# 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 ctypes as ct from functools import reduce # Required in Python 3 import itertools import operator from typing import Any, Dict, Optional, Tuple import numpy as np import torch from torch import Tensor from bitsandbytes.utils import pack_dict_to_tensor, unpack_tensor_to_dict from .cextension import COMPILED_WITH_CUDA, lib # math.prod not compatible with python < 3.8 def prod(iterable): return reduce(operator.mul, iterable, 1) name2qmap = {} if COMPILED_WITH_CUDA: """C FUNCTIONS FOR OPTIMIZERS""" str2optimizer32bit = { "adam": ( lib.cadam32bit_grad_fp32, lib.cadam32bit_grad_fp16, lib.cadam32bit_grad_bf16, ), "momentum": ( lib.cmomentum32bit_grad_32, lib.cmomentum32bit_grad_16, ), "rmsprop": ( lib.crmsprop32bit_grad_32, lib.crmsprop32bit_grad_16, ), "lion": ( lib.clion32bit_grad_fp32, lib.clion32bit_grad_fp16, lib.clion32bit_grad_bf16, ), "adagrad": ( lib.cadagrad32bit_grad_32, lib.cadagrad32bit_grad_16, ), } str2optimizer8bit = { "adam": ( lib.cadam_static_8bit_grad_32, lib.cadam_static_8bit_grad_16, ), "momentum": ( lib.cmomentum_static_8bit_grad_32, lib.cmomentum_static_8bit_grad_16, ), "rmsprop": ( lib.crmsprop_static_8bit_grad_32, lib.crmsprop_static_8bit_grad_16, ), "lion": ( lib.clion_static_8bit_grad_32, lib.clion_static_8bit_grad_16, ), "lamb": ( lib.cadam_static_8bit_grad_32, lib.cadam_static_8bit_grad_16, ), "lars": ( lib.cmomentum_static_8bit_grad_32, lib.cmomentum_static_8bit_grad_16, ), } str2optimizer8bit_blockwise = { "adam": ( lib.cadam_8bit_blockwise_grad_fp32, lib.cadam_8bit_blockwise_grad_fp16, lib.cadam_8bit_blockwise_grad_bf16, ), "momentum": ( lib.cmomentum_8bit_blockwise_grad_fp32, lib.cmomentum_8bit_blockwise_grad_fp16, ), "rmsprop": ( lib.crmsprop_8bit_blockwise_grad_fp32, lib.crmsprop_8bit_blockwise_grad_fp16, ), "lion": ( lib.clion_8bit_blockwise_grad_fp32, lib.clion_8bit_blockwise_grad_fp16, lib.clion_8bit_blockwise_grad_bf16, ), "adagrad": ( lib.cadagrad_8bit_blockwise_grad_fp32, lib.cadagrad_8bit_blockwise_grad_fp16, ), } class GlobalPageManager: _instance = None def __init__(self): raise RuntimeError("Call get_instance() instead") def initialize(self): self.paged_tensors = [] @classmethod def get_instance(cls): if cls._instance is None: cls._instance = cls.__new__(cls) cls._instance.initialize() return cls._instance def prefetch_all(self, to_cpu=False): # assume the first added, will be the # ones that are used first, so swap them in last # in the case they are evicted again for t in self.paged_tensors[::-1]: prefetch_tensor(t, to_cpu) class CUBLAS_Context: _instance = None def __init__(self): raise RuntimeError("Call get_instance() instead") def initialize(self): self.context = {} @classmethod def get_instance(cls): if cls._instance is None: cls._instance = cls.__new__(cls) cls._instance.initialize() return cls._instance def get_context(self, device): if device.index not in self.context: prev_device = torch.cuda.current_device() torch.cuda.set_device(device) self.context[device.index] = ct.c_void_p(lib.get_context()) torch.cuda.set_device(prev_device) return self.context[device.index] class Cusparse_Context: _instance = None def __init__(self): raise RuntimeError("Call get_instance() instead") def initialize(self): self.context = ct.c_void_p(lib.get_cusparse()) @classmethod def get_instance(cls): if cls._instance is None: cls._instance = cls.__new__(cls) cls._instance.initialize() return cls._instance dtype2bytes = {} dtype2bytes[torch.float32] = 4 dtype2bytes[torch.float16] = 2 dtype2bytes[torch.bfloat16] = 2 dtype2bytes[torch.uint8] = 1 dtype2bytes[torch.int8] = 1 FIRST_CUDA_DEVICE = torch.device('cuda', index=0) def get_paged(*shape, dtype=torch.float32, device=FIRST_CUDA_DEVICE): num_bytes = dtype2bytes[dtype]*prod(shape) cuda_ptr = lib.cget_managed_ptr(ct.c_size_t(num_bytes)) c_ptr = ct.cast(cuda_ptr, ct.POINTER(ct.c_int)) new_array = np.ctypeslib.as_array(c_ptr, shape=shape) out = torch.frombuffer(new_array, dtype=dtype, count=prod(shape)).view(shape) out.is_paged = True out.page_deviceid = device.index return out def prefetch_tensor(A, to_cpu=False): assert A.is_paged, 'Only paged tensors can be prefetched!' if to_cpu: deviceid = -1 else: deviceid = A.page_deviceid num_bytes = dtype2bytes[A.dtype]*A.numel() lib.cprefetch(get_ptr(A), ct.c_size_t(num_bytes), ct.c_int32(deviceid)) def elementwise_func(func_name, A, B, value, prefetch=True): func = None if A.dtype == torch.float32: func = getattr(lib, f'c{func_name}_fp32', None) cvalue = ct.c_float(value) elif A.dtype == torch.uint8: func = getattr(lib, f'c{func_name}_uint8', None) cvalue = ct.c_uint8(value) if func is None: raise NotImplementedError(f'Function not implemented: {func_name}') is_managed = getattr(A, 'is_managed', False) if is_managed and prefetch: prefetch_tensor(A) if B is not None: prefetch_tensor(B) func(get_ptr(A), get_ptr(B), cvalue, ct.c_int64(A.numel())) if A.is_paged or B.is_paged: # paged function are fully asynchronous # if we return from this function, we want to the tensor # to be in the correct state, that is the final state after the # operation occurred. So we synchronize. torch.cuda.synchronize() def fill(A, value, device=None, prefetch=True): elementwise_func('fill', A, None, value) def arange(A, device=None): elementwise_func('arange', A, None, 0) def _mul(A, B, device=None): elementwise_func('_mul', A, B, 0) def create_linear_map(signed=True, total_bits=8, add_zero=True): sign = (-1.0 if signed else 0.0) total_values = 2**total_bits if add_zero or total_bits < 8: # add a zero # since we simulate less bits by having zeros in the data type, we # we need to center the quantization around zero and as such lose # a single value total_values = (2**total_bits if not signed else 2**total_bits-1) values = torch.linspace(sign, 1.0, total_values) gap = 256 - values.numel() if gap == 0: return values else: l = values.numel()//2 # noqa: E741 return torch.Tensor(values[:l].tolist() + [0]*gap + values[l:].tolist()) def create_normal_map(offset=0.9677083, use_extra_value=True): try: from scipy.stats import norm except ImportError as ie: raise ImportError( "Scipy is required for `create_normal_map`. " "Install `bitsandbytes` with the `[test]` extra." ) from ie if use_extra_value: # one more positive value, this is an asymmetric type v1 = norm.ppf(torch.linspace(offset, 0.5, 9)[:-1]).tolist() v2 = [0]*(256-15) ## we have 15 non-zero values in this data type v3 = (-norm.ppf(torch.linspace(offset, 0.5, 8)[:-1])).tolist() else: v1 = norm.ppf(torch.linspace(offset, 0.5, 8)[:-1]).tolist() v2 = [0]*(256-14) ## we have 14 non-zero values in this data type v3 = (-norm.ppf(torch.linspace(offset, 0.5, 8)[:-1])).tolist() v = v1 + v2 + v3 values = torch.Tensor(v) values = values.sort().values values /= values.max() assert values.numel() == 256 return values def create_fp8_map(signed=True, exponent_bits=5, precision_bits=2, total_bits=8): e = exponent_bits p = precision_bits has_sign = 1 if signed else 0 assert e+p == total_bits-has_sign # the exponent is biased to 2^(e-1) -1 == 0 evalues = [] pvalues = [] for i, val in enumerate(range(-(2**(exponent_bits-has_sign)), 2**(exponent_bits-has_sign), 1)): evalues.append(2**val) values = [] lst = list(itertools.product([0, 1], repeat=precision_bits)) #for ev in evalues: bias = 2**(exponent_bits-1) for evalue in range(2**(exponent_bits)): for bit_pattern in lst: value = (1 if evalue != 0 else 0) for i, pval in enumerate(list(bit_pattern)): value += pval*(2**-(i+1)) if evalue == 0: # subnormals value = value*2**-(bias) else: # normals value = value*2**-(evalue-bias-1) values.append(value) if signed: values.append(-value) assert len(values) == 2**total_bits values.sort() if total_bits < 8: gap = 256 - len(values) for i in range(gap): values.append(0) values.sort() code = torch.Tensor(values) code /= code.max() return code def create_dynamic_map(signed=True, max_exponent_bits=7, total_bits=8): """ 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 non_sign_bits = total_bits - (1 if signed else 1) additional_items = 2 ** (non_sign_bits - max_exponent_bits) - 1 for i in range(max_exponent_bits): fraction_items = int(2 ** (i + non_sign_bits - max_exponent_bits) + 1 if signed else 2 ** (i + non_sign_bits - max_exponent_bits + 1) + 1) boundaries = torch.linspace(0.1, 1, fraction_items) means = (boundaries[:-1] + boundaries[1:]) / 2.0 data += ((10 ** (-(max_exponent_bits - 1) + i)) * means).tolist() if signed: data += (-(10 ** (-(max_exponent_bits - 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 ** (-(max_exponent_bits - 1) + i)) * means).tolist() if signed: data += (-(10 ** (-(max_exponent_bits - 1) + i)) * means).tolist() data.append(0) data.append(1.0) assert len(data) == 2**total_bits gap = 256 - len(data) for i in range(gap): data.append(0) data.sort() return Tensor(data) def create_quantile_map(A, total_bits=8): q = estimate_quantiles(A, num_quantiles=2**total_bits-1) q = q.tolist() q.append(0) gap = 256 - len(q) for i in range(gap): q.append(0) q.sort() q = Tensor(q) q = q/q.abs().max() return q def get_special_format_str(): if not torch.cuda.is_available(): return 'col_turing' major, _minor = torch.cuda.get_device_capability() if major <= 7: return "col_turing" if major == 8: return "col_ampere" return "col_turing" def is_on_gpu(tensors): on_gpu = True gpu_ids = set() for t in tensors: if t is None: continue # NULL pointers are fine is_paged = getattr(t, 'is_paged', False) on_gpu &= (t.device.type == 'cuda' or is_paged) if not is_paged: gpu_ids.add(t.device.index) if not on_gpu: raise TypeError(f'All input tensors need to be on the same GPU, but found some tensors to not be on a GPU:\n {[(t.shape, t.device) for t in tensors]}') if len(gpu_ids) > 1: raise TypeError(f'Input tensors need to be on the same GPU, but found the following tensor and device combinations:\n {[(t.shape, t.device) for t in tensors]}') return on_gpu def get_ptr(A: Optional[Tensor]) -> Optional[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.data_ptr()) def pre_call(device): prev_device = torch.cuda.current_device() torch.cuda.set_device(device) return prev_device def post_call(prev_device): torch.cuda.set_device(prev_device) def get_transform_func(dtype, orderA, orderOut, transpose=False): name = f'ctransform_{(8 if dtype == torch.int8 else 32)}_{orderA}_to_{orderOut}_{"t" if transpose else "n"}' if not hasattr(lib, name): print(name) raise ValueError( f"Transform function not supported: {orderA} to {orderOut} for data type {dtype} and transpose={transpose}" ) else: return getattr(lib, name) def get_transform_buffer( shape, dtype, device, to_order, from_order="row", transpose=False ): # init_func = torch.empty init_func = torch.zeros dims = len(shape) if dims == 2: rows = shape[0] elif dims == 3: rows = shape[0] * shape[1] cols = shape[-1] state = (shape, to_order) if transpose: # swap dims tmp = rows rows = cols cols = tmp state = (shape[::-1], to_order) if to_order == "row" or to_order == "col": return init_func(shape, dtype=dtype, device=device), state elif to_order == "col32": # blocks of 32 columns (padded) cols = 32 * ((cols + 31) // 32) return init_func((rows, cols), dtype=dtype, device=device), state elif to_order == "col_turing": # blocks of 32 columns and 8 rows cols = 32 * ((cols + 31) // 32) rows = 8 * ((rows + 7) // 8) return init_func((rows, cols), dtype=dtype, device=device), state elif to_order == "col_ampere": # blocks of 32 columns and 32 rows cols = 32 * ((cols + 31) // 32) rows = 32 * ((rows + 31) // 32) return init_func((rows, cols), dtype=dtype, device=device), state else: raise NotImplementedError(f"To_order not supported: {to_order}") def nvidia_transform( A, to_order, from_order="row", out=None, transpose=False, state=None, ld=None, ): if state is None: state = (A.shape, from_order) else: from_order = state[1] if out is None: out, new_state = get_transform_buffer( state[0], A.dtype, A.device, to_order, state[1] ) else: new_state = (state[1], to_order) func = get_transform_func(A.dtype, from_order, to_order, transpose) shape = state[0] if len(shape) == 2: dim1 = ct.c_int32(shape[0]) dim2 = ct.c_int32(shape[1]) elif ld is not None: n = prod(shape) dim1 = prod([shape[i] for i in ld]) dim2 = ct.c_int32(n // dim1) dim1 = ct.c_int32(dim1) else: dim1 = ct.c_int32(shape[0] * shape[1]) dim2 = ct.c_int32(shape[2]) ptr = CUBLAS_Context.get_instance().get_context(A.device) func(ptr, get_ptr(A), get_ptr(out), dim1, dim2) return out, new_state def estimate_quantiles(A: Tensor, out: Optional[torch.Tensor] = None, offset: float = 1 / 512, num_quantiles=256) -> 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/(2*num_quantiles) num_quantiles : int The number of equally spaced quantiles. Returns ------- torch.Tensor: The 256 quantiles in float32 datatype. ''' if A.numel() < 256: raise NotImplementedError(f'Quantile estimation needs at least 256 values in the Tensor, but Tensor had only {A.numel()} values.') if num_quantiles > 256: raise NotImplementedError(f"Currently only a maximum of 256 equally spaced quantiles are supported, but the argument num_quantiles={num_quantiles}") if num_quantiles < 256 and offset == 1/(512): # override default arguments offset = 1/(2*num_quantiles) if out is None: out = torch.zeros((256,), dtype=torch.float32, device=A.device) is_on_gpu([A, out]) device = pre_call(A.device) if A.dtype == torch.float32: lib.cestimate_quantiles_fp32(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 NotImplementedError(f"Not supported data type {A.dtype}") post_call(device) if num_quantiles < 256: step = round(256/num_quantiles) idx = torch.linspace(0, 255, num_quantiles).long().to(A.device) out = out[idx] return out class QuantState: """container for quantization state components to work with Params4bit and similar classes""" valid_quant_types = ('fp4', 'nf4') valid_qs_type_keys = [f"bitsandbytes__{x}" for x in valid_quant_types] valid_qs_keys = ['absmax', 'quant_map', 'nested_absmax', 'nested_quant_map', 'quant_state', 'quant_type', 'blocksize', 'dtype', 'shape', 'nested_blocksize', 'nested_dtype', 'nested_offset'] def __init__(self, absmax, shape=None, code=None, blocksize=None, quant_type=None, dtype=None, offset=None, state2=None): self.absmax = absmax self.shape = shape self.code = code self.dtype = dtype self.blocksize = blocksize self.quant_type = quant_type self.offset = offset self.state2 = state2 self.nested = state2 is not None def __get_item__(self, idx): """ ensures compatibility with older quant state scheme with nested lists. assumes the following layout: state = [qabsmax, input_shape, A.dtype, blocksize, [offset, state2], quant_type] state2 = [absmax, input_shape, A.dtype, blocksize, None, quant_type] """ if self.nested: list_repr = [self.absmax, self.shape, self.dtype, self.blocksize, [self.offset, self.state2], self.quant_type] else: list_repr = [self.absmax, self.shape, self.dtype, self.blocksize, None, self.quant_type] return list_repr[idx] @classmethod def from_dict(cls, qs_dict: Dict[str, Any], device: torch.device) -> 'QuantState': """ unpacks components of state_dict into QuantState where necessary, convert into strings, torch.dtype, ints, etc. qs_dict: based on state_dict, with only relevant keys, striped of prefixes. item with key `quant_state.bitsandbytes__[nf4/fp4]` may contain minor and non-tensor quant state items. """ # unpacking tensor with non-tensor components qs_key = [k for k, v in qs_dict.items() if "quant_state" in k and isinstance(v, torch.Tensor)] if not len(qs_key) and 'quant_type' not in qs_dict: raise ValueError("Expected packed or unpacked quant_state items, found neither") elif len(qs_key) != 1 or qs_key[0].split(".")