Add Lowering for FlexAttention Backwards

[drisspg]

Summary

What does this PR do?

It enables Inductor to actually generate the fused flex attention kernel for the backwards

I did some other things along the way:

• Abstract out the 'build_subgraph_buffer' subroutine and make it reusable between flex attention and flex_attention backwards. In total we need too build 3 subgraphs for fwd + bwd. 1 for the fwd graph and then 2 in the bwd. The FAv2 algorithm recomputes the parts of the forward (more efficiently since we already have the row_max via logsumexp), therefore we need to inline both the fwd graph and the joint graph in the bwds kernel.
• The version of the backwards kernel is from a somewhat older version of the triton tutorial implementation. I think that we should update in a follow up to a newer version. Notably the blocks need to be square for this to work as currently implemented. I am sure there are many opportunities for optimization.
• I didnt correctly register the decomp table + IndexMode when I landed: Add backwards support to FlexAttention #123902, this remedies that.
• The rel_bias helper func was reversed in terms of causality. I updated and then add a test specific for "future causal" attention.
• This PRs but the main point that I think still needs to be worked out is the store_output call. I have it hacked up to be 'fake' but I dont think we want to land that and likely want to just have a mutated 'dq' and a stored_output 'dk'
• I also needed to update the TritonTemplateKernel to actually accept multiple subgraphs (modifications)
• I updated the benchmark to also profile bwds performance

Benchmark Numbers:

The current implementation is not parallelizing over ctx length in the bwd
FWD Speedups

Type	Speedup	shape	score_mod	dtype
Average	0.991
Max	1.182	(16, 16, 4096, 64)	noop	torch.bfloat16
Min	0.796	(2, 16, 512, 256)	head_bias	torch.bfloat16

BWD Speedups

Type	Speedup	shape	score_mod	dtype
Average	0.291
Max	0.652	(8, 16, 512, 64)	head_bias	torch.bfloat16
Min	0.073	(2, 16, 4096, 128)	head_bias	torch.bfloat16

