957 lines
41 KiB
Python
957 lines
41 KiB
Python
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/dia/modular_dia.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_dia.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 Nari Labs and HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Callable, Optional, Union
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import torch
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from torch import nn
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from ...activations import ACT2FN
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from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
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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_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_attention_mask_for_sdpa
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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 (
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BaseModelOutput,
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BaseModelOutputWithPastAndCrossAttentions,
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Seq2SeqLMOutput,
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Seq2SeqModelOutput,
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)
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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 (
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TransformersKwargs,
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auto_docstring,
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can_return_tuple,
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is_torch_flex_attn_available,
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is_torchdynamo_compiling,
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logging,
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)
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from .configuration_dia import DiaConfig, DiaDecoderConfig, DiaEncoderConfig
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from .generation_dia import DiaGenerationMixin
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if is_torch_flex_attn_available():
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from ...integrations.flex_attention import make_flex_block_causal_mask
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logger = logging.get_logger(__name__)
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@auto_docstring
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class DiaPreTrainedModel(PreTrainedModel):
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config: DiaConfig
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base_model_prefix = "model"
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supports_gradient_checkpointing = True
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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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_can_compile_fullgraph = True
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main_input_name = "input_ids"
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_no_split_modules = ["DiaEncoderLayer", "DiaDecoderLayer"]
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class DiaMultiChannelEmbedding(nn.Module):
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"""In order to efficiently compute the audio embedding from the 9 different channels,
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we vectorize the embedding process by using a single embedding layer and an offset.
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Example:
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- num_embeds = 4
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- vocab_size = 8
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- num_channels = 3
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We would have offsets = [0, 8, 16]
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If audio_codes = [0, 1, 2, 3], [1, 3, 4, 7], [5, 6, 7, 8],
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then tokens = audio_codes + offsets
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= [0, 1, 2, 3, 9, 11, 12, 15, 21, 22, 23, 24]
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This allows us to use a single embedding layer for all channels.
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"""
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def __init__(self, config: DiaDecoderConfig):
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super().__init__()
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self.embed = nn.Embedding(config.vocab_size * config.num_channels, config.hidden_size)
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self.hidden_size = config.hidden_size
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self.num_channels = config.num_channels
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offsets = torch.arange(config.num_channels, dtype=torch.long) * config.vocab_size # (C,)
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self.register_buffer("offsets", offsets, persistent=False)
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def forward(self, audio_codes: torch.Tensor) -> torch.Tensor:
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tokens = (audio_codes + self.offsets.to(audio_codes.device)).squeeze(1)
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embeds = self.embed(tokens).view(tokens.shape[0], audio_codes.shape[1], -1, self.hidden_size)
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return embeds.sum(dim=2)
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class DiaMLP(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.config = config
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self.gate_up_proj = nn.Linear(config.hidden_size, 2 * config.intermediate_size, bias=False)
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self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
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self.activation_fn = ACT2FN[config.hidden_act]
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def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
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up_states = self.gate_up_proj(hidden_states)
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gate, up_states = up_states.chunk(2, dim=-1)
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up_states = up_states * self.activation_fn(gate)
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return self.down_proj(up_states)
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@use_kernel_forward_from_hub("RMSNorm")
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class DiaRMSNorm(nn.Module):
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def __init__(self, hidden_size, eps=1e-6):
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"""
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DiaRMSNorm 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 DiaRotaryEmbedding(nn.Module):
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def __init__(self, config: DiaConfig, device=None):
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super().__init__()
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# BC: "rope_type" was originally "type"
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if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
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self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
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else:
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self.rope_type = "default"
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self.max_seq_len_cached = config.max_position_embeddings
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self.original_max_seq_len = config.max_position_embeddings
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self.config = config
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self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
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inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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self.original_inv_freq = self.inv_freq
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@torch.no_grad()
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@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
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def forward(self, x, position_ids):
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inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
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position_ids_expanded = position_ids[:, None, :].float()
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device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
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with torch.autocast(device_type=device_type, enabled=False): # Force float32
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freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
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emb = torch.cat((freqs, freqs), dim=-1)
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cos = emb.cos() * self.attention_scaling
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sin = emb.sin() * self.attention_scaling
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return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
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def rotate_half(x):
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"""Rotates half the hidden dims of the input."""
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x1 = x[..., : x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2 :]
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return torch.cat((-x2, x1), dim=-1)
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
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"""Applies Rotary Position Embedding to the query and key tensors.
