Remove trailing whitespace & ensure newline at EOF

CHANGELOG.md

@@ -49,7 +49,7 @@ Features:
 Bug fixes:
  - Fixed a bug where weight decay was incorrectly applied to 32-bit Adam. #13
  - Fixed an unsafe use of eval. #8
- - Fixed a bug where the StableEmbedding layer 32-bit optimizer override would not work without registering the whole model first (`bnb.optim.GlobalOptimManager.get_instance().register_parameters(model.parameters())`).  #13 #15 
+ - Fixed a bug where the StableEmbedding layer 32-bit optimizer override would not work without registering the whole model first (`bnb.optim.GlobalOptimManager.get_instance().register_parameters(model.parameters())`).  #13 #15
 
 Docs:
  - Added instructions how to solve "\_\_fatbinwrap_" errors.

CONTRIBUTING.md

@@ -28,4 +28,4 @@ outlined on that page and do not file a public issue.
 
 ## License
 By contributing to bitsandbytes, you agree that your contributions will be licensed
-under the LICENSE file in the root directory of this source tree.
+under the LICENSE file in the root directory of this source tree.

Makefile

@@ -26,14 +26,14 @@ INCLUDE :=  -I $(CUDA_HOME)/include -I $(ROOT_DIR)/csrc -I $(CONDA_PREFIX)/inclu
 LIB := -L $(CUDA_HOME)/lib64 -lcudart -lcublas -lcublasLt -lcurand -lcusparse -L $(CONDA_PREFIX)/lib
 
 # NVIDIA NVCC compilation flags
-COMPUTE_CAPABILITY := -gencode arch=compute_35,code=sm_35 # Kepler 
+COMPUTE_CAPABILITY := -gencode arch=compute_35,code=sm_35 # Kepler
-COMPUTE_CAPABILITY += -gencode arch=compute_37,code=sm_37 # Kepler 
+COMPUTE_CAPABILITY += -gencode arch=compute_37,code=sm_37 # Kepler
 COMPUTE_CAPABILITY += -gencode arch=compute_50,code=sm_50 # Maxwell
 COMPUTE_CAPABILITY += -gencode arch=compute_52,code=sm_52 # Maxwell
 COMPUTE_CAPABILITY += -gencode arch=compute_60,code=sm_60 # Pascal
 COMPUTE_CAPABILITY += -gencode arch=compute_61,code=sm_61 # Pascal
 COMPUTE_CAPABILITY += -gencode arch=compute_70,code=sm_70 # Volta
-COMPUTE_CAPABILITY += -gencode arch=compute_72,code=sm_72 # Volta 
+COMPUTE_CAPABILITY += -gencode arch=compute_72,code=sm_72 # Volta
 
 # CUDA 9.2 supports CC 3.0, but CUDA >= 11.0 does not
 CC_CUDA92 := -gencode arch=compute_30,code=sm_30
@@ -58,38 +58,38 @@ CC_cublasLt111 += -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' --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 
+	$(NVCC) $(COMPUTE_CAPABILITY) -Xcompiler '-fPIC' -dlink $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o -o $(BUILD_DIR)/link.o
 	$(GPP) -std=c++14 -DBUILD_CUDA -shared -fPIC $(INCLUDE) $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o $(BUILD_DIR)/link.o $(FILES_CPP) -o ./bitsandbytes/libbitsandbytes_cuda$(CUDA_VERSION).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) -D NO_CUBLASLT
-	$(NVCC) $(COMPUTE_CAPABILITY) $(CC_CUDA92) -Xcompiler '-fPIC' -dlink $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o -o $(BUILD_DIR)/link.o 
+	$(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 -DBUILD_CUDA -shared -fPIC $(INCLUDE) $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o $(BUILD_DIR)/link.o $(FILES_CPP) -o ./bitsandbytes/libbitsandbytes_cuda$(CUDA_VERSION)_nocublaslt.so $(LIB)
 
 cuda10x_nomatmul: $(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) -D NO_CUBLASLT
-	$(NVCC) $(COMPUTE_CAPABILITY) $(CC_CUDA10x) -Xcompiler '-fPIC' -dlink $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o -o $(BUILD_DIR)/link.o 
+	$(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 -DBUILD_CUDA -shared -fPIC $(INCLUDE) $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o $(BUILD_DIR)/link.o $(FILES_CPP) -o ./bitsandbytes/libbitsandbytes_cuda$(CUDA_VERSION)_nocublaslt.so $(LIB)
 
 cuda110_nomatmul: $(BUILD_DIR) env
 	$(NVCC) $(COMPUTE_CAPABILITY) $(CC_CUDA110) -Xcompiler '-fPIC' --use_fast_math -Xptxas=-v -dc $(FILES_CUDA) $(INCLUDE) $(LIB) --output-directory $(BUILD_DIR) -D NO_CUBLASLT
-	$(NVCC) $(COMPUTE_CAPABILITY) $(CC_CUDA110) -Xcompiler '-fPIC' -dlink $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o -o $(BUILD_DIR)/link.o 
+	$(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 -DBUILD_CUDA -shared -fPIC $(INCLUDE) $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o $(BUILD_DIR)/link.o $(FILES_CPP) -o ./bitsandbytes/libbitsandbytes_cuda$(CUDA_VERSION)_nocublaslt.so $(LIB)
 
 cuda11x_nomatmul: $(BUILD_DIR) env
 	$(NVCC) $(COMPUTE_CAPABILITY) $(CC_CUDA11x) -Xcompiler '-fPIC' --use_fast_math -Xptxas=-v -dc $(FILES_CUDA) $(INCLUDE) $(LIB) --output-directory $(BUILD_DIR) -D NO_CUBLASLT
-	$(NVCC) $(COMPUTE_CAPABILITY) $(CC_CUDA11x) -Xcompiler '-fPIC' -dlink $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o -o $(BUILD_DIR)/link.o 
+	$(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 -DBUILD_CUDA -shared -fPIC $(INCLUDE) $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o $(BUILD_DIR)/link.o $(FILES_CPP) -o ./bitsandbytes/libbitsandbytes_cuda$(CUDA_VERSION)_nocublaslt.so $(LIB)
 
 cuda110: $(BUILD_DIR) env
 	$(NVCC) $(CC_cublasLt110) -Xcompiler '-fPIC' --use_fast_math -Xptxas=-v -dc $(FILES_CUDA) $(INCLUDE) $(LIB) --output-directory $(BUILD_DIR)
-	$(NVCC) $(CC_cublasLt110) -Xcompiler '-fPIC' -dlink $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o -o $(BUILD_DIR)/link.o 
+	$(NVCC) $(CC_cublasLt110) -Xcompiler '-fPIC' -dlink $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o -o $(BUILD_DIR)/link.o
 	$(GPP) -std=c++14 -DBUILD_CUDA -shared -fPIC $(INCLUDE) $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o $(BUILD_DIR)/link.o $(FILES_CPP) -o ./bitsandbytes/libbitsandbytes_cuda$(CUDA_VERSION).so $(LIB)
 
 cuda11x: $(BUILD_DIR) env
 	$(NVCC) $(CC_cublasLt111) -Xcompiler '-fPIC' --use_fast_math -Xptxas=-v -dc $(FILES_CUDA) $(INCLUDE) $(LIB) --output-directory $(BUILD_DIR)
-	$(NVCC) $(CC_cublasLt111) -Xcompiler '-fPIC' -dlink $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o -o $(BUILD_DIR)/link.o 
+	$(NVCC) $(CC_cublasLt111) -Xcompiler '-fPIC' -dlink $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o -o $(BUILD_DIR)/link.o
 	$(GPP) -std=c++14 -DBUILD_CUDA -shared -fPIC $(INCLUDE) $(BUILD_DIR)/ops.o $(BUILD_DIR)/kernels.o $(BUILD_DIR)/link.o $(FILES_CPP) -o ./bitsandbytes/libbitsandbytes_cuda$(CUDA_VERSION).so $(LIB)
 
 cpuonly: $(BUILD_DIR) env
@@ -117,7 +117,7 @@ $(ROOT_DIR)/dependencies/cub:
 	cd dependencies/cub; git checkout 1.11.0
 
 clean:
-	rm build/* 
+	rm build/*
 
 cleaneggs:
 	rm -rf *.egg*

README.md

@@ -1,6 +1,6 @@
 # bitsandbytes
 
-The bitsandbytes is a lightweight wrapper around CUDA custom functions, in particular 8-bit optimizers, matrix multiplication (LLM.int8()), and quantization functions. 
+The bitsandbytes is a lightweight wrapper around CUDA custom functions, in particular 8-bit optimizers, matrix multiplication (LLM.int8()), and quantization functions.
 
 
 
@@ -48,7 +48,7 @@ out = linear(x.to(torch.float16))
 
 Requirements: anaconda, cudatoolkit, pytorch
 
-Hardware requirements: 
+Hardware requirements:
  - LLM.int8(): NVIDIA Turing (RTX 20xx; T4) or Ampere GPU (RTX 30xx; A4-A100); (a GPU from 2018 or older).
  - 8-bit optimizers and quantization: NVIDIA Maxwell GPU or newer (>=GTX 9XX).
 
@@ -87,7 +87,7 @@ Note that by default all parameter tensors with less than 4096 elements are kept
 ```
 # parameter tensors with less than 16384 values are optimized in 32-bit
 # it is recommended to use multiplies of 4096
-adam = bnb.optim.Adam8bit(model.parameters(), min_8bit_size=16384) 
+adam = bnb.optim.Adam8bit(model.parameters(), min_8bit_size=16384)
 ```
 
 ### Change Bits and other Hyperparameters for Individual Parameters

bitsandbytes/autograd/_functions.py

@@ -15,7 +15,7 @@ tensor = torch.Tensor
 
 """
     This class pools outlier dimensions across layers.
-    This is particularly important for small models where outlier features 
+    This is particularly important for small models where outlier features
     are less systematic and occur with low frequency.
 """
 class GlobalOutlierPooler(object):

bitsandbytes/functional.py

@@ -1133,7 +1133,7 @@ def igemm(
     ptr = CUBLAS_Context.get_instance().get_context(A.device)
 
     # B^T @ A^T = C^T
-    # [km, nk -> mn] 
+    # [km, nk -> mn]
     is_on_gpu([B, A, out])
     lib.cigemm(ptr, ct.c_bool(transposed_B), ct.c_bool(transposed_A), ct.c_int32(m), ct.c_int32(n), ct.c_int32(k),
                get_ptr(B), get_ptr(A), get_ptr(out), ct.c_int32(lda), ct.c_int32(ldb), ct.c_int32(ldc))

bitsandbytes/nn/modules.py

@@ -267,7 +267,7 @@ class Linear8bitLt(nn.Linear):
                 self.weight.data = self.state.CxB
             elif self.state.memory_efficient_backward and self.state.CxB is not None:
                 # For memory efficient backward, we convert 8-bit row major to turing/ampere format at each inference pass.
-                # Thus, we delete CxB from the state. 
+                # Thus, we delete CxB from the state.
                 del self.state.CxB
 
         return out

compile_from_source.md

@@ -4,7 +4,7 @@ Basic steps.
 1. `make [target]` where `[target]` is among `cuda92, cuda10x, cuda110, cuda11x, cpuonly`
 2. `CUDA_VERSION=XXX python setup.py install`
 
-To run these steps you will need to have the nvcc compiler installed that comes with a CUDA installation. If you use anaconda (recommended) then you can figure out which version of CUDA you are using with PyTorch via the command `conda list | grep cudatoolkit`. Then you can install the nvcc compiler by downloading and installing the same CUDA version from the [CUDA toolkit archive](https://developer.nvidia.com/cuda-toolkit-archive). 
+To run these steps you will need to have the nvcc compiler installed that comes with a CUDA installation. If you use anaconda (recommended) then you can figure out which version of CUDA you are using with PyTorch via the command `conda list | grep cudatoolkit`. Then you can install the nvcc compiler by downloading and installing the same CUDA version from the [CUDA toolkit archive](https://developer.nvidia.com/cuda-toolkit-archive).
 
 For your convenience, there is an installation script in the root directory that installs CUDA 11.1 locally and configures it automatically. After installing you should add the `bin` sub-directory to the `$PATH` variable to make the compiler visible to your system. To do this you can add this to your `.bashrc` by executing these commands:
 ```bash
@@ -13,7 +13,7 @@ echo "export PATH=$PATH:/usr/local/cuda/bin/" >> ~/.bashrc
 source ~/.bashrc
 ```
 
-By default, the Makefile will look at your `CUDA_HOME` environmental variable to find your CUDA version for compiling the library. If this path is not set it is inferred from the path of your `nvcc` compiler. 
+By default, the Makefile will look at your `CUDA_HOME` environmental variable to find your CUDA version for compiling the library. If this path is not set it is inferred from the path of your `nvcc` compiler.
 
 Either `nvcc` needs to be in path for the `CUDA_HOME` variable needs to be set to the CUDA directory root (e.g. `/usr/local/cuda`) in order for compilation to succeed
 

csrc/cpu_ops.cpp

@@ -62,7 +62,7 @@ void quantize_cpu(float *code, float *A, float *absmax, unsigned char *out, long
 
       for (int i = 0; i < valid_chunks; i++)
           int err = pthread_join(threads[i], NULL);
-      
+
       free(threads);
       for (int i = 0; i < valid_chunks; i++)
           free(args[i]);

csrc/kernels.cu

@@ -1,6 +1,6 @@
-// Copyright (c) Facebook, Inc. and its affiliates. 
+// Copyright (c) Facebook, Inc. and its affiliates.
-//   
+//
-// This source code is licensed under the MIT license found in the 
+// This source code is licensed under the MIT license found in the
 // LICENSE file in the root directory of this source tree.
 
 #include <kernels.cuh>
@@ -303,7 +303,7 @@ __global__ void kCompressMax(T * __restrict__ const A, T* out, unsigned char* ou
   if(threadIdx.x % 32 < 8)
   {
     // offset: 8 values per 256 input values
-    // 
+    //
     int offset = BLOCK_SIZE*blockIdx.x*BLOCK_SIZE/32*8;
   }
 
@@ -572,7 +572,7 @@ __global__ void kDequantize(float *code, unsigned char *A, float *out, const int
 
 template<typename T, int OPTIMIZER, int BLOCK_SIZE, int NUM_VALS>
 __launch_bounds__(BLOCK_SIZE/NUM_VALS, 1)
-__global__ void kPreconditionOptimizer32bit2State(T* g, T* p, 
+__global__ void kPreconditionOptimizer32bit2State(T* g, T* p,
                 float* state1, float* state2, float *unorm,
                 const float beta1, const float beta2, const float eps, const float weight_decay,
                 const int step, const float lr, const float gnorm_scale, const int n)
@@ -620,7 +620,7 @@ __global__ void kPreconditionOptimizer32bit2State(T* g, T* p,
       {
           switch(OPTIMIZER)
           {
-              case ADAM: 
+              case ADAM:
                   s1_vals[j] = s1_vals[j]*beta1 + ((1.0f -beta1)*((float)g_vals[j]));
                   s2_vals[j] = s2_vals[j]*beta2 + ((1.0f -beta2)*(((float)g_vals[j])*((float)g_vals[j])));
                   s1_vals[j] *= correction1;
@@ -651,7 +651,7 @@ __global__ void kPreconditionOptimizer32bit2State(T* g, T* p,
 
 template<typename T, int OPTIMIZER>
 __launch_bounds__(TH, 1)
-__global__ void kOptimizer32bit2State(T* g, T* p, 
+__global__ void kOptimizer32bit2State(T* g, T* p,
                 float* state1, float* state2, float *unorm, const float max_unorm, const 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, const bool skip_zeros, const int n)
@@ -714,7 +714,7 @@ __global__ void kOptimizer32bit2State(T* g, T* p,
       {
           switch(OPTIMIZER)
           {
-              case ADAM: 
+              case ADAM:
 									if(!skip_zeros || (skip_zeros && ((float)g_vals[j] != 0.0f)))
 									{
 										s1_vals[j] = s1_vals[j]*beta1 + ((1.0f -beta1)*((float)g_vals[j]));
@@ -739,7 +739,7 @@ __global__ void kOptimizer32bit2State(T* g, T* p,
 
 template<typename T, int OPTIMIZER, int BLOCK_SIZE, int NUM_VALS>
 __launch_bounds__(BLOCK_SIZE/NUM_VALS, 1)
-__global__ void kPreconditionOptimizer32bit1State(T* g, T* p, 
+__global__ void kPreconditionOptimizer32bit1State(T* g, T* p,
                 float* state1, float *unorm,
                 const float beta1, const float eps, const float weight_decay,
                 const int step, const float lr, const float gnorm_scale, const int n)
@@ -781,19 +781,19 @@ __global__ void kPreconditionOptimizer32bit1State(T* g, T* p,
       {
           switch(OPTIMIZER)
           {
-              case MOMENTUM: 
+              case MOMENTUM:
                   if(step == 1)
                     s1_vals[j] = (float)g_vals[j]; // state update
                   else
                     s1_vals[j] = s1_vals[j]*beta1 + ((float)g_vals[j]); // state update
                   s1_vals[j] = s1_vals[j]*s1_vals[j]; // update norm
                   break;
-              case RMSPROP: 
+              case RMSPROP:
                   s1_vals[j] = s1_vals[j]*beta1 + ((1.0f-beta1)*((float)g_vals[j])*((float)g_vals[j])); // state update
                   s1_vals[j] = __fdividef((float)g_vals[j],sqrtf(s1_vals[j])+eps); // update value
                   s1_vals[j] = s1_vals[j]*s1_vals[j]; // update norm
                   break;
-              case ADAGRAD: 
+              case ADAGRAD:
                   s1_vals[j] = s1_vals[j] + ((float)g_vals[j])*((float)g_vals[j]); // state update
                   s1_vals[j] = __fdividef((float)g_vals[j],sqrtf(s1_vals[j])+eps); // update value
                   s1_vals[j] = s1_vals[j]*s1_vals[j]; // update norm
@@ -817,7 +817,7 @@ __global__ void kPreconditionOptimizer32bit1State(T* g, T* p,
 
