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Unverified Commit 06029dd6 authored by Titus's avatar Titus Committed by GitHub
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Merge pull request #1081 from akx/ruff-format 

Reformat Python code with Ruff
parents fd723b78 5a4263f4
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Showing with 928 additions and 916 deletions
bitsandbytes/triton/dequantize_rowwise.py
View file @ 06029dd6
......@@ -5,9 +5,10 @@ import torch
from bitsandbytes.triton.triton_utils import is_triton_available
if not is_triton_available():
def dequantize_rowwise(x: torch.Tensor, state_x: torch.Tensor): return None
else:
def dequantize_rowwise(x: torch.Tensor, state_x: torch.Tensor):
return None
else:
import triton
import triton.language as tl
......@@ -15,21 +16,21 @@ else:
# TODO: autotune this better.
@triton.autotune(
configs=[
triton.Config({}, num_stages=1, num_warps=8),
triton.Config({}, num_stages=2, num_warps=8),
triton.Config({}, num_stages=4, num_warps=8),
triton.Config({}, num_stages=8, num_warps=8),
triton.Config({}, num_stages=1),
triton.Config({}, num_stages=2),
triton.Config({}, num_stages=4),
triton.Config({}, num_stages=8),
triton.Config({}, num_warps=1),
triton.Config({}, num_warps=2),
triton.Config({}, num_warps=4),
triton.Config({}, num_warps=8),
],
key=['n_elements']
configs=[
triton.Config({}, num_stages=1, num_warps=8),
triton.Config({}, num_stages=2, num_warps=8),
triton.Config({}, num_stages=4, num_warps=8),
triton.Config({}, num_stages=8, num_warps=8),
triton.Config({}, num_stages=1),
triton.Config({}, num_stages=2),
triton.Config({}, num_stages=4),
triton.Config({}, num_stages=8),
triton.Config({}, num_warps=1),
triton.Config({}, num_warps=2),
triton.Config({}, num_warps=4),
triton.Config({}, num_warps=8),
],
key=["n_elements"],
)
@triton.jit
def _dequantize_rowwise(
......@@ -51,7 +52,6 @@ else:
output = max_val * x * inv_127
tl.store(output_ptr + offsets, output, mask=row_mask)
def dequantize_rowwise(x: torch.Tensor, state_x: torch.Tensor):
output = torch.empty(*x.shape, device=x.device, dtype=torch.float16)
......@@ -60,5 +60,5 @@ else:
assert x.is_cuda and output.is_cuda
n_elements = output.numel()
grid = lambda meta: (x.shape[0],)
_dequantize_rowwise[grid](x, state_x, output, 1./127, n_elements, BLOCK_SIZE=x.shape[1], P2=P2)
_dequantize_rowwise[grid](x, state_x, output, 1.0 / 127, n_elements, BLOCK_SIZE=x.shape[1], P2=P2)
return output
bitsandbytes/triton/int8_matmul_mixed_dequantize.py
View file @ 06029dd6
......@@ -3,14 +3,14 @@ import torch
from bitsandbytes.triton.triton_utils import is_triton_available
if not is_triton_available():
def int8_matmul_mixed_dequantize(a, b, state_x, state_w, bias): return None
else:
def int8_matmul_mixed_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
# 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
......@@ -27,58 +27,83 @@ else:
for block_n in [32, 64, 128, 256]:
num_warps = 2 if block_n <= 64 else 4
configs.append(
triton.Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': 1},
num_stages=num_stages, num_warps=num_warps))
triton.Config(
{"BLOCK_M": block_m, "BLOCK_N": block_n, "BLOCK_K": block_k, "SPLIT_K": 1},
num_stages=num_stages,
num_warps=num_warps,
),
)
# split_k
for split_k in [2, 4, 8, 16]:
configs.append(triton.Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': split_k},
num_stages=num_stages, num_warps=num_warps, pre_hook=init_to_zero('C')))
configs.append(
triton.Config(
{"BLOCK_M": block_m, "BLOCK_N": block_n, "BLOCK_K": block_k, "SPLIT_K": split_k},
num_stages=num_stages,
num_warps=num_warps,
pre_hook=init_to_zero("C"),
),
)
return configs
@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),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=5, num_warps=2),
triton.Config({"BLOCK_M": 128, "BLOCK_N": 256, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=3, num_warps=8),
triton.Config({"BLOCK_M": 256, "BLOCK_N": 128, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=3, num_warps=8),
triton.Config({"BLOCK_M": 256, "BLOCK_N": 64, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=4, num_warps=4),
triton.Config({"BLOCK_M": 64, "BLOCK_N": 256, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=4, num_warps=4),
triton.Config({"BLOCK_M": 128, "BLOCK_N": 128, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=4, num_warps=4),
triton.Config({"BLOCK_M": 128, "BLOCK_N": 64, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=4, num_warps=4),
triton.Config({"BLOCK_M": 64, "BLOCK_N": 128, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=4, num_warps=4),
triton.Config({"BLOCK_M": 128, "BLOCK_N": 32, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=4, num_warps=4),
triton.Config({"BLOCK_M": 64, "BLOCK_N": 32, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=5, num_warps=2),
# good for int8
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=5, num_warps=2),
triton.Config({"BLOCK_M": 128, "BLOCK_N": 256, "BLOCK_K": 128, "SPLIT_K": 1}, num_stages=3, num_warps=8),
triton.Config({"BLOCK_M": 256, "BLOCK_N": 128, "BLOCK_K": 128, "SPLIT_K": 1}, num_stages=3, num_warps=8),
triton.Config({"BLOCK_M": 256, "BLOCK_N": 64, "BLOCK_K": 128, "SPLIT_K": 1}, num_stages=4, num_warps=4),
triton.Config({"BLOCK_M": 64, "BLOCK_N": 256, "BLOCK_K": 128, "SPLIT_K": 1}, num_stages=4, num_warps=4),
triton.Config({"BLOCK_M": 128, "BLOCK_N": 128, "BLOCK_K": 128, "SPLIT_K": 1}, num_stages=4, num_warps=4),
triton.Config({"BLOCK_M": 128, "BLOCK_N": 64, "BLOCK_K": 64, "SPLIT_K": 1}, num_stages=4, num_warps=4),
triton.Config({"BLOCK_M": 64, "BLOCK_N": 128, "BLOCK_K": 64, "SPLIT_K": 1}, num_stages=4, num_warps=4),
triton.Config({"BLOCK_M": 128, "BLOCK_N": 32, "BLOCK_K": 64, "SPLIT_K": 1}, num_stages=4, num_warps=4),
triton.Config({"BLOCK_M": 64, "BLOCK_N": 32, "BLOCK_K": 64, "SPLIT_K": 1}, num_stages=5, num_warps=2),
*get_configs_io_bound(),
],
key=['M', 'N', 'K'],
prune_configs_by={
'early_config_prune': early_config_prune,
'perf_model': estimate_matmul_time,
'top_k': 10
key=["M", "N", "K"],
prune_configs_by={"early_config_prune": early_config_prune, "perf_model": estimate_matmul_time, "top_k": 10},
)
@triton.heuristics(
{
"EVEN_K": lambda args: args["K"] % (args["BLOCK_K"] * args["SPLIT_K"]) == 0,
},
)
@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,
stride_am, stride_ak,
stride_bk, stride_bn,
stride_cm, stride_cn,
BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
GROUP_M: tl.constexpr, SPLIT_K: tl.constexpr, EVEN_K: tl.constexpr,
ACC_TYPE: tl.constexpr
):
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,
BLOCK_M: tl.constexpr,
BLOCK_N: tl.constexpr,
BLOCK_K: tl.constexpr,
GROUP_M: tl.constexpr,
SPLIT_K: tl.constexpr,
EVEN_K: tl.constexpr,
ACC_TYPE: tl.constexpr,
):
# matrix multiplication
pid = tl.program_id(0)
pid_z = tl.program_id(1)
......@@ -115,13 +140,13 @@ else:
b = tl.load(B)
else:
k_remaining = K - k * (BLOCK_K * SPLIT_K)
a = tl.load(A, mask=rk[None, :] < k_remaining, other=0.)
