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modify linear8bitlt to support pre-turing architectures

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hotfix_bnb
justheuristic 1 year ago
parent b93739df9c
commit 8ddde143b5
2 changed files with 198 additions and 3 deletions
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165
src/petals/utils/linear8bitlt_patch.py
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@ -9,11 +9,12 @@ Based on: https://github.com/TimDettmers/bitsandbytes/blob/main/csrc/kernels.cu#
Exact match tests: see $REPO/tests/test_linear8bitlt.py
"""
import dataclasses
import warnings
from typing import Optional, Tuple
import bitsandbytes.functional as F
import torch
from bitsandbytes.autograd._functions import MatMul8bitLt, MatmulLtState
from bitsandbytes.autograd._functions import MatMul8bitLt, MatmulLtState, GlobalOutlierPooler, prod
from bitsandbytes.nn import Linear8bitLt
@ -88,7 +89,7 @@ class CustomLinear8bitLt(Linear8bitLt):
out = custom_matmul8bitlt(x, self.weight, bias=self.bias, state=self.state)
if not self.state.has_fp16_weights:
if self.state.CB is not None:
if self.state.CB is not None and self.state.CxB is not None:
# we converted 8-bit row major to turing/ampere format in the first inference pass
# we no longer need the row-major weight
del self.state.CB
@ -99,6 +100,7 @@ class CustomLinear8bitLt(Linear8bitLt):
@dataclasses.dataclass(init=True)
class CustomMatmulLtState(MatmulLtState):
tile_indices: Optional[torch.Tensor] = None
force_no_igemmlt: bool = False
def get_tile_size(self):
assert self.formatB in (
@ -123,9 +125,166 @@ def custom_matmul8bitlt(
class CustomMatMul8bitLt(MatMul8bitLt):
# forward is the same as in inference-only CxB
# forward is the same, but we added the fallback for pre-turing GPUs
# backward is mostly the same, but adds one extra clause (see "elif state.CxB is not None")
@staticmethod
def forward(ctx, A, B, out=None, bias=None, state=CustomMatmulLtState):
using_igemmlt = torch.cuda.get_device_capability(device=A.device) >= (7, 5) and not state.force_no_igemmlt
# default to pytorch behavior if inputs are empty
ctx.is_empty = False
if prod(A.shape) == 0:
ctx.is_empty = True
ctx.A = A
ctx.B = B
ctx.bias = bias
if A.shape[-1] == B.shape[0]:
return torch.empty(A.shape[:-1]+B.shape[1:], dtype=A.dtype, device=A.device)
else:
return torch.empty(A.shape[:-1]+B.shape[:1], dtype=A.dtype, device=A.device)
# 1. Quantize A
# 2. Quantize B
# 3. Matmul
# 4. Mixed-precision decomposition matmul
# 5. Save state
formatB = state.formatB
input_shape = A.shape
if state.outlier_pool is None:
state.outlier_pool = GlobalOutlierPooler.get_instance()
# Cast A to fp16
if A.dtype != torch.float16:
warnings.warn(f"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization")
# 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.to(torch.float16), threshold=state.threshold
)
if state.threshold > 0.0 and coo_tensorA is not None:
if state.has_fp16_weights:
idx = torch.unique(coo_tensorA.colidx).long()
CA[:, idx] = 0
CAt[:, idx] = 0
subA = A[:, idx]
state.subB = B[:, idx].t().contiguous()
state.idx = idx
else:
if state.CxB is None and using_igemmlt:
# 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)
else:
if not state.has_fp16_weights and state.CxB is None and using_igemmlt:
state.CxB, state.SB = F.transform(state.CB, to_order=formatB)
subA = None
# 2. Quantize B
if state.has_fp16_weights:
has_grad = True if (getattr(B, "grad", None) is not None) else False
is_transposed = not B.is_contiguous() and B.shape[0] == B.stride(1)
if is_transposed:
B = B.contiguous()
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.to(torch.float16))
if using_igemmlt:
state.CxB, state.SB = F.transform(CB, to_order=formatB)
else:
state.CB = CB
else:
has_grad = False
if coo_tensorA is not None and not state.has_fp16_weights:
# extract outliers
outlier_idx = torch.unique(coo_tensorA.colidx)
state.idx = outlier_idx
# state.outlier_pool.add_outliers(outlier_idx, A.shape[-1])
# if state.use_pool and state.outlier_pool.model_dim == A.shape[-1]:
# # do not use pool for 2nd FFN layer
# state.idx = state.outlier_pool.get_current_outlier_idx().to(A.device)
# else:
# state.idx = outlier_idx
if state.CxB is not None:
outliers = F.extract_outliers(state.CxB, state.SB, state.idx.int())
else:
outliers = state.CB[:, state.idx.long()].clone()
state.subB = (
(outliers * state.SCB.view(-1, 1) / 127.0)
.t()
.contiguous()
.to(A.dtype)
)
CA[:, state.idx.long()] = 0
CAt[:, state.idx.long()] = 0
subA = A[:, state.idx.long()]
shapeB = state.SB[0] if state.SB else B.shape
if len(input_shape) == 3:
output_shape = (input_shape[0], input_shape[1], shapeB[0])
else:
output_shape = (input_shape[0], shapeB[0])
# 3. Matmul
if using_igemmlt:
C32A, SA = F.transform(CA, "col32")
out32, Sout32 = F.igemmlt(C32A, state.CxB, SA, state.SB)
if bias is None or bias.dtype == torch.float16:
output = F.mm_dequant(out32, Sout32, SCA, state.SCB, bias=bias)
output = output.to(A.dtype)
else: # apply bias separately
output = F.mm_dequant(out32, Sout32, SCA, state.SCB, bias=None)
output = output.to(A.dtype).add_(bias)
else:
A_wo_outliers = A.clone()
if state.idx is not None:
A_wo_outliers[:, state.idx.long()] = 0
output = torch.nn.functional.linear(A_wo_outliers, state.CB.to(A.dtype))
output = output.mul_(state.SCB.unsqueeze(0).mul(1.0 / 127.0))
if bias is not None:
output = output.add_(bias)
# we apply the fused bias here
# 4. Mixed-precision decomposition matmul
if coo_tensorA is not None and subA is not None:
output += torch.matmul(subA, state.subB)
# 5. Save state
ctx.state = state
ctx.formatB = formatB
ctx.grad_shape = input_shape
ctx.dtype_A, ctx.dtype_B, ctx.dtype_bias = A.dtype, B.dtype, None if bias is None else bias.dtype
if any(ctx.needs_input_grad[:2]):
ctx.tensors = (CAt, subA)
ctx.tensor_states = (SCAt, state.idx)
else:
ctx.tensors = [None, None]
ctx.tensor_states = (None, None)
ctx.save_for_backward(None, None)
clone_func = torch.clone if len(output_shape) == 3 else lambda x : x
return clone_func(output.view(output_shape))
@staticmethod
def backward(ctx, grad_output):
if ctx.is_empty:

