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728 lines
30 KiB
728 lines
30 KiB
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/lasr/modular_lasr.py.
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# Do NOT edit this file manually as any edits will be overwritten by the generation of
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# the file from the modular. If any change should be done, please apply the change to the
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# modular_lasr.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# Copyright 2025 The HuggingFace Inc. team and Google LLC. 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 collections.abc import Callable
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from dataclasses import dataclass
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from typing import Optional
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import torch
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from torch import nn
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from ...activations import ACT2FN
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from ...integrations import use_kernel_func_from_hub, use_kernelized_func
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from ...masking_utils import create_bidirectional_mask
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import BaseModelOutput, CausalLMOutput
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from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from ...processing_utils import Unpack
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from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple
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from ...utils.generic import check_model_inputs, maybe_autocast
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from .configuration_lasr import LasrCTCConfig, LasrEncoderConfig
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class LasrEncoderSubsampling(nn.Module):
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def __init__(self, config: LasrEncoderConfig):
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super().__init__()
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self.dense_0 = nn.Linear(config.num_mel_bins, config.hidden_size)
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self.conv_0 = nn.Conv1d(
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config.hidden_size,
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config.hidden_size,
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kernel_size=config.subsampling_conv_kernel_size,
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stride=config.subsampling_conv_stride,
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)
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self.conv_1 = nn.Conv1d(
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config.hidden_size,
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config.subsampling_conv_channels,
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kernel_size=config.subsampling_conv_kernel_size,
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stride=config.subsampling_conv_stride,
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)
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self.dense_1 = nn.Linear(config.subsampling_conv_channels, config.hidden_size)
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self.act_fn = nn.ReLU()
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def forward(self, input_features: torch.Tensor) -> torch.Tensor:
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hidden_states = self.act_fn(self.dense_0(input_features))
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hidden_states = hidden_states.transpose(1, 2)
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hidden_states = self.act_fn(self.conv_0(hidden_states))
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hidden_states = self.act_fn(self.conv_1(hidden_states))
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hidden_states = hidden_states.transpose(1, 2)
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return self.dense_1(hidden_states)
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class LasrEncoderRotaryEmbedding(nn.Module):
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inv_freq: torch.Tensor # fix linting for `register_buffer`
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def __init__(self, config: LasrEncoderConfig, device=None):
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super().__init__()
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self.max_seq_len_cached = config.max_position_embeddings
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self.original_max_seq_len = config.max_position_embeddings
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self.config = config
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self.rope_type = self.config.rope_parameters["rope_type"]
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rope_init_fn: Callable = self.compute_default_rope_parameters
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if self.rope_type != "default":
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rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
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inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
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self.register_buffer("inv_freq", inv_freq, persistent=False)
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self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
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@staticmethod
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def compute_default_rope_parameters(
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config: LasrEncoderConfig | None = None,
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device: Optional["torch.device"] = None,
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seq_len: int | None = None,
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) -> tuple["torch.Tensor", float]:
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"""
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Computes the inverse frequencies according to the original RoPE implementation
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Args:
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config ([`~transformers.PreTrainedConfig`]):
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The model configuration.
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device (`torch.device`):
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The device to use for initialization of the inverse frequencies.
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seq_len (`int`, *optional*):
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The current sequence length. Unused for this type of RoPE.
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Returns:
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Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
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post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
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"""
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base = config.rope_parameters["rope_theta"]
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dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
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attention_factor = 1.0 # Unused in this type of RoPE
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# Compute the inverse frequencies
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inv_freq = 1.0 / (
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base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
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)
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return inv_freq, attention_factor
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@torch.no_grad()
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@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
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def forward(self, x, position_ids):
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inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
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position_ids_expanded = position_ids[:, None, :].float()
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device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
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with maybe_autocast(device_type=device_type, enabled=False): # Force float32
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freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
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emb = torch.cat((freqs, freqs), dim=-1)
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cos = emb.cos() * self.attention_scaling
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sin = emb.sin() * self.attention_scaling
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return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
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def rotate_half(x):
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"""Rotates half the hidden dims of the input."""
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x1 = x[..., : x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2 :]
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return torch.cat((-x2, x1), dim=-1)
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@use_kernel_func_from_hub("rotary_pos_emb")
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def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
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"""Applies Rotary Position Embedding to the query and key tensors.
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Args:
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q (`torch.Tensor`): The query tensor.
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k (`torch.Tensor`): The key tensor.
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cos (`torch.Tensor`): The cosine part of the rotary embedding.
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sin (`torch.Tensor`): The sine part of the rotary embedding.
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unsqueeze_dim (`int`, *optional*, defaults to 1):
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The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
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sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
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that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
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k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
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cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
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the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
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Returns:
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`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
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"""
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cos = cos.unsqueeze(unsqueeze_dim)
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sin = sin.unsqueeze(unsqueeze_dim)
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q_embed = (q * cos) + (rotate_half(q) * sin)
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k_embed = (k * cos) + (rotate_half(k) * sin)
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return q_embed, k_embed
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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"""
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This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
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num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
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"""
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batch, num_key_value_heads, slen, head_dim = hidden_states.shape
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if n_rep == 1:
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return hidden_states
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hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
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return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
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def eager_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: torch.Tensor | None,
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scaling: float,
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dropout: float = 0.0,
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**kwargs: Unpack[TransformersKwargs],
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):
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key_states = repeat_kv(key, module.num_key_value_groups)
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value_states = repeat_kv(value, module.num_key_value_groups)
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attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
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if attention_mask is not None:
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causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
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attn_weights = attn_weights + causal_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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attn_output = torch.matmul(attn_weights, value_states)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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@use_kernelized_func(apply_rotary_pos_emb)
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class LasrEncoderAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(self, config: LasrEncoderConfig, layer_idx: int):
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super().__init__()
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self.config = config
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self.layer_idx = layer_idx
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self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
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self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
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self.scaling = self.head_dim**-0.5
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self.attention_dropout = config.attention_dropout
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self.is_causal = False
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self.q_proj = nn.Linear(
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config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
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)
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self.k_proj = nn.Linear(
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config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
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)
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self.v_proj = nn.Linear(
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config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
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)
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self.o_proj = nn.Linear(
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config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
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)
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
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attention_mask: torch.Tensor | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple[torch.Tensor, torch.Tensor]:
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.head_dim)
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query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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cos, sin = position_embeddings
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
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self.config._attn_implementation, eager_attention_forward
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)
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attn_output, attn_weights = attention_interface(
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self,
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query_states,
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key_states,
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value_states,
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attention_mask,
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dropout=0.0 if not self.training else self.attention_dropout,
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scaling=self.scaling,
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**kwargs,
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)
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attn_output = attn_output.reshape(*input_shape, -1).contiguous()
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attn_output = self.o_proj(attn_output)
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return attn_output, attn_weights
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class LasrEncoderConvolutionModule(nn.Module):
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def __init__(self, config: LasrEncoderConfig, module_config=None):
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"""
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Args:
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config (LasrEncoderConfig): Configuration for the model.
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module_config (dict): Configuration for the module (e.g., encoder or decoder).
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"""
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super().__init__()
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channels = config.hidden_size
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# kernel_size should be an odd number for 'SAME' padding
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if module_config is None:
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# e.g. using `LasrEncoderEncoderConfig` in src/transformers/models/lasr_encoder/configuration_lasr_encoder.py
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kernel_size = config.conv_kernel_size
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self.activation = ACT2FN[getattr(config, "hidden_act", "silu")]
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else:
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kernel_size = module_config["kernel_size"]
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self.activation = ACT2FN[module_config.get("activation", "silu")]
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self.padding = "same"
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self.pointwise_conv1 = nn.Conv1d(
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channels, 2 * channels, kernel_size=1, stride=1, padding=0, bias=config.convolution_bias
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)
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self.depthwise_conv = nn.Conv1d(
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channels,
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channels,
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kernel_size,
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stride=1,
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padding=self.padding,
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groups=channels,
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bias=config.convolution_bias,
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)
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self.norm = nn.BatchNorm1d(config.hidden_size, momentum=config.batch_norm_momentum)
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self.pointwise_conv2 = nn.Conv1d(
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channels, channels, kernel_size=1, stride=1, padding=0, bias=config.convolution_bias
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)
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def forward(self, hidden_states, attention_mask=None):
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"""
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Compute convolution module.