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Args:
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q (`torch.Tensor`): The query tensor.
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k (`torch.Tensor`): The key tensor.
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cos (`torch.Tensor`): The cosine part of the rotary embedding.
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sin (`torch.Tensor`): The sine part of the rotary embedding.
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position_ids (`torch.Tensor`, *optional*):
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Deprecated and unused.
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unsqueeze_dim (`int`, *optional*, defaults to 1):
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The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
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sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
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that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
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k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
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cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
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the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
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Returns:
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`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
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"""
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cos = cos.unsqueeze(unsqueeze_dim)
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sin = sin.unsqueeze(unsqueeze_dim)
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q_embed = (q * cos) + (rotate_half(q) * sin)
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k_embed = (k * cos) + (rotate_half(k) * sin)
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return q_embed, k_embed
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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"""
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This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
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num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
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"""
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batch, num_key_value_heads, slen, head_dim = hidden_states.shape
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if n_rep == 1:
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return hidden_states
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hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
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return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
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def eager_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: Optional[torch.Tensor],
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scaling: float,
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dropout: float = 0.0,
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**kwargs: Unpack[TransformersKwargs],
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):
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key_states = repeat_kv(key, module.num_key_value_groups)
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value_states = repeat_kv(value, module.num_key_value_groups)
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attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
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if attention_mask is not None:
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causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
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attn_weights = attn_weights + causal_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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attn_output = torch.matmul(attn_weights, value_states)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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class DiaSelfAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(self, config: Union[DiaEncoderConfig, DiaDecoderConfig], layer_idx: int, is_causal: bool = False):
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super().__init__()
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self.config = config
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self.layer_idx = layer_idx
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self.hidden_size = config.hidden_size
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self.num_heads = self.config.num_attention_heads
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self.num_key_value_heads = self.config.num_key_value_heads or self.num_heads
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self.num_key_value_groups = self.num_heads // self.num_key_value_heads
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self.head_dim = getattr(config, "head_dim", config.hidden_size // self.num_heads)
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self.scaling = 1
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self.attention_dropout = 0.0
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self.is_causal = is_causal
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self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
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self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: tuple[torch.Tensor, torch.Tensor],
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attention_mask: Optional[torch.Tensor],
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past_key_value: Optional[Cache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple[torch.Tensor, torch.Tensor]:
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.head_dim)
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query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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cos, sin = position_embeddings
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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if past_key_value is not None:
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# sin and cos are specific to RoPE models; cache_position needed for the static cache
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
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attention_interface: Callable = eager_attention_forward
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if self.config._attn_implementation != "eager":
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attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
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attn_output, attn_weights = attention_interface(
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self,
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query_states,
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key_states,
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value_states,
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attention_mask,
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dropout=0.0 if not self.training else self.attention_dropout,
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scaling=self.scaling,
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**kwargs,
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)
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attn_output = attn_output.reshape(*input_shape, -1).contiguous()
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attn_output = self.o_proj(attn_output)
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return attn_output, attn_weights
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class DiaCrossAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(self, config: DiaDecoderConfig, layer_idx: int):
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super().__init__()
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self.config = config
