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Support --load_in_8bit on pre-Turing GPUs (#113)
- Linear8bitLt now supports for pre-turing GPUs by temporarily upcasting quantized weights. - added a test for linear8bitlt accuracy with the new fallback, the accuracy is similar than the real thing, (slightly better due to non-quantized A) - performance is roughly halfway between the default mode and memory_efficient_backward Alternatives considered: - cupy - slow, casting to float internally - triton - fast but unstable af. every 3rd attempt to matmul is a segfault - bnb.functional.igemm (no lt) - "CuBLAS Error 8" on old GPUs Co-authored-by: Aleksandr Borzunov <borzunov.alexander@gmail.com>
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justheuristic
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@ -9,11 +9,12 @@ Based on: https://github.com/TimDettmers/bitsandbytes/blob/main/csrc/kernels.cu#
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Exact match tests: see $REPO/tests/test_linear8bitlt.py
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"""
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import dataclasses
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import warnings
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from typing import Optional, Tuple
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import bitsandbytes.functional as F
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import torch
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from bitsandbytes.autograd._functions import MatMul8bitLt, MatmulLtState
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from bitsandbytes.autograd._functions import GlobalOutlierPooler, MatMul8bitLt, MatmulLtState, prod
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from bitsandbytes.nn import Linear8bitLt
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@ -88,7 +89,7 @@ class CustomLinear8bitLt(Linear8bitLt):
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out = custom_matmul8bitlt(x, self.weight, bias=self.bias, state=self.state)
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if not self.state.has_fp16_weights:
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if self.state.CB is not None:
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if self.state.CB is not None and self.state.CxB is not None:
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# we converted 8-bit row major to turing/ampere format in the first inference pass
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# we no longer need the row-major weight
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del self.state.CB
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@ -99,6 +100,7 @@ class CustomLinear8bitLt(Linear8bitLt):
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@dataclasses.dataclass(init=True)
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class CustomMatmulLtState(MatmulLtState):
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tile_indices: Optional[torch.Tensor] = None
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force_no_igemmlt: bool = False
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def get_tile_size(self):
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assert self.formatB in (
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@ -123,9 +125,155 @@ def custom_matmul8bitlt(
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class CustomMatMul8bitLt(MatMul8bitLt):
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# forward is the same as in inference-only CxB
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# forward is the same, but we added the fallback for pre-turing GPUs
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# backward is mostly the same, but adds one extra clause (see "elif state.CxB is not None")
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@staticmethod
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def forward(ctx, A, B, out=None, bias=None, state=CustomMatmulLtState):
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using_igemmlt = torch.cuda.get_device_capability(device=A.device) >= (7, 5) and not state.force_no_igemmlt
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# default to pytorch behavior if inputs are empty
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ctx.is_empty = False
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if prod(A.shape) == 0:
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ctx.is_empty = True
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ctx.A = A
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ctx.B = B
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ctx.bias = bias
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if A.shape[-1] == B.shape[0]:
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return torch.empty(A.shape[:-1] + B.shape[1:], dtype=A.dtype, device=A.device)
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else:
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return torch.empty(A.shape[:-1] + B.shape[:1], dtype=A.dtype, device=A.device)
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# 1. Quantize A
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# 2. Quantize B
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# 3. Matmul
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# 4. Mixed-precision decomposition matmul
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# 5. Save state
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formatB = state.formatB
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input_shape = A.shape
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if state.outlier_pool is None:
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state.outlier_pool = GlobalOutlierPooler.get_instance()
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# Cast A to fp16
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if A.dtype != torch.float16:
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warnings.warn(f"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization")
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# 1. Quantize A
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if len(A.shape) == 3:
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A = A.view(-1, A.shape[-1]).contiguous()
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CA, CAt, SCA, SCAt, coo_tensorA = F.double_quant(A.to(torch.float16), threshold=state.threshold)
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if state.threshold > 0.0 and coo_tensorA is not None:
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if state.has_fp16_weights:
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idx = torch.unique(coo_tensorA.colidx).long()
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CA[:, idx] = 0
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CAt[:, idx] = 0
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subA = A[:, idx]
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state.subB = B[:, idx].t().contiguous()
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state.idx = idx
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else:
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if state.CxB is None and using_igemmlt:
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# B in in 8-bit row-major, we can transform it back to 16-bit to extract outlier dimensions
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# we also need to convert it to the turing/ampere format
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state.CxB, state.SB = F.transform(state.CB, to_order=formatB)
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else:
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if not state.has_fp16_weights and state.CxB is None and using_igemmlt:
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state.CxB, state.SB = F.transform(state.CB, to_order=formatB)
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subA = None
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# 2. Quantize B
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if state.has_fp16_weights:
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has_grad = True if (getattr(B, "grad", None) is not None) else False
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is_transposed = not B.is_contiguous() and B.shape[0] == B.stride(1)
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if is_transposed:
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B = B.contiguous()
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if (state.is_training and not has_grad) or state.CxB is None:
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state.reset_grads()
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(
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CB,
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state.CBt,
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state.SCB,
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state.SCBt,
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coo_tensorB,