 template<typename T, int OPTIMIZER>
 __launch_bounds__(TH, 1)
-__global__ void kOptimizer32bit1State(T *g, T *p, 
+__global__ void kOptimizer32bit1State(T *g, T *p,
                 float *state1, float *unorm, const float max_unorm, const float param_norm,
                 const float beta1, const float eps, const float weight_decay,
                 const int step, const float lr, const float gnorm_scale, const bool skip_zeros, const int n)
@@ -880,7 +880,7 @@ __global__ void kOptimizer32bit1State(T *g, T *p,
 					{
 						switch(OPTIMIZER)
 						{
-								case MOMENTUM: 
+								case MOMENTUM:
 										if(step == 1)
 											s1_vals[j] = (float)g_vals[j];
 										else
@@ -888,11 +888,11 @@ __global__ void kOptimizer32bit1State(T *g, T *p,
 
 										p_vals[j] = ((float)p_vals[j]) + update_scale*(-lr*(s1_vals[j]));
 										break;
-								case RMSPROP: 
+								case RMSPROP:
 										s1_vals[j] = s1_vals[j]*beta1 + ((1.0f-beta1)*((float)g_vals[j])*((float)g_vals[j]));
 										p_vals[j] = ((float)p_vals[j]) - update_scale*(lr*__fdividef((float)g_vals[j],sqrtf((float)s1_vals[j])+eps));
 										break;
-								case ADAGRAD: 
+								case ADAGRAD:
 										s1_vals[j] = s1_vals[j] + ((float)g_vals[j])*((float)g_vals[j]);
 										p_vals[j] = ((float)p_vals[j]) - lr*__fdividef((float)g_vals[j],sqrtf((float)s1_vals[j])+eps);
 										break;
@@ -1154,12 +1154,12 @@ kOptimizerStatic8bit2State(T* p, T* const g, unsigned char* state1, unsigned cha
 template<typename T, int OPTIMIZER>
 __global__ void
 __launch_bounds__(NUM_THREADS, 2)
-kPreconditionOptimizerStatic8bit1State(T* p, T* __restrict__ const g, unsigned char*__restrict__  const state1, 
+kPreconditionOptimizerStatic8bit1State(T* p, T* __restrict__ const g, unsigned char*__restrict__  const state1,
                 float *unorm,
-                const float beta1, 
+                const float beta1,
                 const float eps, const int step,
-                float* __restrict__ const quantiles1, 
+                float* __restrict__ const quantiles1,
-                float* max1, float* new_max1, 
+                float* max1, float* new_max1,
                 const float weight_decay,
                 const float gnorm_scale, const int n)
 {
@@ -1209,7 +1209,7 @@ kPreconditionOptimizerStatic8bit1State(T* p, T* __restrict__ const g, unsigned c
             s1_vals[j] = smem_quantiles1[m_c1[j]]*max1[0];
             switch(OPTIMIZER)
             {
-                case MOMENTUM: 
+                case MOMENTUM:
                     if(step == 1)
                       s1_vals[j] = (float)g_vals[j];
                     else
@@ -1217,7 +1217,7 @@ kPreconditionOptimizerStatic8bit1State(T* p, T* __restrict__ const g, unsigned c
                     if(unorm != NULL)
                       local_unorm += s1_vals[j]*s1_vals[j];
                     break;
-              case RMSPROP: 
+              case RMSPROP:
                     s1_vals[j] = s1_vals[j]*beta1 + ((1.0f-beta1)*(g_val*g_val));
                   break;
             }
@@ -1242,10 +1242,10 @@ template<typename T, int OPTIMIZER>
 __global__ void
 kOptimizerStatic8bit1State(T* p, T* const g, unsigned char* state1,
                 const float *unorm, const float max_unorm, const float param_norm,
-                const float beta1, 
+                const float beta1,
                 const float eps, const int step, const float lr,
-                float* __restrict__ const quantiles1, 
+                float* __restrict__ const quantiles1,
-                float* max1, float* new_max1, 
+                float* max1, float* new_max1,
                 float weight_decay,
                 const float gnorm_scale, const int n)
 {
@@ -1311,7 +1311,7 @@ kOptimizerStatic8bit1State(T* p, T* const g, unsigned char* state1,
 
             switch(OPTIMIZER)
             {
-                case MOMENTUM: 
+                case MOMENTUM:
                   if(step == 1)
                     s1_vals[j] = g_vals[j];
                   else
@@ -1319,7 +1319,7 @@ kOptimizerStatic8bit1State(T* p, T* const g, unsigned char* state1,
 
                   p_vals[j] = ((float)p_vals[j]) + (-lr*update_scale*(s1_vals[j]));
                   break;
-              case RMSPROP: 
+              case RMSPROP:
                   s1_vals[j] = s1_vals[j]*beta1 + ((1.0f-beta1)*(g_val*g_val));
                   p_vals[j] = ((float)p_vals[j]) - (lr*__fdividef(g_val,sqrtf(s1_vals[j])+eps));
                   break;
@@ -1399,7 +1399,7 @@ kOptimizerStatic8bit2StateBlockwise(T* p, T* __restrict__ const g, unsigned char
                 const float beta1, const float beta2,
                 const float eps, const int step, const float lr,
                 float* __restrict__ const quantiles1, float* __restrict__ const quantiles2,
-                float* absmax1, float* absmax2, 
+                float* absmax1, float* absmax2,
                 float weight_decay,
                 const float gnorm_scale, const bool skip_zeros, const int n)
 {
@@ -1543,7 +1543,7 @@ kOptimizerStatic8bit2StateBlockwise(T* p, T* __restrict__ const g, unsigned char
         StoreT(temp_storage.storeh).Store(&(p[i]), g_vals, valid_items);
 
