reran black with linelength 80 for greater readability

bitsandbytes/__init__.py

@@ -3,8 +3,13 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
-from .autograd._functions import (MatmulLtState, bmm_cublas, matmul,
-                                  matmul_cublas, mm_cublas)
+from .autograd._functions import (
+    MatmulLtState,
+    bmm_cublas,
+    matmul,
+    matmul_cublas,
+    mm_cublas,
+)
 from .cextension import COMPILED_WITH_CUDA
 from .nn import modules
 

bitsandbytes/autograd/_functions.py

@@ -111,7 +111,9 @@ class MatMul8bit(torch.autograd.Function):
                     qgrad_output, S1 = F.vectorwise_quant(
                         grad_output, dim=dims, quant_type=quant_type
                     )
-                    qA, S2 = F.vectorwise_quant(A, dim=dims, quant_type=quant_type)
+                    qA, S2 = F.vectorwise_quant(
+                        A, dim=dims, quant_type=quant_type
+                    )
                     igrad_B = F.igemm(qA.permute(permute_dim), qgrad_output)
                     grad_B = F.vectorwise_mm_dequant(
                         igrad_B,
@@ -146,7 +148,11 @@ class MatMul8bit(torch.autograd.Function):
                 qB, S3 = F.vectorwise_quant(B, dim=dim_B, quant_type=quant_type)
                 igrad_A = F.igemm(qgrad_output, qB.permute(permute_dim))
                 grad_A = F.vectorwise_mm_dequant(
-                    igrad_A, S1, S3.permute(permute_dim), grad_output.dtype, quant_type
+                    igrad_A,
+                    S1,
+                    S3.permute(permute_dim),
+                    grad_output.dtype,
+                    quant_type,
                 )
 
         return grad_A, grad_B, None, None, None
@@ -211,7 +217,9 @@ class MatMul8bitLt(torch.autograd.Function):
         # 1. Quantize A
         if len(A.shape) == 3:
             A = A.view(-1, A.shape[-1]).contiguous()
-        CA, CAt, SCA, SCAt, coo_tensorA = F.double_quant(A, threshold=state.threshold)
+        CA, CAt, SCA, SCAt, coo_tensorA = F.double_quant(
+            A, threshold=state.threshold
+        )
 
         if state.threshold > 0.0 and coo_tensorA is not None:
             if state.has_fp16_weights:
@@ -225,7 +233,9 @@ class MatMul8bitLt(torch.autograd.Function):
                 if state.CxB is None:
                     # B in in 8-bit row-major, we can transform it back to 16-bit to extract outlier dimensions
                     # we also need to convert it to the turing/ampere format
-                    state.CxB, state.SB = F.transform(state.CB, to_order=formatB)
+                    state.CxB, state.SB = F.transform(
+                        state.CB, to_order=formatB
+                    )
                     # state.B = (state.CB.float()*(state.SCB.view(-1, 1)/127)).half()
                 # if state.threshold > 0.0 and coo_tensorA is not None and state.idx is None and state.CB is not None:
                 #    # generate outlier index and subB
@@ -259,7 +269,13 @@ class MatMul8bitLt(torch.autograd.Function):
 
             if (state.is_training and not has_grad) or state.CxB is None:
                 state.reset_grads()
-                CB, state.CBt, state.SCB, state.SCBt, coo_tensorB = F.double_quant(B)
+                (
+                    CB,
+                    state.CBt,
+                    state.SCB,
+                    state.SCBt,
+                    coo_tensorB,
+                ) = F.double_quant(B)
                 state.CxB, state.SB = F.transform(CB, to_order=formatB)
         else:
             has_grad = False
@@ -277,7 +293,10 @@ class MatMul8bitLt(torch.autograd.Function):
             #    state.idx = outlier_idx
             outliers = F.extract_outliers(state.CxB, state.SB, state.idx.int())
             state.subB = (
-                (outliers * state.SCB.view(-1, 1) / 127.0).t().contiguous().half()
+                (outliers * state.SCB.view(-1, 1) / 127.0)
+                .t()
+                .contiguous()
+                .half()
             )
             CA[:, state.idx.long()] = 0
             CAt[:, state.idx.long()] = 0
@@ -325,10 +344,14 @@ class MatMul8bitLt(torch.autograd.Function):
         SCAt, idx = ctx.tensor_states
         formatB = ctx.formatB
         state = ctx.state
-        assert state.has_fp16_weights, "Backprop only supported for fp16 weights."
+        assert (
+            state.has_fp16_weights
+        ), "Backprop only supported for fp16 weights."
 
         if len(grad_output.shape) == 3:
-            grad_output = grad_output.view(-1, grad_output.shape[-1]).contiguous()
+            grad_output = grad_output.view(
+                -1, grad_output.shape[-1]
+            ).contiguous()
 
         grad_A = grad_B = None
 
@@ -359,7 +382,11 @@ matmul = MatMul8bitLt.apply
 
 
 def matmul(
-    A: tensor, B: tensor, out: tensor = None, state: MatmulLtState = None, threshold=0.0
+    A: tensor,
+    B: tensor,
+    out: tensor = None,
+    state: MatmulLtState = None,
+    threshold=0.0,
 ):
     state = state or MatmulLtState()
     if threshold > 0.0:

bitsandbytes/cuda_setup.py

 """
-build is dependent on
-- compute capability
-    - dependent on GPU family
+extract factors the build is dependent on:
+[X] compute capability  
+    [ ] TODO: Q - What if we have multiple GPUs of different makes?
 - CUDA version
 - Software:
     - CPU-only: only CPU quantization functions (no optimizer, no matrix multipl)
@@ -19,6 +19,8 @@ evaluation:
 """
 
 import ctypes
+import shlex
+import subprocess
 from os import environ as env
 from pathlib import Path
 from typing import Set, Union
@@ -26,10 +28,31 @@ from typing import Set, Union
 from .utils import print_err, warn_of_missing_prerequisite
 
 
+def execute_and_return(command_string: str) -> Tuple[str, str]:
+    def _decode(subprocess_err_out_tuple):
+        return tuple(
+            to_decode.decode("UTF-8").strip()
+            for to_decode in subprocess_err_out_tuple
+        )
+
+    def execute_and_return_decoded_std_streams(command_string):
+        return _decode(
+            subprocess.Popen(
+                shlex.split(command_string),
+                stdout=subprocess.PIPE,
+                stderr=subprocess.PIPE,
+            ).communicate()
+        )
+
+    std_out, std_err = execute_and_return_decoded_std_streams()
+    return std_out, std_err
+
+
 def check_cuda_result(cuda, result_val):
     if result_val != 0:
+        # TODO: undefined name 'error_str'
         cuda.cuGetErrorString(result_val, ctypes.byref(error_str))
-        print(f"Count not initialize CUDA - failure!")
+        print("Count not initialize CUDA - failure!")
         raise Exception("CUDA exception!")
     return result_val
 
@@ -53,7 +76,9 @@ def get_compute_capability():
 
     result = ctypes.c_int()
     device = ctypes.c_int()
+    # TODO: local variable 'context' is assigned to but never used
     context = ctypes.c_void_p()
+    # TODO: local variable 'error_str' is assigned to but never used
     error_str = ctypes.c_char_p()
 
     result = check_cuda_result(cuda, cuda.cuInit(0))
@@ -61,7 +86,9 @@ def get_compute_capability():
     result = check_cuda_result(cuda, cuda.cuDeviceGetCount(ctypes.byref(nGpus)))
     ccs = []
     for i in range(nGpus.value):
-        result = check_cuda_result(cuda, cuda.cuDeviceGet(ctypes.byref(device), i))
+        result = check_cuda_result(
+            cuda, cuda.cuDeviceGet(ctypes.byref(device), i)
+        )
         result = check_cuda_result(
             cuda,
             cuda.cuDeviceComputeCapability(
@@ -114,11 +141,15 @@ def get_cuda_runtime_lib_path(
     } - non_existent_directories
 
     if len(cuda_runtime_libs) > 1:
-        err_msg = f"Found duplicate {CUDA_RUNTIME_LIB} files: {cuda_runtime_libs}.."
+        err_msg = (
+            f"Found duplicate {CUDA_RUNTIME_LIB} files: {cuda_runtime_libs}.."
+        )
         raise FileNotFoundError(err_msg)
 
     elif len(cuda_runtime_libs) < 1:
-        err_msg = f"Did not find {CUDA_RUNTIME_LIB} files: {cuda_runtime_libs}.."
+        err_msg = (
+            f"Did not find {CUDA_RUNTIME_LIB} files: {cuda_runtime_libs}.."
+        )
         raise FileNotFoundError(err_msg)
 
     single_cuda_runtime_lib_dir = next(iter(cuda_runtime_libs))

bitsandbytes/functional.py

@@ -17,14 +17,29 @@ if COMPILED_WITH_CUDA:
     """C FUNCTIONS FOR OPTIMIZERS"""
     str2optimizer32bit = {}
     str2optimizer32bit["adam"] = (lib.cadam32bit_g32, lib.cadam32bit_g16)
-    str2optimizer32bit["momentum"] = (lib.cmomentum32bit_g32, lib.cmomentum32bit_g16)
-    str2optimizer32bit["rmsprop"] = (lib.crmsprop32bit_g32, lib.crmsprop32bit_g16)
-    str2optimizer32bit["adagrad"] = (lib.cadagrad32bit_g32, lib.cadagrad32bit_g16)
-    str2optimizer32bit["lars"] = (lib.cmomentum32bit_g32, lib.cmomentum32bit_g16)
+    str2optimizer32bit["momentum"] = (
+        lib.cmomentum32bit_g32,
+        lib.cmomentum32bit_g16,
+    )
+    str2optimizer32bit["rmsprop"] = (
+        lib.crmsprop32bit_g32,
+        lib.crmsprop32bit_g16,
+    )
+    str2optimizer32bit["adagrad"] = (
+        lib.cadagrad32bit_g32,
+        lib.cadagrad32bit_g16,
+    )
+    str2optimizer32bit["lars"] = (
+        lib.cmomentum32bit_g32,
+        lib.cmomentum32bit_g16,
+    )
     str2optimizer32bit["lamb"] = (lib.cadam32bit_g32, lib.cadam32bit_g16)
 
