Merge pull request #76 from tomaarsen/cleanup
Cleanup involving a handful of failures, some optimization and a lot of code quality improvements
This commit is contained in:
Tim Dettmers
GitHub
commit
f0ec93d016
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@ -3,6 +3,7 @@
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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from . import cuda_setup, utils
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from .autograd._functions import (
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MatmulLtState,
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bmm_cublas,
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@ -12,7 +13,6 @@ from .autograd._functions import (
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)
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from .cextension import COMPILED_WITH_CUDA
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from .nn import modules
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from . import cuda_setup, utils
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if COMPILED_WITH_CUDA:
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from .optim import adam
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@ -1,6 +1,3 @@
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# from bitsandbytes.debug_cli import cli
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# cli()
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import os
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import sys
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from warnings import warn
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@ -31,8 +28,8 @@ print()
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from . import COMPILED_WITH_CUDA, PACKAGE_GITHUB_URL
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from .cuda_setup.main import get_compute_capabilities, get_cuda_lib_handle
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from .cuda_setup.env_vars import to_be_ignored
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from .cuda_setup.main import get_compute_capabilities, get_cuda_lib_handle
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print_header("POTENTIALLY LIBRARY-PATH-LIKE ENV VARS")
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for k, v in os.environ.items():
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@ -1,12 +1,13 @@
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import operator
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import warnings
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import torch
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import bitsandbytes.functional as F
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from dataclasses import dataclass
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from functools import reduce # Required in Python 3
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import torch
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import bitsandbytes.functional as F
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# math.prod not compatible with python < 3.8
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def prod(iterable):
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return reduce(operator.mul, iterable, 1)
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@ -18,7 +19,7 @@ tensor = torch.Tensor
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This is particularly important for small models where outlier features
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are less systematic and occur with low frequency.
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"""
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class GlobalOutlierPooler(object):
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class GlobalOutlierPooler:
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_instance = None
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def __init__(self):
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@ -49,8 +50,9 @@ class GlobalOutlierPooler(object):
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class MatMul8bit(torch.autograd.Function):
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@staticmethod
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def forward(ctx, A, B, out=None, quant_type="vector", precision=[8, 8, 8]):
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def forward(ctx, A, B, out=None, quant_type="vector", precision=None):
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if precision is None:
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precision = [8, 8, 8]
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if precision[0] != 8:
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with torch.no_grad():
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output = torch.matmul(A, B)
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@ -1,11 +1,11 @@
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import ctypes as ct
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import torch
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from pathlib import Path
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from warnings import warn
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import torch
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class CUDASetup(object):
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class CUDASetup:
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_instance = None
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def __init__(self):
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@ -1,2 +1,6 @@
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from .paths import CUDA_RUNTIME_LIB, extract_candidate_paths, determine_cuda_runtime_lib_path
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from .main import evaluate_cuda_setup
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from .paths import (
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CUDA_RUNTIME_LIB,
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determine_cuda_runtime_lib_path,
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extract_candidate_paths,
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)
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@ -17,11 +17,13 @@ evaluation:
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"""
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import ctypes
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import torch
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from .paths import determine_cuda_runtime_lib_path
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from bitsandbytes.cextension import CUDASetup
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from .paths import determine_cuda_runtime_lib_path
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def check_cuda_result(cuda, result_val):
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# 3. Check for CUDA errors
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@ -48,7 +50,7 @@ def get_cuda_version(cuda, cudart_path):
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minor = (version-(major*1000))//10
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if major < 11:
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CUDASetup.get_instance().add_log_entry('CUDA SETUP: CUDA version lower than 11 are currenlty not supported for LLM.int8(). You will be only to use 8-bit optimizers and quantization routines!!')
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CUDASetup.get_instance().add_log_entry('CUDA SETUP: CUDA version lower than 11 are currently not supported for LLM.int8(). You will be only to use 8-bit optimizers and quantization routines!!')
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return f'{major}{minor}'
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@ -129,7 +131,7 @@ def evaluate_cuda_setup():
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failure = True
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cuda_setup.add_log_entry("WARNING: No libcudart.so found! Install CUDA or the cudatoolkit package (anaconda)!", is_warning=True)
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else:
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cuda_setup.add_log_entry((f"CUDA SETUP: CUDA runtime path found: {cudart_path}"))
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cuda_setup.add_log_entry(f"CUDA SETUP: CUDA runtime path found: {cudart_path}")
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if cc == '' or cc is None:
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failure = True
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@ -1,6 +1,7 @@
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import errno
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from pathlib import Path
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from typing import Set, Union
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from bitsandbytes.cextension import CUDASetup
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from .env_vars import get_potentially_lib_path_containing_env_vars
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@ -1,26 +0,0 @@
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import typer
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cli = typer.Typer()
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@cli.callback()
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def callback():
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"""
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Awesome Portal Gun
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"""
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@cli.command()
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def shoot():
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"""
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Shoot the portal gun
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"""
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typer.echo("Shooting portal gun")
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@cli.command()
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def load():
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"""
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Load the portal gun
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"""
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typer.echo("Loading portal gun")
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@ -3,17 +3,19 @@
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import ctypes as ct
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import itertools
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import operator
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import random
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import torch
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import itertools
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import math
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from functools import reduce # Required in Python 3
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from typing import Tuple
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from torch import Tensor
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from .cextension import COMPILED_WITH_CUDA, lib
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from functools import reduce # Required in Python 3
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# math.prod not compatible with python < 3.8
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def prod(iterable):
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@ -84,7 +86,7 @@ if COMPILED_WITH_CUDA:
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)
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class CUBLAS_Context(object):
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class CUBLAS_Context:
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_instance = None
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def __init__(self):
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@ -114,7 +116,7 @@ class CUBLAS_Context(object):
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return self.context[device.index]
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class Cusparse_Context(object):
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class Cusparse_Context:
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_instance = None
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def __init__(self):
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@ -264,12 +266,11 @@ def create_quantile_map(A, total_bits=8):
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def get_special_format_str():
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if not torch.cuda.is_available(): return 'col_turing'
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major, minor = torch.cuda.get_device_capability()
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major, _minor = torch.cuda.get_device_capability()
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if major <= 7:
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return "col_turing"
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elif major == 8:
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if major == 8:
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return "col_ampere"
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else:
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return "col_turing"
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@ -397,8 +398,6 @@ def nvidia_transform(
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dim2 = ct.c_int32(shape[2])
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ptr = CUBLAS_Context.get_instance().get_context(A.device)
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ptrA = get_ptr(A)
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ptrOut = get_ptr(out)
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func(ptr, get_ptr(A), get_ptr(out), dim1, dim2)
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return out, new_state
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@ -1053,7 +1052,7 @@ def histogram_scatter_add_2d(
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maxdim1 = ct.c_int32(histogram.shape[0])
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n = ct.c_int32(index1.numel())
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is_on_gpu([histogram, index1, index2d, source])
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is_on_gpu([histogram, index1, index2, source])
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lib.chistogram_scatter_add_2d(get_ptr(histogram), get_ptr(index1), get_ptr(index2), get_ptr(source), maxdim1, n)
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def check_matmul(A, B, out, transposed_A, transposed_B, expected_type=torch.int8):
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@ -1512,7 +1511,7 @@ def get_colrow_absmax(
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return row_stats, col_stats, nnz_block_ptr