         //  quantizaztion: 2.67/1.70  -> 3.4/3.3
-        # pragma unroll N_PER_TH 
+        # pragma unroll N_PER_TH
         for(unsigned int j = 0; j < N_PER_TH; j++)
         {
             c1s[j] = quantize_2D<1>(quadrants1, smem_quantiles1[lane_id], __fdividef(s1_vals[j],new_local_abs_max1));
@@ -1656,16 +1656,16 @@ kOptimizerStatic8bit1StateBlockwise(T* p, T* __restrict__ const g, unsigned char
 
 							switch(OPTIMIZER)
 							{
-									case MOMENTUM: 
+									case MOMENTUM:
 										if(step == 1)
 											s1_vals[j] = g_val;
 										else
 											s1_vals[j] = (s1_vals[j]*beta1) + g_val;
 										break;
-									case RMSPROP: 
+									case RMSPROP:
 										s1_vals[j] = s1_vals[j]*beta1 + ((1.0f-beta1)*(g_val*g_val));
 										break;
-									case ADAGRAD: 
+									case ADAGRAD:
 										s1_vals[j] = s1_vals[j] + (g_val*g_val);
 										break;
 							}
@@ -1696,14 +1696,14 @@ kOptimizerStatic8bit1StateBlockwise(T* p, T* __restrict__ const g, unsigned char
 						{
 							switch(OPTIMIZER)
 							{
-									case MOMENTUM: 
+									case MOMENTUM:
 										p_vals[j] = ((float)p_vals[j]) - lr*(s1_vals[j]);
 										break;
-									case RMSPROP: 
+									case RMSPROP:
 										g_val = g_vals[j];
 										p_vals[j] = ((float)p_vals[j]) - lr*(__fdividef(g_val, sqrtf(s1_vals[j])+eps));
 										break;
-									case ADAGRAD: 
+									case ADAGRAD:
 										g_val = g_vals[j];
 										p_vals[j] = ((float)p_vals[j]) - lr*(__fdividef(g_val, sqrtf(s1_vals[j])+eps));
 										break;
@@ -1716,7 +1716,7 @@ kOptimizerStatic8bit1StateBlockwise(T* p, T* __restrict__ const g, unsigned char
         StoreT(temp_storage.storeh).Store(&(p[i]), p_vals, valid_items);
 
         //  quantizaztion: 2.67/1.70  -> 3.4/3.3
-        # pragma unroll N_PER_TH 
+        # pragma unroll N_PER_TH
         for(unsigned int j = 0; j < N_PER_TH; j++)
         {
             c1s[j] = quantize_2D<1>(quadrants1, smem_quantiles1[lane_id], __fdividef(s1_vals[j],new_local_abs_max1));
@@ -1893,9 +1893,9 @@ template <int ITEMS_PER_THREAD, int SUBTILE_ROWS, int THREADS>__global__ void kd
 {
 
   // Strategy: To dequantize we need to load col/row statistics. This can be very expensive
-  // since different row/col stats need to be loaded with each thread. 
+  // since different row/col stats need to be loaded with each thread.
   // (1, bad algorithm) Loading 32 items per thread would only occur 1 row load, but this increases register pressure
-  // and would lead to low global load utilization. 
+  // and would lead to low global load utilization.
   // (2, bad algorithm) If each thread loads some columns and multiple rows one needs to do lot of row loads
   // for each thread and this is duplicated by a factor of 32/num-cols-per-thread.
   // (3, good algorithm) Combining (1) and (2) we use sub-tiles of size 32xk in shared memory per threadblock.
@@ -1903,7 +1903,7 @@ template <int ITEMS_PER_THREAD, int SUBTILE_ROWS, int THREADS>__global__ void kd
   // We can run for example 32x128 sub-tiles and warp-strided loads of 4 elements so that each thread has
   // the same col statistic but needs to load 4 row stats from shared memory. To prevent bank conflicts
   // we use a block-striped shared memory config [1, 31, 63, 95] so no bank conflicts happen during the
-  // shared memory loads. 
+  // shared memory loads.
 
   // data is in 32 column-tile major with tile width 32 columns and numRows rows
   // L1. Load sub-tile row/col statistics. Each thread only holds 1 col, load rows into shared memory.
@@ -2140,7 +2140,7 @@ template <int THREADS, int ITEMS_PER_THREAD, int TILE_ROWS, int TILE_COLS, int T
 
 
   // To have efficient loads and stores if we transpose we need 128 consequitive bytes which at 1 byte are 128 values
-  // As such we need: 
+  // As such we need:
   // at least 32*4 shared memory tiles for col32; preferably 32*32
   // at least 32*6 shared memory tiles for col32_ampere: preferably 32*32
   // at least 32*8 shared memory tiles for col4_turing: preferably 32*32
@@ -2150,7 +2150,7 @@ template <int THREADS, int ITEMS_PER_THREAD, int TILE_ROWS, int TILE_COLS, int T
   // we have 64k sharded mem per SM in Turing which is 8 blocks per SM which is 2*8 = 32 warps = 100% occupancy
   // for turing and 50% for A100 and 75% for RTX 30s / A40 which is probably good enough
   // register pressure should be low with: 8 registers from local memoryh per block and 64 registers per SM
-  // 
+  //
   // to make the shared memory work with that occupancy we might need to union the block loads/stores
 
   // each block loads TILE_COLs columns and TILE_ROW rows
@@ -2239,7 +2239,7 @@ template <int THREADS, int ITEMS_PER_THREAD, int TILE_ROWS, int TILE_COLS, int T
 
           switch(FORMAT)
           {
-              case COL32: 
+              case COL32:
                 if(TRANSPOSE)
                 {
                   // data lies in shared memory in the following way:
@@ -2264,7 +2264,7 @@ template <int THREADS, int ITEMS_PER_THREAD, int TILE_ROWS, int TILE_COLS, int T
 