     str2optimizer8bit = {}
-    str2optimizer8bit["adam"] = (lib.cadam_static_8bit_g32, lib.cadam_static_8bit_g16)
+    str2optimizer8bit["adam"] = (
+        lib.cadam_static_8bit_g32,
+        lib.cadam_static_8bit_g16,
+    )
     str2optimizer8bit["momentum"] = (
         lib.cmomentum_static_8bit_g32,
         lib.cmomentum_static_8bit_g16,
@@ -33,7 +48,10 @@ if COMPILED_WITH_CUDA:
         lib.crmsprop_static_8bit_g32,
         lib.crmsprop_static_8bit_g16,
     )
-    str2optimizer8bit["lamb"] = (lib.cadam_static_8bit_g32, lib.cadam_static_8bit_g16)
+    str2optimizer8bit["lamb"] = (
+        lib.cadam_static_8bit_g32,
+        lib.cadam_static_8bit_g16,
+    )
     str2optimizer8bit["lars"] = (
         lib.cmomentum_static_8bit_g32,
         lib.cmomentum_static_8bit_g16,
@@ -137,7 +155,9 @@ def create_dynamic_map(signed=True, n=7):
     if not signed:
         additional_items = 2 * additional_items
     for i in range(n):
-        fraction_items = 2 ** (i + 7 - n) + 1 if signed else 2 ** (i + 7 - n + 1) + 1
+        fraction_items = (
+            2 ** (i + 7 - n) + 1 if signed else 2 ** (i + 7 - n + 1) + 1
+        )
         boundaries = torch.linspace(0.1, 1, fraction_items)
         means = (boundaries[:-1] + boundaries[1:]) / 2.0
         data += ((10 ** (-(n - 1) + i)) * means).tolist()
@@ -272,7 +292,13 @@ def get_transform_buffer(
 
 
 def nvidia_transform(
-    A, to_order, from_order="row", out=None, transpose=False, state=None, ld=None
+    A,
+    to_order,
+    from_order="row",
+    out=None,
+    transpose=False,
+    state=None,
+    ld=None,
 ):
     if state is None:
         state = (A.shape, from_order)
@@ -352,7 +378,11 @@ def estimate_quantiles(
 
 
 def quantize_blockwise(
-    A: Tensor, code: Tensor = None, absmax: Tensor = None, rand=None, out: Tensor = None
+    A: Tensor,
+    code: Tensor = None,
+    absmax: Tensor = None,
+    rand=None,
+    out: Tensor = None,
 ) -> Tensor:
     """
     Quantize tensor A in blocks of size 4096 values.
@@ -629,7 +659,9 @@ def dequantize_no_absmax(A: Tensor, code: Tensor, out: Tensor = None) -> Tensor:
     """
     if out is None:
         out = torch.zeros_like(A, dtype=torch.float32)
-    lib.cdequantize(get_ptr(code), get_ptr(A), get_ptr(out), ct.c_int(A.numel()))
+    lib.cdequantize(
+        get_ptr(code), get_ptr(A), get_ptr(out), ct.c_int(A.numel())
+    )
     return out
 
 
@@ -1005,7 +1037,9 @@ def histogram_scatter_add_2d(
     )
 
 
-def check_matmul(A, B, out, transposed_A, transposed_B, expected_type=torch.int8):
+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:
@@ -1097,7 +1131,11 @@ def check_matmul(A, B, out, transposed_A, transposed_B, expected_type=torch.int8
 
 
 def igemm(
-    A: Tensor, B: Tensor, out: Tensor = None, transposed_A=False, transposed_B=False
+    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:
@@ -1193,7 +1231,11 @@ def igemm(
 
 
 def batched_igemm(
-    A: Tensor, B: Tensor, out: Tensor = None, transposed_A=False, transposed_B=False
+    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(
@@ -1392,9 +1434,13 @@ def mm_dequant(
     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)
+        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)
+        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}"
@@ -1440,13 +1486,13 @@ def get_colrow_absmax(
     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
-        )
+        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
-        )
+        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(
@@ -1462,7 +1508,13 @@ def get_colrow_absmax(
 
     prev_device = pre_call(A.device)
     lib.cget_col_row_stats(
-        ptrA, ptrRowStats, ptrColStats, ptrNnzrows, ct.c_float(threshold), rows, cols
+        ptrA,
+        ptrRowStats,
+        ptrColStats,
+        ptrNnzrows,
+        ct.c_float(threshold),
+        rows,
+        cols,
     )
     post_call(prev_device)
 
@@ -1526,7 +1578,9 @@ class CSCSparseTensor(object):
 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 = 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(
@@ -1540,10 +1594,14 @@ def coo2csc(cooA):
     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 = 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)
+    return CSCSparseTensor(
+        cooA.rows, cooA.cols, cooA.nnz, colptr, rowidx, values
+    )
 
 
 def coo_zeros(rows, cols, nnz, device, dtype=torch.half):
@@ -1568,7 +1626,9 @@ def double_quant(
         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)
+        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)
@@ -1663,7 +1723,13 @@ def get_special_format_str():
 
 
 def transform(
-    A, to_order, from_order="row", out=None, transpose=False, state=None, ld=None
+    A,
+    to_order,
+    from_order="row",
+    out=None,
+    transpose=False,
+    state=None,
+    ld=None,
 ):
     if state is None:
         state = (A.shape, from_order)
@@ -1716,7 +1782,9 @@ def transform(
 
 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)
+        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
@@ -1982,7 +2050,9 @@ def extract_outliers(A, SA, idx):
     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)
+    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])

bitsandbytes/nn/modules.py

@@ -2,8 +2,19 @@
 #
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
-from typing import (Any, Callable, Dict, Iterator, Mapping, Optional, Set,
-                    Tuple, TypeVar, Union, overload)
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Iterator,
+    Mapping,
+    Optional,
+    Set,
+    Tuple,
+    TypeVar,
+    Union,
+    overload,
+)
 
 import torch
 import torch.nn.functional as F
@@ -131,7 +142,12 @@ class Embedding(torch.nn.Embedding):
 
 class Int8Params(torch.nn.Parameter):
     def __new__(
-        cls, data=None, requires_grad=True, has_fp16_weights=False, CB=None, SCB=None
+        cls,
+        data=None,
+        requires_grad=True,
+        has_fp16_weights=False,
+        CB=None,
+        SCB=None,
     ):
         cls.has_fp16_weights = has_fp16_weights
         cls.CB = None
@@ -186,7 +202,9 @@ class Int8Params(torch.nn.Parameter):
             return self.cuda(device)
         else:
             new_param = Int8Params(
-                super().to(device=device, dtype=dtype, non_blocking=non_blocking),
+                super().to(
+                    device=device, dtype=dtype, non_blocking=non_blocking
+                ),
                 requires_grad=self.requires_grad,
                 has_fp16_weights=self.has_fp16_weights,
             )
@@ -206,7 +224,9 @@ class Linear8bitLt(nn.Linear):
         threshold=0.0,
         index=None,
     ):
-        super(Linear8bitLt, self).__init__(input_features, output_features, bias)
+        super(Linear8bitLt, self).__init__(
+            input_features, output_features, bias
+        )
         self.state = bnb.MatmulLtState()
         self.index = index
 
@@ -215,7 +235,9 @@ class Linear8bitLt(nn.Linear):
         if threshold > 0.0 and not has_fp16_weights:
             self.state.use_pool = True
 
-        self.weight = Int8Params(self.weight.data, has_fp16_weights=has_fp16_weights)
+        self.weight = Int8Params(
+            self.weight.data, has_fp16_weights=has_fp16_weights
+        )
 
     def init_8bit_state(self):
         self.state.CB = self.weight.CB

bitsandbytes/optim/adagrad.py

@@ -23,7 +23,9 @@ class Adagrad(Optimizer1State):
         if not 0.0 <= lr:
             raise ValueError("Invalid learning rate: {}".format(lr))
         if not 0.0 <= weight_decay:
-            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
+            raise ValueError(
+                "Invalid weight_decay value: {}".format(weight_decay)
+            )
         if not 0.0 <= eps:
             raise ValueError("Invalid epsilon value: {}".format(eps))
         if initial_accumulator_value != 0.0:
@@ -63,7 +65,9 @@ class Adagrad8bit(Optimizer1State):
         if not 0.0 <= lr:
             raise ValueError("Invalid learning rate: {}".format(lr))
         if not 0.0 <= weight_decay:
-            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
+            raise ValueError(
+                "Invalid weight_decay value: {}".format(weight_decay)
+            )
         if not 0.0 <= eps:
             raise ValueError("Invalid epsilon value: {}".format(eps))
         if initial_accumulator_value != 0.0:
@@ -104,7 +108,9 @@ class Adagrad32bit(Optimizer1State):
         if not 0.0 <= lr:
             raise ValueError("Invalid learning rate: {}".format(lr))
         if not 0.0 <= weight_decay:
-            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
+            raise ValueError(
+                "Invalid weight_decay value: {}".format(weight_decay)
+            )
         if not 0.0 <= eps:
             raise ValueError("Invalid epsilon value: {}".format(eps))
         if initial_accumulator_value != 0.0:

bitsandbytes/optim/adam.py

@@ -140,7 +140,11 @@ class AnalysisAdam(torch.optim.Optimizer):
         savedir=None,
     ):
         defaults = dict(
-            lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad
+            lr=lr,
+            betas=betas,
+            eps=eps,
+            weight_decay=weight_decay,
+            amsgrad=amsgrad,
         )
         super(AnalysisAdam, self).__init__(params, defaults)
         self.analysis = bnb_analysis
@@ -198,7 +202,9 @@ class AnalysisAdam(torch.optim.Optimizer):
                     state["relerrors"] = torch.zeros(
                         (256, 256), device=p_data_fp32.device
                     )
-                    state["counts"] = torch.zeros((256, 256), device=p_data_fp32.device)
+                    state["counts"] = torch.zeros(
+                        (256, 256), device=p_data_fp32.device
+                    )
                     if amsgrad:
                         # Maintains max of all exp. moving avg. of sq. grad. values
                         state["max_exp_avg_sq"] = torch.zeros_like(p_data_fp32)
@@ -214,7 +220,9 @@ class AnalysisAdam(torch.optim.Optimizer):
                 beta1, beta2 = group["betas"]
                 bias_correction1 = 1 - beta1 ** state["step"]
                 bias_correction2 = 1 - beta2 ** state["step"]
-                step_size = group["lr"] * math.sqrt(bias_correction2) / bias_correction1
+                step_size = (
+                    group["lr"] * math.sqrt(bias_correction2) / bias_correction1
+                )
                 e = state["abserrors"]
                 rele = state["relerrors"]
                 counts = state["counts"]
@@ -235,7 +243,10 @@ class AnalysisAdam(torch.optim.Optimizer):
                 denom = exp_avg_sq.sqrt().add_(group["eps"])
                 update_fp32 = exp_avg / denom
 
