920 lines
42 KiB
Python
920 lines
42 KiB
Python
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Callable, Optional, Union
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import torch
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import torch.nn as nn
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from transformers.utils.generic import OutputRecorder, check_model_inputs
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from ...activations import ACT2FN
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from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
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from ...configuration_utils import PretrainedConfig
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from ...generation import GenerationMixin
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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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BaseModelOutputWithPast,
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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_config_validation
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from ...processing_utils import Unpack
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from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
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from ..glm.modeling_glm import GlmAttention, GlmRotaryEmbedding, apply_rotary_pos_emb
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from ..llama.modeling_llama import LlamaDecoderLayer, LlamaModel, eager_attention_forward
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from ..whisper.modeling_whisper import WhisperModel, shift_tokens_right
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logger = logging.get_logger(__name__)
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class MoonshineConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`MoonshineModel`]. It is used to instantiate a Moonshine
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the Moonshine
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[UsefulSensors/moonshine-tiny](https://huggingface.co/UsefulSensors/moonshine-tiny).
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 32768):
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Vocabulary size of the Moonshine model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`MoonshineModel`].
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hidden_size (`int`, *optional*, defaults to 288):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 1152):
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Dimension of the MLP representations.
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encoder_num_hidden_layers (`int`, *optional*, defaults to 6):
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Number of hidden layers in the Transformer encoder.
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decoder_num_hidden_layers (`int`, *optional*, defaults to 6):
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Number of hidden layers in the Transformer decoder.
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encoder_num_attention_heads (`int`, *optional*, defaults to 8):
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Number of attention heads for each attention layer in the Transformer encoder.
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decoder_num_attention_heads (`int`, *optional*, defaults to 8):
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Number of attention heads for each attention layer in the Transformer decoder.
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encoder_num_key_value_heads (`int`, *optional*):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`encoder_num_key_value_heads=encoder_num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`encoder_num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details, check out [this
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paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
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`num_attention_heads`.
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decoder_num_key_value_heads (`int`, *optional*):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`decoder_num_key_value_heads=decoder_num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`decoder_num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details, check out [this
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paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
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`decoder_num_attention_heads`.
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pad_head_dim_to_multiple_of (`int`, *optional*):
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Pad head dimension in encoder and decoder to the next multiple of this value. Necessary for using certain
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optimized attention implementations.
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encoder_hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder.
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decoder_hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to 512):
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The maximum sequence length that this model might ever be used with.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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decoder_start_token_id (`int`, *optional*, defaults to 1):
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Corresponds to the "<|startoftranscript|>" token, which is automatically used when no `decoder_input_ids`
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are provided to the `generate` function. It is used to guide the model`s generation process depending on
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the task.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models).
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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rope_scaling (`Dict`, *optional*):
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Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
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and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
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accordingly.
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Expected contents:
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`rope_type` (`str`):
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The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
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'llama3'], with 'default' being the original RoPE implementation.
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`factor` (`float`, *optional*):
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Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
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most scaling types, a `factor` of x will enable the model to handle sequences of length x *
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original maximum pre-trained length.
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`original_max_position_embeddings` (`int`, *optional*):
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Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
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pretraining.
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`attention_factor` (`float`, *optional*):
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Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
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computation. If unspecified, it defaults to value recommended by the implementation, using the
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`factor` field to infer the suggested value.
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`beta_fast` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
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ramp function. If unspecified, it defaults to 32.
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`beta_slow` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
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ramp function. If unspecified, it defaults to 1.
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`short_factor` (`list[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to short contexts (<
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`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
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size divided by the number of attention heads divided by 2
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`long_factor` (`list[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to long contexts (<
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`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
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size divided by the number of attention heads divided by 2
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`low_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
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`high_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
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partial_rotary_factor (`float`, *optional*, defaults to 0.9):
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Percentage of the query and keys which will have rotary embedding.
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is_encoder_decoder (`bool`, *optional*, defaults to `True`):
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Whether the model is used as an encoder/decoder or not.
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attention_bias (`bool`, *optional*, defaults to `False`):
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Whether to use a bias in the query, key, value and output projection layers during self-attention.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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bos_token_id (`int`, *optional*, defaults to 1):
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Denotes beginning of sequences token id.
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eos_token_id (`int`, *optional*, defaults to 2):
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Denotes end of sequences token id.
