ciCic/llama-3.2-1B-Instruct-AWQ

Represents

A quantized version of Llama 3.2 1B Instruct with Activation-aware Weight Quantization (AWQ)[https://github.com/mit-han-lab/llm-awq]

Use with transformers/autoawq

Starting with

• transformers==4.45.1
• accelerate==0.34.2
• torch==2.3.1
• numpy==2.0.0
• autoawq==0.2.6

Experimented with

• OS = Windows
• GPU = Nvidia GeForce RTX 3080 10gb
• CPU = Intel Core i5-9600K
• RAM = 32GB

For CUDA users

AutoAWQ

NOTE: this example uses fuse_layers=True to fuse attention and mlp layers together for faster inference

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer

quant_id = "ciCic/llama-3.2-1B-Instruct-AWQ"
model = AutoAWQForCausalLM.from_quantized(quant_id, fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(quant_id, trust_remote_code=True)

streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

# Declare prompt
prompt = "You're standing on the surface of the Earth. "\
        "You walk one mile south, one mile west and one mile north. "\
        "You end up exactly where you started. Where are you?"

# Tokenization of the prompt
tokens = tokenizer(
   prompt, 
    return_tensors='pt'
).input_ids.cuda()

# Generate output in a streaming fashion
generation_output = model.generate(
    tokens, 
    streamer=streamer,
    max_new_tokens=512
)

Transformers

from transformers import AutoTokenizer, TextStreamer, AutoModelForCausalLM
import torch

quant_id = "ciCic/llama-3.2-1B-Instruct-AWQ"
tokenizer = AutoTokenizer.from_pretrained(quant_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    quant_id, 
    torch_dtype=torch.float16, 
    low_cpu_mem_usage=True,
    device_map="cuda"
)

streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

# Convert prompt to tokens
prompt = "You're standing on the surface of the Earth. "\
        "You walk one mile south, one mile west and one mile north. "\
        "You end up exactly where you started. Where are you?"

tokens = tokenizer(
   prompt, 
    return_tensors='pt'
).input_ids.cuda()

# Generate output
generation_output = model.generate(
    tokens, 
    streamer=streamer,
    max_new_tokens=512
)

Issue/Solution

• torch.from_numpy fails
  • This might be due to certain issues within torch==2.3.1 .cpp files. Since AutoAWQ uses torch version 2.3.1, instead of most recent, this issue might occur within module marlin.py -> def _get_perms()
  • Module path: Python\Python311\site-packages\awq\modules\linear\marlin.py
  • Solution:
    • there are several operations to numpy (cpu) then back to tensor (gpu) which could be completely replaced by tensor without having to use numpy, this will solve (temporarily) the from_numpy() issue

def _get_perms():
    perm = []
    for i in range(32):
        perm1 = []
        col = i // 4
        for block in [0, 1]:
            for row in [
                2 * (i % 4),
                2 * (i % 4) + 1,
                2 * (i % 4 + 4),
                2 * (i % 4 + 4) + 1,
            ]:
                perm1.append(16 * row + col + 8 * block)

        for j in range(4):
            perm.extend([p + 256 * j for p in perm1])

    # perm = np.array(perm)
    perm = torch.asarray(perm)
    # interleave = np.array([0, 2, 4, 6, 1, 3, 5, 7])
    interleave = torch.asarray([0, 2, 4, 6, 1, 3, 5, 7])
    perm = perm.reshape((-1, 8))[:, interleave].ravel()
    # perm = torch.from_numpy(perm)
    scale_perm = []
    for i in range(8):
        scale_perm.extend([i + 8 * j for j in range(8)])
    scale_perm_single = []
    for i in range(4):
        scale_perm_single.extend([2 * i + j for j in [0, 1, 8, 9, 16, 17, 24, 25]])
    return perm, scale_perm, scale_perm_single