scan and apply_layers

Add the lowering of scan to HLO While op.

Introduce apply_layers which can sequentially apply a bunch of layers
using scan underneath.

Beef up unit tests including linear layers and decoders.

add regression test for parameter_id_tensor_mapping

add test_apply_layers.py to test shell scripts

correctly import decoder model from examples

examples/decoder_only_model.py

@@ -7,16 +7,16 @@
 from torch import nn
 
 
-# the default config is intentionally kept low to make it runable on a sigle tpu v2-8 core.
+# the default config is intentionally kept low to make it runnable on a single tpu v2-8 core.
 @dataclass
 class DecoderOnlyConfig:
   hidden_size: int = 1024
   num_hidden_layers: int = 2
   num_attention_heads: int = 8
   num_key_value_heads: int = 4
-  intermediate_size = 32 * 1024
-  vocab_size = 3200
-  use_flash_attention = False
+  intermediate_size: int = 32 * 1024
+  vocab_size: int = 3200
+  use_flash_attention: bool = False
 
 
 def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:

test/run_tests.sh

@@ -197,6 +197,7 @@ function run_xla_op_tests2 {
   run_test "$CDIR/pjrt/test_dtypes.py"
   run_test "$CDIR/test_while_loop.py"
   run_test "$CDIR/test_scan.py"
+  run_test "$CDIR/test_apply_layers.py"
   run_test "$CDIR/test_autocast.py"
   run_test "$CDIR/eager/test_eager.py"
   run_test "$CDIR/eager/test_eager_with_xla_compile.py"

