[Full DTensor] Initial skeleton for full_dtensor mode #2049
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fegin
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This PR provides a skelet This PR introduces an initial prototype and skeleton for fully DTensor-based training. The current codebase builds upon SimpleFSDP, but we anticipate developing our own Reparameterization to better serve our specific use case. There are several reasons why SimpleFSDP's Reparameterization is insufficient. For instance, the current parallelize_buffers() implementation in this PR will not function correctly when additional parallelization strategies are applied. Despite these limitations, this PR provides a starting point for experimenting with a full DTensor trainer. Accuracy verification: HSDP SimpleFSDP v.s. FSDP2 ``` python3 scripts/loss_compare.py . . \ --baseline-options='--activation_checkpoint.mode="none" --parallelism.data_parallel_replicate_degree=2' \ --test-options='--model.name full_dtensor.llama3 --activation_checkpoint.mode="none" --parallelism.data_parallel_replicate_degree=2' \ --test-train-file=torchtitan.experiments.full_dtensor.train \ --steps=10 --assert-equal --no-seed-checkpoint ``` ``` [LOSS_COMPARE] [LOSS_COMPARE] Asserting losses are equal... [LOSS_COMPARE] Baseline log: /tmp/baseline_training.log [LOSS_COMPARE] Test log: /tmp/test_training.log [LOSS_COMPARE] Extracted 100 steps from baseline log [LOSS_COMPARE] Extracted 100 steps from test log test_losses_equal (__main__.assert_losses_equal.<locals>.LossEqualityTest.test_losses_equal) ... ok ---------------------------------------------------------------------- Ran 1 test in 0.000s OK ``` Note that, `--no-seed-checkpoint` is used because when seed-checkpoint is used, we got accuracy mismatch. ghstack-source-id: 67cd703 Pull-Request: #2049
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fegin
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This PR provides a skelet This PR introduces an initial prototype and skeleton for fully DTensor-based training. The current codebase builds upon SimpleFSDP, but we anticipate developing our own Reparameterization to better serve our specific use case. There are several reasons why SimpleFSDP's Reparameterization is insufficient. For instance, the current parallelize_buffers() implementation in this PR will not function correctly when additional parallelization strategies are applied. Despite these limitations, this PR provides a starting point for experimenting with a full DTensor trainer. Accuracy verification: HSDP SimpleFSDP v.s. FSDP2 ``` python3 scripts/loss_compare.py . . \ --baseline-options='--activation_checkpoint.mode="none" --parallelism.data_parallel_replicate_degree=2' \ --test-options='--model.name full_dtensor.llama3 --activation_checkpoint.mode="none" --parallelism.data_parallel_replicate_degree=2' \ --test-train-file=torchtitan.experiments.full_dtensor.train \ --steps=10 --assert-equal --no-seed-checkpoint ``` ``` [LOSS_COMPARE] [LOSS_COMPARE] Asserting losses are equal... [LOSS_COMPARE] Baseline log: /tmp/baseline_training.log [LOSS_COMPARE] Test log: /tmp/test_training.log [LOSS_COMPARE] Extracted 100 steps from baseline log [LOSS_COMPARE] Extracted 100 steps from test log test_losses_equal (__main__.assert_losses_equal.<locals>.LossEqualityTest.test_losses_equal) ... ok ---------------------------------------------------------------------- Ran 1 test in 0.000s OK ``` Note that, `--no-seed-checkpoint` is used because when seed-checkpoint is used, we got accuracy mismatch. ghstack-source-id: 6cf9b5e Pull-Request: #2049
fegin
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This PR provides a skelet This PR introduces an initial prototype and skeleton for fully DTensor-based training. The current codebase builds upon SimpleFSDP, but we anticipate developing our own Reparameterization to better serve our specific use case. There are several reasons why SimpleFSDP's Reparameterization is insufficient. For instance, the current parallelize_buffers() implementation in this PR will not function correctly when additional parallelization strategies are applied. Despite these limitations, this PR provides a starting point for experimenting with a full DTensor trainer. Accuracy verification: HSDP SimpleFSDP v.s. FSDP2 ``` python3 scripts/loss_compare.py . . \ --baseline-options='--activation_checkpoint.mode="none" --parallelism.data_parallel_replicate_degree=2' \ --test-options='--model.name full_dtensor.llama3 --activation_checkpoint.mode="none" --parallelism.data_parallel_replicate_degree=2' \ --test-train-file=torchtitan.experiments.full_dtensor.train \ --steps=10 --assert-equal --no-seed-checkpoint ``` ``` [LOSS_COMPARE] [LOSS_COMPARE] Asserting losses are equal... [LOSS_COMPARE] Baseline log: /tmp/baseline_training.log [LOSS_COMPARE] Test log: /tmp/test_training.log [LOSS_COMPARE] Extracted 100 steps from baseline log [LOSS_COMPARE] Extracted 100 steps from test log test_losses_equal (__main__.assert_losses_equal.<locals>.LossEqualityTest.test_losses_equal) ... ok ---------------------------------------------------------------------- Ran 1 test in 0.000s OK ``` Note that, `--no-seed-checkpoint` is used because when seed-checkpoint is used, we got accuracy mismatch. ghstack-source-id: 0d3e3f0 Pull-Request: #2049
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Stack from ghstack (oldest at bottom):
This PR provides a skelet
This PR introduces an initial prototype and skeleton for fully DTensor-based training. The current codebase builds upon SimpleFSDP, but we anticipate developing our own parameterization to better serve our specific use case. There are several reasons why SimpleFSDP's parameterization is insufficient. For instance, the current parallelize_buffers() implementation in this PR will not function correctly when additional parallelization strategies are applied. Despite these limitations, this PR provides a starting point for experimenting with a full DTensor trainer.
Accuracy verification:
HSDP
SimpleFSDP v.s. FSDP2
Note that,
--no-seed-checkpointis used because when seed-checkpoint is used, we got accuracy mismatch.