import copy import torch import torch.nn as nn from torch.ao.quantization import ( QConfigAny, QuantType, ) from torch.ao.quantization.backend_config import ( DTypeWithConstraints, ) from torch.ao.quantization.fake_quantize import ( FakeQuantizeBase, FixedQParamsFakeQuantize, ) from torch.ao.quantization.observer import ( FixedQParamsObserver, ObserverBase, ) from torch.ao.quantization.qconfig import ( float16_static_qconfig, float16_dynamic_qconfig, qconfig_equals, ) from torch.ao.quantization.stubs import DeQuantStub from torch.ao.quantization.utils import ( activation_is_statically_quantized, ) from torch.ao.quantization.observer import _is_activation_post_process from torch.ao.quantization.qconfig_mapping import QConfigMapping from torch.fx import GraphModule, map_arg from torch.fx.graph import ( Graph, Node, ) from .custom_config import PrepareCustomConfig # importing the lib so that the quantized_decomposed ops are registered from ._decomposed import quantized_decomposed_lib # noqa: F401 from typing import Callable, Optional, List, Dict, Any, Set, Tuple, Union, Type from dataclasses import dataclass from collections import namedtuple import operator import warnings # TODO: revisit this list. Many helper methods shouldn't be public __all__ = [ "all_node_args_except_first", "all_node_args_have_no_tensors", "assert_and_get_unique_device", "collect_producer_nodes", "create_getattr_from_value", "create_node_from_old_node_preserve_meta", "EMPTY_ARG_DICT", "get_custom_module_class_keys", "get_linear_prepack_op_for_dtype", "get_new_attr_name_with_prefix", "get_non_observable_arg_indexes_and_types", "get_qconv_prepack_op", "get_skipped_module_name_and_classes", "graph_module_from_producer_nodes", "maybe_get_next_module", "NodeInfo", "node_arg_is_bias", "node_arg_is_weight", "NON_OBSERVABLE_ARG_DICT", "NON_QUANTIZABLE_WEIGHT_OPS", "return_arg_list", "ObservedGraphModuleAttrs", ] NON_QUANTIZABLE_WEIGHT_OPS = {torch.nn.functional.layer_norm, torch.nn.functional.group_norm, torch.nn.functional.instance_norm} @dataclass class ObservedGraphModuleAttrs: node_name_to_qconfig: Dict[str, QConfigAny] node_name_to_scope: Dict[str, Tuple[str, type]] prepare_custom_config: PrepareCustomConfig equalization_node_name_to_qconfig: Dict[str, Any] qconfig_mapping: QConfigMapping is_qat: bool observed_node_names: Set[str] is_observed_standalone_module: bool = False standalone_module_input_quantized_idxs: Optional[List[int]] = None standalone_module_output_quantized_idxs: Optional[List[int]] = None def node_arg_is_weight(node: Node, arg: Any) -> bool: """Returns if node arg is weight""" weight_index = None if "target_dtype_info" in node.meta: weight_index = node.meta["target_dtype_info"].get("weight_index", None) if weight_index is not None and weight_index < len(node.args) and node.args[weight_index] is arg: return True return node.kwargs.get("weight") is arg def node_arg_is_bias(node: Node, arg: Any) -> bool: """Returns if node arg is bias""" bias_index = None if "target_dtype_info" in node.meta: bias_index = node.meta["target_dtype_info"].get("bias_index", None) if bias_index is not None and bias_index < len(node.args) and node.args[bias_index] is arg: return True return node.kwargs.get("bias") is arg def get_custom_module_class_keys(custom_module_mapping: Dict[QuantType, Dict[Type, Type]]) -> List[Any]: r""" Get all the unique custom module keys in the custom config dict e.g. Input: { QuantType.STATIC: { CustomModule1: ObservedCustomModule }, QuantType.DYNAMIC: { CustomModule2: DynamicObservedCustomModule }, QuantType.WEIGHT_ONLY: { CustomModule3: WeightOnlyObservedCustomModule }, } Output: # extract the keys across all inner STATIC, DYNAMIC, and WEIGHT_ONLY dicts [CustomModule1, CustomModule2, CustomModule3] """ # using set to dedup float_custom_module_classes : Set[Any] = set() for quant_mode in [QuantType.STATIC, QuantType.DYNAMIC, QuantType.WEIGHT_ONLY]: quant_mode_custom_module_config = custom_module_mapping.get(quant_mode, {}) quant_mode_custom_module_classes = set(quant_mode_custom_module_config.keys()) float_custom_module_classes |= quant_mode_custom_module_classes return list(float_custom_module_classes) def get_linear_prepack_op_for_dtype(dtype): if dtype == torch.float16: return torch.ops.quantized.linear_prepack_fp16 elif dtype == torch.qint8: return torch.ops.quantized.linear_prepack else: raise Exception("can't get linear prepack op for dtype:", dtype) def get_qconv_prepack_op(conv_op: Callable) -> Callable: prepack_ops = { torch.nn.functional.conv1d: torch.ops.quantized.conv1d_prepack, torch.nn.functional.conv2d: torch.ops.quantized.conv2d_prepack, torch.nn.functional.conv3d: torch.ops.quantized.conv3d_prepack, torch.nn.functional.conv_transpose1d: torch.ops.quantized.conv_transpose1d_prepack, torch.nn.functional.conv_transpose2d: torch.ops.quantized.conv_transpose2d_prepack, torch.nn.functional.conv_transpose3d: torch.ops.quantized.conv_transpose3d_prepack, } prepack_op = prepack_ops.get(conv_op, None) assert prepack_op, f"Didn't find prepack op for {conv_op}" return prepack_op # Returns a function that can get a new attribute name for module with given # prefix, for example, # >> get_new_observer_name = get_new_attr_name_with_prefix('_observer') # >> new_name = get_new_observer_name(module) # new_name will be an unused attribute name on module, e.g. `_observer_1` def get_new_attr_name_with_prefix(prefix: str) -> Callable: prefix = prefix.replace(".", "_") def get_new_attr_name(module: torch.nn.Module): def get_attr_name(i: int): return prefix + str(i) i = 0 attr_name = get_attr_name(i) while hasattr(module, attr_name): i += 1 attr_name = get_attr_name(i) return attr_name return get_new_attr_name def collect_producer_nodes(node: Node) -> Optional[List[Node]]: r''' Starting from a target node, trace back until we hit inpu or getattr node. This is used to extract the chain of operators starting from getattr to the target node, for example def forward(self, x): observed = self.observer(self.weight) return F.linear(x, observed) collect_producer_nodes(observed) will either return a list of nodes that produces the observed node or None if we can't extract a self contained graph without free variables(inputs of the forward function). ''' nodes = [node] frontier = [node] while frontier: node = frontier.pop() all_args = list(node.args) + list(node.kwargs.values()) for arg in all_args: if not isinstance(arg, Node): continue if arg.op == 'placeholder': # hit input, can't fold in this case return None nodes.append(arg) if not (arg.op == 'call_function' and arg.target == getattr): frontier.append(arg) return nodes def graph_module_from_producer_nodes( root: GraphModule, producer_nodes: List[Node]) -> GraphModule: r''' Construct a graph module from extracted producer nodes from `collect_producer_nodes` function Args: root: the root module for the original graph producer_nodes: a list of nodes we use to construct the graph Return: A graph module constructed from the producer nodes ''' assert len(producer_nodes) > 0, 'list of producer nodes can not be empty' # since we traced back from node to getattr producer_nodes.reverse() graph = Graph() env: Dict[Any, Any] = {} def load_arg(a): return map_arg(a, lambda node: env[node]) for producer_node in producer_nodes: env[producer_node] = graph.node_copy(producer_node, load_arg) graph.output(load_arg(producer_nodes[-1])) graph_module = GraphModule(root, graph) return graph_module def assert_and_get_unique_device(module: torch.nn.Module) -> Any: """ Returns the unique device for a module, or None if no device is found. Throws an error if multiple devices are detected. """ devices = {p.device for p in module.parameters()} | \ {p.device for p in module.buffers()} """ As a temp workaround for AIMP HHC publish we added CPU