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import random |
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from typing import Optional, Tuple, Union |
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import math |
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import torch |
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import torch.nn.functional as F |
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from einops import rearrange |
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from torch import nn |
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from transformers import PreTrainedModel |
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from transformers.activations import ACT2FN |
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from transformers.modeling_outputs import MoeModelOutputWithPast, MoeCausalLMOutputWithPast |
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from .configuration_aurora import AuroraConfig |
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from .flow_loss import FlowLoss |
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from .modality_connector import ModalityConnector, VisionEncoder, TextEncoder |
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from .prototype_retriever import PrototypeRetriever |
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from .ts_generation_mixin import TSGenerationMixin |
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from .util_functions import resample, Transpose, causal_attention_mask, RoPE_decoder |
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class AuroraPatchEmbedding(nn.Module): |
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def __init__(self, config: AuroraConfig): |
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super().__init__() |
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self.proj_layer = nn.Linear(config.token_len, config.hidden_size, bias=False) |
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self.token_len = config.token_len |
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self.threshold_ratio = config.threshold_ratio |
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self.mask_ratio = config.mask_ratio |
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def _freq_masking(self, x): |
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x_fft = torch.fft.rfft(x, dim=-1) |
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x_ifft_list = [] |
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for ratio in self.threshold_ratio: |
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temp = x_fft.clone() |
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truncation = int(temp.shape[-1] * ratio) |
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if random.random() > self.mask_ratio: |
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temp[:, :truncation] = 0 |
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else: |
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temp[:, truncation:] = 0 |
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x_ifft = torch.fft.irfft(temp, dim=-1) |
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x_ifft_list.append(x_ifft) |
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x_ifft = torch.stack(x_ifft_list, dim=0) |
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return rearrange(x_ifft, 's b l -> (s b) l') |
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def _predict(self, x, inference_token_len=48): |
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input_length = x.shape[-1] |
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padding_length = (inference_token_len - (input_length % |
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inference_token_len)) % inference_token_len |
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x = F.pad(x, (padding_length, 0)) |
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x = x.unfold(dimension=-1, size=inference_token_len, |
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step=inference_token_len) |
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resampled_weight = resample(old=self.proj_layer.weight.data, new_patch_len=inference_token_len) |
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output = F.linear(x, resampled_weight) |
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return output, None |
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def forward(self, x, inference_token_len=48): |
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if not self.training: |
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return self._predict(x, inference_token_len) |
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input_length = x.shape[-1] |
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padding_length = (self.token_len - (input_length % |
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self.token_len)) % self.token_len |
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x = F.pad(x, (padding_length, 0)) |
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x_masked = self._freq_masking(x) |
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x_origin = x.unfold(dimension=-1, size=self.token_len, |
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step=self.token_len) |
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output_origin = self.proj_layer(x_origin) |
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x_masked = x_masked.unfold(dimension=-1, size=self.token_len, |
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step=self.token_len) |
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output_masked = self.proj_layer(x_masked) |
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return output_origin, output_masked |
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class AuroraAttention(nn.Module): |
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def __init__(self, config: AuroraConfig, layer_idx: Optional[int] = None, rope: bool = False): |
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super().__init__() |
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self.layer_idx = layer_idx |
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self.hidden_size = config.hidden_size |
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self.num_heads = config.num_attention_heads |
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self.head_dim = self.hidden_size // self.num_heads |
