mirror of
https://github.com/invoke-ai/InvokeAI
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2a35d93a4d
**Service Dependencies** Services that depend on other services now access those services via the `Invoker` object. This object is provided to the service as a kwarg to its `start()` method. Until now, most services did not utilize this feature, and several services required their dependencies to be initialized and passed in on init. Additionally, _all_ services are now registered as invocation services - including the low-level services. This obviates issues with inter-dependent services we would otherwise experience as we add workflow storage. **Database Access** Previously, we were passing in a separate sqlite connection and corresponding lock as args to services in their init. A good amount of posturing was done in each service that uses the db. These objects, along with the sqlite startup and cleanup logic, is now abstracted into a simple `SqliteDatabase` class. This creates the shared connection and lock objects, enables foreign keys, and provides a `clean()` method to do startup db maintenance. This is not a service as it's only used by sqlite services.
305 lines
11 KiB
Python
305 lines
11 KiB
Python
# Copyright 2023 Lincoln D. Stein <lincoln.stein@gmail.com>
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"""Utility to collect execution time and GPU usage stats on invocations in flight
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Usage:
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statistics = InvocationStatsService(graph_execution_manager)
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with statistics.collect_stats(invocation, graph_execution_state.id):
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... execute graphs...
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statistics.log_stats()
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Typical output:
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[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> Graph stats: c7764585-9c68-4d9d-a199-55e8186790f3
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[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> Node Calls Seconds VRAM Used
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[2023-08-02 18:03:04,507]::[InvokeAI]::INFO --> main_model_loader 1 0.005s 0.01G
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[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> clip_skip 1 0.004s 0.01G
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[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> compel 2 0.512s 0.26G
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[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> rand_int 1 0.001s 0.01G
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[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> range_of_size 1 0.001s 0.01G
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[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> iterate 1 0.001s 0.01G
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[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> metadata_accumulator 1 0.002s 0.01G
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[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> noise 1 0.002s 0.01G
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[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> t2l 1 3.541s 1.93G
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[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> l2i 1 0.679s 0.58G
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[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> TOTAL GRAPH EXECUTION TIME: 4.749s
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[2023-08-02 18:03:04,508]::[InvokeAI]::INFO --> Current VRAM utilization 0.01G
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The abstract base class for this class is InvocationStatsServiceBase. An implementing class which
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writes to the system log is stored in InvocationServices.performance_statistics.
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"""
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import time
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from abc import ABC, abstractmethod
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from contextlib import AbstractContextManager
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from dataclasses import dataclass, field
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from typing import Dict
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import psutil
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import torch
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import invokeai.backend.util.logging as logger
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from invokeai.app.services.invoker import Invoker
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from invokeai.backend.model_management.model_cache import CacheStats
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from ..invocations.baseinvocation import BaseInvocation
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from .model_manager_service import ModelManagerServiceBase
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# size of GIG in bytes
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GIG = 1073741824
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@dataclass
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class NodeStats:
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"""Class for tracking execution stats of an invocation node"""
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calls: int = 0
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time_used: float = 0.0 # seconds
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max_vram: float = 0.0 # GB
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cache_hits: int = 0
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cache_misses: int = 0
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cache_high_watermark: int = 0
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@dataclass
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class NodeLog:
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"""Class for tracking node usage"""
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# {node_type => NodeStats}
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nodes: Dict[str, NodeStats] = field(default_factory=dict)
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class InvocationStatsServiceBase(ABC):
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"Abstract base class for recording node memory/time performance statistics"
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# {graph_id => NodeLog}
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_stats: Dict[str, NodeLog]
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_cache_stats: Dict[str, CacheStats]
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ram_used: float
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ram_changed: float
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@abstractmethod
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def __init__(self):
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"""
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Initialize the InvocationStatsService and reset counters to zero
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"""
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pass
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@abstractmethod
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def collect_stats(
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self,
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invocation: BaseInvocation,
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graph_execution_state_id: str,
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) -> AbstractContextManager:
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"""
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Return a context object that will capture the statistics on the execution
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of invocaation. Use with: to place around the part of the code that executes the invocation.
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:param invocation: BaseInvocation object from the current graph.
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:param graph_execution_state: GraphExecutionState object from the current session.
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"""
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pass
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@abstractmethod
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def reset_stats(self, graph_execution_state_id: str):
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"""
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Reset all statistics for the indicated graph
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:param graph_execution_state_id
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"""
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pass
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@abstractmethod
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def reset_all_stats(self):
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"""Zero all statistics"""
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pass
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@abstractmethod
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def update_invocation_stats(
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self,
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graph_id: str,
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invocation_type: str,
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time_used: float,
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vram_used: float,
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):
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"""
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Add timing information on execution of a node. Usually
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used internally.
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:param graph_id: ID of the graph that is currently executing
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:param invocation_type: String literal type of the node
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:param time_used: Time used by node's exection (sec)
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:param vram_used: Maximum VRAM used during exection (GB)
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"""
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pass
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@abstractmethod
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def log_stats(self):
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"""
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Write out the accumulated statistics to the log or somewhere else.
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"""
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pass
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@abstractmethod
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def update_mem_stats(
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self,
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ram_used: float,
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ram_changed: float,
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):
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"""
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Update the collector with RAM memory usage info.
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:param ram_used: How much RAM is currently in use.
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:param ram_changed: How much RAM changed since last generation.
