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apex-master/tests/L1/transformer/pipeline_parallel_fwd_bwd_ucc_async.py

@@ -0,0 +1,219 @@
+import os
+import logging
+import itertools
+from typing import Optional, Tuple, List
+import unittest
+
+import torch
+from torch.testing._internal import common_utils
+from torch.testing._internal import common_cuda
+from torch.testing._internal import common_distributed
+
+from apex._autocast_utils import _get_autocast_dtypes
+from apex.transformer import parallel_state
+from apex.transformer.pipeline_parallel import utils as pp_utils
+from apex.transformer.pipeline_parallel.schedules.common import (
+    FwdStepFunc,
+    build_model,
+    _get_params_for_weight_decay_optimization,
+)
+from apex.transformer.pipeline_parallel.schedules.fwd_bwd_no_pipelining import (
+    forward_backward_no_pipelining,
+)
+from apex.transformer.pipeline_parallel.schedules.fwd_bwd_pipelining_with_interleaving import (
+    _forward_backward_pipelining_with_interleaving,
+)
+from apex.transformer.pipeline_parallel.schedules.fwd_bwd_pipelining_without_interleaving import (
+    forward_backward_pipelining_without_interleaving,
+)
+from apex.transformer.testing.distributed_test_base import UccDistributedTestBase
+from apex.transformer.testing import commons as testing_utils
+
+
+logging.getLogger("torch").setLevel(logging.WARNING)
+logging.getLogger("apex").setLevel(logging.WARNING)
+
+
+def _get_default_world_sizes_model_parallel_world_size(pipeline_model_parallel_world_size: Optional[int] = None
+    ) -> Tuple[int, int, int]:
+    # TODO: revisit if we can fold this into the class for skip logic / avoid duplication
+    # of world size computation
+    world_size = torch.cuda.device_count()
+    tensor_model_parallel_world_size = 1
+    data_parallel_size = 1 + (world_size >= 8 and world_size % 2 == 0)
+
+    if pipeline_model_parallel_world_size is None:
+        pipeline_model_parallel_world_size =  world_size // (tensor_model_parallel_world_size * data_parallel_size)
+    else:
+        data_parallel_size = world_size // (tensor_model_parallel_world_size * pipeline_model_parallel_world_size)
+
+    return tensor_model_parallel_world_size, data_parallel_size, pipeline_model_parallel_world_size
+
+
+class UccPipelineParallelForwardBackwardProf(UccDistributedTestBase):
+
+    # The purpose of this class is to test and confirm asynchronous communication via profiling.
+    # Having that in mind, it is safe to skip all the numerical checks.
+    # For unit testing with numerical checks please refer to `tests/L0/run_transformer/test_pipeline_parallel_fwd_bwd.py`.
+
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        self.GLOBAL_BATCH_SIZE = 1024
+        self.MICRO_BATCH_SIZE = 64
+        self.HIDDEN_SIZE = 256
+        self.NUM_FWD_BWD_ITERATIONS = 4
+        self.deallocate_options = (False,)
+        self.dtypes = (torch.float32,)
+
+    @property
+    def world_size(self) -> int:
+        return min(torch.cuda.device_count(), 8)
+
+    def _forward_backward_test_impl(
+        self,
+        forward_only: bool,
+        fwd_bwd_func: FwdStepFunc,
+        pipeline_model_parallel_world_size: Optional[int],
+        virtual_pipeline_model_parallel_size: Optional[int],
+        async_comm: bool = False,
+        *,
+        default_backend: Optional[str] = None,
+        p2p_backend: Optional[str] = None,
+    ) -> None:
+        if fwd_bwd_func == _forward_backward_pipelining_with_interleaving:
+            self.assertIsNotNone(virtual_pipeline_model_parallel_size)
+            self.assertGreater(virtual_pipeline_model_parallel_size, 1)
+        dtype_options = self.dtypes or [torch.float32, torch.double] + _get_autocast_dtypes()
+
+        for dtype, deallocate_pipeline_outputs in itertools.product(
+            dtype_options, self.deallocate_options,
+        ):
+            grad_scaler = (
+                torch.amp.GradScaler('cuda', init_scale=4.0)
+                if dtype == torch.half
+                else None
+            )
+
+            (tensor_model_parallel_world_size,
+            data_parallel_size,
+            pipeline_model_parallel_world_size) = _get_default_world_sizes_model_parallel_world_size(pipeline_model_parallel_world_size)
+
+            parallel_state.initialize_model_parallel(
+                tensor_model_parallel_size_=tensor_model_parallel_world_size,
+                pipeline_model_parallel_size_=pipeline_model_parallel_world_size,
+                virtual_pipeline_model_parallel_size_=virtual_pipeline_model_parallel_size,
+                default_backend=default_backend,
+                p2p_backend=p2p_backend,
+            )
+            pp_utils._reconfigure_microbatch_calculator(
+                rank=parallel_state.get_tensor_model_parallel_rank(),
+                rampup_batch_size=None,
+                global_batch_size=self.GLOBAL_BATCH_SIZE,
+                micro_batch_size=self.MICRO_BATCH_SIZE,