Full Data

shape	score_mod	dtype	fwd_eager_time	fwd_compiled_time	bwd_eager_time	bwd_compiled_time	fwd_speedup	bwd_speedup
(2, 16, 512, 64)	noop	torch.bfloat16	19.936	19.092	57.851	193.564	1.044	0.299
(2, 16, 512, 64)	causal_mask	torch.bfloat16	19.955	19.497	57.662	206.278	1.024	0.280
(2, 16, 512, 64)	relative_bias	torch.bfloat16	19.455	21.297	57.674	195.219	0.913	0.295
(2, 16, 512, 64)	head_bias	torch.bfloat16	19.958	21.289	57.674	193.859	0.938	0.298
(2, 16, 512, 128)	noop	torch.bfloat16	28.157	28.615	82.831	454.211	0.984	0.182
(2, 16, 512, 128)	causal_mask	torch.bfloat16	28.154	28.444	83.091	432.083	0.990	0.192
(2, 16, 512, 128)	relative_bias	torch.bfloat16	28.722	27.897	83.175	446.789	1.030	0.186
(2, 16, 512, 128)	head_bias	torch.bfloat16	28.299	27.673	83.052	459.179	1.023	0.181
(2, 16, 512, 256)	noop	torch.bfloat16	41.167	50.504	175.019	1083.545	0.815	0.162
(2, 16, 512, 256)	causal_mask	torch.bfloat16	41.656	51.933	175.078	1171.176	0.802	0.149
(2, 16, 512, 256)	relative_bias	torch.bfloat16	41.697	50.722	175.159	1097.312	0.822	0.160
(2, 16, 512, 256)	head_bias	torch.bfloat16	41.690	52.387	175.184	1097.336	0.796	0.160
(2, 16, 1024, 64)	noop	torch.bfloat16	39.232	37.454	127.847	612.430	1.047	0.209
(2, 16, 1024, 64)	causal_mask	torch.bfloat16	39.930	39.599	127.755	665.359	1.008	0.192
(2, 16, 1024, 64)	relative_bias	torch.bfloat16	39.417	41.304	127.902	614.990	0.954	0.208
(2, 16, 1024, 64)	head_bias	torch.bfloat16	39.965	42.034	127.953	613.273	0.951	0.209
(2, 16, 1024, 128)	noop	torch.bfloat16	63.964	71.024	226.510	1637.669	0.901	0.138
(2, 16, 1024, 128)	causal_mask	torch.bfloat16	63.843	72.451	226.750	1558.949	0.881	0.145
(2, 16, 1024, 128)	relative_bias	torch.bfloat16	64.301	70.487	226.651	1610.063	0.912	0.141
(2, 16, 1024, 128)	head_bias	torch.bfloat16	64.033	71.394	226.676	1668.511	0.897	0.136
(2, 16, 1024, 256)	noop	torch.bfloat16	129.348	141.390	507.337	4405.175	0.915	0.115
(2, 16, 1024, 256)	causal_mask	torch.bfloat16	129.538	145.680	507.178	4768.874	0.889	0.106
(2, 16, 1024, 256)	relative_bias	torch.bfloat16	129.438	142.782	507.004	4401.002	0.907	0.115
(2, 16, 1024, 256)	head_bias	torch.bfloat16	129.058	146.242	507.547	4434.251	0.883	0.114
(2, 16, 4096, 64)	noop	torch.bfloat16	481.606	409.120	1440.890	14147.269	1.177	0.102
(2, 16, 4096, 64)	causal_mask	torch.bfloat16	480.227	438.847	1434.419	14973.386	1.094	0.096
(2, 16, 4096, 64)	relative_bias	torch.bfloat16	480.831	458.104	1432.935	14193.253	1.050	0.101
(2, 16, 4096, 64)	head_bias	torch.bfloat16	480.749	452.497	1437.040	14084.869	1.062	0.102
(2, 16, 4096, 128)	noop	torch.bfloat16	872.534	848.275	2600.895	35156.849	1.029	0.074
(2, 16, 4096, 128)	causal_mask	torch.bfloat16	872.647	868.279	2587.581	31919.531	1.005	0.081
(2, 16, 4096, 128)	relative_bias	torch.bfloat16	871.484	827.644	2593.989	34805.634	1.053	0.075
(2, 16, 4096, 128)	head_bias	torch.bfloat16	871.422	856.437	2602.482	35708.591	1.017	0.073
(2, 16, 4096, 256)	noop	torch.bfloat16	1904.497	1758.183	6122.416	66754.593	1.083	0.092
(2, 16, 4096, 256)	causal_mask	torch.bfloat16	1911.174	1762.821	6113.207	72759.392	1.084	0.084
(2, 16, 4096, 256)	relative_bias	torch.bfloat16	1911.254	1727.108	6123.530	66577.988	1.107	0.092
(2, 16, 4096, 256)	head_bias	torch.bfloat16	1916.977	1801.804	6118.158	67359.680	1.064	0.091
