[quantization] Introduce smse_for_gptq

This PR introduces smse_for_gptq to improve accuracy.

TICO-DCO-1.0-Signed-off-by: s.malakhov <s.malakhov@partner.samsung.com>

Author: stamalakhov

tico/quantization/algorithm/fpi_gptq/fpi_gptq.py

@@ -32,30 +32,7 @@
 )
 
 from tico.quantization.algorithm.gptq.quant import quantize, Quantizer
-
-
-def iterate_GPTQ(scale, zero, maxq, W, Hinv, max_num_of_iters=50):
-
-    cur_weights = W.clone()
-    mults = torch.pow(torch.diag(Hinv), -1)
-    Hinv_U = torch.triu(Hinv, diagonal=1)
-
-    init_weights = W.clone()
-    for _ in range(max_num_of_iters):
-        cur_Q = quantize(cur_weights, scale, zero, maxq)
-
-        d_W = torch.mul((cur_weights - cur_Q), mults)
-        cur_weights = init_weights - torch.matmul(d_W, Hinv_U)
-        del d_W, cur_Q
-        d_W = cur_Q = None
-
-    del init_weights
-    init_weights = None
-
-    cur_Q = quantize(cur_weights, scale, zero, maxq)
-
-    return cur_Q, cur_weights
-
+from tico.quantization.algorithm.fpi_gptq.util import quantize, iterate_GPTQ
 
 class FPI_GPTQ:
     def __init__(self, layer):

tico/quantization/algorithm/fpi_gptq/util.py

@@ -0,0 +1,50 @@
+# Copyright IST-DASLab. 2025. (commit: 2d65066). GitHub repository.
+# Retrieved from https://github.com/IST-DASLab/gptq. Licensed under the
+# Apache License 2.0.
+
+# Copyright (c) 2026 Samsung Electronics Co., Ltd. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# https://github.com/IST-DASLab/gptq/blob/2d65066/quant.py
+
+import torch
+
+def quantize(x, scale, zero, maxq):
+    if maxq < 0:
+        return (x > scale / 2).float() * scale + (x < zero / 2).float() * zero
+    q = torch.clamp(torch.round(x / scale) + zero, 0, maxq)
+    return scale * (q - zero)
+
+
+def iterate_GPTQ(scale, zero, maxq, W, Hinv, max_num_of_iters=50):
+
+    cur_weights = W.clone()
+    mults = torch.pow(torch.diag(Hinv), -1)
+    Hinv_U = torch.triu(Hinv, diagonal=1)
+
+    init_weights = W.clone()
+    for _ in range(max_num_of_iters):
+        cur_Q = quantize(cur_weights, scale, zero, maxq)
+
+        d_W = torch.mul((cur_weights - cur_Q), mults)
+        cur_weights = init_weights - torch.matmul(d_W, Hinv_U)
+        del d_W, cur_Q
+        d_W = cur_Q = None
+
+    del init_weights
+    init_weights = None
+
+    cur_Q = quantize(cur_weights, scale, zero, maxq)
+
+    return cur_Q, cur_weights

tico/quantization/algorithm/gptq/gptq.py

@@ -361,7 +361,9 @@ def fasterquant(
         H = torch.cholesky_inverse(H)
         H = torch.linalg.cholesky(H, upper=True)
         Hinv = H
-
+        
+        self.quantizer.update(W, Hinv, perm)
+    
         assert isinstance(Hinv, torch.Tensor)
         for i1 in range(0, self.columns, blocksize):
             i2 = min(i1 + blocksize, self.columns)

tico/quantization/algorithm/gptq/quant.py

@@ -21,6 +21,7 @@
 import torch
 import torch.nn as nn
 
+from tico.quantization.algorithm.fpi_gptq.util import iterate_GPTQ
 
 def quantize(x, scale, zero, maxq):
     if maxq < 0:
@@ -101,7 +102,7 @@ def find_params(self, x, weight=False):
             else:
                 self.zero = torch.round(-xmin / self.scale)
 