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) = F.double_quant(B.to(torch.float16))
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if using_igemmlt:
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state.CxB, state.SB = F.transform(CB, to_order=formatB)
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else:
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state.CB = CB
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else:
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has_grad = False
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if coo_tensorA is not None and not state.has_fp16_weights:
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# extract outliers
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outlier_idx = torch.unique(coo_tensorA.colidx)
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state.idx = outlier_idx
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# state.outlier_pool.add_outliers(outlier_idx, A.shape[-1])
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# if state.use_pool and state.outlier_pool.model_dim == A.shape[-1]:
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# # do not use pool for 2nd FFN layer
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# state.idx = state.outlier_pool.get_current_outlier_idx().to(A.device)
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# else:
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# state.idx = outlier_idx
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if state.CxB is not None:
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outliers = F.extract_outliers(state.CxB, state.SB, state.idx.int())
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else:
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outliers = state.CB[:, state.idx.long()].clone()
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state.subB = (outliers * state.SCB.view(-1, 1) / 127.0).t().contiguous().to(A.dtype)
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CA[:, state.idx.long()] = 0
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CAt[:, state.idx.long()] = 0
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subA = A[:, state.idx.long()]
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shapeB = state.SB[0] if state.SB else B.shape
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if len(input_shape) == 3:
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output_shape = (input_shape[0], input_shape[1], shapeB[0])
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else:
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output_shape = (input_shape[0], shapeB[0])
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# 3. Matmul
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if using_igemmlt:
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C32A, SA = F.transform(CA, "col32")
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out32, Sout32 = F.igemmlt(C32A, state.CxB, SA, state.SB)
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if bias is None or bias.dtype == torch.float16:
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# we apply the fused bias here
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output = F.mm_dequant(out32, Sout32, SCA, state.SCB, bias=bias)
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output = output.to(A.dtype)
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else: # apply bias separately
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output = F.mm_dequant(out32, Sout32, SCA, state.SCB, bias=None)
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output = output.to(A.dtype).add_(bias)
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else:
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A_wo_outliers = A.clone()
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if state.idx is not None:
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A_wo_outliers[:, state.idx.long()] = 0
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output = torch.nn.functional.linear(A_wo_outliers, state.CB.to(A.dtype))
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output = output.mul_(state.SCB.unsqueeze(0).mul(1.0 / 127.0))
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if bias is not None:
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output = output.add_(bias)
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# 4. Mixed-precision decomposition matmul
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if coo_tensorA is not None and subA is not None:
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output += torch.matmul(subA, state.subB)
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# 5. Save state
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ctx.state = state
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ctx.formatB = formatB
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ctx.grad_shape = input_shape
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ctx.dtype_A, ctx.dtype_B, ctx.dtype_bias = A.dtype, B.dtype, None if bias is None else bias.dtype
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if any(ctx.needs_input_grad[:2]):
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ctx.tensors = (CAt, subA)
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ctx.tensor_states = (SCAt, state.idx)
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else:
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ctx.tensors = [None, None]
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ctx.tensor_states = (None, None)
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ctx.save_for_backward(None, None)
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clone_func = torch.clone if len(output_shape) == 3 else lambda x: x
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return clone_func(output.view(output_shape))
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@staticmethod
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def backward(ctx, grad_output):
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if ctx.is_empty:
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@ -71,3 +71,38 @@ def test_linear_exact_match():
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assert not linear_custom.state.has_fp16_weights
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assert linear_custom.state.CB is None
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assert linear_custom.state.CxB is not None
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="this test requires a GPU")
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def test_linear_no_igemmlt():
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linear = torch.nn.Linear(1024, 3072)
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x = torch.randn(3, 1024, dtype=torch.half)
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linear_custom = CustomLinear8bitLt(
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linear.in_features,
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linear.out_features,
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linear.bias is not None,
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has_fp16_weights=False,
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threshold=6.0,
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)
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linear_custom.state.force_no_igemmlt = True
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linear_custom.weight = bnb.nn.Int8Params(
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linear.weight.data.clone(), requires_grad=False, has_fp16_weights=False
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).to(linear.weight.dtype)
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linear_custom.bias = linear.bias
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linear_custom.cuda()
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linear.half().cuda()
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x_ref = x.clone().cuda().requires_grad_(True)
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x_ours = x.clone().cuda().requires_grad_(True)
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fx_ref = linear(x_ref).float()
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grad_proj = torch.randn_like(fx_ref)
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(fx_ref * grad_proj).mean().backward()
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fx_ours = linear_custom(x_ours).float()
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(fx_ours * grad_proj).mean().backward()
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assert torch.allclose(fx_ref, fx_ours, atol=0.02)
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assert torch.allclose(x_ref.grad, x_ours.grad, atol=0.01)
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assert not linear_custom.state.has_fp16_weights
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assert linear_custom.state.CB is not None
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assert linear_custom.state.CxB is None
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