Remove CustomLinear8bitLt (#297)

This became a part of https://github.com/TimDettmers/bitsandbytes/releases/tag/0.37.0.

src/petals/utils/convert_block.py

@@ -1,6 +1,7 @@
 """
 Tools for converting transformer blocks, applying quantization and/or tensor parallelism
 """
+import os
 import re
 from typing import Sequence
 
@@ -75,17 +76,16 @@ def replace_8bit_linear(model: nn.Module, threshold=6.0):
     """
 
     # Import bitsandbytes only when necessary, so Petals runs on platforms not supported by bitsandbytes
+    os.environ["BITSANDBYTES_NOWELCOME"] = "1"
     import bitsandbytes as bnb
 
-    from petals.utils.linear8bitlt_patch import CustomLinear8bitLt
-
     for n, module in model.named_children():
         if len(list(module.children())) > 0:
             replace_8bit_linear(module, threshold)
 
         if isinstance(module, torch.nn.Linear) and n not in ["lm_head", "score"]:
             assert module.weight.device.type == "cpu", f"expected linear layers on CPU, got {module.weight.device}"
-            model._modules[n] = CustomLinear8bitLt(
+            model._modules[n] = bnb.nn.Linear8bitLt(
                 module.in_features,
                 module.out_features,
                 module.bias is not None,

