diff options
| -rw-r--r-- | Makefile | 19 | ||||
| -rw-r--r-- | bitsandbytes/__init__.py | 11 | ||||
| -rw-r--r-- | bitsandbytes/cextension.py | 13 | ||||
| -rw-r--r-- | bitsandbytes/functional.py | 52 | ||||
| -rw-r--r-- | bitsandbytes/optim/__init__.py | 19 | ||||
| -rw-r--r-- | bitsandbytes/optim/rmsprop.py | 2 | ||||
| -rw-r--r-- | compile_from_source.md | 4 | ||||
| -rw-r--r-- | csrc/common.cpp | 39 | ||||
| -rw-r--r-- | csrc/common.h | 23 | ||||
| -rw-r--r-- | csrc/cpu_ops.cpp | 57 | ||||
| -rw-r--r-- | csrc/cpu_ops.h | 9 | ||||
| -rw-r--r-- | csrc/ops.cu | 122 | ||||
| -rw-r--r-- | csrc/ops.cuh | 10 | ||||
| -rw-r--r-- | csrc/pythonInterface.c | 11 | ||||
| -rw-r--r-- | setup.py | 22 |
15 files changed, 226 insertions, 187 deletions
@@ -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: diff --git a/bitsandbytes/__init__.py b/bitsandbytes/__init__.py index 6e29322..02ca804 100644 --- a/bitsandbytes/__init__.py +++ b/bitsandbytes/__init__.py @@ -2,9 +2,14 @@ # # 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 -__pdoc__ = {'libBitsNBytes' : False, +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 - } + } diff --git a/bitsandbytes/cextension.py b/bitsandbytes/cextension.py new file mode 100644 index 0000000..63d627e --- /dev/null +++ b/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 diff --git a/bitsandbytes/functional.py b/bitsandbytes/functional.py index fbd7564..ab4e565 100644 --- a/bitsandbytes/functional.py +++ b/bitsandbytes/functional.py @@ -3,38 +3,38 @@ # 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 = {} -''' C FUNCTIONS FOR OPTIMIZERS ''' - -str2optimizer32bit = {} -str2optimizer32bit['adam'] = (lib.cadam32bit_g32, lib.cadam32bit_g16) -str2optimizer32bit['momentum'] = (lib.cmomentum32bit_g32, lib.cmomentum32bit_g16) -str2optimizer32bit['rmsprop'] = (lib.crmsprop32bit_g32, lib.crmsprop32bit_g16) -str2optimizer32bit['adagrad'] = (lib.cadagrad32bit_g32, lib.cadagrad32bit_g16) -str2optimizer32bit['lars'] = (lib.cmomentum32bit_g32, lib.cmomentum32bit_g16) -str2optimizer32bit['lamb'] = (lib.cadam32bit_g32, lib.cadam32bit_g16) - -str2optimizer8bit = {} -str2optimizer8bit['adam'] = (lib.cadam_static_8bit_g32, lib.cadam_static_8bit_g16) -str2optimizer8bit['momentum'] = (lib.cmomentum_static_8bit_g32, lib.cmomentum_static_8bit_g16) -str2optimizer8bit['rmsprop'] = (lib.crmsprop_static_8bit_g32, lib.crmsprop_static_8bit_g16) -str2optimizer8bit['lamb'] = (lib.cadam_static_8bit_g32, lib.cadam_static_8bit_g16) -str2optimizer8bit['lars'] = (lib.cmomentum_static_8bit_g32, lib.cmomentum_static_8bit_g16) - -str2optimizer8bit_blockwise = {} -str2optimizer8bit_blockwise['adam'] = (lib.cadam_8bit_blockwise_fp32, lib.cadam_8bit_blockwise_fp16) -str2optimizer8bit_blockwise['momentum'] = (lib.cmomentum_8bit_blockwise_fp32, lib.cmomentum_8bit_blockwise_fp16) -str2optimizer8bit_blockwise['rmsprop'] = (lib.crmsprop_8bit_blockwise_fp32, lib.crmsprop_8bit_blockwise_fp16) -str2optimizer8bit_blockwise['adagrad'] = (lib.cadagrad_8bit_blockwise_fp32, lib.cadagrad_8bit_blockwise_fp16) +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) + str2optimizer32bit['rmsprop'] = (lib.crmsprop32bit_g32, lib.crmsprop32bit_g16) + str2optimizer32bit['adagrad'] = (lib.cadagrad32bit_g32, lib.cadagrad32bit_g16) + str2optimizer32bit['lars'] = (lib.cmomentum32bit_g32, lib.cmomentum32bit_g16) + str2optimizer32bit['lamb'] = (lib.cadam32bit_g32, lib.cadam32bit_g16) + + str2optimizer8bit = {} + str2optimizer8bit['adam'] = (lib.cadam_static_8bit_g32, lib.cadam_static_8bit_g16) + str2optimizer8bit['momentum'] = (lib.cmomentum_static_8bit_g32, lib.cmomentum_static_8bit_g16) + str2optimizer8bit['rmsprop'] = (lib.crmsprop_static_8bit_g32, lib.crmsprop_static_8bit_g16) + str2optimizer8bit['lamb'] = (lib.cadam_static_8bit_g32, lib.cadam_static_8bit_g16) + str2optimizer8bit['lars'] = (lib.cmomentum_static_8bit_g32, lib.cmomentum_static_8bit_g16) + + str2optimizer8bit_blockwise = {} + str2optimizer8bit_blockwise['adam'] = (lib.cadam_8bit_blockwise_fp32, lib.cadam_8bit_blockwise_fp16) + str2optimizer8bit_blockwise['momentum'] = (lib.cmomentum_8bit_blockwise_fp32, lib.cmomentum_8bit_blockwise_fp16) + str2optimizer8bit_blockwise['rmsprop'] = (lib.crmsprop_8bit_blockwise_fp32, lib.crmsprop_8bit_blockwise_fp16) + str2optimizer8bit_blockwise['adagrad'] = (lib.cadagrad_8bit_blockwise_fp32, lib.cadagrad_8bit_blockwise_fp16) optimal_normal = [-0.9939730167388916, -0.8727636337280273, -0.8097418546676636, -0.7660024166107178, -0.7318882346153259, -0.6793879270553589, -0.657649040222168, -0.6385974884033203, -0.6211113333702087, -0.5901028513908386, -0.5762918591499329, -0.5630806684494019, -0.5509274005889893, -0.5394591689109802, -0.5283197164535522, -0.517780065536499, -0.5074946284294128, -0.4980469048023224, -0.48867011070251465, -0.48003149032592773, -0.47125306725502014, -0.4629971981048584, -0.4547359049320221, -0.446626216173172, -0.43902668356895447, -0.43158355355262756, -0.4244747757911682, -0.4173796474933624, -0.41038978099823, -0.4055633544921875, -0.4035947024822235, -0.39701032638549805, -0.39057496190071106, -0.38439232110977173, -0.3782760500907898, -0.3721940815448761, -0.3661896586418152, -0.3604033589363098, -0.354605108499527, -0.34892538189888, -0.34320303797721863, -0.3376772701740265, -0.3323028087615967, -0.3269782066345215, -0.32166096568107605, -0.316457599401474, -0.3112771809101105, -0.3061025142669678, -0.30106794834136963, -0.2961243987083435, -0.2912728488445282, -0.28644347190856934, -0.28165507316589355, -0.2769731283187866, -0.2722635865211487, -0.26779335737228394, -0.26314786076545715, -0.2586647868156433, -0.2541804611682892, -0.2496625930070877, -0.24527113139629364, -0.24097171425819397, -0.23659978806972504, -0.23218469321727753, -0.22799566388130188, -0.22380566596984863, -0.21965542435646057, -0.2154538631439209, -0.2113603949546814, -0.20735277235507965, -0.20334717631340027, -0.19932441413402557, -0.19530178606510162, -0.19136647880077362, -0.18736697733402252, -0.18337111175060272, -0.17951400578022003, -0.1757056713104248, -0.17182783782482147, -0.1680615097284317, -0.16431649029254913, -0.16053077578544617, -0.15685945749282837, -0.15298527479171753, -0.1493264138698578, -0.14566898345947266, -0.14188314974308014, -0.13819937407970428, -0.1344561129808426, -0.1306886374950409, -0.1271020770072937, -0.12346585839986801, -0.11981867253780365, -0.11614970862865448, -0.11256207525730133, -0.10889036953449249, -0.10525048524141312, -0.1016591489315033, -0.09824034571647644, -0.09469068050384521, -0.0911419615149498, -0.08773849159479141, -0.08416644483804703, -0.08071305602788925, -0.07720902562141418, -0.07371306419372559, -0.07019119709730148, -0.06673648208379745, -0.06329209357500076, -0.059800852090120316, -0.0564190037548542, -0.05296570807695389, -0.049522045999765396, -0.04609023034572601, -0.04262964054942131, -0.039246633648872375, -0.03577171266078949, -0.03236335143446922, -0.028855687007308006, -0.02542758360505104, -0.022069433704018593, -0.018754752352833748, -0.015386369079351425, -0.01194947212934494, -0.008439815603196621, -0.004995611496269703, -0.0016682245768606663, 0.0, 0.0015510577941313386, 0.005062474869191647, 0.008417150937020779, 0.011741090565919876, 0.015184164978563786, 0.018582714721560478, 0.02204744517803192, 0.025471193715929985, 0.02889077737927437, 0.0323684960603714, 0.03579240292310715, 0.039281025528907776, 