use inplace=True mode for WOQ (#1557)

* use inplace mode for WOQ

Signed-off-by: xin3he <[email protected]>

neural_compressor/adaptor/torch_utils/autoround/model_wrapper.py

@@ -127,23 +127,20 @@ def __init__(
                     dtype=self.float_type,
                 ).to(device),
             )
-            self.scales = self.scales.T
             self.register_buffer(
                 "qweight",
                 torch.zeros(
                     (math.ceil(in_features / self.n_pack), out_features),
                     dtype=self.compression_dtype,
                 ).to(device),
             )
-            self.qweight = self.qweight.T
             self.register_buffer(
                 "qzeros",
                 torch.zeros(
                     (math.ceil(self.in_features / self.groupsize), math.ceil(self.out_features / self.n_pack)),
                     dtype=self.compression_dtype,
                 ).to(device),
             )
-            self.qzeros = self.qzeros.T
             self.register_buffer("bias", torch.zeros(self.out_features, dtype=self.float_type).to(device))
         else:
             self.compression_dtype = compression_dtype
@@ -193,6 +190,10 @@ def __init__(
                 self.bias = None
 
     def pack(self, int_weight, scale, zp, bias):
+        if self.use_optimum_format:
+            self.scales = self.scales.t_().contiguous()
+            self.qweight = self.qweight.t_().contiguous()
+            self.qzeros = self.qzeros.t_().contiguous()
         int_weight = int_weight.to(self.device)
         if self.use_optimum_format and zp is None:
             # to avoid overflow
@@ -206,8 +207,8 @@ def pack(self, int_weight, scale, zp, bias):
         assert scale.shape == self.scales.shape, "Scale shape is mismatched."
         self.scales = scale.type(self.float_type).to(self.device)
         if not self.use_optimum_format and self.compression_dim == 0:
-            int_weight = int_weight.T
-            self.qweight = self.qweight.T
+            int_weight = int_weight.t_().contiguous()
+            self.qweight = self.qweight.t_().contiguous()
         origin_shape = int_weight.shape
         target_shape = self.qweight.shape
         assert origin_shape[0] == target_shape[0], "output channels mismatch, please check."
@@ -223,15 +224,15 @@ def pack(self, int_weight, scale, zp, bias):
                 tmp[:, e] = tmp[:, e] << (self.bits * e)
                 self.qweight[:, j] |= tmp[:, e]
         if not self.use_optimum_format and self.compression_dim == 0:
-            self.qweight = self.qweight.T
+            self.qweight = self.qweight.t_().contiguous()
 
         if zp is not None:
             zp = zp.to(self.device)
             if self.use_optimum_format:
                 zp -= 1
             if self.use_optimum_format or self.compression_dim == 0:
-                zp = zp.T
-                self.qzeros = self.qzeros.T
+                zp = zp.t_().contiguous()
+                self.qzeros = self.qzeros.t_().contiguous()
             assert hasattr(self, "qzeros"), "zp is not set when initializing."
             target_shape = self.qzeros.shape
             for j in range(target_shape[1]):
@@ -243,16 +244,16 @@ def pack(self, int_weight, scale, zp, bias):
                     tmp[:, e] = tmp[:, e] << (self.bits * e)
                     self.qzeros[:, j] |= tmp[:, e]
             if self.use_optimum_format or self.compression_dim == 0:
-                self.qzeros = self.qzeros.T
+                self.qzeros = self.qzeros.t_().contiguous()
         if self.use_optimum_format:
-            self.scales = self.scales.T
-            self.qweight = self.qweight.T
-            self.qzeros = self.qzeros.T
+            self.scales = self.scales.t_().contiguous()
+            self.qweight = self.qweight.t_().contiguous()
+            self.qzeros = self.qzeros.t_().contiguous()
 
     def recover(self):
         logger.debug(f"Recovering {self} weight")
-        scales = self.scales.T if self.use_optimum_format else self.scales
-        qweight = self.qweight.T if self.use_optimum_format else self.qweight
+        scales = self.scales.t_().contiguous() if self.use_optimum_format else self.scales
+        qweight = self.qweight.t_().contiguous() if self.use_optimum_format else self.qweight
 
