,
|
|
attention_mask=attention_mask,
|
|
cache_position=cache_position,
|
|
past_key_values=past_key_values,
|
|
position_ids=position_ids,
|
|
)
|
|
|
|
hidden_states = inputs_embeds
|
|
position_embeddings = self.rotary_emb(hidden_states, position_ids)
|
|
|
|
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
|
|
hidden_states = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=causal_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_values,
|
|
cache_position=cache_position,
|
|
position_embeddings=position_embeddings,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class AriaTextForCausalLM(AriaTextPreTrainedModel, GenerationMixin):
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
_tp_plan = {"lm_head": "colwise_rep"}
|
|
_pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
|
|
|
|
def __init__(self, config: AriaTextConfig):
|
|
super().__init__(config)
|
|
self.model = AriaTextModel(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
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Cache] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
logits_to_keep: Union[int, torch.Tensor] = 0,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> CausalLMOutputWithPast:
|
|
r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, AriaTextForCausalLM
|
|
|
|
>>> model = AriaTextForCausalLM.from_pretrained("meta-aria_text/AriaText-2-7b-hf")
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("meta-aria_text/AriaText-2-7b-hf")
|
|
|
|
>>> 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,
|
|
)
|
|
|
|
|
|
@dataclass
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
Base class for Aria causal language model (or autoregressive) outputs.
|
|
"""
|
|
)
|
|
class AriaCausalLMOutputWithPast(ModelOutput):
|
|
r"""
|
|
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
|
|
Language modeling loss (for next-token prediction).
|
|
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
|
|
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
|
|
past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
|
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)`)
|
|
|
|
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
|
|
`past_key_values` input) to speed up sequential decoding.
|
|
image_hidden_states (`torch.FloatTensor`, *optional*):
|
|
A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
|
|
image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
|
|
"""
|
|
|
|
loss: Optional[torch.FloatTensor] = None
|
|
logits: Optional[torch.FloatTensor] = None
|
|
past_key_values: Optional[list[torch.FloatTensor]] = None
|
|
hidden_states: Optional[tuple[torch.FloatTensor]] = None
|
|
attentions: Optional[tuple[torch.FloatTensor]] = None
|
|
image_hidden_states: Optional[torch.FloatTensor] = None
|
|
|
|
|
|
@dataclass
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
Base class for Aria outputs, with hidden states and attentions.
|
|
"""
|
|
)
|
|
class AriaModelOutputWithPast(BaseModelOutputWithPast):
|
|
r"""
|
|
past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
|
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)`)
|
|
|
|
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
|
|
`past_key_values` input) to speed up sequential decoding.
|
|
image_hidden_states (`torch.FloatTensor`, *optional*):
|
|
A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
|
|
image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
|
|
"""
|
|
|
|
image_hidden_states: Optional[torch.FloatTensor] = None
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
The Aria model which consists of a vision backbone and a language model, without a language modeling head.
|
|
"""
|
|
)
|
|
class AriaModel(AriaPreTrainedModel):
|
|
_checkpoint_conversion_mapping = {"language_model.model": "language_model"}
|
|
|
|
def __init__(self, config: AriaConfig):
|
|
super().__init__(config)
|
|
self.vision_tower = AutoModel.from_config(config.vision_config)
|
|
self.multi_modal_projector = AriaProjector(config)
|
|
self.language_model = AutoModel.from_config(config.text_config)
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.language_model.get_input_embeddings()
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.language_model.set_input_embeddings(value)
|
|
|
|
def set_decoder(self, decoder):
|
|
self.language_model = decoder
|
|
|
|
def get_decoder(self):
|
|
return self.language_model
|
|
|
|
def get_image_features(
|
|
self,
|
|
pixel_values: torch.FloatTensor,
|
|
pixel_mask: Optional[torch.FloatTensor] = None,
|
|
vision_feature_layer: int = -1,
|
|
):
|
|
"""
|
|
Obtains image last hidden states from the vision tower and apply multimodal projection.
|
|
|
|
Args:
|
|
pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`):
|
|
The tensors corresponding to the input images.
|
|
pixel_mask (`torch.FloatTensor]`, *optional*):
|
|
The tensors corresponding to the input image mask.
|
|
vision_feature_layer (`Union[int, list[int]]`, *optional*):
|
|
The index of the layer to select the vision feature. If multiple indices are provided,
|
|
the vision feature of the corresponding indices will be concatenated to form the
|
|
vision features.
|
|
Returns:
|
|
image_features (`torch.Tensor`): Image feature tensor of shape `(num_images, image_length, embed_dim)`).
