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# torchao

[![Open In Colab: Torchao Demo](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/en/quantization/torchao.ipynb)

[torchao](https://github.com/pytorch/ao) is a PyTorch architecture optimization library with support for custom high performance data types, quantization, and sparsity. It is composable with native PyTorch features such as [torch.compile](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) for even faster inference and training.

See the table below for additional torchao features.

| Feature | Description |
|--------|-------------|
| **Quantization Aware Training (QAT)** | Train quantized models with minimal accuracy loss (see [QAT README](https://github.com/pytorch/ao/blob/main/torchao/quantization/qat/README.md)) |
| **Float8 Training** | High-throughput training with float8 formats (see [torchtitan](https://github.com/pytorch/torchtitan/blob/main/docs/float8.md) and [Accelerate](https://huggingface.co/docs/accelerate/usage_guides/low_precision_training#configuring-torchao) docs) |
| **Sparsity Support** | Semi-structured (2:4) sparsity for faster inference (see [Accelerating Neural Network Training with Semi-Structured (2:4) Sparsity](https://pytorch.org/blog/accelerating-neural-network-training/) blog post) |
| **Optimizer Quantization** | Reduce optimizer state memory with 4 and 8-bit variants of Adam |
| **KV Cache Quantization** | Enables long context inference with lower memory (see [KV Cache Quantization](https://github.com/pytorch/ao/blob/main/torchao/_models/llama/README.md)) |
| **Custom Kernels Support** | use your own `torch.compile` compatible ops |
| **FSDP2** | Composable with FSDP2 for training|

> [!TIP]
> Refer to the torchao [README.md](https://github.com/pytorch/ao#torchao-pytorch-architecture-optimization) for more details about the library.

torchao supports the [quantization techniques](https://github.com/pytorch/ao/blob/main/torchao/quantization/README.md) below.

- A16W8 Float8 Dynamic Quantization
- A16W8 Float8 WeightOnly Quantization
- A8W8 Int8 Dynamic Quantization
- A16W8 Int8 Weight Only Quantization
- A16W4 Int4 Weight Only Quantization
- A16W4 Int4 Weight Only Quantization + 2:4 Sparsity
- Autoquantization

torchao also supports module level configuration by specifying a dictionary from fully qualified name of module and its corresponding quantization config. This allows skip quantizing certain layers and using different quantization config for different modules.

Check the table below to see if your hardware is compatible.

| Component | Compatibility |
|----------|----------------|
| CUDA Versions | ✅ cu118, cu126, cu128 |
| XPU Versions | ✅ pytorch2.8 |
| CPU | ✅ change `device_map="cpu"` (see examples below) |

Install torchao from PyPi or the PyTorch index with the following commands.

<hfoptions id="install torchao">
<hfoption id="PyPi">

```bash
# Updating 🤗 Transformers to the latest version, as the example script below uses the new auto compilation
# Stable release from Pypi which will default to CUDA 12.6
pip install --upgrade torchao transformers
```

</hfoption>
<hfoption id="PyTorch Index">
Stable Release from the PyTorch index

```bash
pip install torchao --index-url https://download.pytorch.org/whl/cu126 # options are cpu/cu118/cu126/cu128
```

</hfoption>
</hfoptions>

torchao >= 0.15.0 is required. The string-based API (e.g., `TorchAoConfig("int4_weight_only")`) has been removed — use `AOBaseConfig` objects instead (see examples below).

## Quantization examples

TorchAO provides a variety of quantization configurations. Each configuration can be further customized with parameters such as `group_size`, `scheme`, and `layout` to optimize for specific hardware and model architectures.

For a complete list of available configurations, see the [quantization API documentation](https://github.com/pytorch/ao/blob/main/torchao/quantization/quant_api.py).

You can manually choose the quantization types and settings or automatically select the quantization types.

Create a [`TorchAoConfig`] and specify the quantization type and `group_size` of the weights to quantize (for int8 weight only and int4 weight only). Set the `cache_implementation` to `"static"` to automatically [torch.compile](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) the forward method.

We'll show examples for recommended quantization methods based on hardwares, e.g. A100 GPU, H100 GPU, CPU.

> [!WARNING]
> torchao automatically compiles the model during the first inference if we set `cache_implementation="static"`. The model is recompiled every time batch size or `max_new_tokens` is modified. Pass `disable_compile=True` in [`~GenerationMixin.generate`] to quantize without compilation.