src/petals/utils/linear8bitlt_patch.py

@@ -1,334 +0,0 @@
-"""
-A patch to bitsandbytes 0.34.0 that introduces an option to run backward pass in default (fast) matrix layout.
-Authors: modification by @borzunov, original code by @timdettmers. Please disregard commit authors in this file.
-
-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: https://colab.research.google.com/drive/1EJ0MKifajXSSVq7O2_QGwtb0l6gRAGrh?usp=sharing
-Based on: https://github.com/TimDettmers/bitsandbytes/blob/main/csrc/kernels.cu#L2130-L2136
-Exact match tests: see $REPO/tests/test_linear8bitlt.py
-"""
-import dataclasses
-import logging
-from typing import Optional, Tuple
-
-import bitsandbytes.functional as F
-import torch
-from bitsandbytes.autograd._functions import GlobalOutlierPooler, MatMul8bitLt, MatmulLtState, prod
-from bitsandbytes.nn import Linear8bitLt
-
-
-def get_inverse_transform_indices(transform_tile: callable, tile_size: Tuple[int, int]):
-    """
-    Compute a permutation of indices that invert the specified (tiled) matrix transformation
-
-    :param transform_tile: a function that applies forward transform to a tensor of shape [dim1, dim2]
-    :param tile_size: higher-level tile dimensions, i.e. (8, 32) for Turing and (32, 32) for Ampere
-    :note: we assume that tile_transform applies to a cpu-based int8 tensor of shape tile_size
-    :example: transform_tile function for the turing layout (bitsandbytes.functional as F)
-    :returns: indices
-    """
-    d1, d2 = tile_size
-    assert 0 < d1 * d2 < 2**64
-    tile_indices = torch.arange(d1 * d2, dtype=torch.int64).view(d1, d2)
-    # encode each position in tile as a tuple of <= 8 unique bytes
-    permuted_tile_indices = torch.zeros_like(tile_indices)
-    for i in range(8):
-        # select i-th byte, apply transformation and trace where each index ended up
-        ith_dim_indices = torch.div(tile_indices, 256**i, rounding_mode="trunc") % 256
-        sample_tile_i = (ith_dim_indices - 128).to(torch.int8).contiguous()
-        assert torch.all(sample_tile_i.int() + 128 == ith_dim_indices), "int overflow"
-        permuted_tile_i = transform_tile(sample_tile_i)
-        ith_permuted_indices = permuted_tile_i.to(tile_indices.dtype) + 128
-        permuted_tile_indices += ith_permuted_indices * (256**i)
-        if d1 * d2 < 256**i:
-            break  # if all indices fit in i bytes, stop early
-    return permuted_tile_indices
-
-
-def undo_layout(permuted_tensor: torch.Tensor, tile_indices: torch.LongTensor) -> torch.Tensor:
-    """
-    Undo a tiled permutation such as turing or ampere layout
-
-    :param permuted_tensor: torch tensor in a permuted layout
-    :param tile_indices: reverse transformation indices, from get_inverse_transform_indices
-    :return: contiguous row-major tensor
-    """
-    (rows, cols), (tile_rows, tile_cols) = permuted_tensor.shape, tile_indices.shape
-    assert rows % tile_rows == cols % tile_cols == 0, "tensor must contain a whole number of tiles"
-    tensor = permuted_tensor.reshape(-1, tile_indices.numel()).t()
-    outputs = torch.empty_like(tensor)  # note: not using .index_copy because it was slower on cuda
-    outputs[tile_indices.flatten()] = tensor
-    outputs = outputs.reshape(tile_rows, tile_cols, cols // tile_cols, rows // tile_rows)
-    outputs = outputs.permute(3, 0, 2, 1)  # (rows // tile_rows, tile_rows), (cols // tile_cols, tile_cols)
-    return outputs.reshape(rows, cols).contiguous()
-
-
-# the rest of this file is just a patch to bitsandbytes that modifies Linear8bitLt and dependencies
-
-
-class CustomLinear8bitLt(Linear8bitLt):
-    def __init__(self, *args, memory_efficient_backward: bool = False, **kwargs):
-        assert not memory_efficient_backward, "memory_efficient_backward is no longer used"
-        super().__init__(*args, **kwargs)
-        old_state, self.state = self.state, CustomMatmulLtState()
-        self.state.threshold = old_state.threshold
-        self.state.has_fp16_weights = old_state.has_fp16_weights
-        self.state.memory_efficient_backward = old_state.memory_efficient_backward
-        if old_state.threshold > 0.0 and not old_state.has_fp16_weights:
-            self.state.use_pool = True
-
-    def forward(self, x: torch.Tensor):
-        self.state.is_training = self.training
-        if self.weight.CB is not None:
-            self.init_8bit_state()
-
-        # weights are cast automatically as Int8Params, but the bias has to be cast manually
-        if self.bias is not None and self.bias.dtype != x.dtype:
-            self.bias.data = self.bias.data.to(x.dtype)
-
-        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 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
-                self.weight.data = self.state.CxB
-        return out
-
-
-@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 (
-            "col_turing",
-            "col_ampere",
-        ), f"please find this assert and manually enter tile size for {self.formatB}"
-        return (8, 32) if self.formatB == "col_turing" else (32, 32)
-
-
-def custom_matmul8bitlt(
-    A: torch.Tensor,
-    B: torch.Tensor,
-    out: torch.Tensor = None,
-    state: CustomMatmulLtState = None,
-    threshold=0.0,
-    bias=None,
-):
-    state = state or MatmulLtState()
-    if threshold > 0.0:
-        state.threshold = threshold
-    return CustomMatMul8bitLt.apply(A, B, out, bias, state)
-
-
-class CustomMatMul8bitLt(MatMul8bitLt):
-    # 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:
-            logging.debug(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:
-                # we apply the fused bias here
-                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)
-
-        # 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:
-            bias_grad = None if ctx.bias is None else torch.zeros_like(ctx.bias)
-            return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, bias_grad, None
-        req_gradA, req_gradB, _, req_gradBias, _ = ctx.needs_input_grad
-        CAt, subA = ctx.tensors
-        SCAt, idx = ctx.tensor_states
-        formatB = ctx.formatB
-        state = ctx.state
-        grad_A = grad_B = grad_bias = None
-
-        if req_gradBias:
-            # compute grad_bias first before changing grad_output dtype
-            grad_bias = grad_output.sum(0, dtype=ctx.dtype_bias)
-
-        # Cast grad_output to fp16
-        if len(grad_output.shape) == 3:
-            grad_output = grad_output.reshape(-1, grad_output.shape[-1]).contiguous()
-
-        Cgrad, Cgradt, SCgrad, SCgradt, coo_tensor = F.double_quant(grad_output.to(torch.float16))
-        if req_gradB:
-            CxAt, SAt = F.transform(CAt, formatB, transpose=True)
-            C32grad, Sgrad = F.transform(Cgradt, "col32", transpose=True)
-            gradB32, SgradB32 = F.igemmlt(C32grad, CxAt, Sgrad, SAt)
-            grad_B = F.mm_dequant(gradB32, SgradB32, SCgradt, SCAt)
-            if state.threshold > 0.0 and subA is not None:
-                grad_B[:, idx] += torch.matmul(grad_output.t(), subA)
-
-        if req_gradA:
-            if state.CBt is not None:
-                C32grad, Sgrad = F.transform(Cgrad, "col32")
-                if state.CxBt is None:
-                    state.CxBt, state.SBt = F.transform(state.CBt, to_order=formatB, transpose=True)
-                gradA32, SgradA32 = F.igemmlt(C32grad, state.CxBt, Sgrad, state.SBt)
-                grad_A = F.mm_dequant(gradA32, SgradA32, SCgrad, state.SCBt).view(ctx.grad_shape).to(ctx.dtype_A)
-
-            elif state.CB is not None:
-                CB = state.CB.to(ctx.dtype_A, copy=True).mul_(state.SCB.unsqueeze(1).mul(1.0 / 127.0))
-                grad_A = torch.matmul(grad_output, CB).view(ctx.grad_shape).to(ctx.dtype_A)
-            elif state.CxB is not None:
-
-                if state.tile_indices is None:
-                    order, tile_size = state.formatB, state.get_tile_size()
-                    transform = lambda x: F.transform(x.cuda(), from_order="row", to_order=order)[0].to(x.device)
-                    with torch.no_grad():
-                        state.tile_indices = get_inverse_transform_indices(transform, tile_size).to(state.CxB.device)
-
-                CB = (
-                    undo_layout(state.CxB, state.tile_indices)
-                    .to(ctx.dtype_A)
-                    .mul_(state.SCB.unsqueeze(1).mul(1.0 / 127.0))
-                )
-                grad_A = torch.matmul(grad_output, CB).view(ctx.grad_shape).to(ctx.dtype_A)
-            else:
-                raise Exception("State must contain either CBt or CB or CxB matrix for backward")
-
-        return grad_A, grad_B, None, grad_bias, None

