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"""PyTorch OpenAI GPT-2 model modified with MultiQuery attention""" |
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from typing import Optional, Tuple, Union |
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import torch |
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import torch.utils.checkpoint |
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from torch import nn |
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from torch.cuda.amp import autocast |
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from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions |
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from transformers.pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_conv1d_layer |
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from transformers.utils import logging |
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from transformers.models.gpt2.modeling_gpt2 import GPT2Model, GPT2Block, GPT2PreTrainedModel, GPT2LMHeadModel |
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from .configuration_gpt2_mq import GPT2CustomConfig, MULTI_QUERY |
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logger = logging.get_logger(__name__) |
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class GPT2MQAttention(nn.Module): |
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def __init__(self, config, is_cross_attention=False, layer_idx=None): |
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super().__init__() |
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assert config.attention_head_type == MULTI_QUERY |
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max_positions = config.max_position_embeddings |
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self.register_buffer( |
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"bias", |
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torch.tril(torch.ones((max_positions, max_positions), dtype=torch.uint8)).view( |
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1, 1, max_positions, max_positions |
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), |
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) |
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self.register_buffer("masked_bias", torch.tensor(-1e4)) |
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self.embed_dim = config.hidden_size |
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self.num_heads = config.num_attention_heads |
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self.head_dim = self.embed_dim // self.num_heads |
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self.split_size = self.embed_dim |
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if self.head_dim * self.num_heads != self.embed_dim: |
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raise ValueError( |
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f"`embed_dim` must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" |
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f" {self.num_heads})." |
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) |
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self.scale_attn_weights = config.scale_attn_weights |
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if is_cross_attention: |
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raise NotImplementedError("Cross-attention not implemented for MQA") |
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self.is_cross_attention = is_cross_attention |
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self.scale_attn_by_inverse_layer_idx = config.scale_attn_by_inverse_layer_idx |
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self.layer_idx = layer_idx |
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self.reorder_and_upcast_attn = config.reorder_and_upcast_attn |
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if self.is_cross_attention: |
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self.c_attn = Conv1D(2 * self.embed_dim, self.embed_dim) |
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self.q_attn = Conv1D(self.embed_dim, self.embed_dim) |
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else: |
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self.q_attn = Conv1D(self.embed_dim, self.embed_dim) |
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self.kv_attn = Conv1D(2 * self.head_dim, self.embed_dim) |
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self.c_proj = Conv1D(self.embed_dim, self.embed_dim) |
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self.attn_dropout = nn.Dropout(config.attn_pdrop) |
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self.resid_dropout = nn.Dropout(config.resid_pdrop) |
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self.pruned_heads = set() |
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def prune_heads(self, heads): |
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if len(heads) == 0: |
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return |
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heads, index = find_pruneable_heads_and_indices(heads, self.num_heads, self.head_dim, self.pruned_heads) |
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index_attn = torch.cat([index, index + self.split_size, index + (2 * self.split_size)]) |
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self.c_attn = prune_conv1d_layer(self.c_attn, index_attn, dim=1) |
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self.c_proj = prune_conv1d_layer(self.c_proj, index, dim=0) |
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self.split_size = (self.split_size // self.num_heads) * (self.num_heads - len(heads)) |
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self.num_heads = self.num_heads - len(heads) |
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self.pruned_heads = self.pruned_heads.union(heads) |
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def _attn(self, query, key, value, attention_mask=None, head_mask=None): |
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batch_size = query.size(0) |
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query_length = query.size(1) // self.num_heads |
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key_length = key.size(2) |
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attn_weights = torch.bmm(query, key) |
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attn_weights = attn_weights.view(batch_size, self.num_heads, query_length, key_length) |
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if self.scale_attn_weights: |
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attn_weights = attn_weights / torch.tensor( |
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value.size(-1) ** 0.5, dtype=attn_weights.dtype, device=attn_weights.device |
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) |
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if self.scale_attn_by_inverse_layer_idx: |
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attn_weights = attn_weights / float(self.layer_idx + 1) |
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if not self.is_cross_attention: |
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causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length].to(torch.bool) |
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mask_value = torch.finfo(attn_weights.dtype).min |
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mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device) |
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attn_weights = torch.where(causal_mask, attn_weights, mask_value) |
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if attention_mask is not None: |
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attn_weights = attn_weights + attention_mask |
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attn_weights = nn.functional.softmax(attn_weights, dim=-1) |
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attn_weights = attn_weights.type(value.dtype) |
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attn_weights = self.attn_dropout(attn_weights) |
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if head_mask is not None: |
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attn_weights = attn_weights * head_mask |