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class COOSparseTensor(object):
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class COOSparseTensor:
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def __init__(self, rows, cols, nnz, rowidx, colidx, values):
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assert rowidx.dtype == torch.int32
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assert colidx.dtype == torch.int32
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@ -1529,7 +1528,7 @@ class COOSparseTensor(object):
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self.values = values
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class CSRSparseTensor(object):
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class CSRSparseTensor:
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def __init__(self, rows, cols, nnz, rowptr, colidx, values):
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assert rowptr.dtype == torch.int32
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assert colidx.dtype == torch.int32
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@ -1546,7 +1545,7 @@ class CSRSparseTensor(object):
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self.values = values
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class CSCSparseTensor(object):
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class CSCSparseTensor:
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def __init__(self, rows, cols, nnz, colptr, rowidx, values):
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assert colptr.dtype == torch.int32
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assert rowidx.dtype == torch.int32
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@ -1710,8 +1709,6 @@ def transform(A, to_order, from_order='row', out=None, transpose=False, state=No
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dim1 = ct.c_int32(shape[0] * shape[1])
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dim2 = ct.c_int32(shape[2])
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ptrA = get_ptr(A)
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ptrOut = get_ptr(out)
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is_on_gpu([A, out])
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if to_order == 'col32':
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if transpose:
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@ -2,24 +2,11 @@
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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from typing import (
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Any,
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Callable,
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Dict,
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Iterator,
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Mapping,
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Optional,
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Set,
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Tuple,
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TypeVar,
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Union,
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overload,
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)
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from typing import Optional, TypeVar, Union, overload
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import torch
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import torch.nn.functional as F
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from torch import Tensor, device, dtype, nn
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from torch.nn.parameter import Parameter
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import bitsandbytes as bnb
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from bitsandbytes.optim import GlobalOptimManager
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@ -39,7 +26,7 @@ class StableEmbedding(torch.nn.Embedding):
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sparse: bool = False,
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_weight: Optional[Tensor] = None,
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) -> None:
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super(StableEmbedding, self).__init__(
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super().__init__(
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num_embeddings,
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embedding_dim,
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padding_idx,
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@ -96,7 +83,7 @@ class Embedding(torch.nn.Embedding):
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sparse: bool = False,
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_weight: Optional[Tensor] = None,
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) -> None:
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super(Embedding, self).__init__(
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super().__init__(
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num_embeddings,
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embedding_dim,
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padding_idx,
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@ -225,7 +212,7 @@ class Linear8bitLt(nn.Linear):
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threshold=0.0,
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index=None,
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):
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super(Linear8bitLt, self).__init__(
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super().__init__(
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input_features, output_features, bias
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)
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self.state = bnb.MatmulLtState()
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|
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@ -5,12 +5,11 @@
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from bitsandbytes.cextension import COMPILED_WITH_CUDA
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from .adagrad import Adagrad, Adagrad8bit, Adagrad32bit
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from .adam import Adam, Adam8bit, Adam32bit
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from .adamw import AdamW, AdamW8bit, AdamW32bit
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from .sgd import SGD, SGD8bit, SGD32bit
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from .lars import LARS, LARS8bit, LARS32bit, PytorchLARS
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from .lamb import LAMB, LAMB8bit, LAMB32bit
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from .rmsprop import RMSprop, RMSprop8bit, RMSprop32bit
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from .adagrad import Adagrad, Adagrad8bit, Adagrad32bit
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from .lars import LARS, LARS8bit, LARS32bit, PytorchLARS
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from .optimizer import GlobalOptimManager
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from .rmsprop import RMSprop, RMSprop8bit, RMSprop32bit
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from .sgd import SGD, SGD8bit, SGD32bit
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|
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@ -21,18 +21,18 @@ class Adagrad(Optimizer1State):
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block_wise=True,
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):
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if not 0.0 <= lr:
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raise ValueError("Invalid learning rate: {}".format(lr))
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raise ValueError(f"Invalid learning rate: {lr}")
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if not 0.0 <= weight_decay:
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raise ValueError(
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"Invalid weight_decay value: {}".format(weight_decay)
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f"Invalid weight_decay value: {weight_decay}"
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)
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if not 0.0 <= eps:
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raise ValueError("Invalid epsilon value: {}".format(eps))
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raise ValueError(f"Invalid epsilon value: {eps}")
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if initial_accumulator_value != 0.0:
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raise ValueError("Initial accumulator value != 0.0 not supported!")
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if lr_decay != 0.0:
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raise ValueError("Lr Decay != 0.0 not supported!")
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super(Adagrad, self).__init__(
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super().__init__(
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"adagrad",
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params,
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lr,
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|
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@ -63,19 +63,19 @@ class Adagrad8bit(Optimizer1State):
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block_wise=True,
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):
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if not 0.0 <= lr:
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raise ValueError("Invalid learning rate: {}".format(lr))
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raise ValueError(f"Invalid learning rate: {lr}")
|
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if not 0.0 <= weight_decay:
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raise ValueError(
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"Invalid weight_decay value: {}".format(weight_decay)
|
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f"Invalid weight_decay value: {weight_decay}"
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)
|
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if not 0.0 <= eps:
|
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raise ValueError("Invalid epsilon value: {}".format(eps))
|
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raise ValueError(f"Invalid epsilon value: {eps}")
|
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if initial_accumulator_value != 0.0:
|
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raise ValueError("Initial accumulator value != 0.0 not supported!")
|
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if lr_decay != 0.0:
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raise ValueError("Lr Decay != 0.0 not supported!")
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assert block_wise
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super(Adagrad8bit, self).__init__(
|
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super().__init__(
|
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"adagrad",
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params,
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lr,
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|
|
@ -106,18 +106,18 @@ class Adagrad32bit(Optimizer1State):
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block_wise=True,
|
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):
|
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if not 0.0 <= lr:
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raise ValueError("Invalid learning rate: {}".format(lr))
|
||||
raise ValueError(f"Invalid learning rate: {lr}")
|
||||
if not 0.0 <= weight_decay:
|
||||
raise ValueError(
|
||||
"Invalid weight_decay value: {}".format(weight_decay)
|
||||
f"Invalid weight_decay value: {weight_decay}"
|
||||
)
|
||||
if not 0.0 <= eps:
|
||||
raise ValueError("Invalid epsilon value: {}".format(eps))
|
||||
raise ValueError(f"Invalid epsilon value: {eps}")
|
||||
if initial_accumulator_value != 0.0:
|
||||
raise ValueError("Initial accumulator value != 0.0 not supported!")
|
||||
if lr_decay != 0.0:
|
||||
raise ValueError("Lr Decay != 0.0 not supported!")
|
||||
super(Adagrad32bit, self).__init__(
|
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super().__init__(
|
||||
"adagrad",
|
||||
params,
|
||||
lr,
|
||||
|
|
|
|||
|
|
@ -28,7 +28,7 @@ class Adam(Optimizer2State):
|
|||
percentile_clipping=100,
|
||||
block_wise=True,
|
||||
):
|
||||
super(Adam, self).__init__(
|
||||
super().__init__(
|
||||
"adam",
|
||||
params,
|
||||
lr,
|
||||
|
|
@ -57,7 +57,7 @@ class Adam8bit(Optimizer2State):
|
|||
percentile_clipping=100,
|
||||
block_wise=True,
|
||||
):
|
||||
super(Adam8bit, self).__init__(
|
||||
super().__init__(
|
||||
"adam",
|
||||
params,
|
||||
lr,
|
||||
|
|
@ -86,7 +86,7 @@ class Adam32bit(Optimizer2State):
|
|||
percentile_clipping=100,
|
||||
block_wise=True,
|
||||
):
|
||||
super(Adam32bit, self).__init__(
|
||||
super().__init__(
|
||||
"adam",
|
||||
params,
|
||||
lr,
|
||||
|
|
@ -146,7 +146,7 @@ class AnalysisAdam(torch.optim.Optimizer):
|
|||
weight_decay=weight_decay,
|
||||
amsgrad=amsgrad,
|
||||
)
|
||||
super(AnalysisAdam, self).__init__(params, defaults)
|
||||
super().__init__(params, defaults)
|
||||
self.analysis = bnb_analysis
|
||||
self.savedir = savedir
|
||||
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@ class AdamW(Optimizer2State):
|
|||
percentile_clipping=100,
|
||||
block_wise=True,
|
||||
):
|
||||
super(AdamW, self).__init__(
|
||||
super().__init__(
|
||||
"adam",
|
||||
params,
|
||||
lr,
|
||||
|
|
@ -49,7 +49,7 @@ class AdamW8bit(Optimizer2State):
|
|||
percentile_clipping=100,
|
||||
block_wise=True,
|
||||
):
|
||||
super(AdamW8bit, self).__init__(
|
||||
super().__init__(
|
||||
"adam",
|
||||
params,
|
||||
lr,
|
||||
|
|
@ -78,7 +78,7 @@ class AdamW32bit(Optimizer2State):
|
|||
percentile_clipping=100,
|
||||
block_wise=True,
|
||||
):
|
||||
super(AdamW32bit, self).__init__(
|
||||
super().__init__(
|
||||
"adam",
|
||||
params,
|
||||
lr,
|
||||
|
|
|
|||
|
|
@ -23,7 +23,7 @@ class LAMB(Optimizer2State):
|
|||
block_wise=False,
|
||||
max_unorm=1.0,
|
||||
):
|
||||
super(LAMB, self).__init__(
|
||||
super().__init__(
|
||||
"lamb",
|
||||
params,
|
||||
lr,
|
||||
|
|
@ -56,7 +56,7 @@ class LAMB8bit(Optimizer2State):
|
|||
block_wise=False,
|
||||
max_unorm=1.0,
|
||||
):
|
||||
super(LAMB8bit, self).__init__(
|
||||
super().__init__(
|
||||
"lamb",
|
||||
params,
|
||||
lr,
|
||||
|
|
@ -89,7 +89,7 @@ class LAMB32bit(Optimizer2State):
|
|||
block_wise=False,
|
||||
max_unorm=1.0,
|
||||
):
|
||||
super(LAMB32bit, self).__init__(
|
||||
super().__init__(
|
||||
"lamb",
|
||||
params,
|
||||
lr,
|
||||
|
|
|
|||
|
|
@ -25,9 +25,9 @@ class LARS(Optimizer1State):
|
|||
):
|
||||
if momentum == 0:
|
||||
raise NotImplementedError(
|
||||
f"LARS without momentum is not supported!"
