Blog post on bitsandbytes integration on Hugging Face

_blog.yml

@@ -1120,7 +1120,6 @@
     - guide
 
 
-
 - local: skops
   title: Introducing Skops
   author: merve
@@ -1132,3 +1131,13 @@
     - announcement
     - guide
 
+
+- local: hf-bitsandbytes-integration
+  title: "A Gentle Introduction to 8-bit Matrix Multiplication for transformers at scale using transformers, accelerate and bitsandbytes"
+  author: ybelkada
+  thumbnail: /blog/assets/96_hf_bitsandbytes_integration/thumbnail_blue.png
+  date: August 12, 2022
+  tags:
+    - nlp
+    - llm
+    - quantization

assets/96_hf_bitsandbytes_integration/example.py

@@ -0,0 +1,41 @@
+import torch
+import torch.nn as nn
+
+from bitsandbytes.nn import Linear8bitLt
+
+# Utility function
+
+def get_model_memory_footprint(model):
+    r"""
+        Partially copied and inspired from: https://discuss.pytorch.org/t/gpu-memory-that-model-uses/56822/2
+    """
+    return sum([param.nelement() * param.element_size() for param in model.parameters()])
+
+# Main script
+
+fp16_model = nn.Sequential(
+    nn.Linear(64, 64),
+    nn.Linear(64, 64)
+).to(torch.float16)
+
+# Train and save your model!
+
+torch.save(fp16_model.state_dict(), "model.pt")
+
+# Define your int8 model!
+
+int8_model = nn.Sequential(
+    Linear8bitLt(64, 64, has_fp16_weights=False),
+    Linear8bitLt(64, 64, has_fp16_weights=False)
+)
+
+int8_model.load_state_dict(torch.load("model.pt"))
+int8_model = int8_model.to(0) # Quantization happens here
+
+input_ = torch.randn(8, 64, dtype=torch.float16)
+hidden_states = int8_model(input_.to(0))
+
+mem_int8 = get_model_memory_footprint(int8_model)
+mem_fp16 = get_model_memory_footprint(fp16_model)
+
+print(f"Relative difference: {mem_fp16/mem_int8}")