b = tl.load(B, mask=rk[:, None] < k_remaining, other=0.)
a = tl.load(A, mask=rk[None, :] < k_remaining, other=0.0)
b = tl.load(B, mask=rk[:, None] < k_remaining, other=0.0)
acc += tl.dot(a, b)
A += BLOCK_K * SPLIT_K * stride_ak
B += BLOCK_K * SPLIT_K * stride_bk
acc = (w_factor * (x_factor * (acc * divfactor)))
acc = w_factor * (x_factor * (acc * divfactor))
acc = acc.to(C.dtype.element_ty)
# conditionally add bias
......@@ -137,10 +162,9 @@ else:
else:
tl.atomic_add(C, acc, mask=mask)
def int8_matmul_mixed_dequantize(a, b, state_x, state_w, bias):
device = a.device
divfactor = 1. / (127. * 127.)
divfactor = 1.0 / (127.0 * 127.0)
has_bias = 0 if bias is None else 1
# handle non-contiguous inputs if necessary
if a.stride(0) > 1 and a.stride(1) > 1:
......@@ -154,12 +178,28 @@ else:
# allocates output
c = torch.empty((M, N), device=device, dtype=torch.float16)
# accumulator types
ACC_TYPE = tl.float32 #if a.dtype in [torch.float16, torch.bfloat16, torch.float32] else tl.int32
ACC_TYPE = tl.float32 # if a.dtype in [torch.float16, torch.bfloat16, torch.float32] else tl.int32
# launch int8_matmul_mixed_dequantize kernel
grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']), META['SPLIT_K'])
_int8_matmul_mixed_dequantize[grid](a, b, c, bias, state_x, state_w, M, N, K, divfactor, has_bias,
a.stride(0), a.stride(1),
b.stride(0), b.stride(1),
c.stride(0), c.stride(1),
GROUP_M=8, ACC_TYPE=ACC_TYPE)
grid = lambda META: (triton.cdiv(M, META["BLOCK_M"]) * triton.cdiv(N, META["BLOCK_N"]), META["SPLIT_K"])
_int8_matmul_mixed_dequantize[grid](
a,
b,
c,
bias,
state_x,
state_w,
M,
N,
K,
divfactor,
has_bias,
a.stride(0),
a.stride(1),
b.stride(0),
b.stride(1),
c.stride(0),
c.stride(1),
GROUP_M=8,
ACC_TYPE=ACC_TYPE,
)
return c
bitsandbytes/triton/int8_matmul_rowwise_dequantize.py
View file @ 06029dd6
......@@ -3,7 +3,9 @@ import torch
from bitsandbytes.triton.triton_utils import is_triton_available
if not is_triton_available():
def int8_matmul_rowwise_dequantize(a, b, state_x, state_w, bias): return None
def int8_matmul_rowwise_dequantize(a, b, state_x, state_w, bias):
return None
else:
import triton
import triton.language as tl
......@@ -17,7 +19,6 @@ else:
def init_to_zero(name):
return lambda nargs: nargs[name].zero_()
def get_configs_io_bound():
configs = []
for num_stages in [2, 3, 4, 5, 6]:
......@@ -26,58 +27,83 @@ else:
for block_n in [32, 64, 128, 256]:
num_warps = 2 if block_n <= 64 else 4
configs.append(
triton.Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': 1},
num_stages=num_stages, num_warps=num_warps))
triton.Config(
{"BLOCK_M": block_m, "BLOCK_N": block_n, "BLOCK_K": block_k, "SPLIT_K": 1},
num_stages=num_stages,
num_warps=num_warps,
),
)
# split_k
for split_k in [2, 4, 8, 16]:
configs.append(triton.Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': split_k},
num_stages=num_stages, num_warps=num_warps, pre_hook=init_to_zero('C')))
configs.append(
triton.Config(
{"BLOCK_M": block_m, "BLOCK_N": block_n, "BLOCK_K": block_k, "SPLIT_K": split_k},
num_stages=num_stages,
num_warps=num_warps,
pre_hook=init_to_zero("C"),
),
)
return configs
@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),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=5, num_warps=2),