|
||||
"LARS without momentum is not supported!"
|
||||
)
|
||||
super(LARS, self).__init__(
|
||||
super().__init__(
|
||||
"lars",
|
||||
params,
|
||||
lr,
|
||||
|
|
@ -59,9 +59,9 @@ class LARS8bit(Optimizer1State):
|
|||
):
|
||||
if momentum == 0:
|
||||
raise NotImplementedError(
|
||||
f"LARS without momentum is not supported!"
|
||||
"LARS without momentum is not supported!"
|
||||
)
|
||||
super(LARS8bit, self).__init__(
|
||||
super().__init__(
|
||||
"lars",
|
||||
params,
|
||||
lr,
|
||||
|
|
@ -93,9 +93,9 @@ class LARS32bit(Optimizer1State):
|
|||
):
|
||||
if momentum == 0:
|
||||
raise NotImplementedError(
|
||||
f"LARS without momentum is not supported!"
|
||||
"LARS without momentum is not supported!"
|
||||
)
|
||||
super(LARS32bit, self).__init__(
|
||||
super().__init__(
|
||||
"lars",
|
||||
params,
|
||||
lr,
|
||||
|
|
@ -123,12 +123,12 @@ class PytorchLARS(Optimizer):
|
|||
max_unorm=0.02,
|
||||
):
|
||||
if lr < 0.0:
|
||||
raise ValueError("Invalid learning rate: {}".format(lr))
|
||||
raise ValueError(f"Invalid learning rate: {lr}")
|
||||
if momentum < 0.0:
|
||||
raise ValueError("Invalid momentum value: {}".format(momentum))
|
||||
raise ValueError(f"Invalid momentum value: {momentum}")
|
||||
if weight_decay < 0.0:
|
||||
raise ValueError(
|
||||
"Invalid weight_decay value: {}".format(weight_decay)
|
||||
f"Invalid weight_decay value: {weight_decay}"
|
||||
)
|
||||
|
||||
defaults = dict(
|
||||
|
|
@ -143,10 +143,10 @@ class PytorchLARS(Optimizer):
|
|||
raise ValueError(
|
||||
"Nesterov momentum requires a momentum and zero dampening"
|
||||
)
|
||||
super(PytorchLARS, self).__init__(params, defaults)
|
||||
super().__init__(params, defaults)
|
||||
|
||||
def __setstate__(self, state):
|
||||
super(PytorchLARS, self).__setstate__(state)
|
||||
super().__setstate__(state)
|
||||
for group in self.param_groups:
|
||||
group.setdefault("nesterov", False)
|
||||
|
||||
|
|
@ -181,7 +181,7 @@ class PytorchLARS(Optimizer):
|
|||
state = self.state[p]
|
||||
d_p = p.grad
|
||||
if weight_decay != 0:
|
||||
d_p = d_p.add(param, alpha=weight_decay)
|
||||
d_p = d_p.add(p, alpha=weight_decay)
|
||||
|
||||
if momentum != 0:
|
||||
buf = state.get("momentum_buffer", None)
|
||||
|
|
|
|||
|
|
@ -12,13 +12,13 @@ import torch
|
|||
import bitsandbytes.functional as F
|
||||
|
||||
|
||||
class MockArgs(object):
|
||||
class MockArgs:
|
||||
def __init__(self, initial_data):
|
||||
for key in initial_data:
|
||||
setattr(self, key, initial_data[key])
|
||||
|
||||
|
||||
class GlobalOptimManager(object):
|
||||
class GlobalOptimManager:
|
||||
_instance = None
|
||||
|
||||
def __init__(self):
|
||||
|
|
@ -56,9 +56,9 @@ class GlobalOptimManager(object):
|
|||
"""
|
||||
Overrides initial optimizer config for specific parameters.
|
||||
|
||||
The key-values of the optimizer config for the input parameters are overidden
|
||||
The key-values of the optimizer config for the input parameters are overridden
|
||||
This can be both, optimizer parameters like "betas", or "lr" or it can be
|
||||
8-bit specific paramters like "optim_bits", "percentile_clipping".
|
||||
8-bit specific parameters like "optim_bits", "percentile_clipping".
|
||||
|
||||
Parameters
|
||||
----------
|
||||
|
|
@ -93,13 +93,12 @@ class GlobalOptimManager(object):
|
|||
|
||||
class Optimizer8bit(torch.optim.Optimizer):
|
||||
def __init__(self, params, defaults, optim_bits=32):
|
||||
super(Optimizer8bit, self).__init__(params, defaults)
|
||||
super().__init__(params, defaults)
|
||||
self.initialized = False
|
||||
self.name2qmap = {}
|
||||
|
||||
self.mng = GlobalOptimManager.get_instance()
|
||||
self.non_castable_tensor_keys = set(
|
||||
[
|
||||
self.non_castable_tensor_keys = {
|
||||
"qmap1",
|
||||
"qmap2",
|
||||
"max1",
|
||||
|
|
@ -112,8 +111,7 @@ class Optimizer8bit(torch.optim.Optimizer):
|
|||
"absmax1",
|
||||
"absmax2",
|
||||
"unorm_vec",
|
||||
]
|
||||
)
|
||||
}
|
||||
|
||||
if optim_bits == 8:
|
||||
self.fill_qmap()
|
||||
|
|
@ -123,7 +121,7 @@ class Optimizer8bit(torch.optim.Optimizer):
|
|||
self.name2qmap["udynamic"] = F.create_dynamic_map(signed=False)
|
||||
|
||||
def __setstate__(self, state):
|
||||
super(Optimizer8bit, self).__setstate__(state)
|
||||
super().__setstate__(state)
|
||||
|
||||
def load_state_dict(self, state_dict):
|
||||
r"""Loads the optimizer state.
|
||||
|
|
@ -155,8 +153,8 @@ class Optimizer8bit(torch.optim.Optimizer):
|
|||
id_map = {
|
||||
old_id: p
|
||||
for old_id, p in zip(
|
||||
chain.from_iterable((g["params"] for g in saved_groups)),
|
||||
chain.from_iterable((g["params"] for g in groups)),
|
||||
chain.from_iterable(g["params"] for g in saved_groups),
|
||||
chain.from_iterable(g["params"] for g in groups),
|
||||
)
|
||||
}
|
||||
|
||||
|
|
@ -284,11 +282,11 @@ class Optimizer8bit(torch.optim.Optimizer):
|
|||
return config
|
||||
|
||||
def init_state(self, group, p, gindex, pindex):
|
||||
raise NotImplementedError(f"init_state method needs to be overidden")
|
||||
raise NotImplementedError("init_state method needs to be overridden")
|
||||
|
||||
def update_step(self, group, p, gindex, pindex):
|
||||
raise NotImplementedError(
|
||||
f"The update_step method needs to be overidden"
|
||||
"The update_step method needs to be overridden"
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -310,9 +308,9 @@ class Optimizer2State(Optimizer8bit):
|
|||
skip_zeros=False,
|
||||
):
|
||||
if not 0.0 <= lr:
|
||||
raise ValueError("Invalid learning rate: {}".format(lr))
|
||||
raise ValueError(f"Invalid learning rate: {lr}")
|
||||
if not 0.0 <= eps:
|
||||
raise ValueError("Invalid epsilon value: {}".format(eps))
|
||||
raise ValueError(f"Invalid epsilon value: {eps}")
|
||||
if isinstance(betas, str):
|
||||
# format: '(beta1, beta2)'
|
||||
betas = betas.replace("(", "").replace(")", "").strip().split(",")
|
||||
|
|
@ -324,10 +322,10 @@ class Optimizer2State(Optimizer8bit):
|
|||
)
|
||||
if not 0.0 <= weight_decay:
|
||||
raise ValueError(
|
||||
"Invalid weight_decay value: {}".format(weight_decay)
|
||||
f"Invalid weight_decay value: {weight_decay}"
|
||||
)
|
||||
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
|
||||
super(Optimizer2State, self).__init__(params, defaults, optim_bits)
|
||||
super().__init__(params, defaults, optim_bits)
|
||||
|
||||
if args is None:
|
||||
args = {}
|
||||
|
|
@ -542,9 +540,9 @@ class Optimizer1State(Optimizer8bit):
|
|||
skip_zeros=False,
|
||||
):
|
||||
if not 0.0 <= lr:
|
||||
raise ValueError("Invalid learning rate: {}".format(lr))
|
||||
raise ValueError(f"Invalid learning rate: {lr}")
|
||||
if not 0.0 <= eps:
|
||||
raise ValueError("Invalid epsilon value: {}".format(eps))
|
||||
raise ValueError(f"Invalid epsilon value: {eps}")
|
||||
for i in range(len(betas)):
|
||||
if not 0.0 <= betas[i] < 1.0:
|
||||
raise ValueError(
|
||||
|
|
@ -552,10 +550,10 @@ class Optimizer1State(Optimizer8bit):
|
|||
)
|
||||
if not 0.0 <= weight_decay:
|
||||
raise ValueError(
|
||||
"Invalid weight_decay value: {}".format(weight_decay)
|
||||
f"Invalid weight_decay value: {weight_decay}"
|
||||
)
|
||||
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
|
||||
super(Optimizer1State, self).__init__(params, defaults, optim_bits)
|
||||
super().__init__(params, defaults, optim_bits)
|
||||
|
||||
if args is None:
|
||||
args = {}
|
||||
|
|
|
|||
|
|
@ -23,11 +23,11 @@ class RMSprop(Optimizer1State):
|
|||
):
|
||||
if alpha == 0:
|
||||
raise NotImplementedError(
|
||||
f"RMSprop with alpha==0.0 is not supported!"
