542 lines
23 KiB
Python
542 lines
23 KiB
Python
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/voxtral/modular_voxtral.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_voxtral.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 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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import math
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from typing import Callable, Optional, Union
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import torch
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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
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from ...generation import GenerationMixin
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPast, CausalLMOutputWithPast
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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, logging
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from ...utils.generic import check_model_inputs
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from ..auto import AutoModel, AutoModelForCausalLM
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from .configuration_voxtral import VoxtralConfig, VoxtralEncoderConfig
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logger = logging.get_logger(__name__)
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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: Optional[float] = None,
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dropout: float = 0.0,
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head_mask: Optional[torch.Tensor] = None,
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**kwargs,
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):
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if scaling is None:
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scaling = query.size(-1) ** -0.5
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attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
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if attention_mask is not None and attention_mask.ndim == 4:
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attn_weights = attn_weights + attention_mask[:, :, :, : key.shape[-2]]
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attn_weights = nn.functional.softmax(attn_weights, dim=-1)
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if head_mask is not None:
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attn_weights = attn_weights * head_mask.view(1, -1, 1, 1)
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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)
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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 VoxtralAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(
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self,
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embed_dim: int,
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num_heads: int,
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dropout: float = 0.0,
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is_decoder: bool = False,
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bias: bool = True,
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is_causal: bool = False,
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layer_idx: Optional[int] = None,
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config: Optional[VoxtralConfig] = None,
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):
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super().__init__()
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self.embed_dim = embed_dim
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self.num_heads = num_heads
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self.dropout = dropout
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self.head_dim = embed_dim // num_heads
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self.config = config
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if (self.head_dim * num_heads) != self.embed_dim:
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raise ValueError(
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f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
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f" and `num_heads`: {num_heads})."
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)
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self.scaling = self.head_dim**-0.5
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self.is_decoder = is_decoder
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self.is_causal = is_causal
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if layer_idx is None and is_decoder:
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logger.warning_once(
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f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and "
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"will to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
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"when creating this class."
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)
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self.layer_idx = layer_idx
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self.k_proj = nn.Linear(embed_dim, embed_dim, bias=False)
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self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
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self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
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self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
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def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
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return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
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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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layer_head_mask: Optional[torch.Tensor] = None,
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output_attentions: bool = False,
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**kwargs,
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
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"""Input shape: Batch x Time x Channel"""
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bsz, tgt_len, _ = hidden_states.size()
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# Scaling is susceptible to floating point arithmetics' inprecisions
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# which can lead to different results (this is dependent from model
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# to model, e.g. whisper is one such case). We therefore keep the
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# original order of scaling to follow the original implementation
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# and enforce no scaling (1.0) in the attention call below.
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query_states = self._shape(self.q_proj(hidden_states) * self.scaling, tgt_len, bsz)
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key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
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value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
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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.dropout,
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scaling=1.0,
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output_attentions=output_attentions,
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head_mask=layer_head_mask,
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**kwargs,
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)
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attn_output = attn_output.reshape(bsz, tgt_len, -1).contiguous()
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attn_output = self.out_proj(attn_output)
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return attn_output, attn_weights
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class VoxtralEncoderLayer(GradientCheckpointingLayer):
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def __init__(self, config: VoxtralConfig):
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super().__init__()
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self.embed_dim = config.d_model
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self.self_attn = VoxtralAttention(
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embed_dim=self.embed_dim,
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num_heads=config.encoder_attention_heads,
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dropout=config.attention_dropout,
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config=config,
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)
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self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
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self.dropout = config.dropout
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self.activation_fn = ACT2FN[config.activation_function]
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self.activation_dropout = config.activation_dropout
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self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
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self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
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self.final_layer_norm = nn.LayerNorm(self.embed_dim)
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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: torch.Tensor,
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layer_head_mask: torch.Tensor,
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output_attentions: bool = False,
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) -> torch.Tensor:
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"""
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Args:
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hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
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attention_mask (`torch.FloatTensor`): attention mask of size
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`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
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layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
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`(encoder_attention_heads,)`.
