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""" PyTorch Phi model.""" |
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import math |
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from typing import List, Optional, Tuple, Union |
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|
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import torch |
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import torch.nn.functional as F |
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import torch.utils.checkpoint |
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from torch import nn |
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss |
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|
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from transformers.activations import ACT2FN |
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from transformers.cache_utils import Cache, DynamicCache |
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from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask |
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from transformers.modeling_outputs import ( |
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BaseModelOutputWithPast, |
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CausalLMOutputWithPast, |
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SequenceClassifierOutputWithPast, |
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TokenClassifierOutput, |
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) |
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from transformers.modeling_utils import PreTrainedModel |
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from transformers.utils import ( |
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add_code_sample_docstrings, |
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add_start_docstrings, |
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add_start_docstrings_to_model_forward, |
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is_flash_attn_2_available, |
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is_flash_attn_greater_or_equal_2_10, |
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logging, |
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replace_return_docstrings, |
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) |
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from .configuration_phi import PhiConfig |
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try: |
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from flash_attn import flash_attn_func, flash_attn_varlen_func |
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from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input |
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except: |
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pass |
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logger = logging.get_logger(__name__) |
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_CHECKPOINT_FOR_DOC = "microsoft/phi-2" |
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_CONFIG_FOR_DOC = "PhiConfig" |
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PHI_PRETRAINED_MODEL_ARCHIVE_LIST = [ |
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"microsoft/phi-2", |
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] |
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def _get_unpad_data(attention_mask): |
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seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) |
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indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() |
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max_seqlen_in_batch = seqlens_in_batch.max().item() |
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cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)) |
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return ( |
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indices, |
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cu_seqlens, |
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max_seqlen_in_batch, |
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) |
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class PhiRotaryEmbedding(nn.Module): |
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def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None): |
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super().__init__() |
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self.dim = dim |
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self.max_position_embeddings = max_position_embeddings |
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self.base = base |
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inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) |
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self.register_buffer("inv_freq", inv_freq, persistent=False) |
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self._set_cos_sin_cache( |
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seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype() |
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) |
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def _set_cos_sin_cache(self, seq_len, device, dtype): |
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self.max_seq_len_cached = seq_len |
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t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) |
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freqs = torch.outer(t, self.inv_freq) |
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emb = torch.cat((freqs, freqs), dim=-1) |
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self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) |
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self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) |
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def forward(self, x, seq_len=None): |
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if seq_len > self.max_seq_len_cached: |