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Args:
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hidden_states (`torch.Tensor` of shape `(batch, time, channels)`): Input tensor.
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attention_mask (`torch.Tensor` of shape `(batch, 1, time, time)`): Attention mask.
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Returns:
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`torch.Tensor`: Output tensor of shape `(batch, time, channels)`.
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"""
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# exchange the temporal dimension and the feature dimension
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hidden_states = hidden_states.transpose(1, 2)
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# GLU mechanism, (batch_size, 2*channel, dim)
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hidden_states = self.pointwise_conv1(hidden_states)
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# (batch_size, channel, dim)
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hidden_states = nn.functional.glu(hidden_states, dim=1)
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# Apply padding mask before convolution
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if attention_mask is not None:
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if attention_mask.dtype == torch.bool:
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all_masked_rows = torch.all(~attention_mask, dim=2)
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else:
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all_masked_rows = torch.all(~(attention_mask == 0.0), dim=2)
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hidden_states = hidden_states.masked_fill(all_masked_rows, 0.0)
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# 1D Depthwise Conv
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hidden_states = self.depthwise_conv(hidden_states)
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hidden_states = self.norm(hidden_states)
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hidden_states = self.activation(hidden_states)
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hidden_states = self.pointwise_conv2(hidden_states)
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return hidden_states.transpose(1, 2)
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class LasrEncoderFeedForward(nn.Module):
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def __init__(self, config: LasrEncoderConfig):
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super().__init__()
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self.linear1 = nn.Linear(config.hidden_size, config.intermediate_size, bias=config.attention_bias)
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self.activation = ACT2FN[config.hidden_act]
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self.linear2 = nn.Linear(config.intermediate_size, config.hidden_size, bias=config.attention_bias)
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self.activation_dropout = config.activation_dropout
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def forward(self, hidden_states):
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hidden_states = self.activation(self.linear1(hidden_states))
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hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
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hidden_states = self.linear2(hidden_states)
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return hidden_states
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class LasrEncoderBlock(GradientCheckpointingLayer):
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def __init__(self, config: LasrEncoderConfig, layer_idx: int):
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super().__init__()
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self.gradient_checkpointing = False
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self.feed_forward1 = LasrEncoderFeedForward(config)
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self.self_attn = LasrEncoderAttention(config, layer_idx)
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self.conv = LasrEncoderConvolutionModule(config)
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self.feed_forward2 = LasrEncoderFeedForward(config)
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self.norm_feed_forward1 = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, bias=False)
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self.norm_self_att = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, bias=False)
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self.norm_conv = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, bias=False)
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self.norm_feed_forward2 = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, bias=False)
