add fp8 vgg16 example

examples/int8/training/vgg16/main.py

@@ -25,6 +25,12 @@
 PARSER.add_argument("--drop-ratio", default=0.0, type=float, help="Dropout ratio")
 PARSER.add_argument("--momentum", default=0.9, type=float, help="Momentum")
 PARSER.add_argument("--weight-decay", default=5e-4, type=float, help="Weight decay")
+PARSER.add_argument(
+    "--fp8-epochs",
+    default=0,
+    type=int,
+    help="Enable FP8 and specify the number of epochs after the regular training to quantize the model to FP8",
+)
 PARSER.add_argument(
     "--ckpt-dir",
     default="/tmp/vgg16_ckpts",
@@ -167,6 +173,50 @@ def main():
                 ckpt_dir=args.ckpt_dir,
             )
 
+    if args.fp8_epochs > 0:
+        print("[PTQ] Quantizing model to FP8...")
+        import modelopt.torch.quantization as mtq
+        import torch_tensorrt as torchtrt
+        from modelopt.torch.quantization.utils import export_torch_mode
+
+        def calibrate_loop(model):
+            # calibrate on a small number of batches
+            for fp8_ep in range(args.fp8_epochs):
+                print("Epoch: [%5d / %5d]" % (fp8_ep + 1, args.fp8_epochs))
+                total = 0
+                correct = 0
+                loss = 0.0
+                for data, labels in training_dataloader:
+                    data, labels = data.cuda(), labels.cuda(non_blocking=True)
+                    out = model(data)
+                    loss += crit(out, labels)
+                    preds = torch.max(out, 1)[1]
+                    total += labels.size(0)
+                    correct += (preds == labels).sum().item()
+
+                print(
+                    "Test Loss: {:.5f} Test Acc: {:.2f}%".format(
+                        loss / total, 100 * correct / total
+                    )
+                )
+
+        quant_cfg = mtq.FP8_DEFAULT_CFG
+        mtq.quantize(model, quant_cfg, forward_loop=calibrate_loop)
+        # model has FP8 qdq nodes at this point
+        with torch.no_grad():
+            with export_torch_mode():
+                input_tensor = images.cuda()
+                exp_program = torch.export.export(model, (input_tensor,))
+                trt_model = torchtrt.dynamo.compile(
+                    exp_program,
+                    inputs=[input_tensor],
+                    enabled_precisions={torch.float8_e4m3fn},
+                    min_block_size=1,
+                    debug=False,
+                )
+                outputs_trt = trt_model(input_tensor)
+                print("TRT outputs:\n", outputs_trt)
+
 
 def train(model, dataloader, crit, opt, epoch):
     model.train()

examples/int8/training/vgg16/requirements.txt

@@ -4,3 +4,5 @@ nvidia-pyindex
 --extra-index-url https://pypi.nvidia.com
 pytorch-quantization
 tqdm
+nvidia-modelopt
+--extra-index-url https://pypi.nvidia.com