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318 lines
12 KiB
318 lines
12 KiB
from dataclasses import asdict
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from typing import Any, Dict, List, NamedTuple, Tuple
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import torch
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import torch.fx
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from torch.fx.node import Argument, Node, Target
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from torch.utils._pytree import tree_map
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from colossalai.fx._compatibility import compatibility
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from colossalai.fx.profiler import (
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GraphInfo,
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activation_size,
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calculate_fwd_in,
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calculate_fwd_out,
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calculate_fwd_tmp,
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profile_function,
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profile_method,
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profile_module,
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)
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@compatibility(is_backward_compatible=True)
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class TensorMetadata(NamedTuple):
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# TensorMetadata is a structure containing pertinent information
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# about a tensor within a PyTorch program.
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shape: torch.Size
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dtype: torch.dtype
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requires_grad: bool
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stride: Tuple[int]
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numel: int
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is_tensor: bool
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# TODO: we can add a list of sharding spec here, and record the sharding
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# behaviour by appending sharding spec into list.
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def _extract_tensor_metadata(result: torch.Tensor) -> TensorMetadata:
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"""
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Extract a TensorMetadata NamedTuple describing `result`.
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"""
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shape = result.shape
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dtype = result.dtype
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requires_grad = result.requires_grad
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stride = result.stride()
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numel = result.numel()
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is_tensor = True
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return TensorMetadata(shape, dtype, requires_grad, stride, numel, is_tensor)
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@compatibility(is_backward_compatible=True)
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class MetaInfoProp(torch.fx.Interpreter):
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"""
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Execute an FX graph Node-by-Node with meta tensor and
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record the memory usage, FLOPs, and type of the result
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into the corresponding node.
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Usage:
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BATCH_SIZE = 2
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DIM_IN = 4
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DIM_HIDDEN = 16
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DIM_OUT = 16
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model = torch.nn.Sequential(
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torch.nn.Linear(DIM_IN, DIM_HIDDEN),
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torch.nn.Linear(DIM_HIDDEN, DIM_OUT),
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)
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input_sample = torch.rand(BATCH_SIZE, DIM_IN)
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gm = symbolic_trace(model)
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interp = MetaInfoProp(gm)
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interp.run(input_sample)
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print(interp.summary(format='kb')) # don't panic if some statistics are 0.00 MB
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# output of above code is
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Op type Op Forward FLOPs Backward FLOPs FWD_OUT FWD_TMP BWD_OUT BWD_TMP
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----------- ------- --------------- ---------------- --------- --------- --------- ---------
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placeholder input_1 0 FLOPs 0 FLOPs 0.00 KB 0.00 KB 0.00 KB 0.00 KB
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call_module _0 128 FLOPs 288 FLOPs 0.12 KB 0.00 KB 0.34 KB 0.00 KB
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call_module _1 512 FLOPs 1,056 FLOPs 0.12 KB 0.00 KB 1.19 KB 0.00 KB
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output output 0 FLOPs 0 FLOPs 0.00 KB 0.00 KB 0.00 KB 0.00 KB
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Args:
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module (GraphModule): The module to be executed
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"""
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_is_proped: bool = False
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@compatibility(is_backward_compatible=True)
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def run_node(self, n: Node) -> Any:
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"""
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Run a specific node ``n`` and return the result.
