Patch Linear8bit to enable CxB backward (#111)
A patch to bitsandbytes 0.34.0 that introduces an option to run backward pass in default (fast) matrix layout. Authors: cxb inversion by @borzunov, original 8bit code by @timdettmers * optimized layout inversion code by @borzunov ([original code](https://colab.research.google.com/drive/1EJ0MKifajXSSVq7O2_QGwtb0l6gRAGrh?usp=sharing)) to use less forward calls * implemented CustomLinear8bitLt, a child of Linear8bitLt that can do backward without CB * added exact match tests for layouts and linear layers: see tests/test_linear8bitlt.py * switched petals to the new layer type Core idea: layouts apply the same permutation to every tile in the matrix. We can treat this as (batched) gather ops. Reshape input tensor so that ij-th gather operation op will apply to ij-th elements in each tile. Prototype: Layout info: https://github.com/TimDettmers/bitsandbytes/blob/main/csrc/kernels.cu#L2130-L2136 Co-authored-by: Alexander Borzunov <hxrussia@gmail.com> Co-authored-by: Aleksandr Borzunov <borzunov.alexander@gmail.com> Co-authored-by: Tim Dettmers <tim.dettmers@gmail.com>pull/112/head
justheuristic
2 years ago
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088713912d
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"""
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A patch to bitsandbytes 0.34.0 that introduces an option to run backward pass in default (fast) matrix layout.
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Authors: modification by @borzunov, original code by @timdettmers. Please disregard commit authors in this file.
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Core idea: layouts apply the same permutation to every tile in the matrix. We can treat this as (batched) gather ops.
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Reshape input tensor so that ij-th gather operation op will apply to ij-th elements in each tile.
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Prototype: https://colab.research.google.com/drive/1EJ0MKifajXSSVq7O2_QGwtb0l6gRAGrh?usp=sharing
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Based on: https://github.com/TimDettmers/bitsandbytes/blob/main/csrc/kernels.cu#L2130-L2136
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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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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.nn import Linear8bitLt
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def get_inverse_transform_indices(transform_tile: callable, tile_size: Tuple[int, int]):
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"""
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Compute a permutation of indices that invert the specified (tiled) matrix transformation
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:param transform_tile: a function that applies forward transform to a tensor of shape [dim1, dim2]
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:param tile_size: higher-level tile dimensions, i.e. (8, 32) for Turing and (32, 32) for Ampere
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:note: we assume that tile_transform applies to a cpu-based int8 tensor of shape tile_size
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:example: transform_tile function for the turing layout (bitsandbytes.functional as F)
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:returns: indices
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"""
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d1, d2 = tile_size
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assert 0 < d1 * d2 < 2**64
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tile_indices = torch.arange(d1 * d2, dtype=torch.int64).view(d1, d2)
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# encode each position in tile as a tuple of <= 8 unique bytes
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permuted_tile_indices = torch.zeros_like(tile_indices)
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for i in range(8):
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# select i-th byte, apply transformation and trace where each index ended up
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ith_dim_indices = torch.div(tile_indices, 256**i, rounding_mode="trunc") % 256
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sample_tile_i = (ith_dim_indices - 128).to(torch.int8).contiguous()
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assert torch.all(sample_tile_i.int() + 128 == ith_dim_indices), "int overflow"
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permuted_tile_i = transform_tile(sample_tile_i)
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ith_permuted_indices = permuted_tile_i.to(tile_indices.dtype) + 128
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permuted_tile_indices += ith_permuted_indices * (256**i)
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if d1 * d2 < 256**i:
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break # if all indices fit in i bytes, stop early
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return permuted_tile_indices
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def undo_layout(permuted_tensor: torch.Tensor, tile_indices: torch.LongTensor) -> torch.Tensor:
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"""
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Undo a tiled permutation such as turing or ampere layout
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:param permuted_tensor: torch tensor in a permuted layout
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:param tile_indices: reverse transformation indices, from get_inverse_transform_indices
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:return: contiguous row-major tensor
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"""
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(rows, cols), (tile_rows, tile_cols) = permuted_tensor.shape, tile_indices.shape
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assert rows % tile_rows == cols % tile_cols == 0, "tensor must contain a whole number of tiles"
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tensor = permuted_tensor.reshape(-1, tile_indices.numel()).t()
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outputs = torch.empty_like(tensor) # note: not using .index_copy because it was slower on cuda
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outputs[tile_indices.flatten()] = tensor