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self.norm_out = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, bias=False)
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self.feed_forward_residual_weights = config.feed_forward_residual_weights
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self.conv_residual_weights = config.conv_residual_weights
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: torch.Tensor | None = None,
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position_embeddings: torch.Tensor | None = None,
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**kwargs: Unpack[TransformersKwargs],
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) -> torch.Tensor:
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residual = hidden_states
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hidden_states = self.feed_forward1(self.norm_feed_forward1(hidden_states))
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hidden_states = (
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self.feed_forward_residual_weights[0] * residual + self.feed_forward_residual_weights[1] * hidden_states
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)
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normalized_hidden_states = self.norm_self_att(hidden_states)
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attn_output, _ = self.self_attn(
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hidden_states=normalized_hidden_states,
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attention_mask=attention_mask,
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position_embeddings=position_embeddings,
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**kwargs,
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)
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hidden_states = hidden_states + attn_output
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conv_output = self.conv(self.norm_conv(hidden_states), attention_mask=attention_mask)
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hidden_states = self.conv_residual_weights[0] * hidden_states + self.conv_residual_weights[1] * conv_output
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residual = hidden_states
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hidden_states = self.feed_forward2(self.norm_feed_forward2(hidden_states))
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hidden_states = (
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self.feed_forward_residual_weights[0] * residual + self.feed_forward_residual_weights[1] * hidden_states
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)
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hidden_states = self.norm_out(hidden_states)
|
|
|
|
return hidden_states
|
|
|
|
|
|
@auto_docstring
|
|
class LasrPreTrainedModel(PreTrainedModel):
|
|
config: LasrCTCConfig
|
|
base_model_prefix = "model"
|
|
main_input_name = "input_features"
|
|
input_modalities = "audio"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["LasrEncoderBlock"]
|
|
_supports_flat_attention_mask = True
|
|
_supports_sdpa = True
|
|
# padding is incompatible with flex attention as the resulting mask cannot be used to apply padding
|
|
_supports_flex_attn = False
|
|
|
|
# TODO: @eustlb, add support when flash attention supports custom attention bias
|
|
_supports_flash_attn = False
|
|
|
|
_can_compile_fullgraph = True
|
|
_supports_attention_backend = True
|
|
_can_record_outputs = {
|
|
"hidden_states": LasrEncoderBlock,
|
|
"attentions": LasrEncoderAttention,
|
|
}
|
|
|
|
@torch.no_grad()
|
|
def _init_weights(self, module):
|
|
super()._init_weights(module)
|
|
|
|
def _get_subsampling_output_length(self, input_lengths: torch.Tensor):
|
|
encoder_config = self.config.encoder_config if isinstance(self.config, LasrCTCConfig) else self.config
|
|
kernel_size = encoder_config.subsampling_conv_kernel_size
|
|
stride = encoder_config.subsampling_conv_stride
|
|
|
|
num_layers = 2
|
|
for _ in range(num_layers):
|
|
input_lengths = (input_lengths - kernel_size) // stride + 1
|
|
|
|
return input_lengths
|
|
|
|
def _get_output_attention_mask(self, attention_mask: torch.Tensor, target_length: int | None = None):
|
|
"""
|
|
Convert the input attention mask to its subsampled form. `target_length` sets the desired output length, useful
|
|
when the attention mask length differs from `sum(-1).max()` (i.e., when the longest sequence in the batch is padded)
|
|
"""
|
|
output_lengths = self._get_subsampling_output_length(attention_mask.sum(-1))
|
|
# Use target_length if provided, otherwise use max length in batch
|
|
max_length = target_length if target_length is not None else output_lengths.max()
|
|
attention_mask = torch.arange(max_length, device=attention_mask.device) < output_lengths[:, None]
|
|
return attention_mask
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
The LasrEncoder model, based on the Conformer architecture](https://arxiv.org/abs/2005.08100).
