Added blocksizes 2048, 1024, and 512 to blockwise quant.

bitsandbytes/cextension.py

@@ -52,8 +52,13 @@ class CUDASetup(object):
         self.add_log_entry('python setup.py install')
 
     def initialize(self):
-        self.cuda_setup_log = []
+        self.has_printed = False
         self.lib = None
+        self.run_cuda_setup()
+
+    def run_cuda_setup(self):
+        self.initialized = True
+        self.cuda_setup_log = []
 
         from .cuda_setup.main import evaluate_cuda_setup
         binary_name, cudart_path, cuda, cc, cuda_version_string = evaluate_cuda_setup()
@@ -89,7 +94,9 @@ class CUDASetup(object):
             else:
                 self.add_log_entry(f"CUDA SETUP: Loading binary {binary_path}...")
                 self.lib = ct.cdll.LoadLibrary(binary_path)
-        except:
+                print(self.lib)
+        except Exception as ex:
+            self.add_log_entry(str(ex))
             self.print_log_stack()
 
     def add_log_entry(self, msg, is_warning=False):

bitsandbytes/functional.py

@@ -130,10 +130,10 @@ class Cusparse_Context(object):
         return cls._instance
 
 
-def create_linear_map(signed=True, bits=8):
+def create_linear_map(signed=True, total_bits=8):
     sign = (-1.0 if signed else 0.0)
 
-    values = torch.linspace(sign, 1.0, 2**bits)
+    values = torch.linspace(sign, 1.0, 2**total_bits)
     gap = 256 - values.numel()
     if gap == 0:
         return values
@@ -457,6 +457,7 @@ def quantize_blockwise(A: Tensor, code: Tensor = None, absmax: Tensor = None, ra
         The quantization state to undo the quantization.
     """
 
+
     if code is None:
         if "dynamic" not in name2qmap:
             name2qmap["dynamic"] = create_dynamic_map().to(A.device)
@@ -474,8 +475,11 @@ def quantize_blockwise(A: Tensor, code: Tensor = None, absmax: Tensor = None, ra
         out = torch.zeros_like(A, dtype=torch.uint8)
 
     if A.device.type != 'cpu':
+        assert blocksize in [4096, 2048, 1024, 512]
         is_on_gpu([code, A, absmax, out, rand])
+        cblocksize = ct.c_int32(blocksize)
         if rand is not None:
+            assert blocksize==4096
             assert rand.numel() >= 1024
             rand_offset = random.randint(0, 1023)
             if A.dtype == torch.float32:
@@ -483,18 +487,14 @@ def quantize_blockwise(A: Tensor, code: Tensor = None, absmax: Tensor = None, ra
             elif A.dtype == torch.float16:
                 lib.cquantize_blockwise_stochastic_fp16(get_ptr(code), get_ptr(A),get_ptr(absmax), get_ptr(out), get_ptr(rand), ct.c_int32(rand_offset), ct.c_int(A.numel()))
             else:
-                raise ValueError(
-                    f"Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}"
-                )
+                raise ValueError(f"Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}")
         else:
             if A.dtype == torch.float32:
-                lib.cquantize_blockwise_fp32(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out),ct.c_int(A.numel()))
+                lib.cquantize_blockwise_fp32(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out), cblocksize, ct.c_int(A.numel()))
             elif A.dtype == torch.float16:
-                lib.cquantize_blockwise_fp16(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out),ct.c_int(A.numel()))
+                lib.cquantize_blockwise_fp16(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out), cblocksize, ct.c_int(A.numel()))
             else:
-                raise ValueError(
-                    f"Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}"
-                )
+                raise ValueError(f"Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}")
     else:
         # cpu
         assert rand is None

csrc/kernels.cu

@@ -428,16 +428,16 @@ __global__ void kQuantize(float * code, float * __restrict__ const A, unsigned c
 }
 
 template<typename T, int BLOCK_SIZE, int NUM_PER_TH, int STOCHASTIC>
-__launch_bounds__(TH, 4)
+//__launch_bounds__(TH, 4)
 __global__ void kQuantizeBlockwise(float * code, T * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n)
 {
   const int n_full = gridDim.x * BLOCK_SIZE;
   int valid_items = 0;
   const int base_idx = (blockIdx.x * BLOCK_SIZE);
 
-  T vals[NUM];
-  float rand_vals[NUM];
-  unsigned char qvals[NUM];
+  T vals[NUM_PER_TH];
+  float rand_vals[NUM_PER_TH];
+  unsigned char qvals[NUM_PER_TH];
   //float local_abs_max = -FLT_MAX;
   float local_abs_max = 0.0f;
   int local_rand_idx = 0;
@@ -517,8 +517,8 @@ __global__ void kDequantizeBlockwise(float *code, unsigned char * __restrict__ c
   int valid_items = 0;
   const int base_idx = (blockIdx.x * BLOCK_SIZE);
 
