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class VitsConvFlow(nn.Module):
def __init__(self, config: VitsConfig):
super().__init__()
self.filter_channels = config.hidden_size
self.half_channels = config.depth_separable_channels // 2
self.num_bins = config.duration_predictor_flow_bins
self.tail_bound = config.duration_... | 3,253 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
batch_size, channels, length = first_half.shape
hidden_states = hidden_states.reshape(batch_size, channels, -1, length).permute(0, 1, 3, 2)
unnormalized_widths = hidden_states[..., : self.num_bins] / math.sqrt(self.filter_channels)
unnormalized_heights = hidden_states[..., self.num_bins : 2 * s... | 3,253 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
class VitsElementwiseAffine(nn.Module):
def __init__(self, config: VitsConfig):
super().__init__()
self.channels = config.depth_separable_channels
self.translate = nn.Parameter(torch.zeros(self.channels, 1))
self.log_scale = nn.Parameter(torch.zeros(self.channels, 1))
def forwar... | 3,254 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
class VitsStochasticDurationPredictor(nn.Module):
def __init__(self, config):
super().__init__()
embed_dim = config.speaker_embedding_size
filter_channels = config.hidden_size
self.conv_pre = nn.Conv1d(filter_channels, filter_channels, 1)
self.conv_proj = nn.Conv1d(filter_ch... | 3,255 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
self.post_conv_pre = nn.Conv1d(1, filter_channels, 1)
self.post_conv_proj = nn.Conv1d(filter_channels, filter_channels, 1)
self.post_conv_dds = VitsDilatedDepthSeparableConv(
config,
dropout_rate=config.duration_predictor_dropout,
)
self.post_flows = nn.ModuleLis... | 3,255 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
if not reverse:
hidden_states = self.post_conv_pre(durations)
hidden_states = self.post_conv_dds(hidden_states, padding_mask)
hidden_states = self.post_conv_proj(hidden_states) * padding_mask
random_posterior = (
torch.randn(durations.size(0), 2, duration... | 3,255 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
log_determinant_posterior_sum += torch.sum(
(nn.functional.logsigmoid(first_half) + nn.functional.logsigmoid(-first_half)) * padding_mask, [1, 2]
)
logq = (
torch.sum(-0.5 * (math.log(2 * math.pi) + (random_posterior**2)) * padding_mask, [1, 2])
- ... | 3,255 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
nll = torch.sum(0.5 * (math.log(2 * math.pi) + (latents**2)) * padding_mask, [1, 2]) - log_determinant_sum
return nll + logq
else:
flows = list(reversed(self.flows))
flows = flows[:-2] + [flows[-1]] # remove a useless vflow
latents = (
torch.rand... | 3,255 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
class VitsDurationPredictor(nn.Module):
def __init__(self, config):
super().__init__()
kernel_size = config.duration_predictor_kernel_size
filter_channels = config.duration_predictor_filter_channels
self.dropout = nn.Dropout(config.duration_predictor_dropout)
self.conv_1 = n... | 3,256 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
if global_conditioning is not None:
global_conditioning = torch.detach(global_conditioning)
inputs = inputs + self.cond(global_conditioning)
inputs = self.conv_1(inputs * padding_mask)
inputs = torch.relu(inputs)
inputs = self.norm_1(inputs.transpose(1, -1)).transpose(1,... | 3,256 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
class VitsAttention(nn.Module):
"""Multi-headed attention with relative positional representation."""
def __init__(self, config: VitsConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.dropout = config.attention_dropou... | 3,257 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.use_bias)
self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.use_bias)
self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.use_bias)
self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias... | 3,257 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Ten... | 3,257 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_sta... | 3,257 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, se... | 3,257 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
if layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
f" {layer_head_mask.size()}"
)
attn_weights = la... | 3,257 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_we... | 3,257 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
if self.window_size is not None:
value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, src_len)
relative_weights = self._absolute_position_to_relative_position(attn_probs)
rel_pos_bias = torch.matmul(relative_weights, value_relative_embeddings)
attn_ou... | 3,257 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
def _get_relative_embeddings(self, relative_embeddings, length):
pad_length = max(length - (self.window_size + 1), 0)
if pad_length > 0:
relative_embeddings = nn.functional.pad(relative_embeddings, [0, 0, pad_length, pad_length, 0, 0])
slice_start_position = max((self.window_size + ... | 3,257 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
