from functools import partial from typing import Optional, Tuple, Union import torch import torch.nn.functional as F import torch.utils.checkpoint from packaging import version from torch import nn from torch.nn import CrossEntropyLoss from transformers.activations import ACT2FN from transformers.modeling_outputs import ( BaseModelOutputWithPast, CausalLMOutputWithPast, ) from transformers.modeling_utils import PreTrainedModel from transformers.utils import logging from .configuration_llama import CustomLlamaConfig try: from apex.megatron_layer_norm import MixedFusedLayerNorm as LayerNorm except ImportError: from torch.nn import LayerNorm USE_FLASH_ATTN = False try: import flash_attn if version.parse(flash_attn.__version__) >= version.parse("2.1.0"): USE_FLASH_ATTN = True from flash_attn import flash_attn_func, flash_attn_varlen_func from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input except ImportError: pass logger = logging.get_logger(__name__) def _get_unpad_data(attention_mask): seqlens_in_batch = (attention_mask).sum(dim=-1, dtype=torch.int32) indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() max_seqlen_in_batch = seqlens_in_batch.max().item() cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)) return ( indices, cu_seqlens, max_seqlen_in_batch, ) class RMSNorm(torch.nn.Module): def __init__(self, dim: int, eps: float = 1e-6): """ Initialize the RMSNorm normalization layer. Args: dim (int): The dimension of the input tensor. eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6. Attributes: eps (float): A small value added to the denominator for numerical stability. weight (nn.Parameter): Learnable scaling parameter. """ super().__init__() self.eps = eps self.weight = nn.Parameter(torch.ones(dim)) def _norm(self, x): """ Apply the RMSNorm normalization to the input tensor. Args: x (torch.Tensor): The input tensor. Returns: torch.Tensor: The normalized tensor. """ return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) def forward(self, x): """ Forward pass through the RMSNorm layer. Args: x (torch.Tensor): The input tensor. Returns: torch.Tensor: The output tensor after applying RMSNorm. """ output = self._norm(x.float()).type_as(x) return output * self.weight def get_norm(config: CustomLlamaConfig): norm_type = config.norm_type if norm_type == 'rms_norm': return partial(RMSNorm, eps=config.layernorm_epsilon) elif norm_type == 'layer_norm': return partial(LayerNorm, eps=config.layernorm_epsilon) else: raise ValueError(f'Unsupported norm type: {norm_type}') # Copied from transformers.models.bart.modeling_bart._make_causal_mask def _make_causal_mask( input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0, ): """ 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): """ 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 ) class RotaryEmbedding(torch.nn.Module): def __init__(self, dim, base=10000, compress=1.0): super().__init__() self.inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim)) self.seq_len_cached = 0 self.cos_cached = None self.sin_cached = None self.compress = compress def forward(self, x, seq_len): if seq_len > self.seq_len_cached: self.seq_len_cached = seq_len self.inv_freq = self.inv_freq.to(x.device) t = ( torch.arange(seq_len, device=self.inv_freq.device, dtype=self.inv_freq.dtype) * self.compress ) freqs = torch.einsum("i,j->ij", t, self.inv_freq) emb = torch.cat((freqs, freqs), dim=-1).to(x.device) self.cos_cached = emb.cos()[None, None, :, :] self.sin_cached = emb.sin()[None, None, :, :] return self.cos_cached[:seq_len, ...], self.sin_cached[:seq_len, ...] # rotary pos emb helpers: def rotate_half(x): x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2:] return torch.cat((-x2, x1), dim=-1) @torch.jit.script def apply_rotary_pos_emb(q, k, cos, sin, offset: int = 0): cos, sin = ( cos[..., offset: q.shape[-2] + offset, :], sin[..., offset: q.shape[-2] + offset, :], ) q_embed = (q.float() * cos) + (rotate_half(q.float()) * sin) k_embed = (k.float() * cos) + (rotate_half(k.float()) * sin) return q_embed.to(q.dtype), k_embed.to(k.dtype) def apply_rotary_pos_emb_torch( q, k, cos, sin, offset: int = 0 ): # jitting fails with bf16 cos, sin = ( cos[..., offset: q.shape[-2] + offset, :], sin[..., offset: q.shape[-2] + offset, :], ) q_embed = (q.float() * cos) + (rotate_half(q.float()) * sin) k_embed = (k.float() * cos) + (rotate_half(k.float()) * sin) return q_embed.to(q.dtype), k_embed.to(k.dtype) class CustomLlamaAttention(nn.Module): def __init__(self, config: CustomLlamaConfig): super().