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Gustavo de Rosa
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31
README.md
31
README.md
@ -33,6 +33,7 @@ To encourage the model to write more concise answers, you can also try the follo
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Instruct: Write a detailed analogy between mathematics and a lighthouse.
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Output: Mathematics is like a lighthouse. Just as a lighthouse guides ships safely to shore, mathematics provides a guiding light in the world of numbers and logic. It helps us navigate through complex problems and find solutions. Just as a lighthouse emits a steady beam of light, mathematics provides a consistent framework for reasoning and problem-solving. It illuminates the path to understanding and helps us make sense of the world around us.
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```
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where the model generates the text after "Output:".
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### Chat Format:
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@ -65,36 +66,19 @@ def print_prime(n):
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primes.append(num)
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print(primes)
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```
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where the model generates the text after the comments.
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**Notes:**
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* Phi-2 is intended for QA, chat, and code purposes. The model-generated text/code should be treated as a starting point rather than a definitive solution for potential use cases. Users should be cautious when employing these models in their applications.
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* Direct adoption for production tasks without evaluation is out of scope of this project. As a result, the Phi-2 model has not been tested to ensure that it performs adequately for any production-level application. Please refer to the limitation sections of this document for more details.
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* If you are using `transformers>=4.36.0`, always load the model with `trust_remote_code=True` to prevent side-effects.
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* If you are using `transformers<4.37.0`, always load the model with `trust_remote_code=True` to prevent side-effects.
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## Sample Code
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There are four types of execution mode:
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1. FP16 / Flash-Attention / CUDA:
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```python
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model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", torch_dtype="auto", flash_attn=True, flash_rotary=True, fused_dense=True, device_map="cuda", trust_remote_code=True)
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```
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2. FP16 / CUDA:
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```python
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model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", torch_dtype="auto", device_map="cuda", trust_remote_code=True)
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```
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3. FP32 / CUDA:
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```python
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model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", torch_dtype=torch.float32, device_map="cuda", trust_remote_code=True)
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```
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4. FP32 / CPU:
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```python
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model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", torch_dtype=torch.float32, device_map="cpu", trust_remote_code=True)
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```
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To ensure the maximum compatibility, we recommend using the second execution mode (FP16 / CUDA), as follows:
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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@ -114,9 +98,6 @@ text = tokenizer.batch_decode(outputs)[0]
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print(text)
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```
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**Remark:** In the generation function, our model currently does not support beam search (`num_beams > 1`).
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Furthermore, in the forward pass of the model, we currently do not support outputting hidden states or attention values, or using custom input embeddings.
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## Limitations of Phi-2
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* Generate Inaccurate Code and Facts: The model may produce incorrect code snippets and statements. Users should treat these outputs as suggestions or starting points, not as definitive or accurate solutions.
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34
config.json
34
config.json
@ -1,32 +1,34 @@
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{
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"_name_or_path": "microsoft/phi-2",
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"activation_function": "gelu_new",
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"architectures": [
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"PhiForCausalLM"
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],
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"attn_pdrop": 0.0,
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"auto_map": {
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"AutoConfig": "configuration_phi.PhiConfig",
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"AutoModelForCausalLM": "modeling_phi.PhiForCausalLM"
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},
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"attention_dropout": 0.0,
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"bos_token_id": null,
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"embd_pdrop": 0.0,
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"flash_attn": false,
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"flash_rotary": false,
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"fused_dense": false,
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"img_processor": null,
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"eos_token_id": null,
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"hidden_act": "gelu_new",
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"hidden_size": 2560,
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-05,
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"model_type": "phi-msft",
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"n_embd": 2560,
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"n_head": 32,
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"n_head_kv": null,
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"n_inner": null,
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"n_layer": 32,
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"n_positions": 2048,
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"intermediate_size": 10240,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 2048,
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"model_type": "phi",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 32,
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"partial_rotary_factor": 0.4,
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"qk_layernorm": false,
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"resid_pdrop": 0.1,
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"rotary_dim": 32,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.35.2",
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"transformers_version": "4.37.0.dev0",
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"use_cache": true,
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"vocab_size": 51200
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}
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@ -1,62 +1,193 @@
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT license.