check.remove it later. T163614564 """ if {torch.device("cpu"), torch.device("meta")} == devices: warnings.warn("Both 'meta' and 'cpu' are present in the list of devices. Module can have one device. We Select 'cpu'.") devices = {torch.device("cpu")} "" assert len(devices) <= 1, ( "prepare only works with cpu or single-device CUDA modules, " f"but got devices {devices}" ) device = next(iter(devices)) if len(devices) > 0 else None return device def create_getattr_from_value(module: torch.nn.Module, graph: Graph, prefix: str, value: Any) -> Node: """ Given a value of any type, creates a getattr node corresponding to the value and registers the value as a buffer to the module. """ get_new_attr_name = get_new_attr_name_with_prefix(prefix) attr_name = get_new_attr_name(module) device = assert_and_get_unique_device(module) new_value = value.clone().detach() if isinstance(value, torch.Tensor) \ else torch.tensor(value, device=device) module.register_buffer(attr_name, new_value) # Create get_attr with value attr_node = graph.create_node("get_attr", attr_name) return attr_node def all_node_args_have_no_tensors(node: Node, modules: Dict[str, torch.nn.Module], cache: Dict[Node, bool]) -> bool: """ If we know for sure that all of this node's args have no tensors (are primitives), return True. If we either find a tensor or are not sure, return False. Note: this function is not exact. """ if cache and node in cache: return cache[node] result = False # will be overwritten if not isinstance(node, Node): result = True elif node.op == 'placeholder': result = False elif node.op == 'call_module': assert isinstance(node.target, str) if _is_activation_post_process(modules[node.target]): result = all_node_args_have_no_tensors(node.args[0], modules, cache) # type: ignore[arg-type] elif node.op == 'call_module': result = False elif node.op == 'call_function' and node.target is operator.getitem: result = all_node_args_have_no_tensors(node.args[0], modules, cache) # type: ignore[arg-type] elif node.op == 'get_attr': result = False elif node.target is getattr and node.args[1] in ['ndim', 'shape']: # x1 = x0.ndim result = True elif node.op == 'call_method' and node.target == 'size': # x1 = x0.size(0) result = True else: found_one_tensor = False for arg in node.args: if isinstance(arg, list): for list_el in arg: if isinstance(list_el, Node): this_list_el_args_have_no_tensors = \ all_node_args_have_no_tensors(list_el, modules, cache) found_one_tensor = found_one_tensor or \ (not this_list_el_args_have_no_tensors) # If found_one_tensor is True, there is no point in # recursing further as the end result will always # be True. # TODO(future PR): remove this entire function and # change to dtype inference without recursion. if found_one_tensor: result = not found_one_tensor if cache: cache[node] = result return result elif isinstance(arg, int): pass else: if isinstance(arg, Node): this_arg_args_have_no_tensors = all_node_args_have_no_tensors(arg, modules, cache) found_one_tensor = found_one_tensor or \ (not this_arg_args_have_no_tensors) # If found_one_tensor is True, there is no point in # recursing further as the end result will always # be True. # TODO(future PR): remove this entire function and # change to dtype inference without recursion. if found_one_tensor: result = not found_one_tensor if cache: cache[node] = result return result else: found_one_tensor = True result = not found_one_tensor if cache: cache[node] = result return result def all_node_args_except_first(node: Node) -> List[int]: """ Returns all node arg indices after first """ return list(range(1, len(node.args))) def return_arg_list(arg_indices: List[int]) -> Callable[[Node], List[int]]: """ Constructs a function that takes a node as arg and returns the arg_indices that are valid for node.args """ def arg_indices_func(node: Node) -> List[int]: return [i for i in arg_indices if i < len(node.args)] return arg_indices_func NodeInfo = namedtuple("NodeInfo", "op target") # this dict identifies which indices of a node are non tensors # so that they can be propagated correctly since inserting observers # for them would cause errors NON_OBSERVABLE_ARG_DICT: Dict[NodeInfo, Dict[Union[type, torch.dtype], Callable[[Node], List[int]]]] = { NodeInfo("call_method", "masked_fill") : { torch.bool: return_arg_list([1]), float: return_arg_list([2]) }, NodeInfo("call_method", "permute") : { int: all_node_args_except_first }, NodeInfo("call_method", "repeat") : { int: all_node_args_except_first }, NodeInfo("call_method", "reshape") : { int: all_node_args_except_first }, NodeInfo("call_method", "size") : { int: return_arg_list([1]) }, NodeInfo("call_method", "transpose") : { int: all_node_args_except_first }, NodeInfo("call_method", torch.transpose) : { int: all_node_args_except_first }, NodeInfo("call_method", "unsqueeze") : { int: return_arg_list([1]) }, NodeInfo("call_method", "unsqueeze_") : { int: return_arg_list([1]) }, NodeInfo("call_method", torch.unsqueeze) : { int: return_arg_list([1]) }, NodeInfo("call_method", "view") : { int: all_node_args_except_first }, } EMPTY_ARG_DICT: Dict[Union[type, torch.dtype], Callable[[Node], List[int]]] = {} def get_non_observable_arg_indexes_and_types(node: Node) -> Dict[Union[type, torch.dtype], Callable[[Node], List[int]]]: """ Returns a dict with of non float tensor types as keys and values which correspond to a function to retrieve the list (which takes the node as an argument) """ info = NodeInfo(node.op, node.target) return NON_OBSERVABLE_ARG_DICT.get(info, EMPTY_ARG_DICT) def maybe_get_next_module( node: Node, modules: Dict[str, nn.Module], target_module_type: Optional[Type[nn.Module]] = None, target_functional_type: Any = None, ) -> Optional[Node]: """ Gets the next module that matches what is needed in is_target_module_type if it exists Args: node: The node whose users we want to look at target_module_type: Module type that we want to check target_functional_type: Functional type that we want to check """ for user in node.users.keys(): if user.op == 'call_module' and target_module_type is not None and \ isinstance(modules[str(user.target)], target_module_type): return user elif (user.op == 'call_function' and target_functional_type is not None and user.target == target_functional_type): return user return None def create_node_from_old_node_preserve_meta( quantized_graph: Graph, create_node_args: Tuple[Any, ...], old_node: Node, ) -> Node: """ Creates `new_node` and copies the necessary metadata to it from `old_node`. """ new_node = quantized_graph.create_node(*create_node_args) new_node.stack_trace = old_node.stack_trace return new_node def get_skipped_module_name_and_classes( prepare_custom_config: PrepareCustomConfig, is_standalone_module: bool) -> Tuple[List[str], List[Type[Any]]]: skipped_module_names = copy.copy(prepare_custom_config.non_traceable_module_names) skipped_module_classes = copy.copy(prepare_custom_config.non_traceable_module_classes) if not is_standalone_module: # standalone module and custom module config are applied in top level module skipped_module_names += list(prepare_custom_config.standalone_module_names.keys()) skipped_module_classes += list(prepare_custom_config.standalone_module_classes.keys()) skipped_module_classes += get_custom_module_class_keys(prepare_custom_config.float_to_observed_mapping) return skipped_module_names, skipped_module_classes def _is_custom_module_lstm( node: Node, named_modules: Dict[str, torch.nn.Module], qconfig: QConfigAny = None, # QuantizeHandler, but we cannot include the type here due to circular imports qhandler: Optional[Any] = None, ) -> bool: """ Return whether this refers to the custom module LSTM flow. """ mod = _get_module(node, named_modules) if qconfig is not None and qhandler is not None: assert