Added k<256 quantile estimate.

Author: TimDettmers

bitsandbytes/functional.py

@@ -182,7 +182,7 @@ def create_fp8_map(signed=True, exponent_bits=5, precision_bits=2, total_bits=8)
 
 
 
-def create_dynamic_map(signed=True, n=7):
+def create_dynamic_map(signed=True, max_exponent_bits=7, total_bits=8):
     """
     Creates the dynamic quantiztion map.
 
@@ -203,28 +203,32 @@ def create_dynamic_map(signed=True, n=7):
     # these are additional items that come from the case
     # where all the exponent bits are zero and no
     # indicator bit is present
-    additional_items = 2 ** (7 - n) - 1
+    non_sign_bits = total_bits - (1 if signed else 0)
+    additional_items = 2 ** (non_sign_bits - max_exponent_bits) - 1
     if not signed:
         additional_items = 2 * additional_items
-    for i in range(n):
-        fraction_items = (
-            2 ** (i + 7 - n) + 1 if signed else 2 ** (i + 7 - n + 1) + 1
-        )
+    for i in range(max_exponent_bits):
+        fraction_items = int((2 ** (i + non_sign_bits - max_exponent_bits) + 1 if signed else 2 ** (i + non_sign_bits - max_exponent_bits + 1) + 1))
         boundaries = torch.linspace(0.1, 1, fraction_items)
         means = (boundaries[:-1] + boundaries[1:]) / 2.0
-        data += ((10 ** (-(n - 1) + i)) * means).tolist()
+        data += ((10 ** (-(max_exponent_bits - 1) + i)) * means).tolist()
         if signed:
-            data += (-(10 ** (-(n - 1) + i)) * means).tolist()
+            data += (-(10 ** (-(max_exponent_bits - 1) + i)) * means).tolist()
 
-    if additional_items > 0:
-        boundaries = torch.linspace(0.1, 1, additional_items + 1)
-        means = (boundaries[:-1] + boundaries[1:]) / 2.0
-        data += ((10 ** (-(n - 1) + i)) * means).tolist()
-        if signed:
-            data += (-(10 ** (-(n - 1) + i)) * means).tolist()
+        if additional_items > 0:
+            boundaries = torch.linspace(0.1, 1, additional_items + 1)
+            means = (boundaries[:-1] + boundaries[1:]) / 2.0
+            data += ((10 ** (-(max_exponent_bits - 1) + i)) * means).tolist()
+            if signed:
+                data += (-(10 ** (-(max_exponent_bits - 1) + i)) * means).tolist()
 
     data.append(0)
     data.append(1.0)
+
+    gap = 256 - len(data)
+    for i in range(gap):
+        data.append(0)
+
     data.sort()
     return Tensor(data)
 
@@ -371,9 +375,7 @@ def nvidia_transform(
     return out, new_state
 
 
-def estimate_quantiles(
-    A: Tensor, out: Tensor = None, offset: float = 1 / 512
-) -> Tensor:
+def estimate_quantiles(A: Tensor, out: Tensor = None, offset: float = 1 / 512, num_quantiles=256) -> Tensor:
     '''
     Estimates 256 equidistant quantiles on the input tensor eCDF.
 
@@ -393,25 +395,36 @@ def estimate_quantiles(
     out : torch.Tensor
         Tensor with the 256 estimated quantiles.
     offset : float
-        The offset for the first and last quantile from 0 and 1. Default: 1/512
+        The offset for the first and last quantile from 0 and 1. Default: 1/(2*num_quantiles)
+    num_quantiles : int
+        The number of equally spaced quantiles.
 
