Merge branch 'main' into composability-v2

.github/workflows/test-check-transformers.yaml

@@ -3,6 +3,8 @@ on:
   pull_request:
     branches: main
     types: [ labeled, synchronize ]
+  push:
+    branches: main
 
 env:
   CADENCE: "commit"
@@ -15,7 +17,7 @@ env:
 jobs:
   transformers-tests:
     runs-on: gcp-k8s-vllm-l4-solo
-    if: contains(github.event.pull_request.labels.*.name, 'ready')
+    if: contains(github.event.pull_request.labels.*.name, 'ready') || github.event_name == 'push'
     steps:
       - uses: actions/setup-python@v5
         with:

examples/multimodal_vision/phi3_vision_example.py

@@ -0,0 +1,93 @@
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoProcessor
+
+from llmcompressor.modifiers.quantization import GPTQModifier
+from llmcompressor.transformers import oneshot
+from llmcompressor.transformers.utils.data_collator import phi3_vision_data_collator
+
+# Load model.
+model_id = "microsoft/Phi-3-vision-128k-instruct"
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype="auto",
+    trust_remote_code=True,
+    _attn_implementation="eager",
+)
+processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
+processor.chat_template = processor.tokenizer.chat_template
+
+# Oneshot arguments
+DATASET_ID = "lmms-lab/flickr30k"
+DATASET_SPLIT = "test[:512]"
+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))
+
+
+# Apply chat template
+def preprocess(example):
+    messages = [{"role": "user", "content": "<|image_1|>\nWhat does the image show?"}]
+    return {
+        "text": processor.apply_chat_template(
+            messages,
+            add_generation_prompt=True,
+        ),
+        "images": example["image"],
+    }
+
+
+ds = ds.map(preprocess)
+
+
+# # Tokenize inputs.
+def tokenize(sample):
+    return processor(
+        text=sample["text"],
+        images=sample["images"],
+        padding=False,
+        max_length=MAX_SEQUENCE_LENGTH,
+        truncation=True,
+    )
+
+
+# long data lengths produced by the phi3_vision processor
+# can lead to integer overflows when mapping, avoid with writer_batch_size
+ds = ds.map(tokenize, writer_batch_size=1, remove_columns=ds.column_names)
+
+
+# Recipe
+recipe = [
+    GPTQModifier(
+        targets="Linear",
+        scheme="W4A16",
+        sequential_targets=["Phi3DecoderLayer"],
+        ignore=["lm_head", "re:model.vision_embed_tokens.*"],
+    ),
+]
+
+# Perform oneshot
+oneshot(
+    model=model,
+    dataset=ds,
+    recipe=recipe,
+    max_seq_length=MAX_SEQUENCE_LENGTH,
+    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
+    trust_remote_code_model=True,
+    data_collator=phi3_vision_data_collator,
+)
+
+# Confirm generations of the quantized model look sane.
+print("========== SAMPLE GENERATION ==============")
+input_ids = processor(text="Hello my name is", return_tensors="pt").input_ids.to("cuda")
+output = model.generate(input_ids, max_new_tokens=20)
+print(processor.decode(output[0]))
+print("==========================================")
+
+# Save to disk compressed.
+SAVE_DIR = model_id.split("/")[1] + "-W4A16-G128"
+model.save_pretrained(SAVE_DIR, save_compressed=True)
+processor.save_pretrained(SAVE_DIR)

