Add Phi-1 and Phi-1_5 (#26170)

* only dir not even init

* init

* tokenizer removed and reference of codegen added

* modeling file updated a lot remaining app_rotary_emb

* conversion script done

* conversion script fixed, a lot of factoring done and most tests pass

* added token_clf and extractive_QA_head

* integration tests pass

* flash attn tests pass!

* config done

* more docs in modeling file

* some style fix

* style and others

* doc test error fix

* more doc fix

* some attention fixes

* most fixes

* style and other fixes

* docs fix and config

* doc fix

* some comments

* conversion script updated

* conversion script updated

* Revert "conversion script updated"

This reverts commit e92378c54084ec0747041b113083d1746ecb6c7f.

* final comments

* add Phi to language_modeling.md

* edit phi.md file

* rebase and fix

* removed phi-1.5 example

* changed model_type from 'phi'->'mixformer-sequential'

* small change

* small change

* revert \small change

* changed mixformer-sequential->phi

* small change

* added phi-1.5 example instead of phi-1

* doc test might pass now

* rebase and small change

* added the dropout layer

* more fixes

* modified .md file

* very very small doc change

src/transformers/models/phi/configuration_phi.py

+# 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.
+
+""" Phi model configuration"""
+
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+PHI_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "susnato/phi-1_dev": "https://huggingface.co/susnato/phi-1_dev/resolve/main/config.json",
+    "susnato/phi-1_5_dev": "https://huggingface.co/susnato/phi-1_5_dev/resolve/main/config.json",
+}
+
+
+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
+    [susnato/phi-1_dev](https://huggingface.co/susnato/phi-1_dev).
+
+    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.
+        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. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
+            is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalPersimmon/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This
+            is an experimental feature, subject to breaking API changes in future versions.
+        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("susnato/phi-1_dev")
+
+    >>> # 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,
+        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
+        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
+        self._rope_scaling_validation()
+
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    # Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")