__init__() self.num_attention_heads = config.num_attention_heads self.num_kv_heads = config.num_kv_heads self.hidden_size = config.hidden_size self.head_size = self.hidden_size // self.num_attention_heads self.rotary_ndims = int(self.head_size * config.rotary_pct) self.max_positions = config.max_position_embeddings self.rotary_emb = RotaryEmbedding( self.rotary_ndims, base=config.rotary_emb_base, compress=config.rotary_compress, ) self.norm_factor = torch.sqrt( torch.tensor(self.head_size, dtype=torch.float32) ).to(torch.get_default_dtype()) if self.use_gqa: self.query_dense = nn.Linear( config.hidden_size, config.hidden_size, bias=getattr(config, "qkv_proj_bias", True) ) self.key_value_dense = nn.Linear( config.hidden_size, self.head_size * 2 * config.num_kv_heads, bias=getattr(config, "qkv_proj_bias", True), ) else: self.query_key_value = nn.Linear( config.hidden_size, 3 * config.hidden_size, bias=getattr(config, "qkv_proj_bias", True), ) self.dense = nn.Linear( config.hidden_size, config.hidden_size, bias=getattr(config, "out_proj_bias", True), ) self.apply_rotary_fn = ( apply_rotary_pos_emb_torch if config.torch_dtype == torch.bfloat16 else apply_rotary_pos_emb ) @property def use_gqa(self): return self.num_kv_heads < self.num_attention_heads def forward( self, hidden_states, attention_mask, head_mask=None, layer_past=None, use_cache=False, output_attentions=False, ): has_layer_past = layer_past is not None if self.use_gqa: # Compute Q # [batch, seq_len, hidden_size] --> [batch_size, seq_len, (num_heads * head_size)] q = self.query_dense(hidden_states) # [batch_size, seq_len, (num_heads * head_size)] # --> [batch, seq_len, num_attention_heads, head_size] new_q_shape = q.size()[:-1] + \ (self.num_attention_heads, self.head_size) q = q.view(*new_q_shape) # Compute KV # [batch, seq_len, hidden_size] --> [batch_size, seq_len, (num_attention_groups * 2 * head_size)] kv = self.key_value_dense(hidden_states) # [batch, seq_len, (num_attention_groups * 2 * head_size)] # --> [batch, seq_len, num_attention_groups, 2 * head_size] new_kv_shape = kv.size()[:-1] + ( self.num_kv_heads, 2 * self.head_size, ) kv = kv.view(*new_kv_shape) # [batch, num_attention_heads, seq_len, head_size] query = q.permute(0, 2, 1, 3) # [batch, num_attention_groups, seq_len, head_size] key = kv[..., : self.head_size].permute(0, 2, 1, 3) value = kv[..., self.head_size:].permute(0, 2, 1, 3) else: # Compute QKV # Attention heads [batch, seq_len, hidden_size] # --> [batch, seq_len, (np * 3 * head_size)] qkv = self.query_key_value(hidden_states) # [batch, seq_len, (num_heads * 3 * head_size)] # --> [batch, seq_len, num_heads, 3 * head_size] new_qkv_shape = qkv.size()[:-1] + ( self.num_attention_heads, 3 * self.head_size, ) qkv = qkv.view(*new_qkv_shape) # [batch, seq_len, num_attention_heads, 3 * head_size] --> 3 [batch, num_attention_heads, seq_len, head_size] query = qkv[..., : self.head_size].permute(0, 2, 1, 3) key = qkv[..., self.head_size: 2 * self.head_size].permute(0, 2, 1, 3) value = qkv[..., 2 * self.head_size:].permute(0, 2, 1, 3) # Compute rotary embeddings on rotary_ndims query_rot = query[..., : self.rotary_ndims] query_pass = query[..., self.rotary_ndims:] key_rot = key[..., : self.rotary_ndims] key_pass = key[..., self.rotary_ndims:] # Compute token offset for rotary embeddings (when decoding) seq_len = key.shape[-2] offset = 0 if has_layer_past: offset = layer_past[0].shape[-2] seq_len += offset cos, sin = self.rotary_emb(value, seq_len=seq_len) query, key = self.apply_rotary_fn( query_rot, key_rot, cos, sin, offset=offset) query = torch.cat((query, query_pass), dim=-1) key = torch.cat((key, key_pass), dim=-1) # Cache QKV values if has_layer_past: past_key = layer_past[0] past_value = layer_past[1] key = torch.cat((past_key, key), dim=-2) value = torch.cat((past_value, value), dim=-2) present = (key, value) if use_cache else None if USE_FLASH_ATTN: # Compute attention attn_output, attn_weights = self._flash_attn( query, key, value, attention_mask, head_mask ) # from [batch_size, ] attn_output = attn_output.reshape( attn_output.size(0), attn_output.size(1), self.hidden_size).contiguous() else: # Compute attention attn_output, attn_weights = self._attn( query, key, value, attention_mask, head_mask ) # Reshape outputs attn_output = self._merge_heads( attn_output, self.num_attention_heads, self.head_size ) attn_output = self.dense(attn_output) outputs = (attn_output, present) if output_attentions: outputs += (attn_weights,) return outputs @classmethod def _split_heads(cls, tensor, num_attention_heads, attn_head_size): """ Splits hidden dim into attn_head_size and num_attention_heads """ # tensor: [bs, seq_len, hidden_size] new_shape = tensor.size()[:-1] + (num_attention_heads, attn_head_size) # -> [bs, seq_len, num_attention_heads, attn_head_size] tensor = tensor.view(new_shape) # -> [bs, num_attention_heads, seq_len, attn_head_size] tensor = tensor.permute(0, 2, 1, 3) return tensor @classmethod def _merge_heads(cls, tensor, num_attention_heads, attn_head_size): """ Merges attn_head_size dim and num_attn_heads dim into hidden dim """ # tensor [bs, num_attention_heads, seq_len, attn_head_size] tensor = tensor.permute(0, 2, 1, 3).contiguous() # -> [bs, seq_len, num_attention_heads, attn_head_size] tensor = tensor.view( tensor.size(0), tensor.size( 1), num_attention_heads * attn_head_size ) # -> [bs, seq_len, hidden_size] return tensor def _attn(self, query, key, value, attention_mask=None, head_mask=None): # q: [bs, num_attention_heads, seq_len, attn_head_size] # k,v: [bs, num_attention_groups, seq_len, attn_head_size] # compute causal mask from causal mask buffer batch_size, num_attention_heads, query_length, attn_head_size = query.size() _, num_attention_groups, key_length, _ = key.size() group_size = num_attention_heads // num_attention_groups if not self.use_gqa: assert group_size == 1 # repeat key and value, so we can use normal MHA algorithm key = ( key.view(batch_size, num_attention_groups, 1, key_length, attn_head_size) .repeat(1, 1, group_size, 1, 1) .view(batch_size, num_attention_heads, key_length, attn_head_size) ) value = ( value.view(batch_size, num_attention_groups, 1, key_length, attn_head_size) .repeat(1, 1, group_size, 1, 1) .view(batch_size, num_attention_heads, key_length, attn_head_size) ) query = query.view( batch_size * num_attention_heads, query_length, attn_head_size ) key = key.view(batch_size * num_attention_heads, key_length, attn_head_size) attn_scores = torch.zeros( batch_size * num_attention_heads, query_length, key_length, dtype=query.dtype, device=key.device, ) attn_scores = torch.baddbmm( attn_scores, query, key.transpose(1, 2), beta=1.0, alpha=( torch.tensor( 1.0, dtype=self.norm_factor.dtype, device=self.norm_factor.device ) / self.norm_factor ), ) attn_scores = attn_scores.view( batch_size, num_attention_heads, query_length, key_length ) mask_value = torch.finfo(attn_scores.dtype).min # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`. # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device` mask_value = torch.tensor(mask_value, dtype=attn_scores.dtype).to( attn_scores.device ) if attention_mask is not None: # Apply the attention mask attn_scores = attn_scores + attention_mask attn_weights = nn.functional.softmax(attn_scores, dim=-1) attn_weights = attn_weights.to(value.dtype) # Mask heads if we want to if head_mask is not None: attn_weights = attn_weights * head_mask attn_output = torch.matmul(attn_weights, value) return attn_output, attn_weights def _flash_attn(self, query, key, value, attention_mask=None, head_mask=None): assert head_mask is None, "head_mask is not supported in _flash_attn" # q: [bs, num_attention_heads, seq_len, attn_head_size] # k,v: [bs, num_attention_groups, seq_len, attn_head_size] # flash_attn need the layout to be [batch_size, sequence_length, num_heads, head_dim] query = query.transpose(1, 2) key = key.transpose(1, 2) value = value.transpose(1, 2) query_length = query.size(1) causal = query_length != 1 if attention_mask is not None: batch_size = query.size(0) query, key, value, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( query, key, value, attention_mask, query_length ) cu_seqlens_q, cu_seqlens_k = cu_seq_lens max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens attn_output_unpad = flash_attn_varlen_func( query, key, value, cu_seqlens_q=cu_seqlens_q, cu_seqlens_k=cu_seqlens_k, max_seqlen_q=max_seqlen_in_batch_q, max_seqlen_k=max_seqlen_in_batch_k, dropout_p=0, causal=causal, ) attn_output = pad_input( attn_output_unpad, indices_q, batch_size, query_length) else: attn_output = flash_attn_func( query, key, value, 0, causal=causal ) return attn_output, None def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data( attention_mask) batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape num_attention_heads = query_layer.shape[2] key_layer = index_first_axis( key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k ) value_layer = index_first_axis( value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k ) if query_length == kv_seq_len: query_layer = index_first_axis( query_layer.reshape(batch_size * kv_seq_len, num_attention_heads, head_dim), indices_k ) cu_seqlens_q = cu_seqlens_k max_seqlen_in_batch_q = max_seqlen_in_batch_k indices_q = indices_k elif query_length == 1: max_seqlen_in_batch_q = 1 cu_seqlens_q = torch.arange( batch_size + 1, dtype=torch.int32, device=query_layer.device ) # There is a memcpy here, that is very bad. indices_q = cu_seqlens_q[:-1] query_layer = query_layer.squeeze(1) else: # The -q_len: slice assumes left padding. attention_mask = attention_mask[:, -query_length:] query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input( query_layer, attention_mask) return ( query_layer, key_layer, value_layer, indices_q, (cu_seqlens_q, cu_seqlens_k), (max_seqlen_in_batch_q, max_seqlen_in_batch_k), ) def swiglu(x): x1, x2 = x.chunk(2, dim=(x.ndim - 1)) return x1 * torch.nn.functional.silu(x2) def get_activation(act_name: str): if act_name == "gelu": return ACT2FN["gelu_new"] elif act_name == "swiglu": return swiglu else: return ACT2FN[act_name] class CustomLlamaMLP(nn.Module): def __init__(self, config): super().__init__() h_to_4h_out_channels = ( config.ffn_hidden_size * 2 if config.hidden_act == "swiglu" else config.ffn_hidden_size ) self.dense_h_to_4h = nn.Linear( config.hidden_size, h_to_4h_out_channels, bias=getattr(config, "mlp_fc1_bias", True) ) self.dense_4h_to_h = nn.Linear( config.ffn_hidden_size, config.hidden_size, bias=getattr(config, "mlp_fc2_bias", True) ) self.act = get_activation(config.hidden_act) def forward(self, hidden_states): hidden_states = self.dense_h_to_4h(hidden_states) hidden_states = self.act(hidden_states) hidden_states = self.dense_4h_to_h(hidden_states) return hidden_states class CustomLlamaLayer(nn.Module): def __init__(self, config): super().__init__() norm_func = get_norm(config) self.input_layernorm = norm_func(config.hidden_size) self.post_attention_layernorm = norm_func(config.hidden_size) self.attention = CustomLlamaAttention(config) self.mlp = CustomLlamaMLP(config) def forward( self, hidden_states, attention_mask=None, head_mask=None, use_cache=False, layer_past=None, output_attentions=False, ): attn_in = self.input_layernorm(hidden_states) attention_layer_outputs = self.attention( attn_in, attention_mask=attention_mask, layer_past=layer_past, head_mask=head_mask, use_cache=use_cache, output_attentions=output_attentions, ) attn_output = attention_layer_outputs[ 0 ] # output_attn: attn_output, present, (attn_weights) outputs = attention_layer_outputs[1:] # pseudocode: # x = x + attn(ln1(x)) # x = x + mlp(ln2(x)) attn_output = attn_output + hidden_states mlp_input = self.post_attention_layernorm(attn_output) mlp_output = self.mlp(mlp_input) hidden_states = mlp_output + attn_output if use_cache: outputs = ( hidden_states, ) + outputs # hidden_states, present, (attn_weights) else: # hidden_states, (attn_weights) outputs = (hidden_states,) + outputs[1:] return outputs class CustomLlamaPreTrainedModel(PreTrainedModel): config_class = CustomLlamaConfig base_model_prefix = "lm" _no_split_modules = ["CustomLlamaLayer"] class CustomLlamaModel(CustomLlamaPreTrainedModel): def __init__(self, config): super().__init__(config) self.config = config self.embed_in = nn.Embedding(config.vocab_size, config.hidden_size) self.layers = nn.ModuleList( [CustomLlamaLayer(config) for _ in range(config.num_layers)] ) norm_func = get_norm(config) self.final_layer_norm = norm_func(config.hidden_size) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embed_in def set_input_embeddings(self, value): self.embed_in = value # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask def _prepare_decoder_attention_mask( self, attention_mask, input_shape, inputs_embeds, past_key_values_length ): # create causal mask # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] combined_attention_mask = None if input_shape[-1] > 1: 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] 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.