Feat (brevitas_examples/llm): load from checkpoint

src/brevitas_examples/llm/README.md

@@ -55,8 +55,8 @@ usage: main.py [-h] [--config CONFIG] [--model MODEL] [--seed SEED]
                [--load-awq LOAD_AWQ]
                [--export-target {None,onnx_qcdq,torch_qcdq,sharded_torchmlir_group_weight,sharded_packed_torchmlir_group_weight}]
                [--export-prefix EXPORT_PREFIX]
-               [--checkpoint-name CHECKPOINT_NAME] [--fuse-sequences]
-               [--learned-round {None,linear_round}]
+               [--checkpoint-name CHECKPOINT_NAME] [--load-checkpoint]
+               [--fuse-sequences] [--learned-round {None,linear_round}]
                [--learned-round-fast-update] [--few-shot-eval]
                [--few-shot-compile] [--few-shot-zeroshot]
                [--few-shot-limit FEW_SHOT_LIMIT]
@@ -202,6 +202,8 @@ options:
   --checkpoint-name CHECKPOINT_NAME
                         Filename to save checkpoint. If `None`, no checkpoint
                         is saved (default: None)
+  --load-checkpoint     Boolean flag to load_checkpoint, uses checkpoint_name.
+                        Default False)
   --fuse-sequences      Whether to merge the dataset sequences in case they
                         are shorter than the requested number of samples per
                         sequence. This is useful in case you would like to

src/brevitas_examples/llm/config/default_template.yml

@@ -43,6 +43,7 @@ learned_round_scale_lr: 0.01
 learned_round_scale_momentum: 0.9
 ln_affine_merge: false
 load_awq: null
+load_checkpoint: false
 model: facebook/opt-125m
 no_float16: false
 no_quantize: false

src/brevitas_examples/llm/main.py

@@ -20,6 +20,7 @@
 from brevitas.export import export_torch_qcdq
 from brevitas.export.inference.manager import quant_inference_mode
 from brevitas.export.onnx.standard.qcdq.manager import StdQCDQONNXManager
+from brevitas.graph import load_quant_model_mode
 from brevitas.graph.equalize import GraphRotationEqualization
 from brevitas.graph.equalize import LayerwiseActivationRotation
 from brevitas.graph.quantize import layerwise_quantize
@@ -327,8 +328,12 @@ def quantize_llm(args):
     if args.act_equalization is not None:
         offload_model(model)
         print(f"Apply act equalization (SmoothQuant) with alpha {args.act_equalization_alpha}")
+        if args.load_checkpoint:
+            loader = [calibration_loader[0]]
+        else:
+            loader = calibration_loader
         apply_act_equalization(
-            model, args.act_equalization, calibration_loader, alpha=args.act_equalization_alpha)
+            model, args.act_equalization, loader, alpha=args.act_equalization_alpha)
         print("Act equalization applied.")
         remove_hooks(model)
 
@@ -423,18 +428,24 @@ def quantize_llm(args):
     for k, v in dict_hooks.items():
         k._hf_hook.post_forward = v
 
-    if args.act_calibration:
+    if args.act_calibration and not args.load_checkpoint:
         print("Apply act calibration...")
         apply_calibration(model, calibration_loader)
         print("Act calibration applied.")
 
     if args.learned_round:
         print("Applying learned round...")
+        if args.load_checkpoint:
+            iters = 1
+            loader = [calibration_loader[0]]
+        else:
+            iters = args.learned_round_iters
+            loader = calibration_loader
         remove_hooks(model)
         apply_learned_round(
             model,
-            calibration_loader,
-            iters=args.learned_round_iters,
+            loader,
+            iters=iters,
             block_name_attribute=args.gpxq_block_name,
             learn_scale=args.learned_round_scale,
             scale_optimizer_class='sgd',
@@ -446,7 +457,13 @@ def quantize_llm(args):
 
         model = offload_model(model)
 
-    if args.gptq:
+    if args.load_checkpoint:
+        remove_hooks(model)
+        with load_quant_model_mode(model):
+            model.load_state_dict(torch.load(args.checkpoint_name, map_location='cpu'))
+        model = offload_model(model)
+
+    if args.gptq and not args.load_checkpoint:
         print("Applying GPTQ...")
         apply_gptq(
             model,
@@ -459,7 +476,7 @@ def quantize_llm(args):
             max_accumulator_tile_size=args.gpxq_max_accumulator_tile_size)
         print("GPTQ applied.")
 
-    if args.gpfq:
+    if args.gpfq and not args.load_checkpoint:
         print("Applying GPFQ...")
         apply_gpfq(
             model,
@@ -470,7 +487,7 @@ def quantize_llm(args):
             max_accumulator_tile_size=args.gpxq_max_accumulator_tile_size)
         print("GPFQ applied.")
 
-    if args.bias_corr:
+    if args.bias_corr and not args.load_checkpoint:
         print("Applying bias correction...")
         apply_bias_correction(model, calibration_loader)
         print("Bias correction applied.")
@@ -507,7 +524,7 @@ def quantize_llm(args):
         print(results)
     remove_hooks(model)
 
-    if args.checkpoint_name is not None:
+    if args.checkpoint_name is not None and not args.load_checkpoint:
         print(f"Saving checkpoint to {args.checkpoint_name}")
         torch.save(model.state_dict(), args.checkpoint_name)
 
@@ -808,6 +825,10 @@ def parse_args(args, override_defaults={}):
         default=None,
         help="Filename to save checkpoint. If `None`, no checkpoint is saved (default: %(default)s)"
     )
+    parser.add_argument(
+        '--load-checkpoint',
+        action="store_true",
+        help='Boolean flag to load_checkpoint, uses checkpoint_name. Default %(default)s)')
     parser.add_argument(
         "--fuse-sequences",
         action="store_true",