src/transformers/models/phi/convert_phi_weights_to_hf.py

+# 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.
+
+"""
+Weights conversion script for Phi
+
+This script downloads both Phi-1 and Phi-1.5 checkpoints to "checkpoint_path" and then converts the weights to
+HugfgingFace model's format and saves them in "pytorch_dump_folder_path".
+"""
+
+import argparse
+import gc
+import os
+
+import torch
+from huggingface_hub import hf_hub_download
+
+from transformers import PhiConfig, PhiForCausalLM
+
+
+_MODELS = {
+    "microsoft/phi-1": "https://huggingface.co/microsoft/phi-1/blob/main/pytorch_model.bin",
+    "microsoft/phi-1_5": "https://huggingface.co/microsoft/phi-1_5/blob/main/pytorch_model.bin",
+}
+
+
+PHI_MAPPING = {
+    "layers.0.wte.weight": "model.embed_tokens.weight",
+    "layers.25.linear.bias": "lm_head.bias",
+    "layers.25.linear.weight": "lm_head.weight",
+    "layers.25.ln.bias": "model.final_layernorm.bias",
+    "layers.25.ln.weight": "model.final_layernorm.weight",
+    "layers": "model.layers",
+    "ln": "input_layernorm",
+    "mixer": "self_attn",
+    "Wqkv": "query_key_value",
+    "out_proj": "dense",
+}
+
+
+def convert_weights(original_weights, mapping, config):
+    converted_weights = {}
+    original_weights_keys = sorted(original_weights.keys())
+
+    # we change names (1-24) -> layers(0-23) for Phi model layers
+    range_change = {
+        f"layers.{k}.": f"layers.{v}."
+        for k, v in zip(range(1, config.num_hidden_layers + 1), range(0, config.num_hidden_layers))
+    }
+
+    mapping.update(**range_change)
+
+    for original_weights_key in original_weights_keys:
+        new_key = original_weights_key
+
+        if "rotary_emb" in new_key:
+            continue
+
+        if "Wqkv" in new_key:
+            if "weight" in new_key:
+                weight = original_weights[new_key]
+                weights_shape = weight.shape
+                weight = (
+                    weight.view(3, config.num_attention_heads, -1, config.hidden_size)
+                    .transpose(0, 1)
+                    .reshape(*weights_shape)
+                )
+                original_weights[new_key] = weight
+            elif "bias" in new_key:
+                bias = original_weights[new_key]
+                bias_shape = bias.shape
+                bias = bias.view(3, config.num_attention_heads, -1).transpose(0, 1).reshape(*bias_shape)
+                original_weights[new_key] = bias
+
+        for k, v in mapping.items():
+            if k in new_key:
+                new_key = new_key.replace(k, v)
+
+        converted_weights[new_key] = original_weights.pop(original_weights_key)
+
+    return converted_weights
+
+
+def _download(url: str, root: str):
+    repo_id = f"{url.split('/')[3]}/{url.split('/')[4]}"
+    filename = f"{url.split('/')[-1]}"
+    hf_hub_download(
+        repo_id=repo_id,
+        filename=filename,
+        force_filename=root,
+        local_dir_use_symlinks=False,
+    )
+
+
+def convert_phi_weights(checkpoint_path, pytorch_dump_folder_path, use_cuda, save_weights_directly):
+    device = "cuda" if torch.cuda.is_available() and use_cuda else "cpu"
+    for each_model_name, each_model_url in _MODELS.items():
+        converted_checkpoint = {}
+
+        model_path = os.path.join(checkpoint_path, each_model_name + "_" + each_model_url.split("/")[-1])
+        if not os.path.exists(model_path):
+            print(f"\n{each_model_name} was not found! Downloading it to {model_path}")
+            _download(url=each_model_url, root=model_path)
+        model_checkpoint = torch.load(model_path, map_location=device)
+        model_type = each_model_name.split("/")[1]  # phi-1 or phi-1_5
+        config = PhiConfig.from_pretrained(f"susnato/{model_type}_dev")
+
+        # Converting the weights
+        converted_checkpoint.update(**convert_weights(model_checkpoint, PHI_MAPPING, config))
+
+        # Save either the whole model or the converted weights
+        if save_weights_directly:
+            save_weights_path = os.path.join(
+                pytorch_dump_folder_path, each_model_name.split("/")[-1] + "_" + each_model_url.split("/")[-1]
+            )
+            torch.save(converted_checkpoint, save_weights_path)
+            print(f"Model weights saved at {save_weights_path}!")
+
+        else:
+            model = PhiForCausalLM(config).to(device)
+            model.load_state_dict(converted_checkpoint, strict=True)
+            save_model_path = os.path.join(pytorch_dump_folder_path, model_type)
+            model.save_pretrained(save_model_path)
+            print(f"Model saved at {save_model_path}!")
+
+            # release GPU memory for the 2nd model if cuda was used.
+            del config, model
+
+        # release GPU memory for the 2nd model if cuda was used.
+        del model_checkpoint, converted_checkpoint
+        if use_cuda:
+            torch.cuda.empty_cache()
+        gc.collect()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    # # Required parameters
+    parser.add_argument(
+        "--checkpoint_path", type=str, help="Path to the folder of downloaded checkpoints. (Please enter full path)"
+    )
+    parser.add_argument(
+        "--pytorch_dump_folder_path",
+        default=None,
+        type=str,
+        help="Path to the output PyTorch model. (Please enter full path)",
+    )
+    parser.add_argument(
+        "--use_cuda",
+        default=False,
+        type=bool,
+        help="Whether to load the weights on GPU during conversion or not, False by default",
+    )
+    parser.add_argument(
+        "--save_weights_directly",
+        default=True,
+        type=bool,
+        help="Whether to save the weights directly after conversion or load the weight to the Phi model and then save "
+        "the Phi model along with weights. True by default",
+    )
+
+    args = parser.parse_args()
+    convert_phi_weights(args.checkpoint_path, args.pytorch_dump_folder_path, args.use_cuda, args.save_weights_directly)

src/transformers/utils/dummy_pt_objects.py

@@ -6172,6 +6172,44 @@ class PersimmonPreTrainedModel(metaclass=DummyObject):
         requires_backends(self, ["torch"])
 
 
+PHI_PRETRAINED_MODEL_ARCHIVE_LIST = None
+
+
+class PhiForCausalLM(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class PhiForSequenceClassification(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class PhiForTokenClassification(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class PhiModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class PhiPreTrainedModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 

