Added k-bit linear quantization.

bitsandbytes/functional.py

@@ -130,11 +130,17 @@ class Cusparse_Context(object):
         return cls._instance
 
 
-def create_linear_map(signed=True):
+def create_linear_map(signed=True, bits=8):
-    if signed:
+    sign = (-1.0 if signed else 0.0)
-        return torch.linspace(-1.0, 1.0, 256)
+
+    values = torch.linspace(sign, 1.0, 2**bits)
+    gap = 256 - values.numel()
+    if gap == 0:
+        return values
     else:
-        return torch.linspace(0.0, 1.0, 256)
+        l = values.numel()//2
+        #return torch.Tensor(values[:l].tolist() + [-1e-6]*((gap//2)-1) + [0]*2 + [1e-6]*((gap//2)-1) + values[l:].tolist())
+        return torch.Tensor(values[:l].tolist() + [0]*gap + values[l:].tolist())
 
 
 def create_fp8_map(signed=True, exponent_bits=5, precision_bits=2):

tests/test_functional.py

@@ -2091,3 +2091,53 @@ def test_fp8_quant():
         print(3, sum(abserr)/len(abserr))
         print(3, sum(relerr)/len(relerr))
 
+
+def test_few_bit_quant():
+
+    for bits in range(2, 9):
+        code = F.create_linear_map(True, bits=bits).cuda()
+        assert code.numel() == 256
+        print(bits)
+        for i in range(100):
+
+            values = torch.randn(1, 24, device='cuda')
+            values /= values.abs().max()
+            #values[values.abs() < 1e-6] += 1e-5
+
+            q1 = []
+            v1 = []
+            for v in values[0]:
+                idx = torch.abs(v-code).argmin()
+                q1.append(idx.item())
+                v1.append(code[idx].item())
+
+            q1 = torch.Tensor(q1).cuda()
+            v1 = torch.Tensor(v1).cuda()
+
+            q2, S2 = F.quantize(values, code=code)
+            v2 = F.dequantize(q2, S2)
+
+            idx = torch.isclose(q1.int(), q2.int())
+            if idx.sum():
+                # some weird cases
+                err1 = torch.abs(v1-values).mean()
+                err2 = torch.abs(v2-values).mean()
+                assert err2 <= err1
+
+            else:
+                torch.testing.assert_allclose(q1, q2)
+
+    #print(e_bits, p_bits)
+    #abserr = []
+    #relerr = []
+    #for i in range(100):
+    #    A1 = torch.randn(1024, 1024, device="cuda")
+    #    C, SC = F.quantize_blockwise(A1, code=code)
+    #    A2 = F.dequantize_blockwise(C, SC)
+    #    diff = torch.abs(A1 - A2)
+    #    reldiff = diff/torch.abs(A1+1e-8)
+    #    abserr.append(diff.mean().item())
+    #    relerr.append(reldiff.mean().item())
+    #    #assert diff < 0.0075
+    #print(sum(abserr)/len(abserr))
+    #print(sum(relerr)/len(relerr))