[PP] Add DualPipeV schedule #159591
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[PP] Add DualPipeV schedule #159591
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/159591
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Added the DualPipeV schedule according to https://github.com/deepseek-ai/DualPipe <img width="3168" height="486" alt="image" src="https://github.com/user-attachments/assets/5c2d61cc-f7d9-4af6-9542-cfb638f2567e" /> This schedule doesn't perform the actual "overlap" during execution, but provides the scaffolding and schedule definition we need to run it E2E in torchtitan. Supporting the overlapped operation will be worked on in following PRs. Tests: ```sh python test/distributed/pipelining/test_schedule_multiproc.py -k test_v_shape_schedules python test/distributed/pipelining/test_schedule.py -k test_pipeline_order_for_v_schedules ``` Also tested in TorchTitan and is running. cc awgu wanchaol fegin fduwjj wz337 wconstab d4l3k pragupta [ghstack-poisoned]
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Some changes to validation code and visualizer to support a new computation type that will be used in DualPipeV (see #159591) The IR looks like: ``` [0F0, 0F1, 0F2, 0F3, 0F4, 0F5, 0F6, 7F0, 7I0, 7W0, 7F1, 7I1, 7W1, 7F2, 7I2, 7W2, 7F3, (0F7;7B3)OVERLAP_F_B, (7F4;0B0)OVERLAP_F_B, (0F8;7B4)OVERLAP_F_B, (7F5;0B1)OVERLAP_F_B, (0F9;7B5)OVERLAP_F_B, (7F6;0B2)OVERLAP_F_B, 7B6, (7F7;0B3)OVERLAP_F_B, 7B7, (7F8;0B4)OVERLAP_F_B, 7B8, (7F9;0B5)OVERLAP_F_B, 7B9, 0I6, 0W6, 0I7, 0W7, 0I8, 0W8, 0I9, 0W9] [1F0, 1F1, 1F2, 1F3, 1F4, 6F0, 1F5, 6F1, 6I0, 6W0, 6F2, 6I1, 6W1, 6F3, (1F6;6B2)OVERLAP_F_B, (6F4;1B0)OVERLAP_F_B, (1F7;6B3)OVERLAP_F_B, (6F5;1B1)OVERLAP_F_B, (1F8;6B4)OVERLAP_F_B, (6F6;1B2)OVERLAP_F_B, (1F9;6B5)OVERLAP_F_B, (6F7;1B3)OVERLAP_F_B, 6B6, (6F8;1B4)OVERLAP_F_B, 6B7, (6F9;1B5)OVERLAP_F_B, 6B8, 1B6, 6I9, 1I7, 6W9, 1I8, 1W7, 1I9, 1W8, 1W9] [2F0, 2F1, 2F2, 5F0, 2F3, 5F1, 2F4, 5F2, 5I0, 5W0, 5F3, (2F5;5B1)OVERLAP_F_B, (5F4;2B0)OVERLAP_F_B, (2F6;5B2)OVERLAP_F_B, (5F5;2B1)OVERLAP_F_B, (2F7;5B3)OVERLAP_F_B, (5F6;2B2)OVERLAP_F_B, (2F8;5B4)OVERLAP_F_B, (5F7;2B3)OVERLAP_F_B, (2F9;5B5)OVERLAP_F_B, (5F8;2B4)OVERLAP_F_B, 5B6, (5F9;2B5)OVERLAP_F_B, 5B7, 2B6, 5B8, 2I7, 5I9, 2I8, 2W7, 2I9, 5W9, 2W8, 2W9] [3F0, 4F0, 3F1, 4F1, 3F2, 4F2, 3F3, 4F3, 3F4, 4B0, (4F4;3B0)OVERLAP_F_B, (3F5;4B1)OVERLAP_F_B, (4F5;3B1)OVERLAP_F_B, (3F6;4B2)OVERLAP_F_B, (4F6;3B2)OVERLAP_F_B, (3F7;4B3)OVERLAP_F_B, (4F7;3B3)OVERLAP_F_B, (3F8;4B4)OVERLAP_F_B, (4F8;3B4)OVERLAP_F_B, (3F9;4B5)OVERLAP_F_B, (4F9;3B5)OVERLAP_F_B, 4B6, 3B6, 4B7, 3B7, 4I8, 3I8, 4I9, 3I9, 4W8, 3W8, 4W9, 3W9] ``` In this PR, the schedule execution will just treat the OVERLAP_F_B as two separate operations of F and B (so there is no actual overlap). The next step is to allow users to create a custom function to plug in what this operation does. https://github.com/pytorch/pytorch/blob/814629043a0c31441bc3749204c97f1e24fa3462/torch/distributed/pipelining/schedules.py#L1205-L1216 cc awgu wanchaol fegin fduwjj wz337 wconstab d4l3k pragupta [ghstack-poisoned]