# Reshape and slice out the padded elements.
x_final = x_flat.view([batch_heads, length + 1, 2 * length - 1])
x_final = x_final[:, :length, length - 1 :]
return x_final
def _absolute_position_to_relative_position(self, x):
batch_heads, length, _ = x.size()
# Pad along colum... | 3,257 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
class VitsFeedForward(nn.Module):
def __init__(self, config):
super().__init__()
self.conv_1 = nn.Conv1d(config.hidden_size, config.ffn_dim, config.ffn_kernel_size)
self.conv_2 = nn.Conv1d(config.ffn_dim, config.hidden_size, config.ffn_kernel_size)
self.dropout = nn.Dropout(config.ac... | 3,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
hidden_states = hidden_states * padding_mask
if self.padding is not None:
hidden_states = nn.functional.pad(hidden_states, self.padding)
hidden_states = self.conv_1(hidden_states)
hidden_states = self.act_fn(hidden_states)
hidden_states = self.dropout(hidden_states)
... | 3,258 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
class VitsEncoderLayer(nn.Module):
def __init__(self, config: VitsConfig):
super().__init__()
self.attention = VitsAttention(config)
self.dropout = nn.Dropout(config.hidden_dropout)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.feed_forwar... | 3,259 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
residual = hidden_states
hidden_states = self.feed_forward(hidden_states, padding_mask)
hidden_states = self.dropout(hidden_states)
hidden_states = self.final_layer_norm(residual + hidden_states)
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weig... | 3,259 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
class VitsEncoder(nn.Module):
def __init__(self, config: VitsConfig):
super().__init__()
self.config = config
self.layers = nn.ModuleList([VitsEncoderLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
self.layerdrop = config.layerdrop... | 3,260 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
# expand attention_mask
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _prepare_4d_attention_mask(attention_mask, hidden_states.dtype)
hidden_states = hidden_states * padding_mask
synced_gpus = is_deepspeed_zero3_e... | 3,260 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
skip_the_layer = self.training and (dropout_probability < self.layerdrop)
if not skip_the_layer or synced_gpus:
# under fsdp or deepspeed zero3 all gpus must run in sync
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_check... | 3,260 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
hidden_states = hidden_states * padding_mask
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v... | 3,260 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
class VitsTextEncoder(nn.Module):
"""
Transformer encoder that uses relative positional representation instead of absolute positional encoding.
"""
def __init__(self, config: VitsConfig):
super().__init__()
self.config = config
self.embed_tokens = nn.Embedding(config.vocab_size,... | 3,261 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
def forward(
self,
input_ids: torch.Tensor,
padding_mask: torch.FloatTensor,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = True,
) -> Union[Tu... | 3,261 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
stats = self.project(last_hidden_state.transpose(1, 2)).transpose(1, 2) * padding_mask
prior_means, prior_log_variances = torch.split(stats, self.config.flow_size, dim=2)
if not return_dict:
outputs = (last_hidden_state, prior_means, prior_log_variances) + encoder_outputs[1:]
re... | 3,261 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
class VitsPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = VitsConfig
base_model_prefix = "vits"
main_input_name = "input_ids"
supports_gradient_checkpointing... | 3,262 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.LayerNor... | 3,262 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
class VitsModel(VitsPreTrainedModel):
def __init__(self, config: VitsConfig):
super().__init__(config)
self.config = config
self.text_encoder = VitsTextEncoder(config)
self.flow = VitsResidualCouplingBlock(config)
self.decoder = VitsHifiGan(config)
if config.use_stoc... | 3,263 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
# Initialize weights and apply final processing
self.post_init()
def get_encoder(self):
return self.text_encoder
@add_start_docstrings_to_model_forward(VITS_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=VitsModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self... | 3,263 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
```python
>>> from transformers import VitsTokenizer, VitsModel, set_seed
>>> import torch
>>> tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-eng")
>>> model = VitsModel.from_pretrained("facebook/mms-tts-eng")
>>> inputs = tokenizer(text="Hello - my dog is cute", r... | 3,263 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
if labels is not None:
raise NotImplementedError("Training of VITS is not supported yet.")
