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+# Quantization-Aware Training (QAT)
+
+Quantization-Aware Training (QAT) refers to applying fake quantization during the
+training or fine-tuning process, such that the final quantized model will exhibit
+higher accuracies and perplexities. Fake quantization refers to rounding the float
+values to quantized values without actually casting them to dtypes with lower
+bit-widths, in contrast to post-training quantization (PTQ), which does cast the
+quantized values to lower bit-width dtypes, e.g.:
+
+```
+# PTQ: x_q is quantized and cast to int8
+# scale and zero point (zp) refer to parameters used to quantize x_float
+# qmin and qmax refer to the range of quantized values
+x_q = (x_float / scale + zp).round().clamp(qmin, qmax).cast(int8)
+
+# QAT: x_fq is still in float
+# Fake quantize simulates the numerics of quantize + dequantize
+x_fq = (x_float / scale + zp).round().clamp(qmin, qmax)
+x_fq = (x_fq - zp) * scale
+```
+
+## API
+
+torchao currently supports two QAT schemes for linear layers:
+- int8 per token dynamic activations + int4 per group weights
+- int4 per group weights
+
+QAT typically involves applying a transformation to your model before and after training.
+In torchao, these are represented as the prepare and convert steps: (1) prepare inserts
+fake quantize operations into linear layers, and (2) convert transforms the fake quantize
+operations to actual quantize and dequantize operations after training, thereby producing
+a quantized model (dequantize operations are typically fused with linear after lowering).
+Between these two steps, training can proceed exactly as before.
+
+
+
+To use QAT in torchao, apply the prepare step using the appropriate Quantizer before
+training, then apply the convert step after training for inference or generation.
+For example, on a single GPU:
+
+```python
+import torch
+from torchtune.models.llama3 import llama3
+from torchao.quantization.prototype.qat import Int8DynActInt4WeightQATQuantizer
+
+# Smaller version of llama3 to fit in a single GPU
+model = llama3(
+ vocab_size=4096,
+ num_layers=16,
+ num_heads=16,
+ num_kv_heads=4,
+ embed_dim=2048,
+ max_seq_len=2048,
+).cuda()
+
+# Quantizer for int8 dynamic per token activations +
+# int4 grouped per channel weights, only for linear layers
+qat_quantizer = Int8DynActInt4WeightQATQuantizer()
+
+# Insert "fake quantize" operations into linear layers.
+# These operations simulate quantization numerics during
+# training without performing any dtype casting
+model = qat_quantizer.prepare(model)
+
+# Standard training loop
+optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-5)
+loss_fn = torch.nn.CrossEntropyLoss()
+for i in range(10):
+ example = torch.randint(0, 4096, (2, 16)).cuda()
+ target = torch.randn((2, 16, 4096)).cuda()
+ output = model(example)
+ loss = loss_fn(output, target)
+ loss.backward()
+ optimizer.step()
+ optimizer.zero_grad()
+
+# Convert fake quantize to actual quantize operations
+# The quantized model has the exact same structure as the
+# quantized model produced in the corresponding PTQ flow
+# through `Int8DynActInt4WeightQuantizer`
+model = qat_quantizer.convert(model)
+
+# inference or generate
+```
+
+Users can also leverage our integration with [torchtune](https://github.com/pytorch/torchtune)
+and apply quantized-aware fine-tuning as follows:
+
+```
+tune run --nproc_per_node 8 qat_distributed --config llama3/8B_qat_full
+```
+
+For more detail, please refer to [this QAT tutorial](https://pytorch.org/torchtune/main/tutorials/qat_finetune.html).
+
+
+## Evaluation Results
+
+Evaluation was performed on 6-8 A100 GPUs (80GB each) using the torchtune QAT
+integration described above. We fine-tune [Llama3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
+on the [C4 dataset](https://huggingface.co/datasets/allenai/c4) (en subset)
+for 5000 steps using a group size of 256 for the weights. Note that extensive
+hyperparameter tuning may further improve these results.
+
+Results for int8 per token dynamic activations + int4 per group weights, using a learning rate of 2e-5:
+
+| | hellaswag
(acc) | hellaswag
(acc_norm) | wikitext
(word_perplexity) | wikitext
(byte_perplexity) | wikitext
(bits_per_byte) |
+| ---------------- | ------ | ------ | ------ | ------ | ------ |
+| No quantization | 57.86% | 76.60% | 8.905 | 1.505 | 0.590 |
+| PTQ | 51.74% | 70.66% | 11.878 | 1.588 | 0.668 |
+| QAT (quantized) | 57.25% | 76.51% | 9.859 | 1.534 | 0.617 |
+| PTQ degradation | -6.11% | -5.94% | +2.973 | +0.083 | +0.078 |
+| QAT degradation | -0.61% | -0.21% | +0.947 | +0.029 | +0.027 |
+
+Results for int4 per group weights, using a learning rate of 2e-6. For this quantization scheme, the
+quantized path uses the more efficient [int4 tinygemm kernel](https://github.com/pytorch/pytorch/blob/a672f6c84e318bbf455f13dfdd3fd7c68a388bf5/aten/src/ATen/native/cuda/int4mm.cu#L1097).
+
+| | hellaswag
(acc) | hellaswag
(acc_norm) | wikitext
(word_perplexity) | wikitext
(byte_perplexity) | wikitext
(bits_per_byte) |
+| ---------------- | -------- | ------- | ------ | ------ | ------ |
+| No quantization | 57.16% | 77.02% | 8.858 | 1.504 | 0.589 |
+| PTQ | 55.06% | 74.24% | 10.311 | 1.547 | 0.630 |
+| QAT (quantized) | 55.86% | 75.06% | 10.134 | 1.542 | 0.625 |
+| PTQ degradation | -2.10% | -2.78% | +1.453 | +0.043 | +0.041 |
+| QAT degradation | -1.30% | -1.96% | +1.276 | +0.038 | +0.036 |
+
+For more details, please refer to [this blog post](https://pytorch.org/blog/quantization-aware-training).
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