from typing import Any, Dict, List, NamedTuple, Optional, Tuple, Union import numpy as np import torch from torch import nn from torch.nn import functional as F from transformers import PretrainedConfig, PreTrainedModel from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast class DecoderInput(NamedTuple): hidden_states: torch.Tensor position_ids: torch.Tensor attention_mask: Optional[torch.Tensor] = None past_key_values: Optional[List[torch.FloatTensor]] = None output_hidden_states: Optional[bool] = False output_attentions: Optional[bool] = False use_cache: Optional[bool] = False gradient_checkpointing: bool = False class DecoderOutput(NamedTuple): hidden_states: torch.Tensor all_hidden_states: Optional[Tuple[torch.Tensor, ...]] all_self_attns: Optional[Tuple[torch.Tensor, ...]] next_decoder_cache: Optional[Tuple[torch.Tensor, ...]] class PlamoConfig(PretrainedConfig): # type: ignore model_type: str = "plamo" def __init__( self, vocab_size: int = 32000, hidden_size: int = 4096, intermediate_size: int = 13312, num_hidden_layers: int = 32, num_attention_heads: int = 32, num_key_value_heads: Optional[int] = None, max_position_embeddings: int = 2048, initializer_range: float = 0.02, rms_norm_eps: float = 1e-6, use_cache: bool = True, tokenizer_class: str = "PlamoTokenizer", pad_token_id: Optional[int] = None, bos_token_id: int = 1, eos_token_id: int = 2, n_shared_head: int = 8, tie_word_embeddings: bool = False, **kwargs: Any, ) -> None: self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads # for backward compatibility if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.n_shared_head = n_shared_head super().__init__( tokenizer_class=tokenizer_class, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs, ) # Copied from transformers.models.bart.modeling_bart._make_causal_mask def _make_causal_mask( input_ids_shape: Tuple[int, int], dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0 ) -> torch.Tensor: """ Make causal mask used for bi-directional self-attention. """ bsz, tgt_len = input_ids_shape mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) mask_cond = torch.arange(mask.size(-1), device=device) mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) mask = mask.to(dtype) if past_key_values_length > 0: mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1) return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) # Copied from transformers.models.bart.modeling_bart._expand_mask def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None) -> torch.Tensor: """ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. """ bsz, src_len = mask.size() tgt_len = tgt_len if tgt_len is not None else src_len expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) inverted_mask = 1.0 - expanded_mask return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) # type: ignore class RotaryEmbedding(torch.nn.Module): def __init__( self, dim: int, max_position_embeddings: int = 2048, base: int = 10000, device: Optional[torch.device] = None ) -> None: super().__init__() self.dim = dim self.max_position_embeddings = max_position_embeddings self.base = base inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) # Build here to make `torch.jit.trace` work. self._set_cos_sin_cache( seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() ) def _set_cos_sin_cache(self, seq_len: int, device: Any, dtype: Any) -> None: self.max_seq_len_cached = seq_len t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) # type: ignore freqs = torch.einsum("i,j->ij", t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False) self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False) def forward(self, x: torch.Tensor, seq_len: int) -> Tuple[torch.Tensor, torch.Tensor]: # x: [bs, num_attention_heads, seq_len, head_size] if seq_len > self.max_seq_len_cached: self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) return ( self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype), # type: ignore self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype), # type: ignore ) def _rotate_half(x: torch.Tensor) -> torch.Tensor: """Rotates half the hidden dims of the input.""" x1 = x[..., : x.shape[-1] // 2] x2 = x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) def _rotary_pos_emb(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, position_ids: torch.Tensor) -> torch.Tensor: # The first two dimensions of cos and sin are always 1, so we can `squeeze` them. cos = cos.squeeze(1).squeeze(0) # [seq_len, dim] sin = sin.squeeze(1).squeeze(0) # [seq_len, dim] cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim] x_embed = (x * cos) + (_rotate_half(x) * sin) return x_embed class RMSNorm(nn.Module): def __init__(self, hidden_size: int, eps: float = 1e-6) -> None: super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: input_dtype = hidden_states.dtype hidden_states = hidden_states.to(torch.float32) variance = hidden_states.pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) return self.weight * hidden_states.to(input_dtype) class Attention(torch.nn.Module): def __init__(self, config: PlamoConfig) -> None: super().