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Merge branch 'main' into remove_unused_code

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Tim Dettmers 2022-09-05 16:29:25 -07:00 committed by GitHub
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14
README.md
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@ -23,12 +23,12 @@ Resources:
1. Comment out torch.nn.Linear: ``#linear = torch.nn.Linear(...)``
2. Add bnb 8-bit linear light module: ``linear = bnb.nn.Linear8bitLt(...)`` (base arguments stay the same)
3. There are two modes:
- Mixed 8-bit training with 16-bit main weights. Pass the argument ``use_fp16_weights=True`` (default)
- Int8 inference. Pass the argument ``use_fp16_weights=False``
- Mixed 8-bit training with 16-bit main weights. Pass the argument ``has_fp16_weights=True`` (default)
- Int8 inference. Pass the argument ``has_fp16_weights=False``
4. To use the full LLM.int8() method, use the ``threshold=k`` argument. We recommend ``k=6.0``.
```python
# LLM.int8()
linear = bnb.nn.Linear8bitLt(dim1, dim2, bias=True, use_fp16_weights=False, threshold=6.0)
linear = bnb.nn.Linear8bitLt(dim1, dim2, bias=True, has_fp16_weights=False, threshold=6.0)
# inputs need to be fp16
out = linear(x.to(torch.float16))
```
@ -115,7 +115,8 @@ We thank Fabio Cannizzo for his work on [FastBinarySearch](https://github.com/fa
## How to cite us
If you found this library and found LLM.int8() useful, please consider citing our work:
```
```bibtex
@article{dettmers2022llmint8,
title={LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale},
author={Dettmers, Tim and Lewis, Mike and Belkada, Younes and Zettlemoyer, Luke},
@ -124,8 +125,9 @@ If you found this library and found LLM.int8() useful, please consider citing ou
}
```
For 8-bit optimizers or quantization routines please consider citing the following work.
```
For 8-bit optimizers or quantization routines, please consider citing the following work:
```bibtex
@article{dettmers2022optimizers,
title={8-bit Optimizers via Block-wise Quantization},
author={Dettmers, Tim and Lewis, Mike and Shleifer, Sam and Zettlemoyer, Luke},

8
bitsandbytes/cuda_setup/main.py
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@ -26,7 +26,7 @@ def check_cuda_result(cuda, result_val):
if result_val != 0:
error_str = ctypes.c_char_p()
cuda.cuGetErrorString(result_val, ctypes.byref(error_str))
raise Exception(f"CUDA exception! Error code: {error_str.value.decode()}")
print(f"CUDA exception! Error code: {error_str.value.decode()}")
def get_cuda_version(cuda, cudart_path):
# https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART____VERSION.html#group__CUDART____VERSION
@ -55,7 +55,7 @@ def get_cuda_lib_handle():
cuda = ctypes.CDLL("libcuda.so")
except OSError:
# TODO: shouldn't we error or at least warn here?
raise Exception('CUDA SETUP: ERROR! libcuda.so not found! Do you have a CUDA driver installed? If you are on a cluster, make sure you are on a CUDA machine!')
print('CUDA SETUP: WARNING! libcuda.so not found! Do you have a CUDA driver installed? If you are on a cluster, make sure you are on a CUDA machine!')
return None
check_cuda_result(cuda, cuda.cuInit(0))
@ -116,6 +116,10 @@ def evaluate_cuda_setup():
print('For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link')
print('='*80)
binary_name = "libbitsandbytes_cpu.so"
#if not torch.cuda.is_available():
#print('No GPU detected. Loading CPU library...')
#return binary_name
cudart_path = determine_cuda_runtime_lib_path()
if cudart_path is None:
print(

24
bitsandbytes/functional.py
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@ -184,14 +184,9 @@ def create_dynamic_map(signed=True, n=7):
def get_special_format_str():
if not torch.cuda.is_available(): return 'col_turing'
major, minor = torch.cuda.get_device_capability()
if major < 7:
print(
f"Device with CUDA capability of {major} not supported for 8-bit matmul. Device has no tensor cores!"
