test_modules.py

from itertools import product

import pytest
import torch
from torch import nn

import bitsandbytes as bnb


class MockArgs:
    def __init__(self, initial_data):
        for key in initial_data:
            setattr(self, key, initial_data[key])


class MLP8bit(torch.nn.Module):
    def __init__(self, dim1, dim2, has_fp16_weights=True, memory_efficient_backward=False, threshold=0.0):
        super().__init__()
        self.fc1 = bnb.nn.Linear8bitLt(
            dim1, dim2, has_fp16_weights=has_fp16_weights, memory_efficient_backward=memory_efficient_backward,
            threshold=threshold
        )
        self.fc2 = bnb.nn.Linear8bitLt(
            dim2, dim1, has_fp16_weights=has_fp16_weights, memory_efficient_backward=memory_efficient_backward,
            threshold=threshold
        )

    def forward(self, x):
        x = self.fc1(x)
        x = self.fc2(x)
        return x


def get_args():
    args = MockArgs([])
    args.quant_type = "vector"
    args.use_8bit_training = "full"
    args.clip_freq = 9999
    return args


def assert_all_approx_close(a, b, atol=1e-8, rtol=1e-5, count=10):
    idx = torch.isclose(a, b, rtol, atol)
    sumval = (idx == 0).sum().item()
    if sumval > count:
        print(f"Too many values not close: assert {sumval} < {count}")
        torch.testing.assert_allclose(a, b, rtol, atol)


class LinearFunction(torch.autograd.Function):
    @staticmethod
    def get_8bit_linear_trimmed(x, stochastic=False, trim_value=3.0):
        round_func = (
            LinearFunction.round_stoachastic if stochastic else torch.round
        )
        norm = math.sqrt(math.pi) / math.sqrt(2.0)
        # std = torch.abs(x).mean()*norm
        std = torch.std(x)
        max1 = std * trim_value
        x = x / max1 * 127
        x = round_func(x)
        x[x > 127] = 127
        x[x < -127] = -127
        x = x / 127 * max1

        return x

    def quant(x, quant_type, dim=1):
        if quant_type == "linear":
            max1 = torch.abs(x).max().float()
            xq = torch.round(x / max1 * 127).to(torch.int8)
            return xq, max1
        elif quant_type == "vector":
            max1 = torch.amax(torch.abs(x), dim=dim, keepdim=True)
            xq = torch.round(x / max1 * 127).to(torch.int8)
            return xq, max1
        elif quant_type == "min-max":
            maxA = torch.amax(x, dim=dim, keepdim=True).float()
            minA = torch.amin(x, dim=dim, keepdim=True).float()
            scale = (maxA - minA) / 2.0
            xq = torch.round(127 * (x - minA - scale) / scale).to(torch.int8)
            return xq, (minA.float(), scale.float())
        else:
            return None

    def dequant(xq, S1, S2, dtype, quant_type):
        if quant_type == "linear":
            norm = S1 * S2 / (127 * 127)
            # double cast needed to prevent overflows
            return (xq.float() * norm).to(dtype)
        elif quant_type == "vector":
            x = xq.float()
            if len(xq.shape) == 2 and len(S1.shape) == 3:
                S1 = S1.squeeze(0)
            if len(xq.shape) == 2 and len(S2.shape) == 3:
                S2 = S2.squeeze(0)
            # print(x.shape, S1.shape, S2.shape)
            if len(S1.shape) == 2:
                x *= S1.t() / 127
            else:
                x *= S1 / 127
            x *= S2 / 127
            return x.to(dtype)
        else:
            return None

    def dequant_min_max(xq, A, B, SA, SB, dtype):
        offset = B.float().t().sum(0) * (SA[0] + SA[1])
        x = xq.float()
        if len(xq.shape) == 2 and len(SB.shape) == 3:
            SB = SB.squeeze(0)
        if len(xq.shape) == 2 and len(SA.shape) == 3:
            SA = SA.squeeze(0)
        if len(SB.shape) == 2:
            x *= SB.t() / 127
        else:
            x *= SB / 127
        x *= SA[1] / 127
        x += offset
        return x.to(dtype)

    def get_8bit_linear(x, stochastic=False):
        round_func = (
            LinearFunction.round_stoachastic if stochastic else torch.round
        )
        max1 = torch.abs(x).max()
        x = x / max1 * 127
        x = round_func(x) / 127 * max1
        # x = torch.round(x)/128*max1
        return x

    @staticmethod
    def get_8bit_vector_wise(x, dim, stochastic=False):
        round_func = (
            LinearFunction.round_stoachastic if stochastic else torch.round
        )
        max1 = torch.amax(torch.abs(x), dim=dim, keepdim=True)
        max1[max1 == 0] = 1.0
        x = (x * 127) / max1
        x = round_func(x) / 127 * max1
        return x

    @staticmethod
    def round_stoachastic(x):
        sign = torch.sign(x)
        absx = torch.abs(x)
        decimal = absx - torch.floor(absx)
        rdm = torch.rand_like(decimal)
        return sign * (torch.floor(absx) + (rdm < decimal).to(x.dtype))

    @staticmethod
    def fake_8bit_storage(w, exponent_bits):
        code = bnb.functional.create_dynamic_map(n=exponent_bits).to(w.device)
        absmax, C = bnb.functional.quantize_blockwise(w.data, code=code)
        out = bnb.functional.dequantize_blockwise(absmax, C, code)
        out = out.half()
        w.copy_(out)
        return out

    @staticmethod
    def fake_8bit_storage_quantile(w, args):
        code = bnb.functional.estimate_quantiles(w.data, offset=args.offset)
        # C = bnb.functional.quantize_no_absmax(code, w)
        # out = bnb.functional.dequantize_no_absmax(code, C, out=w.data)
        # print(out)
        # out = out.half()
        code /= torch.max(torch.abs(code))
        absmax, C = bnb.functional.quantize_blockwise(w.data, code=code)
        out = bnb.functional.dequantize_blockwise(absmax, C, code)
        out = out.half()
        w.copy_(out)
        return out

    @staticmethod
    def fake_8bit_storage_stoachstic(w):
        rand = torch.rand(1024, device=w.device)
        absmax, C = bnb.functional.quantize_blockwise(w.data, rand=rand)
        out = bnb.functional.dequantize_blockwise(absmax, C)
        out = out.half()
        w.copy_(out)
        return out

    @staticmethod
    def fake_8bit_storage_with_max(w, topk=8):
        blocked_w = einops.rearrange(w.flatten(), "(h b) -> h b", b=256)
        max_val, idx = torch.sort(torch.abs(blocked_w), dim=1, descending=True)
        idx = idx[:, :topk]
        max_val = max_val[:, :topk]

        mask = torch.zeros_like(blocked_w)
        mask.scatter_(dim=1, index=idx, src=torch.ones_like(max_val))
        mask = mask.bool()

        # 1. zero out max values
        # 2. quantize + dequantize
        # 3. write back max values
        # 4. copy matrix back to weight

        values = blocked_w[mask]
        blocked_w[mask] = 0

        code = bnb.functional.create_dynamic_map()
        code = code.to(w.device)
        absmax, C = bnb.functional.quantize_blockwise(blocked_w.data)
        bnb.functional.dequantize_blockwise(absmax, C, out=blocked_w)

        blocked_w[mask] = values

        unblocked_w = blocked_w.flatten().view(w.shape)

        w.copy_(unblocked_w)
        return unblocked_w

    @staticmethod
    def forward(ctx, x, weight, bias=None, args=None):
        if args.use_8bit_training != "off":
            weight8, S1 = LinearFunction.quant(weight, args.quant_type, dim=1)
            x8, S2 = LinearFunction.quant(x, args.quant_type, dim=2)
            outputq = bnb.functional.igemm(x8, weight8.t())
            output = LinearFunction.dequant(
                outputq, S1, S2, x.dtype, args.quant_type
            )
            # if torch.rand(1) < 0.01:
            # output32 = torch.matmul(x, weight.t())
            # err = torch.abs(output-output32).float()
            # relerr = err/(torch.abs(output32).float()+1e-8)
            # print(f'{err.mean().item():.4f}, {relerr.mean().item():.4f}', args.quant_type, 'forward', proxy)
        else:
            # output = torch.matmul(x, weight.t())
            output = torch.einsum("bsi,oi->bso", x, weight)

        ctx.save_for_backward(x, weight, bias)
        ctx.args = args

        if bias is not None:
            output += bias.unsqueeze(0).expand_as(output)
        return output

    @staticmethod
    def backward(ctx, grad_output):
        x, weight, bias = ctx.saved_tensors
        args = ctx.args
        stochastic = False
        grad_input = grad_weight = grad_bias = None
        if bias is not None and ctx.needs_input_grad[2]:
            grad_bias = grad_output.sum(0)

        # weight and x are already 8bit
        # -> transform grad_output to 8-bit
        if args.use_8bit_training == "forward+wgrad":
            grad_output8, S1 = LinearFunction.quant(
                grad_output, args.quant_type, dim=[0, 1]
            )
            x8, S2 = LinearFunction.quant(x, args.quant_type, dim=[0, 1])
            grad_weight8 = bnb.functional.igemm(grad_output8, x8)
            grad_weight = LinearFunction.dequant(
                grad_weight8, S1, S2, grad_output.dtype, args.quant_type
            )

            # grad_weight32 = torch.einsum('bso,bsi->oi', grad_output, x)

            grad_input = grad_output.matmul(weight)
        elif args.use_8bit_training == "full":
            grad_output8, S1 = LinearFunction.quant(
                grad_output, args.quant_type, dim=[0, 1]
            )
            x8, S2 = LinearFunction.quant(x, args.quant_type, dim=[0, 1])
            grad_weight8 = torch.zeros_like(weight, dtype=torch.int32)
            bnb.functional.igemm(grad_output8, x8, out=grad_weight8)
            grad_weight = LinearFunction.dequant(
                grad_weight8, S1, S2, grad_output.dtype, args.quant_type
            )

            grad_output8, S1 = LinearFunction.quant(
                grad_output, args.quant_type, dim=2
            )
            weight8, S3 = LinearFunction.quant(weight, args.quant_type, dim=0)
            grad_input8 = bnb.functional.igemm(grad_output8, weight8)
            grad_input = LinearFunction.dequant(
                grad_input8, S1, S3, grad_output.dtype, args.quant_type
            )

        else:
            grad_input = grad_output.matmul(weight)
            grad_weight = torch.einsum("bsi,bso->oi", x, grad_output)

        return grad_input, grad_weight, grad_bias, None


class Linear8bit(nn.Module):
    def __init__(self, input_features, output_features, bias=True, args=None):
        super().__init__()
        self.input_features = input_features
        self.output_features = output_features
        self.args = args

        self.weight = nn.Parameter(torch.empty(output_features, input_features))
        if bias:
            self.bias = nn.Parameter(torch.empty(output_features))
        else:
            self.register_parameter("bias", None)

        torch.nn.init.xavier_uniform_(self.weight)
        if self.bias is not None:
            torch.nn.init.zeros_(self.bias)

    def forward(self, x):
        self.args.training = self.training

        return LinearFunction.apply(x, self.weight, self.bias, self.args)


threshold = [0.0, 3.0]
values = threshold
names = [f"threshold_{vals}" for vals in values]


@pytest.mark.parametrize("threshold", values, ids=names)
def test_linear8bitlt_inference(threshold):
    l1 = bnb.nn.Linear8bitLt(32, 64, threshold=threshold).cuda().half()
    assert l1.weight.device.type == "cuda"
    assert l1.weight.dtype == torch.float16

    l1.eval()
    for i in range(100):
        b1 = torch.randn(16, 8, 32, device="cuda").half()
        o1 = l1(b1)
        if i == 1:
            assert l1.state.CxB is not None


def test_linear8bitlt_accumulated_gradient():
    l1 = torch.nn.Sequential(*[bnb.nn.Linear8bitLt(32, 32).cuda().half() for i in range(2)])
    l2 = torch.nn.Sequential(*[torch.nn.Linear(32, 32).cuda().half() for i in range(2)])
    l2[0].weight = torch.nn.Parameter(l1[0].weight.clone())
    l2[0].bias = torch.nn.Parameter(l1[0].bias.clone())
    l2[1].weight = torch.nn.Parameter(l1[1].weight.clone())
    l2[1].bias = torch.nn.Parameter(l1[1].bias.clone())
    opt1 = bnb.optim.Adam8bit(l1.parameters(), lr=0.001)
    opt2 = bnb.optim.Adam8bit(l2.parameters(), lr=0.001)

    acc_steps = 10

    for i in range(10):
        b1 = torch.randn(16, 8, 32, device="cuda").half()
        o1 = l1(b1)
        o2 = l2(b1)
        loss1 = o1.mean()
        loss2 = o2.mean()
        loss1.backward()
        loss2.backward()
        if i == 2:
            assert l1[0].state.CxB is not None
            assert l1[1].state.CxB is not None

        if i > 0 and i % acc_steps == 0:
            opt1.step()
            opt1.zero_grad(True)
            opt2.step()
            opt2.zero_grad(True)
            assert_all_approx_close(
                l1[0].weight, l2[0].weight, rtol=1.05, atol=0.01, count=2
            )
            assert_all_approx_close(
                l1[1].weight, l2[1].weight, rtol=1.05, atol=0.01, count=2
            )
            # we do this copy because otherwise we have small divergences over time that add up
            l1[0].weight.data.copy_(l2[0].weight.data)
            l1[1].weight.data.copy_(l2[1].weight.data)
        else:
            torch.testing.assert_allclose(l1[0].weight.grad, l2[0].weight.grad)
            torch.testing.assert_allclose(l1[1].weight.grad, l2[1].weight.grad)


@pytest.mark.parametrize("threshold", [0.0, 2.0])
@pytest.mark.parametrize("memory_efficient_backward", [False])
def test_linear8bitlt_no_fp16_weights(threshold, memory_efficient_backward):
    l1 = (bnb.nn.Linear8bitLt( 32, 64, threshold=threshold, has_fp16_weights=False, memory_efficient_backward=memory_efficient_backward).cuda().half())
    assert l1.weight.dtype == torch.int8

    l1.eval()
    for i in range(100):
        b1 = torch.randn(16, 8, 32, device="cuda").half()
        o1 = l1(b1)
        assert o1.dtype == torch.float16

    mlp = MLP8bit(32, 64, threshold=threshold, has_fp16_weights=False).cuda()
    assert mlp.fc1.weight.dtype == torch.int8
    assert mlp.fc2.weight.dtype == torch.int8

    for i in range(100):
        b1 = torch.randn(16, 8, 32, device="cuda").half()
        o1 = mlp(b1)
        assert o1.dtype == torch.float16
        if threshold > 0:
            assert mlp.fc1.state.idx is not None
        if threshold > 0:
            assert mlp.fc2.state.idx is not None

    mlp = (
        MLP8bit(32, 64, threshold=threshold, has_fp16_weights=False)
        .cuda()
        .half()
    )
    assert mlp.fc1.weight.dtype == torch.int8
    assert mlp.fc2.weight.dtype == torch.int8

    for i in range(100):
        b1 = torch.randn(16, 8, 32, device="cuda").half()
        o1 = mlp(b1)
        assert o1.dtype == torch.float16
        if threshold > 0:
            assert mlp.fc1.state.idx is not None
        if threshold > 0:
            assert mlp.fc2.state.idx is not None

    mlp = (
        MLP8bit(32, 64, threshold=threshold, has_fp16_weights=False)
        .half()
        .cuda()
    )

    for i in range(100):
        b1 = torch.randn(16, 8, 32, device="cuda").half()
        o1 = mlp(b1)
        assert o1.dtype == torch.float16
        if threshold > 0:
            assert mlp.fc1.state.idx is not None
        if threshold > 0:
            assert mlp.fc2.state.idx is not None
    assert mlp.fc1.weight.dtype == torch.int8
    assert mlp.fc2.weight.dtype == torch.int8

    mlp = ( MLP8bit( 32, 64, threshold=threshold, has_fp16_weights=False, memory_efficient_backward=memory_efficient_backward).half().to("cuda"))

    for i in range(100):
        b1 = torch.randn(16, 8, 32, device="cuda").half()
        o1 = mlp(b1)
        assert o1.dtype == torch.float16
        if threshold > 0:
            assert mlp.fc1.state.idx is not None
        if threshold > 0:
            assert mlp.fc2.state.idx is not None
    assert mlp.fc1.weight.dtype == torch.int8
    assert mlp.fc2.weight.dtype == torch.int8
    assert mlp.fc1.weight.device.type == "cuda"
    assert mlp.fc2.weight.device.type == "cuda"

    mlp = MLP8bit(
            32, 64, threshold=threshold, has_fp16_weights=False, memory_efficient_backward=memory_efficient_backward
        )
    w1, w2 = mlp.fc1.weight.clone().cuda(), mlp.fc2.weight.clone().cuda()  # grab weights before quantization,
    mlp = mlp.cuda().half()  # and this line triggers quantization