|
|
"""
|
|
vision_feature_layer = (
|
|
vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
|
|
)
|
|
patch_attention_mask = self._create_patch_attention_mask(pixel_mask)
|
|
image_outputs = self.vision_tower(
|
|
pixel_values, patch_attention_mask=patch_attention_mask, output_hidden_states=True
|
|
)
|
|
image_attn_mask = None
|
|
if patch_attention_mask is not None:
|
|
flattened_mask = patch_attention_mask.flatten(1)
|
|
image_attn_mask = torch.logical_not(flattened_mask)
|
|
|
|
selected_image_feature = image_outputs.hidden_states[vision_feature_layer]
|
|
image_features = self.multi_modal_projector(selected_image_feature, attn_mask=image_attn_mask)
|
|
return image_features
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
pixel_values: torch.FloatTensor = None,
|
|
pixel_mask: 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,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
**kwargs: Unpack[FlashAttentionKwargs],
|
|
) -> Union[tuple, AriaModelOutputWithPast]:
|
|
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
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.get_input_embeddings()(input_ids)
|
|
|
|
# 2. Merge text and images
|
|
if pixel_values is not None and inputs_embeds.shape[1] != 1:
|
|
if input_ids is None:
|
|
special_image_mask = inputs_embeds == self.get_input_embeddings()(
|
|
torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
|
|
)
|
|
special_image_mask = special_image_mask.all(-1)
|
|
else:
|
|
special_image_mask = input_ids == self.config.image_token_id
|
|
|
|
n_image_tokens = (special_image_mask).sum(dim=1).sum(dim=0)
|
|
special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
|
|
image_features = self.get_image_features(
|
|
pixel_values=pixel_values,
|
|
pixel_mask=pixel_mask,
|
|
vision_feature_layer=self.config.vision_feature_layer,
|
|
)
|
|
n_images, n_features_per_image = image_features.shape[0], image_features.shape[1]
|
|
n_image_features = n_images * n_features_per_image
|
|
if n_image_tokens != n_image_features:
|
|
raise ValueError(
|
|
f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
|
|
)
|
|
|
|
image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
|
|
inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
|
|
|
|
outputs = self.language_model(
|
|
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=True,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
|
|
return AriaModelOutputWithPast(
|
|
last_hidden_state=outputs.last_hidden_state,
|
|
past_key_values=outputs.past_key_values if use_cache else None,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
image_hidden_states=image_features if pixel_values is not None else None,
|
|
)
|
|
|
|
def _create_patch_attention_mask(self, pixel_mask):
|
|
if pixel_mask is None:
|
|
return None
|
|
|
|
patches_subgrid = pixel_mask.unfold(
|
|
dimension=1,
|
|
size=self.vision_tower.config.patch_size,
|
|
step=self.vision_tower.config.patch_size,
|
|
)
|
|
patches_subgrid = patches_subgrid.unfold(
|
|
dimension=2,
|
|
size=self.vision_tower.config.patch_size,
|
|
step=self.vision_tower.config.patch_size,
|
|
)
|
|
return (patches_subgrid.sum(dim=(-1, -2)) > 0).bool()
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
Aria model for conditional generation tasks.
|
|
|
|
This model combines a vision tower, a multi-modal projector, and a language model
|
|
to perform tasks that involve both image and text inputs.
|
|
"""
|
|
)
|
|
class AriaForConditionalGeneration(AriaPreTrainedModel, GenerationMixin):
|
|
_checkpoint_conversion_mapping = {
|
|
"^language_model.model": "model.language_model",
|
|
"^vision_tower": "model.vision_tower",
|
|
"^multi_modal_projector": "model.multi_modal_projector",
|
|
"^language_model.lm_head": "lm_head",
|
|
}
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
|
|
def __init__(self, config: AriaConfig):
|
|
super().__init__(config)
|
|
self.model = AriaModel(config)
|
|
self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.model.get_input_embeddings()
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.model.set_input_embeddings(value)
|
|
|
|
def get_output_embeddings(self) -> nn.Module:
|
|
return self.lm_head
|
|
|
|
def set_decoder(self, decoder):
|
|
self.model.set_decoder(decoder)
|
|
|
|
def get_decoder(self):
|
|
return self.model.get_decoder
|
|
|
|
def get_image_features(
|
|
self,
|
|
pixel_values: torch.FloatTensor,
|
|
pixel_mask: Optional[torch.FloatTensor] = None,
|
|
vision_feature_layer: int = -1,
|
|
):
|
|
return self.model.get_image_features(
|
|
pixel_values=pixel_values,
|
|
pixel_mask=pixel_mask,
|
|
vision_feature_layer=vision_feature_layer,
|
|
)
|
|
|
|
# Make modules available throught conditional class for BC
|
|
@property
|
|
def language_model(self):
|
|
return self.model.language_model
|
|
|
|
@property
|
|
def vision_tower(self):
|
|
return self.model.vision_tower
|
|
|
|
@property
|
|
def multi_modal_projector(self):
|
|
return self.model.multi_modal_projector
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
pixel_values: torch.FloatTensor = None,
|
|
pixel_mask: 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,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
logits_to_keep: Union[int, torch.Tensor] = 0,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> Union[tuple, AriaCausalLMOutputWithPast]:
|
|
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 `model.image_token_id` (where `model` is your instance of `AriaForConditionalGeneration`).