|
|
"""
|
|
)
|
|
class LasrEncoder(LasrPreTrainedModel):
|
|
config: LasrEncoderConfig
|
|
base_model_prefix = "encoder"
|
|
|
|
def __init__(self, config: LasrEncoderConfig):
|
|
super().__init__(config)
|
|
self.gradient_checkpointing = False
|
|
|
|
self.dropout = config.dropout
|
|
self.dropout_positions = config.dropout_positions
|
|
self.layerdrop = config.layerdrop
|
|
|
|
self.subsampler = LasrEncoderSubsampling(config)
|
|
self.rotary_emb = LasrEncoderRotaryEmbedding(config)
|
|
self.layers = nn.ModuleList(
|
|
[LasrEncoderBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
|
|
)
|
|
self.out_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, bias=False)
|
|
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
@check_model_inputs()
|
|
@can_return_tuple
|
|
def forward(
|
|
self,
|
|
input_features: torch.Tensor,
|
|
attention_mask: torch.Tensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> BaseModelOutput:
|
|
r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoProcessor, LasrEncoder
|
|
>>> from datasets import load_dataset, Audio
|
|
|
|
>>> model_id = TODO
|
|
>>> processor = AutoProcessor.from_pretrained(model_id)
|
|
>>> encoder = ParakeetEncoder.from_pretrained(model_id)
|
|
|
|
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
|
|
>>> ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))
|
|
|
|
>>> inputs = processor(ds[0]["audio"]["array"])
|
|
>>> encoder_outputs = encoder(**inputs)
|
|
|
|
>>> print(encoder_outputs.last_hidden_state.shape)
|
|
```
|
|
"""
|
|
|
|
hidden_states = self.subsampler(input_features)
|
|
cos, sin = self.rotary_emb(
|
|
hidden_states, torch.arange(hidden_states.shape[1], device=hidden_states.device).unsqueeze(0)
|
|
)
|
|
|
|
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
|
|
cos = nn.functional.dropout(cos, p=self.dropout_positions, training=self.training)
|
|
sin = nn.functional.dropout(sin, p=self.dropout_positions, training=self.training)
|
|
|
|
if attention_mask is not None:
|
|
attention_mask = self._get_output_attention_mask(attention_mask, target_length=hidden_states.shape[1])
|
|
|
|
attention_mask = create_bidirectional_mask(
|
|
config=self.config,
|
|
input_embeds=hidden_states,
|
|
attention_mask=attention_mask,
|
|
)
|
|
|
|
for encoder_layer in self.layers:
|
|
# add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
|
|
to_drop = False
|
|
if self.training:
|
|
dropout_probability = torch.rand([])
|
|
if dropout_probability < self.layerdrop: # skip the layer
|
|
to_drop = True
|
|
|
|
if not to_drop:
|
|
hidden_states = encoder_layer(
|
|
hidden_states,
|
|
attention_mask=attention_mask,
|
|
position_embeddings=(cos, sin),
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = self.out_norm(hidden_states)
|
|
|
|
return BaseModelOutput(last_hidden_state=hidden_states)
|
|
|
|
|
|
@dataclass
|
|
class LasrGenerateOutput(ModelOutput):
|
|
"""
|
|
Outputs of Lasr models.
|
|
|
|
Args:
|
|
sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
|
The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
|
|
if all batches finished early due to the `eos_token_id`.
|
|
logits (`tuple(torch.FloatTensor)` *optional*, returned when `output_logits=True`):
|
|
Unprocessed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
|
|
at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
|
|
each generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
|
|
attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True`):
|
|
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
|
|
`torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
|
|
hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True`):
|
|
Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
|
|
`torch.FloatTensor` of shape `(batch_size, generated_length, hidden_size)`.
|
|
"""
|
|
|
|
sequences: torch.LongTensor
|
|
logits: tuple[torch.FloatTensor] | None = None
|
|
attentions: tuple[tuple[torch.FloatTensor]] | None = None
|
|
hidden_states: tuple[tuple[torch.FloatTensor]] | None = None
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
Lasr Encoder with a Connectionist Temporal Classification (CTC) head.