### H100 GPU

<hfoptions id="examples-H100-GPU">
<hfoption id="float8-dynamic-and-weight-only">

```py
import torch
from transformers import TorchAoConfig, AutoModelForCausalLM, AutoTokenizer
from torchao.quantization import Float8DynamicActivationFloat8WeightConfig, Float8WeightOnlyConfig

quant_config = Float8DynamicActivationFloat8WeightConfig()
# or float8 weight only quantization
# quant_config = Float8WeightOnlyConfig()
quantization_config = TorchAoConfig(quant_type=quant_config)

# Load and quantize the model
quantized_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-8B-Instruct",
    dtype="auto",
    device_map="auto",
    quantization_config=quantization_config
)

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
input_text = "What are we having for dinner?"
input_ids = tokenizer(input_text, return_tensors="pt").to(quantized_model.device, quantized_model.dtype)

# auto-compile the quantized model with `cache_implementation="static"` to get speed up
output = quantized_model.generate(**input_ids, max_new_tokens=10, cache_implementation="static")
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

</hfoption>
<hfoption id="int4-weight-only">

```py
import torch
from transformers import TorchAoConfig, AutoModelForCausalLM, AutoTokenizer
from torchao.quantization import GemliteUIntXWeightOnlyConfig

# We integrated with gemlite, which optimizes for batch size N on A100 and H100
quant_config = GemliteUIntXWeightOnlyConfig(group_size=128)
quantization_config = TorchAoConfig(quant_type=quant_config)

# Load and quantize the model
quantized_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-8B-Instruct",
    dtype="auto",
    device_map="auto",
    quantization_config=quantization_config
)

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
input_text = "What are we having for dinner?"
input_ids = tokenizer(input_text, return_tensors="pt").to(quantized_model.device, quantized_model.dtype)

# auto-compile the quantized model with `cache_implementation="static"` to get speed up
output = quantized_model.generate(**input_ids, max_new_tokens=10, cache_implementation="static")
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

</hfoption>
</hfoptions>

### A100 GPU

<hfoptions id="examples-A100-GPU">
<hfoption id="int8-dynamic-and-weight-only">

```py
import torch
from transformers import TorchAoConfig, AutoModelForCausalLM, AutoTokenizer
from torchao.quantization import Int8DynamicActivationInt8WeightConfig, Int8WeightOnlyConfig

quant_config = Int8DynamicActivationInt8WeightConfig()
# or int8 weight only quantization
# quant_config = Int8WeightOnlyConfig()
quantization_config = TorchAoConfig(quant_type=quant_config)

# Load and quantize the model
quantized_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-8B-Instruct",
    dtype="auto",
    device_map="auto",
    quantization_config=quantization_config
)

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
input_text = "What are we having for dinner?"
input_ids = tokenizer(input_text, return_tensors="pt").to(quantized_model.device, quantized_model.dtype)

# auto-compile the quantized model with `cache_implementation="static"` to get speed up
output = quantized_model.generate(**input_ids, max_new_tokens=10, cache_implementation="static")
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

</hfoption>

<hfoption id="int4-weight-only">

```py
import torch
from transformers import TorchAoConfig, AutoModelForCausalLM, AutoTokenizer
from torchao.quantization import GemliteUIntXWeightOnlyConfig, Int4WeightOnlyConfig

# For batch size N, we recommend gemlite, which may require autotuning
# default is 4 bit, 8 bit is also supported by passing `bit_width=8`
quant_config = GemliteUIntXWeightOnlyConfig(group_size=128)

# For batch size 1, we also have custom tinygemm kernel that's only optimized for this
# We can set `use_hqq` to `True` for better accuracy
# quant_config = Int4WeightOnlyConfig(group_size=128, use_hqq=True)

quantization_config = TorchAoConfig(quant_type=quant_config)

# Load and quantize the model
quantized_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-8B-Instruct",
    dtype="auto",
    device_map="auto",
    quantization_config=quantization_config
)

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
input_text = "What are we having for dinner?"
input_ids = tokenizer(input_text, return_tensors="pt").to(quantized_model.device, quantized_model.dtype)