-                if p_data_fp32.numel() <= 8192 or p_data_fp32.numel() > 50000 * 1000:
+                if (
+                    p_data_fp32.numel() <= 8192
+                    or p_data_fp32.numel() > 50000 * 1000
+                ):
                     # embedding layer or too small
                     p_data_fp32 += -step_size * update_fp32
                 else:
@@ -274,7 +285,9 @@ class AnalysisAdam(torch.optim.Optimizer):
                         # 3. dequantize
                         # Error will be calculated automatically!
                     else:
-                        raise ValueError(f"Invalid analysis value: {self.analysis}!")
+                        raise ValueError(
+                            f"Invalid analysis value: {self.analysis}!"
+                        )
 
                     denom = state2.sqrt().add_(group["eps"])
                     update_8bit = state1 / denom
@@ -296,7 +309,9 @@ class AnalysisAdam(torch.optim.Optimizer):
                         if self.savedir != "" and state["step"] % 100 == 0:
                             if not os.path.exists(self.savedir):
                                 os.makedirs(self.savedir)
-                            shapestr = "_".join([str(dim) for dim in p_data_fp32.shape])
+                            shapestr = "_".join(
+                                [str(dim) for dim in p_data_fp32.shape]
+                            )
                             pathe = os.path.join(
                                 self.savedir, f"{p_id}_{shapestr}_abserr.pkl"
                             )

bitsandbytes/optim/lars.py

@@ -24,7 +24,9 @@ class LARS(Optimizer1State):
         max_unorm=0.02,
     ):
         if momentum == 0:
-            raise NotImplementedError(f"LARS without momentum is not supported!")
+            raise NotImplementedError(
+                f"LARS without momentum is not supported!"
+            )
         super(LARS, self).__init__(
             "lars",
             params,
@@ -56,7 +58,9 @@ class LARS8bit(Optimizer1State):
         max_unorm=0.02,
     ):
         if momentum == 0:
-            raise NotImplementedError(f"LARS without momentum is not supported!")
+            raise NotImplementedError(
+                f"LARS without momentum is not supported!"
+            )
         super(LARS8bit, self).__init__(
             "lars",
             params,
@@ -88,7 +92,9 @@ class LARS32bit(Optimizer1State):
         max_unorm=0.02,
     ):
         if momentum == 0:
-            raise NotImplementedError(f"LARS without momentum is not supported!")
+            raise NotImplementedError(
+                f"LARS without momentum is not supported!"
+            )
         super(LARS32bit, self).__init__(
             "lars",
             params,
@@ -121,7 +127,9 @@ class PytorchLARS(Optimizer):
         if momentum < 0.0:
             raise ValueError("Invalid momentum value: {}".format(momentum))
         if weight_decay < 0.0:
-            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
+            raise ValueError(
+                "Invalid weight_decay value: {}".format(weight_decay)
+            )
 
         defaults = dict(
             lr=lr,
@@ -132,7 +140,9 @@ class PytorchLARS(Optimizer):
             max_unorm=max_unorm,
         )
         if nesterov and (momentum <= 0 or dampening != 0):
-            raise ValueError("Nesterov momentum requires a momentum and zero dampening")
+            raise ValueError(
+                "Nesterov momentum requires a momentum and zero dampening"
+            )
         super(PytorchLARS, self).__init__(params, defaults)
 
     def __setstate__(self, state):

bitsandbytes/optim/optimizer.py

@@ -46,9 +46,13 @@ class GlobalOptimManager(object):
         for group_index, group in enumerate(param_groups):
             for p_index, p in enumerate(group["params"]):
                 if id(p) in self.pid2config:
-                    self.index2config[(group_index, p_index)] = self.pid2config[id(p)]
+                    self.index2config[(group_index, p_index)] = self.pid2config[
+                        id(p)
+                    ]
 
-    def override_config(self, parameters, key=None, value=None, key_value_dict=None):
+    def override_config(
+        self, parameters, key=None, value=None, key_value_dict=None
+    ):
         """
         Overrides initial optimizer config for specific parameters.
 
@@ -136,7 +140,8 @@ class Optimizer8bit(torch.optim.Optimizer):
 
         if len(groups) != len(saved_groups):
             raise ValueError(
-                "loaded state dict has a different number of " "parameter groups"
+                "loaded state dict has a different number of "
+                "parameter groups"
             )
         param_lens = (len(g["params"]) for g in groups)
         saved_lens = (len(g["params"]) for g in saved_groups)
@@ -192,7 +197,9 @@ class Optimizer8bit(torch.optim.Optimizer):
             new_group["params"] = group["params"]
             return new_group
 
-        param_groups = [update_group(g, ng) for g, ng in zip(groups, saved_groups)]
+        param_groups = [
+            update_group(g, ng) for g, ng in zip(groups, saved_groups)
+        ]
         self.__setstate__({"state": state, "param_groups": param_groups})
 
     def to_gpu(self):
@@ -222,9 +229,9 @@ class Optimizer8bit(torch.optim.Optimizer):
                         # found the matching parameter
                         # init override
                         self.mng.pid2config[id(p)] = config
-                        self.mng.index2config[(gindex, pindex)] = self.mng.pid2config[
-                            id(p)
-                        ]
+                        self.mng.index2config[
+                            (gindex, pindex)
+                        ] = self.mng.pid2config[id(p)]
                         found = True
 
     @torch.no_grad()
@@ -280,7 +287,9 @@ class Optimizer8bit(torch.optim.Optimizer):
         raise NotImplementedError(f"init_state method needs to be overidden")
 
     def update_step(self, group, p, gindex, pindex):
-        raise NotImplementedError(f"The update_step method needs to be overidden")
+        raise NotImplementedError(
+            f"The update_step method needs to be overidden"
+        )
 
 
 class Optimizer2State(Optimizer8bit):
@@ -310,9 +319,13 @@ class Optimizer2State(Optimizer8bit):
             betas = [float(b) for b in betas]
         for i in range(len(betas)):
             if not 0.0 <= betas[i] < 1.0:
-                raise ValueError(f"Invalid beta parameter at index {i}: {betas[i]}")
+                raise ValueError(
+                    f"Invalid beta parameter at index {i}: {betas[i]}"
+                )
         if not 0.0 <= weight_decay:
-            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
+            raise ValueError(
+                "Invalid weight_decay value: {}".format(weight_decay)
+            )
         defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
         super(Optimizer2State, self).__init__(params, defaults, optim_bits)
 
@@ -351,7 +364,9 @@ class Optimizer2State(Optimizer8bit):
         state = self.state[p]
         state["step"] = 0
 
-        if dtype == torch.float32 or (dtype == torch.uint8 and p.numel() < 4096):
+        if dtype == torch.float32 or (
+            dtype == torch.uint8 and p.numel() < 4096
+        ):
             state["state1"] = torch.zeros_like(
                 p,
                 memory_format=torch.preserve_format,
@@ -368,8 +383,12 @@ class Optimizer2State(Optimizer8bit):
             if state["step"] == 0:
                 if "dynamic" not in self.name2qmap:
                     self.fill_qmap()
-                self.name2qmap["dynamic"] = self.name2qmap["dynamic"].to(p.device)
-                self.name2qmap["udynamic"] = self.name2qmap["udynamic"].to(p.device)
+                self.name2qmap["dynamic"] = self.name2qmap["dynamic"].to(
+                    p.device
+                )
+                self.name2qmap["udynamic"] = self.name2qmap["udynamic"].to(
+                    p.device
+                )
 
             state["state1"] = torch.zeros_like(
                 p,
@@ -399,11 +418,15 @@ class Optimizer2State(Optimizer8bit):
                     (blocks,), dtype=torch.float32, device=p.device
                 )
             else:
-                state["max1"] = torch.zeros((1,), dtype=torch.float32, device=p.device)
+                state["max1"] = torch.zeros(
+                    (1,), dtype=torch.float32, device=p.device
+                )
                 state["new_max1"] = torch.zeros(
                     (1,), dtype=torch.float32, device=p.device
                 )
-                state["max2"] = torch.zeros((1,), dtype=torch.float32, device=p.device)
+                state["max2"] = torch.zeros(
+                    (1,), dtype=torch.float32, device=p.device
+                )
                 state["new_max2"] = torch.zeros(
                     (1,), dtype=torch.float32, device=p.device
                 )
@@ -470,7 +493,9 @@ class Optimizer2State(Optimizer8bit):
                 state["new_max2"],
                 config["weight_decay"],
                 gnorm_scale=gnorm_scale,
-                unorm_vec=state["unorm_vec"] if config["max_unorm"] > 0.0 else None,
+                unorm_vec=state["unorm_vec"]
+                if config["max_unorm"] > 0.0
+                else None,
                 max_unorm=config["max_unorm"],
             )
 
@@ -522,9 +547,13 @@ class Optimizer1State(Optimizer8bit):
             raise ValueError("Invalid epsilon value: {}".format(eps))
         for i in range(len(betas)):
             if not 0.0 <= betas[i] < 1.0:
-                raise ValueError(f"Invalid beta parameter at index {i}: {betas[i]}")
+                raise ValueError(
+                    f"Invalid beta parameter at index {i}: {betas[i]}"
+                )
         if not 0.0 <= weight_decay:
-            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
+            raise ValueError(
+                "Invalid weight_decay value: {}".format(weight_decay)
+            )
         defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
         super(Optimizer1State, self).__init__(params, defaults, optim_bits)
 