examples/multimodal_vision/qwen2_vl_example.py

@@ -0,0 +1,123 @@
+import base64
+from io import BytesIO
+
+from datasets import load_dataset
+from qwen_vl_utils import process_vision_info
+from transformers import AutoProcessor
+
+from llmcompressor.modifiers.quantization import GPTQModifier
+from llmcompressor.transformers import oneshot
+from llmcompressor.transformers.tracing import TraceableQwen2VLForConditionalGeneration
+from llmcompressor.transformers.utils.data_collator import qwen2_vl_data_collator
+
+# Load model.
+model_id = "Qwen/Qwen2-VL-2B-Instruct"
+model = TraceableQwen2VLForConditionalGeneration.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype="auto",
+)
+processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
+
+# Oneshot arguments
+DATASET_ID = "lmms-lab/flickr30k"
+DATASET_SPLIT = {"calibration": "test[:512]"}
+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)
+
+
+# Apply chat template and tokenize inputs.
+def preprocess_and_tokenize(example):
+    # preprocess
+    buffered = BytesIO()
+    example["image"].save(buffered, format="PNG")
+    encoded_image = base64.b64encode(buffered.getvalue())
+    encoded_image_text = encoded_image.decode("utf-8")
+    base64_qwen = f"data:image;base64,{encoded_image_text}"
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image", "image": base64_qwen},
+                {"type": "text", "text": "What does the image show?"},
+            ],
+        }
+    ]
+    text = processor.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+    image_inputs, video_inputs = process_vision_info(messages)
+
+    # tokenize
+    return processor(
+        text=[text],
+        images=image_inputs,
+        videos=video_inputs,
+        padding=False,
+        max_length=MAX_SEQUENCE_LENGTH,
+        truncation=True,
+    )
+
+
+ds = ds.map(preprocess_and_tokenize, remove_columns=ds["calibration"].column_names)
+
+# Recipe
+recipe = [
+    GPTQModifier(
+        targets="Linear",
+        scheme="W4A16",
+        sequential_targets=["Qwen2VLDecoderLayer"],
+        ignore=["lm_head", "re:visual.*"],
+    ),
+]
+
+# Perform oneshot
+oneshot(
+    model=model,
+    tokenizer=model_id,
+    dataset=ds,
+    recipe=recipe,
+    max_seq_length=MAX_SEQUENCE_LENGTH,
+    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
+    trust_remote_code_model=True,
+    data_collator=qwen2_vl_data_collator,
+)
+
+# Confirm generations of the quantized model look sane.
+print("========== SAMPLE GENERATION ==============")
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "http://images.cocodataset.org/train2017/000000231895.jpg",
+            },
+            {"type": "text", "text": "Please describe the animal in this image\n"},
+        ],
+    }
+]
+prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
+image_inputs, video_inputs = process_vision_info(messages)
+inputs = processor(
+    text=[prompt],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=False,
+    max_length=MAX_SEQUENCE_LENGTH,
+    truncation=True,
+    return_tensors="pt",
+).to("cuda")
+output = model.generate(**inputs, max_new_tokens=100)
+print(processor.decode(output[0], skip_special_tokens=True))
+print("==========================================")
+
+
+# Save to disk compressed.
+SAVE_DIR = model_id.split("/")[1] + "-W4A16-G128"
+model.save_pretrained(SAVE_DIR, save_compressed=True)
+processor.save_pretrained(SAVE_DIR)

src/llmcompressor/modifiers/quantization/gptq/base.py

@@ -67,7 +67,8 @@ class GPTQModifier(Modifier, HooksMixin):
             - run_sequential / run_layer_sequential / run_basic
                 - make_empty_hessian
                 - accumulate_hessian
-                - quantize_weight
+        - on_sequential_batch_end
+            - quantize_weight
         - on_finalize
             - remove_hooks()
             - model.apply(freeze_module_quantization)
@@ -191,7 +192,7 @@ def on_initialize_structure(self, state: State, **kwargs):
         if self._quantization_modifier:
             self._quantization_modifier.on_initialize_structure(state, **kwargs)
 
-    def on_initialize(self, state: "State", **kwargs) -> bool:
+    def on_initialize(self, state: State, **kwargs) -> bool:
         """
         Initialize and run the GPTQ algorithm on the current state
 
@@ -271,7 +272,7 @@ def on_initialize(self, state: "State", **kwargs) -> bool:
                 run_basic(state.model, state.data.calib, self)
                 return True
 
-    def on_finalize(self, state: "State", **kwargs) -> bool:
+    def on_finalize(self, state: State, **kwargs) -> bool:
         """
         disable the quantization observers used by the OBCQ algorithm
 

src/llmcompressor/transformers/tracing/__init__.py

@@ -5,9 +5,13 @@
 from .mllama import (
     MllamaForConditionalGeneration as TraceableMllamaForConditionalGeneration,
 )
+from .qwen2_vl import (
+    Qwen2VLForConditionalGeneration as TraceableQwen2VLForConditionalGeneration,
+)
 
 __all__ = [
     "TraceableLlavaForConditionalGeneration",
     "TraceableMllamaForConditionalGeneration",
     "TraceableMistralForCausalLM",
+    "TraceableQwen2VLForConditionalGeneration",
 ]