# auto-compile the quantized model with `cache_implementation="static"` to get speed up
output = quantized_model.generate(**input_ids, max_new_tokens=10, cache_implementation="static")
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

</hfoption>
</hfoptions>

### Intel XPU

<hfoptions id="examples-Intel-XPU">
<hfoption id="int8-dynamic-and-weight-only">

```py
import torch
from transformers import TorchAoConfig, AutoModelForCausalLM, AutoTokenizer
from torchao.quantization import Int8DynamicActivationInt8WeightConfig, Int8WeightOnlyConfig

quant_config = Int8DynamicActivationInt8WeightConfig()
# or int8 weight only quantization
# quant_config = Int8WeightOnlyConfig()
quantization_config = TorchAoConfig(quant_type=quant_config)

# Load and quantize the model
quantized_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-8B-Instruct",
    dtype="auto",
    device_map="auto",
    quantization_config=quantization_config
)

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
input_text = "What are we having for dinner?"
input_ids = tokenizer(input_text, return_tensors="pt").to(quantized_model.device, quantized_model.dtype)

# auto-compile the quantized model with `cache_implementation="static"` to get speed up
output = quantized_model.generate(**input_ids, max_new_tokens=10, cache_implementation="static")
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

</hfoption>

<hfoption id="int4-weight-only">

```py
import torch
from transformers import TorchAoConfig, AutoModelForCausalLM, AutoTokenizer
from torchao.quantization import Int4WeightOnlyConfig


quant_config = Int4WeightOnlyConfig(group_size=128, int4_packing_format="plain_int32")
quantization_config = TorchAoConfig(quant_type=quant_config)

# Load and quantize the model
quantized_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-8B-Instruct",
    dtype="auto",
    device_map="auto",
    quantization_config=quantization_config
)

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
input_text = "What are we having for dinner?"
input_ids = tokenizer(input_text, return_tensors="pt").to(quantized_model.device).to(quantized_model.dtype)

# auto-compile the quantized model with `cache_implementation="static"` to get speed up
output = quantized_model.generate(**input_ids, max_new_tokens=10, cache_implementation="static")
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

</hfoption>
</hfoptions>

### CPU

<hfoptions id="examples-CPU">
<hfoption id="int8-dynamic-and-weight-only">

```py
import torch
from transformers import TorchAoConfig, AutoModelForCausalLM, AutoTokenizer
from torchao.quantization import Int8DynamicActivationInt8WeightConfig, Int8WeightOnlyConfig

quant_config = Int8DynamicActivationInt8WeightConfig()
# quant_config = Int8WeightOnlyConfig()
quantization_config = TorchAoConfig(quant_type=quant_config)

# Load and quantize the model
quantized_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-8B-Instruct",
    dtype="auto",
    device_map="cpu",
    quantization_config=quantization_config
)

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
input_text = "What are we having for dinner?"
input_ids = tokenizer(input_text, return_tensors="pt").to(quantized_model.device, quantized_model.dtype)

# auto-compile the quantized model with `cache_implementation="static"` to get speed up
output = quantized_model.generate(**input_ids, max_new_tokens=10, cache_implementation="static")
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

</hfoption>
<hfoption id="int4-weight-only">

> [!TIP]
> CPU int4 weight-only quantization requires torchao version 0.15.0 or later.

```py
import torch
from transformers import TorchAoConfig, AutoModelForCausalLM, AutoTokenizer
from torchao.prototype.quantization.int4 import PrototypeInt4WeightOnlyConfig

quantization_config = TorchAoConfig(PrototypeInt4WeightOnlyConfig(group_size=128, int4_choose_qparams_algorithm="tinygemm"))

# Load and quantize the model
quantized_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-8B-Instruct",
    dtype="auto",
    device_map="cpu",
    quantization_config=quantization_config
)

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
input_text = "What are we having for dinner?"
input_ids = tokenizer(input_text, return_tensors="pt").to(quantized_model.device).to(quantized_model.dtype)

# auto-compile the quantized model with `cache_implementation="static"` to get speed up
output = quantized_model.generate(**input_ids, max_new_tokens=10, cache_implementation="static")
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

