diff --git a/src/transformers/models/phi/configuration_phi.py b/src/transformers/models/phi/configuration_phi.py index 6c871b7ea54f..31df3c0a5705 100644 --- a/src/transformers/models/phi/configuration_phi.py +++ b/src/transformers/models/phi/configuration_phi.py @@ -1,3 +1,9 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/phi/modular_phi.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_phi.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # coding=utf-8 # Copyright 2023 Microsoft and the HuggingFace Inc. team. All rights reserved. # @@ -13,14 +19,9 @@ # See the License for the specific language governing permissions and # limitations under the License. -"""Phi model configuration""" from ...configuration_utils import PretrainedConfig from ...modeling_rope_utils import rope_config_validation -from ...utils import logging - - -logger = logging.get_logger(__name__) class PhiConfig(PretrainedConfig): diff --git a/src/transformers/models/phi/modeling_phi.py b/src/transformers/models/phi/modeling_phi.py index 8e60798e857f..858f22773376 100644 --- a/src/transformers/models/phi/modeling_phi.py +++ b/src/transformers/models/phi/modeling_phi.py @@ -1,3 +1,9 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/phi/modular_phi.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_phi.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # coding=utf-8 # Copyright 2023 Microsoft and the HuggingFace Inc. team. All rights reserved. # @@ -13,13 +19,11 @@ # See the License for the specific language governing permissions and # limitations under the License. -"""PyTorch Phi model.""" import math from typing import List, Optional, Tuple, Union import torch -import torch.utils.checkpoint from packaging import version from torch import nn from torch.nn import CrossEntropyLoss @@ -28,6 +32,7 @@ from ...cache_utils import Cache, DynamicCache, StaticCache from ...generation import GenerationMixin from ...modeling_attn_mask_utils import AttentionMaskConverter +from ...modeling_flash_attention_utils import _flash_attention_forward from ...modeling_outputs import ( BaseModelOutputWithPast, CausalLMOutputWithPast, @@ -41,7 +46,6 @@ add_start_docstrings, add_start_docstrings_to_model_forward, get_torch_version, - is_flash_attn_2_available, is_flash_attn_greater_or_equal_2_10, logging, replace_return_docstrings, @@ -49,17 +53,11 @@ from .configuration_phi import PhiConfig -if is_flash_attn_2_available(): - from ...modeling_flash_attention_utils import _flash_attention_forward - - logger = logging.get_logger(__name__) - _CHECKPOINT_FOR_DOC = "microsoft/phi-1" _CONFIG_FOR_DOC = "PhiConfig" -# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Phi class PhiRotaryEmbedding(nn.Module): def __init__( self, @@ -147,7 +145,21 @@ def forward(self, x, position_ids): return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) -# Copied from transformers.models.llama.modeling_llama.rotate_half +class PhiMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.activation_fn = ACT2FN[config.hidden_act] + self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) + self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) + hidden_states = self.fc2(hidden_states) + return hidden_states + + def rotate_half(x): """Rotates half the hidden dims of the input.""" x1 = x[..., : x.shape[-1] // 2] @@ -155,7 +167,6 @@ def rotate_half(x): return torch.cat((-x2, x1), dim=-1) -# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): """Applies Rotary Position Embedding to the query and key tensors. @@ -183,23 +194,6 @@ def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): return q_embed, k_embed -# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Phi -class PhiMLP(nn.Module): - def __init__(self, config): - super().__init__() - self.config = config - self.activation_fn = ACT2FN[config.hidden_act] - self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) - self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) - - def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: - hidden_states = self.fc1(hidden_states) - hidden_states = self.activation_fn(hidden_states) - hidden_states = self.fc2(hidden_states) - return hidden_states - - -# Copied from transformers.models.llama.modeling_llama.repeat_kv with llama->phi def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: """ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, @@ -352,7 +346,6 @@ class PhiFlashAttention2(PhiAttention): flash attention and deal with padding tokens in case the input contains any of them. """ - # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) @@ -704,12 +697,12 @@ class PhiPreTrainedModel(PreTrainedModel): base_model_prefix = "model" supports_gradient_checkpointing = True _no_split_modules = ["PhiDecoderLayer"] - _skip_keys_device_placement = "past_key_values" + _skip_keys_device_placement = ["past_key_values"] _supports_flash_attn_2 = True _supports_sdpa = True _supports_cache_class = True - _supports_static_cache = True _supports_quantized_cache = True + _supports_static_cache = True def _init_weights(self, module): std = self.config.initializer_range @@ -816,17 +809,19 @@ def __init__(self, config: PhiConfig): 