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Calls into placeholder, get_attr, call_function,
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call_method, call_module, or output depending
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on ``node.op``
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Args:
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n (Node): The Node to execute
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Returns:
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Any: The result of executing ``n``
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"""
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self._is_proped = True
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result, meta_info = super().run_node(n)
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def extract_tensor_meta(obj):
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if isinstance(obj, torch.Tensor):
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return _extract_tensor_metadata(obj)
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else:
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return TensorMetadata(None, None, False, None, 0, False)
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tensor_meta = tree_map(extract_tensor_meta, result)
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n.meta['tensor_meta'] = tensor_meta
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n.meta = {**n.meta, **asdict(meta_info)} # extend MetaInfo to `n.meta`
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# TODO: the attribute node_size should be removed in the future
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setattr(n, 'node_size', activation_size(n.meta.get('fwd_in', 0)) + activation_size(n.meta.get('fwd_tmp', 0)))
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n.meta['type'] = type(result)
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# retain the autograd graph
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for param in self.module.parameters():
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param.grad = None
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return result
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# Main Node running APIs
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@compatibility(is_backward_compatible=True)
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def placeholder(self, target: 'Target', args: Tuple[Argument, ...], kwargs: Dict[str, Any]) -> Any:
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"""
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Execute a ``placeholder`` node. Note that this is stateful:
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``Interpreter`` maintains an internal iterator over
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arguments passed to ``run`` and this method returns
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next() on that iterator.
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Args:
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target (Target): The call target for this node. See
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`Node <https://pytorch.org/docs/master/fx.html#torch.fx.Node>`__ for
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details on semantics
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args (Tuple): Tuple of positional args for this invocation
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kwargs (Dict): Dict of keyword arguments for this invocation
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Returns:
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result (Any): The argument value that was retrieved
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meta_info (MetaInfo): The memory cost and FLOPs estimated with `MetaTensor`.
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"""
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return super().placeholder(target, args, kwargs), GraphInfo()
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@compatibility(is_backward_compatible=True)
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def get_attr(self, target: 'Target', args: Tuple[Argument, ...], kwargs: Dict[str, Any]) -> Any:
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"""
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Execute a ``get_attr`` node. Will retrieve an attribute
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value from the ``Module`` hierarchy of ``self.module``.
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Args:
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target (Target): The call target for this node. See
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`Node <https://pytorch.org/docs/master/fx.html#torch.fx.Node>`__ for
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details on semantics
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args (Tuple): Tuple of positional args for this invocation
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kwargs (Dict): Dict of keyword arguments for this invocation
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Return:
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result (Any): The argument value that was retrieved
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meta_info (MetaInfo): The memory cost and FLOPs estimated with `MetaTensor`.
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"""
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return super().get_attr(target, args, kwargs), GraphInfo()
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@compatibility(is_backward_compatible=True)
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def call_function(self, target: 'Target', args: Tuple[Argument, ...], kwargs: Dict[str, Any]) -> Any:
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"""
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Execute a ``call_function`` node with meta tensor and return the result and its meta profile.
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Args:
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target (Target): The call target for this node. See
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`Node <https://pytorch.org/docs/master/fx.html#torch.fx.Node>`__ for
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details on semantics
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args (Tuple): Tuple of positional args for this invocation
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kwargs (Dict): Dict of keyword arguments for this invocation
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Return
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result (Any): The argument value that was retrieved
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meta_info (MetaInfo): The memory cost and FLOPs estimated with `MetaTensor`.
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"""
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assert not isinstance(target, str)
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return profile_function(target)(*args, **kwargs)
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@compatibility(is_backward_compatible=True)
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def call_method(self, target: 'Target', args: Tuple[Argument, ...], kwargs: Dict[str, Any]) -> Any:
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"""
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Execute a ``call_method`` node with meta tensor and return the result and its meta profile.
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Args:
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target (Target): The call target for this node. See
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`Node <https://pytorch.org/docs/master/fx.html#torch.fx.Node>`__ for
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details on semantics
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args (Tuple): Tuple of positional args for this invocation
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kwargs (Dict): Dict of keyword arguments for this invocation
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Return
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result (Any): The argument value that was retrieved
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meta_info (MetaInfo): The memory cost and FLOPs estimated with `MetaTensor`.
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"""
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return profile_method(target)(*args, **kwargs)
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@compatibility(is_backward_compatible=True)
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def call_module(self, target: 'Target', args: Tuple[Argument, ...], kwargs: Dict[str, Any]) -> Any:
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"""
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Execute a ``call_module`` node with meta tensor and return the result and its meta profile.