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output_attentions (`bool`, *optional*):
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Whether or not to return the attentions tensors of all attention layers. See `attentions` under
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returned tensors for more detail.
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"""
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residual = hidden_states
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hidden_states = self.self_attn_layer_norm(hidden_states)
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hidden_states, attn_weights = self.self_attn(
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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layer_head_mask=layer_head_mask,
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output_attentions=output_attentions,
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)
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hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
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hidden_states = residual + hidden_states
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residual = hidden_states
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hidden_states = self.final_layer_norm(hidden_states)
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hidden_states = self.activation_fn(self.fc1(hidden_states))
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hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
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hidden_states = self.fc2(hidden_states)
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hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
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hidden_states = residual + hidden_states
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if hidden_states.dtype == torch.float16:
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clamp_value = torch.finfo(hidden_states.dtype).max - 1000
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hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
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return hidden_states, attn_weights
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@auto_docstring
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class VoxtralPreTrainedModel(PreTrainedModel):
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config: VoxtralConfig
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base_model_prefix = "model"
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supports_gradient_checkpointing = True
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_no_split_modules = None
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_skip_keys_device_placement = "past_key_values"
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_supports_flash_attn = True
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_supports_sdpa = True
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_supports_flex_attn = True
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_supports_cache_class = True
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_supports_attention_backend = True
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_can_compile_fullgraph = True
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def _init_weights(self, module):
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# important: this ported version of Voxtral isn't meant for training from scratch - only
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# inference and fine-tuning - so the proper init weights code has been removed
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std = (
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self.config.initializer_range
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if hasattr(self.config, "initializer_range")
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else self.config.audio_config.initializer_range
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)
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if isinstance(module, (nn.Linear, nn.Conv1d)):
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module.weight.data.normal_(mean=0.0, std=std)
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if module.bias is not None:
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module.bias.data.zero_()
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elif isinstance(module, nn.LayerNorm):
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module.weight.data.fill_(1.0)
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module.bias.data.zero_()
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elif isinstance(module, nn.Embedding):
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module.weight.data.normal_(mean=0.0, std=std)
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if module.padding_idx is not None:
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module.weight.data[module.padding_idx].zero_()
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@auto_docstring(
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custom_intro="""
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The Voxtral encoder, which is a Whisper encoder.
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"""
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)
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class VoxtralEncoder(VoxtralPreTrainedModel):
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"""
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Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
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[`VoxtralEncoderLayer`].
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Args:
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config: VoxtralEncoderConfig
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"""
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# Ignore copy
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config: VoxtralEncoderConfig
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main_input_name = "input_features"
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_no_split_modules = ["VoxtralEncoderLayer"]
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_can_record_outputs = {
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"attentions": VoxtralAttention,
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"hidden_states": VoxtralEncoderLayer,
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}
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def __init__(self, config: VoxtralEncoderConfig):
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super().__init__(config)
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self.dropout = config.dropout
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self.layerdrop = config.encoder_layerdrop
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embed_dim = config.d_model
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self.num_mel_bins = config.num_mel_bins
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self.padding_idx = config.pad_token_id
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self.max_source_positions = config.max_source_positions
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self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
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self.conv1 = nn.Conv1d(self.num_mel_bins, embed_dim, kernel_size=3, padding=1)
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self.conv2 = nn.Conv1d(embed_dim, embed_dim, kernel_size=3, stride=2, padding=1)
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self.embed_positions = nn.Embedding(self.max_source_positions, embed_dim)
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self.embed_positions.requires_grad_(False)
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self.layers = nn.ModuleList([VoxtralEncoderLayer(config) for _ in range(config.encoder_layers)])
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self.layer_norm = nn.LayerNorm(config.d_model)
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# Ignore copy
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self.avg_pooler = nn.AvgPool1d(2, stride=2)
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self.gradient_checkpointing = False
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# Initialize weights and apply final processing
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self.post_init()
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def _freeze_parameters(self):
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for param in self.parameters():
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param.requires_grad = False
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self._requires_grad = False
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def get_input_embeddings(self) -> nn.Module:
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return self.conv1
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def set_input_embeddings(self, value: nn.Module):
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self.conv1 = value
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@check_model_inputs
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def forward(
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self,
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input_features,
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attention_mask=None,
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**kwargs: Unpack[TransformersKwargs],
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):
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r"""
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Args:
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input_features (`torch.LongTensor` of shape `(batch_size, feature_size, sequence_length)`):
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Float values of mel features extracted from the raw speech waveform. Raw speech waveform can be
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obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a
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`numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into
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`input_features`, the [`AutoFeatureExtractor`] should be used for extracting the mel features, padding
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and conversion into a tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`]
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attention_mask (`torch.Tensor`)`, *optional*):
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Voxtral does not support masking of the `input_features`, this argument is preserved for compatibility,
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but it is not used. By default the silence in the input log mel spectrogram are ignored.