|
||||
"RMSprop with alpha==0.0 is not supported!"
|
||||
)
|
||||
if centered:
|
||||
raise NotImplementedError(f"Centered RMSprop is not supported!")
|
||||
super(RMSprop, self).__init__(
|
||||
raise NotImplementedError("Centered RMSprop is not supported!")
|
||||
super().__init__(
|
||||
"rmsprop",
|
||||
params,
|
||||
lr,
|
||||
|
|
@ -59,11 +59,11 @@ class RMSprop8bit(Optimizer1State):
|
|||
):
|
||||
if alpha == 0:
|
||||
raise NotImplementedError(
|
||||
f"RMSprop with alpha==0.0 is not supported!"
|
||||
"RMSprop with alpha==0.0 is not supported!"
|
||||
)
|
||||
if centered:
|
||||
raise NotImplementedError(f"Centered RMSprop is not supported!")
|
||||
super(RMSprop8bit, self).__init__(
|
||||
raise NotImplementedError("Centered RMSprop is not supported!")
|
||||
super().__init__(
|
||||
"rmsprop",
|
||||
params,
|
||||
lr,
|
||||
|
|
@ -96,11 +96,11 @@ class RMSprop32bit(Optimizer1State):
|
|||
|
||||
if alpha == 0:
|
||||
raise NotImplementedError(
|
||||
f"RMSprop with alpha==0.0 is not supported!"
|
||||
"RMSprop with alpha==0.0 is not supported!"
|
||||
)
|
||||
if centered:
|
||||
raise NotImplementedError(f"Centered RMSprop is not supported!")
|
||||
super(RMSprop32bit, self).__init__(
|
||||
raise NotImplementedError("Centered RMSprop is not supported!")
|
||||
super().__init__(
|
||||
"rmsprop",
|
||||
params,
|
||||
lr,
|
||||
|
|
|
|||
|
|
@ -21,8 +21,8 @@ class SGD(Optimizer1State):
|
|||
block_wise=True,
|
||||
):
|
||||
if momentum == 0:
|
||||
raise NotImplementedError(f"SGD without momentum is not supported!")
|
||||
super(SGD, self).__init__(
|
||||
raise NotImplementedError("SGD without momentum is not supported!")
|
||||
super().__init__(
|
||||
"momentum",
|
||||
params,
|
||||
lr,
|
||||
|
|
@ -52,8 +52,8 @@ class SGD8bit(Optimizer1State):
|
|||
block_wise=True,
|
||||
):
|
||||
if momentum == 0:
|
||||
raise NotImplementedError(f"SGD without momentum is not supported!")
|
||||
super(SGD8bit, self).__init__(
|
||||
raise NotImplementedError("SGD without momentum is not supported!")
|
||||
super().__init__(
|
||||
"momentum",
|
||||
params,
|
||||
lr,
|
||||
|
|
@ -83,8 +83,8 @@ class SGD32bit(Optimizer1State):
|
|||
block_wise=True,
|
||||
):
|
||||
if momentum == 0:
|
||||
raise NotImplementedError(f"SGD without momentum is not supported!")
|
||||
super(SGD32bit, self).__init__(
|
||||
raise NotImplementedError("SGD without momentum is not supported!")
|
||||
super().__init__(
|
||||
"momentum",
|
||||
params,
|
||||
lr,
|
||||
|
|
|
|||
|
|
@ -121,5 +121,3 @@ template <int THREADS, int ITEMS_PER_THREAD, int TILE_ROWS, int TILE_COLS, int T
|
|||
template <int FORMAT> __global__ void kExtractOutliers(char *A, int *idx, char *out, int idx_size, int rowsA, int colsA, int tiledRowsA, int tiledColsA);
|
||||
|
||||
#endif
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -290,4 +290,3 @@ extern "C"
|
|||
void cquantize_blockwise_cpu_fp32(float *code, float *A, float *absmax, unsigned char *out, long long blocksize, long long n){ quantize_cpu(code, A, absmax, out, blocksize, n); }
|
||||
void cdequantize_blockwise_cpu_fp32(float *code, unsigned char *A, float *absmax, float *out, long long blocksize, long long n){ dequantize_cpu(code, A, absmax, out, blocksize, n); }
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -76,6 +76,3 @@ if [[ -n "$CUDA_VERSION" ]]; then
|
|||
else
|
||||
echo ""
|
||||
fi
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
3
setup.py
3
setup.py
|
|
@ -26,9 +26,6 @@ setup(
|
|||
keywords="gpu optimizers optimization 8-bit quantization compression",
|
||||
url="https://github.com/TimDettmers/bitsandbytes",
|
||||
packages=find_packages(),
|
||||
entry_points={
|
||||
"console_scripts": ["debug_cuda = bitsandbytes.debug_cli:cli"],
|
||||
},
|
||||
package_data={"": libs},
|
||||
long_description=read("README.md"),
|
||||
long_description_content_type="text/markdown",
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
from itertools import product, permutations
|
||||
from itertools import permutations, product
|
||||
|
||||
import pytest
|
||||
import torch
|
||||
|
|
@ -27,7 +27,7 @@ str_values = list(
|
|||
)
|
||||
)
|
||||
names = [
|
||||
"dim1_{0}_dim2_{1}_dim3_{2}_dim4_{3}_func_{4}_dtype_{5}_requires_grad_{6}_transpose_{7}".format(
|
||||
"dim1_{}_dim2_{}_dim3_{}_dim4_{}_func_{}_dtype_{}_requires_grad_{}_transpose_{}".format(
|
||||
*vals
|
||||
)
|
||||
for vals in str_values
|
||||
|
|
@ -286,7 +286,7 @@ str_values = list(
|
|||
has_bias
|
||||
)
|
||||
)
|
||||
names = ["dim1_{0}_dim2_{1}_dim3_{2}_dim4_{3}_func_{4}_dtype_{5}_requires_grad_{6}_transpose_{7}_decomp_{8}_has_fp16_weights_{9}_has_bias_{10}".format(*vals) for vals in str_values]
|
||||
names = ["dim1_{}_dim2_{}_dim3_{}_dim4_{}_func_{}_dtype_{}_requires_grad_{}_transpose_{}_decomp_{}_has_fp16_weights_{}_has_bias_{}".format(*vals) for vals in str_values]
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
|
|
|
|||
|
|
@ -1,13 +1,13 @@
|
|||
import os
|
||||
import pytest
|
||||
import bitsandbytes as bnb
|
||||
|
||||
from typing import List, NamedTuple
|
||||
|
||||
import pytest
|
||||
|
||||
import bitsandbytes as bnb
|
||||
from bitsandbytes.cuda_setup import (
|
||||
CUDA_RUNTIME_LIB,
|
||||
evaluate_cuda_setup,
|
||||
determine_cuda_runtime_lib_path,
|
||||
evaluate_cuda_setup,
|
||||
extract_candidate_paths,
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -28,7 +28,7 @@ def assert_all_approx_close(a, b, rtol=1e-3, atol=1e-3, count=0):
|
|||
|
||||
class FFN(torch.nn.Module):
|
||||
def __init__(self, input_features, hidden_size, bias=True):
|
||||
super(FFN, self).__init__()
|
||||
super().__init__()
|
||||
self.fc1 = torch.nn.Linear(input_features, hidden_size, bias=bias)
|
||||
self.fc2 = torch.nn.Linear(hidden_size, input_features, bias=bias)
|
||||
|
||||
|
|
@ -42,7 +42,7 @@ class FFN(torch.nn.Module):
|
|||
return x
|
||||
|
||||
|
||||
class Timer(object):
|
||||
class Timer:
|
||||
def __init__(self):
|
||||
self.starts = {}
|
||||
self.ends = {}
|
||||
|
|
@ -69,7 +69,7 @@ class Timer(object):
|
|||
self.ends.pop(name)
|
||||
|
||||
if print_ms and name in self.agg:
|
||||
print("{0} took: {1:.5f}s".format(name, self.agg[name] / 1000.0))
|
||||
print(f"{name} took: {self.agg[name] / 1000.0:.5f}s")
|
||||
|
||||
return self.agg[name]
|
||||
|
||||
|
|
@ -302,7 +302,7 @@ batched = [False, True]
|
|||
values = list(product(dim1, dim2, methods, batched))
|
||||
values_names = list(product(dim1, dim2, method_names, batched))
|
||||
names = [
|
||||
"dim1_{0}_dim2_{1}_quant_{2}_batched_{3}".format(*vals)
|
||||
"dim1_{}_dim2_{}_quant_{}_batched_{}".format(*vals)
|
||||
for vals in values_names
|
||||
]
|
||||
|
||||
|
|