tests/models/phi/test_modeling_phi.py

+# 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.
+
+""" Testing suite for the PyTorch Phi model. """
+
+
+import unittest
+
+from transformers import PhiConfig, is_torch_available
+from transformers.testing_utils import require_torch, slow, torch_device
+
+from ...generation.test_utils import GenerationTesterMixin
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        PhiForCausalLM,
+        PhiForSequenceClassification,
+        PhiForTokenClassification,
+        PhiModel,
+    )
+
+
+# Copied from tests.models.llama.test_modeling_llama.LlamaModelTester with Llama->Phi
+class PhiModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        seq_length=7,
+        is_training=True,
+        use_input_mask=True,
+        use_token_type_ids=False,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=32,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=512,
+        type_vocab_size=16,
+        type_sequence_label_size=2,
+        initializer_range=0.02,
+        num_labels=3,
+        num_choices=4,
+        pad_token_id=0,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.is_training = is_training
+        self.use_input_mask = use_input_mask
+        self.use_token_type_ids = use_token_type_ids
+        self.use_labels = use_labels
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.type_sequence_label_size = type_sequence_label_size
+        self.initializer_range = initializer_range
+        self.num_labels = num_labels
+        self.num_choices = num_choices
+        self.pad_token_id = pad_token_id
+        self.scope = scope
+
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = random_attention_mask([self.batch_size, self.seq_length])
+
+        token_type_ids = None
+        if self.use_token_type_ids:
+            token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
+
+        sequence_labels = None
+        token_labels = None
+        choice_labels = None
+        if self.use_labels:
+            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+            token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
+            choice_labels = ids_tensor([self.batch_size], self.num_choices)
+
+        config = self.get_config()
+
+        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+
+    def get_config(self):
+        return PhiConfig(
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            hidden_act=self.hidden_act,
+            hidden_dropout_prob=self.hidden_dropout_prob,
+            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+            max_position_embeddings=self.max_position_embeddings,
+            type_vocab_size=self.type_vocab_size,
+            is_decoder=False,
+            initializer_range=self.initializer_range,
+            pad_token_id=self.pad_token_id,
+        )
+
+    def create_and_check_model(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = PhiModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask)
+        result = model(input_ids)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+
+    def create_and_check_model_as_decoder(
+        self,
+        config,
+        input_ids,
+        token_type_ids,
+        input_mask,
+        sequence_labels,
+        token_labels,
+        choice_labels,
+        encoder_hidden_states,
+        encoder_attention_mask,
+    ):
+        config.add_cross_attention = True
+        model = PhiModel(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(
+            input_ids,
+            attention_mask=input_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+        )
+        result = model(
+            input_ids,
+            attention_mask=input_mask,
+            encoder_hidden_states=encoder_hidden_states,
+        )
+        result = model(input_ids, attention_mask=input_mask)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+
+    def create_and_check_for_causal_lm(
+        self,
+        config,
+        input_ids,
+        token_type_ids,
+        input_mask,
+        sequence_labels,
+        token_labels,
+        choice_labels,
+        encoder_hidden_states,
+        encoder_attention_mask,
+    ):
+        model = PhiForCausalLM(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, labels=token_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
+
+    def create_and_check_decoder_model_past_large_inputs(
+        self,
+        config,
+        input_ids,
+        token_type_ids,
+        input_mask,
+        sequence_labels,
+        token_labels,
+        choice_labels,
+        encoder_hidden_states,
+        encoder_attention_mask,
+    ):
+        config.is_decoder = True
+        config.add_cross_attention = True
+        model = PhiForCausalLM(config=config)
+        model.to(torch_device)
+        model.eval()
+
+        # first forward pass
+        outputs = model(
+            input_ids,
+            attention_mask=input_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            use_cache=True,
+        )
+        past_key_values = outputs.past_key_values
+
+        # create hypothetical multiple next token and extent to next_input_ids
+        next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
+        next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
+
+        # append to next input_ids and
+        next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
+        next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
+
+        output_from_no_past = model(
+            next_input_ids,
+            attention_mask=next_attention_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            output_hidden_states=True,
+        )["hidden_states"][0]
+        output_from_past = model(
+            next_tokens,
+            attention_mask=next_attention_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            past_key_values=past_key_values,
+            output_hidden_states=True,
+        )["hidden_states"][0]
+
+        # select random slice
+        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