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_attn_weights = attn_weights.view(batch_size, self.num_heads * query_length, key_length) |
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attn_output = torch.bmm(_attn_weights, value) |
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attn_output = attn_output.view(batch_size, self.num_heads, query_length, self.head_dim) |
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return attn_output, attn_weights |
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def _upcast_and_reordered_attn(self, query, key, value, attention_mask=None, head_mask=None): |
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bsz, num_heads, q_seq_len, dk = query.size() |
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_, _, k_seq_len, _ = key.size() |
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attn_weights = torch.empty(bsz * num_heads, q_seq_len, k_seq_len, dtype=torch.float32, device=query.device) |
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scale_factor = 1.0 |
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if self.scale_attn_weights: |
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scale_factor /= float(value.size(-1)) ** 0.5 |
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if self.scale_attn_by_inverse_layer_idx: |
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scale_factor /= float(self.layer_idx + 1) |
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with autocast(enabled=False): |
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q, k = query.reshape(-1, q_seq_len, dk), key.transpose(-1, -2).reshape(-1, dk, k_seq_len) |
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attn_weights = torch.baddbmm(attn_weights, q.float(), k.float(), beta=0, alpha=scale_factor) |
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attn_weights = attn_weights.reshape(bsz, num_heads, q_seq_len, k_seq_len) |
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if not self.is_cross_attention: |
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query_length, key_length = query.size(-2), key.size(-2) |
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causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length].bool() |
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mask_value = torch.finfo(attn_weights.dtype).min |
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mask_value = torch.tensor(mask_value, dtype=attn_weights.dtype).to(attn_weights.device) |
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attn_weights = torch.where(causal_mask, attn_weights, mask_value) |
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if attention_mask is not None: |
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attn_weights = attn_weights + attention_mask |
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attn_weights = nn.functional.softmax(attn_weights, dim=-1) |
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if attn_weights.dtype != torch.float32: |
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raise RuntimeError("Error with upcasting, attn_weights does not have dtype torch.float32") |
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attn_weights = attn_weights.type(value.dtype) |
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attn_weights = self.attn_dropout(attn_weights) |
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if head_mask is not None: |
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attn_weights = attn_weights * head_mask |
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attn_output = torch.matmul(attn_weights, value) |
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return attn_output, attn_weights |
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def _split_heads(self, tensor, num_heads, attn_head_size): |
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""" |
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Splits hidden_size dim into attn_head_size and num_heads |
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""" |
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new_shape = tensor.size()[:-1] + (num_heads, attn_head_size) |
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tensor = tensor.view(new_shape) |
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return tensor.permute(0, 2, 1, 3) |
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def _merge_heads(self, tensor, num_heads, attn_head_size): |
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""" |
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Merges attn_head_size dim and num_attn_heads dim into hidden_size |
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""" |
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tensor = tensor.permute(0, 2, 1, 3).contiguous() |
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new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,) |
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return tensor.view(new_shape) |
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def forward( |
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self, |
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hidden_states: Optional[Tuple[torch.FloatTensor]], |
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layer_past: Optional[Tuple[torch.Tensor]] = None, |
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attention_mask: Optional[torch.FloatTensor] = None, |
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head_mask: Optional[torch.FloatTensor] = None, |
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encoder_hidden_states: Optional[torch.Tensor] = None, |
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encoder_attention_mask: Optional[torch.FloatTensor] = None, |
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use_cache: Optional[bool] = False, |
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output_attentions: Optional[bool] = False, |
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) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]], ...]: |
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if encoder_hidden_states is not None: |
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raise NotImplementedError("Cross-attention not implemented for MQA") |
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if not hasattr(self, "q_attn"): |
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raise ValueError( |
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"If class is used as cross attention, the weights `q_attn` have to be defined. " |
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"Please make sure to instantiate class with `GPT2Attention(..., is_cross_attention=True)`." |
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) |
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query = self.q_attn(hidden_states) |
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key, value = self.c_attn(encoder_hidden_states).split(self.split_size, dim=2) |
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attention_mask = encoder_attention_mask |
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else: |
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query = self.q_attn(hidden_states) |
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key, value = self.kv_attn(hidden_states).split(self.head_dim, dim=2) |
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batch_size, seq_length = query.shape[:2] |
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query = query.view(batch_size, seq_length, self.num_heads, self.head_dim).permute([0, 2, 1, 3]) |
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query = query.reshape(batch_size, self.num_heads * seq_length, self.head_dim) |
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key = key.permute(0, 2, 1) |
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if layer_past is not None: |
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past_key, past_value = layer_past |
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key = torch.cat((past_key, key), dim=-1) |
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value = torch.cat((past_value, value), dim=-2) |
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if use_cache is True: |
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present = (key, value) |
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else: |