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[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]: r""" past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). """ 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 ) use_cache = use_cache if use_cache is not None else self.config.use_cache if input_ids is not None and inputs_embeds is not None: raise ValueError( "You cannot specify both input_ids and inputs_embeds at the same time" ) elif input_ids is not None: input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError( "You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape 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 else: past_key_values = tuple([None] * self.config.num_layers) if inputs_embeds is None: inputs_embeds = self.embed_in(input_ids) # Attention mask. if attention_mask is None: attention_mask = torch.ones( (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device, ) # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_layers x num_heads] # and head_mask is converted to shape [num_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_layers) if USE_FLASH_ATTN: attention_mask = attention_mask if ( attention_mask is not None and 0 in attention_mask) else None else: attention_mask = self._prepare_decoder_attention_mask( attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length, ) hidden_states = inputs_embeds presents = () if use_cache else None all_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None for i, (layer, layer_past) in enumerate(zip(self.layers, past_key_values)): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) outputs = layer( hidden_states, attention_mask=attention_mask, head_mask=head_mask[i], layer_past=layer_past, use_cache=use_cache, output_attentions=output_attentions, ) hidden_states = outputs[0] if use_cache is True: presents = presents + (outputs[1],) if output_attentions: all_attentions = all_attentions + \ (outputs[2 if use_cache else 1],) hidden_states = self.final_layer_norm(hidden_states) # Add last hidden state if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple( v for v in [hidden_states, presents, all_hidden_states, all_attentions] if v is not None ) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_attentions, ) class CustomLlamaForCausalLM(CustomLlamaPreTrainedModel): _tied_weights_keys = ["embed_out.weight"] _keys_to_ignore_on_load_unexpected = [ r"lm.layers.\d+.attention.rotary_emb.inv_freq" ] def __init__(self, config): super().__init__(config) self.lm = CustomLlamaModel(config) self.embed_out = nn.Linear( config.hidden_size, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def get_output_embeddings(self): return self.embed_out def set_output_embeddings(self, new_embeddings): self.embed_out = new_embeddings def forward( self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, head_mask: Optional[torch.FloatTensor] = None, past_key_values: Optional[Tuple[Tuple[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""" past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional tensors are only required when the model is used as a decoder in a Sequence to Sequence model. Contains pre-computed hidden-states (key and values in the self-attention blocks that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). ```""" return_dict = ( return_dict if return_dict is not None else self.config.use_return_dict ) outputs = self.lm( input_ids, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) hidden_states = outputs[0] lm_logits = self.embed_out(hidden_states) lm_loss = None if labels is not None: # we are doing next-token prediction; shift prediction scores and input ids by one shift_logits = lm_logits[:, :-1, :].contiguous() labels = labels[:, 1:].contiguous() loss_fct = CrossEntropyLoss() lm_loss = loss_fct( shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1) ) if not return_dict: output = (lm_logits,) + outputs[1:] return ((lm_loss,) + output) if lm_loss is not None else output return CausalLMOutputWithPast( loss=lm_loss, logits=lm_logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def prepare_inputs_for_generation( self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **model_kwargs ): input_shape = input_ids.shape # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly if attention_mask is None: attention_mask = input_ids.new_ones(input_shape) # cut decoder_input_ids if past is used if past_key_values and past_key_values[0] is not None: input_ids = input_ids[:, -1:] if inputs_embeds is not None and past_key_values is None: model_inputs = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids} model_inputs.update( { "attention_mask": attention_mask, "past_key_values": past_key_values } ) return model_inputs def _reorder_cache(self, past_key_values, beam_idx): reordered_past = () for layer_past in past_key_values: reordered_past += ( tuple( past_state.index_select(0, beam_idx) for past_state in layer_past[:2] ) + layer_past[2:], ) return reordered_past