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"""
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expected_seq_length = self.config.max_source_positions * self.conv1.stride[0] * self.conv2.stride[0]
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if input_features.shape[-1] != expected_seq_length:
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raise ValueError(
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f"Qwen2Audio expects the mel input features to be of length {expected_seq_length}, but found {input_features.shape[-1]}. Make sure to pad the input mel features to {expected_seq_length}."
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)
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input_features = input_features.to(dtype=self.conv1.weight.dtype, device=self.conv1.weight.device)
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inputs_embeds = nn.functional.gelu(self.conv1(input_features))
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inputs_embeds = nn.functional.gelu(self.conv2(inputs_embeds))
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inputs_embeds = inputs_embeds.permute(0, 2, 1)
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embed_pos = self.embed_positions.weight
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hidden_states = (inputs_embeds + embed_pos).to(inputs_embeds.dtype)
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hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
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for idx, encoder_layer in enumerate(self.layers):
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layer_outputs = encoder_layer(
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hidden_states,
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attention_mask=attention_mask,
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layer_head_mask=None,
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)
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hidden_states = layer_outputs[0]
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hidden_states = self.layer_norm(hidden_states)
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return BaseModelOutput(
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last_hidden_state=hidden_states,
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)
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# Ignore copy
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def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor):
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"""
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Computes the output length of the convolutional layers and the output length of the audio encoder
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"""
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input_lengths = (input_lengths - 1) // 2 + 1
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output_lengths = (input_lengths - 2) // 2 + 1
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return input_lengths, output_lengths
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class VoxtralMultiModalProjector(nn.Module):
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def __init__(self, config: VoxtralConfig):
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super().__init__()
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self.linear_1 = nn.Linear(config.audio_config.intermediate_size, config.text_config.hidden_size, bias=False)
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self.act = ACT2FN[config.projector_hidden_act]
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self.linear_2 = nn.Linear(config.text_config.hidden_size, config.text_config.hidden_size, bias=False)
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def forward(self, audio_features):
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hidden_states = self.linear_1(audio_features)
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hidden_states = self.act(hidden_states)
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hidden_states = self.linear_2(hidden_states)
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return hidden_states
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@auto_docstring(
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custom_intro="""
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The Voxtral model, which consists of Whisper encoder, a multi-modal projector and a LLama language model.
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"""
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)
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class VoxtralForConditionalGeneration(VoxtralPreTrainedModel, GenerationMixin):
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_tied_weights_keys = ["lm_head.weight"]
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_tp_plan = {"lm_head": "colwise_rep"}
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_pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
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_keep_in_fp32_modules_strict = ["embed_positions"]
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def __init__(self, config):
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super().__init__(config)
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self.vocab_size = config.text_config.vocab_size
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self.audio_tower = AutoModel.from_config(config.audio_config)
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self.language_model = AutoModelForCausalLM.from_config(config.text_config)
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self.multi_modal_projector = VoxtralMultiModalProjector(config)
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# Initialize weights and apply final processing
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self.post_init()
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def get_input_embeddings(self):
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return self.language_model.get_input_embeddings()
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def set_input_embeddings(self, value):
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self.language_model.set_input_embeddings(value)
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def get_output_embeddings(self):
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return self.language_model.get_output_embeddings()
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def set_output_embeddings(self, new_embeddings):
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self.language_model.set_output_embeddings(new_embeddings)
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def set_decoder(self, decoder):
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self.language_model.set_decoder(decoder)
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def get_decoder(self):
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return self.language_model.get_decoder()
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def get_audio_embeds(self, input_features: torch.FloatTensor):
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"""
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This method is used to get the audio embeddings from input features (a log mel spectrogram), meaning inferring the audio encoder and the multi-modal projector.
