# Copyright 2025 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Callable, Optional, Tuple, Union import torch import torch.nn as nn from ...activations import ACT2FN from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache from ...configuration_utils import PretrainedConfig from ...generation import GenerationMixin from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_attention_mask_for_sdpa from ...modeling_flash_attention_utils import FlashAttentionKwargs from ...modeling_layers import GradientCheckpointingLayer from ...modeling_outputs import ( BaseModelOutput, BaseModelOutputWithPast, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput, ) from ...modeling_rope_utils import rope_config_validation from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel from ...processing_utils import Unpack from ...utils import auto_docstring, can_return_tuple, logging from ..glm.modeling_glm import GlmAttention, GlmRotaryEmbedding, apply_rotary_pos_emb from ..llama.modeling_llama import LlamaDecoderLayer, LlamaModel, eager_attention_forward from ..whisper.modeling_whisper import WhisperModel, shift_tokens_right logger = logging.get_logger(__name__) class MoonshineConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`MoonshineModel`]. It is used to instantiate a Moonshine model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Moonshine [UsefulSensors/moonshine-tiny](https://huggingface.co/UsefulSensors/moonshine-tiny). Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 32768): Vocabulary size of the Moonshine model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`MoonshineModel`]. hidden_size (`int`, *optional*, defaults to 288): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 1152): Dimension of the MLP representations. encoder_num_hidden_layers (`int`, *optional*, defaults to 6): Number of hidden layers in the Transformer encoder. decoder_num_hidden_layers (`int`, *optional*, defaults to 6): Number of hidden layers in the Transformer decoder. encoder_num_attention_heads (`int`, *optional*, defaults to 8): Number of attention heads for each attention layer in the Transformer encoder. decoder_num_attention_heads (`int`, *optional*, defaults to 8): Number of attention heads for each attention layer in the Transformer decoder. encoder_num_key_value_heads (`int`, *optional*): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `encoder_num_key_value_heads=encoder_num_attention_heads`, the model will use Multi Head Attention (MHA), if `encoder_num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `num_attention_heads`. decoder_num_key_value_heads (`int`, *optional*): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `decoder_num_key_value_heads=decoder_num_attention_heads`, the model will use Multi Head Attention (MHA), if `decoder_num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `decoder_num_attention_heads`. pad_head_dim_to_multiple_of (`int`, *optional*): Pad head dimension in encoder and decoder to the next multiple of this value. Necessary for using certain optimized attention implementations. encoder_hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder. decoder_hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. decoder_start_token_id (`int`, *optional*, defaults to 1): Corresponds to the "<|startoftranscript|>" token, which is automatically used when no `decoder_input_ids` are provided to the `generate` function. It is used to guide the model`s generation process depending on the task. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly. Expected contents: `rope_type` (`str`): The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation. `factor` (`float`, *optional*): Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In most scaling types, a `factor` of x will enable the model to handle sequences of length x * original maximum pre-trained length. `original_max_position_embeddings` (`int`, *optional*): Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during pretraining. `attention_factor` (`float`, *optional*): Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention computation. If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value. `beta_fast` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear ramp function. If unspecified, it defaults to 32. `beta_slow` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear ramp function. If unspecified, it defaults to 1. `short_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to short contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `long_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to long contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `low_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE `high_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE partial_rotary_factor (`float`, *optional*, defaults to 0.9): Percentage of the query and keys which will have rotary embedding. is_encoder_decoder (`bool`, *optional*, defaults