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self.attention_dropout = config.dropout_rate |
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self.q_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=True) |
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self.k_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=True) |
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self.v_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=True) |
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self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False) |
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self.rope = rope |
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def _scaled_dot_product_attention(self, Q, K, V, bias=None, attn_mask=None): |
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attn_scores = torch.matmul(Q, K.transpose(-2, -1)) |
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attn_scores = attn_scores / math.sqrt(Q.size(-1)) |
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if attn_mask is not None: |
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if attn_mask.dtype == torch.bool: |
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attn_scores = attn_scores.masked_fill(attn_mask, float('-inf')) |
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else: |
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attn_scores = attn_scores + attn_mask |
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if bias is not None: |
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if attn_scores.shape[0] > bias.shape[0]: |
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bias = bias.repeat(attn_scores.shape[0] // bias.shape[0], 1, 1, 1) |
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attn_scores += bias |
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attn_weights = F.softmax(attn_scores, dim=-1) |
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if self.attention_dropout > 0.0 and self.training: |
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attn_weights = F.dropout(attn_weights, p=self.attention_dropout) |
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attn_output = torch.matmul(attn_weights, V) |
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return attn_output, attn_scores |
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def forward( |
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self, |
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hidden_states: torch.Tensor, |
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key_embedding: torch.Tensor = None, |
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value_embedding: torch.Tensor = None, |
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attention_mask: Optional[torch.Tensor] = None, |
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output_attentions: bool = False, |
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bias: torch.Tensor = None, |
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**kwargs, |
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) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: |
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bsz, q_len, _ = hidden_states.size() |
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if key_embedding is None: |
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key_embedding = hidden_states |
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if value_embedding is None: |
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value_embedding = hidden_states |
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_, k_len, _ = key_embedding.size() |
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_, v_len, _ = value_embedding.size() |
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query_states = self.q_proj(hidden_states) |
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key_states = self.k_proj(key_embedding) |
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value_states = self.v_proj(value_embedding) |
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query_states = query_states.view( |
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bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) |
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key_states = key_states.view( |
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bsz, k_len, self.num_heads, self.head_dim).transpose(1, 2) |
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value_states = value_states.view( |
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bsz, v_len, self.num_heads, self.head_dim).transpose(1, 2) |
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if self.rope: |
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query_states, key_states = RoPE_decoder(query_states, key_states) |
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attn_output, attn_scores = self._scaled_dot_product_attention( |
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Q=query_states, K=key_states, V=value_states, bias=bias, |
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attn_mask=attention_mask) |
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attn_output = attn_output.transpose(1, 2).contiguous() |
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attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) |
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attn_output = self.o_proj(attn_output) |
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if not output_attentions: |
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attn_scores = None |
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return attn_output, attn_scores |
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class AuroraFFN(nn.Module): |
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def __init__(self, hidden_size: int, intermediate_size: int, hidden_act: str): |
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super().__init__() |
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self.ffn = nn.Sequential(nn.Linear(hidden_size, intermediate_size), |
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ACT2FN[hidden_act], |
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nn.Linear(intermediate_size, hidden_size)) |
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def forward(self, hidden_state): |
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return self.ffn(hidden_state) |