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self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype) |
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return ( |
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self.cos_cached[:seq_len].to(dtype=x.dtype), |
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self.sin_cached[:seq_len].to(dtype=x.dtype), |
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) |
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class PhiLinearScalingRotaryEmbedding(PhiRotaryEmbedding): |
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"""PhiRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" |
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def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): |
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self.scaling_factor = scaling_factor |
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super().__init__(dim, max_position_embeddings, base, device) |
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def _set_cos_sin_cache(self, seq_len, device, dtype): |
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self.max_seq_len_cached = seq_len |
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t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) |
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t = t / self.scaling_factor |
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freqs = torch.outer(t, self.inv_freq) |
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emb = torch.cat((freqs, freqs), dim=-1) |
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self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) |
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self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) |
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class PhiDynamicNTKScalingRotaryEmbedding(PhiRotaryEmbedding): |
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"""PhiRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" |
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def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): |
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self.scaling_factor = scaling_factor |
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super().__init__(dim, max_position_embeddings, base, device) |
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def _set_cos_sin_cache(self, seq_len, device, dtype): |
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self.max_seq_len_cached = seq_len |
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if seq_len > self.max_position_embeddings: |
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base = self.base * ( |
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(self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1) |
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) ** (self.dim / (self.dim - 2)) |
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inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)) |
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self.register_buffer("inv_freq", inv_freq, persistent=False) |
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t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype) |
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freqs = torch.outer(t, self.inv_freq) |
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emb = torch.cat((freqs, freqs), dim=-1) |
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self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False) |
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self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False) |
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def rotate_half(x): |
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"""Rotates half the hidden dims of the input.""" |
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x1 = x[..., : x.shape[-1] // 2] |
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x2 = x[..., x.shape[-1] // 2 :] |
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return torch.cat((-x2, x1), dim=-1) |
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1): |
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"""Applies Rotary Position Embedding to the query and key tensors. |
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Args: |
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q (`torch.Tensor`): The query tensor. |
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k (`torch.Tensor`): The key tensor. |
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cos (`torch.Tensor`): The cosine part of the rotary embedding. |
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sin (`torch.Tensor`): The sine part of the rotary embedding. |
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position_ids (`torch.Tensor`): |
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The position indices of the tokens corresponding to the query and key tensors. For example, this can be |
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used to pass offsetted position ids when working with a KV-cache. |
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unsqueeze_dim (`int`, *optional*, defaults to 1): |
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The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and |
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sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note |
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that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and |
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k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes |
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cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have |
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the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. |
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Returns: |
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`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. |