0.0427563451230526, 0.04619763046503067, 0.04968220740556717, 0.05326594039797783, 0.05679265409708023, 0.060245808213949203, 0.06372645497322083, 0.06721872836351395, 0.0706876739859581, 0.0742349922657013, 0.07774098962545395, 0.08123527467250824, 0.08468879014253616, 0.08810535818338394, 0.09155989438295364, 0.09498448669910431, 0.0985206812620163, 0.10206405073404312, 0.10563778132200241, 0.10921968519687653, 0.11284469068050385, 0.11653254181146622, 0.12008969485759735, 0.12368203699588776, 0.1272617131471634, 0.13089501857757568, 0.134552001953125, 0.1382799744606018, 0.14194637537002563, 0.14563234150409698, 0.14930322766304016, 0.15303383767604828, 0.1567956507205963, 0.16050070524215698, 0.16431072354316711, 0.16813558340072632, 0.17204202711582184, 0.1758781224489212, 0.17973239719867706, 0.1836014688014984, 0.18753431737422943, 0.19138391315937042, 0.19535475969314575, 0.19931404292583466, 0.20333819091320038, 0.20738255977630615, 0.21152682602405548, 0.21568812429904938, 0.21978361904621124, 0.22393859922885895, 0.22814159095287323, 0.23241068422794342, 0.23675410449504852, 0.24123944342136383, 0.24569889903068542, 0.2500703036785126, 0.25904011726379395, 0.26349544525146484, 0.2682226300239563, 0.272907555103302, 0.2774306833744049, 0.28220856189727783, 0.2869136929512024, 0.2916390895843506, 0.29649388790130615, 0.30142995715141296, 0.3065022826194763, 0.3114383816719055, 0.31648796796798706, 0.3216581642627716, 0.32700115442276, 0.3322487473487854, 0.33778008818626404, 0.3431521952152252, 0.3487405776977539, 0.3543166518211365, 0.3601346015930176, 0.36605337262153625, 0.37217751145362854, 0.378179669380188, 0.3843980133533478, 0.3906566798686981, 0.39714935421943665, 0.40357843041419983, 0.4104187488555908, 0.4171563684940338, 0.42418959736824036, 0.43136918544769287, 0.4389212429523468, 0.44673123955726624, 0.45457619428634644, 0.4627031683921814, 0.47130417823791504, 0.4798591434955597, 0.48897242546081543, 0.4979848861694336, 0.5, 0.5076631307601929, 0.5177803635597229, 0.5282770991325378, 0.5392990112304688, 0.5506287813186646, 0.5632893443107605, 0.5764452815055847, 0.5903191566467285, 0.6051878333091736, 0.6209936141967773, 0.6382884979248047, 0.6573970913887024, 0.6795773506164551, 0.7037051916122437, 0.7327037453651428, 0.7677436470985413, 0.8111193776130676, 0.875165581703186, 1.0] @@ -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), diff --git a/bitsandbytes/optim/__init__.py b/bitsandbytes/optim/__init__.py index 5e73414..42b5bc0 100644 --- a/bitsandbytes/optim/__init__.py +++ b/bitsandbytes/optim/__init__.py @@ -2,11 +2,16 @@ # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -from .adam import Adam, Adam8bit, Adam32bit -from .adamw import AdamW, AdamW8bit, AdamW32bit -from .sgd import SGD, SGD8bit, SGD32bit -from .lars import LARS, LARS8bit, LARS32bit, PytorchLARS -from .lamb import LAMB, LAMB8bit, LAMB32bit -from .rmsprop import RMSprop, RMSprop8bit, RMSprop32bit -from .adagrad import Adagrad, Adagrad8bit, Adagrad32bit + +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 + from .lars import LARS, LARS8bit, LARS32bit, PytorchLARS + from .lamb import LAMB, LAMB8bit, LAMB32bit + from .rmsprop import RMSprop, RMSprop8bit, RMSprop32bit + from .adagrad import Adagrad, Adagrad8bit, Adagrad32bit + from .optimizer import GlobalOptimManager diff --git a/bitsandbytes/optim/rmsprop.py b/bitsandbytes/optim/rmsprop.py index 7909d5d..0f1ffaa 100644 --- a/bitsandbytes/optim/rmsprop.py +++ b/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) diff --git a/compile_from_source.md b/compile_from_source.md index c6a8b18..71b0c09 100644 --- a/compile_from_source.md +++ b/compile_from_source.md @@ -1,12 +1,12 @@ # Compiling