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class AuroraDecoderLayer(nn.Module): |
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def __init__(self, config: AuroraConfig, layer_idx: int): |
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super().__init__() |
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self.self_attn = AuroraAttention(config, layer_idx, rope=False) |
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self.cross_attn = AuroraAttention(config, layer_idx, rope=True) |
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self.ffn_layer = AuroraFFN( |
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hidden_size=config.hidden_size, |
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intermediate_size=config.intermediate_size, |
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hidden_act=config.hidden_act |
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) |
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if config.norm_mode == 'batch': |
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self.norm1 = nn.Sequential(Transpose(1, 2), nn.BatchNorm1d(config.hidden_size), Transpose(1, 2)) |
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self.norm2 = nn.Sequential(Transpose(1, 2), nn.BatchNorm1d(config.hidden_size), Transpose(1, 2)) |
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self.norm3 = nn.Sequential(Transpose(1, 2), nn.BatchNorm1d(config.hidden_size), Transpose(1, 2)) |
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else: |
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self.norm1 = torch.nn.LayerNorm(config.hidden_size) |
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self.norm2 = torch.nn.LayerNorm(config.hidden_size) |
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self.norm3 = torch.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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cross_states: torch.Tensor, |
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output_attentions: Optional[bool] = False, |
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**kwargs, |
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) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]: |
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residual = hidden_states |
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num_token = hidden_states.shape[1] |
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attention_mask = causal_attention_mask(num_token).to(hidden_states.device) |
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hidden_states, self_attn_weights = self.self_attn( |
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hidden_states=hidden_states, |
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attention_mask=attention_mask, |
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output_attentions=output_attentions, |
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) |
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x_attn = residual + self.norm1(hidden_states) |
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x_cross, cross_attn_weights = self.cross_attn(hidden_states=x_attn, key_embedding=cross_states, |
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value_embedding=cross_states) |
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x_cross = self.norm2(x_cross) + x_attn |
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output_states = self.ffn_layer(x_cross) |
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output_states = self.norm3(output_states) + x_cross |
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if not output_attentions: |
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self_attn_weights = None |
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cross_attn_weights = None |
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return output_states, self_attn_weights, cross_attn_weights |
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class AuroraEncoderLayer(nn.Module): |
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def __init__(self, config: AuroraConfig, layer_idx: int): |
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super().__init__() |
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self.self_attn = AuroraAttention(config, layer_idx, rope=False) |
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self.ffn_layer = AuroraFFN( |
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hidden_size=config.hidden_size, |
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intermediate_size=config.intermediate_size, |
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hidden_act=config.hidden_act |
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) |
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if config.norm_mode == 'batch': |
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self.norm1 = nn.Sequential(Transpose(1, 2), nn.BatchNorm1d(config.hidden_size), Transpose(1, 2)) |
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self.norm2 = nn.Sequential(Transpose(1, 2), nn.BatchNorm1d(config.hidden_size), Transpose(1, 2)) |
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else: |
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self.norm1 = torch.nn.LayerNorm(config.hidden_size) |
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self.norm2 = torch.nn.LayerNorm(config.hidden_size) |
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self.dropout_1 = nn.Dropout(config.dropout_rate) |
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self.dropout_2 = nn.Dropout(config.dropout_rate) |
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def forward( |
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self, |
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hidden_states: torch.Tensor, |
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output_attentions: Optional[bool] = False, |
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bias: torch.Tensor = None, |
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**kwargs |
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) -> Tuple[torch.FloatTensor, torch.FloatTensor]: |
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residual = hidden_states |
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hidden_states, self_attn_weights = self.self_attn( |
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hidden_states=hidden_states, |
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output_attentions=output_attentions, |