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# coding=utf-8
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# Copyright 2023 Microsoft and the HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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from typing import Optional
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""" Phi model configuration"""
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from transformers import PretrainedConfig
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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PHI_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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"microsoft/phi-2": "https://huggingface.co/microsoft/phi-2/resolve/main/config.json",
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}
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class PhiConfig(PretrainedConfig):
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"""Phi configuration."""
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r"""
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This is the configuration class to store the configuration of a [`PhiModel`]. It is used to instantiate an Phi
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the Phi
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[microsoft/phi-1](https://huggingface.co/microsoft/phi-1).
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model_type = "phi-msft"
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attribute_map = {
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"max_position_embeddings": "n_positions",
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"hidden_size": "n_embd",
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"num_attention_heads": "n_head",
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"num_hidden_layers": "n_layer",
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}
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 51200):
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Vocabulary size of the Phi model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`PhiModel`].
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hidden_size (`int`, *optional*, defaults to 2048):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 8192):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 24):
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Number of hidden layers in the Transformer decoder.
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num_attention_heads (`int`, *optional*, defaults to 32):
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Number of attention heads for each attention layer in the Transformer decoder.
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num_key_value_heads (`int`, *optional*):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details checkout [this
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paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
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`num_attention_heads`.
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resid_pdrop (`float`, *optional*, defaults to 0.0):
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Dropout probability for mlp outputs.
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embd_pdrop (`int`, *optional*, defaults to 0.0):
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The dropout ratio for the embeddings.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio after computing the attention scores.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu_new"`):
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The non-linear activation function (function or string) in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to 2048):
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The maximum sequence length that this model might ever be used with. Phi-1 and Phi-1.5 supports up to 2048
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tokens.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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layer_norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the rms normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether to tie weight embeddings
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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rope_scaling (`Dict`, *optional*):
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Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
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strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
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is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
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`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
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these scaling strategies behave:
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https://www.reddit.com/r/LocalPersimmon/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This
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is an experimental feature, subject to breaking API changes in future versions.
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partial_rotary_factor (`float`, *optional*, defaults to 0.5):
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Percentage of the query and keys which will have rotary embedding.
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qk_layernorm (`bool`, *optional*, defaults to `False`):
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Whether or not to normalize the Queries and Keys after projecting the hidden states.
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bos_token_id (`int`, *optional*, defaults to 1):
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Denotes beginning of sequences token id.
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eos_token_id (`int`, *optional*, defaults to 2):
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Denotes end of sequences token id.
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Example:
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```python
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>>> from transformers import PhiModel, PhiConfig
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>>> # Initializing a Phi-1 style configuration
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>>> configuration = PhiConfig.from_pretrained("microsoft/phi-1")
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>>> # Initializing a model from the configuration
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>>> model = PhiModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "phi"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size: int = 50304,
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n_positions: int = 2048,
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n_embd: int = 1024,
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n_layer: int = 20,
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n_inner: Optional[int] = None,
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n_head: int = 16,
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n_head_kv: Optional[int] = None,
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rotary_dim: Optional[int] = 32,
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activation_function: Optional[str] = "gelu_new",
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flash_attn: bool = False,
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flash_rotary: bool = False,
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fused_dense: bool = False,
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attn_pdrop: float = 0.0,
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embd_pdrop: float = 0.0,
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resid_pdrop: float = 0.0,
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layer_norm_epsilon: float = 1e-5,
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initializer_range: float = 0.02,
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tie_word_embeddings: bool = False,
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pad_vocab_size_multiple: int = 64,
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**kwargs
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) -> None:
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self.vocab_size = int(math.ceil(vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple)