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Args:
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target (Target): The call target for this node. See
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`Node <https://pytorch.org/docs/master/fx.html#torch.fx.Node>`__ for
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details on semantics
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args (Tuple): Tuple of positional args for this invocation
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kwargs (Dict): Dict of keyword arguments for this invocation
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Return
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result (Any): The argument value that was retrieved
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meta_info (MetaInfo): The memory cost and FLOPs estimated with `MetaTensor`.
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"""
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# Retrieve executed args and kwargs values from the environment
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# Execute the method and return the result
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assert isinstance(target, str)
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submod = self.fetch_attr(target)
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return profile_module(submod)(*args, **kwargs)
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@compatibility(is_backward_compatible=True)
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def output(self, target: 'Target', args: Tuple[Argument, ...], kwargs: Dict[str, Any]) -> Any:
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"""
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Execute an ``output`` node. This really just retrieves
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the value referenced by the ``output`` node and returns it.
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Args:
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target (Target): The call target for this node. See
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`Node <https://pytorch.org/docs/master/fx.html#torch.fx.Node>`__ for
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details on semantics
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args (Tuple): Tuple of positional args for this invocation
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kwargs (Dict): Dict of keyword arguments for this invocation
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Return:
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result (Any): The argument value that was retrieved
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meta_info (MetaInfo): The memory cost and FLOPs estimated with `MetaTensor`.
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"""
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if hasattr(args[0], '_tensor'):
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return args[0], GraphInfo(fwd_in=[args[0]._tensor])
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return args[0], GraphInfo(save_fwd_in=True)
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def propagate(self, *args):
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"""
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Run `module` via interpretation and return the result and
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record the shape and type of each node.
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Args:
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*args (Tensor): the sample input.
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Returns:
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Any: The value returned from executing the Module
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"""
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return super().run(*args)
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def summary(self, unit: str = 'MB') -> str:
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"""
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Summarizes the memory and FLOPs statistics of the `GraphModule` in
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tabular format. Note that this API requires the ``tabulate`` module
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to be installed.
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"""
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# https://github.com/pytorch/pytorch/blob/master/torch/fx/graph.py
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try:
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from tabulate import tabulate
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except ImportError:
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print("`summary` relies on the library `tabulate`, "
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"which could not be found on this machine. Run `pip "
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"install tabulate` to install the library.")
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assert self._is_proped, "Please call `interp.run(input)` before calling `interp.summary()`."
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# Build up a list of summary information for each node
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node_summaries: List[List[Any]] = []
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def mem_repr(mem: int) -> str:
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unit_divisor_map = {
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'kb': 1024,
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'mb': 1024**2,
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'gb': 1024**3,
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'tb': 1024**4,
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}
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return f"{mem / unit_divisor_map[unit.lower()]:.2f} {unit.upper()}"
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def flops_repr(flop: int) -> str:
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return f"{flop:,} FLOPs"
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for node in self.module.graph.nodes:
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node: Node
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node_summaries.append([
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node.op,
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str(node),
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flops_repr(node.meta['fwd_flop']),
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flops_repr(node.meta['bwd_flop']),
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mem_repr(calculate_fwd_in(node)),
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mem_repr(calculate_fwd_out(node)),
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mem_repr(calculate_fwd_tmp(node)),
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mem_repr(node.meta['bwd_mem_out']),
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mem_repr(node.meta['bwd_mem_tmp']),
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])
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# Use the ``tabulate`` library to create a well-formatted table
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# presenting our summary information
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headers: List[str] = [
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'Op type',
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'Op',
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'Forward FLOPs',
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'Backward FLOPs',
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'FWD_IN',
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'FWD_OUT',
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'FWD_TMP',
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'BWD_OUT',
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'BWD_TMP',
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]
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return tabulate(node_summaries, headers=headers, stralign='right')
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