Add support to convert from EfficientQAT/GPTQv2/exllamav2 weights to …

…gguf

convert-hf-to-gguf-t-mac.py

@@ -1483,6 +1483,33 @@ def real_quantize_tensor(w, n_bit=8, zero_point=True, q_group_size=-1):
         return w, scales, zeros
 
 
+def unpack_gptqv2(qweight, scales, qzeros):
+    """
+    Unpack GPTQv2
+    Return T-MAC biased uint8 weight [0, 2 ** bits), fp16 scales, biased fp16 zeros, bits, group_size
+    """
+    assert qweight.dtype == "int32"
+    assert qzeros.dtype == "int32"
+
+    bits = 32 // (scales.shape[1] // qzeros.shape[1])
+    K = qweight.shape[0] * (32 // bits)
+    M = qweight.shape[1]
+    group_size = K // scales.shape[0]
+
+    # Unpack qweight
+    qweights = [(qweight >> bit_offset) & ((1 << bits) - 1) for bit_offset in range(0, 32, bits)]
+    w = np.stack(qweights, axis=1).reshape(K, M).T.astype("uint8")
+
+    scales = scales.T
+
+    # Unpack qzeros
+    zeros = [(qzeros >> bit_offset) & ((1 << bits) - 1) for bit_offset in range(0, 32, bits)]
+    zeros = np.stack(zeros, axis=-1).reshape(K // group_size, M).T.astype(scales.dtype)
+    zeros = (zeros - (2 ** (bits - 1))) * scales
+
+    return w, scales, zeros, bits, group_size
+
+
 @Model.register("LlamaForCausalLM", "MistralForCausalLM", "MixtralForCausalLM")
 class LlamaModel(Model):
     model_arch = gguf.MODEL_ARCH.LLAMA
@@ -1528,11 +1555,58 @@ def write_tensors(self):
         n_kv_head = self.hparams.get("num_key_value_heads")
         n_experts = self.hparams.get("num_local_experts")
         experts = dict()
+
+        quant_dict = {}
+        # Store scales and qzeros to dict to be later preprocessed
+        # Save memory by not storing qweight
+        for name, data_torch in self.get_tensors():
+            if name.endswith(".scales") or name.endswith(".qzeros"):
+                data = data_torch.numpy()
+                quant_dict[name] = data
+
         for name, data_torch in self.get_tensors():
             # we don't need these
             if name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")):
                 continue
 
+            # should be converted with qweight together
+            if name.endswith(".scales") or name.endswith(".qzeros") or name.endswith(".g_idx"):
+                continue
+
+            if name.endswith(".qweight"):
+                qweight = data_torch.numpy()
+                scales = quant_dict[name.replace(".qweight", ".scales")]
+                qzeros = quant_dict[name.replace(".qweight", ".qzeros")]
+                w, scales, zeros, bits, group_size = unpack_gptqv2(qweight, scales, qzeros)
+                if name.endswith("q_proj.qweight"):
+                    w = permute(w, n_head, n_head)
+                    scales = permute(scales, n_head, n_head)
+                    zeros = permute(zeros, n_head, n_head)
+                if name.endswith("k_proj.qweight"):
+                    w = permute(w, n_head, n_kv_head)
+                    scales = permute(scales, n_head, n_kv_head)
+                    zeros = permute(zeros, n_head, n_kv_head)
+                data_shape = w.shape
+                new_name = tensor_map.get_name(name.replace(".qweight", ".weight"), try_suffixes=(".weight", ".bias"))
+
+                if self.ftype == LlamaFType.MOSTLY_I2:
+                    to_dtype = gguf.GGMLQuantizationType.I2
+                    data = preprocess_for_t_mac(w, scales, zeros, bits=bits)
+                    assert bits == 2, "Currently we only support 2-bit quantized model. 4-bit will soon be added."
+                else:
+                    to_dtype = gguf.GGMLQuantizationType.F32
+                    w = w.astype("float32").reshape(-1, group_size)
+                    scales = scales.astype("float32").reshape(-1, 1)
+                    zeros = zeros.astype("float32").reshape(-1, 1)
+                    data = (w - (zeros / scales + (2 ** (bits - 1)))) * scales
+                    if self.ftype == LlamaFType.MOSTLY_F16:
+                        to_dtype = gguf.GGMLQuantizationType.F16
+                        data = data.astype("float16")
+
+                logger.info(f"{new_name}, n_dims = {data_torch.ndim}, {data_torch.dtype} --> {to_dtype.name}")
+                self.gguf_writer.add_tensor(new_name, data, raw_shape=data_shape, raw_dtype=to_dtype)
+                continue
+
             old_dtype = data_torch.dtype
 
             # convert any unsupported data types to float32
@@ -3213,7 +3287,7 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument("--model-name", type=str, default=None, help="name of the model")
     parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
     parser.add_argument("--kcfg", type=str, default="", help="Path to T-MAC kcfg.ini")
-    parser.add_argument("--quant-type", type=str, default="bitnet", choices=["bitnet", "bitdistiller"])
+    parser.add_argument("--quant-type", type=str, default="bitnet", choices=["bitnet", "bitdistiller", "gptqv2"])
     parser.add_argument("--group-size", type=int, default=128)
 
     return parser.parse_args()