triton.Config({"BLOCK_M": 128, "BLOCK_N": 256, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=3, num_warps=8),
triton.Config({"BLOCK_M": 256, "BLOCK_N": 128, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=3, num_warps=8),
triton.Config({"BLOCK_M": 256, "BLOCK_N": 64, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=4, num_warps=4),
triton.Config({"BLOCK_M": 64, "BLOCK_N": 256, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=4, num_warps=4),
triton.Config({"BLOCK_M": 128, "BLOCK_N": 128, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=4, num_warps=4),
triton.Config({"BLOCK_M": 128, "BLOCK_N": 64, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=4, num_warps=4),
triton.Config({"BLOCK_M": 64, "BLOCK_N": 128, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=4, num_warps=4),
triton.Config({"BLOCK_M": 128, "BLOCK_N": 32, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=4, num_warps=4),
triton.Config({"BLOCK_M": 64, "BLOCK_N": 32, "BLOCK_K": 32, "SPLIT_K": 1}, num_stages=5, num_warps=2),
# good for int8
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=5, num_warps=2),
triton.Config({"BLOCK_M": 128, "BLOCK_N": 256, "BLOCK_K": 128, "SPLIT_K": 1}, num_stages=3, num_warps=8),
triton.Config({"BLOCK_M": 256, "BLOCK_N": 128, "BLOCK_K": 128, "SPLIT_K": 1}, num_stages=3, num_warps=8),
triton.Config({"BLOCK_M": 256, "BLOCK_N": 64, "BLOCK_K": 128, "SPLIT_K": 1}, num_stages=4, num_warps=4),
triton.Config({"BLOCK_M": 64, "BLOCK_N": 256, "BLOCK_K": 128, "SPLIT_K": 1}, num_stages=4, num_warps=4),
triton.Config({"BLOCK_M": 128, "BLOCK_N": 128, "BLOCK_K": 128, "SPLIT_K": 1}, num_stages=4, num_warps=4),
triton.Config({"BLOCK_M": 128, "BLOCK_N": 64, "BLOCK_K": 64, "SPLIT_K": 1}, num_stages=4, num_warps=4),
triton.Config({"BLOCK_M": 64, "BLOCK_N": 128, "BLOCK_K": 64, "SPLIT_K": 1}, num_stages=4, num_warps=4),
triton.Config({"BLOCK_M": 128, "BLOCK_N": 32, "BLOCK_K": 64, "SPLIT_K": 1}, num_stages=4, num_warps=4),
triton.Config({"BLOCK_M": 64, "BLOCK_N": 32, "BLOCK_K": 64, "SPLIT_K": 1}, num_stages=5, num_warps=2),
*get_configs_io_bound(),
],
key=['M', 'N', 'K'],
prune_configs_by={
'early_config_prune': early_config_prune,
'perf_model': estimate_matmul_time,
'top_k': 10
key=["M", "N", "K"],
prune_configs_by={"early_config_prune": early_config_prune, "perf_model": estimate_matmul_time, "top_k": 10},
)
@triton.heuristics(
{
"EVEN_K": lambda args: args["K"] % (args["BLOCK_K"] * args["SPLIT_K"]) == 0,
},
)
@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,
stride_am, stride_ak,
stride_bk, stride_bn,
stride_cm, stride_cn,
BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
GROUP_M: tl.constexpr, SPLIT_K: tl.constexpr, EVEN_K: tl.constexpr,
ACC_TYPE: tl.constexpr
):
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,
BLOCK_M: tl.constexpr,
BLOCK_N: tl.constexpr,
BLOCK_K: tl.constexpr,
GROUP_M: tl.constexpr,
SPLIT_K: tl.constexpr,
EVEN_K: tl.constexpr,
ACC_TYPE: tl.constexpr,
):
# matrix multiplication
pid = tl.program_id(0)
pid_z = tl.program_id(1)
......@@ -114,13 +140,13 @@ else:
b = tl.load(B)
else:
k_remaining = K - k * (BLOCK_K * SPLIT_K)
a = tl.load(A, mask=rk[None, :] < k_remaining, other=0.)