[-1] not in cls.valid_qs_type_keys: raise ValueError(f"There should be exactly one `quant_state` item with ending from {cls.valid_qs_type_keys}.\nDetected {qs_key}.") # unpacking minor and non-tensor quant state items if necessary if len(qs_key) == 1: first_qs_key = qs_key[0] qs_dict.update(unpack_tensor_to_dict(qs_dict.pop(first_qs_key))) qs_dict = {k.split('.')[-1]: v for k, v in qs_dict.items()} # strip prefixes assert set(qs_dict.keys()).issubset(cls.valid_qs_keys) if 'nested_absmax' in qs_dict: offset = torch.tensor(float(qs_dict['nested_offset'])).to(device) state2 = cls( absmax=qs_dict['nested_absmax'].to(device), blocksize=qs_dict['nested_blocksize'], code=qs_dict['nested_quant_map'].to(device), dtype=getattr(torch, qs_dict['nested_dtype']), ) else: offset, state2 = None, None quant_state = cls( quant_type=qs_dict['quant_type'], absmax=qs_dict['absmax'].to(device), blocksize=qs_dict['blocksize'], code=qs_dict['quant_map'].to(device), dtype=getattr(torch, qs_dict['dtype']), shape=torch.Size(qs_dict['shape']) if qs_dict['shape'] is not None else None, offset=offset, state2=state2, ) return quant_state def as_dict(self, packed=False): """ returns dict of tensors and strings to use in serialization via _save_to_state_dict() param: packed -- returns dict[str, torch.Tensor] for state_dict fit for safetensors saving """ qs_dict = { 'quant_type': self.quant_type, 'absmax': self.absmax, 'blocksize': self.blocksize, 'quant_map': self.code, 'dtype': str(self.dtype).strip('torch.'), 'shape': tuple(self.shape), } if self.nested: qs_dict.update({ 'nested_absmax': self.state2.absmax, 'nested_blocksize': self.state2.blocksize, 'nested_quant_map': self.state2.code.clone(), # un-shared to avoid restoring it after shared tensors are removed by safetensors 'nested_dtype': str(self.state2.dtype).strip('torch.'), 'nested_offset': self.offset.item(), }) if not packed: return qs_dict # packed format allows serialization of non-tensor components, critical for saving in safetensors format qs_packed_dict = {k: v for k, v in qs_dict.items() if isinstance(v, torch.Tensor)} non_tensor_dict = {k: v for k, v in qs_dict.items() if not isinstance(v, torch.Tensor)} qs_packed_dict["quant_state." + "bitsandbytes__" + self.quant_type] = pack_dict_to_tensor(non_tensor_dict) return qs_packed_dict def to(self, device): # make sure the quantization state is on the right device self.absmax = self.absmax.to(device) if self.nested: self.offset = self.offset.to(device) self.state2.absmax = self.state2.absmax.to(device) self.state2.code = self.state2.code.to(device) def __eq__(self, other): if not isinstance(other, QuantState): return False return ( torch.allclose(self.absmax, other.absmax, atol=1e-6) and self.shape == other.shape and torch.allclose(self.code, other.code, atol=1e-6) and self.dtype == other.dtype and self.blocksize == other.blocksize and self.quant_type == other.quant_type and (self.offset == other.offset if self.offset is not None and other.offset is not None else self.offset is other.offset) and (self.state2 == other.state2 if self.state2 is not None and other.state2 is not None else self.state2 is other.state2) ) def quantize_blockwise( A: Tensor, code: Optional[torch.Tensor] = None, absmax: Optional[torch.Tensor] = None, out: Optional[torch.Tensor] = None, blocksize=4096, nested=False, ) -> Tuple[Tensor, QuantState]: """ 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. 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"] if absmax is None: n = A.numel() blocks = n // blocksize blocks += 1 if n % blocksize > 0 else 0 absmax = torch.zeros((blocks,), device=A.device, dtype=torch.float32) if out is None: out = torch.zeros_like(A, dtype=torch.uint8) if A.device.type != 'cpu': assert blocksize in [4096, 2048, 1024, 512, 256, 128, 64] cblocksize = ct.c_int32(blocksize) prev_device = pre_call(A.device) code = code.to(A.device) is_on_gpu([code, A, out, absmax]) if A.dtype == torch.float32: lib.cquantize_blockwise_fp32(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out), cblocksize, ct.c_int(A.numel())) elif A.dtype == torch.float16: lib.cquantize_blockwise_fp16(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out), cblocksize, ct.c_int(A.numel())) elif A.dtype == torch.bfloat16: lib.cquantize_blockwise_bf16(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out), cblocksize, ct.c_int(A.numel())) else: raise ValueError(f"Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}") post_call(A.device) else: # cpu code = code.cpu() lib.cquantize_blockwise_cpu_fp32(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_longlong(blocksize), ct.c_longlong(A.numel())) if nested: offset = absmax.mean() absmax -= offset qabsmax, state2 = quantize_blockwise(absmax, blocksize=blocksize, nested=False) quant_state = QuantState(absmax=qabsmax, code=code, blocksize=blocksize, dtype=A.dtype, offset=offset, state2=state2) else: quant_state = QuantState(absmax=absmax, code=code, blocksize=blocksize, dtype=A.dtype) return out, quant_state def dequantize_blockwise( A: Tensor, quant_state: Optional[QuantState] = None, absmax: Optional[torch.Tensor] = None, code: Optional[torch.Tensor] = None, out: Optional[torch.Tensor] = None, blocksize: int = 4096, nested=False ) -> 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 : QuantState Object with code, absmax and other quantization state components. 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"] if quant_state is None: quant_state = QuantState(absmax=absmax, code=code, blocksize=blocksize, dtype=torch.float32) absmax = quant_state.absmax if quant_state.nested: absmax = dequantize_blockwise(quant_state.absmax, quant_state.state2) absmax += quant_state.offset if absmax.dtype != torch.float32: absmax = absmax.float() if out is None: out = torch.empty(A.shape, dtype=quant_state.dtype, device=A.device) if A.device.type != 'cpu': device = pre_call(A.device) code = quant_state.code.to(A.device) if quant_state.blocksize not in [2048, 4096, 1024, 512, 256, 128, 64]: raise ValueError(f"The blockwise of {quant_state.blocksize} is not supported. Supported values: [2048, 4096, 1024, 512, 256, 128, 64]") is_on_gpu([A, absmax, out]) if out.dtype == torch.float32: lib.cdequantize_blockwise_fp32(get_ptr(quant_state.code), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(quant_state.blocksize), ct.c_int(A.numel())) elif out.dtype == torch.float16: lib.cdequantize_blockwise_fp16(get_ptr(quant_state.code), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(quant_state.blocksize), ct.c_int(A.numel())) elif out.dtype == torch.bfloat16: lib.cdequantize_blockwise_bf16(get_ptr(quant_state.code), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(quant_state.blocksize), ct.c_int(A.numel())) else: raise ValueError(f"Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}") post_call(A.device) else: code = quant_state.code.cpu() lib.cdequantize_blockwise_cpu_fp32(get_ptr(code), get_ptr(A), get_ptr(quant_state.absmax), get_ptr(out), ct.c_longlong(quant_state.blocksize), ct.c_longlong(A.numel())) return out def get_4bit_type(typename, device=None, blocksize=64): if device is None: device = 'cuda' data = None if typename == 'nf4': ''' Implements the NF4 data type. Constructs a quantization data type where each bin has equal area under a standard normal distribution N(0, 1) that is normalized into the range [-1, 1]. For more information read the paper: QLoRA: Efficient Finetuning of Quantized LLMs (https://arxiv.org/abs/2305.14314) Implementation of the NF4 data type in bitsandbytes can be found in the `create_normal_map` function in the `functional.py` file: https://github.com/TimDettmers/bitsandbytes/blob/main/bitsandbytes/functional.py#L236. ''' data = [-1.0, -0.6961928009986877, -0.5250730514526367, -0.39491748809814453, -0.28444138169288635, -0.18477343022823334, -0.09105003625154495, 