@ -360,7 +360,7 @@ seq_dim = torch.randint(16, 256, size=(n,)).tolist()
|
|||
transpose = [(False, False), (False, True), (True, False), (True, True)]
|
||||
values = list(product(hidden_dim, batch_dim, transpose, seq_dim))
|
||||
names = [
|
||||
"hidden_dim_{0}_batch_dim_{1},transpose_{2}_seq_dim_{3}".format(*vals)
|
||||
"hidden_dim_{}_batch_dim_{},transpose_{}_seq_dim_{}".format(*vals)
|
||||
for vals in values
|
||||
]
|
||||
|
||||
|
|
@ -425,7 +425,7 @@ hidden_dim = torch.randint(32, 1024 * 4, size=(n,)).tolist()
|
|||
batch_dim = torch.randint(2, 16, size=(n,)).tolist()
|
||||
values = list(product(seq_dim, hidden_dim, batch_dim))
|
||||
names = [
|
||||
"seq_dim{0}_hidden_dim{1}_batch_dim{2}".format(*vals) for vals in values
|
||||
"seq_dim{}_hidden_dim{}_batch_dim{}".format(*vals) for vals in values
|
||||
]
|
||||
|
||||
|
||||
|
|
@ -457,7 +457,7 @@ batch_dim = torch.randint(2, 16, size=(n,)).tolist()
|
|||
transpose = [False, True]
|
||||
values = list(product(seq_dim, hidden_dim, batch_dim, transpose))
|
||||
names = [
|
||||
"seq_dim={0}_hidden_dim={1}_batch_dim={2}_transpose{3}".format(*vals)
|
||||
"seq_dim={}_hidden_dim={}_batch_dim={}_transpose{}".format(*vals)
|
||||
for vals in values
|
||||
]
|
||||
|
||||
|
|
@ -542,7 +542,7 @@ dim4 = torch.randint(32, 256, size=(n,)).tolist()
|
|||
transpose = [(False, False), (True, False), (False, True), (True, True)]
|
||||
values = list(product(dim1, dim2, dim3, dim4, transpose))
|
||||
names = [
|
||||
"dim1_{0}_dim2_{1}_dim3_{2}_dim4_{3}_transpose_{4}".format(*vals)
|
||||
"dim1_{}_dim2_{}_dim3_{}_dim4_{}_transpose_{}".format(*vals)
|
||||
for vals in values
|
||||
]
|
||||
|
||||
|
|
@ -580,7 +580,7 @@ dim1 = torch.randint(1, 64, size=(n,)).tolist()
|
|||
dim2 = torch.randint(32, 128, size=(n,)).tolist()
|
||||
dim3 = torch.randint(32, 256, size=(n,)).tolist()
|
||||
values = list(product(dim1, dim2, dim3))
|
||||
names = ["dim1_{0}_dim2_{1}_dim3_{2}".format(*vals) for vals in values]
|
||||
names = ["dim1_{}_dim2_{}_dim3_{}".format(*vals) for vals in values]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("dim1, dim2, dim3", values, ids=names)
|
||||
|
|
@ -609,7 +609,7 @@ transpose = [False]
|
|||
dims = [2, 3]
|
||||
values = list(product(dim1, dim2, dim3, dims, dtype, a_order, out_order, transpose))
|
||||
|
||||
names = ["dim1_{0}_dim2_{1}_dim3_{2}_dims_{3}_dtype_{4}_orderA_{5}_orderOut_{6}_transpose_{7}".format(*vals)for vals in values]
|
||||
names = ["dim1_{}_dim2_{}_dim3_{}_dims_{}_dtype_{}_orderA_{}_orderOut_{}_transpose_{}".format(*vals)for vals in values]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("dim1, dim2, dim3, dims, dtype, orderA, orderOut, transpose",values,ids=names)
|
||||
|
|
@ -691,7 +691,7 @@ ldb = [0]
|
|||
# ldb = list(range(256, 1*1024, 256))
|
||||
values = list(product(dim1, dim2, dim3, dim4, dims, ldb))
|
||||
names = [
|
||||
"dim1_{0}_dim2_{1}_dim3_{2}_dim4_{3}_dims_{4}_ldb_{5}".format(*vals)
|
||||
"dim1_{}_dim2_{}_dim3_{}_dim4_{}_dims_{}_ldb_{}".format(*vals)
|
||||
for vals in values
|
||||
]
|
||||
|
||||
|
|
@ -739,7 +739,7 @@ dims = (2,)
|
|||
# ldb = list(range(256, 1*1024, 256))
|
||||
values = list(product(dim1, dim2, dim3, dim4, dims))
|
||||
names = [
|
||||
"dim1_{0}_dim2_{1}_dim3_{2}_dim4_{3}_dims_{4}".format(*vals)
|
||||
"dim1_{}_dim2_{}_dim3_{}_dim4_{}_dims_{}".format(*vals)
|
||||
for vals in values
|
||||
]
|
||||
|
||||
|
|
@ -797,7 +797,7 @@ values = [
|
|||
|
||||
# values = list(product(batch, seq, model, hidden))
|
||||
names = [
|
||||
"batch_{0}_seq_{1}_model_{2}_hidden_{3}".format(*vals) for vals in values
|
||||
"batch_{}_seq_{}_model_{}_hidden_{}".format(*vals) for vals in values
|
||||
]
|
||||
|
||||
|
||||
|
|
@ -965,7 +965,7 @@ dims = (2,)
|
|||
formatB = ["col_turing", "col_ampere"]
|
||||
has_bias = [True, False]
|
||||
values = list(product(dim1, dim4, dims, formatB, has_bias))
|
||||
names = ["dim1_{0}_dim4_{1}_dims_{2}_formatB_{3}_has_bias_{4}".format(*vals) for vals in values]
|
||||
names = ["dim1_{}_dim4_{}_dims_{}_formatB_{}_has_bias_{}".format(*vals) for vals in values]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("dim1, dim4, dims, formatB, has_bias", values, ids=names)
|
||||
|
|
@ -1015,7 +1015,7 @@ dim2 = [1 * 1024]
|
|||
dims = (2,)
|
||||
# ldb = list(range(256, 1*1024, 256))
|
||||
values = list(product(dim1, dim2, dims))
|
||||
names = ["dim1_{0}_dim2_{1}_dims_{2}".format(*vals) for vals in values]
|
||||
names = ["dim1_{}_dim2_{}_dims_{}".format(*vals) for vals in values]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("dim1, dim2, dims", values, ids=names)
|
||||
|
|
@ -1071,7 +1071,7 @@ dim1 = torch.randint(1, 4 * 1024, size=(n,)).tolist()
|
|||
dim2 = torch.randint(1, 4 * 1024, size=(n,)).tolist()
|
||||
|
||||
values = list(product(dim1, dim2))
|
||||
names = ["dim1_{0}_dim2_{1}".format(*vals) for vals in values]
|
||||
names = ["dim1_{}_dim2_{}".format(*vals) for vals in values]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("dim1, dim2", values, ids=names)
|
||||
|
|
@ -1118,7 +1118,7 @@ dim4 = torch.randint(1, 4 * 1024, size=(n,)).tolist()
|
|||
inner = torch.randint(1, 4 * 1024, size=(n,)).tolist()
|
||||
|
||||
values = list(zip(dim1, dim4, inner))
|
||||
names = ["dim1_{0}_dim4_{1}_inner_{2}".format(*vals) for vals in values]
|
||||
names = ["dim1_{}_dim4_{}_inner_{}".format(*vals) for vals in values]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("dim1, dim4, inner", values, ids=names)
|
||||
|
|
@ -1162,7 +1162,7 @@ dim4 = torch.randint(1, 4 * 1024, size=(n,)).tolist()
|
|||
inner = torch.randint(1, 4 * 1024, size=(n,)).tolist()
|
||||
|
||||
values = list(zip(dim1, dim4, inner))
|
||||
names = ["dim1_{0}_dim4_{1}_inner_{2}".format(*vals) for vals in values]
|
||||
names = ["dim1_{}_dim4_{}_inner_{}".format(*vals) for vals in values]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("dim1, dim4, inner", values, ids=names)
|
||||
|
|
@ -1237,7 +1237,7 @@ inner = [12288 * 4, 4096 * 4]
|
|||
dim4 = [12288, 4096]
|
||||
|
||||
values = list(zip(dim1, dim4, inner))
|
||||
names = ["dim1_{0}_dim4_{1}_inner_{2}".format(*vals) for vals in values]
|
||||
names = ["dim1_{}_dim4_{}_inner_{}".format(*vals) for vals in values]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("dim1, dim4, inner", values, ids=names)
|
||||
|
|
@ -1303,7 +1303,7 @@ values = list(
|
|||
product(dim1, dim2, dim3, dims, dtype, a_order, out_order, transpose)
|
||||
)
|
||||
names = [
|
||||
"dim1_{0}_dim2_{1}_dim3_{2}_dims_{3}_dtype_{4}_orderA_{5}_orderOut_{6}_{7}".format(
|
||||
"dim1_{}_dim2_{}_dim3_{}_dims_{}_dtype_{}_orderA_{}_orderOut_{}_{}".format(
|
||||
*vals
|
||||
)
|
||||
for vals in values
|
||||
|
|
@ -1354,7 +1354,7 @@ a_order = ["col_turing"]
|
|||