to `True`): Whether the model is used as an encoder/decoder or not. attention_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in the query, key, value and output projection layers during self-attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. bos_token_id (`int`, *optional*, defaults to 1): Denotes beginning of sequences token id. eos_token_id (`int`, *optional*, defaults to 2): Denotes end of sequences token id. Example: ```python >>> from transformers import MoonshineModel, MoonshineConfig >>> # Initializing a Moonshine style configuration >>> configuration = MoonshineConfig().from_pretrained("UsefulSensors/moonshine-tiny") >>> # Initializing a model from the configuration >>> model = MoonshineModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "moonshine" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { "num_key_value_heads": "encoder_num_key_value_heads", "num_attention_heads": "encoder_num_attention_heads", "num_hidden_layers": "encoder_num_hidden_layers", } def __init__( self, vocab_size=32768, hidden_size=288, intermediate_size=1152, encoder_num_hidden_layers=6, decoder_num_hidden_layers=6, encoder_num_attention_heads=8, decoder_num_attention_heads=8, encoder_num_key_value_heads=None, decoder_num_key_value_heads=None, pad_head_dim_to_multiple_of=None, encoder_hidden_act="gelu", decoder_hidden_act="silu", max_position_embeddings=512, initializer_range=0.02, decoder_start_token_id=1, use_cache=True, rope_theta=10000.0, rope_scaling=None, partial_rotary_factor=0.9, is_encoder_decoder=True, attention_bias=False, attention_dropout=0.0, bos_token_id=1, eos_token_id=2, **kwargs, ): self.vocab_size = vocab_size self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.encoder_num_hidden_layers = encoder_num_hidden_layers self.decoder_num_hidden_layers = decoder_num_hidden_layers self.encoder_num_attention_heads = encoder_num_attention_heads self.decoder_num_attention_heads = decoder_num_attention_heads if encoder_num_key_value_heads is None: encoder_num_key_value_heads = encoder_num_attention_heads self.encoder_num_key_value_heads = encoder_num_key_value_heads if decoder_num_key_value_heads is None: decoder_num_key_value_heads = decoder_num_attention_heads self.decoder_num_key_value_heads = decoder_num_key_value_heads self.pad_head_dim_to_multiple_of = pad_head_dim_to_multiple_of self.encoder_hidden_act = encoder_hidden_act self.decoder_hidden_act = decoder_hidden_act self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.decoder_start_token_id = decoder_start_token_id self.use_cache = use_cache self.rope_theta = rope_theta self.rope_scaling = rope_scaling self.partial_rotary_factor = partial_rotary_factor self.is_encoder_decoder = is_encoder_decoder self.attention_bias = attention_bias self.attention_dropout = attention_dropout # Validate the correctness of rotary position embeddings parameters rope_config_validation(self) super().__init__( bos_token_id=bos_token_id, eos_token_id=eos_token_id, is_encoder_decoder=is_encoder_decoder, decoder_start_token_id=decoder_start_token_id, **kwargs, ) class MoonshineEncoderMLP(nn.Module): def __init__(self, config, hidden_act): super().__init__() self.config = config self.activation_fn = ACT2FN[hidden_act] self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.fc1(hidden_states) hidden_states = self.activation_fn(hidden_states) hidden_states = self.fc2(hidden_states) return hidden_states class MoonshineDecoderMLP(nn.Module): def __init__(self, config, hidden_act): super().__init__() self.config = config self.activation_fn = ACT2FN[hidden_act] self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size * 2) self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.fc1(hidden_states) hidden_states, gate = hidden_states.chunk(2, dim=-1) hidden_states = self.activation_fn(gate) * hidden_states hidden_states = self.fc2(hidden_states) return hidden_states class MoonshineAttention(GlmAttention): def __init__( self, config: MoonshineConfig, layer_idx: int, is_causal: bool, num_attention_heads: int, num_key_value_heads: int, ): config.update({"num_attention_heads": num_attention_heads, "num_key_value_heads": num_key_value_heads}) super().__init__(config, layer_idx) self.is_causal = is_causal self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) # Pad head dimension to the next specified multiple. if self.config.pad_head_dim_to_multiple_of is not None: target_multiple = self.config.pad_head_dim_to_multiple_of target_head_dim = target_multiple * ((self.head_dim + target_multiple - 1) // target_multiple) self.head_dim_padding = target_head_dim - self.head_dim else: self.head_dim_padding = 0 def forward( self, hidden_states: torch.Tensor, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, attention_mask: Optional[torch.Tensor] = None, past_key_value: Optional[Cache] = None, cache_position: Optional[torch.LongTensor] = None, key_value_states: Optional[torch.Tensor] = None, **kwargs: Unpack[FlashAttentionKwargs], ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: bsz, q_len = hidden_states.shape[:-1] query_states = ( self.q_proj(hidden_states).view(bsz, q_len, self.config.num_key_value_heads, self.head_dim).transpose(1, 2) ) is_cross_attention = key_value_states is not None if past_key_value is not None: is_updated = past_key_value.is_updated.get(self.layer_idx) if is_cross_attention: # after the first generated id, we can subsequently re-use all key/value_states from cache past_key_value.is_updated[self.layer_idx] = True past_key_value = past_key_value.cross_attention_cache else: past_key_value = past_key_value.self_attention_cache # use key_value_states if cross attention current_states = key_value_states if key_value_states is not None else hidden_states if is_cross_attention and past_key_value and is_updated: key_states = past_key_value.key_cache[self.layer_idx] value_states = past_key_value.value_cache[self.layer_idx] else: key_states = ( self.k_proj(current_states) .view(bsz, -1, self.config.num_key_value_heads, self.head_dim) .transpose(1, 2) ) value_states = ( self.v_proj(current_states) .view(bsz, -1, self.config.num_key_value_heads, self.head_dim) .transpose(1, 2) ) if is_cross_attention and past_key_value is not None: key_states, value_states = past_key_value.update( key_states, value_states, self.layer_idx, {"cache_position": cache_position} ) if not is_cross_attention: cos, sin = position_embeddings query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_value is not None: cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} key_states, value_states = past_key_value.update( key_states, value_states, self.layer_idx, cache_kwargs ) attention_interface: Callable = eager_attention_forward if self.config._attn_implementation != "eager": if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False): logger.warning_once( "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to " 'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' ) else: attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] is_causal = True if self.is_causal and attention_mask is None and q_len > 1 else False if self.head_dim_padding > 0: query_states = torch.nn.functional.pad(query_states, (0, self.head_dim_padding)) key_states = torch.nn.functional.pad(key_states, (0, self.head_dim_padding)) value_states = torch.nn.functional.pad(value_states, (0, self.head_dim_padding)) attn_output, attn_weights = attention_interface( self, query_states, key_states, value_states, attention_mask, dropout=0.0 if not self.training else self.attention_dropout, scaling=self.scaling, is_causal=is_causal, **kwargs, ) if self.head_dim_padding > 0: attn_output = attn_output[..., : -self.head_dim_padding] attn_output = attn_output.reshape(bsz, q_len, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights class MoonshineRotaryEmbedding(GlmRotaryEmbedding): pass class MoonshineEncoderLayer(LlamaDecoderLayer): def __init__(self, config: MoonshineConfig, layer_idx: int): super().__init__(config, layer_idx) self.self_attn = MoonshineAttention( config=config, layer_idx=layer_idx, is_causal=False, num_attention_heads=config.encoder_num_attention_heads, num_key_value_heads=config.encoder_num_key_value_heads, ) self.mlp = MoonshineEncoderMLP(config, config.encoder_hidden_act) self.input_layernorm = nn.LayerNorm(config.hidden_size, bias=False) self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, bias=False) class MoonshineDecoderLayer(GradientCheckpointingLayer): def __init__(self, config: MoonshineConfig, layer_idx: Optional[int] = None): super().__init__() self.hidden_size = config.hidden_size self.self_attn = MoonshineAttention( config=config, layer_idx=layer_idx, is_causal=True, num_attention_heads=config.decoder_num_attention_heads, num_key_value_heads=config.decoder_num_key_value_heads, ) self.encoder_attn = MoonshineAttention( config=config, layer_idx=layer_idx, is_causal=False, num_attention_heads=config.decoder_num_attention_heads, num_key_value_heads=config.decoder_num_key_value_heads, ) self.mlp = MoonshineDecoderMLP(config, config.decoder_hidden_act) self.input_layernorm = nn.LayerNorm(config.hidden_size, bias=False) self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, bias=False) self.final_layernorm = nn.LayerNorm(config.hidden_size, bias=False) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, encoder_position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Cache] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False, cache_position: Optional[torch.LongTensor] = None, position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46 encoder_position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, **kwargs, ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: residual = hidden_states hidden_states = self.input_layernorm(hidden_states) # Self Attention hidden_states, self_attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, **kwargs, ) hidden_states = residual + hidden_states # Cross-Attention Block cross_attn_weights = None if encoder_hidden_states is not None: residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states, cross_attn_weights = self.encoder_attn( hidden_states=hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, past_key_value=past_key_value, output_attentions=output_attentions, use_cache=use_cache, ) hidden_states = residual + hidden_states # Fully Connected residual = hidden_states hidden_states = self.final_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights, cross_attn_weights) return outputs @auto_docstring class MoonshinePreTrainedModel(PreTrainedModel): config_class = MoonshineConfig base_model_prefix = "model" main_input_name = "input_values" supports_gradient_checkpointing = True _no_split_modules = ["MoonshineEncoderLayer", "MoonshineDecoderLayer"] _supports_flash_attn_2 = True _supports_sdpa = True _supports_cache_class = True _supports_static_cache = True def _init_weights(self, module): std = self.config.initializer_range if isinstance(module, (nn.Linear, nn.Conv1d)): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, (nn.GroupNorm, nn.LayerNorm)): module.weight.data.fill_(1.0) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor): """ Computes the output length of the convolutional layers """ output_conv1_length = int((input_lengths - 127) / 64 + 1) output_conv2_length = int((output_conv1_length - 7) / 3 + 1) output_conv3_length = int((output_conv2_length - 3) / 2 + 1) return output_conv3_length class MoonshineEncoder(MoonshinePreTrainedModel): """ Transformer encoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MoonshineEncoderLayer`] Args: config: MoonshineConfig """ main_input_name = "input_values" def __init__(self, config: MoonshineConfig): super().__init__(config) self.config = config embed_dim = config.hidden_size self.conv1 = nn.Conv1d(1, embed_dim, kernel_size=127, stride=64, bias=False) self.conv2 = nn.Conv1d(embed_dim, 2 * embed_dim, kernel_size=7, stride=3) self.conv3 = nn.Conv1d(2 * embed_dim, embed_dim, kernel_size=3, stride=2) self.groupnorm = nn.GroupNorm(num_groups=1, num_channels=embed_dim, eps=1e-5) self.rotary_emb = MoonshineRotaryEmbedding(config=config) self.layers = nn.ModuleList( [MoonshineEncoderLayer(config, idx) for idx in range(config.encoder_num_hidden_layers)] ) self.layer_norm = nn.LayerNorm(embed_dim, bias=False) self.gradient_checkpointing = False self.post_init() def get_input_embeddings(self) -> nn.Module: return self.conv1 def set_input_embeddings(self, value: nn.Module): self.conv1 = value @can_return_tuple def forward( self, input_values: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, **flash_attn_kwargs: Unpack[FlashAttentionKwargs], ) -> BaseModelOutputWithPast: r""" Args: input_values (`torch.FloatTensor` of shape `(batch_size, audio_length)`): Float values of 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_values`, the [`AutoFeatureExtractor`] should be used for padding and conversion into a tensor of type `torch.FloatTensor`. attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding indices in `input_values`. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ 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 ) if input_values is None: raise ValueError("You must specify input_values.") # conv downsampling input_values = input_values.unsqueeze(1) hidden_states = nn.functional.tanh(self.conv1(input_values)) hidden_states = self.groupnorm(hidden_states) hidden_states = nn.functional.gelu(self.conv2(hidden_states)) hidden_states = nn.functional.gelu(self.conv3(hidden_states)) hidden_states = hidden_states.permute(0, 2, 1) # attention mask downsampling if attention_mask is not None: mask_len = self._get_feat_extract_output_lengths(attention_mask.shape[-1]) downsample_stride = 64 * 3 * 2 # conv strides attention_mask = attention_mask[..., ::downsample_stride][..., :mask_len] if self.config._attn_implementation == "flash_attention_2": attention_mask = attention_mask if (attention_mask == 0.0).any() else None # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward elif self.config._attn_implementation == "sdpa" and not output_attentions: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] attention_mask = _prepare_4d_attention_mask_for_sdpa(attention_mask, hidden_states.dtype) else: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] attention_mask = _prepare_4d_attention_mask(attention_mask, hidden_states.dtype) position_ids = torch.arange(0, hidden_states.shape[1], device=hidden_states.device).unsqueeze(0) # create position embeddings to be shared across the decoder layers position_embeddings = self.rotary_emb(hidden_states, position_ids) # encoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None for encoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = encoder_layer( hidden_states, attention_mask=attention_mask, position_ids=position_ids, output_attentions=output_attentions, position_embeddings=position_embeddings, **flash_attn_kwargs, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attns += (layer_outputs[1],) hidden_states = self.layer_norm(hidden_states) # add hidden states from the last encoder layer if output_hidden_states: all_hidden_states += (hidden_states,) return BaseModelOutputWithPast( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attns, ) class MoonshineDecoder(LlamaModel): main_input_name = "input_ids" def __init__(self, config: MoonshineConfig): super().