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Some changes to validation code and visualizer to support a new computation type that will be used in DualPipeV (see #159591) The IR looks like: ``` [0F0, 0F1, 0F2, 0F3, 0F4, 0F5, 0F6, 7F0, 7I0, 7W0, 7F1, 7I1, 7W1, 7F2, 7I2, 7W2, 7F3, (0F7;7B3)OVERLAP_F_B, (7F4;0B0)OVERLAP_F_B, (0F8;7B4)OVERLAP_F_B, (7F5;0B1)OVERLAP_F_B, (0F9;7B5)OVERLAP_F_B, (7F6;0B2)OVERLAP_F_B, 7B6, (7F7;0B3)OVERLAP_F_B, 7B7, (7F8;0B4)OVERLAP_F_B, 7B8, (7F9;0B5)OVERLAP_F_B, 7B9, 0I6, 0W6, 0I7, 0W7, 0I8, 0W8, 0I9, 0W9] [1F0, 1F1, 1F2, 1F3, 1F4, 6F0, 1F5, 6F1, 6I0, 6W0, 6F2, 6I1, 6W1, 6F3, (1F6;6B2)OVERLAP_F_B, (6F4;1B0)OVERLAP_F_B, (1F7;6B3)OVERLAP_F_B, (6F5;1B1)OVERLAP_F_B, (1F8;6B4)OVERLAP_F_B, (6F6;1B2)OVERLAP_F_B, (1F9;6B5)OVERLAP_F_B, (6F7;1B3)OVERLAP_F_B, 6B6, (6F8;1B4)OVERLAP_F_B, 6B7, (6F9;1B5)OVERLAP_F_B, 6B8, 1B6, 6I9, 1I7, 6W9, 1I8, 1W7, 1I9, 1W8, 1W9] [2F0, 2F1, 2F2, 5F0, 2F3, 5F1, 2F4, 5F2, 5I0, 5W0, 5F3, (2F5;5B1)OVERLAP_F_B, (5F4;2B0)OVERLAP_F_B, (2F6;5B2)OVERLAP_F_B, (5F5;2B1)OVERLAP_F_B, (2F7;5B3)OVERLAP_F_B, (5F6;2B2)OVERLAP_F_B, (2F8;5B4)OVERLAP_F_B, (5F7;2B3)OVERLAP_F_B, (2F9;5B5)OVERLAP_F_B, (5F8;2B4)OVERLAP_F_B, 5B6, (5F9;2B5)OVERLAP_F_B, 5B7, 2B6, 5B8, 2I7, 5I9, 2I8, 2W7, 2I9, 5W9, 2W8, 2W9] [3F0, 4F0, 3F1, 4F1, 3F2, 4F2, 3F3, 4F3, 3F4, 4B0, (4F4;3B0)OVERLAP_F_B, (3F5;4B1)OVERLAP_F_B, (4F5;3B1)OVERLAP_F_B, (3F6;4B2)OVERLAP_F_B, (4F6;3B2)OVERLAP_F_B, (3F7;4B3)OVERLAP_F_B, (4F7;3B3)OVERLAP_F_B, (3F8;4B4)OVERLAP_F_B, (4F8;3B4)OVERLAP_F_B, (3F9;4B5)OVERLAP_F_B, (4F9;3B5)OVERLAP_F_B, 4B6, 3B6, 4B7, 3B7, 4I8, 3I8, 4I9, 3I9, 4W8, 3W8, 4W9, 3W9] ``` In this PR, the schedule execution will just treat the OVERLAP_F_B as two separate operations of F and B (so there is no actual overlap). The next step is to allow users to create a custom function to plug in what this operation does. https://github.com/pytorch/pytorch/blob/814629043a0c31441bc3749204c97f1e24fa3462/torch/distributed/pipelining/schedules.py#L1205-L1216 cc awgu wanchaol fegin fduwjj wz337 wconstab d4l3k pragupta [ghstack-poisoned]
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Some changes to validation code and visualizer to support a new computation type that will be used in DualPipeV (see #159591) The IR looks like: ``` [0F0, 0F1, 0F2, 0F3, 0F4, 0F5, 0F6, 7F0, 7I0, 7W0, 7F1, 7I1, 7W1, 7F2, 7I2, 7W2, 7F3, (0F7;7B3)OVERLAP_F_B, (7F4;0B0)OVERLAP_F_B, (0F8;7B4)OVERLAP_F_B, (7F5;0B1)OVERLAP_F_B, (0F9;7B5)OVERLAP_F_B, (7F6;0B2)OVERLAP_F_B, 7B6, (7F7;0B3)OVERLAP_F_B, 7B7, (7F8;0B4)OVERLAP_F_B, 7B8, (7F9;0B5)OVERLAP_F_B, 7B9, 0I6, 0W6, 0I7, 0W7, 0I8, 0W8, 0I9, 0W9] [1F0, 1F1, 1F2, 1F3, 1F4, 6F0, 1F5, 6F1, 6I0, 6W0, 6F2, 6I1, 6W1, 