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109 lines
3.9 KiB
Python
109 lines
3.9 KiB
Python
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.clone(), requires_grad=False, has_fp16_weights=False).to(
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linear.weight.dtype
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)
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linear8bitlt.bias = linear.bias
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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(
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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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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 = linear_custom(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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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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