Added SmeLU #263

kashif · 2022-04-07T18:58:39Z

What does this PR do?

Fixes #262 .

Before submitting

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blefaudeux · 2022-04-07T20:46:09Z

excellent, thanks @kashif ! Couple of comments, I hope that helps, there's a trick with the definition of beta I think

kashif · 2022-04-07T20:47:55Z

right i dont think the beta default arg would work as it is... I was just about to ask you how to deal with that?

blefaudeux · 2022-04-07T20:50:49Z

xformers/triton/k_activations.py

+
+
+@triton.jit
+def smelu(x, beta=2.0):


I don't think that you can pass a default param with triton actually, it only works with a subset of the python syntax and my guess is that this is out of it (cc @ptillet). Something could be worth trying, having a getter for this kernel, like the following

def get_smelu_kernel(beta: float = 2.0): @triton.jit def smelu(x): pass # use beta here, but maybe that this will fail at the JIT phase

If that does not work,

for a start we could have a fixed beta, then iterate on the implementation to expose it (completely fine by me)

could be that the activation kernel take another parameter, which in that case would be the beta value, or that we figure out with Phil how to generate the kernel code on the fly with the proper beta

thanks @blefaudeux I'll give it a try... a bit late here so wanted to give it a shot in the morning 😴

checking this with Philippe, the default value should work actually, maybe that it needs to be : float ? or similar

ah ok! cool let me check

To be clear, what should work (for now) is default arguments for tl.constexpr annotated arguments, and with triton 2.0 :p I'm not too sure about Triton 1.x

ah right... i'm on triton 1.x at the moment...

we need to update to triton2.. CI is blocking right now, I hope to get that sorted out this week end

blefaudeux · 2022-04-19T16:38:09Z

rebasing this on top of #272 should work @kashif

kashif · 2022-04-19T16:48:00Z

ah cool! having a look

blefaudeux · 2022-04-28T05:34:33Z

hey @kashif let me know if I can help

kashif · 2022-04-28T10:13:22Z

so I tried

def get_smelu_kernel(x, beta: float = 2.0):
    @triton.jit
    def smelu(x, beta):
        """
        SmeLU_ activation -  Smooth ReLU

        .. _SmeLU: https://arxiv.org/pdf/2202.06499.pdf
        """
        zero = 0.0
        four = 4.0
        beta = beta.to(x.dtype)

        output = (x + beta) * (x + beta) / (four.to(x.dtype) * beta)
        relu = tl.where(x >= beta, x, zero.to(x.dtype))
        return tl.where(tl.abs(x) <= beta, output, relu)

    smelu(x, beta)

but that didnt work either so I am setting the beta param to 2.0 for now.

blefaudeux · 2022-04-28T15:58:32Z

if you can I would really recommend setting up pre-commit, it helps with all the linting. Some explanations here

blefaudeux · 2022-05-05T15:47:12Z

Thanks for the updates @kashif, looks good to me, we can always iterate to expose beta down the line. It looks like the errors are unrelated to your changes, maybe dependent on main having changed, could you try to rebase ? I can do that also if you'd like

blefaudeux

LGTM, we need to solve these CI issues (guess is that it's waiting for a rebase), thanks a lot @kashif !

kashif · 2022-05-05T15:48:10Z

thank you! I learned a lot and now i have a 3090Ti to test on!

kashif · 2022-05-05T15:48:50Z

i was just heading out to eat... can you kindly rebase?

blefaudeux · 2022-05-05T18:27:29Z

hmm, thanks for the update Kashif, looks like the errors are still there, I don't understand how they can be related. I'll have a look

blefaudeux · 2022-05-09T21:21:49Z

Hmm, I can repro the CI error but it should be unrelated to your changes @kashif, it means there's something wrong either in the triton stack or in the cuda kernels :( I'm trying to sort that out

blefaudeux · 2022-05-09T21:27:21Z

ok, at least I got something wrong: @fmassa, if I run CUDA_LAUNCH_BLOCKING=1 pytest tests -x -v in an env which does not have triton (so no triton kernel is launched), I'm getting that, which points to an issue in the sparse kernels. Rings a bell ?

tests/test_block_factory.py::test_xformer_decoder_block[device0-post-MixtureOfExperts-global-False-relu-1-True-0.1-0.0] PASSED                                                                                                                                                                                                                                                                                                          [ 73%]
tests/test_block_factory.py::test_xformer_decoder_block[device0-post-MixtureOfExperts-global-False-relu-1-True-0.1-0.1] FAILED                                                                                                                                                                                                                                                                                                          [ 73%]

