Add a CPU-only build option

Makefile

@@ -10,10 +10,10 @@ NVCC := $(CUDA_HOME)/bin/nvcc
 ###########################################
 
 CSRC := $(ROOT_DIR)/csrc
-BUILD_DIR:= $(ROOT_DIR)/cuda_build
+BUILD_DIR:= $(ROOT_DIR)/build
 
 FILES_CUDA := $(CSRC)/ops.cu $(CSRC)/kernels.cu
-FILES_CPP := $(CSRC)/pythonInterface.c
+FILES_CPP := $(CSRC)/common.cpp $(CSRC)/cpu_ops.cpp $(CSRC)/pythonInterface.c
 
 INCLUDE :=  -I $(CUDA_HOME)/include -I $(ROOT_DIR)/csrc -I $(CONDA_PREFIX)/include -I $(ROOT_DIR)/dependencies/cub -I $(ROOT_DIR)/include
 LIB := -L $(CUDA_HOME)/lib64 -lcudart -lcuda -lcublas -lcurand -lcusparse -L $(CONDA_PREFIX)/lib
@@ -46,27 +46,30 @@ CC_CUDA11x += -gencode arch=compute_86,code=sm_86
 all: $(ROOT_DIR)/dependencies/cub $(BUILD_DIR) env
 	$(NVCC) $(COMPUTE_CAPABILITY) -Xcompiler '-fPIC' --use_fast_math -Xptxas=-v -dc $(FILES_CUDA) $(INCLUDE) $(LIB) --output-directory $(BUILD_DIR)
 	$(NVCC) $(COMPUTE_CAPABILITY) -Xcompiler '-fPIC' -dlink $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o -o $(BUILD_DIR)/link.o 
-	$(GPP) -std=c++14 -shared -fPIC $(INCLUDE) $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o $(BUILD_DIR)/link.o $(FILES_CPP) -o ./bitsandbytes/libbitsandbytes.so $(LIB)
+	$(GPP) -std=c++14 -DBUILD_CUDA -shared -fPIC $(INCLUDE) $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o $(BUILD_DIR)/link.o $(FILES_CPP) -o ./bitsandbytes/libbitsandbytes.so $(LIB)
 
 cuda92: $(ROOT_DIR)/dependencies/cub $(BUILD_DIR) env
 	$(NVCC) $(COMPUTE_CAPABILITY) $(CC_CUDA92) -Xcompiler '-fPIC' --use_fast_math -Xptxas=-v -dc $(FILES_CUDA) $(INCLUDE) $(LIB) --output-directory $(BUILD_DIR)
 	$(NVCC) $(COMPUTE_CAPABILITY) $(CC_CUDA92) -Xcompiler '-fPIC' -dlink $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o -o $(BUILD_DIR)/link.o 
-	$(GPP) -std=c++14 -shared -fPIC $(INCLUDE) $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o $(BUILD_DIR)/link.o $(FILES_CPP) -o ./bitsandbytes/libbitsandbytes.so $(LIB)
+	$(GPP) -std=c++14 -DBUILD_CUDA -shared -fPIC $(INCLUDE) $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o $(BUILD_DIR)/link.o $(FILES_CPP) -o ./bitsandbytes/libbitsandbytes.so $(LIB)
 
 cuda10x: $(ROOT_DIR)/dependencies/cub $(BUILD_DIR) env
 	$(NVCC) $(COMPUTE_CAPABILITY) $(CC_CUDA10x) -Xcompiler '-fPIC' --use_fast_math -Xptxas=-v -dc $(FILES_CUDA) $(INCLUDE) $(LIB) --output-directory $(BUILD_DIR)
 	$(NVCC) $(COMPUTE_CAPABILITY) $(CC_CUDA10x) -Xcompiler '-fPIC' -dlink $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o -o $(BUILD_DIR)/link.o 
-	$(GPP) -std=c++14 -shared -fPIC $(INCLUDE) $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o $(BUILD_DIR)/link.o $(FILES_CPP) -o ./bitsandbytes/libbitsandbytes.so $(LIB)
+	$(GPP) -std=c++14 -DBUILD_CUDA -shared -fPIC $(INCLUDE) $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o $(BUILD_DIR)/link.o $(FILES_CPP) -o ./bitsandbytes/libbitsandbytes.so $(LIB)
 
 cuda110: $(BUILD_DIR) env
 	$(NVCC) $(COMPUTE_CAPABILITY) $(CC_CUDA110) -Xcompiler '-fPIC' --use_fast_math -Xptxas=-v -dc $(FILES_CUDA) $(INCLUDE) $(LIB) --output-directory $(BUILD_DIR)
 	$(NVCC) $(COMPUTE_CAPABILITY) $(CC_CUDA110) -Xcompiler '-fPIC' -dlink $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o -o $(BUILD_DIR)/link.o 
-	$(GPP) -std=c++14 -shared -fPIC $(INCLUDE) $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o $(BUILD_DIR)/link.o $(FILES_CPP) -o ./bitsandbytes/libbitsandbytes.so $(LIB)
+	$(GPP) -std=c++14 -DBUILD_CUDA -shared -fPIC $(INCLUDE) $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o $(BUILD_DIR)/link.o $(FILES_CPP) -o ./bitsandbytes/libbitsandbytes.so $(LIB)
 