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self.layer_idx = layer_idx
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self.hidden_size = config.hidden_size
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self.cross_hidden_size = config.cross_hidden_size
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self.num_heads = self.config.cross_num_attention_heads
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self.num_key_value_heads = self.config.cross_num_key_value_heads
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self.num_key_value_groups = self.num_heads // self.num_key_value_heads
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self.head_dim = config.cross_head_dim
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self.scaling = 1
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self.attention_dropout = 0.0
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self.is_causal = False
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self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
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self.k_proj = nn.Linear(self.cross_hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.v_proj = nn.Linear(self.cross_hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
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self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
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def forward(
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self,
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hidden_states: torch.Tensor,
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cross_attention_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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past_key_values: Optional[EncoderDecoderCache] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.head_dim)
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cross_shape = (*cross_attention_states.shape[:-1], -1, self.head_dim)
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query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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is_updated = past_key_values.is_updated.get(self.layer_idx) if past_key_values is not None else False
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if past_key_values is not None and is_updated:
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# reuse k,v, cross_attentions
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key_states = past_key_values.cross_attention_cache.layers[self.layer_idx].keys
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value_states = past_key_values.cross_attention_cache.layers[self.layer_idx].values
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else:
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key_states = self.k_proj(cross_attention_states).view(cross_shape).transpose(1, 2)
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value_states = self.v_proj(cross_attention_states).view(cross_shape).transpose(1, 2)
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if past_key_values is not None:
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# save all states to the cache
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key_states, value_states = past_key_values.cross_attention_cache.update(
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key_states,
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value_states,
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self.layer_idx,
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)
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# set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
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past_key_values.is_updated[self.layer_idx] = True
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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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scaling=self.scaling,
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**kwargs,
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)
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attn_output = attn_output.reshape((*input_shape, -1)).contiguous()
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attn_output = self.o_proj(attn_output)
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return attn_output, attn_weights
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class DiaEncoderLayer(GradientCheckpointingLayer):
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def __init__(self, config: DiaEncoderConfig, layer_idx: int):
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super().__init__()
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self.pre_sa_norm = DiaRMSNorm(config.hidden_size, eps=config.norm_eps)
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self.self_attention = DiaSelfAttention(config, layer_idx, is_causal=False)
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self.post_sa_norm = DiaRMSNorm(config.hidden_size, eps=config.norm_eps)
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self.mlp = DiaMLP(config)
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
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attention_mask: Optional[torch.Tensor] = None,
|
|
**kwargs: Unpack[FlashAttentionKwargs],
|
|
) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
|
|
residual = hidden_states
|
|
normed_states = self.pre_sa_norm(hidden_states)
|
|
self_attn_output, self_attn_weights = self.self_attention(
|
|
normed_states,
|
|
position_embeddings=position_embeddings,
|
|
attention_mask=attention_mask,
|
|
**kwargs,
|
|
)
|
|
hidden_states = residual + self_attn_output
|
|
|
|
residual = hidden_states
|
|
normed_states = self.post_sa_norm(hidden_states)
|
|
mlp_out = self.mlp(normed_states)
|
|
hidden_states = residual + mlp_out
|
|
|
|
return hidden_states, self_attn_weights
|
|
|
|
|
|
class DiaEncoder(DiaPreTrainedModel):
|
|
def __init__(self, config: DiaEncoderConfig):
|
|
super().__init__(config)
|
|
self.config = config
|
|
|
|
self.embedding = nn.Embedding(config.vocab_size, config.hidden_size)
|
|
self.layers = nn.ModuleList(
|
|
[DiaEncoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
|
)
|
|
self.norm = DiaRMSNorm(config.hidden_size, eps=config.norm_eps)
|
|
self.rotary_embeddings = DiaRotaryEmbedding(config)
|
|
|
|
@auto_docstring
|
|
@can_return_tuple
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
output_hidden_states: Optional[bool] = False,
|
|
**kwargs: Unpack[FlashAttentionKwargs],
|
|
) -> Union[BaseModelOutput, tuple]:
|
|
hidden_states = self.embedding(input_ids)
|
|
|
|
# RoPE
|
|
# Note: We expect right padding and hence always generate
|
|
# the position ids on the fly to reduce preparation overhead
|
|
position_ids = torch.arange(input_ids.shape[-1], device=input_ids.device)[None, :]
|
|
position_embeddings = self.rotary_embeddings(hidden_states, position_ids)
|
|
|
|
attention_mask = self._update_full_mask(
|
|
attention_mask,
|
|
hidden_states,
|
|
)
|
|
|
|
encoder_states = () if output_hidden_states else None
|
|
all_attentions = () if output_attentions else None
|
|
|
|
for encoder_layer in self.layers:
|
|
if output_hidden_states:
|
|
encoder_states = encoder_states + (hidden_states,)
|
|
|
|
layer_outputs = encoder_layer(
|
|
hidden_states,
|
|
position_embeddings=position_embeddings,
|
|
attention_mask=attention_mask,
|
|
**kwargs,
|
|
)
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if output_attentions:
|
|
all_attentions = all_attentions + (layer_outputs[1],)
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
|
|
if output_hidden_states:
|
|
encoder_states += (hidden_states,)
|
|
|
|
return BaseModelOutput(
|
|
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
|
|
)
|
|
|
|
# Copied from transformers.models.bart.modeling_bart.BartPreTrainedModel._update_full_mask
|
|
def _update_full_mask(
|
|
self,
|
|
attention_mask: Union[torch.Tensor, None],
|
|
inputs_embeds: torch.Tensor,
|
|
):
|
|
if attention_mask is not None:
|
|
if self.config._attn_implementation == "flash_attention_2":
|
|
attention_mask = attention_mask if 0 in attention_mask else None
|
|
elif self.config._attn_implementation == "sdpa":
