Diff converter v2 (#30868)

* current working example! * commit regex and result file * update * nit * push the conversion file * oups * roadmap and nits * attempt diffs for 3 files * persimmon * nit * add diff file that is the same as the modeling_llama.py * fix rope nits * updates * updates with converted versions * give some breathing space to the code * delete * update * update * push the actual result * update regex patterns * update regex patterns * fix some issues * fix some issues * fix some issues * updates * updates * updates * updates * updates * revert changes done to llama * updates * update gemma * updates * oups * current state * current state * update * ouiiii * nit * clear diffs * nit * fixup * update * doc 🚀 * 🔥 * for now use gemma * deal with comments * style * handle funtions * deal with assigns * todos * process inheritage * keep decorators? * 🤗 * deal with duplicates * fixup * correctly remove duplicate code * run ruff post script * ruff deals pretty well with imports, let's leave it to him * ah maybe not lol * for now remove all imports from child. * nit * conversion of llama * okay * convert starcoder2 * synch with main * update llama diff * updates * https://docs.astral.sh/ruff/rules/redefined-while-unused/ fixes the imports, bit needs later version of ruff * updates * okay actual state * non zero exit * update! * revert unrelated * remove other diff files * updates * cleanup * update * less diff! * stash * current updates * updates * No need for call * finished fining deps * update * current changes * current state * current state * new status * nit * finally * fixes * nits * order is now expected * use logger info instead of prints * fixup * up * nit * update * nits * update * correct merge * update * update * update * add warning * update caution message * update * better merging strategy * copy class statements :wink * fixups * nits * update * Apply suggestions from code review Co-authored-by: amyeroberts <[email protected]> * nits * smaller header * do cleanup some stuff * even simpler header? * fixup * updates * ruff * update examples * nit * TODO * state * OUUUUUUF * current state * nits * final state * add a readme * fixup * remove diff llama * fix * nit * dummy noy funny * ruff format tests src utils --check * everless diffs * less diffs and fix test * fixes * naming nit? * update converter and add supper example * nits * updated for function signatures * update * update * add converted dummies * autoformat * single target assign fix * fixup * fix some imports * fixes * don't push them * `# noqa: F841` --------- Co-authored-by: amyeroberts <[email protected]>
huggingface · May 31, 2024 · 96eb062 · 96eb062
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diff --git a/examples/diff-conversion/README.md b/examples/diff-conversion/README.md
@@ -0,0 +1,20 @@
+# Using the `diff_converter` linter
+
+`pip install libcst` is a must!
+
+# `sh examples/diff-conversion/convert_examples.sh` to get the converted outputs
+
+The diff converter is a new `linter` specific to `transformers`. It allows us to unpack inheritance in python to convert a modular `diff` file like `diff_gemma.py` into a `single model single file`. 
+
+Examples of possible usage are available in the `examples/diff-conversion`, or `diff_gemma` for a full model usage.
+
+`python utils/diff_model_converter.py --files_to_parse "/Users/arthurzucker/Work/transformers/examples/diff-conversion/diff_my_new_model2.py"`
+
+## How it works
+We use the `libcst` parser to produce an AST representation of the `diff_xxx.py` file. For any imports that are made from `transformers.models.modeling_xxxx` we parse the source code of that module, and build a class dependency mapping, which allows us to unpack the difference dependencies.
+
+The code from the `diff` file and the class dependency mapping are "merged" to produce the single model single file. 
+We use ruff to automatically remove the potential duplicate imports.
+
+## Why we use libcst instead of the native AST?
+AST is super powerful, but it does not keep the `docstring`, `comment` or code formatting. Thus we decided to go with `libcst`
diff --git a/examples/diff-conversion/convert_examples.sh b/examples/diff-conversion/convert_examples.sh
@@ -0,0 +1,10 @@
+#!/bin/bash
+
+# Iterate over each file in the current directory
+for file in examples/diff-conversion/diff_*; do
+    # Check if it's a regular file
+    if [ -f "$file" ]; then
+        # Call the Python script with the file name as an argument
+        python utils/diff_model_converter.py --files_to_parse "$file"
+    fi
+done
diff --git a/examples/diff-conversion/diff_dummy.py b/examples/diff-conversion/diff_dummy.py
@@ -0,0 +1,44 @@
+from math import log
+from typing import List, Optional, Tuple, Union
+
+import torch
+
+from transformers import Cache
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.models.llama.modeling_llama import LlamaModel
+
+
+def _pre_process_input(input_ids):
+    print(log(input_ids))
+    return input_ids
+
+
+# example where we need some deps and some functions
+class DummyModel(LlamaModel):
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[Cache, 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, CausalLMOutputWithPast]:
+        input_ids = _pre_process_input(input_ids)
+
+        return super().forward(
+            None,
+            attention_mask,
+            position_ids,
+            past_key_values,
+            inputs_embeds,
+            use_cache,
+            output_attentions,
+            output_hidden_states,
+            return_dict,
+            cache_position,
+        )
diff --git a/examples/diff-conversion/diff_my_new_model.py b/examples/diff-conversion/diff_my_new_model.py
@@ -0,0 +1,14 @@
+from transformers.models.llama.configuration_llama import LlamaConfig
+
+
+# Example where we only want to only add a new config argument and new arg doc
+# here there is no `ARG` so we are gonna take parent doc
+class MyNewModelConfig(LlamaConfig):
+    r"""
+    mlp_bias (`bool`, *optional*, defaults to `False`)
+    """
+
+    def __init__(self, mlp_bias=True, new_param=0, **super_kwargs):
+        self.mlp_bias = mlp_bias
+        self.new_param = new_param
+        super().__init__(self, **super_kwargs)
diff --git a/examples/diff-conversion/diff_my_new_model2.py b/examples/diff-conversion/diff_my_new_model2.py
@@ -0,0 +1,31 @@
+from transformers.models.gemma.modeling_gemma import GemmaForSequenceClassification
+from transformers.models.llama.configuration_llama import LlamaConfig
+
+
+# Example where we only want to only modify the docstring
+class MyNewModel2Config(LlamaConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`GemmaModel`]. It is used to instantiate an Gemma
+    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 Gemma-7B.