test/test_apply_layers.py

@@ -0,0 +1,149 @@
+import sys
+import os
+example_folder = os.path.dirname(os.path.dirname(os.path.abspath(
+    sys.argv[0]))) + "/examples"
+sys.path.append(example_folder)
+from decoder_only_model import DecoderOnlyConfig, DecoderOnlyModel  # type:ignore
+
+import sys
+import unittest
+from typing import Iterable
+
+import torch
+
+import torch_xla
+from torch_xla.experimental.apply_layers import apply_layers
+
+from test_utils import XlaTestCase  # type:ignore
+
+
+class ApplyLayersTest(XlaTestCase):
+
+  def setUp(self):
+    super().setUp()
+
+    self.device = torch_xla.device()
+
+  def test_empty_layers(self):
+    layers = []
+    input_data = torch.randn(64).to(self.device)
+    torch_xla.sync()
+    output = apply_layers(layers, input_data.clone())
+    super().compareResults(output, input_data, abs_err=0.0001, rel_err=0.01)
+
+  def test_linear_layers(self):
+    # We want to apply these layers sequentially
+    import torch.nn as nn
+    layers = [nn.Linear(64, 64).to(self.device) for _ in range(10)]
+    input_data = torch.randn(64).to(self.device)
+
+    from copy import deepcopy
+    scan_layers = deepcopy(layers)
+    loop_layers = deepcopy(layers)
+    torch_xla.sync()
+
+    output = apply_layers(scan_layers, input_data.clone())
+    output.sum().backward()
+
+    # Test that the result is the same as for loop.
+    loop_output = input_data.clone()
+    from copy import deepcopy
+    for layer in loop_layers:
+      loop_output = layer(loop_output)
+    torch_xla.sync()
+
+    super().compareResults(loop_output, output, abs_err=0.0001, rel_err=0.01)
+
+    loop_output.sum().backward()
+    torch_xla.sync()
+
+    # Test that the gradients are the same too.
+    for layer_scan, layer_loop in zip(scan_layers, loop_layers):
+      super().compareResults(
+          layer_scan.weight.grad,
+          layer_loop.weight.grad,
+          abs_err=0.0001,
+          rel_err=0.01)
+      super().compareResults(
+          layer_scan.bias.grad,
+          layer_loop.bias.grad,
+          abs_err=0.0001,
+          rel_err=0.01)
+
+  def test_decoder_model(self):
+    # Define a decoder model that composes the decoder model in the example,
+    # but adds the ability to run the layers with the `scan` operator.
+    class DecoderOnlyModelWithScan(torch.nn.Module):
+
+      def __init__(self, **kwargs):
+        super(DecoderOnlyModelWithScan, self).__init__()
+        self.decoder = DecoderOnlyModel(**kwargs)
+
+      @property
+      def layers(self) -> Iterable[torch.nn.Module]:
+        return self.decoder.layers
+
+      def forward(
+          self,
+          input_ids: torch.Tensor,
+      ) -> torch.Tensor:
+        return self.decoder.forward(input_ids)
+
+      def forward_scan(
+          self,
+          input_ids: torch.Tensor,
+      ) -> torch.Tensor:
+        inputs_embeds = self.decoder.embed_tokens(input_ids)
+        # embed positions
+        assert isinstance(inputs_embeds, torch.Tensor)
+        # decoder layers
+        hidden_states = apply_layers(self.decoder.layers, inputs_embeds)
+        hidden_states = self.decoder.norm(hidden_states)
+        # [B, S, H] -> [B, S, V]
+        return self.decoder.output(hidden_states)
+
+    # Make it smaller for fast model run and comparisons.
+    config = DecoderOnlyConfig(
+        hidden_size=128, intermediate_size=8 * 128, vocab_size=256)
+    model = DecoderOnlyModelWithScan(config=config).to(self.device)
+    batch_size = 2
+    sequence_length = 8
+
+    # Generate random input_ids within the range of the vocabulary size
+    input_ids = torch.randint(0, config.vocab_size,
+                              (batch_size, sequence_length)).to(self.device)
+
+    from copy import deepcopy
+    loop_model = deepcopy(model)
+    scan_model = deepcopy(model)
+    torch_xla.sync()
+
+    # Run the loop-based model.
+    loop_output = loop_model(input_ids.clone())
+    loop_output.sum().backward()
+    torch_xla.sync()
+
+    # Run again, this time using `scan`
+    scan_output = scan_model.forward_scan(input_ids.clone())
+    scan_output.sum().backward()
+    torch_xla.sync()
+
+    # Compare results
+    super().compareResults(scan_output, loop_output, abs_err=0.05, rel_err=0.01)
+
+    # Check gradients
+    for layer_scan, layer_loop in zip(scan_model.layers, loop_model.layers):
+      for (name,
+           param_scan), (name2,
+                         param_loop) in zip(layer_scan.named_parameters(),
+                                            layer_loop.named_parameters()):
+        assert name == name2
+        if param_scan.grad is not None or param_loop.grad is not None:
+          super().compareResults(
+              param_scan.grad, param_loop.grad, abs_err=0.1, rel_err=0.05)
+          print(f"Pass: {name} {param_scan.shape}")
+
+
+if __name__ == '__main__':
+  test = unittest.main()
+  sys.exit(0 if test.result.wasSuccessful() else 1)

test/test_operations.py

@@ -21,6 +21,7 @@
 import itertools
 import math
 from numbers import Number
+from functools import reduce
 import numpy
 import random
 import re
@@ -2597,6 +2598,29 @@ def test_api(self):
     mapping = ctx.parameter_id_tensor_mapping()
     self.assertEqual(len(mapping), 2)
 
+  def test_get_parameters_scalar(self):
+    """Scalar tensors parameters may be shared in the HLO graph if their
+    numerical values are equal. `parameter_id_tensor_mapping` needs to handle
+    that appropriately.
+    """
+
+    device = xm.xla_device()
+    tensors = []
+    for i in range(10):
+      # Add three copies of the same value.
+      tensors.append(torch.tensor(i, device=device))
+      tensors.append(torch.tensor(i, device=device))
+      tensors.append(torch.tensor(i, device=device))
+    result = reduce(lambda a, b: a + b, tensors)
+    ctx = torch_xla._XLAC.lowering.LoweringContext()
+    ctx.build([result])
+    mapping = ctx.parameter_id_tensor_mapping()
+
+    import json
+    hlo_json = json.loads(ctx.hlo_json())
+    num_parameters = len(hlo_json["hostProgramShape"]["parameters"])
+    self.assertEqual(len(mapping), num_parameters)
+
 
 class TestGeneric(test_utils.XlaTestCase):
 

test/test_scan.py

@@ -1,11 +1,14 @@
 import sys
 import unittest
-import torch_xla
+from functools import reduce
+
 import torch
+from torch.utils._pytree import tree_map, tree_flatten, tree_iter, tree_leaves, PyTree
+
+import torch_xla
 from torch_xla.experimental.scan import scan
-from torch.utils._pytree import tree_map, tree_flatten, tree_iter
 
-from test_utils import XlaTestCase
+from test_utils import XlaTestCase  # type:ignore
 
 
 def _loopy_scan(fn, init, xs):
@@ -24,6 +27,8 @@ def _loopy_scan(fn, init, xs):
 class ScanTest(XlaTestCase):
 
   def setUp(self):
+    super().setUp()
+
     self.device = torch_xla.device()
 
   def compare_pytree(self, expected_pytree, actual_pytree):
@@ -32,31 +37,54 @@ def compare_pytree(self, expected_pytree, actual_pytree):
     assert expected_spec == actual_spec
     super().compareResults(flat_expected_pytree, flat_actual_pytree)
 
-  def run_test(self, step_fn, init, xs):
+  def run_test(self, fn, init: PyTree, xs: PyTree):
+    """Compares the result of scanning with `fn` with our optimized HLO implementation
+    against a for loop implementation. Checks both output values and gradients.
+    """
     # Actual output
-    final_carry, ys = scan(step_fn, init, xs)
+    init_scan = tree_map(lambda v: v.detach().requires_grad_(), init)
+    xs_scan = tree_map(lambda v: v.detach().requires_grad_(), xs)
+    final_carry, ys = scan(fn, init_scan, xs_scan)
+    # Add up all leaves in `ys` and `backward()` once.
+    reduce(lambda a, b: a + b, map(lambda v: v.sum(), tree_leaves(ys)),
+           torch.tensor(0.0)).backward()
     torch_xla.sync()
 
     # Expected output
-    expected_final_carry, expected_ys = _loopy_scan(step_fn, init, xs)
+    init_loop = tree_map(lambda v: v.detach().requires_grad_(), init)
+    xs_loop = tree_map(lambda v: v.detach().requires_grad_(), xs)
+    expected_final_carry, expected_ys = _loopy_scan(fn, init_loop, xs_loop)
+    # Add up all leaves in `ys` and `backward()` once.
+    reduce(lambda a, b: a + b, map(lambda v: v.sum(), tree_leaves(expected_ys)),
+           torch.tensor(0.0)).backward()
     torch_xla.sync()
 
-    # Compare
+    # Compare values
     self.compare_pytree(expected_final_carry, final_carry)
     self.compare_pytree(expected_ys, ys)
 
+    # Compare gradients
+    self.compare_pytree(
+        tree_map(lambda v: v.grad, init_scan),
+        tree_map(lambda v: v.grad, init_loop))
+    self.compare_pytree(
+        tree_map(lambda v: v.grad, xs_scan), tree_map(lambda v: v.grad,
+                                                      xs_loop))
+
     return final_carry, ys
 
-  def test_scan_forward_simple(self):
+  def test_scan_simple(self):
     """This test uses `scan` to implement `torch.cumsum`."""
 
     def step_fn(carry, x):
       new_carry = carry + x
       y = new_carry
       return new_carry, y
 
-    init = torch.tensor([0.0, 0.0], device=self.device)
-    xs = torch.tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], device=self.device)
+    init = torch.tensor([0.0, 0.0], requires_grad=True, device=self.device)
+    xs = torch.tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]],
+                      requires_grad=True,
+                      device=self.device)
     final_carry, ys = self.run_test(step_fn, init, xs)
 
     # Also ensure that our loop-based scan is correct, with manual checks
@@ -80,26 +108,30 @@ def test_scan_incompatible_length(self):
     with self.assertRaises(ValueError):
       scan(lambda a, b: (a, b), init, (xs_1, xs_2))
 
-  def test_scan_forward_tuples(self):
+  def test_scan_tuples(self):
     """Test scanning over the leading axis of a tuple of tensors simultaneously,
     which is a simple PyTree."""
 