|
|
# output_attentions=True & head_mask can not be supported when using SDPA, fall back to
|
|
# the manual implementation that requires a 4D causal mask in all cases.
|
|
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
|
attention_mask = _prepare_4d_attention_mask_for_sdpa(attention_mask, inputs_embeds.dtype)
|
|
elif self.config._attn_implementation == "flex_attention":
|
|
if isinstance(attention_mask, torch.Tensor):
|
|
attention_mask = make_flex_block_causal_mask(attention_mask, is_causal=False)
|
|
else:
|
|
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
|
attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype)
|
|
|
|
return attention_mask
|
|
|
|
|
|
class DiaDecoderLayer(GradientCheckpointingLayer):
|
|
def __init__(self, config: DiaDecoderConfig, layer_idx: int):
|
|
super().__init__()
|
|
self.embed_dim = config.hidden_size
|
|
self.self_attention = DiaSelfAttention(config, layer_idx, is_causal=True)
|
|
self.cross_attention = DiaCrossAttention(config, layer_idx)
|
|
self.pre_sa_norm = DiaRMSNorm(config.hidden_size, eps=config.norm_eps)
|
|
self.pre_ca_norm = DiaRMSNorm(config.hidden_size, eps=config.norm_eps)
|
|
self.pre_mlp_norm = DiaRMSNorm(config.hidden_size, eps=config.norm_eps)
|
|
self.mlp = DiaMLP(config)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
past_key_values: Optional[EncoderDecoderCache] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
**kwargs,
|
|
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]:
|
|
self_attn_cache = past_key_values
|
|
if isinstance(self_attn_cache, EncoderDecoderCache):
|
|
self_attn_cache = self_attn_cache.self_attention_cache
|
|
|
|
residual = hidden_states
|
|
normed_states = self.pre_sa_norm(hidden_states)
|
|
self_attn_output, self_attn_weights = self.self_attention(
|
|
normed_states,
|
|
position_embeddings,
|
|
attention_mask,
|
|
# Needs to be an arg in order to function properly
|
|
# on inplace operations to be carried (e.g. compile)
|
|
self_attn_cache,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
hidden_states = residual + self_attn_output
|
|
|
|
residual = hidden_states
|
|
normed_states = self.pre_ca_norm(hidden_states)
|
|
cross_states, cross_attn_weights = self.cross_attention(
|
|
normed_states,
|
|
encoder_hidden_states,
|
|
attention_mask=encoder_attention_mask,
|
|
past_key_values=past_key_values,
|
|
**kwargs,
|
|
)
|
|
hidden_states = residual + cross_states
|
|
|
|
residual = hidden_states
|
|
normed_states = self.pre_mlp_norm(hidden_states)
|
|
mlp_out = self.mlp(normed_states)
|
|
hidden_states = residual + mlp_out
|
|
|
|
return hidden_states, self_attn_weights, cross_attn_weights
|
|
|
|
|
|
class DiaDecoder(DiaPreTrainedModel):
|
|
"""Transformer Decoder Stack using DenseGeneral."""