__init__() self.config = config self.hidden_size = config.hidden_size head_dim = self.hidden_size // config.num_attention_heads self.max_position_embeddings = config.max_position_embeddings self.q_num_heads = config.num_attention_heads self.qk_dim = self.v_dim = head_dim self.k_num_heads = self.v_num_heads = int(np.ceil(self.q_num_heads / config.n_shared_head)) self.q_proj = nn.Linear(self.hidden_size, self.q_num_heads * self.qk_dim, bias=False) self.k_proj = nn.Linear(self.hidden_size, self.k_num_heads * self.qk_dim, bias=False) self.v_proj = nn.Linear(self.hidden_size, self.v_num_heads * self.v_dim, bias=False) self.o_proj = nn.Linear(self.q_num_heads * self.v_dim, self.hidden_size, bias=False) self.rotary_emb = RotaryEmbedding(self.qk_dim, max_position_embeddings=self.max_position_embeddings) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, output_attentions: bool = False, use_cache: bool = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor, torch.Tensor]]]: bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states).view(bsz, q_len, self.q_num_heads, self.qk_dim).transpose(1, 2) key_states = self.k_proj(hidden_states).view(bsz, q_len, self.k_num_heads, self.qk_dim).transpose(1, 2) value_states = self.v_proj(hidden_states).view(bsz, q_len, self.v_num_heads, self.v_dim).transpose(1, 2) def _expand_kv(t: torch.Tensor, repeat: int, target: int) -> torch.Tensor: return t.repeat(1, repeat, 1, 1)[:, :target] # expand shared kv assert self.k_num_heads == self.v_num_heads key_states = _expand_kv(key_states, self.config.n_shared_head, self.q_num_heads) value_states = _expand_kv(value_states, self.config.n_shared_head, self.q_num_heads) kv_seq_len = key_states.shape[-2] if past_key_value is not None: kv_seq_len += past_key_value[0].shape[-2] cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) assert position_ids is not None query_states = _rotary_pos_emb(query_states, cos, sin, position_ids) key_states = _rotary_pos_emb(key_states, cos, sin, position_ids) # [bsz, nh, t, hd] if past_key_value is not None: # reuse k, v, self_attention key_states = torch.cat([past_key_value[0], key_states], dim=2) value_states = torch.cat([past_key_value[1], value_states], dim=2) past_key_value = (key_states, value_states) if use_cache else None attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask=attention_mask) attn_output = attn_output.transpose(1, 2) attn_output = attn_output.reshape(bsz, q_len, self.q_num_heads * self.v_dim) attn_output = self.o_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights, past_key_value class MLP(nn.Module): def __init__(self, config: PlamoConfig) -> None: 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.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) self.act_fn = torch.nn.functional.silu def forward(self, x: torch.Tensor) -> torch.Tensor: return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) # type: ignore class PlamoDecoderLayer(torch.nn.Module): def __init__(self, config: PlamoConfig) -> None: super().__init__() self.config = config self.hidden_size = config.hidden_size self.self_attn = Attention(config) self.mlp = MLP(config) self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: Optional[bool] = False, use_cache: Optional[bool] = False, ) -> Tuple[Any, ...]: # from LlamaDecoder residual = hidden_states hidden_states = self.norm(hidden_states) # Self Attention hidden_states_sa, 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, output_attentions=output_attentions, use_cache=use_cache, ) # Fully Connected hidden_states_mlp = self.mlp(hidden_states) # Residual hidden_states = residual + hidden_states_sa + hidden_states_mlp outputs: Any = (hidden_states,) if output_attentions: outputs += (self_attn_weights,) if use_cache: outputs += (present_key_value,) return outputs # type: ignore class PlamoDecoder(torch.nn.Module): def __init__(self, config: PlamoConfig) -> None: super().