                     // each 32 columns we have new tile
                     // each tile has size outRows*32 and base_row is done in increments of 32
-                    offset = base_row*outRows; 
+                    offset = base_row*outRows;
                     out[offset + (base_col + jrow + subrow_loop_row)*32 + threadIdx.x] = data;
                   }
                 }
@@ -2310,7 +2310,7 @@ template <int THREADS, int ITEMS_PER_THREAD, int TILE_ROWS, int TILE_COLS, int T
                     // we increase by row_tile_column every 32 columns
                     // base_row increase in increments of 32
                     //int row_tile_column = 256*outRows/8; // there are outRows/8 row tiles, and each tile is 256 elements
-                    //int col_offset = (base_row/32)*row_tile_column; 
+                    //int col_offset = (base_row/32)*row_tile_column;
                     // -> we can remove the divisions to speed up compute since outRows is always a multiple of 8
                     // 256*outRows/8*base_row/32 = outRows*base_row
                     int col_offset = outRows*base_row;
@@ -2347,7 +2347,7 @@ template <int THREADS, int ITEMS_PER_THREAD, int TILE_ROWS, int TILE_COLS, int T
                     // this happends every 8 rows anew (subrow % 8)
                     // one writes 4 columns at once that is (col % 4) for the particular index in the subtile
                     int subcol = warp_lane;
-                    
+
                     // add local offset (4x4 sub-tile)
                     if(subrow % 2 == 1)
                       // odd
@@ -2387,7 +2387,7 @@ template <int THREADS, int ITEMS_PER_THREAD, int TILE_ROWS, int TILE_COLS, int T
 											// we increase by row_tile_column every 32 columns
 											// base_row increase in increments of 32
 											//int row_tile_column = 1024*outRows/32; // there are outRows/32 row tiles, and each tile is 1024 elements
-											//int col_offset = (base_row/32)*row_tile_column; 
+											//int col_offset = (base_row/32)*row_tile_column;
 											// -> we can remove the divisions to speed up compute since outRows is always a multiple of 8
 											// 1024*outRows/32*base_row/32 = outRows*base_row
 											int col_offset = outRows*base_row;
@@ -2445,7 +2445,7 @@ template <int THREADS, int ITEMS_PER_THREAD, int TILE_ROWS, int TILE_COLS, int T
 #define C 1.0f/127.0f
 #define MAX_SPARSE_COUNT 32
 #define SMEM_SIZE 8*256
-template <typename T, int SPMM_ITEMS, int BITS> 
+template <typename T, int SPMM_ITEMS, int BITS>
 __global__ void kspmm_coo_very_sparse_naive(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, T *B, half *out, float * __restrict__ const dequant_stats, int nnz, int rowsA, int rowsB, int colsB)
 {
 
@@ -2575,7 +2575,7 @@ __global__ void kspmm_coo_very_sparse_naive(int *max_count, int *max_idx, int *o
           #pragma unroll num_items
           for(int k = 0; k < num_items; k++)
             local_valC[(j/num_items) + k] = (float)local_valC[(j/num_items) + k] + (float)local_valOut[k];
-            
+
           reinterpret_cast<float4*>(out)[idx_val/num_items] = reinterpret_cast<float4(&)[num_items]>(local_valC)[j/num_items];
       }
       else
@@ -2589,11 +2589,11 @@ __global__ void kspmm_coo_very_sparse_naive(int *max_count, int *max_idx, int *o
 
     idx_col_B += blockDim.x*SPMM_ITEMS;
     local_idx_col_B_offset += blockDim.x*SPMM_ITEMS;
-  } 
+  }
 }
 
 template <int FORMAT> __global__ void kExtractOutliers(char *A, int *idx, char *out, int idx_size, int rowsA, int colsA, int tiledRowsA, int tiledColsA)
-{  
+{
 	int local_colidx = idx[blockIdx.x];
 
 	if(FORMAT==COL_TURING)
@@ -2653,7 +2653,7 @@ template <int FORMAT> __global__ void kExtractOutliers(char *A, int *idx, char *
 			out[out_idx] = val;
 		}
 	}
-} 
+}
 
 //==============================================================
 //                   TEMPLATE DEFINITIONS

csrc/kernels.cuh

@@ -1,6 +1,6 @@
-// Copyright (c) Facebook, Inc. and its affiliates. 
+// Copyright (c) Facebook, Inc. and its affiliates.
-//   
+//
-// This source code is licensed under the MIT license found in the 
+// This source code is licensed under the MIT license found in the
 // LICENSE file in the root directory of this source tree.
 
 #include <float.h>
@@ -18,49 +18,49 @@ template<typename T, int BLOCK_SIZE, int NUM_PER_TH, int STOCHASTIC> __global__
 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 OPTIMIZER, int BLOCK_SIZE, int NUM_VALS>
-__global__ void kPreconditionOptimizer32bit2State(T* g, T* p, 
+__global__ void kPreconditionOptimizer32bit2State(T* g, T* p,
                 float* state1, float* state2, float *unorm,
                 const float beta1, const float beta2, const float eps, const float weight_decay,
                 const int step, const float lr, const float gnorm_scale, const int n);
 
 template<typename T, int OPTIMIZER>
-__global__ void kOptimizer32bit2State(T* g, T* p, 
+__global__ void kOptimizer32bit2State(T* g, T* p,
                 float* state1, float* state2, float *unorm, const float max_unorm, const 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, const bool skip_zeros, const int n);
 
 template<typename T, int OPTIMIZER, int BLOCK_SIZE, int NUM_VALS>
-__global__ void kPreconditionOptimizer32bit1State(T* g, T* p, 
+__global__ void kPreconditionOptimizer32bit1State(T* g, T* p,
                 float* state1, float *unorm,
                 const float beta1, const float eps, const float weight_decay,
                 const int step, const float lr, const float gnorm_scale, const int n);
 
 template<typename T, int OPTIMIZER>
-__global__ void kOptimizer32bit1State(T* g, T* p, 
+__global__ void kOptimizer32bit1State(T* g, T* p,
                 float* state1,  float *unorm, const float max_unorm, const float param_norm,
                 const float beta1, const float eps, const float weight_decay,
                 const int step, const float lr, const float gnorm_scale, const bool skip_zeros, const int n);
 
