[fx/tuning] tune performance on rotor with meta info. (#1599)

colossalai/fx/passes/algorithms/ckpt_solver_rotor.py

@@ -1,8 +1,7 @@
 from typing import List, Tuple
-import torch
+from torch.fx import Node
-from torch.fx import GraphModule, Node
 from colossalai.fx.graph_module import ColoGraphModule
-from colossalai.fx.profiler import parameter_size
+from colossalai.fx.profiler import activation_size, parameter_size
 import math
 from .linearize import linearize
 from .utils import *
@@ -31,7 +30,7 @@ def _compute_table(chain: Chain, mmax) -> Tuple:
     # Build table
     opt = [[{} for _ in range(chain.length + 1)] for _ in range(mmax + 1)]
     what = [[{} for _ in range(chain.length + 1)] for _ in range(mmax + 1)]
-    ## Last one is a dict because its indices go from i to l. Renumbering will wait for C implementation
+    # Last one is a dict because its indices go from i to l. Renumbering will wait for C implementation
 
     # Initialize borders of the tables for lmax-lmin = 0
     for m in range(mmax + 1):
@@ -115,43 +114,6 @@ def _discretize(mem_unit, values):
     return [math.ceil(value / mem_unit) for value in values]
 
 
-def _compute_size(obj: torch.Tensor) -> int:
-    return obj.numel() * obj.element_size()
-
-
-def _compute_output_size(node: List[Node]) -> int:
-    """Compute the output size of a node
-
-    Args:
-        node (List[Node]): node, list of torch.fx.Node
-
-    Returns:
-        int: output size
-    """
-
-    return node[-1].meta['tensor_meta'].numel * torch.tensor([],
-                                                             dtype=node[-1].meta['tensor_meta'].dtype).element_size()
-
-
-def _get_inplace(node: Node) -> bool:
-    """Get the inplace argument from torch.fx.Node
-
-    Args:
-        node (Node): torch.fx.Node
-
-    Returns:
-        bool: indicates whether this op is inplace
-    """
-
-    is_inplace = False
-    if node.op == "call_function":
-        is_inplace = node.kwargs.get("inplace", False)
-    elif node.op == "call_module":
-        is_inplace = getattr(node.graph.owning_module.get_submodule(node.target), "inplace", False)
-
-    return is_inplace
-
-
 def _fwd_xbar(node: List[Node]) -> int:
     """Get the forward xbar of a node
 
@@ -221,46 +183,33 @@ def _get_bwd_mem_tmp(node: List[Node]) -> int:
         for k, v in deps.items():
             if v > 0:
                 deps_size += k.meta['bwd_mem_out']
+            if v == float('-inf'):
+                deps_size -= k.meta['fwd_mem_tmp'] + k.meta['fwd_mem_out']
 
         return deps_size
 
     bwd_mem_tmp = 0
     deps = {}
 
-    # add all the users for last node into deps,
-    # as those nodes' gradient out will be stored in memory
-    for child in node[-1].users:
-        deps[child] = 1
     for n in reversed(node):
+        deps[n] = len(n.all_input_nodes)
         bwd_mem_tmp = max(bwd_mem_tmp, _get_deps_size() + n.meta['bwd_mem_tmp'])
 
-        deps[n] = len(n.all_input_nodes)
         for child in n.users:
             if child in deps:
                 deps[child] -= 1
-
+                if deps[child] <= 0:
-        for key in list(deps.keys()):
+                    deps[child] = float('-inf')    # free
-            if deps[key] == 0:
-                del deps[key]
 
     return bwd_mem_tmp
 
 
-def _construct_chain(node_list: List[List[Node]], data, mem_unit: int) -> Chain:
+def _construct_chain(node_list: List[List[Node]], input, mem_unit: int) -> Chain:
 