0.0, 0.07958029955625534, 0.16093020141124725, 0.24611230194568634, 0.33791524171829224, 0.44070982933044434, 0.5626170039176941, 0.7229568362236023, 1.0] elif typename == 'fp4': # 0b000 = 0 # 0b001 = 0.0625 # 0b010 = 8 # 0b011 = 12 # 0b100 = 4 # 0b101 = 6 # 0b110 = 2 # 0b111 = 3 # can also be created with bnb.functional.create_fp8_map(signed=True, exponent_bits=2, precision_bits=1, total_bits=4) data = [0, 0.0625, 8.0, 12.0, 4.0, 6.0, 2.0, 3.0, -0, -0.0625, -8.0, -12.0, -4.0, -6.0, -2.0, -3.0] elif typename == 'int4': data = [7, 6, 5, 4, 3, 2, 1, 0, -0, -1, -2, -3, -4, -5, -6, -7] elif typename == 'af4': # Taken from: NF4 Isn't Information Theoretically Optimal (and that's Good) # https://arxiv.org/abs/2306.06965 if blocksize == 64: data = [-1., -0.69441008, -0.51243739, -0.3736951, -0.25607552, -0.14982478, -0.04934812, 0., 0.04273164, 0.12934483, 0.21961274, 0.31675666, 0.42563882, 0.55496234, 0.72424863, 1.][::-1] else: raise NotImplementedError('4-bit AbnormalFloats currently only support blocksize 64.') if data is None: raise NotImplementedError(f'Typename {typename} not supported') data = Tensor(data) data /= data.abs().max() assert data.numel() == 16 return data.to(device) def quantize_fp4(A: Tensor, absmax: Optional[torch.Tensor] = None, out: Optional[torch.Tensor] = None, blocksize=64, compress_statistics=False, quant_storage=torch.uint8): return quantize_4bit(A, absmax, out, blocksize, compress_statistics, 'fp4', quant_storage) def quantize_nf4(A: Tensor, absmax: Optional[torch.Tensor] = None, out: Optional[torch.Tensor] = None, blocksize=64, compress_statistics=False, quant_storage=torch.uint8): return quantize_4bit(A, absmax, out, blocksize, compress_statistics, 'nf4', quant_storage) def quantize_4bit( A: Tensor, absmax: Optional[torch.Tensor] = None, out: Optional[torch.Tensor] = None, blocksize=64, compress_statistics=False, quant_type='fp4', quant_storage=torch.uint8, ) -> Tuple[Tensor, QuantState]: """ Quantize tensor A in blocks of 4-bit values. Quantizes tensor A by dividing it into blocks which are independently quantized to FP4. Parameters ---------- A : torch.Tensor The input tensor. absmax : torch.Tensor The absmax values. out : torch.Tensor The output tensor. blocksize : int The blocksize used in quantization. quant_type : str The 4-bit quantization data type {fp4, nf4} Returns ------- torch.Tensor: Tensor with packed 4-bit values. tuple(torch.Tensor, torch.Size, torch.dtype, int): The quantization state to undo the quantization. """ if A.device.type != 'cuda': raise NotImplementedError(f'Device type not supported for FP4 quantization: {A.device.type}') if quant_type not in ['fp4', 'nf4']: raise NotImplementedError(f'4-bit quantization data type {quant_type} is not implemented.') n = A.numel() input_shape = A.shape if absmax is None: blocks = n // blocksize blocks += 1 if n % blocksize > 0 else 0 absmax = torch.zeros((blocks,), device=A.device, dtype=torch.float32) if out is None: mod = dtype2bytes[quant_storage] * 2 out = torch.zeros(((n+1)//mod, 1), dtype=quant_storage, device=A.device) assert blocksize in [4096, 2048, 1024, 512, 256, 128, 64] prev_device = pre_call(A.device) is_on_gpu([A, out, absmax]) if A.dtype == torch.float32: if quant_type == 'fp4': lib.cquantize_blockwise_fp32_fp4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int32(blocksize), ct.c_int(n)) else: lib.cquantize_blockwise_fp32_nf4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int32(blocksize), ct.c_int(n)) elif A.dtype == torch.float16: if quant_type == 'fp4': lib.cquantize_blockwise_fp16_fp4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int32(blocksize), ct.c_int(n)) else: lib.cquantize_blockwise_fp16_nf4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int32(blocksize), ct.c_int(n)) elif A.dtype == torch.bfloat16: if quant_type == 'fp4': lib.cquantize_blockwise_bf16_fp4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int32(blocksize), ct.c_int(n)) else: lib.cquantize_blockwise_bf16_nf4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int32(blocksize), ct.c_int(n)) else: raise ValueError(f"Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}") post_call(A.device) code = get_4bit_type(quant_type, device=A.device) if compress_statistics: offset = absmax.mean() absmax -= offset qabsmax, state2 = quantize_blockwise(absmax, blocksize=256) del absmax state = QuantState(absmax=qabsmax, shape=input_shape, dtype=A.dtype, blocksize=blocksize, code=code, quant_type=quant_type, offset=offset, state2=state2) else: state = QuantState(absmax=absmax, shape=input_shape, dtype=A.dtype, blocksize=blocksize, code=code, quant_type=quant_type, ) return out, state def dequantize_fp4(A: Tensor, quant_state: Optional[QuantState] = None, absmax: Optional[torch.Tensor] = None, out: Optional[torch.Tensor] = None, blocksize: int = 64) -> Tensor: return dequantize_4bit(A, quant_state, absmax, out, blocksize, 'fp4') def dequantize_nf4(A: Tensor, quant_state: Optional[QuantState] = None, absmax: Optional[torch.Tensor] = None, out: Optional[torch.Tensor] = None, blocksize: int = 64) -> Tensor: return dequantize_4bit(A, quant_state, absmax, out, blocksize, 'nf4') def dequantize_4bit(A: Tensor, quant_state: Optional[QuantState] = None, absmax: Optional[torch.Tensor] = None, out: Optional[torch.Tensor] = None, blocksize: int = 64, quant_type='fp4') -> Tensor: """ Dequantizes FP4 blockwise quantized values. Dequantizes the tensor A with maximum absolute values absmax in blocks of size blocksize. Parameters ---------- A : torch.Tensor The input tensor (packed 4-bit values). quant_state : QuantState object with quantisation stats, incl. absmax values, original tensor shape and original dtype. absmax : torch.Tensor The absmax values. out : torch.Tensor Dequantized output tensor. blocksize : int The blocksize used in quantization. quant_type : str The 4-bit quantization data type {fp4, nf4} Returns ------- torch.Tensor: Dequantized tensor. """ if blocksize not in [2048, 4096, 1024, 512, 256, 128, 64]: raise ValueError(f"The blockwise of {blocksize} is not supported. Supported values: [2048, 4096, 1024, 512, 256, 128, 64]") if quant_type not in ['fp4', 'nf4']: raise NotImplementedError(f'4-bit quantization data type {quant_type} is not implemented.') if quant_state is None: assert absmax is not None and out is not None quant_state = QuantState(absmax=absmax, shape=out.shape, dtype=out.dtype, blocksize=blocksize, quant_type=quant_type) else: absmax = quant_state.absmax if quant_state.nested: absmax = dequantize_blockwise(quant_state.absmax, quant_state.state2) absmax += quant_state.offset if absmax.dtype != torch.float32: absmax = absmax.float() if out is None: out = torch.empty(quant_state.shape, dtype=quant_state.dtype, device=A.device) n = out.numel() device = pre_call(A.device) is_on_gpu([A, absmax, out]) if out.dtype == torch.float32: if quant_state.quant_type == 'fp4': lib.cdequantize_blockwise_fp32_fp4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(quant_state.blocksize), ct.c_int(n)) else: lib.cdequantize_blockwise_fp32_nf4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(quant_state.blocksize), ct.c_int(n)) elif out.dtype == torch.float16: if quant_state.quant_type == 'fp4': lib.cdequantize_blockwise_fp16_fp4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(quant_state.blocksize), ct.c_int(n)) else: lib.cdequantize_blockwise_fp16_nf4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(quant_state.blocksize), ct.c_int(n)) elif out.dtype == torch.bfloat16: if quant_state.quant_type == 'fp4': lib.cdequantize_blockwise_bf16_fp4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(quant_state.blocksize), ct.c_int(n)) else: lib.cdequantize_blockwise_bf16_nf4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(quant_state.blocksize), ct.c_int(n)) else: raise ValueError(f"Blockwise quantization only supports 16/32-bit floats, but got {A.dtype}") post_call(A.device) is_transposed = (True if A.shape[0] == 1 else False) if is_transposed: return out.t() else: return out def quantize( A: Tensor, code: Optional[torch.Tensor] = None, out: Optional[torch.Tensor] = None, ) -> Tuple[Tensor, Tuple[Tensor, 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() if absmax.dtype != torch.float32: absmax = absmax.float() inp = A / absmax out = quantize_no_absmax(inp, code, out) return out, (absmax, code) def dequantize( A: Tensor, state: Optional[Tuple[Tensor, Tensor]] = None, absmax: Optional[torch.Tensor] = None, code: Optional[torch.Tensor] = None, out: Optional[torch.Tensor] = None, ) -> Tensor: assert state is not None or absmax is not None if code is None and 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 state is None: state = (absmax, code) out = dequantize_no_absmax(A, state[1], out) return out * state[0] def quantize_no_absmax(A: Tensor, code: Tensor, out: Optional[torch.