 template<typename T, int OPTIMIZER>
 __global__ void
-kPreconditionOptimizerStatic8bit1State(T* p, T* __restrict__ const g, unsigned char*__restrict__  const state1, 
+kPreconditionOptimizerStatic8bit1State(T* p, T* __restrict__ const g, unsigned char*__restrict__  const state1,
                 float *unorm,
-                const float beta1, 
+                const float beta1,
-                const float eps, const int step, 
+                const float eps, const int step,
-                float* __restrict__ const quantiles1, 
+                float* __restrict__ const quantiles1,
-                float* max1, float* new_max1, 
+                float* max1, float* new_max1,
                 const float weight_decay,
                 const float gnorm_scale, const int n);
 
 
 template<typename T, int OPTIMIZER>
 __global__ void
-kOptimizerStatic8bit1State(T* p, T* const g, unsigned char* state1, 
+kOptimizerStatic8bit1State(T* p, T* const g, unsigned char* state1,
                 const float *unorm, const float max_unorm, const float param_norm,
-                const float beta1, 
+                const float beta1,
-                const float eps, const int step, const float lr, 
+                const float eps, const int step, const float lr,
-                float* __restrict__ const quantiles1, 
+                float* __restrict__ const quantiles1,
-                float* max1, float* new_max1, 
+                float* max1, float* new_max1,
                 float weight_decay, const float gnorm_scale, const int n);
 
 
@@ -70,7 +70,7 @@ __global__ void
 kPreconditionOptimizerStatic8bit2State(T* p, T* __restrict__ const g, unsigned char*__restrict__  const state1, unsigned char* __restrict__ const state2,
                 float *unorm,
                 const float beta1, const float beta2,
-                const float eps, const int step, 
+                const float eps, const int step,
                 float* __restrict__ const quantiles1, float* __restrict__ const quantiles2,
                 float* max1, float* max2, float* new_max1, float* new_max2,
                 const float gnorm_scale, const int n);
@@ -81,7 +81,7 @@ __global__ void
 kOptimizerStatic8bit2State(T* p, T* const g, unsigned char* state1, unsigned char* state2,
                 const float *unorm, const float max_unorm, const float param_norm,
                 const float beta1, const float beta2,
-                const float eps, const int step, const float lr, 
+                const float eps, const int step, const float lr,
                 float* __restrict__ const quantiles1, float* __restrict__ const quantiles2,
                 float* max1, float* max2, float* new_max1, float* new_max2,
                 float weight_decay, const float gnorm_scale, const int n);
@@ -121,5 +121,3 @@ template <int THREADS, int ITEMS_PER_THREAD, int TILE_ROWS, int TILE_COLS, int T
 template <int FORMAT> __global__ void kExtractOutliers(char *A, int *idx, char *out, int idx_size, int rowsA, int colsA, int tiledRowsA, int tiledColsA);
 
 #endif
-
-

csrc/ops.cu

@@ -1,6 +1,6 @@
-// Copyright (c) Facebook, Inc. and its affiliates. 
+// Copyright (c) Facebook, Inc. and its affiliates.
-//   
+//
-// This source code is licensed under the MIT license found in the 
+// This source code is licensed under the MIT license found in the
 // LICENSE file in the root directory of this source tree.
 
 #include <ops.cuh>
@@ -212,7 +212,7 @@ void gemmex(Context *context, bool transposeA, bool transposeB, int m, int n, in
 
 }
 
-void strided_gemmex(Context *context, bool transposeA, bool transposeB, int m, int n, int k, void *A, void *B, void *C, int lda, int ldb, int ldc, 
+void strided_gemmex(Context *context, bool transposeA, bool transposeB, int m, int n, int k, void *A, void *B, void *C, int lda, int ldb, int ldc,
                     long long int strideA, long long int strideB, long long int strideC, int batchCount)
 {
   const int falpha = 1;
@@ -322,7 +322,7 @@ template <typename T, int SRC, int TARGET, bool transpose, int DTYPE> void trans
   cublasLtOrder_t orderOut = get_order<TARGET>();
   int ldA = get_leading_dim<SRC>(dim1, dim2);
   int ldOut = get_leading_dim<TARGET>(dim1, dim2);
-  
+
   cublasLtMatrixLayout_t A_desc = NULL, out_desc = NULL;
   cublasLtMatrixTransformDesc_t A2Out_desc = NULL;
   cublasOperation_t opTranspose = CUBLAS_OP_T;
@@ -368,7 +368,7 @@ template void transform<int8_t, ROW, COL_AMPERE, false, 8>(cublasLtHandle_t ltHa
 template void transform<int8_t, COL32, ROW, false, 8>(cublasLtHandle_t ltHandle, int8_t *A, int8_t *out, int dim1, int dim2);
 template void transform<int32_t, COL32, ROW, false, 32>(cublasLtHandle_t ltHandle, int32_t *A, int32_t *out, int dim1, int dim2);
 
-template <int FORMATB, int DTYPE_OUT, int SCALE_ROWS> int igemmlt(cublasLtHandle_t ltHandle, int m, int n, int k, const int8_t *A, const int8_t *B, void *C, float *row_scale, int lda, int ldb, int ldc) 
+template <int FORMATB, int DTYPE_OUT, int SCALE_ROWS> int igemmlt(cublasLtHandle_t ltHandle, int m, int n, int k, const int8_t *A, const int8_t *B, void *C, float *row_scale, int lda, int ldb, int ldc)
 {
 #ifdef NO_CUBLASLT
   cout << "" << endl;

csrc/ops.cuh

@@ -1,6 +1,6 @@
-// Copyright (c) Facebook, Inc. and its affiliates. 
+// Copyright (c) Facebook, Inc. and its affiliates.
-//   
+//
-// This source code is licensed under the MIT license found in the 
+// This source code is licensed under the MIT license found in the
 // LICENSE file in the root directory of this source tree.
 
