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bitsandbytes-rocm/bitsandbytes/functional.py

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# 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
import itertools
import operator
import random
import torch
import itertools
import math
from scipy.stats import norm
import numpy as np
from functools import reduce # Required in Python 3
from typing import Tuple
from torch import Tensor
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 = {}
str2optimizer32bit["adam"] = (lib.cadam32bit_grad_fp32, lib.cadam32bit_grad_fp16, lib.cadam32bit_grad_bf16)
str2optimizer32bit["momentum"] = (
lib.cmomentum32bit_grad_32,
lib.cmomentum32bit_grad_16,
)
str2optimizer32bit["rmsprop"] = (
lib.crmsprop32bit_grad_32,
lib.crmsprop32bit_grad_16,
)
str2optimizer32bit["lion"] = (lib.clion32bit_grad_fp32, lib.clion32bit_grad_fp16, lib.clion32bit_grad_bf16)
str2optimizer32bit["adagrad"] = (
lib.cadagrad32bit_grad_32,
lib.cadagrad32bit_grad_16,
)
str2optimizer8bit = {}
str2optimizer8bit["adam"] = (
lib.cadam_static_8bit_grad_32,
lib.cadam_static_8bit_grad_16,
)
str2optimizer8bit["momentum"] = (
lib.cmomentum_static_8bit_grad_32,
lib.cmomentum_static_8bit_grad_16,
)
str2optimizer8bit["rmsprop"] = (
lib.crmsprop_static_8bit_grad_32,
lib.crmsprop_static_8bit_grad_16,
)
str2optimizer8bit["lion"] = (
lib.clion_static_8bit_grad_32,
lib.clion_static_8bit_grad_16,
)
str2optimizer8bit["lamb"] = (
lib.cadam_static_8bit_grad_32,
lib.cadam_static_8bit_grad_16,
)
str2optimizer8bit["lars"] = (
lib.cmomentum_static_8bit_grad_32,
lib.cmomentum_static_8bit_grad_16,
)
str2optimizer8bit_blockwise = {}
str2optimizer8bit_blockwise["adam"] = (
lib.cadam_8bit_blockwise_grad_fp32,
lib.cadam_8bit_blockwise_grad_fp16,
lib.cadam_8bit_blockwise_grad_bf16,
)
str2optimizer8bit_blockwise["momentum"] = (
lib.cmomentum_8bit_blockwise_grad_fp32,
lib.cmomentum_8bit_blockwise_grad_fp16,
)
str2optimizer8bit_blockwise["rmsprop"] = (
lib.crmsprop_8bit_blockwise_grad_fp32,
lib.crmsprop_8bit_blockwise_grad_fp16,
)
str2optimizer8bit_blockwise["lion"] = (
lib.clion_8bit_blockwise_grad_fp32,
lib.clion_8bit_blockwise_grad_fp16,
lib.clion_8bit_blockwise_grad_bf16,
)
str2optimizer8bit_blockwise["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 hte
# 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
def get_paged(*shape, dtype=torch.float32, device=torch.device('cuda', index=0)):
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 occured. 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
return torch.Tensor(values[:l].tolist() + [0]*gap + values[l:].tolist())
def create_normal_map(offset=0.9677083, use_extra_value=True):
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 0)
additional_items = 2 ** (non_sign_bits - max_exponent_bits) - 1
if not signed:
additional_items = 2 * additional_items
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)
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: 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.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: 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
def quantize_blockwise(A: Tensor, code: Tensor = None, absmax: Tensor = None, out: Tensor = None, blocksize=4096, nested=False) -> 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.
