intel
diff --git a/‎docs/source/quantization_weight_only.md
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+15-5Lines changed: 15 additions & 5 deletions b/‎docs/source/quantization_weight_only.md
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+15-5Lines changed: 15 additions & 5 deletions
diff --git a/‎neural_compressor/adaptor/torch_utils/model_wrapper.py
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+136-100Lines changed: 136 additions & 100 deletions b/‎neural_compressor/adaptor/torch_utils/model_wrapper.py
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+136-100Lines changed: 136 additions & 100 deletions
diff --git a/‎neural_compressor/adaptor/torch_utils/weight_only.py
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+2Lines changed: 2 additions & 0 deletions b/‎neural_compressor/adaptor/torch_utils/weight_only.py
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+2Lines changed: 2 additions & 0 deletions
@@ -96,6 +96,14 @@ To support low memory inference, Neural Compressor implemented WeightOnlyLinear,
 |  compression_dtype  |       torch.int32       |  Data type for compressed dtype, select from [torch.int8\|16\|32\|64]   |
 |  compression_dim  |       1       |   0 means output channel while 1 means input channel   |
 |  scale_dtype  |       torch.float32       |  Data type for scale and bias   |
+|  use_hf_format  |     False       |  Whether to use the popular format present on HuggingFace hub   |
+
+**Note:** HuggingFace format is quite special, the main differences are as follows:
+
+> 1: Compression Dimension: weight = 1, zero = 0 and both are transposed.   
+> 2: Zero Point: zero_point-= 1 before compression. zero_point is always required even for sym.    
+> 3: Group Index: Use the same number for a group instead of recording channel order.    
+
 
 ### **User Code Example**
 ```python
@@ -119,12 +127,14 @@ conf = PostTrainingQuantConfig(
 )
 q_model = quantization.fit(model, conf, eval_func=eval_func)
 q_model.save("saved_results")
-compressed_model = q_model.export_compressed_model(
-    compression_dtype=torch.int32,
-    compression_dim=1,
-    scale_dtype=torch.float16,
-)
+compressed_model = q_model.export_compressed_model()
 torch.save(compressed_model.state_dict(), "compressed_model.pt")
+# or
+model = Model()
+compressed_model = export_compressed_model(
+    model,
+    saved_dir="saved_results",
+)
 ```
 
 The saved_results folder contains two files: `best_model.pt` and `qconfig.json`, and the generated q_model is a fake quantized model.
 
@@ -215,10 +215,12 @@ def __init__(
         scale_dtype=torch.float32,
         compression_dtype=torch.int32,
         compression_dim=1,
-        gptq_perm=False,
+        g_idx=False,
         device="cpu",
+        use_hf_format=False,
     ):
         super().__init__()
+        self.use_hf_format = use_hf_format
         self.dtype = dtype
         if "int" not in self.dtype:  # for nf4, fp4
             from neural_compressor.adaptor.torch_utils.weight_only import FLOAT_MAPPING, INT_MAPPING
@@ -249,69 +251,105 @@ def __init__(
         assert compression_dim in [0, 1], (
             "Only support 0 or 1 as compression dimension, " + "0 is output channel, 1 is input channel."
         )
-        self.register_buffer(
-            "scale",
-            torch.zeros(
-                (out_features, math.ceil(in_features / self.groupsize)),
-                dtype=self.float_type,
-            ).to(device),
-        )
-        if compression_dim == 1:
+        if self.use_hf_format:
             self.register_buffer(
-                "packed_weight",
+                "scales",
                 torch.zeros(
-                    (out_features, math.ceil(in_features / self.n_pack)),
+                    (math.ceil(in_features / self.groupsize), out_features),
+                    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.compressed_dtype,
                 ).to(device),
             )
-            if zp:
-                self.register_buffer(
-                    "packed_zp",
-                    torch.zeros(
-                        (self.out_features, math.ceil(self.in_features / self.groupsize / self.n_pack)),
-                        dtype=self.compressed_dtype,
-                    ).to(device),
-                )
-        else:
+            self.qweight = self.qweight.T
             self.register_buffer(
-                "packed_weight",
+                "qzeros",
                 torch.zeros(
-                    (math.ceil(out_features / self.n_pack), in_features),
+                    (math.ceil(self.in_features / self.groupsize), math.ceil(self.out_features / self.n_pack)),
                     dtype=self.compressed_dtype,
                 ).to(device),
             )
-            if zp:
+            self.qzeros = self.qzeros.T
+        else:
+            self.register_buffer(
+                "scales",
+                torch.zeros(
+                    (out_features, math.ceil(in_features / self.groupsize)),
+                    dtype=self.float_type,
+                ).to(device),
+            )
+            if compression_dim == 1:
                 self.register_buffer(
-                    "packed_zp",
+                    "qweight",
                     torch.zeros(
-                        (math.ceil(self.out_features / self.n_pack), math.ceil(self.in_features / self.groupsize)),
+                        (out_features, math.ceil(in_features / self.n_pack)),
                         dtype=self.compressed_dtype,
                     ).to(device),
                 )
+                if zp:
+                    self.register_buffer(
+                        "qzeros",
+                        torch.zeros(
+                            (self.out_features, math.ceil(self.in_features / self.groupsize / self.n_pack)),
+                            dtype=self.compressed_dtype,
+                        ).to(device),
+                    )
+            else:
+                self.register_buffer(
+                    "qweight",
+                    torch.zeros(
+                        (math.ceil(out_features / self.n_pack), in_features),
+                        dtype=self.compressed_dtype,
+                    ).to(device),
+                )
+                if zp:
+                    self.register_buffer(
+                        "qzeros",
+                        torch.zeros(
+                            (math.ceil(self.out_features / self.n_pack), math.ceil(self.in_features / self.groupsize)),
+                            dtype=self.compressed_dtype,
+                        ).to(device),
+                    )
+        if g_idx:
+            self.register_buffer("g_idx", torch.zeros(in_features, dtype=torch.int32).to(device))
+        else:
+            self.g_idx = None
         if bias:
             self.register_buffer("bias", torch.zeros(self.out_features, dtype=self.float_type).to(device))
         else:
             self.bias = None
-        if gptq_perm:
-            self.register_buffer("gptq_perm", torch.zeros(in_features, dtype=torch.int32).to(device))
-        else:
-            self.gptq_perm = None
 
