import sys import copy import logging import threading import heapq import time import traceback from enum import Enum import inspect from typing import List, Literal, NamedTuple, Optional import torch import nodes import comfy.model_management from comfy_execution.graph import get_input_info, ExecutionList, DynamicPrompt, ExecutionBlocker from comfy_execution.graph_utils import is_link, GraphBuilder from comfy_execution.caching import HierarchicalCache, LRUCache, CacheKeySetInputSignature, CacheKeySetID from comfy.cli_args import args class ExecutionResult(Enum): SUCCESS = 0 FAILURE = 1 PENDING = 2 class DuplicateNodeError(Exception): pass class IsChangedCache: def __init__(self, dynprompt, outputs_cache): self.dynprompt = dynprompt self.outputs_cache = outputs_cache self.is_changed = {} def get(self, node_id): if node_id in self.is_changed: return self.is_changed[node_id] node = self.dynprompt.get_node(node_id) class_type = node["class_type"] class_def = nodes.NODE_CLASS_MAPPINGS[class_type] if not hasattr(class_def, "IS_CHANGED"): self.is_changed[node_id] = False return self.is_changed[node_id] if "is_changed" in node: self.is_changed[node_id] = node["is_changed"] return self.is_changed[node_id] # Intentionally do not use cached outputs here. We only want constants in IS_CHANGED input_data_all, _ = get_input_data(node["inputs"], class_def, node_id, None) try: is_changed = _map_node_over_list(class_def, input_data_all, "IS_CHANGED") node["is_changed"] = [None if isinstance(x, ExecutionBlocker) else x for x in is_changed] except Exception as e: logging.warning("WARNING: {}".format(e)) node["is_changed"] = float("NaN") finally: self.is_changed[node_id] = node["is_changed"] return self.is_changed[node_id] class CacheSet: def __init__(self, lru_size=None): if lru_size is None or lru_size == 0: self.init_classic_cache() else: self.init_lru_cache(lru_size) self.all = [self.outputs, self.ui, self.objects] # Useful for those with ample RAM/VRAM -- allows experimenting without # blowing away the cache every time def init_lru_cache(self, cache_size): self.outputs = LRUCache(CacheKeySetInputSignature, max_size=cache_size) self.ui = LRUCache(CacheKeySetInputSignature, max_size=cache_size) self.objects = HierarchicalCache(CacheKeySetID) # Performs like the old cache -- dump data ASAP def init_classic_cache(self): self.outputs = HierarchicalCache(CacheKeySetInputSignature) self.ui = HierarchicalCache(CacheKeySetInputSignature) self.objects = HierarchicalCache(CacheKeySetID) def recursive_debug_dump(self): result = { "outputs": self.outputs.recursive_debug_dump(), "ui": self.ui.recursive_debug_dump(), } return result def get_input_data(inputs, class_def, unique_id, outputs=None, dynprompt=None, extra_data={}): valid_inputs = class_def.INPUT_TYPES() input_data_all = {} missing_keys = {} for x in inputs: input_data = inputs[x] input_type, input_category, input_info = get_input_info(class_def, x) def mark_missing(): missing_keys[x] = True input_data_all[x] = (None,) if is_link(input_data) and (not input_info or not input_info.get("rawLink", False)): input_unique_id = input_data[0] output_index = input_data[1] if outputs is None: mark_missing() continue # This might be a lazily-evaluated input cached_output = outputs.get(input_unique_id) if cached_output is None: mark_missing() continue if output_index >= len(cached_output): mark_missing() continue obj = cached_output[output_index] input_data_all[x] = obj elif input_category is not None: input_data_all[x] = [input_data] if "hidden" in valid_inputs: h = valid_inputs["hidden"] for x in h: if h[x] == "PROMPT": input_data_all[x] = [dynprompt.get_original_prompt() if dynprompt is not None else {}] if h[x] == "DYNPROMPT": input_data_all[x] = [dynprompt] if h[x] == "EXTRA_PNGINFO": input_data_all[x] = [extra_data.get('extra_pnginfo', None)] if h[x] == "UNIQUE_ID": input_data_all[x] = [unique_id] return input_data_all, missing_keys map_node_over_list = None #Don't hook this please def _map_node_over_list(obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None): # check if node wants the lists input_is_list = getattr(obj, "INPUT_IS_LIST", False) if len(input_data_all) == 0: max_len_input = 0 else: max_len_input = max(len(x) for x in input_data_all.values()) # get a slice of inputs, repeat last input when list isn't long enough def slice_dict(d, i): return {k: v[i if len(v) > i else -1] for k, v in d.items()} results = [] def process_inputs(inputs, index=None): if allow_interrupt: nodes.before_node_execution() execution_block = None for k, v in inputs.items(): if isinstance(v, ExecutionBlocker): execution_block = execution_block_cb(v) if execution_block_cb else v break if execution_block is None: if pre_execute_cb is not None and index is not None: pre_execute_cb(index) results.append(getattr(obj, func)(**inputs)) else: results.append(execution_block) if input_is_list: process_inputs(input_data_all, 0) elif max_len_input == 0: process_inputs({}) else: for i in range(max_len_input): input_dict = slice_dict(input_data_all, i) process_inputs(input_dict, i) return results def merge_result_data(results, obj): # check which outputs need concatenating output = [] output_is_list = [False] * len(results[0]) if hasattr(obj, "OUTPUT_IS_LIST"): output_is_list = obj.OUTPUT_IS_LIST # merge node execution results for i, is_list in zip(range(len(results[0])), output_is_list): if is_list: value = [] for o in results: if isinstance(o[i], ExecutionBlocker): value.append(o[i]) else: value.extend(o[i]) output.append(value) else: output.append([o[i] for o in results]) return output def get_output_data(obj, input_data_all, execution_block_cb=None, pre_execute_cb=None): results = [] uis = [] subgraph_results = [] return_values = _map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb) has_subgraph = False for i in range(len(return_values)): r = return_values[i] if isinstance(r, dict): if 'ui' in r: uis.append(r['ui']) if 'expand' in r: # Perform an expansion, but do not append results has_subgraph = True new_graph = r['expand'] result = r.get("result", None) if isinstance(result, ExecutionBlocker): result = tuple([result] * len(obj.RETURN_TYPES)) subgraph_results.append((new_graph, result)) elif 'result' in r: result = r.get("result", None) if isinstance(result, ExecutionBlocker): result = tuple([result] * len(obj.RETURN_TYPES)) results.append(result) subgraph_results.append((None, result)) else: if isinstance(r, ExecutionBlocker): r = tuple([r] * len(obj.RETURN_TYPES)) results.append(r) subgraph_results.append((None, r)) if has_subgraph: output = subgraph_results elif len(results) > 0: output = merge_result_data(results, obj) else: output = [] ui = dict() if len(uis) > 0: ui = {k: [y for x in uis for y in x[k]] for k in uis[0].keys()} return output, ui, has_subgraph def format_value(x): if x is None: return None elif isinstance(x, (int, float, bool, str)): return x else: return str(x) def execute(server, dynprompt, caches, current_item, extra_data, executed, prompt_id, execution_list, pending_subgraph_results): unique_id = current_item real_node_id = dynprompt.get_real_node_id(unique_id) display_node_id = dynprompt.get_display_node_id(unique_id) parent_node_id = dynprompt.get_parent_node_id(unique_id) inputs = dynprompt.get_node(unique_id)['inputs'] class_type = dynprompt.get_node(unique_id)['class_type'] class_def = nodes.NODE_CLASS_MAPPINGS[class_type] if caches.outputs.get(unique_id) is not None: if server.client_id is not None: cached_output = caches.ui.get(unique_id) or {} server.send_sync("executed", { "node": unique_id, "display_node": display_node_id, "output": cached_output.get("output",None), "prompt_id": prompt_id }, server.client_id) return (ExecutionResult.SUCCESS, None, None) input_data_all = None try: if unique_id in pending_subgraph_results: cached_results = pending_subgraph_results[unique_id] resolved_outputs = [] for is_subgraph, result