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)
state = [qabsmax, code, blocksize, nested, A.dtype, offset, state2]
else:
state = [absmax, code, blocksize, nested, A.dtype, None, None]
return out, state
def dequantize_blockwise(
A: Tensor,
quant_state: Tuple[Tensor, Tensor] = None,
absmax: Tensor = None,
code: Tensor = None,
out: 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 : 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"]
if quant_state is None:
quant_state = (absmax, code, blocksize, False, torch.float32, None, None)
absmax, code, blocksize, nested, dtype, offset, state2 = quant_state
if absmax.dtype != torch.float32: absmax = absmax.float()
if nested:
absmax = dequantize_blockwise(absmax, state2)
absmax += offset
if out is None:
out = torch.empty(A.shape, dtype=dtype, device=A.device)
if A.device.type != 'cpu':
device = pre_call(A.device)
code = code.to(A.device)
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]")
is_on_gpu([A, absmax, out])
if out.dtype == torch.float32:
lib.cdequantize_blockwise_fp32(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(blocksize), ct.c_int(A.numel()))
elif out.dtype == torch.float16:
lib.cdequantize_blockwise_fp16(get_ptr(code), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(blocksize), ct.c_int(A.numel()))
elif out.dtype == torch.bfloat16:
lib.cdequantize_blockwise_bf16(get_ptr(code), get_ptr(A), get_ptr(absmax), 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}")
post_call(A.device)
else:
code = code.cpu()
lib.cdequantize_blockwise_cpu_fp32(get_ptr(quant_state[1]), get_ptr(A), get_ptr(quant_state[0]), get_ptr(out), ct.c_longlong(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':
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
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(f'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: Tensor = None, out: Tensor = None, blocksize=64, compress_statistics=False):
return quantize_4bit(A, absmax, out, blocksize, compress_statistics, 'fp4')
def quantize_nf4(A: Tensor, absmax: Tensor = None, out: Tensor = None, blocksize=64, compress_statistics=False):
return quantize_4bit(A, absmax, out, blocksize, compress_statistics, 'nf4')
def quantize_4bit(A: Tensor, absmax: Tensor = None, out: Tensor = None, blocksize=64, compress_statistics=False, quant_type='fp4') -> Tensor:
"""
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 (8-bit).
blocksize : int
The blocksize used in quantization.
quant_type : str
The 4-bit quantization data type {fp4, nf4}
Returns
-------
torch.Tensor:
The 8-bit 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:
out = torch.zeros(((n+1)//2, 1), dtype=torch.uint8, 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)
datatype = 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 = [qabsmax, input_shape, A.dtype, blocksize, [offset, state2], quant_type, datatype]
else:
state = [absmax, input_shape, A.dtype, blocksize, None, quant_type, datatype]
return out, state
def dequantize_fp4(A: Tensor, quant_state: Tuple[Tensor, Tensor] = None, absmax: Tensor = None, out: Tensor = None, blocksize: int = 64) -> Tensor:
return dequantize_4bit(A, quant_state, absmax, out, blocksize, 'fp4')
def dequantize_nf4(A: Tensor, quant_state: Tuple[Tensor, Tensor] = None, absmax: Tensor = None, out: Tensor = None, blocksize: int = 64) -> Tensor:
return dequantize_4bit(A, quant_state, absmax, out, blocksize, 'nf4')
def dequantize_4bit(A: Tensor,quant_state: Tuple[Tensor, Tensor] = None, absmax: Tensor = None, out: 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 8-bit tensor (packed 4-bit values).
quant_state : tuple(torch.Tensor, torch.Size, torch.dtype)
Tuple of 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
shape = out.shape
dtype = out.dtype
else:
absmax, shape, dtype, blocksize, compressed_stats, quant_type, data_type = quant_state
if compressed_stats is not None:
if absmax.dtype != torch.float32: absmax = absmax.float()
offset, state2 = compressed_stats
absmax = dequantize_blockwise(absmax, state2)
absmax += offset
if out is None:
out = torch.empty(shape, dtype=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_type == 'fp4':
lib.cdequantize_blockwise_fp32_fp4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(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(blocksize), ct.c_int(n))
elif out.dtype == torch.float16:
if quant_type == 'fp4':
lib.cdequantize_blockwise_fp16_fp4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(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(blocksize), ct.c_int(n))
elif out.dtype == torch.bfloat16:
if quant_type == 'fp4':
lib.cdequantize_blockwise_bf16_fp4(get_ptr(None), get_ptr(A), get_ptr(absmax), get_ptr(out), ct.c_int(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(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: 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()
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,
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.
'''
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: 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: 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,
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: 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: 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(f'state cannot None. gem_4bit( ) requires the state from quantize_4bit( )')
if A.numel() != A.shape[-1]:
raise ValueError(f'Dimensions of A are invalid. Must be a vector with the leading dimensions of "1", e.g. [1, 1, 2048]')
Bshape = state[1]
bout = Bshape[0]
absmax, shape, dtype, blocksize, compressed_stats, quant_type, data_type = state
if compressed_stats is not None:
offset, state2 = compressed_stats
absmax = dequantize_blockwise(absmax, state2)
absmax += 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[-1]])
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 == torch.uint8:
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[-1]), get_ptr(out), lda, ldb, ldc, ct.c_int32(state[3]))
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[-1]), get_ptr(out), lda, ldb, ldc, ct.c_int32(state[3]))
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[-1]), get_ptr(out), lda, ldb, ldc, ct.c_int32(state[3]))
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: 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: 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 == 1:
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
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