     fwd_time = []
     bwd_time = []
-
+    xbar_sizes = [activation_size(input)]
-    if isinstance(data, torch.Tensor):
+    x_sizes = [activation_size(input)]
-        xbar_sizes = [_compute_size(data)]
-        x_sizes = [_compute_size(data)]
-    elif isinstance(data, list) or isinstance(data, tuple):
-        xbar_sizes = [sum([_compute_size(obj) for obj in data])]
-        x_sizes = [sum([_compute_size(obj) for obj in data])]
-    elif isinstance(data, dict):
-        xbar_sizes = [sum([_compute_size(obj) for obj in data.values()])]
-        x_sizes = [sum([_compute_size(obj) for obj in data.values()])]
-
     # currently we can't get the temp memory needed in fwd
     tmp_fwd = [0] * len(node_list)
     tmp_bwd = []
@@ -268,14 +217,10 @@ def _construct_chain(node_list: List[List[Node]], data, mem_unit: int) -> Chain:
     for idx, node in enumerate(node_list):
         fwd_time.append(_fwd_time(node))
         bwd_time.append(_bwd_time(node))
-        x_sizes.append(_compute_output_size(node))
+        x_sizes.append(node[-1].meta['fwd_mem_out'])
         xbar_sizes.append(max(x_sizes[-1], _fwd_xbar(node)))
         tmp_bwd.append(_get_bwd_mem_tmp(node))
 
-        # if a node with only one inplace op, we need to let x_bar = 0
-        if len(node) == 1 and _get_inplace(node[0]):
-            xbar_sizes[-1] = 0
-
     bwd_time.append(0)
 
     # currently we view loss backward temp as zero
@@ -381,7 +326,7 @@ def solver_rotor(gm: ColoGraphModule,
                  mem_limit: int,
                  mem_slots: int = 500,
                  cnode: List[str] = None,
-                 eps: float = 0.02) -> ColoGraphModule:
+                 eps: float = 0.0) -> ColoGraphModule:
     """solver that automatically find activation checkpoint in rotor's manner
 
     Args:
@@ -390,7 +335,7 @@ def solver_rotor(gm: ColoGraphModule,
         mem_limit (int): memory budget in Byte.
         mem_slots (int, optional): number of slots for discretizing memory budget. Defaults to 500.
         cnode (List[Node], optional): common node list for linearize. Defaults to None.
-        eps (float): epsilon for memory decay. Defaults to 0.02
+        eps (float): epsilon for memory decay. Defaults to 0.0
 
     Returns:
         ColoGraphModule: annotated ColoGraphModuled with __sequence__ attribute

colossalai/fx/passes/algorithms/linearize.py

@@ -1,5 +1,6 @@
 from typing import List, Any
 from torch.fx import GraphModule, Node
+from colossalai.fx.profiler import is_inplace
 
 # Common nodes are type of nodes that could be seen as attributes and remain
 # unchanged throughout the whole model, it will be used several times by
@@ -41,6 +42,9 @@ def linearize(gm: GraphModule, cnode: List[str] = None) -> List[List[Node]]:
     Returns:
         List[List[Node]]: List of list, each inside list of Node presents
         the actual 'node' in linearized manner.
+
+    Remarks:
+        We merge the inplace ops into the previous node.
     """
 
     def _is_sink() -> bool:
@@ -50,7 +54,7 @@ def linearize(gm: GraphModule, cnode: List[str] = None) -> List[List[Node]]:
             bool
         """
 
-        return not sum([v for _, v in deps.items()])
+        return not sum([v for _, v in deps.items()]) and not any(map(is_inplace, n.users))
 
     # make sure that item in cnode is valid
     if cnode:

colossalai/fx/profiler/__init__.py

@@ -7,4 +7,4 @@ else:
     from .experimental import meta_profiler_function, meta_profiler_module, profile_function, profile_method, profile_module
 
 from .dataflow import GraphInfo
-from .memory import parameter_size, activation_size
+from .memory import parameter_size, activation_size, is_inplace

colossalai/fx/profiler/dataflow.py

@@ -1,16 +1,17 @@
 from dataclasses import dataclass
 from enum import Enum
-from functools import partial
 from typing import Dict
 from torch.fx import Graph, Node
-from .memory import activation_size
+from .memory import activation_size, is_inplace
+from . import META_COMPATIBILITY
+if META_COMPATIBILITY:
+    from .memory import NORMALIZATION_ATEN, CLONE_ATEN
 
 
 class Phase(Enum):
     FORWARD = 0
-    LOSS = 1
+    BACKWARD = 1
-    BACKWARD = 2
+    PLACEHOLDER = 2
-    PLACEHOLDER = 3
 
 
 @dataclass
@@ -86,8 +87,10 @@ def autograd_graph_analysis(graph: Graph) -> GraphInfo:
     def _peak_memory(deps: Dict[Node, int]):
         peak_mem = 0
         for k, v in deps.items():
-            if v > 0:
+            if v > 0 and is_phase(k, Phase.BACKWARD) and not any(map(is_inplace, k.users)):
                 peak_mem += activation_size(k.meta['out'])
+            if v <= float('-inf') and is_saved(k) and (k.target not in NORMALIZATION_ATEN):
+                peak_mem -= activation_size(k.meta['out'])
         return peak_mem
 
     # deps is used to track all the memory dependencies of the graph.
@@ -96,7 +99,7 @@ def autograd_graph_analysis(graph: Graph) -> GraphInfo:
 
     for n in graph.nodes:
         n: Node
-        if is_saved(n) and not any(map(partial(is_phase, phase=Phase.LOSS), n.users)):
+        if is_saved(n) and (n.target not in NORMALIZATION_ATEN) or any(map(lambda x: x.target in CLONE_ATEN, n.users)):
             # A forward tensor who is marked `save` but is not
             # an input to `loss` should be saved during forward.
             # If the tensor is a placeholder, then it belongs to `fwd_mem_in`.
@@ -110,13 +113,14 @@ def autograd_graph_analysis(graph: Graph) -> GraphInfo:
                 graph_info.fwd_mem_tmp += activation_size(n.meta['out'])
         elif is_phase(n, Phase.BACKWARD):
             if len(n.users):
-                # liveness analysis is only used in backward
-                deps[n] = len(n.users)
                 graph_info.bwd_mem_tmp = max(graph_info.bwd_mem_tmp, _peak_memory(deps))
+            else:
+                # TODO: some of the bwd_mem_out might be model parameters.
+                # basically a backward node without user is a `grad_out` node
+                graph_info.bwd_mem_out += activation_size(n.meta['out'])
         for input_n in n.all_input_nodes:
             if input_n in deps:
                 deps[input_n] -= 1
-            else:
+                if deps[input_n] <= 0:
-                # basically a backward node without user is a `grad_out` node
+                    deps[input_n] = float('-inf')
-                graph_info.bwd_mem_out += activation_size(n.meta['out'])
     return graph_info

colossalai/fx/profiler/memory.py

@@ -1,9 +1,10 @@
 import torch
+from torch.fx import Node
 from typing import Union, Dict, List, Tuple
 from operator import add, floordiv, getitem, mul, neg, setitem, sub, pos
 from . import META_COMPATIBILITY
 
-__all__ = ['activation_size', 'parameter_size']
+__all__ = ['activation_size', 'parameter_size', 'is_inplace']
 
 if META_COMPATIBILITY:
     aten = torch.ops.aten
@@ -21,6 +22,7 @@ if META_COMPATIBILITY:
         aten.bernoulli_.float,
 
     # inplace reshaping
+        aten.copy_.default,
         aten.detach.default,
         aten.t.default,
         aten.transpose.int,
@@ -28,7 +30,17 @@ if META_COMPATIBILITY:
         aten._unsafe_view.default,
     ]
 