)
assert major >= 7
if major == 7:
if major <= 7:
return "col_turing"
elif major == 8:
return "col_ampere"
@ -1667,21 +1662,6 @@ def double_quant(
return out_row, out_col, row_stats, col_stats, coo_tensor
def get_special_format_str():
major, minor = torch.cuda.get_device_capability()
if major < 7:
print(
f"Device with CUDA capability of {major} not supported for 8-bit matmul. Device has no tensor cores!"
)
assert major >= 7
if major == 7: return 'col_turing'
elif major == 8: return 'col_ampere'
else: return 'col_turing'
def transform(A, to_order, from_order='row', out=None, transpose=False, state=None, ld=None):
prev_device = pre_call(A.device)
if state is None: state = (A.shape, from_order)

1
bitsandbytes/optim/__init__.py
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@ -5,7 +5,6 @@
from bitsandbytes.cextension import COMPILED_WITH_CUDA
if COMPILED_WITH_CUDA:
from .adam import Adam, Adam8bit, Adam32bit
from .adamw import AdamW, AdamW8bit, AdamW32bit
from .sgd import SGD, SGD8bit, SGD32bit

6
csrc/ops.cu
View File

@ -371,7 +371,11 @@ template void transform<int32_t, COL32, ROW, false, 32>(cublasLtHandle_t ltHandl
template <int FORMATB, int DTYPE_OUT, int SCALE_ROWS> int igemmlt(cublasLtHandle_t ltHandle, int m, int n, int k, const int8_t *A, const int8_t *B, void *C, float *row_scale, int lda, int ldb, int ldc)
{
#ifdef NO_CUBLASLT
printf("ERROR: Your GPU does not support Int8 Matmul!");
cout << "" << endl;
cout << "=============================================" << endl;
cout << "ERROR: Your GPU does not support Int8 Matmul!" << endl;
cout << "=============================================" << endl;
cout << "" << endl;
assert(false);
return 0;

2
setup.py
View File

@ -18,7 +18,7 @@ def read(fname):
setup(
name=f"bitsandbytes",
version=f"0.32.1",
version=f"0.32.2",
author="Tim Dettmers",
author_email="dettmers@cs.washington.edu",
description="8-bit optimizers and matrix multiplication routines.",

2
tests/test_autograd.py
View File

@ -40,6 +40,7 @@ names = [
ids=names,
)
def test_matmul(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose):
if not torch.cuda.is_available(): pytest.skip('No GPU found.')
if dim2 > 0:
dim2 = dim2 - (dim2 % 16)
dim3 = dim3 - (dim3 % 16)
@ -306,6 +307,7 @@ def test_matmullt(
has_fp16_weights,
has_bias
):
if not torch.cuda.is_available(): pytest.skip('No GPU found.')
dimA = (dim2, dim3) if not transpose[0] else (dim3, dim2)
dimB = (dim3, dim4) if not transpose[1] else (dim4, dim3)
outlier_dim = torch.randint(0, dimA[1], size=(dimA[1] // 8,), device="cuda")

48
tests/test_functional.py
View File

@ -1813,16 +1813,16 @@ def test_spmm_coo_dequant(dim1, dim2, dtype):
batch_size = 1
seqdim = 2048
seqdim = 1
values = []
values.append((batch_size, seqdim, 768, 4 * 768))
#values.append((batch_size, seqdim, 768, 4 * 768))
# values.append((batch_size, seqdim, 1024, 4*1024))
# values.append((batch_size, seqdim, 1536, 4*1536))
# values.append((batch_size, seqdim, 2048, 4*2048))
# values.append((batch_size, seqdim, 2560, 4*2560))
# values.append((batch_size, seqdim, 4096, 4*4096))
# values.append((batch_size, seqdim, 5140, 4*5140))
# values.append((batch_size, seqdim, 12288, 4*12288))
values.append((batch_size, seqdim, 12288, 4*12288))
names = [
"batch_{0}_seq_{1}_model_{2}_hidden_{3}".format(*vals) for vals in values
]
@ -1830,6 +1830,7 @@ names = [
@pytest.mark.parametrize("batch, seq, model, hidden", values, ids=names)
def test_bench_matmul(batch, seq, model, hidden):