-    def pack(self, int_weight, scale, zp, bias, gptq_perm=None):
+    def pack(self, int_weight, scale, zp, bias, g_idx=None):
         int_weight = int_weight.to(self.device)
+        if self.use_hf_format and zp is None:
+            # to avoid overflow
+            int_weight = int_weight.type(torch.int32)
+            shift_bias = 2 ** (self.bits - 1)
+            int_weight += shift_bias
+            zp = torch.zeros_like(scale, dtype=torch.uint8) + shift_bias
         if bias is not None:
             assert hasattr(self, "bias"), "bias is not set when initializing."
             self.bias = bias.type(self.float_type).to(self.device)
-        if gptq_perm is not None:
-            assert hasattr(self, "gptq_perm"), "gptq_perm is not set when initializing."
-            self.gptq_perm = gptq_perm.type(torch.int32).to(self.device)
-        assert scale.shape == self.scale.shape, "Scale shape is mismatched."
-        self.scale = scale.type(self.float_type).to(self.device)
-        if self.compression_dim == 0:
+        if g_idx is not None:
+            assert hasattr(self, "g_idx"), "g_idx is not set when initializing."
+            self.g_idx = g_idx.type(torch.int32).to(self.device)
+            if self.use_hf_format:
+                invperm = torch.argsort(self.g_idx)
+                self.g_idx = invperm // self.groupsize
+                self.g_idx = self.g_idx.type(torch.int32).to(self.device)
+        assert scale.shape == self.scales.shape, "Scale shape is mismatched."
+        self.scales = scale.type(self.float_type).to(self.device)
+        if not self.use_hf_format and self.compression_dim == 0:
             int_weight = int_weight.T
-            self.packed_weight = self.packed_weight.T
+            self.qweight = self.qweight.T
         origin_shape = int_weight.shape
-        target_shape = self.packed_weight.shape
+        target_shape = self.qweight.shape
         assert origin_shape[0] == target_shape[0], "output channels mismatch, please check."
         mask = torch.tensor(2**self.bits - 1, dtype=self.compressed_dtype).to(self.device)
 