 cuda11x: $(BUILD_DIR) env
 	$(NVCC) $(COMPUTE_CAPABILITY) $(CC_CUDA11x) -Xcompiler '-fPIC' --use_fast_math -Xptxas=-v -dc $(FILES_CUDA) $(INCLUDE) $(LIB) --output-directory $(BUILD_DIR)
 	$(NVCC) $(COMPUTE_CAPABILITY) $(CC_CUDA11x) -Xcompiler '-fPIC' -dlink $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o -o $(BUILD_DIR)/link.o 
-	$(GPP) -std=c++14 -shared -fPIC $(INCLUDE) $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o $(BUILD_DIR)/link.o $(FILES_CPP) -o ./bitsandbytes/libbitsandbytes.so $(LIB)
+	$(GPP) -std=c++14 -DBUILD_CUDA -shared -fPIC $(INCLUDE) $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o $(BUILD_DIR)/link.o $(FILES_CPP) -o ./bitsandbytes/libbitsandbytes.so $(LIB)
+
+cpuonly: $(BUILD_DIR) env
+	$(GPP) -std=c++14 -shared -fPIC -I $(ROOT_DIR)/csrc -I $(ROOT_DIR)/include $(FILES_CPP) -o ./bitsandbytes/libbitsandbytes.so
 
 env:
 	@echo "ENVIRONMENT"
@@ -80,7 +83,7 @@ env:
 	@echo "============================"
 
 $(BUILD_DIR):
-	mkdir -p cuda_build
+	mkdir -p build
 	mkdir -p dependencies
 
 $(ROOT_DIR)/dependencies/cub:

bitsandbytes/__init__.py

@@ -2,8 +2,13 @@
 #   
 # This source code is licensed under the MIT license found in the 
 # LICENSE file in the root directory of this source tree.
-from .optim import adam
+
 from .nn import modules
+from cextension import COMPILED_WITH_CUDA
+
+if COMPILED_WITH_CUDA:
+    from .optim import adam
+
 __pdoc__ = {'libBitsNBytes': False,
             'optim.optimizer.Optimizer8bit': False,
             'optim.optimizer.MockArgs': False

bitsandbytes/cextension.py

@@ -0,0 +1,13 @@
+import ctypes as ct
+import os
+from warnings import warn
+
+lib = ct.cdll.LoadLibrary(os.path.dirname(__file__) + '/libbitsandbytes.so')
+
+try:
+    lib.cadam32bit_g32
+    COMPILED_WITH_CUDA = True
+except AttributeError:
+    warn("The installed version of bitsandbytes was compiled without GPU support. "
+         "8-bit optimizers and GPU quantization are unavailable.")
+    COMPILED_WITH_CUDA = False

bitsandbytes/functional.py

@@ -3,18 +3,18 @@
 # 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 os
 import random
 from typing import Tuple
 
 import torch
 from torch import Tensor
 
-lib = ct.cdll.LoadLibrary(os.path.dirname(__file__) + '/libbitsandbytes.so')
+from cextension import lib, COMPILED_WITH_CUDA
+
 name2qmap = {}
 
+if COMPILED_WITH_CUDA:
     ''' C FUNCTIONS FOR OPTIMIZERS '''
-
     str2optimizer32bit = {}
     str2optimizer32bit['adam'] = (lib.cadam32bit_g32, lib.cadam32bit_g16)
     str2optimizer32bit['momentum'] = (lib.cmomentum32bit_g32, lib.cmomentum32bit_g16)
@@ -138,7 +138,7 @@ def estimate_quantiles(A: Tensor, out: Tensor=None, offset: float=1/512) -> Tens
     elif A.dtype == torch.float16:
         lib.cestimate_quantiles_fp16(get_ptr(A), get_ptr(out), ct.c_float(offset), ct.c_int(A.numel()))
     else:
-        raise NotImplementError(f'Not supported data type {A.dtype}')
+        raise NotImplementedError(f'Not supported data type {A.dtype}')
     return out
 
 def quantize_blockwise(A: Tensor, code: Tensor=None, absmax: Tensor=None, rand=None, out: Tensor=None) -> Tensor:
@@ -384,7 +384,7 @@ def optimizer_update_32bit(optimizer_name:str, g: Tensor, p: Tensor, state1: Ten
         param_norm = torch.norm(p.data.float())
 
     if optimizer_name not in str2optimizer32bit:
-        raise NotImplementError(f'Optimizer not implemented: {optimizer_name}. Choices: {",".join(str2optimizer32bit.keys())}')
+        raise NotImplementedError(f'Optimizer not implemented: {optimizer_name}. Choices: {",".join(str2optimizer32bit.keys())}')
 
     if g.dtype == torch.float32 and state1.dtype == torch.float32:
         str2optimizer32bit[optimizer_name][0](get_ptr(g), get_ptr(p), get_ptr(state1), get_ptr(state2), get_ptr(unorm_vec), ct.c_float(max_unorm),

bitsandbytes/optim/__init__.py

@@ -2,6 +2,10 @@
 #   
 # This source code is licensed under the MIT license found in the 
 # LICENSE file in the root directory of this source tree.
+
+from bitsandbytes.cextension import COMPILED_WITH_CUDA
+
+if COMPILED_WITH_CUDA:
     from .adam import Adam, Adam8bit, Adam32bit
     from .adamw import AdamW, AdamW8bit, AdamW32bit
     from .sgd import SGD, SGD8bit, SGD32bit