-  T vals[NUM];
-  unsigned char qvals[NUM];
+  T vals[NUM_PER_TH];
+  unsigned char qvals[NUM_PER_TH];
   float local_abs_max = -FLT_MAX;
 
   typedef cub::BlockLoad<unsigned char, THREADS, NUM_PER_TH, cub::BLOCK_LOAD_WARP_TRANSPOSE> LoadChar;
@@ -2791,11 +2791,21 @@ template __global__ void kQuantizeBlockwise<half, 4096, 4, 0>(float * code, half
 template __global__ void kQuantizeBlockwise<float, 4096, 4, 0>(float * code, float * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
 template __global__ void kQuantizeBlockwise<half, 4096, 4, 1>(float * code, half * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
 template __global__ void kQuantizeBlockwise<float, 4096, 4, 1>(float * code, float * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
+template __global__ void kQuantizeBlockwise<half, 2048, 4, 0>(float * code, half * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
+template __global__ void kQuantizeBlockwise<float, 2048, 4, 0>(float * code, float * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
+template __global__ void kQuantizeBlockwise<half, 1024, 4, 0>(float * code, half * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
+template __global__ void kQuantizeBlockwise<float, 1024, 4, 0>(float * code, float * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
+template __global__ void kQuantizeBlockwise<half, 512, 2, 0>(float * code, half * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
+template __global__ void kQuantizeBlockwise<float, 512, 2, 0>(float * code, float * __restrict__ const A, float *absmax, unsigned char *out, float * __restrict__ const rand, const int rand_offset, const int n);
 
 template __global__ void kDequantizeBlockwise<half, 4096, 1024, 4>(float *code, unsigned char * __restrict__ const A, float * __restrict__ const absmax, half *out, const int n);
 template __global__ void kDequantizeBlockwise<float, 4096, 1024, 4>(float *code, unsigned char * __restrict__ const A, float * __restrict__ const absmax, float *out, const int n);
 template __global__ void kDequantizeBlockwise<half, 2048, 512, 4>(float *code, unsigned char * __restrict__ const A, float * __restrict__ const absmax, half *out, const int n);
 template __global__ void kDequantizeBlockwise<float, 2048, 512, 4>(float *code, unsigned char * __restrict__ const A, float * __restrict__ const absmax, float *out, const int n);
+template __global__ void kDequantizeBlockwise<half, 1024, 256, 4>(float *code, unsigned char * __restrict__ const A, float * __restrict__ const absmax, half *out, const int n);
+template __global__ void kDequantizeBlockwise<float, 1024, 256, 4>(float *code, unsigned char * __restrict__ const A, float * __restrict__ const absmax, float *out, const int n);
+template __global__ void kDequantizeBlockwise<half, 512, 256, 2>(float *code, unsigned char * __restrict__ const A, float * __restrict__ const absmax, half *out, const int n);
+template __global__ void kDequantizeBlockwise<float, 512, 256, 2>(float *code, unsigned char * __restrict__ const A, float * __restrict__ const absmax, float *out, const int n);
 
 
 

csrc/ops.cu

@@ -50,11 +50,23 @@ void dequantize(float *code, unsigned char *A, float *out, int n)
   CUDA_CHECK_RETURN(cudaPeekAtLastError());
 }
 
-template <typename T, int STOCHASTIC> void quantizeBlockwise(float * code, T *A, float *absmax, unsigned char *out, float *rand, int rand_offset, const int n)
+template <typename T, int STOCHASTIC> void quantizeBlockwise(float * code, T *A, float *absmax, unsigned char *out, float *rand, int rand_offset, int blocksize, const int n)
 {
-  int num_blocks = n/4096;
-  num_blocks = n % 4096 == 0 ? num_blocks : num_blocks + 1;
-  kQuantizeBlockwise<T, 4096, 4, STOCHASTIC><<<num_blocks, 1024>>>(code, A, absmax, out, rand, rand_offset, n);
+  int num_blocks = n/blocksize;
+  num_blocks = n % blocksize == 0 ? num_blocks : num_blocks + 1;
+  if(STOCHASTIC == 1)
+    assert(blocksize == 4096);
+
+  if(blocksize == 4096)
+    kQuantizeBlockwise<T, 4096, 4, STOCHASTIC><<<num_blocks, 1024>>>(code, A, absmax, out, rand, rand_offset, n);
+  else if(blocksize == 2048)
+    kQuantizeBlockwise<T, 2048, 4, 0><<<num_blocks, 512>>>(code, A, absmax, out, rand, rand_offset, n);
+  else if(blocksize == 1024)
+    kQuantizeBlockwise<T, 1024, 4, 0><<<num_blocks, 256>>>(code, A, absmax, out, rand, rand_offset, n);
+  else if(blocksize == 512)
+    kQuantizeBlockwise<T, 512, 2, 0><<<num_blocks, 256>>>(code, A, absmax, out, rand, rand_offset, n);
+
+
   CUDA_CHECK_RETURN(cudaPeekAtLastError());
 }
 