out_order = ["row"]
|
||||
values = list(product(dim1, dim2, dtype, a_order, out_order))
|
||||
names = [
|
||||
"dim1_{0}_dim2_{1}_dtype_{2}_orderA_{3}_orderOut_{4}".format(*vals)
|
||||
"dim1_{}_dim2_{}_dtype_{}_orderA_{}_orderOut_{}".format(*vals)
|
||||
for vals in values
|
||||
]
|
||||
|
||||
|
|
@ -1380,7 +1380,7 @@ dim2 = torch.randint(1, 4 * 1024, size=(n,)).tolist()
|
|||
# dim2 = [5]
|
||||
|
||||
values = list(product(dim1, dim2))
|
||||
names = ["dim1_{0}_dim2_{1}".format(*vals) for vals in values]
|
||||
names = ["dim1_{}_dim2_{}".format(*vals) for vals in values]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("dim1, dim2", values, ids=names)
|
||||
|
|
@ -1417,7 +1417,7 @@ dim2 = torch.randint(1, 1 * 1024, size=(n,)).tolist()
|
|||
# dim2 = [11]
|
||||
transposed_B = [False, True]
|
||||
values = list(product(dim1, dim2, transposed_B))
|
||||
names = ["dim1_{0}_dim2_{1}_transposed_B_{2}".format(*vals) for vals in values]
|
||||
names = ["dim1_{}_dim2_{}_transposed_B_{}".format(*vals) for vals in values]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("dim1, dim2, transposed_B", values, ids=names)
|
||||
|
|
@ -1498,7 +1498,7 @@ n = 2
|
|||
dim1 = torch.randint(256, 1 * 1024, size=(n,)).tolist()
|
||||
dim2 = torch.randint(256, 1 * 1024, size=(n,)).tolist()
|
||||
values = list(product(dim1, dim2))
|
||||
names = ["dim1_{0}_dim2_{1}".format(*vals) for vals in values]
|
||||
names = ["dim1_{}_dim2_{}".format(*vals) for vals in values]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("dim1, dim2", values, ids=names)
|
||||
|
|
@ -1563,7 +1563,7 @@ dtype = [torch.float16]
|
|||
out_function = ["zeros", "ones"]
|
||||
values = list(product(dim1, dim2, dtype, out_function))
|
||||
names = [
|
||||
"dim1_{0}_dim2_{1}_dtype_{2}_out_func_{3}".format(*vals) for vals in values
|
||||
"dim1_{}_dim2_{}_dtype_{}_out_func_{}".format(*vals) for vals in values
|
||||
]
|
||||
|
||||
|
||||
|
|
@ -1680,7 +1680,7 @@ dim2 = [2048]
|
|||
# dim2 = [2]
|
||||
dtype = [torch.int8]
|
||||
values = list(product(dim1, dim2, dtype))
|
||||
names = ["dim1_{0}_dim2_{1}_dtype_{2}".format(*vals) for vals in values]
|
||||
names = ["dim1_{}_dim2_{}_dtype_{}".format(*vals) for vals in values]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("dim1, dim2, dtype", values, ids=names)
|
||||
|
|
@ -1796,7 +1796,7 @@ values.append((batch_size, seqdim, 768, 4 * 768))
|
|||
# values.append((batch_size, seqdim, 5140, 4*5140))
|
||||
#values.append((batch_size, seqdim, 12288, 4*12288))
|
||||
names = [
|
||||
"batch_{0}_seq_{1}_model_{2}_hidden_{3}".format(*vals) for vals in values
|
||||
"batch_{}_seq_{}_model_{}_hidden_{}".format(*vals) for vals in values
|
||||
]
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@ from torch import nn
|
|||
import bitsandbytes as bnb
|
||||
|
||||
|
||||
class MockArgs(object):
|
||||
class MockArgs:
|
||||
def __init__(self, initial_data):
|
||||
for key in initial_data:
|
||||
setattr(self, key, initial_data[key])
|
||||
|
|
@ -15,7 +15,7 @@ class MockArgs(object):
|
|||
|
||||
class MLP8bit(torch.nn.Module):
|
||||
def __init__(self, dim1, dim2, has_fp16_weights=True, memory_efficient_backward=False, threshold=0.0):
|
||||
super(MLP8bit, self).__init__()
|
||||
super().__init__()
|
||||
self.fc1 = bnb.nn.Linear8bitLt(
|
||||
dim1, dim2, has_fp16_weights=has_fp16_weights, memory_efficient_backward=memory_efficient_backward,
|
||||
threshold=threshold
|
||||
|
|
@ -289,7 +289,7 @@ class LinearFunction(torch.autograd.Function):
|
|||
|
||||
class Linear8bit(nn.Module):
|
||||
def __init__(self, input_features, output_features, bias=True, args=None):
|
||||
super(Linear8bit, self).__init__()
|
||||
super().__init__()
|
||||
self.input_features = input_features
|
||||
self.output_features = output_features
|
||||
self.args = args
|
||||
|
|
@ -312,7 +312,7 @@ class Linear8bit(nn.Module):
|
|||
|
||||
threshold = [0.0, 3.0]
|
||||
values = threshold
|
||||
names = ["threshold_{0}".format(vals) for vals in values]
|
||||
names = [f"threshold_{vals}" for vals in values]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("threshold", values, ids=names)
|
||||
|
|
@ -378,7 +378,7 @@ def test_linear8bitlt_accumulated_gradient():
|
|||
|
||||
threshold = [0.0, 2.0]
|
||||
values = threshold
|
||||
names = ["threshold_{0}".format(vals) for vals in values]
|
||||
names = [f"threshold_{vals}" for vals in values]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("threshold", values, ids=names)
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@ k = 20
|
|||
|
||||
|
||||
def get_temp_dir():
|
||||
path = "/tmp/autoswap/{0}".format(str(uuid.uuid4()))
|
||||
path = f"/tmp/autoswap/{str(uuid.uuid4())}"
|
||||
os.makedirs(path, exist_ok=True)
|
||||
return path
|
||||
|
||||
|
|
@ -116,7 +116,7 @@ gtype = [torch.float32, torch.float16]
|
|||
optimizer_names = ["adam", "momentum", "rmsprop", "lars"]
|
||||
values = list(product(dim1, dim2, gtype, optimizer_names))
|
||||
names = [
|
||||
"dim1_{0}_dim2_{1}_gtype_{2}_optim_{3}".format(*vals) for vals in values
|
||||
"dim1_{}_dim2_{}_gtype_{}_optim_{}".format(*vals) for vals in values
|
||||
]
|
||||
|
||||
|
||||
|
|
@ -187,7 +187,7 @@ dim1 = [1024]
|
|||
dim2 = [32, 1024, 4097]
|
||||
gtype = [torch.float32, torch.float16]
|
||||
values = list(product(dim1, dim2, gtype))
|
||||
names = ["dim1_{0}_dim2_{1}_gtype_{2}".format(*vals) for vals in values]
|
||||
names = ["dim1_{}_dim2_{}_gtype_{}".format(*vals) for vals in values]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("dim1, dim2, gtype", values, ids=names)
|
||||
|
|
@ -250,7 +250,7 @@ optimizer_names = [
|
|||
]
|
||||
values = list(product(dim1, dim2, gtype, optimizer_names))
|
||||
names = [
|
||||
"dim1_{0}_dim2_{1}_gtype_{2}_optim_{3}".format(*vals) for vals in values
|
||||
"dim1_{}_dim2_{}_gtype_{}_optim_{}".format(*vals) for vals in values
|
||||
]
|
||||
|
||||
|
||||
|
|
@ -391,7 +391,7 @@ gtype = [torch.float32]
|
|||
optim_bits = [32, 8]
|
||||
values = list(product(dim1, dim2, gtype, optim_bits))
|
||||
names = [
|
||||
"dim1_{0}_dim2_{1}_gtype_{2}_optim_bits_{3}".format(*vals)
|
||||
"dim1_{}_dim2_{}_gtype_{}_optim_bits_{}".format(*vals)
|
||||
for vals in values
|
||||
]
|
||||
|
||||
|
|
@ -495,7 +495,7 @@ gtype = [torch.float32, torch.float16]
|
|||
optimizer_names = ["adam8bit_blockwise"]
|
||||
values = list(product(dim1, dim2, gtype, optimizer_names))
|
||||
names = [
|
||||
"dim1_{0}_dim2_{1}_gtype_{2}_optim_{3}".format(*vals) for vals in values
|
||||
"dim1_{}_dim2_{}_gtype_{}_optim_{}".format(*vals) for vals in values
|
||||
]
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,149 +0,0 @@
|
|||
./setup.py:20:10: F541 f-string is missing placeholders