in cached_results: if not is_subgraph: resolved_outputs.append(result) else: resolved_output = [] for r in result: if is_link(r): source_node, source_output = r[0], r[1] node_output = caches.outputs.get(source_node)[source_output] for o in node_output: resolved_output.append(o) else: resolved_output.append(r) resolved_outputs.append(tuple(resolved_output)) output_data = merge_result_data(resolved_outputs, class_def) output_ui = [] has_subgraph = False else: input_data_all, missing_keys = get_input_data(inputs, class_def, unique_id, caches.outputs, dynprompt, extra_data) if server.client_id is not None: server.last_node_id = display_node_id server.send_sync("executing", { "node": unique_id, "display_node": display_node_id, "prompt_id": prompt_id }, server.client_id) obj = caches.objects.get(unique_id) if obj is None: obj = class_def() caches.objects.set(unique_id, obj) if hasattr(obj, "check_lazy_status"): required_inputs = _map_node_over_list(obj, input_data_all, "check_lazy_status", allow_interrupt=True) required_inputs = set(sum([r for r in required_inputs if isinstance(r,list)], [])) required_inputs = [x for x in required_inputs if isinstance(x,str) and ( x not in input_data_all or x in missing_keys )] if len(required_inputs) > 0: for i in required_inputs: execution_list.make_input_strong_link(unique_id, i) return (ExecutionResult.PENDING, None, None) def execution_block_cb(block): if block.message is not None: mes = { "prompt_id": prompt_id, "node_id": unique_id, "node_type": class_type, "executed": list(executed), "exception_message": f"Execution Blocked: {block.message}", "exception_type": "ExecutionBlocked", "traceback": [], "current_inputs": [], "current_outputs": [], } server.send_sync("execution_error", mes, server.client_id) return ExecutionBlocker(None) else: return block def pre_execute_cb(call_index): GraphBuilder.set_default_prefix(unique_id, call_index, 0) output_data, output_ui, has_subgraph = get_output_data(obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb) if len(output_ui) > 0: caches.ui.set(unique_id, { "meta": { "node_id": unique_id, "display_node": display_node_id, "parent_node": parent_node_id, "real_node_id": real_node_id, }, "output": output_ui }) if server.client_id is not None: server.send_sync("executed", { "node": unique_id, "display_node": display_node_id, "output": output_ui, "prompt_id": prompt_id }, server.client_id) if has_subgraph: cached_outputs = [] new_node_ids = [] new_output_ids = [] new_output_links = [] for i in range(len(output_data)): new_graph, node_outputs = output_data[i] if new_graph is None: cached_outputs.append((False, node_outputs)) else: # Check for conflicts for node_id in new_graph.keys(): if dynprompt.has_node(node_id): raise DuplicateNodeError(f"Attempt to add duplicate node {node_id}. Ensure node ids are unique and deterministic or use graph_utils.GraphBuilder.") for node_id, node_info in new_graph.items(): new_node_ids.append(node_id) display_id = node_info.get("override_display_id", unique_id) dynprompt.add_ephemeral_node(node_id, node_info, unique_id, display_id) # Figure out if the newly created node is an output node class_type = node_info["class_type"] class_def = nodes.NODE_CLASS_MAPPINGS[class_type] if hasattr(class_def, 'OUTPUT_NODE') and class_def.OUTPUT_NODE == True: new_output_ids.append(node_id) for i in range(len(node_outputs)): if is_link(node_outputs[i]): from_node_id, from_socket = node_outputs[i][0], node_outputs[i][1] new_output_links.append((from_node_id, from_socket)) cached_outputs.append((True, node_outputs)) new_node_ids = set(new_node_ids) for cache in caches.all: cache.ensure_subcache_for(unique_id, new_node_ids).clean_unused() for node_id in new_output_ids: execution_list.add_node(node_id) for link in new_output_links: execution_list.add_strong_link(link[0], link[1], unique_id) pending_subgraph_results[unique_id] = cached_outputs return (ExecutionResult.PENDING, None, None) caches.outputs.set(unique_id, output_data) except comfy.model_management.InterruptProcessingException as iex: logging.info("Processing interrupted") # skip formatting inputs/outputs error_details = { "node_id": real_node_id, } return (ExecutionResult.FAILURE, error_details, iex) except Exception as ex: typ, _, tb = sys.exc_info() exception_type = full_type_name(typ) input_data_formatted = {} if input_data_all is not None: input_data_formatted = {} for name, inputs in input_data_all.items(): input_data_formatted[name] = [format_value(x) for x in inputs] logging.error(f"!!! Exception during processing !!! {ex}") logging.error(traceback.format_exc()) error_details = { "node_id": real_node_id, "exception_message": str(ex), "exception_type": exception_type, "traceback": traceback.format_tb(tb), "current_inputs": input_data_formatted } if isinstance(ex, comfy.model_management.OOM_EXCEPTION): logging.error("Got an OOM, unloading all loaded models.") comfy.model_management.unload_all_models() return (ExecutionResult.FAILURE, error_details, ex) executed.add(unique_id) return (ExecutionResult.SUCCESS, None, None) class PromptExecutor: def __init__(self, server, lru_size=None): self.lru_size = lru_size self.server = server self.reset() def reset(self): self.caches = CacheSet(self.lru_size) self.status_messages = [] self.success = True def add_message(self, event, data: dict, broadcast: bool): data = { **data, "timestamp": int(time.time() * 1000), } self.status_messages.append((event, data)) if self.server.client_id is not None or broadcast: self.server.send_sync(event, data, self.server.client_id) def handle_execution_error(self, prompt_id, prompt, current_outputs, executed, error, ex): node_id = error["node_id"] class_type = prompt[node_id]["class_type"] # First, send back the status to the frontend depending # on the exception type if isinstance(ex, comfy.model_management.InterruptProcessingException): mes = { "prompt_id": prompt_id, "node_id": node_id, "node_type": class_type, "executed": list(executed), } self.add_message("execution_interrupted", mes, broadcast=True) else: mes = { "prompt_id": prompt_id, "node_id": node_id, "node_type": class_type, "executed": list(executed), "exception_message": error["exception_message"], "exception_type": error["exception_type"], "traceback": error["traceback"], "current_inputs": error["current_inputs"], "current_outputs": list(current_outputs), } self.add_message("execution_error", mes, broadcast=False) def execute(self, prompt, prompt_id, extra_data={}, execute_outputs=[]): nodes.interrupt_processing(False) if "client_id" in extra_data: self.server.client_id = extra_data["client_id"] else: self.server.client_id = None self.status_messages = [] self.add_message("execution_start", { "prompt_id": prompt_id}, broadcast=False) with torch.inference_mode(): dynamic_prompt = DynamicPrompt(prompt) is_changed_cache = IsChangedCache(dynamic_prompt, self.caches.outputs) for cache in self.caches.all: cache.set_prompt(dynamic_prompt, prompt.keys(), is_changed_cache) cache.clean_unused() cached_nodes = [] for node_id in prompt: if self.caches.outputs.get(node_id) is not None: cached_nodes.append(node_id) comfy.model_management.cleanup_models_gc() self.add_message("execution_cached", { "nodes": cached_nodes, "prompt_id": prompt_id}, broadcast=False) pending_subgraph_results = {} executed = set() execution_list = ExecutionList(dynamic_prompt, self.caches.outputs) current_outputs = self.caches.outputs.all_node_ids() for node_id in list(execute_outputs): execution_list.add_node(node_id) while not execution_list.is_empty(): node_id, error, ex = execution_list.stage_node_execution() if error is not None: self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex) break result, error, ex = execute(self.server, dynamic_prompt, self.caches, node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results) self.success = result != ExecutionResult.FAILURE if result == ExecutionResult.FAILURE: self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex) break elif result == ExecutionResult.PENDING: execution_list.unstage_node_execution() else: # result == ExecutionResult.SUCCESS: execution_list.complete_node_execution() else: # Only execute when the while-loop ends without break self.add_message("execution_success", { "prompt_id": prompt_id }, broadcast=False) ui_outputs = {} meta_outputs = {} all_node_ids = self.caches.ui.all_node_ids() for node_id in all_node_ids: ui_info = self.caches.ui.get(node_id) if ui_info is not None: ui_outputs[node_id] = ui_info["output"] meta_outputs[node_id] = ui_info["meta"] self.history_result = { "outputs": ui_outputs, "meta": meta_outputs, } self.server.last_node_id = None if comfy.model_management.DISABLE_SMART_MEMORY: comfy.model_management.unload_all_models() def validate_inputs(prompt, item, validated): unique_id = item if unique_id in validated: return validated[unique_id] inputs = prompt[unique_id]['inputs'] class_type = prompt[unique_id]['class_type'] obj_class = nodes.NODE_CLASS_MAPPINGS[class_type] class_inputs = obj_class.INPUT_TYPES() valid_inputs = set(class_inputs.get('required',{})).union(set(class_inputs.get('optional',{}))) errors = [] valid = True validate_function_inputs = [] validate_has_kwargs = False if hasattr(obj_class, "VALIDATE_INPUTS"): argspec = inspect.getfullargspec(obj_class.VALIDATE_INPUTS) validate_function_inputs = argspec.args validate_has_kwargs = argspec.varkw is not None received_types = {} for x in valid_inputs: type_input, input_category, extra_info = get_input_info(obj_class, x) assert extra_info is not None if x not in inputs: if input_category == "required": error = { "type": "required_input_missing", "message": "Required input is missing", "details": f"{x}", "extra_info": { "input_name": x } } errors.append(error) continue val = inputs[x] info = (type_input, extra_info) if isinstance(val, list): if len(val) != 2: error = { "type": "bad_linked_input", "message": "Bad linked input, must be a length-2 list of [node_id, slot_index]", "details": f"{x}", "extra_info": { "input_name": x, "input_config": info, "received_value": val } } errors.append(error) continue o_id = val[0] o_class_type = prompt[o_id]['class_type'] r = nodes.NODE_CLASS_MAPPINGS[o_class_type].RETURN_TYPES received_type = r[val[1]] received_types[x] = received_type if 'input_types' not in validate_function_inputs and received_type != type_input: details = f"{x}, {received_type} != {type_input}" error = { "type": "return_type_mismatch", "message": "Return type mismatch between linked nodes", "details": details, "extra_info": { "input_name": x, "input_config": info, "received_type": received_type, "linked_node": val } } errors.append(error) continue try: r = validate_inputs(prompt, o_id, validated) if r[0] is False: # `r` will be set in `validated[o_id]` already valid = False continue except Exception as ex: typ, _, tb = sys.exc_info() valid = False exception_type = full_type_name(typ) reasons = [{ "type": "exception_during_inner_validation", "message": "Exception when validating inner node", "details": str(ex), "extra_info": { "input_name": x, "input_config": info, "exception_message": str(ex), "exception_type": exception_type, "traceback": traceback.format_tb(tb), "linked_node": val } }] validated[o_id] = (False, reasons, o_id) continue else: try: if type_input == "INT": val = int(val) inputs[x] = val if type_input == "FLOAT": val = float(val) inputs[x] = val if type_input == "STRING": val = str(val) inputs[x] = val if type_input == "BOOLEAN": val = bool(val) inputs[x] = val except Exception as ex: error = { "type": "invalid_input_type", "message": f"Failed to convert an input value to a {type_input} value", "details": f"{x}, {val}, {ex}", "extra_info": { "input_name": x, "input_config": info, "received_value": val, "exception_message": str(ex) } } errors.append(error) continue if x not in validate_function_inputs and