__init__(config) self.norm = nn.LayerNorm(config.hidden_size, bias=False) self.layers = nn.ModuleList( [MoonshineDecoderLayer(config, idx) for idx in range(config.decoder_num_hidden_layers)] ) def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Cache] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, **flash_attn_kwargs: Unpack[FlashAttentionKwargs], ) -> Union[Tuple, BaseModelOutputWithPast]: r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. encoder_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding indices in `encoder_hidden_states`. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) """ 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 (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if self.gradient_checkpointing and self.training and use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`." ) use_cache = False if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) if use_cache and past_key_values is None: self_attention_cache = DynamicCache() cross_attention_cache = DynamicCache() past_key_values = EncoderDecoderCache(self_attention_cache, cross_attention_cache) if cache_position is None: past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 cache_position = torch.arange( past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device ) if position_ids is None: position_ids = cache_position.unsqueeze(0) causal_mask = self._update_causal_mask( attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions ) hidden_states = inputs_embeds # create position embeddings to be shared across the decoder layers position_embeddings = self.rotary_emb(hidden_states, position_ids) # decoder layers 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 # attention mask downsampling if encoder_attention_mask is not None: mask_len = encoder_hidden_states.shape[-2] downsample_stride = 64 * 3 * 2 # conv strides encoder_attention_mask = encoder_attention_mask[..., ::downsample_stride][..., :mask_len] if self.config._attn_implementation == "flash_attention_2": encoder_attention_mask = encoder_attention_mask if (encoder_attention_mask == 0.0).any() else None # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward elif self.config._attn_implementation == "sdpa" and not output_attentions: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] encoder_attention_mask = _prepare_4d_attention_mask_for_sdpa( encoder_attention_mask, hidden_states.dtype, hidden_states.shape[-2] ) else: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] encoder_attention_mask = _prepare_4d_attention_mask( encoder_attention_mask, hidden_states.dtype, hidden_states.shape[-2] ) for decoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) layer_outputs = decoder_layer( hidden_states, attention_mask=causal_mask, encoder_attention_mask=encoder_attention_mask, encoder_hidden_states=encoder_hidden_states, position_ids=position_ids, past_key_value=past_key_values, output_attentions=output_attentions, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, **flash_attn_kwargs, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attns += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) hidden_states = self.norm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) return BaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, past_key_values=past_key_values if use_cache else None, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions, ) class MoonshineModel(WhisperModel): @can_return_tuple @auto_docstring def forward( self, input_values: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.LongTensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.LongTensor] = None, encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, past_key_values: Optional[Union[EncoderDecoderCache, Tuple[torch.FloatTensor]]] = None, decoder_inputs_embeds: Optional[Tuple[torch.FloatTensor]] = None, decoder_position_ids: Optional[Tuple[torch.LongTensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, ) -> Seq2SeqModelOutput: r""" input_values (`torch.FloatTensor` of shape `(batch_size, audio_length)`): Float values of 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_values`, the [`AutoFeatureExtractor`] should be used for padding and conversion into a tensor of type `torch.FloatTensor`. decoder_input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) decoder_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix. decoder_position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) Example: ```python >>> import torch >>> from transformers import AutoFeatureExtractor, MoonshineModel >>> from datasets import load_dataset >>> model = MoonshineModel.from_pretrained("UsefulSensors/moonshine-tiny") >>> feature_extractor = AutoFeatureExtractor.from_pretrained("UsefulSensors/moonshine-tiny") >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt") >>> input_values = inputs.input_values >>> decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id >>> last_hidden_state = model(input_values, decoder_input_ids=decoder_input_ids).last_hidden_state >>> list(last_hidden_state.shape) [1, 2, 288] ``` """ 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 encoder_outputs is None: encoder_outputs: BaseModelOutput = self.encoder( input_values, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True 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, ) # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn) decoder_outputs: BaseModelOutputWithPastAndCrossAttentions = self.decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, encoder_attention_mask=attention_mask, encoder_hidden_states=encoder_outputs.last_hidden_state, past_key_values=past_key_values, inputs_embeds=decoder_inputs_embeds, position_ids=decoder_position_ids, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, cache_position=cache_position, ) 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.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) @auto_docstring( custom_intro=""" The Moonshine Model with a language modeling head. Can be used for automatic speech recognition. """ ) class MoonshineForConditionalGeneration(MoonshinePreTrainedModel, GenerationMixin): _tied_weights_keys = ["proj_out.weight"] def __init__(self, config: MoonshineConfig): super().__init__(config) self.model = MoonshineModel(config) self.proj_out = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def get_encoder(self): return self.model.get_encoder() def get_decoder(self): return self.model.get_decoder() def get_output_embeddings(self): return self.proj_out def set_output_embeddings(self, new_embeddings): self.proj_out = new_embeddings def get_input_embeddings(self) -> nn.Module: return self.model.get_input_embeddings() @can_return_tuple @auto_docstring def forward( self, input_values: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.LongTensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None, decoder_attention_mask: Optional[torch.LongTensor] = None, encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, past_key_values: Optional[Union[EncoderDecoderCache, Tuple[torch.FloatTensor]]] = None, decoder_inputs_embeds: Optional[Tuple[torch.FloatTensor]] = None, decoder_position_ids: Optional[Tuple[torch.LongTensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, labels: Optional[torch.LongTensor] = None, ) -> Seq2SeqLMOutput: r""" input_values (`torch.FloatTensor` of shape `(batch_size, audio_length)`): Float values of 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_values`, the [`AutoFeatureExtractor`] should be used for padding and conversion into a tensor of type `torch.FloatTensor`. decoder_input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) decoder_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix. decoder_position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. Example: ```python >>> import torch >>> from transformers import AutoProcessor, MoonshineForConditionalGeneration >>> from datasets import load_dataset >>> processor = AutoProcessor.from_pretrained("UsefulSensors/moonshine-tiny") >>> model = MoonshineForConditionalGeneration.from_pretrained("UsefulSensors/moonshine-tiny") >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> inputs = processor(ds[0]["audio"]["array"], return_tensors="pt") >>> input_values = inputs.input_values >>> generated_ids = model.generate(input_values, max_new_tokens=100) >>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] >>> transcription 'Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.' ```""" if labels is not None: if decoder_input_ids is None and decoder_inputs_embeds is None: decoder_input_ids = shift_tokens_right( labels, self.config.pad_token_id, self.config.decoder_start_token_id ) outputs: Seq2SeqModelOutput = self.model( input_values, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, encoder_outputs=encoder_outputs, decoder_attention_mask=decoder_attention_mask, past_key_values=past_key_values, decoder_inputs_embeds=decoder_inputs_embeds, decoder_position_ids=decoder_position_ids, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, cache_position=cache_position, ) logits = self.proj_out(outputs.last_hidden_state) loss = None if labels is not None: loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size) return Seq2SeqLMOutput( loss=loss, logits=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__ = [ "MoonshineConfig", "MoonshineModel", "MoonshinePreTrainedModel", "MoonshineForConditionalGeneration", ]