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""" |
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cos = cos[position_ids].unsqueeze(unsqueeze_dim) |
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sin = sin[position_ids].unsqueeze(unsqueeze_dim) |
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q_embed = (q * cos) + (rotate_half(q) * sin) |
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k_embed = (k * cos) + (rotate_half(k) * sin) |
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return q_embed, k_embed |
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class PhiMLP(nn.Module): |
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def __init__(self, config): |
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super().__init__() |
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self.config = config |
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self.activation_fn = ACT2FN[config.hidden_act] |
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self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) |
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self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) |
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: |
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hidden_states = self.fc1(hidden_states) |
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hidden_states = self.activation_fn(hidden_states) |
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hidden_states = self.fc2(hidden_states) |
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return hidden_states |
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def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: |
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""" |
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This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, |
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num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) |
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""" |
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batch, num_key_value_heads, slen, head_dim = hidden_states.shape |
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if n_rep == 1: |
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return hidden_states |
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hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) |
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return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) |
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class PhiAttention(nn.Module): |
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"""Multi-headed attention from 'Attention Is All You Need' paper""" |
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def __init__(self, config: PhiConfig, layer_idx: Optional[int] = None): |
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super().__init__() |
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self.config = config |
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self.layer_idx = layer_idx |
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if layer_idx is None: |
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logger.warning_once( |
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f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will " |
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"to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " |
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"when creating this class." |
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) |
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|
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self.attention_dropout = config.attention_dropout |
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self.hidden_size = config.hidden_size |
|
self.num_heads = config.num_attention_heads |
|
self.head_dim = self.hidden_size // self.num_heads |
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self.num_key_value_heads = config.num_key_value_heads |
|
self.num_key_value_groups = self.num_heads // self.num_key_value_heads |
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self.max_position_embeddings = config.max_position_embeddings |
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self.rope_theta = config.rope_theta |
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self.partial_rotary_factor = config.partial_rotary_factor |
|
self.is_causal = True |
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|
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if (self.head_dim * self.num_heads) != self.hidden_size: |
|
raise ValueError( |
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f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" |
|
f" and `num_heads`: {self.num_heads})." |
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) |
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|
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self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True) |
|
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True) |
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self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True) |
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self.dense = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=True) |
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|
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self.qk_layernorm = config.qk_layernorm |
|
if self.qk_layernorm: |
|
self.q_layernorm = nn.LayerNorm( |
|
config.hidden_size // self.num_heads, eps=config.layer_norm_eps, elementwise_affine=True |
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) |
|
self.k_layernorm = nn.LayerNorm( |
|
config.hidden_size // self.num_heads, eps=config.layer_norm_eps, elementwise_affine=True |
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) |
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|
|
self._init_rope() |
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|
|