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present = None |
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if self.reorder_and_upcast_attn: |
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raise NotImplementedError("Reorder and upcast attention not implemented for MQA") |
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attn_output, attn_weights = self._upcast_and_reordered_attn(query, key, value, attention_mask, head_mask) |
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else: |
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attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask) |
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attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim) |
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attn_output = self.c_proj(attn_output) |
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attn_output = self.resid_dropout(attn_output) |
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outputs = (attn_output, present) |
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if output_attentions: |
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outputs += (attn_weights,) |
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return outputs |
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class GPT2CustomBlock(GPT2Block): |
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def __init__(self, config: GPT2CustomConfig, layer_idx=None): |
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super().__init__(config, layer_idx) |
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if config.attention_head_type == MULTI_QUERY: |
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self.attn = GPT2MQAttention(config, layer_idx=layer_idx) |
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if config.add_cross_attention: |
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raise NotImplementedError("Cross-attention not implemented for MQA") |
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class GPT2CustomModel(GPT2Model): |
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config_class = GPT2CustomConfig |
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def __init__(self, config): |
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GPT2PreTrainedModel.__init__(self, config) |
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self.embed_dim = config.hidden_size |
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self.wte = nn.Embedding(config.vocab_size, self.embed_dim) |
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self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim) |
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self.drop = nn.Dropout(config.embd_pdrop) |
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self.h = nn.ModuleList([GPT2CustomBlock(config, layer_idx=i) for i in range(config.num_hidden_layers)]) |
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self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon) |
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self.model_parallel = False |
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self.device_map = None |
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self.gradient_checkpointing = False |
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self.post_init() |
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def forward( |
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self, |
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input_ids: Optional[torch.LongTensor] = None, |
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past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, |
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attention_mask: Optional[torch.FloatTensor] = None, |
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token_type_ids: Optional[torch.LongTensor] = None, |
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position_ids: Optional[torch.LongTensor] = None, |
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head_mask: Optional[torch.FloatTensor] = None, |
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inputs_embeds: Optional[torch.FloatTensor] = None, |
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encoder_hidden_states: Optional[torch.Tensor] = None, |
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encoder_attention_mask: Optional[torch.FloatTensor] = None, |
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use_cache: Optional[bool] = 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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) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]: |
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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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use_cache = use_cache if use_cache is not None else self.config.use_cache |
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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("You cannot specify both input_ids and inputs_embeds at the same time") |
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elif input_ids is not None: |
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input_shape = input_ids.size() |
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input_ids = input_ids.view(-1, input_shape[-1]) |
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batch_size = input_ids.shape[0] |
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elif inputs_embeds is not None: |
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input_shape = inputs_embeds.size()[:-1] |
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batch_size = inputs_embeds.shape[0] |
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else: |
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raise ValueError("You have to specify either input_ids or inputs_embeds") |
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device = input_ids.device if input_ids is not None else inputs_embeds.device |
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if token_type_ids is not None: |
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token_type_ids = token_type_ids.view(-1, input_shape[-1]) |
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if position_ids is not None: |
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position_ids = position_ids.view(-1, input_shape[-1]) |
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if past_key_values is None: |
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past_length = 0 |
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past_key_values = tuple([None] * len(self.h)) |
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else: |
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past_length = past_key_values[0][0].size(-1) |
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if position_ids is None: |
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position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device) |
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position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1]) |
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if attention_mask is not None: |
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if batch_size <= 0: |
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raise ValueError("batch_size has to be defined and > 0") |
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attention_mask = attention_mask.view(batch_size, -1) |
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attention_mask = attention_mask[:, None, None, :] |
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attention_mask = attention_mask.to(dtype=self.dtype) |
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attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min |
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if self.config.add_cross_attention and encoder_hidden_states is not None: |
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encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() |
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encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) |
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if encoder_attention_mask is None: |
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encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) |