-    def step_fn(carry, x):
+    def fn(carry, x):
       carry1, carry2 = carry
       x1, x2 = x
       new_carry1 = carry1 + x1.sum()
       new_carry2 = carry2 + x2.sum()
-      y1 = x1 * 2
-      y2 = x2 * 2
+      y1 = x1 * 2 + torch.sum(new_carry1)
+      y2 = x2 * 2 + torch.sum(new_carry2)
       return (new_carry1, new_carry2), (y1, y2)
 
-    init = (torch.tensor([0.0], device=self.device),
-            torch.tensor([1.0, 2.0], device=self.device))
+    init = (torch.tensor([0.0], requires_grad=True, device=self.device),
+            torch.tensor([1.0, 2.0], requires_grad=True, device=self.device))
 
-    xs = (torch.tensor([[1.0, 2.0], [3.0, 4.0]], device=self.device),
-          torch.tensor([[5.0, 6.0, 7.0], [8.0, 9.0, 10.0]], device=self.device))
+    xs = (torch.tensor([[1.0, 2.0], [3.0, 4.0]],
+                       requires_grad=True,
+                       device=self.device),
+          torch.tensor([[5.0, 6.0, 7.0], [8.0, 9.0, 10.0]],
+                       requires_grad=True,
+                       device=self.device))
 
-    self.run_test(step_fn, init, xs)
+    self.run_test(fn, init, xs)
 
 
 if __name__ == '__main__':

test/tpu/run_tests.sh

@@ -25,6 +25,7 @@ python3 test/pjrt/test_dtypes.py
 python3 test/pjrt/test_dynamic_plugin_tpu.py
 python3 test/test_while_loop.py
 python3 test/test_scan.py
+python3 test/test_apply_layers.py
 python3 test/test_pallas.py
 python3 test/test_pallas_spmd.py
 python3 test/test_input_output_aliases.py

torch_xla/csrc/init_python_bindings.cpp

@@ -1064,7 +1064,6 @@ class PyLoweringContext {
   // etc.)
   std::unordered_map<int64_t, at::Tensor> GetParameterIdTensorMapping() {
     // Find parameters in the lowering
-    const std::vector<size_t>& param_ids = lowering_ctx.GetParameterSequence();
     const std::vector<torch::lazy::BackendDataPtr>& device_data =
         lowering_ctx.GetParametersData();
 
@@ -1081,7 +1080,9 @@ class PyLoweringContext {
       at::ScalarType dtype =
           MaybeUpcastToHostTorchType(literal.shape().element_type());
       at::Tensor input = MakeTensorFromXlaLiteral(literal, dtype);
-      results[param_ids[i]] = input;
+      std::optional param_id = lowering_ctx.GetParameterId(device_data[i]);
+      XLA_CHECK(param_id.has_value());
+      results[param_id.value()] = input;
     }
     return results;
   }
@@ -1104,12 +1105,13 @@ class PyLoweringContext {
     torch::lazy::BackendData::Handle handle = data->GetHandle();
 
     // Linearly search parameters and compare opaque handles
-    const std::vector<size_t>& param_ids = lowering_ctx.GetParameterSequence();
     const std::vector<torch::lazy::BackendDataPtr>& device_data =
         lowering_ctx.GetParametersData();
     for (int i = 0; i < device_data.size(); ++i) {
       if (device_data[i]->GetHandle() == handle) {
-        return param_ids[i];
+        std::optional param_id = lowering_ctx.GetParameterId(device_data[i]);
+        XLA_CHECK(param_id.has_value());
+        return param_id.value();
       }
     }
     return -1;