         device = scales.device
         fp32_weight = torch.zeros(self.out_features, self.in_features, dtype=self.float_type).to(device)
@@ -264,8 +265,8 @@ def recover(self):
         # unpack weight
         weight = torch.zeros(self.out_features, self.in_features, dtype=weight_dtype).to(device)
         if not self.use_optimum_format and self.compression_dim == 0:
-            weight = weight.T
-            qweight = qweight.T
+            weight = weight.t_().contiguous()
+            qweight = qweight.t_().contiguous()
         origin_shape = weight.shape
         target_shape = qweight.shape
         for j in range(target_shape[1]):
@@ -280,7 +281,7 @@ def recover(self):
                     tmp &= mask  # remove sign bit
                 weight[:, index] = tmp.type(weight_dtype)
         if not self.use_optimum_format and self.compression_dim == 0:
-            weight = weight.T
+            weight = weight.t_().contiguous()
         if "int" not in self.dtype:
             new_weight = torch.zeros(self.out_features, self.in_features).to(device)
             for k, v in self.int2float_mapping.items():
@@ -290,10 +291,10 @@ def recover(self):
         if hasattr(self, "qzeros"):
             zp_dtype = self.compression_dtype  # to avoid overflow when weight-zp
             zp = torch.zeros(scales.shape, dtype=zp_dtype).to(device)
-            qzeros = self.qzeros.T if self.use_optimum_format else self.qzeros
+            qzeros = self.qzeros.t_().contiguous() if self.use_optimum_format else self.qzeros
             if self.use_optimum_format or self.compression_dim == 0:
-                zp = zp.T
-                qzeros = qzeros.T
+                zp = zp.t_().contiguous()
+                qzeros = qzeros.t_().contiguous()
             origin_shape = zp.shape
             target_shape = qzeros.shape
             for j in range(target_shape[1]):
@@ -307,7 +308,7 @@ def recover(self):
                     tmp &= mask
                     zp[:, index] = tmp.type(zp_dtype)
             if self.use_optimum_format or self.compression_dim == 0:
-                zp = zp.T
+                zp = zp.t_().contiguous()
             if self.use_optimum_format:
                 # zp -= 1 may cause zp == -1, after recover it becomes 2**self.bits - 1
                 zp += 1

neural_compressor/adaptor/torch_utils/model_wrapper.py

@@ -327,9 +327,9 @@ def __init__(
 
     def pack(self, int_weight, scale, zp, bias, g_idx=None):
         if self.use_optimum_format:
-            self.scales = self.scales.T
-            self.qweight = self.qweight.T
-            self.qzeros = self.qzeros.T
+            self.scales = self.scales.t_().contiguous()
+            self.qweight = self.qweight.t_().contiguous()
+            self.qzeros = self.qzeros.t_().contiguous()
         int_weight = int_weight.to(self.device)
         if self.use_optimum_format and zp is None:
             # to avoid overflow
@@ -350,8 +350,8 @@ def pack(self, int_weight, scale, zp, bias, g_idx=None):
         assert scale.shape == self.scales.shape, "Scale shape is mismatched."
         self.scales = scale.type(self.float_type).to(self.device)
         if not self.use_optimum_format and self.compression_dim == 0:
-            int_weight = int_weight.T
-            self.qweight = self.qweight.T
+            int_weight = int_weight.t_().contiguous()
+            self.qweight = self.qweight.t_().contiguous()
         origin_shape = int_weight.shape
         target_shape = self.qweight.shape
         assert origin_shape[0] == target_shape[0], "output channels mismatch, please check."
@@ -367,15 +367,15 @@ def pack(self, int_weight, scale, zp, bias, g_idx=None):
                 tmp[:, e] = tmp[:, e] << (self.bits * e)
                 self.qweight[:, j] |= tmp[:, e]
         if not self.use_optimum_format and self.compression_dim == 0:
-            self.qweight = self.qweight.T
+            self.qweight = self.qweight.t_().contiguous()
 