iters = 128
formatB = F.get_special_format_str()
A = torch.randn(batch, seq, model, device="cuda").half()
@ -1848,28 +1849,33 @@ def test_bench_matmul(batch, seq, model, hidden):
linearMixedBit.eval()
# warmup
for i in range(100):
for i in range(iters):
torch.matmul(A, B.t())
torch.cuda.synchronize()
print("")
torch.cuda.synchronize()
t0 = time.time()
for i in range(100):
for i in range(iters):
torch.matmul(A, B.t())
torch.cuda.synchronize()
print(
f"pytorch: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s"
f"pytorch fp16: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s"
)
torch.cuda.synchronize()
t0 = time.time()
for i in range(100):
for i in range(iters):
bnb.matmul(A, B)
torch.cuda.synchronize()
print(
f"bnb lt: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s"
)
print(f"CB -> CxB conversion (each iteration): [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s")
torch.cuda.synchronize()
t0 = time.time()
for i in range(iters):
bnb.matmul(A, B, threshold=6.0)
torch.cuda.synchronize()
print(f"CB -> CxB conversion + threshold: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s")
CA, CAt, SCA, SCAt, coo_tensorA = F.double_quant(A, threshold=0.0)
C32A, SA = F.transform(CA, "col32")
@ -1877,18 +1883,16 @@ def test_bench_matmul(batch, seq, model, hidden):
CxB, SB = F.transform(CB, to_order=formatB)
torch.cuda.synchronize()
t0 = time.time()
for i in range(100):
for i in range(iters):
out32, Sout32 = F.igemmlt(C32A, CxB, SA, SB)
torch.cuda.synchronize()
print(
f"igemmlt: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s"
)
print(f"no overhead matmul-lt: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s")
BA, statsB = F.vectorwise_quant(B, dim=1)
CxB, SB = F.nvidia_transform(CB, to_order=formatB)
torch.cuda.synchronize()
t0 = time.time()
for i in range(100):
for i in range(iters):
A2 = A.view(-1, A.shape[-1]).contiguous()
CA, statsA = F.vectorwise_quant(A2, dim=1)
C32A, SA = F.nvidia_transform(CA, "col32")
@ -1896,15 +1900,13 @@ def test_bench_matmul(batch, seq, model, hidden):
Cout, Sout = F.nvidia_transform(out32, "row", state=Sout32)
F.vectorwise_mm_dequant(Cout, statsA, statsB.t())
torch.cuda.synchronize()
print(
f"vector pytorch + nvidia: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s"
)
#print(f"vector pytorch + nvidia: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s")
BA, statsB = F.vectorwise_quant(B, dim=1, quant_type="linear")
CxB, SB = F.nvidia_transform(CB, to_order=formatB)
torch.cuda.synchronize()
t0 = time.time()
for i in range(100):
for i in range(iters):
A2 = A.view(-1, A.shape[-1]).contiguous()
CA, statsA = F.vectorwise_quant(A2, dim=1, quant_type="linear")
C32A, SA = F.nvidia_transform(CA, "col32")
@ -1912,14 +1914,12 @@ def test_bench_matmul(batch, seq, model, hidden):
Cout, Sout = F.nvidia_transform(out32, "row", state=Sout32)
out = Cout * statsB * statsA * (1.0 / (127 * 127))
torch.cuda.synchronize()
print(
f"linear pytorch + nvidia: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s"
)
#print(f"linear pytorch + nvidia: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s")
linear8bit(A)
torch.cuda.synchronize()
t0 = time.time()
for i in range(100):
for i in range(iters):
linear8bit(A)
torch.cuda.synchronize()
print(
@ -1929,7 +1929,7 @@ def test_bench_matmul(batch, seq, model, hidden):
linearMixedBit(A)
torch.cuda.synchronize()
t0 = time.time()
for i in range(100):
for i in range(iters):
linearMixedBit(A)
torch.cuda.synchronize()
print(