def _init_rope(self): |
|
if self.config.rope_scaling is None: |
|
self.rotary_emb = PhiRotaryEmbedding( |
|
int(self.partial_rotary_factor * self.head_dim), |
|
max_position_embeddings=self.max_position_embeddings, |
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base=self.rope_theta, |
|
) |
|
else: |
|
scaling_type = self.config.rope_scaling["type"] |
|
scaling_factor = self.config.rope_scaling["factor"] |
|
if scaling_type == "linear": |
|
self.rotary_emb = PhiLinearScalingRotaryEmbedding( |
|
int(self.partial_rotary_factor * self.head_dim), |
|
max_position_embeddings=self.max_position_embeddings, |
|
scaling_factor=scaling_factor, |
|
base=self.rope_theta, |
|
) |
|
elif scaling_type == "dynamic": |
|
self.rotary_emb = PhiDynamicNTKScalingRotaryEmbedding( |
|
int(self.partial_rotary_factor * self.head_dim), |
|
max_position_embeddings=self.max_position_embeddings, |
|
scaling_factor=scaling_factor, |
|
base=self.rope_theta, |
|
) |
|
else: |
|
raise ValueError(f"Unknown RoPE scaling type {scaling_type}") |
|
|
|
|
|
@torch.autocast("cpu", enabled=False) |
|
@torch.autocast("cuda", enabled=False) |
|
def forward( |
|
self, |
|
hidden_states: torch.Tensor, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
past_key_value: Optional[Cache] = None, |
|
output_attentions: bool = False, |
|
use_cache: bool = False, |
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: |
|
bsz, q_len, _ = hidden_states.size() |
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|
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query_states = self.q_proj(hidden_states) |
|
key_states = self.k_proj(hidden_states) |
|
value_states = self.v_proj(hidden_states) |
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|
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if self.qk_layernorm: |
|
query_states = self.q_layernorm(query_states) |
|
key_states = self.k_layernorm(key_states) |
|
|
|
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) |
|
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) |
|
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) |
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|
|
kv_seq_len = key_states.shape[-2] |
|
if past_key_value is not None: |
|
if self.layer_idx is None: |
|
raise ValueError( |
|
f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} " |
|
"for auto-regressive decoding with k/v caching, please make sure to initialize the attention class " |
|
"with a layer index." |
|
) |
|
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) |
|
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) |
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|
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query_rot, query_pass = ( |
|
query_states[..., : self.rotary_emb.dim], |
|
query_states[..., self.rotary_emb.dim :], |
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) |
|
key_rot, key_pass = ( |
|
key_states[..., : self.rotary_emb.dim], |
|
key_states[..., self.rotary_emb.dim :], |
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) |
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query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids) |
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query_states = torch.cat((query_rot, query_pass), dim=-1) |
|
key_states = torch.cat((key_rot, key_pass), dim=-1) |
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|
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if past_key_value is not None: |
|
cache_kwargs = {"sin": sin, "cos": cos, "partial_rotation_size": self.rotary_emb.dim} |
|
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) |
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|
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key_states = repeat_kv(key_states, self.num_key_value_groups) |
|
value_states = repeat_kv(value_states, self.num_key_value_groups) |
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|
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attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) |
|
|
|
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): |
|
raise ValueError( |
|
f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is" |
|
f" {attn_weights.size()}" |
|
) |
|
|
|
if attention_mask is not None: |
|
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): |
|
raise ValueError( |
|
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" |
|
) |
|
attn_weights = attn_weights + attention_mask |
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|
|
|
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(value_states.dtype) |
|
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) |
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|
|
attn_output = torch.matmul(attn_weights, value_states) |
|
|
|
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): |
|
raise ValueError( |
|
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" |
|
f" {attn_output.size()}" |
|
) |
|
|
|
attn_output = attn_output.transpose(1, 2).contiguous() |
|
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) |
|
|
|
attn_output = self.dense(attn_output) |
|
|
|
if not output_attentions: |
|
attn_weights = None |
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|
|
return attn_output, attn_weights, past_key_value |
|
|
|
|
|
class PhiFlashAttention2(PhiAttention): |
|
""" |
|
Phi flash attention module. This module inherits from `PhiAttention` as the weights of the module stays |
|
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of |
|
flash attention and deal with padding tokens in case the input contains any of them. |
|
""" |
|
|
|
|
|
def __init__(self, *args, **kwargs): |
|
super().__init__(*args, **kwargs) |
|
|
|
|
|
|
|
|
|
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() |
|
|
|
def forward( |
|
self, |
|
hidden_states: torch.Tensor, |
|
attention_mask: Optional[torch.LongTensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
past_key_value: Optional[Cache] = None, |
|
output_attentions: bool = False, |
|
use_cache: bool = False, |
|
**kwargs, |
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: |
|
|
|
|
|
output_attentions = False |
|
|
|