|
|
Tokens with indices set to `model.image_token_id` are ignored (masked), the loss is only
|
|
computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> import requests
|
|
>>> import torch
|
|
>>> from PIL import Image
|
|
>>> from io import BytesIO
|
|
|
|
>>> from transformers import AutoProcessor, AutoModel
|
|
>>> from transformers.image_utils import load_image
|
|
|
|
>>> # Note that passing the image urls (instead of the actual pil images) to the processor is also possible
|
|
>>> image1 = load_image("https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg")
|
|
>>> image2 = load_image("https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg")
|
|
>>> image3 = load_image("https://cdn.britannica.com/68/170868-050-8DDE8263/Golden-Gate-Bridge-San-Francisco.jpg")
|
|
|
|
>>> processor = AutoProcessor.from_pretrained("Rhymes-AI/Aria")
|
|
>>> model = AutoModel.from_pretrained("Rhymes-AI/Aria", torch_dtype=torch.bfloat16, device_map="auto")
|
|
|
|
>>> # Create inputs
|
|
>>> messages = [
|
|
... {
|
|
... "role": "user",
|
|
... "content": [
|
|
... {"type": "image"},
|
|
... {"type": "text", "text": "In this image, we can see the city of New York, and more specifically the Statue of Liberty."},
|
|
... {"type": "image"},
|
|
... {"type": "text", "text": "What can we see in this image?"},
|
|
... ]
|
|
... },
|
|
... {
|
|
... "role": "user",
|
|
... "content": [
|
|
... {"type": "image"},
|
|
... {"type": "text", "text": "In which city is that bridge located?"},
|
|
... ]
|
|
... }
|
|
... ]
|
|
|
|
>>> prompts = [processor.apply_chat_template([message], add_generation_prompt=True) for message in messages]
|
|
>>> images = [[image1, image2], [image3]]
|
|
>>> inputs = processor(text=prompts, images=images, padding=True, return_tensors="pt").to(model.device)
|
|
|
|
>>> # Generate
|
|
>>> generated_ids = model.generate(**inputs, max_new_tokens=256)
|
|
>>> generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
|
|
|
|
>>> print(generated_texts[0])
|
|
Assistant: There are buildings, trees, lights, and water visible in this image.
|
|
|
|
>>> print(generated_texts[1])
|
|
Assistant: The bridge is in San Francisco.
|
|
```"""
|
|
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,
|
|
pixel_values=pixel_values,
|
|
pixel_mask=pixel_mask,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = outputs[0]
|
|
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
|
|
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
|
logits = self.lm_head(hidden_states[:, slice_indices, :])
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(
|
|
logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
|
|
)
|
|
|
|
return AriaCausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
def prepare_inputs_for_generation(
|
|
self,
|
|
input_ids,
|
|
past_key_values=None,
|
|
inputs_embeds=None,
|
|
pixel_values=None,
|
|
pixel_mask=None,
|
|
attention_mask=None,
|
|
cache_position=None,
|
|
logits_to_keep=None,
|
|
**kwargs,
|
|
):
|
|
model_inputs = super().prepare_inputs_for_generation(
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
attention_mask=attention_mask,
|
|
cache_position=cache_position,
|
|
logits_to_keep=logits_to_keep,
|
|
**kwargs,
|
|
)
|
|
|
|
if cache_position[0] == 0:
|
|
# If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
|
|
# Otherwise we need pixel values to be passed to model
|
|
model_inputs["pixel_values"] = pixel_values
|
|
model_inputs["pixel_mask"] = pixel_mask
|
|
|
|
return model_inputs
|
|
|
|
|
|
__all__ = [
|
|
"AriaForConditionalGeneration",
|
|
"AriaPreTrainedModel",
|
|
"AriaTextPreTrainedModel",
|
|
"AriaTextModel",
|
|
"AriaModel",
|
|
"AriaTextForCausalLM",
|
|
]
|