|
|
|
|
def __init__(self, config: DiaDecoderConfig):
|
|
super().__init__(config)
|
|
self.num_channels = config.num_channels
|
|
self.vocab_size = config.vocab_size
|
|
self.embeddings = DiaMultiChannelEmbedding(config)
|
|
self.rotary_embeddings = DiaRotaryEmbedding(config)
|
|
self.layers = nn.ModuleList(
|
|
[DiaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
|
)
|
|
self.norm = DiaRMSNorm(config.hidden_size, eps=config.norm_eps)
|
|
|
|
@auto_docstring
|
|
@can_return_tuple
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[EncoderDecoderCache] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
output_hidden_states: Optional[bool] = False,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
**kwargs,
|
|
) -> Union[BaseModelOutputWithPastAndCrossAttentions, tuple]:
|
|
r"""
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length, num_codebooks)`):
|
|
The original `decoder_input_ids` in 3D shape to facilitate more efficient computations.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
"""
|
|
|
|
batch_size, seq_length = input_ids.size()[:-1]
|
|
past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
if cache_position is None:
|
|
cache_position = torch.arange(
|
|
past_key_values_length, past_key_values_length + seq_length, device=input_ids.device
|
|
)
|
|
if position_ids is None:
|
|
position_ids = cache_position[None, :]
|
|
|
|
# RoPE
|
|
hidden_states = self.embeddings(input_ids)
|
|
position_embeddings = self.rotary_embeddings(hidden_states, position_ids)
|
|
|
|
if attention_mask is None and not is_torchdynamo_compiling():
|
|
# required mask seq length can be calculated via length of past cache
|
|
mask_seq_length = past_key_values_length + seq_length
|
|
attention_mask = torch.ones(batch_size, mask_seq_length, device=input_ids.device)
|
|
|
|
attention_mask = create_causal_mask(
|
|
config=self.config,
|
|
input_embeds=hidden_states,
|
|
attention_mask=attention_mask,
|
|
cache_position=cache_position,
|
|
past_key_values=past_key_values,
|
|
position_ids=position_ids,
|
|
)
|
|
encoder_attention_mask = self._update_cross_attn_mask(
|
|
encoder_hidden_states,
|
|
encoder_attention_mask,
|
|
hidden_states.shape[:2],
|
|
hidden_states,
|
|
)
|
|
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attns = () if output_attentions else None
|
|
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
|
|
|
|
for layer in self.layers:
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
layer_outputs = layer(
|
|
hidden_states,
|
|
position_embeddings,
|
|
attention_mask,
|
|
encoder_hidden_states,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
past_key_values=past_key_values,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if output_attentions:
|
|
all_self_attns = all_self_attns + (layer_outputs[1],)
|
|
|
|
if encoder_hidden_states is not None:
|
|
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
return BaseModelOutputWithPastAndCrossAttentions(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attns,
|
|
cross_attentions=all_cross_attentions,
|
|
)
|
|
|
|
# Copied from transformers.models.bart.modeling_bart.BartPreTrainedModel._update_cross_attn_mask
|
|
def _update_cross_attn_mask(
|
|
self,
|
|
encoder_hidden_states: Union[torch.Tensor, None],
|
|
encoder_attention_mask: Union[torch.Tensor, None],
|
|
input_shape: torch.Size,
|
|
inputs_embeds: torch.Tensor,
|
|
):
|
|
# expand encoder attention mask
|
|
if encoder_hidden_states is not None and encoder_attention_mask is not None:
|
|
if self.config._attn_implementation == "flash_attention_2":
|
|
encoder_attention_mask = encoder_attention_mask if 0 in encoder_attention_mask else None
|
|
elif self.config._attn_implementation == "sdpa":
|
|
# output_attentions=True & cross_attn_head_mask can not be supported when using SDPA, and we fall back on
|
|
# the manual implementation that requires a 4D causal mask in all cases.
|
|
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
|
encoder_attention_mask = _prepare_4d_attention_mask_for_sdpa(
|
|
encoder_attention_mask,
|
|
inputs_embeds.dtype,
|
|
tgt_len=input_shape[-1],
|
|
)
|
|
elif self.config._attn_implementation == "flex_attention":
|
|
if isinstance(encoder_attention_mask, torch.Tensor):
|
|
encoder_attention_mask = make_flex_block_causal_mask(
|
|
encoder_attention_mask,
|
|
query_length=input_shape[-1],
|
|
is_causal=False,
|
|
)
|
|
else:
|
|
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
|
encoder_attention_mask = _prepare_4d_attention_mask(
|
|
encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
|
|
)
|
|
|
|
return encoder_attention_mask
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
The bare Dia model outputting raw hidden-states without any specific head on top.
|
|
"""
|
|
)
|
|
class DiaModel(DiaPreTrainedModel):
|
|
def __init__(self, config: DiaConfig):
|
|
super().__init__(config)
|
|
self.config = config
|
|
self.encoder = DiaEncoder(config.encoder_config)
|
|
self.decoder = DiaDecoder(config.decoder_config)
|
|
self.post_init()
|
|
|
|
def get_encoder(self):
|
|
return self.encoder
|
|
|
|
def get_decoder(self):
|
|
return self.decoder
|
|
|
|
@auto_docstring
|
|
@can_return_tuple
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.LongTensor] = None,
|
|
decoder_input_ids: Optional[torch.LongTensor] = None,
|
|
decoder_position_ids: Optional[torch.LongTensor] = None,
|
|
decoder_attention_mask: Optional[torch.LongTensor] = None,
|
|
encoder_outputs: Optional[Union[BaseModelOutput, tuple]] = None,
|
|
past_key_values: Optional[EncoderDecoderCache] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
**kwargs,
|
|
) -> Union[tuple, Seq2SeqModelOutput]:
|
|
r"""
|
|
decoder_input_ids (`torch.LongTensor` of shape `(batch_size * num_codebooks, target_sequence_length)
|
|
or (batch_size, target_sequence_length, num_codebooks)`, *optional*):
|
|
1. (batch_size * num_codebooks, target_sequence_length): corresponds to the general use case where
|
|
the audio input codebooks are flattened into the batch dimension. This also aligns with the flat-
|
|
tened audio logits which are used to calculate the loss.
