Kylesayrs/gptq batched updates

examples/quantization_w4a16/llama3_example.py

@@ -9,7 +9,7 @@
 
 model = SparseAutoModelForCausalLM.from_pretrained(
     MODEL_ID,
-    device_map="auto",
+    device_map="cuda:0",
     torch_dtype="auto",
 )
 tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
@@ -20,7 +20,7 @@
 
 # Select number of samples. 512 samples is a good place to start.
 # Increasing the number of samples can improve accuracy.
-NUM_CALIBRATION_SAMPLES = 512
+NUM_CALIBRATION_SAMPLES = 160 #2048
 MAX_SEQUENCE_LENGTH = 2048
 
 # Load dataset and preprocess.
@@ -55,7 +55,7 @@ def tokenize(sample):
 
 # 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"])
+recipe = GPTQModifier(targets="Linear", scheme="W4A16", ignore=["lm_head"], batch_size=-1, dampening_frac=0.5)
 
 # Apply algorithms.
 oneshot(

examples/quantization_w4a16/vision2_example.py

@@ -0,0 +1,83 @@
+from datasets import load_dataset
+from transformers import AutoProcessor, MllamaForConditionalGeneration
+
+from llmcompressor.modifiers.quantization import GPTQModifier
+from llmcompressor.transformers import oneshot
+
+# Select model and load it.
+MODEL_ID = "meta-llama/Llama-3.2-11B-Vision-Instruct"
+
+model = MllamaForConditionalGeneration.from_pretrained(
+    MODEL_ID,
+    device_map="cuda:0",
+    torch_dtype="auto",
+)
+processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
+
+# 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 = 160 #2048
+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": processor.apply_chat_template(
+            example["messages"],
+            tokenize=False,
+        )
+    }
+
+
+ds = ds.map(preprocess)
+
+
+# Tokenize inputs.
+def tokenize(sample):
+    return processor(
+        None,
+        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"], batch_size=1, dampening_frac=0.5)
+
+# Apply algorithms.
+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,
+)
+
+# Confirm generations of the quantized model look sane.
+print("\n\n")
+print("========== SAMPLE GENERATION ==============")
+input_ids = processor("Hello my name is", return_tensors="pt").input_ids.to("cuda")
+output = model.generate(input_ids, max_new_tokens=100)
+print(processor.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)
+processor.save_pretrained(SAVE_DIR)

examples/quantization_w4a16/vision_example.py

@@ -0,0 +1,88 @@
+from datasets import load_dataset
+from transformers import AutoProcessor
+
+from llmcompressor.modifiers.quantization import GPTQModifier
+from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
+
+# Select model and load it.
+MODEL_ID = "meta-llama/Llama-3.2-11B-Vision-Instruct"
+
+model = SparseAutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    device_map="cuda:0",
+    torch_dtype="auto",
+)
+breakpoint()
+processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
+
+# Select calibration dataset.
+DATASET_ID = "lmms-lab/flickr30k"
+DATASET_SPLIT = "test[:165]"
+
+# Select number of samples. 512 samples is a good place to start.
+# Increasing the number of samples can improve accuracy.
+NUM_CALIBRATION_SAMPLES = 165 #2048
+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):
+    messages = [
+        [
+            {
+                "role": "user", 
+                "content": [
+                    {"type": "image"},
+                    {"type": "text", "text": "What does the image show?"}
+                ]
+            }
+        ],
+    ]
+    return {
+        "text": processor.apply_chat_template(
+            messages,
+            add_generation_prompt=True,
+        ),
+    }
+
+
+ds = ds.map(preprocess)
+
+
+# Tokenize inputs.
+def tokenize(sample):
+    return processor(sample["image"], sample["text"], add_special_tokens=False, return_tensors="pt", max_length=MAX_SEQUENCE_LENGTH)
+
+
+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"], batch_size=-1, dampening_frac=0.5)
+
+# Apply algorithms.
+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,
+)
+
+# Confirm generations of the quantized model look sane.
+print("\n\n")
+print("========== SAMPLE GENERATION ==============")
+input_ids = processor("Hello my name is", return_tensors="pt").input_ids.to("cuda")
+output = model.generate(input_ids, max_new_tokens=100)
+print(processor.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)
+processor.save_pretrained(SAVE_DIR)

shubhra.py

@@ -0,0 +1,92 @@
+from datasets import load_dataset
+from transformers import AutoProcessor, MllamaForConditionalGeneration, LlavaForConditionalGeneration
+
+from llmcompressor.modifiers.quantization import GPTQModifier
+from llmcompressor.transformers import oneshot, wrap_hf_model_class
+import os
+
+# Load model.
+#model_id = "meta-llama/Llama-3.2-11B-Vision-Instruct"
+model_id = "mgoin/pixtral-12b"
+model_class = wrap_hf_model_class(LlavaForConditionalGeneration)
+model = model_class.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)
+
+print("Loading dataset")
+DATASET_ID = "lmms-lab/flickr30k"
+DATASET_SPLIT = "test[:128]"
+
+NUM_CALIBRATION_SAMPLES = 1#128
+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))
+
+print("Preprocessing samples")
+def preprocess(example):
+    messages = [
+        [
+            {
+                "role": "user", 
+                "content": [
+                    {"type": "image"},
+                    {"type": "text", "text": "What does the image show?"}
+                ]
+            }
+        ],
+    ]
+    return {
+        "text": processor.apply_chat_template(
+            messages,
+            add_generation_prompt=True,
+        ),
+    }
+
+ds = ds.map(preprocess)
+
+
+# Tokenize inputs.
+def tokenize(sample):
+    return processor(sample["image"], sample["text"], add_special_tokens=False, return_tensors="pt")
+
+
+ds = ds.map(tokenize, remove_columns=ds.column_names)
+print(ds)
+
+print("Setting up quantization params")
+# Configure the quantization algorithm and scheme.
+# In this case, we:
+#   * quantize the weights to fp8 with per channel via ptq
+#   * quantize the activations to fp8 with dynamic per token
+#ignore=["re:.*lm_head", "re:model.vision_embed_tokens.*"]
+#ignore=["re:.*lm_head", "re:multi_modal_projector.*", "re:vision_model.*", "re:language_model.*cross_attn.*"],
+ignore=["re:.*lm_head", "re:multi_modal_projector.*", "re:vision_model.*"]
+
+recipe = [
+    # SmoothQuantModifier(smoothing_strength=0.8, ignore=ignore),
+    GPTQModifier(targets="Linear", scheme="W8A8", ignore=ignore),
+]
+
+save_name = model_id.split("/")[1] + "-W8A8"
+save_path = os.path.join("./my_test/", save_name)
+print("Starting quantization")
+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,
+    output_dir=save_path,
+)
+
+#processor.save_pretrained(save_path)
+
+# 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("==========================================")