Implement a function to collect the model's execution stats.

Author: Xreki

graph_net/torch/collect_stats.py

@@ -0,0 +1,203 @@
+import argparse
+import os
+import importlib
+from typing import Type
+from dataclasses import dataclass, field
+from collections import defaultdict
+
+import torch
+from torch.fx.passes.shape_prop import ShapeProp
+from graph_net.torch import utils
+
+
+def is_single_model_dir(model_dir):
+    return os.path.isfile(f"{model_dir}/graph_net.json")
+
+
+def load_class_from_file(file_path: str, class_name: str) -> Type[torch.nn.Module]:
+    spec = importlib.util.spec_from_file_location("unnamed", file_path)
+    unnamed = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(unnamed)
+    model_class = getattr(unnamed, class_name, None)
+    return model_class
+
+
+def get_input_dict(model_path, device):
+    inputs_params = utils.load_converted_from_text(f"{model_path}")
+    params = inputs_params["weight_info"]
+    for tensor_meta in params.values():
+        if hasattr(tensor_meta, "device"):
+            tensor_meta.device = device
+    return {
+        k: utils.replay_tensor(v).to(torch.device(device)) for k, v in params.items()
+    }
+
+
+@dataclass
+class OpStat:
+    op_name: str
+    dtype: set[str] = field(default_factory=set)
+    count: int = 0
+
+
+def collect_op_stats(model, input_dict):
+    # Use meta tensors as input to avoid actually running the model
+    meta_input_dict = {}
+    for name, x in input_dict.items():
+        meta_input_dict[name] = (
+            torch.empty_like(x, device="meta") if isinstance(x, torch.Tensor) else x
+        )
+
+    # FX symbolic trace
+    traced = torch.fx.symbolic_trace(model)
+    # print(traced.graph)
+
+    node_outputs = {}
+    op_stats = {}
+    for node in traced.graph.nodes:
+        op_name = None
+        dtype = None
+        if node.op == "placeholder":
+            node_outputs[node.name] = meta_input_dict[node.target]
+            op_name = node.op
+            dtype = node_outputs[node.name].dtype
+        elif node.op in ["call_function", "call_method", "call_module"]:
+            node_args = []
+            for arg in node.args:
+                node_args.append(
+                    node_outputs[arg.name] if hasattr(arg, "name") else arg
+                )
+            node_kwargs = {}
+            for k, v in node.kwargs.items():
+                node_kwargs[k] = node_outputs[v.name] if hasattr(v, "name") else v
+
+            if node.op == "call_module":
+                # classname of module
+                submod = dict(traced.named_modules())[node.target]
+                op_name = submod.__class__.__name__
+                try:
+                    out = submod(*node_args, **node_kwargs)
+                    node_outputs[node.name] = out
+                    dtype = out.dtype if isinstance(out, torch.Tensor) else None
+                except Exception:
+                    node_outputs[node.name] = None
+            elif node.op in ["call_function", "call_method"]:
+                op_name = (
+                    node.target.__name__ if node.op == "call_function" else node.target
+                )
+                try:
+                    out = node.target(*node_args, **node_kwargs)
+                    node_outputs[node.name] = out
+                    dtype = out.dtype if isinstance(out, torch.Tensor) else None
+                except Exception:
+                    print(f"Dtype inference failed: op_name={op_name}")
+                    node_outputs[node.name] = None
+        elif node.op == "output":
+            op_name = node.op
+            node_args = []
+            for arg in node.args:
+                node_args.append(
+                    node_outputs[arg.name] if hasattr(arg, "name") else arg
+                )
+            node_outputs[node.name] = node_args[0] if len(node_args) == 1 else node_args
+            dtype = (
+                node_args[0].dtype if isinstance(node_args[0], torch.Tensor) else None
+            )
+        else:
+            assert False
+
+        if op_name is not None:
+            dtype_str = str(dtype).replace("torch.", "") if dtype is not None else None
+            if op_stats.get(op_name, None) is None:
+                op_stats[op_name] = OpStat(op_name, {dtype_str}, 1)
+            else:
+                op_stats[op_name].dtype.add(dtype_str)
+                op_stats[op_name].count = op_stats[op_name].count + 1
+    return op_stats
+
+
+def collect_model_stats(model_path, device):
+    print(f"Collect information for {model_path}")
+    model_class = load_class_from_file(
+        os.path.join(model_path, "model.py"), "GraphModule"
+    )
+    model = model_class()
+    input_dict = get_input_dict(model_path, device)
+
+    num_ops = 0
+    num_inputs = 0
+    num_outputs = 0
+    dtype = set()
+    op_stats = collect_op_stats(model, input_dict)
+    for op_name, stat in op_stats.items():
+        if op_name == "placeholder":
+            num_inputs += stat.count
+        elif op_name == "output":
+            num_outputs += stat.count
+        else:
+            num_ops += stat.count
+        for v in stat.dtype:
+            if v is not None:
+                dtype.add(v)
+
+    # num_params_in_gb = 0
+    # for name, tensor in input_dict.items():
+    #   if isinstance
+
+    print(f"Information of {model_path}:")
+    print(f"- num_inputs  : {num_inputs}")
+    print(f"- num_outputs : {num_outputs}")
+    print(f"- num_ops     : {num_ops}")
+    print(f"- dtype       : {dtype}")
+
+
+def main(args):
+    if args.model_path is not None:
+        assert os.path.isdir(args.model_path)
+        assert is_single_model_dir(args.model_path)
+        collect_model_stats(args.model_path, args.device)
+    else:
+        graph_net_samples_path = (
+            (graph_net.torch.samples_util.get_default_samples_directory())
+            if args.graph_net_samples_path is None
+            else args.graph_net_samples_path
+        )
+        for root, dirs, files in os.walk(graph_net_samples_path):
+            if is_single_model_dir(root):
+                collect_model_stats(root, args.device)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Validate a computation graph sample. return 0 if success"
+    )
+    parser.add_argument(
+        "--device",
+        type=str,
+        required=False,
+        default="cuda",
+        help="Device for testing the compiler (e.g., 'cpu' or 'cuda')",
+    )
+    parser.add_argument(
+        "--model-path",
+        type=str,
+        required=False,
+        default=None,
+        help="Computation graph sample directory. e.g '../../samples/torch/resnet18'",
+    )
+    parser.add_argument(
+        "--graph-net-samples-path",
+        type=str,
+        required=False,
+        default=None,
+        help="GraphNet samples directory. e.g '../../samples'",
+    )
+    parser.add_argument(
+        "--workspace",
+        default=os.environ.get("GRAPH_NET_EXTRACT_WORKSPACE", "./workspace"),
+        help="temporary directory for validation (default: env var GRAPH_NET_EXTRACT_WORKSPACE). ",
+    )
+    args = parser.parse_args()
+    os.environ["GRAPH_NET_EXTRACT_WORKSPACE"] = args.workspace
+
+    main(args=args)

graph_net/torch/test_compiler.py

@@ -1,4 +1,3 @@
-from . import utils
 import argparse
 import importlib.util
 import inspect
@@ -14,6 +13,8 @@
 import json
 import numpy as np
 import platform
+
+from graph_net.torch import utils
 from graph_net.torch.backend.graph_compiler_backend import GraphCompilerBackend
 from graph_net.torch.backend.tvm_backend import TvmBackend
 from graph_net.torch.backend.xla_backend import XlaBackend