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Added is_available_triton guard.

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This commit is contained in:
Tim Dettmers 2023-04-12 12:10:34 -07:00
parent 7140c01405
commit c3d87e4435
7 changed files with 572 additions and 536 deletions
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4
bitsandbytes/research/autograd/_functions.py
View File

@ -184,7 +184,7 @@ class MatMulFP8Global(torch.autograd.Function):
return grad_A, grad_B, None, None, None, None, None
class MatMul8bitMixed(torch.autograd.Function):
class SwitchBackBnb(torch.autograd.Function):
@staticmethod
def forward(ctx, A, B, out=None, bias=None, state=MatmulLtState()):
# default to pytorch behavior if inputs are empty
@ -408,4 +408,4 @@ def switchback_bnb(
state = state or MatmulLtState()
if threshold > 0.0:
state.threshold = threshold
return MatMul8bitMixed.apply(A, B, out, bias, state)
return SwitchBackBnb.apply(A, B, out, bias, state)

28
bitsandbytes/triton/dequantize_rowwise.py
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@ -1,14 +1,20 @@
import math
import torch
import time
import triton
import triton.language as tl
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
from bitsandbytes.triton.triton_utils import is_triton_available
# rowwise quantize
if not is_triton_available():
def dequantize_rowwise(x: torch.Tensor, state_x: torch.Tensor): return None
else:
# TODO: autotune this better.
@triton.autotune(
import triton
import triton.language as tl
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
# rowwise quantize
# TODO: autotune this better.
@triton.autotune(
configs=[
triton.Config({}, num_stages=1, num_warps=8),
triton.Config({}, num_stages=2, num_warps=8),
@ -24,9 +30,9 @@ from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_tim
triton.Config({}, num_warps=8),
],
key=['n_elements']
)
@triton.jit
def _dequantize_rowwise(
)
@triton.jit
def _dequantize_rowwise(
x_ptr,
state_x,
output_ptr,
@ -34,7 +40,7 @@ def _dequantize_rowwise(
n_elements,
BLOCK_SIZE: tl.constexpr,
P2: tl.constexpr,
):
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
arange = tl.arange(0, P2)
@ -46,7 +52,7 @@ def _dequantize_rowwise(
tl.store(output_ptr + offsets, output, mask=row_mask)
def dequantize_rowwise(x: torch.Tensor, state_x: torch.Tensor):
def dequantize_rowwise(x: torch.Tensor, state_x: torch.Tensor):
output = torch.empty(*x.shape, device=x.device, dtype=torch.float16)
P2 = int(2 ** (math.ceil(math.log2(x.shape[1]))))

37
bitsandbytes/triton/int8_matmul_mixed_dequanitze.py
View File

@ -1,19 +1,24 @@
import torch
from bitsandbytes.triton.triton_utils import is_triton_available
import triton
import triton.language as tl
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
if not is_triton_available():
def int8_matmul_mixed_dequanitze(a, b, state_x, state_w, bias): return None
else:
import triton
import triton.language as tl
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
# This is a matmul kernel based on triton.ops.matmul
# It is modified to support rowwise quantized input and global quantized weight
# It's purpose is fused matmul then dequantize
# It does support bias.
# This is a matmul kernel based on triton.ops.matmul
# It is modified to support rowwise quantized input and global quantized weight
# It's purpose is fused matmul then dequantize
# It does support bias.
def init_to_zero(name):
def init_to_zero(name):
return lambda nargs: nargs[name].zero_()
def get_configs_io_bound():
def get_configs_io_bound():
configs = []
for num_stages in [2, 3, 4, 5, 6]:
for block_m in [16, 32]:
@ -30,7 +35,7 @@ def get_configs_io_bound():
return configs
@triton.autotune(
@triton.autotune(
configs=[
# basic configs for compute-bound matmuls
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
@ -59,12 +64,12 @@ def get_configs_io_bound():
'perf_model': estimate_matmul_time,
'top_k': 10
},
)
@triton.heuristics({
)
@triton.heuristics({
'EVEN_K': lambda args: args['K'] % (args['BLOCK_K'] * args['SPLIT_K']) == 0,
})
@triton.jit
def _int8_matmul_mixed_dequantize(A, B, C, bias, state_x_ptr, state_w_ptr, M, N, K, divfactor: tl.constexpr, has_bias : tl.constexpr,
})
@triton.jit
def _int8_matmul_mixed_dequantize(A, B, C, bias, state_x_ptr, state_w_ptr, M, N, K, divfactor: tl.constexpr, has_bias : tl.constexpr,
stride_am, stride_ak,
stride_bk, stride_bn,
stride_cm, stride_cn,
@ -131,7 +136,7 @@ def _int8_matmul_mixed_dequantize(A, B, C, bias, state_x_ptr, state_w_ptr, M, N,
tl.atomic_add(C, acc, mask=mask)
def int8_matmul_mixed_dequanitze(a, b, state_x, state_w, bias):
def int8_matmul_mixed_dequanitze(a, b, state_x, state_w, bias):
device = a.device
divfactor = 1. / (127. * 127.)
has_bias = 0 if bias is None else 1