+        output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
+        output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
+
+        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
+
+        # test that outputs are equal for slice
+        self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        (
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = config_and_inputs
+        inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
+        return config, inputs_dict
+
+
+@require_torch
+class PhiModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (
+        (PhiModel, PhiForCausalLM, PhiForSequenceClassification, PhiForTokenClassification)
+        if is_torch_available()
+        else ()
+    )
+    all_generative_model_classes = (PhiForCausalLM,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": PhiModel,
+            "text-classification": PhiForSequenceClassification,
+            "text-generation": PhiForCausalLM,
+            "token-classification": PhiForTokenClassification,
+            "zero-shot": PhiForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
+
+    test_headmasking = False
+    test_pruning = False
+
+    # Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.setUp with Llama->Phi
+    def setUp(self):
+        self.model_tester = PhiModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=PhiConfig, hidden_size=37)
+
+    # Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_config
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    # Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_model
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    # Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_llama_sequence_classification_model with Llama->Phi,llama->phi
+    def test_phi_sequence_classification_model(self):
+        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.num_labels = 3
+        input_ids = input_dict["input_ids"]
+        attention_mask = input_ids.ne(1).to(torch_device)
+        sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
+        model = PhiForSequenceClassification(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
+        self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
+
+    # Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_llama_sequence_classification_model_for_single_label with Llama->Phi,llama->phi
+    def test_phi_sequence_classification_model_for_single_label(self):
+        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.num_labels = 3
+        config.problem_type = "single_label_classification"
+        input_ids = input_dict["input_ids"]
+        attention_mask = input_ids.ne(1).to(torch_device)
+        sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
+        model = PhiForSequenceClassification(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
+        self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
+
+    # Copied from tests.models.llama.test_modeling_llama.LlamaModelTest.test_llama_sequence_classification_model_for_multi_label with Llama->Phi,llama->phi
+    def test_phi_sequence_classification_model_for_multi_label(self):
+        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.num_labels = 3
+        config.problem_type = "multi_label_classification"
+        input_ids = input_dict["input_ids"]
+        attention_mask = input_ids.ne(1).to(torch_device)
+        sequence_labels = ids_tensor(
+            [self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size
+        ).to(torch.float)
+        model = PhiForSequenceClassification(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
+        self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
+
+
+@slow
+@require_torch
+class PhiIntegrationTest(unittest.TestCase):
+    def test_model_phi_1_logits(self):
+        input_ids = {
+            "input_ids": torch.tensor(
+                [[1212, 318, 281, 1672, 2643, 290, 428, 318, 257, 1332]], dtype=torch.long, device=torch_device
+            )
+        }
+
+        model = PhiForCausalLM.from_pretrained("susnato/phi-1_dev").to(torch_device)
+        model.eval()
+
+        output = model(**input_ids).logits
+
+        # fmt: off
+        EXPECTED_OUTPUT = torch.tensor([[2.2671,  6.7684, -2.0107, -1.2440, -1.5335, -2.3828,  6.9186,  6.4245, 3.1548,  0.9998,  0.0760,  4.4653,  4.9857,  4.2956,  1.2308, -1.4178, 0.1361,  0.5191, -0.5699, -2.2201, -3.0750, -3.9600, -4.5936, -3.7394, -2.7777,  6.1874, -0.4148, -1.5684, -0.5967,  0.2395], [1.7004,  4.0383,  0.0546,  0.4530, -0.3619, -0.9021,  1.8355,  1.3587, 1.2406,  2.5775, -0.8834,  5.1910,  4.2565,  4.1406,  3.0752, -0.9099, 1.1595,  0.0264,  0.3243, -1.1803, -1.3945, -2.1406, -3.9939, -1.4438, -2.9546,  3.9204,  1.0851, -1.0598, -1.7819, -0.4827]]).to(torch_device)
+        # fmt: on
+
+        self.assertTrue(torch.allclose(EXPECTED_OUTPUT, output[0, :2, :30], atol=1e-4, rtol=1e-4))
+
+    def test_model_phi_1_5_logits(self):
+        input_ids = {
+            "input_ids": torch.tensor(
+                [[1212, 318, 281, 1672, 2643, 290, 428, 318, 257, 1332]], dtype=torch.long, device=torch_device
+            )
+        }
+
+        model = PhiForCausalLM.from_pretrained("susnato/phi-1_5_dev").to(torch_device)
+        model.eval()
+
+        output = model(**input_ids).logits
+
+        # fmt: off
+        EXPECTED_OUTPUT = torch.tensor([[12.2922, 13.3507,  8.6963,  9.1355,  9.3502,  9.2667, 14.2027, 13.1363, 13.5446, 11.1337,  9.9279, 16.7195, 13.0768, 14.9141, 11.9965,  8.0233, 10.3129, 10.6118, 10.0204,  9.3827,  8.8344,  8.2806,  8.0153,  8.0540, 7.0964, 16.5743, 11.1256,  9.6987, 11.4770, 10.5440], [12.3323, 14.6050,  8.9986,  8.1580,  9.5654,  6.6728, 12.5966, 12.6662, 12.2784, 11.7522,  8.2039, 16.3102, 11.2203, 13.6088, 12.0125,  9.1021, 9.8216, 10.0987,  9.0926,  8.4260,  8.8009,  7.6547,  6.8075,  7.7881, 7.4501, 15.7451, 10.5053,  8.3129, 10.0027,  9.2612]]).to(torch_device)
+        # fmt: on
+
+        self.assertTrue(torch.allclose(EXPECTED_OUTPUT, output[0, :2, :30], atol=1e-4, rtol=1e-4))