================================================================================================================================================================================================================== FAILURES ===================================================================================================================================================================================================================
_________________________________________________________________________________________________________________________________________________________________________ test_xformer_decoder_block[device0-post-MixtureOfExperts-global-False-relu-1-True-0.1-0.1] __________________________________________________________________________________________________________________________________________________________________________

attention_name = 'global', rotary_embeddings = False, feedforward_name = 'MixtureOfExperts', heads = 1, attn_dropout = 0.1, residual_dropout = 0.1, causal = True, activation = 'relu', layer_norm_style = 'post', device = device(type='cuda')

    @pytest.mark.parametrize("attn_dropout", [0.0, 0.1])
    @pytest.mark.parametrize("residual_dropout", [0.0, 0.1])
    @pytest.mark.parametrize("causal", [True, False])
    @pytest.mark.parametrize("heads", [1, 2])
    @pytest.mark.parametrize("activation", [a.value for a in Activation])
    @pytest.mark.parametrize("rotary_embeddings", [False, True])
    @pytest.mark.parametrize("attention_name", ATTENTION_REGISTRY.keys())
    @pytest.mark.parametrize("feedforward_name", FEEDFORWARD_REGISTRY.keys())
    @pytest.mark.parametrize("layer_norm_style", ["pre", "post"])
    @pytest.mark.parametrize("device", DEVICES)
    @pytest.mark.skipif(
        not torch.cuda.is_available(), reason="This test requires a CUDA device"
    )
    def test_xformer_decoder_block(
        attention_name: str,
        rotary_embeddings: bool,
        feedforward_name: str,
        heads: int,
        attn_dropout: float,
        residual_dropout: float,
        causal: bool,
        activation: Activation,
        layer_norm_style: str,
        device: torch.device,
    ):
    
        block_size = 16
    
        attention_config = {
            "name": attention_name,
            "dropout": attn_dropout,
            "causal": causal,
            "window_size": SEQ // 8 + 1,
            "seq_len": SEQ,
            "dim_head": MODEL // heads,
            "attention_query_mask": torch.rand((SEQ, 1)) < GLOBAL_ATTENTION_RATIO,
            "layout": torch.eye(SEQ // block_size, SEQ // block_size, dtype=torch.long),
            "block_size": block_size,
            "num_rules": 2,  # Compositional Attention
        }
    
        multi_head_config = {
            "num_heads": heads,
            "dim_model": MODEL,
            "residual_dropout": residual_dropout,
            "attention": attention_config,
            "use_rotary_embeddings": rotary_embeddings,
        }
    
        feedforward_config = {
            "name": feedforward_name,
            "dim_model": MODEL,
            "dropout": DROPOUT,
            "activation": activation,
            "hidden_layer_multiplier": 4,
            "number_of_experts": 4,
            "gate": "top_2",
        }
    
        if feedforward_name == "MixtureOfExperts":
            init_torch_distributed_local()
    
        position_encoding_config = {
            "name": "sine",
            "dim_model": MODEL,
            "seq_len": SEQ,
            "vocab_size": VOCAB_SIZE,
        }
    
        encoder_block_config = xFormerEncoderConfig(
            dim_model=MODEL,
            multi_head_config=multi_head_config,
            feedforward_config=feedforward_config,
            position_encoding_config=position_encoding_config,
            layer_norm_style=layer_norm_style,
        )
    
        decoder_block_config = xFormerDecoderConfig(
            dim_model=MODEL,
            multi_head_config_masked=multi_head_config,
            multi_head_config_cross=multi_head_config,
            feedforward_config=feedforward_config,
            position_encoding_config=position_encoding_config,
            layer_norm_style=layer_norm_style,
        )
    
        # Test that the whole block can be instantiated
        encoder_block = xFormerEncoderBlock.from_config(encoder_block_config).to(device)
        decoder_block = xFormerDecoderBlock.from_config(decoder_block_config).to(device)
    