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. ''' prev_device = pre_call(A.device) if out is None: out = torch.zeros_like(A, dtype=torch.uint8) is_on_gpu([A, out]) lib.cquantize(get_ptr(code), get_ptr(A), get_ptr(out), ct.c_int(A.numel())) post_call(prev_device) return out def dequantize_no_absmax(A: Tensor, code: Tensor, out: Optional[torch.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. ''' prev_device = pre_call(A.device) if out is None: out = torch.zeros_like(A, dtype=torch.float32) is_on_gpu([code, A, out]) lib.cdequantize(get_ptr(code), get_ptr(A), get_ptr(out), ct.c_int(A.numel())) post_call(prev_device) return out def optimizer_update_32bit( optimizer_name: str, g: Tensor, p: Tensor, state1: Tensor, beta1: float, eps: float, step: int, lr: float, state2: Optional[torch.Tensor] = None, beta2: float = 0.0, weight_decay: float = 0.0, gnorm_scale: float = 1.0, unorm_vec: Optional[torch.Tensor] = None, max_unorm: float = 0.0, skip_zeros=False, ) -> 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. unorm_vec : torch.Tensor The tensor for the update norm. max_unorm : float The maximum update norm relative to the weight norm. skip_zeros : bool Whether to skip zero-valued gradients or not (default: False). """ param_norm = 0.0 if max_unorm > 0.0: param_norm = torch.norm(p.data.float()) optim_func = None if g.dtype == torch.float32: optim_func = str2optimizer32bit[optimizer_name][0] elif g.dtype == torch.float16: optim_func = str2optimizer32bit[optimizer_name][1] elif (g.dtype == torch.bfloat16 and len(str2optimizer32bit[optimizer_name])==3): optim_func = str2optimizer32bit[optimizer_name][2] else: raise ValueError(f"Gradient+optimizer bit data type combination not supported: grad {g.dtype}, optimizer {state1.dtype}") is_on_gpu([g, p, state1, state2, unorm_vec]) prev_device = pre_call(g.device) optim_func( 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_bool(skip_zeros), ct.c_int32(g.numel())) post_call(prev_device) 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: Optional[torch.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. unorm_vec : torch.Tensor The tensor for the update norm. max_unorm : float The maximum update norm relative to the weight norm. """ param_norm = 0.0 if max_unorm > 0.0: param_norm = torch.norm(p.data.float()) prev_device = pre_call(g.device) is_on_gpu([g, p, state1, state2, unorm_vec, qmap1, qmap2, max1, max2, new_max1, new_max2]) 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}" ) post_call(prev_device) 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, skip_zeros=False, ) -> None: optim_func = None prev_device = pre_call(g.device) is_on_gpu([g, p, state1, state2, qmap1, qmap2, absmax1, absmax2]) if g.dtype == torch.float32 and state1.dtype == torch.uint8: optim_func = str2optimizer8bit_blockwise[optimizer_name][0] elif g.dtype == torch.float16 and state1.dtype == torch.uint8: optim_func = str2optimizer8bit_blockwise[optimizer_name][1] elif (g.dtype == torch.bfloat16 and state1.dtype == torch.uint8 and len(str2optimizer8bit_blockwise[optimizer_name])==3): optim_func = str2optimizer8bit_blockwise[optimizer_name][2] else: raise ValueError( f"Gradient+optimizer bit data type combination not supported: grad {g.dtype}, optimizer {state1.dtype}" ) post_call(prev_device) is_on_gpu([p, g, state1, state2, qmap1, qmap2, absmax1, absmax2]) prev_device = pre_call(g.device) optim_func( 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_bool(skip_zeros), ct.c_int32(g.numel()), ) post_call(prev_device) 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). """ prev_device = pre_call(grad.device) is_on_gpu([grad, gnorm_vec]) 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!") post_call(prev_device) 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()) is_on_gpu([histogram, index1, index2, source]) lib.chistogram_scatter_add_2d(get_ptr(histogram), get_ptr(index1), get_ptr(index2), get_ptr(source), maxdim1, n) def check_matmul(A, B, out, transposed_A, transposed_B, expected_type=torch.int8): if not torch.cuda.is_initialized(): torch.cuda.init() if A.dtype != expected_type or B.dtype != expected_type: raise TypeError( f"Expected torch.int8 input tensors A and B, but got {A.dtype} and {B.dtype}" ) sA = A.shape sB = B.shape tA = transposed_A tB = transposed_B correct = True if len(sA) == 2 and len(sB) == 2: if not tA and not tB and A.shape[1] != B.shape[0]: correct = False elif tA and not tB and A.shape[0] != B.shape[0]: correct = False elif tA and tB and A.shape[0] != B.shape[1]: correct = False elif not tA and tB and A.shape[1] != B.shape[1]: correct = False elif len(sA) == 3 and len(sB) == 2: if not tA and not tB and A.shape[2] != B.shape[0]: correct = False elif tA and not tB and A.shape[1] != B.shape[0]: correct = False elif tA and tB and A.shape[1] != B.shape[1]: correct = False elif not tA and tB and A.shape[2] != B.shape[1]: correct = False elif len(sA) == 3 and len(sB) == 3: if not tA and not tB and A.shape[2] != B.shape[1]: correct = False elif tA and not tB and A.shape[1] != B.shape[1]: correct = False elif tA and tB and A.shape[1] != B.shape[2]: correct = False elif not tA and tB and A.shape[2] != B.shape[2]: correct = False if out is not None: sout = out.shape # special case common in backprop if not correct and len(sA) == 3 and len(sB) == 3: if ( sout[0] == sA[2] and sout[1] == sB[2] and sA[0] == sB[0] and sA[1] == sB[1] ): correct = True else: if len(sA) == 2 and len(sB) == 2: if not tA and not tB: sout = (sA[0], sB[1]) elif tA and tB: sout = (sA[1], sB[0]) elif tA and not tB: sout = (sA[1], sB[1]) elif not tA and tB: sout = (sA[0], sB[0]) elif len(sA) == 3 and len(sB) == 2: if not tA and not tB: sout = (sA[0], sA[1], sB[1]) elif tA and tB: sout = (sA[0], sA[2], sB[0]) elif tA and not tB: sout = (sA[0], sA[2], sB[1]) elif not tA and tB: sout = (sA[0], sA[1], sB[0]) elif len(sA) == 3 and len(sB) == 3: if not tA and not tB: sout = (sA[0], sA[1], sB[2]) elif tA and tB: sout = (sA[0], sA[2], sB[1]) elif tA and not tB: sout = (sA[0], sA[2], sB[2]) elif not tA and tB: sout = (sA[0], sA[1], sB[1]) if not correct: raise ValueError( f"Tensor dimensions incorrect for matrix mulitiplication: A x B: {sA} x {sB} with transpose for A x B: {tA} x {tB}." ) return sout def gemv_4bit( A: Tensor, B: Tensor, out: Optional[torch.Tensor] = None, transposed_A=False, transposed_B=False, state=None ): prev_device = pre_call(A.device) #sout = check_matmul(A, B, out, transposed_A, transposed_B, expected_type=A.dtype) if state is None: raise ValueError('state cannot None. gem_4bit( ) requires the state from quantize_4bit( )') if A.numel() != A.shape[-1]: raise ValueError('Dimensions of A are invalid. Must be a vector with the leading dimensions of "1", e.g. [1, 1, 2048]') Bshape = state.shape bout = Bshape[0] absmax = state.absmax if