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Args:
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input_features (`torch.FloatTensor`):
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|
Float values of mel features extracted from the raw speech waveform. Raw speech waveform can be
|
|
obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a
|
|
`numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into
|
|
`input_features`, the [`AutoFeatureExtractor`] should be used for extracting the mel features, padding
|
|
and conversion into a tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`]
|
|
|
|
Returns:
|
|
`torch.FloatTensor`:
|
|
The audio embeddings.
|
|
"""
|
|
audio_outputs = self.audio_tower(input_features)
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|
audio_hidden_states = audio_outputs.last_hidden_state
|
|
audio_hidden_states = audio_hidden_states.reshape(-1, self.config.audio_config.intermediate_size)
|
|
audio_embeds = self.multi_modal_projector(audio_hidden_states)
|
|
return audio_embeds
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
input_features: Optional[torch.FloatTensor] = 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 VoxtralForConditionalGeneration, AutoProcessor
|
|
>>> import torch
|
|
|
|
>>> device = "cuda" if torch.cuda.is_available() else "cpu"
|
|
>>> repo_id = "mistralai/Voxtral-Mini-3B-2507"
|
|
|
|
>>> processor = AutoProcessor.from_pretrained(repo_id)
|
|
>>> model = VoxtralForConditionalGeneration.from_pretrained(repo_id, torch_dtype=torch.bfloat16, device_map=device)
|
|
|
|
>>> conversation = [
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{
|
|
"type": "audio",
|
|
"url": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/dude_where_is_my_car.wav",
|
|
},
|
|
{"type": "text", "text": "What can you tell me about this audio?"},
|
|
],
|
|
}
|
|
]
|
|
|
|
>>> inputs = processor.apply_chat_template(conversation)
|
|
>>> inputs = inputs.to(device, dtype=torch.bfloat16)
|
|
|
|
>>> outputs = model.generate(**inputs, max_new_tokens=30)
|
|
>>> processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
|
|
["This audio is a humorous conversation between two friends, likely in English, where one of them is trying to figure out what the other's tattoo says."]
|
|
```"""
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.get_input_embeddings()(input_ids)
|
|
|
|
if input_features is not None:
|
|
audio_embeds = self.get_audio_embeds(input_features)
|
|
|
|
# replace text-audio token placeholders with audio embeddings
|
|
audio_token_mask = input_ids == self.config.audio_token_id
|
|
inputs_embeds[audio_token_mask] = audio_embeds
|
|
|
|
outputs: BaseModelOutputWithPast = self.language_model(
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
labels=labels,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
logits_to_keep=logits_to_keep,
|
|
**kwargs,
|
|
)
|
|
return outputs
|
|
|
|
def prepare_inputs_for_generation(self, *args, **kwargs):
|
|
# Overwritten -- we should not pass input_features when we are in cached decoding stage
|
|
|
|
input_features = kwargs.pop("input_features", None)
|
|
cache_position = kwargs.get("cache_position")
|
|
|
|
model_inputs = super().prepare_inputs_for_generation(*args, **kwargs)
|
|
|
|
if cache_position is not None and cache_position[0] == 0:
|
|
# input_features should only be passed when we are not in cached decoding stage
|
|
model_inputs["input_features"] = input_features
|
|
|
|
return model_inputs
|
|
|
|
|
|
__all__ = ["VoxtralPreTrainedModel", "VoxtralEncoder", "VoxtralForConditionalGeneration"]
|