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Quantization

Typically quantization algorithms will have different schemes for how the activation and weights are quantized so A16W8 for instance means the activations are quantized to 16 bits wheras the weights are quantized to 8 bits. Trying out different quantization schemes in torchao is generally a 1 line change. Note: exact APIs are not stable, we may change them in the future.

Benchmarks

Benchmarks and evaluation are run on a machine with a single NVIDIA-A100-80GB GPU using the scripts for generation and eval. Evaluation was done using the lm_eval library for tasks/data. The models used were meta-llama/Llama-2-7b-chat-hf and meta-llama/Meta-Llama-3-8B.

Model Technique wikitext-perplexity Tokens/Second Memory Bandwidth (GB/s) Peak Memory (GB) Model Size (GB)
Llama-2-7B Base (bfloat16) 12.212 107.38 1418.93 13.88 13.21
int8dq 12.262 9.61 63.67 8.61 6.62
int8wo 12.204 170.83 1131.18 8.95 6.62
fp6 12.369 117.89 584.57 6.52 4.96
int4wo-64 12.843 201.14 751.42 4.87 3.74
int4wo-64-GPTQ 12.527 201.14 751.42 4.87 3.74
autoquant-int4hqq 12.825 209.19 804.32 4.89 3.84
Llama-3-8B Base (bfloat16) 7.441 95.64 1435.54 16.43 15.01
int8dq 7.581 8.61 64.75 9.24 7.52
int8wo 7.447 153.03 1150.80 10.42 7.52
fp6 7.661 161.58 910.02 7.72 5.63
int4wo-64 8.316 180.80 763.33 6.88 4.22
int4wo-64-GPTQ 7.921 180.80 763.33 6.88 4.22
autoquant-int4hqq 8.110 188.41 800.58 7.14 4.25

note: Int8 dynamic quantization works best on compute bound models like SAM whereas Llama with batchsize=1 tends to be memory bound, thus the rather low performance.

For int4 we make heavy use of tinygemm of torch.ops.aten._weight_int4pack_mm to bitpack into a layout optimized for tensor cores

And a quick crash course on inference quantization to help parse the above table. Int4 quantization is an ambiguous term because there's the dtype in which a layer is represented and then the dtype in which the computation is done. For example, if you're using Weight-Only (wo) int4 quantization that means that the layer will be upcasted to a larger dtype like fp16 so an int4 matrix multiplication is defined as F.linear(input, weight.to(input.dtype)). Dynamic quantization (DQ) primarily targets activations, enabling on-the-fly quantization from higher precision formats like bf16 to lower precision formats such as int8. This process, when supported by hardware, allows for direct computation, such as performing F.linear(input, weight). Naive quantization algorithms are also notoriously sensitive to outliers so we also typically set a group size that applies a scale factor per group of 64 elements in the case of int4wo-64.

Autoquantization

Autoquantization is a tool to automatically determine the best way to apply quantization to your model by comparing the performance of each quantization technique to each layer for the input types and shapes you care about.

import torch
import torchao
from torchao.quantization import  DEFAULT_INT4_AUTOQUANT_CLASS_LIST

# Plug in your model and example input
model = torch.nn.Sequential(torch.nn.Linear(32, 64)).cuda().to(torch.bfloat16)
input = torch.randn(32,32, dtype=torch.bfloat16, device='cuda')
use_autoquant_default = True

if use_autoquant_default:
    # perform autoquantization and torch.compile with default settings
    model = torchao.autoquant(torch.compile(model, mode='max-autotune'))
elif not use_autoquant_default:
    # perform autoquantization and torch.compile with int4 support
    model = torchao.autoquant(torch.compile(model, mode='max-autotune'), qtensor_class_list=DEFAULT_INT4_AUTOQUANT_CLASS_LIST)

# pass in an input which is used in order to pick fastest quantization operations
# and apply torch compilation.
model(input)

When used as in the example above, when the autoquant api is called alongside torch.compile, autoquant sets up the model so that when its run on the next input, the autoquantization and torch.compile processes leave you with a heavily optimized model.

