[r2.3] backport: auto-sharding PoC (#6719)

docs/spmd.md

@@ -492,4 +492,33 @@ generated_table = visualize_sharding(sharding, use_color=False)
 
 You could use these examples on TPU/GPU/CPU single-host and modify it to run on multi-host. And you could modify it to sharding-style `tiled`, `partial_replication` and `replicated`.
 
+### Auto-Sharding
+We are introducing a new PyTorch/XLA SPMD feature, called ``auto-sharding``, [RFC](https://github.com/pytorch/xla/issues/6322). This is an experimental feature in `r2.3` and `nightly`, that supports `XLA:TPU` and a single TPUVM host.
 
+PyTorch/XLA auto-sharding can be enabled by one of the following:
+- Setting envvar `XLA_SPMD_AUTO=1`
+- Calling the SPMD API in the beginning of your code:
+```python
+import torch_xla.runtime as xr
+xr.use_spmd(auto=True)
+```
+- Calling `pytorch.distributed._tensor.distribute_module` with `auto-policy` and `xla`:
+```python
+import torch_xla.runtime as xr
+from torch.distributed._tensor import DeviceMesh, distribute_module
+from torch_xla.distributed.spmd import auto_policy
+
+device_count = xr.global_runtime_device_count()
+device_mesh = DeviceMesh("xla", list(range(device_count)))
+
+# Currently, model should be loaded to xla device via distribute_module.
+model = MyModule()  # nn.module
+sharded_model = distribute_module(model, device_mesh, auto_policy)
+```
+
+Optionally, one can set the following options/env-vars to control the behvaior of
+the XLA-based auto-sharding pass:
+- `XLA_AUTO_USE_GROUP_SHARDING`: group resharding of the parameters. Set by default.
+- `XLA_AUTO_SPMD_MESH`: logical mesh shape to be used for auto-sharding. For example,
+`XLA_AUTO_SPMD_MESH=2,2` corresponds to a 2-by-2 mesh with 4 global devices. If unset,
+a default device mesh shape of `num_devices,1` will be used.

test/run_tests.sh

@@ -226,6 +226,8 @@ function run_xla_op_tests3 {
   run_test "$CDIR/spmd/test_xla_distributed_checkpoint.py"
   run_test "$CDIR/spmd/test_xla_spmd_python_api_interaction.py"
   run_test "$CDIR/spmd/test_dtensor_integration.py"
+  run_test "$CDIR/spmd/test_dtensor_integration2.py"
+  run_test "$CDIR/spmd/test_xla_auto_sharding.py"
   run_test "$CDIR/test_operations_hlo.py" "$@" --verbosity=$VERBOSITY
   run_test "$CDIR/test_input_output_aliases.py"
   run_test "$CDIR/test_torch_distributed_xla_backend.py"

test/spmd/args_parse.py

@@ -39,6 +39,7 @@ def parse_common_options(datadir=None,
   parser.add_argument('--async_closures', action='store_true')
   parser.add_argument('--debug', action='store_true')
   parser.add_argument('--profile', action='store_true')
+  parser.add_argument('--auto_spmd', action='store_true')
   if opts:
     for name, aopts in opts:
       parser.add_argument(name, **aopts)

test/spmd/test_dtensor_integration.py

@@ -4,11 +4,13 @@
 import torch
 from torch import nn
 import torch.optim as optim
-from torch.distributed._tensor import DeviceMesh, Shard
+from torch.distributed._tensor import (DeviceMesh, Shard, distribute_tensor,
+                                       distribute_module)
 import torch_xla
+import torch_xla.debug.metrics as met
 import torch_xla.runtime as xr
 import torch_xla.core.xla_model as xm
-from torch_xla.distributed.spmd import xla_distribute_tensor, xla_distribute_module
+from torch_xla.distributed.spmd import auto_policy
 
 import unittest
 
@@ -19,7 +21,6 @@ class DTensorIntegrationTest(test_xla_sharding_base.XlaShardingTest):
 