bitsandbytes/optim/rmsprop.py

@@ -31,6 +31,6 @@ class RMSprop32bit(Optimizer1State):
         if alpha == 0:
             raise NotImplementedError(f'RMSprop with alpha==0.0 is not supported!')
         if centered:
-            raise NotImplementError(f'Centered RMSprop is not supported!')
+            raise NotImplementedError(f'Centered RMSprop is not supported!')
         super(RMSprop32bit, self).__init__('rmsprop', params, lr, (alpha, momentum), eps,
                 weight_decay, 32, args, min_8bit_size, percentile_clipping, block_wise)

csrc/common.cpp

@@ -0,0 +1,39 @@
+#include <common.h>
+#include <float.h>
+
+void *quantize_block(void *arguments) {
+    // 1. find absmax in block
+    // 2. divide input value by absmax to normalize into [-1.0, 1.0]
+    // 3. do binary search to find the closest value
+    // 4. check minimal distance
+    // 5. store index
+
+    struct quantize_block_args *args = (quantize_block_args *) arguments;
+
+    // 1. find absmax in block
+    float absmax_block = -FLT_MAX;
+    for (int i = args->block_idx; i < args->block_end; i++)
+        absmax_block = fmax(absmax_block, fabs(args->A[i]));
+
+    args->absmax[args->block_idx / BLOCK_SIZE] = absmax_block;
+
+    for (int i = args->block_idx; i < args->block_end; i++) {
+        // 2. divide input value by absmax to normalize into [-1.0, 1.0]
+        // 3. do binary search to find the closest value
+        float normed_value = args->A[i] / absmax_block;
+        int idx = args->bin_searcher->scalar(normed_value);
+
+        // 4. check minimal distance
+        // The binary search returns always the value to the left, which might not be the closest value
+        if (idx < 255) {
+            float dist_left = fabs(normed_value - (args->code[idx]));
+            float dist_right = fabs(normed_value - (args->code[idx + 1]));
+            if (dist_right < dist_left) { idx += 1; }
+        }
+
+        // 5. store index
+        args->out[i] = (unsigned char) idx;
+    }
+
+    return NULL;
+}

csrc/common.h

@@ -0,0 +1,23 @@
+#include <BinSearch.h>
+
+#ifndef common
+#define common
+
+using namespace BinSearch;
+
+struct quantize_block_args {
+    BinAlgo<Scalar, float, Direct2> *bin_searcher;
+    float *code;
+    float *A;
+    float *absmax;
+    unsigned char *out;
+    int block_end;
+    int block_idx;
+    int threadidx;
+};
+
+#define BLOCK_SIZE 4096
+
+void *quantize_block(void *arguments);
+
+#endif

csrc/cpu_ops.cpp

@@ -0,0 +1,57 @@
+#include <BinSearch.h>
+#include <pthread.h>
+#include <common.h>
+
+using namespace BinSearch;
+
+void dequantize_cpu(float *code, unsigned char *A, float *absmax, float *out, int n) {
+    for (int block_idx = 0; block_idx < n; block_idx += BLOCK_SIZE) {
+        int valid_items = n - block_idx >= BLOCK_SIZE ? BLOCK_SIZE : n - block_idx;
+        int block_end = block_idx + valid_items;
+        for (int i = block_idx; i < block_end; i++)
+            out[i] = code[A[i]] * absmax[block_idx / BLOCK_SIZE];
+    }
+}
+
+void quantize_cpu(float *code, float *A, float *absmax, unsigned char *out, int n) {
+
+    // the default code is has range [-0.993, 1.0] which can cause an error in the binary search algorithm used below
+    code[0] = -1.0f;
+
+    int num_blocks = n / BLOCK_SIZE;
+    num_blocks += n % BLOCK_SIZE == 0 ? 0 : 1;
+
+    pthread_t *threads = (pthread_t *) malloc(sizeof(pthread_t) * num_blocks);
+    struct quantize_block_args **args = (quantize_block_args **) malloc(num_blocks * sizeof(quantize_block_args *));
+
+    for (int i = 0; i < num_blocks; i++)
+        args[i] = (quantize_block_args *) malloc(sizeof(quantize_block_args));
+
+    const uint32 elements_code = 256;
+    BinAlgo<Scalar, float, Direct2> bin_searcher(code, elements_code);
+
+    for (int block_idx = 0; block_idx < n; block_idx += BLOCK_SIZE) {
+        int valid_items = n - block_idx >= BLOCK_SIZE ? BLOCK_SIZE : n - block_idx;
+        int block_end = block_idx + valid_items;
+
+        struct quantize_block_args *arg = args[block_idx / BLOCK_SIZE];
+        arg->bin_searcher = &bin_searcher;
+        arg->code = code;
+        arg->A = A;
+        arg->absmax = absmax;
+        arg->out = out;
+        arg->block_end = block_end;
+        arg->block_idx = block_idx;
+        arg->threadidx = block_idx / BLOCK_SIZE;
+
+        pthread_create(&threads[block_idx / BLOCK_SIZE], NULL, &quantize_block, (void *) arg);
+    }
+
+    for (int i = 0; i < num_blocks; i++)
+        int err = pthread_join(threads[i], NULL);
+
+    free(threads);
+    for (int i = 0; i < num_blocks; i++)
+        free(args[i]);
+    free(args);
+}

