Added FP8 quantization map.

bitsandbytes/functional.py

@@ -6,6 +6,7 @@ import ctypes as ct
 import operator
 import random
 import torch
+import itertools
 
 from typing import Tuple
 from torch import Tensor
@@ -136,6 +137,39 @@ def create_linear_map(signed=True):
         return torch.linspace(0.0, 1.0, 256)
 
 
+def create_fp8_map(signed=True, exponent_bits=5, precision_bits=2):
+    e = exponent_bits
+    p = precision_bits
+    assert e+p == 7
+    # the exponent is biased to 2^(e-1) -1 == 0
+    evalues = []
+    pvalues = []
+    for i, val in enumerate(range(-((2**(exponent_bits-1))), 2**(exponent_bits-1), 1)):
+        evalues.append(2**val)
+
+
+    lst = list(itertools.product([0, 1], repeat=precision_bits))
+    for bit_pattern in lst:
+        value = 1
+        for i, pval in enumerate(list(bit_pattern)):
+            value += pval*(2**-(i+1))
+        pvalues.append(value)
+
+    assert len(evalues)*len(pvalues) == 128
+    values = []
+    for ev in evalues:
+        for pv in pvalues:
+            values.append(-ev*pv)
+            values.append(ev*pv)
+    values.sort()
+    code = torch.Tensor(values)
+    code /= code.max()
+    code[127] = 0
+
+    return code
+
+
+
 def create_dynamic_map(signed=True, n=7):
     """
     Creates the dynamic quantiztion map.

tests/test_functional.py

@@ -2040,3 +2040,54 @@ def test_blockwise_cpu_large():
                 assert diffs[-1] < 0.011
             # print(sum(diffs)/len(diffs))
             # print(sum(reldiffs)/len(reldiffs))
+
+
+
+def test_fp8_quant():
+    for e_bits in range(1, 7):
+        p_bits = 7-e_bits
+        code = F.create_fp8_map(True, e_bits, p_bits).cuda()
+
+        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))
+
+        abserr = []
+        relerr = []
+        for i in range(100):
+            A1 = torch.rand(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))
+
+        abserr = []
+        relerr = []
+        for i in range(100):
+            A1 = torch.randn(1024, 1024, device="cuda")
+            C, SC = F.quantize_blockwise(A1)
+            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(3, sum(abserr)/len(abserr))
+        print(3, sum(relerr)/len(relerr))
+