@@ -66,6 +78,11 @@ template<typename T> void dequantizeBlockwise(float *code, unsigned char *A, flo
     kDequantizeBlockwise<T, 4096, 1024, 4><<<num_blocks, 4096/4>>>(code, A, absmax, out, n);
   else if(blocksize == 2048)
     kDequantizeBlockwise<T, 2048, 512, 4><<<num_blocks, 2048/4>>>(code, A, absmax, out, n);
+  else if(blocksize == 1024)
+    kDequantizeBlockwise<T, 1024, 256, 4><<<num_blocks, 1024/4>>>(code, A, absmax, out, n);
+  else if(blocksize == 512)
+    kDequantizeBlockwise<T, 512, 256, 2><<<num_blocks, 512/2>>>(code, A, absmax, out, n);
+
   CUDA_CHECK_RETURN(cudaPeekAtLastError());
 }
 
@@ -659,10 +676,10 @@ template void transformRowToFormat<COL_AMPERE, 1>(char * A, char *out, int rows,
 template void estimateQuantiles(half *A, float *code, float offset, int n);
 template void estimateQuantiles(float *A, float *code, float offset, int n);
 
-template void quantizeBlockwise<half, 0>(float * code, half *A, float *absmax, unsigned char *out, float* rand, int rand_offset, const int n);
-template void quantizeBlockwise<float, 0>(float * code, float *A, float *absmax, unsigned char *out, float* rand, int rand_offset, const int n);
-template void quantizeBlockwise<half, 1>(float * code, half *A, float *absmax, unsigned char *out, float* rand, int rand_offset, const int n);
-template void quantizeBlockwise<float, 1>(float * code, float *A, float *absmax, unsigned char *out, float* rand, int rand_offset, const int n);
+template void quantizeBlockwise<half, 0>(float * code, half *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
+template void quantizeBlockwise<float, 0>(float * code, float *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
+template void quantizeBlockwise<half, 1>(float * code, half *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
+template void quantizeBlockwise<float, 1>(float * code, float *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
 template void dequantizeBlockwise<half>(float *code, unsigned char *A, float *absmax, half *out, int blocksize, const int n);
 template void dequantizeBlockwise<float>(float *code, unsigned char *A, float *absmax, float *out, int blocksize, const int n);
 

csrc/ops.cuh

@@ -128,7 +128,7 @@ template <typename T> void estimateQuantiles(T *A, float *code, float offset, in
 
 void quantize(float *code, float *A, unsigned char *out, int n);
 void dequantize(float *code, unsigned char *A, float *out, int n);
-template <typename T, int STOCHASTIC> void quantizeBlockwise(float * code, T *A, float *absmax, unsigned char *out, float* rand, int rand_offset, const int n);
+template <typename T, int STOCHASTIC> void quantizeBlockwise(float * code, T *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
 template<typename T> void dequantizeBlockwise(float *code, unsigned char *A, float *absmax, T *out, int block_size, const int n);
 
 template<typename T, int OPTIMIZER> void optimizer32bit(T* g, T* p, 

csrc/pythonInterface.c

@@ -75,10 +75,10 @@ MAKE_BLOCKWISE8(adagrad, ADAGRAD, float, 32)
 void percentileClipping_g32(float * g, float *gnorm_vec, int step, const int n){ percentileClipping<float>(g, gnorm_vec, step, n); }
 void percentileClipping_g16(half * g, float *gnorm_vec, int step, const int n){ percentileClipping<half>(g, gnorm_vec, step, n); }
 