bsz, q_len, _ = hidden_states.size() |
|
|
|
query_states = self.q_proj(hidden_states) |
|
key_states = self.k_proj(hidden_states) |
|
value_states = self.v_proj(hidden_states) |
|
|
|
if self.qk_layernorm: |
|
query_states = self.q_layernorm(query_states) |
|
key_states = self.k_layernorm(key_states) |
|
|
|
|
|
|
|
|
|
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) |
|
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) |
|
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) |
|
|
|
kv_seq_len = key_states.shape[-2] |
|
if past_key_value is not None: |
|
kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) |
|
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) |
|
|
|
|
|
query_rot, query_pass = ( |
|
query_states[..., : self.rotary_emb.dim], |
|
query_states[..., self.rotary_emb.dim :], |
|
) |
|
key_rot, key_pass = ( |
|
key_states[..., : self.rotary_emb.dim], |
|
key_states[..., self.rotary_emb.dim :], |
|
) |
|
|
|
query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids) |
|
|
|
|
|
query_states = torch.cat((query_rot, query_pass), dim=-1) |
|
key_states = torch.cat((key_rot, key_pass), dim=-1) |
|
|
|
if past_key_value is not None: |
|
cache_kwargs = {"sin": sin, "cos": cos, "partial_rotation_size": self.rotary_emb.dim} |
|
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) |
|
|
|
|
|
|
|
query_states = query_states.transpose(1, 2) |
|
key_states = key_states.transpose(1, 2) |
|
value_states = value_states.transpose(1, 2) |
|
|
|
attn_dropout = self.attention_dropout if self.training else 0.0 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
if query_states.dtype == torch.float32: |
|
if torch.is_autocast_enabled(): |
|
target_dtype = torch.get_autocast_gpu_dtype() |
|
|
|
elif hasattr(self.config, "_pre_quantization_dtype"): |
|
target_dtype = self.config._pre_quantization_dtype |
|
else: |
|
target_dtype = self.q_proj.weight.dtype |
|
|
|
logger.warning_once( |
|
f"The input hidden states seems to be silently casted in float32, this might be related to" |
|
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" |
|
f" {target_dtype}." |
|
) |
|
|
|
query_states = query_states.to(target_dtype) |
|
key_states = key_states.to(target_dtype) |
|
value_states = value_states.to(target_dtype) |
|
|
|
attn_output = self._flash_attention_forward( |
|
query_states, key_states, value_states, attention_mask, q_len, dropout=attn_dropout, softmax_scale=None |
|
) |
|
|
|
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() |
|
attn_output = self.dense(attn_output) |
|
|
|
if not output_attentions: |
|
attn_weights = None |
|
|
|
return attn_output, attn_weights, past_key_value |
|
|
|
|
|
def _flash_attention_forward( |
|
self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None |
|
): |
|
""" |
|
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token |
|
first unpad the input, then computes the attention scores and pad the final attention scores. |
|
|
|
Args: |
|
query_states (`torch.Tensor`): |
|
Input query states to be passed to Flash Attention API |
|
key_states (`torch.Tensor`): |
|
Input key states to be passed to Flash Attention API |
|
value_states (`torch.Tensor`): |
|
Input value states to be passed to Flash Attention API |
|
attention_mask (`torch.Tensor`): |
|
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the |
|
position of padding tokens and 1 for the position of non-padding tokens. |
|
dropout (`int`, *optional*): |
|
Attention dropout |
|
softmax_scale (`float`, *optional*): |
|
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) |
|
""" |
|
if not self._flash_attn_uses_top_left_mask: |
|
causal = self.is_causal |
|
else: |
|
|
|
causal = self.is_causal and query_length != 1 |
|
|
|
|
|
if attention_mask is not None: |
|
batch_size = query_states.shape[0] |
|
query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( |
|
query_states, key_states, value_states, 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_states, |
|
key_states, |
|
value_states, |
|
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=dropout, |
|
softmax_scale=softmax_scale, |
|
causal=causal, |
|
) |
|
|
|
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) |
|
else: |
|
attn_output = flash_attn_func( |
|
query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal |
|
) |
|
|
|
return attn_output |
|
|
|
|
|
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 |
|
|
|
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, self.num_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 |
|
) |
|
indices_q = cu_seqlens_q[:-1] |
|
query_layer = query_layer.squeeze(1) |
|
else: |
|
|
|
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), |
|
) |
|
|
|
|
|
PHI_ATTENTION_CLASSES = { |
|
"eager": PhiAttention, |
|
"flash_attention_2": PhiFlashAttention2, |
|
} |
|
|
|
|
|
class PhiDecoderLayer(nn.Module): |
|
def __init__(self, config: PhiConfig, layer_idx: int): |
|
super().__init__() |
|
self.self_attn = PHI_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx=layer_idx) |
|
self.mlp = PhiMLP(config) |
|
self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) |
|
self.resid_dropout = nn.Dropout(config.resid_pdrop) |
|
|
|
def forward( |
|
self, |
|
hidden_states: torch.Tensor, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
output_attentions: Optional[bool] = False, |
|
use_cache: Optional[bool] = False, |
|
past_key_value: Optional[Tuple[torch.Tensor]] = None, |
|
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: |
|
""" |
|
Args: |
|
hidden_states (`torch.FloatTensor`): |
|
input to the layer of shape `(batch, seq_len, embed_dim)` |
|
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size |
|
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. |
|
position_ids (`torch.LongTensor` of shape `({0})`, *optional*): |
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range |
|
`[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) |
|
output_attentions (`bool`, *optional*): |
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under |
|
returned tensors for more detail. |
|
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`). |
|