-        if self.mse is not None:
+        if self.mse is not None and self.mse != "smse_for_gptq" and self.mse != "mse_for_gptq":
             best = torch.full([x.shape[0]], float("inf"), device=dev)
             for i in range(int(self.maxshrink * self.grid)):
                 p = 1 - i / self.grid
@@ -151,6 +152,77 @@ def find_params(self, x, weight=False):
             self.scale = self.scale.unsqueeze(0)
             self.zero = self.zero.unsqueeze(0)
 
+    def update(self, x, Hinv, perm):
+        if self.mse is None or self.mse != "smse_for_gptq":
+            return
+
+        shape = x.shape
+        if self.perchannel:
+            x = x.flatten(1)
+        else:
+            x = x.flatten().unsqueeze(0)
+
+        dev = x.device
+        tmp = torch.zeros(x.shape[0], device=dev)
+        xmin = torch.minimum(x.min(1)[0], tmp)
+        xmax = torch.maximum(x.max(1)[0], tmp)
+
+        if self.sym:
+            xmax = torch.maximum(torch.abs(xmin), xmax)
+            tmp = xmin < 0
+            if torch.any(tmp):
+                xmin[tmp] = -xmax[tmp]
+        tmp = (xmin == 0) & (xmax == 0)
+        xmin[tmp] = -1
+        xmax[tmp] = +1
+        if self.maxq < 0:
+            self.scale = xmax
+            self.zero = xmin
+        else:
+            self.scale = (xmax - xmin) / self.maxq
+            if self.sym:
+                self.zero = torch.full_like(self.scale, (self.maxq + 1) / 2)  # type: ignore[arg-type]
+            else:
+                self.zero = torch.round(-xmin / self.scale)
+
+        sensitivity = None
+        if self.sensitivity is not None:
+            sensitivity = self.sensitivity.to(Hinv.dtype).to(dev)
+            if perm is not None:
+                sensitivity = sensitivity[:, perm.to(dev)]
+
+        num_of_iters = 15
+        best = torch.full([x.shape[0]], float("inf"), device=dev)
+        for i in range(int(self.maxshrink * self.grid)):
+            p = 1 - i / self.grid
+            xmin1 = p * xmin
+            xmax1 = p * xmax
+            scale1 = (xmax1 - xmin1) / self.maxq
+            zero1 = torch.round(-xmin1 / scale1) if not self.sym else self.zero
+            q, _ = iterate_GPTQ(
+                scale1.unsqueeze(1),
+                zero1.unsqueeze(1),
+                self.maxq,
+                x,
+                Hinv,
+                max_num_of_iters=num_of_iters,
+            )
+            assert sensitivity is not None
+            assert self.mse == "smse_for_gptq"
+            err = ((q - x) ** 2) * sensitivity.to(q.device)
+            
+            err = err
+            err = torch.sum(err, 1)
+            tmp = err < best
+            if torch.any(tmp):
+                best[tmp] = err[tmp]
+                self.scale[tmp] = scale1[tmp]
+                self.zero[tmp] = zero1[tmp]
+
+        shape = [-1] + [1] * (len(shape) - 1)
+        self.scale = self.scale.reshape(shape)
+        self.zero = self.zero.reshape(shape)
+
     def quantize(self, x):
         if self.ready():
             return quantize(x, self.scale, self.zero, self.maxq)

tico/quantization/algorithm/gptq/utils.py

@@ -163,7 +163,11 @@ def compute_sensitivity_info(self):
         if self.show_progress is True:
             print("Calibrating sensitivity")
         for inputs, targets in tqdm.tqdm(data_loader, disable=not self.show_progress):
-            model.zero_grad()
+            model.zero_grad(set_to_none=True)
+            if model.device.type != "cpu":
+                torch.cuda.empty_cache()
+                torch.cuda.synchronize()
+
             if isinstance(inputs, torch.Tensor):
                 inp_ids = inputs.squeeze(0)  # remove redundant batch dimension
                 logits = model(inp_ids.to(model.device)).logits
@@ -215,6 +219,11 @@ def compute_sensitivity_info(self):
         for name in modules_to_process:
             sensitivity[name] /= len(data_loader)
 