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bias=bias |
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) |
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x_attn = self.norm1(residual + self.dropout_1(hidden_states)) |
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output_states = self.ffn_layer(x_attn) |
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output_states = self.norm2(self.dropout_2(output_states) + x_attn) |
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if not output_attentions: |
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self_attn_weights = None |
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return output_states, self_attn_weights |
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class AuroraPredictHead(nn.Module): |
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def __init__(self, config: AuroraConfig): |
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super().__init__() |
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self.output_proj = nn.Linear(config.hidden_size, config.token_len, bias=False) |
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self.dropout = nn.Dropout(config.dropout_rate) |
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def _predict(self, hidden_states: torch.Tensor, inference_token_len=48): |
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resampled_weight = resample(old=self.output_proj.weight.data.T, new_patch_len=inference_token_len).T |
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output = F.linear(hidden_states, resampled_weight) |
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return output |
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def forward( |
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self, |
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hidden_states: torch.Tensor, |
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inference_token_len: int = 48, |
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**kwargs |
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) -> torch.FloatTensor: |
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if not self.training: |
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return self._predict(hidden_states, inference_token_len) |
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return self.output_proj(self.dropout(hidden_states)) |
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class AuroraPreTrainedModel(PreTrainedModel): |
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config_class = AuroraConfig |
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base_model_prefix = "model" |
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supports_gradient_checkpointing = True |
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_no_split_modules = ["AuroraEncoderLayer", "AuroraDecoderLayer"] |
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_supports_flash_attn_2 = True |
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_supports_sdpa = False |
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_supports_cache_class = False |
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class AuroraModel(nn.Module): |
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def __init__(self, config: AuroraConfig): |
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super().__init__() |
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self.embed_layer = AuroraPatchEmbedding(config) |
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self.enc_layers = nn.ModuleList( |
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[AuroraEncoderLayer(config, layer_idx) |
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for layer_idx in range(config.num_enc_layers)] |
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) |
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self.dec_layers = nn.ModuleList( |
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[AuroraDecoderLayer(config, layer_idx) |
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for layer_idx in range(config.num_dec_layers)] |
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) |
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self.mask_num = len(config.threshold_ratio) |
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self.gradient_checkpointing = False |
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self.VisionEncoder = VisionEncoder(config) |
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self.TextEncoder = TextEncoder(config) |
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self.ModalityConnector = ModalityConnector(config) |
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self.VisionGuider = AuroraAttention(config) |
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self.TextGuider = AuroraAttention(config) |
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self.W = nn.Parameter(torch.eye(config.num_distill)) |
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self.fuse = nn.Linear(config.hidden_size, config.hidden_size) |
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def forward( |
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self, |
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input_ids: torch.FloatTensor = None, |
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attention_mask: Optional[torch.Tensor] = None, |
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text_input_ids: Optional[torch.FloatTensor] = None, |
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text_attention_mask: Optional[torch.FloatTensor] = None, |
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text_token_type_ids: Optional[torch.FloatTensor] = None, |
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vision_ids: Optional[torch.FloatTensor] = None, |
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inputs_embeds: Optional[torch.FloatTensor] = None, |
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output_attentions: Optional[bool] = None, |
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output_hidden_states: Optional[bool] = None, |
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return_dict: Optional[bool] = None, |
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predict_token_num: Optional[int] = None, |
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inference_token_len: Optional[int] = None, |
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) -> Union[Tuple, MoeModelOutputWithPast]: |