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Example:
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```python
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>>> from transformers import MoonshineModel, MoonshineConfig
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>>> # Initializing a Moonshine style configuration
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>>> configuration = MoonshineConfig().from_pretrained("UsefulSensors/moonshine-tiny")
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>>> # Initializing a model from the configuration
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>>> model = MoonshineModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "moonshine"
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keys_to_ignore_at_inference = ["past_key_values"]
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attribute_map = {
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"num_key_value_heads": "encoder_num_key_value_heads",
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"num_attention_heads": "encoder_num_attention_heads",
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"num_hidden_layers": "encoder_num_hidden_layers",
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}
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def __init__(
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self,
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vocab_size=32768,
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hidden_size=288,
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intermediate_size=1152,
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encoder_num_hidden_layers=6,
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decoder_num_hidden_layers=6,
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encoder_num_attention_heads=8,
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decoder_num_attention_heads=8,
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encoder_num_key_value_heads=None,
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decoder_num_key_value_heads=None,
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pad_head_dim_to_multiple_of=None,
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encoder_hidden_act="gelu",
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decoder_hidden_act="silu",
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max_position_embeddings=512,
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initializer_range=0.02,
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decoder_start_token_id=1,
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use_cache=True,
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rope_theta=10000.0,
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rope_scaling=None,
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partial_rotary_factor=0.9,
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is_encoder_decoder=True,
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attention_bias=False,
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attention_dropout=0.0,
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bos_token_id=1,
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eos_token_id=2,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.encoder_num_hidden_layers = encoder_num_hidden_layers
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self.decoder_num_hidden_layers = decoder_num_hidden_layers
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self.encoder_num_attention_heads = encoder_num_attention_heads
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self.decoder_num_attention_heads = decoder_num_attention_heads
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if encoder_num_key_value_heads is None:
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encoder_num_key_value_heads = encoder_num_attention_heads
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self.encoder_num_key_value_heads = encoder_num_key_value_heads
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if decoder_num_key_value_heads is None:
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decoder_num_key_value_heads = decoder_num_attention_heads
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self.decoder_num_key_value_heads = decoder_num_key_value_heads
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self.pad_head_dim_to_multiple_of = pad_head_dim_to_multiple_of
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self.encoder_hidden_act = encoder_hidden_act
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self.decoder_hidden_act = decoder_hidden_act
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self.max_position_embeddings = max_position_embeddings
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self.initializer_range = initializer_range
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self.decoder_start_token_id = decoder_start_token_id
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self.partial_rotary_factor = partial_rotary_factor
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self.is_encoder_decoder = is_encoder_decoder
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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# Validate the correctness of rotary position embeddings parameters
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rope_config_validation(self)
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super().__init__(
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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is_encoder_decoder=is_encoder_decoder,
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decoder_start_token_id=decoder_start_token_id,
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**kwargs,
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)
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class MoonshineEncoderMLP(nn.Module):
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def __init__(self, config, hidden_act):
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super().__init__()
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self.config = config
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self.activation_fn = ACT2FN[hidden_act]
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self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
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self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states = self.fc1(hidden_states)
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hidden_states = self.activation_fn(hidden_states)
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hidden_states = self.fc2(hidden_states)
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return hidden_states
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class MoonshineDecoderMLP(nn.Module):
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def __init__(self, config, hidden_act):
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super().__init__()
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self.config = config
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self.activation_fn = ACT2FN[hidden_act]
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self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size * 2)
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self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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hidden_states = self.fc1(hidden_states)
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hidden_states, gate = hidden_states.chunk(2, dim=-1)
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hidden_states = self.activation_fn(gate) * hidden_states
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hidden_states = self.fc2(hidden_states)
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return hidden_states
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class MoonshineAttention(GlmAttention):
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def __init__(
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self,
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config: MoonshineConfig,
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layer_idx: int,
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is_causal: bool,
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num_attention_heads: int,
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num_key_value_heads: int,
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):
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config.update({"num_attention_heads": num_attention_heads, "num_key_value_heads": num_key_value_heads})
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super().__init__(config, layer_idx)
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self.is_causal = is_causal
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self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
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# Pad head dimension to the next specified multiple.
