[GPTQ Enhence] Add GPTQ int8 weight unpack function

neural_speed/convert/common.py

@@ -207,6 +207,8 @@ def unpack_weight(qweight, scales, qzeros, q_config):
             return unpack_gptq_weight_4bits(qweight, scales, qzeros, q_config)
         elif qbits == 3:
             return unpack_gptq_weight_3bits(qweight, scales, qzeros, q_config)
+        elif qbits == 8:
+            return unpack_gptq_weight_8bits(qweight, scales, qzeros, q_config)
 
         return ValueError(f"Unsupported q_config[bits]: {qbits}")
 
@@ -215,6 +217,51 @@ def unpack_weight(qweight, scales, qzeros, q_config):
     raise ValueError(f"Unsupported quant_method: {quant_method}")
 
 
+def unpack_gptq_weight_8bits(qweight, scales, qzeros, q_config):
+    sym = q_config['sym']
+    group_size = q_config['group_size']
+    bits = q_config['bits']
+    s32_bits = 32
+
+    assert bits == 8
+    # Int32 can store 8 * 4bits data. This is the offset for each data.
+    wf = torch.tensor(list(range(0, s32_bits, bits)), dtype=torch.int32).unsqueeze(0)
+    zeros = torch.bitwise_right_shift(torch.unsqueeze(qzeros, 2).expand(-1, -1, 32 // bits),
+                                      wf.unsqueeze(0)).to(torch.int16 if bits == 8 else torch.int8)
+    torch.bitwise_and(zeros, (2**bits) - 1, out=zeros)
+
+    if bits == 8:
+        zeros = zeros.to(torch.int8 if sym else torch.uint8)
+
+    zeros = zeros + 1
+    try:
+        zeros = zeros.reshape(scales.shape)
+    except:
+        # zeros and scales have different iteam numbers.
+        # remove 1 (due to 0 + 1 in line 68)
+        zeros = zeros[zeros !=1]
+        zeros = zeros.reshape(scales.shape)
+
+    if not sym and bits == 8:
+        zeros = (zeros.to(torch.int32) - 128).to(torch.int8)
+
+    weight = torch.bitwise_right_shift(torch.unsqueeze(qweight, 1).expand(-1, 32 // bits, -1),
+                                       wf.unsqueeze(-1)).to(torch.int16 if bits == 8 else torch.int8)
+    torch.bitwise_and(weight, (2**bits) - 1, out=weight)
+
+    if bits == 8:
+        # due to INC add shift bias for sym
+        if sym:
+            shift_bias = 2**(bits - 1)
+            weight -= shift_bias
+        weight = weight.to(torch.int8 if sym else torch.uint8)
+        # due to INC asym return torch.uint8 but backend request int8,
+        # change it to int8 with offset 128
+        if not sym:
+            weight = (weight.to(torch.int32) - 128).to(torch.int8)
+    return weight, scales, zeros
+
+
 def unpack_gptq_weight_4bits(qweight, scales, qzeros, q_config):
     group_size = q_config['group_size']
     bits = q_config['bits']

neural_speed/convert/convert_quantized_llama.py

@@ -106,7 +106,8 @@ def main(args_in: Optional[List[str]] = None) -> None:
     out_path = args.outfile.as_posix()
     model_path = args.model.as_posix()
 
-    model, config, quantize_config = load_quantized_model(model_path)
+    #model, config, quantize_config = load_quantized_model(model_path)
+    model, config, quantize_config = load_quantized_safetensors(model_path)
     f = open(out_path, "wb")
 
     # 1. write hparams