   @classmethod
   def setUpClass(cls):
-    xr.use_spmd()
     super().setUpClass()
 
   def test_xla_distribute_tensor(self):
@@ -33,8 +34,7 @@ def test_xla_distribute_tensor(self):
           3,
           requires_grad=requires_grad,
           device=xm.xla_device())
-      dist_tensor = xla_distribute_tensor(tensor_to_shard, device_mesh,
-                                          shard_spec)
+      dist_tensor = distribute_tensor(tensor_to_shard, device_mesh, shard_spec)
       # TODO(yeounoh) switch to DTensor API when XLAShardedTensor inherits DTensor
       assert type(dist_tensor).__name__ == "XLAShardedTensor"
       assert len(dist_tensor.sharding_spec) > 0
@@ -47,65 +47,68 @@ def test_xla_distribute_tensor(self):
         self.assertTrue(dist_tensor.global_tensor.requires_grad)
         self.assertTrue(dist_tensor.is_leaf)
 
-  def test_xla_distribute_module(self):
+  def test_optimizer_step_with_sharding(self):
+    # Use simple linear model to test model parameter sharding
     model = self.SimpleLinear().to(xm.xla_device())
 
+    # Running the same mark_sharding test with xla_distribute_tensor instead
     device_count = xr.global_runtime_device_count()
     device_mesh = DeviceMesh("xla", list(range(device_count)))
+    shard_spec = [Shard(0)]
+    distribute_tensor(model.fc1.weight, device_mesh, shard_spec)
+    sharding_spec = torch_xla._XLAC._get_xla_sharding_spec(model.fc1.weight)
 
-    def shard_params(mod_name, mod, mesh):
-      shard_spec = [Shard(0)]
-      # annoate fc1 and fc2
-      if isinstance(mod, nn.Linear):
-        for name, param in mod.named_parameters():
-          dist_param = xla_distribute_tensor(param, mesh, shard_spec)
-
-    sharded_model = xla_distribute_module(model, device_mesh, shard_params)
-    self.assertTrue(
-        torch_xla._XLAC._get_xla_sharding_spec(sharded_model.fc1.weight) != "")
-    self.assertTrue(
-        torch_xla._XLAC._get_xla_sharding_spec(sharded_model.fc2.weight) != "")
-
-    sharded_model.train()
+    model.train()
     optimizer = optim.SGD(model.parameters(), lr=0.1)
     data = torch.randn(128, 128).to(xm.xla_device())
     target = torch.zeros(128).to(xm.xla_device())
     loss_fn = nn.CrossEntropyLoss()
-    for i in range(3):
+    for _ in range(3):
       optimizer.zero_grad()
       output = model(data)
       loss = loss_fn(output, target)
       loss.backward()
       optimizer.step()
       xm.mark_step()
+      # Sharding is persisted across mark_step calls, and test if the sharded computation
+      # can repeat more than once without crashing.
+      self.assertEqual(sharding_spec,
+                       torch_xla._XLAC._get_xla_sharding_spec(model.fc1.weight))
 
-  def test_optimizer_step_with_sharding(self):
-    # Use simple linear model to test model parameter sharding
+  def test_xla_distribute_module(self):
     model = self.SimpleLinear().to(xm.xla_device())
 
-    # Running the same mark_sharding test with xla_distribute_tensor instead
     device_count = xr.global_runtime_device_count()
     device_mesh = DeviceMesh("xla", list(range(device_count)))
-    shard_spec = [Shard(0)]
-    xla_distribute_tensor(model.fc1.weight, device_mesh, shard_spec)
-    sharding_spec = torch_xla._XLAC._get_xla_sharding_spec(model.fc1.weight)
 
-    model.train()
-    optimizer = optim.SGD(model.parameters(), lr=0.1)
+    def shard_params(mod_name, mod, mesh):
+      shard_spec = [Shard(0)]
+      # annoate fc1 and fc2
+      if isinstance(mod, nn.Linear):
+        for name, param in mod.named_parameters():
+          dist_param = distribute_tensor(param, mesh, shard_spec)
+
+    sharded_model = distribute_module(model, device_mesh, shard_params)
+    self.assertTrue(
+        torch_xla._XLAC._get_xla_sharding_spec(sharded_model.fc1.weight) != "")
+    self.assertTrue(
+        torch_xla._XLAC._get_xla_sharding_spec(sharded_model.fc2.weight) != "")
+
+    sharded_model.train()
+    optimizer = optim.SGD(sharded_model.parameters(), lr=0.1)
     data = torch.randn(128, 128).to(xm.xla_device())
     target = torch.zeros(128).to(xm.xla_device())
     loss_fn = nn.CrossEntropyLoss()
-    for i in range(3):
+    for _ in range(3):
       optimizer.zero_grad()
-      output = model(data)
+      output = sharded_model(data)
       loss = loss_fn(output, target)
       loss.backward()
       optimizer.step()
       xm.mark_step()
-      # Sharding is persisted across mark_step calls, and test if the sharded computation
-      # can repeat more than once without crashing.
-      self.assertEqual(sharding_spec,
-                       torch_xla._XLAC._get_xla_sharding_spec(model.fc1.weight))
+    # Should run with SPMD mode, ExecuteReplicated.
+    self.assertTrue(met.counter_value("ExecuteReplicated") > 0)
+    self.assertTrue(met.counter_value("ExecuteComputation") is None)
 