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encoder_attention_mask = self.invert_attention_mask(encoder_attention_mask) |
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else: |
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encoder_attention_mask = None |
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head_mask = self.get_head_mask(head_mask, self.config.n_layer) |
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if inputs_embeds is None: |
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inputs_embeds = self.wte(input_ids) |
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position_embeds = self.wpe(position_ids) |
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hidden_states = inputs_embeds + position_embeds |
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if token_type_ids is not None: |
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token_type_embeds = self.wte(token_type_ids) |
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hidden_states = hidden_states + token_type_embeds |
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hidden_states = self.drop(hidden_states) |
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output_shape = input_shape + (hidden_states.size(-1),) |
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presents = () if use_cache else None |
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all_self_attentions = () if output_attentions else None |
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all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None |
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all_hidden_states = () if output_hidden_states else None |
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for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): |
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if self.model_parallel: |
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torch.cuda.set_device(hidden_states.device) |
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if layer_past is not None: |
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layer_past = tuple(past_state.to(hidden_states.device) for past_state in layer_past) |
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if attention_mask is not None: |
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attention_mask = attention_mask.to(hidden_states.device) |
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if isinstance(head_mask, torch.Tensor): |
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head_mask = head_mask.to(hidden_states.device) |
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if output_hidden_states: |
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all_hidden_states = all_hidden_states + (hidden_states,) |
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if self.gradient_checkpointing and self.training: |
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if use_cache: |
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logger.warning( |
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"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." |
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) |
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use_cache = False |
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def create_custom_forward(module): |
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def custom_forward(*inputs): |
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return module(*inputs, use_cache, output_attentions) |
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return custom_forward |
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outputs = torch.utils.checkpoint.checkpoint( |
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create_custom_forward(block), |
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hidden_states, |
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None, |
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attention_mask, |
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head_mask[i], |
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encoder_hidden_states, |
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encoder_attention_mask, |
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) |
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else: |
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outputs = block( |
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hidden_states, |
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layer_past=layer_past, |
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attention_mask=attention_mask, |
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head_mask=head_mask[i], |
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encoder_hidden_states=encoder_hidden_states, |
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encoder_attention_mask=encoder_attention_mask, |
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use_cache=use_cache, |
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output_attentions=output_attentions, |
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) |
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hidden_states = outputs[0] |
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if use_cache is True: |
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presents = presents + (outputs[1],) |
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if output_attentions: |
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all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],) |
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if self.config.add_cross_attention: |
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all_cross_attentions = all_cross_attentions + (outputs[3 if use_cache else 2],) |
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if self.model_parallel: |
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for k, v in self.device_map.items(): |
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if i == v[-1] and "cuda:" + str(k) != self.last_device: |
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hidden_states = hidden_states.to("cuda:" + str(k + 1)) |
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hidden_states = self.ln_f(hidden_states) |
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hidden_states = hidden_states.view(output_shape) |
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if output_hidden_states: |
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all_hidden_states = all_hidden_states + (hidden_states,) |
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if not return_dict: |
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return tuple( |
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v |
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for v in [hidden_states, presents, all_hidden_states, all_self_attentions, all_cross_attentions] |
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if v is not None |
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) |
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return BaseModelOutputWithPastAndCrossAttentions( |
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last_hidden_state=hidden_states, |
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past_key_values=presents, |
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hidden_states=all_hidden_states, |
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attentions=all_self_attentions, |
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cross_attentions=all_cross_attentions, |
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) |
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class GPT2LMHeadCustomModel(GPT2LMHeadModel): |
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config_class = GPT2CustomConfig |
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def __init__(self, config): |
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GPT2PreTrainedModel.__init__(self, config) |
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self.transformer = GPT2CustomModel(config) |
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self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) |
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self.model_parallel = False |
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self.device_map = None |
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self.post_init() |