Add qlora nf4 (#8782)

* add nf4

* dequant nf4

* style

python/llm/src/bigdl/llm/ggml/model/llama/llama_cpp.py

@@ -999,6 +999,22 @@ _lib.ggml_dequantize_q4_0.argtypes = [
 _lib.ggml_quantize_q4_0.restype = None
 
 
+# def ggml_dequantize_nf4(
+#     src: ctypes.c_void_p,
+#     dst: ctypes.c_void_p,
+#     k: ctypes.c_int,
+# ):
+#     _lib.ggml_dequantize_nf4(src, dst, k)
+#
+#
+# _lib.ggml_dequantize_nf4.argtypes = [
+#     ctypes.c_void_p,
+#     ctypes.c_void_p,
+#     ctypes.c_int,
+# ]
+# _lib.ggml_dequantize_nf4.restype = None
+
+
 def ggml_compute_forward_mul_mat_q_fp32(src_0_ne,  # type: ctypes.Array[ctypes.c_int64]
                                         src_0_data,  # type: ctypes.c_void_p
                                         src_0_qtype,  # type: int

python/llm/src/bigdl/llm/ggml/quantize.py

@@ -29,7 +29,8 @@ ggml_tensor_qtype = {"sym_int4": 2,   # q4_0 in ggml
                      "asym_int4": 3,  # q4_1 in ggml
                      "sym_int5": 6,   # q5_0 in ggml
                      "asym_int5": 7,  # q5_1 in ggml
-                     "sym_int8": 8}   # q8_0 in ggml
+                     "sym_int8": 8,   # q8_0 in ggml
+                     "nf4": 10}
 
 _llama_quantize_type = {"q4_0": 2,
                         "q4_1": 3,

python/llm/src/bigdl/llm/transformers/low_bit_linear.py

@@ -257,6 +257,8 @@ class LowBitLinear(nn.Linear):
         else:
             # CPU logic
             # todo may need to set a different number on different platforms
+            invalidInputError(self.qtype != ggml_tensor_qtype["nf4"],
+                              "NF4 quantization is currently not supported on CPU")
             if IS_SERVER and (not IS_SPR) and \
                     self.qtype == SYM_INT4 and x_2d.shape[0] >= TORCH_LINEAR_THRESHOLD:
                 x0_fp32 = ggml_int4_convert_fp32(x0, self.weight_shape, self.weight_length)