When model(input) is called, (under the hood) the tool does a preliminary run with the input where each linear layer keeps track of the different shapes and types of activations that it sees. Once the preliminary run is complete, the next step is to check each linear layer and benchmark the tracked shapes for different types of quantization techniques in order to pick the fastest one, attempting to take into account fusions where possible. Finally once the best class is found for each layer, the next step is to apply the necessary quantization technique to each layer, before finally allowing the normal torch.compile process to occur on the now quantized model. By default the api only uses int8 techniques, i.e. it chooses between no quantization, int8 dynamic quantization and int8 weight only quantization for each layer, though there is also an option add int4 quantization which can be used for maximum performance or to avoid perf regressions from int4_weight_only() since for certain (compute bound) regimes, int4 weight only quantization can be very slow.

Sometimes it is desirable to reuse a quantization plan that autoquant came up with. torchao.quantization.AUTOQUANT_CACHE is a dictionary holding autoquant's benchmark results. We can save it and restore it later, which will cause autoquant to choose the same quantization methods.

import pickle
import torchao.quantization

# After the first forward pass (when quantization was done)
from torchao.quantization.autoquant import AUTOQUANT_CACHE
with open("quantization-cache.pkl", "wb") as f:
    pickle.dump(AUTOQUANT_CACHE)

# On load
from torchao.quantization.autoquant import AUTOQUANT_CACHE
with open("quantization-cache.pkl", "rb") as f:
    AUTOQUANT_CACHE.update(pickle.load(f))

Quantization Techniques

While the above autoquant api tries multiple quantization techniques to find the best combination for your model, the techniques themselves can be applied individually. While there are a large variety of quantization apis, the following techniques have been thoroughly tested and perform well for the metrics they seek to optimize. Each are examples of affine quantization

A16W4 WeightOnly Quantization

# for torch 2.4+
from torchao.quantization import quantize_, int4_weight_only
group_size = 32

# you can enable [hqq](https://ithub.com/mobiusml/hqq/tree/master) quantization which is expected to improves accuracy through
# use_hqq flag for `int4_weight_only` quantization
use_hqq = False
quantize_(model, int4_weight_only(group_size=group_size, use_hqq=use_hqq))

# for torch 2.2.2 and 2.3
from torchao.quantization.quant_api import change_linear_weights_to_int4_woqtensors
change_linear_weights_to_int4_woqtensors(model)

Note: The quantization error incurred by applying int4 quantization to your model can be fairly significant, so using external techniques like GPTQ may be necessary to obtain a usable model.

A16W8 Int8 WeightOnly Quantization

# for torch 2.4+
from torchao.quantization import quantize_, int8_weight_only
quantize_(model, int8_weight_only())

# for torch 2.2.2 and 2.3
from torchao.quantization.quant_api import change_linear_weights_to_int8_woqtensors
change_linear_weights_to_int8_woqtensors(model)

A8W8 Int8 Dynamic Quantization

# for torch 2.4+
from torchao.quantization import quantize_, int8_dynamic_activation_int8_weight
quantize_(model, int8_dynamic_activation_int8_weight())

# for torch 2.2.2 and 2.3
from torchao.quantization.quant_api import change_linear_weights_to_int8_dqtensors
change_linear_weights_to_int8_dqtensors(model)

A16W6 Floating Point WeightOnly Quantization

# for torch 2.4+
from torchao.quantization import quantize_, fpx_weight_only
quantize_(model, fpx_weight_only(3, 2))

You can find more information here. It should be noted where most other TorchAO apis and benchmarks have focused on applying techniques on top of a bf16 model, performance, fp6 works primarily with the fp16 dtype.

Affine Quantization Details

Affine quantization refers to the type of quantization that maps from high precision floating point numbers to quantized numbers (low precision integer or floating point dtypes) with an affine transformation, i.e.: quantized_val = high_preicsion_float_val / scale + zero_point where scale and zero_point are quantization parameters for some granularity and based on some data (also some dtypes may not require a zero_point). Each of the techniques in the above section qualify as Affine Quantization.

Quantization Primitives

We used to have different quantize and dequantize operators for quantization with different granularities. But in the end these can all be expressed with a block_size argument with different settings, so we unified existing quant primitives to choose_qparams_affine, quantize_affine and dequantize_affine that can represent symmetric/asymmetric per tensor/channel/token/channel_group quantization, this can be used to implement the unified quantized tensor subclass.