+        model.zero_grad(set_to_none=True)
+        if model.device.type != "cpu":
+            torch.cuda.synchronize()
+            torch.cuda.empty_cache()
+                
         model = model.to(dtype)
 
         return sensitivity

tico/quantization/wrapq/examples/quantize_full_qmodel_with_gptq.py

@@ -40,6 +40,7 @@
 from lm_eval.utils import make_table
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
+import numpy as np
 import tico
 
 from tico.quantization import convert, prepare
@@ -98,6 +99,51 @@ def inject_gptq_qparams(
         obs.load_qparams(quantizer.scale, quantizer.zero, lock=True)
 
 
+def evaluate_ppl_of_model_on_dataset(model, dataset, device: str = "cuda"):
+    if hasattr(model, "device") and model.device.type != device.type:
+        if hasattr(model, "to"):
+            model.to(device)
+    nlls = []
+    with torch.no_grad():
+        for batch in tqdm.tqdm(dataset):
+            if isinstance(batch, torch.Tensor):
+                batch = batch.to(device)
+                output = model(
+                    batch.to(device),
+                )
+            else:
+                raise RuntimeError("Unknown input in ppl_eval_on_dataset")
+
+            if hasattr(output, "logits"):
+                lm_logits = output.logits
+            elif len(output) > 1:
+                lm_logits = torch.tensor(output[0])
+            else:
+                lm_logits = torch.tensor(output)
+
+            if torch.isfinite(lm_logits).all():
+                shift_logits = lm_logits[:, :-1, :].contiguous()
+                if isinstance(batch, torch.Tensor):
+                    shift_labels = batch[:, 1:].contiguous()
+                else:
+                    assert isinstance(batch, tuple)
+                    shift_labels = batch[0][:, 1:].contiguous()
+                loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
+                loss = loss_fct(
+                    shift_logits.reshape(-1, shift_logits.size(-1)),
+                    shift_labels.view(-1),
+                )
+                nlls.append(loss)
+                del shift_logits, shift_labels
+                shift_logits = shift_labels = None  # type: ignore[assignment]
+
+            del batch, lm_logits, output
+            lm_logits = output = batch = None  # noqa: F841
+            torch.cuda.empty_cache()
+
+    ppl = np.exp(torch.cat(nlls, dim=-1).mean().item())
+    return ppl
+
 # -------------------------------------------------------------------------
 # Save model in circle format
 # -------------------------------------------------------------------------
@@ -296,7 +342,7 @@ def main():
         "--gptq_mse",
         type=str,
         default=None,
-        choices=["mse", "smse"],
+        choices=["mse", "smse", "smse_for_gptq"],
         help="Whether and how to use mse in gptq (none/mse/smse/)",
     )
     parser.add_argument(
@@ -410,6 +456,13 @@ def main():
         j = i + seqlen
         inp = train_ids[:, i:j]
         calib_inputs.append(inp.cpu())
+        
+    train_ppl_fp32 = evaluate_ppl_of_model_on_dataset(
+            model, calib_inputs, device=device
+        )
+    print("\n┌── Wikitext-2 train perplexity ─────────────")
+    print(f"│ FP32 : {train_ppl_fp32:8.2f}")
+    print("└───────────────────────────────────────────")
 
     # -------------------------------------------------------------------------
     # Run GPTQ (weight-only) pass
@@ -418,7 +471,9 @@ def main():
         print("Applying GPTQ …")
 
         sens = None
-        if args.gptq_mse is not None and args.gptq_mse == "smse":
+        if args.gptq_mse is not None and (
+            args.gptq_mse == "smse" or args.gptq_mse == "smse_for_gptq"
+        ):
             if args.sensitivity_path is not None:
                 sens = torch.load(args.sensitivity_path)
             else:
@@ -446,6 +501,13 @@ def main():
     if not args.no_PTQ:
         q_m = quantize_using_PTQ(q_m, calib_inputs, args)
 
+    train_ppl_ioqdtype = evaluate_ppl_of_model_on_dataset(
+            q_m, calib_inputs, device=device
+        )
+    print("\n┌── Wikitext-2 train perplexity ─────────────")
+    print(f"│ int16 : {train_ppl_ioqdtype:8.2f}")
+    print("└───────────────────────────────────────────")
+    
     # after PTQ quantizer only fixed-length input sequences are valid
     evaluate(q_m, tokenizer, dataset_test, args)