state.nested: absmax = dequantize_blockwise(state.absmax, state.state2) absmax += state.offset if out is None: if len(A.shape) == 3: out = torch.empty(size=(A.shape[0], A.shape[1], bout), dtype=A.dtype, device=A.device) else: out = torch.empty(size=(A.shape[0], bout), dtype=A.dtype, device=A.device) n = 1 m = Bshape[0] k = Bshape[1] lda = Bshape[0] ldc = Bshape[0] ldb = (A.shape[-1]+1)//2 is_on_gpu([B, A, out, absmax, state.code]) m = ct.c_int32(m) n = ct.c_int32(n) k = ct.c_int32(k) lda = ct.c_int32(lda) ldb = ct.c_int32(ldb) ldc = ct.c_int32(ldc) if B.dtype in [torch.uint8, torch.bfloat16, torch.float16, torch.float32]: if A.dtype == torch.float16: lib.cgemm_4bit_inference_naive_fp16(m, n, k, get_ptr(A), get_ptr(B), get_ptr(absmax), get_ptr(state.code), get_ptr(out), lda, ldb, ldc, ct.c_int32(state.blocksize)) elif A.dtype == torch.bfloat16: lib.cgemm_4bit_inference_naive_bf16(m, n, k, get_ptr(A), get_ptr(B), get_ptr(absmax), get_ptr(state.code), get_ptr(out), lda, ldb, ldc, ct.c_int32(state.blocksize)) elif A.dtype == torch.float32: lib.cgemm_4bit_inference_naive_fp32(m, n, k, get_ptr(A), get_ptr(B), get_ptr(absmax), get_ptr(state.code), get_ptr(out), lda, ldb, ldc, ct.c_int32(state.blocksize)) else: raise NotImplementedError(f'Matmul not implemented for data type {A.dtype}') else: raise NotImplementedError(f'Matmul not implemented for data type {A.dtype}') post_call(prev_device) return out def igemm( A: Tensor, B: Tensor, out: Optional[torch.Tensor] = None, transposed_A=False, transposed_B=False, ): sout = check_matmul(A, B, out, transposed_A, transposed_B) if out is None: out = torch.zeros(size=sout, dtype=torch.int32, device=A.device) if len(A.shape) == 3 and len(B.shape) == 3: if A.shape[0] == B.shape[0] and A.shape[2] == B.shape[1]: return batched_igemm(A, B, out) sA = A.shape sB = B.shape if transposed_A and len(sA) == 2: sA = (sA[1], sA[0]) elif transposed_A and len(sA) == 3: sA = (sA[0], sA[2], sA[0]) if transposed_B and len(sB) == 2: sB = (sB[1], sB[0]) elif transposed_B and len(sB) == 3: sB = (sB[0], sB[2], sB[0]) # this is a mess: cuBLAS expect column major, but PyTorch is row major. # So to perform the matrix multiplication, we have to treat A, B, and C matrices # (transpose of row major is column major) # This means we compute B^T A^T = C^T and we explicitly switch the dimensions of each of these # matrices in the input arguments for cuBLAS # column major: A @ B = C: [m, k] @ [k, n] = [m, n] # row major: B^T @ A^T = C^T: [m, k] @ [k, n] = [m, n] # column major with row major layout: B^T @ A^T = C^T: [k, m] @ [n, k] = [n, m] if len(sB) == 2: if B.stride()[0] == B.shape[1]: transposed_B = False elif B.stride()[1] == B.shape[0]: transposed_B = True if len(A.shape) == 2: if A.stride()[0] == A.shape[1]: transposed_A = False elif A.stride()[1] == A.shape[0]: transposed_A = True else: if A.stride()[1] == A.shape[2]: transposed_A = False elif A.stride()[2] == A.shape[1]: transposed_A = True if len(sA) == 2: n = sA[0] ldb = A.stride()[1 if transposed_A else 0] elif len(sA) == 3 and len(sB) == 2: n = sA[0] * sA[1] ldb = sA[2] m = sB[1] k = sB[0] lda = B.stride()[(1 if transposed_B else 0)] ldc = sB[1] elif len(sB) == 3: # special case assert len(sA) == 3 if not (sA[0] == sB[0] and sA[1] == sB[1]): raise ValueError( f"Only bsi,bso->io supported for tensor contractions, but dims for A x B were: {sA} x {sB}" ) transposed_A = True transposed_B = False m = sB[2] n = sA[2] k = sB[0] * sB[1] lda = m ldb = sA[2] ldc = m ptr = CUBLAS_Context.get_instance().get_context(A.device) # B^T @ A^T = C^T # [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)) return out def batched_igemm( A: Tensor, B: Tensor, out: Optional[torch.Tensor] = None, transposed_A=False, transposed_B=False, ): if not len(A.shape) == 3 or not len(B.shape) == 3: raise ValueError( f"Expected 3-dimensional tensors for bmm, but got shapes A and B: {A.shape} and {B.shape}" ) sout = check_matmul(A, B, out, transposed_A, transposed_B) if out is None: out = torch.zeros(size=sout, dtype=torch.int32, device=A.device) if B.is_contiguous(): lda = B.stride()[1] transposed_A = False else: s = B.stride() if s[0] != B.shape[0]: B = B.contiguous() lda = B.stride()[1] elif s[2] == B.shape[1]: transposed_A = True lda = B.stride()[2] else: if s[2] == 1: B = B.contiguous() lda = B.stride()[1] elif s[1] == 1: B = B.contiguous() lda = B.stride()[1] else: B = B.contiguous() lda = B.stride()[1] if A.is_contiguous(): ldb = A.stride()[1] transposed_B = False else: s = A.stride() if s[0] != A.shape[0]: A = A.contiguous() ldb = A.stride()[1] transposed_B = False elif s[2] == A.shape[1]: ldb = A.stride()[2] transposed_B = True else: A = A.contiguous() ldb = A.stride()[1] transposed_B = False # this is a mess: cuBLAS expect column major, but PyTorch is row major. # So to perform the matrix multiplication, we have to treat A, B, and C matrices # (transpose of row major is column major) # This means we compute B^T A^T = C^T and we explicitly switch the dimensions of each of these # matrices in the input arguments for cuBLAS # column major: A @ B = C: [batch, m, k] @ [batch, k, n] = [batch, m, n] # row major: B^T @ A^T = C^T: [batch, m, k] @ [batch, k, n] = [batch, m, n] # column major with row major layout: B^T @ A^T = C^T: [batch, k, m] @ [batch, n, k] = [batch, n, m] num_batch = A.shape[0] n = A.shape[1] m = B.shape[2] k = B.shape[1] ldc = m strideA = B.shape[1] * B.shape[2] strideB = A.shape[1] * A.shape[2] strideC = A.shape[1] * B.shape[2] ptr = CUBLAS_Context.get_instance().get_context(A.device) is_on_gpu([B, A, out]) lib.cbatched_igemm(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), ct.c_long(strideA), ct.c_long(strideB), ct.c_long(strideC), ct.c_uint32(num_batch)) return out def igemmlt(A, B, SA, SB, out=None, Sout=None, dtype=torch.int32): shapeA = SA[0] shapeB = SB[0] dimsA = len(shapeA) dimsB = len(shapeB) assert dimsB == 2, 'Only two dimensional matrices are supported for argument B' if dimsA == 2: m = shapeA[0] elif dimsA == 3: m = shapeA[0] * shapeA[1] rows = n = shapeB[0] assert prod(list(shapeA)) > 0, f'Input tensor dimensions need to be > 0: {shapeA}' # if the tensor is empty, return a transformed empty tensor with the right dimensions if shapeA[0] == 0 and dimsA == 2: return torch.empty((0, shapeB[0]), device=A.device, dtype=torch.float16) elif shapeA[1] == 0 and dimsA == 3: return torch.empty(tuple(shapeA[:2] + [shapeB[0]]), device=A.device, dtype=torch.float16) if dimsA == 2 and out is None: out, Sout = get_transform_buffer( (shapeA[0], shapeB[0]), dtype, A.device, "col32", "row" ) elif dimsA == 3 and out is None: out, Sout = get_transform_buffer( (shapeA[0], shapeA[1], shapeB[0]), dtype, A.device, "col32", "row" ) assert dimsB != 3, "len(B.shape)==3 not supported" assert A.device.type == "cuda" assert B.device.type == "cuda" assert A.dtype == torch.int8 assert B.dtype == torch.int8 assert out.dtype == dtype assert SA[1] == "col32" assert SB[1] in ["col_turing", "col_ampere"] assert Sout[1] == "col32" assert ( shapeA[-1] == shapeB[-1] ), f"Matmullt only supports A @ B^T. Inner matrix dimensions do not match: A @ B = {shapeA} @ {shapeB}" formatB = SB[1] prev_device = A.device torch.cuda.set_device(A.device) ptr = CUBLAS_Context.get_instance().get_context(A.device) ptrA = get_ptr(A) ptrB = get_ptr(B) ptrC = get_ptr(out) k = shapeA[-1] lda = ct.c_int32(m * 32) if formatB == "col_turing": # turing: tiles with rows filled up to multiple of 8 rows by 32 columns # n = rows ldb = ct.c_int32(((rows + 7) // 8) * 8 * 32) else: # ampere: tiles with rows filled up to multiple of 32 rows by 32 columns # n = rows ldb = ct.c_int32(((rows + 31) // 32) * 32 * 32) ldc = ct.c_int32(m * 32) m = ct.c_int32(m) n = ct.c_int32(n) k = ct.c_int32(k) has_error = 0 ptrRowScale = get_ptr(None) is_on_gpu([A, B, out]) if formatB == 'col_turing': if dtype == torch.int32: has_error = lib.cigemmlt_turing_32( ptr, m, n, k, ptrA, ptrB, ptrC, ptrRowScale, lda, ldb, ldc ) else: has_error = lib.cigemmlt_turing_8( ptr, m, n, k, ptrA, ptrB, ptrC, ptrRowScale, lda, ldb, ldc ) elif formatB == "col_ampere": if dtype == torch.int32: has_error = lib.cigemmlt_ampere_32( ptr, m, n, k, ptrA, ptrB, ptrC, ptrRowScale, lda, ldb, ldc ) else: has_error = lib.cigemmlt_ampere_8( ptr, m, n, k, ptrA, ptrB, ptrC, ptrRowScale, lda, ldb, ldc ) if has_error == 100: # `ERR_NOT_IMPLEMENTED` is defined as 100 in `ops.cu` raise NotImplementedError("igemmlt not available (probably built with NO_CUBLASLT)") if has_error: print(f'A: {shapeA}, B: {shapeB}, C: {Sout[0]}; (lda, ldb, ldc): {(lda, ldb, ldc)}; (m, n, k): {(m, n, k)}') raise Exception('cublasLt ran into an error!') torch.cuda.set_device(prev_device) return out, Sout def mm_dequant( A, quant_state, row_stats, col_stats, out=None, new_row_stats=None, new_col_stats=None, bias=None ): assert A.dtype == torch.int32 if bias is not None: assert bias.dtype == torch.float16 out_shape = quant_state[0] if len(out_shape) == 3: out_shape = (out_shape[0] * out_shape[1], out_shape[2]) if out is None: out = torch.empty(out_shape, dtype=torch.float16, device=A.device) if new_row_stats is None: new_row_stats = torch.empty( out_shape[0], dtype=torch.float32, device=A.device ) if new_col_stats is None: new_col_stats = torch.empty( out_shape[1], dtype=torch.float32, device=A.device ) assert ( new_row_stats.shape[0] == row_stats.shape[0] ), f"{new_row_stats.shape} vs {row_stats.shape}" assert ( new_col_stats.shape[0] == col_stats.shape[0] ), f"{new_col_stats.shape} vs {col_stats.shape}" prev_device = pre_call(A.device) ptrA = get_ptr(A) ptrOut = get_ptr(out) ptrRowStats = get_ptr(row_stats) ptrColStats = get_ptr(col_stats) ptrNewRowStats = get_ptr(new_row_stats) ptrNewColStats = get_ptr(new_col_stats) ptrBias = get_ptr(bias) numRows = ct.c_int32(out_shape[0]) numCols = ct.c_int32(out_shape[1]) is_on_gpu([A, row_stats, col_stats, out, new_row_stats, new_col_stats, bias]) lib.cdequant_mm_int32_fp16(ptrA, ptrRowStats, ptrColStats, ptrOut, ptrNewRowStats, ptrNewColStats, ptrBias, numRows, numCols) post_call(prev_device) return out def get_colrow_absmax( A, row_stats=None, col_stats=None, nnz_block_ptr=None, threshold=0.0 ): assert A.dtype == torch.float16 device = A.device cols = A.shape[-1] if len(A.shape) == 3: rows = A.shape[0] * A.shape[1] else: rows = A.shape[0] col_tiles = (cols + 255) // 256 tiled_rows = ((rows + 15) // 16) * 16 if row_stats is None: row_stats = torch.empty( (rows,), dtype=torch.float32, device=device ).fill_(-50000.0) if col_stats is None: col_stats = torch.empty( (cols,), dtype=torch.float32, device=device ).fill_(-50000.0) if nnz_block_ptr is None and threshold > 0.0: nnz_block_ptr = torch.zeros( ((tiled_rows * col_tiles) + 1,), dtype=torch.int32, device=device ) ptrA = get_ptr(A) ptrRowStats = get_ptr(row_stats) ptrColStats = get_ptr(col_stats) ptrNnzrows = get_ptr(nnz_block_ptr) rows = ct.c_int32(rows) cols = ct.c_int32(cols) prev_device = pre_call(A.device) is_on_gpu([A, row_stats, col_stats, nnz_block_ptr]) lib.cget_col_row_stats(ptrA, ptrRowStats, ptrColStats, ptrNnzrows, ct.c_float(threshold), rows, cols) post_call(prev_device) if threshold > 0.0: nnz_block_ptr.cumsum_(0) return row_stats, col_stats, nnz_block_ptr class COOSparseTensor: def __init__(self, rows, cols, nnz, rowidx, colidx, values): assert rowidx.dtype == torch.int32 assert colidx.dtype == torch.int32 assert values.dtype == torch.float16 assert values.numel() == nnz assert rowidx.numel() == nnz assert colidx.numel() == nnz self.rows = rows self.cols = cols self.nnz = nnz self.rowidx = rowidx self.colidx = colidx self.values = values class CSRSparseTensor: def __init__(self, rows, cols, nnz, rowptr, colidx, values): assert rowptr.dtype == torch.int32 assert colidx.dtype == torch.int32 assert values.dtype == torch.float16 assert values.numel() == nnz assert colidx.numel() == nnz assert rowptr.numel() == rows + 1 self.rows = rows self.cols = cols self.nnz = nnz self.rowptr = rowptr self.colidx = colidx self.values = values class CSCSparseTensor: def __init__(self, rows, cols, nnz, colptr, rowidx, values): assert colptr.dtype == torch.int32 assert rowidx.dtype == torch.int32 assert values.dtype == torch.float16 assert values.numel() == nnz assert rowidx.numel() == nnz assert colptr.numel() == cols + 1 self.rows = rows self.cols = cols self.nnz = nnz self.colptr = colptr self.rowidx = rowidx self.values = values def coo2csr(cooA): values, counts = torch.unique(cooA.rowidx, return_counts=True) values.add_(1) rowptr = torch.zeros( (cooA.rows + 1,), dtype=torch.int32, device=cooA.rowidx.device ) rowptr.scatter_(index=values.long(), src=counts.int(), dim=0) rowptr.cumsum_(0) return CSRSparseTensor( cooA.rows, cooA.cols, cooA.nnz, rowptr, cooA.colidx, cooA.values ) def coo2csc(cooA): val, col2rowidx = torch.sort(cooA.colidx) rowidx = cooA.rowidx[col2rowidx] values = cooA.values[col2rowidx] colvalues, counts = torch.unique(val, return_counts=True) colvalues.add_(1) colptr = torch.zeros( (cooA.cols + 1,), dtype=torch.int32, device=cooA.colidx.device ) colptr.scatter_(index=colvalues.long(), src=counts.int(), dim=0) colptr.cumsum_(0) return CSCSparseTensor( cooA.rows, cooA.cols, cooA.nnz, colptr, rowidx, values ) def coo_zeros(rows, cols, nnz, device, dtype=torch.half): rowidx = torch.zeros((nnz,), dtype=torch.int32, device=device) colidx = torch.zeros((nnz,), dtype=torch.int32, device=device) values = torch.zeros((nnz,), dtype=dtype, device=device) return COOSparseTensor(rows, cols, nnz, rowidx, colidx, values) def double_quant( A, col_stats=None, row_stats=None, out_col=None, out_row=None, threshold=0.0 ): device = A.device assert A.dtype == torch.half assert device.type == "cuda" prev_device = pre_call(A.device) cols = A.shape[-1] if len(A.shape) == 3: rows = A.shape[0] * A.shape[1] else: rows = A.shape[0] if row_stats is None or col_stats is None: row_stats, col_stats, nnz_row_ptr = get_colrow_absmax( A, threshold=threshold ) if out_col is None: out_col = torch.zeros(A.shape, device=device, dtype=torch.int8) if out_row is None: out_row = torch.zeros(A.shape, device=device, dtype=torch.int8) coo_tensor = None ptrA = get_ptr(A) ptrColStats = get_ptr(col_stats) ptrRowStats = get_ptr(row_stats) ptrOutCol = get_ptr(out_col) ptrOutRow = get_ptr(out_row) is_on_gpu([A, col_stats, row_stats, out_col, out_row]) if threshold > 0.0: nnz = nnz_row_ptr[-1].item() if nnz > 0: coo_tensor = coo_zeros( A.shape[0], A.shape[1], nnz_row_ptr[-1].item(), device ) ptrRowIdx = get_ptr(coo_tensor.rowidx) ptrColIdx = get_ptr(coo_tensor.colidx) ptrVal = get_ptr(coo_tensor.values) ptrRowPtr = get_ptr(nnz_row_ptr) lib.cdouble_rowcol_quant( ptrA, ptrRowStats, ptrColStats, ptrOutCol, ptrOutRow, ptrRowIdx, ptrColIdx, ptrVal, ptrRowPtr, ct.c_float(threshold), ct.c_int32(rows), ct.c_int32(cols), ) val, idx = torch.sort(coo_tensor.rowidx) coo_tensor.rowidx = val coo_tensor.colidx = coo_tensor.colidx[idx] coo_tensor.values = coo_tensor.values[idx] else: lib.cdouble_rowcol_quant( ptrA, ptrRowStats, ptrColStats, ptrOutCol, ptrOutRow, None, None, None, None, ct.c_float(0.0), ct.c_int32(rows), ct.c_int32(cols), ) else: lib.cdouble_rowcol_quant( ptrA, ptrRowStats, ptrColStats, ptrOutCol, ptrOutRow, None, None, None, None, ct.c_float(threshold), ct.c_int32(rows), ct.c_int32(cols), ) post_call(prev_device) return out_row, out_col, row_stats, col_stats, coo_tensor def transform(A, to_order, from_order='row', out=None, transpose=False, state=None, ld=None): prev_device = pre_call(A.device) if state is None: state = (A.shape, from_order) else: from_order = state[1] if out is None: out, new_state = get_transform_buffer(state[0], A.dtype, A.device, to_order, state[1], transpose) else: new_state = (state[0], to_order) # (shape, order) shape = state[0] if len(shape) == 2: dim1 = ct.c_int32(shape[0]) dim2 = ct.c_int32(shape[1]) else: dim1 = ct.c_int32(shape[0] * shape[1]) dim2 = ct.c_int32(shape[2]) is_on_gpu([A, out]) if to_order == 'col32': if transpose: lib.ctransform_row2col32T(get_ptr(A), get_ptr(out), dim1, dim2) else: lib.ctransform_row2col32(get_ptr(A), get_ptr(out), dim1, dim2) elif to_order == "col_turing": if transpose: lib.ctransform_row2turingT(get_ptr(A), get_ptr(out), dim1, dim2) else: lib.ctransform_row2turing(get_ptr(A), get_ptr(out), dim1, dim2) elif to_order == "col_ampere": if transpose: lib.ctransform_row2ampereT(get_ptr(A), get_ptr(out), dim1, dim2) else: lib.ctransform_row2ampere(get_ptr(A), get_ptr(out), dim1, dim2) elif to_order == "row": if from_order == "col_turing": lib.ctransform_turing2row(get_ptr(A), get_ptr(out), dim1, dim2) elif from_order == "col_ampere": lib.ctransform_ampere2row(get_ptr(A), get_ptr(out), dim1, dim2) else: raise NotImplementedError(f'Transform function not implemented: From {from_order} to {to_order}') post_call(prev_device) return out, new_state def spmm_coo(cooA, B, out=None): if out is None: out = torch.empty( (cooA.rows, B.shape[1]), device=B.device, dtype=B.dtype ) nnz = cooA.nnz assert cooA.rowidx.numel() == nnz assert cooA.colidx.numel() == nnz assert cooA.values.numel() == nnz assert cooA.cols == B.shape[0] transposed_B = False if B.is_contiguous() else True ldb = B.stride()[(1 if transposed_B else 0)] ldc = B.shape[1] ptr = Cusparse_Context.get_instance().context ptrRowidx = get_ptr(cooA.rowidx) ptrColidx = get_ptr(cooA.colidx) ptrValues = get_ptr(cooA.values) ptrB = get_ptr(B) ptrC = get_ptr(out) cnnz = ct.c_int32(cooA.nnz) crowsA = ct.c_int32(cooA.rows) ccolsA = ct.c_int32(cooA.cols) ccolsB = ct.c_int32(B.shape[1]) cldb = ct.c_int32(ldb) cldc = ct.c_int32(ldc) is_on_gpu([cooA.rowidx, cooA.colidx, cooA.values, B, out]) lib.cspmm_coo(ptr, ptrRowidx, ptrColidx, ptrValues, cnnz, crowsA, ccolsA, ccolsB, cldb, ptrB, cldc, ptrC, ct.c_bool(transposed_B)) return out def spmm_coo_very_sparse(cooA, B, dequant_stats=None, out=None): if out is None: out = torch.zeros( (cooA.rows, B.shape[1]), device=B.device, dtype=cooA.values.dtype ) nnz = cooA.nnz prev_device = pre_call(B.device) assert cooA.rowidx.numel() == nnz assert cooA.colidx.numel() == nnz assert cooA.values.numel() == nnz assert cooA.cols == B.shape[0], f"{cooA.cols} vs {B.shape}" transposed_B = False if B.is_contiguous() else True ldb = B.stride()[(1 if transposed_B else 0)] ldc = B.shape[1] values, counts = torch.unique(cooA.rowidx, return_counts=True) offset = counts.cumsum(0).int() max_count, max_idx = torch.sort(counts, descending=True) max_idx = max_idx.int() max_count = max_count.int() assert ( max_count[0] <= 32 ), f"Current max count per row is 8 but found {max_count[0]}." assert B.dtype in [torch.float16, torch.int8] ptrOffset = get_ptr(offset) ptrMaxCount = get_ptr(max_count) ptrMaxIdx = get_ptr(max_idx) ptrRowidx = get_ptr(cooA.rowidx) ptrColidx = get_ptr(cooA.colidx) ptrValues = get_ptr(cooA.values) ptrB = get_ptr(B) ptrC = get_ptr(out) ptrDequantStats = get_ptr(dequant_stats) cnnz_rows = ct.c_int32(counts.numel()) cnnz = ct.c_int32(cooA.nnz) crowsA = ct.c_int32(cooA.rows) ccolsA = ct.c_int32(cooA.cols) crowsB = ct.c_int32(B.shape[1]) ccolsB = ct.c_int32(B.shape[1]) cldb = ct.c_int32(ldb) cldc = ct.c_int32(ldc) is_on_gpu([cooA.rowidx, cooA.colidx, cooA.values, B, out, dequant_stats]) if B.dtype == torch.float16: lib.cspmm_coo_very_sparse_naive_fp16( ptrMaxCount, ptrMaxIdx, ptrOffset, ptrRowidx, ptrColidx, ptrValues, ptrB, ptrC, ptrDequantStats, cnnz_rows, cnnz, crowsA, crowsB, ccolsB, ) elif B.dtype == torch.int8: lib.cspmm_coo_very_sparse_naive_int8( ptrMaxCount, ptrMaxIdx, ptrOffset, ptrRowidx, ptrColidx, ptrValues, ptrB, ptrC, ptrDequantStats, cnnz_rows, cnnz, crowsA, crowsB, ccolsB, ) # else: assertion error post_call(prev_device) return out C = 127.0 def vectorwise_quant(x, dim=1, quant_type="vector"): if quant_type == "linear": max1 = torch.abs(x).max().float() xq = torch.round(x / max1 * 127).to(torch.int8) return xq, max1 elif quant_type in ["vector", "row"]: max1 = torch.amax(torch.abs(x), dim=dim, keepdim=True) xq = torch.round(x * (C / max1)).to(torch.int8) return xq, max1 elif quant_type == "zeropoint": dtype = x.dtype x = x.float() dyna = x.max() - x.min() if dyna == 0: dyna = 1 qx = 255.0 / dyna minx = x.min() zpx = torch.round(minx * qx) x = torch.round(qx * x - zpx) + zpx return x, qx elif quant_type in ["vector-zeropoint", "row-zeropoint"]: dtype = x.dtype x = x.float() dyna = torch.amax(x, dim=dim, keepdim=True) - torch.amin( x, dim=dim, keepdim=True ) dyna[dyna == 0] = 1 qx = 255.0 / dyna minx = torch.amin(x, dim=dim, keepdim=True) zpx = torch.round(minx * qx) x = torch.round(qx * x - zpx) + zpx return x, qx elif quant_type == "truncated-vector": with torch.no_grad(): absx = torch.abs(x) max1 = torch.amax(absx, dim=dim, keepdim=True) max1 = max1 * 0.7 idx = absx > max1.expand_as(absx) sign = torch.sign(x[idx]) x[idx] = max1.expand_as(absx)[idx] * sign xq = torch.round(x / max1 * C).to(torch.int8) return xq, max1 else: return None def vectorwise_dequant(xq, max1, quant_type="vector"): if quant_type == "vector": x = (xq / C * max1).to(torch.float32) return x else: return None def vectorwise_mm_dequant(xq, S1, S2, dtype=torch.half, quant_type="vector"): if quant_type == "linear": norm = S1 * S2 / (C * C) # double cast needed to prevent overflows return (xq.float() * norm).to(dtype) elif quant_type == "zeropoint": norm = 1.0 / (S1 * S2) return (xq.float() * norm).to(dtype) elif quant_type == "row-zeropoint": norm = 1.0 / (S1 * S2) x = xq.float() if len(S1.shape) == 3 and len(x.shape) == 2: S1 = S1.squeeze(0) if len(S2.shape) == 3 and len(x.shape) == 2: S2 = S2.squeeze(0) if len(S1.shape) == 2: x *= norm else: x *= norm return x.to(dtype) elif quant_type == "vector-zeropoint": x = xq.float() if len(S1.shape) == 3 and len(x.shape) == 2: S1 = S1.squeeze(0) if len(S2.shape) == 3 and len(x.shape) == 2: S2 = S2.squeeze(0) if len(S1.shape) == 2: x *= 1.0 / S1 else: x *= 1.0 / S1 x *= 1.0 / S2.t() return x.to(dtype) elif quant_type == "row": x = xq.float() if len(S1.shape) == 3 and len(x.shape) == 2: S1 = S1.squeeze(0) if len(S2.shape) == 3 and len(x.shape) == 2: S2 = S2.squeeze(0) if len(S1.shape) == 2: x *= S1 * S2 / (C * C) else: x *= S1 * S2 / (C * C) return x.to(dtype) elif quant_type in ["truncated-vector", "vector"]: x = xq.float() if len(S1.shape) == 3 and len(x.shape) == 2: S1 = S1.squeeze(0) if len(S2.shape) == 3 and len(x.shape) == 2: S2 = S2.squeeze(0) if len(S1.shape) == 2: x *= S1 / C else: x *= S1 / C x *= S2 / C return x.to(dtype) else: return None def dequant_min_max(xq, A, B, SA, SB, dtype=torch.half): offset = B.float().t().sum(0) * (SA[0] + SA[1]) x = xq.float() if len(xq.shape) == 2 and len(SB.shape) == 3: SB = SB.squeeze(0) if len(SB.shape) == 2: x *= SB.t() / 127 else: x *= SB / 127 x *= SA[1] / 127 x += offset return x.to(dtype) def extract_outliers(A, SA, idx): shapeA = SA[0] formatA = SA[1] assert formatA in ["col_turing", "col_ampere"] assert A.device.type == "cuda" out = torch.zeros( (shapeA[0], idx.numel()), dtype=torch.int8, device=A.device ) idx_size = ct.c_int32(idx.numel()) rows = ct.c_int32(shapeA[0]) cols = ct.c_int32(shapeA[1]) ptrA = get_ptr(A) ptrIdx = get_ptr(idx) ptrOut = get_ptr(out) prev_device = pre_call(A.device) if formatA == 'col_turing': lib.cextractOutliers_turing(ptrA, ptrIdx, ptrOut, idx_size, rows, cols) elif formatA == "col_ampere": lib.cextractOutliers_ampere(ptrA, ptrIdx, ptrOut, idx_size, rows, cols) post_call(prev_device) return out def pipeline_test(A, batch_size): out = torch.zeros_like(A) lib.cpipeline_test(get_ptr(A), get_ptr(out), ct.c_size_t(A.numel()), ct.c_size_t(batch_size)) return out