(8, 16, 512, 64)	noop	torch.bfloat16	44.984	43.974	170.276	262.259	1.023	0.649
(8, 16, 512, 64)	causal_mask	torch.bfloat16	45.001	46.265	170.509	274.893	0.973	0.620
(8, 16, 512, 64)	relative_bias	torch.bfloat16	45.466	48.211	170.606	262.759	0.943	0.649
(8, 16, 512, 64)	head_bias	torch.bfloat16	45.481	48.435	170.267	261.265	0.939	0.652
(8, 16, 512, 128)	noop	torch.bfloat16	72.565	74.736	313.220	773.126	0.971	0.405
(8, 16, 512, 128)	causal_mask	torch.bfloat16	72.015	75.755	313.311	775.513	0.951	0.404
(8, 16, 512, 128)	relative_bias	torch.bfloat16	72.105	74.189	313.806	769.238	0.972	0.408
(8, 16, 512, 128)	head_bias	torch.bfloat16	72.005	74.364	313.509	775.237	0.968	0.404
(8, 16, 512, 256)	noop	torch.bfloat16	138.656	165.453	663.707	2672.067	0.838	0.248
(8, 16, 512, 256)	causal_mask	torch.bfloat16	139.096	172.613	663.593	2926.538	0.806	0.227
(8, 16, 512, 256)	relative_bias	torch.bfloat16	139.500	168.417	663.938	2658.629	0.828	0.250
(8, 16, 512, 256)	head_bias	torch.bfloat16	139.776	173.549	662.920	2667.266	0.805	0.249
(8, 16, 1024, 64)	noop	torch.bfloat16	134.883	125.004	484.706	1195.254	1.079	0.406
(8, 16, 1024, 64)	causal_mask	torch.bfloat16	134.297	132.875	485.420	1234.953	1.011	0.393
(8, 16, 1024, 64)	relative_bias	torch.bfloat16	134.839	139.231	485.470	1198.556	0.968	0.405
(8, 16, 1024, 64)	head_bias	torch.bfloat16	133.822	136.449	485.608	1189.198	0.981	0.408
(8, 16, 1024, 128)	noop	torch.bfloat16	235.470	234.765	886.094	2662.944	1.003	0.333
(8, 16, 1024, 128)	causal_mask	torch.bfloat16	236.305	241.382	886.293	2646.984	0.979	0.335
(8, 16, 1024, 128)	relative_bias	torch.bfloat16	236.414	233.980	885.250	2642.178	1.010	0.335
(8, 16, 1024, 128)	head_bias	torch.bfloat16	237.176	239.040	885.754	2665.242	0.992	0.332
(8, 16, 1024, 256)	noop	torch.bfloat16	504.445	517.855	1978.956	9592.906	0.974	0.206
(8, 16, 1024, 256)	causal_mask	torch.bfloat16	502.428	536.002	1978.611	10607.342	0.937	0.187
(8, 16, 1024, 256)	relative_bias	torch.bfloat16	503.396	523.960	1977.993	9539.284	0.961	0.207
(8, 16, 1024, 256)	head_bias	torch.bfloat16	503.818	536.014	1980.131	9576.262	0.940	0.207
(8, 16, 4096, 64)	noop	torch.bfloat16	1970.139	1674.930	5750.940	16724.134	1.176	0.344
(8, 16, 4096, 64)	causal_mask	torch.bfloat16	1959.036	1775.056	5780.512	17390.350	1.104	0.332
(8, 16, 4096, 64)	relative_bias	torch.bfloat16	1947.198	1773.869	5780.643	16779.699	1.098	0.345
(8, 16, 4096, 64)	head_bias	torch.bfloat16	1963.935	1829.502	5780.018	16703.259	1.073	0.346
(8, 16, 4096, 128)	noop	torch.bfloat16	3582.711	3362.623	10436.069	36415.565	1.065	0.287
(8, 16, 4096, 128)	causal_mask	torch.bfloat16	3581.504	3499.472	10346.869	36164.959	1.023	0.286
(8, 16, 4096, 128)	relative_bias	torch.bfloat16	3589.779	3337.849	10529.621	36261.696	1.075	0.290
(8, 16, 4096, 128)	head_bias	torch.bfloat16	3602.265	3436.444	10458.660	36507.790	1.048	0.286
(8, 16, 4096, 256)	noop	torch.bfloat16	7695.923	7126.275	24643.009	140949.081	1.080	0.175
(8, 16, 4096, 256)	causal_mask	torch.bfloat16	7679.939	7186.252	24538.105	157156.067	1.069	0.156
(8, 16, 4096, 256)	relative_bias	torch.bfloat16	7681.374	6994.832	24549.713	140077.179	1.098	0.175
(8, 16, 4096, 256)	head_bias	torch.bfloat16	7679.822	7212.278	24627.823	140675.003	1.065	0.175