isinstance(qhandler, torch.ao.quantization.fx.quantize_handler.QuantizeHandler) # type: ignore[attr-defined] return isinstance(mod, torch.nn.LSTM) and \ activation_is_statically_quantized(qconfig) and \ qhandler.is_custom_module() else: return isinstance(mod, torch.ao.nn.quantizable.LSTM) def _is_custom_module_mha( node: Node, named_modules: Dict[str, torch.nn.Module], qconfig: QConfigAny = None, # QuantizeHandler, but we cannot include the type here due to circular imports qhandler: Optional[Any] = None, ) -> bool: """ Return whether this refers to the custom module MultiheadAttention flow. """ mod = _get_module(node, named_modules) if qconfig is not None and qhandler is not None: assert isinstance(qhandler, torch.ao.quantization.fx.quantize_handler.QuantizeHandler) # type: ignore[attr-defined] return isinstance(mod, torch.nn.MultiheadAttention) and \ activation_is_statically_quantized(qconfig) and \ qhandler.is_custom_module() else: return isinstance(mod, torch.ao.nn.quantizable.MultiheadAttention) def _get_module(node: Node, named_modules: Dict[str, torch.nn.Module]) -> Optional[torch.nn.Module]: """ If `node` refers to a call_module node, return the module, else None. """ if node.op == "call_module" and str(node.target) in named_modules: return named_modules[str(node.target)] else: return None def _insert_dequant_stub( node: Node, model: torch.nn.Module, named_modules: Dict[str, torch.nn.Module], graph: Graph, ) -> Node: """ Attach a `DeQuantStub` to the model and create a node that calls this `DeQuantStub` on the output of `node`, similar to how observers are inserted. """ prefix = "dequant_stub_" get_new_dequant_stub_name = get_new_attr_name_with_prefix(prefix) dequant_stub_name = get_new_dequant_stub_name(model) dequant_stub = DeQuantStub() setattr(model, dequant_stub_name, dequant_stub) named_modules[dequant_stub_name] = dequant_stub with graph.inserting_after(node): return graph.call_module(dequant_stub_name, (node,)) def _insert_dequant_stubs_for_custom_module_lstm_output( node: Node, model: torch.nn.Module, named_modules: Dict[str, torch.nn.Module], graph: Graph, ) -> Node: """ Insert DeQuantStubs after each internal output node of custom module LSTM. Custom module LSTM outputs are nested tuples of the structure (output, (hidden0, hidden1)), Since we cannot dequantize a tuple as a whole, we must first break down the tuple into its components through `getitem`. This function transforms the graph as follows: (1) Split the LSTM node into (output, (hidden0, hidden1)) (2) Insert a DeQuantStub after each internal node (3) Recombine the DeQuantStubs into the same structure as before (4) Reroute all consumers of the original LSTM node and its sub-nodes (e.g. lstm[0]) Before: lstm_output | v original_user(s) After: lstm_output / \\ / (getitem) \\ / \\ v v output hidden | / \\ (DeQuantStub) (getitem) | / \\ v v v output_dq hidden0 hidden1 | | | | (DeQuantStub) (DeQuantStub) | | | | v v | hidden0_dq hidden1_dq | \\ / | (tuple) | \\ / | v v | hidden_dq \\ / \\ (tuple) / v v lstm_output_dq | v original_user(s) For step (4), reroute all users of the original LSTM node(s) as follows: lstm_output -> lstm_output_dq lstm_output[0] -> output_dq lstm_output[1] -> hidden_dq lstm_output[1][0] -> hidden0_dq lstm_output[1][1] -> hidden1_dq Return the node `lstm_output_dq`. """ # (1) Split the LSTM node into (output, (hidden0, hidden1)) # (2) Insert a DeQuantStub after each internal node with graph.inserting_after(node): output = graph.call_function(operator.getitem, (node, 0)) output_dq = _insert_dequant_stub(output, model, named_modules, graph) with graph.inserting_after(output_dq): hidden = graph.call_function(operator.getitem, (node, 1)) with graph.inserting_after(hidden): hidden0 = graph.call_function(operator.getitem, (hidden, 0)) hidden0_dq = _insert_dequant_stub(hidden0, model, named_modules, graph) with graph.inserting_after(hidden0_dq): hidden1 = graph.call_function(operator.getitem, (hidden, 1)) hidden1_dq = _insert_dequant_stub(hidden1, model, named_modules, graph) # (3) Recombine the DeQuantStubs into the same structure as before with graph.inserting_after(hidden1_dq): hidden_dq = graph.call_function(tuple, ([hidden0_dq, hidden1_dq],)) with graph.inserting_after(hidden_dq): lstm_output_dq = graph.call_function(tuple, ([output_dq, hidden_dq],)) # (4) Reroute all consumers of the original LSTM node and its sub-nodes for user in list(node.users.keys()): if user != output and user != hidden: user.replace_input_with(node, lstm_output_dq) # The getitem and tuple nodes we added here may interfere with reference quantized # pattern matching, so we need to redirect the consumers of internal nodes to the # corresponding nodes with DeQuantStubs (e.g. lstm_output_dq[0] -> output_dq) attached, # in order to preserve reference patterns like "dequantize - consumer - quantize". _reroute_tuple_getitem_pattern(graph) return lstm_output_dq def _maybe_get_custom_module_lstm_from_node_arg( arg: Node, named_modules: Dict[str, torch.nn.Module], ) -> Optional[Node]: """ Given an argument of a node, if the argument refers to the path through which the node is a consumer of custom module LSTM, return the custom module LSTM node, or None otherwise. This is used to determine whether a node is a consumer of custom module LSTM, and, if so, skip inserting input observers for this node. This is because custom module LSTM produces quantized outputs, so inserting an input observer for the consumer of custom module LSTM would unnecessarily quantize the outputs again. lstm -> consumer In practice, however, custom module LSTM outputs a tuple (output, (hidden0, hidden1)) with DeQuantStubs attached to each internal node (see `_insert_dequant_stubs_for_custom_module_lstm_output`). This tuple can be consumed in one of four ways: lstm -> getitem -> DeQuantStub -> consumer # consume lstm[0] lstm -> getitem -> getitem -> DeQuantStub -> tuple -> consumer # consume lstm[1] lstm -> getitem -> getitem -> DeQuantStub -> consumer # consume lstm[1][0] or lstm[1][1] lstm -> getitem -> DeQuantStub -> tuple -> consumer # consume lstm Thus, we must match against the above patterns instead of simply checking the parent node to determine whether this node is a consumer of a custom module LSTM. """ def match_dq(a): return isinstance(_get_module(a, named_modules), DeQuantStub) def match_lstm(a): return _is_custom_module_lstm(a, named_modules) def match_getitem(a): return a.op == "call_function" and a.target == operator.getitem def match_tuple(a): return a.op == "call_function" and a.target == tuple def _match_pattern(match_pattern: List[Callable]) -> Optional[Node]: """ Traverse up the graph and match the args one by one. If there is a match, return the last matched node, or None otherwise. """ a = arg for i, match in enumerate(match_pattern): if not match(a): return None # Match next arg, for tuple the arg is a tuple of a list, e.g. ([dq_1, other_node],) if i < len(match_pattern) - 1: if match == match_tuple: a = a.args[0][0] # type: ignore[assignment,index] else: a = a.args[0] # type: ignore[assignment] return a all_match_patterns = [ [match_dq, match_getitem, match_lstm], [match_tuple, match_dq, match_getitem, match_getitem, match_lstm], [match_dq, match_getitem, match_getitem, match_lstm], [match_tuple, match_dq, match_getitem, match_lstm], ] for p in all_match_patterns: matched_node = _match_pattern(p) if matched_node is not None: return matched_node return None def _reroute_tuple_getitem_pattern(graph: Graph): """ Search for patterns where N consecutive `tuple` call_function nodes are followed by N consecutive `getitem` call_function nodes that are "reverses" of the `tuple` nodes. If we find this pattern, reroute the consumers of the last `getitem` to skip these N `tuple` and `getitem` nodes. Before: a b c | \\ / \\ tuple \\ / tuple | getitem(1) | getitem(0) | d After: b | d """ def find_patterns( node: Node, index_stack: List[int], current_pattern: List[Node], matched_patterns: List[List[Node]], seen: Set[Tuple[Node, Tuple[int, ...]]]): """ Traverse the graph recursively to match for the N-tuple - N-getitem patterns, starting at the given node. We use a stack to keep track of the expected `getitem` indices, since these are reversed from the `tuple` indices. In the above example, the stack after (b -> tuple -> tuple) will be [0, 1], which will be popped by getitem(1) first and then by getitem(0). TODO: traverse upwards from the output and handle the case when tuple is not a separate node, e.g. graph.call_function(operator.getitem, args=(a, (b, c))) """ if len(index_stack) == 0 and len(current_pattern) > 0: matched_patterns.append(copy.copy(current_pattern)) current_pattern.clear() # Avoid duplicating work state = (node, tuple(index_stack)) if state in seen: return seen.add(state) # Iterate through users of this node to find tuple/getitem nodes to match for user in node.users: if user.op == "call_function" and user.target == tuple: for i, user_arg in enumerate(user.args[0]): # type: ignore[arg-type] if user_arg == node: index_stack.append(i) current_pattern.append(user) find_patterns(user, index_stack, current_pattern, matched_patterns, seen) elif user.op == "call_function" and user.target == operator.getitem: if len(index_stack) > 0: if user.args[1] == index_stack[-1]: index_stack.pop() current_pattern.append(user) find_patterns(user, index_stack, current_pattern, matched_patterns, seen) return matched_patterns # Collect all matched patterns matched_patterns: List[List[Node]] = [] seen: Set[Tuple[Node, Tuple[int, ...]]] = set() # (node, index_stack) for node in graph.nodes: find_patterns(node, [], [], matched_patterns, seen) # For each pattern, redirect all consumers of the last getitem node to the correct input # of the first tuple node for pattern in matched_patterns: first_tuple = pattern[0] last_getitem = pattern[-1] assert first_tuple.op == "call_function" and first_tuple.target == tuple assert last_getitem.op == "call_function" and last_getitem.target == operator.getitem last_getitem_index = last_getitem.args[1] new_input = first_tuple.args[0][last_getitem_index] # type: ignore[index] for user in list(last_getitem.users.keys()): user.replace_input_with(last_getitem, new_input) def _get_observer_from_activation_post_process( activation_post_process: Union[ObserverBase, FakeQuantizeBase], ) -> ObserverBase: """ If `activation_post_process` is an observer, return the observer. If `activation_post_process` is a fake quantize, return the internal observer. """ if isinstance(activation_post_process, ObserverBase): return activation_post_process else: assert isinstance(activation_post_process, FakeQuantizeBase) return activation_post_process.activation_post_process # type: ignore[return-value] def _qconfig_satisfies_dtype_config_constraints( qconfig: QConfigAny, dtype_with_constraints: DTypeWithConstraints, is_activation: bool = True) -> bool: """ Return whether `qconfig` satisfies the following constraints from the backend, specified through the activation and weight DTypeWithConstraints. 1. QConfig specified a quantization range that falls within the backend's, if any 2. QConfig specified a min scale value that is >= the backend's, if any 3. QConfig specified a FixedQParamsObserver or FixedQParamsFakeQuantize that has scale and zero point that match the backend's, if any If `is_activation` is True, we check `qconfig.activation`, else we check `qconfig.weight`. If `qconfig` or `dtype_with_constraints.dtype` is None, or the dtypes do not match, return True. """ # TODO: log warnings only when the user enabled a debug flag def _activation_post_process_satisfies_dtype_config_constraints( activation_post_process: Union[ObserverBase, FakeQuantizeBase], dtype_with_constraints: DTypeWithConstraints, debug_string: str) -> bool: observer = _get_observer_from_activation_post_process(activation_post_process) app_quant_min = getattr(observer, "quant_min", None) app_quant_max = getattr(observer, "quant_max", None) # TODO: for now, just use the existing eps value as scale_min. In the future, we should # resolve the differences between the two, either by renaming eps or some other way app_scale_min = getattr(observer, "eps", None) backend_quant_min = dtype_with_constraints.quant_min_lower_bound backend_quant_max = dtype_with_constraints.quant_max_upper_bound backend_scale_min = dtype_with_constraints.scale_min_lower_bound backend_scale_exact_match = dtype_with_constraints.scale_exact_match backend_zero_point_exact_match = dtype_with_constraints.zero_point_exact_match # check quantization ranges if backend_quant_min is not None and backend_quant_max is not None: if app_quant_min is None or app_quant_max is None: warnings.warn(f"QConfig {debug_string} must specify 'quant_min' and 'quant_max', ignoring {qconfig}") return False elif app_quant_min < backend_quant_min or app_quant_max > backend_quant_max: warnings.warn( f"QConfig {debug_string} quantization range must fall within the backend's:\n" f"QConfig range = ({app_quant_min}, {app_quant_max}), " f"BackendConfig range = ({backend_quant_min}, {backend_quant_max}), " f"ignoring {qconfig}" ) return False # check scale min if backend_scale_min is not None: if app_scale_min is None: warnings.warn(f"QConfig {debug_string} must specify 'eps', ignoring {qconfig}") return False if app_scale_min < backend_scale_min: warnings.warn( f"QConfig {debug_string} eps ({app_scale_min}) must be greater than or equal to " f"the backend's min scale value ({backend_scale_min}), ignoring {qconfig}" ) return False # check fixed scale and zero point if backend_scale_exact_match is not None and backend_zero_point_exact_match is not None: # For tests only, accept the following qconfigs for now # TODO: handle fp16 qconfigs properly for accepted_qconfig in [float16_static_qconfig, float16_dynamic_qconfig]: if qconfig_equals(qconfig, accepted_qconfig): return True suggestion_str = ( "Please use torch.ao.quantization.get_default_qconfig_mapping or " "torch.ao.quantization.get_default_qat_qconfig_mapping. Example:\n" " qconfig_mapping = get_default_qconfig_mapping(\"fbgemm\")\n" " model = prepare_fx(model, qconfig_mapping, example_inputs)" ) if not isinstance(activation_post_process, FixedQParamsObserver) and \ not isinstance(activation_post_process, FixedQParamsFakeQuantize): warnings.warn( f"QConfig must specify a FixedQParamsObserver or a FixedQParamsFakeQuantize " f"for fixed qparams ops, ignoring {qconfig}.\n{suggestion_str}" ) return False if observer.scale != backend_scale_exact_match or observer.zero_point != backend_zero_point_exact_match: warnings.warn( f"QConfig fixed scale ({observer.scale}) and zero point ({observer.zero_point}) " f"do not match the backend's ({backend_scale_exact_match} and {backend_zero_point_exact_match}), " f"ignoring {qconfig}.\n{suggestion_str}" ) return False return True if qconfig is None or dtype_with_constraints.dtype is None: return True activation_post_process_ctr = qconfig.activation if is_activation else qconfig.weight debug_string = "activation" if is_activation else "weight" satisfies_constraints = True if activation_post_process_ctr is not None: activation_post_process = activation_post_process_ctr() assert _is_activation_post_process(activation_post_process) # If dtypes don't match, don't check the activation_post_process and return True early if activation_post_process.dtype != dtype_with_constraints.dtype: return True satisfies_constraints = _activation_post_process_satisfies_dtype_config_constraints( activation_post_process, dtype_with_constraints, debug_string) return satisfies_constraints