if attention_mask is not None:
input_padding_mask = attention_mask.unsqueeze(-1).float()
else:
input_padding_mask = torch.ones_like(input_ids).unsqueeze(-1).float()
if... | 3,263 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
text_encoder_output = self.text_encoder(
input_ids=input_ids,
padding_mask=input_padding_mask,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
... | 3,263 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
if self.config.use_stochastic_duration_prediction:
log_duration = self.duration_predictor(
hidden_states,
input_padding_mask,
speaker_embeddings,
reverse=True,
noise_scale=self.noise_scale_duration,
)
else:
... | 3,263 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
# Create a padding mask for the output lengths of shape (batch, 1, max_output_length)
indices = torch.arange(predicted_lengths.max(), dtype=predicted_lengths.dtype, device=predicted_lengths.device)
output_padding_mask = indices.unsqueeze(0) < predicted_lengths.unsqueeze(1)
output_padding_mask = ... | 3,263 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
# Reconstruct an attention tensor of shape (batch, 1, out_length, in_length)
attn_mask = torch.unsqueeze(input_padding_mask, 2) * torch.unsqueeze(output_padding_mask, -1)
batch_size, _, output_length, input_length = attn_mask.shape
cum_duration = torch.cumsum(duration, -1).view(batch_size * inpu... | 3,263 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
prior_latents = prior_means + torch.randn_like(prior_means) * torch.exp(prior_log_variances) * self.noise_scale
latents = self.flow(prior_latents, output_padding_mask, speaker_embeddings, reverse=True)
spectrogram = latents * output_padding_mask
waveform = self.decoder(spectrogram, speaker_embe... | 3,263 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
class OlmoeRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-5):
"""
OlmoeRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
... | 3,264 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
class OlmoeRotaryEmbedding(nn.Module):
def __init__(self, config: OlmoeConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_type", co... | 3,265 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
def _dynamic_frequency_update(self, position_ids, device):
"""
dynamic RoPE layers should recompute `inv_freq` in the following situations:
1 - growing beyond the cached sequence length (allow scaling)
2 - the current sequence length is in the original scale (avoid losing precision with ... | 3,265 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset
# This .to() is needed if the model has been moved to a device after being initialized (because
# the buffer is automatically moved, but not the original copy)
self.original_inv_f... | 3,265 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
# Core RoPE block
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
position_ids_expanded = position_ids[:, None, :].float()
# Force float32 (see https://github.com/huggingface/transformers/pull/29285)
device_type = x.device.type
device... | 3,265 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
class OlmoeMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self... | 3,266 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
class OlmoeAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: OlmoeConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
if layer_idx is None:
log... | 3,267 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
self.attention_dropout = config.attention_dropout
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
self.num_key_value_heads = config.num_key_value_heads
self.num_key_value_groups = self.num_heads ... | 3,267 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=c... | 3,267 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position... | 3,267 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
if self.config.clip_qkv is not None:
query_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
key_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
value_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
query_states = query_... | 3,267 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, ca... | 3,267 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
# upcast attention to fp32
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
attn_output = torch.matmul(attn_weights, value_states)
i... | 3,267 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
class OlmoeFlashAttention2(OlmoeAttention):
"""
OLMoE flash attention module. This module inherits from `OlmoeAttention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
flash attention and deal with pa... | 3,268 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-... | 3,268 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_posi... | 3,268 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
query_states = self.q_norm(self.q_proj(hidden_states))
key_states = self.k_norm(self.k_proj(hidden_states))
value_states = self.v_proj(hidden_states)
if self.config.clip_qkv is not None:
query_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv)
key_states.c... | 3,268 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
cos, sin = position_embeddings
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_... | 3,268 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
# therefore the input hidden states gets silently casted in float32. Hence, we need
# cast them back in the correct dtype just to be sure everything works as expected.
# This might slowdown training & inference ... | 3,268 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
logger.warning_once(
f"The input hidden states seems to be silently casted in float32, this might be related to"
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
f" {target_dtype}."