__init__() self.layers = torch.nn.ModuleList([PlamoDecoderLayer(config) for _ in range(config.num_hidden_layers)]) def forward(self, x: DecoderInput) -> DecoderOutput: all_hidden_states: Optional[Tuple[torch.Tensor, ...]] = () if x.output_hidden_states else None all_self_attns: Optional[Tuple[torch.Tensor, ...]] = () if x.output_attentions else None next_decoder_cache: Optional[Tuple[torch.Tensor, ...]] = () if x.use_cache else None hidden_states = x.hidden_states for idx, decoder_layer in enumerate(self.layers): if x.output_hidden_states: assert all_hidden_states is not None all_hidden_states += (hidden_states,) past_key_value = x.past_key_values[idx] if x.past_key_values is not None else None if self.training and x.gradient_checkpointing: def create_custom_forward(module): # type: ignore def custom_forward(*inputs): # type: ignore # None for past_key_value return module(*inputs, x.output_attentions, None) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(decoder_layer), # type: ignore hidden_states, x.attention_mask, x.position_ids, None, ) else: layer_outputs = decoder_layer( hidden_states, attention_mask=x.attention_mask, position_ids=x.position_ids, past_key_value=past_key_value, output_attentions=x.output_attentions, use_cache=x.use_cache, ) hidden_states = layer_outputs[0] if x.use_cache: cache = layer_outputs[2 if x.output_attentions else 1] assert cache is not None assert next_decoder_cache is not None next_decoder_cache += (cache,) if x.output_attentions: assert layer_outputs[1] is not None assert all_self_attns is not None all_self_attns += (layer_outputs[1],) return DecoderOutput(hidden_states, all_hidden_states, all_self_attns, next_decoder_cache) class PlamoPreTrainedModel(PreTrainedModel): # type: ignore config_class = PlamoConfig _no_split_modules: List[str] base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["PlamoDecoderLayer"] _skip_keys_device_placement = "past_key_values" _keys_to_ignore_on_load_unexpected = [r"decoder\.version"] def _init_weights(self, module: torch.nn.Module) -> None: std = self.config.initializer_range if isinstance(module, nn.Linear): module.weight.data.normal_(mean=0.0, std=std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=std) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() def _set_gradient_checkpointing(self, module: torch.nn.Module, value: bool = False) -> None: module.gradient_checkpointing = value # type: ignore class PlamoModel(PlamoPreTrainedModel): def __init__(self, config: PlamoConfig): super().__init__(config) self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) self.layers = PlamoDecoder(config) # type: ignore self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self) -> torch.nn.Embedding: return self.embed_tokens def set_input_embeddings(self, value: torch.nn.Embedding) -> None: self.embed_tokens = value # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask def _prepare_decoder_attention_mask( self, attention_mask: torch.Tensor, input_shape: Tuple[int, int], inputs_embeds: Optional[torch.FloatTensor], past_key_values_length: int, ) -> Optional[torch.Tensor]: # create causal mask # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] combined_attention_mask: Optional[torch.Tensor] = None if input_shape[-1] > 1: assert inputs_embeds is not None combined_attention_mask = _make_causal_mask( input_shape, inputs_embeds.dtype, device=inputs_embeds.device, past_key_values_length=past_key_values_length, ) if attention_mask is not None: # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] assert inputs_embeds is not None expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to( inputs_embeds.device ) combined_attention_mask = ( expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask ) return combined_attention_mask def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPast]: assert input_ids is not None 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 ) 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_return_dict # retrieve input_ids and inputs_embeds if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") elif input_ids is not None: batch_size, seq_length = input_ids.shape else: raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") seq_length_with_past = seq_length past_key_values_length = 0 if past_key_values is not None: past_key_values_length = past_key_values[0][0].shape[2] seq_length_with_past = seq_length_with_past + past_key_values_length if position_ids is None: device = input_ids.device position_ids = torch.arange( past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device ) position_ids = position_ids.unsqueeze(0).view(-1, seq_length) else: position_ids = position_ids.view(-1, seq_length).long() if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) # embed positions if attention_mask is None: attention_mask = torch.ones( (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device ) attention_mask = self._prepare_decoder_attention_mask( attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length ) hidden_states = inputs_embeds if self.gradient_checkpointing and self.training: if use_cache: use_cache = False # decoder layers out = self.layers( DecoderInput( hidden_states, position_ids, attention_mask, past_key_values, output_hidden_states, output_attentions, use_cache, self.gradient_checkpointing, ) ) assert isinstance(out, DecoderOutput) hidden_states = out.hidden_states all_hidden_states = out.all_hidden_states all_self_attns = out.all_self_attns next_decoder_cache = out.next_decoder_cache hidden_states = self.norm(hidden_states) # add hidden states from the last decoder layer if output_hidden_states: assert all_hidden_states is not None all_hidden_states += (hidden_states,) next_cache = next_decoder_cache if use_cache else None 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 BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=next_cache, hidden_states=all_hidden_states, attentions=all_self_attns, ) class PlamoForCausalLM(PlamoPreTrainedModel): def __init__(self, config: PretrainedConfig) -> None: super().__init__(config) self.model = PlamoModel(config) self.lm_head: torch.nn.Module = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self) -> torch.nn.Embedding: return self.model.embed_tokens def set_input_embeddings(self, value: torch.nn.Embedding) -> None: self.model.embed_tokens = value def get_output_embeddings(self) -> torch.nn.Module: return self.lm_head def set_output_embeddings(self, new_embeddings: torch.nn.Module) -> None: self.lm_head = new_embeddings def set_decoder(self, decoder: PlamoModel) -> None: self.model = decoder def get_decoder(self) -> PlamoModel: return self.model def forward( # type: ignore self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: r""" Args: 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]`. Returns: Example: ```python >>> from transformers import AutoTokenizer, LlamaForCausalLM >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS) >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER) >>> prompt = "Hey, are you consciours? Can you talk to me?" >>> inputs = tokenizer(prompt, return_tensors="pt") >>> # Generate >>> generate_ids = model.generate(inputs.input_ids, max_length=30) >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you." ```""" assert input_ids is not None 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 # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] logits = self.lm_head(hidden_states) loss = None if labels is not None: # Shift so that tokens < n predict n shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = nn.CrossEntropyLoss() shift_logits = shift_logits.view(-1, self.config.vocab_size) shift_labels = shift_labels.view(-1) # Enable model parallelism shift_labels = shift_labels.to(shift_logits.device) loss = loss_fct(shift_logits, shift_labels) if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def prepare_inputs_for_generation( self, input_ids: torch.Tensor, past_key_values: Optional[List[torch.FloatTensor]] = None, attention_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, **kwargs: Any, ) -> Dict[str, Any]: if past_key_values: input_ids = input_ids[:, -1:] position_ids = kwargs.get("position_ids", None) if attention_mask is not None and position_ids is None: # create position_ids on the fly for batch generation position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) if past_key_values: position_ids = position_ids[:, -1].unsqueeze(-1) # if `inputs_embeds` are passed, we only want to use them in the 1st generation step if inputs_embeds is not None and past_key_values is None: model_inputs: Dict[str, Any] = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids} model_inputs.update( { "position_ids": position_ids, "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), "attention_mask": attention_mask, } ) return model_inputs @staticmethod def _reorder_cache(past_key_values: List[torch.FloatTensor], beam_idx: int) -> Tuple[Any, ...]: reordered_past: Tuple[Any, ...] = () for layer_past in past_key_values: reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) return reordered_past