        # Check that the dimensions make sense, to a FW pass
        inputs = torch.rand(BATCH, SEQ, device=device)
>       encoded = encoder_block(inputs)

tests/test_block_factory.py:222: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
../../.conda/envs/xformers_dev/lib/python3.8/site-packages/torch/nn/modules/module.py:1110: in _call_impl
    return forward_call(*input, **kwargs)
xformers/factory/block_factory.py:207: in forward
    x = self.wrap_att(inputs=[q, k, v], att_mask=att_mask)
../../.conda/envs/xformers_dev/lib/python3.8/site-packages/torch/nn/modules/module.py:1110: in _call_impl
    return forward_call(*input, **kwargs)
xformers/components/residual.py:120: in forward
    x = self.sublayer(inputs=inputs, **kwargs)
../../.conda/envs/xformers_dev/lib/python3.8/site-packages/torch/nn/modules/module.py:1110: in _call_impl
    return forward_call(*input, **kwargs)
xformers/components/residual.py:68: in forward
    return residue + self.layer(*inputs, **kwargs)
../../.conda/envs/xformers_dev/lib/python3.8/site-packages/torch/nn/modules/module.py:1110: in _call_impl
    return forward_call(*input, **kwargs)
xformers/components/multi_head_dispatch.py:213: in forward
    y = self.attention(q, k, v, **kw_mask_args)
../../.conda/envs/xformers_dev/lib/python3.8/site-packages/torch/nn/modules/module.py:1110: in _call_impl
    return forward_call(*input, **kwargs)
xformers/components/attention/global_tokens.py:115: in forward
    att = scaled_dot_product_attention(
xformers/components/attention/core.py:230: in scaled_dot_product_attention
    att = scaled_query_key_softmax(q, k, att_mask=att_mask)
xformers/components/attention/core.py:209: in scaled_query_key_softmax
    att = _softmax(att, causal=is_causal)
xformers/components/attention/core.py:98: in _softmax
    return a.softmax()
xformers/components/attention/_sputnik_sparse.py:96: in softmax
    out = torch.nn.functional.softmax(self._mat, -1)
../../.conda/envs/xformers_dev/lib/python3.8/site-packages/torch/nn/functional.py:1779: in softmax
    return handle_torch_function(softmax, (input,), input, dim=dim, _stacklevel=_stacklevel, dtype=dtype)
../../.conda/envs/xformers_dev/lib/python3.8/site-packages/torch/overrides.py:1390: in handle_torch_function
    result = torch_func_method(public_api, types, args, kwargs)
xformers/sparse/csr_tensor.py:322: in __torch_function__
    return cls._softmax(args[0], kwargs["dim"])
xformers/sparse/csr_tensor.py:135: in _softmax
    out = _csr_ops._SparseSoftmax.apply(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

ctx = <torch.autograd.function._SparseSoftmaxBackward object at 0x7f36522afe40>, m = 64, n = 64, row_indices = tensor([48, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 32, 49,
        50, 51, 52, 53, 54, 55, 56, 57...15,  0, 17, 18, 19, 20, 21,
        22, 23, 24, 25, 26, 27, 28, 29, 30, 31], device='cuda:0',
       dtype=torch.int32), values = tensor([], device='cuda:0', size=(2, 0), grad_fn=<_sddmmBackward>)
row_offsets = tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], device='cuda:0',
       dtype=torch.int32), column_indices = tensor([], device='cuda:0', dtype=torch.int32)

    @staticmethod
    def forward(ctx, m, n, row_indices, values, row_offsets, column_indices):
>       out = torch.ops.xformers.sparse_softmax_sputnik(
            m, n, row_indices, values, row_offsets, column_indices
        )
E       RuntimeError: CUDA error: invalid configuration argument

xformers/sparse/_csr_ops.py:56: RuntimeError
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Captured stderr call -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
WARNING:root:Local experts no specified but world size of 1
WARNING:root:Assuming that all experts are local
WARNING:root:Local experts no specified but world size of 1
WARNING:root:Assuming that all experts are local
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Captured log call --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
WARNING  root:mixture_of_experts.py:108 Local experts no specified but world size of 1
WARNING  root:mixture_of_experts.py:109 Assuming that all experts are local
WARNING  root:mixture_of_experts.py:108 Local experts no specified but world size of 1
WARNING  root:mixture_of_experts.py:109 Assuming that all experts are local
============================================================================================================================================================================================================== warnings summary ===============================================================================================================================================================================================================
tests/test_attention_patterns.py::test_local_1d_pattern[50-3]
  /home/lefaudeux/.conda/envs/xformers_dev/lib/python3.8/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2144.)
    return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]

tests/test_block_factory.py: 9012 tests with warnings
  /home/lefaudeux/Git/xformers/xformers/components/positional_embedding/sine.py:39: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
    div = torch.exp(-math.log(10000) * (2 * (dim // 2) / self.dim_model))