tests/test_linear8bitlt.py

@@ -1,108 +0,0 @@
-import bitsandbytes as bnb
-import pytest
-import torch
-from bitsandbytes import functional as F
-
-from petals.utils.linear8bitlt_patch import CustomLinear8bitLt, get_inverse_transform_indices, undo_layout
-
-
-@pytest.mark.skipif(
-    not torch.cuda.is_available() or torch.cuda.get_device_capability() < (7, 5),
-    reason="this test requires a turing-generation or newer GPU, see bitsandbytes docs",
-)
-def test_layout_exact_match():
-    x = (torch.randn(14336 * 3, 14336) * 10).to(torch.int8).cuda()
-    for tile_size, order in ((8, 32), "col_turing"), ((32, 32), "col_ampere"):
-        transform = lambda x: F.transform(x.cuda(), from_order="row", to_order=order)[0].to(x.device)
-        tile_indices = get_inverse_transform_indices(transform, tile_size)
-        cxb = transform(x)
-
-        torch.cuda.synchronize()
-        restored_x = undo_layout(cxb, tile_indices)
-        torch.cuda.synchronize()
-        assert restored_x.is_contiguous()
-        assert torch.all(torch.eq(restored_x, x))
-
-
-@pytest.mark.skipif(
-    not torch.cuda.is_available() or torch.cuda.get_device_capability() < (7, 5),
-    reason="this test requires a turing-generation or newer GPU, see bitsandbytes docs",
-)
-def test_linear_exact_match():
-    linear = torch.nn.Linear(1024, 3072)
-    x = torch.randn(3, 1024, dtype=torch.half)
-    linear8bitlt = bnb.nn.Linear8bitLt(
-        linear.in_features,
-        linear.out_features,
-        linear.bias is not None,
-        has_fp16_weights=False,
-        threshold=6.0,
-        memory_efficient_backward=True,
-    )
-    linear8bitlt.weight = bnb.nn.Int8Params(linear.weight.data.clone(), requires_grad=False, has_fp16_weights=False).to(
-        linear.weight.dtype
-    )
-    linear8bitlt.bias = linear.bias
-    linear8bitlt.cuda()
-
-    linear_custom = CustomLinear8bitLt(
-        linear.in_features,
-        linear.out_features,
-        linear.bias is not None,
-        has_fp16_weights=False,
-        threshold=6.0,
-    )
-    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()
-
-    x_ref = x.clone().cuda().requires_grad_(True)
-    x_ours = x.clone().cuda().requires_grad_(True)
-    fx_ref = linear8bitlt(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.equal(fx_ref, fx_ours)
-    assert torch.allclose(x_ref.grad, x_ours.grad)
-    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