not validate_has_kwargs: if "min" in extra_info and val < extra_info["min"]: error = { "type": "value_smaller_than_min", "message": "Value {} smaller than min of {}".format(val, extra_info["min"]), "details": f"{x}", "extra_info": { "input_name": x, "input_config": info, "received_value": val, } } errors.append(error) continue if "max" in extra_info and val > extra_info["max"]: error = { "type": "value_bigger_than_max", "message": "Value {} bigger than max of {}".format(val, extra_info["max"]), "details": f"{x}", "extra_info": { "input_name": x, "input_config": info, "received_value": val, } } errors.append(error) continue if isinstance(type_input, list): if val not in type_input: input_config = info list_info = "" # Don't send back gigantic lists like if they're lots of # scanned model filepaths if len(type_input) > 20: list_info = f"(list of length {len(type_input)})" input_config = None else: list_info = str(type_input) error = { "type": "value_not_in_list", "message": "Value not in list", "details": f"{x}: '{val}' not in {list_info}", "extra_info": { "input_name": x, "input_config": input_config, "received_value": val, } } errors.append(error) continue if len(validate_function_inputs) > 0 or validate_has_kwargs: input_data_all, _ = get_input_data(inputs, obj_class, unique_id) input_filtered = {} for x in input_data_all: if x in validate_function_inputs or validate_has_kwargs: input_filtered[x] = input_data_all[x] if 'input_types' in validate_function_inputs: input_filtered['input_types'] = [received_types] #ret = obj_class.VALIDATE_INPUTS(**input_filtered) ret = _map_node_over_list(obj_class, input_filtered, "VALIDATE_INPUTS") for x in input_filtered: for i, r in enumerate(ret): if r is not True and not isinstance(r, ExecutionBlocker): details = f"{x}" if r is not False: details += f" - {str(r)}" error = { "type": "custom_validation_failed", "message": "Custom validation failed for node", "details": details, "extra_info": { "input_name": x, } } errors.append(error) continue if len(errors) > 0 or valid is not True: ret = (False, errors, unique_id) else: ret = (True, [], unique_id) validated[unique_id] = ret return ret def full_type_name(klass): module = klass.__module__ if module == 'builtins': return klass.__qualname__ return module + '.' + klass.__qualname__ def validate_prompt(prompt): outputs = set() for x in prompt: if 'class_type' not in prompt[x]: error = { "type": "invalid_prompt", "message": f"Cannot execute because a node is missing the class_type property.", "details": f"Node ID '#{x}'", "extra_info": {} } return (False, error, [], []) class_type = prompt[x]['class_type'] class_ = nodes.NODE_CLASS_MAPPINGS.get(class_type, None) if class_ is None: error = { "type": "invalid_prompt", "message": f"Cannot execute because node {class_type} does not exist.", "details": f"Node ID '#{x}'", "extra_info": {} } return (False, error, [], []) if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE is True: outputs.add(x) if len(outputs) == 0: error = { "type": "prompt_no_outputs", "message": "Prompt has no outputs", "details": "", "extra_info": {} } return (False, error, [], []) good_outputs = set() errors = [] node_errors = {} validated = {} for o in outputs: valid = False reasons = [] try: m = validate_inputs(prompt, o, validated) valid = m[0] reasons = m[1] except Exception as ex: typ, _, tb = sys.exc_info() valid = False exception_type = full_type_name(typ) reasons = [{ "type": "exception_during_validation", "message": "Exception when validating node", "details": str(ex), "extra_info": { "exception_type": exception_type, "traceback": traceback.format_tb(tb) } }] validated[o] = (False, reasons, o) if valid is True: good_outputs.add(o) else: logging.error(f"Failed to validate prompt for output {o}:") if len(reasons) > 0: logging.error("* (prompt):") for reason in reasons: logging.error(f" - {reason['message']}: {reason['details']}") errors += [(o, reasons)] for node_id, result