chore: import upstream snapshot with attribution

deepspeed/compile/graph_param.py

@@ -0,0 +1,103 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Tuple
+from functools import reduce
+
+import torch
+from torch.fx import Graph, Node
+
+from .fx import get_output_node
+from .util import get_param_nodes, get_input_nodes
+
+
+@dataclass
+class DSGraphParam:
+    name: str
+    shape: torch.Size
+    dtype: torch.dtype
+    device: torch.device
+    node: Node
+    allgather_node: Node
+    release_node: Node
+    param: torch.Tensor
+    numel: int = field(init=False)
+
+    def __post_init__(self):
+        self.numel = reduce(lambda x, y: x * y, self.shape)
+
+
+class DSGraphParamManager:
+
+    def __init__(self, fw_graph: Graph, sample_inputs: Any, index_to_ds_ids: List[Tuple[int, int, int]]):
+        self._fw_graph = fw_graph
+        self._bw_graph = None
+        self._params: Dict[str, DSGraphParam] = {}
+        self._param_name_to_grad: Dict[str, Node] = {}
+        self._ds_ids: Dict[str, int] = {}
+
+        param_nodes = get_param_nodes(fw_graph, index_to_ds_ids)
+        self._param_names = [pn.name for pn in param_nodes]
+        self._param_indices = [i for i, _, _ in index_to_ds_ids]
+
+        param_inputs = [sample_inputs[i] for i, _, _ in index_to_ds_ids]
+        ds_ids = [ds_id for _, ds_id, _ in index_to_ds_ids]
+        ds_shapes = [ds_shape for _, _, ds_shape in index_to_ds_ids]
+
+        for pn, pi, ds_id, ds_shape in zip(param_nodes, param_inputs, ds_ids, ds_shapes):
+            self._params[pn.name] = DSGraphParam(name=pn.name,
+                                                 shape=ds_shape,
+                                                 dtype=pi.dtype,
+                                                 device=pi.device,
+                                                 node=pn,
+                                                 allgather_node=None,
+                                                 release_node=None,
+                                                 param=pi)
+            self._ds_ids[pn.name] = ds_id
+
+    def get_bwd_mapping(self, bw_graph: Graph):
+        self._bw_graph = bw_graph
+
+        output_node = get_output_node(bw_graph)
+        param_nodes_bw = [n for n in self._bw_graph.nodes if n.name in self.param_names]
+        grad_outputs = [output_node.args[0][i] for i in self._param_indices]
+        param_name_to_grad = {param_name: grad for param_name, grad in zip(self.param_names, grad_outputs)}
+        return param_nodes_bw, param_name_to_grad
+
+    @property
+    def param_names(self) -> List[str]:
+        return self._param_names
+
+    @property
+    def params(self) -> Dict[str, DSGraphParam]:
+        return self._params
+
+    @property
+    def ds_ids(self) -> Dict[str, int]:
+        return self._ds_ids
+
+    def get_grad_name(self, param_name) -> str:
+        assert self._param_name_to_grad is not None, "Backward graph is not added yet"
+        return self._param_name_to_grad[param_name]
+
+    def replace_fake_tensors_with_real_params(self, sample_inputs: List[Any], bw_graph: Graph) -> List[Any]:
+        """Replace fake tensors in sample_inputs with real parameters from DSGraphParamManager
+
+        Args:
+            sample_inputs: The input tensors that may contain fake tensors
+            bw_graph: The backward graph to get parameter mapping from (if in backward pass)
+        """
+        replaced_inputs = list(sample_inputs)
+
+        # For backward pass, get the parameter nodes and their mapping
+        param_nodes_bw, _ = self.get_bwd_mapping(bw_graph)
+        param_names_bw = [n.name for n in param_nodes_bw]
+
+        for i, inp in enumerate(get_input_nodes(bw_graph)):
+            if inp.name in param_names_bw:
+                replaced_inputs[i] = self._params[inp.name].param
+
+        return replaced_inputs