         if zp is not None:
             zp = zp.to(self.device)
             if self.use_optimum_format:
                 zp -= 1
             if self.use_optimum_format or self.compression_dim == 0:
-                zp = zp.T
-                self.qzeros = self.qzeros.T
+                zp = zp.t_().contiguous()
+                self.qzeros = self.qzeros.t_().contiguous()
             assert hasattr(self, "qzeros"), "zp is not set when initializing."
             target_shape = self.qzeros.shape
             for j in range(target_shape[1]):
@@ -387,16 +387,16 @@ def pack(self, int_weight, scale, zp, bias, g_idx=None):
                     tmp[:, e] = tmp[:, e] << (self.bits * e)
                     self.qzeros[:, j] |= tmp[:, e]
             if self.use_optimum_format or self.compression_dim == 0:
-                self.qzeros = self.qzeros.T
+                self.qzeros = self.qzeros.t_().contiguous()
         if self.use_optimum_format:
-            self.scales = self.scales.T
-            self.qweight = self.qweight.T
-            self.qzeros = self.qzeros.T
+            self.scales = self.scales.t_().contiguous()
+            self.qweight = self.qweight.t_().contiguous()
+            self.qzeros = self.qzeros.t_().contiguous()
 
     def recover(self):
         logger.debug(f"Recovering {self} weight")
-        scales = self.scales.T if self.use_optimum_format else self.scales
-        qweight = self.qweight.T if self.use_optimum_format else self.qweight
+        scales = self.scales.t_().contiguous() if self.use_optimum_format else self.scales
+        qweight = self.qweight.t_().contiguous() if self.use_optimum_format else self.qweight
 
         device = scales.device
         fp32_weight = torch.zeros(self.out_features, self.in_features, dtype=self.float_type).to(device)
@@ -411,8 +411,8 @@ def recover(self):
         # unpack weight
         weight = torch.zeros(self.out_features, self.in_features, dtype=weight_dtype).to(device)
         if not self.use_optimum_format and self.compression_dim == 0:
-            weight = weight.T
-            qweight = qweight.T
+            weight = weight.t_().contiguous()
+            qweight = qweight.t_().contiguous()
         origin_shape = weight.shape
         target_shape = qweight.shape
         for j in range(target_shape[1]):
@@ -427,7 +427,7 @@ def recover(self):
                     tmp &= mask  # remove sign bit
                 weight[:, index] = tmp.type(weight_dtype)
         if not self.use_optimum_format and self.compression_dim == 0:
-            weight = weight.T
+            weight = weight.t_().contiguous()
         if "int" not in self.dtype:
             new_weight = torch.zeros(self.out_features, self.in_features).to(device)
             for k, v in self.int2float_mapping.items():
@@ -437,10 +437,10 @@ def recover(self):
         if hasattr(self, "qzeros"):
             zp_dtype = self.compression_dtype  # to avoid overflow when weight-zp
             zp = torch.zeros(scales.shape, dtype=zp_dtype).to(device)
-            qzeros = self.qzeros.T if self.use_optimum_format else self.qzeros
+            qzeros = self.qzeros.t_().contiguous() if self.use_optimum_format else self.qzeros
             if self.use_optimum_format or self.compression_dim == 0:
-                zp = zp.T
-                qzeros = qzeros.T
+                zp = zp.t_().contiguous()
+                qzeros = qzeros.t_().contiguous()
             origin_shape = zp.shape
             target_shape = qzeros.shape
             for j in range(target_shape[1]):
@@ -454,7 +454,7 @@ def recover(self):
                     tmp &= mask
                     zp[:, index] = tmp.type(zp_dtype)
             if self.use_optimum_format or self.compression_dim == 0:
-                zp = zp.T
+                zp = zp.t_().contiguous()
             if self.use_optimum_format:
                 # zp -= 1 may cause zp == -1, after recover it becomes 2**self.bits - 1
                 zp += 1

neural_compressor/adaptor/torch_utils/weight_only.py

@@ -330,16 +330,17 @@ def search_clip(m, num_bits=4, group_size=32, scheme="asym", data_type="int", en
     history = []
     for i_s in range(int(max_shrink * n_grid)):
         ratio = 1 - i_s / n_grid  # 1, 0.805-1.0
-        cur_weight = quant_weight(
-            m.weight.data,
+        quant_weight(
+            m.weight.data,  # in-place mode
             num_bits=num_bits,
             group_size=group_size,
             scheme=scheme,
             data_type=data_type,
             full_range=enable_full_range,
             quantile=ratio,
         )
-        loss = (org_weight - cur_weight).float().pow(2).mean().item()
+        loss = (org_weight - m.weight.data).float().pow(2).mean().item()
+        m.weight.data.copy_(org_weight)
         history.append(loss)
         is_best = loss < best_error
         if is_best:
@@ -429,14 +430,17 @@ def rtn_quantize(
             if num_bits <= 0:
                 logger.info(f"Skip {name}")
                 continue
-            weight = m.weight.T if group_dim == 0 else m.weight
+            # contiguous is not an in-place op and returns Tensor instead of Parameter, so set it back to m.weight.data.
+            # transpose should be executed on Parameter level because Param.data.t_() is not an in-place op.
+            # Parameter.T is an in-place op while Tensor.T is not.
+            m.weight.data = m.weight.t_().data.contiguous() if group_dim == 0 else m.weight.data
             if enable_mse_search:
                 quantile = search_clip(m, num_bits, group_size, scheme, data_type, enable_full_range)
             if return_int:
                 from .model_wrapper import WeightOnlyLinear
 