36
tests/test_linear8bitlt.py
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@ -71,3 +71,39 @@ def test_linear_exact_match():
assert not linear_custom.state.has_fp16_weights
assert linear_custom.state.CB is None
assert linear_custom.state.CxB is not None
@pytest.mark.skipif(not torch.cuda.is_available(), reason="this test requires a GPU")
def test_linear_no_igemmlt():
linear = torch.nn.Linear(1024, 3072)
x = torch.randn(3, 1024, dtype=torch.half)
linear_custom = CustomLinear8bitLt(
linear.in_features,
linear.out_features,
linear.bias is not None,
has_fp16_weights=False,
threshold=6.0,
)
linear_custom.state.force_no_igemmlt = True
linear_custom.weight = bnb.nn.Int8Params(linear.weight.data.clone(), requires_grad=False, has_fp16_weights=False).to(
linear.weight.dtype
)
linear_custom.bias = linear.bias
linear_custom.cuda()
linear.half().cuda()
x_ref = x.clone().cuda().requires_grad_(True)
x_ours = x.clone().cuda().requires_grad_(True)
fx_ref = linear(x_ref).float()
grad_proj = torch.randn_like(fx_ref)
(fx_ref * grad_proj).mean().backward()
fx_ours = linear_custom(x_ours).float()
(fx_ours * grad_proj).mean().backward()
assert torch.allclose(fx_ref, fx_ours, atol=0.02)
assert torch.allclose(x_ref.grad, x_ours.grad, atol=0.01)
assert not linear_custom.state.has_fp16_weights
assert linear_custom.state.CB is not None
assert linear_custom.state.CxB is None

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