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518 lines
22 KiB
518 lines
22 KiB
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# This file was automatically generated from src/transformers/models/glmasr/modular_glmasr.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_glmasr.py file directly. One of our CI enforces this.
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
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# Copyright 2025 the HuggingFace 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 collections.abc import Callable
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from typing import Optional
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from ...activations import ACT2FN
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from ...cache_utils import Cache
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from ...generation import GenerationMixin
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from ...integrations import use_kernelized_func
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import BaseModelOutputWithPooling, CausalLMOutputWithPast
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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 TransformersKwargs, auto_docstring, is_torch_available
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from ...utils.generic import can_return_tuple, check_model_inputs, maybe_autocast
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from ..auto import AutoModel, AutoModelForCausalLM
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from .configuration_glmasr import GlmAsrConfig, GlmAsrEncoderConfig
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if is_torch_available():
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import torch
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from torch import nn
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class GlmAsrRotaryEmbedding(nn.Module):
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inv_freq: torch.Tensor # fix linting for `register_buffer`
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def __init__(self, config: GlmAsrConfig, 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: GlmAsrConfig | 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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partial_rotary_factor = config.rope_parameters.get("partial_rotary_factor", 1.0)
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head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
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dim = int(head_dim * partial_rotary_factor)
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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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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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def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
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cos = cos.unsqueeze(unsqueeze_dim)
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sin = sin.unsqueeze(unsqueeze_dim)
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rotary_dim = cos.shape[-1]
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q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:]
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k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:]
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# Apply rotary embeddings on the first half or full tensor
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q_embed = (q_rot * cos) + (rotate_half(q_rot) * sin)
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k_embed = (k_rot * cos) + (rotate_half(k_rot) * sin)
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# Concatenate back to full shape
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q_embed = torch.cat([q_embed, q_pass], dim=-1)
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k_embed = torch.cat([k_embed, k_pass], dim=-1)
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return q_embed, k_embed
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@use_kernelized_func(apply_rotary_pos_emb)
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class GlmAsrAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(self, config: GlmAsrConfig, 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(config.hidden_size, config.num_attention_heads * self.head_dim, bias=True)
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self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False)
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self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True)
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self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=True)
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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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**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=None,
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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 GlmAsrMLP(nn.Module):
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def __init__(self, config):
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super().__init__()
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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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self.act_fn = ACT2FN[config.hidden_act]
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def forward(self, hidden_states: torch.Tensor):
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hidden_states = self.fc1(hidden_states)
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hidden_states = self.act_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 GlmAsrEncoderLayer(GradientCheckpointingLayer):
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def __init__(self, config: GlmAsrConfig, layer_idx: int):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.self_attn = GlmAsrAttention(config=config, layer_idx=layer_idx)
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self.mlp = GlmAsrMLP(config)
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self.input_layernorm = nn.LayerNorm(config.hidden_size)
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self.post_attention_layernorm = nn.LayerNorm(config.hidden_size)
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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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**kwargs: Unpack[TransformersKwargs],
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) -> torch.Tensor:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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# Self Attention
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hidden_states, _ = self.self_attn(
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hidden_states=hidden_states,
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position_embeddings=position_embeddings,
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**kwargs,
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)
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hidden_states = residual + hidden_states
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# Fully Connected
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residual = hidden_states
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hidden_states = self.post_attention_layernorm(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = residual + hidden_states
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return hidden_states
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@auto_docstring
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class GlmAsrPreTrainedModel(PreTrainedModel):
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config: GlmAsrConfig
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base_model_prefix = "model"
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input_modalities = ("audio", "text")
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supports_gradient_checkpointing = True
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_no_split_modules = ["GlmAsrAttention"]
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_skip_keys_device_placement = "past_key_values"
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_supports_flash_attn = True
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_supports_sdpa = True
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# TODO: @eustlb, this is what WhisperEncoder should look like
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class GlmAsrEncoder(GlmAsrPreTrainedModel):
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config: GlmAsrEncoderConfig
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main_input_name = "input_features"
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input_modalities = "audio"