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states |
|
""" |
|
|
|
residual = hidden_states |
|
|
|
hidden_states = self.input_layernorm(hidden_states) |
|
|
|
|
|
attn_outputs, 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, |
|
) |
|
attn_outputs = self.resid_dropout(attn_outputs) |
|
|
|
feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states)) |
|
hidden_states = attn_outputs + feed_forward_hidden_states + residual |
|
outputs = (hidden_states,) |
|
|
|
if output_attentions: |
|
outputs += (self_attn_weights,) |
|
|
|
if use_cache: |
|
outputs += (present_key_value,) |
|
|
|
return outputs |
|
|
|
|
|
PHI_START_DOCSTRING = r""" |
|
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the |
|
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads |
|
etc.) |
|
|
|
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. |
|
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage |
|
and behavior. |
|
|
|
Parameters: |
|
config ([`PhiConfig`]): |
|
Model configuration class with all the parameters of the model. Initializing with a config file does not |
|
load the weights associated with the model, only the configuration. Check out the |
|
[`~PreTrainedModel.from_pretrained`] method to load the model weights. |
|
""" |
|
|
|
|
|
@add_start_docstrings( |
|
"The bare Phi Model outputting raw hidden-states without any specific head on top.", |
|
PHI_START_DOCSTRING, |
|
) |
|
class PhiPreTrainedModel(PreTrainedModel): |
|
config_class = PhiConfig |
|
base_model_prefix = "model" |
|
supports_gradient_checkpointing = True |
|
_no_split_modules = ["PhiDecoderLayer"] |
|
_skip_keys_device_placement = "past_key_values" |
|
_supports_flash_attn_2 = True |
|
_supports_cache_class = True |
|
|
|
def _init_weights(self, module): |
|
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_() |
|
|
|
|
|
PHI_INPUTS_DOCSTRING = r""" |
|
Args: |
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): |
|
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide |
|
it. |
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and |
|
[`PreTrainedTokenizer.__call__`] for details. |
|
|
|
[What are input IDs?](../glossary#input-ids) |
|
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): |
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: |
|
|
|
- 1 for tokens that are **not masked**, |
|
- 0 for tokens that are **masked**. |
|
|
|
[What are attention masks?](../glossary#attention-mask) |
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and |
|
[`PreTrainedTokenizer.__call__`] for details. |
|
|
|
If `past_key_values` is used, optionally only the last `input_ids` have to be input (see |
|
`past_key_values`). |
|
|
|
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] |
|
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more |
|
information on the default strategy. |
|
|
|
- 1 indicates the head is **not masked**, |
|
- 0 indicates the head is **masked**. |
|
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): |
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, |
|
config.n_positions - 1]`. |
|
|
|
[What are position IDs?](../glossary#position-ids) |
|
past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): |
|
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention |
|
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` |
|
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. |
|
|
|
Two formats are allowed: |
|
- a [`~cache_utils.Cache`] instance; |
|
- 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)`). This is also known as the legacy |
|
cache format. |
|
|
|
The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the |
|
legacy cache format will be returned. |
|
|
|
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't |
|
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` |
|
of shape `(batch_size, sequence_length)`. |
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): |
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This |
|
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the |
|
model's internal embedding lookup matrix. |
|
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 (`bool`, *optional*): |
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned |
|
tensors for more detail. |
|
output_hidden_states (`bool`, *optional*): |
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for |
|
more detail. |
|
return_dict (`bool`, *optional*): |
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. |
|
""" |
|
|
|
|
|
@add_start_docstrings( |
|
"The bare Phi Model outputting raw hidden-states without any specific head on top.", |
|
PHI_START_DOCSTRING, |
|
) |
|
class PhiModel(PhiPreTrainedModel): |
|
""" |
|
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`PhiDecoderLayer`] |
|
|
|
Args: |
|
config: PhiConfig |
|
""" |
|
|
|
def __init__(self, config: PhiConfig): |
|
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.embed_dropout = nn.Dropout(config.embd_pdrop) |
|
self.layers = nn.ModuleList( |
|
[PhiDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] |
|
) |
|
self.final_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) |
|
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" |
|
|
|
self.gradient_checkpointing = False |
|
|
|
self.post_init() |
|
|
|
def get_input_embeddings(self): |
|
return self.embed_tokens |
|
|
|
def set_input_embeddings(self, value): |
|
self.embed_tokens = value |
|
|
|
@add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING) |
|
def forward( |
|
self, |
|
input_ids: torch.LongTensor = None, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
position_ids: Optional[torch.LongTensor] = 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]: |
|
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 |
|
|
|
|
|
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: |
|
batch_size, seq_length = input_ids.shape[:2] |
|
elif inputs_embeds is not None: |
|
batch_size, seq_length = inputs_embeds.shape[:2] |
|
else: |
|
raise ValueError("You have to specify either input_ids or inputs_embeds") |
|
|
|
past_key_values_length = 0 |
|
|
|
if self.gradient_checkpointing and self.training: |
|
if use_cache: |
|
logger.warning_once( |