</hfoption>
</hfoptions>

### Per Module Quantization

#### 1. Skip quantization for certain layers

With `FqnToConfig` we can specify a default configuration for all layers while skipping quantization for certain layers.

```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig

model_id = "meta-llama/Llama-3.1-8B-Instruct"

from torchao.quantization import Int4WeightOnlyConfig, FqnToConfig
config = Int4WeightOnlyConfig(group_size=128)

# set default to int4 (for linears), and skip quantizing `model.layers.0.self_attn.q_proj`
quant_config = FqnToConfig({"_default": config, "model.layers.0.self_attn.q_proj": None})
quantization_config = TorchAoConfig(quant_type=quant_config)
quantized_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", dtype=torch.bfloat16, quantization_config=quantization_config)
# lm_head is not quantized and model.layers.0.self_attn.q_proj is not quantized
print("quantized model:", quantized_model)
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Manual Testing
prompt = "Hey, are you conscious? Can you talk to me?"
inputs = tokenizer(prompt, return_tensors="pt").to(quantized_model.device, quantized_model.dtype)
generated_ids = quantized_model.generate(**inputs, max_new_tokens=128)
output_text = tokenizer.batch_decode(
    generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
```

#### 2. Quantizing different layers with different quantization configs (no regex)

```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig

model_id = "facebook/opt-125m"

from torchao.quantization import Int4WeightOnlyConfig, FqnToConfig, Int8DynamicActivationInt4WeightConfig, IntxWeightOnlyConfig, PerAxis, MappingType

weight_dtype = torch.int8
granularity = PerAxis(0)
mapping_type = MappingType.ASYMMETRIC
embedding_config = IntxWeightOnlyConfig(
    weight_dtype=weight_dtype,
    granularity=granularity,
    mapping_type=mapping_type,
)
linear_config = Int8DynamicActivationInt4WeightConfig(group_size=128)
quant_config = FqnToConfig({"_default": linear_config, "model.decoder.embed_tokens": embedding_config, "model.decoder.embed_positions": None})
# set `include_embedding` to True in order to include embedding in quantization
# when `include_embedding` is True, we'll remove input embedding from `modules_not_to_convert` as well
quantization_config = TorchAoConfig(quant_type=quant_config, include_embedding=True)
quantized_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cpu", dtype=torch.bfloat16, quantization_config=quantization_config)
print("quantized model:", quantized_model)
# make sure embedding is quantized
print("embed_tokens weight:", quantized_model.model.decoder.embed_tokens.weight)
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Manual Testing
prompt = "Hey, are you conscious? Can you talk to me?"
inputs = tokenizer(prompt, return_tensors="pt").to("cpu", quantized_model.dtype)
generated_ids = quantized_model.generate(**inputs, max_new_tokens=128, cache_implementation="static")
output_text = tokenizer.batch_decode(
    generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
```