-void quantizeBlockwise_fp16(float * code, half *A, float *absmax, unsigned char *out, const int n){ quantizeBlockwise<half, 0>(code, A, absmax, out, NULL, 0, n); }
-void quantizeBlockwise_fp32(float * code, float *A, float *absmax, unsigned char *out, const int n){ quantizeBlockwise<float, 0>(code, A, absmax, out, NULL, 0, n); }
-void quantizeBlockwise_stochastic_fp16(float * code, half *A, float *absmax, unsigned char *out, float* rand, int rand_offset, const int n){ quantizeBlockwise<half, 1>(code, A, absmax, out, rand, rand_offset, n); }
-void quantizeBlockwise_stochastic_fp32(float * code, float *A, float *absmax, unsigned char *out, float* rand, int rand_offset, const int n){ quantizeBlockwise<float, 1>(code, A, absmax, out, rand, rand_offset, n); }
+void quantizeBlockwise_fp16(float * code, half *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise<half, 0>(code, A, absmax, out, NULL, 0, blocksize, n); }
+void quantizeBlockwise_fp32(float * code, float *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise<float, 0>(code, A, absmax, out, NULL, 0, blocksize, n); }
+void quantizeBlockwise_stochastic_fp16(float * code, half *A, float *absmax, unsigned char *out, float* rand, int rand_offset, const int n){ quantizeBlockwise<half, 1>(code, A, absmax, out, rand, rand_offset, 4096, n); }
+void quantizeBlockwise_stochastic_fp32(float * code, float *A, float *absmax, unsigned char *out, float* rand, int rand_offset, const int n){ quantizeBlockwise<float, 1>(code, A, absmax, out, rand, rand_offset, 4096, n); }
 
 void dequantizeBlockwise_fp16(float *code, unsigned char *A, float *absmax, half *out, int blocksize, const int n){ dequantizeBlockwise<half>(code, A, absmax, out, blocksize, n); } \
 void dequantizeBlockwise_fp32(float *code, unsigned char *A, float *absmax, float *out, int blocksize, const int n){ dequantizeBlockwise<float>(code, A, absmax, out, blocksize, n); }
@@ -140,8 +140,8 @@ extern "C"
 	void cestimate_quantiles_fp16(half *A, float *code, float offset, int n){ estimateQuantiles_fp16(A, code, offset, n); }
 	void cquantize(float *code, float *A, unsigned char *out, int n){ quantize(code, A, out, n); }
 	void cdequantize(float *code, unsigned char *A, float *out, int n){ dequantize(code, A, out, n); }
-  void cquantize_blockwise_fp16(float * code, half *A, float *absmax, unsigned char *out, const int n){ quantizeBlockwise_fp16(code, A, absmax, out, n); }
-  void cquantize_blockwise_fp32(float * code, float *A, float *absmax, unsigned char *out, const int n){ quantizeBlockwise_fp32(code, A, absmax, out, n); }
+  void cquantize_blockwise_fp16(float * code, half *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise_fp16(code, A, absmax, out, blocksize, n); }
+  void cquantize_blockwise_fp32(float * code, float *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise_fp32(code, A, absmax, out, blocksize, n); }
   void cquantize_blockwise_stochastic_fp16(float * code, half *A, float *absmax, unsigned char *out, float *rand, int rand_offset, const int n){ quantizeBlockwise_stochastic_fp16(code, A, absmax, out, rand, rand_offset, n); }
   void cquantize_blockwise_stochastic_fp32(float * code, float *A, float *absmax, unsigned char *out, float *rand, int rand_offset, const int n){ quantizeBlockwise_stochastic_fp32(code, A, absmax, out, rand, rand_offset, n); }
 

tests/test_functional.py

@@ -151,30 +151,41 @@ def test_dynamic_quantization():
 