     Returns
     -------
     torch.Tensor:
         The 256 quantiles in float32 datatype.
     '''
+    if A.numel() < 256: raise NotImplementedError(f'Quantile estimation needs at least 256 values in the Tensor, but Tensor had only {A.numel()} values.')
+    if num_quantiles > 256: raise NotImplementedError(f"Currently only a maximum of 256 equally spaced quantiles are supported, but the argument num_quantiles={num_quantiles}")
+    if num_quantiles < 256 and offset == 1/(512):
+        # override default arguments
+        offset = 1/(2*num_quantiles)
+
     if out is None: out = torch.zeros((256,), dtype=torch.float32, device=A.device)
     is_on_gpu([A, out])
+    device = pre_call(A.device)
     if A.dtype == torch.float32:
-        lib.cestimate_quantiles_fp32(
-            get_ptr(A), get_ptr(out), ct.c_float(offset), ct.c_int(A.numel())
-        )
+        lib.cestimate_quantiles_fp32(get_ptr(A), get_ptr(out), ct.c_float(offset), ct.c_int(A.numel()))
     elif A.dtype == torch.float16:
-        lib.cestimate_quantiles_fp16(
-            get_ptr(A), get_ptr(out), ct.c_float(offset), ct.c_int(A.numel())
-        )
+        lib.cestimate_quantiles_fp16(get_ptr(A), get_ptr(out), ct.c_float(offset), ct.c_int(A.numel()))
     else:
         raise NotImplementedError(f"Not supported data type {A.dtype}")
+    post_call(device)
+
+    if num_quantiles < 256:
+        idx = torch.linspace(0, 255, num_quantiles).long().to(A.device)
+        out = out[idx]
+
     return out
 
 

tests/test_functional.py

@@ -6,9 +6,11 @@
 import einops
 import pytest
 import torch
+import numpy as np
 
 import bitsandbytes as bnb
 from bitsandbytes import functional as F
+from scipy.stats import norm
 
 torch.set_printoptions(
     precision=5, sci_mode=False, linewidth=120, edgeitems=20, threshold=10000
@@ -2094,19 +2096,34 @@ def test_fp8_quant():
 
 def test_few_bit_quant():
 
+    print('')
     for bits in range(2, 9):
-        for method in ['linear', 'fp8']:
+        print('='*30, bits, '='*30)
+        for method in ['linear', 'fp8', 'dynamic', 'quantile']:
+            abserrs = []
+            relerrs = []
             code = None
             if method == 'linear':
                 code = F.create_linear_map(True, bits=bits).cuda()
             elif method == 'fp8':
                 ebits = math.ceil(bits/2)
                 pbits = bits-ebits-1
                 code = F.create_fp8_map(True, ebits, pbits, bits).cuda()
-                print(ebits, pbits, bits)
-                print(code)
+            elif method == 'dynamic':
+                code = F.create_dynamic_map(True, bits-0, bits).cuda()
+            elif method == 'quantile':
+                values = torch.randn(2048, 2048, device='cuda')
+                q = F.estimate_quantiles(values, offset= 1/(2*(2**bits)), num_quantiles=2**bits)
+                gap = 256-q.numel()
+                q = q.tolist()
+                for i in range(gap):
+                    q.append(0)
+                q = torch.Tensor(q).cuda()
+
+                q /= q.abs().max()
+                code, idx = torch.sort(q)
+            print(method, (code==0).sum())
             assert code.numel() == 256
-            print(bits)
             for i in range(10):
 
                 values = torch.randn(1, 32, device='cuda')
@@ -2127,11 +2144,25 @@ def test_few_bit_quant():
                 v2 = F.dequantize(q2, S2)
 
                 idx = torch.isclose(q1.int(), q2.int())
+                err2 = torch.abs(v2-values)
+                abserrs.append(err2.mean().item())
+                relerrs.append((err2/(1e-10+values).abs()).mean().item())
                 if idx.sum():
                     # some weird cases
                     err1 = torch.abs(v1-values).mean()
-                    err2 = torch.abs(v2-values).mean()
-                    assert err2 <= err1
+                    assert err2.mean() <= err1
 
                 else:
                     torch.testing.assert_allclose(q1, q2)
+            print(method, 'abserr:', sum(abserrs)/len(abserrs), 'relerr:', sum(relerrs)/len(relerrs))
+
+
+def test_kbit_quantile_estimation():
+    for i in range(100):
+        data = torch.randn(1024, 1024, device='cuda')
+        for bits in range(2, 9):
+            p = np.linspace(1.3e-4, 1-1.3e-4, 2**bits)
+            val1 = torch.Tensor(norm.ppf(p)).cuda()
+            val2 = F.estimate_quantiles(data, offset=0, num_quantiles=2**bits)
+            err = torch.abs(val1-val2).mean()
+            assert err < 0.035