@@ -323,121 +361,112 @@ def pack(self, int_weight, scale, zp, bias, gptq_perm=None):
             for e in range(tmp.shape[1]):
                 tmp[:, e] &= mask
                 tmp[:, e] = tmp[:, e] << (self.bits * e)
-                self.packed_weight[:, j] |= tmp[:, e]
-        if self.compression_dim == 0:
-            self.packed_weight = self.packed_weight.T
+                self.qweight[:, j] |= tmp[:, e]
+        if not self.use_hf_format and self.compression_dim == 0:
+            self.qweight = self.qweight.T
 
         if zp is not None:
             zp = zp.to(self.device)
-            if self.compression_dim == 0:
+            if self.use_hf_format:
+                zp -= 1
+            if self.use_hf_format or self.compression_dim == 0:
                 zp = zp.T
-                self.packed_zp = self.packed_zp.T
-            assert hasattr(self, "packed_zp"), "zp is not set when initializing."
-            target_shape = self.packed_zp.shape
+                self.qzeros = self.qzeros.T
+            assert hasattr(self, "qzeros"), "zp is not set when initializing."
+            target_shape = self.qzeros.shape
             for j in range(target_shape[1]):
                 start = self.n_pack * j
                 end = self.n_pack * (j + 1)
                 tmp = zp[:, start:end].type(self.compressed_dtype)
                 for e in range(tmp.shape[1]):
                     tmp[:, e] &= mask
                     tmp[:, e] = tmp[:, e] << (self.bits * e)
-                    self.packed_zp[:, j] |= tmp[:, e]
-            if self.compression_dim == 0:
-                self.packed_zp = self.packed_zp.T
+                    self.qzeros[:, j] |= tmp[:, e]
+            if self.use_hf_format or self.compression_dim == 0:
+                self.qzeros = self.qzeros.T
+        if self.use_hf_format:
+            self.scales = self.scales.T
+            self.qweight = self.qweight.T
+            self.g_idx = self.g_idx
+            self.qzeros = self.qzeros.T
 
     def recover(self):
         logger.debug(f"Recovering {self} weight")
-        device = self.scale.device
+        if self.use_hf_format:
+            # Prevent broken id links of self.scales and self.scales
+            self.scales = self.scales.T
+            self.qweight = self.qweight.T
+            self.g_idx = self.g_idx
+            self.qzeros = self.qzeros.T
+        device = self.scales.device
+        fp32_weight = torch.zeros(self.out_features, self.in_features, dtype=self.float_type).to(device)
+        if self.g_idx is None:
+            # used for recovering fp32_weight
+            self.g_idx = torch.tensor([i // self.groupsize for i in range(self.in_features)], dtype=torch.int32)
         mask = torch.tensor(2**self.bits - 1, dtype=self.compressed_dtype).to(device)
-        if hasattr(self, "packed_zp"):
+        if hasattr(self, "qzeros"):
             weight_dtype = torch.uint8
         else:
             weight_dtype = torch.int8
         # unpack weight
         weight = torch.zeros(self.out_features, self.in_features, dtype=weight_dtype).to(device)
-        packed_weight = self.packed_weight
-        if self.compression_dim == 0:
+        qweight = self.qweight
+        if not self.use_hf_format and self.compression_dim == 0:
             weight = weight.T
-            packed_weight = packed_weight.T
+            qweight = qweight.T
         origin_shape = weight.shape
-        target_shape = packed_weight.shape
+        target_shape = qweight.shape
         for j in range(target_shape[1]):
             for e in range(self.n_pack):
                 index = j * self.n_pack + e
                 if index >= origin_shape[1]:
                     continue
-                tmp = packed_weight[:, j]
+                tmp = qweight[:, j]
                 tmp = tmp << (self.compress_bits - self.bits * (e + 1))
                 tmp = tmp >> self.compress_bits - self.bits
                 if weight_dtype == torch.uint8:
                     tmp &= mask  # remove sign bit
                 weight[:, index] = tmp.type(weight_dtype)
-        if self.compression_dim == 0:
+        if not self.use_hf_format and self.compression_dim == 0:
             weight = weight.T
         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():
                 new_weight += torch.where(weight == k, v, 0)
             weight = new_weight
         # unpack zero_point
-        if hasattr(self, "packed_zp"):
+        if hasattr(self, "qzeros"):
             zp_dtype = self.compressed_dtype  # to avoid overflow when weight-zp
-            zp = torch.zeros(self.scale.shape, dtype=zp_dtype).to(device)
-            packed_zp = self.packed_zp
-            if self.compression_dim == 0:
+            zp = torch.zeros(self.scales.shape, dtype=zp_dtype).to(device)
+            qzeros = self.qzeros
+            if self.use_hf_format or self.compression_dim == 0:
                 zp = zp.T
-                packed_zp = packed_zp.T
+                qzeros = qzeros.T
             origin_shape = zp.shape
-            target_shape = packed_zp.shape
+            target_shape = qzeros.shape
             for j in range(target_shape[1]):
                 for e in range(self.n_pack):
                     index = j * self.n_pack + e
                     if index >= origin_shape[1]:
                         continue
-                    tmp = packed_zp[:, j]
+                    tmp = qzeros[:, j]
                     tmp = tmp << (self.compress_bits - self.bits * (e + 1))
                     tmp = tmp >> self.compress_bits - self.bits
                     tmp &= mask
                     zp[:, index] = tmp.type(zp_dtype)
-            if self.compression_dim == 0:
+            if self.use_hf_format or self.compression_dim == 0:
                 zp = zp.T
+            if self.use_hf_format:
+                # zp -= 1 may cause zp == -1, after recover it becomes 2**self.bits - 1
+                zp += 1
+                zp = torch.where(zp > (2**self.bits - 1), 0, zp)
             # recover fp32 weight with int_weight, scale, and zero_point
-            left_element = self.in_features % self.groupsize
-            if left_element != 0:
-                split_index = self.in_features // self.groupsize * self.groupsize
-                weight1 = weight[:, :-split_index].reshape(-1, self.groupsize)
-                scale1 = self.scale[:, :-1].reshape(-1, 1)
-                zp1 = zp[:, :-1].reshape(-1, 1)
-                weight1 = ((weight1 - zp1) * scale1).reshape(self.out_features, -1)
-                weight2 = weight[:, -split_index:]
-                scale2 = self.scale[:, -1:]
-                zp2 = zp[:, -1].reshape(-1, 1)
-                weight2 = (weight2 - zp2) * scale2
-                fp32_weight = torch.cat((weight1, weight2), dim=1)
-            else:
-                weight = weight.reshape(-1, self.groupsize)
-                scale = self.scale.reshape(-1, 1)
-                zp = zp.reshape(-1, 1)
-                fp32_weight = ((weight - zp) * scale).reshape(self.out_features, -1)
+            for idx in range(self.in_features):
+                fp32_weight[:, idx] = (weight[:, idx] - zp[:, self.g_idx[idx]]) * self.scales[:, self.g_idx[idx]]
         else:
             # recover fp32 weight with int_weight, scale
-            left_element = self.in_features % self.groupsize
-            if left_element != 0:
-                split_index = self.in_features // self.groupsize * self.groupsize
-                weight1 = weight[:, :split_index].reshape(-1, self.groupsize)
-                scale1 = self.scale[:, :-1].reshape(-1, 1)
-                weight1 = (weight1 * scale1).reshape(self.out_features, -1)
-                weight2 = weight[:, split_index:]
-                scale2 = self.scale[:, -1:]
-                weight2 = weight2 * scale2
-                fp32_weight = torch.cat((weight1, weight2), dim=1)
-            else:
-                weight = weight.reshape(-1, self.groupsize)
-                scale = self.scale.reshape(-1, 1)
-                fp32_weight = (weight * scale).reshape(self.out_features, -1)
-        if self.gptq_perm is not None:
-            invperm = torch.argsort(self.gptq_perm)
-            fp32_weight = fp32_weight[:, invperm]
+            for idx in range(self.in_features):
+                fp32_weight[:, idx] = weight[:, idx] * self.scales[:, self.g_idx[idx]]
         return fp32_weight
 
     def forward(self, input):
@@ -453,9 +482,16 @@ def forward(self, input):
             return F.linear(input, weight, self.bias)
 
     def extra_repr(self) -> str:
-        return "in_features={}, out_features={}, bits={}, group_size={}, bias={}".format(
-            self.in_features, self.out_features, self.bits, self.groupsize, self.bias is not None
+        tmp_str = "in_features={}, out_features={}, bits={}, group_size={}, bias={}".format(
+            self.in_features,
+            self.out_features,
+            self.bits,
+            self.groupsize,
+            self.bias is not None,
         )
+        if self.use_hf_format:
+            tmp_str += ", use_hf_format=True"
+        return tmp_str
 
 
 class FakeAffineTensorQuantFunction(Function):
 
@@ -396,6 +396,7 @@ def rtn_quantize(
         compression_dim = kwargs.get("compression_dim", 1)
         scale_dtype = kwargs.get("scale_dtype", torch.float32)
         device = kwargs.get("device", "cpu")
+        use_hf_format = kwargs.get("use_hf_format", False)
     for name, m in model.named_modules():
         if m.__class__.__name__ not in supported_layers:
             continue
@@ -451,6 +452,7 @@ def rtn_quantize(
                 compression_dim=compression_dim,
                 scale_dtype=scale_dtype,
                 device=device,
+                use_hf_format=use_hf_format,
             )
             new_module.pack(int_weight, scale, zp, m.bias)
             if name == "":