 
 if __name__ == '__main__':

test/spmd/test_dtensor_integration2.py

@@ -0,0 +1,58 @@
+import os
+import sys
+
+import torch
+from torch import nn
+import torch.optim as optim
+from torch.distributed._tensor import (DeviceMesh, Shard, distribute_tensor,
+                                       distribute_module)
+import torch_xla
+import torch_xla.debug.metrics as met
+import torch_xla.runtime as xr
+import torch_xla.core.xla_model as xm
+from torch_xla.distributed.spmd import auto_policy
+
+import unittest
+
+import test_xla_sharding_base
+
+
+# This integration test passes when run independently.
+class DTensorIntegrationTest2(test_xla_sharding_base.XlaShardingTest):
+
+  @classmethod
+  def setUpClass(cls):
+    super().setUpClass()
+
+  @unittest.skipUnless(xr.device_type() in ["TPU", "CPU"],
+                       "Auto-sharding currently supports TPU device.")
+  def test_xla_distribute_module_auto(self):
+    device_count = xr.global_runtime_device_count()
+    device_mesh = DeviceMesh("xla", list(range(device_count)))
+
+    # Use torch_xla.distributed.spmd.auto_policy to enable auto-sharding;
+    # Currently, model should be loaded to xla device via distribute_module.
+    model = self.SimpleLinear()
+    sharded_model = distribute_module(model, device_mesh, auto_policy)
+    sharded_model.train()
+    self.assertTrue(torch_xla._XLAC._xla_get_auto_sharding())
+
+    optimizer = optim.SGD(sharded_model.parameters(), lr=0.1)
+    data = torch.randn(128, 128).to(xm.xla_device())
+    target = torch.zeros(128).to(xm.xla_device())
+    loss_fn = nn.CrossEntropyLoss()
+    for _ in range(5):
+      optimizer.zero_grad()
+      output = sharded_model(data)
+      loss = loss_fn(output, target)
+      loss.backward()
+      optimizer.step()
+      xm.mark_step()
+    # Should compile with auto-sharding, we expect up to 3 times
+    cnt = met.counter_value("CompileWithAutoSharding")
+    self.assertTrue((cnt is not None) and (cnt <= 3))
+
+
+if __name__ == '__main__':
+  test = unittest.main()
+  sys.exit(0 if test.result.wasSuccessful() else 1)

test/spmd/test_dynamo_spmd.py

@@ -41,7 +41,6 @@ class DynamoSpmdInferenceTest(test_xla_sharding_base.XlaShardingTest):
 
   @classmethod
   def setUpClass(cls):
-    xr.use_spmd()
     super().setUpClass()
 
   def test_dynamo_spmd_basic(self):

test/spmd/test_spmd_graph_dump.py

@@ -18,7 +18,6 @@ class SpmdGraphDumpTest(test_xla_sharding_base.XlaShardingTest):
 
   @classmethod
   def setUpClass(cls):
-    xr.use_spmd()
     super().setUpClass()
 
   def test_dump_with_output_sharding(self):

test/spmd/test_train_spmd_imagenet.py

@@ -86,6 +86,8 @@
 import torch_xla.test.test_utils as test_utils
 import torch_xla.distributed.spmd as xs
 
+xr.use_spmd(auto=FLAGS.auto_spmd)
+
 DEFAULT_KWARGS = dict(
     batch_size=128,
     test_set_batch_size=64,

test/spmd/test_train_spmd_linear_model.py

@@ -36,6 +36,8 @@
 FLAGS = args_parse.parse_common_options(
     batch_size=128, num_epochs=1, opts=MODEL_OPTS.items())
 