Note: these primitive ops supports two "types" of quantization, distinguished by whether zero_point is in floating point domain or integer domain. See docstrings for choose_qparams for more details.

Quantized Tensor Subclass

We also have a unified quantized tensor subclass that implements how to get a quantized tensor from floating point tensor and what does it mean to call linear ops on an instance of the tensor, e.g. F.linear and aten.addmm, with this we could dispatch to different operators (e.g. int4mm op) based on device (cpu, cuda) and quantization settings (int4, int8) and also packing formats (e.g. format optimized for cpu int4 mm kernel)

Layouts

We extended the layout concept to represent different packing formats for a tensor. AffineQuantizedTensor supports plain and tensor_core_tiled layout. plain layout is used for int8_weight_only and int8_dynamic_activation_int8_weight and also as a default layout. tensor_core_tiled layout is used for int4_weight_only quantization and is packing the weights in a format that is compatible with tinygemm int4mm kernels.

Full Affine Quantization Flow Example

Let's use int4 weight only quantization that's targeting tinygemm int4 weight only quantized matmul as an example:

import torch
from torchao.quantization.quant_primitives import MappingType, ZeroPointDomain
from torchao.dtypes import to_affine_quantized_intx
import copy
from torchao.quantization.quant_api import (
    quantize_,
    int4_weight_only,
)

class ToyLinearModel(torch.nn.Module):
    def __init__(self, m=64, n=32, k=64):
        super().__init__()
        self.linear1 = torch.nn.Linear(m, n, bias=False)
        self.linear2 = torch.nn.Linear(n, k, bias=False)

    def example_inputs(self, batch_size=1, dtype=torch.float32, device="cpu"):
        return (torch.randn(batch_size, self.linear1.in_features, dtype=dtype, device=device),)

    def forward(self, x):
        x = self.linear1(x)
        x = self.linear2(x)
        return x

dtype = torch.bfloat16
m = ToyLinearModel(1024, 1024, 1024).eval().to(dtype).to("cuda")
m_bf16 = copy.deepcopy(m)
example_inputs = m.example_inputs(dtype=dtype, device="cuda")

m_bf16 = torch.compile(m_bf16, mode='max-autotune')
# apply int4 weight only quant (compatible with tinygemm int4 weight only quant mm kernel in torchao)
group_size = 32
# only works for torch 2.4+
quantize_(m, int4_weight_only(group_size=group_size))

# temporary workaround for tensor subclass + torch.compile
# NOTE: this is only need for torch version < 2.5+
from torchao.utils import TORCH_VERSION_AT_LEAST_2_5
from torchao.utils import unwrap_tensor_subclass
if not TORCH_VERSION_AT_LEAST_2_5:
    unwrap_tensor_subclass(m)
# compile the model to improve performance
m = torch.compile(m, mode='max-autotune')

# benchmark to see the speedup
from torchao.utils import benchmark_model

num_runs = 100
torch._dynamo.reset()
bf16_time = benchmark_model(m_bf16, num_runs, example_inputs)
print(f"bf16 mean time: {bf16_time}")
int4_time = benchmark_model(m, num_runs, example_inputs)
print(f"int4 weight only quantized mean time: {int4_time}")
print(f"speedup: {bf16_time / int4_time}")

# output (1xA100 GPU machine)
bf16 mean time: 71.457685546875
int4 weight only quantized mean time: 31.4580908203125
speedup: 2.2715200981216173

What we do underlying the APIs are roughly the following:

from torchao.dtypes import to_affine_quantized_intx
def int8wo_quant(weight):
    return to_affine_quantized_intx(weight, MappingType.SYMMETRIC, (1, weight.shape[1]), torch.int8, eps=torch.finfo(torch.float32).eps, zero_point_dtype=torch.int64)

for module, name in model.named_modules():
    if isinstance(module, torch.nn.Linear):
        # optional filtering for module name, shape etc.
        m.weight = nn.Parameter(int8wo_quant(module.weight))