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions |
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output_hidden_states = ( |
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states |
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) |
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
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if input_ids is not None and inputs_embeds is not None: |
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raise ValueError( |
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"You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") |
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elif input_ids is not None: |
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batch_size, seq_length = input_ids.shape |
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elif inputs_embeds is not None: |
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batch_size, seq_length, _ = inputs_embeds.shape |
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else: |
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raise ValueError( |
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"You have to specify either decoder_input_ids or decoder_inputs_embeds") |
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if inference_token_len is None: |
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inference_token_len = self.config.token_len |
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masked_embeds = None |
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if inputs_embeds is None: |
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inputs_embeds, masked_embeds = self.embed_layer(input_ids, inference_token_len) |
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if masked_embeds is None: |
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x_enc = inputs_embeds |
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else: |
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x_enc = torch.concat([inputs_embeds, masked_embeds], dim=0) |
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if vision_ids is not None: |
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vision_features = self.VisionEncoder(vision_ids, type='real') |
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else: |
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vision_features = self.VisionEncoder(input_ids, type='pseudo') |
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_, attn_vision = self.VisionGuider( |
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inputs_embeds, |
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vision_features, |
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vision_features, |
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output_attentions=True |
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) |
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if text_input_ids is not None: |
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text_features = self.TextEncoder({'input_ids': text_input_ids, 'attention_mask': text_attention_mask, |
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'token_type_ids': text_token_type_ids}) |
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_, attn_text = self.TextGuider( |
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inputs_embeds, |
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text_features, |
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text_features, |
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output_attentions=True |
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) |
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else: |
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text_features = None |
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attn_text = None |
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if attn_text is not None: |
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guided_bias = torch.einsum("bhik,kl,bhjl->bhij", attn_vision, self.W, attn_text) |
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else: |
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guided_bias = None |
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all_hidden_states = () if output_hidden_states else None |
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all_self_attns = () if output_attentions else None |
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for encoder_layer in self.enc_layers: |
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if output_hidden_states: |
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all_hidden_states += (x_enc,) |
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if self.gradient_checkpointing and self.training: |
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layer_outputs = self._gradient_checkpointing_func( |
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encoder_layer.__call__, |
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x_enc, |
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output_attentions, |
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guided_bias |
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) |
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else: |
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layer_outputs = encoder_layer( |
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x_enc, |
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output_attentions=output_attentions, |
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|
bias=guided_bias |
|
|
) |
|
|
|
|
|
x_enc = layer_outputs[0] |
|
|
|
|
|
if output_attentions: |
|
|
all_self_attns += (layer_outputs[1],) |
|
|
|
|
|
if x_enc.shape[0] > batch_size: |
|
|
x_enc, x_rec = torch.split(x_enc, [batch_size, x_enc.shape[0] - batch_size], dim=0) |
|
|
x_rec = rearrange(x_rec, '(s b) n d -> s b n d', s=self.mask_num) |
|
|
x_rec = x_rec.mean(0) |
|
|
else: |
|
|
x_rec = None |
|
|
|
|
|
decay_weights = 0.5 ** torch.arange(predict_token_num) |
|
|
decay_weights = decay_weights.unsqueeze(0).unsqueeze(-1).to(x_enc.device) |
|
|
|
|
|
from_text, from_vision = self.ModalityConnector(x_enc, text_features, vision_features) |
|
|
if from_text is not None: |
|
|
x_enc = x_enc + self.fuse(from_vision + from_text) |
|
|
else: |
|
|
x_enc = x_enc + self.fuse(from_vision) |
|
|
|
|
|
last_token = x_enc[:, -1:, :] |
|
|
x_dec = decay_weights * last_token.repeat(1, predict_token_num, 1) |