-    __all__ += ['INPLACE_ATEN', 'WEIRD_OPS']
+    NORMALIZATION_ATEN = [
+        aten.native_batch_norm.default,
+        aten.native_layer_norm.default,
+    # aten.max_pool2d_with_indices.default,
+    ]
+
+    CLONE_ATEN = [
+        aten.clone.default,
+    ]
+
+    __all__ += ['INPLACE_ATEN', 'WEIRD_OPS', 'NORMALIZATION_ATEN', 'CLONE_ATEN']
 
 else:
     # TODO fill out the inplace ops
@@ -106,3 +118,23 @@ def parameter_size(mod: torch.nn.Module) -> int:
     for param in mod.parameters():
         param_size += param.numel() * torch.tensor([], dtype=param.dtype).element_size()
     return param_size
+
+
+def is_inplace(n: Node):
+    """Get the inplace argument from torch.fx.Node
+
+    Args:
+        node (Node): torch.fx.Node
+
+    Returns:
+        bool: indicates whether this op is inplace
+    """
+    inplace = False
+    if n.op == "call_function":
+        inplace = n.kwargs.get("inplace", False)
+        if META_COMPATIBILITY and n.target in INPLACE_ATEN:
+            inplace = True
+    elif n.op == "call_module":
+        inplace = getattr(n.graph.owning_module.get_submodule(n.target), "inplace", False)
+
+    return inplace

colossalai/fx/profiler/opcount.py

@@ -222,6 +222,7 @@ flop_mapping = {
     aten._adaptive_avg_pool2d_backward.default: elementwise_flop_counter(0, 1),
     aten._adaptive_avg_pool3d.default: elementwise_flop_counter(1, 0),
     aten._adaptive_avg_pool3d_backward.default: elementwise_flop_counter(0, 1),
+    aten.embedding_dense_backward.default: elementwise_flop_counter(0, 1),
 }
 
 elementwise_flop_aten = [

colossalai/fx/profiler/profiler.py

@@ -1,12 +1,10 @@
-from dataclasses import dataclass
-from enum import auto
 from typing import Callable, Any, Dict, Tuple
 import torch
 from torch.fx import Graph, Node
 from torch.fx.node import Argument, Target
 from torch.utils._pytree import tree_map
 from .dataflow import GraphInfo, autograd_graph_analysis, Phase
-from .memory import WEIRD_OPS, activation_size
+from .memory import WEIRD_OPS
 from .tensor import MetaTensor
 from .opcount import flop_mapping
 
@@ -23,7 +21,7 @@ def is_autogradable(x):
     return isinstance(x, torch.Tensor) and x.is_floating_point()
 
 
-def _profile(target: Callable, *args, inplace=False, **kwargs) -> Tuple[Any, ...]:
+def _profile(target: Callable, *args, **kwargs) -> Tuple[Any, ...]:
     """
     Profile a Callable function with args and kwargs.
 
@@ -42,7 +40,6 @@ def _profile(target: Callable, *args, inplace=False, **kwargs) -> Tuple[Any, ...
     # `flop_count`` serves as a global dictionary to store results.
     flop_count = {
         Phase.FORWARD: 0,
-        Phase.LOSS: 0,
         Phase.BACKWARD: 0,
     }
 
@@ -71,6 +68,10 @@ def _profile(target: Callable, *args, inplace=False, **kwargs) -> Tuple[Any, ...
             kwargs_node = tree_map(get_node, kwargs)
             node = subgraph.create_node('call_function', func, args_node, kwargs_node)
 
+            # do not allocate on `cpu`
+            if 'device' in kwargs:
+                kwargs['device'] = 'meta'
+
             def unwrap(x):
                 # if x is a `nn.Parameter`, we can first wrap it with `FlopTensor`
                 if isinstance(x, torch.Tensor) and not hasattr(x, '_tensor'):
@@ -101,13 +102,13 @@ def _profile(target: Callable, *args, inplace=False, **kwargs) -> Tuple[Any, ...
     if target not in WEIRD_OPS:
 
         def wrap(x):
-            return FlopTensor(x.detach().requires_grad_(
+            return FlopTensor(
-                True)) if is_autogradable(x) and not inplace and not hasattr(x, '_tensor') else x
+                x.detach().requires_grad_(True)) if is_autogradable(x) and not hasattr(x, '_tensor') else x
     else:
 
         def wrap(x):
-            return FlopTensor(x.detach().requires_grad_(
+            return FlopTensor(
-                False)) if is_autogradable(x) and not inplace and not hasattr(x, '_tensor') else x
+                x.detach().requires_grad_(False)) if is_autogradable(x) and not hasattr(x, '_tensor') else x
 
