test_transformers.py

# 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,
    set_default_dtype
)
from torch.testing._internal.common_cuda import TEST_CUDA, SM80OrLater

if TEST_FAIRSEQ:
    import fairseq.models.transformer as fairseq_transformer

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 masks that are passed.
        If the passed key padding mask is left aligned or mask_check=False, test that nested tensors are used
        (sparsity fastpath), otherwise use fastpath with traditional tensors.
        Also test that fast path is executed with both key padding mask and attention mask passed at the same time.
        """

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

        def _test_fastpath(model, key_padding_mask, mock_return_value, attn_mask=None, 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=key_padding_mask, mask=attn_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_key_padding_mask = torch.Tensor([[0, 0, 1]]).to(torch.bool)
        not_aligned_key_padding_mask = torch.Tensor([[1, 0, 1]]).to(torch.bool)
        attn_mask = torch.Tensor([[1, 0, 1], [0, 1, 0], [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_key_padding_mask, nested_tensor_return_value, nested_tensors=True)

        # Not aligned mask results in fastpath
        _test_fastpath(model, not_aligned_key_padding_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_key_padding_mask, tensor_return_value, nested_tensors=False)
        _test_fastpath(model, not_aligned_key_padding_mask, tensor_return_value, nested_tensors=False)
        # Fast path is taken if both attention mask and key padding mask are present
        _test_fastpath(model, aligned_key_padding_mask, tensor_return_value, attn_mask=attn_mask, 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_key_padding_mask, nested_tensor_return_value, nested_tensors=True)
        _test_fastpath(model, not_aligned_key_padding_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))

    # Test failing MHA when bias was NoneType
    def test_bias_is_none(self):
        x = torch.rand((1, 5, 10))
        model = torch.nn.modules.activation.MultiheadAttention(10, 1, bias=False, batch_first=True)
        model.eval()
        model(x, x, x)
        # completes without error


# 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()