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." |
|
) |
|
use_cache = False |
|
|
|
if use_cache: |
|
use_legacy_cache = not isinstance(past_key_values, Cache) |
|
if use_legacy_cache: |
|
past_key_values = DynamicCache.from_legacy_cache(past_key_values) |
|
past_key_values_length = past_key_values.get_usable_length(seq_length) |
|
|
|
if position_ids is None: |
|
device = input_ids.device if input_ids is not None else inputs_embeds.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) |
|
|
|
if inputs_embeds is None: |
|
inputs_embeds = self.embed_tokens(input_ids) |
|
|
|
inputs_embeds = self.embed_dropout(inputs_embeds) |
|
|
|
|
|
if self._use_flash_attention_2: |
|
|
|
attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None |
|
else: |
|
|
|
attention_mask = _prepare_4d_causal_attention_mask( |
|
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length |
|
) |
|
|
|
hidden_states = inputs_embeds |
|
|
|
|
|
all_hidden_states = () if output_hidden_states else None |
|
all_self_attns = () if output_attentions else None |
|
next_decoder_cache = None |
|
|
|
for decoder_layer in self.layers: |
|
if output_hidden_states: |
|
all_hidden_states += (hidden_states,) |
|
|
|
if self.gradient_checkpointing and self.training: |
|
layer_outputs = self._gradient_checkpointing_func( |
|
decoder_layer.__call__, |
|
hidden_states, |
|
attention_mask, |
|
position_ids, |
|
past_key_values, |
|
output_attentions, |
|
) |
|
else: |
|
layer_outputs = decoder_layer( |
|
hidden_states, |
|
attention_mask=attention_mask, |
|
position_ids=position_ids, |
|
past_key_value=past_key_values, |
|
output_attentions=output_attentions, |
|
use_cache=use_cache, |
|
) |
|
|
|
hidden_states = layer_outputs[0] |
|
|
|
if use_cache: |
|
next_decoder_cache = layer_outputs[2 if output_attentions else 1] |
|
|
|
if output_attentions: |
|
all_self_attns += (layer_outputs[1],) |
|
|
|
hidden_states = self.final_layernorm(hidden_states) |
|
|
|
|
|
if output_hidden_states: |
|
all_hidden_states += (hidden_states,) |
|
|
|
next_cache = None |
|
if use_cache: |
|
next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache |
|
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 PhiForCausalLM(PhiPreTrainedModel): |
|
_tied_weights_keys = ["lm_head.weight"] |
|
|
|
|
|
def __init__(self, config): |
|
super().__init__(config) |
|
self.model = PhiModel(config) |
|
self.vocab_size = config.vocab_size |
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=True) |
|
|
|
|
|
self.post_init() |
|
|
|
|
|
def get_input_embeddings(self): |
|
return self.model.embed_tokens |
|
|
|
|
|
def set_input_embeddings(self, value): |
|
self.model.embed_tokens = value |
|
|
|
|
|
def get_output_embeddings(self): |
|
return self.lm_head |
|
|
|
|
|
def set_output_embeddings(self, new_embeddings): |
|
self.lm_head = new_embeddings |
|
|
|
|
|
def set_decoder(self, decoder): |
|
self.model = decoder |
|
|
|
|
|
def get_decoder(self): |
|
return self.model |
|
|
|
@add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING) |
|
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) |
|
def forward( |
|
self, |
|
input_ids: torch.LongTensor = None, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
position_ids: Optional[torch.LongTensor] = 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, PhiForCausalLM |
|
|
|
>>> model = PhiForCausalLM.from_pretrained("microsoft/phi-1") |
|
>>> tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1") |
|
|
|
>>> prompt = "This is an example script ." |
|
>>> 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] |
|
'This is an example script .\n\n\n\nfrom typing import List\n\ndef find_most_common_letter(words: List[str' |
|
```""" |
|
|
|
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 |
|
|
|
|
|
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) |
|
logits = logits.float() |
|
|
|
loss = None |
|
if labels is not None: |
|
|
|
shift_logits = logits[..., :-1, :].contiguous() |
|
shift_labels = labels[..., 1:].contiguous() |
|
|
|
loss_fct = CrossEntropyLoss() |
|
shift_logits = shift_logits.view(-1, self.config.vocab_size) |
|
shift_labels = shift_labels.view(-1) |
|
|
|
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, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs |
|
): |
|
if past_key_values is not None: |
|
if isinstance(past_key_values, Cache): |
|
cache_length = past_key_values.get_seq_length() |
|
past_length = past_key_values.seen_tokens |
|
max_cache_length = past_key_values.get_max_length() |
|
else: |
|
cache_length = past_length = past_key_values[0][0].shape[2] |
|
max_cache_length = None |
|
|
|
|
|
|
|
|
|
|
|
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]: |
|
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] |
|
|
|
|
|
elif past_length < input_ids.shape[1]: |
|
input_ids = input_ids[:, past_length:] |
|
|
|
|
|
|
|
if ( |
|
max_cache_length is not None |
|
and attention_mask is not None |
|
and cache_length + input_ids.shape[1] > max_cache_length |
|
): |
|
attention_mask = attention_mask[:, -max_cache_length:] |
|
|
|
position_ids = kwargs.get("position_ids", None) |
|
if attention_mask is not None and position_ids is None: |
|
|
|
position_ids = attention_mask.long().cumsum(-1) - 1 |
|
position_ids.masked_fill_(attention_mask == 0, 1) |
|
if past_key_values: |
|
position_ids = position_ids[:, -input_ids.shape[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( |
|
{ |
|
"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, beam_idx): |
|
reordered_past = () |
|
for layer_past in past_key_values: |
|
reordered_past += ( |
|
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past), |
|
) |
|
return reordered_past |
|
|
|
|
|
@add_start_docstrings( |
|
""" |
|
The PhiModel with a sequence classification head on top (linear layer). |
|
|
|
[`PhiForSequenceClassification`] uses the last token in order to do the classification, as other causal models |
|
(e.g. GPT-2) do. |
|
|
|
Since it does classification on the last token, it requires to know the position of the last token. If a |
|
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If |
|