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.rotary_emb = PhiRotaryEmbedding(config=config) + self.gradient_checkpointing = False + if getattr(config, "pretraining_tp", 1) != 1: + logger.warn("`pretraining_tp` is deprecated, please use `model.tensor_parallel` instead.") + self.embed_dropout = nn.Dropout(config.embd_pdrop) + 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._use_sdpa = config._attn_implementation == "sdpa" - self.gradient_checkpointing = False # Initialize weights and apply final processing self.post_init() @@ -963,7 +958,6 @@ def forward( attentions=all_self_attns, ) - # Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask def _update_causal_mask( self, attention_mask: torch.Tensor, @@ -1030,7 +1024,6 @@ def _update_causal_mask( return causal_mask @staticmethod - # Copied from transformers.models.llama.modeling_llama.LlamaModel._prepare_4d_causal_attention_mask_with_cache_position def _prepare_4d_causal_attention_mask_with_cache_position( attention_mask: torch.Tensor, sequence_length: int, @@ -1089,8 +1082,8 @@ def _prepare_4d_causal_attention_mask_with_cache_position( class PhiForCausalLM(PhiPreTrainedModel, GenerationMixin): _tied_weights_keys = ["lm_head.weight"] + _tp_plan = {"lm_head": "colwise_rep"} - # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Phi,bias=False->bias=True def __init__(self, config): super().__init__(config) self.model = PhiModel(config) @@ -1100,27 +1093,21 @@ def __init__(self, config): # Initialize weights and apply final processing self.post_init() - # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_input_embeddings def get_input_embeddings(self): return self.model.embed_tokens - # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings def set_input_embeddings(self, value): self.model.embed_tokens = value - # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings def get_output_embeddings(self): return self.lm_head - # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_output_embeddings def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings - # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_decoder def set_decoder(self, decoder): self.model = decoder - # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_decoder def get_decoder(self): return self.model @@ -1131,7 +1118,7 @@ def forward( 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, + past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, @@ -1172,7 +1159,6 @@ def forward( >>> 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 @@ -1216,7 +1202,7 @@ def forward( @add_start_docstrings( """ - The PhiModel with a sequence classification head on top (linear layer). + The Phi Model transformer 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. @@ -1229,7 +1215,6 @@ def forward( """, PHI_START_DOCSTRING, ) -# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with LLAMA->PHI,Llama->Phi with self.transformer->self.model, transformer_outputs->model_outputs class PhiForSequenceClassification(PhiPreTrainedModel): def __init__(self, config): super().__init__(config) @@ -1268,7 +1253,7 @@ def forward( """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict - model_outputs = self.model( + transformer_outputs = self.model( input_ids, attention_mask=attention_mask, position_ids=position_ids, @@ -1279,7 +1264,7 @@ def forward( output_hidden_states=output_hidden_states, return_dict=return_dict, ) - hidden_states = model_outputs[0] + hidden_states = transformer_outputs[0] logits = self.score(hidden_states) if input_ids is not None: @@ -1307,35 +1292,33 @@ def forward( loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config) if not return_dict: - output = (pooled_logits,) + model_outputs[1:] + output = (pooled_logits,) + transformer_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, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_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. + The Phi Model transformer 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, ) -# Copied from transformers.models.mpt.modeling_mpt.MptForTokenClassification with MPT->PHI,Mpt->Phi,self.transformer->self.model,transformer_outputs->model_outputs 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: + if getattr(config, "classifier_dropout", None) is not None: classifier_dropout = config.classifier_dropout - elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None: + elif getattr(config, "hidden_dropout", None) is not None: classifier_dropout = config.hidden_dropout else: classifier_dropout = 0.1 @@ -1345,6 +1328,12 @@ def __init__(self, config: PhiConfig): # Initialize weights and apply final processing 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) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, @@ -1363,7 +1352,7 @@ def forward( output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, **deprecated_arguments, - ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]: + ) -> Union[Tuple, TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., diff --git a/src/transformers/models/phi/modular_phi.py b/src/transformers/models/phi/modular_phi.py new file mode 100644 index 000000000000..a2cbca698a78 --- /dev/null +++ b/src/transformers/models/phi/modular_phi.py @@ -0,0 +1,942 @@ +# coding=utf-8 +# Copyright 2023 Microsoft and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import math +from typing import List, Optional, Tuple, Union + +import torch +from packaging import version +from torch import nn +from torch.nn import CrossEntropyLoss + +from ...cache_utils import Cache, DynamicCache +from ...configuration_utils import PretrainedConfig +from ...modeling_flash_attention_utils import _flash_attention_forward +from ...modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, + TokenClassifierOutput, +) +from ...modeling_rope_utils import rope_config_validation +from ...utils import ( + get_torch_version, + is_flash_attn_greater_or_equal_2_10, + logging, +) +from ..clip.modeling_clip import CLIPMLP +from ..gemma.modeling_gemma import GemmaForCausalLM +from ..llama.modeling_llama import ( + LlamaForSequenceClassification, + LlamaForTokenClassification, + LlamaModel, + LlamaRotaryEmbedding, + apply_rotary_pos_emb, + repeat_kv, +) + + +_CONFIG_FOR_DOC = "PhiConfig" +_CHECKPOINT_FOR_DOC = "microsoft/phi-1" + +logger = logging.get_logger(__name__) + + +class PhiConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`PhiModel`]. It is used to instantiate an Phi + model according to the specified arguments, defining the model architecture. Instantiating a configuration with the + defaults will yield a similar configuration to that of the Phi + [microsoft/phi-1](https://huggingface.co/microsoft/phi-1). + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + vocab_size (`int`, *optional*, defaults to 51200): + Vocabulary size of the Phi model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`PhiModel`]. + hidden_size (`int`, *optional*, defaults to 2048): + Dimension of the hidden representations. + intermediate_size (`int`, *optional*, defaults to 8192): + Dimension of the MLP representations. + num_hidden_layers (`int`, *optional*, defaults to 24): + Number of hidden layers in the Transformer decoder. + num_attention_heads (`int`, *optional*, defaults to 32): + Number of attention heads for each attention layer in the Transformer decoder. + num_key_value_heads (`int`, *optional*): + This is the number of key_value heads that should be used to implement Grouped Query Attention. If + `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if + `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When + converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed + by meanpooling all the original heads within that group. For more details checkout [this + paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to + `num_attention_heads`. + resid_pdrop (`float`, *optional*, defaults to 0.0): + Dropout probability for mlp outputs. + embd_pdrop (`int`, *optional*, defaults to 0.0): + The dropout ratio for the embeddings. + attention_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio after computing the attention scores. + hidden_act (`str` or `function`, *optional*, defaults to `"gelu_new"`): + The non-linear activation function (function or string) in the decoder. + max_position_embeddings (`int`, *optional*, defaults to 2048): + The maximum sequence length that this model might ever be used with. Phi-1 and Phi-1.5 supports up to 2048 + tokens. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + layer_norm_eps (`float`, *optional*, defaults to 1e-05): + The epsilon used by the rms normalization layers. + use_cache (`bool`, *optional*, defaults to `True`): + Whether or not the model should return the last key/values attentions (not used by all models). Only + relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not. + tie_word_embeddings (`bool`, *optional*, defaults to `False`): + Whether to tie weight embeddings + rope_theta (`float`, *optional*, defaults to 10000.0): + The base period of the RoPE embeddings. + rope_scaling (`Dict`, *optional*): + Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type + and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value + accordingly. + Expected contents: + `rope_type` (`str`): + The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', + 'llama3'], with 'default' being the original RoPE implementation. + `factor` (`float`, *optional*): + Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In + most scaling types, a `factor` of x will enable the model to handle sequences of length x * + original maximum pre-trained length. + `original_max_position_embeddings` (`int`, *optional*): + Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during + pretraining. + `attention_factor` (`float`, *optional*): + Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention + computation. If unspecified, it defaults to value recommended by the implementation, using the + `factor` field to infer the suggested value. + `beta_fast` (`float`, *optional*): + Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear + ramp function. If unspecified, it defaults to 32. + `beta_slow` (`float`, *optional*): + Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear + ramp function. If unspecified, it defaults to 1. + `short_factor` (`List[float]`, *optional*): + Only used with 'longrope'. The scaling factor to be applied