csrc/cpu_ops.h

@@ -0,0 +1,9 @@
+#ifndef BITSANDBYTES_CPU_OPS_H
+#define BITSANDBYTES_CPU_OPS_H
+
+
+void quantize_cpu(float *code, float *A, float *absmax, unsigned char *out, int n);
+
+void dequantize_cpu(float *code, unsigned char *A, float *absmax, float *out, int n);
+
+#endif

csrc/ops.cu

@@ -8,125 +8,14 @@
 #include <cub/device/device_scan.cuh>
 #include <limits>
 #include <BinSearch.h>
+#include <common.h>
 
 
 using namespace BinSearch;
 using std::cout;
 using std::endl;
 
-#define BLOCK_SIZE 4096
+void histogramScatterAdd2D(float *histogram, int *index1, int *index2, float *src, int maxidx1, int n) {
-
-struct quantize_block_args
-{
-  BinAlgo<Scalar, float, Direct2> *bin_searcher;
-  float *code;
-  float *A;
-  float *absmax;
-  unsigned char *out;
-  int block_end;
-  int block_idx;
-  int threadidx;
-};
-
-void *quantize_block(void *arguments)
-{
-  // 1. find absmax in block
-  // 2. divide input value by absmax to normalize into [-1.0, 1.0]
-  // 3. do binary search to find the closest value
-  // 4. check minimal distance
-  // 5. store index
-
-  struct quantize_block_args *args = (quantize_block_args*)arguments;
-
-  // 1. find absmax in block
-  float absmax_block = -FLT_MAX;
-  for (int i = args->block_idx; i < args->block_end; i++)
-    absmax_block = fmax(absmax_block, fabs(args->A[i]));
-
-  args->absmax[args->block_idx/BLOCK_SIZE] = absmax_block;
-
-  for (int i = args->block_idx; i < args->block_end; i++)
-  {
-    // 2. divide input value by absmax to normalize into [-1.0, 1.0]
-    // 3. do binary search to find the closest value
-    float normed_value = args->A[i]/absmax_block;
-    int idx = args->bin_searcher->scalar(normed_value);
-
-    // 4. check minimal distance
-    // The binary search returns always the value to the left, which might not be the closest value
-    if(idx < 255)
-    {
-      float dist_left = fabs(normed_value-(args->code[idx]));
-      float dist_right = fabs(normed_value-(args->code[idx+1]));
-      if(dist_right < dist_left){ idx+=1; }
-    }
-
-    // 5. store index
-    args->out[i] = (unsigned char)idx;
-  }
-
-  return NULL;
-}
-
-void quantize_cpu(float *code, float *A, float *absmax, unsigned char *out, int n)
-{
-
-  // the default code is has range [-0.993, 1.0] which can cause an error in the binary search algorithm used below
-  code[0] = -1.0f; 
-
-  int num_blocks = n/BLOCK_SIZE;
-  num_blocks += n % BLOCK_SIZE == 0 ? 0 : 1;
-
-  pthread_t *threads = (pthread_t*)malloc(sizeof(pthread_t)*num_blocks);
-  struct quantize_block_args **args = (quantize_block_args**)malloc(num_blocks*sizeof(quantize_block_args*));
-
-  for(int i = 0; i < num_blocks; i++)
-    args[i] = (quantize_block_args*)malloc(sizeof(quantize_block_args));
-
-  const uint32 elements_code = 256;
-  BinAlgo<Scalar, float, Direct2> bin_searcher(code, elements_code);
-
-  for(int block_idx = 0; block_idx < n; block_idx+=BLOCK_SIZE)
-  {
-    int valid_items = n-block_idx >= BLOCK_SIZE ? BLOCK_SIZE : n - block_idx;
-    int block_end = block_idx + valid_items;
-
-    struct quantize_block_args *arg = args[block_idx/BLOCK_SIZE];
-    arg->bin_searcher = &bin_searcher;
-    arg->code = code;
-    arg->A = A;
-    arg->absmax = absmax;
-    arg->out = out;
-    arg->block_end = block_end;
-    arg->block_idx = block_idx;
-    arg->threadidx = block_idx/BLOCK_SIZE;
- 
-    pthread_create(&threads[block_idx/BLOCK_SIZE], NULL, &quantize_block, (void *)arg);
-  }
-
-  for(int i = 0; i < num_blocks; i++)
-    int err = pthread_join(threads[i], NULL);
-
-  free(threads);
-  for(int i = 0; i < num_blocks; i++)
-    free(args[i]);
-  free(args);
-}
-
-
-void dequantize_cpu(float *code, unsigned char *A, float *absmax, float *out, int n)
-{
-  for(int block_idx = 0; block_idx < n; block_idx+=BLOCK_SIZE)
-  {
-    int valid_items = n-block_idx >= BLOCK_SIZE ? BLOCK_SIZE : n - block_idx;
-    int block_end = block_idx + valid_items;
-    for (int i = block_idx; i < block_end; i++)
-      out[i] = code[A[i]]*absmax[block_idx/BLOCK_SIZE];
-  }
-}
-
-void histogramScatterAdd2D(float* histogram, int *index1, int *index2, float *src, int maxidx1, int n)
-{
     int threads = 512;
     int blocks = n / threads;
     blocks = n % threads == 0 ? blocks : blocks + 1;
@@ -134,8 +23,8 @@ void histogramScatterAdd2D(float* histogram, int *index1, int *index2, float *sr
     CUDA_CHECK_RETURN(cudaPeekAtLastError());
 }
 