        # note: quantization for activation need to be applied after the weight quantization
        # quantization activation (needed by dynamic quantization)
        input_quant_func = int8wo_quant  # specify how input activation is quantized
        module.weight = nn.Parameter(to_linear_activation_quantized(module.weight, input_quant_func))

Workaround with unwrap_tensor_subclass for export, AOTI and torch.compile (pytorch 2.4 and before only)

The model/tensor subclass should also be compatible with AOTI and torch.export, currently we can support torch.export.export and torch.aot_compile with the following workaround:

from torchao.utils import unwrap_tensor_subclass
m_unwrapped = unwrap_tensor_subclass(m)


# export
m = torch.export.export(m_unwrapped, example_inputs).module()

# aot_compile
torch._export.aot_compile(m_unwrapped, example_inputs)

For torch.compile, if you are using pytorch nightly or pytorch 2.5+, you won't need to use unwrap_tensor_subclass in order to be compatible with torch.compile, but if you use 2.4 or before, you'll need to use unwrap_tensor_subclass as well to be able to run torch.compile on the quantized model.

Note that the workaround will not be needed after pytorch/pytorch#129682 is fixed.

Other Available Quantization Techniques

KV Cache Quantization

We've added kv cache quantization and other features in order to enable long context length (and necessarily memory efficient) inference.

In practice these features alongside int4 weight only quantization allow us to reduce peak memory by ~55%, meaning we can Llama3.1-8B inference with a 130k context length with only 18.9 GB of peak memory. More details can be found here

Sparse-Marlin

Sparse-Marlin 2:4 is an optimized GPU kernel that extends the Mixed Auto-Regressive Linear (Marlin) dense kernel to support 4-bit quantized weights and 2:4 sparsity for extremely high performance.

Model Technique Tokens/Second Memory Bandwidth (GB/s) Peak Memory (GB) Model Size (GB)
Llama-3-8B Base (bfloat16) 95.64 1435.54 16.43 15.01
int8wo 153.03 1150.80 10.42 7.52
int4wo-64 180.80 763.33 6.88 4.22
int4wo-64-sparse-marlin 226.02 689.20 5.32 3.05

More details can be found here

UINTx Quantization

We're trying to develop kernels for low bit quantization for intx quantization formats. While the current performance is not ideal, we're hoping to continue to iterate on these kernels to improve their performance.

Model Technique wikitext-perplexity Tokens/Second Memory Bandwidth (GB/s) Peak Memory (GB) Model Size (GB)
Llama-2-7B Base (bfloat16) 12.212 107.38 1418.93 13.88 13.21
uintx-4-64-hqq 12.775 50.99 200.08 6.29 3.92
uintx-2-8-hqq 24.500 40.25 265.95 9.24 6.61
Llama-3-8B Base (bfloat16) 7.441 95.64 1435.54 16.43 15.01
uintx-4-64-hqq 8.124 47.85 213.24 11.85 4.46
uintx-2-8-hqq 39.605 34.83 261.42 14.99 7.51

You try can out these apis with the quantize_ api as above alongside the constructor uintx_weight_only an example can be found in in torchao/_models/llama/generate.py.

Automatic Inductor Configuration

The quantize_ and autoquant apis now automatically use our recommended inductor configuration setings. You can mimic the same configuration settings for your own experiments by using the torchao.quantization.utils.recommended_inductor_config_setter to replicate our recommended configuration settings. Alternatively if you wish to disable these recommended settings, you can use the key word argument set_inductor_config and set it to false in the quantize_ or autoquant apis to prevent assignment of those configuration settings. You can also overwrite these configuration settings after they are assigned if you so desire, as long as they are overwritten before passing any inputs to the torch.compiled model. This means that previous flows which referenced a variety of inductor configurations that needed to be set are now outdated, though continuing to manually set those same inductor configurations is unlikely to cause any issues.

(prototype) A16W8 Float8 WeightOnly Quantization

# for torch 2.5+
from torchao.quantization import quantize_, float8_weight_only
quantize_(model, float8_weight_only())

This API works today but has not been extensively tested and benchmarked yet. Hardware with CUDA compute capability 8.9 or greater is required.