(16, 16, 512, 64)	noop	torch.bfloat16	80.126	78.291	333.719	541.165	1.023	0.617
(16, 16, 512, 64)	causal_mask	torch.bfloat16	80.065	81.696	333.779	551.113	0.980	0.606
(16, 16, 512, 64)	relative_bias	torch.bfloat16	80.138	86.715	333.364	542.118	0.924	0.615
(16, 16, 512, 64)	head_bias	torch.bfloat16	80.415	85.204	333.294	536.840	0.944	0.621
(16, 16, 512, 128)	noop	torch.bfloat16	134.964	138.025	607.093	1333.102	0.978	0.455
(16, 16, 512, 128)	causal_mask	torch.bfloat16	134.192	141.523	606.269	1424.318	0.948	0.426
(16, 16, 512, 128)	relative_bias	torch.bfloat16	135.711	138.639	606.283	1327.974	0.979	0.457
(16, 16, 512, 128)	head_bias	torch.bfloat16	135.552	140.555	607.107	1347.370	0.964	0.451
(16, 16, 512, 256)	noop	torch.bfloat16	275.113	315.144	1301.583	5268.153	0.873	0.247
(16, 16, 512, 256)	causal_mask	torch.bfloat16	274.867	328.106	1302.513	5770.594	0.838	0.226
(16, 16, 512, 256)	relative_bias	torch.bfloat16	276.052	321.770	1302.904	5241.920	0.858	0.249
(16, 16, 512, 256)	head_bias	torch.bfloat16	271.409	328.839	1302.142	5266.037	0.825	0.247
(16, 16, 1024, 64)	noop	torch.bfloat16	260.489	237.463	955.884	1817.558	1.097	0.526
(16, 16, 1024, 64)	causal_mask	torch.bfloat16	262.378	254.350	955.280	1843.807	1.032	0.518
(16, 16, 1024, 64)	relative_bias	torch.bfloat16	261.338	268.253	956.038	1820.036	0.974	0.525
(16, 16, 1024, 64)	head_bias	torch.bfloat16	262.153	264.156	956.023	1810.076	0.992	0.528
(16, 16, 1024, 128)	noop	torch.bfloat16	476.475	461.413	1760.578	4306.521	1.033	0.409
(16, 16, 1024, 128)	causal_mask	torch.bfloat16	473.794	479.178	1761.277	4619.439	0.989	0.381
(16, 16, 1024, 128)	relative_bias	torch.bfloat16	473.839	463.282	1758.692	4290.562	1.023	0.410
(16, 16, 1024, 128)	head_bias	torch.bfloat16	472.979	472.896	1763.086	4367.931	1.000	0.404
(16, 16, 1024, 256)	noop	torch.bfloat16	1014.184	1026.764	3922.997	19104.147	0.988	0.205
(16, 16, 1024, 256)	causal_mask	torch.bfloat16	1013.217	1039.046	3928.382	21086.281	0.975	0.186
(16, 16, 1024, 256)	relative_bias	torch.bfloat16	1008.519	1015.278	3922.133	18980.652	0.993	0.207
(16, 16, 1024, 256)	head_bias	torch.bfloat16	1011.360	1047.542	3931.245	19069.172	0.965	0.206
(16, 16, 4096, 64)	noop	torch.bfloat16	3929.850	3325.667	11411.704	23344.280	1.182	0.489
(16, 16, 4096, 64)	causal_mask	torch.bfloat16	3885.262	3581.544	11390.515	23725.639	1.085	0.480
(16, 16, 4096, 64)	relative_bias	torch.bfloat16	3865.737	3537.308	11489.901	23406.330	1.093	0.491
(16, 16, 4096, 64)	head_bias	torch.bfloat16	3880.530	3665.249	11484.411	23299.496	1.059	0.493
(16, 16, 4096, 128)	noop	torch.bfloat16	7030.306	6745.715	20621.264	57464.096	1.042	0.359
(16, 16, 4096, 128)	causal_mask	torch.bfloat16	7095.414	7034.385	20410.656	61660.511	1.009	0.331
(16, 16, 4096, 128)	relative_bias	torch.bfloat16	7084.779	6686.497	20315.161	57243.969	1.060	0.355
(16, 16, 4096, 128)	head_bias	torch.bfloat16	7075.367	6863.305	20494.385	58481.953	1.031	0.350
(16, 16, 4096, 256)	noop	torch.bfloat16	15612.741	14297.482	55306.847	281161.865	1.092	0.197
(16, 16, 4096, 256)	causal_mask	torch.bfloat16	15326.592	14263.878	55227.806	313063.232	1.075	0.176
(16, 16, 4096, 256)	relative_bias	torch.bfloat16	15297.963	14007.379	54558.029	279529.175	1.092	0.195
(16, 16, 4096, 256)	head_bias	torch.bfloat16	15216.160	14276.027	55081.581	280996.826	1.066	0.196

cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @peterbell10 @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @chauhang

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✅ You can merge normally! (7 Unrelated Failures)

As of commit 69e8b7a with merge base 9689532 ():

FLAKY - The following jobs failed but were likely due to flakiness present on trunk:

• inductor-periodic / cuda12.1-py3.10-gcc9-sm86-periodic-dynamo-benchmarks / test (aot_eager_torchbench, 1, 2, linux.g5.4xlarge.nvidia.gpu) (gh) (similar failure)
  hf_BigBird
• inductor-periodic / cuda12.1-py3.10-gcc9-sm86-periodic-dynamo-benchmarks / test (dynamic_aot_eager_torchbench, 1, 2, linux.g5.4xlarge.nvidia.gpu) (gh) (similar failure)
  hf_BigBird
• inductor-periodic / cuda12.1-py3.10-gcc9-sm86-periodic-dynamo-benchmarks / test (dynamo_eager_torchbench, 1, 2, linux.g5.4xlarge.nvidia.gpu) (gh) (similar failure)
  hf_BigBird

BROKEN TRUNK - The following job failed but were present on the merge base:

👉 Rebase onto the `viable/strict` branch to avoid these failures

• Lint / lintrunner-noclang / linux-job (gh) (trunk failure)
  >>> Lint for test/test_flop_counter.py:

UNSTABLE - The following jobs failed but were likely due to flakiness present on trunk and has been marked as unstable:

• inductor / cuda12.1-py3.10-gcc9-sm86 / test (aot_inductor_torchbench, 1, 2, linux.g5.4xlarge.nvidia.gpu, unstable) (gh) (#120841)
  hf_BigBird
• inductor / cuda12.1-py3.10-gcc9-sm86 / test (dynamic_inductor_torchbench, 1, 2, linux.g5.4xlarge.nvidia.gpu, unstable) (gh) (#120841)
  hf_BigBird
• inductor / cuda12.1-py3.10-gcc9-sm86 / test (inductor_torchbench, 1, 2, linux.g5.4xlarge.nvidia.gpu, unstable) (gh) (#120841)
  hf_BigBird

This comment was automatically generated by Dr. CI and updates every 15 minutes.

[drisspg]

need to figure out this one

[drisspg]

I think we want indent width, but probably definitely not fake_output lol

[pytorchmergebot]

@drisspg your PR has been successfully reverted.

[drisspg]

@huydhn hmmm so what exactly do I need to do?

[clee2000]

@huydhn hmmm so what exactly do I need to do?

