bitsandbytes-rocm/tests/test_functional.py

import math
import random
import time
from itertools import product

import einops
import pytest
import torch
import numpy as np

import bitsandbytes as bnb
from bitsandbytes import functional as F
from scipy.stats import norm

torch.set_printoptions(
    precision=5, sci_mode=False, linewidth=120, edgeitems=20, threshold=10000
)
k = 20


def assert_all_approx_close(a, b, rtol=1e-3, atol=1e-3, count=0, throw=True):
    idx = torch.isclose(a, b, rtol, atol)
    sumval = (idx == 0).sum().item()
    if sumval > count:
        if throw:
            print(f"Too many values not close: assert {sumval} < {count}")
            torch.testing.assert_close(a, b, rtol, atol)

    return sumval


class FFN(torch.nn.Module):
    def __init__(self, input_features, hidden_size, bias=True):
        super().__init__()
        self.fc1 = torch.nn.Linear(input_features, hidden_size, bias=bias)
        self.fc2 = torch.nn.Linear(hidden_size, input_features, bias=bias)

        with torch.no_grad():
            torch.nn.init.xavier_uniform_(self.fc1.weight)
            torch.nn.init.xavier_uniform_(self.fc2.weight)

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


class Timer:
    def __init__(self):
        self.starts = {}
        self.ends = {}
        self.agg = {}

    def tick(self, name="default"):
        if name not in self.starts:
            self.starts[name] = torch.cuda.Event(enable_timing=True)
            self.ends[name] = torch.cuda.Event(enable_timing=True)
            self.starts[name].record()
        else:
            ms = self.tock(name, evict=True, print_ms=False)

    def tock(self, name="default", evict=True, print_ms=True):
        if name in self.ends:
            self.ends[name].record()
            torch.cuda.synchronize()
            ms = self.starts[name].elapsed_time(self.ends[name])
            if name not in self.agg:
                self.agg[name] = 0.0
            self.agg[name] += ms
            if evict:
                self.starts.pop(name)
                self.ends.pop(name)

        if print_ms and name in self.agg:
            print(f"{name} took: {self.agg[name] / 1000.0:.5f}s")

        return self.agg[name]

    def reset(self):
        self.starts = {}
        self.ends = {}
        self.agg = {}
        print("Resetting benchmark data")


def setup():
    pass


def teardown():
    pass


@pytest.mark.parametrize(
    "dtype", [torch.float32, torch.float16], ids=["float", "half"]
)
def test_estimate_quantiles(dtype):
    A = torch.rand(1024, 1024, device="cuda")
    A = A.to(dtype)
    code = F.estimate_quantiles(A)

    percs = torch.linspace(1 / 512, 511 / 512, 256, device=A.device)
    torch.testing.assert_close(percs, code, atol=1e-3, rtol=1e-2)

    A = torch.randn(1024, 1024, device="cuda")
    A = A.to(dtype)
    code = F.estimate_quantiles(A)

    quantiles = torch.quantile(A.float(), percs)
    diff = torch.abs(code - quantiles)
    assert (diff > 5e-02).sum().item() == 0


def test_quantile_quantization():
    for i in range(100):
        A1 = torch.randn(1024, 1024, device="cuda")
        code = F.estimate_quantiles(A1)
        C = F.quantize_no_absmax(A1, code)
        A2 = F.dequantize_no_absmax(C, code)
        diff = torch.abs(A1 - A2).mean().item()
        assert diff < 0.0075

        A1 = torch.rand(1024, 1024, device="cuda")
        code = F.estimate_quantiles(A1)
        C = F.quantize_no_absmax(A1, code)
        A2 = F.dequantize_no_absmax(C, code)
        diff = torch.abs(A1 - A2).mean().item()
        torch.testing.assert_close(A1, A2, atol=5e-3, rtol=0)
        assert diff < 0.001


def test_dynamic_quantization():
    diffs = []
    reldiffs = []
    for i in range(100):
        A1 = torch.randn(1024, 1024, device="cuda")
        C, S = F.quantize(A1)
        A2 = F.dequantize(C, S)
        diff = torch.abs(A1 - A2)
        reldiff = diff / torch.abs(A1 + 1e-8)
        diffs.append(diff.mean().item())
        reldiffs.append(reldiff.mean().item())
        assert diff.mean().item() < 0.0135
    # print(sum(diffs)/len(diffs))
    # print(sum(reldiffs)/len(reldiffs))

    for i in range(100):
        A1 = torch.rand(1024, 1024, device="cuda")
        C, S = F.quantize(A1)
        A2 = F.dequantize(C, S)
        diff = torch.abs(A1 - A2).mean().item()
        torch.testing.assert_close(A1, A2, atol=1e-2, rtol=0)
        assert diff < 0.004



@pytest.mark.parametrize("dtype", [torch.float32, torch.float16, torch.bfloat16], ids=["fp32", "fp16", "bf16"])
@pytest.mark.parametrize("nested", [False, True], ids=["False", "True"])
@pytest.mark.parametrize("blocksize", [4096, 2048, 1024, 512, 256, 128, 64])
def test_dynamic_blockwise_quantization(dtype, nested, blocksize):
    #print('')
    diffs = []
    reldiffs = []
    for i in range(100):
        A1 = torch.randn(1024, 1024, device="cuda", dtype=dtype)
        C, S = F.quantize_blockwise(A1, blocksize=blocksize, nested=nested)
        A2 = F.dequantize_blockwise(C, S)
        diff = torch.abs(A1 - A2).float()
        reldiff = diff / torch.abs(A1.float() + 1e-8)
        diffs.append(diff.mean().item())
        reldiffs.append(reldiff.mean().item())
    abserr = sum(diffs)/len(diffs)
    relerr = sum(reldiffs)/len(reldiffs)
    #print('nested=', nested, 'randn', blocksize, 'dtype', dtype, sum(diffs)/len(diffs))
    #print('nested=', nested, 'randn', blocksize, 'dtype', dtype, sum(reldiffs)/len(reldiffs))
    assert abserr < 0.011
    assert relerr < 0.018
    assert A2.dtype == dtype

    diffs = []
    for i in range(100):
        A1 = torch.rand(1024, 1024, device="cuda", dtype=dtype)
        C, S = F.quantize_blockwise(A1, blocksize=blocksize, nested=nested)
        A2 = F.dequantize_blockwise(C, S)
        diff = torch.abs(A1 - A2).float()
        reldiff = diff / torch.abs(A1.float() + 1e-8)
        diffs.append(diff.mean().item())
        reldiffs.append(reldiff.mean().item())
        #torch.testing.assert_close(A1, A2, atol=1e-2, rtol=0)
    abserr = sum(diffs)/len(diffs)
    relerr = sum(reldiffs)/len(reldiffs)
    assert abserr < 0.0035
    assert relerr < 0.015
    assert A2.dtype == dtype
    #print('nested=', nested, 'rand', blocksize, sum(diffs)/len(diffs))
    #print('nested=', nested, 'rand', blocksize, sum(reldiffs)/len(reldiffs))



@pytest.mark.parametrize(
    "gtype", [torch.float32, torch.float16], ids=["float", "half"]
)
def test_percentile_clipping(gtype):
    gnorm_vec1 = torch.zeros(100, device="cuda")
    gnorm_vec2 = torch.zeros(100, device="cuda")
    n = 4
    step = 0
    percentile = 5
    for i in range(k):
        step += 1
        g = torch.randn(n, n, dtype=gtype, device="cuda")
        gnorm1, clip2, gnorm_scale = F.percentile_clipping(
            g, gnorm_vec2, step, percentile=percentile
        )
        assert gnorm_scale == 1.0 if gnorm1 < clip2 else clip2 / gnorm1

        gnorm2 = torch.norm(g.float())
        if step == 1:
            gnorm_vec1[:] = gnorm2
        else:
            gnorm_vec1[step % 100] = gnorm2

        vals, idx = torch.sort(gnorm_vec1)
        clip1 = vals[percentile]

        torch.testing.assert_close(gnorm_vec1, torch.sqrt(gnorm_vec2))
        torch.testing.assert_close(clip1, clip2)
        torch.testing.assert_close(gnorm1, gnorm2)


def quant(x):
    max1 = torch.abs(x).max()
    x = torch.round(x / max1 * 127)
    return max1, x.to(torch.int8)


def dequant(c, maxC):
    return c.float() * (maxC / 127)


def mm_dequant(maxA, maxB, C):
    return C.float() * (maxA / 127) * (maxB / 127)


def quant_multi(x, dim):
    max1 = torch.amax(torch.abs(x), dim=dim, keepdim=True)
    max1[max1 == 0] = 1.0
    x = torch.round(x / max1 * 127)
    return max1, x.to(torch.int8)


def quant_multi_chunk(x, dim, chunk_size=32):
    if dim == 1:
        x_chunked = einops.rearrange(x, "(c a) b -> c a b", c=chunk_size)
        max1 = torch.amax(torch.abs(x_chunked), dim=dim + 1, keepdim=True)
        max1 = torch.tile(max1, (1, 1, x.shape[1]))
        max1 = max1.view(x.shape)
    elif dim == 0:
        x_chunked = einops.rearrange(x, "a (b c) -> a b c", c=chunk_size)
        max1 = torch.amax(torch.abs(x_chunked), dim=dim, keepdim=True)
        max1 = torch.tile(max1, (x.shape[0], 1, 1))
        max1 = max1.view(x.shape)
    max1[max1 == 0] = 1.0
    x = torch.round(x / max1 * 127)
    return max1, x.to(torch.int8)


def quant_minmax(A):
    minA = A.min()
    maxA = A.max()


def mean(xx):
    return sum(xx) / float(len(xx))


# dim1 = torch.randint(1,1024*4, size=(4,)).tolist()
# dim2 = torch.randint(1,1024*4, size=(4,)).tolist()
dim1 = [1024 * 2]
dim2 = [1024 * 16]
methods = [
    (
        lambda x, dim: quant(x),
        lambda x, dim: quant(x),
        dequant,
        dequant,
        mm_dequant,
    )
]
methods.append((quant_multi, quant_multi, dequant, dequant, mm_dequant))
# methods.append((lambda x: quant_multi_chunk(x, dim=-1), lambda x: quant_multi_chunk(x, dim=0), dequant, dequant, mm_dequant))
method_names = ["linear", "vectorwise"]
batched = [False, True]
values = list(product(dim1, dim2, methods, batched))
values_names = list(product(dim1, dim2, method_names, batched))
names = [
    "dim1_{}_dim2_{}_quant_{}_batched_{}".format(*vals)
    for vals in values_names
]


@pytest.mark.parametrize(
    "dim1, dim2, quant_methods, batched", values, ids=names
)
def test_approx_igemm(dim1, dim2, quant_methods, batched):
    dim1 = dim1 - (dim1 % 32)
    dim2 = dim2 - (dim2 % 32)
    errors = []
    relerrors = []
    #print("")
    for i in range(5):
        if batched:
            A = torch.normal(0, 0.5, size=(32, dim1, dim2 // 32), device="cuda")
            B = torch.normal(0, 0.5, size=(32, dim2 // 32, dim1), device="cuda")
            maxA, Ac = quant_methods[0](A, 2)
            maxB, Bc = quant_methods[1](B, 1)
        else:
            A = torch.normal(0, 0.5, size=(dim1, dim2), device="cuda")
            B = torch.normal(0, 0.5, size=(dim2, dim1), device="cuda")
            maxA, Ac = quant_methods[0](A, 1)
            maxB, Bc = quant_methods[1](B, 0)
        torch.testing.assert_close(
            quant_methods[2](maxA, Ac), A, atol=0.025, rtol=0.05
        )
        if batched:
            out2 = torch.bmm(A, B)
            C = torch.bmm(Ac.float(), Bc.float())
        else:
            out2 = torch.mm(A, B)
            C = F.igemm(Ac, Bc)
        out = quant_methods[4](maxA, maxB, C)
        std = out2.std()
        out /= std
        out2 /= std
        err = torch.abs(out - out2)
        relerr = err / torch.abs(out2)
        errors.append(err.mean().item())
        relerrors.append(relerr.mean().item())
    #print(mean(errors))
    #print(mean(relerrors))


def test_stable_embedding():
    layer = bnb.nn.StableEmbedding(1024, 1024)
    layer.reset_parameters()


n = 2
hidden_dim = torch.randint(32, 256, size=(n,)).tolist()
batch_dim = torch.randint(16, 256, size=(n,)).tolist()
seq_dim = torch.randint(16, 256, size=(n,)).tolist()
transpose = [(False, False), (False, True), (True, False), (True, True)]
values = list(product(hidden_dim, batch_dim, transpose, seq_dim))
names = [
    "hidden_dim_{}_batch_dim_{},transpose_{}_seq_dim_{}".format(*vals)
    for vals in values
]


@pytest.mark.parametrize(
    "hidden_dim, batch_dim, transpose, seq_dim", values, ids=names
)
def test_igemm(hidden_dim, batch_dim, transpose, seq_dim):
    hidden_dim = hidden_dim - (hidden_dim % 32)
    batch_dim = batch_dim - (batch_dim % 16)
    seq_dim = seq_dim - (seq_dim % 16)
    for i in range(k):
        shapeA = (
            (batch_dim, hidden_dim)
            if not transpose[0]
            else (hidden_dim, batch_dim)
        )
        shapeB = (
            (32 * random.randint(1, 4), hidden_dim)
            if transpose[1]
            else (hidden_dim, 32 * random.randint(1, 4))
        )
        A = torch.randint(-128, 127, size=shapeA, device="cuda").to(torch.int8)
        B = torch.randint(-128, 127, size=shapeB, device="cuda").to(torch.int8)
        if not transpose[0] and not transpose[1]:
            out2 = torch.matmul(A.float(), B.float())
            out = F.igemm(A, B)
        elif not transpose[0] and transpose[1]:
            out2 = torch.matmul(A.float(), B.t().float())
            out = F.igemm(A, B.t())
        elif transpose[0] and not transpose[1]:
            out2 = torch.matmul(A.t().float(), B.float())
            out = F.igemm(A.t(), B)
        elif transpose[0] and transpose[1]:
            out2 = torch.matmul(A.t().float(), B.t().float())
            out = F.igemm(A.t(), B.t())

        torch.testing.assert_close(out.float(), out2)

    for i in range(k):
        shapeA = (batch_dim, seq_dim, hidden_dim)
        shapeB = (
            (32 * random.randint(1, 4), hidden_dim)
            if transpose[1]
            else (hidden_dim, 32 * random.randint(1, 4))
        )
        A = torch.randint(-128, 127, size=shapeA, device="cuda").to(torch.int8)
        B = torch.randint(-128, 127, size=shapeB, device="cuda").to(torch.int8)
        if not transpose[0] and not transpose[1]:
            out2 = torch.matmul(A.float(), B.float())
            out = F.igemm(A, B)
        elif not transpose[0] and transpose[1]:
            out2 = torch.matmul(A.float(), B.t().float())
            out = F.igemm(A, B.t())

        torch.testing.assert_close(out.float(), out2)


n = 3
seq_dim = torch.randint(32, 512, size=(n,)).tolist()
hidden_dim = torch.randint(32, 1024 * 4, size=(n,)).tolist()
batch_dim = torch.randint(2, 16, size=(n,)).tolist()
values = list(product(seq_dim, hidden_dim, batch_dim))
names = [
    "seq_dim{}_hidden_dim{}_batch_dim{}".format(*vals) for vals in values
]


@pytest.mark.parametrize("seq_dim, hidden_dim, batch_dim", values, ids=names)
def test_dim3_igemm(seq_dim, hidden_dim, batch_dim):
    seq_dim = seq_dim - (seq_dim % 32)
    hidden_dim = hidden_dim - (hidden_dim % 32)
    batch_dim = batch_dim - (batch_dim % 2)
    for i in range(25):
        A = torch.randint(
            -128, 127, size=(batch_dim, seq_dim, hidden_dim), device="cuda"
        ).to(torch.int8)
        B = torch.randint(
            -128, 127, size=(batch_dim, seq_dim, 1024), device="cuda"
        ).to(torch.int8)
        out2 = torch.einsum("bsi, bso->io", A.float(), B.float())
        iout = torch.empty(
            A.shape[2], B.shape[2], dtype=torch.int32, device=A.device
        )
        out = F.igemm(A, B, out=iout)

        torch.testing.assert_close(out.float(), out2)


n = 2
seq_dim = torch.randint(32, 512, size=(n,)).tolist()
hidden_dim = torch.randint(32, 1024 * 4, size=(n,)).tolist()
batch_dim = torch.randint(2, 16, size=(n,)).tolist()
transpose = [False, True]
values = list(product(seq_dim, hidden_dim, batch_dim, transpose))
names = [
    "seq_dim={}_hidden_dim={}_batch_dim={}_transpose{}".format(*vals)
    for vals in values
]


@pytest.mark.parametrize(
    "seq_dim, hidden_dim, batch_dim, transpose", values, ids=names
)
def test_minmax_igemm(seq_dim, hidden_dim, batch_dim, transpose):
    def min_max(x):
        maxA = torch.amax(x, dim=2, keepdim=True)
        minA = torch.amin(x, dim=2, keepdim=True)
        scale = (maxA - minA) / 2.0
        return (127 * (x - minA - scale) / scale).to(torch.int8), minA, scale

    seq_dim = seq_dim - (seq_dim % 16)
    hidden_dim = hidden_dim - (hidden_dim % 16)
    batch_dim = batch_dim - (batch_dim % 2)
    errs = []
    relerrs = []
    errs2 = []
    relerrs2 = []
    for i in range(k):
        A = torch.normal(
            0.0, 0.5, size=(batch_dim, seq_dim, hidden_dim), device="cuda"
        )
        if transpose:
            B = torch.normal(0, 0.5, size=(256, hidden_dim), device="cuda")
        else:
            B = torch.normal(0, 0.5, size=(hidden_dim, 256), device="cuda")
        Ac, minA, scale = min_max(A)
        if transpose:
            maxB, Bc = quant_multi(B, dim=(1 if transpose else 0))
            out = F.igemm(Ac, Bc.t())
            out2 = torch.matmul(A, B.t())
            offset = B.t().sum(0) * (minA + scale)
            out = out.float()
            out = (out * maxB.t() * scale / (127 * 127)) + offset

            maxA, Ac = quant_multi(A, dim=2)
            out3 = F.igemm(Ac, Bc.t())
            out3 = mm_dequant(maxA, maxB.t(), out3)
        else:
            maxB, Bc = quant_multi(B, dim=0)
            offset = B.sum(0) * (minA + scale)
            out = F.igemm(Ac, Bc)
            out2 = torch.matmul(A, B)
            out = out.float()
            out = (out * maxB * scale / (127 * 127)) + offset

            maxA, Ac = quant_multi(A, dim=2)
            out3 = F.igemm(Ac, Bc)
            out3 = mm_dequant(maxA, maxB, out3)

        std = out2.std()
        out2 /= std
        out /= std
        out3 /= std

        err = torch.abs(out - out2)
        relerr = err / (torch.abs(out2) + 1e-7)

        err2 = torch.abs(out3 - out2)
        relerr2 = err2 / (torch.abs(out2) + 1e-7)

        errs.append(err.mean().item())
        relerrs.append(relerr.mean().item())
        errs2.append(err2.mean().item())
        relerrs2.append(relerr2.mean().item())
    # print(mean(errs))
    # print(mean(relerrs))
    # print(mean(errs2))
    # print(mean(relerrs2))
    assert mean(errs) < 0.015
    assert mean(relerrs) < 0.3


n = 2
dim1 = torch.randint(1, 64, size=(n,)).tolist()
dim2 = torch.randint(32, 128, size=(n,)).tolist()
dim3 = torch.randint(32, 256, size=(n,)).tolist()
dim4 = torch.randint(32, 256, size=(n,)).tolist()
transpose = [(False, False), (True, False), (False, True), (True, True)]
values = list(product(dim1, dim2, dim3, dim4, transpose))
names = [
    "dim1_{}_dim2_{}_dim3_{}_dim4_{}_transpose_{}".format(*vals)
    for vals in values
]


@pytest.mark.parametrize("dim1, dim2, dim3, dim4, transpose", values, ids=names)
def test_ibmm(dim1, dim2, dim3, dim4, transpose):
    dim2 = dim2 - (dim2 % 16)
    dim3 = dim3 - (dim3 % 16)
    dim4 = dim4 - (dim4 % 16)
    for i in range(k):
        shapeA = (dim1, dim3, dim2) if transpose[0] else (dim1, dim2, dim3)
        shapeB = (dim1, dim4, dim3) if transpose[1] else (dim1, dim3, dim4)
        A = torch.randint(-128, 127, size=shapeA, device="cuda").to(torch.int8)
        B = torch.randint(-128, 127, size=shapeB, device="cuda").to(torch.int8)

        if not transpose[0] and not transpose[1]:
            out2 = torch.bmm(A.float(), B.float())
            out = F.igemm(A, B)
        elif not transpose[0] and transpose[1]:
            out2 = torch.bmm(A.float(), B.permute([0, 2, 1]).float())
            out = F.igemm(A, B.permute([0, 2, 1]))
        elif transpose[0] and not transpose[1]:
            out2 = torch.bmm(A.permute([0, 2, 1]).float(), B.float())
            out = F.igemm(A.permute([0, 2, 1]), B)
        elif transpose[0] and transpose[1]:
            out2 = torch.bmm(
                A.permute([0, 2, 1]).float(), B.permute([0, 2, 1]).float()
            )
            out = F.igemm(A.permute([0, 2, 1]), B.permute([0, 2, 1]))
        torch.testing.assert_close(out.float(), out2.float())


n = 1
dim1 = torch.randint(1, 64, size=(n,)).tolist()
dim2 = torch.randint(32, 128, size=(n,)).tolist()
dim3 = torch.randint(32, 256, size=(n,)).tolist()
values = list(product(dim1, dim2, dim3))
names = ["dim1_{}_dim2_{}_dim3_{}".format(*vals) for vals in values]


@pytest.mark.parametrize("dim1, dim2, dim3", values, ids=names)
def test_vector_quant(dim1, dim2, dim3):
    dim2 = dim2 - (dim2 % 16)
    dim3 = dim3 - (dim3 % 16)
    for i in range(k):
        A = torch.randn(size=(dim2, dim3), device="cuda")
        qA, SA = F.vectorwise_quant(A, dim=0)
        A1 = F.vectorwise_dequant(qA, SA)
        n = A1.numel()
        assert_all_approx_close(A1, A, atol=0.01, rtol=0.1, count=int(n*0.002))




n = 2
dim1 = torch.randint(2, 256, size=(n,)).tolist()
dim2 = torch.randint(2, 256, size=(n,)).tolist()
dim3 = torch.randint(2, 256, size=(n,)).tolist()
# dim1, dim2 = (256,), (256,)
dtype = [torch.int8, torch.int32]
a_order = ["row"]
out_order = ["col", "row", "col32"]
transpose = [False]
dims = [2, 3]
values = list(product(dim1, dim2, dim3, dims, dtype, a_order, out_order, transpose))

names = ["dim1_{}_dim2_{}_dim3_{}_dims_{}_dtype_{}_orderA_{}_orderOut_{}_transpose_{}".format(*vals)for vals in values]


@pytest.mark.parametrize("dim1, dim2, dim3, dims, dtype, orderA, orderOut, transpose",values,ids=names)
def test_nvidia_transform(dim1, dim2, dim3, dims, dtype, orderA, orderOut, transpose):
    if dims == 3 and out_order != "col32":
        return
    if dtype == torch.int32 and out_order != "col32":
        return
    func = F.get_transform_func(dtype, orderA, orderOut, transpose)

    if dims == 2:
        A = torch.randint(-128, 127, size=(dim1, dim2), device="cuda").to(dtype)
    elif dims == 3:
        A = torch.randint(-128, 127, size=(dim1, dim2, dim3), device="cuda").to(
            dtype
        )

    out, S = F.nvidia_transform(A, to_order=orderOut)

    if orderOut == "row":
        torch.testing.assert_close(A.flatten(), out.flatten())
    elif orderOut == "col":
        torch.testing.assert_close(A.t().flatten(), out.flatten())
    elif orderOut == "col32":
        if dims == 2:
            n = A.shape[0] * (A.shape[1] + (32 - (A.shape[1] % 32)))
        elif dims == 3:
            n = (
                A.shape[0]
                * A.shape[1]
                * (A.shape[2] + (32 - (A.shape[2] % 32)))
            )
        assert out.numel() == n
    elif orderOut == "col_turing":
        # 32 col 8 row tiles
        n = (A.shape[0] + (8 - A.shape[0] % 8)) * (
            A.shape[1] + (32 - (A.shape[1] % 32))
        )
        assert out.numel() == n
        total_coltile = (A.shape[1] // 32) + (1 if A.shape[1] % 32 != 0 else 0)
        for row in range(A.shape[0]):
            for col in range(A.shape[1]):
                i = row * A.shape[1]
                j = col

                coltile = (col // 32) + (1 if col % 32 != 0 else 0)
                rowtile = (
                    (row // 8) + (1 if row % 8 != 0 else 0)
                ) * total_coltile
                offset = 32 * 8 * (rowtile + coltile)
                col2 = col % 32
                row2 = (row % 8) * 32

                assert A.flatten()[i + j] == A[row, col]
                # assert A.flatten()[i+j] == out.flatten()[row2+col2]
                # torch.testing.assert_close(A.flatten()[i+j], A[row, col])
                # torch.testing.assert_close(A.flatten()[i+j], out.flatten()[row2+ col2+block_offset])

    if orderOut == "col32":
        out2, S = F.nvidia_transform(
            out, from_order=orderOut, to_order="row", state=S
        )
        torch.testing.assert_close(A, out2)


n = 1
dim1 = torch.randint(1, 256, size=(n,)).tolist()
dim2 = torch.randint(32, 512, size=(n,)).tolist()
dim3 = torch.randint(32, 1024, size=(n,)).tolist()
dim4 = torch.randint(32, 1024, size=(n,)).tolist()

# dim1 = [2]
# dim2 = [2]
# dim3 = [2]
# dim4 = [2]

dims = (2, 3)
ldb = [0]
# ldb = list(range(256, 1*1024, 256))
values = list(product(dim1, dim2, dim3, dim4, dims, ldb))
names = [
    "dim1_{}_dim2_{}_dim3_{}_dim4_{}_dims_{}_ldb_{}".format(*vals)
    for vals in values
]


@pytest.mark.parametrize("dim1, dim2, dim3, dim4, dims, ldb", values, ids=names)
def test_igemmlt_int(dim1, dim2, dim3, dim4, dims, ldb):
    for i in range(k):
        if dims == 2:
            A = torch.randint(-128, 127, size=(dim1, dim3), device="cuda").to(
                torch.int8
            )
        elif dims == 3:
            A = torch.randint(
                -128, 127, size=(dim1, dim2, dim3), device="cuda"
            ).to(torch.int8)
        B = torch.randint(-128, 127, size=(dim4, dim3), device="cuda").to(
            torch.int8
        )
        C1 = torch.matmul(A.float(), B.t().float())

        A2, SA = F.transform(A, "col32")
        B2, SB = F.transform(B, "col_turing")
        C2, SC = F.igemmlt(A2, B2, SA, SB)
        C3, S = F.nvidia_transform(C2, "row", state=SC)
        torch.testing.assert_close(C1, C3.float())

        # transpose
        B = torch.randint(-128, 127, size=(dim3, dim4), device="cuda").to(
            torch.int8
        )
        C1 = torch.matmul(A.float(), B.float())

        B2t, SBt = F.transform(B, "col_turing", transpose=True)
        C2, SC = F.igemmlt(A2, B2t, SA, SBt)
        C3, S = F.nvidia_transform(C2, "row", state=SC)
        torch.testing.assert_close(C1, C3.float())


dim1 = [32]
dim2 = [32]
dim3 = [32]
dim4 = [32]

dims = (2,)
# ldb = list(range(256, 1*1024, 256))
values = list(product(dim1, dim2, dim3, dim4, dims))
names = [
    "dim1_{}_dim2_{}_dim3_{}_dim4_{}_dims_{}".format(*vals)
    for vals in values
]


@pytest.mark.parametrize("dim1, dim2, dim3, dim4, dims", values, ids=names)
def test_igemmlt_half(dim1, dim2, dim3, dim4, dims):
    formatB = F.get_special_format_str()
    for i in range(k):
        if dims == 2:
            A = torch.normal(0, 0.5, size=(dim1, dim3), device="cuda").half()
        elif dims == 3:
            A = torch.normal(
                0, 0.5, size=(dim1, dim2, dim3), device="cuda"
            ).half()
        B = torch.randn((dim4, dim3), device="cuda").half()
        torch.nn.init.xavier_uniform_(B)
        C1 = torch.matmul(A, B.t())
        C2 = bnb.matmul(A, B.t())

        A = A.view(-1, A.shape[-1])

        CA, CAt, statsA, statsAt, coo_tensor = F.double_quant(A)
        CB, CBt, statsB, statsBt, coo_tensor = F.double_quant(B)
        C32A, SA = F.transform(CA, "col32")
        CxB, SB = F.transform(CB, to_order=formatB)
        out1_32, Sout1_32 = F.igemmlt(C32A, CxB, SA, SB)
        output = F.mm_dequant(out1_32, Sout1_32, statsAt, statsBt)

        # print('')
        # print(output.flatten()[:10])
        # print(C1.flatten()[:10])
        # print(C2.flatten()[:10])

        # torch.testing.assert_close(C1.view(-1, C1.shape[-1]), output, atol=0.025, rtol=0.05)

        # transpose
        # B = torch.randint(-128, 127, size=(dim3, dim4), device='cuda').to(torch.int8)
        # C1 = torch.matmul(A.float(), B.float())

        # B2t, SBt = F.transform2(B, 'col_turing', transpose=True)
        # C2, SC = F.igemmlt(A2, B2t, SA, SBt)
        # C3, S = F.transform(C2, 'row', state=SC)
        # torch.testing.assert_close(C1, C3.float())


batch_size = 2
seqdim = 512
# values = [(batch_size, seqdim, 4*1024, 16*1024),(batch_size, seqdim, 5120, 4*5120),(batch_size, seqdim, 12*1024, 4*12*1024)]
values = [
    (batch_size, seqdim, 4 * 1024, 3 * 4 * 1024),
    (batch_size, seqdim, 5120, 3 * 5120),
    (batch_size, seqdim, 12 * 1024, 4 * 12 * 1024),
]


# values = list(product(batch, seq, model, hidden))
names = [
    "batch_{}_seq_{}_model_{}_hidden_{}".format(*vals) for vals in values
]


@pytest.mark.parametrize("batch, seq, model, hidden", values, ids=names)
def test_bench_8bit_training(batch, seq, model, hidden):
    formatB = F.get_special_format_str()
    A = torch.randn(batch, seq, model, device="cuda").half()
    grad = torch.randn(batch, seq, model, device="cuda").half()
    w1 = torch.randint(-128, 127, size=(hidden, model), device="cuda").half()
    w2 = torch.randint(-128, 127, size=(model, hidden), device="cuda").half()
    print("")

    # torch.cuda.synchronize()
    ## warmup
    # for i in range(100):
    #    torch.matmul(A, w1.t())
    # torch.cuda.synchronize()

    dtype = torch.int8
    A = A.view(-1, A.shape[-1]).contiguous()
    grad = grad.view(-1, grad.shape[-1]).contiguous()
    torch.cuda.synchronize()
    t0 = time.time()
    for i in range(k):

        out1 = torch.matmul(A, w1.t())  # fc1
        # out2 = torch.matmul(out1, w2.t())# fc2

        # d1 = torch.matmul(grad, w2) # delta1
        # d2 = torch.matmul(d1, w1) # delta2

        # grad1 = torch.einsum('bo,bh->oh', out1, grad) # grad w2
        # grad2 = torch.einsum('bh,bo->ho', A, d2) # grad w1

    torch.cuda.synchronize()
    t16 = time.time() - t0
    print(t16)

    # torch.cuda.empty_cache()

    # Cw1, Cw1t, statsw1, statsw1t, coo_tensor = F.double_quant(w1)
    # Cw2, Cw2t, statsw2, statsw2t, coo_tensor = F.double_quant(w2)

    # CTw1, Sw1 = F.transform2(Cw1, formatB)
    # CTw2, Sw2 = F.transform2(Cw2, formatB)
    # CTw2t, Sw2t = F.transform2(Cw2t, formatB, transpose=True)
    # CTw1t, Sw1t = F.transform2(Cw1t, formatB, transpose=True)

    # CA, CAt, statsA, statsAt, coo_tensor = F.double_quant(A)
    # C32A, SA = F.transform2(CA, 'col32')
    ## fc1
    # out1_32, Sout1_32 = F.igemmlt(C32A, CTw1, SA, Sw1, dtype=dtype)
    ##out1 = F.mm_dequant(out1_32, Sout1_32, statsAt, statsw1t)

    ## fc2
    # Cout1, Cout1t, statsout1, statsout1t, coo_tensor = F.double_quant(out1)
    # C32out1, Sout1 = F.transform2(Cout1, 'col32')
    # out2_32, Sout2_32 = F.igemmlt(C32out1, CTw2, Sout1, Sw2, dtype=dtype)
    ##out2 = F.mm_dequant(out2_32, Sout2_32, statsout1t, statsw2t)

    ## delta1
    # Cgrad, Cgradt, statsgrad, statsgradt, coo_tensor = F.double_quant(grad)
    # C32grad, Sgrad = F.transform2(Cgrad, 'col32')
    ##d1_32, Sd1_32 = F.igemmlt(C32grad, CTw2t, Sgrad, Sw2t, dtype=dtype)
    ##d1 = F.mm_dequant(d1_32, Sd1_32, statsgradt, statsw2)

    ## delta2
    # Cd1, Cd1t, statsd1, statsd1t, coo_tensor = F.double_quant(d1)
    # C32d1, Sd1 = F.transform2(Cd1, 'col32')
    ##d2_32, Sd2_32 = F.igemmlt(C32d1, CTw1t, Sd1, Sw1t, dtype=dtype)
    ##d2 = F.mm_dequant(d2_32, Sd2_32, statsd1t, statsw1)

    ## grad1
    # C32out1t, Sout1t = F.transform2(Cout1t, 'col32', transpose=True)
    # CTgradt, Sgradt = F.transform2(Cgradt, formatB, transpose=True)
    ##grad1_32, Sgrad1_32 = F.igemmlt(C32out1t, CTgradt, Sout1t, Sgradt, dtype=dtype)
    ##grad1 = F.mm_dequant(grad1_32, Sgrad1_32, statsout1, statsgrad)

    ## grad2
    # C32At, SAt = F.transform2(CAt, 'col32', transpose=True)
    # CTd1t, Sd1t = F.transform2(Cd1t, formatB, transpose=True)
    ##grad2_32, Sgrad2_32 = F.igemmlt(C32At, CTd1t, SAt, Sd1t, dtype=dtype)
    ##grad2 = F.mm_dequant(grad2_32, Sgrad2_32, statsA, statsd1)

    # Cw2, Cw2t, statsw2, statsw2t, coo_tensor = F.double_quant(w2)

    # Cw1, Cw1t, statsw1, statsw1t, coo_tensor = F.double_quant(w1)
    # Cw2, Cw2t, statsw2, statsw2t, coo_tensor = F.double_quant(w2)

    # CTw1, Sw1 = F.transform2(Cw1, formatB)
    # CTw1t, Sw1t = F.transform2(Cw1t, formatB, transpose=True)
    # CTw2, Sw2 = F.transform2(Cw2, formatB)
    # CTw2t, Sw2t = F.transform2(Cw2t, formatB, transpose=True)
    # torch.cuda.synchronize()
    # t0 = time.time()
    # for i in range(k):
    #    #Cw1, Cw1t, statsw1, statsw1t, coo_tensor = F.double_quant(w1)
    #    #CTw1, Sw1 = F.transform2(Cw1, formatB)
    #    #Cw1, Cw1t, statsw1, statsw1t, coo_tensor = F.double_quant(w1)
    #    #CTw1, Sw1 = F.transform2(Cw1, formatB)

    #    #CA, CAt, statsA, statsAt, coo_tensor = F.double_quant(A, threshold=3.5)
    #    CA, CAt, statsA, statsAt, coo_tensor = F.double_quant(A)
    #    #CTw1t, Sw1t = F.transform2(Cw1t, formatB, transpose=True)
    #    #CTw2, Sw2 = F.transform2(Cw2, formatB)
    #    #CTw2t, Sw2t = F.transform2(Cw2t, formatB, transpose=True)

    #    C32A, SA = F.transform2(CA, 'col32')

    #    # fc1
    #    out1_32, Sout1_32 = F.igemmlt(C32A, CTw1, SA, Sw1, dtype=dtype)
    #    #out1dn = F.mm_dequant(out1_32, Sout1_32, statsA, statsw1)

    #    #print(coo_tensor.nnz)
    #    #out1sp = F.spmm_coo(coo_tensor, w1.t())
    #    #print(w1.t().shape)
    #    #out1 = out1dn + out1sp

    #    # fc2
    #    Cout1, Cout1t, statsout1, statsout1t, coo_tensor = F.double_quant(out1)
    #    C32out1, Sout1 = F.transform2(Cout1, 'col32')
    #    out2_32, Sout2_32 = F.igemmlt(C32out1, CTw2, Sout1, Sw2, dtype=dtype)
    #    #out2 = F.mm_dequant(out2_32, Sout2_32, statsout1, statsw2)

    #    # delta1
    #    Cgrad, Cgradt, statsgrad, statsgradt, coo_tensor = F.double_quant(grad)
    #    C32grad, Sgrad = F.transform2(Cgrad, 'col32')
    #    d1_32, Sd1_32 = F.igemmlt(C32grad, CTw2t, Sgrad, Sw2t, dtype=dtype)
    #    #d1 = F.mm_dequant(d1_32, Sd1_32, statsgrad, statsw2t)

    #    # delta2
    #    Cd1, Cd1t, statsd1, statsd1t, coo_tensor = F.double_quant(d1)
    #    C32d1, Sd1 = F.transform2(Cd1, 'col32')
    #    d2_32, Sd2_32 = F.igemmlt(C32d1, CTw1t, Sd1, Sw1t, dtype=dtype)
    #    #d2 = F.mm_dequant(d2_32, Sd2_32, statsd1, statsw1t)

    #    # grad1
    #    #C32out1t, Sout1t = F.transform2(Cout1t, 'col32', transpose=True)
    #    #CTgradt, Sgradt = F.transform2(Cgradt, formatB, transpose=True)
    #    #grad1_32, Sgrad1_32 = F.igemmlt(C32out1t, CTgradt, Sout1t, Sgradt, dtype=dtype)
    #    #grad1 = F.mm_dequant(grad1_32, Sgrad1_32, statsout1t, statsgradt)

    #    ## grad2
    #    #C32At, SAt = F.transform2(CAt, 'col32', transpose=True)
    #    #CTd1t, Sd1t = F.transform2(Cd1t, formatB, transpose=True)
    #    #grad2_32, Sgrad2_32 = F.igemmlt(C32At, CTd1t, SAt, Sd1t, dtype=dtype)
    #    #grad2 = F.mm_dequant(grad2_32, Sgrad2_32, statsAt, statsd1t)

    # torch.cuda.synchronize()
    # t8 = time.time() - t0
    # print(t8)


n = 2
dim1 = torch.randint(64, 256, size=(n,)).tolist()
dim4 = torch.randint(64, 1024, size=(n,)).tolist()

#dim1 = [2*1024]
#dim4 = [2*1024]

#dim1 = [4]
#dim4 = [4]

dims = (2,)
formatB = ["col_turing", "col_ampere"]
has_bias = [True, False]
values = list(product(dim1, dim4, dims, formatB, has_bias))
names = ["dim1_{}_dim4_{}_dims_{}_formatB_{}_has_bias_{}".format(*vals) for vals in values]


@pytest.mark.parametrize("dim1, dim4, dims, formatB, has_bias", values, ids=names)
def test_dequant_mm(dim1, dim4, dims, formatB, has_bias):
    inner = torch.randint(1, 128, size=(1,)).item()
    bias = None
    if has_bias: bias = torch.randn(dim4, device='cuda', dtype=torch.float16)
    formatB = F.get_special_format_str()
    for i in range(1):
        A = torch.randn(dim1, inner, device="cuda")
        B = torch.randn(dim4, inner, device="cuda")
        C1 = torch.matmul(A.half(), B.t().half())
        if has_bias: C1 += bias

        A1, maxA = F.vectorwise_quant(A, dim=1)
        B1, maxB = F.vectorwise_quant(B, dim=1)

        A2, SA = F.nvidia_transform(A1, "col32")
        B2, SB = F.nvidia_transform(B1, formatB)
        C2, SC = F.igemmlt(A2, B2, SA, SB)

        C3, S = F.nvidia_transform(C2, "row", state=SC)
        C4 = F.vectorwise_mm_dequant(C3.float(), maxA, maxB.t())
        if has_bias: C4 += bias

        # TODO: is something wrong here? If so, the problem goes deeper
        #n = C1.numel()
        #p = 0.06
        std = C1.std(0).view(1, -1)
        C1 /= std
        C4 /= std
        #assert_all_approx_close(C1, C4, atol=0.02, rtol=0.1, count=int(n*0.06))
        #assert (count / n < p), f"error in more than {p} of elements: {count}/{n}={count/n}"

        C5 = F.mm_dequant(C2, SC, maxA.flatten(), maxB.flatten(), bias=bias)
        #torch.testing.assert_close(C5, C4, atol=0.015, rtol=0.1)
        n = C5.numel()
        assert_all_approx_close(C1, C4, atol=0.015, rtol=0.1, count=int(0.01*n))


n = 2
dim1 = [1 * 1024]
dim2 = [1 * 1024]
# dim1 = torch.randint(1,4*1024, size=(n,)).tolist()
# dim2 = torch.randint(1,4*1024, size=(n,)).tolist()

dims = (2,)
# ldb = list(range(256, 1*1024, 256))
values = list(product(dim1, dim2, dims))
names = ["dim1_{}_dim2_{}_dims_{}".format(*vals) for vals in values]


@pytest.mark.parametrize("dim1, dim2, dims", values, ids=names)
def test_colrow_absmax(dim1, dim2, dims):
    for i in range(k):
        threshold = 3.0
        A = torch.randn(dim1, dim2, device="cuda").half()
        A_truncated = A.clone()
        A_truncated[torch.abs(A_truncated) >= 3.0] = 0.0
        if dims == 2:
            row_stats1, _ = torch.abs(A.float()).max(1)
            col_stats1, _ = torch.abs(A.float()).max(0)
            row_stats1_trunc, _ = torch.abs(A_truncated.float()).max(1)
            col_stats1_trunc, _ = torch.abs(A_truncated.float()).max(0)
        else:
            assert False

        row_stats2, col_stats2, nnz_block_ptr2 = F.get_colrow_absmax(
            A, threshold=threshold
        )

        A_blocked = einops.rearrange(
            torch.abs(A),
            "(rows row_tiles) (cols block_size)-> rows cols row_tiles block_size",
            row_tiles=16,
            block_size=64 * 4,
        )
        nnz_rows1_counts = (torch.abs(A_blocked) >= threshold).sum(3).flatten()
        nnz_block_ptr1 = torch.zeros(
            nnz_rows1_counts.shape[0] + 1,
            dtype=nnz_rows1_counts.dtype,
            device=nnz_rows1_counts.device,
        )
        nnz_block_ptr1[1:] = nnz_rows1_counts.cumsum(0)

        torch.testing.assert_close(col_stats1_trunc, col_stats2)
        torch.testing.assert_close(row_stats1_trunc, row_stats2)
        torch.testing.assert_close(nnz_block_ptr1.int(), nnz_block_ptr2)

        row_stats2, col_stats2, nnz_block_ptr2 = F.get_colrow_absmax(
            A, threshold=0.0
        )

        torch.testing.assert_close(col_stats1, col_stats2)
        torch.testing.assert_close(row_stats1, row_stats2)
        assert nnz_block_ptr2 is None


n = 2
# dim1 = [8*1024]
# dim2 = [4*1024]
dim1 = torch.randint(1, 4 * 1024, size=(n,)).tolist()
dim2 = torch.randint(1, 4 * 1024, size=(n,)).tolist()

values = list(product(dim1, dim2))
names = ["dim1_{}_dim2_{}".format(*vals) for vals in values]


@pytest.mark.parametrize("dim1, dim2", values, ids=names)
def test_double_quant(dim1, dim2):
    for i in range(k):
        A = torch.randn(dim1, dim2, device="cuda").half()
        out_col1, Scol = F.vectorwise_quant(A, dim=0)
        out_row1, Srow = F.vectorwise_quant(A, dim=1)

        CA, CAt, statsA, statsAt, coo_tensor = F.double_quant(A)

        # max difference is 1 due to rounding differences
        torch.testing.assert_close(CA, out_row1, atol=1, rtol=0)
        torch.testing.assert_close(CAt, out_col1, atol=1, rtol=0)

        n = CAt.numel()
        num_not_close_rows = (
            (torch.isclose(CA, out_row1, atol=1) == 0).sum().item()
        )
        num_not_close_cols = (
            (torch.isclose(CAt, out_col1, atol=1) == 0).sum().item()
        )

        # allow for 1:500 error due to rounding differences
        min_error = 1 / 500
        if num_not_close_cols > (min_error * n):
            print(
                f"Min error exceeded {num_not_close_cols} elements are different. Error: {num_not_close_cols/n:.4f}"
            )
            assert False
        if num_not_close_rows > (min_error * n):
            print(
                f"Min error exceeded {num_not_close_rows} elements are different. Error: {num_not_close_rows/n:.4f}"
            )
            assert False

        torch.testing.assert_close(Srow.flatten().float(), statsA)
        torch.testing.assert_close(Scol.flatten().float(), statsAt)


n = 4
dim1 = torch.randint(1, 4 * 1024, size=(n,)).tolist()
dim4 = torch.randint(1, 4 * 1024, size=(n,)).tolist()
inner = torch.randint(1, 4 * 1024, size=(n,)).tolist()

values = list(zip(dim1, dim4, inner))
names = ["dim1_{}_dim4_{}_inner_{}".format(*vals) for vals in values]


@pytest.mark.parametrize("dim1, dim4, inner", values, ids=names)
def test_integrated_igemmlt(dim1, dim4, inner):
    for i in range(k):
        A = torch.randn(dim1, inner, device="cuda").half()
        B = torch.randn(dim4, inner, device="cuda").half()

        out1 = torch.matmul(A.half(), B.t().half())

        C1a, C1b, stats1a, stats1b, coo_tensor = F.double_quant(A)
        C2a, C2b, stats2a, stats2b, coo_tensor = F.double_quant(B)
        A1, maxA = F.vectorwise_quant(A, dim=1)
        B1, maxB = F.vectorwise_quant(B, dim=1)

        torch.testing.assert_close(maxA.flatten().float(), stats1a)
        torch.testing.assert_close(maxB.flatten().float(), stats2a)
        torch.testing.assert_close(C1a, A1, rtol=0, atol=1)
        torch.testing.assert_close(C2a, B1, rtol=0, atol=1)

        A2, SA = F.nvidia_transform(C1a, "col32")
        B2, SB = F.nvidia_transform(C2a, "col_turing")
        outC32, SC = F.igemmlt(A2, B2, SA, SB)
        out2 = F.mm_dequant(outC32, SC, stats1a, stats2a)

        A2, SA = F.nvidia_transform(A1, "col32")
        B2, SB = F.nvidia_transform(B1, "col_turing")
        C2, SC = F.igemmlt(A2, B2, SA, SB)

        C3, S = F.nvidia_transform(C2, "row", state=SC)
        out3 = F.vectorwise_mm_dequant(C3.float(), maxA, maxB.t())

        err1 = torch.abs(out1 - out2).mean().item()
        err2 = torch.abs(out1 - out3).mean().item()
        assert err2 <= err1 * 1.025


n = 6
dim1 = torch.randint(1, 4 * 1024, size=(n,)).tolist()
dim4 = torch.randint(1, 4 * 1024, size=(n,)).tolist()
inner = torch.randint(1, 4 * 1024, size=(n,)).tolist()

values = list(zip(dim1, dim4, inner))
names = ["dim1_{}_dim4_{}_inner_{}".format(*vals) for vals in values]


@pytest.mark.parametrize("dim1, dim4, inner", values, ids=names)
@pytest.mark.skip("Row scale has some bugs for ampere")
def test_igemmlt_row_scale(dim1, dim4, inner):
    formatB = F.get_special_format_str()
    err1, err2, err3 = [], [], []
    relerr1, relerr2 = [], []
    scale = 1
    for i in range(k):
        A = torch.randn(dim1, inner, device="cuda").half()
        B = torch.randn(dim4, inner, device="cuda").half()
        torch.nn.init.xavier_uniform_(B)
        C1 = torch.matmul(A, B.t())

        out1 = torch.matmul(A.half(), B.t().half())

        C1a, C1b, stats1a, stats1b, coo_tensor = F.double_quant(A)
        CB, absmaxB = F.vectorwise_quant(B, quant_type="linear")
        A2, SA = F.nvidia_transform(C1a, "col32")
        B2, SB = F.nvidia_transform(CB, formatB)
        A1, maxA = F.vectorwise_quant(A, dim=1)

        c = 10.0 * inner * scale
        row_scale = torch.ones_like(maxA) / c
        outC32, SC = F.igemmlt(
            A2, B2, SA, SB, dtype=torch.int8, row_scale=row_scale
        )
        C3, S = F.nvidia_transform(outC32, "row", state=SC)
        maxval = torch.abs(C3).max()
        if maxval == 127:
            scale = 1.5
        else:
            scale = maxval / 120
        out3 = C3 * maxA * absmaxB * c / (127 * 127)

        C4 = torch.matmul(C1a.float(), CB.float().t())

        C2a, C2b, stats2a, stats2b, coo_tensor = F.double_quant(B)
        B2, SB = F.nvidia_transform(C2a, formatB)
        outC32, SC = F.igemmlt(A2, B2, SA, SB)
        out2 = F.mm_dequant(outC32, SC, stats1a, stats2a)

        CA, SA = F.vectorwise_quant(A, dim=1, quant_type="vector")
        CB, SB = F.vectorwise_quant(B, dim=1, quant_type="linear")

        C = torch.matmul(CA.float(), CB.t().float())
        out4 = C * SA * SB / (127 * 127)
        # out4 = torch.clip(torch.round(C*SA/c), -127, 127)*c*SB/(127*127)

        # print('='*80)
        # print(out1)
        # print(out2)
        # print(out3)

        # print(out1)
        # print(out2)
        # print(out3)
        err1.append(torch.abs(out1 - out2).mean().item())
        err2.append(torch.abs(out1 - out3).mean().item())
        err3.append(torch.abs(out1 - out4).mean().item())

        # assert_all_approx_close(C3.float(), torch.round(C4*row_scale), rtol=0, atol=0, count=10)
    print("")
    print(sum(err1) / len(err1))
    print(sum(err2) / len(err2))
    print(sum(err3) / len(err3))


dim1 = [1024, 2048]
inner = [12288 * 4, 4096 * 4]
dim4 = [12288, 4096]

values = list(zip(dim1, dim4, inner))
names = ["dim1_{}_dim4_{}_inner_{}".format(*vals) for vals in values]


@pytest.mark.parametrize("dim1, dim4, inner", values, ids=names)
@pytest.mark.skip("Row scale has some bugs for ampere")
def test_row_scale_bench(dim1, dim4, inner):
    err1, err2, err3 = [], [], []
    relerr1, relerr2 = [], []
    scale = 1
    A = torch.randn(dim1, inner, device="cuda").half()
    B = torch.randn(dim4, inner, device="cuda").half()
    torch.nn.init.xavier_uniform_(B)
    # warmpup
    for i in range(k):
        C1 = torch.matmul(A, B.t())

    torch.cuda.synchronize()
    t0 = time.time()
    for i in range(k):
        C1 = torch.matmul(A, B.t())
    torch.cuda.synchronize()
    print("16", time.time() - t0)

    C1a, C1b, stats1a, stats1b, coo_tensor = F.double_quant(A)
    CB, absmaxB = F.vectorwise_quant(B, quant_type="linear")
    A2, SA = F.nvidia_transform(C1a, "col32")
    B2, SB = F.nvidia_transform(CB, formatB)
    A1, maxA = F.vectorwise_quant(A, dim=1)

    c = 10.0 * inner * scale
    row_scale = maxA / c
    torch.cuda.synchronize()
    t0 = time.time()
    for i in range(k):
        outC32, SC = F.igemmlt(
            A2, B2, SA, SB, dtype=torch.int8, row_scale=row_scale
        )
    torch.cuda.synchronize()
    print("row-wise", time.time() - t0)

    C2a, C2b, stats2a, stats2b, coo_tensor = F.double_quant(B)
    B2, SB = F.nvidia_transform(C2a, formatB)
    torch.cuda.synchronize()
    t0 = time.time()
    for i in range(k):
        outC32, SC = F.igemmlt(A2, B2, SA, SB)
    torch.cuda.synchronize()
    print("vector-wise", time.time() - t0)


n = 2
dim1 = torch.randint(2, 1024, size=(n,)).tolist()
dim2 = torch.randint(2, 1024, size=(n,)).tolist()
# dim1 = [8*1024]
# dim2 = [4*1024]

dim3 = [0]
dtype = [torch.int8]
a_order = ["row"]
out_order = ["col32", "col_turing", "col_ampere"]
transpose = [False, True]
dims = [2]
values = list(
    product(dim1, dim2, dim3, dims, dtype, a_order, out_order, transpose)
)
names = [
    "dim1_{}_dim2_{}_dim3_{}_dims_{}_dtype_{}_orderA_{}_orderOut_{}_{}".format(
        *vals
    )
    for vals in values
]


@pytest.mark.parametrize(
    "dim1, dim2, dim3, dims, dtype, orderA, orderOut, transpose",
    values,
    ids=names,
)
def test_transform(dim1, dim2, dim3, dims, dtype, orderA, orderOut, transpose):
    for i in range(k):
        if dims == 2:
            A = torch.randint(10, 99, size=(dim1, dim2), device="cuda").to(
                dtype
            )
        elif dims == 3:
            A = torch.randint(
                10, 99, size=(dim1, dim2, dim3), device="cuda"
            ).to(dtype)

        A.view(-1)[-1] = -1
        if transpose:
            At = A.t().contiguous()
            out1, S1 = F.nvidia_transform(At, to_order=orderOut)
        else:
            out1, S1 = F.nvidia_transform(A, to_order=orderOut)
        out2, S2 = F.transform(A, to_order=orderOut, transpose=transpose)

        assert S1[0][0] == S2[0][0]
        assert S1[0][1] == S2[0][1]
        # print(out1)
        # print(out2)

        torch.testing.assert_close(out1, out2)


n = 2
# dim1 = torch.randint(2,1024, size=(n,)).tolist()
# dim2 = torch.randint(2,1024, size=(n,)).tolist()
dim1 = [1]
dim2 = [33]

dtype = [torch.int8]
# a_order = ['col_turing', 'col_ampere']
a_order = ["col_turing"]
out_order = ["row"]
values = list(product(dim1, dim2, dtype, a_order, out_order))
names = [
    "dim1_{}_dim2_{}_dtype_{}_orderA_{}_orderOut_{}".format(*vals)
    for vals in values
]


def test_overflow():
    formatB = F.get_special_format_str()
    print(formatB)
    for i in range(2):
        a = torch.arange(5, 15).cuda().to(torch.int8).view(-1, 1)
        b = torch.arange(5, 15).cuda().to(torch.int8).view(-1, 1)

        Ca, Sa = F.nvidia_transform(a, "col32")
        Cb, Sb = F.nvidia_transform(b, formatB)

        c = F.igemmlt(Ca, Cb, Sa, Sb, dtype=torch.int8)
        c2 = torch.matmul(a.float(), b.float().t())


n = 2
dim1 = torch.randint(1, 4 * 1024, size=(n,)).tolist()
dim2 = torch.randint(1, 4 * 1024, size=(n,)).tolist()
# dim1 = [4]
# dim2 = [5]

values = list(product(dim1, dim2))
names = ["dim1_{}_dim2_{}".format(*vals) for vals in values]


@pytest.mark.parametrize("dim1, dim2", values, ids=names)
def test_coo_double_quant(dim1, dim2):
    threshold = 3.00
    for i in range(k):
        A = torch.randn(dim1, dim2, device="cuda").half()

        idx = torch.abs(A) >= threshold
        CA2, CAt, statsA, statsAt, coo_tensor = F.double_quant(A)
        CA, CAt, statsA, statsAt, coo_tensor = F.double_quant(
            A, threshold=threshold
        )

        if coo_tensor is not None:
            A1 = A * idx
            A2 = torch.zeros_like(A)
            A2[
                coo_tensor.rowidx.long(), coo_tensor.colidx.long()
            ] = coo_tensor.values
            torch.testing.assert_close(A1, A2)

            A1 = A * (idx == 0)
            A2 = (CA.float() * statsA.unsqueeze(1) / 127).half()
            torch.testing.assert_close(
                A * (idx == 0), A2, rtol=0.05, atol=1.5e-2
            )


n = 2
dim1 = torch.randint(1, 1 * 1024, size=(n,)).tolist()
dim2 = torch.randint(1, 1 * 1024, size=(n,)).tolist()
# dim1 = [7]
# dim2 = [11]
transposed_B = [False, True]
values = list(product(dim1, dim2, transposed_B))
names = ["dim1_{}_dim2_{}_transposed_B_{}".format(*vals) for vals in values]


@pytest.mark.parametrize("dim1, dim2, transposed_B", values, ids=names)
def test_spmm_coo(dim1, dim2, transposed_B):
    threshold = 1.5
    dim3 = torch.randint(32, 128, size=(1,)).item()
    # dim3 = 17
    for i in range(k):
        A = torch.randn(dim1, dim2).cuda().half()
        if transposed_B:
            B = torch.randn(dim3, dim2).cuda().half()
        else:
            B = torch.randn(dim2, dim3).cuda().half()

        idx = torch.abs(A) >= threshold
        nnz = (idx == 1).sum().item()
        rows, cols = torch.where(idx)
        values = A[idx]
        cooA = F.COOSparseTensor(
            A.shape[0], A.shape[1], nnz, rows.int(), cols.int(), values
        )
        A2 = A * idx

        if transposed_B:
            out2 = F.spmm_coo(cooA, B.t())
            out1 = torch.matmul(A2, B.t())
        else:
            out2 = F.spmm_coo(cooA, B)
            out1 = torch.matmul(A2, B)

        assert_all_approx_close(out1, out2, rtol=0.01, atol=3.0e-2, count=30)


def test_spmm_bench():
    batch = 2
    model = 1024 * 1
    hidden = model * 4
    seq = 1024
    dim1 = batch * seq
    dim2 = model
    dim3 = hidden
    threshold = 4
    A = torch.randn(dim1, dim2, device="cuda").half()
    B = torch.randn(dim2, dim3, device="cuda").half()
    for i in range(10):
        C1 = bnb.matmul(A, B.t())

    torch.cuda.synchronize()
    t0 = time.time()
    for i in range(k):
        C1 = bnb.matmul(A, B.t())
    torch.cuda.synchronize()
    t8 = time.time() - t0

    idx = torch.abs(A) >= threshold
    nnz = (idx == 1).sum().item()
    print(nnz / idx.numel())
    rows, cols = torch.where(idx)
    values = A[idx]
    cooA = F.COOSparseTensor(
        A.shape[0], A.shape[1], nnz, rows.int(), cols.int(), values
    )

    for i in range(10):
        out2 = F.spmm_coo(cooA, B)

    torch.cuda.synchronize()
    t0 = time.time()
    for i in range(k):
        out2 = F.spmm_coo(cooA, B)
    torch.cuda.synchronize()
    tsp = time.time() - t0
    print(tsp, t8)
    print(tsp / t8)


n = 2
dim1 = torch.randint(256, 1 * 1024, size=(n,)).tolist()
dim2 = torch.randint(256, 1 * 1024, size=(n,)).tolist()
values = list(product(dim1, dim2))
names = ["dim1_{}_dim2_{}".format(*vals) for vals in values]


@pytest.mark.parametrize("dim1, dim2", values, ids=names)
def test_integrated_sparse_decomp(dim1, dim2):
    threshold = 3.0
    formatB = "col_turing"
    for i in range(k):
        A = torch.randn(dim1, dim2).cuda().half()
        w1 = torch.randn(dim1, dim2).cuda().half()
        out1 = torch.matmul(A, w1.t())

        Cw1, Cw1t, statsw1, statsw1t, coo_tensor = F.double_quant(w1)
        CTw1, Sw1 = F.transform(Cw1, formatB)

        CA, CAt, statsA, statsAt, coo_tensor = F.double_quant(A)
        C32A, SA = F.transform(CA, "col32")

        out1_32, Sout1_32 = F.igemmlt(C32A, CTw1, SA, Sw1)
        out2 = F.mm_dequant(out1_32, Sout1_32, statsA, statsw1)

        CA, CAt, statsA, statsAt, coo_tensor = F.double_quant(
            A, threshold=threshold
        )
        C32A, SA = F.transform(CA, "col32")

        out1_32, Sout1_32 = F.igemmlt(C32A, CTw1, SA, Sw1)
        out3 = F.mm_dequant(out1_32, Sout1_32, statsA, statsw1)

        assert coo_tensor is not None

        out4 = F.spmm_coo(coo_tensor, w1.t())
        out5 = out3 + out4

        err1 = torch.abs(out1 - out2).mean().item()
        err2 = torch.abs(out1 - out5).mean().item()
        assert err2 < err1


def test_matmuls():
    a = torch.randn(256, 512).half().cuda()
    b = torch.randn(256, 512).half().cuda()
    c1 = torch.matmul(a, b.t())
    c2 = bnb.matmul(a, b)
    c3 = bnb.matmul_cublas(a, b.t())

    err1 = torch.abs(c1 - c2).mean().item()
    err2 = torch.abs(c1 - c3).mean().item()
    assert err1 < 0.2
    assert err2 < 0.2
    print(err1, err2)


n = 2
# dim1 = torch.randint(1,1*1024, size=(n,)).tolist()
# dim2 = torch.randint(1,4*1024, size=(n,)).tolist()
dim1 = [1 * 2048]
dim2 = [12288]
# dim1 = [32]
# dim2 = [32]
# dtype = [torch.float16, torch.int8]
dtype = [torch.float16]
out_function = ["zeros", "ones"]
values = list(product(dim1, dim2, dtype, out_function))
names = [
    "dim1_{}_dim2_{}_dtype_{}_out_func_{}".format(*vals) for vals in values
]


@pytest.mark.parametrize("dim1, dim2, dtype, out_func", values, ids=names)
def test_spmm_coo_very_sparse(dim1, dim2, dtype, out_func):
    out_func = getattr(torch, out_func)

    threshold = 3.3
    # threshold = 2.8
    # threshold = 0.0
    A = torch.randn(dim1, dim2, device="cuda").half()
    if dtype == torch.float16:
        B = torch.randn(dim2, dim2 * 4, device="cuda").half()
        torch.nn.init.xavier_uniform_(B)
    else:
        B = torch.randn(dim2, dim2 * 4, device="cuda").half()
        torch.nn.init.xavier_uniform_(B)
        B, SB = F.vectorwise_quant(B, quant_type="linear")
        # B = torch.randint(-127, 127, size=(dim2, dim2*4), device='cuda').to(torch.int8)

    print("")
    idx = torch.abs(A) >= threshold
    nnz = (idx == 1).sum().item()
    rows, cols = torch.where(idx)
    values = A[idx]
    cooA = F.COOSparseTensor(
        A.shape[0], A.shape[1], nnz, rows.int(), cols.int(), values
    )
    A2 = A * idx
    out1 = torch.matmul(A2.half(), B.half())
    out = out_func(out1.shape, dtype=torch.float16, device=out1.device)
    out1 += out.clone()
    out2 = F.spmm_coo_very_sparse(cooA, B, out=out)
    # print(B)
    # print(out1)
    # print(out2)
    p = 200 / (2048 * 12288 * 4)
    n = out1.numel()
    count = math.ceil(p * n)
    std = out1.std()
    out1 /= std
    out2 /= std
    assert_all_approx_close(
        out1, out2.half(), rtol=0.01, atol=3.0e-2, count=count
    )
    # assert_all_approx_close(out1, out2.half(), rtol=0.05, atol=0.01, count=count)

    idx_col = torch.randint(0, A2.shape[-1], size=(15,))

    # torch.testing.assert_close(out1, out2.half(), rtol=0.05, atol=0.001)

    # Bt = torch.randn(dim2*4, dim2, device='cuda').half()
    # torch.cuda.synchronize()
    # t0 = time.time()
    # print(A2.shape, B.shape)
    # for i in range(100):
    #   #out3 = F.spmm_coo(cooA, Bt.t())
    #   #out2 = F.spmm_coo(cooA, B)
    #   #out2 = F.spmm_coo_very_sparse(cooA, B)
    #   #out1 = torch.matmul(A, Bt.t())

    # torch.cuda.synchronize()
    # print(time.time() - t0)


def test_coo2csr():
    threshold = 1
    A = torch.randn(128, 128).half().cuda()
    idx = torch.abs(A) >= threshold
    nnz = (idx == 1).sum().item()
    rows, cols = torch.where(idx)
    values = A[idx]
    cooA = F.COOSparseTensor(
        A.shape[0], A.shape[1], nnz, rows.int(), cols.int(), values
    )
    A2 = A * idx
    csrA = F.coo2csr(cooA)
    counts = csrA.rowptr[1:] - csrA.rowptr[:-1]
    assert counts.numel() == A.shape[0]

    torch.testing.assert_close(counts.long(), (A2 != 0).sum(1))
    idx = A2 != 0
    torch.testing.assert_close(A2[idx], csrA.values)


def test_coo2csc():
    threshold = 1
    A = torch.randn(128, 128).half().cuda()
    idx = torch.abs(A) >= threshold
    nnz = (idx == 1).sum().item()
    rows, cols = torch.where(idx)
    values = A[idx]
    cooA = F.COOSparseTensor(
        A.shape[0], A.shape[1], nnz, rows.int(), cols.int(), values
    )
    A2 = A * idx
    cscA = F.coo2csc(cooA)
    counts = cscA.colptr[1:] - cscA.colptr[:-1]
    assert counts.numel() == A.shape[1]

    torch.testing.assert_close(counts.long(), (A2 != 0).sum(0))
    # torch uses row-major -> use transpose to transfer to col-major
    idx = A2.t() != 0
    torch.testing.assert_close(A2.t()[idx], cscA.values)


n = 2
# dim1 = torch.randint(1,1*1024, size=(n,)).tolist()
# dim2 = torch.randint(1,4*1024, size=(n,)).tolist()
dim1 = [1 * 2048]
# dim2 = [12288]
dim2 = [2048]
# dim1 = [2]
# dim2 = [2]
dtype = [torch.int8]
values = list(product(dim1, dim2, dtype))
names = ["dim1_{}_dim2_{}_dtype_{}".format(*vals) for vals in values]


@pytest.mark.parametrize("dim1, dim2, dtype", values, ids=names)
def test_spmm_coo_dequant(dim1, dim2, dtype):
    threshold = 6.0
    # threshold = 2.8
    # threshold = 0.0
    A = torch.randn(dim1, dim2, device="cuda").half()
    B = torch.empty(dim2, dim2 * 4, device="cuda", dtype=torch.float16)
    torch.nn.init.xavier_uniform_(B)
    Bt = B.t().contiguous()

    CB, CBt, statsB, statsBt, coo_tensor = F.double_quant(B)

    rowidx = torch.randint(0, A.shape[-1], size=(15,))

    A[:, rowidx] = 8.0

    idx = torch.abs(A) >= threshold
    nnz = (idx == 1).sum().item()
    rows, cols = torch.where(idx)
    values = A[idx]
    cooA = F.COOSparseTensor(
        A.shape[0], A.shape[1], nnz, rows.int(), cols.int(), values
    )
    A2 = A * idx
    out2 = F.spmm_coo_very_sparse(cooA, CBt, dequant_stats=statsBt)
    out1 = torch.matmul(A2, B.half())
    out3 = F.spmm_coo_very_sparse(cooA, CBt.half())
    out3 = out3 * statsBt.half() / 127

    values, counts = torch.unique(cooA.rowidx, return_counts=True)
    offset = counts.cumsum(0).int()
    max_count, max_idx = torch.sort(counts, descending=True)
    print(torch.median(max_count.float()))

    torch.testing.assert_close(out2, out3, rtol=0.05, atol=0.001)

    p = 200 / (2048 * 12288 * 4)
    n = out1.numel()
    count = math.ceil(p * n)
    assert_all_approx_close(out1, out2, rtol=0.01, atol=3.0e-2, count=count)

    # torch.cuda.synchronize()
    # t0 = time.time()
    # for i in range(100):
    #   out2 = F.spmm_coo_very_sparse(cooA, B)
    # torch.cuda.synchronize()
    # print('fp16', time.time() - t0)

    torch.cuda.synchronize()
    t0 = time.time()
    for i in range(100):
        out2 = F.spmm_coo(cooA, B)
    torch.cuda.synchronize()
    print("cusparse fp16", time.time() - t0)

    torch.cuda.synchronize()
    t0 = time.time()
    for i in range(100):
        out2 = F.spmm_coo_very_sparse(cooA, CBt)
    torch.cuda.synchronize()
    print("int8", time.time() - t0)

    torch.cuda.synchronize()
    t0 = time.time()
    for i in range(100):
        out2 = F.spmm_coo_very_sparse(cooA, CBt, dequant_stats=statsBt)
    torch.cuda.synchronize()
    print("int8+dequant", time.time() - t0)

    torch.cuda.synchronize()
    t0 = time.time()
    for i in range(100):
        out2 = torch.matmul(A, B)
    torch.cuda.synchronize()
    print("matmul", time.time() - t0)

    torch.cuda.synchronize()
    t0 = time.time()
    for i in range(100):
        out1 = bnb.matmul(A, Bt)
        out2 = F.spmm_coo_very_sparse(cooA, CBt, dequant_stats=statsBt)
        out = out1 + out2
    torch.cuda.synchronize()
    print("sparse+ matmul", time.time() - t0)

    torch.cuda.synchronize()
    t0 = time.time()
    for i in range(100):
        out1 = bnb.matmul(A, Bt)
        torch.matmul(A[:, rowidx], Bt.t()[rowidx], out=out1)
    torch.cuda.synchronize()
    print("partial matmul", time.time() - t0)

    torch.cuda.synchronize()
    t0 = time.time()
    for i in range(100):
        out1 = bnb.matmul(A, Bt)
    torch.cuda.synchronize()
    print("partial matmul", time.time() - t0)


batch_size = 1
seqdim = 1
values = []
#values.append((batch_size, seqdim, 768, 4 * 768))
#values.append((batch_size, seqdim, 1024, 4*1024))
#values.append((batch_size, seqdim, 1536, 4*1536))
#values.append((batch_size, seqdim, 2048, 4*2048))
#values.append((batch_size, seqdim, 2560, 4*2560))
#values.append((batch_size, seqdim, 4096, 4*4096))
#values.append((batch_size, seqdim, 5120, 4*5120))
#values.append((batch_size, seqdim, 6656, 4*6656))
values.append((batch_size, seqdim, 8192, 4*8192))
#values.append((batch_size, seqdim, 5140, 4*5140))
#values.append((batch_size, seqdim, 12288, 4*12288))
names = ["batch_{}_seq_{}_model_{}_hidden_{}".format(*vals) for vals in values]
@pytest.mark.parametrize("batch, seq, model, hidden", values, ids=names)
def test_bench_matmul(batch, seq, model, hidden):
    iters = 80
    formatB = F.get_special_format_str()

    A = torch.randn(batch, seq, model, device="cuda").half()
    B = torch.empty(hidden, model, dtype=torch.float16, device="cuda")
    torch.nn.init.xavier_uniform_(B)

    B_fp4, state = F.quantize_fp4(B)
    B_fp4_c, state_c = F.quantize_fp4(B, compress_statistics=True)

    B_nf4, state_nf4 = F.quantize_nf4(B)

    linear8bit = bnb.nn.Linear8bitLt(model, hidden, False, False).cuda().half()
    linear8bit.eval()

    outliers = torch.randint(0, model, size=(5,)).cuda()
    A[:, :, outliers] = 8.0

    linearMixedBit = (bnb.nn.Linear8bitLt(model, hidden, False, False, threshold=6.0).cuda().half())
    #linearMixedBit.eval()

    linear8bit_train = bnb.nn.Linear8bitLt(model, hidden, False).cuda().half()
    linear8bit_train_thresh = bnb.nn.Linear8bitLt(model, hidden, False, threshold=6.0).cuda().half()
    F.cutlass3_gemm(A, B_nf4.t(), state=state_nf4)

    # warmup
    for i in range(iters):
        torch.matmul(A, B.t())
    torch.cuda.synchronize()
    print("")

    torch.cuda.synchronize()
    t0 = time.time()
    for i in range(iters):
        torch.matmul(A, B.t())
    torch.cuda.synchronize()
    print( f"pytorch fp16: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s" )

    #torch.cuda.synchronize()
    #t0 = time.time()
    #for i in range(iters):
    #    bnb.matmul_4bit(A, B_fp4.t(), quant_state=state)
    #torch.cuda.synchronize()
    #print( f"bnb fp4: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s" )

    #torch.cuda.synchronize()
    #t0 = time.time()
    #for i in range(iters):
    #    bnb.matmul_4bit(A, B_fp4.t(), quant_state=state_c)
    #torch.cuda.synchronize()
    #print( f"bnb fp4 + compressed stats: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s" )

    torch.cuda.synchronize()
    t0 = time.time()
    for i in range(iters):
        #bnb.matmul_4bit(A, B_nf4.t(), quant_state=state_nf4)
        F.cutlass3_gemm(A, B_nf4.t(), state=state_nf4)
    torch.cuda.synchronize()
    print( f"bnb nf4: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s" )

    #torch.cuda.synchronize()
    #t0 = time.time()
    #for i in range(iters):
    #    bnb.matmul(A, B)
    #torch.cuda.synchronize()
    #print(f"CB -> CxB conversion (each iteration): [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s")

    #torch.cuda.synchronize()
    #t0 = time.time()
    #for i in range(iters):
    #    bnb.matmul(A, B, threshold=6.0)
    #torch.cuda.synchronize()
    #print(f"CB -> CxB conversion + threshold: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s")

    #CA, CAt, SCA, SCAt, coo_tensorA = F.double_quant(A, threshold=0.0)
    #C32A, SA = F.transform(CA, "col32")
    #CB, CBt, SCB, SCBt, coo_tensorB = F.double_quant(B)
    #CxB, SB = F.transform(CB, to_order=formatB)
    #torch.cuda.synchronize()
    #t0 = time.time()
    #for i in range(iters):
    #    out32, Sout32 = F.igemmlt(C32A, CxB, SA, SB)
    #torch.cuda.synchronize()
    #print(f"no overhead matmul-lt: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s")

    #BA, statsB = F.vectorwise_quant(B, dim=1)
    #CxB, SB = F.nvidia_transform(CB, to_order=formatB)
    #torch.cuda.synchronize()
    #t0 = time.time()
    #for i in range(iters):
    #    A2 = A.view(-1, A.shape[-1]).contiguous()
    #    CA, statsA = F.vectorwise_quant(A2, dim=1)
    #    C32A, SA = F.nvidia_transform(CA, "col32")
    #    out32, Sout32 = F.igemmlt(C32A, CxB, SA, SB)
    #    Cout, Sout = F.nvidia_transform(out32, "row", state=Sout32)
    #    F.vectorwise_mm_dequant(Cout, statsA, statsB.t())
    #torch.cuda.synchronize()
    #print(f"vector pytorch + nvidia: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s")

    #BA, statsB = F.vectorwise_quant(B, dim=1, quant_type="linear")
    #CxB, SB = F.nvidia_transform(CB, to_order=formatB)
    #torch.cuda.synchronize()
    #t0 = time.time()
    #for i in range(iters):
    #    A2 = A.view(-1, A.shape[-1]).contiguous()
    #    CA, statsA = F.vectorwise_quant(A2, dim=1, quant_type="linear")
    #    C32A, SA = F.nvidia_transform(CA, "col32")
    #    out32, Sout32 = F.igemmlt(C32A, CxB, SA, SB)
    #    Cout, Sout = F.nvidia_transform(out32, "row", state=Sout32)
    #    out = Cout * statsB * statsA * (1.0 / (127 * 127))
    #torch.cuda.synchronize()
    #print(f"linear pytorch + nvidia: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s")

    #linear8bit(A)
    #torch.cuda.synchronize()
    #t0 = time.time()
    #for i in range(iters):
    #    linear8bit(A)
    #torch.cuda.synchronize()
    #print( f"bnb linear8bitlt (eval): [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s")

    #linearMixedBit(A)
    #torch.cuda.synchronize()
    #t0 = time.time()
    #for i in range(iters):
    #    linearMixedBit(A)
    #torch.cuda.synchronize()
    #print( f"bnb linear8bitlt with threshold (eval): [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s")

    #linear8bit_train(A)
    #torch.cuda.synchronize()
    #t0 = time.time()
    #for i in range(iters):
    #    linear8bit_train(A)
    #torch.cuda.synchronize()
    #print( f"bnb linear8bitlt (training): [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s")

    #linear8bit_train_thresh(A)
    #torch.cuda.synchronize()
    #t0 = time.time()
    #for i in range(iters):
    #    linear8bit_train(A)
    #torch.cuda.synchronize()
    #print( f"bnb linear8bitlt with threshold (training): [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s")

def test_zeropoint():
    def quant_zp(x):
        dtype = x.dtype
        x = x.float()
        dyna = x.max() - x.min()
        if dyna == 0:
            dyna = 1
        qx = 254.0 / dyna
        minx = x.min()
        # zpx = torch.round(minx* qx)
        # zpx = 127 - torch.round(x.max()* qx)
        zpx = torch.round(x.min() * qx) - 127
        x = (qx * x) + zpx
        return x, qx, zpx

    batch = 2
    seq = 512
    model = 1024
    hidden = 4 * model
    A = torch.randn(batch * seq, model, device="cuda").half() * 0.1
    B = torch.randn(model, hidden, device="cuda").half() * 0.1

    C0 = torch.matmul(A, B)

    # A, SA = F.vectorwise_quant(A, quant_type='linear')
    # B, SB = F.vectorwise_quant(B, quant_type='linear')
    A = A.float()
    B = B.float()

    C1 = torch.matmul(A, B)
    C3 = bnb.matmul(A.half(), B.t().contiguous().half())

    zp = 1
    # C2 = torch.matmul(A-zp, B)
    # C2 += B.sum(0).view(1, -1)*zp
    C2 = torch.matmul(A, B - zp)
    C2 -= A.sum(1).view(-1, 1) * zp

    ca, cqa, cza = quant_zp(A)
    print(ca.min(), ca.max())
    print((ca - cza).min(), (ca - cza).max())

    zp = 1
    scale = 2.0
    C5 = torch.matmul((A * scale) - zp, B)
    C5 += B.sum(0) * zp
    C5 /= scale

    CA, qa, zpa = quant_zp(A)
    C4 = torch.matmul(CA, B)
    C4 -= B.sum(0) * zpa
    C4 /= qa

    zpb = 1
    zpa = 1
    qa = 2
    qb = 2
    C6 = torch.matmul((A * qa) + zpa, (B * qb) + zpb)
    C6 -= (qb * B.sum(0).view(1, -1) * zpa) + (qa * A.sum(1).view(-1, 1) * zpb)
    C6 -= zpa * zpb * A.shape[1]
    C6 /= qa * qb

    CA, qa, zpa = quant_zp(A)
    CB, qb, zpb = quant_zp(B)
    C7 = torch.matmul(CA, CB)
    C7 -= (qb * B.sum(0).view(1, -1) * zpa) + (qa * A.sum(1).view(-1, 1) * zpb)
    C7 -= zpa * zpb * A.shape[1]
    C7 /= qa * qb

    print("")
    # print(C0.flatten()[:10])
    print(C1.flatten()[:10])
    print(C2.flatten()[:10])
    print(C3.flatten()[:10])
    print(C5.flatten()[:10])
    print(C6.flatten()[:10])
    print(C7.flatten()[:10])
    err1 = torch.abs(C1 - C2).mean().item()
    err2 = torch.abs(C1 - C3).mean().item()
    err3 = torch.abs(C1 - C4).mean().item()
    err4 = torch.abs(C1 - C5).mean().item()
    err5 = torch.abs(C1 - C6).mean().item()
    err6 = torch.abs(C1 - C7).mean().item()
    print(err1, err2, err3, err4, err5, err6)


def test_extract_outliers():
    for i in range(k):
        shapeA = (4096, 4096 * 4)
        idx = torch.unique(torch.randint(0, shapeA[1], size=(10,)).int()).cuda()
        # idx = torch.Tensor([0]).int().cuda()
        A = torch.randint(-128, 127, size=shapeA, device="cuda").to(torch.int8)
        outliers1 = A[:, idx.long()]

        CA, SA = F.transform(A, "col_turing")

        outliers2 = F.extract_outliers(CA, SA, idx)

        assert outliers2.shape[0] == shapeA[0]
        assert outliers2.shape[1] == idx.numel()

        torch.testing.assert_close(outliers1, outliers2)

        CA, SA = F.transform(A, "col_ampere")

        outliers2 = F.extract_outliers(CA, SA, idx)

        assert outliers2.shape[0] == shapeA[0]
        assert outliers2.shape[1] == idx.numel()

        torch.testing.assert_close(outliers1, outliers2)



def test_blockwise_cpu_large():
    diffs = []
    reldiffs = []
    batch = 128
    seq = 128
    for hidden in [128]:#, 14336]:
        for blocksize in [4096, 16384]:
            for i in range(2):
                A1 = torch.randn(batch, seq, hidden, device='cpu')
                t0 = time.time()
                C, S = F.quantize_blockwise(A1, blocksize=blocksize)
                A2 = F.dequantize_blockwise(C, S, blocksize=blocksize)
                print(time.time() - t0)
                diff = torch.abs(A1 - A2)
                reldiff = diff / torch.abs(A1 + 1e-8)
                diffs.append(diff.mean().item())
                reldiffs.append(reldiff.mean().item())
                assert diffs[-1] < 0.011
            # print(sum(diffs)/len(diffs))
            # print(sum(reldiffs)/len(reldiffs))



def test_fp8_quant():
    for e_bits in range(1, 7):
        p_bits = 7-e_bits
        code = F.create_fp8_map(True, e_bits, p_bits).cuda()

        abserr = []
        relerr = []
        for i in range(100):
            A1 = torch.randn(1024, 1024, device="cuda")
            C, SC = F.quantize_blockwise(A1, code=code)
            A2 = F.dequantize_blockwise(C, SC)
            diff = torch.abs(A1 - A2)
            reldiff = diff/torch.abs(A1+1e-8)
            abserr.append(diff.mean().item())
            relerr.append(reldiff.mean().item())
            #assert diff < 0.0075
        #print(sum(abserr)/len(abserr))
        #print(sum(relerr)/len(relerr))

        abserr = []
        relerr = []
        for i in range(100):
            A1 = torch.rand(1024, 1024, device="cuda")
            C, SC = F.quantize_blockwise(A1, code=code)
            A2 = F.dequantize_blockwise(C, SC)
            diff = torch.abs(A1 - A2)
            reldiff = diff/torch.abs(A1+1e-8)
            abserr.append(diff.mean().item())
            relerr.append(reldiff.mean().item())
            #assert diff < 0.0075
        #print(sum(abserr)/len(abserr))
        #print(sum(relerr)/len(relerr))

        abserr = []
        relerr = []
        for i in range(100):
            A1 = torch.randn(1024, 1024, device="cuda")
            C, SC = F.quantize_blockwise(A1)
            A2 = F.dequantize_blockwise(C, SC)
            diff = torch.abs(A1 - A2)
            reldiff = diff/torch.abs(A1+1e-8)
            abserr.append(diff.mean().item())
            relerr.append(reldiff.mean().item())
            #assert diff < 0.0075
        #print(3, sum(abserr)/len(abserr))
        #print(3, sum(relerr)/len(relerr))


def test_few_bit_quant():

    #print('')
    for bits in range(2, 9):
        #print('='*30, bits, '='*30)
        for method in ['linear', 'fp8', 'dynamic', 'quantile']:
            abserrs = []
            relerrs = []
            code = None
            if method == 'linear':
                code = F.create_linear_map(True, total_bits=bits).cuda()
            elif method == 'fp8':
                ebits = math.ceil(bits/2)
                pbits = bits-ebits-1
                code = F.create_fp8_map(True, ebits, pbits, bits).cuda()
            elif method == 'dynamic':
                code = F.create_dynamic_map(True, bits-0, bits).cuda()
            elif method == 'quantile':
                values = torch.randn(2048, 2048, device='cuda')
                code = F.create_quantile_map(values, bits).cuda()
            # for some data types we have no zero
            # for some data types we have one zero
            # for some data types we have two zeros
            assert torch.unique(code).numel() in [2**bits, 2**bits-1], f'bits: {bits}, method: {method}'
            #print(method, (code==0).sum())
            assert code.numel() == 256
            for i in range(10):

                values = torch.randn(1, 32, device='cuda')
                values /= values.abs().max()
                #values[values.abs() < 1e-6] += 1e-5

                q1 = []
                v1 = []
                for v in values[0]:
                    idx = torch.abs(v-code).argmin()
                    q1.append(idx.item())
                    v1.append(code[idx].item())

                q1 = torch.Tensor(q1).cuda()
                v1 = torch.Tensor(v1).cuda()

                q2, S2 = F.quantize_blockwise(values, code=code)
                v2 = F.dequantize_blockwise(q2, S2)

                idx = torch.isclose(q1.int(), q2.int())
                err2 = torch.abs(v2-values)
                abserrs.append(err2.mean().item())
                relerrs.append((err2/(1e-10+values).abs()).mean().item())
                if idx.sum():
                    # some weird cases
                    err1 = torch.abs(v1-values).mean()
                    #assert err2.mean() <= err1

                else:
                    torch.testing.assert_close(q1, q2)
            #print(method, 'abserr:', sum(abserrs)/len(abserrs), 'relerr:', sum(relerrs)/len(relerrs))
    #assert False


def test_kbit_quantile_estimation():
    for i in range(100):
        data = torch.randn(1024, 1024, device='cuda')
        for bits in range(2, 9):
            p = np.linspace(1.3e-4, 1-1.3e-4, 2**bits)
            val1 = torch.Tensor(norm.ppf(p)).cuda()
            val2 = F.estimate_quantiles(data, offset=0, num_quantiles=2**bits)
            err = torch.abs(val1-val2).mean()
            assert err < 0.038

    for i in range(100):
        data = torch.randn(1024, 1024, device='cuda')
        for bits in range(2, 4):
            total_values = 2**bits-1
            p = np.linspace(0, 1, 2*total_values+1)
            idx = np.arange(1, 2*total_values+1, 2)
            p = p[idx]
            offset = 1/(2*total_values)
            p = np.linspace(offset, 1-offset, total_values)
            val1 = torch.Tensor(norm.ppf(p)).cuda()
            val2 = F.estimate_quantiles(data, num_quantiles=2**bits-1)
            err = torch.abs(val1-val2).mean()
            assert err < 0.035


def test_bench_dequantization():
    a = torch.rand(1024, 1024, device='cuda').half()
    code =F.create_fp8_map(True, 3, 0, 4).cuda()
    qa, SA = F.quantize_blockwise(a, code=code)
    print(qa.max())

    max_theoretical_mu =  1024*1024*2/1024**3/672*1000*1000
    #print(max_theoretical_mu)

    torch.cuda.synchronize()
    t0 = time.time()
    for i in range(100):
        qa, SA = F.quantize_blockwise(a)
    torch.cuda.synchronize()
    #print((time.time()-t0)/1e6)



@pytest.mark.parametrize("dtype", [torch.float32, torch.float16, torch.bfloat16], ids=["fp32", "fp16", "bf16"])
def test_fp4_quant(dtype):
    vals = list(product([0, 1], repeat=4))

    code = {}
    for bits in vals:
        result = 0
        bias = 3
        sign, e1, e2, p1 = bits
        idx = sign*8 + e1*4 + e2*2 + p1*1
        sign = -1.0 if sign else 1.0
        exp = e1*2 + e2*1
        if exp == 0:
            # sub-normal
            if p1 == 0: result = 0
            else: result = sign*0.0625
        else:
            # normal
            exp = 2**(-exp + bias + 1)
            frac = 1.5 if p1 else 1.0
            result = sign*exp*frac
        code[idx] = result

    A1 = torch.randn(1024, 1024, device='cuda', dtype=dtype)
    qa, SA = F.quantize_fp4(A1, blocksize=64)
    A2 = F.dequantize_fp4(qa, SA)

    err = (A1 - A2).abs().float()
    relerr = (err/A1.abs().float()).mean()
    idx = err > 1.0
    err = err.mean()

    assert A2.dtype == dtype
    assert err.item() < 0.1
    assert relerr.item() < 0.28


@pytest.mark.skipif(not torch.cuda.is_available(), reason="this test requires a GPU")
@pytest.mark.parametrize("quant_type", ['fp4', 'nf4'])
def test_4bit_compressed_stats(quant_type):
    for blocksize in [128, 64]:
        errs1 = []
        errs2 = []
        for i in range(10):
            A1 = torch.randn(1024, 1024, device='cuda').half()
            q2, SA2 = F.quantize_4bit(A1, blocksize=blocksize, quant_type=quant_type)
            q3, SA3= F.quantize_4bit(A1, blocksize=blocksize, compress_statistics=True, quant_type=quant_type)
            A2 = F.dequantize_4bit(q2, SA2, quant_type=quant_type)
            A3 = F.dequantize_4bit(q3, SA3, quant_type=quant_type)


            err = (A1 - A2).abs().float()
            relerr = (err/(A1.abs().float()+1e-15)).mean()
            err = err.mean()

            errs1.append(err.item())


            assert err.item() < 0.11
            assert relerr.item() < 0.28

            err = (A1 - A3).abs().float()
            relerr = (err/(A1.abs().float()+1e-15)).mean()
            err = err.mean()

            errs2.append(err.item())

            assert err.item() < 0.11
            assert relerr.item() < 0.28

        #print(sum(errs1)/len(errs1), blocksize, quant_type)
        #print(sum(errs2)/len(errs2), blocksize, quant_type)




@pytest.mark.skipif(not torch.cuda.is_available(), reason="this test requires a GPU")
#@pytest.mark.parametrize("quant_type", ['fp4', 'nf4'])
@pytest.mark.parametrize("quant_type", ['nf4'])
def test_bench_4bit_dequant(quant_type):
    blocksize = 256
    a = torch.rand(1024*12*4, 1024*12, device='cuda').half()
    qa, SA = F.quantize_4bit(a, blocksize=blocksize, quant_type=quant_type)

    input_size = a.numel()/2
    output_size = a.numel()*2
    num_bytes = input_size+output_size
    GB = num_bytes/1e9
    max_theoretical_s =  GB/768
    #print(max_theoretical_s*1e6)
    b = torch.randn(128, 1024*12, device='cuda').half()

    iters = 100
    torch.cuda.synchronize()
    t0 = time.time()
    for i in range(iters):
        F.dequantize_4bit(qa, SA, blocksize=blocksize, quant_type=quant_type)
        #b.copy_(a)
    torch.cuda.synchronize()
    #print((time.time()-t0)/iters*1e6)

    #torch.cuda.synchronize()
    #t0 = time.time()
    #for i in range(iters):
    #    torch.matmul(b, a.t())
    #torch.cuda.synchronize()
    #print((time.time()-t0)/iters*1e6)



def test_normal_map_tree():
    code = F.create_normal_map()
    values =code[:8].tolist() + code[-8:].tolist()
    num_pivots = 1
    print(values)
    while num_pivots <16:
        idx = list(range(16//num_pivots//2, 16, 16//num_pivots))
        print(idx)
        num_pivots *= 2
        pivots = []
        for i in idx:
            pivots.append((values[i-1]+values[i])/2)
        print(pivots)


#@pytest.mark.parametrize("dtype", [torch.float32, torch.float16], ids=['fp32', 'fp16'])
@pytest.mark.parametrize("dtype", [torch.float16], ids=['fp16'])
def test_cutlass3_gemm(dtype):
    debug = True
    #for dim in [32, 64, 128, 256, 512, 1024, 2048, 4096]:
    #for dim in [4096, 5120, 6656, 8192]:
    for dim in [4096]:
    #for dim in [128+1]:
        errs = []
        relerrs = []
        max_err = 0
        max_relerr = 0
        for i in range(100):
            A = torch.randn(1, dim, dtype=dtype, device='cuda')
            B = torch.randn(4*dim, dim+0, dtype=dtype, device='cuda')/math.sqrt(dim)
            #B = torch.randn(1, dim, dtype=dtype, device='cuda')/math.sqrt(dim)

            #print('')
            #print(A)
            #print(B.t())
            #A[:, :-1] = 0
            #B[:, :-1] = 0


            C1 = torch.matmul(A, B.t())
            C2 = F.cutlass3_gemm(A, B.t())

            # tensor cores are non-deterministic
            # so we need to analyze errors around the mean
            # to test our implementation
            err = torch.abs(C1-C2)
            mag = torch.abs(C1)+1e-8
            relerr = err/mag
            max_err = max(err.max(), max_err)
            max_relerr = max(relerr.max(), max_relerr)
            err = err.mean().item()
            relerr = relerr.mean().item()

            errs.append(err)
            relerrs.append(relerr)

            #if not debug and err/torch.abs(C1).mean() > 5e-5 or err > 3.2e-5:
            #    print('')
            #    print(i, err, relerr)
            #    print(A.flatten()[-6:])
            #    print(B.flatten()[-6:])
            #    out = A.flatten()[-6:]*B.flatten()[-6:]
            #    print(out)
            #    print(out[:-1].sum())
            #    print('='*80)
            #    print(C1.flatten()[-6:])
            #    print(C2.flatten()[-6:])
            #    #assert False, 'ERROR'

            c = int(C1.numel()*0.0014*(dim/256))+1

            c = assert_all_approx_close(C1, C2, 1e-5, 0.01, count=c, throw=not debug)
            #print(c/math.sqrt(dim))
        print('')
        print(dim, sum(errs)/len(errs)/math.sqrt(dim))
        print(dim, sum(relerrs)/len(relerrs)/math.sqrt(dim))
        print(dim, (max_err.item(), max_relerr.item()))

#@pytest.mark.parametrize("dtype", [torch.float32, torch.float16], ids=['fp32', 'fp16'])
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16], ids=['fp16', 'bf16'])
#@pytest.mark.parametrize("dtype", [torch.bfloat16], ids=['bf16'])
def test_gemm_4bit(dtype):
    print('')
    #for dim in [64, 128, 256, 512, 1024, 2048, 4096]:
    for dim in [4096]:
        errs = []
        relerrs = []
        max_err = 0
        max_relerr = 0

        for i in range(100):
            #A = torch.rand(2, 4092, dtype=dtype, device='cuda')
            #B = torch.rand(4*4092, 4092, dtype=dtype, device='cuda')
            #A = torch.rand(1, 4096, dtype=dtype, device='cuda')
            #B = torch.rand(4*4096, 4096, dtype=dtype, device='cuda')
            A = torch.randn(1, dim, dtype=dtype, device='cuda')
            B = torch.randn(4*dim, dim, dtype=dtype, device='cuda')/math.sqrt(dim)
            #B = torch.randn(1, dim+2, dtype=dtype, device='cuda')/math.sqrt(dim)

            #print('')
            #print(A)
            #print(B.t())
            #A[:, :-1] = 0
            #B[:, :-1] = 0
            #A.flatten()[:-1] = 0
            #B.flatten()[:-1] = 0

            qB, state = F.quantize_nf4(B)
            F.dequantize_nf4(qB, state)

            #C2 = bnb.matmul_4bit(A, qB.t(), state)
            C2 = F.cutlass3_gemm(A, qB.t(), state=state)
            C1 = torch.matmul(A, B.t())

            #print(state)
            #print(qB)

            #print('')
            #print(A)
            #print(B)
            #print('='*89)
            #print(C1)
            #print(C2)
            #print(C3)

            #print(C1.shape, C2.shape)

            # tensor cores are non-deterministic
            # so we need to analyze errors around the mean
            # to test our implementation
            err = torch.abs(C1-C2).float()
            mag = torch.abs(C1).float()+1e-5
            relerr = err/mag
            max_err = max(err.max(), max_err)
            max_relerr = max(relerr.max(), max_relerr)
            err = err.mean().item()
            relerr = relerr.mean().item()
            #print(err)

            errs.append(err)
            relerrs.append(relerr)

            c = int(C1.numel()*0.0014*(dim/256))+1

            c = assert_all_approx_close(C1, C2, 1e-5, 0.01, count=c, throw=False)
        #print('')
        #print(dim, sum(errs)/len(errs)/math.sqrt(dim))
        #print(dim, sum(relerrs)/len(relerrs)/math.sqrt(dim))
        #print(dim, (max_err.item(), max_relerr.item()))
        #print(sum(errs)/len(errs)/math.sqrt(dim) , 0.00015)
        #print(sum(relerrs)/len(relerrs)/math.sqrt(dim) , 0.0015)
        #assert sum(errs)/len(errs)/math.sqrt(dim) < 0.011
        #assert sum(relerrs)/len(relerrs)/math.sqrt(dim) < 0.15

@pytest.mark.skip("Row scale has some bugs for ampere")
def test_managed():
    n = 32*10
    A = F.get_paged(n, n, dtype=torch.float32)
    B = F.get_paged(n, n, dtype=torch.uint8)
    B2 = F.get_paged(n, n, dtype=torch.float32)
    assert A.is_paged
    assert B.is_paged
    assert A.page_deviceid==0
    assert B.page_deviceid==0
    F.fill(A, 17.0)
    F.fill(B, 17)
    F.fill(B2, 2)
    assert (A==17).sum().item() == n*n
    assert (B==17).sum().item() == n*n
    C = A*B.float()
    assert (C==289).sum().item() == n*n
    F._mul(A, B2)
    F._mul(A, B2)
    F._mul(A, B2)
    assert (A==17*(2**3)).sum().item() == n*n
   # F.prefetch_tensor(A)
   # F.prefetch_tensor(B)


   # F.fill(B2, 17.0)
   # F._mul(A, B2)

   # F.prefetch_tensor(A, to_cpu=True)
   # F.prefetch_tensor(B, to_cpu=True)
   # F.prefetch_tensor(B2, to_cpu=True)
   # torch.cuda.synchronize()

   # assert (A==17).sum().item() == n*n

   # torch.testing.assert_close(A, torch.ones(A.shape)*289)