#### 3. Quantizing different layers with different quantization configs (with regex)

We can also use regex to specify the config for all modules that has `module_fqn` that
matches the regex, all regex should start with `re:`, for example `re:layers\..*\.gate_proj` will
match all layers like `layers.0.gate_proj`. See [here](https://github.com/pytorch/ao/blob/2fe0ca0899c730c528efdbec8886feaa38879f39/torchao/quantization/quant_api.py#L2392) for docs.

```py
import logging

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig

# Configure logging to see warnings and debug information
logging.basicConfig(
    level=logging.INFO, format="%(name)s - %(levelname)s - %(message)s"
)

# Enable specific loggers that might contain the serialization warnings
logging.getLogger("transformers").setLevel(logging.INFO)
logging.getLogger("torchao").setLevel(logging.INFO)
logging.getLogger("safetensors").setLevel(logging.INFO)
logging.getLogger("huggingface_hub").setLevel(logging.INFO)

model_id = "facebook/opt-125m"

from torchao.quantization import (
    Float8DynamicActivationFloat8WeightConfig,
    Int4WeightOnlyConfig,
    IntxWeightOnlyConfig,
    PerRow,
    PerAxis,
    FqnToConfig,
    Float8Tensor,
    Int4TilePackedTo4dTensor,
    IntxUnpackedToInt8Tensor,
)

float8dyn = Float8DynamicActivationFloat8WeightConfig(granularity=PerRow())
int4wo = Int4WeightOnlyConfig(int4_packing_format="tile_packed_to_4d")
intxwo = IntxWeightOnlyConfig(weight_dtype=torch.int8, granularity=PerAxis(0))

qconfig_dict = {
    # highest priority
    "model.decoder.layers.3.self_attn.q_proj": int4wo,
    "model.decoder.layers.3.self_attn.k_proj": int4wo,
    "model.decoder.layers.3.self_attn.v_proj": int4wo,
    # vllm
    "model.decoder.layers.3.self_attn.qkv_proj": int4wo,

    "re:model\.decoder\.layers\..+\.self_attn\.q_proj": float8dyn,
    "re:model\.decoder\.layers\..+\.self_attn\.k_proj": float8dyn,
    "re:model\.decoder\.layers\..+\.self_attn\.v_proj": float8dyn,
    # this should not take effect and we'll fallback to _default
    # since no full mach (missing `j` in the end)
    "re:model\.decoder\.layers\..+\.self_attn\.out_pro": float8dyn,
    # vllm
    "re:model\.decoder\.layers\..+\.self_attn\.qkv_proj": float8dyn,

    "_default": intxwo,
}
quant_config = FqnToConfig(qconfig_dict)
quantization_config = TorchAoConfig(quant_type=quant_config)
quantized_model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,
    quantization_config=quantization_config,
)
print("quantized model:", quantized_model)
tokenizer = AutoTokenizer.from_pretrained(model_id)
for i in range(12):
    if i == 3:
        assert isinstance(quantized_model.model.decoder.layers[i].self_attn.q_proj.weight, Int4TilePackedTo4dTensor)
        assert isinstance(quantized_model.model.decoder.layers[i].self_attn.k_proj.weight, Int4TilePackedTo4dTensor)
        assert isinstance(quantized_model.model.decoder.layers[i].self_attn.v_proj.weight, Int4TilePackedTo4dTensor)
    else:
        assert isinstance(quantized_model.model.decoder.layers[i].self_attn.q_proj.weight, Float8Tensor)
        assert isinstance(quantized_model.model.decoder.layers[i].self_attn.k_proj.weight, Float8Tensor)
        assert isinstance(quantized_model.model.decoder.layers[i].self_attn.v_proj.weight, Float8Tensor)
    assert isinstance(quantized_model.model.decoder.layers[i].self_attn.out_proj.weight, IntxUnpackedToInt8Tensor)

# Manual Testing
prompt = "What are we having for dinner?"
print("Prompt:", prompt)
inputs = tokenizer(
    prompt,
    return_tensors="pt",
).to(quantized_model.device, quantized_model.dtype)
# setting temperature to 0 to make sure result deterministic
generated_ids = quantized_model.generate(**inputs, max_new_tokens=128, temperature=0)

correct_output_text = tokenizer.batch_decode(
    generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print("Response:", correct_output_text[0][len(prompt) :])


# Load model from saved checkpoint
reloaded_model = AutoModelForCausalLM.from_pretrained(
    save_to,
    device_map=torch.accelerator.current_accelerator(),
    torch_dtype=torch.bfloat16,
    # quantization_config=quantization_config,
)

generated_ids = reloaded_model.generate(**inputs, max_new_tokens=128, temperature=0)
output_text = tokenizer.batch_decode(
    generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print("Response:", output_text[0][len(prompt) :])

assert(correct_output_text == output_text)
```

## Serialization

Saving the quantized model with `save_pretrained` (in [safetensors](https://huggingface.co/docs/safetensors/en/index) format) is only supported for torchao >= v0.15. For any version below, it is only possible to manually save as unsafe `.bin` checkpoints with [torch.save](https://docs.pytorch.org/docs/stable/generated/torch.save.html).

<hfoptions id="serialization-examples">
<hfoption id="save-locally">

```py
# torchao >= 0.15
output_dir = "llama3-8b-int4wo-128"
quantized_model.save_pretrained("llama3-8b-int4wo-128")
```

</hfoption>
<hfoption id="push-to-huggingface-hub">

```py
# torchao >= 0.15
USER_ID = "your_huggingface_user_id"
REPO_ID = "llama3-8b-int4wo-128"
quantized_model.push_to_hub(f"{USER_ID}/llama3-8b-int4wo-128")
tokenizer.push_to_hub(f"{USER_ID}/llama3-8b-int4wo-128")
```

```py
# torchao < 0.15 -> unsafe serialization
filename = "llama3-8b-int4wo-128/pytorch_model.bin"
torch.save(quantized_model.state_dict(), filename)
```