-template <typename T> void estimateQuantiles(T *A, float *code, float offset, int n)
+template<typename T>
-{
+void estimateQuantiles(T *A, float *code, float offset, int n) {
     int blocks = n / 4096;
     blocks = n % 4096 == 0 ? blocks : blocks + 1;
     CUDA_CHECK_RETURN(cudaMemset(code, 0, 256 * sizeof(float)));
@@ -143,32 +32,30 @@ template <typename T> void estimateQuantiles(T *A, float *code, float offset, in
     CUDA_CHECK_RETURN(cudaPeekAtLastError());
 }
 
-void quantize(float *code, float *A, unsigned char *out, int n)
+void quantize(float *code, float *A, unsigned char *out, int n) {
-{
     int blocks = n / 1024;
     blocks = n % 1024 == 0 ? blocks : blocks + 1;
     kQuantize<<<blocks, 1024>>>(code, A, out, n);
     CUDA_CHECK_RETURN(cudaPeekAtLastError());
 }
 
-void dequantize(float *code, unsigned char *A, float *out, int n)
+void dequantize(float *code, unsigned char *A, float *out, int n) {
-{
     int blocks = n / 1024;
     blocks = n % 1024 == 0 ? blocks : blocks + 1;
     kDequantize<<<blocks, 1024>>>(code, A, out, 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, const int n) {
     int blocks = n / 4096;
     blocks = n % 4096 == 0 ? blocks : blocks + 1;
     kQuantizeBlockwise < T, 4096, 4, STOCHASTIC ><<<blocks, 1024>>>(code, A, absmax, out, rand, rand_offset, n);
     CUDA_CHECK_RETURN(cudaPeekAtLastError());
 }
 
-template<typename T> void dequantizeBlockwise(float *code, unsigned char *A, float *absmax, T *out, int blocksize, const int n)
+template<typename T>
-{
+void dequantizeBlockwise(float *code, unsigned char *A, float *absmax, T *out, int blocksize, const int n) {
     int blocks = n / blocksize;
     blocks = n % blocksize == 0 ? blocks : blocks + 1;
     if (blocksize == 4096)
@@ -178,43 +65,45 @@ template<typename T> void dequantizeBlockwise(float *code, unsigned char *A, flo
     CUDA_CHECK_RETURN(cudaPeekAtLastError());
 }
 
-template<typename T, int OPTIMIZER> void optimizer32bit(T* g, T* p, 
+template<typename T, int OPTIMIZER>
+void optimizer32bit(T *g, T *p,
                     float *state1, float *state2, float *unorm, float max_unorm, float param_norm,
                     const float beta1, const float beta2, const float eps, const float weight_decay,
-                const int step, const float lr, const float gnorm_scale, bool skip_zeros, const int n)
+                    const int step, const float lr, const float gnorm_scale, bool skip_zeros, const int n) {
-{
     int blocks = n / 4096;
     blocks = n % 4096 == 0 ? blocks : blocks + 1;
-	switch(OPTIMIZER)
+    switch (OPTIMIZER) {
-	{
         case ADAM:
-      if(max_unorm > 0.0f)
+            if (max_unorm > 0.0f) {
-			{ 
                 CUDA_CHECK_RETURN(cudaMemset(unorm, 0, 1 * sizeof(float)));
-        kPreconditionOptimizer32bit2State<T, OPTIMIZER, 4096, 8><<<blocks, 512>>>(g, p, state1, state2, unorm, beta1, beta2, eps, weight_decay, step, lr, gnorm_scale, n);
+                kPreconditionOptimizer32bit2State < T, OPTIMIZER, 4096,
+                        8 ><<<blocks, 512>>>(g, p, state1, state2, unorm, beta1, beta2, eps, weight_decay, step, lr, gnorm_scale, n);
                 CUDA_CHECK_RETURN(cudaPeekAtLastError());
             }
-			kOptimizer32bit2State<T, OPTIMIZER><<<blocks, 1024>>>(g, p, state1, state2, unorm, max_unorm, param_norm, beta1, beta2, eps, weight_decay, step, lr, gnorm_scale, skip_zeros, n);
+            kOptimizer32bit2State < T,
+                    OPTIMIZER ><<<blocks, 1024>>>(g, p, state1, state2, unorm, max_unorm, param_norm, beta1, beta2, eps, weight_decay, step, lr, gnorm_scale, skip_zeros, n);
             CUDA_CHECK_RETURN(cudaPeekAtLastError());
             break;
         case MOMENTUM:
         case RMSPROP:
         case ADAGRAD:
 