6F3, (1F6;6B2)OVERLAP_F_B, (6F4;1B0)OVERLAP_F_B, (1F7;6B3)OVERLAP_F_B, (6F5;1B1)OVERLAP_F_B, (1F8;6B4)OVERLAP_F_B, (6F6;1B2)OVERLAP_F_B, (1F9;6B5)OVERLAP_F_B, (6F7;1B3)OVERLAP_F_B, 6B6, (6F8;1B4)OVERLAP_F_B, 6B7, (6F9;1B5)OVERLAP_F_B, 6B8, 1B6, 6I9, 1I7, 6W9, 1I8, 1W7, 1I9, 1W8, 1W9] [2F0, 2F1, 2F2, 5F0, 2F3, 5F1, 2F4, 5F2, 5I0, 5W0, 5F3, (2F5;5B1)OVERLAP_F_B, (5F4;2B0)OVERLAP_F_B, (2F6;5B2)OVERLAP_F_B, (5F5;2B1)OVERLAP_F_B, (2F7;5B3)OVERLAP_F_B, (5F6;2B2)OVERLAP_F_B, (2F8;5B4)OVERLAP_F_B, (5F7;2B3)OVERLAP_F_B, (2F9;5B5)OVERLAP_F_B, (5F8;2B4)OVERLAP_F_B, 5B6, (5F9;2B5)OVERLAP_F_B, 5B7, 2B6, 5B8, 2I7, 5I9, 2I8, 2W7, 2I9, 5W9, 2W8, 2W9] [3F0, 4F0, 3F1, 4F1, 3F2, 4F2, 3F3, 4F3, 3F4, 4B0, (4F4;3B0)OVERLAP_F_B, (3F5;4B1)OVERLAP_F_B, (4F5;3B1)OVERLAP_F_B, (3F6;4B2)OVERLAP_F_B, (4F6;3B2)OVERLAP_F_B, (3F7;4B3)OVERLAP_F_B, (4F7;3B3)OVERLAP_F_B, (3F8;4B4)OVERLAP_F_B, (4F8;3B4)OVERLAP_F_B, (3F9;4B5)OVERLAP_F_B, (4F9;3B5)OVERLAP_F_B, 4B6, 3B6, 4B7, 3B7, 4I8, 3I8, 4I9, 3I9, 4W8, 3W8, 4W9, 3W9] ``` In this PR, the schedule execution will just treat the OVERLAP_F_B as two separate operations of F and B (so there is no actual overlap). The next step is to allow users to create a custom function to plug in what this operation does. https://github.com/pytorch/pytorch/blob/814629043a0c31441bc3749204c97f1e24fa3462/torch/distributed/pipelining/schedules.py#L1205-L1216 Pull Request resolved: #158978 Approved by: https://github.com/wconstab
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| self.pipeline_order[rank] = rank_ops | ||
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| # Initialize the pipeline order with communication necessary to run with _PipelineScheduleRuntime | ||
| self._load_actions(self.pipeline_order) |
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regretting that this function name doesn't make it clear that it's running all the passes to insert comms.
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I can update it in a follow up PR! I also was making a dumb mistake of calling _load_action twice which doesn't error out but leads to numerics issues, so im going to add validation to check for this
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| weight_key = (actual_stage_index, _ComputationType.BACKWARD_WEIGHT) | ||
| counters[weight_key] = counters.get(weight_key, 0) + 1 | ||
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| # Step 1: F0 |
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generally, is there some matching between the 6 steps and the rank-based formulas you used here, and the paper/code? it isn't that obvious how this compares to the logic in dualpipev
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The logic for dualpipev is here (https://github.com/deepseek-ai/DualPipe/blob/3da1bbea53606543d7f5f232338fc58096db30e3/dualpipe/dualpipev.py#L331-L396). So this schedule copies that (minus their communication operations, which we put in ourselves).