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_no_split_modules = ["GlmAsrEncoderLayer"]
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_can_record_outputs = {
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"hidden_states": GlmAsrEncoderLayer,
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"attentions": GlmAsrAttention,
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}
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def __init__(self, config: GlmAsrEncoderConfig):
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super().__init__(config)
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self.conv1 = nn.Conv1d(config.num_mel_bins, config.hidden_size, kernel_size=3, padding=1)
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self.conv2 = nn.Conv1d(config.hidden_size, config.hidden_size, kernel_size=3, stride=2, padding=1)
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self.layers = nn.ModuleList(
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[GlmAsrEncoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
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)
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self.norm = nn.LayerNorm(config.hidden_size)
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self.rotary_emb = GlmAsrRotaryEmbedding(config=config)
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self.gradient_checkpointing = False
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self.post_init()
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@check_model_inputs
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@auto_docstring
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def forward(self, input_features, **kwargs: Unpack[TransformersKwargs]):
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inputs_embeds = nn.functional.gelu(self.conv1(input_features))
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inputs_embeds = nn.functional.gelu(self.conv2(inputs_embeds))
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inputs_embeds = inputs_embeds.transpose(1, 2)
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hidden_states = inputs_embeds
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position_embeddings = self.rotary_emb(
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hidden_states, position_ids=torch.arange(hidden_states.shape[1], device=hidden_states.device)[None, :]
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)
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for encoder_layer in self.layers:
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hidden_states = encoder_layer(hidden_states, position_embeddings=position_embeddings, **kwargs)
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hidden_states = self.norm(hidden_states)
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return BaseModelOutputWithPooling(last_hidden_state=hidden_states)
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class GlmAsrMultiModalProjector(nn.Module):
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"""
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Audio adaptor (small MLP) that projects GlmAsrEncoder features
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to the LLM embedding space so they can replace `<sound>` tokens.
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"""
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def __init__(self, config: GlmAsrConfig):
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super().__init__()
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self.linear_1 = nn.Linear(config.audio_config.intermediate_size, config.text_config.hidden_size * 2)
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self.act = ACT2FN[config.projector_hidden_act]
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self.linear_2 = nn.Linear(config.text_config.hidden_size * 2, config.text_config.hidden_size)
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def forward(self, audio_features):
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hidden_states = self.linear_1(audio_features)
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hidden_states = self.act(hidden_states)
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hidden_states = self.linear_2(hidden_states)
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return hidden_states
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@auto_docstring(
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custom_intro="""
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The GlmAsr model which consists of a fine-tuned Whisper encoder, a multi-modal projector and a Llama language model.
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"""
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)
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class GlmAsrForConditionalGeneration(GlmAsrPreTrainedModel, GenerationMixin):
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_keep_in_fp32_modules_strict = None
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_tp_plan = None
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_pp_plan = None
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def __init__(self, config):
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super().__init__(config)
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self.vocab_size = config.text_config.vocab_size
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self.audio_tower = AutoModel.from_config(config.audio_config)
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self.language_model = AutoModelForCausalLM.from_config(config.text_config)
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self.multi_modal_projector = GlmAsrMultiModalProjector(config)
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# Initialize weights and apply final processing
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self.post_init()
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def get_input_embeddings(self):
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return self.language_model.get_input_embeddings()
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def set_input_embeddings(self, value):
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self.language_model.set_input_embeddings(value)
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def get_output_embeddings(self):
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return self.language_model.get_output_embeddings()
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def set_output_embeddings(self, new_embeddings):
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self.language_model.set_output_embeddings(new_embeddings)
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def set_decoder(self, decoder):
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self.language_model.set_decoder(decoder)
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def get_decoder(self):
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return self.language_model.get_decoder()
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@can_return_tuple
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@auto_docstring(
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custom_intro="Compute audio embeddings from log-mel input features using the audio encoder and multi-modal projector."
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)
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def get_audio_features(
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self,
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input_features: torch.FloatTensor,
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input_features_mask: torch.Tensor,
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**kwargs: Unpack[TransformersKwargs],
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) -> tuple | BaseModelOutputWithPooling:
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r"""
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input_features (`torch.FloatTensor`):
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Float values of mel features extracted from the raw speech waveform. Raw speech waveform can be
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obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]` or a
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`numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into
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`input_features`, the [`AutoFeatureExtractor`] should be used for extracting the mel features, padding
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and conversion into a tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`]
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input_features_mask (`torch.Tensor` of shape `(batch_size, feature_sequence_length)`):
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Mask to avoid performing attention on padded feature indices.