@@ -563,7 +592,9 @@ class Optimizer1State(Optimizer8bit):
         state = self.state[p]
         state["step"] = 0
 
-        if dtype == torch.float32 or (dtype == torch.uint8 and p.numel() < 4096):
+        if dtype == torch.float32 or (
+            dtype == torch.uint8 and p.numel() < 4096
+        ):
             state["state1"] = torch.zeros_like(
                 p,
                 memory_format=torch.preserve_format,
@@ -574,7 +605,9 @@ class Optimizer1State(Optimizer8bit):
             if state["step"] == 0:
                 if "dynamic" not in self.name2qmap:
                     self.fill_qmap()
-                self.name2qmap["dynamic"] = self.name2qmap["dynamic"].to(p.device)
+                self.name2qmap["dynamic"] = self.name2qmap["dynamic"].to(
+                    p.device
+                )
 
             state["state1"] = torch.zeros_like(
                 p,
@@ -593,7 +626,9 @@ class Optimizer1State(Optimizer8bit):
                     (blocks,), dtype=torch.float32, device=p.device
                 )
             else:
-                state["max1"] = torch.zeros((1,), dtype=torch.float32, device=p.device)
+                state["max1"] = torch.zeros(
+                    (1,), dtype=torch.float32, device=p.device
+                )
                 state["new_max1"] = torch.zeros(
                     (1,), dtype=torch.float32, device=p.device
                 )

bitsandbytes/optim/rmsprop.py

@@ -22,7 +22,9 @@ class RMSprop(Optimizer1State):
         block_wise=True,
     ):
         if alpha == 0:
-            raise NotImplementedError(f"RMSprop with alpha==0.0 is not supported!")
+            raise NotImplementedError(
+                f"RMSprop with alpha==0.0 is not supported!"
+            )
         if centered:
             raise NotImplementedError(f"Centered RMSprop is not supported!")
         super(RMSprop, self).__init__(
@@ -56,7 +58,9 @@ class RMSprop8bit(Optimizer1State):
         block_wise=True,
     ):
         if alpha == 0:
-            raise NotImplementedError(f"RMSprop with alpha==0.0 is not supported!")
+            raise NotImplementedError(
+                f"RMSprop with alpha==0.0 is not supported!"
+            )
         if centered:
             raise NotImplementedError(f"Centered RMSprop is not supported!")
         super(RMSprop8bit, self).__init__(
@@ -91,7 +95,9 @@ class RMSprop32bit(Optimizer1State):
     ):
 
         if alpha == 0:
-            raise NotImplementedError(f"RMSprop with alpha==0.0 is not supported!")
+            raise NotImplementedError(
+                f"RMSprop with alpha==0.0 is not supported!"
+            )
         if centered:
             raise NotImplementedError(f"Centered RMSprop is not supported!")
         super(RMSprop32bit, self).__init__(

bitsandbytes/utils.py

+import sys
 import shlex
 import subprocess
-import sys
 
 
 def execute_and_return(command_string: str) -> Tuple[str, str]:

quicktest.py

@@ -14,23 +14,31 @@ def test_igemmlt(dim1, dim2, dim3, dim4, dims, ldb):
                 torch.int8
             )
         elif dims == 3:
-            A = torch.randint(-128, 127, size=(dim1, dim2, dim3), device="cuda").to(
-                torch.int8
-            )
-        B = torch.randint(-128, 127, size=(dim4, dim3), device="cuda").to(torch.int8)
+            A = torch.randint(
+                -128, 127, size=(dim1, dim2, dim3), device="cuda"
+            ).to(torch.int8)
+        B = torch.randint(-128, 127, size=(dim4, dim3), device="cuda").to(
+            torch.int8
+        )
         C1 = torch.matmul(A.float(), B.t().float())
 
         A2, SA = F.transform(A, "col32")
         B2, SB = F.transform(B, "colx")
         if dims == 2:
             C2, SC = F.transform(
-                torch.zeros(A.shape[0], B.shape[0], dtype=torch.int32, device="cuda"),
+                torch.zeros(
+                    A.shape[0], B.shape[0], dtype=torch.int32, device="cuda"
+                ),
                 "col32",
             )
         else:
             C2, SC = F.transform(
                 torch.zeros(
-                    A.shape[0], A.shape[1], B.shape[0], dtype=torch.int32, device="cuda"
+                    A.shape[0],
+                    A.shape[1],
+                    B.shape[0],
+                    dtype=torch.int32,
+                    device="cuda",
                 ),
                 "col32",
             )

tests/test_autograd.py

@@ -18,9 +18,13 @@ req_grad_str = ["FF", "TF", "TT", "FT"]
 transpose = [(False, False), (False, True), (True, True), (True, False)]
 str_transpose = ["FF", "FT", "TT", "TF"]
 dtype = [torch.float32, torch.float16]
-values = list(product(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose))
+values = list(
+    product(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose)
+)
 str_values = list(
-    product(dim1, dim2, dim3, dim4, str_funcs, dtype, req_grad_str, str_transpose)
+    product(
+        dim1, dim2, dim3, dim4, str_funcs, dtype, req_grad_str, str_transpose
+    )
 )
 names = [
     "dim1_{0}_dim2_{1}_dim3_{2}_dim4_{3}_func_{4}_dtype_{5}_requires_grad_{6}_transpose_{7}".format(
@@ -31,7 +35,9 @@ names = [
 
 
 @pytest.mark.parametrize(
-    "dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose", values, ids=names
+    "dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose",
+    values,
+    ids=names,
 )
 def test_matmul(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose):
     dim2 = dim2 - (dim2 % 16)
@@ -79,7 +85,9 @@ def test_matmul(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose):
                 A.grad = None
                 B.grad = None
 
-                loss_torch = torch.nn.functional.mse_loss(out_torch, target).mean()
+                loss_torch = torch.nn.functional.mse_loss(
+                    out_torch, target
+                ).mean()
                 loss_torch.backward()
                 gradA2 = A.grad
                 gradB2 = B.grad
@@ -87,25 +95,35 @@ def test_matmul(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose):
                 B.grad = None
 
             if req_grad[0]:
-                torch.testing.assert_allclose(gradA1, gradA2, atol=0.015, rtol=0.1)
+                torch.testing.assert_allclose(
+                    gradA1, gradA2, atol=0.015, rtol=0.1
+                )
             if req_grad[1]:
                 n = gradB1.numel()
                 idx = torch.isclose(gradB1, gradB2, atol=0.06, rtol=0.3)
                 assert (idx == 0).sum().item() < n * 0.1
                 idx = torch.isclose(gradB1, gradB2, atol=0.10, rtol=0.3)
                 assert (idx == 0).sum().item() < n * 0.02
-                torch.testing.assert_allclose(gradB1, gradB2, atol=0.18, rtol=0.3)
+                torch.testing.assert_allclose(
+                    gradB1, gradB2, atol=0.18, rtol=0.3
+                )
 
         # batched matrix multiply
         if funcs[0] in [torch.bmm, torch.matmul]:
             A = torch.randn(
-                size=(dim1, dim2, dim3), device="cuda", requires_grad=req_grad[0]
+                size=(dim1, dim2, dim3),
+                device="cuda",
+                requires_grad=req_grad[0],
             )
             B = torch.randn(
-                size=(dim1, dim3, dim4), device="cuda", requires_grad=req_grad[1]
+                size=(dim1, dim3, dim4),
+                device="cuda",
+                requires_grad=req_grad[1],
             )
             target = torch.randn(
-                size=(dim1, dim2, dim4), device="cuda", requires_grad=req_grad[1]
+                size=(dim1, dim2, dim4),
+                device="cuda",
+                requires_grad=req_grad[1],
             )
             torch.nn.init.xavier_uniform_(B)
 
@@ -115,7 +133,9 @@ def test_matmul(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose):
             n = out_bnb.numel()
             idx = torch.isclose(out_bnb, out_torch, atol=0.01, rtol=0.1)
             assert (idx == 0).sum().item() < n * 0.01
-            torch.testing.assert_allclose(out_bnb, out_torch, atol=0.027, rtol=0.2)
+            torch.testing.assert_allclose(
+                out_bnb, out_torch, atol=0.027, rtol=0.2
+            )
 
             if any(req_grad):
                 out_bnb.data.copy_(out_torch)
@@ -127,7 +147,9 @@ def test_matmul(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose):
                 A.grad = None
                 B.grad = None
 
-                loss_torch = torch.nn.functional.mse_loss(out_torch, target).mean()
+                loss_torch = torch.nn.functional.mse_loss(
+                    out_torch, target
+                ).mean()
                 loss_torch.backward()
                 gradA2 = A.grad
                 gradB2 = B.grad
@@ -135,7 +157,9 @@ def test_matmul(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose):
                 B.grad = None
 
             if req_grad[0]:
-                torch.testing.assert_allclose(gradA1, gradA2, atol=0.015, rtol=0.1)
+                torch.testing.assert_allclose(
+                    gradA1, gradA2, atol=0.015, rtol=0.1
+                )
             if req_grad[1]:
                 n = gradB1.numel()
                 idx = torch.isclose(gradB1, gradB2, atol=0.06, rtol=0.3)
@@ -146,12 +170,16 @@ def test_matmul(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose):
         if funcs[0] in [torch.matmul]:
             dim1 = dim1 - (dim1 % 16)
             A = torch.randn(
-                size=(dim1, dim2, dim3), device="cuda", requires_grad=req_grad[0]
+                size=(dim1, dim2, dim3),
+                device="cuda",
+                requires_grad=req_grad[0],
             )
             dimB = (dim4, dim3) if transpose[1] else (dim3, dim4)
             B = torch.randn(size=dimB, device="cuda", requires_grad=req_grad[1])
             target = torch.randn(
-                size=(dim1, dim2, dim4), device="cuda", requires_grad=req_grad[1]
+                size=(dim1, dim2, dim4),
+                device="cuda",
+                requires_grad=req_grad[1],
             )
             torch.nn.init.xavier_uniform_(B)
 
