From 7439924891496025edf60c9da6a782f362a50c70 Mon Sep 17 00:00:00 2001 From: Tim Dettmers Date: Tue, 5 Oct 2021 19:16:20 -0700 Subject: Initial commit --- bitsandbytes/functional.py | 531 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 531 insertions(+) create mode 100644 bitsandbytes/functional.py (limited to 'bitsandbytes/functional.py') diff --git a/bitsandbytes/functional.py b/bitsandbytes/functional.py new file mode 100644 index 0000000..65c697d --- /dev/null +++ b/bitsandbytes/functional.py @@ -0,0 +1,531 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +import os +import random +import math +import ctypes as ct +import torch +from torch import Tensor +from typing import Tuple + +lib = ct.cdll.LoadLibrary(os.path.dirname(__file__) + '/libbitsandbytes.so') +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['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) + +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, 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0.9925699159502983] + +def create_linear_map(signed=True): + if signed: + return torch.linspace(-1.0, 1.0, 256) + else: + return torch.linspace(0.0, 1.0, 256) + +def create_dynamic_map(signed=True, n=7): + ''' + Creates the dynamic quantiztion map. + + The dynamic data type is made up of a dynamic exponent and + fraction. As the exponent increase from 0 to -7 the number + of bits available for the fraction shrinks. + + This is a generalization of the dynamic type where a certain + number of the bits and be reserved for the linear quantization + region (the fraction). n determines the maximum number of + exponent bits. + + For more details see + (8-Bit Approximations for Parallelism in Deep Learning)[https://arxiv.org/abs/1511.04561] + ''' + + data = [] + # these are additional items that come from the case + # where all the exponent bits are zero and no + # indicator bit is present + additional_items = 2**(7-n)-1 + if not signed: additional_items = 2*additional_items + for i in range(n): + fraction_items = 2**(i+7-n)+1 if signed else 2**(i+7-n+1)+1 + boundaries = torch.linspace(0.1, 1, fraction_items) + means = (boundaries[:-1]+boundaries[1:])/2.0 + data += ((10**(-(n-1)+i))*means).tolist() + if signed: + data += (-(10**(-(n-1)+i))*means).tolist() + + if additional_items > 0: + boundaries = torch.linspace(0.1, 1, additional_items+1) + means = (boundaries[:-1]+boundaries[1:])/2.0 + data += ((10**(-(n-1)+i))*means).tolist() + if signed: + data += (-(10**(-(n-1)+i))*means).tolist() + + data.append(0) + data.append(1.0) + data.sort() + return Tensor(data) + +def get_ptr(A: Tensor) -> ct.c_void_p: + ''' + Get the ctypes pointer from a PyTorch Tensor. + + Parameters + ---------- + A : torch.tensor + The PyTorch tensor. + + Returns + ------- + ctypes.c_void_p + ''' + if A is None: return None + else: return ct.c_void_p(A.data.storage().data_ptr()) + +def estimate_quantiles(A: Tensor, out: Tensor=None, offset: float=1/512) -> Tensor: + ''' + Estimates 256 equidistant quantiles on the input tensor eCDF. + + Uses SRAM-Quantiles algorithm to quickly estimate 256 equidistant quantiles + via the eCDF of the input tensor `A`. This is a fast but approximate algorithm + and the extreme quantiles close to 0 and 1 have high variance / large estimation + errors. These large errors can be avoided by using the offset variable which trims + the distribution. The default offset value of 1/512 ensures minimum entropy encoding -- it + trims 1/512 = 0.2% from each side of the distrivution. An offset value of 0.01 to 0.02 + usually has a much lower error but is not a minimum entropy encoding. Given an offset + of 0.02 equidistance points in the range [0.02, 0.98] are used for the quantiles. + + Parameters + ---------- + A : torch.Tensor + The input tensor. Any shape. + out : torch.Tensor + Tensor with the 256 estimated quantiles. + offset : float + The offset for the first and last quantile from 0 and 1. Default: 1/512 + + Returns + ------- + torch.Tensor: + The 256 quantiles in float32 datatype. + ''' + if out is None: out = torch.zeros((256,), dtype=torch.float32, device=A.device) + if A.dtype == torch.float32: + lib.cestimate_quantiles_fp32(get_ptr(A), get_ptr(out), ct.c_float(offset), ct.c_int(A.numel())) + 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}') + return out + +def quantize_blockwise(A: Tensor, code: Tensor=None, absmax: Tensor=None, rand=None, out: Tensor=None) -> Tensor: + ''' + Quantize tensor A in blocks of size 4096 values. + + Quantizes tensor A by dividing it into blocks of 4096 values. + Then the absolute maximum value within these blocks is calculated + for the non-linear quantization. + + Parameters + ---------- + A : torch.Tensor + The input tensor. + code : torch.Tensor + The quantization map. + absmax : torch.Tensor + The absmax values. + rand : torch.Tensor + The tensor for stochastic rounding. + out : torch.Tensor + The output tensor (8-bit). + + Returns + ------- + torch.Tensor: + The 8-bit tensor. + tuple(torch.Tensor, torch.Tensor): + The quantization state to undo the quantization. + ''' + + if code is None: + if 'dynamic' not in name2qmap: name2qmap['dynamic'] = create_dynamic_map().to(A.device) + code = name2qmap['dynamic'] + code = code.to(A.device) + + if absmax is None: + n = A.numel() + num_blocks = 4096 + blocks = n//num_blocks + blocks += 1 if n % num_blocks > 0 else 0 + absmax = torch.zeros((blocks,), device=A.device) + + if out is None: out = torch.zeros_like(A, dtype=torch.uint8) + + + if A.device.type != 'cpu': + if rand is not None: + assert rand.numel() >= 1024 + rand_offset = random.randint(0, 1023) + if A.dtype == torch.float32: + lib.cquantize_blockwise_stochastic_fp32(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out), get_ptr(rand), ct.c_int32(rand_offset), ct.c_int(A.numel())) + elif A.dtype == torch.float16: + lib.cquantize_blockwise_stochastic_fp16(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out), get_ptr(rand), ct.c_int32(rand_offset), ct.c_int(A.numel())) + else: + raise ValueError(f'Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}') + else: + if A.dtype == torch.float32: + lib.cquantize_blockwise_fp32(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(A.numel())) + elif A.dtype == torch.float16: + lib.cquantize_blockwise_fp16(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(A.numel())) + else: + raise ValueError(f'Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}') + else: + # cpu + assert rand is None + lib.cquantize_blockwise_cpu_fp32(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(A.numel())) + + return out, (absmax, code) + +def dequantize_blockwise(A: Tensor, quant_state: Tuple[Tensor, Tensor]=None, + absmax: Tensor=None, code: Tensor=None, out: Tensor=None, + blocksize: int=4096) -> Tensor: + ''' + Dequantizes blockwise quantized values. + + Dequantizes the tensor A with maximum absolute values absmax in + blocks of size 4096. + + Parameters + ---------- + A : torch.Tensor + The input 8-bit tensor. + quant_state : tuple(torch.Tensor, torch.Tensor) + Tuple of code and absmax values. + absmax : torch.Tensor + The absmax values. + code : torch.Tensor + The quantization map. + out : torch.Tensor + Dequantized output tensor (default: float32) + + + Returns + ------- + torch.Tensor: + Dequantized tensor (default: float32) + ''' + assert quant_state is not None or absmax is not None + if code is None and quant_state is None: + if 'dynamic' not in name2qmap: name2qmap['dynamic'] = create_dynamic_map().to(A.device) + code = name2qmap['dynamic'] + code = code.to(A.device) + + if out is None: out = torch.zeros_like(A, dtype=torch.float32) + if quant_state is None: quant_state = (absmax, code) + + if blocksize not in [2048, 4096]: + raise ValueError(f'The blockwise of {blocksize} is not supported. Supported values: [2048 4096]') + + if A.device.type != 'cpu': + if out.dtype == torch.float32: + lib.cdequantize_blockwise_fp32(get_ptr(quant_state[1]), get_ptr(A), get_ptr(quant_state[0]), get_ptr(out), ct.c_int(blocksize), ct.c_int(A.numel())) + elif out.dtype == torch.float16: + lib.cdequantize_blockwise_fp16(get_ptr(quant_state[1]), get_ptr(A), get_ptr(quant_state[0]), get_ptr(out), ct.c_int(blocksize), ct.c_int(A.numel())) + else: + raise ValueError(f'Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}') + else: + lib.cdequantize_blockwise_cpu_fp32(get_ptr(quant_state[1]), get_ptr(A), get_ptr(quant_state[0]), get_ptr(out), ct.c_int(A.numel())) + + + return out + + +def quantize(A: Tensor, code: Tensor=None, out: Tensor=None) -> Tensor: + if code is None: + if 'dynamic' not in name2qmap: name2qmap['dynamic'] = create_dynamic_map().to(A.device) + code = name2qmap['dynamic'] + code = code.to(A.device) + + absmax = torch.abs(A).max() + inp = A/absmax + out = quantize_no_absmax(inp, code, out) + return out, (absmax, code) + +def dequantize(A: Tensor, quant_state: Tuple[Tensor, Tensor]=None, absmax: Tensor=None, code: Tensor=None, out: Tensor=None) -> Tensor: + assert quant_state is not None or absmax is not None + if code is None and quant_state is None: + if 'dynamic' not in name2qmap: name2qmap['dynamic'] = create_dynamic_map().to(A.device) + code = name2qmap['dynamic'] + code = code.to(A.device) + + if quant_state is None: quant_state = (absmax, code) + out = dequantize_no_absmax(A, quant_state[1], out) + return out*quant_state[0] + +def quantize_no_absmax(A: Tensor, code: Tensor, out: Tensor=None) -> Tensor: + ''' + Quantizes input tensor to 8-bit. + + Quantizes the 32-bit input tensor `A` to the 8-bit output tensor + `out` using the quantization map `code`. + + Parameters + ---------- + A : torch.Tensor + The input tensor. + code : torch.Tensor + The quantization map. + out : torch.Tensor, optional + The output tensor. Needs to be of type byte. + + Returns + ------- + torch.Tensor: + Quantized 8-bit tensor. + ''' + if out is None: out = torch.zeros_like(A, dtype=torch.uint8) + lib.cquantize(get_ptr(code), get_ptr(A), get_ptr(out), ct.c_int(A.numel())) + return out + +def dequantize_no_absmax(A: Tensor, code: Tensor, out: Tensor=None) -> Tensor: + ''' + Dequantizes the 8-bit tensor to 32-bit. + + Dequantizes the 8-bit tensor `A` to the 32-bit tensor `out` via + the quantization map `code`. + + Parameters + ---------- + A : torch.Tensor + The 8-bit input tensor. + code : torch.Tensor + The quantization map. + out : torch.Tensor + The 32-bit output tensor. + + Returns + ------- + torch.Tensor: + 32-bit output tensor. + ''' + if out is None: out = torch.zeros_like(A, dtype=torch.float32) + lib.cdequantize(get_ptr(code), get_ptr(A), get_ptr(out), ct.c_int(A.numel())) + return out + +def optimizer_update_32bit(optimizer_name:str, g: Tensor, p: Tensor, state1: Tensor, + beta1: float, eps: float, step: int, lr: float, + state2: Tensor=None, beta2: float=0.0, + weight_decay: float=0.0, gnorm_scale: float=1.0, + unorm_vec: Tensor=None, max_unorm: float=0.0) -> None: + ''' + Performs an inplace optimizer update with one or two optimizer states. + + Universal optimizer update for 32-bit state and 32/16-bit gradients/weights. + + Parameters + ---------- + optimizer_name : str + The name of the optimizer: {adam}. + g : torch.Tensor + Gradient tensor. + p : torch.Tensor + Parameter tensor. + state1 : torch.Tensor + Optimizer state 1. + beta1 : float + Optimizer beta1. + eps : float + Optimizer epsilon. + weight_decay : float + Weight decay. + step : int + Current optimizer step. + lr : float + The learning rate. + state2 : torch.Tensor + Optimizer state 2. + beta2 : float + Optimizer beta2. + gnorm_scale : float + The factor to rescale the gradient to the max clip value. + ''' + + param_norm = 0.0 + if max_unorm > 0.0: + 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())}') + + 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), + ct.c_float(param_norm), ct.c_float(beta1), ct.c_float(beta2), ct.c_float(eps), ct.c_float(weight_decay), + ct.c_int32(step), ct.c_float(lr), ct.c_float(gnorm_scale), ct.c_int32(g.numel())) + elif g.dtype == torch.float16 and state1.dtype == torch.float32: + str2optimizer32bit[optimizer_name][1](get_ptr(g), get_ptr(p), get_ptr(state1), get_ptr(state2), get_ptr(unorm_vec), ct.c_float(max_unorm), + ct.c_float(param_norm), ct.c_float(beta1), ct.c_float(beta2), ct.c_float(eps), ct.c_float(weight_decay), + ct.c_int32(step), ct.c_float(lr), ct.c_float(gnorm_scale), ct.c_int32(g.numel())) + else: + raise ValueError(f'Gradient+optimizer bit data type combination not supported: grad {g.dtype}, optimizer {state1.dtype}') + +def optimizer_update_8bit(optimizer_name: str, g: Tensor, p: Tensor, state1: Tensor, state2: Tensor, + beta1: float, beta2: float, eps: float, + step: int, lr: float, qmap1: Tensor, qmap2: Tensor, + max1: Tensor, max2: Tensor, new_max1: Tensor, new_max2: Tensor, + weight_decay: float=0.0, gnorm_scale: float=1.0, + unorm_vec: Tensor=None, max_unorm: float=0.0) -> None: + ''' + Performs an inplace Adam update. + + Universal Adam update for 32/8-bit state and 32/16-bit gradients/weights. + Uses AdamW formulation if weight decay > 0.0. + + Parameters + ---------- + optimizer_name : str + The name of the optimizer. Choices {adam, momentum} + g : torch.Tensor + Gradient tensor. + p : torch.Tensor + Parameter tensor. + state1 : torch.Tensor + Adam state 1. + state2 : torch.Tensor + Adam state 2. + beta1 : float + Adam beta1. + beta2 : float + Adam beta2. + eps : float + Adam epsilon. + weight_decay : float + Weight decay. + step : int + Current optimizer step. + lr : float + The learning rate. + qmap1 : torch.Tensor + Quantization map for first Adam state. + qmap2 : torch.Tensor + Quantization map for second Adam state. + max1 : torch.Tensor + Max value for first Adam state update. + max2 : torch.Tensor + Max value for second Adam state update. + new_max1 : torch.Tensor + Max value for the next Adam update of the first state. + new_max2 : torch.Tensor + Max value for the next Adam update of the second state. + gnorm_scale : float + The factor to rescale the gradient to the max clip value. + ''' + + param_norm = 0.0 + if max_unorm > 0.0: + param_norm = torch.norm(p.data.float()) + + if g.dtype == torch.float32 and state1.dtype == torch.uint8: + str2optimizer8bit[optimizer_name][0](get_ptr(p), get_ptr(g), get_ptr(state1), get_ptr(state2), + get_ptr(unorm_vec), ct.c_float(max_unorm), ct.c_float(param_norm), + ct.c_float(beta1), ct.c_float(beta2), ct.c_float(eps), + ct.c_int32(step), ct.c_float(lr), + get_ptr(qmap1), get_ptr(qmap2), + get_ptr(max1), get_ptr(max2), get_ptr(new_max1), get_ptr(new_max2), + ct.c_float(weight_decay),ct.c_float(gnorm_scale), ct.c_int32(g.numel())) + elif