From afc8abfc980343d2bfc9a02f1eef7ffcc5ec66f1 Mon Sep 17 00:00:00 2001 From: andrewor14 Date: Mon, 5 Aug 2024 09:14:09 -0700 Subject: [PATCH] Add QAT README --- torchao/quantization/prototype/qat/README.md | 125 ++++++++++++++++++ .../prototype/qat/images/qat_diagram.png | Bin 0 -> 239599 bytes 2 files changed, 125 insertions(+) create mode 100644 torchao/quantization/prototype/qat/README.md create mode 100644 torchao/quantization/prototype/qat/images/qat_diagram.png diff --git a/torchao/quantization/prototype/qat/README.md b/torchao/quantization/prototype/qat/README.md new file mode 100644 index 0000000000..4be16ef041 --- /dev/null +++ b/torchao/quantization/prototype/qat/README.md @@ -0,0 +1,125 @@ +# 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. + +![qat](images/qat_diagram.png) + +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). diff --git a/torchao/quantization/prototype/qat/images/qat_diagram.png b/torchao/quantization/prototype/qat/images/qat_diagram.png new file mode 100644 index 0000000000000000000000000000000000000000..3d990e2bf19ef1aa7e65a8dd07e4b71cf8882a2a GIT binary patch literal 239599 zcmeFabyStx+CB_RkPeYX1cR1RIu~F8B1m_KASvA}rM7^glt^zxkj_PibVw-;l7iCR z_00!R_t_iIJAQk7e;&p_7EjGN@4W77-VgpEcmEC^HYGL+3JRX&-J9~@M=lD=nLf<3 z;7CE_LK_MSc7~~hgq);=1g+d-D`Qi0BNUXoe}qL~$SF*dHdMM=31G2ag2{{H-EzU9 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