 
 def test_dynamic_blockwise_quantization():
-    diffs = []
-    reldiffs = []
-    for i in range(100):
-        A1 = torch.randn(1024, 1024, device="cuda")
-        C, S = F.quantize_blockwise(A1)
-        A2 = F.dequantize_blockwise(C, S)
-        diff = torch.abs(A1 - A2)
-        reldiff = diff / torch.abs(A1 + 1e-8)
-        diffs.append(diff.mean().item())
-        reldiffs.append(reldiff.mean().item())
-        assert diffs[-1] < 0.011
-    # print(sum(diffs)/len(diffs))
-    # print(sum(reldiffs)/len(reldiffs))
-
-    diffs = []
-    for i in range(100):
-        A1 = torch.rand(1024, 1024, device="cuda")
-        C, S = F.quantize_blockwise(A1)
-        A2 = F.dequantize_blockwise(C, S)
-        diff = torch.abs(A1 - A2).mean().item()
-        assert diff < 0.0033
-        diffs.append(diff)
-        torch.testing.assert_allclose(A1, A2, atol=1e-2, rtol=0)
-    # print(sum(diffs)/len(diffs))
+    #print('')
+    for blocksize in [4096, 2048, 1024, 512]:
+        diffs = []
+        reldiffs = []
+        for i in range(100):
+            A1 = torch.randn(1024, 1024, device="cuda")
+            C, S = F.quantize_blockwise(A1)
+            A2 = F.dequantize_blockwise(C, S)
+            diff = torch.abs(A1 - A2)
+            reldiff = diff / torch.abs(A1 + 1e-8)
+            diffs.append(diff.mean().item())
+            reldiffs.append(reldiff.mean().item())
+        abserr = sum(diffs)/len(diffs)
+        relerr = sum(reldiffs)/len(reldiffs)
+        assert abserr < 0.011
+        assert relerr < 0.018
+        #print('randn', blocksize, sum(diffs)/len(diffs))
+        #print('randn', blocksize, sum(reldiffs)/len(reldiffs))
+
+        diffs = []
+        for i in range(100):
+            A1 = torch.rand(1024, 1024, device="cuda")
+            C, S = F.quantize_blockwise(A1)
+            A2 = F.dequantize_blockwise(C, S)
+            diff = torch.abs(A1 - A2)
+            reldiff = diff / torch.abs(A1 + 1e-8)
+            diffs.append(diff.mean().item())
+            reldiffs.append(reldiff.mean().item())
+            torch.testing.assert_allclose(A1, A2, atol=1e-2, rtol=0)
+        abserr = sum(diffs)/len(diffs)
+        relerr = sum(reldiffs)/len(reldiffs)
+        assert abserr < 0.0035
+        assert relerr < 0.015
+        #print('rand', blocksize, sum(diffs)/len(diffs))
+        #print('rand', blocksize, sum(reldiffs)/len(reldiffs))
 
 
 def test_dynamic_blockwise_stochastic_quantization():
@@ -1618,17 +1629,6 @@ def test_spmm_coo_very_sparse(dim1, dim2, dtype, out_func):
     # print(time.time() - t0)
 
 
-def test_layout():
-    a1 = torch.rand(16, 64, device="cuda", dtype=torch.float16)
-    a1 = torch.arange(16 * 64, device="cuda").reshape(16, 64).byte()
-    a2, s2 = F.transform(a1, "col_turing")
-    print(a2.shape)
-
-    print(a1.flatten()[8 * 64 : 8 * 64 + 32])
-    for i in range(4):
-        print(a2.flatten()[i * 8 * 32 : i * 8 * 32 + 32], 0)
-
-
 def test_coo2csr():
     threshold = 1
     A = torch.randn(128, 128).half().cuda()
@@ -2062,8 +2062,8 @@ def test_fp8_quant():
             abserr.append(diff.mean().item())
             relerr.append(reldiff.mean().item())
             #assert diff < 0.0075
-        print(sum(abserr)/len(abserr))
-        print(sum(relerr)/len(relerr))
+        #print(sum(abserr)/len(abserr))
+        #print(sum(relerr)/len(relerr))
 
         abserr = []
         relerr = []
@@ -2076,8 +2076,8 @@ def test_fp8_quant():
             abserr.append(diff.mean().item())
             relerr.append(reldiff.mean().item())
             #assert diff < 0.0075
-        print(sum(abserr)/len(abserr))
-        print(sum(relerr)/len(relerr))
+        #print(sum(abserr)/len(abserr))
+        #print(sum(relerr)/len(relerr))
 
         abserr = []
         relerr = []
@@ -2090,21 +2090,21 @@ def test_fp8_quant():
             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))
+        #print(3, sum(abserr)/len(abserr))
+        #print(3, sum(relerr)/len(relerr))
 
 
 def test_few_bit_quant():
 
-    print('')
+    #print('')
     for bits in range(2, 9):
-        print('='*30, bits, '='*30)
+        #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()
+                code = F.create_linear_map(True, total_bits=bits).cuda()
             elif method == 'fp8':
                 ebits = math.ceil(bits/2)
                 pbits = bits-ebits-1
@@ -2122,7 +2122,7 @@ def test_few_bit_quant():
 
                 q /= q.abs().max()
                 code, idx = torch.sort(q)
-            print(method, (code==0).sum())
+            #print(method, (code==0).sum())
             assert code.numel() == 256
             for i in range(10):
 
@@ -2154,7 +2154,7 @@ def test_few_bit_quant():
 
                 else:
                     torch.testing.assert_allclose(q1, q2)
-            print(method, 'abserr:', sum(abserrs)/len(abserrs), 'relerr:', sum(relerrs)/len(relerrs))
+            #print(method, 'abserr:', sum(abserrs)/len(abserrs), 'relerr:', sum(relerrs)/len(relerrs))
 
 
 def test_kbit_quantile_estimation():