-      if(max_unorm > 0.0f)
+            if (max_unorm > 0.0f) {
-			{ 
                 CUDA_CHECK_RETURN(cudaMemset(unorm, 0, 1 * sizeof(float)));
-				kPreconditionOptimizer32bit1State<T, OPTIMIZER, 4096, 8><<<blocks, 512>>>(g, p, state1, unorm, beta1, eps, weight_decay, step, lr, gnorm_scale, n);
+                kPreconditionOptimizer32bit1State < T, OPTIMIZER, 4096,
+                        8 ><<<blocks, 512>>>(g, p, state1, unorm, beta1, eps, weight_decay, step, lr, gnorm_scale, n);
                 CUDA_CHECK_RETURN(cudaPeekAtLastError());
             }
 
-			kOptimizer32bit1State<T, OPTIMIZER><<<blocks, 1024>>>(g, p, state1, unorm, max_unorm, param_norm, beta1, eps, weight_decay, step, lr, gnorm_scale, skip_zeros, n);
+            kOptimizer32bit1State < T,
+                    OPTIMIZER ><<<blocks, 1024>>>(g, p, state1, unorm, max_unorm, param_norm, beta1, eps, weight_decay, step, lr, gnorm_scale, skip_zeros, n);
             CUDA_CHECK_RETURN(cudaPeekAtLastError());
             break;
     }
 }
 
-template<typename T, int OPTIMIZER> void optimizerStatic8bit(T* p, T* g,
+template<typename T, int OPTIMIZER>
+void optimizerStatic8bit(T *p, T *g,
                          unsigned char *state1, unsigned char *state2,
                          float *unorm, float max_unorm, float param_norm,
                          float beta1, float beta2,
@@ -222,21 +111,21 @@ template<typename T, int OPTIMIZER> void optimizerStatic8bit(T* p, T* g,
                          float *quantiles1, float *quantiles2,
                          float *max1, float *max2, float *new_max1, float *new_max2,
                          float weight_decay,
-                const float gnorm_scale, int n)
+                         const float gnorm_scale, int n) {
-{
     int blocks = n / 4096;
     blocks = n % 4096 == 0 ? blocks : blocks + 1;
 
     if (max_unorm > 0.0f) { CUDA_CHECK_RETURN(cudaMemset(unorm, 0, 1 * sizeof(float))); }
 
-	switch(OPTIMIZER)
+    switch (OPTIMIZER) {
-	{
         case ADAM:
             CUDA_CHECK_RETURN(cudaMemset(new_max1, 0, 1 * sizeof(float)));
             CUDA_CHECK_RETURN(cudaMemset(new_max2, 0, 1 * sizeof(float)));
-			kPreconditionOptimizerStatic8bit2State<T, OPTIMIZER><<<blocks, 256>>>(p, g, state1, state2, unorm, beta1, beta2, eps, step, quantiles1, quantiles2, max1, max2, new_max1, new_max2, gnorm_scale, n);
+            kPreconditionOptimizerStatic8bit2State < T,
+                    OPTIMIZER ><<<blocks, 256>>>(p, g, state1, state2, unorm, beta1, beta2, eps, step, quantiles1, quantiles2, max1, max2, new_max1, new_max2, gnorm_scale, n);
             CUDA_CHECK_RETURN(cudaPeekAtLastError());
-			kOptimizerStatic8bit2State<T, OPTIMIZER><<<blocks, 1024>>>(p, g, state1, state2, unorm, max_unorm, param_norm, beta1, beta2, eps, step, lr,
+            kOptimizerStatic8bit2State < T,
+                    OPTIMIZER ><<<blocks, 1024>>>(p, g, state1, state2, unorm, max_unorm, param_norm, beta1, beta2, eps, step, lr,
             quantiles1, quantiles2, max1, max2, new_max1, new_max2, weight_decay, gnorm_scale, n);
             CUDA_CHECK_RETURN(cudaPeekAtLastError());
             break;
@@ -244,7 +133,8 @@ template<typename T, int OPTIMIZER> void optimizerStatic8bit(T* p, T* g,
         case RMSPROP:
         case ADAGRAD:
             CUDA_CHECK_RETURN(cudaMemset(new_max1, 0, 1 * sizeof(float)));
-			kPreconditionOptimizerStatic8bit1State<T, OPTIMIZER><<<blocks, 256>>>(p, g, state1, unorm, beta1, eps, step, quantiles1, max1, new_max1, weight_decay, gnorm_scale, n);
+            kPreconditionOptimizerStatic8bit1State < T,
+                    OPTIMIZER ><<<blocks, 256>>>(p, g, state1, unorm, beta1, eps, step, quantiles1, max1, new_max1, weight_decay, gnorm_scale, n);
             CUDA_CHECK_RETURN(cudaPeekAtLastError());
             kOptimizerStatic8bit1State < T, OPTIMIZER ><<<blocks, 1024>>>(p, g, state1, unorm, max_unorm, param_norm, beta1, eps, step, lr,
             quantiles1, max1, new_max1, weight_decay, gnorm_scale, n);
@@ -260,18 +150,19 @@ template<typename T, int OPTIMIZER> void optimizerStatic8bit(T* p, T* g,
 #define BLOCKSIZE_1STATE 2048
 #define NUM_1STATE 8
 