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"""
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pass
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class InvocationStatsService(InvocationStatsServiceBase):
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"""Accumulate performance information about a running graph. Collects time spent in each node,
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as well as the maximum and current VRAM utilisation for CUDA systems"""
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_invoker: Invoker
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def __init__(self):
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# {graph_id => NodeLog}
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self._stats: Dict[str, NodeLog] = {}
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self._cache_stats: Dict[str, CacheStats] = {}
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self.ram_used: float = 0.0
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self.ram_changed: float = 0.0
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def start(self, invoker: Invoker) -> None:
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self._invoker = invoker
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class StatsContext:
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"""Context manager for collecting statistics."""
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invocation: BaseInvocation
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collector: "InvocationStatsServiceBase"
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graph_id: str
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start_time: float
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ram_used: int
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model_manager: ModelManagerServiceBase
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def __init__(
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self,
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invocation: BaseInvocation,
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graph_id: str,
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model_manager: ModelManagerServiceBase,
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collector: "InvocationStatsServiceBase",
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):
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"""Initialize statistics for this run."""
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self.invocation = invocation
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self.collector = collector
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self.graph_id = graph_id
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self.start_time = 0.0
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self.ram_used = 0
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self.model_manager = model_manager
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def __enter__(self):
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self.start_time = time.time()
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if torch.cuda.is_available():
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torch.cuda.reset_peak_memory_stats()
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self.ram_used = psutil.Process().memory_info().rss
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if self.model_manager:
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self.model_manager.collect_cache_stats(self.collector._cache_stats[self.graph_id])
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def __exit__(self, *args):
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"""Called on exit from the context."""
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ram_used = psutil.Process().memory_info().rss
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self.collector.update_mem_stats(
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ram_used=ram_used / GIG,
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ram_changed=(ram_used - self.ram_used) / GIG,
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)
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self.collector.update_invocation_stats(
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graph_id=self.graph_id,
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invocation_type=self.invocation.type, # type: ignore - `type` is not on the `BaseInvocation` model, but *is* on all invocations
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time_used=time.time() - self.start_time,
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vram_used=torch.cuda.max_memory_allocated() / GIG if torch.cuda.is_available() else 0.0,
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)
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def collect_stats(
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self,
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invocation: BaseInvocation,
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graph_execution_state_id: str,
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) -> StatsContext:
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if not self._stats.get(graph_execution_state_id): # first time we're seeing this
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self._stats[graph_execution_state_id] = NodeLog()
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self._cache_stats[graph_execution_state_id] = CacheStats()
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return self.StatsContext(invocation, graph_execution_state_id, self._invoker.services.model_manager, self)
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def reset_all_stats(self):
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"""Zero all statistics"""
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self._stats = {}
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def reset_stats(self, graph_execution_id: str):
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try:
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self._stats.pop(graph_execution_id)
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except KeyError:
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logger.warning(f"Attempted to clear statistics for unknown graph {graph_execution_id}")
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def update_mem_stats(
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self,
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ram_used: float,
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ram_changed: float,
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):
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self.ram_used = ram_used
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self.ram_changed = ram_changed
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def update_invocation_stats(
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self,
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graph_id: str,
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invocation_type: str,
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time_used: float,
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vram_used: float,
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):
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if not self._stats[graph_id].nodes.get(invocation_type):
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self._stats[graph_id].nodes[invocation_type] = NodeStats()
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stats = self._stats[graph_id].nodes[invocation_type]
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stats.calls += 1
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stats.time_used += time_used
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stats.max_vram = max(stats.max_vram, vram_used)
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def log_stats(self):
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completed = set()
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errored = set()
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for graph_id, node_log in self._stats.items():
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try:
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current_graph_state = self._invoker.services.graph_execution_manager.get(graph_id)
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except Exception:
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errored.add(graph_id)
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continue
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if not current_graph_state.is_complete():
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continue
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total_time = 0
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logger.info(f"Graph stats: {graph_id}")
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logger.info(f"{'Node':>30} {'Calls':>7}{'Seconds':>9} {'VRAM Used':>10}")
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for node_type, stats in self._stats[graph_id].nodes.items():
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logger.info(f"{node_type:>30} {stats.calls:>4} {stats.time_used:7.3f}s {stats.max_vram:4.3f}G")
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total_time += stats.time_used
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cache_stats = self._cache_stats[graph_id]
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hwm = cache_stats.high_watermark / GIG
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tot = cache_stats.cache_size / GIG
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loaded = sum([v for v in cache_stats.loaded_model_sizes.values()]) / GIG
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logger.info(f"TOTAL GRAPH EXECUTION TIME: {total_time:7.3f}s")
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logger.info("RAM used by InvokeAI process: " + "%4.2fG" % self.ram_used + f" ({self.ram_changed:+5.3f}G)")
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logger.info(f"RAM used to load models: {loaded:4.2f}G")
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if torch.cuda.is_available():
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logger.info("VRAM in use: " + "%4.3fG" % (torch.cuda.memory_allocated() / GIG))
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logger.info("RAM cache statistics:")
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logger.info(f" Model cache hits: {cache_stats.hits}")
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logger.info(f" Model cache misses: {cache_stats.misses}")
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logger.info(f" Models cached: {cache_stats.in_cache}")
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logger.info(f" Models cleared from cache: {cache_stats.cleared}")
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logger.info(f" Cache high water mark: {hwm:4.2f}/{tot:4.2f}G")
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completed.add(graph_id)
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for graph_id in completed:
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del self._stats[graph_id]
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del self._cache_stats[graph_id]
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for graph_id in errored:
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del self._stats[graph_id]
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del self._cache_stats[graph_id]
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