from source Basic steps. -1. `make cudaXXX` where `cudaXXX` is among `cuda92, cuda10x, cuda110, cuda11x` +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). -For your convenience, there is a install script int he 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: +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 `$PATH` variable to make the compiler visible to your system. To do this you can add this to your `.bashrc` by executing these commands: ```bash echo "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64/" >> ~/.bashrc echo "export PATH=$PATH:/usr/local/cuda/bin/" >> ~/.bashrc diff --git a/csrc/common.cpp b/csrc/common.cpp new file mode 100644 index 0000000..972602b --- /dev/null +++ b/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; +} diff --git a/csrc/common.h b/csrc/common.h new file mode 100644 index 0000000..2f25a58 --- /dev/null +++ b/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 diff --git a/csrc/cpu_ops.cpp b/csrc/cpu_ops.cpp new file mode 100644 index 0000000..89de52d --- /dev/null +++ b/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); +} diff --git a/csrc/cpu_ops.h b/csrc/cpu_ops.h new file mode 100644 index 0000000..57145a9 --- /dev/null +++ b/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 diff --git a/csrc/ops.cu b/csrc/ops.cu index 9691241..40c185c 100644 --- a/csrc/ops.cu +++ b/csrc/ops.cu @@ -8,123 +8,13 @@ #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 - -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; @@ -178,7 +68,7 @@ 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) @@ -189,7 +79,7 @@ template<typename T, int OPTIMIZER> void optimizer32bit(T* g, T* p, { case ADAM: 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); CUDA_CHECK_RETURN(cudaPeekAtLastError()); @@ -202,7 +92,7 @@ template<typename T, int OPTIMIZER> void optimizer32bit(T* g, T* p, case ADAGRAD: 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); CUDA_CHECK_RETURN(cudaPeekAtLastError()); @@ -218,7 +108,7 @@ 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, @@ -261,7 +151,7 @@ template<typename T, int OPTIMIZER> void optimizerStatic8bit(T* p, T* g, #define NUM_1STATE 8 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, bool skip_zeros, int n) { diff --git a/csrc/ops.cuh b/csrc/ops.cuh index 1bc13fb..8fb4cec 100644 --- a/csrc/ops.cuh +++ b/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 - - - - - - - diff --git a/csrc/pythonInterface.c b/csrc/pythonInterface.c index e0b0d59..c2fed6b 100644 --- a/csrc/pythonInterface.c +++ b/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 +153,10 @@ 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); } } - @@ -6,27 +6,27 @@ 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']}", - version = "0.26.0", - author = "Tim Dettmers", - author_email = "dettmers@cs.washington.edu", - description = ("8-bit optimizers and quantization routines."), - license = "MIT", - keywords = "gpu optimizers optimization 8-bit quantization compression", - url = "http://packages.python.org/bitsandbytes", + name="bitsandbytes", + version=f"0.26.0+{version}", + author="Tim Dettmers", + author_email="dettmers@cs.washington.edu", + description="8-bit optimizers and quantization routines.", + license="MIT", + keywords="gpu optimizers optimization 8-bit quantization compression", + url="http://packages.python.org/bitsandbytes", packages=find_packages(), package_data={'': ['libbitsandbytes.so']}, long_description=read('README.md'), - long_description_content_type = 'text/markdown', + long_description_content_type='text/markdown', classifiers=[ "Development Status :: 4 - Beta", 'Topic :: Scientific/Engineering :: Artificial Intelligence' ], ) - |