37
bitsandbytes/triton/int8_matmul_rowwise_dequantize.py
View File

@ -1,19 +1,24 @@
import torch
import triton
import triton.language as tl
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
from bitsandbytes.triton.triton_utils import is_triton_available
# This is a matmul kernel based on triton.ops.matmul
# It is modified to support rowwise quantized input and columnwise quantized weight
# It's purpose is fused matmul then dequantize
# It does support bias.
if not is_triton_available():
def int8_matmul_rowwise_dequantize(a, b, state_x, state_w, bias): return None
else:
import triton
import triton.language as tl
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
def init_to_zero(name):
# This is a matmul kernel based on triton.ops.matmul
# It is modified to support rowwise quantized input and columnwise quantized weight
# It's purpose is fused matmul then dequantize
# It does support bias.
def init_to_zero(name):
return lambda nargs: nargs[name].zero_()
def get_configs_io_bound():
def get_configs_io_bound():
configs = []
for num_stages in [2, 3, 4, 5, 6]:
for block_m in [16, 32]:
@ -30,7 +35,7 @@ def get_configs_io_bound():
return configs
@triton.autotune(
@triton.autotune(
configs=[
# basic configs for compute-bound matmuls
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
@ -59,12 +64,12 @@ def get_configs_io_bound():
'perf_model': estimate_matmul_time,
'top_k': 10
},
)
@triton.heuristics({
)
@triton.heuristics({
'EVEN_K': lambda args: args['K'] % (args['BLOCK_K'] * args['SPLIT_K']) == 0,
})
@triton.jit
def _int8_matmul_rowwise_dequantize(A, B, C, bias, state_x_ptr, state_w_ptr, M, N, K, divfactor, has_bias : tl.constexpr,
})
@triton.jit
def _int8_matmul_rowwise_dequantize(A, B, C, bias, state_x_ptr, state_w_ptr, M, N, K, divfactor, has_bias : tl.constexpr,
stride_am, stride_ak,
stride_bk, stride_bn,
stride_cm, stride_cn,
@ -130,7 +135,7 @@ def _int8_matmul_rowwise_dequantize(A, B, C, bias, state_x_ptr, state_w_ptr, M,
tl.atomic_add(C, acc, mask=mask)
def int8_matmul_rowwise_dequantize(a, b, state_x, state_w, bias):
def int8_matmul_rowwise_dequantize(a, b, state_x, state_w, bias):
divfactor = 1. / (127. * 127.)
has_bias = 0 if bias is None else 1

28
bitsandbytes/triton/quantize_columnwise_and_transpose.py
View File

@ -1,14 +1,20 @@
import math
import torch
import time
import triton
import triton.language as tl
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
from bitsandbytes.triton.triton_utils import is_triton_available
# This kernel does fused columnwise quantization and transpose.
if not is_triton_available():
def quantize_columnwise_and_transpose(x: torch.Tensor): return None
else:
# TODO: autotune this better.
@triton.autotune(
import triton
import triton.language as tl
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
# This kernel does fused columnwise quantization and transpose.
# TODO: autotune this better.
@triton.autotune(
configs=[
triton.Config({}, num_stages=1),
triton.Config({}, num_stages=2),
@ -26,9 +32,9 @@ from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_tim
triton.Config({}, num_warps=8),
],
key=['n_elements']
)
@triton.jit
def _quantize_columnwise_and_transpose(
)
@triton.jit
def _quantize_columnwise_and_transpose(
x_ptr,
output_ptr,
output_maxs,
@ -36,7 +42,7 @@ def _quantize_columnwise_and_transpose(
M : tl.constexpr, N : tl.constexpr,
BLOCK_SIZE: tl.constexpr,
P2: tl.constexpr,
):
):
pid = tl.program_id(axis=0)
block_start = pid
p2_arange = tl.arange(0, P2)
@ -53,7 +59,7 @@ def _quantize_columnwise_and_transpose(
tl.store(output_ptr + new_offsets, output, mask=p2_arange_mask)
tl.store(output_maxs + pid, max_val)
def quantize_columnwise_and_transpose(x: torch.Tensor):
def quantize_columnwise_and_transpose(x: torch.Tensor):
M, N = x.shape
output = torch.empty(N, M, device=x.device, dtype=torch.int8)
output_maxs = torch.empty(x.shape[1], device=x.device, dtype=torch.float16)