(prototype) A16W8 Float8 Dynamic Quantization with Rowwise Scaling

# for torch 2.5+
from torchao.quantization.quant_api import quantize_, PerRow, float8_dynamic_activation_float8_weight
quantize_(model, float8_dynamic_activation_float8_weight(granularity=PerRow()))

This API works today but has not been extensively tested and benchmarked yet. Hardware with CUDA compute capability 8.9 or greater is required.

(To be moved to prototype) A16W4 WeightOnly Quantization with GPTQ

from torchao._models._eval import InputRecorder, TransformerEvalWrapper
from torchao.quantization.GPTQ import Int4WeightOnlyGPTQQuantizer
precision = torch.bfloat16
device = "cuda"
checkpoint_file_name = "../gpt-fast/checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth"
checkpoint_path = Path(checkpoint_file_name)
model = Transformer.from_name(checkpoint_path.parent.name)
checkpoint = torch.load(str(checkpoint_path), mmap=True, weights_only=True)
model.load_state_dict(checkpoint, assign=True)
model = model.to(dtype=precision, device="cpu")
model.eval()
tokenizer_path = checkpoint_path.parent / "tokenizer.model"
assert tokenizer_path.is_file(), tokenizer_path
tokenizer = SentencePieceProcessor(  # pyre-ignore[28]
    model_file=str(tokenizer_path)
)
blocksize = 128
percdamp = 0.01
groupsize = 128
calibration_tasks = ["wikitext"]
calibration_limit = 1
calibration_seq_length = 100
input_prep_func = prepare_inputs_for_model
pad_calibration_inputs = False

inputs = InputRecorder(
    tokenizer,
    calibration_seq_length,
    input_prep_func,
    pad_calibration_inputs,
    model.config.vocab_size,
    device="cpu",
).record_inputs(
    calibration_tasks,
    calibration_limit,
).get_inputs()

quantizer = Int4WeightOnlyGPTQQuantizer(
    blocksize,
    percdamp,
    groupsize,
)
model.setup_caches(max_batch_size=1, max_seq_length=calibration_seq_length)
model = quantizer.quantize(model, inputs).cuda()

(To be deprecated) A8W8 Dynamic Quantization

from torchao.quantization.quant_api import Int8DynActInt4WeightQuantizer
quantizer = Int8DynActInt4WeightQuantizer(groupsize=128)
model = quantizer.quantize(model)

This is used in ExecuTorch to quantize llama model right now.

(To be moved to prototype) A8W8 Dynamic Quantization with Smoothquant

We've also implemented a version of smoothquant with the same GEMM format as above. Due to requiring calibration, the API is more complicated.

Example

import torch
from torchao.quantization.smoothquant import swap_linear_with_smooth_fq_linear, smooth_fq_linear_to_inference

# Fuse the int8*int8 -> int32 matmul and subsequent mul op avoiding materialization of the int32 intermediary tensor
torch._inductor.config.force_fuse_int_mm_with_mul = True

# plug in your model
model = get_model()

# convert linear modules to smoothquant
# linear module in calibration mode
swap_linear_with_smooth_fq_linear(model)

# Create a data loader for calibration
calibration_data = get_calibration_data()
calibration_dataset = MyDataset(calibration_data)
calibration_loader = DataLoader(calibration_dataset, batch_size=32, shuffle=True)

# Calibrate the model
model.train()
for batch in calibration_loader:
    inputs = batch
    model(inputs)

# set it to inference mode
smooth_fq_linear_to_inference(model)

# compile the model to improve performance
model = torch.compile(model, mode='max-autotune')
model(input)

Notes

  1. APIs have been hardware tested on A100 and T4(colab)
  2. While these techniques are designed to improve model performance, in some cases the opposite can occur. This is because quantization adds additional overhead to the model that is hopefully made up for by faster matmuls (dynamic quantization) or loading weights faster (weight-only quantization). If your matmuls are small enough or your non-quantized perf isn't bottlenecked by weight load time, these techniques may reduce performance.
  3. Use the PyTorch nightlies so you can leverage tensor subclasses which is preferred over older module swap based methods because it doesn't modify the graph and is generally more composable and flexible.