re-enable int8 for api change (#579)

models/recommendation/pytorch/dlrm/product/dlrm_s_pytorch.py

@@ -97,6 +97,10 @@
 # intel
 import intel_extension_for_pytorch as ipex
 from torch.utils import ThroughputBenchmark
+
+# int8
+from torch.ao.quantization import MinMaxObserver, PerChannelMinMaxObserver, QConfig
+from intel_extension_for_pytorch.quantization import prepare, convert
 # For distributed run
 import extend_distributed as ext_dist
 
@@ -403,12 +407,18 @@ def trace_model(args, dlrm, test_ld):
                 dlrm.emb_l.bfloat16()
             dlrm = ipex.optimize(dlrm, dtype=torch.bfloat16, inplace=True)
         elif args.int8:
-            conf = ipex.quantization.QuantConf(args.int8_configure)
-            dlrm = ipex.quantization.convert(dlrm, conf, (X, lS_o, lS_i))
+            qconfig = QConfig(activation=MinMaxObserver.with_args(qscheme=torch.per_tensor_symmetric, dtype=torch.qint8),
+                weight=PerChannelMinMaxObserver.with_args(dtype=torch.qint8, qscheme=torch.per_channel_symmetric))
+            prepared_dlrm = prepare(dlrm, qconfig, example_inputs=(X, lS_o, lS_i), inplace=True)
+            prepared_dlrm.load_qconf_summary(qconf_summary = args.int8_configure)
+            dlrm = convert(prepared_dlrm)
         else:
             dlrm = ipex.optimize(dlrm, dtype=torch.float, inplace=True)
         if args.int8:
-            dlrm = freeze(dlrm)
+            dlrm = torch.jit.trace(dlrm, [X, lS_o, lS_i])
+            dlrm = torch.jit.freeze(dlrm)
+            dlrm(X, lS_o, lS_i)
+            dlrm(X, lS_o, lS_i)
         else:
             with torch.cpu.amp.autocast(enabled=args.bf16):
                 dlrm = torch.jit.trace(dlrm, (X, lS_o, lS_i), check_trace=True)
@@ -666,6 +676,7 @@ def run():
     parser.add_argument("--ipex-merged-emb", action="store_true", default=False)
     parser.add_argument("--num-warmup-iters", type=int, default=1000)
     parser.add_argument("--int8", action="store_true", default=False)
+    parser.add_argument("--calibration", action="store_true", default=False)
     parser.add_argument("--int8-configure", type=str, default="./int8_configure.json")
     parser.add_argument("--dist-backend", type=str, default="ccl")
 
@@ -743,6 +754,7 @@ def run():
         sigmoid_top=ln_top.size - 2,
         loss_threshold=args.loss_threshold,
     )
+
     if args.ipex_merged_emb:
         dlrm.emb_l = ipex.nn.modules.MergedEmbeddingBagWithSGD.from_embeddingbag_list(dlrm.emb_l, lr=args.learning_rate)
         dlrm.need_linearize_indices_and_offsets = torch.BoolTensor([False])
@@ -805,6 +817,26 @@ def run():
         print("Testing state: accuracy = {:3.3f} %".format(ld_acc_test * 100))
 
     ext_dist.barrier()
+
+    if args.calibration:
+        assert args.load_model != "", "need load weight to do calibration"
+        dlrm.eval()
+        qconfig = QConfig(activation=MinMaxObserver.with_args(qscheme=torch.per_tensor_symmetric, dtype=torch.qint8),
+            weight=PerChannelMinMaxObserver.with_args(dtype=torch.qint8, qscheme=torch.per_channel_symmetric))
+        for j, inputBatch in enumerate(train_ld):
+            X, lS_o, lS_i, T, W, CBPP = unpack_batch(inputBatch)
+            example_inputs = (X, lS_o, lS_i)
+            prepared_dlrm = prepare(dlrm, qconfig, example_inputs=example_inputs, inplace=True)
+            break
+
+        for j, inputBatch in enumerate(train_ld):
+            prepared_dlrm(X, lS_o, lS_i)
+            if j == 2:
+                break
+        prepared_dlrm.save_qconf_summary(qconf_summary = args.int8_configure)
+        print("calibration done, save config file to ", args.int8_configure)
+        exit()
+
     print("time/loss/accuracy (if enabled):")
 
     if args.bf16 and not args.inference_only: