Merge branch 'main' into cleanup

bitsandbytes/cextension.py

@@ -52,8 +52,13 @@ class CUDASetup:
         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 +94,8 @@ class CUDASetup:
             else:
                 self.add_log_entry(f"CUDA SETUP: Loading binary {binary_path}...")
                 self.lib = ct.cdll.LoadLibrary(binary_path)
-        except:
+        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

@@ -3,6 +3,7 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 import ctypes as ct
+import itertools
 import operator
 import random
 from functools import reduce  # Required in Python 3
@@ -130,13 +131,59 @@ class Cusparse_Context:
         return cls._instance
 
 
-def create_linear_map(signed=True):
+def create_linear_map(signed=True, total_bits=8):
-    if signed:
+    sign = (-1.0 if signed else 0.0)
-        return torch.linspace(-1.0, 1.0, 256)
+
-    return torch.linspace(0.0, 1.0, 256)
+    values = torch.linspace(sign, 1.0, 2**total_bits)
+    gap = 256 - values.numel()
+    if gap == 0:
+        return values
+    else:
+        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_dynamic_map(signed=True, n=7):
+def create_fp8_map(signed=True, exponent_bits=5, precision_bits=2, total_bits=8):
+    e = exponent_bits
+    p = precision_bits
+    has_sign = 1 if signed else 0
+    assert e+p == total_bits-has_sign
+    # the exponent is biased to 2^(e-1) -1 == 0
+    evalues = []
+    pvalues = []
+    for i, val in enumerate(range(-((2**(exponent_bits-has_sign))), 2**(exponent_bits-has_sign), 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) == 2**(total_bits-has_sign)
+    values = []
+    for ev in evalues:
+        for pv in pvalues:
+            if signed:
+                values.append(-ev*pv)
+            values.append(ev*pv)
+    if total_bits < 8:
+        gap = 256 - len(values)
+        for i in range(gap):
+            values.append(0)
+    values.sort()
+    code = torch.Tensor(values)
+    code /= code.max()
+    code[127] = 0
+
+    return code
+
+
+
+def create_dynamic_map(signed=True, max_exponent_bits=7, total_bits=8):
     """
     Creates the dynamic quantiztion map.
 
@@ -157,28 +204,32 @@ 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):
+    for i in range(max_exponent_bits):
-        fraction_items = (
+        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))
-            2 ** (i + 7 - n) + 1 if signed else 2 ** (i + 7 - n + 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:
+        if additional_items > 0:
-        boundaries = torch.linspace(0.1, 1, additional_items + 1)
+            boundaries = torch.linspace(0.1, 1, additional_items + 1)
-        means = (boundaries[:-1] + boundaries[1:]) / 2.0
+            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:
+            if signed:
-            data += (-(10 ** (-(n - 1) + i)) * means).tolist()
+                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)
 
@@ -322,9 +373,7 @@ def nvidia_transform(
     return out, new_state
 
 
-def estimate_quantiles(
+def estimate_quantiles(A: Tensor, out: Tensor = None, offset: float = 1 / 512, num_quantiles=256) -> Tensor:
-    A: Tensor, out: Tensor = None, offset: float = 1 / 512
-) -> Tensor:
     '''
     Estimates 256 equidistant quantiles on the input tensor eCDF.
 
@@ -344,25 +393,36 @@ 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(
+        lib.cestimate_quantiles_fp32(get_ptr(A), get_ptr(out), ct.c_float(offset), ct.c_int(A.numel()))
-            get_ptr(A), get_ptr(out), ct.c_float(offset), ct.c_int(A.numel())
-        )
     elif A.dtype == torch.float16:
-        lib.cestimate_quantiles_fp16(
+        lib.cestimate_quantiles_fp16(get_ptr(A), get_ptr(out), ct.c_float(offset), ct.c_int(A.numel()))
-            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
 
 
@@ -395,15 +455,14 @@ 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)
         code = name2qmap["dynamic"]
-        code = code.to(A.device)
 
     if absmax is None:
         n = A.numel()
-        blocksize = (blocksize if A.device.type == 'cpu' else 4096)
         blocks = n // blocksize
         blocks += 1 if n % blocksize > 0 else 0
         absmax = torch.zeros((blocks,), device=A.device)
@@ -412,8 +471,13 @@ 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':
-        is_on_gpu([code, A, absmax, out, rand])
+        assert blocksize in [4096, 2048, 1024, 512]
+        cblocksize = ct.c_int32(blocksize)
+        prev_device = pre_call(A.device)
+        code = code.to(A.device)
         if rand is not None:
+            is_on_gpu([code, A, out, absmax, rand])
+            assert blocksize==4096
             assert rand.numel() >= 1024
             rand_offset = random.randint(0, 1023)
             if A.dtype == torch.float32:
@@ -421,20 +485,19 @@ 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(
+                raise ValueError(f"Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}")
-                    f"Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}"
-                )
         else:
+            is_on_gpu([code, A, out, absmax])
             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(
+                raise ValueError(f"Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}")
-                    f"Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}"
+        post_call(A.device)
-                )
     else:
         # cpu
+        code = code.cpu()
         assert rand is None
         lib.cquantize_blockwise_cpu_fp32(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_longlong(blocksize), ct.c_longlong(A.numel()))
 
@@ -479,27 +542,30 @@ def dequantize_blockwise(
         if "dynamic" not in name2qmap:
             name2qmap["dynamic"] = create_dynamic_map().to(A.device)
         code = name2qmap["dynamic"]
-        code = code.to(A.device)
 
     if out is None:
         out = torch.zeros_like(A, dtype=torch.float32)
     if quant_state is None:
         quant_state = (absmax, code)
+    else:
+        absmax, code = quant_state
 
 
     if A.device.type != 'cpu':
-        if blocksize not in [2048, 4096]:
+        device = pre_call(A.device)
-            raise ValueError(f"The blockwise of {blocksize} is not supported. Supported values: [2048 4096]")
+        code = code.to(A.device)
+        if blocksize not in [2048, 4096, 1024, 512]:
+            raise ValueError(f"The blockwise of {blocksize} is not supported. Supported values: [2048, 4096, 1024, 512]")
         is_on_gpu([A, out])
         if out.dtype == torch.float32:
-            lib.cdequantize_blockwise_fp32(get_ptr(quant_state[1]), get_ptr(A), get_ptr(quant_state[0]), get_ptr(out), ct.c_int(blocksize), ct.c_int(A.numel()))
+            lib.cdequantize_blockwise_fp32(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(blocksize), ct.c_int(A.numel()))
         elif out.dtype == torch.float16:
-            lib.cdequantize_blockwise_fp16(get_ptr(quant_state[1]), get_ptr(A), get_ptr(quant_state[0]), get_ptr(out), ct.c_int(blocksize), ct.c_int(A.numel()))
+            lib.cdequantize_blockwise_fp16(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(blocksize), ct.c_int(A.numel()))
         else:
-            raise ValueError(
+            raise ValueError(f"Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}")
-                f"Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}"
+        post_call(A.device)
-            )
     else:
+        code = code.cpu()
         lib.cdequantize_blockwise_cpu_fp32(get_ptr(quant_state[1]), get_ptr(A), get_ptr(quant_state[0]), get_ptr(out), ct.c_longlong(blocksize), ct.c_longlong(A.numel()))
 
     return out

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];
+  T vals[NUM_PER_TH];
-  float rand_vals[NUM];
+  float rand_vals[NUM_PER_TH];
-  unsigned char qvals[NUM];
+  unsigned char qvals[NUM_PER_TH];
   //float local_abs_max = -FLT_MAX;
   float local_abs_max = 0.0f;
   int local_rand_idx = 0;
@@ -510,15 +510,15 @@ __global__ void kQuantizeBlockwise(float * code, T * __restrict__ const A, float
 }
 
 template<typename T, int BLOCK_SIZE, int THREADS, int NUM_PER_TH>
-__global__ void kDequantizeBlockwise(float *code, unsigned char * __restrict__ const A, float * __restrict__ const absmax, T *out, const int n)
+__global__ void kDequantizeBlockwise(float *code, unsigned char * A, float * absmax, T *out, 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];
+  T vals[NUM_PER_TH];
-  unsigned char qvals[NUM];
+  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;
@@ -526,10 +526,11 @@ __global__ void kDequantizeBlockwise(float *code, unsigned char * __restrict__ c
 
   __shared__ typename LoadChar::TempStorage loadchar;
   __shared__ typename StoreT::TempStorage storet;
-  __shared__ float smem_code[256];
+  //__shared__ float smem_code[256];
+  //float local_code[16];
 
-  if(threadIdx.x < 256)
+  //if(threadIdx.x < 256)
-    smem_code[threadIdx.x] = code[threadIdx.x];
+    //smem_code[threadIdx.x] = code[threadIdx.x];
 
   for (unsigned int i = base_idx; i < n_full; i += gridDim.x*BLOCK_SIZE)
   {
@@ -539,9 +540,10 @@ __global__ void kDequantizeBlockwise(float *code, unsigned char * __restrict__ c
       __syncthreads();
       LoadChar(loadchar).Load(&(A[i]), qvals, valid_items, 128);
 
+      // load code through read-only cache via __ldg
       #pragma unroll NUM_PER_TH
       for(int j = 0; j < NUM_PER_TH; j++)
-        vals[j] = smem_code[qvals[j]]*local_abs_max;
+        vals[j] = __ldg(&code[qvals[j]])*local_abs_max;
 
       __syncthreads();
       StoreT(storet).Store(&(out[i]), vals, valid_items);
@@ -2791,11 +2793,21 @@ 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<half, 4096, 1024, 4>(float *code, unsigned char * A, float * 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<float, 4096, 1024, 4>(float *code, unsigned char * A, float * 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<half, 2048, 512, 4>(float *code, unsigned char * A, float * 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<float, 2048, 512, 4>(float *code, unsigned char * A, float * absmax, float *out, const int n);
+template __global__ void kDequantizeBlockwise<half, 1024, 256, 4>(float *code, unsigned char * A, float * absmax, half *out, const int n);
+template __global__ void kDequantizeBlockwise<float, 1024, 256, 4>(float *code, unsigned char * A, float * absmax, float *out, const int n);
+template __global__ void kDequantizeBlockwise<half, 512, 256, 2>(float *code, unsigned char * A, float * absmax, half *out, const int n);
+template __global__ void kDequantizeBlockwise<float, 512, 256, 2>(float *code, unsigned char * A, float * absmax, float *out, const int n);
 
 
 

csrc/kernels.cuh

@@ -15,7 +15,7 @@ __global__ void kQuantize(float * code, float * __restrict__ const A, unsigned c
 __global__ void kDequantize(float *code, unsigned char *A, float *out, const int n);
 
 template<typename T, int BLOCK_SIZE, int NUM_PER_TH, int STOCHASTIC> __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);
-template<typename T, int BLOCK_SIZE, int THREADS, int NUM_PER_TH> __global__ void kDequantizeBlockwise(float *code, unsigned char * __restrict__ const A, float * __restrict__ const absmax, T *out, const int n);
+template<typename T, int BLOCK_SIZE, int THREADS, int NUM_PER_TH> __global__ void kDequantizeBlockwise(float *code, unsigned char * A, float * absmax, T *out, const int n);
 
 template<typename T, int OPTIMIZER, int BLOCK_SIZE, int NUM_VALS>
 __global__ void kPreconditionOptimizer32bit2State(T* g, T* p,

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;
+  int num_blocks = n/blocksize;
-  num_blocks = n % 4096 == 0 ? num_blocks : num_blocks + 1;
+  num_blocks = n % blocksize == 0 ? num_blocks : num_blocks + 1;
-  kQuantizeBlockwise<T, 4096, 4, STOCHASTIC><<<num_blocks, 1024>>>(code, A, absmax, out, rand, rand_offset, n);
+  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<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, 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, 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, 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_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, const int n){ quantizeBlockwise<float, 0>(code, A, absmax, out, NULL, 0, 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, 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, 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_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, const int n){ quantizeBlockwise_fp32(code, A, absmax, out, 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

@@ -6,12 +6,14 @@ from itertools import product
 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=4, sci_mode=False, linewidth=120, edgeitems=20, threshold=10000
+    precision=5, sci_mode=False, linewidth=120, edgeitems=20, threshold=10000
 )
 k = 20
 
@@ -149,30 +151,41 @@ def test_dynamic_quantization():
 
 
 def test_dynamic_blockwise_quantization():
-    diffs = []
+    #print('')
-    reldiffs = []
+    for blocksize in [4096, 2048, 1024, 512]:
-    for i in range(100):
+        diffs = []
-        A1 = torch.randn(1024, 1024, device="cuda")
+        reldiffs = []
-        C, S = F.quantize_blockwise(A1)
+        for i in range(100):
-        A2 = F.dequantize_blockwise(C, S)
+            A1 = torch.randn(1024, 1024, device="cuda")
-        diff = torch.abs(A1 - A2)
+            C, S = F.quantize_blockwise(A1, blocksize=blocksize)
-        reldiff = diff / torch.abs(A1 + 1e-8)
+            A2 = F.dequantize_blockwise(C, S, blocksize=blocksize)
-        diffs.append(diff.mean().item())
+            diff = torch.abs(A1 - A2)
-        reldiffs.append(reldiff.mean().item())
+            reldiff = diff / torch.abs(A1 + 1e-8)
-        assert diffs[-1] < 0.011
+            diffs.append(diff.mean().item())
-    # print(sum(diffs)/len(diffs))
+            reldiffs.append(reldiff.mean().item())
-    # print(sum(reldiffs)/len(reldiffs))
+        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 = []
+        diffs = []
-    for i in range(100):
+        for i in range(100):
-        A1 = torch.rand(1024, 1024, device="cuda")
+            A1 = torch.rand(1024, 1024, device="cuda")
-        C, S = F.quantize_blockwise(A1)
+            C, S = F.quantize_blockwise(A1, blocksize=blocksize)
-        A2 = F.dequantize_blockwise(C, S)
+            A2 = F.dequantize_blockwise(C, S, blocksize=blocksize)
-        diff = torch.abs(A1 - A2).mean().item()
+            diff = torch.abs(A1 - A2)
-        assert diff < 0.0033
+            reldiff = diff / torch.abs(A1 + 1e-8)
-        diffs.append(diff)
+            diffs.append(diff.mean().item())
-        torch.testing.assert_allclose(A1, A2, atol=1e-2, rtol=0)
+            reldiffs.append(reldiff.mean().item())
-    # print(sum(diffs)/len(diffs))
+            #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():
@@ -1616,17 +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()
@@ -2040,3 +2042,143 @@ 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))
+
+
+def test_few_bit_quant():
+
+    #print('')
+    for bits in range(2, 9):
+        #print('='*30, bits, '='*30)
+        for method in ['linear', 'fp8', 'dynamic', 'quantile']:
+            abserrs = []
+            relerrs = []
+            code = None
+            if method == 'linear':
+                code = F.create_linear_map(True, total_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()
+            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
+            for i in range(10):
+
+                values = torch.randn(1, 32, 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())
+                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()
+                    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
+
+
+def test_bench_dequantization():
+    a = torch.rand(1024, 1024, device='cuda').half()
+    qa, SA = F.quantize_blockwise(a)
+
+    max_theoretical_mu =  1024*1024*2/1024**3/672*1000*1000
+    #print(max_theoretical_mu)
+
+    torch.cuda.synchronize()
+    t0 = time.time()
+    for i in range(100):
+        F.dequantize_blockwise(qa, SA, blocksize=2048)
+    torch.cuda.synchronize()
+    #print((time.time()-t0)/1e6)
+