The overlapped_f_b in the dualpipev is a dummy implementation using an MLP. The DeepSeek team does not provide a concrete implementation for what they described in the paper, but they leverage the tech built in DeepEP for the dispatch + MOE + combine example (https://github.com/deepseek-ai/DeepEP/blob/main/README.md#example-use-in-model-training-or-inference-prefilling)
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should the picture in your PR desc match this picture exactly (from DualPipe? |
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Rename method and add validation Pull Request resolved: #160558 Approved by: https://github.com/wconstab ghstack dependencies: #159591
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Update schedule tests to use `world_size=4`, changes needed: - Move some tests that require world_size=2 to new class - Move helper methods from class level to function level - Update some initialization to pass assert since gradients were super small. Pull Request resolved: #160559 Approved by: https://github.com/wconstab ghstack dependencies: #159591, #160558
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DualPipeV was added to pt-core (pytorch/pytorch#159591) so just adding code to support it in titan To use, in .toml file set: ``` pipeline_parallel_schedule = "DualPipeV" ``` Ideally we don't have this if-statement check, so as a future BE task I can look into removing it
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Added the DualPipeV schedule according to http://github.com/deepseek-ai/DualPipe/blob/main/dualpipe/dualpipev.py#L11 <img width="3633" height="486" alt="image" src="https://github.com/user-attachments/assets/4e843bb9-87cd-4d11-936c-7dfe8ee12f16" /> This schedule doesn't perform the actual "overlap" during execution, but provides the scaffolding and schedule definition we need to run it E2E in torchtitan. Supporting the overlapped operation will be worked on in following PRs. Tests: ```sh python test/distributed/pipelining/test_schedule_multiproc.py -k test_v_shape_schedules python test/distributed/pipelining/test_schedule.py -k test_pipeline_order_for_v_schedules ``` Also tested in TorchTitan and is running. Pull Request resolved: pytorch#159591 Approved by: https://github.com/wconstab
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Rename method and add validation Pull Request resolved: pytorch#160558 Approved by: https://github.com/wconstab ghstack dependencies: pytorch#159591
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Update schedule tests to use `world_size=4`, changes needed: - Move some tests that require world_size=2 to new class - Move helper methods from class level to function level - Update some initialization to pass assert since gradients were super small. Pull Request resolved: pytorch#160559 Approved by: https://github.com/wconstab ghstack dependencies: pytorch#159591, pytorch#160558
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Added the DualPipeV schedule according to http://github.com/deepseek-ai/DualPipe/blob/main/dualpipe/dualpipev.py#L11 <img width="3633" height="486" alt="image" src="https://github.com/user-attachments/assets/4e843bb9-87cd-4d11-936c-7dfe8ee12f16" /> This schedule doesn't perform the actual "overlap" during execution, but provides the scaffolding and schedule definition we need to run it E2E in torchtitan. Supporting the overlapped operation will be worked on in following PRs. Tests: ```sh python test/distributed/pipelining/test_schedule_multiproc.py -k test_v_shape_schedules python test/distributed/pipelining/test_schedule.py -k test_pipeline_order_for_v_schedules ``` Also tested in TorchTitan and is running. Pull Request resolved: pytorch#159591 Approved by: https://github.com/wconstab
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Rename method and add validation Pull Request resolved: pytorch#160558 Approved by: https://github.com/wconstab ghstack dependencies: pytorch#159591
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Update schedule tests to use `world_size=4`, changes needed: - Move some tests that require world_size=2 to new class - Move helper methods from class level to function level - Update some initialization to pass assert since gradients were super small. Pull Request resolved: pytorch#160559 Approved by: https://github.com/wconstab ghstack dependencies: pytorch#159591, pytorch#160558
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Stack from ghstack (oldest at bottom):
Added the DualPipeV schedule according to http://github.com/deepseek-ai/DualPipe/blob/main/dualpipe/dualpipev.py#L11
This schedule doesn't perform the actual "overlap" during execution, but provides the scaffolding and schedule definition we need to run it E2E in torchtitan. Supporting the overlapped operation will be worked on in following PRs.
Tests:
Also tested in TorchTitan and is running.
cc @awgu @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @pragupta