    for i in range(100):
        b1 = torch.randn(16, 8, 32, device="cuda").half()
        o1 = mlp(b1)
        assert o1.dtype == torch.float16
        if threshold > 0:
            assert mlp.fc1.state.idx is not None
        if threshold > 0:
            assert mlp.fc2.state.idx is not None

    assert mlp.fc1.weight.dtype == torch.int8
    assert mlp.fc2.weight.dtype == torch.int8
    assert mlp.fc1.weight.device.type == "cuda"
    assert mlp.fc2.weight.device.type == "cuda"

    if memory_efficient_backward:
        b1 = torch.randn(16, 8, 32, device="cuda", requires_grad=True, dtype=torch.half)
        o1 = mlp(b1)
        assert o1.dtype == torch.float16
        assert o1.requires_grad
        grad_proj = torch.randn_like(o1)

        mlp.zero_grad()
        (o1 * grad_proj).sum().backward()
        grad_ref = grad_proj.flatten(2) @ w2.half() @ w1.half()
        scale = grad_ref.abs().mean()

        torch.testing.assert_allclose(b1.grad, grad_ref, rtol=0, atol=0.05 * scale)
        idx = torch.isclose(b1.grad, grad_ref, atol=0.01 * scale, rtol=0.1)
        assert (idx == 0).sum().item() <= b1.numel() * 0.005


@pytest.mark.parametrize("module", [lambda nin, nout, bias=True: bnb.nn.Linear8bitLt(nin, nout, bias=bias, has_fp16_weights=False), bnb.nn.LinearFP4], ids=['Int8Lt', 'FP4'])
def test_linear_kbit_fp32_bias(module):
    # casts model to fp16 -> int8 automatically
    l1 = module(32, 64).cuda()
    assert l1.weight.dtype in [torch.int8, torch.uint8]
    assert l1.bias.dtype == torch.float32

    for i in range(100):
        b1 = torch.randn(16, 8, 32, device="cuda").half()
        # casts bias to fp32
        o1 = l1(b1)
        assert l1.bias.dtype == torch.float16

    # casts model to fp16 -> int8 automatically
    l1 = module(32, 64, bias=False).cuda()
    assert l1.weight.dtype in [torch.int8, torch.uint8]
    assert l1.bias is None

    for i in range(100):
        b1 = torch.randn(16, 8, 32, device="cuda").half()
        o1 = l1(b1)
        assert l1.bias is None

@pytest.mark.skipif(not torch.cuda.is_available(), reason="this test requires a GPU")
@pytest.mark.parametrize("module", [bnb.nn.Linear8bitLt, bnb.nn.LinearFP4, lambda d1, d2: bnb.nn.LinearFP4(d1, d2, compress_statistics=True)], ids=['Int8Lt', 'FP4', 'FP4+C'])
def test_kbit_backprop(module):
    b = 17
    dim1 = 37
    dim2 = 83

    ref = nn.Sequential(*[torch.nn.Linear(dim1, dim2), torch.nn.Linear(dim2, 10)])
    ref[1].weight.requires_grad = False
    kbit = nn.Sequential(*[torch.nn.Linear(dim1, dim2), module(dim2, 10)])
    kbit[0].weight.detach().copy_(ref[0].weight)
    kbit[1].weight.detach().copy_(ref[1].weight)
    kbit[0].bias.detach().copy_(ref[0].bias)
    kbit[1].bias.detach().copy_(ref[1].bias)
    ref = ref.half().cuda()
    kbit = kbit.half().cuda()

    for i in range(100):
        batch = torch.randn(b, dim1).half().cuda()
        out1 = ref(batch)
        out2 = kbit(batch)
        out1.mean().backward()
        out2.mean().backward()

        grad1 = ref[0].weight.grad
        grad2 = kbit[0].weight.grad
        bgrad1 = ref[0].bias.grad
        bgrad2 = kbit[0].bias.grad

        torch.testing.assert_allclose(grad1, grad2, atol=0.008, rtol=0.05)
        torch.testing.assert_allclose(bgrad1, bgrad2, atol=0.008, rtol=0.05)
        ref.zero_grad()
        kbit.zero_grad()

        assert kbit[0].weight.grad.sum().item() == 0
        assert kbit[0].bias.grad.sum().item() == 0