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if self.config.pad_head_dim_to_multiple_of is not None:
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target_multiple = self.config.pad_head_dim_to_multiple_of
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target_head_dim = target_multiple * ((self.head_dim + target_multiple - 1) // target_multiple)
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self.head_dim_padding = target_head_dim - self.head_dim
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else:
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self.head_dim_padding = 0
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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,
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attention_mask: Optional[torch.Tensor] = None,
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past_key_value: Optional[Cache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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key_value_states: Optional[torch.Tensor] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
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bsz, q_len = hidden_states.shape[:-1]
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query_states = (
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self.q_proj(hidden_states).view(bsz, q_len, self.config.num_key_value_heads, self.head_dim).transpose(1, 2)
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)
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is_cross_attention = key_value_states is not None
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if past_key_value is not None:
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is_updated = past_key_value.is_updated.get(self.layer_idx)
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if is_cross_attention:
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# after the first generated id, we can subsequently re-use all key/value_states from cache
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past_key_value.is_updated[self.layer_idx] = True
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past_key_value = past_key_value.cross_attention_cache
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else:
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past_key_value = past_key_value.self_attention_cache
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# use key_value_states if cross attention
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current_states = key_value_states if key_value_states is not None else hidden_states
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if is_cross_attention and past_key_value and is_updated:
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key_states = past_key_value.layers[self.layer_idx].keys
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value_states = past_key_value.layers[self.layer_idx].values
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else:
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key_states = (
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self.k_proj(current_states)
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.view(bsz, -1, self.config.num_key_value_heads, self.head_dim)
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.transpose(1, 2)
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)
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value_states = (
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self.v_proj(current_states)
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.view(bsz, -1, self.config.num_key_value_heads, self.head_dim)
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.transpose(1, 2)
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)
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if is_cross_attention and past_key_value is not None:
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key_states, value_states = past_key_value.update(
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key_states, value_states, self.layer_idx, {"cache_position": cache_position}
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)
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if not is_cross_attention:
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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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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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key_states, value_states = past_key_value.update(
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key_states, value_states, self.layer_idx, cache_kwargs
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)
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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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is_causal = True if self.is_causal and attention_mask is None and q_len > 1 else False
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if self.head_dim_padding > 0:
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query_states = torch.nn.functional.pad(query_states, (0, self.head_dim_padding))
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key_states = torch.nn.functional.pad(key_states, (0, self.head_dim_padding))
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value_states = torch.nn.functional.pad(value_states, (0, self.head_dim_padding))
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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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is_causal=is_causal,
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**kwargs,
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)
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if self.head_dim_padding > 0:
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attn_output = attn_output[..., : -self.head_dim_padding]
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attn_output = attn_output.reshape(bsz, q_len, -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 MoonshineRotaryEmbedding(GlmRotaryEmbedding):
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pass
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class MoonshineEncoderLayer(LlamaDecoderLayer):
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def __init__(self, config: MoonshineConfig, layer_idx: int):
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super().__init__(config, layer_idx)
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self.self_attn = MoonshineAttention(
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config=config,
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layer_idx=layer_idx,
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is_causal=False,
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num_attention_heads=config.encoder_num_attention_heads,
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num_key_value_heads=config.encoder_num_key_value_heads,
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)
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self.mlp = MoonshineEncoderMLP(config, config.encoder_hidden_act)
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self.input_layernorm = nn.LayerNorm(config.hidden_size, bias=False)
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self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, bias=False)
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class MoonshineDecoderLayer(GradientCheckpointingLayer):
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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,
|
|
use_cache: Optional[bool] = False,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
|
|
encoder_position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
|
residual = hidden_states
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
|
|
hidden_states, _ = self.self_attn(
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_value,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
position_embeddings=position_embeddings,
|
|
**kwargs,
|
|
)
|
|
hidden_states = residual + hidden_states
|
|
|
|
if encoder_hidden_states is not None:
|
|
residual = hidden_states
|
|
hidden_states = self.post_attention_layernorm(hidden_states)
|
|
hidden_states, _ = self.encoder_attn(
|
|
hidden_states=hidden_states,
|
|
key_value_states=encoder_hidden_states,
|
|
attention_mask=encoder_attention_mask,
|
|
past_key_value=past_key_value,
|
|
use_cache=use_cache,
|
|
)
|
|
hidden_states = residual + hidden_states
|
|
|
|
residual = hidden_states
|
|
hidden_states = self.final_layernorm(hidden_states)
|
|
hidden_states = self.mlp(hidden_states)
|
|
hidden_states = residual + hidden_states
|
|
return hidden_states
|
|
|
|
|
|
@auto_docstring
|
|
class MoonshinePreTrainedModel(PreTrainedModel):
|
|
config: MoonshineConfig
|
|
base_model_prefix = "model"
|
|
main_input_name = "input_values"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["MoonshineEncoderLayer", "MoonshineDecoderLayer"]