-template<typename T, int OPTIMIZER> void optimizerStatic8bitBlockwise(T* p, T* g,
+template<typename T, int OPTIMIZER>
+void optimizerStatic8bitBlockwise(T *p, T *g,
                                   unsigned char *state1, unsigned char *state2, float beta1, float beta2, float eps, int step, float lr,
-                float* quantiles1, float* quantiles2, float* absmax1, float* absmax2, float weight_decay, const float gnorm_scale, bool skip_zeros, int n)
+                                  float *quantiles1, float *quantiles2, float *absmax1, float *absmax2, float weight_decay,
-{
+                                  const float gnorm_scale, bool skip_zeros, int n) {
 
     int blocks = 0;
-	switch(OPTIMIZER)
+    switch (OPTIMIZER) {
-	{
         case ADAM:
             blocks = n / BLOCKSIZE_2STATE;
             blocks = n % BLOCKSIZE_2STATE == 0 ? blocks : blocks + 1;
-			kOptimizerStatic8bit2StateBlockwise<T, OPTIMIZER, BLOCKSIZE_2STATE, NUM_2STATE><<<blocks, BLOCKSIZE_2STATE/NUM_2STATE>>>(p, g, state1, state2, beta1, beta2, eps, step, lr,
+            kOptimizerStatic8bit2StateBlockwise < T, OPTIMIZER, BLOCKSIZE_2STATE, NUM_2STATE ><<<blocks, BLOCKSIZE_2STATE /
+                                                                                                         NUM_2STATE>>>(p, g, state1, state2, beta1, beta2, eps, step, lr,
             quantiles1, quantiles2, absmax1, absmax2, weight_decay, gnorm_scale, skip_zeros, n);
             CUDA_CHECK_RETURN(cudaPeekAtLastError());
             break;
@@ -280,7 +171,8 @@ template<typename T, int OPTIMIZER> void optimizerStatic8bitBlockwise(T* p, T* g
         case ADAGRAD:
             blocks = n / BLOCKSIZE_1STATE;
             blocks = n % BLOCKSIZE_1STATE == 0 ? blocks : blocks + 1;
-			kOptimizerStatic8bit1StateBlockwise<T, OPTIMIZER, BLOCKSIZE_1STATE, NUM_1STATE><<<blocks, BLOCKSIZE_1STATE/NUM_1STATE>>>(p, g, state1, beta1, beta2, eps, step, lr,
+            kOptimizerStatic8bit1StateBlockwise < T, OPTIMIZER, BLOCKSIZE_1STATE, NUM_1STATE ><<<blocks, BLOCKSIZE_1STATE /
+                                                                                                         NUM_1STATE>>>(p, g, state1, beta1, beta2, eps, step, lr,
             quantiles1, absmax1, weight_decay, gnorm_scale, skip_zeros, n);
             CUDA_CHECK_RETURN(cudaPeekAtLastError());
             break;
@@ -288,9 +180,8 @@ template<typename T, int OPTIMIZER> void optimizerStatic8bitBlockwise(T* p, T* g
 }
 
 
-
+template<typename T>
-template<typename T> void percentileClipping(T * g, float *gnorm_vec, int step, const int n)
+void percentileClipping(T *g, float *gnorm_vec, int step, const int n) {
-{
     int blocks = n / 2048;
     blocks = n % 2048 == 0 ? blocks : blocks + 1;
     CUDA_CHECK_RETURN(cudaMemset(&gnorm_vec[step % 100], 0, 1 * sizeof(float)));
@@ -304,13 +195,23 @@ template<typename T> void percentileClipping(T * g, float *gnorm_vec, int step,
 //==============================================================
 
 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
-template void quantizeBlockwise<float, 0>(float * code, float *A, float *absmax, unsigned char *out, float* rand, int rand_offset, const int n);
+quantizeBlockwise<half, 0>(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, 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, 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 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);
 
 #define MAKE_optimizer32bit(name, gtype) \
@@ -320,12 +221,19 @@ template void optimizer32bit<gtype, name>(gtype* g, gtype* p, \
                 const int step, const float lr, const float gnorm_scale, const bool skip_zeros, const int n);
 
 MAKE_optimizer32bit(ADAM, half)
+
 MAKE_optimizer32bit(ADAM, float)
+
 MAKE_optimizer32bit(MOMENTUM, half)
+
 MAKE_optimizer32bit(MOMENTUM, float)
+
 MAKE_optimizer32bit(RMSPROP, half)
+
 MAKE_optimizer32bit(RMSPROP, float)
+
 MAKE_optimizer32bit(ADAGRAD, half)
+
 MAKE_optimizer32bit(ADAGRAD, float)
 
 #define MAKE_optimizerStatic8bit(name, gtype) \
@@ -338,11 +246,17 @@ template void optimizerStatic8bit<gtype, name>(gtype* p, gtype* g, unsigned char
                 float weight_decay, \
                 const float gnorm_scale, int n); \
 
+
 MAKE_optimizerStatic8bit(ADAM, half)
+
 MAKE_optimizerStatic8bit(ADAM, float)
+
 MAKE_optimizerStatic8bit(MOMENTUM, half)
+
 MAKE_optimizerStatic8bit(MOMENTUM, float)
+
 MAKE_optimizerStatic8bit(RMSPROP, half)
+
 MAKE_optimizerStatic8bit(RMSPROP, float)
 
