[fx/profiler] provide a table of summary. (#1634)

* [fx/profiling] provide summary for MetaInfoProp.

* [fx/profiler] provide a table of summary.

* [fx] optimize table repr.

colossalai/fx/__init__.py

@@ -1,2 +1,3 @@
 from .tracer import ColoTracer, meta_trace
 from .graph_module import ColoGraphModule
+from .passes import MetaInfoProp

colossalai/fx/passes/__init__.py

@@ -1,2 +1,3 @@
 from .adding_split_node_pass import balanced_split_pass, split_with_split_nodes_pass
-from .shard_1d_pass import column_shard_linear_pass, row_shard_linear_pass
+from .shard_1d_pass import column_shard_linear_pass, row_shard_linear_pass
+from .meta_info_prop import MetaInfoProp

colossalai/fx/passes/meta_info_prop.py

@@ -4,7 +4,7 @@ import torch
 import torch.fx
 from torch.fx.node import Node, Argument, Target
 from torch.utils._pytree import tree_map
-from typing import Any, Tuple, NamedTuple, Dict
+from typing import Any, List, Tuple, NamedTuple, Dict
 from torch.fx._compatibility import compatibility
 from colossalai.fx.profiler import profile_function, profile_module, profile_method, activation_size
 
@@ -48,28 +48,33 @@ class MetaInfoProp(torch.fx.Interpreter):
     Usage:
         BATCH_SIZE = 2
         DIM_IN = 4
+        DIM_HIDDEN = 16
         DIM_OUT = 16
-        model = torch.nn.Linear(DIM_IN, DIM_OUT)
+        model = torch.nn.Sequential(
+            torch.nn.Linear(DIM_IN, DIM_HIDDEN), 
+            torch.nn.Linear(DIM_HIDDEN, DIM_OUT),
+            )
         input_sample = torch.rand(BATCH_SIZE, DIM_IN)
-        orig_output = model(input_sample)
         gm = symbolic_trace(model)
-        MetaInfoProp(gm).run(input_sample)
+        interp = MetaInfoProp(gm)
-
+        interp.run(input_sample)
-        for node in gm.graph.nodes:
+        print(interp.summary(format='kb'))    # don't panic if some statistics are 0.00 MB
-            print(node.name, node.meta['tensor_meta'].dtype,
+        
-                node.meta['tensor_meta'].shape, node.meta['tensor_meta'].numel)
         
         # output of above code is 
-        # input_1 torch.float32 torch.Size([2, 4]) 8
+            Op type       Op    Forward FLOPs    Backward FLOPs    SAVE_FWD_IN    FWD_OUT    FWD_TMP    BWD_OUT    BWD_TMP
-        # weight torch.float32 torch.Size([16, 4]) 64
+        -----------  -------  ---------------  ----------------  -------------  ---------  ---------  ---------  ---------
-        # bias torch.float32 torch.Size([16]) 16
+        placeholder  input_1          0 FLOPs           0 FLOPs          False    0.00 KB    0.00 KB    0.00 KB    0.00 KB
-        # linear torch.float32 torch.Size([2, 16]) 32
+        call_module       _0        128 FLOPs         288 FLOPs           True    0.12 KB    0.00 KB    0.34 KB    0.00 KB
-        # output torch.float32 torch.Size([2, 16]) 32
+        call_module       _1        512 FLOPs       1,056 FLOPs           True    0.12 KB    0.00 KB    1.19 KB    0.00 KB
+             output   output          0 FLOPs           0 FLOPs           True    0.00 KB    0.00 KB    0.00 KB    0.00 KB
     Args:
          module (GraphModule): The module to be executed
 
     """
 
+    _is_proped: bool = False
+
     @compatibility(is_backward_compatible=True)
     def run_node(self, n: Node) -> Any:
         """
@@ -84,6 +89,7 @@ class MetaInfoProp(torch.fx.Interpreter):
         Returns:
             Any: The result of executing ``n``
         """
+        self._is_proped = True
         result, meta_info = super().run_node(n)
 
         def extract_tensor_meta(obj):
@@ -236,3 +242,64 @@ class MetaInfoProp(torch.fx.Interpreter):
             Any: The value returned from executing the Module
         """
         return super().run(*args)
+
+    def summary(self, unit: str = 'MB') -> str:
+        """
+        Summarizes the memory and FLOPs statistics of the `GraphModule` in 
+        tabular format. Note that this API requires the ``tabulate`` module 
+        to be installed.
+        """
+        # https://github.com/pytorch/pytorch/blob/master/torch/fx/graph.py
+        try:
+            from tabulate import tabulate
+        except ImportError:
+            print("`summary` relies on the library `tabulate`, "
+                  "which could not be found on this machine. Run `pip "
+                  "install tabulate` to install the library.")
+
+        assert self._is_proped, "Please call `interp.run(input)` before calling `interp.summary()`."
+
+        # Build up a list of summary information for each node
+        node_summaries: List[List[Any]] = []
+
+        def mem_repr(mem: int) -> str:
+            unit_divisor_map = {
+                'kb': 1024,
+                'mb': 1024**2,
+                'gb': 1024**3,
+                'tb': 1024**4,
+            }
+            return f"{mem / unit_divisor_map[unit.lower()]:.2f} {unit.upper()}"
+
+        def flops_repr(flop: int) -> str:
+            return f"{flop:,} FLOPs"
+
+        for node in self.module.graph.nodes:
+            node: Node
+            node_summaries.append([
+                node.op,
+                str(node),
+                flops_repr(node.meta['fwd_flop']),
+                flops_repr(node.meta['bwd_flop']),
+                node.meta['save_fwd_in'],
+                mem_repr(node.meta['fwd_mem_out']),
+                mem_repr(node.meta['fwd_mem_tmp']),
+                mem_repr(node.meta['bwd_mem_out']),
+                mem_repr(node.meta['bwd_mem_tmp']),
+            ])
+
+        # Use the ``tabulate`` library to create a well-formatted table
+        # presenting our summary information
+        headers: List[str] = [
+            'Op type',
+            'Op',
+            'Forward FLOPs',
+            'Backward FLOPs',
+            'SAVE_FWD_IN',
+            'FWD_OUT',
+            'FWD_TMP',
+            'BWD_OUT',
+            'BWD_TMP',
+        ]
+
+        return tabulate(node_summaries, headers=headers, stralign='right')