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the |
|
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in |
|
each row of the batch). |
|
""", |
|
PHI_START_DOCSTRING, |
|
) |
|
|
|
class PhiForSequenceClassification(PhiPreTrainedModel): |
|
def __init__(self, config): |
|
super().__init__(config) |
|
self.num_labels = config.num_labels |
|
self.model = PhiModel(config) |
|
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) |
|
|
|
|
|
self.post_init() |
|
|
|
def get_input_embeddings(self): |
|
return self.model.embed_tokens |
|
|
|
def set_input_embeddings(self, value): |
|
self.model.embed_tokens = value |
|
|
|
@add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING) |
|
def forward( |
|
self, |
|
input_ids: torch.LongTensor = None, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
position_ids: Optional[torch.LongTensor] = 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, SequenceClassifierOutputWithPast]: |
|
r""" |
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): |
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., |
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If |
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy). |
|
""" |
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
|
|
|
model_outputs = self.model( |
|
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 = model_outputs[0] |
|
logits = self.score(hidden_states) |
|
|
|
if input_ids is not None: |
|
batch_size = input_ids.shape[0] |
|
else: |
|
batch_size = inputs_embeds.shape[0] |
|
|
|
if self.config.pad_token_id is None and batch_size != 1: |
|
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") |
|
if self.config.pad_token_id is None: |
|
sequence_lengths = -1 |
|
else: |
|
if input_ids is not None: |
|
|
|
sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 |
|
sequence_lengths = sequence_lengths % input_ids.shape[-1] |
|
sequence_lengths = sequence_lengths.to(logits.device) |
|
else: |
|
sequence_lengths = -1 |
|
|
|
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] |
|
|
|
loss = None |
|
if labels is not None: |
|
labels = labels.to(logits.device) |
|
if self.config.problem_type is None: |
|
if self.num_labels == 1: |
|
self.config.problem_type = "regression" |
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): |
|
self.config.problem_type = "single_label_classification" |
|
else: |
|
self.config.problem_type = "multi_label_classification" |
|
|
|
if self.config.problem_type == "regression": |
|
loss_fct = MSELoss() |
|
if self.num_labels == 1: |
|
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) |
|
else: |
|
loss = loss_fct(pooled_logits, labels) |
|
elif self.config.problem_type == "single_label_classification": |
|
loss_fct = CrossEntropyLoss() |
|
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) |
|
elif self.config.problem_type == "multi_label_classification": |
|
loss_fct = BCEWithLogitsLoss() |
|
loss = loss_fct(pooled_logits, labels) |
|
if not return_dict: |
|
output = (pooled_logits,) + model_outputs[1:] |
|
return ((loss,) + output) if loss is not None else output |
|
|
|
return SequenceClassifierOutputWithPast( |
|
loss=loss, |
|
logits=pooled_logits, |
|
past_key_values=model_outputs.past_key_values, |
|
hidden_states=model_outputs.hidden_states, |
|
attentions=model_outputs.attentions, |
|
) |
|
|
|
|
|
@add_start_docstrings( |
|
""" |
|
PhiModel with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for |
|
Named-Entity-Recognition (NER) tasks. |
|
""", |
|
PHI_START_DOCSTRING, |
|
) |
|
|
|
class PhiForTokenClassification(PhiPreTrainedModel): |
|
def __init__(self, config: PhiConfig): |
|
super().__init__(config) |
|
self.num_labels = config.num_labels |
|
|
|
self.model = PhiModel(config) |
|
if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None: |
|
classifier_dropout = config.classifier_dropout |
|
elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None: |
|
classifier_dropout = config.hidden_dropout |
|
else: |
|
classifier_dropout = 0.1 |
|
self.dropout = nn.Dropout(classifier_dropout) |
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels) |
|
|
|
|
|
self.post_init() |
|
|
|
@add_start_docstrings_to_model_forward(PHI_INPUTS_DOCSTRING) |
|
@add_code_sample_docstrings( |
|
checkpoint=_CHECKPOINT_FOR_DOC, |
|
output_type=TokenClassifierOutput, |
|
config_class=_CONFIG_FOR_DOC, |
|
) |
|
def forward( |
|
self, |
|
input_ids: Optional[torch.LongTensor] = None, |
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None, |
|
attention_mask: Optional[torch.Tensor] = None, |
|
inputs_embeds: Optional[torch.Tensor] = None, |
|
labels: Optional[torch.Tensor] = None, |
|
use_cache: Optional[bool] = None, |
|
output_attentions: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
return_dict: Optional[bool] = None, |
|
**deprecated_arguments, |
|
) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]: |
|
r""" |
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): |
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., |
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If |
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy). |
|
""" |
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
|
|
|
model_outputs = self.model( |
|
input_ids, |
|
past_key_values=past_key_values, |
|
attention_mask=attention_mask, |
|
inputs_embeds=inputs_embeds, |
|
use_cache=use_cache, |
|
output_attentions=output_attentions, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict, |
|
) |
|
|
|
hidden_states = model_outputs[0] |
|
hidden_states = self.dropout(hidden_states) |
|
logits = self.classifier(hidden_states) |
|
|
|
loss = None |
|
if labels is not None: |
|
|
|
labels = labels.to(logits.device) |
|
batch_size, seq_length = labels.shape |
|
loss_fct = CrossEntropyLoss() |
|
loss = loss_fct( |
|
logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length) |
|
) |
|
|
|
if not return_dict: |
|
output = (logits,) + model_outputs[2:] |
|
return ((loss,) + output) if loss is not None else output |
|
|
|
return TokenClassifierOutput( |
|
loss=loss, |
|
logits=logits, |
|
hidden_states=model_outputs.hidden_states, |
|
attentions=model_outputs.attentions, |
|
) |
|
|