+    e.g. [google/gemma-7b](https://huggingface.co/google/gemma-7b)
+    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 256000):
+            Vocabulary size of the Gemma model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`GemmaModel`]
+    ```python
+    >>> from transformers import GemmaModel, GemmaConfig
+    >>> # Initializing a Gemma gemma-7b style configuration
+    >>> configuration = GemmaConfig()
+    >>> # Initializing a model from the gemma-7b style configuration
+    >>> model = GemmaModel(configuration)
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+
+# Example where alllllll the dependencies are fetched to just copy the entire class
+class MyNewModel2ForSequenceClassification(GemmaForSequenceClassification):
+    pass
diff --git a/examples/diff-conversion/diff_new_model.py b/examples/diff-conversion/diff_new_model.py
@@ -0,0 +1,30 @@
+# Example where we only want to overwrite the defaults of an init
+
+from transformers.models.gemma.configuration_gemma import GemmaConfig
+
+
+class NewModelConfig(GemmaConfig):
+    def __init__(
+        self,
+        vocab_size=256030,
+        hidden_size=64,
+        intermediate_size=90,
+        num_hidden_layers=28,
+        num_attention_heads=16,
+        num_key_value_heads=16,
+        head_dim=256,
+        hidden_act="gelu_pytorch_tanh",
+        hidden_activation=None,
+        max_position_embeddings=1500,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=0,
+        eos_token_id=1,
+        bos_token_id=2,
+        tie_word_embeddings=True,
+        rope_theta=10000.0,
+        attention_bias=False,
+        attention_dropout=0.0,
+    ):
+        super().__init__(self)
diff --git a/examples/diff-conversion/diff_super.py b/examples/diff-conversion/diff_super.py
@@ -0,0 +1,38 @@
+from typing import List, Optional, Tuple, Union
+
+import torch
+
+from transformers import Cache
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.models.llama.modeling_llama import LlamaModel
+
+
+# example where we need some deps and some functions
+class SuperModel(LlamaModel):
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[Cache, 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, CausalLMOutputWithPast]:
+        out = super().forward(
+            input_ids,
+            attention_mask,
+            position_ids,
+            past_key_values,
+            inputs_embeds,
+            use_cache,
+            output_attentions,
+            output_hidden_states,
+            return_dict,
+            cache_position,
+        )
+        out.logits *= 2**4
+        return out
diff --git a/src/transformers/models/gemma/configuration_gemma.py b/src/transformers/models/gemma/configuration_gemma.py
@@ -1,5 +1,12 @@
+#           🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#               This file was automatically generated from <path_to_diff_file.py>.
+#         Do NOT edit this file manually as any edits will be overwritten by the generation of
+#         the file from the diff. If any change should be done, please apply the change to the
+#                                    diff.py file directly.
+#           🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
 # coding=utf-8
-# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+# Copyright 2024 Google Inc. 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.
@@ -12,27 +19,19 @@
 # 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.
-"""Gemma model configuration"""
-
-from ...configuration_utils import PretrainedConfig
-from ...utils import logging
 
 
-logger = logging.get_logger(__name__)
+from transformers import PretrainedConfig
 
 
 class GemmaConfig(PretrainedConfig):
     r"""
     This is the configuration class to store the configuration of a [`GemmaModel`]. It is used to instantiate an Gemma
     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 Gemma-7B.
-
     e.g. [google/gemma-7b](https://huggingface.co/google/gemma-7b)
-
     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 256000):
             Vocabulary size of the Gemma model. Defines the number of different tokens that can be represented by the
@@ -83,16 +82,12 @@ class GemmaConfig(PretrainedConfig):
             Whether to use a bias in the query, key, value and output projection layers during self-attention.
         attention_dropout (`float`, *optional*, defaults to 0.0):
             The dropout ratio for the attention probabilities.
-
     ```python
     >>> from transformers import GemmaModel, GemmaConfig
-
     >>> # Initializing a Gemma gemma-7b style configuration
     >>> configuration = GemmaConfig()
-
     >>> # Initializing a model from the gemma-7b style configuration
     >>> model = GemmaModel(configuration)
-
     >>> # Accessing the model configuration
     >>> configuration = model.config
     ```"""