Implement Q8_0 quantization fully in PyTorch.

This is equivalent to gguf.quantize_q8_0 but doesn't round-trip to
Numpy.

convert_grok.py

@@ -123,12 +123,21 @@ def get_weights(fn):
     assert len(arrays) in (1, 2)
 
 
+def torch_roundf(t: torch.Tensor) -> torch.Tensor:
+    """Round halfway cases away from zero like roundf(3). Cf. gguf/quants.py."""
+    a = abs(t)
+    floored = torch.floor(a)
+    b = floored + torch.floor(2 * (a - floored))
+    return torch.sign(t) * b
+
+
 def quantize_q8_0(tensor: torch.Tensor) -> torch.CharTensor:
-    # equivalent to ggml_quantize_q8_0 in ggml.c (modulo rounding away from zero)
+    # Equivalent to gguf.quantize_q8_0 but PyTorch instead of Numpy.
     assert tensor.shape[1] % QK8_0 == 0
     tensor = tensor.reshape(-1, QK8_0)
     scale = tensor.abs().max(dim=-1, keepdim=True).values / ((1 << 7) - 1)
-    tensor = (tensor / scale).round().clamp(min=-128, max=127).char()
+    iscale = torch.where(scale != 0.0, 1.0 / scale, 0.0)
+    tensor = torch_roundf(tensor * iscale).clamp(min=-128, max=127).char()
     # add scale into each block
     tensor = torch.cat((scale.half().view(torch.int8), tensor), dim=-1)
     return tensor
@@ -175,9 +184,7 @@ def maybe_quantize_tensor(tensor, ggml_type):
     elif ggml_type == gguf.GGMLQuantizationType.F16:
         return tensor.half()
     elif ggml_type == gguf.GGMLQuantizationType.Q8_0:
-        if tensor.device.type == "meta":
-            return quantize_q8_0(tensor)  # Cannot convert into numpy array.
-        return torch.from_numpy(gguf.quantize_q8_0(tensor.numpy()))
+        return quantize_q8_0(tensor)
     elif ggml_type == gguf.GGMLQuantizationType.Q4_0:
         return quantize_q4_0(tensor)
     elif ggml_type == gguf.GGMLQuantizationType.Q4_1: