# Owner(s): ["module: nn"]

import contextlib
import torch
import torch.nn as nn
import torch.nn.functional as F
import unittest
from unittest.mock import patch
import math
from torch.backends.cuda import sdp_kernel
import torch.optim as optim

from torch.testing._internal.common_nn import NNTestCase
from torch.testing._internal.common_utils import (
    TEST_FAIRSEQ,
    run_tests,
    parametrize,
    instantiate_parametrized_tests,
    freeze_rng_state,
    TEST_WITH_CROSSREF,
    TEST_WITH_ROCM,
    IS_WINDOWS,
    slowTest
)
from torch.testing._internal.common_cuda import TEST_CUDA, SM80OrLater

if TEST_FAIRSEQ:
    import fairseq.models.transformer as fairseq_transformer

@contextlib.contextmanager
def set_default_dtype(dtype):
    saved_dtype = torch.get_default_dtype()
    torch.set_default_dtype(dtype)
    try:
        yield
    finally:
        torch.set_default_dtype(saved_dtype)

class TestTransformers(NNTestCase):
    _do_cuda_memory_leak_check = True
    _do_cuda_non_default_stream = True

    device_list = ['cpu']  # TODO: is there a way to do parametrize for this?
    if TEST_CUDA:
        device_list.append('cuda')

    @unittest.skip("4D mask not supported yet - activate when 4D mask supported")
    @unittest.skipIf(not TEST_CUDA, "CUDA unavailable")  # TODO: make this work for both cuda and cpu
    def test_self_attn_TxT_attn_mask(self):
        embed_dim = 16
        num_heads = 4
        batch_size = 10
        tgt_len = 16

        query = torch.rand(batch_size, tgt_len, embed_dim, device="cuda")  # [N, T, D]
        attn_mask = torch.randint(0, 2, (tgt_len, tgt_len)).cuda().float()  # [T, T]
        attn_mask = attn_mask.masked_fill(attn_mask == 0, float('-inf')).masked_fill(attn_mask == 1, float(0.0))

        attn_mask_4d = attn_mask.expand(batch_size, num_heads, tgt_len, tgt_len)

        mta_model = torch.nn.MultiheadAttention(embed_dim, num_heads, batch_first=True).cuda()
        mta_model.eval()

        # Generate 3D results
        with torch.inference_mode():
            output_mask_4d = mta_model(query, query, query, attn_mask=attn_mask_4d)[0]
            output_mask_4d = output_mask_4d.transpose(0, 1)  # [N, T, D]

            output_mask_TxT = mta_model(query, query, query, attn_mask=attn_mask)[0]
            output_mask_TxT = output_mask_TxT.transpose(0, 1)  # [N, T, D]

            self.assertEqual(output_mask_4d, output_mask_TxT)

    @parametrize("device", device_list)
    @slowTest
    def test_train_with_pad_and_catch_error(self, device):
        iters = 100
        pad_mask = torch.tensor([[1, 1, 0, 0]], dtype=torch.bool).to(device)
        layer = nn.TransformerEncoderLayer(
            d_model=2,
            dim_feedforward=4,
            nhead=2,
            batch_first=True,
            activation="gelu",
            dropout=0,
        )
        criterion = nn.MSELoss()
        encoder = nn.TransformerEncoder(layer, 2).to(device)
        optimizer = optim.SGD(encoder.parameters(), lr=0.1, momentum=0.9)
        encoder.train()
        for i in range(iters):
            encoder.train()
            optimizer.zero_grad()
            inputs = torch.cat([torch.randn(1, 2, 2), torch.zeros(1, 2, 2)], dim=1).to(device)

            outputs = encoder(inputs, src_key_padding_mask=pad_mask)

            loss = criterion(outputs[:, 0:2, :], inputs[:, 0:2, :])
            loss.backward()
            optimizer.step()

            with torch.no_grad():
                test = torch.cat([torch.randn(1, 2, 2), torch.zeros(1, 2, 2)], dim=1).to(device)

                # Expect uint8 type not supported
                ex = None
                try:
                    test_train_uint8 = encoder(test, src_key_padding_mask=pad_mask.to(torch.uint8))
                except AssertionError as e:
                    continue
                self.assertFalse(e, "Failed to catch unsupported uint8 type exception")

                test_train_bool = encoder(test, src_key_padding_mask=pad_mask)
                encoder.eval()

                # Expect long type not supported
                ex = None
                try:
                    test_eval_uint8 = encoder(test, src_key_padding_mask=pad_mask.to(torch.int64))
                except AssertionError as e:
                    continue
                self.assertFalse(e, "Failed to catch unsupported Long type exception")

                test_eval_bool = encoder(test, src_key_padding_mask=pad_mask)
                l1_bool = nn.L1Loss()(test_train_bool[:, 0:2, :], test_eval_bool[:, 0:2, :]).item()
                self.assertTrue(l1_bool < 1e-4, "Eval/Train difference in pad_mask BOOL")

    @parametrize("device", device_list)
    @parametrize("nhead", [1, 4, 8])
    def test_transformerencoderlayer_src_mask(self, device, nhead):
        batch_size = 2
        seqlen = 4
        d_model = 8
        dim_feedforward = 32

        model = torch.nn.TransformerEncoderLayer(
            d_model=d_model,
            nhead=nhead,
            dim_feedforward=dim_feedforward,
            batch_first=True).to(device)
        src = torch.rand(batch_size, seqlen, d_model).to(device)  # bs, seqlen, d_model
        src_mask = torch.zeros(seqlen, seqlen).to(torch.bool).to(device)

        model(src, src_mask=src_mask)
        model.eval()
        with torch.no_grad():
            model(src, src_mask=src_mask)

    @parametrize("device", device_list)
    @parametrize("use_torchscript", [False])
    @parametrize("enable_nested_tensor", [True, False])
    @parametrize("use_autocast", [True, False])
    @parametrize("d_model", [12, 256])
    def test_transformerencoder_fastpath(self, device, use_torchscript, enable_nested_tensor, use_autocast, d_model):
        """
        Test TransformerEncoder fastpath output matches slowpath output
        """
        torch.manual_seed(1234)
        nhead = 4
        dim_feedforward = d_model
        batch_first = True

        model = torch.nn.TransformerEncoder(
            torch.nn.TransformerEncoderLayer(
                d_model=d_model,
                nhead=nhead,
                dim_feedforward=dim_feedforward,
                batch_first=batch_first),
            num_layers=2,
            enable_nested_tensor=enable_nested_tensor
        ).to(device).eval()

        if use_torchscript:
            model = torch.jit.script(model)

        # each input is (input, mask)
        input_mask_pairs = [
            (
                torch.rand(3, 2, d_model),
                [
                    [0, 1],
                    [0, 1],
                    [1, 1]
                ]
            ),
            (
                torch.rand(2, 100, d_model),
                [
                    [0] * 98 + [1] * 2,
                    [0] * 90 + [1] * 10
                ]
            ),
            # softmax.cu switches from fast->slowpath at masked seqlen 1024. test 1024.
            (
                torch.rand(2, 1024, d_model),
                [
                    [0] * 1020 + [1] * 4,
                    [0] * 1024,
                ]
            ),
            (
                torch.rand(1, 1026, d_model),
                [[0] * 1024 + [1] * 2]
            ),
            # softmax.cu switches from fast->slowpath at masked seqlen 1024. test range of masks above 1024.
            (
                torch.rand(4, 1040, d_model),
                [
                    [0] * 1024 + [1] * 16,
                    [0] * 1025 + [1] * 15,
                    [0] * 1031 + [1] * 9,
                    [0] * 1040,
                ]
            )
        ]
        input_mask_pairs = [
            (
                torch.tensor(pair[0], device=device, dtype=torch.get_default_dtype()),  # float input
                torch.tensor(pair[1], device=device, dtype=torch.bool)  # bool mask
            ) for pair in input_mask_pairs
        ]

        maybe_autocast = torch.autocast("cuda", dtype=torch.float16) if use_autocast else contextlib.nullcontext()
        with maybe_autocast:
            for input, src_key_padding_mask in input_mask_pairs:
                with torch.no_grad():
                    fastpath_output = model(input, src_key_padding_mask=src_key_padding_mask)
                slowpath_output = model(input, src_key_padding_mask=src_key_padding_mask)  # reference
                # Make sure fastpath_output is same shape as slowpath_output and mask.
                # When enable_nested_tensor=true, fastpath_output may be smaller than input tensor.
                # Eg if input bs=1, seqlen=6, and we mask out 2 tokens, fastpath_output will have bs=1, seqlen=4.
                # Expand back to old size to match.
                bs, true_seqlen, embed_dim = fastpath_output.shape
                expanded_seqlen = src_key_padding_mask.shape[1]
                fastpath_output_expanded = torch.zeros(bs, expanded_seqlen, embed_dim, device=device)
                fastpath_output_expanded[:, :true_seqlen, :] = fastpath_output
                # no garauntees on output corresponding to masked tokens, so they may vary between slow/fast path. set all to 0.
                fastpath_output_expanded = fastpath_output_expanded.masked_fill(src_key_padding_mask.unsqueeze(-1), 0)
                slowpath_output = slowpath_output.masked_fill(src_key_padding_mask.unsqueeze(-1), 0)
                torch.testing.assert_close(fastpath_output_expanded, slowpath_output, rtol=1e-7, atol=1e-5)

    @parametrize("with_no_grad", [True, False])
    @parametrize("training", [True, False])
    @parametrize("enable_nested_tensor", [False])
    @parametrize("device", device_list)
    def test_transformerencoder_square_input(self, with_no_grad, training, enable_nested_tensor, device):
        """
        Test for edge cases when input of shape (batch size, sequence length, embedding dimension) has
        batch size == sequence length
        """
        model = torch.nn.TransformerEncoder(
            torch.nn.TransformerEncoderLayer(d_model=4, nhead=2, dim_feedforward=16, dropout=0.0, batch_first=True),
            num_layers=2,
            enable_nested_tensor=enable_nested_tensor
        ).to(device)

        with torch.no_grad():
            # set constant weights of the model
            for idx, p in enumerate(model.parameters()):
                x = p.data
                sz = x.view(-1).size(0)
                shape = x.shape
                x = torch.cos(torch.arange(0, sz).float().view(shape))
                p.data.copy_(x)

        if training:
            model = model.train()
        else:
            model = model.eval()
        x = torch.arange(0, 16).reshape(2, 2, 4).to(torch.get_default_dtype()).to(device)
        src_mask = torch.Tensor([[0, 1], [0, 0]]).to(torch.bool).to(device)

        if with_no_grad:
            cm = torch.no_grad()
        else:
            cm = contextlib.nullcontext()
        with cm:
            result = model(x, mask=src_mask)

        ref_output = torch.Tensor([[[2.420306205749512, 0.017629241570830, -0.607857942581177, -0.085519507527351],
                                    [2.420306205749512, 0.017629241570830, -0.607857942581177, -0.085519507527351]],
                                   [[2.419836044311523, 0.017548924311996, -0.608187675476074, -0.085347734391689],
                                    [2.419836044311523, 0.017548924311996, -0.608187675476074, -0.085347734391689]]]
                                  ).to(device)
        self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
        torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-5)

    @parametrize("batch_first", [True, False])
    @parametrize("training", [True, False])
    @parametrize("enable_nested_tensor", [True, False])
    @parametrize("device", device_list)
    def test_transformerencoder(self, batch_first, training, enable_nested_tensor, device):
        def get_a_test_layer(activation, batch_first=False):
            d_model = 4
            nhead = 2
            dim_feedforward = 16
            dropout = 0.0

            layer = nn.TransformerEncoderLayer(
                d_model,
                nhead,
                dim_feedforward=dim_feedforward,
                dropout=dropout,
                activation=activation,
                batch_first=batch_first,
            ).to(device)

            with torch.no_grad():
                # set constant weights of the model
                for idx, p in enumerate(layer.parameters()):
                    x = p.data
                    sz = x.view(-1).size(0)
                    shape = x.shape
                    x = torch.cos(torch.arange(0, sz).float().view(shape))
                    p.data.copy_(x)

            return layer

        # this is a deterministic test for TransformerEncoder
        activation = F.relu

        def _test(batch_first, training, enable_nested_tensor):
            def perm_fn(x):
                return x.transpose(1, 0) if batch_first else x

            encoder_layer = get_a_test_layer(activation=activation,
                                             batch_first=batch_first)

            model = nn.TransformerEncoder(encoder_layer, 1).to(device)
            if not training:
                model = model.eval()

            # deterministic input
            encoder_input = perm_fn(torch.tensor([[[0.7462, 0.6653, 0.5679, 0.4891],
                                                   [0.5387, 0.1655, 0.3565, 0.0471]],
                                                  [[0.8335, 0.2799, 0.5031, 0.2947],
                                                   [0.1402, 0.0318, 0.7636, 0.1346]],
                                                  [[0.6333, 0.9344, 0.1376, 0.9938],
                                                   [0.8924, 0.2872, 0.6692, 0.2944]],
                                                  [[0.9897, 0.6915, 0.3154, 0.1733],
                                                   [0.8645, 0.3513, 0.3064, 0.0767]],
                                                  [[0.8117, 0.2366, 0.4838, 0.7881],
                                                   [0.3718, 0.4945, 0.9511, 0.0864]]]
                                                 )).to(device)
            result = model(encoder_input)
            ref_output = perm_fn(torch.tensor([[[2.428589, 0.020835, -0.602055, -0.085249],
                                                [2.427987, 0.021213, -0.602496, -0.084103]],
                                               [[2.424689, 0.019155, -0.604793, -0.085672],
                                                [2.413863, 0.022211, -0.612486, -0.072490]],
                                               [[2.433774, 0.021598, -0.598343, -0.087548],
                                                [2.425104, 0.019748, -0.604515, -0.084839]],
                                               [[2.436185, 0.022682, -0.596625, -0.087261],
                                                [2.433556, 0.021891, -0.598509, -0.086832]],
                                               [[2.416246, 0.017512, -0.610712, -0.082961],
                                                [2.422901, 0.024187, -0.606178, -0.074929]]]
                                              )).to(device)
            self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
            torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-5)

            # all 0 src_mask
            src_mask = torch.zeros([5, 5]).to(device) == 1
            result = model(encoder_input, mask=src_mask)
            self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
            torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-5)

            # all 0
            mask = torch.zeros([2, 5]).to(device) == 1
            result = model(encoder_input, src_key_padding_mask=mask)
            self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
            torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-5)

            mask[0, 1] = 1
            mask[1, 3] = 1
            mask[1, 4] = 1
            # If mask is not left aligned
            # We disable nested tensor
            model.enable_nested_tensor = enable_nested_tensor
            result = model(encoder_input, src_key_padding_mask=mask)
            ref_output = perm_fn(torch.tensor([[[2.429026, 0.020793, -0.601741, -0.085642],
                                                [2.428811, 0.021445, -0.601912, -0.084252]],
                                               [[2.425009, 0.019155, -0.604566, -0.085899],
                                                [2.415408, 0.02249, -0.611415, -0.073]],
                                               [[2.434199, 0.021682, -0.598039, -0.087699],
                                                [2.42598, 0.019941, -0.603896, -0.085091]],
                                               [[2.436457, 0.022736, -0.59643, -0.08736],
                                                [2.434021, 0.022093, -0.598179, -0.08679]],
                                               [[2.416531, 0.017498, -0.610513, -0.083181],
                                                [2.4242, 0.024653, -0.605266, -0.074959]]]
                                              )).to(device)
            self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
            torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-5)

            # test case 2, multiple layers no norm
            model = nn.TransformerEncoder(encoder_layer, 2, enable_nested_tensor=enable_nested_tensor).to(device)
            if not training:
                model = model.eval()
            result = model(encoder_input, src_key_padding_mask=mask)
            ref_output = perm_fn(torch.tensor([[[2.419051, 0.017446, -0.608738, -0.085003],
                                                [2.419102, 0.017452, -0.608703, -0.085026]],
                                               [[2.419043, 0.017445, -0.608744, -0.084999],
                                                [2.419052, 0.017446, -0.608738, -0.085004]],
                                               [[2.419067, 0.017448, -0.608727, -0.085010],
                                                [2.419098, 0.017452, -0.608706, -0.085024]],
                                               [[2.419072, 0.017449, -0.608724, -0.085012],
                                                [2.419119, 0.017455, -0.608691, -0.085034]],
                                               [[2.419019, 0.017442, -0.608761, -0.084989],
                                                [2.419075, 0.017449, -0.608722, -0.085014]]]
                                              )).to(device)
            self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
            torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-5)

            model = nn.TransformerEncoder(encoder_layer, 6, enable_nested_tensor=enable_nested_tensor).to(device)
            if not training:
                model = model.eval()
            result = model(encoder_input, src_key_padding_mask=mask)
            ref_output = perm_fn(torch.tensor([[[2.419101, 0.017453, -0.608703, -0.085025],
                                                [2.419101, 0.017453, -0.608704, -0.085025]],
                                               [[2.419101, 0.017453, -0.608703, -0.085025],
                                                [2.419101, 0.017453, -0.608704, -0.085025]],
                                               [[2.419101, 0.017453, -0.608703, -0.085025],
                                                [2.419101, 0.017453, -0.608704, -0.085025]],
                                               [[2.419101, 0.017453, -0.608703, -0.085025],
                                                [2.419101, 0.017453, -0.608704, -0.085025]],
                                               [[2.419101, 0.017453, -0.608703, -0.085025],
                                                [2.419101, 0.017453, -0.608704, -0.085025]]]
                                              )).to(device)
            self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
            torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-5)

            # test case 3, multiple layers with norm
            # d_model = 4
            norm = nn.LayerNorm(4)
            model = nn.TransformerEncoder(encoder_layer, 2, norm=norm, enable_nested_tensor=enable_nested_tensor).to(device)
            if not training:
                model = model.eval()
            result = model(encoder_input, src_key_padding_mask=mask)
            ref_output = perm_fn(torch.tensor([[[1.695949, -0.357635, -0.893077, -0.445238],
                                                [1.695955, -0.357639, -0.893050, -0.445266]],
                                               [[1.695948, -0.357634, -0.893082, -0.445233],
                                                [1.695950, -0.357635, -0.893077, -0.445238]],
                                               [[1.695951, -0.357636, -0.893069, -0.445246],
                                                [1.695955, -0.357639, -0.893052, -0.445264]],
                                               [[1.695952, -0.357636, -0.893066, -0.445249],
                                                [1.695957, -0.357641, -0.893041, -0.445276]],
                                               [[1.695946, -0.357632, -0.893095, -0.445220],
                                                [1.695952, -0.357637, -0.893065, -0.445251]]]
                                              )).to(device)
            self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
            torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-5)

            model = nn.TransformerEncoder(encoder_layer, 6, norm=norm, enable_nested_tensor=enable_nested_tensor).to(device)
            if not training:
                model = model.eval()
            result = model(encoder_input, src_key_padding_mask=mask)
            ref_output = perm_fn(torch.tensor([[[1.695955, -0.357639, -0.893051, -0.445265],
                                                [1.695955, -0.357639, -0.893051, -0.445265]],
                                               [[1.695955, -0.357639, -0.893051, -0.445265],
                                                [1.695955, -0.357639, -0.893051, -0.445265]],
                                               [[1.695955, -0.357639, -0.893051, -0.445265],
                                                [1.695955, -0.357639, -0.893051, -0.445265]],
                                               [[1.695955, -0.357639, -0.893051, -0.445265],
                                                [1.695955, -0.357639, -0.893051, -0.445265]],
                                               [[1.695955, -0.357639, -0.893051, -0.445265],
                                                [1.695955, -0.357639, -0.893051, -0.445265]]]
                                              )).to(device)
            self.assertEqual(tuple(result.shape), tuple(ref_output.shape))
            torch.testing.assert_close(result, ref_output, rtol=1e-7, atol=1e-5)

        # TODO: remove set default dtype to double by making ref_output more precise.
        # Added because this test was copied from test_nn.py, which has default
        # dtype double. If default dtype is float, tests will say tensors not close because
        # ref output precision too low
        with set_default_dtype(torch.double):
            if training:
                cm = contextlib.nullcontext()
            else:
                cm = torch.no_grad()  # transformer fast path requires no grad
            with cm:
                _test(batch_first, training, enable_nested_tensor)

    @unittest.skipIf(not TEST_FAIRSEQ, "Fairseq not found")
    @unittest.skipIf(not TEST_CUDA, 'CUDA not available')
    def test_decoder_only_layer(self):
        DEFAULT_PADDING_IDX = 0

        class FairseqDecoder(torch.nn.Module):
            def __init__(
                self,
                embed_dim,
                attention_heads,
                ffn_embed_dim,
                num_layers,
                embedding_layer,  # torch.nn.Embedding. Must have a padding_idx field
                dropout=0,
                normalize_before=False,
                torch_encoder=None,  # torch encoder that you can map weights from
                activation="relu",
            ):
                super().__init__()

                cfg = fairseq_transformer.TransformerConfig()
                cfg.decoder.embed_dim = embed_dim
                cfg.decoder.output_dim = embed_dim
                cfg.decoder.attention_heads = attention_heads
                cfg.decoder.ffn_embed_dim = ffn_embed_dim
                cfg.dropout = dropout
                cfg.decoder.normalize_before = normalize_before
                cfg.decoder.layers = num_layers
                # make embedding behavior same as other encoders
                cfg.no_token_positional_embeddings = True
                cfg.no_scale_embedding = True
                cfg.activation_fn = activation

                dictionary = {}  # TODO: verify what this is

                self.decoder = fairseq_transformer.TransformerDecoder(
                    cfg,
                    dictionary,
                    embedding_layer,
                    no_encoder_attn=True,
                    output_projection=None,
                )

                if torch_encoder is not None:
                    self.decoder = torch_to_fairseq(torch_encoder, self.decoder)
                self.decoder = self.decoder.eval().cuda().half()

            def forward(
                self,
                tokens,
                src_lengths=None,
                with_triangle_mask=False,
                incremental_state=None,
            ):
                return self.decoder(
                    prev_output_tokens=tokens,
                    encoder_out=None,
                    incremental_state=incremental_state,
                    features_only=True,
                    full_context_alignment=not with_triangle_mask,
                    alignment_layer=None,
                    alignment_heads=None,
                    src_lengths=src_lengths,
                    return_all_hiddens=False,
                )[0]

        class BetterDecoder(torch.nn.Module):
            """
            Only incremental decoder for now
            """

            def __init__(self, transformer, embedding, pad_idx):
                super().__init__()
                self.transformer = transformer
                self.embedding = embedding
                self.padding_idx = pad_idx

            def forward(
                self,
                x,
                src_mask=None,
                include_padding_mask=True,
                incr_key_lst=None,
                incr_value_lst=None,
                is_incremental_decoding=False,
            ):
                padding_mask = None
                if not x.is_nested and include_padding_mask:
                    padding_mask = x.eq(self.padding_idx)
                if(is_incremental_decoding):
                    x = x[:, -1:]  # only take the last token
                x = self.embedding(x)

                one_encoder_layer = self.transformer.layers[0]
                self_attn = one_encoder_layer.self_attn
                embed_dim = self_attn.embed_dim
                num_heads = self_attn.num_heads

                use_gelu = (
                    one_encoder_layer.activation_relu_or_gelu == 2
                )  # see torch/nn/modules/activation attention impl. 1 == relu, 2 == gelu
                assert (
                    one_encoder_layer.activation_relu_or_gelu != 0
                )  # 0 == not relu or gelu

                norm_first = one_encoder_layer.norm_first


                # TODO: make this a bit less janky. but for now we initialize with an empty tensor.
                if(not is_incremental_decoding):
                    assert len(incr_key_lst) == 0 or incr_key_lst[0] is None
                    assert len(incr_value_lst) == 0 or incr_value_lst[0] is None
                while len(incr_key_lst) <= len(self.transformer.layers):
                    if(is_incremental_decoding):
                        incr_key_lst.append(torch.Tensor([]).cuda().half())
                        incr_value_lst.append(torch.Tensor([]).cuda().half())
                    else:
                        incr_key_lst.append(None)
                        incr_value_lst.append(None)

                for i, layer in enumerate(self.transformer.layers):
                    incr_key = incr_key_lst[i]
                    incr_value = incr_value_lst[i]

                    x, incr_key, incr_value = torch._transformer_decoder_only_layer_fwd(
                        src=x,
                        embed_dim=embed_dim,
                        num_heads=num_heads,
                        qkv_weight=layer.self_attn.in_proj_weight,
                        qkv_bias=layer.self_attn.in_proj_bias,
                        proj_weight=layer.self_attn.out_proj.weight,
                        proj_bias=layer.self_attn.out_proj.bias,
                        use_gelu=use_gelu,
                        norm_first=norm_first,
                        # TODO: layer_norm_eps hardcoded to be same as nn.TransformerEncoder default.
                        # fix by pulling from self_attn.norm1
                        eps=1e-5,
                        norm_weight_1=layer.norm1.weight,
                        norm_bias_1=layer.norm1.bias,
                        norm_weight_2=layer.norm2.weight,
                        norm_bias_2=layer.norm2.bias,
                        ffn_weight_1=layer.linear1.weight,
                        ffn_bias_1=layer.linear1.bias,
                        ffn_weight_2=layer.linear2.weight,
                        ffn_bias_2=layer.linear2.bias,
                        mask=src_mask,
                        incr_key=incr_key,  # altered in place
                        incr_value=incr_value,
                    )

                    # not in-place
                    if(not is_incremental_decoding):
                        incr_key = None
                        incr_value = None
                    incr_key_lst[i] = incr_key
                    incr_value_lst[i] = incr_value

                return x, incr_key_lst, incr_value_lst

        def torch_to_fairseq(torch_encoder, fairseq_encoder):
            for src_layer, dst_layer in zip(torch_encoder.layers, fairseq_encoder.layers):
                w_q, w_k, w_v = src_layer.self_attn.in_proj_weight.chunk(3, dim=0)
                b_q, b_k, b_v = src_layer.self_attn.in_proj_bias.chunk(3, dim=0)

                dst_layer.self_attn.q_proj.weight = torch.nn.Parameter(w_q)
                dst_layer.self_attn.q_proj.bias = torch.nn.Parameter(b_q)
                dst_layer.self_attn.k_proj.weight = torch.nn.Parameter(w_k)
                dst_layer.self_attn.k_proj.bias = torch.nn.Parameter(b_k)
                dst_layer.self_attn.v_proj.weight = torch.nn.Parameter(w_v)
                dst_layer.self_attn.v_proj.bias = torch.nn.Parameter(b_v)

                dst_layer.self_attn.out_proj.weight = src_layer.self_attn.out_proj.weight
                dst_layer.self_attn.out_proj.bias = src_layer.self_attn.out_proj.bias

                dst_layer.fc1.weight = src_layer.linear1.weight
                dst_layer.fc1.bias = src_layer.linear1.bias

                # fairseq may use fusedlayernorm from nvidia apex - diff properties
                dst_layer.self_attn_layer_norm.load_state_dict(src_layer.norm1.state_dict())

                dst_layer.fc2.weight = src_layer.linear2.weight
                dst_layer.fc2.bias = src_layer.linear2.bias

                dst_layer.final_layer_norm.load_state_dict(src_layer.norm2.state_dict())

            return fairseq_encoder

        def set_weights_deterministic(model):
            for idx, p in enumerate(model.parameters()):
                x = p.data
                sz = x.view(-1).size(0)
                shape = x.shape
                x = torch.cos(torch.arange(0, sz).float().view(shape))
                p.data.copy_(x)

        D = 4  # d_model
        H = 2  # nhead
        FD = 16  # dim_feedforward
        V = 100  # vocab size
        L = 2  # num layers

        embedding_layer = torch.nn.Embedding(V, D, DEFAULT_PADDING_IDX)
        layer = torch.nn.TransformerEncoderLayer(
            d_model=D,
            nhead=H,
            dim_feedforward=FD,
            batch_first=True,
            activation="gelu",
        )
        transformer = torch.nn.TransformerEncoder(
            layer,
            num_layers=L,
        ).eval().cuda().half()

        set_weights_deterministic(embedding_layer)
        set_weights_deterministic(transformer)

        better_decoder = (
            BetterDecoder(transformer, embedding_layer, DEFAULT_PADDING_IDX)
            .eval()
            .cuda()
            .half()
        )
        fairseq_decoder = (
            FairseqDecoder(
                D,
                H,
                FD,
                L,
                embedding_layer,
                dropout=0,
                normalize_before=False,
                torch_encoder=transformer,
                activation="gelu",
            )
            .eval()
            .cuda()
            .half()
        )

        tokens = torch.Tensor([
            [5, 6, 7, 8],
            [9, 10, 11, 12]
        ]).to(torch.int).cuda()
        lengths_tensor = torch.Tensor([2, 2]).to(torch.int).cuda()
        # bs = 2, seqlen = 4
        bs, seqlen = tokens.shape

        upper_triangle = torch.zeros(seqlen, seqlen)
        upper_triangle.fill_(-100000000)
        upper_triangle = torch.triu(upper_triangle, 1)
        upper_triangle = upper_triangle.cuda().half()
        upper_triangle_expanded = upper_triangle.unsqueeze(0).unsqueeze(0)
        upper_triangle_expanded = upper_triangle_expanded.expand(
            bs, H, -1, -1
        )

        # test forced decoding
        with torch.no_grad():
            result, _, _ = better_decoder(
                tokens,
                src_mask=upper_triangle_expanded,
                include_padding_mask=False,
                incr_key_lst=[],
                incr_value_lst=[],
                is_incremental_decoding=False,
            )
        ref_output = fairseq_decoder(tokens, lengths_tensor, with_triangle_mask=True)

        self.assertEqual(result.shape, ref_output.shape)
        torch.testing.assert_close(result, ref_output, atol=1e-3, rtol=1e-2)

        # test incremental decoding
        bs, seqlen = tokens.shape

        incr_state = {}
        ref_outputs = [fairseq_decoder(
            tokens[:, :i],
            src_lengths=None,
            with_triangle_mask=False,
            incremental_state=incr_state,
        ) for i in range(1, seqlen + 1)]
        ref_output = torch.stack(ref_outputs)

        incr_key_lst = []
        incr_value_lst = []
        results = []
        for i in range(1, seqlen + 1):
            res, incr_key_lst, incr_value_lst = better_decoder(
                tokens[:, :i],
                src_mask=None,
                include_padding_mask=False,
                incr_key_lst=incr_key_lst,
                incr_value_lst=incr_value_lst,
                is_incremental_decoding=True,
            )
            results.append(res)
        result = torch.stack(results)

        self.assertEqual(result.shape, ref_output.shape)
        torch.testing.assert_close(result, ref_output, atol=1e-3, rtol=1e-2)

    @parametrize("input_dim,attn_mask_dim,is_causal",
                 [(3, None, False), (3, 2, False), (3, 2, True), (3, 3, False), (3, 3, True),
                  (4, None, False), (4, 2, False), (4, 2, True), (4, 4, False), (4, 4, True)],
                 name_fn=lambda input_dim, attn_dim, is_causal: (
                     f"{input_dim}D_input_dim_" + (
                         f"{attn_dim}D_{'causal_' if is_causal else ''}attn_mask"
                         if attn_dim is not None else "no_attn_mask")))
    @parametrize("dropout_p", [0.0, 0.2, 0.5])
    @parametrize("device", device_list)
    @sdp_kernel(enable_flash=False)
    def test_scaled_dot_product_attention(self, device, input_dim, attn_mask_dim, is_causal, dropout_p):
        def sdp_ref(
                q,
                k,
                v,
                attn_mask=None,
                dropout_p=0.0):
            E = q.size(-1)
            q = q / math.sqrt(E)
            # (B, Nt, E) x (B, E, Ns) -> (B, Nt, Ns)
            if attn_mask is not None:
                attn = torch.baddbmm(attn_mask, q, k.transpose(-2, -1))
            else:
                attn = torch.bmm(q, k.transpose(-2, -1))

            attn = torch.nn.functional.softmax(attn, dim=-1)
            if dropout_p > 0.0:
                attn = torch.nn.functional.dropout(attn, p=dropout_p)
            # (B, Nt, Ns) x (B, Ns, E) -> (B, Nt, E)
            output = torch.bmm(attn, v)
            return output, attn
        # TODO: Support cross-device / dtype testing properly when instantiate_device_type_tests() is used.
        dtypes = [torch.double, torch.float]
        for dtype in dtypes:

            def rand_tensor(*shape):
                return torch.randn(shape, device=device, dtype=dtype)

            # This test compares python and C++ implementations of SDP.
            N, N_prime, L, S, E = 5, 2, 4, 3, 6
            if input_dim == 3:
                query = rand_tensor(N, L, E)
                key = rand_tensor(N, S, E)
                value = rand_tensor(N, S, E)
            elif input_dim == 4:
                query = rand_tensor(N, N_prime, L, E)
                key = rand_tensor(N, N_prime, S, E)
                value = rand_tensor(N, N_prime, S, E)
            else:
                self.fail(f'Invalid input_dim {input_dim} encountered in SDP test')

            attn_mask = None
            if attn_mask_dim is not None:
                assert attn_mask_dim in [2, input_dim]
                mask_size = (L, S) if attn_mask_dim == 2 else ((N, L, S) if input_dim == 3 else (N, N_prime, L, S))
                attn_mask = (torch.ones(mask_size, device=device, dtype=torch.bool).tril() if is_causal
                             else torch.randint(0, 2, size=mask_size, device=device, dtype=torch.bool))

            with freeze_rng_state():
                # Python impl only supports float mask and 3D inputs.
                attn_mask_float = attn_mask
                if attn_mask_float is not None:
                    attn_mask_float = torch.zeros_like(attn_mask, dtype=query.dtype)
                    attn_mask_float.masked_fill_(attn_mask.logical_not(), float("-inf"))
                q, k, v = query.view(-1, L, E), key.view(-1, S, E), value.view(-1, S, E)
                a = attn_mask_float
                if a is not None and attn_mask_dim > 3:
                    a = a.view(-1, L, S)
                expected = sdp_ref(q, k, v, attn_mask=a, dropout_p=dropout_p)
                if input_dim > 3:
                    expected = (expected[0].view(-1, N_prime, L, E), expected[1].view(-1, N_prime, L, S))

            need_attn_weights: bool = True
            with freeze_rng_state():
                if is_causal:
                    # NB: Don't pass attn_mask here
                    actual = torch.ops.aten._scaled_dot_product_attention(
                        query, key, value, None, dropout_p, need_attn_weights, is_causal)

                    # Error case: both explicit attn_mask and is_causal are set
                    with self.assertRaisesRegex(RuntimeError,
                                                "Explicit attn_mask should not be set when is_causal=True"):
                        torch.ops.aten._scaled_dot_product_attention(
                            query, key, value, attn_mask, dropout_p, need_attn_weights, is_causal)
                else:
                    actual = torch.ops.aten._scaled_dot_product_attention(
                        query, key, value, attn_mask, dropout_p, need_attn_weights, is_causal)

            # freeze_rng_state() doesn't seem to work outside of CPU, so dropout makes the results incomparable.
            # TODO: Do this skipping in a nicer way once the granular test skipping logic lands.
            if dropout_p == 0.0 or device == 'cpu':
                self.assertEqual(actual, expected)

    @unittest.skipIf(TEST_WITH_CROSSREF, 'Fastpath not available with crossref')
    @torch.no_grad()
    def test_mask_check_fastpath(self):
        """
        Test that fastpath is executed independently of the mask that is passed.
        If the passed mask is left aligned or mask_check=False, test that nested tensors are used (sparsity fastpath),
        otherwise use fastpath with traditional tensors.
        """

        x = torch.Tensor([[[1, 2], [3, 4], [5, 6]]]).to(torch.float)

        def _test_fastpath(model, mask, mock_return_value, nested_tensors=True):
            with patch('torch._transformer_encoder_layer_fwd') as fastpath_mock:
                fastpath_mock.return_value = mock_return_value
                model(x, src_key_padding_mask=mask)

                # If mock was called, fastpath was taken
                self.assertTrue(fastpath_mock.called)

                # If mock was called with nested tensors, sparsity fastpath was taken
                for call_args, _ in fastpath_mock.call_args_list:
                    self.assertEqual(call_args[0].is_nested, nested_tensors)

        encoder_layer = torch.nn.TransformerEncoderLayer(d_model=2, nhead=2, dim_feedforward=8, batch_first=True)

        model = torch.nn.TransformerEncoder(encoder_layer, num_layers=2, enable_nested_tensor=True, mask_check=True)
        model.eval()

        aligned_mask = torch.Tensor([[0, 0, 1]]).to(torch.bool)
        not_aligned_mask = torch.Tensor([[1, 0, 1]]).to(torch.bool)
        nested_tensor_return_value = torch.nested.nested_tensor([torch.ones((2, 2), dtype=torch.float)])
        tensor_return_value = torch.ones((1, 3, 2), dtype=torch.float)

        # Left aligned mask results in sparsity fastpath
        _test_fastpath(model, aligned_mask, nested_tensor_return_value, nested_tensors=True)

        # Not aligned mask results in fastpath
        _test_fastpath(model, not_aligned_mask, tensor_return_value, nested_tensors=False)

        model = torch.nn.TransformerEncoder(encoder_layer, num_layers=2, enable_nested_tensor=False, mask_check=True)
        model.eval()

        # If nested tensor disabled, fastpath is always taken
        _test_fastpath(model, aligned_mask, tensor_return_value, nested_tensors=False)
        _test_fastpath(model, not_aligned_mask, tensor_return_value, nested_tensors=False)


        model = torch.nn.TransformerEncoder(encoder_layer, num_layers=2, enable_nested_tensor=True, mask_check=False)
        model.eval()

        # Mask check disabled results in sparisty fastpath, independently of the mask
        _test_fastpath(model, aligned_mask, nested_tensor_return_value, nested_tensors=True)
        _test_fastpath(model, not_aligned_mask, nested_tensor_return_value, nested_tensors=True)

    @unittest.skipIf(not TEST_CUDA or TEST_WITH_ROCM or IS_WINDOWS, "Flash Attention was not built for this system")
    @parametrize("type", ["dense", "nested"])
    @parametrize("is_contiguous", [True, False])
    def test_scaled_dot_product_attention_fused_kernels(self, type: str, is_contiguous: bool):
        def rand_nt(shape):
            batch, seq_len, num_heads, head_dim = shape
            return torch.nested.nested_tensor([torch.randn(seq_len, num_heads, head_dim,
                                                           device="cuda", dtype=torch.float16) for _ in range(batch)])

        def rand_tensor(shape):
            batch, seq_len, num_heads, head_dim = shape
            return torch.randn(batch, seq_len, num_heads, head_dim, device="cuda", dtype=torch.float16)

        batch, seq_len, num_heads, head_dim = 32, 64, 16, 64
        shape = (batch, seq_len, num_heads, head_dim)
        if type == "dense":
            query = rand_tensor(shape)
            key = rand_tensor(shape)
            value = rand_tensor(shape)
        elif type == "nested":
            query = rand_nt(shape)
            key = rand_nt(shape)
            value = rand_nt(shape)

        # Lets switch seq_len and num_heads
        # B x S X H X D -> B x H x S x D
        query = query.transpose(1, 2)
        key = key.transpose(1, 2)
        value = value.transpose(1, 2)

        if is_contiguous:
            query = query.contiguous()
            key = key.contiguous()
            value = value.contiguous()

        with sdp_kernel(enable_math=False):
            actual = torch.nn.functional._scaled_dot_product_attention(
                query, key, value, attn_mask=None, dropout_p=0.0, need_attn_weights=False, is_causal=False)
        with sdp_kernel(enable_flash=False):
            math_ref = torch.nn.functional._scaled_dot_product_attention(
                query.contiguous(), key.contiguous(), value.contiguous(),
                attn_mask=None, dropout_p=0.0, need_attn_weights=False, is_causal=False)

        self.assertEqual(actual[0].contiguous(), math_ref[0].contiguous(), atol=1e-3, rtol=1e-2)

    @unittest.skipIf(not TEST_CUDA or TEST_WITH_ROCM or IS_WINDOWS, "Flash Attention was not built for this system")
    @parametrize("type", ["dense", "nested"])
    @parametrize("is_contiguous", [True, False])
    def test_scaled_dot_product_attention_fused_kernels_packed(self, type: str, is_contiguous: bool):
        def rand_nt(shape):
            batch, seq_len, num_heads, head_dim = shape
            return torch.nested.nested_tensor([torch.randn(seq_len, 3 * num_heads * head_dim,
                                                           device="cuda", dtype=torch.float16) for _ in range(batch)])

        def rand_tensor(shape):
            batch, seq_len, num_heads, head_dim = shape
            return torch.randn(batch, seq_len, 3 * num_heads * head_dim, device="cuda", dtype=torch.float16)

        batch_size, seq_len, num_heads, head_dim = 32, 64, 16, 64
        shape = (batch_size, seq_len, num_heads, head_dim)

        # Test Packed
        qkv = rand_tensor(shape) if type == "dense" else rand_nt(shape)
        query, key, value = qkv.chunk(3, dim=-1)

        query = query.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
        key = key.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)

        if is_contiguous:
            query = query.contiguous()
            key = key.contiguous()
            value = value.contiguous()

        with sdp_kernel(enable_math=False):
            actual = torch.nn.functional._scaled_dot_product_attention(
                query, key, value, attn_mask=None, dropout_p=0.0, need_attn_weights=False, is_causal=False)
        with sdp_kernel(enable_flash=False):
            math_ref = torch.nn.functional._scaled_dot_product_attention(
                query.contiguous(), key.contiguous(), value.contiguous(),
                attn_mask=None, dropout_p=0.0, need_attn_weights=False, is_causal=False)

        self.assertEqual(actual[0].contiguous(), math_ref[0].contiguous(), atol=2e-3, rtol=1e-2)

    @unittest.skipIf(not TEST_CUDA or TEST_WITH_ROCM or IS_WINDOWS, "Flash Attention was not built for this system")
    @parametrize("type", ["dense", "nested"])
    @parametrize("fused_kernel", ["flash", "mem_efficient"])
    def test_scaled_dot_product_attention_fused_kernels_packed_accuracy(self, type: str, fused_kernel: str):
        if (not SM80OrLater) and fused_kernel == "flash":
            return

        def rand_nt(shape):
            batch, seq_len, num_heads, head_dim = shape
            tensors = [6 * torch.rand((seq_len, 3 * num_heads * head_dim), device="cuda", dtype=torch.float32) - 3
                       for _ in range(batch)]
            return (torch.nested.nested_tensor(tensors, device="cuda", dtype=torch.float32),
                    torch.nested.nested_tensor(tensors, device="cuda", dtype=torch.float16))

        def rand_tensor(shape):
            batch, seq_len, num_heads, head_dim = shape
            tensor = 6 * torch.rand((batch, seq_len, 3 * num_heads * head_dim), device="cuda", dtype=torch.float32) - 3
            return tensor, tensor.to(dtype=torch.float16)

        batch_size, seq_len, num_heads, head_dim = 16, 8, 4, 64
        shape = (batch_size, seq_len, num_heads, head_dim)

