gguf-py : add numpy Q1_0 and Q2_0 de/quantization support (#30630)

* gguf-py : add numpy Q1_0 and Q2_0 de/quantization support

* cont: added q1_0 / q2_0 to the test list removing the small test

Assisted-by: Claude Opus 5.5 <noreply@anthropic.com>

---------

Assisted-by: Claude Opus 5.5 <noreply@anthropic.com>

gguf-py/gguf/quants.py

@@ -654,6 +654,59 @@ class TQ2_0(__Quant, qtype=GGMLQuantizationType.TQ2_0):
         return (d * qs.astype(np.float32))
 
 
+class Q1_0(__Quant, qtype=GGMLQuantizationType.Q1_0):
+    @classmethod
+    def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        # sequential sum like the C reference, np.sum uses pairwise summation and can round differently
+        d = np.cumsum(abs(blocks), axis=-1, dtype=np.float32)[..., -1:] / np.float32(cls.block_size)
+        qs = np.packbits(blocks >= 0, axis=-1, bitorder="little")
+
+        d = d.astype(np.float16).view(np.uint8)
+
+        return np.concatenate([d, qs], axis=-1)
+
+    @classmethod
+    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        d, qs = np.hsplit(blocks, [2])
+
+        d = d.view(np.float16).astype(np.float32)
+
+        qs = np.unpackbits(qs, axis=-1, bitorder="little")
+
+        return np.where(qs != 0, d, -d)
+
+
+class Q2_0(__Quant, qtype=GGMLQuantizationType.Q2_0):
+    @classmethod
+    def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        n_blocks = blocks.shape[0]
+
+        d = abs(blocks).max(axis=-1, keepdims=True)
+        with np.errstate(divide="ignore"):
+            id = np.where(d == 0, 0, 1 / d)
+        qs = (np_roundf(blocks * id) + 1).clip(0, 3).astype(np.uint8)
+
+        qs = qs.reshape((n_blocks, -1, 4)) << np.array([0, 2, 4, 6], dtype=np.uint8)
+        qs = qs[..., 0] | qs[..., 1] | qs[..., 2] | qs[..., 3]
+
+        d = d.astype(np.float16).view(np.uint8)
+
+        return np.concatenate([d, qs], axis=-1)
+
+    @classmethod
+    def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray:
+        n_blocks = blocks.shape[0]
+
+        d, qs = np.hsplit(blocks, [2])
+
+        d = d.view(np.float16).astype(np.float32)
+
+        qs = qs.reshape((n_blocks, -1, 1)) >> np.array([0, 2, 4, 6], dtype=np.uint8)
+        qs = (qs & 0x03).reshape((n_blocks, -1)).astype(np.int8) - np.int8(1)
+
+        return (d * qs.astype(np.float32))
+
+
 class MXFP4(__Quant, qtype=GGMLQuantizationType.MXFP4):
     # e2m1 values (doubled)
     # ref: https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf

gguf-py/tests/test_quants.py

@@ -69,6 +69,7 @@ class GGMLQuants:
             "tq1_0", "tq2_0",
             "mxfp4",
             "nvfp4",
+            "q1_0", "q2_0",
             "iq2_xxs", "iq2_xs", "iq2_s", "iq3_xxs", "iq3_s", "iq1_s", "iq1_m",
             "iq4_nl", "iq4_xs",
         ):