in validated.items(): valid = result[0] reasons = result[1] # If a node upstream has errors, the nodes downstream will also # be reported as invalid, but there will be no errors attached. # So don't return those nodes as having errors in the response. if valid is not True and len(reasons) > 0: if node_id not in node_errors: class_type = prompt[node_id]['class_type'] node_errors[node_id] = { "errors": reasons, "dependent_outputs": [], "class_type": class_type } logging.error(f"* {class_type} {node_id}:") for reason in reasons: logging.error(f" - {reason['message']}: {reason['details']}") node_errors[node_id]["dependent_outputs"].append(o) logging.error("Output will be ignored") if len(good_outputs) == 0: errors_list = [] for o, errors in errors: for error in errors: errors_list.append(f"{error['message']}: {error['details']}") errors_list = "\n".join(errors_list) error = { "type": "prompt_outputs_failed_validation", "message": "Prompt outputs failed validation", "details": errors_list, "extra_info": {} } return (False, error, list(good_outputs), node_errors) return (True, None, list(good_outputs), node_errors) MAXIMUM_HISTORY_SIZE = 10000 class PromptQueue: def __init__(self, server): self.server = server self.mutex = threading.RLock() self.not_empty = threading.Condition(self.mutex) self.task_counter = 0 self.queue = [] self.currently_running = {} self.history = {} self.flags = {} server.prompt_queue = self def put(self, item): with self.mutex: heapq.heappush(self.queue, item) self.server.queue_updated() self.not_empty.notify() def get(self, timeout=None): with self.not_empty: while len(self.queue) == 0: self.not_empty.wait(timeout=timeout) if timeout is not None and len(self.queue) == 0: return None item = heapq.heappop(self.queue) i = self.task_counter self.currently_running[i] = copy.deepcopy(item) self.task_counter += 1 self.server.queue_updated() return (item, i) class ExecutionStatus(NamedTuple): status_str: Literal['success', 'error'] completed: bool messages: List[str] def task_done(self, item_id, history_result, status: Optional['PromptQueue.ExecutionStatus']): with self.mutex: prompt = self.currently_running.pop(item_id) if len(self.history) > MAXIMUM_HISTORY_SIZE: self.history.pop(next(iter(self.history))) status_dict: Optional[dict] = None if status is not None: status_dict = copy.deepcopy(status._asdict()) self.history[prompt[1]] = { "prompt": prompt, "outputs": {}, 'status': status_dict, } self.history[prompt[1]].update(history_result) self.server.queue_updated() def get_current_queue(self): with self.mutex: out = [] for x in self.currently_running.values(): out += [x] return (out, copy.deepcopy(self.queue)) def get_tasks_remaining(self): with self.mutex: return len(self.queue) + len(self.currently_running) def wipe_queue(self): with self.mutex: self.queue = [] self.server.queue_updated() def delete_queue_item(self, function): with self.mutex: for x in range(len(self.queue)): if function(self.queue[x]): if len(self.queue) == 1: self.wipe_queue() else: self.queue.pop(x) heapq.heapify(self.queue) self.server.queue_updated() return True return False def get_history(self, prompt_id=None, max_items=None, offset=-1): with self.mutex: if prompt_id is None: out = {} i = 0 if offset < 0 and max_items is not None: offset = len(self.history) - max_items for k in self.history: if i >= offset: out[k] = self.history[k] if max_items is not None and len(out) >= max_items: break i += 1 return out elif prompt_id in self.history: return {prompt_id: copy.deepcopy(self.history[prompt_id])} else: return {} def wipe_history(self): with self.mutex: self.history = {} def delete_history_item(self, id_to_delete): with self.mutex: self.history.pop(id_to_delete, None) def set_flag(self, name, data): with self.mutex: self.flags[name] = data self.not_empty.notify() def get_flags(self, reset=True): with self.mutex: if reset: ret = self.flags self.flags = {} return ret else: return self.flags.copy()