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self.n_positions = n_positions
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self.n_embd = n_embd
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self.n_layer = n_layer
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self.n_inner = n_inner
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self.n_head = n_head
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self.n_head_kv = n_head_kv
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self.rotary_dim = min(rotary_dim, n_embd // n_head)
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self.activation_function = activation_function
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self.flash_attn = flash_attn
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self.flash_rotary = flash_rotary
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self.fused_dense = fused_dense
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self.attn_pdrop = attn_pdrop
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self.embd_pdrop = embd_pdrop
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self.resid_pdrop = resid_pdrop
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self.layer_norm_epsilon = layer_norm_epsilon
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self.initializer_range = initializer_range
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vocab_size=51200,
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hidden_size=2048,
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intermediate_size=8192,
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num_hidden_layers=24,
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num_attention_heads=32,
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num_key_value_heads=None,
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resid_pdrop=0.0,
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embd_pdrop=0.0,
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attention_dropout=0.0,
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hidden_act="gelu_new",
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max_position_embeddings=2048,
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initializer_range=0.02,
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layer_norm_eps=1e-5,
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use_cache=True,
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tie_word_embeddings=False,
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rope_theta=10000.0,
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rope_scaling=None,
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partial_rotary_factor=0.5,
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qk_layernorm=False,
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bos_token_id=1,
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eos_token_id=2,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.resid_pdrop = resid_pdrop
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self.embd_pdrop = embd_pdrop
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self.attention_dropout = attention_dropout
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self.hidden_act = hidden_act
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self.max_position_embeddings = max_position_embeddings
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self.partial_rotary_factor = partial_rotary_factor
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self.qk_layernorm = qk_layernorm
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self._rope_scaling_validation()
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super().__init__(
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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# Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
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def _rope_scaling_validation(self):
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"""
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Validate the `rope_scaling` configuration.
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"""
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if self.rope_scaling is None:
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return
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if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
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raise ValueError(
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"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
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f"got {self.rope_scaling}"
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)
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rope_scaling_type = self.rope_scaling.get("type", None)
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rope_scaling_factor = self.rope_scaling.get("factor", None)
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if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
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raise ValueError(
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f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
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)
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if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
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raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
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@ -1,4 +1,4 @@
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{
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"_from_model_config": true,
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"transformers_version": "4.35.2"
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"transformers_version": "4.37.0.dev0"
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}
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"total_size": 5559367680
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},
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"weight_map": {
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"lm_head.linear.bias": "model-00002-of-00002.safetensors",
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"lm_head.linear.weight": "model-00002-of-00002.safetensors",
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"lm_head.ln.bias": "model-00002-of-00002.safetensors",
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"lm_head.ln.weight": "model-00002-of-00002.safetensors",
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"transformer.embd.wte.weight": "model-00001-of-00002.safetensors",
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"transformer.h.0.ln.bias": "model-00001-of-00002.safetensors",
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||||
"transformer.h.0.ln.weight": "model-00001-of-00002.safetensors",
|
||||
"transformer.h.0.mixer.Wqkv.bias": "model-00001-of-00002.safetensors",
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||||
"transformer.h.0.mixer.Wqkv.weight": "model-00001-of-00002.safetensors",
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"transformer.h.0.mixer.out_proj.bias": "model-00001-of-00002.safetensors",
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"transformer.h.0.mixer.out_proj.weight": "model-00001-of-00002.safetensors",
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"transformer.h.0.mlp.fc1.bias": "model-00001-of-00002.safetensors",
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"transformer.h.0.mlp.fc1.weight": "model-00001-of-00002.safetensors",
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"transformer.h.0.mlp.fc2.bias": "model-00001-of-00002.safetensors",
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"transformer.h.0.mlp.fc2.weight": "model-00001-of-00002.safetensors",
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"transformer.h.1.ln.bias": "model-00001-of-00002.safetensors",
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"transformer.h.1.ln.weight": "model-00001-of-00002.safetensors",
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"transformer.h.1.mixer.Wqkv.bias": "model-00001-of-00002.safetensors",
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}
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||||
|
||||
2072
modeling_phi.py
2072
modeling_phi.py
File diff suppressed because it is too large
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