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outputs = outputs.reshape(tile_rows, tile_cols, cols // tile_cols, rows // tile_rows)
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outputs = outputs.permute(3, 0, 2, 1) # (rows // tile_rows, tile_rows), (cols // tile_cols, tile_cols)
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return outputs.reshape(rows, cols).contiguous()
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# the rest of this file is just a patch to bitsandbytes that modifies Linear8bitLt and dependencies
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class CustomLinear8bitLt(Linear8bitLt):
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def __init__(self, *args, memory_efficient_backward: bool = False, **kwargs):
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assert not memory_efficient_backward, "memory_efficient_backward is no longer used"
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super().__init__(*args, **kwargs)
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self.state = CustomMatmulLtState(**dataclasses.asdict(self.state))
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def forward(self, x: torch.Tensor):
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self.state.is_training = self.training
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if self.weight.CB is not None:
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self.init_8bit_state()
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# weights are cast automatically as Int8Params, but the bias has to be cast manually
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if self.bias is not None and self.bias.dtype != x.dtype:
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self.bias.data = self.bias.data.to(x.dtype)
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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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# 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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self.weight.data = self.state.CxB
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return out
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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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def get_tile_size(self):
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assert self.formatB in (
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"col_turing",
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"col_ampere",
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), f"please find this assert and manually enter tile size for {self.formatB}"
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return (8, 32) if self.formatB == "col_turing" else "col_ampere"
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def custom_matmul8bitlt(
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A: torch.Tensor,
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B: torch.Tensor,
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out: torch.Tensor = None,
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state: CustomMatmulLtState = None,
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threshold=0.0,
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bias=None,
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):
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state = state or MatmulLtState()
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if threshold > 0.0:
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state.threshold = threshold
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return CustomMatMul8bitLt.apply(A, B, out, bias, state)
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class CustomMatMul8bitLt(MatMul8bitLt):
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# forward is the same as in inference-only CxB
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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 backward(ctx, grad_output):
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if ctx.is_empty:
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bias_grad = None if ctx.bias is None else torch.zeros_like(ctx.bias)
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return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, bias_grad, None
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req_gradA, req_gradB, _, req_gradBias, _ = ctx.needs_input_grad
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CAt, subA = ctx.tensors
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SCAt, idx = ctx.tensor_states
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formatB = ctx.formatB
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state = ctx.state
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grad_A = grad_B = grad_bias = None
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if req_gradBias:
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# compute grad_bias first before changing grad_output dtype
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grad_bias = grad_output.sum(0, dtype=ctx.dtype_bias)
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# Cast grad_output to fp16
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if len(grad_output.shape) == 3:
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grad_output = grad_output.reshape(-1, grad_output.shape[-1]).contiguous()
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Cgrad, Cgradt, SCgrad, SCgradt, coo_tensor = F.double_quant(grad_output.to(torch.float16))
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if req_gradB:
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CxAt, SAt = F.transform(CAt, formatB, transpose=True)
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C32grad, Sgrad = F.transform(Cgradt, "col32", transpose=True)
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gradB32, SgradB32 = F.igemmlt(C32grad, CxAt, Sgrad, SAt)
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grad_B = F.mm_dequant(gradB32, SgradB32, SCgradt, SCAt)
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if state.threshold > 0.0 and subA is not None:
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grad_B[:, idx] += torch.matmul(grad_output.t(), subA)
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if req_gradA:
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if state.CBt is not None:
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C32grad, Sgrad = F.transform(Cgrad, "col32")
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if state.CxBt is None:
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state.CxBt, state.SBt = F.transform(state.CBt, to_order=formatB, transpose=True)
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gradA32, SgradA32 = F.igemmlt(C32grad, state.CxBt, Sgrad, state.SBt)
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grad_A = F.mm_dequant(gradA32, SgradA32, SCgrad, state.SCBt).view(ctx.grad_shape).to(ctx.dtype_A)
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elif state.CB is not None:
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CB = state.CB.to(ctx.dtype_A, copy=True).mul_(state.SCB.unsqueeze(1).mul(1.0 / 127.0))
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grad_A = torch.matmul(grad_output, CB).view(ctx.grad_shape).to(ctx.dtype_A)
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elif state.CxB is not None:
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if state.tile_indices is None:
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order, tile_size = state.formatB, state.get_tile_size()
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transform = lambda x: F.transform(x.cuda(), from_order="row", to_order=order)[0].to(x.device)
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with torch.no_grad():
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state.tile_indices = get_inverse_transform_indices(transform, tile_size).to(state.CxB.device)
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CB = (
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undo_layout(state.CxB, state.tile_indices)
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.to(ctx.dtype_A)
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.mul_(state.SCB.unsqueeze(1).mul(1.0 / 127.0))
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)
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grad_A = torch.matmul(grad_output, CB).view(ctx.grad_shape).to(ctx.dtype_A)
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else:
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raise Exception("State must contain either CBt or CB or CxB matrix for backward")
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return grad_A, grad_B, None, grad_bias, None
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@ -0,0 +1,68 @@
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import bitsandbytes as bnb
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import pytest
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import torch
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from bitsandbytes import functional as F
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from petals.utils.linear8bitlt_patch import CustomLinear8bitLt, get_inverse_transform_indices, undo_layout
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@pytest.mark.skipif(
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not torch.cuda.is_available() or torch.cuda.get_device_capability() < (7, 5),
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reason="this test requires a turing-generation or newer GPU, see bitsandbytes docs",
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)
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def test_layout_exact_match():
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x = (torch.randn(14336 * 3, 14336) * 10).to(torch.int8).cuda()
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for tile_size, order in ((8, 32), "col_turing"), ((32, 32), "col_ampere"):
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transform = lambda x: F.transform(x.cuda(), from_order="row", to_order=order)[0].to(x.device)
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tile_indices = get_inverse_transform_indices(transform, tile_size)
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cxb = transform(x)
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torch.cuda.synchronize()
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restored_x = undo_layout(cxb, tile_indices)
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torch.cuda.synchronize()
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assert restored_x.is_contiguous()
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assert torch.all(torch.eq(restored_x, x))
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@pytest.mark.skipif(
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not torch.cuda.is_available() or torch.cuda.get_device_capability() < (7, 5),
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reason="this test requires a turing-generation or newer GPU, see bitsandbytes docs",
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)
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def test_linear_exact_match():
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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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linear8bitlt = bnb.nn.Linear8bitLt(
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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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memory_efficient_backward=True,
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)
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linear8bitlt.weight = bnb.nn.Int8Params(linear.weight.data, requires_grad=False, has_fp16_weights=False).to(
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linear.weight.dtype
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)
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linear8bitlt.cuda()
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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.weight = bnb.nn.Int8Params(linear.weight.data, requires_grad=False, has_fp16_weights=False).to(
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linear.weight.dtype
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)
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linear8bitlt.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 = linear8bitlt(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 = linear8bitlt(x_ours).float()
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(fx_ours * grad_proj).mean().backward()
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assert torch.equal(fx_ref, fx_ours)
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assert torch.allclose(x_ref.grad, x_ours.grad)
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