|
|
_supports_flash_attn = True
|
|
_supports_sdpa = True
|
|
|
|
_can_compile_fullgraph = True
|
|
# TODO arthur, how do we separate when it cross / self coming from different layer?
|
|
|
|
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"
|
|
_can_record_outputs = {
|
|
"attentions": MoonshineAttention,
|
|
"hidden_states": MoonshineEncoderLayer,
|
|
}
|
|
|
|
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
|
|
|
|
@check_model_inputs
|
|
def forward(
|
|
self,
|
|
input_values: torch.FloatTensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> 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]`, a
|
|
`numpy.ndarray` or a `torch.Tensor`, *e.g.* via the torchcodec libary (`pip install torchcodec`) or
|
|
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)
|
|
"""
|
|
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
|
|
elif self.config._attn_implementation == "sdpa":
|
|
attention_mask = _prepare_4d_attention_mask_for_sdpa(attention_mask, hidden_states.dtype)
|
|
else:
|
|
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)
|
|
position_embeddings = self.rotary_emb(hidden_states, position_ids)
|
|
|
|
for encoder_layer in self.layers:
|
|
hidden_states = encoder_layer(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
position_embeddings=position_embeddings,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = self.layer_norm(hidden_states)
|
|
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
)
|
|
|
|
|
|
class MoonshineDecoder(LlamaModel):
|
|
main_input_name = "input_ids"
|
|
_can_record_outputs = {
|
|
"attentions": OutputRecorder(MoonshineAttention, index=1, layer_name="self_attn"),
|
|
"hidden_states": MoonshineDecoderLayer,
|
|
"cross_attentions": OutputRecorder(MoonshineAttention, index=1, layer_name="encoder_attn"),
|
|
}
|
|
|
|
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)]
|
|
)
|
|
|
|
@check_model_inputs
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
past_key_values: Optional[Cache] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> 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)
|
|
"""
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
|
|
if use_cache and past_key_values is None:
|
|
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 = create_causal_mask(
|
|
config=self.config,
|
|
input_embeds=inputs_embeds,
|
|
attention_mask=attention_mask,
|
|
cache_position=cache_position,
|
|
past_key_values=past_key_values,
|
|
position_ids=position_ids,
|
|
)
|
|
|
|
hidden_states = inputs_embeds
|
|
position_embeddings = self.rotary_emb(hidden_states, position_ids)
|
|
|
|
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
|
|
elif self.config._attn_implementation == "sdpa":
|
|
encoder_attention_mask = _prepare_4d_attention_mask_for_sdpa(
|
|
encoder_attention_mask, hidden_states.dtype, hidden_states.shape[-2]
|
|
)
|
|
else:
|
|
encoder_attention_mask = _prepare_4d_attention_mask(
|
|
encoder_attention_mask, hidden_states.dtype, hidden_states.shape[-2]
|
|
)
|
|
|
|
for decoder_layer in self.layers:
|
|
hidden_states = decoder_layer(
|
|
hidden_states,
|
|
causal_mask,
|
|
encoder_hidden_states, # as a positional argument for gradient checkpointing
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_value=past_key_values,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
position_embeddings=position_embeddings,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = self.norm(hidden_states)
|
|
|
|
return BaseModelOutputWithPastAndCrossAttentions(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values if use_cache else None,
|
|
)
|
|
|
|
|
|
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,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> 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]`, a
|
|
`numpy.ndarray` or a `torch.Tensor`, *e.g.* via the torchcodec libary (`pip install torchcodec`) or
|
|
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_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`
|
|
|
|
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]
|
|
```
|
|
"""
|
|
if encoder_outputs is None:
|
|
encoder_outputs: BaseModelOutput = self.encoder(input_values, attention_mask=attention_mask, **kwargs)
|
|
|
|
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,
|
|
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.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,
|
|
cache_position: Optional[torch.LongTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> 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]`, a
|
|
`numpy.ndarray` or a `torch.Tensor`, *e.g.* via the torchcodec libary (`pip install torchcodec`) or
|
|
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`.
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decoder_position_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`):
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Indices of positions of each input sequence tokens in the position embeddings.
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Used to calculate the position embeddings up to `config.decoder_config.max_position_embeddings`
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Example:
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|
|
```python
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>>> import torch
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>>> from transformers import AutoProcessor, MoonshineForConditionalGeneration
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>>> from datasets import load_dataset
|
|
|
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>>> processor = AutoProcessor.from_pretrained("UsefulSensors/moonshine-tiny")
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>>> model = MoonshineForConditionalGeneration.from_pretrained("UsefulSensors/moonshine-tiny")
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|
|
|
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
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|
|
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>>> inputs = processor(ds[0]["audio"]["array"], return_tensors="pt")
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>>> input_values = inputs.input_values
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|
|
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>>> generated_ids = model.generate(input_values, max_new_tokens=100)
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|
|
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>>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
|
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>>> transcription
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|
'Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.'
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```"""
|
|
|
|
if labels is not None:
|
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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,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
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",
|
|
]
|