|
|
|
|
2. (batch_size, sequence_length, num_codebooks): corresponds to the internally used shape of
|
|
Dia to calculate embeddings and subsequent steps more efficiently.
|
|
|
|
If no `decoder_input_ids` are provided, it will create a tensor of `bos_token_id` with shape
|
|
`(batch_size, 1, num_codebooks)`. Indices can be obtained using the [`DiaProcessor`]. See
|
|
[`DiaProcessor.__call__`] for more details.
|
|
|
|
[What are decoder input IDs?](../glossary#decoder-input-ids)
|
|
decoder_position_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`):
|
|
Indices of positions of each input sequence tokens in the position embeddings.
|
|
Used to calculate the position embeddings up to `config.decoder_config.max_position_embeddings`.
|
|
|
|
[What are position IDs?](../glossary#position-ids)
|
|
"""
|
|
|
|
if input_ids is None and encoder_outputs is None:
|
|
raise ValueError(
|
|
"You should either provide text ids or the cached text encodings. Neither has been found."
|
|
)
|
|
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
|
|
if self.is_gradient_checkpointing and self.training:
|
|
if use_cache:
|
|
logger.warning_once(
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
|
)
|
|
use_cache = False
|
|
|
|
if use_cache and past_key_values is None:
|
|
past_key_values = EncoderDecoderCache(DynamicCache(), DynamicCache())
|
|
|
|
if encoder_outputs is None:
|
|
encoder_outputs = self.encoder(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
**kwargs,
|
|
)
|
|
# If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput
|
|
elif not isinstance(encoder_outputs, BaseModelOutput):
|
|
encoder_outputs = BaseModelOutput(
|
|
last_hidden_state=encoder_outputs[0],
|
|
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
|
|
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
|
|
)
|
|
|
|
# On default we initialize the decoder with bos tokens if nothing has been provided
|
|
bsz, seq_len, channels = (encoder_outputs[0].shape[0], -1, self.config.decoder_config.num_channels)
|
|
if decoder_input_ids is None:
|
|
decoder_input_ids = torch.full(
|
|
size=(bsz, 1, channels), fill_value=self.config.bos_token_id, device=self.device
|
|
)
|
|
# Ensure 3D
|
|
if decoder_input_ids.ndim == 2:
|
|
decoder_input_ids = decoder_input_ids.reshape(bsz, channels, seq_len).transpose(1, 2)
|
|
|
|
decoder_outputs = self.decoder(
|
|
input_ids=decoder_input_ids,
|
|
position_ids=decoder_position_ids,
|
|
attention_mask=decoder_attention_mask,
|
|
encoder_hidden_states=encoder_outputs[0],
|
|
encoder_attention_mask=attention_mask,
|
|
past_key_values=past_key_values,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
|
|
return Seq2SeqModelOutput(
|
|
last_hidden_state=decoder_outputs.last_hidden_state,
|
|
past_key_values=decoder_outputs.past_key_values,
|
|
decoder_hidden_states=decoder_outputs.hidden_states,
|
|
decoder_attentions=decoder_outputs.attentions,
|
|
cross_attentions=decoder_outputs.cross_attentions,
|
|
encoder_last_hidden_state=encoder_outputs[0],
|
|
encoder_hidden_states=encoder_outputs.hidden_states,
|
|
encoder_attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
The Dia model consisting of a (byte) text encoder and audio decoder with a prediction head on top.
