add missing shape for replaced fx node in fx passes

test/stablehlo/export_tinyroberta_unbounded_dynamism.py

@@ -0,0 +1,61 @@
+import os
+
+import numpy as np
+import tensorflow as tf
+import torch
+import torch.nn as nn
+import torch_xla
+from torch.utils import _pytree as pytree
+from torch.export import Dim, export
+from torch_xla.stablehlo import exported_program_to_stablehlo
+from torch_xla.tf_saved_model_integration import \
+    save_torch_module_as_tf_saved_model
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer
+
+os.environ["EXPERIMENTAL_XLA_UNBOUNDED_DYNAMISM"] = "1"
+
+
+class WrapModel(torch.nn.Module):
+
+  def __init__(self):
+    super().__init__()
+    self._model = AutoModelForQuestionAnswering.from_pretrained(
+        "deepset/tinyroberta-squad2")
+
+  def forward(self, input, mask):
+    res = self._model.forward(input, mask)
+    return tuple(
+        x for x in (res.loss, res.start_logits, res.end_logits,
+                    res.hidden_states) if x is not None)
+
+
+def _get_fake_pipeline_model_inputs():
+  tokens_len = 10
+  input_ids = torch.randint(
+      low=0, high=2000, size=(3, tokens_len), dtype=torch.int64)
+  attention_mask = torch.ones((3, tokens_len), dtype=torch.int64)
+  return (input_ids, attention_mask)
+
+
+model = WrapModel()
+args = _get_fake_pipeline_model_inputs()
+dynamic_shapes = ({0: Dim("bs")}, {0: Dim("bs")})
+ep = export(model, args=args, dynamic_shapes=dynamic_shapes)
+
+tmp_dir = "/tmp/tiny_roberta/tiny_roberta_export"
+save_torch_module_as_tf_saved_model(
+    model, args, tmp_dir, dynamic_shapes=dynamic_shapes)
+
+tokens_len = 10
+args = (torch.randint(
+    low=0, high=2000, size=(2, tokens_len),
+    dtype=torch.int64), torch.ones((2, tokens_len), dtype=torch.int64))
+loaded_m = tf.saved_model.load(tmp_dir)
+tf_input = pytree.tree_map_only(torch.Tensor, lambda x: tf.constant(x.numpy()),
+                                args)
+
+tf_output = loaded_m.f(*tf_input)
+with torch.no_grad():
+  torch_output = model(*args)
+  print(np.max(torch_output[0].numpy() - tf_output[0].numpy()))
+  print(np.max(torch_output[1].numpy() - tf_output[1].numpy()))

torch_xla/experimental/unbounded_dynamism_export.py

@@ -113,6 +113,7 @@ def decompose_dynamic_shape_select(gm: GraphModule):
               view_new_shape.append(get_dim_size_node)
           view_args = (slice_node, view_new_shape)
           view_node = graph.call_function(aten.view.default, view_args)
+          view_node.meta['val'] = n.meta['val']
           n.replace_all_uses_with(view_node)
         graph.erase_node(n)
 
@@ -151,6 +152,7 @@ def decompose_split_with_sizes(gm: GraphModule):
         for consumer in consumers:
           assert n.op == "call_function" and consumer.target.__name__ == "getitem"
           slice_idx = consumer.args[1]
+          decomposed_slices[slice_idx].meta['val'] = consumer.meta['val']
           consumer.replace_all_uses_with(decomposed_slices[slice_idx])
 
 
@@ -201,6 +203,7 @@ def flatten_embedding_indices_tensor(gm: GraphModule):
         with graph.inserting_after(n):
           recover_view_args = (n, recover_view_shape)
           recover_view_node = graph.call_function(aten.view, recover_view_args)
+          recover_view_node.meta['val'] = n.meta['val']
           n.replace_all_uses_with(recover_view_node)
           recover_view_node.update_arg(0, n)
 
@@ -337,6 +340,7 @@ def dynamic_unsqueeze_to_view(gm: GraphModule):
         view_args = (unsqueeze_src,
                      view_args[:squeezed_dim] + [1] + view_args[squeezed_dim:])
         view_node = graph.call_function(aten.view, view_args)
+        view_node.meta['val'] = n.meta['val']
         n.replace_all_uses_with(view_node)
         graph.erase_node(n)