                 _, scale, zp = quant_weight(
-                    weight,
+                    m.weight.data,
                     num_bits,
                     group_size,
                     scheme,
@@ -446,9 +450,9 @@ def rtn_quantize(
                     full_range=enable_full_range,
                 )
                 if group_dim == 0:
-                    weight.transpose_(0, 1)
-                scale = scale.T if group_dim == 0 else scale
-                zp = zp.T if group_dim == 0 and zp is not None else zp
+                    m.weight.t_()
+                scale = scale.t_().contiguous() if group_dim == 0 else scale
+                zp = zp.t_().contiguous() if group_dim == 0 and zp is not None else zp
                 new_module = WeightOnlyLinear(
                     m.in_features,
                     m.out_features,
@@ -463,14 +467,14 @@ def rtn_quantize(
                     device=device,
                     use_optimum_format=use_optimum_format,
                 )
-                new_module.pack(weight, scale, zp, m.bias)
+                new_module.pack(m.weight.data, scale, zp, m.bias)
                 if name == "":
                     return new_module
                 else:
                     set_module(model, name, new_module)
             else:
                 quant_weight(
-                    weight,
+                    m.weight.data,
                     num_bits,
                     group_size,
                     scheme,
@@ -479,7 +483,7 @@ def rtn_quantize(
                     full_range=enable_full_range,
                 )
                 if group_dim == 0:
-                    weight.transpose_(0, 1)
+                    m.weight.t_()
             if orig_dtype != torch.float:
                 m = m.to(orig_dtype)
     return model
@@ -651,18 +655,18 @@ def quant_weight_w_scale(weight, scale, zp, group_size=-1):
     if zp is not None:
         zp = zp.to(device)
     if group_size == -1:
-        return torch.round(weight / scale) if zp is None else torch.round(weight / scale + zp)
+        return weight.div_(scale).round_() if zp is None else weight.div_(scale).add_(zp).round_()
     int_weight = torch.zeros(weight.shape).to(device)
     leng = weight.shape[1] // group_size
     tail_flag = False if weight.shape[1] % group_size == 0 else True
     for i in range(leng):
-        int_weight_tmp = weight[:, i * group_size : (i + 1) * group_size] / scale[:, i].unsqueeze(1)
+        int_weight_tmp = weight[:, i * group_size : (i + 1) * group_size].div_(scale[:, i].unsqueeze(1))
         if zp is not None:
-            int_weight_tmp += zp[:, i].unsqueeze(1)
-        int_weight[:, i * group_size : (i + 1) * group_size] = torch.round(int_weight_tmp)
+            int_weight_tmp.add_(zp[:, i].unsqueeze(1))
+        int_weight[:, i * group_size : (i + 1) * group_size].copy_(int_weight_tmp.round_())
     if tail_flag:
-        int_weight_tmp = weight[:, leng * group_size :] / scale[:, -1].unsqueeze(1)
+        int_weight_tmp = weight[:, leng * group_size :].div_(scale[:, -1].unsqueeze(1))
         if zp is not None:
-            int_weight_tmp += zp[:, -1].unsqueeze(1)
-        int_weight[:, leng * group_size :] = torch.round(int_weight_tmp)
+            int_weight_tmp.add_(zp[:, -1].unsqueeze(1))
+        int_weight[:, leng * group_size :].copy_(int_weight_tmp.round_())
     return int_weight

neural_compressor/torch/algorithms/weight_only/__init__.py

@@ -11,3 +11,7 @@
 # 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.
+
+from .utility import *
+from .rtn import rtn_quantize
+from .gptq import gptq_quantize