        # Test Packed
        qkv, qkv_low_precision = rand_tensor(shape) if type == "dense" else rand_nt(shape)
        query, key, value = qkv.chunk(3, dim=-1)
        query_lp, key_lp, value_lp = qkv_low_precision.chunk(3, dim=-1)

        query = query.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
        key = key.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)

        query_lp = query_lp.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
        key_lp = key_lp.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)
        value_lp = value_lp.view(batch_size, -1, num_heads, head_dim).transpose(1, 2)

        if fused_kernel == "flash":
            with sdp_kernel(enable_mem_efficient=False, enable_math=False):
                actual = torch.nn.functional._scaled_dot_product_attention(
                    query_lp, key_lp, value_lp, attn_mask=None, dropout_p=0.0, need_attn_weights=False, is_causal=False)
        elif fused_kernel == "mem_efficient":
            with sdp_kernel(enable_flash=False, enable_math=False):
                actual = torch.nn.functional._scaled_dot_product_attention(
                    query_lp, key_lp, value_lp, attn_mask=None, dropout_p=0.0, need_attn_weights=False, is_causal=False)

        with sdp_kernel(enable_flash=False, enable_mem_efficient=False):
            math_ref_lp = torch.nn.functional._scaled_dot_product_attention(
                query_lp.contiguous(), key_lp.contiguous(), value_lp.contiguous(),
                attn_mask=None, dropout_p=0.0, need_attn_weights=False, is_causal=False)

        with sdp_kernel(enable_flash=False, enable_mem_efficient=False):
            math_query = query.contiguous()
            math_key = key.contiguous()
            math_value = value.contiguous()

            math_ref = torch.nn.functional._scaled_dot_product_attention(
                math_query, math_key, math_value, attn_mask=None, dropout_p=0.0, need_attn_weights=False, is_causal=False)

        actual_test = actual[0]
        math_ref_test = math_ref[0]
        math_ref_lp_test = math_ref_lp[0]

        if actual_test.is_nested:
            actual_test = torch.nested.to_padded_tensor(actual_test.contiguous(), padding=0.0)
            math_ref_test = torch.nested.to_padded_tensor(math_ref_test, padding=0.0)
            math_ref_lp_test = torch.nested.to_padded_tensor(math_ref_lp_test, padding=0.0)

        actual_test = actual_test.to(dtype=torch.float32).contiguous()
        math_ref_test = math_ref_test.to(dtype=torch.float32).contiguous()
        math_ref_lp_test = math_ref_lp_test.to(dtype=torch.float32).contiguous()

        self.assertEqual(math_ref_test, math_ref_lp_test, atol=7e-3, rtol=7e-3)
        self.assertEqual(actual_test, math_ref_test, atol=5e-3, rtol=5e-3)


    @unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
    def test_sdp_runtime_dispatch(self):
        # We will test all the constraints that we know will cause a failure
        # The problem is that any code path that goes down flash_attention
        # will fail on CI/CD becuase it is not compiled with the right flags
        device = 'cuda'
        dtype = torch.float16

        def make_tensor(*size, device=device, dtype=dtype):
            return torch.randn(size, device=device, dtype=dtype)

        with sdp_kernel(enable_flash=False, enable_math=False, enable_mem_efficient=False):
            q, k, v = make_tensor(2, 3, 4), make_tensor(2, 3, 4), make_tensor(2, 3, 4)
            self.assertRaisesRegex(RuntimeError, "No viable backend for scaled_dot_product_attention was found.",
                                   lambda: torch.nn.functional._scaled_dot_product_attention(q, k, v))

        with sdp_kernel(enable_flash=True, enable_mem_efficient=False, enable_math=False):
            # Failures for invalid input

            # Dim is not 4
            q, k, v = make_tensor(2, 3, 4), make_tensor(2, 3, 4), make_tensor(2, 3, 4)
            self.assertRaises(RuntimeError, lambda: torch.nn.functional._scaled_dot_product_attention(
                q, k, v, None, 0.0, False, False))

            # Xformers can now cover this case but will add back in next PR
            # Invalid last_dim size
            q, k, v = make_tensor(2, 2, 3, 4), make_tensor(2, 2, 3, 4), make_tensor(2, 2, 3, 4)
            self.assertRaises(RuntimeError, lambda: torch.nn.functional._scaled_dot_product_attention(
                q, k, v, None, 0.0, False, False))

            # Invalid dtype
            q, k, v = make_tensor(2, 2, 3, 16, dtype=torch.float64), make_tensor(
                2, 2, 3, 16, dtype=torch.float64), make_tensor(2, 2, 3, 16, dtype=torch.float64)
            self.assertRaises(RuntimeError, lambda: torch.nn.functional._scaled_dot_product_attention(
                q, k, v, None, 0.0, False, False))

            q, k, v = make_tensor(2, 2, 3, 16, dtype=torch.float32), make_tensor(
                2, 2, 3, 16, dtype=torch.float32), make_tensor(2, 2, 3, 16, dtype=torch.float32)
            self.assertRaises(RuntimeError, lambda: torch.nn.functional._scaled_dot_product_attention(
                q, k, v, None, 0.0, False, False))

            # Failures for unsupported SDP args
            q, k, v = make_tensor(2, 2, 3, 16), make_tensor(2, 2, 3, 16), make_tensor(2, 2, 3, 16)

            # Needs attention weights
            self.assertRaises(RuntimeError, lambda: torch.nn.functional._scaled_dot_product_attention(
                q, k, v, None, 0.0, True, False))

            # Non-None attention mask
            self.assertRaises(RuntimeError, lambda: torch.nn.functional._scaled_dot_product_attention(
                q, k, v, torch.ones_like(q), 0.0, False, False))

# TODO: Replace this with instantiate_device_type_tests() to take advantage of test framework support for
# cross device / dtype testing.
instantiate_parametrized_tests(TestTransformers)

if __name__ == '__main__':
    run_tests()