g.dtype == torch.float16 and state1.dtype == torch.uint8: + str2optimizer8bit[optimizer_name][1](get_ptr(p), get_ptr(g), get_ptr(state1), get_ptr(state2), + get_ptr(unorm_vec), ct.c_float(max_unorm), ct.c_float(param_norm), + ct.c_float(beta1), ct.c_float(beta2), ct.c_float(eps), + ct.c_int32(step), ct.c_float(lr), + get_ptr(qmap1), get_ptr(qmap2), + get_ptr(max1), get_ptr(max2), get_ptr(new_max1), get_ptr(new_max2), + ct.c_float(weight_decay),ct.c_float(gnorm_scale), ct.c_int32(g.numel())) + else: + raise ValueError(f'Gradient+optimizer bit data type combination not supported: grad {g.dtype}, optimizer {state1.dtype}') + + +def optimizer_update_8bit_blockwise(optimizer_name: str, g: Tensor, p: Tensor, state1: Tensor, state2: Tensor, + beta1: float, beta2: float, eps: float, + step: int, lr: float, qmap1: Tensor, qmap2: Tensor, + absmax1: Tensor, absmax2: Tensor, weight_decay: float=0.0, gnorm_scale: float=1.0) -> None: + + + if g.dtype == torch.float32 and state1.dtype == torch.uint8: + str2optimizer8bit_blockwise[optimizer_name][0](get_ptr(p), get_ptr(g), get_ptr(state1), get_ptr(state2), + ct.c_float(beta1), ct.c_float(beta2), ct.c_float(eps), + ct.c_int32(step), ct.c_float(lr), get_ptr(qmap1), get_ptr(qmap2), + get_ptr(absmax1), get_ptr(absmax2), ct.c_float(weight_decay), ct.c_float(gnorm_scale), ct.c_int32(g.numel())) + elif g.dtype == torch.float16 and state1.dtype == torch.uint8: + str2optimizer8bit_blockwise[optimizer_name][1](get_ptr(p), get_ptr(g), get_ptr(state1), get_ptr(state2), + ct.c_float(beta1), ct.c_float(beta2), ct.c_float(eps), + ct.c_int32(step), ct.c_float(lr), get_ptr(qmap1), get_ptr(qmap2), + get_ptr(absmax1), get_ptr(absmax2), ct.c_float(weight_decay), ct.c_float(gnorm_scale), ct.c_int32(g.numel())) + else: + raise ValueError(f'Gradient+optimizer bit data type combination not supported: grad {g.dtype}, optimizer {state1.dtype}') + + +def percentile_clipping(grad: Tensor, gnorm_vec: Tensor, step: int, percentile: int=5): + """Applies percentile clipping + + grad: torch.Tensor + The gradient tensor. + gnorm_vec: torch.Tensor + Vector of gradient norms. 100 elements expected. + step: int + The current optimiation steps (number of past gradient norms). + + """ + if grad.dtype == torch.float32: + lib.cpercentile_clipping_g32(get_ptr(grad), get_ptr(gnorm_vec), ct.c_int32(step), ct.c_int32(grad.numel())) + elif grad.dtype == torch.float16: + lib.cpercentile_clipping_g16(get_ptr(grad), get_ptr(gnorm_vec), ct.c_int32(step), ct.c_int32(grad.numel())) + else: + raise ValueError(f'Gradient type {grad.dtype} not supported!') + + current_gnorm = torch.sqrt(gnorm_vec[step % 100]) + vals, idx = torch.sort(gnorm_vec) + clip_value = torch.sqrt(vals[percentile]) + gnorm_scale = 1.0 + + if current_gnorm > clip_value: + gnorm_scale = clip_value/current_gnorm + + return current_gnorm, clip_value, gnorm_scale + + +def histogram_scatter_add_2d(histogram: Tensor, index1: Tensor, index2: Tensor, source: Tensor): + assert len(histogram.shape) == 2 + assert histogram.dtype == torch.float32 + assert source.dtype == torch.float32 + assert index1.dtype == torch.int32 + assert index2.dtype == torch.int32 + + assert histogram.device.type == 'cuda' + assert index1.device.type == 'cuda' + assert index2.device.type == 'cuda' + assert source.device.type == 'cuda' + + maxdim1 = ct.c_int32(histogram.shape[0]) + n = ct.c_int32(index1.numel()) + lib.chistogram_scatter_add_2d(get_ptr(histogram), get_ptr(index1), get_ptr(index2), get_ptr(source), maxdim1, n) -- cgit v1.2.3