Remove TraceableMistralForCausalLM (#1052)

## Purpose ##
* Remove changes to `MistralForCausalLM` which was thought to be needed
for Pixtral, but is not

## Changes ##
* Remove `TraceableMistralForCausalLM`
* Remove `TraceableMistralForCausalLM`'s use in
`LlavaForConditionalGeneration`
* Remove some unneeded imports in
`src/llmcompressor/transformers/tracing/llava.py`

## Testing ##
* Ran `examples/multimodal_vision/llava_example.py` to completion
* Ran `examples/multimodal_vision/pixtral_example.py` to completion
* Ran `mixtral_example.py` to completion
* `grep -r 'TraceableMistralForCausalLM' src/ examples/ tests/`
* `grep -r 'TraceableLlavaForConditionalGeneration' src/ examples/
tests/`

<details><summary>mixtral_example.py</summary>

```python3
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer

from llmcompressor.modifiers.quantization import GPTQModifier
from llmcompressor.transformers import oneshot
from llmcompressor.transformers.compression.helpers import calculate_offload_device_map

# Select model and load it.
MODEL_ID = "mistralai/Mixtral-8x7B-Instruct-v0.1"
NUM_GPUS = 1

device_map = calculate_offload_device_map(
    MODEL_ID, reserve_for_hessians=True, num_gpus=NUM_GPUS, torch_dtype="auto"
)

model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    device_map=device_map,
    torch_dtype="auto",
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)

# Select calibration dataset.
DATASET_ID = "HuggingFaceH4/ultrachat_200k"
DATASET_SPLIT = "train_sft"

# Select number of samples. 512 samples is a good place to start.
# Increasing the number of samples can improve accuracy.
NUM_CALIBRATION_SAMPLES = 512
MAX_SEQUENCE_LENGTH = 2048

# Load dataset and preprocess.
ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES))


def preprocess(example):
    return {
        "text": tokenizer.apply_chat_template(
            example["messages"],
            tokenize=False,
        )
    }


ds = ds.map(preprocess)


# Tokenize inputs.
def tokenize(sample):
    return tokenizer(
        sample["text"],
        padding=False,
        max_length=MAX_SEQUENCE_LENGTH,
        truncation=True,
        add_special_tokens=False,
    )


ds = ds.map(tokenize, remove_columns=ds.column_names)

# Configure the quantization algorithm to run.
#   * quantize the weights to 4 bit with GPTQ with a group size 128
recipe = GPTQModifier(targets="Linear", scheme="W4A16", ignore=["lm_head"])

# Apply algorithms.
oneshot(
    model=model,
    dataset=ds,
    recipe=recipe,
    max_seq_length=MAX_SEQUENCE_LENGTH,
    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
)

# Confirm generations of the quantized model look sane.
print("\n\n")
print("========== SAMPLE GENERATION ==============")
input_ids = tokenizer("Hello my name is", return_tensors="pt").input_ids.to("cuda")
output = model.generate(input_ids, max_new_tokens=100)
print(tokenizer.decode(output[0]))
print("==========================================\n\n")

# Save to disk compressed.
SAVE_DIR = MODEL_ID.split("/")[1] + "-W4A16-G128"
model.save_pretrained(SAVE_DIR, save_compressed=True)
tokenizer.save_pretrained(SAVE_DIR)
```
</details>

Signed-off-by: Kyle Sayers <[email protected]>
Co-authored-by: Michael Goin <[email protected]>

src/llmcompressor/transformers/tracing/__init__.py

@@ -1,7 +1,6 @@
 from .llava import (
     LlavaForConditionalGeneration as TraceableLlavaForConditionalGeneration,
 )
-from .mistral import MistralForCausalLM as TraceableMistralForCausalLM
 from .mllama import (
     MllamaForConditionalGeneration as TraceableMllamaForConditionalGeneration,
 )
@@ -12,6 +11,5 @@
 __all__ = [
     "TraceableLlavaForConditionalGeneration",
     "TraceableMllamaForConditionalGeneration",
-    "TraceableMistralForCausalLM",
     "TraceableQwen2VLForConditionalGeneration",
 ]

src/llmcompressor/transformers/tracing/llava.py

@@ -19,20 +19,14 @@
 from typing import List, Optional, Tuple, Union
 
 import torch
-from transformers import AutoModel, AutoModelForCausalLM, LlavaForConditionalGeneration
+from transformers import LlavaForConditionalGeneration
 from transformers.models.llava.configuration_llava import LlavaConfig
 from transformers.models.llava.modeling_llava import (
     LlavaCausalLMOutputWithPast,
-    LlavaMultiModalProjector,
-    LlavaPreTrainedModel,
     logger,
 )
-from transformers.models.mistral.configuration_mistral import MistralConfig
 from transformers.utils.fx import HFProxy
 
-# TRACING: Reuse traceable subclass
-from .mistral import MistralForCausalLM as TraceableMistralForCausalLM
-
 
 # TRACING: The shape of image_features is known and documented by
 # LlavaForConditionalGeneration.get_image_features
@@ -75,22 +69,6 @@ def maybe_install_metadata_inputs_embeds_masked(
 
 # TRACING: override `__init__` and `forward`
 class LlavaForConditionalGeneration(LlavaForConditionalGeneration):
-    def __init__(self, config: LlavaConfig):
-        super(LlavaPreTrainedModel, self).__init__(config)
-        self.vision_tower = AutoModel.from_config(config.vision_config)
-
-        self.multi_modal_projector = LlavaMultiModalProjector(config)
-        self.vocab_size = config.text_config.vocab_size
-
-        # TRACING: Must use TraceableMistralForCausalLM which wraps an untraceable function
-        if isinstance(config.text_config, MistralConfig):
-            self.language_model = TraceableMistralForCausalLM(config.text_config)
-        else:
-            self.language_model = AutoModelForCausalLM.from_config(config.text_config)
-
-        self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
-        self.post_init()
-
     def forward(
         self,
         input_ids: torch.LongTensor = None,