@@ -178,7 +206,9 @@ def test_matmul(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose):
                 A.grad = None
                 B.grad = None
 
-                loss_torch = torch.nn.functional.mse_loss(out_torch, target).mean()
+                loss_torch = torch.nn.functional.mse_loss(
+                    out_torch, target
+                ).mean()
                 loss_torch.backward()
                 gradA2 = A.grad
                 gradB2 = B.grad
@@ -186,7 +216,9 @@ def test_matmul(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose):
                 B.grad = None
 
             if req_grad[0]:
-                torch.testing.assert_allclose(gradA1, gradA2, atol=0.015, rtol=0.1)
+                torch.testing.assert_allclose(
+                    gradA1, gradA2, atol=0.015, rtol=0.1
+                )
             if req_grad[1]:
                 n = gradB1.numel()
                 idx = torch.isclose(gradB1, gradB2, atol=0.06, rtol=0.3)
@@ -258,7 +290,16 @@ names = [
     ids=names,
 )
 def test_matmullt(
-    dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose, decomp, has_fp16_weights
+    dim1,
+    dim2,
+    dim3,
+    dim4,
+    funcs,
+    dtype,
+    req_grad,
+    transpose,
+    decomp,
+    has_fp16_weights,
 ):
     dimA = (dim2, dim3) if not transpose[0] else (dim3, dim2)
     dimB = (dim3, dim4) if not transpose[1] else (dim4, dim3)
@@ -278,7 +319,10 @@ def test_matmullt(
                 size=dimB, device="cuda", requires_grad=req_grad[1], dtype=dtype
             )
             target = torch.randn(
-                size=(dim2, dim4), device="cuda", requires_grad=req_grad[1], dtype=dtype
+                size=(dim2, dim4),
+                device="cuda",
+                requires_grad=req_grad[1],
+                dtype=dtype,
             )
             torch.nn.init.xavier_uniform_(B)
             B2 = B.clone()
@@ -317,14 +361,18 @@ def test_matmullt(
                 if any(req_grad):
                     out_bnb.data.copy_(out_torch)
                     torch.cuda.synchronize()
-                    loss_bnb = torch.nn.functional.mse_loss(out_bnb, target).mean()
+                    loss_bnb = torch.nn.functional.mse_loss(
+                        out_bnb, target
+                    ).mean()
                     loss_bnb.backward()
                     gradA1 = A.grad
                     gradB1 = B.grad
                     A.grad = None
                     B.grad = None
 
-                    loss_torch = torch.nn.functional.mse_loss(out_torch, target).mean()
+                    loss_torch = torch.nn.functional.mse_loss(
+                        out_torch, target
+                    ).mean()
                     loss_torch.backward()
                     gradA2 = A.grad
                     gradB2 = B.grad
@@ -332,7 +380,9 @@ def test_matmullt(
                     B.grad = None
 
                 if req_grad[0]:
-                    torch.testing.assert_allclose(gradA1, gradA2, atol=0.015, rtol=0.1)
+                    torch.testing.assert_allclose(
+                        gradA1, gradA2, atol=0.015, rtol=0.1
+                    )
                 if req_grad[1]:
                     n = gradB1.numel()
                     assert torch.abs(gradB1).sum() > 0.0
@@ -341,4 +391,6 @@ def test_matmullt(
                     assert (idx == 0).sum().item() < n * 0.1
                     idx = torch.isclose(gradB1, gradB2, atol=0.10, rtol=0.3)
                     assert (idx == 0).sum().item() < n * 0.02
-                    torch.testing.assert_allclose(gradB1, gradB2, atol=0.18, rtol=0.3)
+                    torch.testing.assert_allclose(
+                        gradB1, gradB2, atol=0.18, rtol=0.3
+                    )

tests/test_cuda_setup_evaluator.py

@@ -3,8 +3,12 @@ from typing import List, NamedTuple
 
 import pytest
 
-from bitsandbytes.cuda_setup import (CUDA_RUNTIME_LIB, evaluate_cuda_setup,
-                                     get_cuda_runtime_lib_path, tokenize_paths)
+from bitsandbytes.cuda_setup import (
+    CUDA_RUNTIME_LIB,
+    evaluate_cuda_setup,
+    get_cuda_runtime_lib_path,
+    tokenize_paths,
+)
 
 
 class InputAndExpectedOutput(NamedTuple):
@@ -13,11 +17,26 @@ class InputAndExpectedOutput(NamedTuple):
 
 
 HAPPY_PATH__LD_LIB_TEST_PATHS: List[InputAndExpectedOutput] = [
-    (f"some/other/dir:dir/with/{CUDA_RUNTIME_LIB}", f"dir/with/{CUDA_RUNTIME_LIB}"),
-    (f":some/other/dir:dir/with/{CUDA_RUNTIME_LIB}", f"dir/with/{CUDA_RUNTIME_LIB}"),
-    (f"some/other/dir:dir/with/{CUDA_RUNTIME_LIB}:", f"dir/with/{CUDA_RUNTIME_LIB}"),
-    (f"some/other/dir::dir/with/{CUDA_RUNTIME_LIB}", f"dir/with/{CUDA_RUNTIME_LIB}"),
-    (f"dir/with/{CUDA_RUNTIME_LIB}:some/other/dir", f"dir/with/{CUDA_RUNTIME_LIB}"),
+    (
+        f"some/other/dir:dir/with/{CUDA_RUNTIME_LIB}",
+        f"dir/with/{CUDA_RUNTIME_LIB}",
+    ),
+    (
+        f":some/other/dir:dir/with/{CUDA_RUNTIME_LIB}",
+        f"dir/with/{CUDA_RUNTIME_LIB}",
+    ),
+    (
+        f"some/other/dir:dir/with/{CUDA_RUNTIME_LIB}:",
+        f"dir/with/{CUDA_RUNTIME_LIB}",
+    ),
+    (
+        f"some/other/dir::dir/with/{CUDA_RUNTIME_LIB}",
+        f"dir/with/{CUDA_RUNTIME_LIB}",
+    ),
+    (
+        f"dir/with/{CUDA_RUNTIME_LIB}:some/other/dir",
+        f"dir/with/{CUDA_RUNTIME_LIB}",
+    ),
     (
         f"dir/with/{CUDA_RUNTIME_LIB}:other/dir/libcuda.so",
         f"dir/with/{CUDA_RUNTIME_LIB}",

tests/test_functional.py

@@ -86,7 +86,9 @@ def teardown():
     pass
 
 
-@pytest.mark.parametrize("dtype", [torch.float32, torch.float16], ids=["float", "half"])
+@pytest.mark.parametrize(
+    "dtype", [torch.float32, torch.float16], ids=["float", "half"]
+)
 def test_estimate_quantiles(dtype):
     A = torch.rand(1024, 1024, device="cuda")
     A = A.to(dtype)
@@ -190,7 +192,9 @@ def test_dynamic_blockwise_stochastic_quantization():
         )
 
 
-@pytest.mark.parametrize("gtype", [torch.float32, torch.float16], ids=["float", "half"])
+@pytest.mark.parametrize(
+    "gtype", [torch.float32, torch.float16], ids=["float", "half"]
+)
 def test_percentile_clipping(gtype):
     gnorm_vec1 = torch.zeros(100, device="cuda")
     gnorm_vec2 = torch.zeros(100, device="cuda")
@@ -270,7 +274,13 @@ def mean(xx):
 dim1 = [1024 * 2]
 dim2 = [1024 * 16]
 methods = [
-    (lambda x, dim: quant(x), lambda x, dim: quant(x), dequant, dequant, mm_dequant)
+    (
+        lambda x, dim: quant(x),
+        lambda x, dim: quant(x),
+        dequant,
+        dequant,
+        mm_dequant,
+    )
 ]
 methods.append((quant_multi, quant_multi, dequant, dequant, mm_dequant))
 # methods.append((lambda x: quant_multi_chunk(x, dim=-1), lambda x: quant_multi_chunk(x, dim=0), dequant, dequant, mm_dequant))
@@ -279,11 +289,14 @@ 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) for vals in values_names
+    "dim1_{0}_dim2_{1}_quant_{2}_batched_{3}".format(*vals)
+    for vals in values_names
 ]
 
 
-@pytest.mark.parametrize("dim1, dim2, quant_methods, batched", values, ids=names)
+@pytest.mark.parametrize(
+    "dim1, dim2, quant_methods, batched", values, ids=names
+)
 def test_approx_igemm(dim1, dim2, quant_methods, batched):
     dim1 = dim1 - (dim1 % 32)
     dim2 = dim2 - (dim2 % 32)
@@ -339,14 +352,18 @@ names = [
 ]
 
 
-@pytest.mark.parametrize("hidden_dim, batch_dim, transpose, seq_dim", values, ids=names)
+@pytest.mark.parametrize(
+    "hidden_dim, batch_dim, transpose, seq_dim", values, ids=names
+)
 def test_igemm(hidden_dim, batch_dim, transpose, seq_dim):
     hidden_dim = hidden_dim - (hidden_dim % 32)
     batch_dim = batch_dim - (batch_dim % 16)
     seq_dim = seq_dim - (seq_dim % 16)
     for i in range(k):
         shapeA = (
-            (batch_dim, hidden_dim) if not transpose[0] else (hidden_dim, batch_dim)
+            (batch_dim, hidden_dim)
+            if not transpose[0]
+            else (hidden_dim, batch_dim)
         )
         shapeB = (
             (32 * random.randint(1, 4), hidden_dim)
@@ -394,7 +411,9 @@ seq_dim = torch.randint(32, 512, size=(n,)).tolist()
 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]
+names = [
+    "seq_dim{0}_hidden_dim{1}_batch_dim{2}".format(*vals) for vals in values
+]
 
