test_autograd.py

from itertools import product

import pytest
import torch

import bitsandbytes as bnb

n = 1
k = 25
dim1 = torch.randint(16, 64, size=(n,)).tolist()
dim2 = torch.randint(32, 96, size=(n,)).tolist()
dim3 = torch.randint(32, 96, size=(n,)).tolist()
dim4 = torch.randint(32, 96, size=(n,)).tolist()
funcs = [(torch.bmm, bnb.bmm_cublas), (torch.matmul, bnb.matmul_cublas)]
str_funcs = ["bmm", "matmul"]
req_grad = [(False, False), (True, False), (True, True), (False, True)]
req_grad_str = ["FF", "TF", "TT", "FT"]
transpose = [(False, False), (False, True), (True, True), (True, False)]
str_transpose = ["FF", "FT", "TT", "TF"]
dtype = [torch.float32, torch.float16]
values = list(
    product(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose)
)
str_values = list(
    product(
        dim1, dim2, dim3, dim4, str_funcs, dtype, req_grad_str, str_transpose
    )
)
names = [
    "dim1_{0}_dim2_{1}_dim3_{2}_dim4_{3}_func_{4}_dtype_{5}_requires_grad_{6}_transpose_{7}".format(
        *vals
    )
    for vals in str_values
]


@pytest.mark.parametrize(
    "dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose",
    values,
    ids=names,
)
def test_matmul(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose):
    dim2 = dim2 - (dim2 % 16)
    dim3 = dim3 - (dim3 % 16)
    dim4 = dim4 - (dim4 % 16)
    for i in range(k):

        # normal multiply
        if funcs[0] in [torch.mm, torch.matmul]:
            dimA = (dim2, dim3) if not transpose[0] else (dim3, dim2)
            dimB = (dim3, dim4) if not transpose[1] else (dim4, dim3)
            A = torch.randn(size=dimA, device="cuda", requires_grad=req_grad[0])
            B = torch.randn(size=dimB, device="cuda", requires_grad=req_grad[1])
            target = torch.randn(
                size=(dim2, dim4), device="cuda", requires_grad=req_grad[1]
            )
            torch.nn.init.xavier_uniform_(B)

            if not transpose[0] and not transpose[1]:
                out_torch = funcs[0](A, B)
                out_bnb = funcs[1](A, B)
            elif not transpose[0] and transpose[1]:
                out_torch = funcs[0](A, B.t())
                out_bnb = funcs[1](A, B.t())
            elif transpose[0] and not transpose[1]:
                out_torch = funcs[0](A.t(), B)
                out_bnb = funcs[1](A.t(), B)
            elif transpose[0] and transpose[1]:
                out_torch = funcs[0](A.t(), B.t())
                out_bnb = funcs[1](A.t(), B.t())

            n = out_bnb.numel()
            idx = torch.isclose(out_bnb, out_torch, atol=0.01, rtol=0.1)
            assert (idx == 0).sum().item() < n * 0.0175
            idx = torch.isclose(out_bnb, out_torch, atol=0.035, rtol=0.2)
            assert (idx == 0).sum().item() < n * 0.001

            if any(req_grad):
                out_bnb.data.copy_(out_torch)
                torch.cuda.synchronize()
                loss_bnb = torch.nn.functional.mse_loss(out_bnb, target).mean()
                loss_bnb.backward()
                gradA1 = A.grad
                gradB1 = B.grad
                A.grad = None
                B.grad = None

                loss_torch = torch.nn.functional.mse_loss(
                    out_torch, target
                ).mean()
                loss_torch.backward()
                gradA2 = A.grad
                gradB2 = B.grad
                A.grad = None
                B.grad = None

            if req_grad[0]:
                torch.testing.assert_allclose(
                    gradA1, gradA2, atol=0.015, rtol=0.1
                )
            if req_grad[1]:
                n = gradB1.numel()
                idx = torch.isclose(gradB1, gradB2, atol=0.06, rtol=0.3)
                assert (idx == 0).sum().item() < n * 0.1
                idx = torch.isclose(gradB1, gradB2, atol=0.10, rtol=0.3)
                assert (idx == 0).sum().item() < n * 0.02
                torch.testing.assert_allclose(
                    gradB1, gradB2, atol=0.18, rtol=0.3
                )

        # batched matrix multiply
        if funcs[0] in [torch.bmm, torch.matmul]:
            A = torch.randn(
                size=(dim1, dim2, dim3),
                device="cuda",
                requires_grad=req_grad[0],
            )
            B = torch.randn(
                size=(dim1, dim3, dim4),
                device="cuda",
                requires_grad=req_grad[1],
            )
            target = torch.randn(
                size=(dim1, dim2, dim4),
                device="cuda",
                requires_grad=req_grad[1],
            )
            torch.nn.init.xavier_uniform_(B)

            out_torch = funcs[0](A, B)
            out_bnb = funcs[1](A, B)

            n = out_bnb.numel()
            idx = torch.isclose(out_bnb, out_torch, atol=0.01, rtol=0.1)
            assert (idx == 0).sum().item() < n * 0.01
            torch.testing.assert_allclose(
                out_bnb, out_torch, atol=0.027, rtol=0.2
            )

            if any(req_grad):
                out_bnb.data.copy_(out_torch)
                torch.cuda.synchronize()
                loss_bnb = torch.nn.functional.mse_loss(out_bnb, target).mean()
                loss_bnb.backward()
                gradA1 = A.grad
                gradB1 = B.grad
                A.grad = None
                B.grad = None

                loss_torch = torch.nn.functional.mse_loss(
                    out_torch, target
                ).mean()
                loss_torch.backward()
                gradA2 = A.grad
                gradB2 = B.grad
                A.grad = None
                B.grad = None

            if req_grad[0]:
                torch.testing.assert_allclose(
                    gradA1, gradA2, atol=0.015, rtol=0.1
                )
            if req_grad[1]:
                n = gradB1.numel()
                idx = torch.isclose(gradB1, gradB2, atol=0.06, rtol=0.3)
                assert (idx == 0).sum().item() < n * 0.1
                idx = torch.isclose(gradB1, gradB2, atol=0.10, rtol=0.3)
                assert (idx == 0).sum().item() < n * 0.02

        if funcs[0] in [torch.matmul]:
            dim1 = dim1 - (dim1 % 16)
            A = torch.randn(
                size=(dim1, dim2, dim3),
                device="cuda",
                requires_grad=req_grad[0],
            )
            dimB = (dim4, dim3) if transpose[1] else (dim3, dim4)
            B = torch.randn(size=dimB, device="cuda", requires_grad=req_grad[1])
            target = torch.randn(
                size=(dim1, dim2, dim4),
                device="cuda",
                requires_grad=req_grad[1],
            )
            torch.nn.init.xavier_uniform_(B)

            if transpose[1]:
                out_torch = funcs[0](A, B.t())
                out_bnb = funcs[1](A, B.t())
            else:
                out_torch = funcs[0](A, B)
                out_bnb = funcs[1](A, B)

            n = out_bnb.numel()
            idx = torch.isclose(out_bnb, out_torch, atol=0.01, rtol=0.1)
            assert (idx == 0).sum().item() < n * 0.0175
            idx = torch.isclose(out_bnb, out_torch, atol=0.035, rtol=0.2)
            assert (idx == 0).sum().item() < n * 0.001

            if any(req_grad):
                out_bnb.data.copy_(out_torch)
                torch.cuda.synchronize()
                loss_bnb = torch.nn.functional.mse_loss(out_bnb, target).mean()
                loss_bnb.backward()
                gradA1 = A.grad
                gradB1 = B.grad
                A.grad = None
                B.grad = None

                loss_torch = torch.nn.functional.mse_loss(
                    out_torch, target
                ).mean()
                loss_torch.backward()
                gradA2 = A.grad
                gradB2 = B.grad
                A.grad = None
                B.grad = None

            if req_grad[0]:
                torch.testing.assert_allclose(
                    gradA1, gradA2, atol=0.015, rtol=0.1
                )
            if req_grad[1]:
                n = gradB1.numel()
                idx = torch.isclose(gradB1, gradB2, atol=0.06, rtol=0.3)
                assert (idx == 0).sum().item() < n * 0.1
                idx = torch.isclose(gradB1, gradB2, atol=0.10, rtol=0.3)
                assert (idx == 0).sum().item() < n * 0.02


n = 1
k = 3
dim1 = torch.randint(16, 64, size=(n,)).tolist()
dim2 = torch.randint(32, 96, size=(n,)).tolist()
dim3 = torch.randint(32, 96, size=(n,)).tolist()
dim4 = torch.randint(32, 96, size=(n,)).tolist()

# dim1 = (17,)
# dim2 = (7,)
# dim3 = (37,)
# dim4 = (23,)

decomp = [0.0, 6.0]
funcs = [(torch.matmul, bnb.matmul)]
str_funcs = ["matmul"]
req_grad = [(False, False), (True, False), (True, True), (False, True)]
req_grad_str = ["FF", "TF", "TT", "FT"]
transpose = [(False, True), (False, False)]
str_transpose = ["NT", "NN"]
dtype = [torch.float16]
has_fp16_weights = [True, False]
values = list(
    product(
        dim1,
        dim2,
        dim3,
        dim4,
        funcs,
        dtype,
        req_grad,
        transpose,
        decomp,
        has_fp16_weights,
    )
)
str_values = list(
    product(
        dim1,
        dim2,
        dim3,
        dim4,
        str_funcs,
        dtype,
        req_grad_str,
        str_transpose,
        decomp,
        has_fp16_weights,
    )
)
names = [
    "dim1_{0}_dim2_{1}_dim3_{2}_dim4_{3}_func_{4}_dtype_{5}_requires_grad_{6}_transpose_{7}_decomp_{8}_has_fp16_weights_{9}".format(
        *vals
    )
    for vals in str_values
]


@pytest.mark.parametrize(
    "dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose, decomp, has_fp16_weights",
    values,
    ids=names,
)
def test_matmullt(
    dim1,
    dim2,
    dim3,
    dim4,
    funcs,
    dtype,
    req_grad,
    transpose,
    decomp,
    has_fp16_weights,
):
    dimA = (dim2, dim3) if not transpose[0] else (dim3, dim2)
    dimB = (dim3, dim4) if not transpose[1] else (dim4, dim3)
    outlier_dim = torch.randint(0, dimA[1], size=(dimA[1] // 8,), device="cuda")

    for i in range(k):

        # normal multiply
        if funcs[0] in [torch.mm, torch.matmul]:
            A = torch.randn(
                size=dimA, device="cuda", requires_grad=req_grad[0], dtype=dtype
            )
            if decomp == 6.0:
                with torch.no_grad():
                    A[:, outlier_dim] = 6.0
            B = torch.randn(
                size=dimB, device="cuda", requires_grad=req_grad[1], dtype=dtype
            )
            target = torch.randn(
                size=(dim2, dim4),
                device="cuda",
                requires_grad=req_grad[1],
                dtype=dtype,
            )
            torch.nn.init.xavier_uniform_(B)
            B2 = B.clone()

            state = bnb.MatmulLtState()
            state.threshold = decomp
            state.has_fp16_weights = has_fp16_weights
            if not has_fp16_weights:
                if not transpose[0] and not transpose[1]:
                    B2 = B2.t().contiguous()
                (
                    state.CB,
                    CBt,
                    state.SCB,
                    SCBt,
                    coo_tensorB,
                ) = bnb.functional.double_quant(B2)
                B2 = state.CB

            if not transpose[0] and transpose[1]:
                out_torch = funcs[0](A, B.t())
                out_bnb = funcs[1](A, B2, state=state)
            elif not transpose[0] and not transpose[1]:
                out_torch = funcs[0](A, B)
                out_bnb = funcs[1](A, B2.t(), state=state)

            n = out_bnb.numel()
            err = torch.abs(out_bnb - out_torch).mean().item()
            # print(f'abs error {err:.4f}')
            idx = torch.isclose(out_bnb, out_torch, atol=0.01, rtol=0.1)
            assert (idx == 0).sum().item() < n * 0.0175
            idx = torch.isclose(out_bnb, out_torch, atol=0.035, rtol=0.2)
            assert (idx == 0).sum().item() < n * 0.001

            if has_fp16_weights:
                if any(req_grad):
                    out_bnb.data.copy_(out_torch)
                    torch.cuda.synchronize()
                    loss_bnb = torch.nn.functional.mse_loss(
                        out_bnb, target
                    ).mean()
                    loss_bnb.backward()
                    gradA1 = A.grad
                    gradB1 = B.grad
                    A.grad = None
                    B.grad = None

                    loss_torch = torch.nn.functional.mse_loss(
                        out_torch, target
                    ).mean()
                    loss_torch.backward()
                    gradA2 = A.grad
                    gradB2 = B.grad
                    A.grad = None
                    B.grad = None

                if req_grad[0]:
                    torch.testing.assert_allclose(
                        gradA1, gradA2, atol=0.015, rtol=0.1
                    )
                if req_grad[1]:
                    n = gradB1.numel()
                    assert torch.abs(gradB1).sum() > 0.0
                    assert torch.abs(gradB2).sum() > 0.0
                    idx = torch.isclose(gradB1, gradB2, atol=0.06, rtol=0.3)
                    assert (idx == 0).sum().item() < n * 0.1
                    idx = torch.isclose(gradB1, gradB2, atol=0.10, rtol=0.3)
                    assert (idx == 0).sum().item() < n * 0.02
                    torch.testing.assert_allclose(
                        gradB1, gradB2, atol=0.18, rtol=0.3
                    )