to short contexts (< + `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden + size divided by the number of attention heads divided by 2 + `long_factor` (`List[float]`, *optional*): + Only used with 'longrope'. The scaling factor to be applied to long contexts (< + `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden + size divided by the number of attention heads divided by 2 + `low_freq_factor` (`float`, *optional*): + Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE + `high_freq_factor` (`float`, *optional*): + Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE + partial_rotary_factor (`float`, *optional*, defaults to 0.5): + Percentage of the query and keys which will have rotary embedding. + qk_layernorm (`bool`, *optional*, defaults to `False`): + Whether or not to normalize the Queries and Keys after projecting the hidden states. + bos_token_id (`int`, *optional*, defaults to 1): + Denotes beginning of sequences token id. + eos_token_id (`int`, *optional*, defaults to 2): + Denotes end of sequences token id. + + Example: + + ```python + >>> from transformers import PhiModel, PhiConfig + + >>> # Initializing a Phi-1 style configuration + >>> configuration = PhiConfig.from_pretrained("microsoft/phi-1") + + >>> # Initializing a model from the configuration + >>> model = PhiModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "phi" + keys_to_ignore_at_inference = ["past_key_values"] + + def __init__( + self, + vocab_size=51200, + hidden_size=2048, + intermediate_size=8192, + num_hidden_layers=24, + num_attention_heads=32, + num_key_value_heads=None, + resid_pdrop=0.0, + embd_pdrop=0.0, + attention_dropout=0.0, + hidden_act="gelu_new", + max_position_embeddings=2048, + initializer_range=0.02, + layer_norm_eps=1e-5, + use_cache=True, + tie_word_embeddings=False, + rope_theta=10000.0, + rope_scaling=None, + partial_rotary_factor=0.5, + qk_layernorm=False, + bos_token_id=1, + eos_token_id=2, + **kwargs, + ): + self.vocab_size = vocab_size + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + + if num_key_value_heads is None: + num_key_value_heads = num_attention_heads + + self.num_key_value_heads = num_key_value_heads + self.resid_pdrop = resid_pdrop + self.embd_pdrop = embd_pdrop + self.attention_dropout = attention_dropout + self.hidden_act = hidden_act + self.max_position_embeddings = max_position_embeddings + self.initializer_range = initializer_range + self.layer_norm_eps = layer_norm_eps + self.use_cache = use_cache + self.rope_theta = rope_theta + self.rope_scaling = rope_scaling + self.partial_rotary_factor = partial_rotary_factor + self.qk_layernorm = qk_layernorm + # Validate the correctness of rotary position embeddings parameters + # BC: if there is a 'type' field, move it to 'rope_type'. + if self.rope_scaling is not None and "type" in self.rope_scaling: + self.rope_scaling["rope_type"] = self.rope_scaling["type"] + rope_config_validation(self) + + super().__init__( + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) + + +class PhiRotaryEmbedding(LlamaRotaryEmbedding): + pass + + +class PhiMLP(CLIPMLP): + pass + + +class PhiAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: PhiConfig, layer_idx: Optional[int] = None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + if layer_idx is None: + logger.warning_once( + f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will " + "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.attention_dropout = config.attention_dropout + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + self.num_key_value_heads = config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.rope_theta = config.rope_theta + self.rotary_ndims = int(self.head_dim * config.partial_rotary_factor) + self.is_causal = True + + if (self.head_dim * self.num_heads) != self.hidden_size: + raise ValueError( + f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" + f" and `num_heads`: {self.num_heads})." + ) + + 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) + self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True) + self.dense = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=True) + + 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 + ) + self.k_layernorm = nn.LayerNorm( + config.hidden_size // self.num_heads, eps=config.layer_norm_eps, elementwise_affine=True + ) + + self.rotary_emb = PhiRotaryEmbedding(config=self.config) + + 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, + cache_position: Optional[torch.LongTensor] = None, + position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + 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) + + cos, sin = position_embeddings + + # Partial rotary embedding + query_rot, query_pass = ( + query_states[..., : self.rotary_ndims], + query_states[..., self.rotary_ndims :], + ) + key_rot, key_pass = ( + key_states[..., : self.rotary_ndims], + key_states[..., self.rotary_ndims :], + ) + # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor] + query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin) + + # [batch_size, seq_length, num_heads, head_dim] + 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_ndims, + "cache_position": cache_position, + } + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + # Queries and keys upcast to fp32 is required by Phi-2 to avoid overflow + attn_weights = torch.matmul( + query_states.to(torch.float32), key_states.to(torch.float32).transpose(2, 3) + ) / math.sqrt(self.head_dim) + + if attention_mask is not None: + causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] + attn_weights += causal_mask + + # upcast attention to fp32 + 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) + + 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 + + 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) + + # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. + # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. + # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). + 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, + cache_position: Optional[torch.LongTensor] = None, + position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + # PhiFlashAttention2 attention does not support output_attentions + + 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) + + # Flash attention requires the input to have the shape + # batch_size x seq_length x head_dim x hidden_dim + # therefore we just need to keep the original shape + 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) + + cos, sin = position_embeddings + + # Partial rotary embedding + query_rot, query_pass = ( + query_states[..., : self.rotary_ndims], + query_states[..., self.rotary_ndims :], + ) + key_rot, key_pass = ( + key_states[..., : self.rotary_ndims], + key_states[..., self.rotary_ndims :], + ) + # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor] + query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin) + + # [batch_size, seq_length, num_heads, head_dim] + 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_ndims, + "cache_position": cache_position, + } + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache + # to be able to avoid many of these transpose/reshape/view. + 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 + + # In PEFT, usually we cast the layer norms in float32 for training stability reasons + # therefore the input hidden states gets silently casted in float32. Hence, we need + # cast them back in the correct dtype just to be sure everything works as expected. + # This might slowdown training & inference so it is recommended to not cast the LayerNorms + # in fp32. + + if query_states.dtype == torch.float32: + if torch.is_autocast_enabled(): + target_dtype = torch.get_autocast_gpu_dtype() + # Handle the case where the model is quantized + 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 = _flash_attention_forward( + query_states, + key_states, + value_states, + attention_mask, + q_len, + position_ids=position_ids, + dropout=attn_dropout, + softmax_scale=None, + use_top_left_mask=self._flash_attn_uses_top_left_mask, + is_causal=self.is_causal, + ) + + 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 + + +class PhiSdpaAttention(PhiAttention): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.require_contiguous_qkv = version.parse(get_torch_version()) < version.parse("2.2.0") + + """ + SDPA attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from + `PhiAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to + SDPA API. + """ + + # Adapted from PhiAttention.forward + 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, + cache_position: Optional[torch.LongTensor] = None, + position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if output_attentions: + # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented. + logger.warning_once( + "PhiModel is using PhiSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not " + "support `output_attentions=True`. Falling back to the manual attention implementation, but specifying " + "the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can " + 'be removed using the argument `attn_implementation="eager"` when loading the model.' + ) + return super().forward( + 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, + cache_position=cache_position, + position_embeddings=position_embeddings, + ) + + 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) + + cos, sin = position_embeddings + + # Partial rotary embedding + query_rot, query_pass = ( + query_states[..., : self.rotary_ndims], + query_states[..., self.rotary_ndims :], + ) + key_rot, key_pass = ( + key_states[..., : self.rotary_ndims], + key_states[..., self.rotary_ndims :], + ) + # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor] + query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin) + + # [batch_size, seq_length, num_heads, head_dim] + 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_ndims, + "cache_position": cache_position, + } + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + causal_mask = attention_mask + if attention_mask is not None: + causal_mask = causal_mask[:, :, :, : key_states.shape[-2]] + + # SDPA with memory-efficient backend is broken in torch==2.1.2 when using non-contiguous inputs and a custom + # attn_mask, so we need to call `.contiguous()` here. This was fixed in torch==2.2.0. + # Reference: https://github.com/pytorch/pytorch/issues/112577 + if self.require_contiguous_qkv and query_states.device.type == "cuda" and attention_mask is not None: + query_states = query_states.contiguous() + key_states = key_states.contiguous() + value_states = value_states.contiguous() + + # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment + # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling. + is_causal = True if causal_mask is None and q_len > 1 else False + + attn_output = torch.nn.functional.scaled_dot_product_attention( + query_states, + key_states, + value_states, + attn_mask=causal_mask, + dropout_p=self.attention_dropout if self.training else 0.0, + is_causal=is_causal, + ) + + 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) + + return attn_output, None, past_key_value + + +PHI_ATTENTION_CLASSES = { + "eager": PhiAttention, + "flash_attention_2": PhiFlashAttention2, + "sdpa": PhiSdpaAttention, +} + + +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, + cache_position: Optional[torch.LongTensor] = None, + position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC + **kwargs, + ) -> 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 + cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): + Indices depicting the position of the input sequence tokens in the sequence + position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*): + Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`, + with `head_dim` being the embedding dimension of each attention head. + kwargs (`dict`, *optional*): + Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code + into the model + """ + + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + 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, + cache_position=cache_position, + position_embeddings=position_embeddings, + ) + 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 + + +class PhiModel(LlamaModel): + """ + 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) + del self.norm + self.embed_dropout = nn.Dropout(config.embd_pdrop) + self.final_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.rotary_emb = PhiRotaryEmbedding(config=config) + self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" + self._use_sdpa = config._attn_implementation == "sdpa" + + self.post_init() + + 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, + cache_position: Optional[torch.LongTensor] = 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 None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + 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 + + # kept for BC (non `Cache` `past_key_values` inputs) + return_legacy_cache = False + if use_cache and not isinstance(past_key_values, Cache): + return_legacy_cache = True + if past_key_values is None: + past_key_values = DynamicCache() + else: + past_key_values = DynamicCache.from_legacy_cache(past_key_values) + logger.warning_once( + "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and " + "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class " + "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)" + ) + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if cache_position is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + cache_position = torch.arange( + past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device + ) + if position_ids is None: + position_ids = cache_position.unsqueeze(0) + + causal_mask = self._update_causal_mask( + attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions + ) + + inputs_embeds = self.embed_dropout(inputs_embeds) + hidden_states = inputs_embeds + + # create position embeddings to be shared across the decoder layers + position_embeddings = self.rotary_emb(hidden_states, position_ids) + + # decoder layers + 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, + causal_mask, + position_ids, + output_attentions, + use_cache, + past_key_values, + cache_position, + position_embeddings, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_ids=position_ids, + past_key_value=past_key_values, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + position_embeddings=position_embeddings, + ) + + 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) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = next_decoder_cache if use_cache else None + if return_legacy_cache: + next_cache = next_cache.to_legacy_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(GemmaForCausalLM): + def __init__(self, config): + super().__init__(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=True) + + def forward(self, **super_kwargs) -> Union[Tuple, CausalLMOutputWithPast]: + r""" + ```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' + ```""" + return super().forward(**super_kwargs) + + +class PhiForSequenceClassification(LlamaForSequenceClassification): + pass + + +class PhiForTokenClassification(LlamaForTokenClassification): + def __init__(self, config: PhiConfig): + super().__init__(config) + del self.score + self.classifier = nn.Linear(config.hidden_size, config.num_labels) + + 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: + # move labels to correct device to enable model parallelism + 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, + )