diff --git a/src/petals/utils/linear8bitlt_patch.py b/src/petals/utils/linear8bitlt_patch.py index 435bd9f..723b1f7 100644 --- a/src/petals/utils/linear8bitlt_patch.py +++ b/src/petals/utils/linear8bitlt_patch.py @@ -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: diff --git a/tests/test_linear8bitlt.py b/tests/test_linear8bitlt.py index 8b4fe7f..0d07831 100644 --- a/tests/test_linear8bitlt.py +++ b/tests/test_linear8bitlt.py @@ -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 +