|
||||
./setup.py:21:13: F541 f-string is missing placeholders
|
||||
./quicktest.py:5:1: F401 'bitsandbytes as bnb' imported but unused
|
||||
./bitsandbytes/cuda_setup.py:42:56: F821 undefined name 'error_str'
|
||||
./bitsandbytes/cuda_setup.py:43:15: F541 f-string is missing placeholders
|
||||
./bitsandbytes/cuda_setup.py:67:5: F841 local variable 'context' is assigned to but never used
|
||||
./bitsandbytes/cuda_setup.py:68:5: F841 local variable 'error_str' is assigned to but never used
|
||||
./bitsandbytes/cuda_setup.py:76:9: F841 local variable 'result' is assigned to but never used
|
||||
./bitsandbytes/cuda_setup.py:144:13: F841 local variable 'has_gpu' is assigned to but never used
|
||||
./bitsandbytes/functional.py:294:13: F821 undefined name 'math'
|
||||
./bitsandbytes/functional.py:295:16: F821 undefined name 'math'
|
||||
./bitsandbytes/functional.py:303:5: F841 local variable 'ptrA' is assigned to but never used
|
||||
./bitsandbytes/functional.py:304:5: F841 local variable 'ptrOut' is assigned to but never used
|
||||
./bitsandbytes/functional.py:1057:17: W503 line break before binary operator
|
||||
./bitsandbytes/functional.py:1058:17: W503 line break before binary operator
|
||||
./bitsandbytes/functional.py:1059:17: W503 line break before binary operator
|
||||
./bitsandbytes/functional.py:1649:1: F811 redefinition of unused 'get_special_format_str' from line 160
|
||||
./bitsandbytes/functional.py:1687:5: F841 local variable 'ptrA' is assigned to but never used
|
||||
./bitsandbytes/functional.py:1688:5: F841 local variable 'ptrOut' is assigned to but never used
|
||||
./bitsandbytes/functional.py:1802:5: F841 local variable 'ccolsA' is assigned to but never used
|
||||
./bitsandbytes/functional.py:1805:5: F841 local variable 'cldb' is assigned to but never used
|
||||
./bitsandbytes/functional.py:1806:5: F841 local variable 'cldc' is assigned to but never used
|
||||
./bitsandbytes/functional.py:1873:9: F841 local variable 'dtype' is assigned to but never used
|
||||
./bitsandbytes/__init__.py:6:1: F401 '.autograd._functions.MatmulLtState' imported but unused
|
||||
./bitsandbytes/__init__.py:6:1: F401 '.autograd._functions.bmm_cublas' imported but unused
|
||||
./bitsandbytes/__init__.py:6:1: F401 '.autograd._functions.matmul' imported but unused
|
||||
./bitsandbytes/__init__.py:6:1: F401 '.autograd._functions.matmul_cublas' imported but unused
|
||||
./bitsandbytes/__init__.py:6:1: F401 '.autograd._functions.mm_cublas' imported but unused
|
||||
./bitsandbytes/__init__.py:9:1: F401 '.nn.modules' imported but unused
|
||||
./bitsandbytes/__init__.py:12:5: F401 '.optim.adam' imported but unused
|
||||
./bitsandbytes/autograd/_functions.py:5:1: F401 'bitsandbytes as bnb' imported but unused
|
||||
./bitsandbytes/autograd/_functions.py:12:75: W291 trailing whitespace
|
||||
./bitsandbytes/nn/__init__.py:5:1: F401 '.modules.Int8Params' imported but unused
|
||||
./bitsandbytes/nn/__init__.py:5:1: F401 '.modules.Linear8bit' imported but unused
|
||||
./bitsandbytes/nn/__init__.py:5:1: F401 '.modules.Linear8bitLt' imported but unused
|
||||
./bitsandbytes/nn/__init__.py:5:1: F401 '.modules.StableEmbedding' imported but unused
|
||||
./bitsandbytes/nn/modules.py:5:1: F401 'typing.Any' imported but unused
|
||||
./bitsandbytes/nn/modules.py:5:1: F401 'typing.Callable' imported but unused
|
||||
./bitsandbytes/nn/modules.py:5:1: F401 'typing.Dict' imported but unused
|
||||
./bitsandbytes/nn/modules.py:5:1: F401 'typing.Iterator' imported but unused
|
||||
./bitsandbytes/nn/modules.py:5:1: F401 'typing.Mapping' imported but unused
|
||||
./bitsandbytes/nn/modules.py:5:1: F401 'typing.Set' imported but unused
|
||||
./bitsandbytes/nn/modules.py:5:1: F401 'typing.Tuple' imported but unused
|
||||
./bitsandbytes/nn/modules.py:11:1: F401 'torch.nn.parameter.Parameter' imported but unused
|
||||
./bitsandbytes/nn/modules.py:183:13: W503 line break before binary operator
|
||||
./bitsandbytes/nn/modules.py:184:13: W503 line break before binary operator
|
||||
./bitsandbytes/nn/modules.py:272:24: F821 undefined name 'dist'
|
||||
./bitsandbytes/nn/modules.py:272:49: F821 undefined name 'dist'
|
||||
./bitsandbytes/optim/optimizer.py:243:9: F841 local variable 'overflows' is assigned to but never used
|
||||
./bitsandbytes/optim/optimizer.py:280:35: F541 f-string is missing placeholders
|
||||
./bitsandbytes/optim/optimizer.py:283:35: F541 f-string is missing placeholders
|
||||
./bitsandbytes/optim/lars.py:27:39: F541 f-string is missing placeholders
|
||||
./bitsandbytes/optim/lars.py:59:39: F541 f-string is missing placeholders
|
||||
./bitsandbytes/optim/lars.py:91:39: F541 f-string is missing placeholders
|
||||
./bitsandbytes/optim/lars.py:157:13: F841 local variable 'params_with_grad' is assigned to but never used
|
||||
./bitsandbytes/optim/lars.py:158:13: F841 local variable 'd_p_list' is assigned to but never used
|
||||
./bitsandbytes/optim/lars.py:159:13: F841 local variable 'momentum_buffer_list' is assigned to but never used
|
||||
./bitsandbytes/optim/lars.py:174:35: F821 undefined name 'param'
|
||||
./bitsandbytes/optim/__init__.py:9:5: F401 '.adam.Adam' imported but unused
|
||||
./bitsandbytes/optim/__init__.py:9:5: F401 '.adam.Adam8bit' imported but unused
|
||||
./bitsandbytes/optim/__init__.py:9:5: F401 '.adam.Adam32bit' imported but unused
|
||||
./bitsandbytes/optim/__init__.py:10:5: F401 '.adamw.AdamW' imported but unused
|
||||
./bitsandbytes/optim/__init__.py:10:5: F401 '.adamw.AdamW8bit' imported but unused
|
||||
./bitsandbytes/optim/__init__.py:10:5: F401 '.adamw.AdamW32bit' imported but unused
|
||||
./bitsandbytes/optim/__init__.py:11:5: F401 '.sgd.SGD' imported but unused
|
||||
./bitsandbytes/optim/__init__.py:11:5: F401 '.sgd.SGD8bit' imported but unused
|
||||
./bitsandbytes/optim/__init__.py:11:5: F401 '.sgd.SGD32bit' imported but unused
|
||||
./bitsandbytes/optim/__init__.py:12:5: F401 '.lars.LARS' imported but unused
|
||||
./bitsandbytes/optim/__init__.py:12:5: F401 '.lars.LARS8bit' imported but unused
|
||||
./bitsandbytes/optim/__init__.py:12:5: F401 '.lars.LARS32bit' imported but unused
|
||||
./bitsandbytes/optim/__init__.py:12:5: F401 '.lars.PytorchLARS' imported but unused
|
||||
./bitsandbytes/optim/__init__.py:13:5: F401 '.lamb.LAMB' imported but unused
|
||||
./bitsandbytes/optim/__init__.py:13:5: F401 '.lamb.LAMB8bit' imported but unused
|
||||
./bitsandbytes/optim/__init__.py:13:5: F401 '.lamb.LAMB32bit' imported but unused
|
||||
./bitsandbytes/optim/__init__.py:14:5: F401 '.rmsprop.RMSprop' imported but unused
|
||||
./bitsandbytes/optim/__init__.py:14:5: F401 '.rmsprop.RMSprop8bit' imported but unused
|
||||