</hfoption>
</hfoptions>

## Loading quantized models

Loading a quantized model depends on the quantization scheme. For quantization schemes, like int8 and float8, you can quantize the model on any device and also load it on any device. The example below demonstrates quantizing a model on the CPU and then loading it on CUDA or XPU.

```py
import torch
from transformers import TorchAoConfig, AutoModelForCausalLM, AutoTokenizer
from torchao.quantization import Int8WeightOnlyConfig

quant_config = Int8WeightOnlyConfig(group_size=128)
quantization_config = TorchAoConfig(quant_type=quant_config)

# Load and quantize the model
quantized_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-8B-Instruct",
    dtype="auto",
    device_map="cpu",
    quantization_config=quantization_config
)
# save the quantized model
output_dir = "llama-3.1-8b-torchao-int8"
quantized_model.save_pretrained(output_dir)

# reload the quantized model
reloaded_model = AutoModelForCausalLM.from_pretrained(
    output_dir,
    device_map="auto",
    dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
input_text = "What are we having for dinner?"
input_ids = tokenizer(input_text, return_tensors="pt").to(reloaded_model.device.type)

output = reloaded_model.generate(**input_ids, max_new_tokens=10)
print(tokenizer.decode(output[0], skip_special_tokens=True))

```

For int4, the model can only be loaded on the same device it was quantized on because the layout is specific to the device. The example below demonstrates quantizing and loading a model on the CPU.

```py
import torch
from transformers import TorchAoConfig, AutoModelForCausalLM, AutoTokenizer
from torchao.quantization import Int4WeightOnlyConfig
from torchao.quantization.quantize_.workflows import Int4PackingFormat

quant_config = Int4WeightOnlyConfig(group_size=128, int4_packing_format=Int4PackingFormat.PLAIN_INT32)
quantization_config = TorchAoConfig(quant_type=quant_config)

# Load and quantize the model
quantized_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-8B-Instruct",
    dtype="auto",
    device_map="cpu",
    quantization_config=quantization_config
)
# save the quantized model
output_dir = "llama-3.1-8b-torchao-int4-cpu"
quantized_model.save_pretrained(output_dir)

# reload the quantized model
reloaded_model = AutoModelForCausalLM.from_pretrained(
    output_dir,
    device_map="cpu",
    dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
input_text = "What are we having for dinner?"
input_ids = tokenizer(input_text, return_tensors="pt").to(reloaded_model.device.type)

output = reloaded_model.generate(**input_ids, max_new_tokens=10)
print(tokenizer.decode(output[0], skip_special_tokens=True))

```

## Resources

For a better sense of expected performance, view the [benchmarks](https://github.com/pytorch/ao/tree/main/torchao/quantization#benchmarks) for various models with CUDA and XPU backends. You can also run the code below to benchmark a model yourself.

```py
from torch._inductor.utils import do_bench_using_profiling
from typing import Callable

def benchmark_fn(func: Callable, *args, **kwargs) -> float:
    """Thin wrapper around do_bench_using_profiling"""
    no_args = lambda: func(*args, **kwargs)
    time = do_bench_using_profiling(no_args)
    return time * 1e3

MAX_NEW_TOKENS = 1000
print("int4wo-128 model:", benchmark_fn(quantized_model.generate, **input_ids, max_new_tokens=MAX_NEW_TOKENS, cache_implementation="static"))

bf16_model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", dtype=torch.bfloat16)
output = bf16_model.generate(**input_ids, max_new_tokens=10, cache_implementation="static") # auto-compile
print("bf16 model:", benchmark_fn(bf16_model.generate, **input_ids, max_new_tokens=MAX_NEW_TOKENS, cache_implementation="static"))
```

> [!TIP]
> For best performance, you can use recommended settings by calling `torchao.quantization.utils.recommended_inductor_config_setter()`

Refer to [Other Available Quantization Techniques](https://github.com/pytorch/ao/tree/main/torchao/quantization#other-available-quantization-techniques) for more examples and documentation.

## Issues

If you encounter any issues with the Transformers integration, please open an issue on the [Transformers](https://github.com/huggingface/transformers/issues) repository. For issues directly related to torchao, please open an issue on the [torchao](https://github.com/pytorch/ao/issues) repository.