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"""
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audio_outputs = self.audio_tower(input_features, return_dict=True, **kwargs)
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audio_hidden_states = audio_outputs.last_hidden_state
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audio_hidden_states = audio_hidden_states.reshape(
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input_features.shape[0], -1, self.config.audio_config.intermediate_size
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)
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|
audio_embeds = self.multi_modal_projector(audio_hidden_states)
|
|
|
|
audio_lengths = input_features_mask.sum(-1)
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for padding, kernel_size, stride in [(1, 3, 1), (1, 3, 2)]:
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audio_lengths = (audio_lengths + 2 * padding - (kernel_size - 1) - 1) // stride + 1
|
|
merge_factor = 4
|
|
post_lengths = (audio_lengths - merge_factor) // merge_factor + 1
|
|
|
|
valid_mask = torch.arange(audio_embeds.shape[1], device=post_lengths.device)[None, :] < post_lengths[:, None]
|
|
audio_outputs.pooler_output = audio_embeds[valid_mask.to(audio_embeds.device)]
|
|
|
|
return audio_outputs
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor | None = None,
|
|
input_features: torch.FloatTensor | None = None,
|
|
input_features_mask: torch.Tensor | None = None,
|
|
attention_mask: torch.Tensor | None = None,
|
|
position_ids: torch.LongTensor | None = None,
|
|
past_key_values: Cache | None = None,
|
|
inputs_embeds: torch.FloatTensor | None = None,
|
|
labels: torch.LongTensor | None = None,
|
|
use_cache: bool | None = None,
|
|
cache_position: torch.LongTensor | None = None,
|
|
logits_to_keep: int | torch.Tensor = 0,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> CausalLMOutputWithPast:
|
|
r"""
|
|
input_features_mask (`torch.Tensor` of shape `(batch_size, feature_sequence_length)`):
|
|
Mask to avoid performing attention on padding feature indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
|
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
|
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import GlmAsrForConditionalGeneration, AutoProcessor
|
|
|
|
>>> model_id = "zai-org/GLM-ASR-Nano-2512"
|
|
>>> processor = AutoProcessor.from_pretrained(model_id)
|
|
>>> model = GlmAsrForConditionalGeneration.from_pretrained(model_id, dtype="auto", device_map="auto")
|
|
>>> inputs = processor.apply_transcription_request("https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/bcn_weather.mp3")
|
|
|
|
>>> inputs = inputs.to(model.device, dtype=model.dtype)
|
|
|
|
>>> outputs = model.generate(**inputs, do_sample=False, max_new_tokens=500)
|
|
|
|
>>> decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1] :], skip_special_tokens=True)
|
|
>>> print(decoded_outputs)
|
|
```"""
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.get_input_embeddings()(input_ids)
|
|
|
|
if input_features is not None and input_ids is not None:
|
|
audio_embeds = self.get_audio_features(input_features, input_features_mask, return_dict=True).pooler_output
|
|
|
|
# replace text-audio token placeholders with audio embeddings
|
|
audio_token_mask = (input_ids == self.config.audio_token_id).unsqueeze(-1)
|
|
inputs_embeds = inputs_embeds.masked_scatter(
|
|
audio_token_mask.to(inputs_embeds.device), audio_embeds.to(inputs_embeds.device)
|
|
)
|
|
|
|
outputs: CausalLMOutputWithPast = self.language_model(
|
|
inputs_embeds=inputs_embeds,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
past_key_values=past_key_values,
|
|
labels=labels,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
logits_to_keep=logits_to_keep,
|
|
**kwargs,
|
|
)
|
|
return outputs
|
|
|
|
def prepare_inputs_for_generation(self, *args, **kwargs):
|
|
# Overwritten -- we should not pass input_features when we are in cached decoding stage
|
|
|
|
input_features = kwargs.pop("input_features", None)
|
|
input_features_mask = kwargs.pop("input_features_mask", None)
|
|
cache_position = kwargs.get("cache_position")
|
|
|
|
model_inputs = super().prepare_inputs_for_generation(*args, **kwargs)
|
|
|
|
if cache_position is not None and cache_position[0] == 0:
|
|
# input_features should only be passed when we are not in cached decoding stage
|
|
if input_features is not None:
|
|
model_inputs["input_features"] = input_features
|
|
if input_features_mask is not None:
|
|
model_inputs["input_features_mask"] = input_features_mask
|
|
|
|
return model_inputs
|
|
|
|
|
|
__all__ = ["GlmAsrEncoder", "GlmAsrForConditionalGeneration", "GlmAsrPreTrainedModel"]
|