-- Docs: https://docs.pytest.org/en/latest/warnings.html
=========================================================================================================================================================================================================== short test summary info ===========================================================================================================================================================================================================
FAILED tests/test_block_factory.py::test_xformer_decoder_block[device0-post-MixtureOfExperts-global-False-relu-1-True-0.1-0.1] - RuntimeError: CUDA error: invalid configuration argument

blefaudeux · 2022-05-09T21:29:26Z

Note that it happens without fairscale (so without the MixtureOfExperts), same error, on "global" again

fmassa · 2022-05-10T16:29:00Z

I'm looking at the issue.

fmassa · 2022-05-10T16:45:49Z

This should be fixed with #300

Looks like some of the configurations in the test are generating a fully-empty (all zeros) matrix. Might be good to have a look to see if this is intended.

blefaudeux · 2022-05-10T22:00:47Z

For the record, here are my numbers on a desktop 3080 with an incoming fused linear PR (/main numbers should be somewhat close)

--- Type: torch.float16 ---

Units: TFlops/s	B=8, M=512, K=256	B=8, M=512, K=512	B=4, M=512, K=1024	B=2, M=512, K=2048	B=2, M=512, K=4096	B=2, M=512, K=8192
pytorch - smelu - no bias - fw+bw	2.1	3.8	5.8	8.2	10.1	10.4
triton - smelu - no bias - fw+bw	2.9	9.2	10.4	12.0	12.6	13.6
pytorch - smelu - bias - fw+bw	1.9	3.6	5.6	8.0	9.4	11.2
triton - smelu - bias - fw+bw	2.6	8.2	9.9	11.6	10.8	13.2

--- Type: torch.float16 ---

Units: TFlops/s	B=8, M=512, K=256	B=8, M=512, K=512	B=4, M=512, K=1024	B=2, M=512, K=2048	B=2, M=512, K=4096	B=2, M=512, K=8192
pytorch - smelu - no bias - fw	1.8	3.5	5.5	7.9	10.2	10.0
triton - smelu - no bias - fw	11.4	13.6	13.9	13.9	14.1	13.7
pytorch - smelu - bias - fw	1.7	3.2	5.1	7.5	9.9	10.7
triton - smelu - bias - fw	11.4	13.6	14.0	14.0	14.0	12.3

Added SmeLU

2b9c64e

facebook-github-bot added the CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. label Apr 7, 2022

added pytorch class

7bc8ef6

blefaudeux reviewed Apr 7, 2022

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tl.constexpr annotated arguments

bbbf95f

This was referenced Apr 13, 2022

Rotary Embeddings + Triton #265

Open

[backend] 3/3 Triton 2 update #272

Merged

kashif added 3 commits April 21, 2022 18:32

Merge remote-tracking branch 'upstream/main' into smooth-relu

1982d2a

do not change constant beta

1afc663

triton 2.0 fixes

07d4241

kashif added 2 commits April 28, 2022 10:51

Merge remote-tracking branch 'upstream/main' into smooth-relu

e992661

make smelu beta=2.0 default

3ccf685

kashif added 3 commits April 28, 2022 14:19

use buffer for beta

3813fcb

ignore mypy

8591819

revert buffer

a47ab38

kashif added 2 commits May 5, 2022 13:26

Merge remote-tracking branch 'upstream/main' into smooth-relu

83f1078

use beta=2.0 for now

94ed71b

blefaudeux approved these changes May 5, 2022

View reviewed changes

Merge remote-tracking branch 'upstream/main' into smooth-relu

b7209fd

Merge remote-tracking branch 'upstream/main' into smooth-relu

a70661e

fmassa mentioned this pull request May 10, 2022

Fix sddmm2 when nnz=0 #300

Merged

Merge remote-tracking branch 'upstream/main' into smooth-relu

4ddf413

blefaudeux merged commit 837d7c0 into facebookresearch:main May 10, 2022

kashif deleted the smooth-relu branch May 10, 2022 18:24

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Added SmeLU #263

Added SmeLU #263

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Added SmeLU #263

Added SmeLU #263

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kashif commented Apr 7, 2022 • edited Loading

What does this PR do?

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