39
bitsandbytes/triton/quantize_global.py
View File

@ -1,27 +1,34 @@
import math
import torch
import time
import triton
import triton.language as tl
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
from bitsandbytes.triton.triton_utils import is_triton_available
# global quantize
@triton.autotune(
if not is_triton_available():
def quantize_global_transpose(input): return None
def quantize_global(x: torch.Tensor): return None
else:
import triton
import triton.language as tl
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
# global quantize
@triton.autotune(
configs=[
triton.Config({'BLOCK_SIZE': 1024,}, num_warps=4),
triton.Config({'BLOCK_SIZE': 2048,}, num_stages=1),
],
key=['n_elements']
)
@triton.jit
def _quantize_global(
)
@triton.jit
def _quantize_global(
x_ptr,
absmax_inv_ptr,
output_ptr,
n_elements,
BLOCK_SIZE: tl.constexpr,
):
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
@ -31,7 +38,7 @@ def _quantize_global(
output = tl.libdevice.llrint(127. * (x * absmax_inv))
tl.store(output_ptr + offsets, output, mask=mask)
def quantize_global(x: torch.Tensor):
def quantize_global(x: torch.Tensor):
absmax = x.abs().max().unsqueeze(0)
absmax_inv = 1./ absmax
output = torch.empty(*x.shape, device='cuda', dtype=torch.int8)
@ -42,8 +49,8 @@ def quantize_global(x: torch.Tensor):
return output, absmax
# global quantize and transpose
@triton.autotune(
# global quantize and transpose
@triton.autotune(
configs=[
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'GROUP_M': 8}, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'GROUP_M': 8}, num_warps=4),
@ -51,9 +58,9 @@ def quantize_global(x: torch.Tensor):
# ...
],
key=['M', 'N']
)
@triton.jit
def _quantize_global_transpose(A, absmax_inv_ptr, B, stride_am, stride_an, stride_bn, stride_bm, M, N,
)
@triton.jit
def _quantize_global_transpose(A, absmax_inv_ptr, B, stride_am, stride_an, stride_bn, stride_bm, M, N,
BLOCK_M : tl.constexpr,
BLOCK_N : tl.constexpr,
GROUP_M : tl.constexpr):
@ -84,7 +91,7 @@ def _quantize_global_transpose(A, absmax_inv_ptr, B, stride_am, stride_an, strid
tl.store(B, output, mask=mask)
def quantize_global_transpose(input):
def quantize_global_transpose(input):
absmax = input.abs().max().unsqueeze(0)
absmax_inv = 1./ absmax
M, N = input.shape

29
bitsandbytes/triton/quantize_rowwise.py
View File

@ -1,14 +1,21 @@
import math
import torch
import time
import triton
import triton.language as tl
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
# rowwise quantize
from bitsandbytes.triton.triton_utils import is_triton_available
# TODO: autotune this better.
@triton.autotune(
if not is_triton_available():
def quantize_rowwise(x: torch.Tensor): return None
else:
import triton
import triton.language as tl
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
# rowwise quantize
# TODO: autotune this better.
@triton.autotune(
configs=[
triton.Config({}, num_stages=1, num_warps=8),
triton.Config({}, num_stages=2, num_warps=8),
@ -24,16 +31,16 @@ from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_tim
triton.Config({}, num_warps=8),
],
key=['n_elements']
)
@triton.jit
def _quantize_rowwise(
)
@triton.jit
def _quantize_rowwise(
x_ptr,
output_ptr,
output_maxs,
n_elements,
BLOCK_SIZE: tl.constexpr,
P2: tl.constexpr,
):
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
arange = tl.arange(0, P2)
@ -47,7 +54,7 @@ def _quantize_rowwise(
tl.store(output_ptr + offsets, output, mask=row_mask)
tl.store(output_maxs + pid, max_val)
def quantize_rowwise(x: torch.Tensor):
def quantize_rowwise(x: torch.Tensor):
output = torch.empty(*x.shape, device=x.device, dtype=torch.int8)
output_maxs = torch.empty(x.shape[0], device=x.device, dtype=torch.float16)