 
 @pytest.mark.parametrize("seq_dim, hidden_dim, batch_dim", values, ids=names)
@@ -406,11 +425,13 @@ def test_dim3_igemm(seq_dim, hidden_dim, batch_dim):
         A = torch.randint(
             -128, 127, size=(batch_dim, seq_dim, hidden_dim), device="cuda"
         ).to(torch.int8)
-        B = torch.randint(-128, 127, size=(batch_dim, seq_dim, 1024), device="cuda").to(
-            torch.int8
-        )
+        B = torch.randint(
+            -128, 127, size=(batch_dim, seq_dim, 1024), device="cuda"
+        ).to(torch.int8)
         out2 = torch.einsum("bsi, bso->io", A.float(), B.float())
-        iout = torch.empty(A.shape[2], B.shape[2], dtype=torch.int32, device=A.device)
+        iout = torch.empty(
+            A.shape[2], B.shape[2], dtype=torch.int32, device=A.device
+        )
         out = F.igemm(A, B, out=iout)
 
         torch.testing.assert_allclose(out.float(), out2)
@@ -428,7 +449,9 @@ names = [
 ]
 
 
-@pytest.mark.parametrize("seq_dim, hidden_dim, batch_dim, transpose", values, ids=names)
+@pytest.mark.parametrize(
+    "seq_dim, hidden_dim, batch_dim, transpose", values, ids=names
+)
 def test_minmax_igemm(seq_dim, hidden_dim, batch_dim, transpose):
     def min_max(x):
         maxA = torch.amax(x, dim=2, keepdim=True)
@@ -444,7 +467,9 @@ def test_minmax_igemm(seq_dim, hidden_dim, batch_dim, transpose):
     errs2 = []
     relerrs2 = []
     for i in range(k):
-        A = torch.normal(0.0, 0.5, size=(batch_dim, seq_dim, hidden_dim), device="cuda")
+        A = torch.normal(
+            0.0, 0.5, size=(batch_dim, seq_dim, hidden_dim), device="cuda"
+        )
         if transpose:
             B = torch.normal(0, 0.5, size=(256, hidden_dim), device="cuda")
         else:
@@ -504,7 +529,8 @@ 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) for vals in values
+    "dim1_{0}_dim2_{1}_dim3_{2}_dim4_{3}_transpose_{4}".format(*vals)
+    for vals in values
 ]
 
 
@@ -529,7 +555,9 @@ def test_ibmm(dim1, dim2, dim3, dim4, transpose):
             out2 = torch.bmm(A.permute([0, 2, 1]).float(), B.float())
             out = F.igemm(A.permute([0, 2, 1]), B)
         elif transpose[0] and transpose[1]:
-            out2 = torch.bmm(A.permute([0, 2, 1]).float(), B.permute([0, 2, 1]).float())
+            out2 = torch.bmm(
+                A.permute([0, 2, 1]).float(), B.permute([0, 2, 1]).float()
+            )
             out = F.igemm(A.permute([0, 2, 1]), B.permute([0, 2, 1]))
         torch.testing.assert_allclose(out.float(), out2.float())
 
@@ -563,7 +591,9 @@ a_order = ["row"]
 out_order = ["col", "row", "col32"]
 transpose = [False]
 dims = [2, 3]
-values = list(product(dim1, dim2, dim3, dims, dtype, a_order, out_order, transpose))
+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(
@@ -574,9 +604,13 @@ names = [
 
 
 @pytest.mark.parametrize(
-    "dim1, dim2, dim3, dims, dtype, orderA, orderOut, transpose", values, ids=names
+    "dim1, dim2, dim3, dims, dtype, orderA, orderOut, transpose",
+    values,
+    ids=names,
 )
-def test_nvidia_transform(dim1, dim2, dim3, dims, dtype, orderA, orderOut, transpose):
+def test_nvidia_transform(
+    dim1, dim2, dim3, dims, dtype, orderA, orderOut, transpose
+):
     if dims == 3 and out_order != "col32":
         return
     if dtype == torch.int32 and out_order != "col32":
@@ -586,7 +620,9 @@ def test_nvidia_transform(dim1, dim2, dim3, dims, dtype, orderA, orderOut, trans
     if dims == 2:
         A = torch.randint(-128, 127, size=(dim1, dim2), device="cuda").to(dtype)
     elif dims == 3:
-        A = torch.randint(-128, 127, size=(dim1, dim2, dim3), device="cuda").to(dtype)
+        A = torch.randint(-128, 127, size=(dim1, dim2, dim3), device="cuda").to(
+            dtype
+        )
 
     out, S = F.nvidia_transform(A, to_order=orderOut)
 
@@ -598,7 +634,11 @@ def test_nvidia_transform(dim1, dim2, dim3, dims, dtype, orderA, orderOut, trans
         if dims == 2:
             n = A.shape[0] * (A.shape[1] + (32 - (A.shape[1] % 32)))
         elif dims == 3:
-            n = A.shape[0] * A.shape[1] * (A.shape[2] + (32 - (A.shape[2] % 32)))
+            n = (
+                A.shape[0]
+                * A.shape[1]
+                * (A.shape[2] + (32 - (A.shape[2] % 32)))
+            )
         assert out.numel() == n
     elif orderOut == "col_turing":
         # 32 col 8 row tiles
@@ -613,7 +653,9 @@ def test_nvidia_transform(dim1, dim2, dim3, dims, dtype, orderA, orderOut, trans
                 j = col
 
                 coltile = (col // 32) + (1 if col % 32 != 0 else 0)
-                rowtile = ((row // 8) + (1 if row % 8 != 0 else 0)) * total_coltile
+                rowtile = (
+                    (row // 8) + (1 if row % 8 != 0 else 0)
+                ) * total_coltile
                 offset = 32 * 8 * (rowtile + coltile)
                 col2 = col % 32
                 row2 = (row % 8) * 32
@@ -624,7 +666,9 @@ def test_nvidia_transform(dim1, dim2, dim3, dims, dtype, orderA, orderOut, trans
                 # torch.testing.assert_allclose(A.flatten()[i+j], out.flatten()[row2+ col2+block_offset])
 
     if orderOut == "col32":
-        out2, S = F.nvidia_transform(out, from_order=orderOut, to_order="row", state=S)
+        out2, S = F.nvidia_transform(
+            out, from_order=orderOut, to_order="row", state=S
+        )
         torch.testing.assert_allclose(A, out2)
 
 
@@ -657,10 +701,12 @@ def test_igemmlt_int(dim1, dim2, dim3, dim4, dims, ldb):
                 torch.int8
             )
         elif dims == 3:
-            A = torch.randint(-128, 127, size=(dim1, dim2, dim3), device="cuda").to(
-                torch.int8
-            )
-        B = torch.randint(-128, 127, size=(dim4, dim3), device="cuda").to(torch.int8)
+            A = torch.randint(
+                -128, 127, size=(dim1, dim2, dim3), device="cuda"
+            ).to(torch.int8)
+        B = torch.randint(-128, 127, size=(dim4, dim3), device="cuda").to(
+            torch.int8
+        )
         C1 = torch.matmul(A.float(), B.t().float())
 
         A2, SA = F.transform(A, "col32")
@@ -670,7 +716,9 @@ def test_igemmlt_int(dim1, dim2, dim3, dim4, dims, ldb):
         torch.testing.assert_allclose(C1, C3.float())
 
         # transpose
-        B = torch.randint(-128, 127, size=(dim3, dim4), device="cuda").to(torch.int8)
+        B = torch.randint(-128, 127, size=(dim3, dim4), device="cuda").to(
+            torch.int8
+        )
         C1 = torch.matmul(A.float(), B.float())
 
         B2t, SBt = F.transform(B, "col_turing", transpose=True)
@@ -688,7 +736,8 @@ 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) for vals in values
+    "dim1_{0}_dim2_{1}_dim3_{2}_dim4_{3}_dims_{4}".format(*vals)
+    for vals in values
 ]
 
 
@@ -699,7 +748,9 @@ def test_igemmlt_half(dim1, dim2, dim3, dim4, dims):
         if dims == 2:
             A = torch.normal(0, 0.5, size=(dim1, dim3), device="cuda").half()
         elif dims == 3:
-            A = torch.normal(0, 0.5, size=(dim1, dim2, dim3), device="cuda").half()
+            A = torch.normal(
+                0, 0.5, size=(dim1, dim2, dim3), device="cuda"
+            ).half()
         B = torch.randn((dim4, dim3), device="cuda").half()
         torch.nn.init.xavier_uniform_(B)
         C1 = torch.matmul(A, B.t())
@@ -742,7 +793,9 @@ values = [
 
 
 # values = list(product(batch, seq, model, hidden))
-names = ["batch_{0}_seq_{1}_model_{2}_hidden_{3}".format(*vals) for vals in values]
+names = [
+    "batch_{0}_seq_{1}_model_{2}_hidden_{3}".format(*vals) for vals in values
+]
 
 
 @pytest.mark.parametrize("batch, seq, model, hidden", values, ids=names)
@@ -909,7 +962,9 @@ dims = (2,)
 # ldb = list(range(256, 1*1024, 256))
 formatB = ["col_turing", "col_ampere"]
 values = list(product(dim1, dim4, dims, formatB))
-names = ["dim1_{0}_dim4_{1}_dims_{2}_formatB_{3}".format(*vals) for vals in values]
+names = [
+    "dim1_{0}_dim4_{1}_dims_{2}_formatB_{3}".format(*vals) for vals in values
+]
 
 
 @pytest.mark.parametrize("dim1, dim4, dims, formatB", values, ids=names)
@@ -992,7 +1047,9 @@ def test_colrow_absmax(dim1, dim2, dims):
         torch.testing.assert_allclose(row_stats1_trunc, row_stats2)
         torch.testing.assert_allclose(nnz_block_ptr1, nnz_block_ptr2)
 
-        row_stats2, col_stats2, nnz_block_ptr2 = F.get_colrow_absmax(A, threshold=0.0)
+        row_stats2, col_stats2, nnz_block_ptr2 = F.get_colrow_absmax(
+            A, threshold=0.0
+        )
 
         torch.testing.assert_allclose(col_stats1, col_stats2)
         torch.testing.assert_allclose(row_stats1, row_stats2)
@@ -1023,8 +1080,12 @@ def test_double_quant(dim1, dim2):
         torch.testing.assert_allclose(CAt, out_col1, atol=1, rtol=0)
 
         n = CAt.numel()
-        num_not_close_rows = (torch.isclose(CA, out_row1, atol=1) == 0).sum().item()
-        num_not_close_cols = (torch.isclose(CAt, out_col1, atol=1) == 0).sum().item()
+        num_not_close_rows = (
+            (torch.isclose(CA, out_row1, atol=1) == 0).sum().item()
+        )
+        num_not_close_cols = (
+            (torch.isclose(CAt, out_col1, atol=1) == 0).sum().item()
+        )
 