 
@@ -131,7 +131,7 @@ 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> 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, 
+template<typename T, int OPTIMIZER> void optimizer32bit(T* g, T* p,
                 float* state1, float* state2, float *unorm, float max_unorm, float param_norm,
                 float beta1, float beta2, float eps, float weight_decay,
                 int step, float lr, const float gnorm_scale, bool skip_zeros, int n);
@@ -139,15 +139,15 @@ template<typename T, int OPTIMIZER> void optimizer32bit(T* g, T* p,
 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,
-                float eps, int step, float lr, 
+                float eps, int step, float lr,
                 float* quantiles1, float* quantiles2,
                 float* max1, float* max2, float* new_max1, float* new_max2,
                 float weight_decay,
                 const float gnorm_scale, int n);
 
 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, 
+                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, 
+                float* quantiles1, float* quantiles2, float* absmax1, float* absmax2, float weight_decay, const float gnorm_scale,
 								bool skip_zeros, int n);
 
 template<typename T> void percentileClipping(T * g, float *gnorm_vec, int step, const int n);
@@ -155,7 +155,7 @@ template<typename T> void percentileClipping(T * g, float *gnorm_vec, int step,
 void histogramScatterAdd2D(float* histogram, int *index1, int *index2, float *src, int maxidx1, int n);
 
 void gemmex(Context * context, bool transposeA, bool transposeB, int m, int n, int k, void *A, void *B, void *C, int lda, int ldb, int ldc);
-void strided_gemmex(Context *context, bool transposeA, bool transposeB, int m, int n, int k, void *A, void *B, void *C, int lda, int ldb, int ldc, 
+void strided_gemmex(Context *context, bool transposeA, bool transposeB, int m, int n, int k, void *A, void *B, void *C, int lda, int ldb, int ldc,
                     long long int strideA, long long int strideB, long long int strideC, int batchCount);
 
 

csrc/pythonInterface.c

@@ -1,6 +1,6 @@
-// Copyright (c) Facebook, Inc. and its affiliates. 
+// Copyright (c) Facebook, Inc. and its affiliates.
-//   
+//
-// This source code is licensed under the MIT license found in the 
+// 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
@@ -9,7 +9,7 @@
 #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 
+// We use macro functions to expand all the different optimizers. Looks ugly, and is ugly, but its better than to
 // maintain all that boilerplate
 //===================================================================================
 //                               UNMANGLED CALLS
@@ -290,4 +290,3 @@ extern "C"
 	void cquantize_blockwise_cpu_fp32(float *code, float *A, float *absmax, unsigned char *out, long long blocksize, long long n){ quantize_cpu(code, A, absmax, out, blocksize, n); }
 	void cdequantize_blockwise_cpu_fp32(float *code, unsigned char *A, float *absmax, float *out, long long blocksize, long long n){ dequantize_cpu(code, A, absmax, out, blocksize, n); }
 }
-

cuda_install.sh

@@ -76,6 +76,3 @@ if [[ -n "$CUDA_VERSION" ]]; then
 else
   echo ""
 fi
-
-
-

howto_config_override.md

@@ -14,16 +14,16 @@ mng.register_parameters(model.parameters()) # 1. register parameters while still
 
 model = model.cuda()
 # use 8-bit optimizer states for all parameters
-adam = bnb.optim.Adam(model.parameters(), lr=0.001, optim_bits=8) 
+adam = bnb.optim.Adam(model.parameters(), lr=0.001, optim_bits=8)
 
 # 2a. override: the parameter model.fc1.weight now uses 32-bit Adam
-mng.override_config(model.fc1.weight, 'optim_bits', 32) 
+mng.override_config(model.fc1.weight, 'optim_bits', 32)
 
 # 2b. override: the two special layers use
 # sparse optimization + different learning rate + different Adam betas
 mng.override_config([model.special.weight, model.also_special.weight],
-                    key_value_dict ={'is_sparse': True, 'lr': 1e-5, 'betas'=(0.9, 0.98)}) 
+                    key_value_dict ={'is_sparse': True, 'lr': 1e-5, 'betas'=(0.9, 0.98)})
-``` 
+```
 Possible options for the config override are: `betas, eps, weight_decay, lr, optim_bits, min_8bit_size, percentile_clipping, block_wise, max_unorm`
 
 For overrides for particular layers we recommend overriding locally in each module. You can do this by passing the module, the parameter, and its attribute name to the GlobalOptimManager:

include/Algo-Direct-Common.h

@@ -121,7 +121,7 @@ template <unsigned char Gap, typename T>
 struct DirectTraits<true,Gap,T>
 {
     typedef FVec1<SSE, T> fVec1;
-    
+
     static void checkH(T scaler, T H_Times_x0, T xN)
     {
         union {
@@ -177,9 +177,9 @@ struct DirectInfo
             , cst0(fun_t::cst0(H, x[0]))
         {
             myassert(((bws != NULL) && (isAligned(bws,64))), "bucket pointer not allocated or incorrectly aligned");
-            
+
             uint32 nb = 1 + fun_t::f(H, cst0, x[n-1]);
-            
+
             const uint32 npad = Gap-1;
             const uint32 n_sz = n + npad;   // size of padded vector
 
@@ -320,7 +320,7 @@ struct DirectInfo
         T cst0 = fun_t::cst0(H, px[0]);
         const uint32 maxIndex = fun_t::f(H, cst0, px[n-1]);
         buckets.resize(maxIndex + 1);
-        
+
         data = Data(px, n, H, buckets.begin(), (npad? xi.begin(): NULL));
     }
 

include/SIMD.h

@@ -203,7 +203,7 @@ struct IVec<SSE, double> : IVecBase<SSE>
 #if 1
         // takes 4 cycles
         __m128i hi = _mm_shuffle_epi32(vec, 2);  // 1 cycle
-        __m128i s = _mm_add_epi32(vec, hi);      
+        __m128i s = _mm_add_epi32(vec, hi);
         int32 x = _mm_cvtsi128_si32(s);
         return -x;
 #else

tests/test_autograd.py

@@ -336,7 +336,7 @@ def test_matmullt(
             )
             bias = None
             bias2 = None
-            if has_bias: 
+            if has_bias:
                 bias = torch.randn(dim4, device='cuda', dtype=dtype, requires_grad=req_grad[2])
                 bias2 = bias.clone()
             torch.nn.init.xavier_uniform_(B)