+xr.use_spmd(auto=FLAGS.auto_spmd)
+
 
 class SimpleLinear(nn.Module):
 

test/spmd/test_xla_auto_sharding.py

@@ -0,0 +1,74 @@
+import copy
+
+import unittest
+from unittest.mock import patch
+import math
+import numpy as np
+import os
+import sys
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+import torch.optim as optim
+import torch_xla
+import torch_xla.debug.metrics as met
+import torch_xla.runtime as xr
+import torch_xla.core.xla_model as xm
+import torch_xla.debug.metrics as met
+import torch_xla.distributed.spmd as xs
+from torch_xla.distributed.spmd import XLAShardedTensor
+import test_xla_sharding_base
+
+import torch_xla.core.xla_env_vars as xenv
+import torch_xla.utils.utils as xu
+from torch_xla._internal import tpu
+
+
+class XlaAutoShardingTest(test_xla_sharding_base.XlaShardingTest):
+
+  @classmethod
+  def setUpClass(cls):
+    xr.use_spmd(auto=True)
+    super().setUpClass()
+
+  @unittest.skipUnless(xr.device_type() in ["TPU", "CPU"],
+                       "Auto-sharding currently supports TPU & CPU backends.")
+  def test_matmul(self):
+    met.clear_counters()
+    t1 = torch.ones(64, 128)
+    t2 = torch.ones(128, 256)
+    t3 = (t1 @ t2).sum()
+
+    xt1 = t1.to(xm.xla_device())
+    xt2 = t2.to(xm.xla_device())
+    xt3 = (xt1 @ xt2).sum()
+    xm.mark_step()
+    self.assertEqual(met.counter_value("CompileWithAutoSharding"), 1)
+    self.assertTrue(torch.allclose(t3, xt3.cpu()))
+
+  @unittest.skipUnless(xr.device_type() in ["TPU", "CPU"],
+                       "Auto-sharding currently supports TPU & CPU backends.")
+  def test_simple_linear_training(self):
+    met.clear_counters()
+
+    model = self.SimpleLinear().to(xm.xla_device())
+    model.train()
+    optimizer = optim.SGD(model.parameters(), lr=0.1)
+    data = torch.randn(128, 128).to(xm.xla_device())
+    target = torch.zeros(128).to(xm.xla_device())
+    loss_fn = nn.CrossEntropyLoss()
+    for i in range(5):
+      optimizer.zero_grad()
+      output = model(data)
+      loss = loss_fn(output, target)
+      loss.backward()
+      optimizer.step()
+      xm.mark_step()
+    cnt = met.counter_value("CompileWithAutoSharding")
+    self.assertTrue((cnt is not None) and (cnt <= 3))
+
+
+if __name__ == '__main__':
+  test = unittest.main()
+  sys.exit(0 if test.result.wasSuccessful() else 1)

test/spmd/test_xla_distributed_checkpoint.py

@@ -41,7 +41,6 @@ class DistributedCheckpointTestBase(test_xla_sharding_base.XlaShardingTest):
 
   @classmethod
   def setUpClass(cls):
-    xr.use_spmd()
     super().setUpClass()
 
   def _get_sharded_model(self, mesh_shape=None):

test/spmd/test_xla_sharding.py

@@ -28,7 +28,6 @@ class BasicXlaShardingTest(test_xla_sharding_base.XlaShardingTest):
 
   @classmethod
   def setUpClass(cls):
-    xr.use_spmd()
     super().setUpClass()
 
   def test_xla_sharded_tensor(self):
@@ -38,8 +37,6 @@ def test_xla_sharded_tensor(self):
                        device=xm.xla_device())
     xst1 = xs.mark_sharding(xt1, self._get_mesh((1, self.n_devices)),
                             partition_spec)
-
-    # TODO(244003536) add more tests for XLAShardedTensror.
     self.assertTrue(isinstance(xst1, XLAShardedTensor))
 
   def test_xla_sharded_tensor_repr(self):

test/spmd/test_xla_sharding_base.py

@@ -1,3 +1,4 @@
+import os
 import unittest
 import numpy as np
 
@@ -31,6 +32,13 @@ def forward(self, x):
   def setUpClass(cls):
     cls.n_devices = xr.global_runtime_device_count()
     cls.device_ids = np.array(range(cls.n_devices))
+    xr.use_spmd()
+
+  @classmethod
+  def tearDownClass(cls):
+    del os.environ['XLA_USE_SPMD']
+    if 'XLA_AUTO_SPMD' in os.environ:
+      del os.environ['XLA_AUTO_SPMD']
 
   def _get_mesh(self, mesh_shape, device_ids=None, axis_names=None):
     assert type(mesh_shape) is tuple, 'mesh_shape must be Tuple[int]'