|
|
|
|
|
|
|
|
for decoder_layer in self.dec_layers: |
|
|
if output_hidden_states: |
|
|
all_hidden_states += (x_dec,) |
|
|
|
|
|
if self.gradient_checkpointing and self.training: |
|
|
layer_outputs = self._gradient_checkpointing_func( |
|
|
decoder_layer.__call__, |
|
|
x_dec, |
|
|
x_enc, |
|
|
output_attentions=output_attentions, |
|
|
) |
|
|
else: |
|
|
layer_outputs = decoder_layer( |
|
|
x_dec, |
|
|
x_enc, |
|
|
output_attentions=output_attentions |
|
|
) |
|
|
|
|
|
x_dec = layer_outputs[0] |
|
|
|
|
|
if output_attentions: |
|
|
all_self_attns += (layer_outputs[1],) |
|
|
|
|
|
|
|
|
if output_hidden_states: |
|
|
all_hidden_states += (x_dec,) |
|
|
|
|
|
if not return_dict: |
|
|
return tuple( |
|
|
v |
|
|
for v in [x_dec, all_hidden_states, all_self_attns] |
|
|
if v is not None |
|
|
) |
|
|
|
|
|
output_states = (x_rec, x_dec, from_text, from_vision) |
|
|
|
|
|
return MoeModelOutputWithPast( |
|
|
last_hidden_state=output_states, |
|
|
hidden_states=all_hidden_states, |
|
|
attentions=all_self_attns, |
|
|
) |
|
|
|
|
|
|
|
|
class AuroraForPrediction(AuroraPreTrainedModel, TSGenerationMixin): |
|
|
def __init__(self, config: AuroraConfig): |
|
|
super().__init__(config) |
|
|
self.config = config |
|
|
self.model = AuroraModel(config) |
|
|
self.point_loss = torch.nn.MSELoss(reduction='none') |
|
|
self.flow_match = FlowLoss(config.token_len, config.hidden_size, config.flow_loss_depth, config.hidden_size, |
|
|
config.num_sampling_steps) |
|
|
self.linear_head = AuroraPredictHead(config) |
|
|
|
|
|
self.retriever = PrototypeRetriever(config) |
|
|
|
|
|
def set_decoder(self, decoder): |
|
|
self.model = decoder |
|
|
|
|
|
def get_decoder(self): |
|
|
return self.model |
|
|
|
|
|
def forward( |
|
|
self, |
|
|
input_ids: torch.FloatTensor = None, |
|
|
text_input_ids: torch.FloatTensor = None, |
|
|
text_attention_mask: torch.FloatTensor = None, |
|
|
text_token_type_ids: torch.FloatTensor = None, |
|
|
vision_ids: torch.FloatTensor = None, |
|
|
attention_mask: Optional[torch.Tensor] = None, |
|
|
inputs_embeds: Optional[torch.FloatTensor] = None, |
|
|
labels: Optional[torch.FloatTensor] = None, |
|
|
loss_masks: Optional[torch.FloatTensor] = None, |
|
|
mask_y: Optional[torch.FloatTensor] = None, |
|
|
output_attentions: Optional[bool] = None, |
|
|
output_hidden_states: Optional[bool] = None, |
|
|
return_dict: Optional[bool] = None, |
|
|
max_output_length: Optional[int] = None, |
|
|
revin: Optional[bool] = True, |
|
|
num_samples: Optional[int] = 1, |
|
|
inference_token_len: Optional[int] = 48, |
|
|
): |
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions |
|
|
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states |
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
|
|
|
|
|
if labels is not None: |
|
|
if max_output_length is None: |
|
|
max_output_length = labels.shape[1] |
|
|
predict_token_num = math.ceil(max_output_length / self.config.token_len) |
|
|
else: |
|
|
predict_token_num = math.ceil(max_output_length / inference_token_len) |
|
|
|
|
|
if revin: |
|
|
means = input_ids.mean(1, keepdim=True).detach() |
|
|
stdev = input_ids.std(dim=1, keepdim=True, unbiased=False).detach() + 1e-5 |
|
|
input_ids = (input_ids - means) / stdev |
|
|
|
|
|
outputs = self.model( |
|
|
input_ids=input_ids, |
|
|
inputs_embeds=inputs_embeds, |
|
|
text_input_ids=text_input_ids, |
|
|
text_attention_mask=text_attention_mask, |
|
|
text_token_type_ids=text_token_type_ids, |
|
|
vision_ids=vision_ids, |
|
|
output_attentions=output_attentions, |
|
|
output_hidden_states=output_hidden_states, |
|
|
return_dict=return_dict, |
|
|
predict_token_num=predict_token_num, |
|
|
inference_token_len=inference_token_len |
|
|
) |
|
|
|
|
|
hidden_states = outputs[0] if not return_dict else outputs.last_hidden_state |
|
|
x_rec, x_dec, from_text, from_vision = hidden_states |
|
|
|
|
|
if from_text is not None: |
|
|
generated_prototypes = self.retriever(from_text + from_vision, predict_token_num) |
|
|
else: |
|
|
generated_prototypes = self.retriever(from_vision, predict_token_num) |
|
|
|
|
|
loss = None |
|
|
predictions = None |
|
|
eps = 1e2 |
|
|
mask = None |
|
|
if labels is not None: |
|
|
if revin: |
|
|
origin_labels = labels |
|
|
labels = (labels - means) / stdev |
|
|
|
|
|
origin_length = labels.shape[-1] |
|
|
target_length = predict_token_num * self.config.token_len |
|
|
if origin_length < target_length: |
|
|
pad_length = target_length - origin_length |
|
|
labels = F.pad(labels, (0, pad_length)) |
|
|
mask = torch.tensor([1] * origin_length + [0] * pad_length, device=labels.device) |
|
|
mask = mask.unsqueeze(0) |
|
|
|
|
|
reco = rearrange(self.linear_head(x_rec), 'b n p -> b (n p)') |
|
|
fore = rearrange(self.linear_head(x_dec), 'b n p -> b (n p)') |
|
|
if revin: |
|
|
fore = fore * stdev + means |
|
|
|
|
|
reco_loss = self.point_loss(reco[:, :input_ids.shape[-1]], input_ids) |
|
|
fore_loss = self.point_loss(fore[:, :origin_length], origin_labels) |
|
|
reco_loss = reco_loss[reco_loss < eps] |
|
|
fore_loss = fore_loss[fore_loss < eps] |
|
|
point_loss = reco_loss.mean() + fore_loss.mean() |
|
|
|
|
|
shift_labels = labels.unfold( |
|
|
dimension=-1, size=self.config.token_len, step=self.config.token_len) |
|
|
bsz, L, _ = shift_labels.shape |
|
|
shift_labels = shift_labels.reshape( |
|
|
bsz * L, -1).repeat(self.config.diffusion_batch_mul, 1) |
|
|
x_dec = x_dec.reshape( |
|
|
bsz * L, -1).repeat(self.config.diffusion_batch_mul, 1) |
|
|
protos = generated_prototypes.reshape(bsz * L, -1).repeat(self.config.diffusion_batch_mul, 1) |
|
|
flow_loss = self.flow_match(target=shift_labels, z=x_dec.detach(), prototype=protos, eps=eps, mask=mask) |
|
|
loss = point_loss + flow_loss |
|
|
|
|
|
else: |
|
|
predictions = self.flow_match.sample(z=rearrange(x_dec, 'b n d -> (b n) d'), |
|
|
prototype=rearrange(generated_prototypes, 'b n p -> (b n) p'), |
|
|
num_samples=num_samples, |
|
|
inference_token_len=inference_token_len) |
|
|
predictions = rearrange(predictions, '(b n) s p -> b s (n p)', n=predict_token_num)[:, :, |
|
|
:max_output_length] |
|
|
|
|
|
if revin: |
|
|
stdev = stdev.unsqueeze(1).repeat(1, num_samples, 1) |
|
|
means = means.unsqueeze(1).repeat(1, num_samples, 1) |
|
|
predictions = (predictions * stdev) + means |
|
|
|
|
|
return MoeCausalLMOutputWithPast( |
|
|
loss=loss, |
|
|
logits=predictions, |
|
|
hidden_states=outputs.hidden_states, |
|
|
attentions=outputs.attentions, |
|
|
) |
|
|
|