./bitsandbytes/optim/__init__.py:14:5: F401 '.rmsprop.RMSprop32bit' imported but unused
|
||||
./bitsandbytes/optim/__init__.py:15:5: F401 '.adagrad.Adagrad' imported but unused
|
||||
./bitsandbytes/optim/__init__.py:15:5: F401 '.adagrad.Adagrad8bit' imported but unused
|
||||
./bitsandbytes/optim/__init__.py:15:5: F401 '.adagrad.Adagrad32bit' imported but unused
|
||||
./bitsandbytes/optim/__init__.py:17:1: F401 '.optimizer.GlobalOptimManager' imported but unused
|
||||
./bitsandbytes/optim/adam.py:229:21: F841 local variable 'max_exp_avg_sq' is assigned to but never used
|
||||
./bitsandbytes/optim/rmsprop.py:25:39: F541 f-string is missing placeholders
|
||||
./bitsandbytes/optim/rmsprop.py:27:39: F541 f-string is missing placeholders
|
||||
./bitsandbytes/optim/rmsprop.py:59:39: F541 f-string is missing placeholders
|
||||
./bitsandbytes/optim/rmsprop.py:61:39: F541 f-string is missing placeholders
|
||||
./bitsandbytes/optim/rmsprop.py:94:39: F541 f-string is missing placeholders
|
||||
./bitsandbytes/optim/rmsprop.py:96:39: F541 f-string is missing placeholders
|
||||
./bitsandbytes/optim/sgd.py:24:39: F541 f-string is missing placeholders
|
||||
./bitsandbytes/optim/sgd.py:55:39: F541 f-string is missing placeholders
|
||||
./bitsandbytes/optim/sgd.py:86:39: F541 f-string is missing placeholders
|
||||
./tests/test_optim.py:1:1: F401 'ctypes' imported but unused
|
||||
./tests/test_optim.py:199:5: F841 local variable 'mask' is assigned to but never used
|
||||
./tests/test_optim.py:218:9: F841 local variable 'atol' is assigned to but never used
|
||||
./tests/test_optim.py:218:15: F841 local variable 'rtol' is assigned to but never used
|
||||
./tests/test_optim.py:304:17: W503 line break before binary operator
|
||||
./tests/test_optim.py:354:21: W503 line break before binary operator
|
||||
./tests/test_autograd.py:309:13: F841 local variable 'err' is assigned to but never used
|
||||
./tests/test_cuda_setup_evaluator.py:31:9: F821 undefined name 'test_dir'
|
||||
./tests/test_cuda_setup_evaluator.py:33:14: F821 undefined name 'test_input'
|
||||
./tests/test_cuda_setup_evaluator.py:81:32: E203 whitespace before ':'
|
||||
./tests/test_functional.py:55:13: F841 local variable 'ms' is assigned to but never used
|
||||
./tests/test_functional.py:177:5: F841 local variable 'diffs' is assigned to but never used
|
||||
./tests/test_functional.py:178:5: F841 local variable 'reldiffs' is assigned to but never used
|
||||
./tests/test_functional.py:260:5: F841 local variable 'minA' is assigned to but never used
|
||||
./tests/test_functional.py:261:5: F841 local variable 'maxA' is assigned to but never used
|
||||
./tests/test_functional.py:584:5: F841 local variable 'func' is assigned to but never used
|
||||
./tests/test_functional.py:617:17: F841 local variable 'offset' is assigned to but never used
|
||||
./tests/test_functional.py:618:17: F841 local variable 'col2' is assigned to but never used
|
||||
./tests/test_functional.py:619:17: F841 local variable 'row2' is assigned to but never used
|
||||
./tests/test_functional.py:705:9: F841 local variable 'C1' is assigned to but never used
|
||||
./tests/test_functional.py:706:9: F841 local variable 'C2' is assigned to but never used
|
||||
./tests/test_functional.py:715:9: F841 local variable 'output' is assigned to but never used
|
||||
./tests/test_functional.py:750:5: F841 local variable 'formatB' is assigned to but never used
|
||||
./tests/test_functional.py:754:5: F841 local variable 'w2' is assigned to but never used
|
||||
./tests/test_functional.py:763:5: F841 local variable 'dtype' is assigned to but never used
|
||||
./tests/test_functional.py:770:9: F841 local variable 'out1' is assigned to but never used
|
||||
./tests/test_functional.py:1108:5: F841 local variable 'relerr1' is assigned to but never used
|
||||
./tests/test_functional.py:1108:14: F841 local variable 'relerr2' is assigned to but never used
|
||||
./tests/test_functional.py:1114:9: F841 local variable 'C1' is assigned to but never used
|
||||
./tests/test_functional.py:1135:9: F841 local variable 'C4' is assigned to but never used
|
||||
./tests/test_functional.py:1179:5: F841 local variable 'err1' is assigned to but never used
|
||||
./tests/test_functional.py:1179:11: F841 local variable 'err2' is assigned to but never used
|
||||
./tests/test_functional.py:1179:17: F841 local variable 'err3' is assigned to but never used
|
||||
./tests/test_functional.py:1180:5: F841 local variable 'relerr1' is assigned to but never used
|
||||
./tests/test_functional.py:1180:14: F841 local variable 'relerr2' is assigned to but never used
|
||||
./tests/test_functional.py:1192:9: F841 local variable 'C1' is assigned to but never used
|
||||
./tests/test_functional.py:1313:9: F841 local variable 'c' is assigned to but never used
|
||||
./tests/test_functional.py:1314:9: F841 local variable 'c2' is assigned to but never used
|
||||
./tests/test_functional.py:1406:9: F841 local variable 'C1' is assigned to but never used
|
||||
./tests/test_functional.py:1425:9: F841 local variable 'out2' is assigned to but never used
|
||||
./tests/test_functional.py:1542:5: F841 local variable 'idx_col' is assigned to but never used
|
||||
./tests/test_functional.py:1566:30: E203 whitespace before ':'
|
||||
./tests/test_functional.py:1568:38: E203 whitespace before ':'
|
||||
./tests/test_functional.py:1655:5: F841 local variable 'offset' is assigned to but never used
|
||||
./tests/test_functional.py:1706:9: F841 local variable 'out' is assigned to but never used
|
||||
./tests/test_functional.py:1822:9: F841 local variable 'out' is assigned to but never used
|
||||
./tests/test_functional.py:1882:5: F841 local variable 'out2' is assigned to but never used
|
||||
./tests/test_functional.py:1928:9: F841 local variable 'dtype' is assigned to but never used
|
||||
./tests/test_functional.py:1934:9: F841 local variable 'minx' is assigned to but never used
|
||||
./tests/test_functional.py:1948:5: F841 local variable 'C0' is assigned to but never used
|
||||
./tests/test_modules.py:1:1: F401 'itertools.product' imported but unused
|
||||
./tests/test_modules.py:52:9: F841 local variable 'norm' is assigned to but never used
|
||||
./tests/test_modules.py:52:16: F821 undefined name 'math'
|
||||
./tests/test_modules.py:52:26: F821 undefined name 'math'
|
||||
./tests/test_modules.py:52:37: F821 undefined name 'math'
|
||||
./tests/test_modules.py:177:21: F821 undefined name 'einops'
|
||||
./tests/test_modules.py:233:9: F841 local variable 'stochastic' is assigned to but never used
|
||||
./tests/test_modules.py:382:9: F841 local variable 'o1' is assigned to but never used
|
||||
Loading…
Reference in New Issue
Block a user