)
query_s... | 3,268 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
return attn_output, attn_weights, past_key_value | 3,268 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
class OlmoeSdpaAttention(OlmoeAttention):
"""
OLMoE attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`OlmoeAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
""" | 3,269 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
# Adapted from OlmoeAttention.forward
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_c... | 3,269 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
return super().forward(
hidden_states=hidden_states,
attent... | 3,269 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
bsz, q_len, _ = hidden_states.size()
query_states = self.q_norm(self.q_proj(hidden_states))
key_states = self.k_norm(self.k_proj(hidden_states))
value_states = self.v_proj(hidden_states)
if self.config.clip_qkv is not None:
query_states.clamp_(min=-self.config.clip_qkv, max... | 3,269 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, ca... | 3,269 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
# Reference: https://github.com/pytorch/pytorch/issues/112577.
if query_states.device.type == "cuda" and causal_mask is not None:
query_states = query_states.contiguous()... | 3,269 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=causal_mask,
dropout_p=self.attention_dropout if self.training else 0.0,
is_causal=is_causal,
)
attn_output = attn_ou... | 3,269 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
class OlmoeSparseMoeBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.num_experts = config.num_experts
self.top_k = config.num_experts_per_tok
self.norm_topk_prob = config.norm_topk_prob
self.gate = nn.Linear(config.hidden_size, self.num_experts, bias=Fals... | 3,270 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
if self.norm_topk_prob:
routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
# we cast back to the input dtype
rout... | 3,270 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
# Loop over all available experts in the model and perform the computation on each expert
for expert_idx in range(self.num_experts):
expert_layer = self.experts[expert_idx]
idx, top_x = torch.where(expert_mask[expert_idx])
# Index the correct hidden states and compute the ex... | 3,270 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
# However `index_add_` only support torch tensors for indexing so we'll use
# the `top_x` tensor here.
final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
... | 3,270 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
class OlmoeDecoderLayer(nn.Module):
def __init__(self, config: OlmoeConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = OLMOE_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
self.mlp = OlmoeSparseMoeB... | 3,271 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
output_router_logits: Optional[bool] ... | 3,271 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
query_sequence_length, key_sequence_length)` if default attention is used.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_router_logit... | 3,271 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
Indices depicting the position of the input sequence tokens in the sequence
position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
with `head_dim` ... | 3,271 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value... | 3,271 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
if output_router_logits:
outputs += (router_logits,)
return outputs | 3,271 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
class OlmoePreTrainedModel(PreTrainedModel):
config_class = OlmoeConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["OlmoeDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_supp... | 3,272 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
class OlmoeModel(OlmoePreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`OlmoeDecoderLayer`]
Args:
config: OlmoeConfig
"""
def __init__(self, config: OlmoeConfig):
super().__init__(config)
self.padding_idx = config.p... | 3,273 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
def set_input_embeddings(self, value):
self.embed_tokens = value | 3,273 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
@add_start_docstrings_to_model_forward(OLMOE_INPUTS_DOCSTRING)
# Ignore copy
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[... | 3,273 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
)
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_retu... | 3,273 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient check... | 3,273 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
# kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = False
if use_cache and not isinstance(past_key_values, Cache):
return_legacy_cache = True
if past_key_values is None:
past_key_values = DynamicCache()
else:
past... | 3,273 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
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 No... | 3,273 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
if output_hidden_states:
all_hidden_states += (hidden_states,) | 3,273 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
causal_mask,
position_ids,
past_key_values,
... | 3,273 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
position_embeddings=position_embeddings,
) | 3,273 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
if output_attentions:
all_self_attns += (layer_outputs[1],)
if output_router_logits and layer_outputs[-1] is not None:
a... | 3,273 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
if not return_dict:
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
return MoeModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=next_cache,
hidden_states=all_hidden_states,
at... | 3,273 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
# to infer the attention mask.
past_seen_tokens = past_key_values.get_seq_leng... | 3,273 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
dtype, device = input_tensor.dtype, input_tensor.device
sequence_length = input_tensor.shape[1]
if using_static_cache:
target_length = past_key_values.get_max_cache_shape()
else:
target_length = (
attention_mask.shape[-1]
if isinstance(atte... | 3,273 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
if (
self.config._attn_implementation == "sdpa"
and attention_mask is not None
and attention_mask.device.type == "cuda"
and not output_attentions
):
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows whe... | 3,273 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
@staticmethod
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype: torch.dtype,
device: torch.device,
cache_position: torch.Tensor,
batch_size: int,
**kwargs,
):
... | 3,273 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
`(batch_size, 1, query_length, key_value_length)`.
sequence_length (`int`):
The sequence length being processed.
... | 3,273 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
if attention_mask is not None and attention_mask.dim() == 4:
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
causal_mask = attention_mask
else:
min_dtype = torch.finfo(dtype).min
causal_mask = torch.full(... | 3,273 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
) | 3,273 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
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