         # allow for 1:500 error due to rounding differences
         min_error = 1 / 500
@@ -1123,7 +1184,9 @@ def test_igemmlt_row_scale(dim1, dim4, inner):
 
         c = 10.0 * inner * scale
         row_scale = torch.ones_like(maxA) / c
-        outC32, SC = F.igemmlt(A2, B2, SA, SB, dtype=torch.int8, row_scale=row_scale)
+        outC32, SC = F.igemmlt(
+            A2, B2, SA, SB, dtype=torch.int8, row_scale=row_scale
+        )
         C3, S = F.nvidia_transform(outC32, "row", state=SC)
         maxval = torch.abs(C3).max()
         if maxval == 127:
@@ -1204,7 +1267,9 @@ def test_row_scale_bench(dim1, dim4, inner):
     torch.cuda.synchronize()
     t0 = time.time()
     for i in range(k):
-        outC32, SC = F.igemmlt(A2, B2, SA, SB, dtype=torch.int8, row_scale=row_scale)
+        outC32, SC = F.igemmlt(
+            A2, B2, SA, SB, dtype=torch.int8, row_scale=row_scale
+        )
     torch.cuda.synchronize()
     print("row-wise", time.time() - t0)
 
@@ -1230,7 +1295,9 @@ a_order = ["row"]
 out_order = ["col32", "col_turing", "col_ampere"]
 transpose = [False, True]
 dims = [2]
-values = list(product(dim1, dim2, dim3, dims, dtype, a_order, out_order, transpose))
+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(
         *vals
@@ -1240,14 +1307,20 @@ names = [
 
 
 @pytest.mark.parametrize(
-    "dim1, dim2, dim3, dims, dtype, orderA, orderOut, transpose", values, ids=names
+    "dim1, dim2, dim3, dims, dtype, orderA, orderOut, transpose",
+    values,
+    ids=names,
 )
 def test_transform(dim1, dim2, dim3, dims, dtype, orderA, orderOut, transpose):
     for i in range(k):
         if dims == 2:
-            A = torch.randint(10, 99, size=(dim1, dim2), device="cuda").to(dtype)
+            A = torch.randint(10, 99, size=(dim1, dim2), device="cuda").to(
+                dtype
+            )
         elif dims == 3:
-            A = torch.randint(10, 99, size=(dim1, dim2, dim3), device="cuda").to(dtype)
+            A = torch.randint(
+                10, 99, size=(dim1, dim2, dim3), device="cuda"
+            ).to(dtype)
 
         A.view(-1)[-1] = -1
         if transpose:
@@ -1282,7 +1355,9 @@ names = [
 ]
 
 
-@pytest.mark.parametrize("dim1, dim2, dtype, orderA, orderOut", values, ids=names)
+@pytest.mark.parametrize(
+    "dim1, dim2, dtype, orderA, orderOut", values, ids=names
+)
 def test_transform_to_row(dim1, dim2, dtype, orderA, orderOut):
     for i in range(1):
         A = torch.randint(-127, 127, size=(dim1, dim2), device="cuda").to(dtype)
@@ -1332,17 +1407,23 @@ def test_coo_double_quant(dim1, dim2):
 
         idx = torch.abs(A) >= threshold
         CA2, CAt, statsA, statsAt, coo_tensor = F.double_quant(A)
-        CA, CAt, statsA, statsAt, coo_tensor = F.double_quant(A, threshold=threshold)
+        CA, CAt, statsA, statsAt, coo_tensor = F.double_quant(
+            A, threshold=threshold
+        )
 
         if coo_tensor is not None:
             A1 = A * idx
             A2 = torch.zeros_like(A)
-            A2[coo_tensor.rowidx.long(), coo_tensor.colidx.long()] = coo_tensor.values
+            A2[
+                coo_tensor.rowidx.long(), coo_tensor.colidx.long()
+            ] = coo_tensor.values
             torch.testing.assert_allclose(A1, A2)
 
             A1 = A * (idx == 0)
             A2 = (CA.float() * statsA.unsqueeze(1) / 127).half()
-            torch.testing.assert_allclose(A * (idx == 0), A2, rtol=0.05, atol=1.5e-2)
+            torch.testing.assert_allclose(
+                A * (idx == 0), A2, rtol=0.05, atol=1.5e-2
+            )
 
 
 n = 2
@@ -1454,7 +1535,9 @@ def test_integrated_sparse_decomp(dim1, dim2):
         out1_32, Sout1_32 = F.igemmlt(C32A, CTw1, SA, Sw1)
         out2 = F.mm_dequant(out1_32, Sout1_32, statsA, statsw1)
 
-        CA, CAt, statsA, statsAt, coo_tensor = F.double_quant(A, threshold=threshold)
+        CA, CAt, statsA, statsAt, coo_tensor = F.double_quant(
+            A, threshold=threshold
+        )
         C32A, SA = F.transform(CA, "col32")
 
         out1_32, Sout1_32 = F.igemmlt(C32A, CTw1, SA, Sw1)
@@ -1494,7 +1577,9 @@ dim2 = [12288]
 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]
+names = [
+    "dim1_{0}_dim2_{1}_dtype_{2}_out_func_{3}".format(*vals) for vals in values
+]
 
 
 @pytest.mark.parametrize("dim1, dim2, dtype, out_func", values, ids=names)
@@ -1536,7 +1621,9 @@ def test_spmm_coo_very_sparse(dim1, dim2, dtype, out_func):
     std = out1.std()
     out1 /= std
     out2 /= std
-    assert_all_approx_close(out1, out2.half(), rtol=0.01, atol=3.0e-2, count=count)
+    assert_all_approx_close(
+        out1, out2.half(), rtol=0.01, atol=3.0e-2, count=count
+    )
     # assert_all_approx_close(out1, out2.half(), rtol=0.05, atol=0.01, count=count)
 
     idx_col = torch.randint(0, A2.shape[-1], size=(15,))
@@ -1734,7 +1821,9 @@ values.append((batch_size, seqdim, 768, 4 * 768))
 # values.append((batch_size, seqdim, 4096, 4*4096))
 # 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]
+names = [
+    "batch_{0}_seq_{1}_model_{2}_hidden_{3}".format(*vals) for vals in values
+]
 
 
 @pytest.mark.parametrize("batch, seq, model, hidden", values, ids=names)

tests/test_modules.py

@@ -48,7 +48,9 @@ def assert_all_approx_close(a, b, atol=1e-8, rtol=1e-5, count=10):
 class LinearFunction(torch.autograd.Function):
     @staticmethod
     def get_8bit_linear_trimmed(x, stochastic=False, trim_value=3.0):
-        round_func = LinearFunction.round_stoachastic if stochastic else torch.round
+        round_func = (
+            LinearFunction.round_stoachastic if stochastic else torch.round
+        )
         norm = math.sqrt(math.pi) / math.sqrt(2.0)
         # std = torch.abs(x).mean()*norm
         std = torch.std(x)
@@ -116,7 +118,9 @@ class LinearFunction(torch.autograd.Function):
         return x.to(dtype)
 
     def get_8bit_linear(x, stochastic=False):
-        round_func = LinearFunction.round_stoachastic if stochastic else torch.round
+        round_func = (
+            LinearFunction.round_stoachastic if stochastic else torch.round
+        )
         max1 = torch.abs(x).max()
         x = x / max1 * 127
         x = round_func(x) / 127 * max1
@@ -125,7 +129,9 @@ class LinearFunction(torch.autograd.Function):
 
     @staticmethod
     def get_8bit_vector_wise(x, dim, stochastic=False):
-        round_func = LinearFunction.round_stoachastic if stochastic else torch.round
+        round_func = (
+            LinearFunction.round_stoachastic if stochastic else torch.round
+        )
         max1 = torch.amax(torch.abs(x), dim=dim, keepdim=True)
         max1[max1 == 0] = 1.0
         x = (x * 127) / max1
@@ -209,7 +215,9 @@ class LinearFunction(torch.autograd.Function):
             weight8, S1 = LinearFunction.quant(weight, args.quant_type, dim=1)
             x8, S2 = LinearFunction.quant(x, args.quant_type, dim=2)
             outputq = bnb.functional.igemm(x8, weight8.t())
-            output = LinearFunction.dequant(outputq, S1, S2, x.dtype, args.quant_type)
+            output = LinearFunction.dequant(
+                outputq, S1, S2, x.dtype, args.quant_type
+            )
             # if torch.rand(1) < 0.01:
             # output32 = torch.matmul(x, weight.t())
             # err = torch.abs(output-output32).float()
@@ -261,7 +269,9 @@ class LinearFunction(torch.autograd.Function):
                 grad_weight8, S1, S2, grad_output.dtype, args.quant_type
             )
 
-            grad_output8, S1 = LinearFunction.quant(grad_output, args.quant_type, dim=2)
+            grad_output8, S1 = LinearFunction.quant(
+                grad_output, args.quant_type, dim=2
+            )
             weight8, S3 = LinearFunction.quant(weight, args.quant_type, dim=0)
             grad_input8 = bnb.functional.igemm(grad_output8, weight8)
             grad_input = LinearFunction.dequant(
@@ -338,8 +348,12 @@ def test_linear8bit():
         loss2.backward()
         loss3.backward()
 