If you do mean to use >6GB of memory, do something like #126399

(Note for @clee2000, I remember we are still using 2 parallel processed on CUDA sm86 jobs. So this is just FYI and probably not related to your change to increase that number)

We're using 3 for sm86

[drisspg]

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Merge failed

Reason: 19 jobs have failed, first few of them are: inductor / cuda12.1-py3.10-gcc9-sm86 / test (inductor, 1, 1, linux.g5.4xlarge.nvidia.gpu, unstable), inductor / cuda12.1-py3.10-gcc9-sm86 / test (inductor_distributed, 1, 1, linux.g5.12xlarge.nvidia.gpu, unstable), inductor / cuda12.1-py3.10-gcc9-sm86 / test (inductor_huggingface, 1, 1, linux.g5.4xlarge.nvidia.gpu, unstable), inductor / cuda12.1-py3.10-gcc9-sm86 / test (inductor_timm, 1, 2, linux.g5.4xlarge.nvidia.gpu, unstable), inductor / cuda12.1-py3.10-gcc9-sm86 / test (inductor_timm, 2, 2, linux.g5.4xlarge.nvidia.gpu, unstable)

Details for Dev Infra team

Raised by workflow job

[drisspg]

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Repeat contributor may use this command to rebase their PR.

optional arguments:
  -s, --stable          [DEPRECATED] Rebase onto viable/strict
  -b BRANCH, --branch BRANCH
                        Branch you would like to rebase to

Label

usage: @pytorchbot label labels [labels ...]

Adds label to a PR or Issue [Can be used on Issues]

positional arguments:
  labels  Labels to add to given Pull Request or Issue [Can be used on Issues]

Dr CI

usage: @pytorchbot drci 

Update Dr. CI. Updates the Dr. CI comment on the PR in case it's gotten out of sync with actual CI results.

cherry-pick

usage: @pytorchbot cherry-pick --onto ONTO [--fixes FIXES] -c
                               {regression,critical,fixnewfeature,docs,release}

Cherry pick a pull request onto a release branch for inclusion in a release

optional arguments:
  --onto ONTO           Branch you would like to cherry pick onto (Example: release/2.1)
  --fixes FIXES         Link to the issue that your PR fixes (Example: https://github.com/pytorch/pytorch/issues/110666)
  -c {regression,critical,fixnewfeature,docs,release}, --classification {regression,critical,fixnewfeature,docs,release}
                        A machine-friendly classification of the cherry-pick reason.

Close

usage: @pytorchbot close

Close a PR [Can be used on issues]

[drisspg]

@pytorchbot merge -i

[pytorchmergebot]

Merge started

Your change will be merged while ignoring the following 1 checks: Lint / lintrunner-noclang / linux-job

Learn more about merging in the wiki.

Questions? Feedback? Please reach out to the PyTorch DevX Team

Advanced Debugging

Check the merge workflow status
here

[pytorchmergebot]

Merge failed

Reason: 19 jobs have failed, first few of them are: inductor / cuda12.1-py3.10-gcc9-sm86 / test (inductor, 1, 1, linux.g5.4xlarge.nvidia.gpu, unstable), inductor / cuda12.1-py3.10-gcc9-sm86 / test (inductor_distributed, 1, 1, linux.g5.12xlarge.nvidia.gpu, unstable), inductor / cuda12.1-py3.10-gcc9-sm86 / test (inductor_huggingface, 1, 1, linux.g5.4xlarge.nvidia.gpu, unstable), inductor / cuda12.1-py3.10-gcc9-sm86 / test (inductor_timm, 1, 2, linux.g5.4xlarge.nvidia.gpu, unstable), inductor / cuda12.1-py3.10-gcc9-sm86 / test (inductor_timm, 2, 2, linux.g5.4xlarge.nvidia.gpu, unstable)

Details for Dev Infra team

Raised by workflow job

[drisspg]

@pytorchbot merge -f "spoke with Catherine, I added all of flex's test to the serial list, and these failures look unrelated"

[pytorchmergebot]

Merge started

Your change will be merged immediately since you used the force (-f) flag, bypassing any CI checks (ETA: 1-5 minutes). Please use -f as last resort and instead consider -i/--ignore-current to continue the merge ignoring current failures. This will allow currently pending tests to finish and report signal before the merge.

Learn more about merging in the wiki.

Questions? Feedback? Please reach out to the PyTorch DevX Team

Advanced Debugging

Check the merge workflow status
here