b = tl.load(B, mask=rk[:, None] < k_remaining, other=0.)
a = tl.load(A, mask=rk[None, :] < k_remaining, other=0.0)
b = tl.load(B, mask=rk[:, None] < k_remaining, other=0.0)
acc += tl.dot(a, b)
A += BLOCK_K * SPLIT_K * stride_ak
B += BLOCK_K * SPLIT_K * stride_bk
acc = (w_factor * (x_factor * (acc * divfactor)))
acc = w_factor * (x_factor * (acc * divfactor))
acc = acc.to(C.dtype.element_ty)
if has_bias:
......@@ -135,9 +161,8 @@ else:
else:
tl.atomic_add(C, acc, mask=mask)
def int8_matmul_rowwise_dequantize(a, b, state_x, state_w, bias):
divfactor = 1. / (127. * 127.)
divfactor = 1.0 / (127.0 * 127.0)
has_bias = 0 if bias is None else 1
......@@ -154,12 +179,28 @@ else:
# allocates output
c = torch.empty((M, N), device=device, dtype=torch.float16)
# accumulator types
ACC_TYPE = tl.float32 #if a.dtype in [torch.float16, torch.bfloat16, torch.float32] else tl.int32
ACC_TYPE = tl.float32 # if a.dtype in [torch.float16, torch.bfloat16, torch.float32] else tl.int32
# launch int8_matmul_rowwise_dequantize kernel
grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']), META['SPLIT_K'])
_int8_matmul_rowwise_dequantize[grid](a, b, c, bias, state_x, state_w, M, N, K, divfactor, has_bias,
a.stride(0), a.stride(1),
b.stride(0), b.stride(1),
c.stride(0), c.stride(1),
GROUP_M=8, ACC_TYPE=ACC_TYPE)
grid = lambda META: (triton.cdiv(M, META["BLOCK_M"]) * triton.cdiv(N, META["BLOCK_N"]), META["SPLIT_K"])
_int8_matmul_rowwise_dequantize[grid](
a,
b,
c,
bias,
state_x,
state_w,
M,
N,
K,
divfactor,
has_bias,
a.stride(0),
a.stride(1),
b.stride(0),
b.stride(1),
c.stride(0),
c.stride(1),
GROUP_M=8,
ACC_TYPE=ACC_TYPE,
)
return c
bitsandbytes/triton/quantize_columnwise_and_transpose.py
View file @ 06029dd6
......@@ -5,9 +5,10 @@ import torch
from bitsandbytes.triton.triton_utils import is_triton_available
if not is_triton_available():
def quantize_columnwise_and_transpose(x: torch.Tensor): return None
else:
def quantize_columnwise_and_transpose(x: torch.Tensor):
return None
else:
import triton
import triton.language as tl
......@@ -15,23 +16,23 @@ else:
# TODO: autotune this better.
@triton.autotune(
configs=[
triton.Config({}, num_stages=1),
triton.Config({}, num_stages=2),
triton.Config({}, num_stages=4),
triton.Config({}, num_stages=8),
triton.Config({}, num_stages=16),
triton.Config({}, num_stages=1, num_warps=8),
triton.Config({}, num_stages=2, num_warps=8),
triton.Config({}, num_stages=4, num_warps=8),
triton.Config({}, num_stages=8, num_warps=8),
triton.Config({}, num_stages=16, num_warps=8),
triton.Config({}, num_warps=1),
triton.Config({}, num_warps=2),
triton.Config({}, num_warps=4),
triton.Config({}, num_warps=8),
],
key=['n_elements']
configs=[
triton.Config({}, num_stages=1),
triton.Config({}, num_stages=2),
triton.Config({}, num_stages=4),
triton.Config({}, num_stages=8),
triton.Config({}, num_stages=16),
triton.Config({}, num_stages=1, num_warps=8),
triton.Config({}, num_stages=2, num_warps=8),
triton.Config({}, num_stages=4, num_warps=8),
triton.Config({}, num_stages=8, num_warps=8),
triton.Config({}, num_stages=16, num_warps=8),
triton.Config({}, num_warps=1),
triton.Config({}, num_warps=2),
triton.Config({}, num_warps=4),
triton.Config({}, num_warps=8),
],
key=["n_elements"],
)
@triton.jit
def _quantize_columnwise_and_transpose(
......@@ -39,7 +40,8 @@ else:
output_ptr,
output_maxs,
n_elements,
M : tl.constexpr, N : tl.constexpr,
M: tl.constexpr,
N: tl.constexpr,
BLOCK_SIZE: tl.constexpr,
P2: tl.constexpr,
):
......@@ -47,12 +49,12 @@ else:
block_start = pid
p2_arange = tl.arange(0, P2)
p2_arange_mask = p2_arange < M
arange = p2_arange * N
arange = p2_arange * N
offsets = block_start + arange
x = tl.load(x_ptr + offsets, mask=p2_arange_mask)
abs_x = tl.abs(x)
max_val = tl.max(tl.where(p2_arange_mask, abs_x, 0), axis=0)
output = tl.libdevice.llrint(127. * (x / max_val))
output = tl.libdevice.llrint(127.0 * (x / max_val))
new_start = pid * M
new_offsets = new_start + p2_arange
......@@ -68,6 +70,6 @@ else:
assert x.is_cuda and output.is_cuda
n_elements = output.numel()
grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
grid = lambda meta: (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),)
_quantize_columnwise_and_transpose[grid](x, output, output_maxs, n_elements, M, N, BLOCK_SIZE=M, P2=P2)
return output, output_maxs
bitsandbytes/triton/quantize_global.py
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bitsandbytes/triton/quantize_rowwise.py
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bitsandbytes/utils.py
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check_bnb_install.py
View file @ 06029dd6
......@@ -2,14 +2,14 @@ import torch
import bitsandbytes as bnb
p = torch.nn.Parameter(torch.rand(10,10).cuda())
a = torch.rand(10,10).cuda()
p = torch.nn.Parameter(torch.rand(10, 10).cuda())
a = torch.rand(10, 10).cuda()
p1 = p.data.sum().item()
adam = bnb.optim.Adam([p])
out = a*p
out = a * p
loss = out.sum()
loss.backward()
adam.step()
......@@ -17,5 +17,5 @@ adam.step()
p2 = p.data.sum().item()
assert p1 != p2
print('SUCCESS!')
print('Installation was successful!')
print("SUCCESS!")
print("Installation was successful!")
examples/int8_inference_huggingface.py
View file @ 06029dd6
......@@ -2,23 +2,18 @@ import torch
from transformers import LlamaForCausalLM, LlamaTokenizer
MAX_NEW_TOKENS = 128
model_name = 'meta-llama/Llama-2-7b-hf'
model_name = "meta-llama/Llama-2-7b-hf"
text = 'Hamburg is in which country?\n'
text = "Hamburg is in which country?\n"
tokenizer = LlamaTokenizer.from_pretrained(model_name)
input_ids = tokenizer(text, return_tensors="pt").input_ids
max_memory = f'{int(torch.cuda.mem_get_info()[0]/1024**3)-2}GB'
max_memory = f"{int(torch.cuda.mem_get_info()[0]/1024**3)-2}GB"
n_gpus = torch.cuda.device_count()
max_memory = {i: max_memory for i in range(n_gpus)}
model = LlamaForCausalLM.from_pretrained(
model_name,
device_map='auto',
load_in_8bit=True,
max_memory=max_memory
)
model = LlamaForCausalLM.from_pretrained(model_name, device_map="auto", load_in_8bit=True, max_memory=max_memory)
generated_ids = model.generate(input_ids, max_length=MAX_NEW_TOKENS)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
install_cuda.py
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pyproject.toml
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scripts/stale.py
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tests/test_autograd.py
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tests/test_cuda_setup_evaluator.py
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tests/test_functional.py
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tests/test_generation.py
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tests/test_linear4bit.py
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tests/test_linear8bitlt.py
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tests/test_modules.py
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tests/test_optim.py
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