 #define MAKE_optimizerStatic8bitBlockwise(gtype, optim_name) \
@@ -350,14 +264,23 @@ template void optimizerStatic8bitBlockwise<gtype, optim_name>(gtype* p, gtype* g
                 unsigned char* state1, unsigned char* state2, float beta1, float beta2, float eps, int step, float lr,  \
                 float* quantiles1, float* quantiles2, float* absmax1, float* absmax2, float weight_decay, const float gnorm_scale, bool skip_zeros, int n); \
 
+
 MAKE_optimizerStatic8bitBlockwise(half, ADAM);
+
 MAKE_optimizerStatic8bitBlockwise(float, ADAM);
+
 MAKE_optimizerStatic8bitBlockwise(half, MOMENTUM);
+
 MAKE_optimizerStatic8bitBlockwise(float, MOMENTUM);
+
 MAKE_optimizerStatic8bitBlockwise(half, RMSPROP);
+
 MAKE_optimizerStatic8bitBlockwise(float, RMSPROP);
+
 MAKE_optimizerStatic8bitBlockwise(half, ADAGRAD);
+
 MAKE_optimizerStatic8bitBlockwise(float, ADAGRAD);
 
 template void percentileClipping(float *g, float *gnorm_vec, int step, const int n);
+
 template void percentileClipping(half *g, float *gnorm_vec, int step, const int n);

csrc/ops.cuh

@@ -68,16 +68,6 @@ template<typename T, int OPTIMIZER> void optimizerStatic8bitBlockwise(T* p, T* g
 
 template<typename T> void percentileClipping(T * g, float *gnorm_vec, int step, const int n);
 
-void quantize_cpu(float *code, float *A, float *absmax, unsigned char *out, int n);
-void dequantize_cpu(float *code, unsigned char *A, float *absmax, float *out, int n);
-
 void histogramScatterAdd2D(float* histogram, int *index1, int *index2, float *src, int maxidx1, int n);
 
 #endif
-
-
-
-
-
-
-

csrc/pythonInterface.c

@@ -3,7 +3,10 @@
 // This source code is licensed under the MIT license found in the 
 // LICENSE file in the root directory of this source tree.
 
+#if BUILD_CUDA
 #include <ops.cuh>
+#endif
+#include <cpu_ops.h>
 
 // We cannot call templated code from C, so we wrap the template in a C compatible call here if necessary.
 // We use macro functions to expand all the different optimizers. Looks ugly, and is ugly, but its better than to 
@@ -12,6 +15,7 @@
 //                               UNMANGLED CALLS
 //===================================================================================
 
+#if BUILD_CUDA
 void estimateQuantiles_fp32(float *A, float *code, float offset, int n){ estimateQuantiles<float>(A, code, offset, n); }
 void estimateQuantiles_fp16(half *A, float *code, float offset, int n){ estimateQuantiles<half>(A, code, offset, n); }
 
@@ -78,9 +82,11 @@ void quantizeBlockwise_stochastic_fp32(float * code, float *A, float *absmax, un
 
 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); }
+#endif
 
 extern "C"
 {
+#if BUILD_CUDA
 void cestimate_quantiles_fp32(float *A, float *code, float offset, int n){ estimateQuantiles_fp32(A, code, offset, n); }
 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); }
@@ -147,11 +153,11 @@ extern "C"
 
 void cpercentile_clipping_g32(float * g, float *gnorm_vec, int step, const int n){ percentileClipping_g32(g, gnorm_vec, step, n); }
 void cpercentile_clipping_g16(half * g, float *gnorm_vec, int step, const int n){ percentileClipping_g16(g, gnorm_vec, step, n); }
+void chistogram_scatter_add_2d(float* histogram, int *index1, int *index2, float *src, int maxidx1, int n){ histogramScatterAdd2D(histogram, index1, index2, src, maxidx1, n); }
+#endif
 
 void cquantize_blockwise_cpu_fp32(float *code, float *A, float *absmax, unsigned char *out, const int n){ quantize_cpu(code, A, absmax, out, n); }
 void cdequantize_blockwise_cpu_fp32(float *code, unsigned char *A, float *absmax, float *out, const int n){ dequantize_cpu(code, A, absmax, out, n); }
-
-	void chistogram_scatter_add_2d(float* histogram, int *index1, int *index2, float *src, int maxidx1, int n){ histogramScatterAdd2D(histogram, index1, index2, src, maxidx1, n); }
 }
 
 

setup.py

@@ -6,17 +6,18 @@ import os
 from setuptools import setup, find_packages
 
 
-
 def read(fname):
     return open(os.path.join(os.path.dirname(__file__), fname)).read()
 
 
+version = os.getenv("CUDA_VERSION", "cpu")
+
 setup(
-    name = f"bitsandbytes-cuda{os.environ['CUDA_VERSION']}",
+    name="bitsandbytes",
-    version = "0.26.0",
+    version=f"0.26.0+{version}",
     author="Tim Dettmers",
     author_email="dettmers@cs.washington.edu",
-    description = ("8-bit optimizers and quantization routines."),
+    description="8-bit optimizers and quantization routines.",
     license="MIT",
     keywords="gpu optimizers optimization 8-bit quantization compression",
     url="http://packages.python.org/bitsandbytes",
@@ -29,4 +30,3 @@ setup(
         'Topic :: Scientific/Engineering :: Artificial Intelligence'
     ],
 )
-