-        assert_all_approx_close(l1.bias.grad, l2.bias.grad, atol=0.01, rtol=0, count=2)
-        assert_all_approx_close(l3.bias.grad, l2.bias.grad, atol=0.01, rtol=0, count=2)
+        assert_all_approx_close(
+            l1.bias.grad, l2.bias.grad, atol=0.01, rtol=0, count=2
+        )
+        assert_all_approx_close(
+            l3.bias.grad, l2.bias.grad, atol=0.01, rtol=0, count=2
+        )
         assert_all_approx_close(
             l1.weight.grad, l2.weight.grad, atol=0.013, rtol=0.05, count=2
         )
@@ -388,7 +402,9 @@ def test_linear8bitlt_accumulated_gradient():
     l1 = torch.nn.Sequential(
         *[bnb.nn.Linear8bitLt(32, 32).cuda().half() for i in range(2)]
     )
-    l2 = torch.nn.Sequential(*[torch.nn.Linear(32, 32).cuda().half() for i in range(2)])
+    l2 = torch.nn.Sequential(
+        *[torch.nn.Linear(32, 32).cuda().half() for i in range(2)]
+    )
     l2[0].weight = torch.nn.Parameter(l1[0].weight.clone())
     l2[0].bias = torch.nn.Parameter(l1[0].bias.clone())
     l2[1].weight = torch.nn.Parameter(l1[1].weight.clone())
@@ -462,7 +478,11 @@ def test_linear8bitlt_no_fp16_weights(threshold):
         if threshold > 0:
             assert mlp.fc2.state.idx is not None
 
-    mlp = MLP8bit(32, 64, threshold=threshold, has_fp16_weights=False).cuda().half()
+    mlp = (
+        MLP8bit(32, 64, threshold=threshold, has_fp16_weights=False)
+        .cuda()
+        .half()
+    )
     assert mlp.fc1.weight.dtype == torch.int8
     assert mlp.fc2.weight.dtype == torch.int8
 
@@ -475,7 +495,11 @@ def test_linear8bitlt_no_fp16_weights(threshold):
         if threshold > 0:
             assert mlp.fc2.state.idx is not None
 
-    mlp = MLP8bit(32, 64, threshold=threshold, has_fp16_weights=False).half().cuda()
+    mlp = (
+        MLP8bit(32, 64, threshold=threshold, has_fp16_weights=False)
+        .half()
+        .cuda()
+    )
 
     for i in range(100):
         b1 = torch.randn(16, 8, 32, device="cuda").half()
@@ -488,7 +512,11 @@ def test_linear8bitlt_no_fp16_weights(threshold):
     assert mlp.fc1.weight.dtype == torch.int8
     assert mlp.fc2.weight.dtype == torch.int8
 
-    mlp = MLP8bit(32, 64, threshold=threshold, has_fp16_weights=False).half().to("cuda")
+    mlp = (
+        MLP8bit(32, 64, threshold=threshold, has_fp16_weights=False)
+        .half()
+        .to("cuda")
+    )
 
     for i in range(100):
         b1 = torch.randn(16, 8, 32, device="cuda").half()

tests/test_optim.py

@@ -103,20 +103,26 @@ str2statenames["adam8bit_blockwise"] = [
     ("exp_avg", "state1", "qmap1", "absmax1"),
     ("exp_avg_sq", "state2", "qmap2", "absmax2"),
 ]
-str2statenames["momentum8bit"] = [("momentum_buffer", "state1", "qmap1", "max1")]
+str2statenames["momentum8bit"] = [
+    ("momentum_buffer", "state1", "qmap1", "max1")
+]
 str2statenames["momentum8bit_blockwise"] = [
     ("momentum_buffer", "state1", "qmap1", "absmax1")
 ]
 str2statenames["lars8bit"] = [("momentum_buffer", "state1", "qmap1", "max1")]
 str2statenames["rmsprop8bit"] = [("square_avg", "state1", "qmap1", "max1")]
-str2statenames["rmsprop8bit_blockwise"] = [("square_avg", "state1", "qmap1", "absmax1")]
+str2statenames["rmsprop8bit_blockwise"] = [
+    ("square_avg", "state1", "qmap1", "absmax1")
+]
 
 dim1 = [1024]
 dim2 = [32, 1024, 4097, 1]
 gtype = [torch.float32, torch.float16]
 optimizer_names = ["adam", "momentum", "rmsprop", "lars", "lamb"]
 values = list(product(dim1, dim2, gtype, optimizer_names))
-names = ["dim1_{0}_dim2_{1}_gtype_{2}_optim_{3}".format(*vals) for vals in values]
+names = [
+    "dim1_{0}_dim2_{1}_gtype_{2}_optim_{3}".format(*vals) for vals in values
+]
 
 
 @pytest.mark.parametrize("dim1, dim2, gtype, optim_name", values, ids=names)
@@ -203,9 +209,13 @@ def test_global_config(dim1, dim2, gtype):
     eps = 1e-8
 
     bnb.optim.GlobalOptimManager.get_instance().initialize()
-    bnb.optim.GlobalOptimManager.get_instance().override_config(p3, "optim_bits", 8)
+    bnb.optim.GlobalOptimManager.get_instance().override_config(
+        p3, "optim_bits", 8
+    )
 
-    bnb.optim.GlobalOptimManager.get_instance().register_parameters([p1, p2, p3])
+    bnb.optim.GlobalOptimManager.get_instance().register_parameters(
+        [p1, p2, p3]
+    )
     p1 = p1.cuda()
     p2 = p2.cuda()
     p3 = p3.cuda()
@@ -245,7 +255,9 @@ optimizer_names = [
     "rmsprop8bit_blockwise",
 ]
 values = list(product(dim1, dim2, gtype, optimizer_names))
-names = ["dim1_{0}_dim2_{1}_gtype_{2}_optim_{3}".format(*vals) for vals in values]
+names = [
+    "dim1_{0}_dim2_{1}_gtype_{2}_optim_{3}".format(*vals) for vals in values
+]
 
 
 @pytest.mark.parametrize("dim1, dim2, gtype, optim_name", values, ids=names)
@@ -329,8 +341,12 @@ def test_optimizer8bit(dim1, dim2, gtype, optim_name):
                 bnb_optimizer = str2optimizers[optim_name][1]([p2])
                 bnb_optimizer.load_state_dict(torch.load(join(path, "opt.pt")))
                 rm_path(path)
-                torch.testing.assert_allclose(raws1cpy, bnb_optimizer.state[p2][name2])
-                torch.testing.assert_allclose(qmap1, bnb_optimizer.state[p2][qmap])
+                torch.testing.assert_allclose(
+                    raws1cpy, bnb_optimizer.state[p2][name2]
+                )
+                torch.testing.assert_allclose(
+                    qmap1, bnb_optimizer.state[p2][qmap]
+                )
 
                 if "blockwise" in optim_name:
                     s1 = F.dequantize_blockwise(
@@ -349,12 +365,17 @@ def test_optimizer8bit(dim1, dim2, gtype, optim_name):
 
                 num_not_close = (
                     torch.isclose(
-                        torch_optimizer.state[p1][name1], s1, atol=atol, rtol=rtol
+                        torch_optimizer.state[p1][name1],
+                        s1,
+                        atol=atol,
+                        rtol=rtol,
                     )
                     == 0
                 )
                 assert num_not_close.sum().item() < 20
-            torch.testing.assert_allclose(p1, p2.float(), atol=patol, rtol=prtol)
+            torch.testing.assert_allclose(
+                p1, p2.float(), atol=patol, rtol=prtol
+            )
 
         # the parameters diverge quickly. Here we keep them close
         # together so we can test against the Adam error
@@ -375,7 +396,10 @@ dim2 = [32, 1024, 4097]
 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) for vals in values]
+names = [
+    "dim1_{0}_dim2_{1}_gtype_{2}_optim_bits_{3}".format(*vals)
+    for vals in values
+]
 
 
 @pytest.mark.parametrize("dim1, dim2, gtype, optim_bits", values, ids=names)
@@ -391,7 +415,12 @@ def test_adam_percentile_clipping(dim1, dim2, gtype, optim_bits):
     p2 = p1.clone()
     adam1 = bnb.optim.Adam([p1], lr, (beta1, beta2), eps, optim_bits=optim_bits)
     adam2 = bnb.optim.Adam(
-        [p2], lr, (beta1, beta2), eps, optim_bits=optim_bits, percentile_clipping=5
+        [p2],
+        lr,
+        (beta1, beta2),
+        eps,
+        optim_bits=optim_bits,
+        percentile_clipping=5,
     )
 
     gnorm_vec = torch.zeros(100).cuda()
@@ -399,7 +428,9 @@ def test_adam_percentile_clipping(dim1, dim2, gtype, optim_bits):
 
     for i in range(50):
         step += 1
-        g1 = torch.randn(dim1, dim2, device="cuda", dtype=gtype) * 0.1 + (0.01 * i)
+        g1 = torch.randn(dim1, dim2, device="cuda", dtype=gtype) * 0.1 + (
+            0.01 * i
+        )
         g2 = g1.clone()
         p2.grad = g2
 
@@ -430,10 +461,16 @@ def test_adam_percentile_clipping(dim1, dim2, gtype, optim_bits):
         elif optim_bits == 8:
             torch.testing.assert_allclose(p1, p2, atol=1e-4, rtol=1e-3)
             torch.testing.assert_allclose(
-                adam1.state[p1]["state1"], adam2.state[p2]["state1"], atol=2, rtol=1e-3
+                adam1.state[p1]["state1"],
+                adam2.state[p2]["state1"],
+                atol=2,
+                rtol=1e-3,
             )
             torch.testing.assert_allclose(
-                adam1.state[p1]["state2"], adam2.state[p2]["state2"], atol=2, rtol=1e-3
+                adam1.state[p1]["state2"],
+                adam2.state[p2]["state2"],
+                atol=2,
+                rtol=1e-3,
             )
             adam1.state[p1]["state1"].copy_(adam2.state[p2]["state1"])
             adam1.state[p1]["state2"].copy_(adam2.state[p2]["state2"])
@@ -463,7 +500,9 @@ gtype = [torch.float32, torch.float16]
 # optimizer_names = ['lars_apex', 'lars8bit']
 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]
+names = [
+    "dim1_{0}_dim2_{1}_gtype_{2}_optim_{3}".format(*vals) for vals in values
+]
 
 
 @pytest.mark.parametrize("dim1, dim2, gtype, optim_name", values, ids=names)