     # Basically, we need to detach the args and kwargs from the outer graph.
     args = tree_map(wrap, args)
@@ -125,7 +126,7 @@ def _profile(target: Callable, *args, inplace=False, **kwargs) -> Tuple[Any, ...
     tree_map(set_placeholder, kwargs)
 
     def pack(x):
-        if isinstance(x, FlopTensor):
+        if isinstance(x, FlopTensor) and not isinstance(x, torch.nn.Parameter):
             x._node.meta['saved'] = True
         return x
 
@@ -146,10 +147,12 @@ def _profile(target: Callable, *args, inplace=False, **kwargs) -> Tuple[Any, ...
         # If the output is not a floating point `torch.Tensor` or it does not
         # requires grad, then we should not run backward for this node.
         if is_autogradable(out) and out.requires_grad:
-        phase = Phase.LOSS
-        loss = out.sum()
             phase = Phase.BACKWARD
-        loss.backward()
+            if isinstance(out, FlopTensor):
+                out._node.meta['save'] = False
+            grad = torch.empty_like(out._tensor, device='meta') if isinstance(out, FlopTensor) else torch.empty_like(
+                out, device='meta')
+            torch.autograd.backward(out, FlopTensor(grad))
 
     graph_info = autograd_graph_analysis(subgraph)
     graph_info.fwd_flop, graph_info.bwd_flop = flop_count[Phase.FORWARD], flop_count[Phase.BACKWARD]
@@ -172,7 +175,7 @@ def profile_function(target: 'Target') -> Callable:
     Examples:
         >>> input = torch.rand(100, 100, 100, 100, device='meta')
         >>> func = torch.nn.functional.relu
-        >>> output, meta_info = profile_function(func)(input, inplace=False)
+        >>> output, meta_info = profile_function(func)(input)
     """
 
     def f(*args: Tuple[Argument, ...], **kwargs: Dict[str, Any]) -> Any:
@@ -183,7 +186,7 @@ def profile_function(target: 'Target') -> Callable:
             args = tree_map(lambda x: x.to('meta') if isinstance(x, torch.Tensor) else x, args)
             kwargs = tree_map(lambda x: x.to('meta') if isinstance(x, torch.Tensor) else x, kwargs)
             out = func(*args, **kwargs)
-            return out, GraphInfo(out.numel(), out.numel(), activation_size((args, kwargs)), 0, activation_size(out), 0)
+            return out, GraphInfo(out.numel(), out.numel(), 0, 0, 0, 0)
         out, meta = _profile(func, *args, **kwargs)
         return out, meta
 
@@ -201,7 +204,7 @@ def profile_method(target: 'Target') -> Callable:
     def f(*args: Tuple[Argument, ...], **kwargs: Dict[str, Any]) -> Any:
         # execute the method and return the result
         assert isinstance(target, str), f'{target} instance is not str.'
-        out, meta = _profile(target, *args, inplace=False, **kwargs)
+        out, meta = _profile(target, *args, **kwargs)
         return out, meta
 
     return f
@@ -230,8 +233,8 @@ def profile_module(module: torch.nn.Module) -> Callable:
             args = tree_map(lambda x: x.to('meta'), args)
             kwargs = tree_map(lambda x: x.to('meta'), kwargs)
             out = func(*args, **kwargs)
-            return out, GraphInfo(out.numel(), out.numel(), activation_size((args, kwargs)), 0, activation_size(out), 0)
+            return out, GraphInfo(out.numel(), out.numel(), 0, 0, 0, 0)
-        out, meta = _profile(func, *args, inplace=getattr(module, 'inplace', False), **kwargs)
+        out, meta = _profile(func, *args, **kwargs)
         return out, meta
 
     f.__name__ = module.__class__.__name__