|
|
"""
|
|
)
|
|
class DiaForConditionalGeneration(DiaPreTrainedModel, DiaGenerationMixin):
|
|
base_model_prefix = "model"
|
|
|
|
def __init__(self, config: DiaConfig):
|
|
super().__init__(config)
|
|
self.config = config
|
|
self.model = DiaModel(config)
|
|
|
|
self.num_channels = config.decoder_config.num_channels
|
|
self.vocab_size = config.decoder_config.vocab_size
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self.logits_dense = nn.Linear(
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config.decoder_config.hidden_size, (self.num_channels * self.vocab_size), bias=False
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|
)
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|
self.loss_type = "ForMaskedLM"
|
|
|
|
# Initialize weights and apply final processing
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|
self.post_init()
|
|
|
|
def get_encoder(self):
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|
return self.model.get_encoder()
|
|
|
|
def get_decoder(self):
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|
return self.model.get_decoder()
|
|
|
|
@auto_docstring
|
|
@can_return_tuple
|
|
def forward(
|
|
self,
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|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.LongTensor] = None,
|
|
decoder_input_ids: Optional[torch.LongTensor] = None,
|
|
decoder_position_ids: Optional[torch.LongTensor] = None,
|
|
decoder_attention_mask: Optional[torch.LongTensor] = None,
|
|
encoder_outputs: Optional[Union[BaseModelOutput, tuple]] = None,
|
|
past_key_values: Optional[EncoderDecoderCache] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
**kwargs,
|
|
) -> Union[tuple, Seq2SeqLMOutput]:
|
|
r"""
|
|
decoder_input_ids (`torch.LongTensor` of shape `(batch_size * num_codebooks, target_sequence_length)
|
|
or (batch_size, target_sequence_length, num_codebooks)`, *optional*):
|
|
1. (batch_size * num_codebooks, target_sequence_length): corresponds to the general use case where
|
|
the audio input codebooks are flattened into the batch dimension. This also aligns with the flat-
|
|
tened audio logits which are used to calculate the loss.
|
|
|
|
2. (batch_size, sequence_length, num_codebooks): corresponds to the internally used shape of
|
|
Dia to calculate embeddings and subsequent steps more efficiently.
|
|
|
|
If no `decoder_input_ids` are provided, it will create a tensor of `bos_token_id` with shape
|
|
`(batch_size, 1, num_codebooks)`. Indices can be obtained using the [`DiaProcessor`]. See
|
|
[`DiaProcessor.__call__`] for more details.
|
|
|
|
[What are decoder input IDs?](../glossary#decoder-input-ids)
|
|
decoder_position_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`):
|
|
Indices of positions of each input sequence tokens in the position embeddings.
|
|
Used to calculate the position embeddings up to `config.decoder_config.max_position_embeddings`.
|
|
|
|
[What are position IDs?](../glossary#position-ids)
|
|
labels (`torch.LongTensor` of shape `(batch_size * num_codebooks,)`, *optional*):
|
|
Labels for computing the masked language modeling loss. Indices should either be in
|
|
`[0, ..., config.decoder_config.vocab_size - 1]` or -100. Tokens with indices set to `-100`
|
|
are ignored (masked).
|
|
"""
|
|
|
|
outputs = self.model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
decoder_input_ids=decoder_input_ids,
|
|
decoder_position_ids=decoder_position_ids,
|
|
decoder_attention_mask=decoder_attention_mask,
|
|
encoder_outputs=encoder_outputs,
|
|
past_key_values=past_key_values,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
|
|
last_hidden_state = outputs[0]
|
|
batch_size = last_hidden_state.shape[0]
|
|
# 3D <-> 2D makes it necessary to prioritize channel dim
|
|
audio_logits = (
|
|
self.logits_dense(last_hidden_state)
|
|
.view((batch_size, -1, self.num_channels, self.vocab_size))
|
|
.transpose(1, 2)
|
|
.contiguous()
|
|
.view(batch_size * self.num_channels, -1, self.vocab_size)
|
|
)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(logits=audio_logits, labels=labels, vocab_size=self.vocab_size, **kwargs)
|
|
|
|
return Seq2SeqLMOutput(
|
|
loss=loss,
|
|
logits=audio_logits,
|
|
past_key_values=outputs.past_key_values,
|
|
decoder_hidden_states=outputs.decoder_hidden_states,
|
|
decoder_attentions=outputs.decoder_attentions,
|
|
cross_attentions=outputs.cross_attentions,
|
|
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
|
|
encoder_hidden_states=outputs.encoder_hidden_states,
|
|
encoder_attentions=outputs.encoder_attentions,
|
|
)
|
|
|
|
|
|
__all__ = ["DiaModel", "DiaPreTrainedModel", "DiaForConditionalGeneration"]
|