Merge pull request #457 from datamol-io/pipeline_integration

Pipeline integration + Virtual Nodes Edges bug fix

expts/hydra-configs/accelerator/ipu_pipeline.yaml

@@ -0,0 +1,22 @@
+type: ipu
+ipu_config:
+    - deviceIterations(60) # IPU would require large batches to be ready for the model.
+    # 60 for PCQM4mv2
+    # 30 for largemix
+    - replicationFactor(4)
+    # - enableProfiling("graph_analyser")       # The folder where the profile will be stored
+    # - enableExecutableCaching("pop_compiler_cache")
+    - TensorLocations.numIOTiles(128)
+    - _Popart.set("defaultBufferingDepth", 96)
+    - Precision.enableStochasticRounding(True)
+
+ipu_inference_config:
+    # set device iteration and replication factor to 1 during inference
+    # gradient accumulation was set to 1 in the code
+    - deviceIterations(60)
+    - replicationFactor(1)
+    - Precision.enableStochasticRounding(False)
+
+accelerator_kwargs:
+    _accelerator: "ipu"
+    gnn_layers_per_ipu: [4, 4, 4, 4]

expts/hydra-configs/model/mpnn.yaml

@@ -22,3 +22,5 @@ architecture:
       attn_type: "none" # "full-attention", "none"
       # biased_attention: false
       attn_kwargs: null
+    virtual_node: 'sum'
+    use_virtual_edges: true

graphium/config/_loader.py

@@ -260,6 +260,10 @@ def load_architecture(
         graph_output_nn_kwargs=graph_output_nn_kwargs,
         task_heads_kwargs=task_heads_kwargs,
     )
+    # Get accelerator_kwargs if they exist
+    accelerator_kwargs = config["accelerator"].get("accelerator_kwargs", None)
+    if accelerator_kwargs is not None:
+        model_kwargs["accelerator_kwargs"] = accelerator_kwargs
 
     if model_class is FullGraphFinetuningNetwork:
         finetuning_head_kwargs = config["finetuning"].pop("finetuning_head", None)

graphium/config/zinc_default_multitask_pyg.yaml

@@ -181,3 +181,5 @@ architecture:     # The parameters for the full graph network are taken from `co
       dropout: 0.2
       normalization: none
       residual_type: none
+accelerator:
+  type: cpu

graphium/nn/architectures/global_architectures.py

@@ -12,6 +12,7 @@
 from torch import Tensor, nn
 import torch
 from torch_geometric.data import Data
+from omegaconf import DictConfig, OmegaConf
 
 # graphium imports
 from graphium.data.utils import get_keys
@@ -593,6 +594,26 @@ def _check_bad_arguments(self):
         ) and not self.layer_class.layer_supports_edges:
             raise ValueError(f"Cannot use edge features with class `{self.layer_class}`")
 
+    def get_nested_key(self, d, target_key):
+        """
+        Get the value associated with a key in a nested dictionary.
+
+        Parameters:
+        - d: The dictionary to search in
+        - target_key: The key to search for
+
+        Returns:
+        - The value associated with the key if found, None otherwise
+        """
+        if target_key in d:
+            return d[target_key]
+        for key, value in d.items():
+            if isinstance(value, (dict, DictConfig)):
+                nested_result = self.get_nested_key(value, target_key)
+                if nested_result is not None:
+                    return nested_result
+        return None
+
     def _create_layers(self):
         r"""
         Create all the necessary layers for the network.
@@ -639,7 +660,8 @@ def _create_layers(self):
                         this_out_dim_edges = self.full_dims_edges[ii + 1]
                         this_edge_kwargs["out_dim_edges"] = this_out_dim_edges
                     else:
-                        this_out_dim_edges = self.layer_kwargs.get("out_dim_edges")
+                        this_out_dim_edges = self.get_nested_key(self.layer_kwargs, "out_dim_edges")
+                        this_edge_kwargs["out_dim_edges"] = this_out_dim_edges
                     layer_out_dims_edges.append(this_out_dim_edges)
 
             # Create the GNN layer