+                data_parallel_size=parallel_state.get_data_parallel_world_size(),
+            )
+
+            global_batch_shape = (
+                self.GLOBAL_BATCH_SIZE
+                // parallel_state.get_data_parallel_world_size(),
+                self.HIDDEN_SIZE,
+                self.HIDDEN_SIZE,
+            )
+
+            batch = None
+            if parallel_state.is_pipeline_first_stage():
+                batch = (torch.ones(global_batch_shape, dtype=dtype).cuda(), )
+
+            model = build_model(
+                testing_utils.model_provider_func,
+                # Use DDP only when it's better to have
+                wrap_with_ddp=data_parallel_size > 1,
+                virtual_pipeline_model_parallel_size=virtual_pipeline_model_parallel_size,
+                hidden_size=self.HIDDEN_SIZE,
+            )
+
+
+            offset = pipeline_model_parallel_world_size if virtual_pipeline_model_parallel_size is not None else 0
+            for idx, model_module in enumerate(model):
+                model_module = model_module.to(dtype)
+
+            _param_groups = _get_params_for_weight_decay_optimization(model)
+            optimizer = torch.optim.Adam(_param_groups, lr=1e-3)
+
+            pp_utils.update_num_microbatches(0)
+
+            for _ in range(self.NUM_FWD_BWD_ITERATIONS):
+                loss = fwd_bwd_func(
+                    testing_utils.fwd_step_func,
+                    batch,
+                    model,
+                    forward_only=forward_only,
+                    # `tensor_shape` is the shape of micro batch.
+                    tensor_shape=(
+                        self.MICRO_BATCH_SIZE,
+                        self.HIDDEN_SIZE,
+                        self.HIDDEN_SIZE,
+                    ),
+                    dtype=dtype,
+                    async_comm=async_comm,
+                    grad_scaler=grad_scaler,
+                    deallocate_pipeline_output=deallocate_pipeline_outputs,
+                )
+
+            parallel_state.destroy_model_parallel()
+
+    def test_learning_no_pipelining(self):
+        self._forward_backward_test_impl(False, forward_backward_no_pipelining, 1, None)
+
+    def test_inference_no_pipelining(self):
+        self._forward_backward_test_impl(True, forward_backward_no_pipelining, 1, None)
+
+    def test_learning_pipelining_without_interleaving(self):
+        self._forward_backward_test_impl(
+            False, forward_backward_pipelining_without_interleaving, None, None
+        )
+
+    def test_inference_pipelining_without_interleaving(self):
+        self._forward_backward_test_impl(
+            True, forward_backward_pipelining_without_interleaving, None, None
+        )
+
+    def test_learning_async_pipelining_without_interleaving(self):
+        self._forward_backward_test_impl(
+            False, forward_backward_pipelining_without_interleaving, None, None, async_comm=True
+        )
+
+    def test_inference_async_pipelining_without_interleaving(self):
+        self._forward_backward_test_impl(
+            True, forward_backward_pipelining_without_interleaving, None, None, async_comm=True
+        )
+
+    @unittest.skipUnless(_get_default_world_sizes_model_parallel_world_size()[-1] > 2, "Interleaved schedule requires pipeline_model_parallel_world_size > 2")
+    def test_learning_pipelining_with_interleaving(self):
+        self._forward_backward_test_impl(
+            False, _forward_backward_pipelining_with_interleaving, None, virtual_pipeline_model_parallel_size=2
+        )
+
+    @unittest.skipUnless(_get_default_world_sizes_model_parallel_world_size()[-1] > 2, "Interleaved schedule requires pipeline_model_parallel_world_size > 2")
+    def test_inference_pipelining_with_interleaving(self):
+        self._forward_backward_test_impl(
+            True, _forward_backward_pipelining_with_interleaving, None, virtual_pipeline_model_parallel_size=2
+        )
+
+    @unittest.skipUnless(_get_default_world_sizes_model_parallel_world_size()[-1] > 2, "Interleaved schedule requires pipeline_model_parallel_world_size > 2")
+    def test_learning_async_pipelining_with_interleaving(self):
+        self._forward_backward_test_impl(
+            False, _forward_backward_pipelining_with_interleaving, None, virtual_pipeline_model_parallel_size=2, async_comm=True
+        )
+
+    @unittest.skipUnless(_get_default_world_sizes_model_parallel_world_size()[-1] > 2, "Interleaved schedule requires pipeline_model_parallel_world_size > 2")
+    def test_inference_async_pipelining_with_interleaving(self):
+        self._forward_backward_test_impl(
+            True, _forward_backward_pipelining_with_interleaving, None, virtual_pipeline_model_parallel_size=2, async_comm=True
+        )
+
+
+if __name__ == "__main__":
+    os.environ["UCC_TLS"] = "ucp,cuda"
+    common_distributed.TIMEOUT_DEFAULT = 500
+    common_utils.run_tests()