|
|
"""
|
|
)
|
|
class LasrForCTC(LasrPreTrainedModel):
|
|
config: LasrCTCConfig
|
|
|
|
def __init__(self, config: LasrCTCConfig):
|
|
super().__init__(config)
|
|
self.encoder = LasrEncoder(config.encoder_config)
|
|
# Conv rather than linear to be consistent with NeMO decoding layer
|
|
self.ctc_head = nn.Conv1d(config.encoder_config.hidden_size, config.vocab_size, kernel_size=1)
|
|
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
@can_return_tuple
|
|
def forward(
|
|
self,
|
|
input_features: torch.Tensor,
|
|
attention_mask: torch.Tensor | None = None,
|
|
labels: torch.Tensor | None = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> CausalLMOutput:
|
|
r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoProcessor, LasrForCTC
|
|
>>> from datasets import load_dataset, Audio
|
|
|
|
>>> model_id = "nvidia/lasr-ctc-1.1b"
|
|
>>> processor = AutoProcessor.from_pretrained(model_id)
|
|
>>> model = LasrForCTC.from_pretrained(model_id)
|
|
|
|
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
|
|
>>> ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))
|
|
|
|
>>> inputs = processor(ds[0]["audio"]["array"], text=ds[0]["text"])
|
|
>>> outputs = model(**inputs)
|
|
|
|
>>> print(outputs.loss)
|
|
```"""
|
|
|
|
encoder_outputs = self.encoder(
|
|
input_features=input_features,
|
|
attention_mask=attention_mask,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = encoder_outputs.last_hidden_state
|
|
logits = self.ctc_head(hidden_states.transpose(1, 2)).transpose(1, 2)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# retrieve loss input_lengths from attention_mask
|
|
attention_mask = (
|
|
attention_mask if attention_mask is not None else torch.ones_like(input_features, dtype=torch.long)
|
|
)
|
|
input_lengths = self._get_subsampling_output_length(attention_mask.sum(-1))
|
|
|
|
# assuming that padded tokens are filled with -100
|
|
# when not being attended to
|
|
labels_mask = labels != self.config.pad_token_id
|
|
target_lengths = labels_mask.sum(-1)
|
|
flattened_targets = labels.masked_select(labels_mask)
|
|
|
|
# ctc_loss doesn't support fp16
|
|
log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)
|
|
|
|
with torch.backends.cudnn.flags(enabled=False):
|
|
loss = nn.functional.ctc_loss(
|
|
log_probs,
|
|
flattened_targets,
|
|
input_lengths,
|
|
target_lengths,
|
|
blank=self.config.pad_token_id,
|
|
reduction=self.config.ctc_loss_reduction,
|
|
zero_infinity=self.config.ctc_zero_infinity,
|
|
)
|
|
|
|
return CausalLMOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
@torch.no_grad()
|
|
def generate(
|
|
self,
|
|
input_features: torch.Tensor,
|
|
attention_mask: torch.Tensor | None = None,
|
|
return_dict_in_generate: bool = False,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> LasrGenerateOutput | torch.LongTensor:
|
|
r"""
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoProcessor, LasrForCTC
|
|
>>> from datasets import load_dataset, Audio
|
|
|
|
>>> model_id = TODO
|
|
>>> processor = AutoProcessor.from_pretrained(model_id)
|
|
>>> model = LasrForCTC.from_pretrained(model_id)
|
|
|
|
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
|
|
>>> ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))
|
|
|
|
>>> inputs = processor(ds[0]["audio"]["array"], text=ds[0]["text"])
|
|
>>> predicted_ids = model.generate(**inputs)
|
|
>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
|
|
|
|
>>> print(transcription)
|
|
```
|
|
"""
|
|
kwargs["return_dict"] = True
|
|
outputs: CausalLMOutput = self.forward(
|
|
input_features=input_features,
|
|
attention_mask=attention_mask,
|
|
**kwargs,
|
|
)
|
|
|
|
# greedy decoding
|
|
sequences = outputs.logits.argmax(dim=-1)
|
|
|
|
# mask out padded tokens
|
|
if attention_mask is not None:
|
|
attention_mask = self._get_output_attention_mask(attention_mask, target_length=sequences.shape[1])
|
|
sequences[~attention_mask] = self.config.pad_token_id
|
|
|
|
if return_dict_in_generate:
|
|
return LasrGenerateOutput(
|
|
sequences=sequences,
|
|
logits=outputs.logits,
|
|
attentions=outputs.attentions,
|
|
hidden_states=outputs.hidden_states,
|
|
)
|
|
|
|
return sequences
|
|
|
|
|
|
__all__ = ["LasrForCTC", "LasrEncoder", "LasrPreTrainedModel"]
|